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Virus infections are known to induce a transient state of immune suppression often associated with an inhibition of T cell proliferation in response to mitogen or cognate-antigen stimulation . Recently , virus-induced immune suppression has been linked to responses to type 1 interferon ( IFN ) , a signal 3 cytokine that normally can augment the proliferation and differentiation of T cells exposed to antigen ( signal 1 ) and co-stimulation ( signal 2 ) . However , pre-exposure of CD8 T cells to IFN-inducers such as viruses or poly ( I∶C ) prior to antigen signaling is inhibitory , indicating that the timing of IFN exposure is of essence . We show here that CD8 T cells pretreated with poly ( I∶C ) down-regulated the IFN receptor , up-regulated suppressor of cytokine signaling 1 ( SOCS1 ) , and were refractory to IFNβ-induced signal transducers and activators of transcription ( STAT ) phosphorylation . When exposed to a viral infection , these CD8 T cells behaved more like 2-signal than 3-signal T cells , showing defects in short lived effector cell differentiation , reduced effector function , delayed cell division , and reduced levels of survival proteins . This suggests that IFN-pretreated CD8 T cells are unable to receive the positive effects that type 1 IFN provides as a signal 3 cytokine when delivered later in the signaling process . This desensitization mechanism may partially explain why vaccines function poorly in virus-infected individuals .
The fact that virus infections can induce a transient state of immune suppression was first described over a century ago , as patients acutely infected with the measles virus failed to develop a recall response to tuberculin even though they had previously been immunized [1] . Since then , infection with a number of other viruses , including HIV [2] , Lymphocytic choriomeningitis virus ( LCMV ) [3] , CMV [4] and Influenza A [5] have been shown to induce a transient state of immune suppression in humans and animal models [6] , [7] . Although virus-induced immune suppression can affect many aspects of the immune system , it is often associated with a reduced ability of T cells to proliferate in response to mitogens or antigen-specific stimulation . Viruses may induce this suppression by directly infecting cells of the immune system , but they can also induce immune suppression without directly targeting hematopoietic cells . In vitro studies have shown that inhibition of T cell proliferation can be due to death receptor-associated activation-induced cell death ( AICD ) [8] , [9] , impaired antigen presentation [10] , [11] , exposure to immunosuppressive cytokines [12] , and perhaps to competition for limited amounts of cytokine growth factors . Recent in vivo studies from our laboratory showed that type 1 IFN can be a profound and universal factor inducing suppression of T cell proliferation during viral infections if the T cells are exposed to type 1 IFN prior to encountering their cognate ligand [13] . Efficient clonal expansion and differentiation of CD8 T cells is required to develop protective memory CD8 T cells . This requires three signals: a cognate peptide MHC-TCR interaction ( signal 1 ) , co-stimulation ( signal 2 ) , and infection-induced cytokines ( signal 3 ) [14]–[16] . CD8 T cells that encounter antigen and co-simulation undergo programmed cell division , but these two signals alone are not sufficient for full effector cell differentiation and survival into memory [14] , [17] , [18] . CD8 T cells need a third signal , provided by cytokines , including IL-12 or type 1 IFN , for efficient clonal expansion , differentiation into various effector populations , acquisition of cytolytic effector functions , and memory formation [15] , [19] . One in vitro study showed that without IL-12 , CD8 T cells did not proliferate well or develop full effector function [20] . Type 1 IFN , however , can evidently substitute for IL-12 as a signal 3 cytokine [21] , [22] . Signal 3 cytokines are required for efficient clonal expansion in response to antigen , and the infecting pathogen and resulting inflammatory environment determine which cytokine ( s ) provide signal 3 activity [23]–[26] . LCMV-specific CD8 T cells use type 1 IFN as the signal 3 cytokine for effective primary T cell expansion [25] , [27] , [28] , whereas Listeria and VSV depend on both type 1 IFN and IL-12 [23] , [25] , [29] , [30] . Studies showed that IFNαβ Receptor ( R ) KO LCMV-specific transgenic P14 CD8 T cells divided similarly to WT P14 cells but had reduced survival , thereby limiting their overall clonal expansion [27] . In other systems , the addition of adjuvants or IL-12 to activated CD8 T cells promoted their expansion by up-regulating the IκB family member BCL3 , which was found to prolong T cell survival [31]–[33] . Signal 3 cytokines also play an important role in CD8 T cell differentiation into various phenotypic and functional effector populations . Differences in CD8 T cell exposure to co-stimulatory molecules and cytokines can alter their differentiation into early effector cells ( EECs ) , short-lived effector cells ( SLECs ) and memory precursor effector cells ( MPECs ) [17] , [25] , [34] . Recent studies investigating the role for signal 3 cytokines in primary CD8 T cell responses have shown that loss of IL-12R , IFNAR , or both receptors can alter CD8 T cell differentiation , showing reduced SLEC and increased MPEC formation [25] , [35] . Signal 3 cytokines have also been shown to augment the acquisition of CD8 T cell effector functions , including production of cytokines ( IFNγ and TNF ) and CTL activity . In vitro studies showed that without type 1 IFN or IL-12 , CD8 T cells had decreased lytic ability and low levels of granzyme B expression [21] , [27] , [36] . Additional in vivo studies showed reduced granzyme B expression in IFNαβR KO transgenic P14 CD8 T cells compared to WT P14 CD8 T cells [27] . However , not all infection models show the same requirements for the specific signal 3 cytokines in driving these effector functions [25] , [27] . Type 1 IFN signaling is complex in that it can activate multiple downstream pathways , including the JAK/STAT pathway . Engagement of the type 1 IFN receptor promotes phosphorylation of downstream STAT molecules , including STAT1 , 3 , 4 , and 5 [37] . The combination of STAT molecule ( s ) that are phosphorylated and translocated into the nucleus controls the outcome of CD8 T cell activation . Activation of STAT1 downstream of type 1 IFN receptor signaling generally has anti-proliferative effects on CD8 T cells [38] , [39] . In contrast , type 1 IFN-mediated activation of STAT3 and/or STAT5 has anti-apoptotic and pro-mitogenic effects [38] , [40] , [41] . Type 1 IFN signaling via STAT4 promotes both the acquisition of effector function , including IFNγ production , and clonal expansion [21] , [42] . Recent studies showed that during LCMV infection , virus-specific CD8 T cells had decreased total STAT1 levels and increased STAT4 levels , thereby promoting effector T cell differentiation and clonal expansion over anti-proliferative effects [38] , [43] . Thus , type 1 IFN can have both inhibitory and stimulatory effects on CD8 T cell proliferation , and when type 1 IFN provides signal 3 cytokine activity , it has positive effects on CD8 T cell expansion . The timing of exposure of CD8 T cells to all 3 signals is very important , as T cells exposed to virus-induced inflammatory environments prior to cognate antigen respond differently to signals 1 and 2 compared to CD8 T cells from naïve environments [13] , [44] , [45] . Under circumstances when CD8 T cells see antigen and co-stimulation prior to or at the same time as inflammatory cytokines , IL-12 or type 1 IFN have positive effects on T cell differentiation and expansion . However , CD8 T cells pre-exposed to virus-induced inflammatory environments showed reduced proliferation when exposed to cognate antigen [13] , [44] . Virus-induced impaired proliferation could be mimicked by the type 1 IFN-inducer poly ( I∶C ) . In this study , we utilized poly ( I∶C ) to study the mechanism of IFN-mediated virus-induced T cell immune suppression . We sought to investigate whether the IFN-mediated suppression of CD8 T cells is due to type 1 IFN having direct suppressive effects on CD8 T cells or if it inhibits the positive effects IFN has on CD8 T cells . We show here that poly ( I∶C ) -pretreated CD8 T cells are refractory to IFNβ signaling in terms of downstream STAT phosphorylation , suggesting that they are unable to receive positive effects that signal 3 cytokines normally provide during acute infections . Indeed , these out-of-sequence signal 3 CD8 T cells were found to behave more similar to 2-signal-only CD8 T cells rather than T cells that receive all 3 signals in the proper order . Therefore , the inability to respond to signal 3 cytokines limits CD8 T cell expansion and suggests a causative mechanism for reduced vaccine efficacy when administered during acute infections .
Previously , we had shown that CD8 T cells exposed to exogenous cognate antigen 3–9 days , but not 12 days , after initiation of a virus infection proliferated poorly in response to a cognate antigen stimulus , and viruses that induced a strong type 1 IFN response had the greatest suppressive effects [13] . To investigate the mechanism of this virus-induced suppression of T cell proliferation , the IFN-inducer poly ( I∶C ) was used to prime CD8 T cells . Congenic transgenic LCMV-specific P14 CD8 T cells were used here to study virus-specific T cells exposed to the IFN-inducer poly ( I∶C ) prior to infection . As demonstrated in Figure 1A , Ly5 . 1 P14 cells were transferred into Ly5 . 2 B6 hosts that were inoculated with either HBSS or poly ( I∶C ) . One to three days later , splenocytes were isolated , and equal numbers of P14 cells ( enumerated by flow cytometry staining ) were transferred into recipients that were immediately infected with LCMV . Spleens from recipient mice were harvested at the peak of transgenic T cell expansion , day 7 post infection , and the proportion ( Figure 1B and 1C ) and total number ( Figure 1D ) of transgenic P14 cells were then determined . Suppression of proliferation of poly ( I∶C ) -pretreated P14 cells ( black bars ) was greatest at 1 and 2 days of treatment compared to control treated cells ( open bars ) . After 3 days of pre-treatment , clonal expansion of poly ( I∶C ) -pretreated P14 cells was comparable to that of the HBSS-pretreated control P14 cells , indicating that poly ( I∶C ) -mediated suppression of proliferation is a transient effect . Because type 1 IFN is required for efficient clonal expansion of LCMV-specific CD8 T cells , we investigated its role in the reduced proliferation seen in poly ( I∶C ) -pretreated CD8 T cells . We previously showed that impaired proliferation required direct effects of type 1 IFN acting on the T cell [13] , and this is illustrated in Figure 1E , using a similar experimental set up as that described in Figure 1A . Congenic Thy1 . 1 IFNαβR KO P14 CD8 T cells were transferred into Thy1 . 2 mice that were inoculated with either HBSS or poly ( I∶C ) . One day later , equal numbers of transgenic T cells were transferred into mice prior to LCMV infection . The percentage ( Figure 1E ) and total number ( Figure 1F ) of donor IFNαβR KO P14 cells in recipient host mice were determined at various times after LCMV infection . Poly ( I∶C ) -pretreated IFNαβR KO P14 cells proliferated to similar numbers as the control-treated counterparts at both day 7 and 8 post LCMV infection . The fact that these CD8 T cells lacked expression of the IFNαβR and showed no difference in proliferation between HBSS- and poly ( I∶C ) -primed groups suggested that there was a direct role for type 1 IFN on the CD8 T cells in this model of immune suppression . Knowing that type 1 IFN delivered at an optimal time can provide a positive signal 3 to CD8 T cells and enhance their proliferation , we questioned whether an out of sequence early exposure to IFN would interfere with later attempts at IFN signaling . Type 1 IFN can activate multiple downstream STAT molecules including STAT1 , 3 , 4 , and 5 . Because type 1 IFN can have both positive and negative effects on T cell expansion , where recent studies have shown that the specific STAT ( s ) activated dictate the outcome , all of the aforementioned STAT molecules were tested . The phosphorylation of STAT molecules downstream of the type 1 IFN receptor was thus examined in CD8 T cells from mice pretreated with either HBSS or poly ( I∶C ) . Mice were inoculated with either HBSS or poly ( I∶C ) for 1 day , and their splenocytes were isolated and stimulated ex vivo with mouse IFNβ for ∼30 min , followed by phosflow to examine downstream STAT phosphorylation ( Figure 2 ) . In unstimulated ( non-IFNβ-treated ) CD8 T cells , there was very little phosphoSTAT staining , regardless of the pretreatment regimen ( Figure 2A , shaded histograms ) . In T cells from HBSS-treated mice ( open bars ) , the phenotypically naïve CD44lo CD8 T cells responded strongly to IFNβ stimulation and showed phosphoSTAT 1 , 3 , 4 and 5 staining well above the unstimulated controls ( solid line , open histograms in Figure 2A; open bars in Figure 2B–E ) . However , CD44lo CD8 T cells from mice pre-exposed to poly ( I∶C ) for 1 day were unable to respond to IFNβ stimulation and did not phosphorylate any downstream STAT molecules tested ( dashed line open histogram in Figure 2A; black bars in Figures 2B–2E ) . Similar to naïve CD8 T cells , which represent most of the T cells and which are the focus of this study , CD44hi memory phenotype CD8 T cells from poly ( I∶C ) -pretreated mice also showed reduced response to IFNβ stimulation in terms of downstream STAT phosphorylation ( Figure S1 ) . Since STAT phosphorylation is a transient event , a kinetic analysis of STAT phosphorylation in cells from HBSS- or poly ( I∶C ) -inoculated mice stimulated with IFNβ for times ranging from 5 minutes to 2 hours was performed . The poly ( I∶C ) -pretreated CD8 T cells did not phosphorylate downstream STATs above unstimulated controls at any time point tested ( data not shown ) . The lack of IFNβ- induced phosphoSTAT staining in poly ( I∶C ) -pretreated T cells suggests that the T cells are unable to respond to IFN and therefore do not receive either the positive or the negative effects that type 1 IFN can have on lymphocytes . To test the duration of this unresponsiveness to IFNβ stimulation , mice were inoculated with HBSS or poly ( I∶C ) , and after 1 , 2 , or 3 days , their splenocytes were stimulated ex-vivo with IFNβ for ∼30 min before staining for phosphoSTATs . As shown in Figure 2 , the phenotypically naive CD8 T cells from mice pretreated with poly ( I∶C ) for 1 day did not respond to IFNβ stimulation in terms of STAT phosphorylation and this is also shown in Figures 3A–D ( black bars ) . Similarly , CD8 T cells from poly ( I∶C ) -pretreated mice were also less responsive to IFN stimulation when treated 2 days previously compared to controls . However , by 3 days after pretreatment , the CD44lo CD8 T cells from poly ( I∶C ) -treated mice started to regain the ability to respond to IFNβ stimulation and showed downstream STAT phosphorylation above unstimulated controls . At 3 days , the poly ( I∶C ) -pretreated CD8 T cells phosphorylated downstream STATs to similar levels as HBSS-pretreated CD8 T cells for all STATs tested except STAT4 . Memory phenotype CD44hi CD8 T cells pretreated with poly ( I∶C ) showed a similar transient unresponsiveness to IFNβ stimulation as the naïve CD44lo CD8 T cell response seen in Figure 3 ( data not shown ) . These data show that the refractoriness to IFNβ stimulation is transient , with kinetics similar to that of the poly ( I∶C ) -induced impaired proliferation ( Figure 1 ) . To make sure that STAT molecules were available to be phosphorylated , total STAT protein levels in naïve CD8 T cells after different days post HBSS or poly ( I∶C ) inoculation were determined ( Figure 3E–H ) . After 1 , 2 , and 3 days of treatment , total STAT 1 , 3 , 4 and 5 levels in poly ( I∶C ) -pretreated naïve CD8 T cells were similar to , if not higher than , the control-treated cells . Total STAT1 expression was higher in poly ( I∶C ) -pretreated naïve CD8 T cells after 1 day and stayed high through day 3 of treatment , as compared to STAT1 levels in HBSS-treated CD8 T cells . Since STAT1 is an IFN-inducible gene [46] , higher STAT1 protein expression in poly ( I∶C ) -pretreated CD8 T cells was expected . These data indicate that the reduced phosphoSTAT staining found in IFNβ-stimulated poly ( I∶C ) -pretreated CD8 T cells was not due to lower levels of total STAT protein . To test if the poly ( I∶C ) -primed CD8 T cells were unresponsive to other cytokines , splenocytes were stimulated ex vivo with various cytokines for ∼30 min before staining for the appropriate downstream phosphoSTATs . We tested IL-2 , IL-6 , IL-7 , IL-12 , and IL-15 , because these cytokines have positive effects on T cell survival or proliferation . IL-2 , IL-7 , and IL-12 stimulation did not elicit positive phosphoSTAT staining in the control- or poly ( I∶C ) -treated naïve CD8 T cells ( Figure S2 ) . Of these cytokines tested , IL-6 and IL-15 were found to elicit positive phosphoSTAT staining in HBSS-treated naïve CD8 T cells ( open bars ) above the unstimulated control levels ( Figure 4A and B ) . However , unlike poly ( I∶C ) -pretreated naïve CD8 T cells stimulated with IFNβ , poly ( I∶C ) -pretreated naïve CD8 T cells stimulated with IL-6 ( black bars , Figure 4A ) or IL-15 ( black bars , Figure 4B ) responded just as well , in terms of phosphorylating downstream STAT3 and STAT5 , respectively , as their control-treated counterparts . Similar to naïve T cells , CD44hi CD8 T cells also phosphorylated downstream STAT3 and STAT5 in response to IL-6 and IL-15 stimulation ( respectively ) from both the HBSS- and poly ( I∶C ) -treated groups ( Figure S3 ) . Together , these data indicate that poly ( I∶C ) treatment did not make P14 CD8 T cells universally unresponsive to all cytokines; rather , the impairment was instead more specific to type 1 IFN . These data suggest that poly ( I∶C ) -pretreated CD8 T cells , when put into hosts subsequently infected with LCMV , are not able to respond to the type 1 IFN induced by the virus , and thus are unable to receive positive signal 3 cytokine signals . Cytokine signaling must be tightly regulated in order to prevent over-active and prolonged immune activation . A number of different mechanisms are in place to limit cytokine signaling , including reducing cytokine receptor expression , down-regulating expression of signaling protein components , and up-regulating the expression of suppressors of cytokine signaling proteins ( SOCS ) 47 , 48 . Reduced STAT protein levels did not account for the refractoriness to IFNβ simulation seen in the poly ( I∶C ) -pretreated CD8 T cells ( Figure 3 ) . To investigate why naïve CD8 T cells pre-exposed to poly ( I∶C ) were unresponsive to type 1 IFN , but not all cytokines , cytokine receptor expression was determined . At various days post HBSS or poly ( I∶C ) treatment , naïve CD8 T cells were assessed for cytokine receptor signaling components , including portions of the IL-2 , IL-6 , IL-7 , and IL-15 complexes , and these are represented in Figure S4 . The type 1 IFN receptor is comprised of two components , IFNAR1 and IFNAR2 [37] . Naïve CD8 T cells from mice inoculated with poly ( I∶C ) for one day had much lower expression levels of IFNAR1 , compared to the HBSS-treated cells ( Figure 5A–5B ) . However , IFNAR1 expression levels returned to control-treated levels by 2 days post treatment . The CD44hi CD8 T cells had similar kinetics of IFNAR1 expression as the naïve CD8 T cells , showing slightly reduced receptor expression with 1 day treatment of poly ( I∶C ) but not 2 or 3 days of treatment ( Figure S5 ) . Thus , unresponsiveness to type 1 IFN at day 1 correlated with the lack of expression of the IFN receptor . Since poly ( I∶C ) -pretreated CD8 T cells were still less responsive to IFNβ stimulation , as measured by STAT phosphorylation , 2 days after poly ( I∶C ) treatment ( Figure 3 ) , there were likely other suppressive mechanisms to limit IFNβ responsiveness , in addition to the reduced receptor expression shown in Figure 5A and B . SOCS proteins are known to inhibit cytokine receptor signaling by acting at many different steps in the JAK/STAT signaling pathway [47] . SOCS1 inhibits type 1 IFN signaling by binding to the receptor-associated JAK protein TYK2 , thus blunting IFN receptor signaling [49] . Naïve CD44lo CD8 T cells that were sorted from mice inoculated with HBSS or poly ( I∶C ) for 1 day showed a 7-fold relative increase in SOCS1 message expression after poly ( I∶C ) treatment . We next utilized the well-established protocol for staining for phosphorylated proteins to identify intracellular levels of SOCS1 . SOCS1 expression was determined in naïve CD8 T cells from poly ( I∶C ) - or HBSS-inoculated mice 1 , 2 , or 3 days after treatment ( Figure 5C–D ) . Indeed , at both 1 and 2 days after poly ( I∶C ) treatment , naïve CD8 T cells had higher expression of SOCS1 compared to the control-treated cells . However , by 3 days of pretreatment , there was no longer a significant difference in SOCS1 expression between control and poly ( I∶C ) -treated CD8 T cells . CD44hi CD8 T cells also showed increased SOCS1 protein levels ( Figure S5 ) . This suggests that a combination of a decrease in IFNαβR expression and an increase in SOCS1 expression may account for the observed refractoriness to IFNβ stimulation . These results correlated kinetically with refractoriness to IFNβ stimulation ( Figure 3 ) and to the suppressed proliferation seen in poly ( I∶C ) -pretreated CD8 T cells ( Figure 1 ) . These experiments do not definitively parse out the relative contributions of decreased receptor expression vs . inhibitory molecule contribution to T cell refractoriness to IFN stimulation , but the results are very consistent with previous work in more tractable systems studying the mechanism of unresponsiveness to IFN [49]–[51] . Further , they clearly show that this unresponsiveness is not due to decreases in overall STAT protein expression . Because the suppression of proliferation of poly ( I∶C ) -pretreated CD8 T cells correlated with refractoriness to IFNβ stimulation , we hypothesized that poly ( I∶C ) -pretreated CD8 T cells were unable to receive the positive effects that type 1 IFN exerts as a signal 3 cytokine when delivered in the proper sequence . This hypothesis would suggest that poly ( I∶C ) -pretreated P14 cells would behave similarly to 2-signal only CD8 T cells , rather than 3-signal CD8 T cells . Thus , we examined their effector phenotype and their abilities to divide , produce cytokines , degranulate , and express the survival protein BCL3 in response to antigen exposure . Studies have shown that 2-signal CD8 T cells divide similarly to 3-signal CD8 T cells but have defects in survival [27] . We therefore tested whether the impairment in proliferation seen in poly ( I∶C ) -pretreated CD8 T cells was due to division or survival defects . A similar experimental setup as shown in Figure 1A was used , but after inoculation with HBSS or poly ( I∶C ) , congenic P14 CD8 T cells were labeled with CellTrace violet and transferred together into the same recipients to track cell division during LCMV infection . Cells were harvested at days 3 and 4 post infection , and CellTrace violet dilution was measured . Because we were looking at early days post infection , a larger number of transgenic cells was transferred than what would normally be considered physiologically relevant in order to quantify early cell division . Neither the poly ( I∶C ) - nor the HBSS-pretreated P14 cells diluted CellTrace violet in naïve mice , indicating that they did not divide ( Figure 6A ) . At day 3 post infection , the control-treated P14 cells diluted more CellTrace violet , indicating they had undergone more cell divisions compared to the poly ( I∶C ) -pretreated P14 cells ( Figure 6A–C ) . However , by day 4 post LCMV infection , the division profiles of both HBSS-and poly ( I∶C ) -pretreated P14 cells appeared similar . Although the percentage of cells that divided was statistically similar between the two groups ( Figure 6B ) , the proliferation index of poly ( I∶C ) -pretreated P14 cells was lower compared to HBSS-treated cells ( Figure 6C ) . The proliferation index represents the average number of divisions of the cells that have undergone at least one division . These results show that poly ( I∶C ) -pretreated CD8 T cells have a delay in the number of cell divisions . If a delay in cell division were the only thing contributing to the suppression of proliferation , at later time points post infection the expansion of poly ( I∶C ) -pretreated CD8 T cells might eventually reach the same level as the control-treated cells . Therefore , a time course of HBSS- or poly ( I∶C ) -pretreated P14 CD8 T cell expansion in response to LCMV infection was performed . The peak of expansion of poly ( I∶C ) -pretreated P14 CD8 T cells was delayed ( day 9 ) compared to HBSS-pretreated P14 CD8 T cells ( day 7 ) , but the magnitude of the response was still reduced in the poly ( I∶C ) -pretreated cells ( Figure 6D ) . In addition , out-of-sequence P14 cells showed decreased memory frequencies ( Figure 6E ) and number ( data not shown ) compared to their control treated counterparts at multiple time points tested . This suggests that the defects in clonal expansion were not solely due to a delay in cell division but may also be due to other factors such as defects in cell survival . In vitro and in vivo studies by others found that survival of activated T cells in response to signal 3 cytokines and adjuvants was in part due to an increase in the IκB family member BCL3 and that cells lacking signal 3 cytokines have reduced expression of BCL3 [31]–[33] . To determine if the poly ( I∶C ) -treated virus-stimulated T cells resembled two signal only T cells in this respect , the expression of BCL3 was thus determined in HBSS-control or poly ( I∶C ) -pretreated P14 CD8 T cells after LCMV infection . Indeed , a lower percent of poly ( I∶C ) -pretreated P14 cells up-regulated BCL3 than HBSS-treated P14 cells at days 4 , 5 and 6 post LCMV infection ( Figure 6F ) . Additionally , the BCL3 MFI of poly ( I∶C ) -pretreated P14 CD8 T cells was lower than in HBSS control-treated cells ( Figure 6G ) . Combined , these results showed that , similar to 2-signal CD8 T cells , out-of-sequence signal 3-stimulated P14 CD8 T cells had a delay in cell division when compared to CD8 T cells that receive all 3 signals in the correct order , and that they had defects in a survival protein that may limit the ability of these cells to clonally expand . To further support the hypothesis that out-of-sequence signal 3 CD8 T cells do not receive the positive effects that type 1 IFN can have as a signal 3 cytokine during acute virus infection , and thereby contribute to suppression of proliferation , we determined the frequency of poly ( I∶C ) - or HBSS-pretreated P14 cells after cognate peptide stimulation . Congenic P14 mice were directly treated with HBSS or poly ( I∶C ) for 1 day , and their splenocytes were transferred together into the same recipients that were naive , that received 13mer GP33–45 peptide or that were inoculated with LCMV . Here the LCMV infection should induce high levels of type 1 IFN , whereas the peptides would be poor type 1 IFN inducers . Figure 6H shows that at all time points tested , poly ( I∶C ) -pretreated P14 cells expanded to similar levels as HBSS-pretreated P14 cells in mice that only saw antigen ( 13mer GP33–45 ) and did not have a major inflammatory response . However , poly ( I∶C ) -pretreated P14 cells had defects in expansion in response to the IFN-inducing LCMV infection compared to control treated cells ( note the different axis for GP33 peptide or LCMV inoculation ) . Given that poly ( I∶C ) -pretreated P14 cells expanded to similar levels in response to antigen only but had defects in expansion in response to antigen and inflammation ( i . e . live virus infection ) , these results further support our hypothesis that out-of-sequence CD8 T cells are unable to receive positive effects of signal 3 cytokines during acute infections . Signal 3 cytokines have been shown to regulate the differentiation of CD8 T cells into distinct effector populations including EEC , SLEC and MPEC [17] , [24] , [34] , [35] . Therefore , we examined the ability of poly ( I∶C ) -pretreated CD8 T cells to differentiate into EEC , SLEC and MPEC populations , which can be distinguished based on expression of KLRG1 and CD127 [30] , [34] , [52] . A similar experimental model was used as shown in Figure 1A , where WT or IFNαβR KO P14 cells were transferred into mice for 1 day of treatment with poly ( I∶C ) or HBSS prior to a second transfer into congenic hosts that were subsequently inoculated with LCMV . At different days post infection , splenocytes were isolated and stained for KLRG1 and CD127 . At day 7 post infection , the IFNαβR KO P14 CD8 T cells had similar proportions of SLEC ( KLRG1hi , CD127lo ) , MPEC ( KLRG1lo , CD127hi ) and EEC ( KLRG1lo , CD127lo ) , regardless of the pretreatment regime . These data are consistent with other results showing that type 1 IFN is important for SLEC differentiation in various infection models [25] , [35] . However , poly ( I∶C ) -pretreated WT P14 CD8 T cells had reduced proportions of SLEC populations and increased EEC proportions compared to the HBSS-pretreated WT P14 CD8 T cells ( Figure 7A ) . This data supports our hypothesis that the out-of-sequence CD8 T cells behave more similar to 2-signal only CD8 T cells ( IFNαβR KO P14 cells ) in terms of effector cell differentiation . The defect in SLEC differentiation in poly ( I∶C ) -treated cells can be seen as early as day 5 post infection , but is more dramatic at days 6 and 7 post infection ( Figure 7B ) . The proportion of MPECs were generally similar to or slightly elevated in poly ( I∶C ) -pretreated P14 cells as compared to control-treated counterparts at days 5–7 post infection ( data not shown ) . These data show that in addition to CD8 T cells requiring signal 3 cytokines for proper effector cell differentiation , they also need to see the signals in the appropriate order . In some infection models , two-signal CD8 T cells can produce similar proportions of cytokines as compared to 3-signal CD8 T cells ( VSV ) , but other infection models show reduced cytokine production ( Listeria ) [25] , [27] . Therefore , we compared poly ( I∶C ) - and HBSS-treated cells for their ability to produce the effector cytokines TNF and IFNγ . A similar experimental model was used as shown in Figure 1A , where P14 cells were transferred into mice for 1 day of treatment with poly ( I∶C ) or HBSS or P14 mice were treated directly with poly ( I∶C ) or HBSS prior to transferring cells into congenic hosts that were subsequently inoculated with LCMV . At day 5 post infection , splenocytes were isolated and stimulated ex vivo with or without cognate peptide GP33 for 5 hours . Using naïve CD8 T cells , isotype controls and fluorescent minus one staining to distinguish positive vs . negative staining , the poly ( I∶C ) -pretreated P14 cells produced similar frequencies of TNF and IFNγ compared to control-treated P14 cells after in vitro LCMV GP33 peptide stimulation ( Figure 7C ) . Plotting the proportion of double cytokine producers ( TNF and IFNγ ) ( Figure 7D ) revealed no significant difference in the ability of these cells to produce effector cytokines . Using CD107a and b as markers for degranulation , we found that poly ( I∶C ) -pretreated P14 cells stained to a similar extent , if not slightly more , than HBSS-treated cells in response to GP33 peptide stimulation ( Figure 7E–7F ) . However , poly ( I∶C ) -pretreated P14 CD8 T cells had substantially lower levels of granzyme B expression than control-treated cells at day 5 post infection ( Figure 7E ) . Reduced granzyme B expression in the out-of-sequence CD8 T cells was seen as early as day 4 post infection and lasted at least until day 6 post infection ( Figure 7G ) . KLRG1 expression in CD8 T cells is considered a marker for effector function , and there was a positive correlation between KLRG1 expression and granzyme B expression ( R square = 0 . 8563 , p<0 . 0001 ) ( Figure 7H ) . Since granzyme B expression has been used as a correlative marker for cytotoxic capability [36] , this suggests that poly ( I∶C ) -pretreated CD8 T cells have reduced cytolytic function compared to HBSS-treated CD8 T cells . These data showing similar cytokine production but reduced granzyme B expression are consistent with published phenotypes for CD8 T cells that only receive 2 signals [25] , [27] . A new method to study effector cell function is the trogocytosis assay , whereby target cells are labeled with a membrane dye , mixed with cytotoxic effector cells for 1 hr , and then examined for the transfer of dye to a flow cytometry-defined effector cell population [53] , [54] . This is an indicator of how aggressively the effector cells are attacking the targets . Figure S6 shows that the poly ( I∶C ) -pretreated P14 cells , 5 days after LCMV infection , had modest but statistically significant reduced ability to acquire the dye from GP33-pulsed RMA cells , when compared to the HBSS-pretreated donor T cells . Reduced incorporation of the lipid dye is an indicator of reduced effector cell function [55] , [56] . We tested whether these donor poly ( I∶C ) -pretreated T cell responses , which were dramatically reduced in number and modestly reduced in effector function , would affect viral load differently than that in mice receiving HBSS-treated cells . Mice receiving either HBSS-pretreated or poly ( I∶C ) -pretreated P14 CD8 T cells were subsequently infected with LCMV , and viral titers were examined at different time points post infection . As early as day 4 post infection , mice receiving poly ( I∶C ) -pretreated P14 cells had modest but statistically significant increased viral titers compared to mice receiving control treated P14 cells in the fat pad ( 4 . 7±0 . 1 vs . 4 . 3±0 . 08 log pfu , two independent experiments combined for n = 10 per group ) respectively ( p = 0 . 0082 ) . In addition , at day 6 post infection , there was a modest but significant increase in viral load in the spleen and liver in mice receiving poly ( I∶C ) -pretreated P14 CD8 T cells ( 4 . 2±0 . 07 log pfu in the spleen , 4 . 2±0 . 08 log pfu in the liver ) compared to mice receiving HBSS-pretreated P14 cells ( 3 . 8±0 . 12 log pfu in the spleen , 3 . 8±0 . 08 log pfu in the liver ) ( 3 independent experiments combined for n = 14–15 , p = 0 . 0197 ( spleen ) and p = 0 . 0074 ( liver ) ) . These differences in viral titer are admittedly small , but they are statistically significant and occur in environments where normal endogenous host T cell responses are simultaneously occurring .
Transient states of immune suppression occur during many acute viral infections , and it has long been known that individuals should not get vaccinated when they are sick . Virus-induced immune suppression was first noted over 100 years ago [1] , and more recent studies have shown it to be a common element of many viral infections and often be associated with suppressed T cell proliferation in response to antigens and mitogens . In vitro studies had suggested that AICD contributed to this inhibition of T cell proliferation [2] , [8] , [9] , [57] , and other studies implicated impaired antigen-presenting cell function [10] , [11] , [58] , induction of immunosuppressive cytokines like IL-10 , [12] , and perhaps the competition for T cell growth factors could play a role . While studying viral infection models , we recently found that a general mechanism of virus-induced immune suppression could be linked to type 1 IFN , normally induced at high quantities in most viral infections [13] . This was somewhat surprising , given that type 1 IFN has been described as a signal 3 cytokine , which drives the expansion and differentiation of T cells after they have encountered cognate ligand ( signal 1 ) and co-stimulation ( signal 2 ) . The primary observation of the present report is that if T cells are exposed to type 1 IFN inducers before exposure to cognate ligand , they lose their sensitivity to further IFN stimulation and do not receive the benefits of a signal 3 cytokine . Instead , they behave like T cells receiving only two signals , with defects in effector cell differentiation , reduced effector function , lower expression of a pro-survival protein , and limited clonal expansion . Dating back to the early days of IFN therapy in humans , it has long been known that lymphocytes like NK cells become hyporesponsive to treatment , and IFN , like many other cytokines , can render treated cells resistant to further IFN stimulation by down regulating the IFN receptor and by inducing factors like SOCS1 that impair IFN-induced signal transduction [47] , [48] . We show here that this is the case with virus-specific T cells , and that these T cells pre-exposed to IFN fail to derive the benefit of the positive signal 3 effects of IFN signaling . The implications of this phenomenon are widespread . Because IFN is induced so rapidly during viral infections , one can deduce that the T cells that encounter antigen in the first day or two of infection would respond more impressively than “late-comer” virus-specific T cells stimulated later in infection . Thus , the dynamics of how much antigen is synthesized and presented vs . how much and how quickly IFN is induced may dictate the efficacy of the host response . Secondly , the T cell response to many acute infections , at least in mouse models , is relatively ordered and undergoes a rather synchronized contraction from 6–9 days post-infection . How can this occur when the amount of T cell proliferation is a programmed event [59]–[62] and when different T cells should encounter antigen at different time periods ? We would argue that the late-comer T cells , because of their previous exposure to IFN , would not undergo as many divisions and possibly have lower survival properties , thereby enabling them to contract when the rest of the T cells do . Third , we would argue that naïve or memory T cells specific to third party antigens would not respond well to a cognate antigen stimulus if they were first exposed to the IFN milieu of a viral infection and then stimulated with antigen . This failure to respond to recall antigens , such as tetanus toxoid or tuberculin , is a common feature of virus-induced immune suppression in humans , and the weak efficacy of vaccines in already infected individuals may well be a function of the same problem [63]–[65] . Finally , under conditions when a host develops a persistent viral infection there would be a chronic stimulation of the type 1 IFN response , and such hosts would probably not immunologically respond well to either the antigens of the infecting virus or to third party antigens on challenge . This weak response to third party antigens is not only seen during persistent viral infections but also during chronic autoimmune diseases , such as lupus erythematosus , where signal 3 cytokines may be chronically produced [66] , [67] . We therefore suggest that the elimination of signal 3 stimulation by out-of-sequence exposure to the signal 3 stimulant , in this case IFN , would be a common factor disrupting T cell responses in the context of acute or persistent viral infections . This generalized IFN-induced impairment of proliferation is one example of how out-of-sequence signaling can alter responses to cognate antigen exposure . On the other hand , virus-induced inflammatory environments can alter the response of bystander CD8 T cells not specific for the infecting virus to third party cognate antigens by driving the T cells down a different differentiation pathway [45] . Our previous studies showed that transgenic CD8 T cells exposed to virus-induced inflammatory environments were sensitized to undergo rapid effector function such that upon stimulation with cognate antigen they produced cytokines including granzyme B and IFNγ within a few hours and without a need for cell division . The sensitization to rapid effector function was most dramatic with viruses that induced a strong type 1 IFN response , and this event could also be induced by poly ( I∶C ) . We do not know if the CD8 T cells sensitized to rapid effector function are in fact the same cells that ultimately are suppressed in proliferation . However , we do know that these two changes in T cell response to cognate antigen stimulation occur by very distinct mechanisms and occur in virus-induced inflammatory environments; consequently , the impairment of proliferation may contribute to generalized IFN-induced immune suppression , even though there may be an initial transient activation of the T cells . Type 1 IFN can have both stimulatory and inhibitory effects on CD8 T cell proliferation , but here it was initially unclear if poly ( I∶C ) -pretreated CD8 T cells were receiving direct inhibitory signals or fewer stimulatory signals from IFN . Since type 1 IFN signaling can act through multiple STATs , each capable of altering cell fate , it might have been expected that poly ( I∶C ) -pretreated CD8 T cells would have had different STAT phosphorylation in response to IFNβ stimulation . Recent work has shown that virus-specific CD8 T cells down-regulate total STAT1 and up-regulate STAT4 , so that when IFN signals though the IFN receptor the anti-proliferative effects of STAT1 will be overcome by the positive effects mediated through STAT4 [38] , [43] . Therefore , poly ( I∶C ) -pretreated CD8 T cells could have had more pSTAT1 and less pSTAT4 than control-treated cells after IFNβ stimulation . However , this was not the case at the time points studied , as phosphorylation of all tested STATs was reduced ( Figures 2 and 3 ) . The fact that all pSTATs were reduced in poly ( I∶C ) -pretreated CD8 T cells suggested that IFN was not having a direct negative effect other than by desensitizing cells to the positive effects that a later exposure to IFN could mediate . It should be noted that not only were naïve CD8 T cells unresponsive to IFNβ stimulation after poly ( I∶C ) treatment , but CD4 T cells and NK cells were also refractory to further IFNβ stimulation in terms of STAT phosphorylation ( data not shown ) . Type 1 IFN has been shown to act directly on CD4 T cells , NK cells and B cells to promote effector function [68]–[70] , and these results may indicate that in addition to poly ( I∶C ) inducing inhibitory effects on CD8 T cell proliferation , it may also have suppressive effects on other lymphocyte populations that utilize IFN at another time for their activation . Indeed , reduced antibody production by B cells and lower NK cell cytotoxicity have been seen under conditions of virus-induced immune suppression [69] , [71] . Antigen and co-stimulatory molecules provide proper signals for T cell activation and differentiation , but more recent studies have focused on the role for inflammatory cytokines in these processes . We find here an additional layer of complexity in that the timing of T cell exposure to signal 3 cytokines is extremely important . If CD8 T cells are unable to receive the positive effects of type 1 IFN , as shown in this study , they should behave more like T cells that only received 2 signals , rather than 3 signals . This was the case , as the out-of-sequence signal 3 CD8 T cells had defects in SLEC differentiation and effector function . Poly ( I∶C ) -pretreated CD8 T cells degranulated , as shown by CD107a/b staining , but they had reduced granzyme B expression ( Figure 7 ) , suggesting that poly ( I∶C ) -pretreated CD8 T cells have lower cytolytic capabilities at day 5 post-infection . This is consistent with the phenotype of signal 3-lacking T cells but inconsistent with our observation that prior signaling with IFN can sensitize a CD8 T cell to rapid effector function on exposure to cognate ligand . That enhanced effector function , however , was examined shortly after TCR ( a few hours ) stimulation and not examined at day 5 post infection . Thus , out of sequence exposure to IFN may initially stimulate effector function of CD8 T cells but not sustain it as they poorly proliferate . Another hallmark of 2-signal only CD8 T cells is limited clonal expansion , which in many cases is attributed to decreased survival . Although the exact mechanism is unknown , BCL3 prolongs the survival of activated CD8 T cells after signal 3 cytokine addition or CpG adjuvant administration [31]–[33] , [72] . The IFN-induced suppression of proliferation seen here may also have been due to a decrease in survival . This idea was supported by poly ( I∶C ) -pretreated CD8 T cells having lower expression of the pro-survival protein BCL3 ( Figure 6 ) . We show here , that poly ( I∶C ) -pretreated P14 cells also had a delay in cell division compared to HBSS-treated cells in response to LCMV infection ( Figure 6 ) . Interestingly , the delay in cell division of poly ( I∶C ) -pretreated P14 cells is seen at day 3 post infection , but not at day 4 post infection , matching the kinetics of the timing of the ability of CD8 T cells to respond to IFNβ signals by phosphorylating downstream STATs ( Figure 3 ) . The positive effects that an inflammatory environment can have on CD8 T cell expansion is also shown here , whereby P14 T cells expanded ∼20 fold in response to GP33 peptide , but expanded more than 30 , 000 fold in response to LCMV infection . The facts that poly ( I∶C ) -pretreated P14 cells are suppressed in proliferation in response to LCMV infection but not to GP33 peptide stimulation support the idea that the refractoriness to IFN stimulation contributes to reduced expansion ( Figure 6 ) . This mechanism of IFN-induced immune suppression may explain how many virus infections can inhibit T cell responses , by limiting the ability of T cells to receive stimulatory effects from the environment . To summarize , if CD8 T cells see signal 3 first , they become refractory to further IFN stimulation and are unable to receive the positive signals that type 1 IFN can provide when delivered at the proper time after antigen and co-stimulation . This limits their ability to clonally expand , to sustain cytolytic capabilities , and form memory . Our studies show lower proportions and numbers of out-of-sequence CD8 T cells at different stages of memory formation ( Figure 6E ) , including as late as 11 weeks post infection . Preliminary data show that the out-of-sequence CD8 T cells that do form memory are able produce similar proportions of cytokines , when stimulated ex vivo , compared to memory cells from the control environment ( data not shown ) , but the effectiveness of these memory cells has not been further investigated . The efficacy of an out-of-sequence CD8 T cell memory response to secondary challenge is important to study but is beyond the scope of the paper , whose focus was to examine why IFN causes naïve T cells to function poorly during the context of an acute viral infection . Thus , under circumstances when CD8 T cells can receive positive signals , such as during an infection or vaccination with adjuvants , out-of-sequence signals can have a profound effect on CD8 T cell expansion and activation . This out-of-sequence inhibition of T cell proliferation may account for the more general immune suppression seen in many acute virus infections known to induce type 1 IFN . This mechanism of CD8 T cell suppression would be expected to contribute to the reduced efficacy of vaccines when they are administered during an acute infection .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the University of Massachusetts Medical School Institutional Animal Care and Use Committee , Docket # A-305 , Animal Welfare Assurance Number A-3306-01 . C57BL/6J ( Ly5 . 2+ ) male mice were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . Ly5 . 1 and Thy1 . 1 P14 [73] TCR-transgenic mice were bred in the Department of Animal Medicine at the University of Massachusetts Medical School ( UMMS ) . The P14 transgenic mice were bred onto the B6 . IFNαβR KO background to generate P14 CD8 T cells that lacked IFNαβR [13] , [74] . Lymphocytic choriomeningitis virus ( LCMV ) , strain Armstrong , was propagated in baby hamster kidney ( BHK21 ) cells , as previously described [75] , [76] . Mice were injected intraperitoneally ( i . p . ) with 5×104 pfu of LCMV . Organ homogenate viral titers were determined by plaque assay using Vero cells . To activate P14 CD8 T cells without a virus-induced inflammatory response , mice were inoculated intravenously ( i . v . ) with 5 µg ( diluted in HBSS ) of a 13-mer peptide ( GP33–45 ) ( KAVYNFATCGIFA ) from the LCMV glycoprotein . RMA cells were labeled with the minimal GP33 epitope ( KAVYNFATC ) , or the Vaccinia Virus K3L epitope ( YSLPNAGDVI ) at 1 µM concentration . Poly ( I∶C ) was purchased from InvivoGen ( SanDiego , CA ) and diluted in HBSS for a concentration of 1 µg/µl . Mice were either inoculated with 200 µl HBSS or 200 µg poly ( I∶C ) i . p . Mouse IFNβ was purchased from PBL Interferon Source . Cytokines: IL-2 was purchased from BD Biosciences , IL-6 and IL-12 were purchased from R&D , and IL-7 and IL-15 were purchased from PeproTech , INC . Splenocytes were stimulated ex vivo with cytokine concentrations of 1000 U/ml IFNβ , or 10 µg/ml of IL-2 , IL-6 , IL-7 , IL-12 , or IL-15 at 37°C for ∼30 minutes . For the dual transfer experiments , splenocytes ( 1–3×107 ) from WT or IFNαβR KO P14 transgenic mice ( Ly5 . 1+ or Thy1 . 1+ ) were adoptively transferred i . v . into congenic C57BL/6J ( Ly5 . 2+ Thy1 . 2+ ) mice . One day after transfer , mice were inoculated i . p . with HBSS or poly ( I∶C ) , and at various times post treatment ( 1 day∼18–24 hours , 2 days∼40–48 hours , 3 days∼64–72 hours ) spleens were isolated , and the frequency of transgenic P14 CD8 T cells was determined by flow cytometric staining of Vα2 , CD8α , and Ly5 . 1 or Thy1 . 1 . Equal numbers ( ∼10 . 000 ) of P14 CD8 T cells were transferred i . v . into congenic B6 hosts immediately prior to infection with LCMV . For single transfer experiments , Ly5 . 1 or Thy1 . 1 P14 TCR transgenic mice were inoculated with HBSS or poly ( I∶C ) i . p . for various times , after which , the same protocol was used as the dual transfer method to transfer in equal numbers of P14 transgenic T cells . In experiments where control- and poly ( I∶C ) -treated transgenic P14 cells were transferred into the same recipients subsequently infected with LCMV , a total of 10 , 000 P14 cells were transferred . In experiments where HBSS- and poly ( I∶C ) - treated P14 cells were transferred into the same recipients receiving 13-mer GP33–45 peptide , ∼2–4×105 P14 cells were transferred i . v . This higher amount was necessary for the peptide-stimulated cells to be detected . Prior to any adoptive transfer , single cell suspensions were prepared by lysing red blood cells with 0 . 84% NH4Cl solution and washing with HBSS . Where described , cells were labeled with 5 µM CellTrace Violet ( Invitrogen ) by incubating at 37°C for 15 min . Cells were then washed with HBSS at least 2 times prior to adoptive transfer . A larger number of transgenic P14 cells ( ∼1×106 per group ) were transferred into hosts to identify virus-specific cells early after LCMV infection . Spleen leukocytes were stained with a combination of fluorescently labeled monoclonal antibodies ( MAb ) specific for CD8α ( 53-6 . 7 ) , Vα2 TCR ( B20 . 1 ) , Ly5 . 1 ( A20 ) , Thy1 . 1 ( HIS51 ) , CD44 ( IM7 ) KLRG1 ( 2F1 ) , CD127 ( A7R34 ) , and IFNAR1 ( MAR1-5A3 ) for 20 min at 4°C . Intracellular cytokine staining was performed as described previously [45] . Briefly , spleen leukocytes ( 2–4×106 ) were plated with or without 5 µM synthetic peptide stimulation in the presence of GolgiPlug ( BD Pharmingen ) and human rIL-2 for 4 to 5 hours at 37°C . After stimulation , cells were washed in Flow Cytometry Buffer ( 2% FCS in HBSS ) , blocked with α-FcR ( 2 . 4G2 ) and stained with a combination of fluorescently labeled mAbs listed above . After surface staining , spleen leukocytes were fixed and permeabilized with Cytofix/Cytoperm ( BD Bioscience ) for 20 min at 4°C and then stained with a combination of fluorescently labeled MAb specific for TNF ( MP6-XT22 ) , IFNγ ( XMG1 . 2 ) , and Granzyme B ( GB11 , Invitrogen ) . To identify cells undergoing Ag-induced degranulation , splenocytes were stimulated as stated above with addition of CD107a ( 1D4B ) and CD107b ( ABL-93 ) . All MAbs were purchased from eBioscience , SanDiego , CA , BioLegend , San Diego , CA , or BD Bioscience , San Diego , CA . unless otherwise noted . Freshly stained and previously fixed samples were acquired using a BD Bioscience LSR II flow cytometer with FACS Diva software . Data were analyzed with FlowJo software ( Tree Star Inc . , Ashland , OR ) . To determine percent divided and proliferation index , the proliferation function from FlowJo was applied to samples . To identify intracellular proteins ( phospho-specific STATs , total STAT levels , SOCS1 , and BCL3 ) the BD Phosflow Alternative Protocol 1 was used and slightly modified . Generally , spleen leukocytes were isolated , stimulated ( where appropriate ) , fixed , stained for surface molecules , permeabilized , and finally stained for intracellular proteins . Spleens were isolated , and single cell suspensions were prepared . Red blood cells were lysed by addition of 0 . 84% NH4Cl solution , and cells were plated at 2–4×106 cells per well in 96 well round bottom plates . Cells were incubated at 37°C in 100 µl of media ( RPMI supplemented with 10% FCS and pen-strep and L-Glut ) for the indicated times in the presence ( stimulated ) or absence ( unstimulated ) of cytokines . Total volume was brought up to 200 µl before spinning . Cells were fixed with BD cytofix ( BD Bioscience ) on ice for 20 min , washed with Flow Cytometry Buffer and blocked with α-FcR ( 2 . 4G2 ) for 5 min at 4°C . Cells were washed and stained with a variety of fluorescently labeled MAbs for 20 min at 4°C , washed with Flow Cytometry Buffer , and then permeablized with BD Perm buffer III ( BD Bioscience ) for 30 min on ice . Splenocytes were washed and then stained with a combination of fluorescently labeled Abs pY701 STAT1 ( BD Bioscience ) , pY705 STAT3 ( BD Bioscience ) , pY693 STAT4 ( BD Bioscience ) , pY694 STAT5 ( BD Bioscience ) , STAT1 ( clone 1/Stat1; BD Bioscience ) , or unlabeled Abs STAT3 ( 79D7; cell signaling technology ) , STAT4 ( C46B10; cell signaling technology ) , STAT5 ( 3H7; cell signaling technology ) , SOCS1 ( A156; cell signaling technology ) , or BCL3 ( C-14; Santa Cruz Biotechnology ) for 20–30 min at RT in the dark . If antibodies were not fluorescently labeled , cells were washed and then stained with FITC-labeled donkey anti-Rabbit IgG ( Poly4064; BioLegend ) for 15 min at RT in the dark . After intracellular staining , splenocytes were washed and samples were acquired using a BD Bioscience LSR II flow cytometry with FACS Diva software . Data were analyzed with FlowJo software . Naïve CD44lo CD8 T cells were sorted to 98–99% purity using MACS Naïve CD8a+ T cell Isolation Kit ( Miltenyi Biotec ) . RNA was isolated from sorted naïve CD8 T cells with an RNeasy mini kit ( Qiagen ) and concentration was determined . cDNA was generated using the RT2 Easy First Strand Kit ( Qiagen ) and QuantiFast SYBR Green PCR Kit ( Qiagen ) was used to determine the relative mRNA concentrations by quantitative real-time PCR . Primers for Socs1 ( RefSeq Accession number NM_009896 . 2 ) and Actb ( RefSeq Accession number NM_007393 . 3 ) were used . When CD8 T cells kill target cells , they strip off part of the target cell membrane and incorporate it into their own , by a process called trogocytosis [53] , [54] . A trogocytosis assay was thus performed to measure the ability of P14 T cells to attack targets . Effector P14 cells were generated as described in earlier materials and methods sections . Briefly , P14 mice were either HBSS or poly ( I∶C ) treated for 1 day prior to adoptive transfer ∼10 , 000 total P14 cells per group into separate animals subsequently infected with LCMV . Spleens were harvested at day 5 post infection and single cell suspensions were obtained . Targets were RMA cells cultured in complete RPMI and were un-pulsed , pulsed with an irrelevant peptide , K3L ( YSLPNAGDVI ) or pulsed with a specific peptide , GP33 ( KAVYNFATC ) , at 1 µM for ∼90–120 minutes at 37°C . After incubation , target cells were labeled with fluorescent lipids SP-DiIC18 ( 3 ) ( Molecular probes ) and diluted in Diluent C ( Sigma Aldrich ) using the protocol adapted from Daubeuf S . et al [54] . Target cells ( ∼7×105 ) were co-cultured with effector cells ( 1 . 5×106 total splenocytes ) per well for 1 hour at 37°C . Cells were stained with surface antibodies of interest , and samples were acquired using a BD Bioscience LSR II flow cytometry with FACS Diva software . Data were analyzed with FlowJo software . Where appropriate , Students t test and linear regression were calculated using GraphPad InStat software . Significance was set at a P value of 0 . 05; * indicates a P of <0 . 05 , ** a P of <0 . 01 , *** a P of <0 . 001 , and **** a P of <0 . 0001 . All results are expressed as means of +/− standard deviations . | Vaccines are used to protect individuals against infection with a number of different pathogens and depend on the formation of antigen specific memory cells . The efficacy of vaccines can be affected by a number of different factors . It has been known for some time now that suppression of the immune system occurs during acute viral infections . Thus , receiving a vaccine during an acute illness may reduce the efficacy of the vaccine administered . We have identified a common mechanism of immune suppression that may occur with many different pathogens that induce a particular inflammatory response . Any pathogen that induces type 1 interferon could potentially suppress the immune response to a subsequent pathological insult . The mechanism of immune suppression identified here was not having a direct negative effect on lymphocytes , but rather was inhibiting the cells ability to receive positive signals that influence their differentiation , expansion and memory formation . This desensitization mechanism may partially explain why vaccines function poorly in virus-infected individuals . | [
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| 2014 | Out-of-Sequence Signal 3 as a Mechanism for Virus-Induced Immune Suppression of CD8 T Cell Responses |
A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system , e . g . , drug design , metabolic network reconstruction and system biology . When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function , the EC number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology ( IUBMB ) is used . However , each and every one of these applications is critically dependent upon the consistency and reliability of the underlying data for success . We have developed tools for the validation of the EC number classification scheme . In this paper , we present validated data of 3788 enzymatic reactions including 229 sub-subclasses of the EC classification system . Over 80% agreement was found between our assignment and the EC classification . For 61 ( i . e . , only 2 . 5% ) reactions we found that their assignment was inconsistent with the rules of the nomenclature committee; they have to be transferred to other sub-subclasses . We demonstrate that our validation results can be used to initiate corrections and improvements to the EC number classification scheme .
With the several thousand proteins found in each organism a highly developed hierarchical and consistent classification scheme is absolutely essential for a comparison of metabolic capacities of the organisms . Unfortunately such a system exists only for the enzymes and not for the other protein classes but for the enzymes the classification scheme allows an immediate access or the enzyme functional properties including catalysed reaction , substrate specificity , etc . In this respect a quick comparative assessment of enzymatic pathways between organisms is possible even when the enzymes in the different organisms have totally different sequences as long as they belong to the same EC-class . A well reconstructed metabolic network provides a unified platform to integrate all the biological and medical information on genes , enzymes , metabolites , drugs and drug targets for a system level study of the relationship between metabolism and disease . Therefore an accurate representation of biochemical and metabolic networks by mathematical models is one of the major goals of integrative systems biology . Metabolic networks have been constructed for a number of genomes [1] , [2] . An example for the reconstruction process of a metabolic network are schematically shown in Figure 1 . It is essential to integrate information from different databases to get a more complete enzyme list for the reconstruction . The main databases to be taken into account to provide a complete cross-link between genes and their corresponding enzymes are NCBI EntrezGene [3] , Ensembl [4] , KEGG [5] , MetaCyc [6] and BRENDA [7] . The second step of the reconstruction procedure is to fill the gaps resulting from the first step based on information from literature . This step is very time-consuming and it would be therefore highly desirable to make the first step an automatic and reliable procedure . One of the problems is the different substrate specificity of enzymes in different organisms a fact that cannot be really accounted for by any classification system [8] . A further problem is the wide-spread use of incomplete EC numbers such as 1 . - . - . - ( e . g . in UNIPROT entry AK1C3_HUMAN ) . This often occurs because an enzymatic function is inferred from the existence of a certain pair of metabolites or only experimentally shown from a cell extract without a full characterisation of the enzyme with biochemical methods , which is the requirement for the assignment of EC-numbers by the IUBMB Nomenclature Committee [9] . For example , in the UniProt database there are more than 800 proteins annotated with an incomplete EC number [10] . Applications like drug design , ligand docking , or systems biology require the EC number classification to be correct , consistent , and accurate . For these reasons the automatic assignment of EC numbers to enzymatic reactions is a current issue in bioinformatics and requires specific chemical knowledge , therefore just a few approaches have been published to handle the assignment problem . The Kyoto Encyclopedia of Genes and Genomes ( KEGG ) developed a tool for computational assignment of EC numbers published by Kotera et al . [11] . In this approach each reaction formula is decomposed by manual work into sets of corresponding substrate and product molecules , which are called reactant pairs . In the second step every reactant pair is analysed by the structure comparison method SIMCOMP developed by Hattori et al . [12] . Another approach proposed by Körner et al . [13] and Apostolakis et al . [14] considers reaction energetics to predict reaction sites . Lationa et al . [15] introduced an EC number classification method based on self-organizing maps . This approach allows to assign EC numbers at the sub-subclass levels for reactions with accuracies of 70% . One of the authors being the current chairman of the IUBMB nomenclature committee we felt the need to develop a system that allows for a highly reliable classification system that can help to identify the sub-subclass of any given enzyme-catalyzed reaction , allow a quick assignment of new reactions and additionally serve in a retrospective quality control of existing EC-numbers . With ca . 4000 existing EC-numbers this can certainly not be done by hand . In this article we present an efficient and reliable strategy for the automatic classification of enzyme-catalysed biochemical reactions based on the chemical structure of the involved substrates and products .
With one of the authors being the present chairman of the NC-IUBMB it is planned to use this and related tools to identify and remove errors and inconsistencies in the current EC-system and to optimise the system in a transparent and stable way . We plan to develop a tool that assign EC sub-subclasses to new reactions , access to which will be provided to the scientific community in the Internet’ .
We used 3 , 788 different enzyme-catalysed reactions from an in-house-developed Database named BiReDa ( Biochemical Reaction Database ) . The database held exclusively error-free MDL/MOL files as well as stoichiometrically and stereochemically correct reaction data from the BRENDA Database [7] and the KEGG LIGAND database [5] , which have been corrected manually or automatically , if required . The key idea of this approach is to reproduce the classification system given by the IUBMB as closely as possible and not to create new classification rules . The underlying procedure is divided into two steps: | The fundamental understanding of metabolism in organisms which can only be achieved by integrated studies on their biology using a systems biology approach will aid in the design of future metabolic engineering strategies . Metabolic network reconstruction provides insight into the molecular mechanisms of a particular organism . An annotated genome containing the specific metabolic genes found in a particular organism can be used to reconstruct its metabolic network . The correlation between the genome and metabolism is made by searching gene databases or by searching protein databases with a known EC number in order to find the associated gene . The success of the search process is critically dependent upon the consistency and reliability of the underlying data . Therefore we have developed tools which can be used to identify wrong or inconsistent classification of enzymes and help to remove them from the relevant search databases . | [
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| 2010 | Automatic Assignment of EC Numbers |
Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms . We present DEAP , Differential Expression Analysis for Pathways , which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data . DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway . On simulated data , DEAP significantly outperformed traditional methods: with high differential expression , DEAP increased power by two orders of magnitude; with very low differential expression , DEAP doubled the power . DEAP performance was illustrated on two different gene and protein expression studies . DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted . On the interferon study , DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway .
Genes and proteins can be grouped into different categories on the basis of many traits: sequence , function , interactions , etc . . Grouping genes by biological pathway is often the most relevant approach to biologists . For this study , we represent biological pathways as directed graphs , where the nodes are biological compounds and the edges represent their regulatory relationships , either catalytic or inhibitory . A catalytic edge exists when expression of the parent node increases expression of the child node ( i . e . A3 is a parent to child A4 with a catalytic edge , Figure 1 ) . In an inhibitory relationship , expression of the parent node decreases expression of the child node ( i . e . A1 is a parent to child A4 with an inhibitory edge , Figure 1 ) . Further , we define a path as a connected subset of the pathway ( i . e . A3A4A7 is a path , A1A2A3 is not , Figure 1 ) . We use the term path to signify either a simple path or a simple cycle , where the term simple implies no repeated nodes . While biological pathways have long been known , recent experimental data and computational advances have elucidated many previously uncharacterized mechanisms . Repositories contain information about thousands of biological pathways , with each pathway containing up to several hundred proteins [11]–[14] . Identifying the handful of pathways most relevant to a particular data set is an important challenge . The primary assumption of this paper is that biologically relevant pathways are characterized by co-regulated differential expression of their paths . Currently , the most popular approach to connect expression data to pathways is through gene set analysis . Gene set analysis methods consider sets of genes simultaneously as opposed to the gene-by-gene basis commonly used in differential expression analysis . One of the most prominent set-based methods is Gene Set Enrichment Analysis ( GSEA ) , where the identified genes are ranked based on expression values [15] , [16] . Significance of enriched gene sets is determined from a maximum running sum , which is calculated for each gene set by simultaneously walking down the ranked gene list and incrementing or decrementing the score on the basis of set membership . Other approaches calculate set based scores through different metrics and distributions [17]–[21] . Some of these methods compare gene sets relative to others ( known as enrichment analysis or competitive approaches ) while others compare individual gene sets across conditions without regard for other sets ( known as self -contained approaches ) [22] . The major limitation of set-based approaches in their application to pathway datasets is that they neglect the graph structure of the pathway . For example , in Figure 1 , sporadic patterns of expression in nodes A1 . . A8 would prevent identification of significant differential expression by set analysis . Considering the additional information contained in the edges , it becomes clear that A3A4A7 represents a path with similar differential expression from reactants to products . Consequently , A3A4A7 represents a differentially expressed path and may possess biological significance , but is unlikely to be identified as such by set based approaches . We define pathway analysis as any approach which identifies patterns of differential expression in a data set by considering pathway structure . In pathway analysis , researchers are generally interested in identifying pathways associated with a biological condition and determining the components of those pathways that explain the association . Thus , hypothesis testing can be viewed as a two-step procedure: first , test an entire pathway for differential expression; second , identify the path providing the greatest contribution to that differential expression . Recent approaches to pathway analysis test the generic hypothesis of a pathway differential expression without identifying specific paths [23]–[31] . One of the most popular methods for pathway analysis , signalling pathway impact analysis ( SPIA , Table 1 ) combines a set analysis score with a cumulative pathway score [23] , [24] . The pathway score is calculated by summing all edges in the graph . Catalytic and inhibitory relationships are considered by using a multiplier on the expression values . While this score takes into consideration the graph structure of pathways , it includes all possible paths , rather than just differentially expressed paths . For example , in Figure 1 , the SPIA score would be based on the combination of path scores for A3A4A7 , A1A4A7 , A2A5A7 , A3A6A8 , and A3A6A7 and the set score for A1 . . A8 . Protein interaction permutation analysis , designed for siRNA experiments , calculates the significance of the number of interactions in a network for which both genes are “hits” [25] . Recently , Zhao et al . introduced an approach that includes pathway structure in the analysis of genome wide association studies [26] . However , neither of these methods are directly applicable to expression data . Other pathway analysis approaches calculate set enrichment scores , but weight gene products based on their correlation with neighboring genes in the pathway [27] , [28] . Alternatively , other approaches integrate omics data over pathways , but encode all expression data as −1 , 0 , or 1 , limiting the information utilized from experimentation [29] . A mixed linear model presents an advanced approach to the hypothesis test , but is limited to acyclic models and implementation remains complex [30] . Like SPIA , all of these approaches account for the pathway structure as a whole , rather than identifying differentially expressed paths . To our knowledge , popular commercial pathway tools ( i . e . Ingenuity Pathway Analysis , BioBase , GeneGo , Metacore , Ariadne ) currently offer no methods that directly incorporate pathway analysis . High-throughput data analysis typically falls into the category of p>>n problems , where the number of genes or proteins , p , is considerably larger than the number of samples , n . Pathway and gene set analysis methods have the added complexity that gene expression within pathways is often highly correlated . Therefore , the statistical analysis approaches described above typically rely on random permutations of biological replicates in order to preserve expression correlation structure . However , the small sample size limits the number of possible permutations and , hence , the precision of p-value estimates . In addition , permutation tests are only applicable to simple experimental designs . Utilizing a random rotation approach circumvents these issues [32]–[34] . In this study , we present a new pathway analysis method , Differential Expression Analysis for Pathways ( DEAP ) . The primary assumption of DEAP is that patterns of differential expression in paths within a pathway are biologically meaningful . DEAP calculates the path within each pathway with the maximum absolute running sum score where catalytic/inhibitory edges are taken as positive/negative summands . To assess the statistical significance , we use a random rotation . Similar to other pathway analysis methods , DEAP tests a generic hypothesis of overall pathway differential expression . Contrary to current methods , DEAP identifies the most differentially expressed path to provide a refined focus for further biological exploration .
As illustrated in Figure 2 , the DEAP algorithm begins by overlaying expression data onto the pathway graph ( Figure 2 . 1 ) . Every possible path from the graph is independently examined ( Figure 2 . 2 ) . A recursive function calculates the differential expression for each path by adding or subtracting all downstream nodes with catalytic or inhibitory relationships , respectively ( Figure 2 . 3 ) . As an example , the score for the path containing all nodes in the inhibitory string in Figure 3 ( left ) , where green = +1 and red = −1 , is calculated as: ( 1 ) The absolute value of the expression level is utilized as the DEAP score ( Figure 2 . 4 ) to determine the path with maximal differential expression ( Figure 2 . 5 ) . The DEAP algorithm returns both the maximum absolute value and the path associated with that maximum value . The algorithm is formalized in Methods: DEAP Algorithm . DEAP scores for different pathways are not directly comparable due to size and structure differences among pathways . Thus , we employ a self-contained approach which individually assesses the significance of each pathway . Generating a null distribution is complicated by the low number of samples relative to gene identifications and the correlation of gene expression within pathways . Most existing approaches use permutation tests to preserve the correlation between genes; however , small sample size limits their effectiveness . We use random rotation to circumvent these issues [32]–[34] . Our random rotation implementation is applicable to a wide range of complex experimental designs with multiple conditions and replicates . The significance levels are adjusted for multiple comparisons using the false discovery rate method of Storey and Tibshirani [35] . For each pathway in the analysis , DEAP outputs its score , the corresponding p-value , and the path with the maximum absolute score ( see examples in Files S1 , S2 ) . The open source implementation ( licensed under the GNU Lesser General Public License v3 . 0 ) of this algorithm is available in Supplemental Materials ( File S3 ) . Data from the five pathways illustrated in Figure 3 were simulated as described in Methods . Algorithmic performance was measured in terms of power , the percentage of times each differentially expressed pathway was identified as significant ( p<0 . 05 ) , which is equivalent to one minus the type II error rate . The power of DEAP was compared to GSEA and SPIA , the two most popular gene set and pathway analysis methods , respectively . Comparative analysis of these methods included four key parameters: the overall effect ( mean of ‘on’ genes , μ ) , variation in individual gene effects ( σ2g ) , sample size ( n ) , and type I error rate . Regardless of the level of differential expression , DEAP was consistently more powerful than were other approaches ( Figure 4 ) . For small μ values ( low differential expression ) , the power of DEAP was approximately twice that of GSEA and SPIA , demonstrating improved sensitivity . For μ = 1 ( high differential expression ) , DEAP had an increase in power over both GSEA and SPIA of two orders of magnitude . At μ = 1 . 25 , the performance of SPIA improved substantially , approaching that of DEAP on all pathways except the long alternate route where SPIA was confounded by noise ( Figure S1 ) . Across the board , GSEA performed poorly because GSEA did not consider pathway structure and is dependent on comparisons to other pathways . Sample size and within-gene variance also have significant effects on the performance of the algorithms . As sample size ( n ) grew , the power of DEAP relative to other approaches increased , particularly in pathways containing inhibitory edges ( Figure 5 ) . As variance ( σ2g ) increased , DEAP exhibited minor increases in power ( Figure 6 ) . Further , DEAP consistently outperformed GSEA and SPIA as variance increased . To estimate the type I error rate , we simulated random data under the null hypothesis ( μ = 0 , σ2g = 0 , n = 10 ) . The plots in Figure 7 displays type I error rates with respect to the nominal values . SPIA was notably more conservative for every pathway structure . The performance of both GSEA and DEAP was on target; however , DEAP was more conservative on pathways with inhibitory edges ( Figure S4 ) . An additional advantage of DEAP is the ability to identify the maximally differentially expressed path of the pathway . For the simulated data with μ = 1 and μ = 2 , DEAP identified the entire differentially expressed path 99% and 100% of the time , respectively . For example , the long alternate route contains 14 proteins , but DEAP identified the differentially expressed region that contains only four , substantially reducing the search space . In addition to comparing DEAP to GSEA and SPIA , we compared DEAP to several modifications of the DEAP algorithm , which were altered as follows: scores normalized by pathway length; all weights set to +1; and sum taken across the entire pathway . We also compared DEAP to a set-based implementation with rotation . DEAP had substantially higher power than all four approaches ( Table S1 and Figures S1 , S2 , S3 , S4 ) . While simulated pathways provide easily controllable examples to validate DEAP as an appropriate test of the hypothesis , biological pathways bring increased complexity from which the signal must be detected . To validate DEAP on more realistic pathway structures , we simulated activity on biological pathways from the KEGG and Reactome databases [13] , [14] . In the case of KEGG [13] , we simulated data on the TGF-ß signaling pathway to indicate activity in the TGF- ß receptors leading to cell cycle arrest ( Figure 8 ) . In terms of sensitivity to the pathway effect ( μ ) , variance ( σ2g ) , and sample size , DEAP outperformed both GSEA and SPIA on the TGF-ß signaling pathway ( Figure 8 ) . Notably , increased variance diminishes the power of SPIA , but does not affect DEAP , reflecting its ability to identify signal in the noisy environments common in biological experimentation . In the case of Reactome [14] , we simulated data on the post-transcriptional silencing by small RNAs pathway from to indicate RNA cleavage ( Figure 9 ) . DEAP had superior performance over GSEA and SPIA in terms of all tested variables: pathway effect ( μ ) , variance ( σ2g ) , and sample size ( Figure 9 ) . In both sets of simulated data on real biological pathways , the type I error estimate was conservative for DEAP , GSEA , and SPIA ( Figure S5 ) . In addition to DEAP , GSEA , and SPIA , we applied the four alternative formulations of DEAP to both sets of biological pathways and noted the consistently strong performance of DEAP ( Figures S6 , S7 ) . To verify that the simulated data effects are biologically relevant , we also applied DEAP to two sets of biological data on biological pathways . The experimental data are from a transcriptomic study of interferon [36] , [37] and a proteomic study of chronic obstructive pulmonary disease ( COPD ) . We applied DEAP , GSEA , and SPIA to identify differentially expressed pathways from the PANTHER database [11] . Pathway associations with the phenotypes were determined based on a literature review using Google Scholar ( details in Methods: Biological Data Validation ) . We analyzed a microarray expression data of cells of radio-insensitive tumors that had been treated with interferon [36] , [37] . DEAP identified six pathways with known literature associations with interferon while GSEA identified five and SPIA identified none ( Table 2 ) . The two most clearly relevant pathways for this transcriptomics data set were interferon gamma signalling , as the cells had been stimulated with interferon; and JAK STAT signalling , the pathway being studied by the authors of the microarray study [36] , [37] . Unlike GSEA and SPIA , DEAP identified these pathways as significantly differentially expressed . The lack of overlap between the pathways identified by GSEA and DEAP is indicative of the different hypotheses being tested by these two approaches , with GSEA focusing on non-specific differential expression among pathway genes and DEAP focusing on differential expression among pathway connected genes . As such , these two approaches should be viewed as complementary approaches that can be simultaneously utilized to augment biological discovery . Additionally , DEAP analysis of the interferon transcriptomics data uses path identification to reduce the search space for future experimentation . Consider the Notch signalling pathway , which contains 26 proteins and is known to be activated by interferon treatment [38] . GSEA and SPIA both did not identify Notch signalling as significantly differentially expressed due to generally sporadic expression patterns . However , DEAP analysis focused on consistent differential expression of 4 connected nodes and labelled Notch signalling as significantly differentially expressed ( Figure 10 ) . Without identifying the maximally differentially expressed path , the Notch signalling pathway would have been overlooked . Further , future experimentation can now focus on those four proteins exhibiting the most significant differential expression . In order to illustrate DEAP on a different data type , we also analyzed a proteomics study which compared healthy smokers with patients diagnosed with COPD ( Methods: Biological data , Table 3 ) . On this data set , GSEA identified nine pathways , four of which had apparent associations with COPD . SPIA identified only one pathway with significant differential expression . DEAP identified 12 pathways and eight had literature-verified implications with COPD . Of notable clinical relevance to COPD is the inflammation mediated by chemokine and cytokine signalling pathway , which was identified only by DEAP [39] .
DEAP takes into account the graph structure of a pathway and determines the maximally expressed path . Pathway-centric analysis by DEAP is complementary to set-based analysis of other functional categories , as seen in both biological examples ( Tables 2–3 ) . Application of the random rotation approach allows for accurate assessment of statistical significance of the DEAP scores . On simulated data for simulated pathways , DEAP both increased power over existing approaches and accurately controlled the false positive rate . With high differential expression , this translated to a two-fold increase in the power of DEAP over GSEA and SPIA . On simulated data applied to real biological pathways , DEAP showed the strongest performance for all levels of pathway effect , variance , and sample size . Analysis of experimental transcriptomic and proteomic data indicates that DEAP identified important pathways related to a particular disease or condition where other approaches failed , specifically identifying six pathways related to interferon and eight related to COPD . Further , DEAP uniquely identified the most expressed path of the pathway with 100% accuracy in simulated data . Though we demonstrated DEAP on transcriptomics and proteomics studies , DEAP is widely applicable to other omics research areas ( metabolomics , lipidomics , etc . ) and expression technologies ( next generation sequencing , RNAseq , etc . ) . This broad applicability extends from the flexible design of DEAP: the only required inputs are expression levels of biomolecules and corresponding pathways . Appropriate scaling of the expression levels is defined by the user . For instance , RNAseq data is very similar to spectral count proteomics data in that they are both count-based . Thus , RNAseq read counts can be used as input for DEAP in the same manner as peptide spectral counts . Further , RNA transcripts can be used in place of proteins . To identify the most important pathways for further study , pathways can be ranked based on DEAP score significance . Specifically , future studies can be focused on the most differentially expressed paths within the pathways with the lowest false discovery rate , which can be especially beneficial when studying pathways that contain hundreds of biological compounds . Currently , DEAP is being integrated with our proteomics analysis pipeline SPIRE ( http://proteinspire . org ) and expression database MOPED ( http://moped . proteinspire . org ) [10] , [40] ( Table 1 ) . Application of DEAP to existing and future studies has the potential to discover meaningful biological patterns .
Expression data ( presumably on a log scale ) for each gene in a pathway was simulated using a multivariate normal distribution defined in Equation 2: ( 2 ) In this equation d is the indicator of whether a gene is ‘on’ or ‘off’ . The value of d is 0 if the gene is ‘off’ and +1 if the gene is up-regulated and ‘on’ and −1 if the gene is down-regulated and ‘on’ . The value of d is determined by the predefined pathways . The variable μ is the mean of the absolute value of expression for ‘on’ genes and , therefore , represents the ‘pathway effect’ . The value of μ is held constant for each gene in the pathway and across replicate samples . The variable g is assumed to come from a normal distribution with mean 0 and variance σ2g . The variance σ2g measures how much individual gene expression deviates from the overall ‘pathway effect’ , μ . The value of g is randomly generated ( although in many of the simulations is set to 0 ) for each gene in the pathway , but the same value is used for replicate samples . The variable e is assumed to come from a normal distribution with mean 0 and variance 1 and represent random variation in gene expression . The value of e is randomly generated for each combination of gene and sample . The simulations varied the values μ , σ2g , and the sample size ( number of independent samples of pathway data ) . R scripts were used to generate the simulated data [41] . Five diverse pathways were specifically created to test the efficacy of identification by different scoring methods ( Figure 3 ) . Gray colored nodes had unaltered values from a standard normal distribution . Nodes labelled as green and red were sampled with μ values of +X and −X , respectively , where X was a positive number . Simulated data and pathways are available on Dryad: doi:10 . 5061/dryad . qh1pg . Microarray data from a study of cells treated with interferon were acquired from the Gene Expression Omnibus ( GDS3126 ) [6] . The sample was taken from radio-resistant tumors following treatment with a mixture of interferons [36] , [37] . It was hypothesized that interferon and biochemically-related pathways would be stimulated in this data set . The expression value was the logarithm of the case/control ratio . Though microarrays measure mRNA expression , the pathways represent information in terms of proteins . Therefore , the gene identifiers in the microarray data were mapped to UniProt protein identifiers using the UniProt website [42] . Handling the one-to-many relationship of genes and proteins is discussed below ( see Methods: DEAP ) . When duplicate probes existed for the same gene , the expression value utilized for the gene was the arithmetic mean of these probes . The COPD proteomics data can be found at PeptideAtlas ( raw data ) [7] and MOPED ( processed data ) [10] ( moped . proteinspire . org ) . We analyzed data from CD4 and CD8 T-lymphocytes . The control patients were healthy smokers , with an average FEV1/FVC of 82 . 5% . Case patients had been medically diagnosed with COPD and had an average FEV1/FVC of 42 . 0% . A total of 10 cases and 10 controls were utilized in this analysis . Additional experimental details can be found associated with the PeptideAtlas accession numbers in Table S3 . On MOPED , data is stored under the experimental name “steffan_copd . ” The tandem mass spectrometry data were analyzed through SPIRE with the parameters in Table S4 [40] . Protein expression was measured by the number of peptide spectral matches identified for each protein normalized by the total number of spectra in the sample . For pathway analysis , we used the difference between the log normalized expression values . Pathway data were downloaded from the PANTHER database [11] . A total of 165 pathways downloaded in SBML format from PANTHER pathway version 3 . 01 . PANTHER pathways contain information about proteins , biochemicals , and other substrates . For the purposes of data interpretation , the pathways were broken into their protein components using an internally developed python script where connections of proteins through biochemical substrates were maintained as protein-protein interactions PANTHER's internal identifiers were mapped to UniProt identifiers . Ultimately , parsing of the PANTHER pathway database resulted in a graph structure in which each node represented a set of proteins that act as a set of reactants and/or products . Inhibitory or catalytic edges between two sets of proteins were determined as detailed in PANTHER . We used random rotation approach to estimate the null distribution of the test statistics and compute the p-values [32] . Rotation testing has been used recently in gene set analysis as an alternative to permutation and parametric tests [33] , [34] . Rotation tests have an advantage over permutation tests in that they produce reasonable results for small sample sizes and complex experimental designs . Rotation testing assumes that pathway and set data come from independent random samples of a multivariate normal distribution with mean zero under the null hypothesis . A rotation test is carried out by multiplying the original data by a random rotation matrix , calculating the test statistic , and repeating the procedure to generate a null distribution . Adjustments for an overall mean , covariates , or blocking factors are handled by performing the rotations of an orthogonal projection of the original data on to the residual space from a linear model and then transforming the rotated data back . A random rotation matrix was generated by first generating a matrix X of standard normal random variables and then taking the rotation matrix to be the orthogonal matrix Q from the QR decomposition of X . Scripts to carry out rotation testing were written using the R programming language and are available in File S3 , released under the GNU Lesser General Public License v3 . 0 . The user is able to input a custom design matrix which accounts for complex experimental designs with multiple conditions and replicates . Given: a current edge , all other edges in graph , expression values for all proteins: For single channel ( unpaired ) data , define E ( x ) to be the difference between the logarithm of the arithmetic mean of expression values associated with protein x in the two conditions . For two channel ( paired ) data , define E ( x ) to be the arithmetic mean of the log expression ratio ( s ) associated with protein x . The recursive function operates as follows: In DEAP , the maximum order ( by absolute value ) path is used to test the null hypothesis about the expression of the entire pathway . This claim , that the expression of one path answers questions about the expression of the pathway , is justified on two levels . On a biological level , significant fluctuations in activity do not require differential expression of an entire pathway . For example , in Figure 1 , A3A4A7 represents a path with similar expression levels that proceeds all the way from reactants to products , a pattern that seems to be significant . From a logical perspective , consider a pathway , P , as the union of all paths of the pathway , P1 , P2 , … , PK . Each path is completely defined by its set of edges . Note that the k-paths are not entirely disjoint in the sense that some paths might share the nodes and the edges . However , we require each path to have a distinct set of edges . To test the hypothesis of a differentially expressed pathway requires testing whether any of the constituent paths is differentially expressed . This corresponds to testing the family of k-null hypothesis . To control the family wise error rate , we use a maximum order statistic , since the probability of making at least one incorrect decision under the null is equivalent to the probability of the maximum order statistic exceeding the threshold . To approximate a null distribution of the test statistic , s* , we performed n rotations of the data . For each rotation sample , we recompute the DEAP score , si . The p-value is calculated as a proportion of scores that are at least as extreme as the observed score , the proportion of simulated DEAP scores whose value are greater than or equal to the observed DEAP score: The DEAP algorithm was implemented to allow for efficient computation . By maintaining global maximum and minimum values and updating their values as the recursive function proceeds , it is not necessary to examine all paths of the graph independently . Rather , we can initialize DEAP score calculations only at leaf edges , which have no upstream edges pointing to any proteins in their reactant set . To ensure that closed cycles are not missed , we track the edges which have been visited and examine additional edges until the difference of the complete edge set and the already visited edge set is empty . This greatly reduces the number of calculations per graph . Once the recursive function has returned a maximum and minimum score for a particular edge , that score will remain constant regardless of the preceding edge except in the case of cycles ( see paragraph below ) . Therefore , we use a dictionary mapping edges to maximum and minimum scores to prevent duplicative score calculations . After this implementation , score calculations that took several hours on particularly complex pathway structures completed in seconds . In the case of cycles , scores may be dependent on the node of the cycle which is examined first . For these cycles , our current implementation represents a heuristic estimator rather than the exact optimal solution . Bidirectional edges are subject to this same limitation as they are equivalent to a two node cycle . Implementations that determined the exact optimal solution were prohibitively slow for practical application . Except in edge cases , the heuristic implementation will provide approximations of sufficient quality to identify significant patterns of differential expression . Every DEAP score calculation is independent of other DEAP score calculations , so we set up processing for multi-threading . For example , on a 64-bit Intel Core i7-2720QM CPU with 8GB RAM , speed improvements of approximately 4-fold were noted for the score calculation process . Specific running time is highly dependent on expression data set size , experimental design , pathway complexity , and number of rotation testing iterations . Running DEAP on 90 simulated data files each with 10 samples , 1000 proteins , 1000 pathways , and performing 100 data rotations took 72 minutes when multi-threaded and 260 minutes when performed on a single thread . The function tracks edges that have already been examined in a particular recursive cycle to prevent entrance into infinite loops in cyclical pathways . To control for duplicate protein identifiers , summations over the products and reactants were performed on the set of unique expression values rather than for every identifier . For example , if protein A and protein B both had expression levels of 1 . 743 and were both in the same protein set , then it was assumed they were the result of data duplication and 1 . 743 was only added to the score once . This duplication elimination was implemented primarily due to issues arising from redundant protein identifiers and potential mRNA translation into multiple proteins . For instance , the five UniProt identifiers for variants of Histone H3 ( Q6NXT2 , P68431 , Q16695 , Q71DI3 , and P84243 ) are included in the same PANTHER pathway unit and share near identical protein sequences , so their proteomic and transcriptomic identification will be duplicated . The algorithm was implemented in Python and is available in File S3 , released under the GNU Lesser General Public License v3 . 0 . Accuracy of pathway associations with experimental conditions were validated using a Google Scholar literature search . The literature search was performed by searching Google Scholar ( http://scholar . google . com ) for a combination of the pathway name and details of the experimental condition . We continued searching Google Scholar until satisfied that the association was confirmed or felt reasonably certain that there was not yet a literature confirmed association . Once a literature association was confirmed , the most pertinent reference was retained and cited in this manuscript . The DEAP approach is based on the following fundamental assumptions: The GSEAlm package for the R Project , available through BioConductor , was utilized to perform GSEA analysis [44] . Pathways were transformed into a gene set matrix and multi-sample expression data were loaded appropriately . Since GSEA performs test for up- and down-regulation independently , the minimum of these two values was taken and multiplied by two to adjust for a two-tail test . SPIA analysis was performed using the SPIA package for the R Project , available through BioConductor [45] . To convert the pathways into the SPIA format , inhibitory and catalytic relationships were formatted into the inhibition and activation matrices , respectively . Since the SPIA implementation only allowed input of single expression ratios , the arithmetic mean of expression values for each protein was input into SPIA . | The data deluge represents a growing challenge for life sciences . Within this sea of data surely lie many secrets to understanding important biological and medical systems . To quantify important patterns in this data , we present DEAP ( Differential Expression Analysis for Pathways ) . DEAP amalgamates information about biological pathway structure and differential expression to identify important patterns of regulation . On both simulated and biological data , we show that DEAP is able to identify key mechanisms while making significant improvements over existing methodologies . For example , on the interferon study , DEAP uniquely identified both the interferon gamma signalling pathway and the JAK STAT signalling pathway . | [
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| 2013 | Differential Expression Analysis for Pathways |
To be transmitted to vertebrate hosts via the saliva of their vectors , arthropod-borne viruses have to cross several barriers in the mosquito body , including the midgut infection and escape barriers . Yellow fever virus ( YFV ) belongs to the genus Flavivirus , which includes human viruses transmitted by Aedes mosquitoes , such as dengue and Zika viruses . The live-attenuated YFV-17D vaccine has been used safely and efficiently on a large scale since the end of World War II . Early studies have shown , using viral titration from salivary glands of infected mosquitoes , that YFV-17D can infect Aedes aegypti midgut , but does not disseminate to other tissues . Here , we re-visited this issue using a panel of techniques , such as RT-qPCR , Western blot , immunofluorescence and titration assays . We showed that YFV-17D replication was not efficient in Aedes aegypti midgut , as compared to the clinical isolate YFV-Dakar . Viruses that replicated in the midgut failed to disseminate to secondary organs . When injected into the thorax of mosquitoes , viruses succeeded in replicating into midgut-associated tissues , suggesting that , during natural infection , the block for YFV-17D replication occurs at the basal membrane of the midgut . The two barriers associated with Ae . aegypti midgut prevent YFV-17D replication . Our study contributes to our basic understanding of vector–pathogen interactions and may also aid in the development of non-transmissible live virus vaccines .
Arboviruses , which are transmitted among vertebrate hosts by blood-feeding arthropod vectors , put billions of people at risk worldwide . Viral infection in arthropods is usually persistent . Following uptake of an infectious blood meal by a female mosquito , arbovirus must initiate a productive infection of the midgut epithelium , which consists of a single layer of cells [1] . To develop a disseminated infection , virus must then escape the midgut into the haemocoel and infect secondary tissues such as the fat body , trachea and the salivary glands [1] . Finaly , the virus needs to be released into salivary ducts for horizontal transmission to an uninfected vertebrate host [1] . Traditional means of controlling the spread of arbovirus infection include mosquito control and vaccination of susceptible vertebrates . However , in many cases , these measures are either unavailable or ineffective . To successfully implement the strategy of blocking the virus at the arthropod stage , further knowledge of the virus/vector interactions is required . Flaviviruses constitute the most important and diverse group of arthropod-transmitted viruses causing diseases in humans . They are 50 nm-diameter enveloped viruses harboring a single positive-strand RNA genome of around 11 kb . The genome encodes a polyprotein that is cleaved into seven non-structural ( NS ) proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) and three structural proteins: capsid ( C ) , pre-membrane/membrane ( prM/M ) and envelope ( Env ) . The C , M , and Env proteins are incorporated into virions , while NS proteins are not [2 , 3] . NS proteins coordinate RNA replication , viral assembly and modulate innate immune responses . Several members of the Flavivirus genus , such as dengue virus ( DENV ) , yellow fever virus ( YFV ) and Zika virus ( ZIKV ) are highly pathogenic to humans and constitute major global health problems . YFV is responsible for viral hemorrhagic fever resulting in up to 50% fatality [4] . Despite the existence of the safe and effective live-attenuated vaccine YFV-17D , YFV regularly resurges in the African and South American continents , as illustrated by recent outbreaks in Brazil and equatorial Africa [5–7] . The YFV-17D vaccine has been used safely and efficiently on a large scale since the end of World War II [8] . It was developed in the 1930’s by passaging the blood of a human patient in rhesus macaques and later in mouse and chicken embryo tissues [9] . A single dose confers protective immunity for up to 35 years . During the attenuation process , the virus has lost its neurotropic and viscerotropic properties , which account for the major disease manifestations of yellow fever in primates [10 , 11] . The molecular determinants responsible for its virulence attenuation and immunogenicity are poorly understood . We have recently shown that YFV-17D binds and enters mammalian cells more efficiently than a non-attenuated strain , resulting in a higher uptake of viral RNA into the cytoplasm and consequently a greater cytokine-mediated antiviral response [12] . This differential entry process may contribute to attenuation in humans . YFV-17D is also used as a platform for engineering vaccines against other health-threatening flaviviruses , such as vaccines against Japanese encephalitis virus ( JEV ) , West Nile virus ( WNV ) , the four serotypes of DENV , and , more recently , ZIKV [13–16] . These vaccines consist in a YFV-17D backbone in which sequences coding for prM/E proteins are replaced by those of the selected flavivirus . Some of these live-attenuated chimeric vaccines are commercially available [17 , 18] , with variable success [19] . YFV-17D is thus a key component in controlling flaviviral disease and it must not disseminate in mosquitoes . Early studies have shown , using almost exclusively viral titration by plaque assays , that YFV-17D can infect Aedes aegypti midgut [20 , 21] , but does not disseminate to other tissues and fails to be transmitted to a novel host . Here , we re-visited this question using a variety of techniques and showed that not only the midgut escape barrier , but also the midgut infection barrier , restrict YFV-17D replication in its vector .
The YFV-17D vaccine strain ( YF-17D-204 STAMARIL , Sanofi Pasteur , Lyon ) was provided by the Institut Pasteur Medical Center . The YFV-DAK strain ( YFV-Dakar HD 1279 ) was provided by the World Reference Center for Emerging Viruses and Arboviruses ( WRCEVA ) , through the University of Texas Medical Branch at Galveston , USA . Viral stocks were prepared on Vero cells , concentrated by polyethylene glycol 6000 ( Sigma ) precipitation and titrated on Vero cells by plaque assay as described previously [22] . The Aag2 mosquito cell lines ( provided by the teams of M . Flamand and L . Lambrechts , Institut Pasteur , Paris ) are derived from larvae of Aedes aegypti . They were cultured in a humid chamber at 28°C , with no CO2 , in Leibovitz medium ( Gibco Leibovitz's L-15 Medium , Life Technologies ) supplemented with 10% fetal bovine serum ( FBS ) , 2% tryptose phosphate buffer ( Gibco Tryptose Phosphate Broth 1X , Life Technologies ) , 1/100 dilution of the penicillin-streptomycin ( P/S ) stock ( final concentration of 100 units/mL and 100 μg/mL , respectively ) ( Sigma ) and non-essential amino acids ( GibcoTM NEAA 100X MEM , Life Technologies ) . Vero cells , which are African green monkey kidney epithelial cells , were purchased from the American Type Culture Collection ( ATCC ) and used to perform viral titration . They were maintained in Dulbecco's modified Eagle's medium ( DMEM , Invitrogen ) , supplemented with 10% FBS and 1% P/S . Env MAb 4G2 hybridoma cells were kindly provided from P . Desprès ( La Réunion University , Sainte Clotilde ) . Anti-YFV-NS4B and anti-DENV NS1 17A12 ( that recognize YFV-NS1 ) antibodies , were kind gifts from C . M . Rice ( Rockefeller University , NY ) [23] and M . Flamand ( Institut Pasteur , Paris ) [24] , respectively . Anti-actin ( A1978 , Sigma ) and anti-tubulin ( T5168 , Sigma ) antibodies were used as loading controls for mosquito organs and Aag2 cells , respectively . Secondary antibodies were as followed: anti-mouse 680 ( LI-COR Bioscience ) , anti-rabbit 800 ( Thermo Fisher Scientific ) and anti-rabbit Cy3 ( Life Technologies ) . The Paea strain of Ae . aegypti is a laboratory colony originated from mosquitoes collected in French Polynesia in 1960 and conserved in the laboratory since 400–450 generations . Adult mosquitoes were maintained at 25 ± 1°C and 80% relative humidity with a light/dark ratio of 12 h/12 h . The larvae were provided with brewer’s yeast tablets and adults were given continuous access to 10% sucrose solution . Sucrose was removed 24 h prior to the infectious blood meal . The infectious blood meal was comprised of half-human blood and half-viral suspension ( 4 . 107 PFU/mL in the mix ) . The blood donors were randomly selected from a population of healthy volunteers donating blood at the ‘Etablissement Français du Sang’ ( EFS ) , within the framework of an agreement with Institut Pasteur . Experimental procedures with human blood have been approved by EFS Ethical Committees for human research . All samples were collected in accordance with EU standards and national laws . Informed consent was obtained from all donors . Seven day-old female mosquitoes were allowed to feed for 15 min through a collagen membrane covering electric feeders maintained at 37°C ( Hemotek system ) . Blood-fed females were selected and transferred into cardboard boxes protected with mosquito nets . Alternatively , ice-chilled mosquitoes were injected intrathoracically with twice 69 nL of viral stock ( 2 . 5x104 PFU ) with a micro-injector ( Drummond , Nanoject II ) . Mosquitoes were anesthetized on ice at various time-points after infection . They were passed through a 70% ethanol bath and then in a PBS bath before being dissected in a drop of PBS under a magnifying glass using tweezers . The midguts , legs and salivary glands were removed and placed in individual tubes containing sterilized glass beads of a diameter of 0 . 5 mm ( Dutscher ) in a suitable lysis buffer . Experiments were reproduced in triplicate with 5–10 mosquitoes collected at each time-point for dissection . The mosquito midguts , legs or salivary glands were crushed using a tissue homogenizer ( Ozyme , Precellys Evolution ) during twice 15 s at 1000 g . Total RNA was extracted from mosquito tissues with the NucleoSpin RNA II kit ( Macherey-Nagel ) . YFV RNA was quantified using NS3-specific primers and TaqMan probe ( NS3-For CACGGCATGGTTCCTTCCA; NS3-MFAM CAGAGCTGCAAATGTC; NS3-Rev ACTCTTTCCAGCCTTACGCAAA ) with TaqMan RNA-to-CT 1-Step ( Thermo Fisher Scientific ) on a QuantStudio 6 Flex machine ( Applied Biosystems ) . Genome equivalent ( GE ) concentrations were determined by extrapolation from a standard curve generated from serial dilutions of total YFV RNA of a known concentration . Individual midguts and salivary glands were collected in RIPA buffer ( Sigma ) containing protease inhibitors ( Roche Applied Science ) . Tissue lysates were normalized for protein content with Pierce 660nm Protein Assay ( Thermo Scientific ) , boiled in NuPAGE LDS sample buffer ( Thermo Fisher Scientific ) in non-reducing conditions and 32 μg ( midgut ) or 14 μg ( salivary glands ) of proteins ( corresponding to around 10 pooled organs ) were separated by SDS-PAGE ( NuPAGE 4–12% Bis-Tris Gel , Life Technologies ) . Separated proteins were transferred to a nitrocellulose membrane ( Bio-Rad ) . After blocking with PBS-Tween-20 0 . 1% ( PBST ) containing 5% milk for 1 h at RT , the membrane was incubated overnight at 4°C with primary antibodies diluted in blocking buffer . Finally , the membranes were incubated for 1 h at RT with secondary antibodies diluted in blocking buffer , washed , and scanned using an Odyssey CLx infrared imaging system ( LI-COR Bioscience ) . After dissection , individual midgut were deposited on slides , fixed in cold acetone for 15 min and rehydrated in PBS for 15 min . The midguts were then incubated for 2 h in Triton X-100 ( 0 . 2% ) . After washing with PBS , they were incubated for 30 min with PBS + 0 . 1% Tween 20 + 1% BSA . The slides were then incubated overnight at 4°C with anti-YFV-NS4B antibodies diluted 1:1000 in PBS . After washing with PBS , they were incubated for 1 h with secondary antibodies and washed with PBS . The actin network was visualized with phalloidin Alexafluor 488 ( Invitrogen ) . After washing , nuclei were stained using Prolong gold antifade containing 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Invitrogen ) . All preparations were observed with a confocal microscope ( ZEISS LSM 700 inverted ) and images were acquired with the ZEN software . Viral RNA was extracted from viral stocks ( around 1 . 4x109 genomes for YFV-17D and 3 . 4x108 for YFV-DAK ) using Trizol ( Ambion , TRIzol Reagent ) , were re-suspended in RNAse-free water and treated with DNAse with the DNA-free kit ( Ambion ) before being stored at -80°C . Synthesis of cDNAs was carried out with the Maxima H Minus First Strand kit ( Thermo Fisher Scientific ) from 250 ng of viral RNA . Three fragments of the viral genome were amplified by 25 rounds of PCR using the Phusion High-Fidelity DNA Polymerase kit ( NEB ) using primers mainly described previously [25] . New primers targeting the 3'-UTR of the genome were designed for optimal amplification of YFV-17D and YFV-DAK ( Table 1 ) . The PCR products were purified with the NucleoSpin Gel kit and PCR Clean Up ( Macherey-Nagel ) , resuspended in RNAse-free water and stored at -20°C . Libraries were prepared after pooling 400 ng of the three overlapping amplicons , which had a size between 3725 and 3891 pb . The PCR products were fragmented randomly with the NEBNext dsDNA fragmentase kit ( NEB ) and then purified with the AMPure XP Beads kit ( Beckman Coulter , Inc . ) . The Illumina sequencing libraries were prepared with the NEBNext Ultra DNA Library Prep kit ( NEB ) by selecting 400 bp fragments . NEBNext Multiplex Oligos for Illumina primers ( NEB ) were used . Purification was performed with the AMPure XP Beads kit . The Qubit dsDNA BR Assay kit ( Thermo Fisher Scientific ) was used for quantification . Samples from the library , diluted to 4 nM , were sequenced on a NextSeq 500 sequencer ( Illumina ) machine with the NextSeq 500 Mid Output Kit v2 kit ( 150 cycles ) ( Illumina ) , to generate single-end reads of 150 nt . The PhiX control library served as a quality and calibration control in sequencing runs ( Illumina , FC-110-3001 ) . Reads were trimmed for adapters and primer sequences . Low quality reads were filtered using Trim Galore ! ( www . bioinformatics . babraham . ac . uk/projects/trim_galore/ ) with the following parameters: quality 30 , length 75 and stringency 4 . Final reads quality was evaluated using FastQC ( www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) . Reads were aligned on the YFV-Asibi reference genome AY640589 . 1 using BWA [26] and SAMtools [27] . Consensus sequences were obtained using SAMtools mpileup , VarScan mpileup2cns ( min-var-freq 0 . 5 ) and BCFtools consensus [27] . When mapping YFV-DAK sequencing reads on the YFV-Asibi sequence , an uncomplete coverage was observed . We reiterated the mapping of YFV-DAK reads on this intermediate consensus sequence to obtain the final YFV-DAK consensus sequence . Consensus sequence of YFV-17D and YFV-DAK have been deposited on NCBI ( ID numbers MN10624 and MN106242 ) , and sequencing data have been uploaded on SRA ( SRA accession number PRJNA548475 ) . Variant determination was estimated using VarScan mpileup2snp ( min-var-freq 0 . 01 , strand-filter 0 ) with a cutoff of 3% . Data were analyzed using GraphPad Prism 7 . Statistical analyses were performed using two-tailed Fisher's exact test or Mann-Whitney test ( * p < 0 . 05; ** p < 0 . 01; *** p < 0 . 001; **** p < 0 . 0001 , ns , not significant . ) , as indicated .
The replication and dissemination of YFV-17D was studied in the Ae . aegypti strain Paea . The clinical isolate YFV-Dakar HD 1279 ( YFV-DAK ) , whose replication in rhesus macaque is well characterised [28] , was used as a positive control for these experiments . Virus produced on Vero cells were mixed with human blood to prepare a meal containing 4x107 PFU/mL of either YFV-17D or YFV-DAK . Five to ten mosquitoes were collected every 2–3 days until 14 days post-feeding ( dpf ) . Mosquitoes were dissected to separate the midgut from legs and salivary glands . Virus production in these tissues was first assayed by calculating the viral titer by plaque assays on Vero cells . Several whole mosquitoes were also analyzed 20 minutes after feeding to ensure that the mosquitoes ingested a similar amount of viral particles of both viral strains ( Fig 1A and 1B , black dots ) . Around 103 infectious particles of YFV-DAK were detected per midguts 3 dpf ( Fig 1A ) . Viral titers remained high in midguts until 14 dpf . YFV-DAK infectious particles were present in legs as early as 5 dpf and in salivary glands as early as 7 dpf ( Fig 1A ) . This replication pattern is comparable to that of South American and African YFV isolates in the strain Ae . aegypti AE-GOI [29] . Midgut of mosquitoes infection with YFV-17D produced 1 to 2 log less infectious particles than YFV-DAK at 3 dpf ( Fig 1B ) . Infectious particles were detected in a unique leg sample at 14 dpf . No virus was detected in salivary glands of mosquitoes infected with YFV-17D . Thus , by contrast to YFV-DAK , and in agreement with previous studies performed with the Ae . aegypti strains Rexville or Rexville-D ( Rex-D ) [21 , 30–32] , YFV-17D disseminated poorly in the strain Paea . Viral replication was assessed in the midguts , legs and salivary glands by measuring viral RNA quantity over-time by RT-qPCR ( Figs 1C , 1D and S1 ) . Total RNA was also extracted from several whole mosquitoes the same day of the feeding to insure that they had ingested similar amount of infectious particles from both viral strains ( Fig 1C and 1D , black dots ) . Around 107 copies of viral RNA were detected in midguts of mosquitoes infected with YFV-DAK since 3 days ( Figs 1C , S1A and S1C ) . The viral RNA copy number per midgut remained high until 14 dpf , indicating that viral replication had already reached a plateau at an early stage of infection ( Figs 1C , S1A and S1C ) . In agreement with titration assays ( Fig 1A ) , YFV-DAK RNA was detected in legs and salivary glands of mosquitoes around 7 dpf . The quantity of viral RNA detected in these secondary organs increased over time to reach on average 107 copies RNA in legs and 106 copies in salivary glands at 14 dpf ( Figs 1C , S1A and S1C ) . Around 5x105 copies of YFV-17D RNA was detected in 2 out of 5 midguts of blood-feed mosquitoes at 3 dpf ( Fig 1D ) . At 12 dpf , around 107 copies of YFV-17D RNA was detected in 4 out of 8 mosquitoes , which is 10 time less than in YFV-DAK infected moquitoes . YFV-17D RNA was found in legs of 2 mosquitoes among the 46 blood-fed mosquitoes collected during 14 days ( Figs 1D , S1B and S1D ) . No virus was dectected in the salivary glands of these 46 mosquitoes ( Figs 1D , S1B and S1D ) . Therefore , in agreement with our titration assays ( Fig 1B ) and with previous studies performed with Rexville strains of Ae . aegypti [30–32] , YFV-17D disseminated poorly in the strain Paea . The RT-qPCR analyses also revealed that the vaccine strain replicated less efficiently than YFV-DAK in its vector . The percentage of mosquitoes that were positive for viral RNA among the mosquitoes that had taken blood was calculated based on RT-qPCR data obtained from 3 independent experiments ( Fig 1E ) . Significantly less midguts were postivive for YFV-17D RNA than YFV-DAK RNA at days 7 and 14 post-feeding , suggesting that the midgut infection barrier restricts the replication of the vaccine strain . Viral dissemination to legs was defined by the presence of viral RNA in the legs of mosquitoes whose midguts were infected ( Fig 1F ) . YFV-DAK had disseminated in around 40% of infected mosquitoes at 7 dpf and in around 90% of mosquitoes at 14 dpf . At this time , YFV-17D had disseminated in around 10% of them ( Fig 1F ) . YFV-DAK dissemination rates are consistent with the ones reported for the YFV-Asibi strain [32] or clinical isolates from Peru [31] in Rexville mosquitoes . YFV-DAK RNA was detected in salivary glands of approximately 75% of mosquitoes whose midguts were infected , revealing that the virus had efficiently reach these secondary organs ( Fig 1G ) . By constrast , no dissemination in salivary glands was observed in mosquitoes infected with YFV-17D . To investigate the replication ability of the two viral strains further , the presence of viral antigens in pooled midguts and salivary glands of mosquitoes fed on blood containing 4 . 107 PFU/mL of either YFV-17D or YFV-DAK was analyzed by Western blots at days 7 and 14 post-feeding using antibodies against Env and NS1 . The Env protein was detected at both time-points in the midgut of mosquitoes infected with the strain YFV-DAK , in a majority form of around 45 kDa and a minor form of around 35 kDa ( Fig 2A ) . The Env protein was not detected in the salivary glands 7 days after the blood meal but was present as a 45 kDa form 14 days after the blood meal ( Fig 2A ) . These data are in good agreement with the titration and RT-qPCR data presented in Figs 1 and S1 . Like the Env protein , the NS1 protein was detected in the midguts of mosquitoes infected with the YFV-DAK strain at both 7 and 14 dpf ( Fig 2A ) . In midguts , NS1 was detected at the expected size of 45 kDa , but also as heavier forms of around 80 kDa . These forms could represent NS1-2A , a polyprotein precursor consisting of NS1 and a portion of NS2A . This NS1-2A form was previously reported in human SW-13 cells infected with YFV-17D [23] and maybe generated by alternative cleavage sites in the NS2A region upstream from the cleavage site generating the N-terminus of NS2B . Alternatively , they could represent glycosylated versions of NS1 monomer or dimer . NS1 was also detected in the salivary glands of mosquitoes infected with YFV-DAK for 14 days ( Fig 2A ) . No or very little signal was detected by the anti-NS1 or anti-Env antibodies in organs of mosquitoes infected with YFV-17D ( Fig 2A ) . In order to ensure that the antibodies directed against the NS1 and Env proteins recognize YFV-17D proteins , control experiments were performed with the Ae . aegypti Aag2 cells infected for 24 or 48 hours at an MOI of 0 . 1 with both viral strains . Both proteins were well detected in cells infected for 48 hrs , independently of the viral strain used ( Fig 2B ) . Thus , absence of detection of YFV-17D Env and NS1 proteins in the mosquito organs at 7 and 14 dpf is not due to poor recognition of the viral antigens , nor the antibodies used , but reflects a low-level replication . These data confirm our titration and RT-qPCR analyses ( Fig 1 ) . Of note , the YFV-DAK Env was detected as 2 forms in Aag2 cells infected for 48 hours while the YFV-17D Env was detected as a unique form . No YFV-17D proteins were detected at 24 hours post-infection , suggesting that the replication of the vaccine strain is slower in Aag2 cells than the one of YFV-DAK , as in Ae . aegypti ( Fig 1 ) . Finally , to confirm RT-qPCR and Western blot data , immunofluorescence analyses were performed on midgut of mosquitoes fed since 7 days using antibodies againt the viral protein NS4B . YFV-DAK antigens were evenly distributed in foci over the entire epithelium at this time ( Fig 3A ) . By contrast , YFV-17D antigens were found in one or two localized foci in infected midguts . In an attempt to investigate further this uneven distribution of YFV-17D replication sites , the midgut of mosquitoes infected with both viral strains for 3 or 7 days were cut longitudinally into two equal parts . The presence of viral RNA was determined by RT-qPCR analyses performed on individual half midguts ( Fig 3B ) . Among 18 mosquitoes that ingested blood containing YFV-17D , 3 half midguts were positive for YFV-17D RNA at day 3 post-infection and only 2 at day 7 post-infection . This is in agreement for our previous results ( Fig 1E ) . Among these five positive midguts , only one contained YFV-17D RNA in both sections ( Fig 3B ) . As expected based on previous results ( Fig 1E ) , it was easier to obtain midguts positive for YFV-DAK . Twelve out of the 15 midguts that were positive for YFV-DAK RNA contained viral RNA in both sections . These experiments revealed that YFV-17D replication in Ae . aegypti midgut is more confined than YFV-DAK replication . Together , these data show that , by contrast the clinical isolate YFV-DAK , the vaccine strain replicated poorly in , and disseminated poorly from Ae . aegypti midgut . To assess whether YFV-17D could infect Ae . aegypti when delivered via a non-oral route , mosquitoes were inoculated intra-thoracically with 2 . 5x104 PFU of YFV-17D or YFV-DAK , which corresponds to around 10 times less PFU than when mosquitoes are taking around 5 μL of a blood meal containing 4x107 PFU/mL . The presence of viral RNA was analyzed by RT-qPCR 10 days after injection . Mosquitoes infected via a blood meal served as controls . Several whole mosquitoes were also analyzed 20 minutes after feeding or injection to ensure that a similar amount of viral particles of both viral strains were delivered in mosquitoes ( Fig 4 , black boxes ) . In good agreement with our previous experiments ( Fig 1E ) , around 35% of midguts ( 8 out 22 ) were positive for YFV-17D RNA , whereas 81% ( 18 out 22 ) were positive for YFV-DAK RNA at day 10 post feeding ( Fig 4A ) . Moreover , significantly less viral RNA ( around 10 times ) was found in YFV-17D-infected midguts as compared to YFV-DAK-infected midguts ( Fig 4A ) . YFV-DAK RNA was detected in legs and salivary glands of around 50% of these mosquitoes . By contrast , YFV-17D was detected in the legs of a unique mosquito out of 22 and was not detected in salivary glands ( Fig 4A ) , confirming the inability of the vaccine strain to spread to secondary organs when orally delivered . When the midgut barriers were bypassed by injecting Ae . aegypti mosquitoes in the thorax , 100% of midguts were positive for both viral strains and similar amounts of YFV-17D and YFV-DAK RNA were detected in this organ , indicating that both viral strains successfully replicated in midgut-associated tissues when bypassing the lumen ( Fig 4B ) . All legs and salivary glands were positive for YFV-17D and YFV-DAK RNA ( Fig 4B ) , revealing that the two viruses were efficiently infecting secondary tissues once the midgut was bypassed . Of note , significantly more ( around 10 times ) YFV-DAK RNA was detected in salivary glands than YFV-17D RNA ( Fig 4B ) , suggesting that YFV-17D is sensitive to the salivary gland infection barrier . To ensure that viral RNA detected in secondary organs of injected mosquitoes represented replicative RNA and not input viral RNA , UV-treated viral RNA was also injected into the thorax of several mosquitoes . A signal , slightly above the detection threshold , was detected in two organs out of 39 tested ( Fig 4B ) . These data confirm the ability of YFV-17D to replicate as efficiently as YFV-DAK in midgut and secondary organs when mosquitoes were inoculated intra-thoracically . To determine the consensus sequence of the two viral strains , we performed next generation sequencing ( NGS ) analysis of the two viral stocks . Average coverage depths for these alignments were around 1000x ( S1 Table ) and homogeneous along their references . The comparison of the two consensus sequences identified 333 synonymous mutations ( Fig 5A and 5B , blue bars ) and 60 non-synonymous ones ( Fig 5B , red bars ) . These differences were scattered along the genome . Single nucleotide variants ( SNVs ) and their frequency were identified all along the two genomes ( Fig 5C ) . Only the SNVs representing a minimum of 3% of all observations were considered . The genome of YFV-17D contained more SNVs than the one of YFV-DAK ( 50 against 18 ) . A SNV that lies in the NS2A gene of YFV-17D is represented in 44% of the population , but does not induce amino acid change .
Studies conducted shortly after the development of YFV-17D showed that Ae . aegypti fed on vaccinated volunteers or rhesus monkeys were unable to transmit YFV-17D to susceptible monkeys [33] . These results were confirmed five decades later by showing that suckling mice bitten by Ae . aegypti infected with YFV-17D did not exhibit sign of disease [31] . Poor dissemination of YFV-17D to mosquito heads was shown by examining head tissues by immunofluorescence or immunohistochemical studies [31 , 34] . Consistently , titration assays performed on organs of the Rex-D strain of Ae . aegypti revealed that YFV-17D infects the midgut , but does not spread to secondary organs [21 , 32] . Our RT-qPCR , immunofluorescence and titration analyses document the inability of YFV-17D to disseminate in the Paea strain of Ae . aegypti . Our analysis also revealed that YFV-17D replicates poorly in the midgut , as compared to the clinical isolate YFV-DAK . Among the mosquito with midgut positive for YFV-17D RNA , only 10% had viruses that disseminated to their legs and none had viral RNA in their salivary glands . Thus , our data suggest that the YFV-17D strain is not only sensitive to the midgut escape barrier , but also to the midgut infection barrier when orally delivered . When injected into the thorax of mosquitoes , YFV-17D replicated in midgut tissues as efficiently as YFV-DAK . These data suggest that the restriction of YFV-17D replication in the midgut occur at the level of epithelial cells . Our RT-qPCR analyses suggest that the major restriction occurs at a stage prior to viral RNA production . Several mechanisms , not mutually exclusive , could explain this restriction . First , the restriction could occur during viral entry in midgut epithelial cells . The low number of loci revealed by immunofluorescence analysis of YFV-17D-infected midguts suggests that only few cells were initially infected by the vaccine strain and thus supports the hypothesis of an entry defect . Flavivirus entry mechanisms are poorly described in mosquito cells . Neither attachment factor ( s ) nor entry receptor ( s ) are identified yet . As in mammalian cells , the domain III of Env is involved in attachment and entry of flavivirus in mosquito cells [35] . Thus , it is conceivable that YFV-17D Env would have a lower affinity for cell entry factors than YFV-DAK Env . Our NGS analysis revealed that the consensus sequence of the two Env proteins differs from 75 mutations , including 14 non-synonymous mutations . Seven of these non-synonymous mutations lie within the domain III . Finally , in Aag2 cells infected for 48 hours , we detected two forms of YFV-DAK Env under non-reducing conditions and a single form of YFV-17D Env . These differences may reflect a different conformation and may explain a different affinity for a cell entry receptor . In agreement with this hypothesis , when domain III of the Env gene of a YFV able to disseminate was replaced by domain III of the Env gene of YFV-17D , the dissemination of the chimeric virus was strongly inhibited , suggesting an important role in Domain III in this process [21] . These results , however , may be the consequence of chimerization , as it is known that flavivirus chimeras replicate less efficiently than parental viruses [36] . We have recently shown that the YFV-Asibi enters a panel of human cells by canonical endocytosis mechanisms involving clathrin , while YFV-17D enters cells in a clathrin-independent manner [12] . We have shown that the 12 mutations differentiating YFV-Asibi Env from YFV-17D Env are responsible for the differential internalization process . Based on these data , we hypothesized that YFV-17D and YFV-Asibi use different cell receptors [12] . It is therefore possible that the YFV-17D and YFV-DAK strains also use different receptors in mosquito cells and that the receptor used by YFV-17D is poorly expressed at the apical surface of midgut epithelial cells , as compared to the one used by the clinical strain . This hypothesis is consistent with our data showing that YFV-17D succeeded in replicating into midgut-associated tissues when inoculated intra-thoracically . Alternatively , the glycosylation status of the Env protein could play a role in the differential entry abilities of the two strains . Flavivirus Env proteins possess a conserved N-glycosylation motif at amino acid 153/154 . This modification is involved in important viral replication and pathogenesis functions [37] . Mutagenesis studies on many flaviviruses , including the DENV , WNV and ZIKV , indicate that the loss of this N153/154-glycosylation impairs viral replication in the midgut [38–40] . Unlike most flaviviruses , the YFV Env lacks the N153/154-glycosylation canonical site . A second non-canonical N-glycosylation site exists at position 470 . However , it is unlikely that this site is functional because it is located in the hydrophobic carboxy-terminal domain and is therefore inserted into the endoplasmic reticulum membrane . The absence of an accessible N-glycosylation site in YFV-DAK Env therefore indicates that such motif is probably not necessary for replication and dissemination in mosquitoes . We therefore believe that mutations in Env , rather than its glycosylation status , are involved in vector competence . Another mechanism that could explain the low replication of YFV-17D in the midgut of mosquitoes is its inability to escape the antiviral mechanisms in midgut epithelial cells . The RNA interference pathway ( RNAi ) , is a major antiviral defense initiated by the recognition of viral replication intermediates by the Dicer-2 protein [41] . Its efficacy differs within organs , both in Anopheles gambiae [42] and Aedes aegypti [43] . One can envisage that the pathway is particularly efficient against YFV-17D in midgut epithelial cells . This pathway inhibits the replication of DENV and ZIKV viruses in the midgut and salivary glands of mosquitoes [44–46] . Interestingly , Myles and colleagues recently showed that the YFV C protein counteracts the RNA interference pathway in Ae . aegypti by protecting double-stranded viral RNA from Dicer-2-induced cleavage [47] . No amino acid sequence responsible for this effect has been identified . Our NGS analysis revealed that the consensus sequence of the C gene of our two strains of interest differ by 10 mutations , including a non-synonymous one . This unique mutation in C could modulate its RNA interference suppression activity . The NS1 protein , which is a highly conserved glycoprotein secreted by flavivirus-infected cells , enhances DENV and JEV replication in their vectors [48] . It does so by allowing them to escape two important antiviral mechanisms: the production of reactive species of oxygen ( ROS ) and the JAK/STAT pathway [48] . One can envisage that , like the NS1 proteins of DENV and JEV , YFV-DAK NS1 protein could be a potent suppressor of these two antiviral strategies . The NS1 protein of YFV-DAK could also be more expressed and/or secreted than the one of YFV-17D . Our NGS analysis detected more than 60 non-synonymous nucleotide differences along the genome of the two viral strains . These , together with the 8 nucleotide differences in the 3' untranslated region ( UTR ) between the 2 viral strains , could have functional consequences . The higher abundance of variants in YFV-17D genome as compared to YFV-DAK genome was unexpected since a recent study showed that the vaccine strains YFV-17D and YFV-FNV contained fewer variants than their respective parental strains [49] . However , our observations do not inform on the general variability of the genomes since they concern only a small number of nucleotides . Further analysis would be warranted to compare the genetic diversity of YFV-17D and YFV-DAK . Deep sequencing analysis of YFV-17D genome coupled to independent diversity measurements , such as the Simpson 1-D and Shannon entropy indexes , revealed that the vaccine strain lacks quasispecies diversity as compared to its parental strain Asibi [25] . This loss of genetic diversity has been proposed to contribute to YFV-17D attenuation in vaccinated patients [25] . Moreover , recent studies with Venezuelan equine encephalitis virus ( VEEV ) , which belongs to the genus Alphavirus , have revealed that viruses able to disseminate in mosquitoes have an higher diversity than the ones that did not disseminate [50] . Thus , the poor genetic diversity of YFV-17D may contribute to its inability to infect and spread in Ae . aegypti . Additional studies will be needed to identify the molecular mechanism ( s ) responsible for the low replication and dissemination of the YFV-17D vaccine strain in Aedes mosquito . These studies are essential to better understand the interactions between viruses and their vectors and can also contribute to the development of non-transmissible live-attenuated vaccines . | Most flaviviruses , including yellow fever virus ( YFV ) , are transmitted between hosts by mosquito bites . The yellow fever vaccine ( YFV-17D ) is one of the safest and most effective live virus vaccine ever developed . It is also used as a platform for engineering vaccines against other health-threatening flaviviruses , such as Japanese encephalitis , West Nile , dengue and Zika viruses . We studied here the replication and dissemination of YFV-17D in mosquitoes . Our data showing that YFV-17D is unable to disseminate to secondary organs , as compared to a YFV clinical isolate , agree with previous studies . We have expanded on this knowledge by quantifying viral RNA production , viral protein expression , viral distribution and infectivity of YFV-17D in the vector midguts . We show that the midgut is a powerful barrier that inhibits YFV-17D dissemination in mosquitoes . Our study contributes to our basic understanding of the interactions between viruses and their vectors , which is key for conceiving new approaches in inhibiting virus transmission and designing non-transmissible live virus vaccines . | [
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| 2019 | Midgut barriers prevent the replication and dissemination of the yellow fever vaccine in Aedes aegypti |
Evaluating the future consequences of actions is achievable by simulating a mental search tree into the future . Expanding deep trees , however , is computationally taxing . Therefore , machines and humans use a plan-until-habit scheme that simulates the environment up to a limited depth and then exploits habitual values as proxies for consequences that may arise in the future . Two outstanding questions in this scheme are “in which directions the search tree should be expanded ? ” , and “when should the expansion stop ? ” . Here we propose a principled solution to these questions based on a speed/accuracy tradeoff: deeper expansion in the appropriate directions leads to more accurate planning , but at the cost of slower decision-making . Our simulation results show how this algorithm expands the search tree effectively and efficiently in a grid-world environment . We further show that our algorithm can explain several behavioral patterns in animals and humans , namely the effect of time-pressure on the depth of planning , the effect of reward magnitudes on the direction of planning , and the gradual shift from goal-directed to habitual behavior over the course of training . The algorithm also provides several predictions testable in animal/human experiments .
When confronted with several choices , we need to have an evaluation of how good each option is . Each choice has some immediate consequences , but also takes us into a new state where new choices emerge , and so on . Think of chess as an example . One intuitive way to solve a sequential decision-making problem like chess is to prospectively think into the future . This idea , known as model-based planning in the reinforcement learning literature [2] , expands a mental decision-tree by simulating a number of future action sequences . Although this method is accurate ( in terms of statistical efficiency ) , evaluating deep trees is computationally expensive ( in terms of time , working memory , metabolic energy , etc . ) . In chess , for example , it is impossible even for the best supercomputers to expand the tree of all possible strategies up to the end of the game . Therefore , several solutions have been provided in the artificial intelligence literature for how to approximate the values of choices without expanding a search tree to its fullest extent [3] or how to make the best use of limited computational resources to plan better [4] . To avoid the costs of planning altogether , a drastic alternative is to rely on heuristic methods that evaluate choices without any tree expansion . For example , a chess player can evaluate a chess position , without investigating the possibility of that position leading to a win or lose , by simply counting up the values of their pieces—a common heuristic utilized by novice players . Another example of approximate evaluation techniques , widely used in both natural and artificial intelligence . is using habits . This method , known as model-free reinforcement learning [2 , 5] , simply “caches” the average of previously realized rewards ensued by performing each action , and uses the cached values for evaluating those choices should they come up again in the future . Although using such heuristics frees cognitive resources from model-based planning , the downside is their inaccuracy . Habits , for example , take many trials to form , and they are always unreliable in changing environments . Rather than clinging to one of these extreme solutions ( i . e . , full planning vs . heuristics/habits ) , an intelligent agent can instead combine the two in order to harvest the relative advantages ( i . e . , accuracy vs . affordability ) of both techniques [6–9] . This , in theory , is achievable by forward planning up to some depth and then exploiting heuristic values as proxies for consequences that may arise in the further future . That is , when the depth of planning is say d , the agent computes the value of a choice by adding the first d rewards predicted by explicit simulation , to the value of the remaining actions estimated by the heuristic/habitual values . For example , a chess player could think three steps ahead , and then estimate , heuristically , the strength of the position he could achieve after those three moves . This integrative approach has been used in artificial intelligence for example for obtaining super-human Go performance [10] ) . Furthermore , it was shown recently that humans also use this scheme , named plan-until-habit , for integrating planning and habitual processes in a normative way , and that their depth of planning depends on the time-pressure imposed on them [11] . The plan-until-habit ( or plan-until-heuristic , in general ) scheme aims at mitigating the computational costs of planning by appealing to the habitual system after the planning system has sufficiently expanded the decision-tree . Obviously , the first questions to be asked in this framework are “in which directions the decision-tree should be expanded ? ” , and “when should the expansion stop ? ” . In this paper , we present , for the first time , a principled algorithm for optimal tree-expansion in the plan-until-habit framework . The algorithm is based on a speed/accuracy tradeoff: deeper planning leads to more accurate evaluations , but at the cost of slower decision-making . As a proof of concept , we show through simulations how this algorithm expands the decision-tree effectively and efficiently in a simulated grid-world environment . We further show that our algorithm can explain several behavioral patterns in animals and humans , namely the effect of time-pressure on the depth of planning , the effect of reward magnitudes on the direction of planning , and the gradual shift from goal-directed to habitual behavior during training . The algorithms also provide several predictions testable in animal/human experiments .
From an external-observer viewpoint , the questions to be answered by an agent are of the type “what action should be taken ? ” . From a metacognitive perspective , however , the agent should first think about how to think ( e . g . , how deep she should plan ) . In fact , the question she could ask at each step of the planning process is “Should I expand the decision-tree one step further ? ” , and if yes , “In what direction ? ” . To answer these , assume that the agent has already expanded a tree to a certain extent ( Fig 1A ) . This means that the agent knows , possibly with some uncertainties , a few next states to be visited upon taking each action , and the immediate rewards associated with each of those transitions . She can , therefore , sum up the predicted rewards along each trajectory ( i . e . , action-sequence ) and have an estimate of the total rewards to be achieved . On the top of this “total immediate rewards” , each trajectory ends in a frontier state which represents the edge of the current planning horizon along that trajectory . The habitual ( or any other heuristic ) values on this frontier state supposedly reflect the total ( discounted ) rewards to be expected from that point on . Therefore , the sum of “total immediate rewards” and the habitual value of the frontier node provides an estimate of the total expected reward of each trajectory ( Fig 1B ) . Habitual values , however , can be highly unreliable due to the inflexible nature of habit formation . For each given trajectory , therefore , the dependence of its estimated total rewards on uncertain habitual values renders the whole estimation uncertain . If expanding the tree along that trajectory would make value estimation less dependent on habitual values and thus reduce uncertainty , that expansion is worth considering . In this sense , the critical value to be computed for each trajectory is the “value of uncertainty reduction” ( vur ) . vur computation for a trajectory should examine whether a new piece of information , possibly providable by a further expansion of the tree along that trajectory , could change agent’s decision about what action to be taken , and how much extra value is expected to be gained by that policy improvement . vur is , in fact , the expected value of policy improvement-induced rewards , computed over all possible new pieces of information that could be provided by expanding the trajectory one step further ( Fig 1C ) . Although the agent readily possesses those new pieces of information in her memory ( because she has a model of the environment ) , loading them into working memory and taking them into the value-estimation account is worth doing only if the value of uncertainty reduction is more than its cost . Here is the general scheme of our algorithm: at each stage of planning , vur is computed for each trajectory on the search tree ( we discuss later that previously-computed vur-values can be reused later under certain conditions ) . The trajectory with the highest vur is expanded if its vur is bigger than the cost of expansion . Otherwise , the expansion process is terminated and the agent chooses an action ( e . g . , using soft-max rule ) according to the estimated values derived from the tree . In this paper , we assume that the cost of expansion simply reflects the opportunity cost of time . That is , assuming that each expansion takes ϵ time units , the total cost of one expansion is R ¯ ϵ , where R ¯ is the average reward the agent receives in the given environment . As explained before , the main motivation for expanding the tree is reducing value-estimation uncertainties . There could be several reasons for why expansion reduces uncertainty . In many cases , like chess , heuristic estimations become more precise as the game advances . In general , proximity to goal sometimes makes it easier to evaluate the states . Another way that expansion reduces uncertainty , which is the focus of our formal model , is through temporal discounting . By each level of expanding a trajectory , the dependence of its estimated value on the less-reliable habitual system is shifted one step further into the future . As a simplified example , imagine you are in a maze and you have already thought two steps ahead along a certain trajectory , T1 , of actions , and those two steps will take you to the state s′ . You can use the MF value , VMF ( s′ ) of that state to compute the total value of the trajectory: V ( T1 ) = r1 + γ . r2 + γ2 . VMF ( s′ ) , where r1 and r2 are the immediate rewards expected to be received by performing the first and the second actions on the trajectory T1 . Assuming that the estimates of the immediate rewards have zero uncertainty , and that the MF estimates always have variance σ2 ( i . e . , uncertainty ) ) , the total uncertainty of V ( T1 ) will be ( γ2 . σ ) 2 = γ4 . σ2 . Now , if you think one step deeper and expect to land in state s′′ after taking the first three steps of trajectory T2 , then V ( T2 ) = r1 + γ . r2 + γ2 . r3 + γ3 . VMF ( s′′ ) . Therefore , its variance will be ( γ3 . σ ) 2 = γ6 . σ2 . This toy example shows that as a natural consequence of temporal discounting , by increasing the depth of planning , the total uncertainty of trajectories decreases , due to the reduced reliance on uncertain MF values . Therefore , the discount factor is the critical variable that determines the extent of uncertainty reduction by each expansion . In this paper , we only consider environments where the transition between states via actions are deterministic ( i . e . , deterministic transition function for the Markov decision process; See Methods for how this assumption can be relaxed ) . Therefore , the expanded tree , at each point , is a deterministic tree . In order to compute vur , let’s define a strategy in a tree as a combination of actions that an agent can take to reach a leaf in the tree ( see Fig 1 ) , and define a frontier search as the set of all strategies that agent can take in a given tree ( e . g . , the search frontier in Fig 1 is {A1 , A2 , A3 , A4 , A5} ) . Based on this definitions , as shows in the Methods section , the value of uncertainty reduction for strategy Ai , given the search frontier F , can be written as: VUR ( A i | F ) = E μ i * [ max ( μ i * , max A ∈ F - A i E [ V ( A ) ] ) ] ︸ with expansion - max A ∈ F E [ V ( A ) ] ︸ without expansion , ( 1 ) where F − Ai is the set F excluding Ai . According to this equation , computing vur ( Ai|F ) requires μ i * , which is the expected mean of strategy Ai after the potential expansion . However , this variable can be computed before expansion , by μ i * ∼ N ( μ i , ( 1 - γ 2 ) σ i 2 ) ( see Methods section ) , in which γ is the discount factor , and μi and σ i 2 are respectively the mean and the variance of the MF-value distribution for the last action on Ai . In other words , vur is computable based on μ i * , the expectation with respect to the predicted value of Ai after expansion , instead of its realized value which is not available before the expansion ( a more general form of the above equation without reliance on the discount factor is presented in the Methods section ) . The right-hand side of Eq 1 is composed of two parts: the amount of future rewards that are expected to be gained with the expansion of strategy Ai , and the amount expected to be gained without the expansion of Ai . vur is the difference between these two quantities . The without-expansion term is simply the value of the best strategy that is currently available to the agent . In the with-expansion term , the outer ‘max’ operator implies that if after expanding , Ai turns out to be worse than the other available strategies ( F − Ai ) , then the best strategy among the other ones will be taken . Otherwise , Ai will be taken . The agent , however , needs to calculate this term before the expansion of Ai and therefore the term is calculated based on the expectation with respect to the predicted value of Ai after expansion ( denoted by μ i * ) instead of its realized value which is not available before the expansion . It can be shown that in the case of normally distributed MF value functions , Eq 1 has a closed-form solution ( see S1 Text for details ) : VUR ( A i | F ) = { σ i [ ϕ ( μ i - μ β σ i ) - μ i - μ β σ i Φ ( - μ i - μ β σ i ) ] + μ β - μ α if A i is the best strategy σ i [ ϕ ( μ i - μ α σ i ) - μ i - μ α σ i Φ ( - μ i - μ α σ i ) ] otherwise ( 2 ) where μi and σi are , respectively , the mean and the standard deviation of strategy Ai . Furthermore , μα and μβ are the means of the , respectively , first-best and second-best strategies in the currently-expanded tree . First-best and second-best strategies are the strategies that have the highest and the second-highest mean values . Finally , ϕ and Φ are , respectively , the probability density and cumulative distribution functions of a standard normal distribution . A central principle for any meta-control algorithm is that the cost of meta-reasoning ( here , the cost of computing arg maxA VUR ( A|F ) ) should be lower than the cost of expensive reasoning ( here , one-step expansion of the decision-tree ) . In terms of memory cost , tree-expansion would require loading information about the expanding nodes from the long-term to the working memory . Furthermore , it would require engaging an additional working memory slot to store such information . Meta-reasoning , however , has minimal memory cost , since all the variables for computing arg maxA VUR ( A|F ) already exist in the working memory ( i . e . , are in the already-expanded tree ) . In terms of computational-time cost , we should stress that even though we want to find the strategy with the maximum vur value , this does not necessarily require computing vur’s of all strategies at each time step . vur ( Ai|F ) only depends on μi , σi and μα ( or μβ ) . Therefore , vur values can be cached , and reused as long as the aforementioned parameters have not changed ( i . e . , the newly-added strategies are not first- nor second-best strategies ) . From an algorithmic point of view , computing vur of a given Ai can be viewed as a constant time operation . Therefore computing arg maxA vur ( A|F ) is in the order of O ( | F | ) in the worst case , where |F| is the cardinality of F ( i . e . , number of items in the search frontier ) . However , as shown in the appendix , as the tree expands , the expected cost becomes constant ( i . e . , O ( 1 ) ) asymptotically , given that the agent caches previously computed vur values . This is intuitively becuase as the depth of the tree grows , the uncertainty around the value of the to-be-expanded strategy shrinks ( becuase of the discounting factor ) , which makes it less likely that the strategy ( which is not currently the best strategy ) becomes the best one after expnasion ( or second best strategy ) . As such , the chances that a new expansion affects previusly computed vur values becomes smaller and smaller as the tree gets deeper . This rate of decrement is faster than the rate at which new potential strategies are added to the tree as it gets deeper , and therefore overall the number of vur values that need re-computation remains constant as in the limit . Just as a proof of concept , we would like to see whether our method can be beneficial in a setting in which an agent is combining both MF and MB information for efficient planning . For this , we first trained an agent in an episodic grid-world environment where she obtains imperfect estimates of state-values by the model-free system . After training , she utilizes both the MF and the MB systems to use the plan-until-habit scheme , where the MB system is used to construct the tree , and the MF systems is used for estimating the values of state-actions that lie on the frontier of the tree . We predict that the increased accuracy in model-free estimates , as a result of training , would bias the direction of expanding the tree towards better states . The agent starts each episode in the center of a 7 × 7 grid and can choose to go up , down , left , or right at each state . All the transitions are deterministic and are associated with a unit cost . The bottom right cell is the goal state that concludes the episode . This state is not associated with any reward , but is implicitly rewarding since it terminates the costly walk in the grid world . Evidently , the optimal policies are combinations of three right moves and three down moves . Given the structure of the task , for easier geometric interpretation and without loss of generality , the MF system learns state values , rather than state-action values . To apply our plan-until-habit pruning algorithm , we require an MF system that learns not just the mean , but also the variance ( i . e . , uncertainty ) over the state values . In our implementation , the agent estimates the value of a state by generating a number of trajectory samples from the state , similar to the first-visit Monte Carlo method described in [2] , and utilizing the trajectories’ return statistics . However , instead of estimating the Q-values with Monte Carlo averages , we use independent conjugate normal priors and obtain posterior estimates of Q’s , which are conditioned on the trajectory returns ( see S1 Text ) . We obtain N trajectory samples starting from each state , such that each sample consists of a trajectory resulting from a fixed uniform random policy that assigns 1 4 probability to each direction {UP , DOWN , LEFT , RIGHT} . We test our planning model in two different settings . First , we assume the agent has no experience interacting with the environment ( i . e . , N = 0 ) . This condition results in the posterior Q-values having large and equal variances . We compare this with the case where the agent has collected some samples ( i . e . , N = 10 ) , resulting in more accurate estimates of state values . In both cases , we employ the same pruning mechanism , with a variable number of possible tree expansions ( capturing working-memory limitations; see Discussion section ) selected uniformly from [5 , 25] and γ = 0 . 95 . As displayed in Fig 2A , in the no-experience condition , the search tree is explored in all directions almost uniformly . In the second condition , however , the search is directed more towards the goal state as illustrated in Fig 2B . These results are in line with our intuition that the agent prunes more aggressively as she gathers more experience and thus , is better able to judge what the promising states or actions are . Behavioral evidence suggests that humans , when planning , curtail any further evaluation of a sequence of actions as soon as they encounter a large punishment on the sequence [12] . In a behavioral task [12] , subjects were required to plan ahead in order to maximize their income gain . The environment in the task is composed of six states . Each state affords two actions , each of which transitions the subject to another state deterministically . Subjects see their current state on a display and press the ‘U’ or ‘I’ buttons on the keyboard to transition to a different state . In the first phase of the experiment , subjects learn the deterministic transition structure of the environment . In the second phase , transitions are associated with specific gains or losses , which are visually cued to make it easier to remember . At each trial in this stage , subjects are told to take a certain number of actions , varying between 2 and 8 , and collect all the rewards and punishments along their chosen trajectory . This forces them to think ahead and plan in order to find a relatively profitable trajectory among 22 = 4 to 28 = 256 options . For example , in the setting described in Fig 3A , 8 possible trajectories resulting from 3 consecutive actions are displayed . Out of all 12 transitions , 3 of them are associated with a large loss . The magnitude of this loss is manipulated across trials ( from {−140 , −100 , −70} ) such that for certain losses ( i . e . , −100 and −70 ) , Pavlovian pruning results in suboptimal strategies . In other words , pruning a strategy that starts with a −100 or −70 loss would result in discarding the most profitable course of actions , since such actions will eventually lead to highly rewarding states . The results of this experiment show that humans prune infrequently if pruning results in prematurely discarding optimal trajectories . Conversely , they tend to prune liberally when pruning does not eliminate the optimal trajectories . That is , they prune more when the loss on a trajectory is so large ( i . e . , −140 ) that cannot be compensated for by future rewards . We aimed to replicate this task in our simulations . Because in the first part of the experiments subjects learn the transition and the immediate rewards through repetitive exposure , we assume that the agent ( i . e . , our simulation of a subject ) knows the transition and reward structures . Since the immediate state-action rewards are visually cued , subjects , after observing their starting state s and their available actions a1 and a2 , presumably incorporate the immediate rewards of those actions into their planning at no cost . Therefore , we assume that the agent starts the decision tree with two already-expanded actions , with values Q ( ai ) = R ( s , ai ) + γV ( T ( s , ai ) ) , where i ∈ 1 , 2 , and R ( s , a ) and T ( s , a ) are the immediate reward and successor states resulting from taking action a at state s . As in the previous experiment , we obtain the posterior Q-value distributions of the agent through a training stage . Similar to the training phase of the original study , we have the simulated agent interact with the environment for 100 episodes , during which she observes transitions and collects reinforcements . At each trial , the agent is located in a random state and is allowed to make a certain number of moves , which is sampled uniformly from {2 , 3 , 4} . She selects actions following uniform random policy , and stores the mean cumulative reinforcements collected after taking action a at state s , similar to the first-visit Monte Carlo algorithm [2] . Those mean values are then used for obtaining the posterior Q-distributions assuming a conjugate normal distribution as in the previous experiment ( see S1 Text ) . The prior is a normal distribution with mean and standard deviation of 0 and 1000 , respectively . After the training stage , the agent moves on to the pruning state , where she starts at state s and is asked to mentally expand the planning tree for n ∈ {2 , 4 , 6 , 8 , 10 , 12s} steps . We record the frequency with which the agent expands the early branch with the large punishment , which we very between −40 and −140 . Finally , we set γ to 0 . 95 as before . One critical observation in [12] is that subjects prune more frequently as the magnitude of the punishment increases . As shown in Fig 4 , our simulation results account for this pattern . Intuitively , observing a punishment on a trajectory reduces the expected value of the trajectory and thus , reduces the overlap between the value-distribution of that trajectory and that of the best trajectory . When the punishment is large enough , the overlap becomes very small even if the trajectories have highly uncertain value estimates . Small overlap is equivalent to low “value of uncertainty resolution” expected from expanding the unpromising trajectory , because there is a very small chance that the new pieces of information will render the unpromising trajectory better than the currently best strategy . In the simulations , we also vary the maximum number of branches allowed to be expanded , reflecting constraints on the working memory capacity ( see Discussion section ) . Not surprisingly , as the memory capacity is increased , pruning frequency decreases ( Fig 4 ) . Another important aspect of the study is that the likelihood of selecting the optimal sequence of actions by the subjects was affected by three factors: ( i ) subjects were less likely to choose the “Optimal Lookahead” sequence when it contained a large loss , ( ii ) this effect became larger as the size of the loss increased , and ( iii ) the optimal sequence was more likely to be chosen when the tree was shallow ( i . e . , when the subjects were supposed to choose a smaller number of actions ) . These three effects are shown in the top panel of Fig 5 for the data reported in Huys et . al . [12] . The bottom panel displays the prediction of our method based on the simulations in the same task . It can be seen that similar to the actual data , we predict that the subjects will be more successful in picking the optimal sequence when it does not contain a large loss , the tree is shallow and the loss is small ( i . e . , the effect is strongest in the −140 group and the weakest in the −70 group ) . One notable qualitative mismatch between the top and bottom panels is that , our model assigns a higher probability of choosing optimal sequences for smaller depths than what is shown for the actual data on the top panel . This is because , in our setting , the agent is very likely to make enough expansions to find the optimal sequence for a tree of depth 2 , as there are only 22 = 4 possible sequences—which can be spanned with a small number of expansions . The number of expansions are sampled from round ( Gamma ( 4 , 2 ) ) + 1 , where + 1 ensures positivity . Given this distribution , it is often the case that the agent performs enough expansions to find the optimal . However , if we look at the top left plot in Fig 5 , we see that the probability of choosing the optimal sequence is low if it contains a large loss—even for depth of 2 . This might suggest that the subjects do not fully use their “expansion budgets” , if performing expansions do not seem advantageous . The same could be done in our scheme by stopping expansions altogether if the maximum vur is below a threshold . However , we refrained from doing so , and instead used a random number of expansions for simplicity , and for limiting the flexibility of the model to prevent overfitting . Other than this , all other parameters are kept the same as the ones used for generating Fig 3 . Previously , the punishment-induced pruning discussed here was explained assuming that a Pavlovian system , reflexively evoked by large losses , curtails further evaluation of the corresponding sub-tree [12 , 13] . In our computational framework , however , this pruning pattern emerges naturally , rather than devising new mechanisms , from a speed-accuracy tradeoff . Furthermore , the normative nature of our explanation depicts punishment-induced pruning as an adaptive mechanism in the face of cognitive limitations , rather than depicting it an a “maladaptive” Pavlovian response [12] . Several lines of research have shown a transfer of control over behavior from goal-directed to habitual decision-making during the course of learning [14–17] . Previous accounts of interaction between MB and MF algorithms [18 , 19] explained this behavior by showing that the MF value estimates become more and more accurate along the course of experiencing a task . As a result , they eventually become more accurate than MB estimates [18] , or become accurate enough that the extra information that MB planning can provide is not worth the cost of planning [19] . Therefore , a binary transition from goal-directed to habitual responding occurs in behavior . Our model also explains the transition , but also suggests that it is gradual , rather than binary . As MF estimates become more accurate , the variance in strategy values decrease and thus , vur values also decrease monotonically ( see S1 Text for an analytical proof of this effect ) . This implies that an experienced agent would construct a shallower search tree and hence , spends less time planning compared to an inexperienced agent . Furthermore , in contrast to the previous accounts that propose ad-hoc [18] or optimal , but with very strong assumptions ( i . e . , MB tree-expansion has an infinite depth ) , [19] models for MB-MF arbitration mechanisms , our proposed model’s optimality is based on more reasonable assumptions . Our algorithm further predicts that in a plan-until-habit scheme , time-limitation would reduce the depth of planning . That is , time pressure would monotonically limit the total number of branches to be expanded , pressing the agent to switch to habitual/heuristic values at a shallower depth . This is due to the fact that every tree-expansion step is assumed to take a certain amount of time , ϵ . Therefore , our model , for the first time , accounts for recent evidence showing that humans use a plan-until-habit scheme and that time pressure reduces their depth of MB planning [11] , resulting to a relying on habitual responses at a shallower level . In this experimental study [11] , participants first learned the stationary transition structure of the environment in a three-step task . They then navigated through the decision tree , in each trial , to reach their desired terminal state . The rewarding value of the terminal states was non-stationary and changed along the trials , allowing to measure , from participants’ choices , whether or not they use a plan-to-habit scheme; and if they do , what depth of planning they adopt . The experiment imposed a decision time-limit of either 2000 or 700 milliseconds to two different groups of participants . While both groups showed a significant behavioral signature of plan-to-habit responding , participants that experienced a shorter time-limitation showed pruning the tree and switching to MF values at shallower levels . In this section , we qualitatively compare our plan-to-habit pruning algorithm to other methods , such as Monte Carlo tree search .
Finding optimal or near optimal actions requires comparing the expected value of all possible plans that can be taken in the future . This can be achieved by explicitly expanding a model that represents the underlying structure of the environment , followed by calculating the expected value of each plan . However , the computational complexity of this process grows exponentially with the depth of search for optimal plans , which makes it infeasible to implement in all but the smallest environments . Indeed , evidence shows that humans and other animals use alternative ways that have lower computational complexities than explicit search . Examples are using ‘cached’ values of actions instead of recalculating them at each decision point [18] , or using ‘action chunking’ , in which actions span over multiple future states [23] . Here , we suggest that such decision-making strategies are not operating independent of the planning processes , but they interact in order to provide a planning process that adapts its extent according to time and cognitive resource and therefore , scales to complex environments . In particular , the model that we suggest is built upon two bases: ( i ) the planning process is directed toward the parts of the environment’s model that are most likely to benefit from further deliberation , and ( ii ) the planning process uses ‘cached’ action values for the unexpanded ( i . e . , pruned ) parts of the tree . Simulation results showed that the model prunes effectively in a synthetic grid world , and that it explains several patterns reported in humans/animals . Namely , a sequential decision-making task has demonstrated that humans use strategies such as ‘fragmentation’ and ‘hoarding’ , in addition to pruning , for efficient planning . The pruning process , however , was shown to play a significant role on the top of those strategies [13] . Indeed , the data shows that humans stop expanding a branch of the model once they encounter a large punishment . This effect was previously accounted for , in the model-based planning framework , by adding a new parameter that encodes the probability of stopping the search after encountering a large punishment . The model here does not explicitly contain such a parameter , but the pruning effect emerges naturally based on the fact that the value of uncertainty resolution is lower for the branches of the model that start with large punishments and therefore , they are more likely to be pruned . Another component of the model here is using the cached values for unexpanded parts of the model , which is in line with previous works [11 , 12] . The psychological nature of such cached values can be related to either Pavlovian ( as used in [12] ) or instrumental ( as used in [11] ) processes in the brain , depending on whether cached values are coded for state or for state-action pairs , respectively . In the former case , our algorithm represents a collaborative interaction between instrumental model-based and Pavlovian processes [24] . In the latter case , it represents interaction between instrumental model-based and instrumental model-free processes . The theoretical framework we presented here is readily compatible with either case . As discussed in the previous sections , temporal discounting of future rewards ( and punishments ) is a necessary component in the current framework . Reduction of uncertainty is a variable that changes monotonically with the discount factor: the smaller the γ , the less dependence of the value of each strategy on uncertain cached values on the leaves and therefore , the more reduction of uncertainty by deepening the tree . However , when a new piece of information on a leaf at depth d is achieved , its policy-improvement impact on the root-level actions is measured at the root of the tree , thus discounted by a factor γd . Therefore , the smaller the γ is , the less valuable a given uncertainty reduction is . This effect counteracts the above-mentioned effect of γ on the degree of uncertainty reduction . As a result , discount factor has a non-monotonic effect on vur and thus , on the depth of planning . vur is equal to zero for γ-values of zero and one , and reaches a maximum for an intermediate value of γ ( its exact value depends on other parameters ) . In sum , we proposed a principled algorithm for pruning in a plan-until-heuristic scheme . While we showed the ability of the model in accounting for several behavioral patterns in humans/animals , whether or not people use such algorithm requires further direct experiments . Such experiments could test the effect of variables like the mean and the variance of cached values on the probability of expanding a node . On the theoretical front , our algorithm can benefit from several improvements , most notably , from relaxing the assumption that the environment has a deterministic transition structure . In that case , the algorithm could increase the efficiency of the state-of-the-art algorithms that use a plan-until-heuristic scheme in complex games [10] . Furthermore , whereas we simply assume here that planning and action execution cannot be performed in parallel , it is reasonable to assume that agents deliberate over upcoming choices while performing previously chosen actions .
We focus on deterministic Markov decision processes ( MDPs ) . The environment is composed of a finite set of states S; a finite set of actions A; a ( potentially partial ) transition function T : S × A ⇸ S; and a reward function f R : S × A × S → R . The agent interacts with the environment via a ( potentially stochastic ) policy π : S × A ⇸ [ 0 , 1 ] s . t . ∑a π ( s , a ) = 1 for all s , with the goal of maximizing the expected value of the cumulative discounted rewards E [ R t | s t = s ] , where R t = ∑ i = 0 ∞ γ i r t + i , s is the start state , and γ is the discount factor . The state-action values of a policy π are defined as Q π ( s , a ) = E π [ R t | s t = s , a t = a ] . Finally , the optimal state-action values are defined as Q* ( s , a ) = maxπ Qπ ( s , a ) . We assume for now that the model-based ( MB ) system has perfect knowledge of the environment ( i . e . , the reward and transition functions ) ( we will relax this assumption later ) . The agent uses some of this information to build a search tree representation , which relates the current state st to other states that can potentially be occupied in the future . The root of the tree is st , and its immediate children include the one-step-reachable states . Let us illustrate the formation of a search tree . The agent creates a tree node , containing information about her current state st , which becomes the root of the tree , meaning all other nodes will stem directly or indirectly from it . The agent picks an action a available at st to expand , which in turn adds s′ ≔ T ( st , a ) to the tree as a child node of st . Now , if the agent continues planning , she can either expand an action from st , assuming there are more than one action available at st , or she can choose to expand from s′ . The planning process is composed of iteratively selecting an action to expand from the set of unexplored node-action pairs and adding the resulting new state to the tree as a new node . Let us consider the state of a tree at a given time , containing a total number of n unexpanded node-action pairs . This means , there are n trajectories that start from st and terminate at one of the unexpanded state-action pairs . We call each trajectory a “strategy” , denoted by Ai , which is a tuple of state-action pairs , and introduce the search frontier F = {A1 , A2 , … , An} as the set of all strategies for a given tree . We define expanding a strategy A by adding s′ , the immediate successor state of the unexplored state-action pair at the end of A , to the tree and adding the resulting new strategies to the frontier . These new strategies have the form A + 〈s′ , a′〉 , where a′ denotes any action available at s′ , and + is a tuple-concatenation operator . Note that after the expansion , if A is no longer unexplored—that is , has no unexpanded actions—then A is removed from F . This process of tree expansion goes on until an action is taken or the frontier is empty . The latter condition means the tree captures all possible trajectories in the MDP , which can only happen in an episodic MDP where no matter what actions the agent takes , she ends up in a terminal state ( i . e . , the state that ends the episode ) after a finite number of actions . We also assume that the agent has an estimation of the expected cumulative discounted rewards of each state-action pair 〈s , a〉 , encoded by a random variable Q ( s , a ) . A model-free ( MF ) system , for example , can represent such Q-values as random normal variables by tracking the first order statistics ( i . e . , mean ) and second order statistics ( i . e . , variance ) of the values [25 , 26] . Given that state-action values are the expected longterm discounted rewards , any stochastic estimation of it will be normally distributed given the Central Limit Theorem assuming a fixed sampling policy and a reasonable ( fR has finite variance for all 〈s , a , s′〉 ) reward structure . Thus , it is reasonable to represent Q’s as random normal variables . With these settings , and in keeping with the plan-until-habit scheme , the value of a strategy Ai that ends with an 〈sM , aM〉 at depth M with Q ( s M , a M ) ∼ N ( μ s M , a M , σ s M , a M 2 ) can be estimated by V ( A i ) = r 1 + γ r 2 + γ 2 r 3 + ⋯ + γ M - 1 r M + γ M Q ( s M , a M ) , ( 3 ) where each ri corresponds to the MB estimation of reward after taking the ith action in the strategy . Assuming that there is no uncertainty in estimating the immediate rewards ( As discussed later , it is straightforward to relax the assumption of zero uncertainty for immediate rewards ) , r1 , r2 , . . , rM , the total variance of V ( A i ) ∼ N ( μ i , σ i 2 ) is σ i 2 = γ 2 M σ s M , a M 2 . It can be seen that as a strategy gets deeper , MF value distributions ( i . e . , Q’s ) get discounted more , which will form the basis of our method . We seek to compute the value of expanding the tree along Ai . The agent knows that expanding Ai will lead to a new , yet unknown state , sM+1 , where an action aM+1 with the highest Q-value , Q ( sM+1 , aM+1 ) , among other actions of that state exists . This potential expansion will lead to a new strategy , A i * , with its value estimated by: V ( A i * ) = r 1 + γ r 2 + γ 2 r 3 + ⋯ + γ M - 1 r M + γ M r M + 1 + γ M + 1 Q ( s M + 1 , a M + 1 ) . ( 4 ) Note that rM+1 , sM+1 , aM+1 , and Q ( sM+1 , aM+1 ) are unknown prior to expansion . To reflect this , we use the notation V ¯ ( . ) to denote an unknown value estimation: V ¯ ( A i * ) = r 1 + γ r 2 + γ 2 r 3 + ⋯ + γ M - 1 r M + γ M r ¯ M + 1 + γ M + 1 Q ¯ ( s ¯ M + 1 , a ¯ M + 1 ) , ( 5 ) where r ¯ M + 1 and Q ¯ ( s ¯ M + 1 , a ¯ M + 1 ) denote , respectively , the immediate reward and the value distribution of the successor state-action pair , both unknown prior to expansion and thus , denoted with a bar ( ¯ ) . Intuitively , E [ V ( A i ) ] should be equal to E [ V ¯ ( A i * ) ] , because they result from the same information prior to an expansion . Only with the extra information obtained from an expansion , namely after observing r ¯ M + 1 and Q ¯ ( s ¯ M + 1 , a ¯ M + 1 ) , the agent hopes to gain precision . In fact , we assume the agent’s probability estimates are coherent in the sense that her expectations of r ¯ M + 1 and Q ¯ ( s ¯ M + 1 , a ¯ M + 1 ) are in line with E [ V ( A i ) ] . Therefore , we have: E [ V ( A i ) ] = E Q ¯ , r ¯ [ E [ V ¯ ( A i * ) | Q ¯ , r ¯ ] ] , ( 6 ) where we drop the subscript M + 1 of r and arguments s ¯ M + 1 , a ¯ M + 1 of Q ¯ for brevity . This equality is also known as the law of total expectation , and here it suggests that an expansion may change the expected value of V ( A i * ) but not in expectation . We should emphasize that an agent does not necessarily need to obey this , but not doing so might result in inefficiencies . Particularly , if Eq 6 is not obeyed , then a Dutch book may be formed such that the agent would expect to lose value by performing tree expansions . Also , note that , Var Q ¯ , r ¯ [ E [ V ¯ ( A i * ) | Q ¯ , r ¯ ] ] ≥ Var [ E [ V ( A i ) ] ] = 0 , ( 7 ) which means that while the agent knows the exact mean of Ai’s value ( Var [ E [ V ( A i ) ] ] = 0 ) , the mean of the new strategy’s value is unknown prior to expansion . This variability in the expected value of the new strategy creates the possibility that the true ( i . e . , after expansion ) expected value of A i * is even higher than the mean value of the best currently-expanded strategy . In fact , prior to expansion , the agent believes that acting on the basis of its currently-expanded tree will pay her max A ∈ F E [ V ( A ) ] , which is the mean value of the best strategy . However , if the true expected value of A i * is even higher than max A ∈ F E [ V ( A ) ] , then the agent can change her policy and “gain” extra reward . The expectation of this “gain” , given the distribution over the expected value of A i * , computes the value of expanding a strategy . In other words , expanding a strategy will yield a net expected increase ( assuming the expanded strategy has variance in its value ) in the expected value of the best strategy , which we refer to as the value of uncertainty resolution ( vur ) . The vur along the strategy Ai is equal to the expected value of policy improvement-induced reward resulting from observing r ¯ M + 1 and Q ¯ ( s ¯ M + 1 , a ¯ M + 1 ) Formally , given the current state of the search frontier F , vur ( Ai|F ) is simply the difference between the expected value of best strategy after expanding Ai ( i . e . , observing r ¯ M + 1 and Q ¯ ( s ¯ M + 1 , a ¯ M + 1 ) ) and before expanding Ai: VUR ( A i | F ) = E Q ¯ , r ¯ , [ max ( E [ V ¯ ( A i ) | Q ¯ , r ¯ ] , max A ∈ F - A i E [ V ( A ) ] ) ] - max A ∈ F E [ V ( A ) ] ( 8 ) ≥ 0 . ( 9 ) where F − Ai is the set F excluding Ai assuming Ai will be fully explored after expansion , and thus be removed from F . Otherwise , the max should run over F . The second ( with minus ) term in Eq 8 is the expected value of the best strategy in the frontier . The first term is the expected value of the best strategy after expansion . The vur is always non-negative because of Jensen’s inequality: max is convex and thus , the expectation of the max of random variables has to be larger than or equal to the maximum of expectations . In order to progress further analytically , we make an assumption and assert that Var[Q ( sM , aM ) ] = Var[Q ( sM+1 , aM+1 ) ] . That is , we assume that MF value distributions for 〈s , a〉 and its immediate successor state-action pairs have the same uncertainty , possibly because the habitual system has had a similar number of experiences ( i . e . , samples ) of neighboring actions and they are possibly of similar values . We can see in Eq 4 that only Q ( sM+1 , aM+1 ) contributes to the uncertainty in V ( A i * ) . Therefore we have , V ( A i * ) ∼ N ( μ i * , γ 2 M + 2 σ s M + 1 , a M + 1 2 ) ( 10 ) = N ( μ i * , γ 2 ( γ 2 M σ s M , a M 2 ) ) ( 11 ) = N ( μ i * , γ 2 σ i 2 ) , ( 12 ) where μ i * ∈ R is the mean , which we will obtain shortly , and σ i 2 = γ 2 M σ s M , a M 2 is the variance of V ( Ai ) . However , both V ( Ai ) ( magenta curve in Fig 1C ) and V ( A i * ) ( black/grey curves in Fig 1C ) are estimating the value for the same action at the root state , st . Therefore , the value distributions V ( A i ) ∼ N ( μ i , σ i 2 ) and V ( A i * ) ∼ N ( μ i * , γ 2 σ i 2 ) should be consistent as in Eq 6 , implying V ( A i ) = E μ i * [ V ( A i * ) ] , ( 13 ) which can only be satisfied if μ i * ∼ N ( μ i , ( 1 - γ 2 ) σ i 2 ) . ( 14 ) The distribution over μ i * represents the probability distribution of the expected value of a strategy after expansion . This variability comes from the fact that we will have additional pieces of information , namely rM+1 and Q ( sM+1 , aM+1 ) . Note that in equation Eq 10 , the only source of variance in A i * is assumed to be the variance in Q ( sM+1 , aM+1 ) . In other words , the agent is assumed to have no uncertainty in estimating r1 , r2 , . . , and rM . It is straightforward to relax this assumption by keeping track of the variance of r1 , r2 , . . , and rM , denoted by σ r 1 2 , σ r 2 2 , . . , σ r M 2 . In that case , Eq 10 will be replaced by V ( A i * ) ∼ N ( μ i * , σ r 1 2 + γ 2 σ r 2 2 + . . + γ 2 M σ r M + 1 2 + γ 2 M + 2 σ s M + 1 , a M + 1 2 ) ( 15 ) = N ( μ i * , σ i 2 - γ 2 M ( ( γ 2 - 1 ) σ s M , a M 2 + σ r M 2 ) ) , ( 16 ) which gives μ i * ∼ N ( μ i , γ 2 M ( ( 1 - γ 2 ) σ s M , a M 2 - σ r M 2 ) ) ) , ( 17 ) where σ s M , a M 2 = γ - 2 M σ i 2 again . This will take MB imperfection information about the reward function into account . Eq 10 also assumes that the agent has perfect information regarding the transition function . Given that our algorithm is only developed for MDPs with deterministic transition function , this assumption is feasible . Relaxing these assumptions ( i . e . , deterministic , and perfect knowledge of , transition function ) are left for future work . Relaxing the assumption on deterministic transition function would result the estimated value , V ( A i * ) , of the strategy A i * to become a mixture of Gaussians , rather than a simple Gaussian distribution . Computing μ i * and vur for such cases would significantly increase the computational cost of meta-cognition and hence , developing approximation methods would be required . For example , one could resort to Monte Carlo methods , where a set of transitions are sampled from the stochastic transition function , over which the vur is averaged . Given we now know the distribution of μ i * , we can rewrite the vur definition given in Eq 8: VUR ( A i | F ) = E μ i * [ max ( μ i * , max A ∈ F - A i E [ V ( A ) ] ) ] - max A ∈ F E [ V ( A ) ] , ( 18 ) where μ i * is distributed according to Eq 14 and F − Ai is the set F excluding Ai . We show in S1 Text that there is a closed-form solution for vur ( Ai|F ) defined above . Utilizing this uncertainty resolution mechanism , the agent can simply find the most promising strategy to expand , via arg max A i ∈ F VUR ( A i | F ) . The agent can continue expanding the search tree by reducing the uncertainties of the most promising branches until the value gained by expansion is less than the opportunity cost of expanding ( as in [19] ) , or the search can continue until the working memory is full . The latter termination condition could be implemented based on the assumption that the working memory has a limited number of slots [27 , 28] ( e . g . , for storing states of the expanded tree ) . Alternatively , one could assume that the working memory is inherently corrupted by noise , and that the level of this noise increases with the number of items in memory [29] . It is straightforward to incorporate this mechanism into our algorithm: expansion results in the variance of V ( A i * ) to decrease by a factor γ2 , but also increases by an additive factor that is proportional to the number of items ( e . g . states ) currently stored in the working memory . Thus , one can compute when the noise overwhelms the resolved uncertainty . It is noteworthy that in this paper , computing vur is based on the assumption that when the value of expansion is bigger than its cost and thus an expansion should occur , an action will be executed immediately after that expansion . In fact , our model does not compute the value of further expansions following the next potential expansion . Relaxing this assumption would require computing the value of expanding all subsets of available and potentially-emerging strategies . In this case , for a certain subset like T1 , T2 , one needs to compute vur ( T1 , T2|F ) and compare it with B . C , where B = 2 is the number of expansions being considered , and C is the cost of one single expansion . We show in S1 Text ( section “on considering vur values independently” ) that the value of expanding several strategies before performing an action is not necessarily equal to the sum of the value of expanding each of those strategies independently . In general , computing the optimal sequence of expansions for a budget of B would be NP-complete in B , as it reduces to stochastic knapsack problem [30] . Another interesting outcome of this model is that the relationship between vur and γ roughly follows an inverse U-shaped curve . If γ = 0 , then V ( A ) as given in Eq 3 will be a scalar; as such , vur will be 0 . If γ = 1 , then the variance of E [ V ( A * ) ] as given Eq 14 will be zero , which too will result in vur being 0 . The interpretation of these conditions is easy: if you do not care about the future , then no need to plan; and in the latter condition , the agent cannot gain precision by discounting the model-free estimates . | When faced with several choices in complex environments like chess , thinking about all the potential consequences of each choice , infinitely deep into the future , is simply impossible due to time and cognitive limitations . An outstanding question is what is the best direction and depth of thinking about the future ? Here we propose a mathematical algorithm that computes , along the course of planning , the benefit of thinking another step in a given direction into the future , and compares that with the cost of thinking in order to compute the net benefit . We show that this algorithm is consistent with several behavioral patterns observed in humans and animals , suggesting that they , too , make efficient use of their time and cognitive resources when deciding how deep to think . | [
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| 2019 | Optimizing the depth and the direction of prospective planning using information values |
The extensive glycosylation of HIV-1 envelope ( Env ) glycoprotein leaves few glycan-free holes large enough to admit broadly neutralizing antibodies ( bnAb ) . Consequently , most bnAbs must inevitably make some glycan contacts and avoid clashes with others . To investigate how Env glycan maturation regulates HIV sensitivity to bnAbs , we modified HIV-1 pseudovirus ( PV ) using various glycoengineering ( GE ) tools . Promoting the maturation of α-2 , 6 sialic acid ( SA ) glycan termini increased PV sensitivity to two bnAbs that target the V2 apex and one to the interface between Env surface gp120 and transmembrane gp41 subunits , typically by up to 30-fold . These effects were reversible by incubating PV with neuraminidase . The same bnAbs were unusually potent against PBMC-produced HIV-1 , suggesting similar α-2 , 6 hypersialylated glycan termini may occur naturally . Overexpressing β-galactosyltransferase during PV production replaced complex glycans with hybrid glycans , effectively 'thinning' trimer glycan coverage . This increased PV sensitivity to some bnAbs but ablated sensitivity to one bnAb that depends on complex glycans . Other bnAbs preferred small glycans or galactose termini . For some bnAbs , the effects of GE were strain-specific , suggesting that GE had context-dependent effects on glycan clashes . GE was also able to increase the percent maximum neutralization ( i . e . saturation ) by some bnAbs . Indeed , some bnAb-resistant strains became highly sensitive with GE—thus uncovering previously unknown bnAb breadth . As might be expected , the activities of bnAbs that recognize glycan-deficient or invariant oligomannose epitopes were largely unaffected by GE . Non-neutralizing antibodies were also unaffected by GE , suggesting that trimers remain compact . Unlike mature bnAbs , germline-reverted bnAbs avoided or were indifferent to glycans , suggesting that glycan contacts are acquired as bnAbs mature . Together , our results suggest that glycovariation can greatly impact neutralization and that knowledge of the optimal Env glycoforms recognized by bnAbs may assist rational vaccine design .
Neutralizing antibodies ( nAbs ) are likely to be an essential part of the immunity conferred by an effective HIV-1 vaccine [1] . NAbs interfere with HIV-1 infection by binding to functional Envelope glycoprotein ( Env ) spikes consisting of gp120/gp41 trimers arrayed on virion surfaces , thereby blocking receptor engagement and/or membrane fusion . Env trimer surfaces are populated by a dense glycan network that constitutes ~50% of its mass [2–5] . Since anti-glycan Abs are regulated by immunological tolerance , glycans provide a formidable defense against nAbs that , at least initially , must attempt to navigate past to reach underlying protein epitopes [6–8] . Although the space between glycans is sufficient for access by single immunoglobulin domain ligands ( e . g . , soluble CD4 and llama Abs ) and by bovine Abs with very long , protruding heavy chain third complementarity determining loops ( CDRH3s ) [9–11] , glycan-free spaces are typically insufficient for human Abs , that consist of two immunoglobulin chains and less protrusive CDRH3s . Structural and glycan array studies reveal that some bnAbs overcome this problem by contacting composite protein-glycan epitopes [4] . HIV-1 Env's unparalleled sequence diversity presents a daunting challenge for vaccinologists aiming to induce broadly neutralizing antibodies ( bnAbs ) . The heterogeneity of its surface glycans could add an additional layer of difficulty . In mammals , N-linked glycosylation involves >700 genes that can impart a plethora of carbohydrate structures to Asn-X-Ser/Thr sequons ( where X can be any amino acid except for proline ) [12] . Glycosylation ( summarized in Fig 1 ) begins in the endoplasmic reticulum , where an oligomannose glycan precursor ( Glc3Man9GlcNAc2; Glc = glucose , Man = mannose , GlcNAc2 = N-acetylglucosamine ) is transferred to nascent proteins prior to folding ( Fig 1: top row , fourth glycan from left ) . Trimming of mannose termini results in Man5GlcNAc2 , the simplest oligomannose glycan , which may then be modified to form a variety of hybrid or complex glycans that may be fucosylated , galactosylated , sialylated and/or bisected by a central GlcNAc moiety ( the latter is modeled in Fig 1: bottom row , rightmost hybrid glycan ) . The 75–105 N-linked glycoforms on the surface of HIV-1 Env trimers range from untrimmed high mannose glycans to complex , multiantennary glycans . One key regulator of their maturation state is the cellular expression of enzymes that catalyze each step ( Fig 1 ) , which depends on factors including host genetics , age , infection , and pregnancy [13 , 14] . Although early mannose trimming is usually efficient , later steps such as galactosylation and sialylation may be inefficient , so that some complex and hybrid glycans may incompletely mature and lack these termini . Further variation can arise from the covalent bond angle of terminal sialic acid ( SA ) moieties , which may be predominantly α-2 , 3 or α-2 , 6-linked in different species , tissues and cell lines [15–18] . For example , peripheral blood mononuclear cell ( PBMC ) -derived HIV-1 Env is modified mostly with α-2 , 6-linked SAs , whereas that produced in human embryonic kidney ( HEK ) 293T or Chinese hamster ovary ( CHO ) cells bears mostly or exclusively α-2 , 3-linked SAs , respectively [17 , 19 , 20] . Furthermore , 293T and Jurkat cell lines impart a higher proportion of high mannose and hybrid glycans than CHO cells [15] . In humans , α-2 , 8-linked SA may be linked to α-2 , 3 or α-2 , 6-linked SA , usually in neuronal tissue ( Fig 1 ) . The density of surface glycans is so great in some Env domains that α-mannosidase , like bnAbs , has difficulty in gaining access due to steric constraints . This results in an unusually high proportion of immature oligomannose glycoforms ( Man5GlcNAc2 –Man9GlcNAc2 ) , including an oligomannose patch common to all forms of Env [17 , 21 , 22] . In general , as Env sequons "compete" for glycan addition and modification , each may variably become occupied by oligomannose , hybrid or complex glycans or , due to steric competition with neighboring sequons , may occasionally remain unoccupied ( "sequon skipping" ) . Together , the above factors contribute to considerable Env glycodiversity [21 , 23] . HIV-1 bnAbs fall into 5 distinct epitope clusters: V2 apex , V3-glycan , CD4 binding site ( CD4bs ) , gp120-gp41 interface and membrane-proximal external region ( MPER ) , whose epitopes collectively cover a large portion of the trimer's exposed surface [2 , 4 , 24–29] . Since most of these bnAbs make some glycan contacts , it appears possible that glycodiversity could modulate their activities [30] . V2 apex-specific bnAbs include at least five families ( PG9 , CAP256 , CH01 , PGT145 and PCT64-35S ) that exhibit unusually long ( >24 amino acid ) anionic CDRH3s that project outward to penetrate Env’s glycan shield and reach underlying protein [31–41] . In contrast , another V2 apex lineage represented by VRC38 . 01 uses a 16AA non-protruding CDRH3 that binds via side-chain to side-chain contacts [42] . These bnAbs contact the positively charged strand C and conserved glycans , typically at positions N156 and N160 [18 , 21 , 32 , 34 , 39 , 42] . Previous studies showed that PG9 engages SA termini [39 , 43] , recognizing α-2 , 3 SAs in one array [36] , but preferentially binding α-2 , 6 SAs in the context of an intact V1V2 domain [4 , 39] . CAP256 . 09 also binds α-2 , 6 SAs [4 , 40] . In contrast , CH01 recognizes mannosylated V2 peptides [44] . PGT145 does not bind in glycan arrays and appears to be largely insensitive to glycan changes [4] . V2 bnAbs may also ‘clash’ with some glycans . For example , glycans at position 130 of the V1 and C-terminal V2 ( V2’ ) of some strains can limit V2 bnAb sensitivity [42 , 45] . Furthermore , productive bnAb-glycan contacts or clashes may be influenced by the glycodiversity mentioned above , resulting in non-sigmoidal neutralization curves and sub-saturating neutralization , even with excess nAb [4 , 42 , 46 , 47] . V3-glycan bnAbs target the intrinsic mannose patch , usually centered around the N332 glycan [37 , 48–52] . In the absence of this glycan , proximal glycans , e . g . N137 , N156 , N295 and N301 can contribute to binding [53] . In some scenarios , however , glycan clashes may regulate neutralization [30] . Some of these bnAbs also recognize complex glycans [4 , 37 , 48 , 50 , 54] . Most CD4bs bnAbs are subject to possible clashes with a "glycan fence" that includes the N276 glycan of loop D and V5 loop glycans that surround the underlying receptor binding site [8 , 27 , 29 , 55 , 56] . Changes in the composition or maturation state of the glycan fence may regulate virus sensitivity to CD4bs bnAbs [57] . However , some bnAbs ( HJ16 and 179NC75 ) incorporate the N276 glycan in their epitopes [58 , 59] and VRC13 contacts several partially mature glycans in arrays [4] . Gp120-gp41 interface bnAbs exhibit diverse glycan dependencies . 35O22 [60] targets a quaternary epitope involving glycans N88 , N230 , N241 and N625 , and binds oligomannose and complex glycans in arrays [4] . PGT151 recognizes tetra-antennary glycans at positions 611 and 637 , with the N448 glycan playing a regulatory role [3 , 4 , 21 , 61] . 3BC176 does not bind to glycan arrays but is sterically impacted by the N88 glycan [62 , 63] . Conversely , ACS202 and VRC34 . 01 depend on the N88 glycan , but the latter is sterically impeded by the N611 glycan [64 , 65] . 8ANC195 depends on the N234 , N276 and N637 glycans and clashes with the N230 glycan [66–68] . Finally , CAP248-2B binds proximal to interface glycans , but appears to be unaffected by glycoform changes [69] . MPER bnAbs 10E8 , 2F5 , Z13 and 4E10 are not known to recognize glycans [70–72] . However , partial deglycosylation by PNGase F increases 2F5 and 4E10 affinity , perhaps due to the removal of complex glycans at the trimer base [73] . Incomplete neutralization by bnAb 10Ee8 may be due to glycan heterogeneity , particularly at position N625 [74] . Glycoengineering ( GE ) methods , including several outlined in Fig 1 can alter glycan maturation state and involve the use of: i ) glycosylation inhibitors , ii ) glycosyltransferase knockout cell lines , iii ) in vitro enzyme reactions and iv ) glycosyltransferase plasmid co-transfections . To date , only a handful of GE methods have been used to modify HIV-1 [4 , 37 , 75 , 76] . For example , kifunensine , which prevents Man9GlcNAc2 trimming ( Fig 1 ) , decreases HIV-1 sensitivity to some V2 apex mAbs [37 , 39 , 43 , 76 , 77] , but increases sensitivity to PGT125 and 35O22 [60] . Virus production in a knockout cell line lacking functional N-acetylglucosaminyltransferase I ( GNT1- ) increases virus sensitivity to some mAbs [75 , 76] , but decreases PGT151 sensitivity [3 , 4 , 61 , 78] . Aside from these and other anecdotal reports , the effects of GE on HIV-1 neutralization have not been comprehensively investigated . To fill in this knowledge gap , we investigated the effects of 16 GE methods on the sensitivities of 293T cell-produced pseudoviruses ( PVs ) to a large panel of bnAbs . Some bnAbs were dramatically impacted . PG9 and CAP256 . 09 were up to ~30-fold more potent against PVs produced with co-transfected α-2 , 6 sialyltransferase . PGT151 and PGT121 were more potent against PVs with terminal SA removed . 35O22 and CH01 were more potent against PV produced in GNT1- cells . The effects of GE on bnAbs VRC38 . 01 , VRC13 and PGT145 were inconsistent between Env strains , suggesting context-specific glycan clashes . Overexpressing β-galactosyltransferase during PV production 'thinned' glycan coverage , by replacing complex glycans with hybrid glycans . This impacted PV sensitivity to some bnAbs . Maximum percent neutralization by excess bnAb was also improved by GE . Remarkably , some otherwise resistant PVs were rendered sensitive by GE . Germline-reverted versions of some bnAbs usually differed from their mature counterparts , showing glycan indifference or avoidance , suggesting that glycan binding is not germline-encoded but rather , it is gained during affinity maturation . Overall , these GE tools provide new ways to improve bnAb-trimer recognition that may be useful for informing the design of vaccine immunogens to try to elicit similar bnAbs .
Early GE tools included adding inhibitors kifunensine and swainsonine or co-transfecting plasmids expressing N-acetylglucosaminyltransferases 1 and 3 ( GNT1 and 3 ) during PV production in 293T cells . A fifth early GE variant was PV produced in GNT1- cells . The resulting PVs are referred to hereafter as their modification followed by PV . GE-modified PVs were generally resistant to non-nAbs , although the GNT1- PV was mildly sensitive to V3 mAb 14e ( Fig 3 ) . Although F105 reduced swainsonine PV infectivity to a plateau at ~80% in the data shown , this was not observed in repeats . Overall , early GE methods did not markedly affected trimer compactness . We next analyzed 4 prototype V2 apex bnAbs: PG9 , PGT145 , CH01 and VRC38 . 01 . Like PGT145 [4] , VRC38 . 01 was unreactive in oligomannose glycan arrays ( S3 Fig ) . Consistent with previous studies , kifunensine PV was resistant to PG9 and CH01 [37 , 42 , 77] , but had little effect on PGT145 and VRC38 . 01 [42] . Conversely , GNT1- PV was >100-fold more sensitive to CH01 , suggesting that small glycans eliminate binding clashes ( Figs 2A and 3 ) . GNT1- PV was also ~10-fold more sensitive PGT145 , marginally more sensitive to VRC38 . 01 , and marginally more resistant to PG9 . On the other hand , swainsonine , which inhibits D2 and D3 mannose trimming ( Fig 1 ) , increased PG9 sensitivity ( Figs 2A and 3 ) , suggesting preferential recognition of hybrid glycans [4 , 40] . CH01 also preferred swainsonine and GNT3 PVs ( Fig 3 ) . Thus , GE here helps to minimize CH01 binding clashes by replacing complex glycans with smaller glycans like Man5GlcNAc2 ( GNT1- PV ) or hybrid glycans ( swainsonine and GNT3 PVs ) . The natural scarcity of bisected glycans in previous reports [13 , 19 , 21] suggests that GNT3 is typically poorly expressed in 293T cells . In contrast , GNT1 plasmid co-transfection had little effect on V2 bnAbs—or indeed on other bnAbs ( Figs 2A and 3 , S2 Fig ) , suggesting that natural cellular levels of this enzyme are not limiting . Gp120-gp41 interface bnAbs were heavily impacted by early GE , and in diverse ways ( Fig 3 ) . PGT151 potency was reduced against kifunensine , swainsonine and GNT1- PVs , consistent with the importance of tetra-antennary glycan contacts which are eliminated by these GE methods [4 , 61 , 78] . In stark contrast , kifunensine and GNT1- PVs were highly sensitive to 35O22 , facilitating nearly 100% saturating neutralization and suggesting a preference for high mannose glycans . 35O22 was also modestly more potent against swainsonine PV but was less potent against the GNT3 PV . VRC34 . 01 was also more potent against GNT1- and kifunensine PVs ( Figs 2A and 3 ) , although in this case the GNT1- PV was the most sensitive , suggesting that smaller glycans reduce binding clashes . VRC34 . 01 sensitivity was also improved against the GNT3 PV , perhaps due to the replacement of complex glycans with smaller hybrid glycans ( Fig 1 ) . However , swainsonine , which also promotes hybrid glycans , had no effect , suggesting that fine differences in glycan structures are important . Finally , 3BC176 activity was increased against kifunensine , GNT1- and swainsonine PVs but not GNT3 PV . Thus , 35O22 , VRC34 . 01 and 3BC176 activities were all improved by GE tools that reduce glycan size , albeit with unique patterns that reflect their different binding modes . Perhaps unsurprisingly , V3-glycan bnAbs were generally only modestly affected by early GE , as they target the intrinsic mannose patch ( S2 Fig ) . The same was true for CD4bs and MPER bnAbs , in this case because they generally target protein epitopes ( S2 Fig ) . Nevertheless , almost all of these bnAbs were more potent against the GNT1- PV , suggesting that smaller glycans help to minimize binding clashes . PGT125 and PGT128 were even more potent against kifunensine PV , consistent with a preference for untrimmed high mannose glycan . The poor neutralizing activities of CH01 and 35O22 under standard conditions were unexpected ( Fig 3 ) . We investigated whether this was assay-related by checking the activities of both mAbs in the CF2 ( used throughout this study ) and TZM-bl assays . Both mAbs incompletely neutralized the JR-FL PV at high mAb concentrations in both assays ( S4 Fig ) , although the residual infectivity in the CF2 assay plateaued at higher levels . In contrast , VRC01 completely neutralized the PV in both assays , albeit with slightly different IC50s . Although our previous studies have shown these assays yield similar IC50s , modest differences may stem from the higher CCR5 surface density of CF2 cells [79] . Modifying intermediate glycosylation steps did not impact PV resistance to non-nAbs 14e and F105 . The effects on bnAbs were generally modest ( Figs 2A and 3 ) . However , 2-deoxy-2-fluoro-l-fucose ( 2FF ) ( which inhibits core fucosylation; Fig 1 ) dramatically improved CH01 sensitivity , consistent with the increased GNT1- PV sensitivity ( Figs 2A and 3 ) , further underlining a preference for small glycans . The absence of core fucose may facilitate greater glycan flexibility , minimizing clashes . Perhaps for similar reasons , 2FF also markedly increased 35O22 sensitivity and , to a lesser extent , PGT128 , b12 and VRC01 sensitivities , but decreased sensitivities to PGT145 , PGT151 and 3BC176 ( Figs 2A and 3 , S2 Fig ) . As with early GE , mAbs directed to the V3-glycan supersite , CD4bs and MPER epitopes were largely unaffected by other intermediate GE methods ( S2 Fig ) . However , fucosyltransferase 8 ( FUCT8 ) co-transfection modestly decreased PGT128 sensitivity , mirroring the opposite effect of 2FF , suggesting that fucose causes a binding clash . The generally modest impact of FUCT8 , GNT4 and GNT5 co-transfections suggests ample cellular levels of these enzymes are already expressed in the host cells [13] . Late GE had no effect on non-nAbs and only mild effects on 3 of the 4 V2 bnAbs ( Figs 2A and 3 ) . In contrast , β-1 , 4-galactosyltransferase 1 ( B4GALT1 ) co-transfection increased PG9 potency by ~10-fold , suggesting that it helps promote the development of differentiated hybrid or complex glycan termini ( Fig 1 ) [4 , 39] . Conversely , inhibiting galactosylation with 2-deoxy-2-fluoro-d-galactose ( 2FG ) had little impact on V2 bnAbs , except for a moderate decrease in PGT145 sensitivity ( Figs 2A and 3 ) . Sialylation depends on effective galactosylation ( Fig 1 ) . Since the latter may be limiting , we promoted Env sialylation by co-transfecting various sialyltransferase plasmids together with B4GALT [13] . Co-transfection of β-galactoside α-2 , 6-sialyltransferase 1 ( ST6GAL1 ) and B4GALT1 increased PG9 sensitivity ~30-fold ( Figs 2A and 3 ) . In stark contrast , co-transfection of β-galactoside α-2 , 3-sialyltransferase 4 ( ST3GAL4 ) markedly reduced PG9 sensitivity ( Fig 2A ) , suggesting a preference for α-2 , 6 SA termini [4 , 36 , 39 , 43] . Overexpression of N-acetylneuraminide α-2 , 8-sialyltransferase 4 ( ST8SIA4 ) , which transfers additional SA moieties onto SA termini ( Fig 1 ) , also increased PG9 sensitivity ( Figs 2A and 3 ) . However , the increase was not as strong as with the B4GALT1-modification alone . Therefore , we suggest that ST8SIA4 ( used with B4GALT1 ) in fact has a mild inhibitory effect . Finally , the removal of both α-2 , 3- and α-2 , 6-linked SA termini by NA reduced PG9 sensitivity by >5-fold , further emphasizing the role of SA contacts . Gp120-gp41 interface bnAbs were dramatically affected by late GE ( Fig 2A ) . Since PGT151 recognizes tri- and tetra-antennary glycans [3 , 4 , 61] and is adversely affected by kifunensine , swainsonine , GNT1- and 2FF [78] , we were surprised that B4GALT1+/-ST6GAL1 , ST3GAL4 or ST8SIA4 also decreased PGT151 sensitivity ( Figs 2A and 3 ) . 2FG had a mild inhibitory effect , suggesting galactose-dependency . Conversely NA had no impact , suggesting impartiality to SA termini [3 , 4 , 61] . In stark contrast , the same late GE methods dramatically increased 35O22's potency by >100-fold . This was paradoxical , considering the similar enhancing effects of kifunensine , GNT1- and 2FF ( Figs 2A and 3 ) . On the other hand , the slight loss of sensitivity with NA treatment is consistent with previous reports that 35O22 recognizes complex glycans ( N88 , N241 and N625 in the JR-FL strain ) [4 , 21 , 23] . Later , we address the unexpected effects of late modifications on PGT151 and 35O22 sensitivities . BnAbs VRC34 and 3BC176 were more modestly affected by late GE , although the spread of effects was somewhat wider for 3BC176 , with B4GALT1+ST3GAL4-modified PV being the most sensitive ( Figs 2A and 3 ) . B4GALT1+/-ST6GAL1 and NA were found to increase PGT121 sensitivity ( S2 Fig ) . Otherwise late GE generally only moderately affected V3 glycan , CD4bs and MPER bnAbs . However , NA increased 2F5 resistance , perhaps because removing negatively charged SA reduces electrostatic repulsion with the membrane , so that the trimer sits deeper in the membrane . To contextualize the above findings , we extracted Env from GE-modified virus-like particles ( VLPs ) and ran them in blue native PAGE ( BN-PAGE ) -Western blot . Previously , we showed that Env resolves into two bands in BN-PAGE: trimers , largely consisting of functional gp120/gp41 , and monomers , largely consisting of high-mannose uncleaved gp160ΔCT [81 , 82] ( Fig 4 ) . GE caused some marked changes . Kifunensine , GNT1- , GNT3 and NA trimers all migrated relatively slowly ( Fig 4 , compare lanes 1 , 2 , 3 , 5 and 14 ) . This could be due to bulky untrimmed Man9GlcNAc2 glycans ( kifunensine ) , the added mass of a bisecting glycan ( GNT3 ) and , perhaps most importantly , the lack of negatively charged SAs which assist in trimer migration ( kifunensine , GNT1- and NA ) . Conversely , B4GALT1+ST3GAL4/ST6GAL1/ST8SIA4 trimers all migrated faster than the control ( Fig 4 , compare lanes 1 , 15–17 ) , suggesting that additional , negatively charged SA improves trimer migration . GNT1- , kifunensine , 2FG and B4GALT1+ST6GAL1 trimers were all relatively poorly expressed ( Fig 4 , compare lanes 1 , 2 , 3 , 12 and 16 ) . The weaker expression GNT1- and kifunensine trimers may account for the low PV infectivities that we observed earlier ( S1A Fig ) . We also analyzed a concentrated stock of replicating full-length JR-FL virus grown in PBMCs using image enhancement to assist comparison with VLP Env ( Fig 4 , lanes 18 and 21 ) . The PBMC trimer migrated slightly faster than the VLP trimer . This was unexpected , given that the VLP Env ( gp160ΔCT ) lacks 148 C-terminal amino acids , resulting in an expected reduction in trimer mass of ~48 . 9 kDa ( ~16 . 3 x 3 ) . We suggest that the relatively fast mobility of PBMC trimer is driven by Env hypersialylation [19] . To further contextualize the neutralization analysis above , we analyzed GE-modified VLP gp120 and gp41 in SDS-PAGE-Western blots . Fine details can be found in S1 Text and Figs A and B contained therein . Briefly , the key findings were as follows: i ) B4GALT1 increased Env endo H-sensitivity , consistent with the partial replacement of complex glycans with unfucosylated hybrid glycans . This may explain why B4GALT1 unexpectedly reduced PV sensitivity to multiantennary glycan-preferring bnAb PGT151 and increased sensitivity to the small glycan-preferring bnAb 35O22 . ii ) GNT3 , swainsonine and 2FF also increased Env endo H-sensitivity , albeit less effectively than B4GALT1 , suggesting that these methods also replace complex glycans with hybrid glycans , albeit less effectively than B4GALT1 . iii ) Kifunensine and GNT1- Env consisted of relatively homogeneous , endo H-sensitive high mannose glycans , as expected . Given that B4GALT1+/-ST6GAL1 impacts PG9 , 35O22 and PGT151 sensitivities ( Figs 2A and 3 ) and promotes hybrid glycans ( S1 Text ) , we wondered what effect ST6GAL1 alone might have . Unlike B4GALT1 , ST6GAL1 alone did not affect gp120 or gp41 endo H sensitivity , suggesting that it does not promote hybrid glycans ( S5A Fig ) . However , it nevertheless increased PG9 sensitivity to near equivalent levels as B4GALT1+ST6GAL1 PV ( S5B Fig ) . This suggests that cellular galactosylation is sufficient for ST6GAL1 to attach α-2 , 6 SA termini . However , unlike the B4GALT1 treatments , ST6GAL1 alone did not increase 35O22 sensitivity and only modestly decreased PGT151 sensitivity ( S5B Fig ) . As expected , VRC01 was unaffected . Referring to Fig 1 , upon the formation of a hybrid intermediate in the medial Golgi , there is a bifurcation in the pathway , where glycans either mature into complex or hybrid glycoforms . We suggest that B4GALT1 overexpression diverts glycoprotein traffic in the latter direction , so that normally complex glycans ( magenta in S5C Fig ) are replaced by smaller hybrid glycans ( orange in S5C Fig ) , thus "thinning" Env glycan coverage . Co-transfection of ST6GAL1 and B4GALT1 further improves PG9 sensitivity by promoting sialylation ( yellow in S5C Fig ) . According to Fig 1 , swainsonine , 2FF and GNT3 modifications might also divert traffic to the hybrid glycan branch . However , judging from gp41 endo H laddering patterns , B4GALT1 is more effective ( S1 Text ) . To further assess the effects of GE , we next analyzed the vaccine strain BG505 T332N gp160ΔCT ( referred to as 'BG505' hereafter ) , focusing on the GE tools that markedly impacted JR-FL sensitivity: GNT3 , 2FF , swainsonine , B4GALT1 , ST3GAL4 , ST6GAL1 and NA . Kifunensine and GNT1- PV infection was undetectable ( S1B Fig ) . Several other treatments also reduced infection more significantly than they did for JR-FL ( S1A and S1B Fig ) . The effects of GE on BG505 bnAb sensitivity are shown in Fig 2B and S6 Fig . As for JR-FL , BG505 resistance to 14e or F105 was unaffected by GE ( S6 Fig ) . Also , as for JR-FL , PG9 potency was improved against B4GALT1+ST6GAL1 and swainsonine PVs but was reduced against NA PV . In contrast to JR-FL , however , B4GALT1 PV did not show increased PG9 sensitivity and B4GALT1+ST3GAL4 PV did not reduce PG9 sensitivity . Remarkably , B4GALT1+ST6GAL1 increased CAP256 . 09 sensitivity by ~30-fold and NA decreased sensitivity by a similar factor , consistent with evidence that CAP256 . 09 contacts terminal SA [4 , 36 , 40] . As for PG9 , B4GALT1 alone had no effect on CAP256 . 09 , whereas the B4GALT1+ST3GAL4 PV was moderately resistant . PGT145 , CH01 and VRC38 . 01 were largely unaffected by GE , except that 2FF inhibited CH01 ( S6 Fig ) , contrasting sharply with its impact on JR-FL sensitivity ( Fig 3 ) , suggesting strain-specific GE effects . As for the JR-FL strain , BG505 sensitivities to V3-glycan and CD4bs bnAbs were largely unaffected by GE ( Fig 2B , S6 Fig ) . However , VRC13 was a notable exception . Unlike most CD4bs mAbs , VRC13 binds to mono- and biantennary glycans bearing terminal galactose in glycan arrays [4] . Contrasting with the mild effects observed for JR-FL , VRC13 neutralization of the BG505 strain was markedly impacted by GE . NA PV was the most sensitive , followed by the B4GALT1 PV . Conversely , swainsonine and GNT3 PVs were less sensitive . Unlike JR-FL , B4GALT1 overexpression did not markedly affect BG505 sensitivity to PGT151 or 35O22 ( S6 Fig ) , mirroring the similar lack of impact of B4GALT1 on V2 mAbs ( also unlike JR-FL ) . NA slightly enhanced PGT151 sensitivity ( S6 Fig ) —also not observed for JR-FL ( Fig 2 ) . In contrast , B4GALT1+ST6GAL1 mildly inhibited PGT151 , suggesting a negative impact of terminal SA . Swainsonine and , to a lesser extent , GNT3 , both reduced PGT151 sensitivity , consistent with the partial replacement of multi-antennary glycans with hybrid glycans [4] . None of the GE modifications greatly impacted VRC34 . 01 sensitivity ( S6 Fig ) . However , 8ANC195 was hypersensitive to B4GALT1+ST6GAL1 PV and was more resistant to NA PV . This suggests α-2 , 6-linked SA dependency , as we observed for PG9 and CAP256 . 09 . Finally , several GE modifications increased 2F5 sensitivity ( S6 Fig ) but had less impact on 4E10 and 10E8 . Conversely , NA digestion mildly inhibited 2F5 and 4E10 , suggesting that , as for the JR-FL strain , removing negatively charged SA moieties may allow the trimer to sit deeper in the membrane , partially obscuring the MPER . We next examined whether GE-mediated changes in HIV+ plasma sensitivity matched those of mAbs isolated from the same donors . Donor CAP256 plasma neutralization is overwhelmingly mediated by V2 apex bnAbs of the CAP256 lineage [35] . Donor N152 plasma neutralization is largely mediated by the 10E8 bnAb lineage but is also the source of bnAb 35O22 that only modestly contributes to plasma neutralization [60] . B4GALT1+ST6GAL1 increased the sensitivity of BG505 PV to the CAP256 plasma by 100-fold , mirroring the 30-fold increase in CAP256 . 09 sensitivity ( S7A Fig ) . However , a BG505 K169E mutant was resistant to both , consistent with the criticial role of the K169 contact ( S7A Fig ) . In contrast , B4GALT1+ST6GAL1 modified JR-FL PV showed modestly improved sensitivity to the N152 plasma , as observed for 10E8 , but unlike its dramatic effect on 35O22 sensitivity ( S7B and S2 Figs and Fig 3 ) . Thus , the effects of GE on HIV+ plasmas appear to track with predominant bnAb specificities . To formally check that ST6GAL1-mediated increases in sensitivity to PG9 , CAP256 . 09 and 8ANC195 were due to higher numbers of α-2 , 6 SA termini , B4GALT1+/-ST6GAL1 PVs were subsequently treated with NA and then re-tested for sensitivity to these bnAbs . NA effectively reversed the effects of B4GALT1+ST6GAL1 , confirming the importance of SA for these mAbs ( S8 Fig ) . Nevertheless , B4GALT1+NA and B4GALT1+ST6GAL1+NA PVs were not as sensitive as PV treated with NA alone . In contrast , the effects of the same treatments on JR-FL sensitivity to 35O22 was permanent , i . e . it was not reversed by NA ( S8A Fig ) , confirming that SA was not involved in the gain of 35O22 sensitivity with B4GALT1 . PGT151 activity of B4GALT1+/-ST6GAL1 PV was also not fully recovered by NA , again suggesting a permanent effect , although there was a modest gain of sensitivity for B4GALT1 PV ( S8A Fig ) . As noted above , B4GALT1 appeared to have a relatively modest effect on BG505 sensitivity to PGT151 ( compare S8A and S8B Fig ) —a point we return to below . We next examined the effects of GE on two newly reported interface bnAbs ACS202 and CAP248-2B [65 , 69] . Details are given in S2 Text and Figs C and D contained therein . In brief , both nAbs were only modestly affected by GE . B4GALT1 reduced ACS202 sensitivity slightly while slightly increasing CAP248-2B sensitivity , suggesting that smaller glycans may resolve clashes in the latter case . Overall , this is consistent with the diverse binding mechanisms of interface bnAbs , where some are markedly affected by GE in different ways ( PGT151 , 35O22 , 8ANC195 ) , whereas others are marginally affected ( VRC34 . 01 , ACS202 and CAP248-2B ) . We also examined the effects of GE on PG9 and PGT145 developmental relatives . Details are given in S2 Text and Fig E contained therein ) . In brief , glycan proclivities appeared to be consistent within the mature branches of these lineages . We next investigated the possibility that negatively charged poly-SA chains might further impact PV sensitivity to V2 apex and interface bnAbs . This may have been overlooked by our use of B4GALT1+ST8SIA4 above ( Figs 2A and 3 ) , as this lacked ST6GAL1 that might be needed to create α-2 , 6 SA termini as substrates for ST8SIA4 ( Fig 1 ) . Details are provided in S2 Text and Fig F contained therein . In brief , poly SA chains formed by B4GALT1+ST6GAL1+ST8SIA4 triple transfection did not accentuate the effects already observed with B4GALT1+ST6GAL1 or B4GALT1+ST8SIA4 component double treatments , suggesting that extra charge does not improve bnAb binding . Given the differing impact of B4GALT1 on JR-FL and BG505 sensitivities to interface bnAbs 35O22 and PGT151 ( Figs 2 and 3 , S2 and S6 Figs ) , we wondered if B4GALT1-induced endo H-sensitivity observed for JR-FL might occur with B4GALT1 treatment of other strains ( S1 Text , S5 Fig ) . Fourteen VLPs from various clades were produced with or without B4GALT1 , then analyzed by SDS-PAGE-Western blot and probed for gp41 . The gp41 bands of untreated VLPs were , in most cases , largely endo H-resistant , whereas their B4GALT1-modified counterparts were endo H-sensitive , suggesting that it inhibits gp41 glycan maturation ( S9 Fig ) . The sizes of gp41 bands from strains BG505 , JR-FL , WITO and 16055 were relatively small , consistent with their truncated gp41 tails ( gp160ΔCT ) and staining was also relatively strong . Of these four strains , unexpectedly , BG505 and WITO gp41 bands were partially endo H-sensitive even without B4GALT1 modification ( S9A Fig ) . The complex laddering in both cases suggested substantial glycodiversity . It is possible that the particularly high expression of the BG505 and WITO Env clones leads to these unusual band patterns . Overall , these findings raise the possibility that the milder effects of B4GALT1 on BG505 sensitivities to 35O22 and PGT151 as compared to JR-FL may be because hybrid gp41 glycans are already present in the BG505 strain , thus diluting the impact of B4GALT1 co-expression . We next investigated the effect of GE on the sensitivities of the same 14 strains ( Fig 5 ) . Here , the geometric mean IC50 of each bnAb against all PVs was plotted to the right of each graph . Overlapping dots in Fig 5 are resolved in S1 Table , where IC50s are shown in a heat map . Wilcoxon Signed Rank tests were performed using two columns of data , in which the IC50s of a given mAb against control and GE PVs were paired for each strain ( S1 Table ) . Representative mAb titrations are shown in S10 Fig . Env sequences of these and other strains used in this study are shown in S11 Fig . BnAbs were categorized into 4 groups , as follows: In all but one case ( CM244 . ec1 ) , B4GALT1+ST6GAL1 PVs were more PG9-sensitive . 10 B4GALT1+ST6GAL1 PVs were also more sensitive to CAP256 . 09 . Remarkably , the otherwise CAP256 . 09-resistant Q23 . 17 strain was rendered highly sensitive ( S10A Fig , S1 Table ) . Four other PVs: JR-FL , JR-CSF , WITO and REJO were resistant to CAP256 . 09 , due to their lack of the key 169K contact ( S11 Fig ) . B4GALT1 and ST6GAL1 treatments alone also generally increased PV sensitivities to these mAbs . B4GALT1 was , in many cases , less effective for CAP256 . 09 than for PG9 , perhaps because it promotes hybrid glycans that are better tolerated by PG9 [4] . In contrast , NA generally reduced sensitivity to these bnAbs to varying extents . Overall , the improved sensitivity imparted by α-2 , 6 SA-modification is conserved across multiple clades , and we classify these bnAbs as α-2 , 6 SA-dependent . PGT151 sensitivity was consistently , albeit usually modestly , improved by NA , suggesting a conserved preference for terminal galactose [4 , 61] . This was particularly marked for the 16055 strain ( Fig 5 , S10B Fig ) . Interestingly , GNT1- and B4GALT1 both consistently reduced PGT151 sensitivity , although the extent varied considerably between strains . GE also affected neutralization saturation . Thus , GNT1- modification of the JR-CSF strain completely eliminated PGT151 sensitivity , whereas B4GALT1+/-ST6GAL1 reduced saturation to ~50% ( S10B Fig ) . For 8 viruses , PGT151 sensitivities of B4GALT1 PVs were increased by subsequent NA treatment ( compare B4GALT1 and B4GALT1+NA in Fig 5 ) . However , in all cases except BG505 and JR-CSF , IC50s did not reach the sensitivity achieved by NA alone . Thus , in most cases , B4GALT1 reduces PGT151 sensitivity in a manner that is not fully recoverable by subsequent NA treatment . PGT121 was also effective against NA PVs and most B4GALT1+NA PVs . In some cases , resistant ( or nearly resistant ) strains became sensitive with these ( and other ) treatments ( REJO , CNE58 , 16055 and KER2018 . 11 ) , thus increasing PGT121 breadth ( Fig 5 , S1 Table and S10C Fig ) . Three of these 4 strains ( CNE58 excepted ) lack-the canonical N332 glycan , but two have a glycan at position N334 instead ( 16055 excepted ) , raising the possibility that GE can help compensate and restore PGT121 binding ( S11 Fig ) . Overall , these findings suggest that PGT121 prefers galactose termini , consistent with glycan array data [4] . High concentrations of 35O22 did not quite reach an IC50 for all 14 unmodified strains ( Fig 5 ) . However , activity was dramatically and consistently increased in 12 GNT1- PVs tested ( GNT1- modified BG505 and CNE58 PVs were poorly infectious and were therefore omitted; Fig 5 , S1 Table ) . For 9 strains , B4GALT1 PV also increased 35O22 sensitivity . Notably , B4GALT1+/-ST6GAL1 improved 35O22 saturation of JR-CSF and KER2018 . 11 PVs , and GNT1- led to a further increase ( S10D Fig ) . This pattern was the exact reverse of that observed for PGT151 ( S10B Fig ) . Since 35O22 binds to complex and high mannose glycans in arrays , we suggest that its greater activity against GNT1- PVs might be due to improved glycan core binding [4] . Overall , GE consistently improved 35O22 neutralization , in part by improving saturation—a point that we return to later below . Consistent with earlier observations ( Fig 3 and S6 Fig ) , 5 of 5 CH01-sensitive strains became more sensitive with GNT1- modifications . GNT1- modification uncovered CH01 sensitivity in another 5 otherwise resistant strains ( Fig 5 ) . In keeping with the lack of CH01 binding to glycan arrays , it appears that smaller Man5GlcNAc2 glycans consistently minimize clashes . However , two strains ( REJO and 16055 ) remained resistant upon GNT1- modification . Since key CH01 contacts ( N156 and N160 glycans and K171 ) [42] are present , this resistance may be due to remaining glycan clashes . Contrasting with the largely consistent and , in some cases , highly significant ( S1 Table ) patterns above , the most sensitive GE variant for some bnAbs differed between strains . PGT145 potently neutralized many GNT1- PVs but was less effective on Q23 . 17 . NA-treated PVs were also largely sensitive , except for ZM233 . 6 . However , GNT1- and B4GALT1 versions of ZM233 . 6 were highly PGT145-sensitive ( Fig 5; S1 Table ) . V2 bnAbs are subject to potential clashes with glycans at position N130 and in the V2' region ( residues 183–191 , stippled pattern in S11 Fig ) [45] . None of the 14 strains used in Fig 5 have a N130 glycan , although BG505 , BI369 . 9A and ZM233 . 6 each have two sequons in the V2' region , while the other strains have one or none ( S11 Fig ) . The dramatically higher PGT145 sensitivities of GNT1- and B4GALT1-modified BI369 . 9A and ZM233 . 6 PVs may be because glycan clashes are eliminated . Unmodified BG505 is PGT145-sensitive without GE modifications , suggesting that the V2' glycans of this strain do not limit PGT145 access . The outlier status of the ZM233 . 6 strain may be related to its unique lack ( among these strains ) of the N156 glycan ( S11 Fig ) . Overall , PGT145 appears to be glycan-averse and subject to clashes , consistent with its lack of glycan array binding . GE also had variable effects on VRC13 . Half of the GNT1- PVs tested were sensitive . NA also improved sensitivity in most cases . The glycan fence surrounding the primary receptor site [57] , typically consists of 4 to 7 glycans , including variable glycans of the V5 loop ( S11 Fig ) . Although VRC13 may clash with some of these glycans , it may contact others . Therefore , since GE may eliminate clashes or modulate mAb-glycan contacts , it is difficult to unequivocally interpret these patterns . GE also variably affected VRC38 . 01 . Given the lack of glycan array binding , this may be due to glycan clashes . Thus , GNT1- PVs of the REJO and 16055 strains were far more sensitive than control PVs , while the sensitivities of other strains were less affected , and in one case ( T250-4 ) , was lower ( Fig 5 , S1 Table and S10E Fig ) . The effects of B4GALT1+ST6GAL1 also varied . As mentioned above , all the strains in this panel lack the N130 glycan that clashes with VRC38 . 01 binding . These strains also exhibit most if not all known VRC38 . 01 contacts ( S11 Fig ) [42] . The resistance of the CH070 and ZM233 . 6 strains may be related to their lack of a tyrosine at position 173 that may help orient the N156 glycan for binding ( both strains ) and/or the absence of the N156 glycan ( ZM233 . 6 strain; S11 Fig ) . The activities of other , less broadly neutralizing nAbs were also examined against these 14 strains . Although HJ16 neutralized the 16055 PV and was unaffected by GE [21 , 23] , it remarkably enhanced infection by Q23 . 17 and KER2018 . 11 PVs in various formats ( S10F Fig ) . However , this enhancement was reduced ( Q23 . 17 ) or eliminated ( KER2018 . 11 ) against GNT1- versions of these PVs ( S10F Fig ) , implying that HJ16 can activate infection by some strains when they carry complex glycans . WITO sensitivity to 8ANC195 was knocked in by B4GALT1+ST6GAL1 , thereby increasing its breadth ( S10G Fig ) . This effect was partially reversed by NA , as above with the BG505 strain ( S8 Fig ) . Thus , although WITO strain bears the N230 glycan thought to clash with this mAb and lacks the N234 glycan thought to be important for binding , B4GALT1+ST6GAL1 modification was sufficient to allow this mAb to neutralize . In many cases , GE increased the percent maximum neutralization by excess nAb . Perhaps the best examples are 35O22 and CH01 ( Fig 2 ) . Using neutralization data from the 14-virus panel ( Fig 5 ) , we plotted the % of control and GE-modified PVs neutralized to >65% , >90% and >95% saturation by excess bnAb ( 10μg/ml ) . GE dramatically improved saturation by PG9 , CAP256 . 09 , 35O22 and CH01 ( Fig 6 ) . Notably , PG9 neutralized all B4GALT1+ST6GAL1 PVs to >95% saturation . Although the effects of GE on PGT151 and PGT121 were relatively modest , there was also a positive trend . For PGT145 , there was no clear increase in saturation against GNT1- PVs , consistent with the variable effects noted in Fig 5 . Overall , despite the small numbers of viruses analyzed here , we infer that , in many cases , GE improves neutralization saturation , in some cases dramatically , either by eliminating glycan clashes and/or by creating optimal glycan structures for optimal binding . Above , we found that certain bnAbs were more effective against B4GALT1+ST6GAL1 PVs . In primary cells , HIV-1 Env is thought to be naturally modified by terminal α-2 , 6 SA ( contrasting α-2 , 3 SA , as common for 293T cells ) [19] . Furthermore , the relatively fast mobility of PBMC-derived trimers in BN-PAGE ( Fig 4 ) suggest possible hypersialylation . We wondered if these factors might make PBMC virus unusually sensitive to these bnAbs . We therefore investigated the change in IC50 of PVs upon B4GALT1+ST6GAL1-modification and the change in IC50 of 293T cell-produced infectious molecular clones ( IMCs ) upon PBMC passage . B4GALT1+ST6GAL1-modification of 45_01DG5 and T278-50 PVs rendered them highly sensitive to the α-2 , 6 SA-dependent bnAb CAP256 . 25 ( Fig 7 ) . Notably , the 45_01DG5 strain bears a methionine at position 165 of the V2 loop C strand ( S11 Fig ) , suggesting that improved glycan contacts may compensate for suboptimal protein contacts . PBMC passage also increased the IMC sensitivity of both strains to CAP256 . 25 ( Fig 7 ) . B4GALT1+ST6GAL1-modification and PBMC passage also improved YU2 sensitivity to 8ANC195 . However , ADA sensitivity was unaffected . The sensitivities of 45_01DG5 , T278-50 and JR-CSF to PG9 were also increased by B4GALT1+ST6GAL1 modification and PBMC passage . These gains were similar in magnitude to those observed with 8ANC195 on YU2 but less than those with CAP256 . 25 . Although B4GALT1+ST6GAL1-modification marginally improved YU2 and ADA sensitivities to PG9 , there was no clear effect of PBMC passage on IMC sensitivity . In stark contrast to most of the above findings with α-2 , 6 SA-reactive bnAbs , PGT145 and VRC01 IMC sensitivities were in no case improved by PBMC passage . In fact , sensitivities to VRC01 were reduced in all cases and T278-50 sensitivity was completely knocked out for both bnAbs . Consistent with these findings , B4GALT1+ST6GAL1-modification also did not enhance sensitivity to these mAbs . Overall , these observations suggest that PBMC passage modifies IMCs with α-2 , 6 SA in a similar way that B4GALT1+ST6GAL1 modifies PV , increasing sensitivities to bnAbs that depend on these hypersialylated termini . However , the sensitivity of PBMC-grown IMCs was , in most cases , lower than that of B4GALT1+ST6GAL1-modified PV . Given the key role of glycan contacts for CAP256 . 09 and PG9 , we next investigated their role in sensitivity to germline-reverted versions of these bnAbs . Autologous PV from donor CAP256 sampled at 34 weeks was sensitive to an unmutated common ancestor ( UCA ) and I1 intermediate CAP256 bnAb and the mature . 09 and . 25 clones ( Fig 8A ) . However , the effects of GE changed as these Abs matured . The UCA preferentially neutralized the GNT1- and B4GALT1+ST6GAL1 PVs , whereas the I1 intermediate was indifferent to GE and both mature clones showed a strong preference for the B4GALT1+ST6GAL1 PV and weaker neutralization of the GNT1- PV . Together , this suggests that α-2 , 6 SA binding is not germline-encoded but develops during ontogeny . The stronger GNT1- PV sensitivity to the UCA suggests a benefit of reducing glycan clashes . The higher sensitivity of the B4GALT1+ST6GAL1 PV to the UCA is consistent with B4GALT1-induced glycan "thinning" ( replacing complex glycans with hybrid glycans ) that may also reduce clashes . To approximate the PG9 ancestor , we used a germline-reverted heavy chain ( gH ) co-expressed with the mature light chain ( mL ) [34 , 42] . Since autologous viruses were unavailable , we examined heterologous tier 2 strains . GE did not affect the activity of the revertant against the 16055 PV ( Fig 8B ) . However , Q23 . 17 GNT1- PV and , to a lesser extent , B4GALT1+ST6GAL1-modified Q23 . 17 PV were relatively sensitive ( Fig 8B ) . In contrast , the B4GALT1+ST6GAL1 PV of both strains was most sensitive to mature PG9 . Overall , this suggests that the SA binding is not a feature of CAP256 or PG9 mAb ancestors , where smaller glycans may be more important to minimize clashes , at least in some settings . To further investigate the impact of GE on the activities of bnAb ancestors , we used a panel of 9 V2 nAb-sensitive strains [34 , 36 , 40 , 42 , 45] that all lack the N130 glycan and have short , sparsely glycosylated V2' regions ( S11 Fig ) . Additional data for the JR-FL strain is shown in S12 Fig . As we observed above ( Fig 5 ) , CAP256 . 09 preferentially neutralized B4GALT1+ST6GAL1 PVs , but the UCA and I1 variants preferentially neutralized the GNT1- and B4GALT1+ST6GAL1-modified autologous PVs ( Fig 8A and 8C , S2 Table ) . The I1 intermediate also neutralized GNT1- modified 16055 strain and ( marginally ) the T250-4 strain , but no others . Similarly , the PG9 revertant did not exhibit the B4GALT1+ST6GAL1 preference of its mature counterpart ( Fig 8B and 8C ) . For some strains , GNT1- PVs were slightly more sensitive , although this difference was not statistically significant when all 9 strains were considered ( Fig 8C , S2 Table ) . The high sensitivities of many GNT1- PVs to mature CH04 ( a clonal variant of CH01 ) were mirrored by high sensitivities of GNT1- PVs of some strains to its UCA ( WITO , KER2018 and Q23 . 17; Fig 8C ) . The effects of GNT1- on PGT145 mHgL revertant sensitivity were mixed , but generally matched those of mature PGT145 . For example , the revertant neutralized the KER2018 . 11 and JR-FL GNT1- PVs more effectively , as did its mature counterpart , although revertant neutralization did not reach an IC50 titer against KER2018 . 11 ( S12A Fig ) . KER2018 . 11 , C1080 , Q23 . 17 and JR-FL GNT1- PVs were more sensitive to the VRC38 . 01 revertant ( Fig 8C , S12B Fig ) . Similarly , a mixed chain VRC13 mHgL revertant neutralized the JR-FL GNT1- PV more effectively , as did its mature counterpart ( S12C Fig ) . Overall , the sensitivities of GNT1- PVs to PGT145 , VRC38 . 01 and VRC13 varied between strains , generally in concert with their mature counterparts , suggesting that strain-specific clashes are important throughout their development and may , in some cases , be alleviated by PV production in GNT1- cells . Finally , we examined the effects of GE on JR-FL sensitivity to a somatic ancestor and a revertant of PGT121 . Neutralization by the 3H3L ancestor derived from deep sequencing [83] was not appreciably enhanced by NA , unlike mature PGT121 ( S12D Fig ) . Moreover , it was most effective against the GNT1- PV , contrasting sharply with mature PGT121 . Reverted forms of PGT121 , including CDR3mat [84 , 85] all failed to neutralize ( S12D Fig ) . The increased sensitivity of GNT1- trimers to germline-reverted bnAbs raises the possibility that they could be useful as vaccine priming immunogens . To be effective in a vaccine context , it may be important to ascertain that GNT1- trimers are compact and V3-resistant like their unmodified counterparts . This would assuage any concerns of 'off target' responses that could drain focus from desired targets . We investigated this question using HIV-1+ donor plasmas . The N90 plasma ( source of the VRC38 nAb lineage [42] ) neutralized GNT1- JR-FL PV ~30-fold more effectively than the control ( Fig 9A ) . The weakly neutralizing 1648 plasma exhibited a similar increase [86] ( Fig 9B ) . In contrast , the CAP256 plasma neutralized the T250-4 control and GNT1- PVs equivalently [35] ( Fig 9C ) . These differences could reflect the differing glycan proclivities of the bnAbs they contain . However , the high potencies of the N90 and 1648 plasmas against GNT1- PV could also be due to the increased sensitivity of the modified PVs to V3-directed non-nAbs . To investigate , we used peptides to adsorb V3 non-nAbs and a JR-FL A328G mutant which is known to have an overtly V3-sensitive tier 1 phenotype as a reference [79] . V3 mAb 14e neutralized the GNT1- PV somewhat more effectively than the control , whereas the A328G tier 1 mutant was highly sensitive ( Fig 9D ) . Non-nAb F105 ( CD4bs ) also potently neutralized the A328G mutant , but not the GNT1- PV ( Fig 9E ) . The A328G mutant was also sensitive to the N90 plasma , but slightly less so than the GNT1- PV ( Fig 9F ) . Conversely , the A328G PV was more sensitive to the 1648 plasma than the GNT1- PV , suggesting that V3 neutralization dominates when tier 2 nAb titers are weak ( Fig 9G ) . Added V3 peptides fully adsorbed 14e but not F105 activity , as expected ( Fig 9D and 9E ) . They also adsorbed A328G neutralization by the N90 and 1648 plasmas . In stark contrast , however , the sensitivities of GNT1- and control PVs were both unaffected ( Fig 9F and 9G ) . This is important , as it implies that the increased sensitivity of GNT1- modified JR-FL PV to the N90 plasma is largely due to increased sensitivity to tier 2 bnAbs , rather than to increased sensitivity to V3 non-nAbs . Quantitatively , if the IC50 of 14e against GNT1- PV of ~10μg/ml ( Fig 9D ) and infected plasmas contain an estimated ~100–1000μg/ml total of anti-gp120 Abs , the maximum plasma ID50 of 14e-like Abs against the GNT1- PV could be 1:100 . This suggests that the increased N90 plasma ID50 ( ~1:10 , 000 ) against the GNT1- PV ( Fig 8A ) is not due to V3 non-nAbs , as also confirmed by the lack of effect of interfering V3 peptide . In contrast , the ~5-fold reduced A328G mutant sensitivity to the N90 plasma with added V3 peptides suggests that V3 Abs contribute a sizeable fraction of the activity against this tier 1 virus ( Fig 9F ) . Overall , these findings suggest that GNT1- trimers retain a largely V3-resistant , compact tier 2 conformation but can be more sensitive to tier 2 bnAbs and their revertants , making them attractive for use in vaccine priming .
Here we sought to better understand the glycan structures recognized by bnAbs and how this glycoreactivity evolves and might be applied to vaccine design . Our findings suggest that bnAb precursors initially avoid glycans . UCA binding to native trimers may be facilitated when glycan sequons are unusually absent or skipped over ( i . e . "glycan holes" ) [79 , 87 , 88] or when glycan maturation is stunted , minimizing clashes . Efforts to understand the early events in bnAb development are complicated by several factors . First , in many cases , "UCA-triggering" Env strains are unknown , as are the glycans they carry and , indeed , their form ( e . g . gp120/gp41 trimer , gp160 or gp120 monomer ) . Second , many bnAb ancestors are mere approximations [34] , raising questions about how well they represent the behavior of genuine ancestors . Third , UCAs typically exhibit little , if any , neutralizing activity against the triggering viruses , suggesting that more sensitive assays may be needed . In vitro trimer binding assays may be more sensitive and informative . In a recent study , the PCT64 UCA did not neutralize autologous virus , but weakly bound to an autologous GNT1- Env trimer , supporting the preference of bnAb ancestors for minimally glycosylated Env [41] . Similarly , CH04 UCA binding to SOSIP trimers was detected when neutralization was not [34] . Accordingly , we are now investigating the binding of bnAb ancestors to GE-modified VLPs by ELISA . Even these assays may be too stringent: evidence of inferred bnAb ancestor triggering even when they fail to detectably bind to the antigen in vitro attests to the exquisite sensitivity of the earliest stages of bnAb maturation [84 , 85 , 88] . During maturation , some nAbs acquire an ability to bind glycans . Indeed , the paratope electropositivity of VRC01 class and some V2 bnAbs increases with maturation [34 , 35 , 42 , 89] , ostensibly to facilitate interactions with heavily glycosylated HIV-1 Env spikes that often bear negatively charged SA termini . For VRC01-class bnAbs , this may help accommodate the N276 glycan [21] . For CAP256 bnAbs , α-2 , 6 hypersialylated trimers may promote the development of SA contacts . In contrast , for some nAbs such as CH01 and 35O22 , the preference for small glycans does not change and clashes apparently remain unresolved . PGT151 and PGT121 both preferred NA-treated PV . Electrostatic incompatibility with SA does not explain this behavior , as these mAbs recognize complex glycans with galactose termini [4 , 37 , 48 , 50 , 54] and do not preferentially neutralize GNT1- PV . Overall , there was a remarkable agreement between bnAb GE preferences and their reactivity in glycan arrays [4] , with some exceptions: despite the SA-dependency of 8ANC195 observed here , this nAb did not bind in glycan arrays [4] . Similarly , although 35O22 bound complex glycans with SA antennae in arrays , it nevertheless neutralized GNT1- PV highly effectively , perhaps suggesting contact with glycan cores . Our observation that PG9 , CAP256 . 09 and 8ANC195 "punched above their weight" against PBMC-passaged virus is consistent with an earlier report in which PG9 outperformed b12 , 4E10 , 2F5 and VRC01 [90] . This , coupled with fast mobility of PBMC trimers in BN-PAGE and similar increased potency of these bnAbs against B4GALT1+ST6GAL1-modified PV all lead to the same conclusion that these bnAbs are more effective against α-2 , 6 hypersialylated trimers . This has two consequences for vaccine development . First , that these bnAbs may be particularly effective in a clinical setting . Second , that α-2 , 6 hypersialylated trimers may be ideal for boosting as they better match PBMC virus and may promote the development of α-2 , 6 SA-dependent bnAbs or else provide a "closer to real life" glycosylation profile to enable other bnAbs to navigate past them . Another explanation for the outperformance of these mAbs against PBMC-passaged virus could be increased V2 tyrosine sulfation at positions Y173 and Y177 , which may increase sensitivity to trimer-preferring bnAbs [91] . However , the lack of increased PGT145 potency with PBMC-passaging suggests that α-2 , 6 SA modification has a more decisive role in regulating bnAb sensitivity . Sulfation may , however , contribute to the generally higher nAb resistance of PBMC viruses [90 , 92 , 93] . Indeed , the fast migration of PBMC trimers in BN-PAGE could be due to increased sialylation and/or sulfation . Our findings raise the question of what factors are important for bnAb development in natural infection . Clearly , the answer may be multi-factorial and may include virus sequence diversity and host antibody repertoire differences . Host-encoded glycovariation may also be a factor . Thus , it may be that the donors who developed CAP256 and PG9 lineages impart α-2 , 6 SA termini with relatively high efficiency . Conversely , PGT121 and PGT151 may have developed in donors where sialylation was inefficient . 35O22 and CH01 may have developed against viral strains with glycan holes that eliminate clashes or else the donor glycosylation machinery might naturally express Env trimers bearing smaller glycans . Indeed , PBMC-based HIV-1 neutralization assays are notoriously subject to significant inter-donor variability that could in part stem from host glycosylation differences that could toggle bnAb epitope exposure on the Env trimers they express . Our key messages are summarized in Fig 10 . GE was able to increase bnAb potency , saturation and breadth ( Fig 10A ) , revealing the most sensitive glycoforms for prototype bnAbs ( Fig 10B ) and suggesting new prime-boost vaccine strategies ( Fig 10C ) . The diffuse gp120 bands observed in Western blots suggest a possible swarm of glycovariants [21 , 23] . BnAbs may not be able to neutralize all these variants , providing an avenue for virus 'escape' without mutation . However , GE could resolve this and improve bnAb saturation , either by optimizing glycan contacts and/or by eliminating clashes . Remarkably , several otherwise bnAb-resistant strains were rendered sensitive by GE , uncovering previously unappreciated breadth . Quantifying the extent of this increased breadth will require an analysis of larger numbers of GE-modified PVs . Our findings provide new opportunities for affinity-based prime-boost native trimer vaccine strategies [84 , 85] ( Fig 10C ) . Thus , priming immunogens may be derived from hypersensitive strains that are i ) modified to introduce glycan holes at the desired target , ii ) mutated to maximize nAb affinity , iii ) GE-modified to eliminate glycan clashes and iv ) modified to plug glycan holes at unwanted sites . For example , VRC01 priming immunogens might ideally lack the N276 and N463 glycans [7 , 28 , 94] , and be GNT1- modified to further reduce clashes , permitting different angles of approach and improving electrostatic compatibility . In support of GNT1- trimers as priming immunogens , we found that they are recognized poorly by non-nAbs–implying that off-target non-neutralizing responses should be limited . GNT1- modified Q23 . 17 trimers were hypersensitive to PG9 , CH04 and VRC38 . 01 germline-revertants , raising particular interest in this strain for vaccine development . Although concerns have been raised over its tier 1B classification [95] , its resistance to V3 non-nAbs and V2 bnAb hypersensitivity suggest "closed" trimers that may benefit from a naturally short and unglycosylated V2’ region [45 , 88] . Intermediate boosting immunogens ( Fig 10C ) could be the most sensitive GE-modified glycoform for the target bnAb , to encourage the development of glycan contacts . Thus , for example , NA-treated trimers could promote the maturation of PGT121 and PGT151-like bnAbs . Previous work showed that desialylation improves gp120 recognition by some ligands and also improves immunogenicity by reducing electrostatic surface potential [96] . Desialylation may also modify antigen capture and presentation in ways that might promote B cell responses [96] . As mentioned above , the unexpected "glycan thinning" effect of B4GALT1 overexpression ( Fig 10B ) may be useful for intermediate boosting , to both minimize glycan clashes and also promote SA-reactivity ( Fig 10C ) . Mass spectrometry analysis of Env glycans may in future allow us to better understand effects of B4GALT1 and other GE-modifications , providing a clearer basis for their effects and their utility in vaccines . In the current study , we were limited to Western blot analyses both by the relatively poor Env yield , and by the co-presence of immature and unprocessed forms of Env that may contaminate trimer glycoanalysis . However , recent improvements in trimer expression and purification methods should facilitate these analyses in the near future [23 , 97] . As final boosts ( Fig 10C ) , α-2 , 6 hypersialylated trimers may be ideal for reasons already mentioned above . The use of CHO or 293T cells for HIV-1 vaccine production may be limited by their tendency to attach α-2 , 3 SA termini . To address this problem , trimers might be either produced in these cells along with co-expressed ST6GAL1 or could be enzymatically modified after expression to exchange α-2 , 3 SA with α-2 , 6 SA termini . The common development of V2 bnAbs in natural infection , usually with relatively little somatic hypermutation [35 , 36 , 98] is increasing interest in this site as a vaccine target . Indeed , V2-hypersensitive strain trimers [34 , 36 , 42] were recently shown to elicit V2 nAbs [45 , 88] . Although the long CDRH3 loops of PG9 and CAP256 . 09 set a high bar for their use as vaccine blueprints , their relatively high potency in PBMC assays raises their significance , especially if other SA-dependent bnAbs with more common-in-repertoire features can be found . By comparison , V2 bnAbs like CH01 , VRC38 . 01 , BG1 and PCT64-35S have shorter CDRH3 loops . VRC38 . 01 may be of particular interest because it is only moderately impacted by glycan variation , thus blocking one avenue of viral escape [39 , 43] . In future , GE-modified baits may help to recover additional bnAbs from HIV-1-infected donors to inform vaccine design . GE also provides a way to investigate the effects of glycovariation on the tier 2 nAbs now increasingly being elicited by leading candidate vaccines [57] . This may enable us to develop prime-boost vaccination strategies to elicit broadly protective nAbs .
All human plasmas were archived ( i . e . they were not drawn for this project ) . Normal ( uninfected ) human PBMCs with no donor identifiers , sourced from Duke University and NIH blood bank , were used to propagate replicating JR-FL and IMC viruses . Institutional Review Board ( IRB ) approval for this project was obtained through the San Diego Biomedical Research Institute IRB Committee ( approval number: IRB-14-04-JB; Federal Wide Assurance number: 00021327 ) . MAbs were obtained from their producers and the NIH AIDS Reagent Repository . MAbs included the following ( originators given in parentheses ) : 19b , 39F , F2A3 , C011 and 14e ( J . Robinson ) , directed to the gp120 V3 loop [99]; b12 ( D . Burton ) , VRC01 and VRC13 ( J . Mascola ) , 8ANC131 ( M . Nussenzweig ) , HJ16 ( A . Lanzavecchia ) , F105 ( M . Posner ) , directed to epitopes that overlap the CD4bs [67 , 99–101]; PGT121 , PGT125 and PGT128 ( D . Burton ) directed to epitopes involving the base of the V3 loop of gp120 and the N332 glycan [37]; VRC38 . 01 , CAP256 . 09 and CAP256 . 25 ( J . Mascola ) , PG9 , PG16 , PGT145 and PGDM1400 ( D . Burton ) , CH01 and CH04 ( B . Haynes ) , directed to V2 apex epitopes [31 , 33 , 35 , 37 , 38 , 42 , 47 , 77]; PGT151 ( D . Burton ) , 35O22 ( M . Connors ) , VRC34 . 01 ( J . Mascola ) , 8ANC195 and 3BC176 ( M . Nussenzweig ) , ACS202 ( R . Sanders ) and CAP248-2B ( P . Moore ) directed to the gp120-gp41 interface [60–62 , 64 , 65 , 68 , 69]; 4E10 and 2F5 ( H . Katinger ) and 10E8 ( M . Connors ) , directed to the gp41 MPER [72] . Information on these mAbs can be found at the web link: ( www . hiv . lanl . gov ) . Germline revertants and ancestors were also obtained for mAb lineages CAP256 [35] , PG9 [34] , PGT145 [34] VRC38 [42] , CH04 [33] , VRC13 [102] and PGT121 [83–85] . In some cases , variable and J segments were reverted to inferred germline residues , leaving the CDR3 intact . In other cases , UCAs were inferred from nAb ancestors recovered from donors; other ancestors were recovered by deep sequencing . Plasmid pCAGGS was used to express JR-FL gp160ΔCT on VLP surfaces [99] . Gp160ΔCT is truncated at amino acid 709 , leaving a 3 amino acid gp41 cytoplasmic tail . This increases native trimer expression and can be used to produce PVs with similar neutralization sensitivity profiles compared to their full-length gp160 counterparts [99] . Mutants were generated by QuikChange ( Agilent Technologies ) and were numbered according to the HXB2 reference strain [86] . "SOS" mutations ( A501C and T605C ) introduce an intermolecular disulfide bond between gp120 and gp41 [99] . The E168K mutation knocks in PG9 epitope and increases trimer expression [99] , and the N189A mutation removes a sequon that is competitive with N188 , and improves sigmoidal neutralization of V2-targeting nAbs such as PG9 . Plasmids expressing other Env gp160s were obtained from the NIH AIDS repository . Env-deficient sub-genomic plasmid pNL4-3 . Luc . R-E- [99] , pMuLV Gag ( expresses endogenous murine leukemia virus Gag , driven by a CMV promoter ) and pMV-Rev 0932 ( expresses codon-optimized HIV-1 Rev , driven by a CMV promoter ) . We also investigated a series of other Envs , some of which were full-length and others had truncated cytoplasmic tails ( gp160ΔCT ) , as follows . In the following list , clade assignments are given in parentheses . BG505 T332N gp160ΔCT ( A ) , KER2018 . 11 gp160 ( A ) , BI369 . 9A gp160 ( A ) , Q23 . 17 gp160 ( A ) , CM244 . ec1 gp160 ( AE ) , T250-4 gp160 ( AG ) , JR-CSF gp160 ( B ) , WITO 4160 . 33 gp160ΔCT ( B ) , REJO4541 gp160 ( B ) , CH070 . 1 gp160 ( BC ) , ZM233 . 6 gp160 ( C ) , CNE58 gp160 ( C ) , 16055–2 clone 3 gp160ΔCT ( C ) , T278-50 gp160 ( AG ) , and 45_01DG5 gp160 ( B ) . Glycosyltransferase plasmids pEE6 . 4_B4GALT1 ( expresses β-1 , 4 galactosyltransferase 1 ) , pEE14 . 4_ST6GAL1 ( expresses β-galactoside α-2 , 6-sialyltransferase 1 ) and pEE6 . 4_GNT3 ( expresses N-acetylglucosaminyltransferase 3 ) were reported previously [13] . Others were obtained from the DNASU repository: β-1 , 3-N-acetylglucosaminyltransferases 1–5 ( GNT1 , 2 , 4 and 5 ) , α-1 , 6-fucosyltransferase ( FUCT8 ) , β-galactoside α-2 , 3-sialyltransferase 4 ( ST3GAL4 ) or α-N-acetyl-neuraminide α-2 , 8-sialyltransferase 4 ( ST8SIA4 ) . Glycosyltransferase plasmids were co-transfected at a ratio of 1% total transfected DNA . The exception to this was B4GALT1+sialyltransferase ( ST3GAL4 , ST6GAL1 or ST8SIA4 ) co-expression , where B4GALT1 was transfected at 1% and the sialyltransferase at 2 . 5% total transfected DNA . For increasing galactosylation and increasing sialylation , 5 mM D-galactose ( Sigma ) was added to the medium prior , during and post-transfection in conjunction with co-transfection of B4GALT1 . Decoy substrates for blocking fucosylation , 2-deoxy-2-fluoro-l-fucose ( 2FF ) ( Carbosynth ) and galactosylation , 2-deoxy-2-fluoro-d-galactose ( 2FG ) ( Carbosynth ) , were added 4h post transfection at 0 . 4 mM and 1mM , respectively . To block the action of mannosidase 1 , kifunensine was added at 25 μM during and post transfection . Swainsonine was added at 20 μM during and post transfection to block mannosidase 2 activity . Terminal SAs were cleaved by incubating 25 μl of 1 , 000x concentrated PV with 2 μl of NA from C . perfringens ( Sigma , Cat# N5631 , 5u/ml ) and 1 μl of NA from A . ureafaciens ( Roche , Cat # 10269611001 , 10U/ml ) ) for 1 h at 37°C . Both NAs cleave α-2 , 3 , α-2 , 6 and α-2 , 8 SA , but C . perfringens NA preferentially cleaves α-2 , 3 SA , whereas A . ureafaciens NA preferentially cleaves α-2 , 6 SA . PV was then washed with PBS , pelleted and resuspended at 1000 x . VLPs were produced by co-transfecting human embryonic kidney 293T cells ( ATCC ) with an Env-expressing plasmid ( typically pCAGGS JR-FL gp160ΔCT SOS E168K and mutants thereof ) , pMuLV Gag and pMV-Rev 0932 using polyethyleneimine ( PEI Max , Polysciences , Inc . ) , as described previously [99] . Two days later , supernatants were collected , precleared by low speed centrifugation , filtered , and pelleted at 50 , 000 x g in a Sorvall SS34 rotor . To remove residual medium , VLP pellets were diluted with 1ml of PBS , then re-centrifuged at 15 , 000 rpm and resuspended in PBS at 1 , 000 x the original concentration . Replicating JR-FL virus was propagated from a stock provided by the NIH AIDS Reagent Repository ( CAT#395 , donated by Dr Irvin Chen ) , by cell-free infection of uninfected human peripheral blood mononucleocytes ( PBMCs ) activated in RPMI medium containing 20% FBS , 50μg/ml gentamycin , 5μg/ml PHA-P ( Sigma Cat# L1668 ) and 5% IL-2 for 12–24 hours followed by washing and resuspension in RPMI supplemented with only 20% FBS and 5% IL-2 for infection . Virus supernatant was inactivated using 1mM aldrithiol and then was concentrated in the same manner as VLPs for use in gel analyses . HIV-1-infected donor plasmas N90 , N152 , CAP256 , BB34 , 1688 , 1702 , and N160 , and uninfected control plasma 210 have all been described previously [35 , 60 , 72 , 86] . These were obtained from the Laboratory of Immunoregulation , NIAID ( N90 and N152 ) , The National Institute for Communicable Diseases , Johannesburg , South Africa ( CAP256 , BB34 ) and Zeptometrix , Inc . , Buffalo , New York , USA ( 1648 , 1688 , 1702 , N160 , 210 ) . Blue native polyacrylamide gel electrophoresis ( BN-PAGE ) was performed as described previously [99] . Briefly , VLPs were solubilized in 0 . 12% Triton X-100 in 1 mM EDTA . An equal volume of 2x sample buffer ( 100 mM morpholinepropanesulfonic acid ( MOPS ) , 100 mM Tris HCl , pH 7 . 7 , 40% glycerol , and 0 . 1% Coomassie blue ) was added . Samples were then loaded onto a 4–12% Bis-Tris NuPAGE gel ( Invitrogen ) and separated at 4°C for 3 hours at 100V . Proteins were then transferred to polyvinylidene difluoride ( PVDF ) membrane , destained , immersed in blocking buffer ( 4% nonfat milk in PBST ) and probed with an anti-gp120 cocktail ( 39F , F2A3 , C011 and 14e at 1μg/ml ) and/or an anti-gp41 cocktail ( 2F5 and 4E10 at 1μg/ml ) . Blots were then probed by an anti-human Fc alkaline phosphatase conjugate ( Accurate Chemicals ) and developed using SigmaFast BCIP/NBT substrate ( Sigma ) . Env proteins were resolved by reducing SDS-PAGE . Briefly , samples were reduced and denatured by heating at 90°C for 10 min in LDS buffer ( Invitrogen ) prior to loading onto 4–12% Bis-Tris NuPAGE gels ( Invitrogen ) . SDS-PAGE-Western blots were performed as described above for the BN-PAGE Western blotting method . For the cleavage of oligomannose and hybrid glycans , endonuclease H ( endo H ) ( New England Biolabs ) was added to samples after reduction and denaturation , and incubated for 37°C for 1 h prior to SDS-PAGE-Western blotting . Wilcoxon Signed Rank tests were performed on data for each mAb-virus pair , organized into two columns to compare IC50s under control and GE-modified conditions . Oligomannose arrays were printed using Man5GlcNAc2 , Man6GlcNAc2 , Man7GlcNAc2D1 , Man7GlcNAc2D3 , Man8GlcNAc2D1D3 ( D denotes the arms bearing terminal mannose groups; see Fig 1 ) , and Man9GlcNAc2 at 33μM ( Z Biotech ) . GlcNAc2 was printed at the same concentration . Print buffer without glycans was included as a background control . Each array was hydrated for 2 min in ultrapure water and then blocked for 1 h with hydrazide glycan blocking buffer ( Zbiotech ) , rotating at 40 rpm in the dark . Arrays were inserted into a SlideArray holder ( SlideArray ) to partition the array into 24 subarrays . MAbs were diluted to 50 μg/mL in hydrazide glycan assay buffer . PGT128 and biotinylated Concanavalin A were used as positive controls and V3 mAb 19b was used as a negative control . Each mAb was incubated on an individual subarray for 1h and then washed 5 times with PBS/0 . 05% tween 20 ( PBST ) . Subarrays that received biotinylated Concanavalin A were incubated with streptavidin-Cy3 ( Sigma ) . All other wells were incubated with anti-IgG-Cy3 ( Sigma ) for 1h while rotating at 40 rpm covered from light . The arrays were washed 5 times with 70μL of PBST and then washed once with 0 . 01X PBS and then dried . The arrays were scanned with a GenePix 4000B ( Molecular Devices ) scanner at wavelength 532nm using GenePix Pro7 software . The fluorescence within each feature was background subtracted using the local method in GenePix Pro7 software ( Molecular Devices ) . Glycan specific binding = ( glycan binding background-subtracted fluorescence ) − ( print buffer alone background-subtracted fluorescence ) . A JR-FL native Env trimer structure ( PDB: 5FUU ) [3] was used to model native spike glycans . First , atomic clashes in the 5FUU structure were relieved and missing side-chains were rebuilt by running a constrained ROSETTA-relax simulation . Each sequon was decorated with a Man9GlcNAc2 glycan . The glycan at position N637 in gp41 is absent , per evidence that one or other glycans at N625 and N637 remain unoccupied [18] . For any overlapping sequons , e . g . those at N188 and 189 , only the first sequon is occupied . GlycanRelax [103] was used to approximate glycan conformational behavior . For each model , 10 separate GlycanRelax trajectories of 10 , 000 cycles of MonteCarlo trials were carried out . Each gp120 glycan could move independently throughout the GlycanRelax minimization . A single low energy model was generated using PyMOL Software ( Version 1 . 5 . 0 . 4 Schrödinger , LLC ) . | Here we engineered various changes in the sizes and shapes of sugars that decorate HIV surface spike proteins and tested the effects of these changes on virus susceptibility to neutralizing antibodies . In so doing , we were able to define the optimal Env-sugars recognized by prototype bnAbs that recognize various canonical epitope clusters on Env spike proteins . Some bnAbs preferred spike proteins decorated with large , complex glycans . Others preferred smaller glycans that improved their access to underlying protein targets . For similar reasons , germline-reverted versions of bnAbs were also generally more effective when the glycans were small . In some cases , bnAbs acquired an ability to bind to sugars as they matured . A comparison of viruses generated in cell lines and primary cells revealed large differences in bnAb sensitivity , raising questions about clinical relevance of cell line-produced virus for checking vaccine responses and , moreover , the use of these cell lines for manufacturing vaccines . Overall , just as car engines may be modified to be supercharged or hybrid for increased power or efficiency , the sugars of HIV coat proteins may also need to be engineered as 'supercharged' and 'hybrid' or otherwise modified in rational vaccine designs to optimize bnAb recognition . | [
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| 2018 | Glycoengineering HIV-1 Env creates ‘supercharged’ and ‘hybrid’ glycans to increase neutralizing antibody potency, breadth and saturation |
Angiostrongylus costaricensis is a relatively uncharacterized nematode that causes abdominal angiostrongyliasis in Latin America , a human parasitic disease . Currently , no effective pharmacological treatment for angiostrongyliasis exists . Peptidases are known to be druggable targets for a variety of diseases and are essential for several biological processes in parasites . Therefore , this study aimed to systematically characterize the peptidase activity of A . costaricensis in different developmental stages of this parasitic nematode . A library of diverse tetradecapeptides was incubated with cellular lysates from adult worms and from first-stage larvae ( L1 ) and cleaved peptide products were identified by mass spectrometry . Lysates were also treated with class specific peptidase inhibitors to determine which enzyme class was responsible for the proteolytic activity . Peptidase activity from the four major mechanistic classes ( aspartic , metallo , serine and cysteine ) were detected in adult worm lysate , whereas aspartic , metallo and serine-peptidases were found in the larval lysates . In addition , the substrate specificity profile was found to vary at different pH values . The proteolytic activities in adult worm and L1 lysates were characterized using a highly diversified library of peptide substrates and the activity was validated using a selection of fluorescent substrates . Taken together , peptidase signatures for different developmental stages of this parasite has improved our understanding of the disease pathogenesis and may be useful as potential drug targets or vaccine candidates .
Angiostrongylus costaricensis is a zoonotic parasitic nematode that causes human abdominal angiostrongyliasis , a severe gastrointestinal disease . This helminth was first described in patients from Costa Rica in 1971 [1] . Abdominal angiostrongyliasis ( AA ) is currently widespread in Latin America [2 , 3] and cases have also been reported in Africa and Europe [2] . Humans are incidental hosts who become infected following the ingestion of raw mollusks or unwashed vegetables contaminated with the mucous of mollusks containing third-stage larvae ( L3 ) of A . costaricensis . The life cycle requires a mollusk ( i . e . , slugs and/or snails ) as an intermediate host and a rodent definitive host ( i . e genus Rattus and cotton rats ) . Adult worms live inside the mesenteric arteries of infected rodents , where eggs laid by females are carried by the blood stream to the intestinal wall . The eggs hatch as first-stage larvae ( L1 ) , which migrate into the intestinal lumen and are further eliminated with the faeces . L1 are ingested [4] and/or penetrate through the mollusk tegument [5 , 6] and two molts occur inside the intermediate host before the development of the infective L3 stage . To complete its life cycle , L3 larvae must be ingested by rodents , where they follow one of two alternative migratory routes during their development into adult worms: a lymphatic/venous–arterial pathway or a venous portal pathway . Both migratory pathways direct the worms to their final destination , the ileocecal region [3 , 7 , 8] . To date , definitive diagnosis of the infection requires a biopsy to find worms , eggs and/or larvae in histological sections . In the absence of parasitic structures , a probable diagnosis of AA is supported by a histological triplet involving the presence of eosinophilic infiltration in the intestinal wall , granulomatous inflammation and eosinophilic vasculitis [9] . Proposed noninvasive serological diagnostic tests have lacked sensitivity and specificity [10 , 11] . Stool examination is not useful in diagnosing A . costaricensis because the eggs are not shed into human faeces . Furthermore , there is no specific and/or effective pharmacological treatment for this disease . Previous studies have shown that anthelminthics [12–16] , antithrombotic drugs [17] and anti-inflammatory agents [18] are not effective against the nematode parasite . Therefore , the investigation of potential new diagnosis and/or treatment targets for AA is urgently required . Peptidases are proteolytic enzymes that have been suggested as drug targets for parasitic diseases [19–22] . In parasitic helminths , peptidases are involved in host-parasite interactions , parasite immune evasion , life cycle transition and pathogenesis [23–26] . Using gel-based approaches , we have previously confirmed the presence of peptidase activity in L1 and L3 extracts . The gelatinolytic enzymatic activity of L3 larvae could be ascribed to metallo-peptidases , whereas the characterization of the protease activity of L1 larvae was inconclusive . Haemoglobinolytic activity due to aspartyl protease activity was also detected in the crude extracts of adult worms and in larval stages ( L1 and L3 ) [27] . The present work aimed to provide a more detailed understanding of the proteolytic activity of adult worms and L1 lysates of Angiostrongylus costaricensis . To discover the proteolytic activity in these biological samples , lysates were incubated with an equimolar mixture of synthetic tetradecapeptide substrates . Both cleaved and uncleaved peptides were identified using tandem mass spectrometry and substrate specificity profiles were subsequently generated . This method , termed multiplex substrate profiling by mass spectrometry ( MSP-MS ) [28] , has previously been used to discover protease activity in the excretion/secretion products from Schistosoma mansoni [29] and in the gut of Schmidtea mediterranea [30] . Further characterization of peptidase activity was performed using a panel of fluorescent peptide substrates and class-specific peptidase inhibitors .
The following protease inhibitors and peptide substrates were used in this study: Pepstatin A ( Research Products , Mt . Prospect , IL , USA ) , E-64 ( Merck Calbiochem , La Jolla , CA , USA ) , AEBSF ( Sigma Aldrich , St Louis , MO , USA ) , and 1 , 10- phenanthroline ( Acros Organics , Moris Plains , New Jersey , USA ) . H-Gly-Phe-AMC ( 7-amino-4-methylcoumarin ) , H-Tyr-AMC , H-Gly-Arg-AMC , H-Arg-AMC , Z-Arg-Arg-AMC , Z-Val-Arg-AMC , Z-Ala-Val-Asn-AMC , Glutaryl-Gly-Arg-AMC , Z-Val-Ala-Asp-AMC , Z-Val-Ala-Asp-AMC , Z-Val-Ala-Asp-AMC , Z-Val-Ala-Asp-AMC , Suc-Leu-Tyr-AMC and Z-Arg-Leu-Arg-Gly-Gly-AMC were purchased from Bachem ( Torrance , CA , USA ) . Z-Phe-Arg-AMC was purchased from R&D Systems ( Minneapolis , MN , USA ) . H-Ala-AMC , Z-Ala-AMC and Boc-Ala-Gly-Pro-Arg-AMC were purchased from Enzymes Systems Products ( Livermore , CA , USA ) . N-Benzoyl-Phe-Val-Arg-AMC and N-Succinyl-Ala-Pro-Ala-AMC were purchased from Sigma . Z-Arg-Arg-Leu-Arg-AMC and Suc-Pro-Ser-Pro-AMC were from System Peptide Company ( Pudong New Area , Shanghai , China ) . Boc-Leu-Arg-Arg-AMC ( 4-methylcoumarinyl-7-amide ) , Z-Ala-Ala-Asn-AMC and Z-Phe-Ala-AMC were from Peptide Institute ( Ibaraki-Shi , Osaka , Japan ) . Suc-Leu-Leu-Val-Tyr-AMC and Suc-Gly-Pro-Leu-Gly-Pro-AMC were supplied by Peninsula Labs ( Belmont , CA , USA ) . The internally quenched fluorescent substrate peptide substrate Mca-Gly-Lys-Pro-Ile-Leu-Phe-Phe-Arg-Leu-Lys ( DNP ) -DArg-NH2 was purchased from CPC Scientific ( Sunnyvale , CA , USA ) . Stock solutions of substrate or inhibitor were dissolved in DMSO or water and diluted in buffer as required . In all cases , the DMSO concentration in the assays was less than 1% ( v/v ) .
A global and unbiased substrate screen was used to uncover the peptidase substrate specificity in adult A . costaricensis worms . For these initial studies , protein lysates from female worms were used due to their larger size and sufficiently abundant protein levels; however , both male and female worm extracts were used for downstream validation assays . A . costaricensis protein was added to a mixture of 228 physicochemically diverse tetradecapeptides and cleavage of any of the 2 , 964 available peptide bonds within these peptides was detected by LC-MS/MS . When assayed at pH 3 . 0 , cleavage of 77 peptide bonds was discovered after 5 minutes incubation and , by 1200 minutes , a total of 556 cleaved peptide bonds were identified ( S1 File ) . These data show that acid-acting peptidases present in A . costaricensis adults are capable of cleaving almost 20% of the available bonds in this tetradecapeptide library and are therefore likely to be able to degrade many protein substrates into short peptides . Aspartic acid peptidases are highly active at pH 3 . 0 and these enzymes are inhibited by pepstatin A . We therefore incubated worm lysate with this inhibitor prior to the addition of the peptide library . Under these assay conditions , we found a lower number of cleaved peptides at each time interval , when compared to the untreated assay , indicating that one or more aspartic acid peptidases are present in this worm sample . A comparison of the cleaved peptide bonds that are generated in the presence and absence of pepstatin , allowed us to assign each cleavage site as being either the product of an aspartic acid peptidase or the product of other peptidases that are active at pH 3 . 0 but are not inhibited by pepstatin . Using only cleavage products that are detectable in the first 15 minutes of the assay of the DMSO treated assay , we discovered 126 products that are generated by aspartic acid peptidases as these are absent in the pepstatin A treated assay . In addition , 66 cleavage sites are present in both the DMSO and pepstatin A assays and therefore the products of one or more pepstatin-insensitive acid peptidases . The location of each cleavage site within the 14-mer peptides was determined . In general , the aspartic acid peptidases preferentially cleaved the 14-mer substrates between residues 5 and 12 , indicating that they have greater endopeptidase activity than exopeptidase activity ( Fig 1A ) . The pepstatin-insensitive peptidases generally cleaved between position 2 and 3 , and between 12 and 14 indicating that these enzymes are likely to include at least one carboxypeptidase and one di-aminopeptidase . A substrate specificity profile was generated for both types of enzyme activities . The aspartyl peptidase had a strong preference for hydrophobic amino acids Phe , Leu and norleucine ( Nle ) in the P1 site and no tolerance for Val , Ile , Lys , Arg , Gly or Asn . In the P1ʹ position , Phe , Tyr and Nle were preferred while Arg and Thr were frequently found at P2ʹ ( Fig 1B ) . For the substrate specificity associated with the ‘undefined acid peptidase’ there was as preference for cleavage between Asn and Nle , with Asp commonly found at P2ʹ , Phe and Glu at P2 and Leu at P3 ( Fig 1C ) . The specificity of the A . costaricensis aspartic acid peptidase strongly correlates ( Pearson correlation = 0 . 84 ) with the substrate specificity of human cathepsin D , an aspartyl peptidase that is also potently inhibited by pepstatin [32] . In fact , the standard fluorescent substrate for assaying cathepsin D consists of the following P4 to P3ʹ sequence , PILFFRL [33] , which closely matches the optimal substrate sequence for the A . costaricensis aspartic acid . Therefore , to validate the substrate profiling we assayed the female lysate with the cathepsin D substrate and found it to be rapidly hydrolyzed ( Fig 1D ) . In addition , we assayed a lysate of male A . costaricensis worms and found that this enzyme activity is also present , although the specific activity is decreased by 2-fold . Addition of pepstatin to the female lysate completely abolished this activity , while E-64 , a broad-specificity cysteine peptidase inhibitor showed no statistically significant reduction in activity ( 2 . 83 ± 0 . 15% ) ( Fig 1E ) . Taken together , these studies show that one or more aspartic acid peptidases are active in the male and female worms at pH 3 . 0 and that this activity is inhibited by pepstatin . In addition , at least one enzyme with strong di-carboxypeptidase activity is also active at pH 3 . 0 in female A . costaricensis worms . When the worm extracts were assayed at pH 5 . 0 , cleavage of 84 peptide bonds was discovered after 15 minutes incubation and 512 peptide cleavage sites were detected by 1200 minutes ( S1 File ) . Cysteine peptidases of the papain family are generally optimally active at pH 5 . 0 and these enzymes can be inhibited by E-64 . Thus , we incubated the worm lysate with this inhibitor prior to the addition of the peptide library . Under these conditions , treated worm lysates exhibited a reduction in the number of cleaved peptides at all time intervals , when compared to the untreated control , indicating that one or more cysteine peptidases are present in this worm sample . Comparison of cleavage peptide events occurred in the presence and absence of E-64 , allow us to determine each cleavage site as being either cysteine peptidase or other peptidases that are active at pH 5 . 0 but are not inhibited by E-64 ( Fig 2A ) . Using only cleavage products identified after 60 minutes of the assay , 127 cleavage sites were detected as cysteine peptidases ( Fig 2B ) and 81 cleavage sites that are unchanged after E-64 treatment and are therefore the products of an undefined peptidase ( s ) ( Fig 2C ) . Again , the location of each cleavage site within the 14-mer peptides was determined and the cysteine peptidases preferentially cleaved the 14-mer substrates between residues 12 and 14 while the other peptidases active at pH 5 . 0 hydrolyzed bonds within the 14-mer substrate without a clear preference for location ( Fig 2A ) . These data indicate that the cysteine peptidases have mono- and di- carboxypeptidase specificity . A substrate specificity profile was generated for the cysteine peptidase that showed a strong preference for basic amino acids such as Lys and Arg in the P1 and P4 positions ( Fig 2B ) . The substrate specificity associated with the ‘undefined peptidase’ was clearly different from the cysteine peptidase and showed a cleavage preference for norleucine ( Nle ) and Phe or Val in the P1´ position ( Fig 2C ) . The endopeptidase activity of adult worm lysates was also evaluated with 19 synthetic fluorogenic substrates at pH 5 . 0 ( S1 Table ) . A substrate that was preferentially hydrolyzed by both female and male lysates contained the sequence Boc-Ala-Gly-Pro-Arg-AMC ( Fig 2D ) . The location of Arg in the P1 position matches the substrate preference of the E-64 sensitive cysteine peptidases . When the female worm lysate was subsequently incubated with 10 μM of E-64 and assayed with Boc-Ala-Gly-Pro-Arg-AMC , the activity was completed inhibited ( Fig 2E ) . These data show that A . costaricensis cysteine peptidases are active in both male and female worm lysates . These enzymes display broad substrate specificity but under this pH conditions , cleavage generally occurs near the carboxyl terminus of peptide and protein substrates thereby generating single amino acids and dipeptides . We next assayed the female worm lysate at pH 8 . 0 to detect peptidases that are active at neutral pH and a total of 153 cleavage peptide bonds were identified ( S1 File ) . Serine peptidases and metallopeptidases are generally active at neutral and basic pH , therefore , worm lysates were assayed in the presence and the absence of the serine peptidase inhibitor AEBSF and the metallopeptidase inhibitor 1 , 10-phenanthroline . Under each condition , we observed a decrease in the number of cleaved peptides at each time point , when compared to the untreated assay . The cleavage pattern within the 14-mer peptides after 240 min incubation at pH 8 . 0 , in the presence of AEBSF or 1 , 10 phenanthroline is shown in Fig 3A . A total of 80 cleavage sites were associated with serine peptidase and 56 cleavage sites were shown to be generated by metallopeptidase . Serine peptidases showed strong preference for Asp and Tyr at P1 , Arg at P2ʹ site and no tolerance for Arg at P1ʹ position ( Fig 3B ) . Metallopeptidases also showed strong preference for Tyr and Asp at P1 site . In P4 position , Asn was preferred while Arg was frequently observed at P2ʹ site ( Fig 3C ) . The endopeptidase activity of both male and female worm lysates was also evaluated with 19 synthetic fluorogenic substrates at pH 8 . 0 ( S1 Table ) . Several substrates and Suc-Gly-Pro-Leu-Gly-Pro-AMC most found to be hydrolyzed rapidly by peptidases in both samples . Using this substrate the specific activity of peptidases in female lysates was 7 . 3-fold higher than in males ( Fig 3D ) . In female extracts , this proteolytic activity was reduced by 37 ± 2 . 89% in the presence of AEBSF and by 19 ± 2 . 08% with 1 , 10 phenanthroline ( Fig 3E ) indicating that both serine and metallo-peptidases can hydrolyze this substrate . The lysates were further assayed with mono- and di-peptide AMC substrates including Gly-Phe , Tyr , Ala , Arg or Gly-Arg ( S2 Table ) , because many of the cleavage sites found in the tetradecapeptides substrate library were close to the amino termini indicating that one or more aminopeptidases were active in this sample ( Fig 3A ) . Female lysates showed higher specific activity compared with male lysates , with a clear preference for a tyrosine residue in the P1 site . The aminopeptidase activity of female lysates was reduced by 65 ± 2 . 92% in the presence of AEBSF and by 72 ± 0 . 59% in the presence of 1 , 10 phenanthroline . When both inhibitors were included in the assay activity decreased by 86 ± 0 . 72% ( Fig 3F ) . Taken together , these results indicate the presence of serine and metallo-peptidases in A . costaricensis adult worm lysates that have overlapping substrate specificity . 1 , 10 Phenanthroline is known to inhibit Zn-metallopeptidases by chelating the metal ions but it also has an affinity for Ca2+ ions . Therefore , the similarity in specificity between the serine and metallo-peptidases may to due 1 , 10 Phenanthroline inhibiting Ca2+ or Zn2+ dependent serine proteases in the A . costaricensis adult worm lysate . We next evaluated peptidase activity in A . costaricensis L1 lysate to determine if these enzymes differ from the peptidases detect in adults . When L1 lysate was assayed at pH 3 . 0 with the tetradecapeptides library , cleavage of 11 peptide bonds was discovered after 5 minutes incubation and , by 1200 minutes , a total of 138 cleavage peptide bonds were identified ( S1 File ) . When this same assay was performed in the presence of pepstatin , a decrease in the number of cleaved peptides occurred at each time interval , although not all peptide cleavage sites were sensitive to pepstatin . Using only cleavage products that were detectable after 240 minutes incubation , 51 were generated by aspartic acid peptidases ( Fig 4B ) and 34 are unchanged after pepstatin treatment , therefore representing the products of undefined peptidases ( Fig 4C ) . Aspartic acid peptidase preferentially cleaved the 14-mer peptides between residues 3 and 4 and between residues 10 and 14 while the undefined peptidase frequently cleaved between position 1 and 3 and between 13 and 14 ( Fig 4A ) . The location of the cleavage sites differs from what was seen with the aspartyl peptidases present in the adult lysate ( Fig 1A ) . However , the overall substrate specificity preference was similar to the adult lysate peptidases with cleavage occurring between two hydrophobic residues such as Phe and Tyr at P1 and norleucine ( Nle ) at P1ʹ . In addition , these enzymes preferred Glu at P2 and Val at P3ʹ ( Fig 4B ) . For the substrate specificity associated with the ‘undefined acid peptidase’ there was a preference for Trp at P1 , Leu at P4 and Nle at P1ʹ ( Fig 4C ) . We predicted that the acid peptidase may also hydrolyze the internally quenched fluorescent substrates that was designed for human cathepsin D and could be readily hydrolyzed by aspartic acid peptidases in the A . costaricensis adult lysate . When this substrate was assayed with L1 lysate at pH 3 . 0 , cleavage was observed , and this activity was completely inactivated by pepstatin ( Fig 4D ) . These data show that many of the peptidases active in L1 are likely aspartic acid enzymes , as already observed for adult worms . When the L1 lysate was assayed in pH 8 . 0 , a total of 634 cleavage peptide sites were detected within the 14-mer peptides after only 15 minutes . With extended incubation for up to 1200 minutes , 1289 unique peptide bonds were hydrolyzed ( S1 File ) . Similarly , to the pH 8 . 0 studies performed with the adult lysate , the L1 lysate was pre-treated with either AEBSF or 1 , 10 phenanthroline . A total of 302 sites were found to be the product of serine peptidases , while 183 peptide bonds were cleaved by metallopeptidases . In general , the distribution of cleavage sites was similar for both classes of enzymes , and the serine peptidase was dominant , with the notable exception of the bonds between amino acid 1 and 2 , where metallopeptidase activity prevailed ( Fig 5A ) . The serine peptidases showed strong preference for basic or bulky residues ( Arg , Lys , His ) and Trp at P1 position and no tolerance for Gly , Val and Ile . At the P2 site , Leu was preferred and no tolerance for Glu and Asp . At the P3 position , Arg and Ala were preferred , while Arg and Ile were frequently found at P4 . At the P4ʹ site , Pro was preferred while this same amino acid was not tolerated at P1ʹ ( Fig 5B ) . Metallopeptidases showed strong preference for Lys and Phe at the P1 site and Ala at P2ʹ ( Fig 5C ) . Additionally , the endopeptidase activity of L1 lysates was also evaluated with a selection of synthetic fluorogenic substrates at both pH 5 . 0 and 8 . 0 ( S3 Table ) . Suc-Leu-Leu-Val-Tyr-AMC and Boc-Ala-Gly-Pro-Arg-AMC substrates were preferentially hydrolyzed at pH 8 . 0 and all substrates that where hydrolyzed at pH 8 . 0 were also hydrolyzed at pH 5 . 0 . As the specific activity was always higher at pH 8 . 0 , we chose to focus our efforts on assays performed at pH 8 . 0 . The proteolytic activity observed with Suc-Leu-Leu-Val-Tyr-AMC was inhibited by 91 ± 1 . 72% in the presence of AEBSF and by 75 . 0 ± 3 . 66% when using 1 , 10-phenanthroline ( Fig 5D ) . Cleavage of Boc-Ala-Gly-Pro-Arg-AMC was slightly reduced upon treatment with the AEBSF ( 9 . 2 ± 2 . 5% ) , and it was inhibited by 96 . 0 ± 0 . 25% in the presence of 1 , 10-phenanthroline ( Fig 5E ) . These results indicate that both serine and metallo-peptidases are responsible for cleavage of Suc-Leu-Leu-Val-Tyr-AMC and while metallopeptidases cleave the Boc-Ala-Gly-Pro-Arg-AMC substrate . Taken together , our data indicate that multiple proteases are present in L1 and adult worm lysates of A . costaricensis; they are capable of cleaving a diverse set of peptide bonds over a broad pH range .
The present work shows that A . costaricensis expresses many different peptidases in adult and larval stages . Proteolytic enzymes are receiving increasing attention as potential therapeutic targets or as diagnostic markers for various diseases [34–36] . In helminths , they have been implicated in a broad range of biological process [35 , 37–39] . Very little is known about the function of proteolytic enzymes in A . costaricensis , therefore biochemical characterization of their proteolytic enzymes may provide insights into parasite-host interaction mechanisms involved in the establishment and development of abdominal angiostrongyliasis . Using a wide variety of peptide substrates and several classes of specific peptidase inhibitors , aspartic acid , cysteine , serine and metallo-peptidases from A . costaricensis were detected and partially characterized . The cysteine peptidase specificity in adult worms matched papain-type peptidases found in mammalian cells . Human cathepsin B and L are inhibited by E-64 and show preference for positively charged amino acids at the P1 position , in addition to hydrophobic amino acids at the P2 position [40] . Cysteine peptidases have been implicated in multiple functions that are essential to the biology of parasitic organisms , such as antigen presentation , digestion , immune invasion , hemoglobin hydrolysis and host invasion [41] . Cathepsins B and L perform the majority of digestive function in helminths [37 , 41] and RT-PCR data analysis of Angiostrongylus cantonensis showed that cysteine peptidase transcripts are present in larval stages and adult worms [42] . In addition , similarly to cathepsin B , which was shown to have carboxypeptidase activity [43] , most activity attributed to cysteine peptidases in A . costaricensis worm extract occurred at the C-terminal side of the substrates . Human cathepsin B and L do not tolerate Pro at the P2 position and therefore it is unexpected that the fluorescent substrate Boc-Ala-Gly-Pro-Arg-AMC was rapidly cleaved by the worm cysteine peptidases . However , it is possible that A . costaricensis also express cysteine peptidases with a substrate specificity similar to human cathepsin K , as this enzyme preferentially cleaves substrates that contain proline at P2 and Arg at P1 [43] . A gut associated cathepsin B cysteine peptidase was identified in juvenile and adult worms of Angiostrongylus cantonensis , suggesting that these enzymes are involved in nutrition [44 , 45] . The biological function of A . costaricensis cysteine peptidases remains unknown , but this study has confirmed that these enzymes are active in the adult worm . Aspartic peptidases are a group of evolutionarily conserved proteolytic enzymes that have been characterized in many parasites [35 , 46 , 47] . Aspartic peptidases trigger a multienzyme cooperative cascade of hemoglobin proteolysis inside the guts of nematodes and other parasites . They are responsible for cleaving the intact hemoglobin before it can be further processed by additional digestive peptidases [35] . Using a fluorescence substrate developed for human cathepsin D , this study confirmed that A . costaricensis adult worms and L1 larvae express one or more enzymes that are functionally related to human cathepsin-D . These enzymes are inhibited by pepstatin and preferentially hydrolyze protein between two hydrophobic amino acids . Our previous data showed that aspartic peptidase inhibitors could block hemoglobin degradation by A . costaricensis [27] , therefore this proteolytic activity is likely to be required for parasite nutrition by the nematode . Interestingly , initial activity profiling of ticks Ixodes ricinus gut lysates indicated that hemoglobinolysis is optimal at acid pH , suggesting that proteolysis is mediated by peptidases belonging to the aspartic and/or cysteine peptidase classes which are known to operate optimally at acid pH [48] . Blood-feeding pathogens digest hemoglobin as a source of nutrition but little is known about this process in Angiostrongylus costaricensis . In helminths , hemoglobin usually is digested by a cascade of aspartic and cysteine peptidases [49–51] . In adult schistosome worms , for example , digestion of blood requires a combination of cysteine and aspartic peptidases [35 , 52] . In female worms , there are at least two serine peptidases which are active at pH 8 . 0 . The specificity profile of the AEBSF-sensitive enzymes showed a preference for Asp or Tyr at P1 . It is unlikely that the same enzyme is hydrolyzing both amino acid residues . The serine peptidase cleaving substrates showing Asp at the P1 site is likely to be an endopeptidase; the other enzyme is an aminopeptidase that prefers amino terminal Tyr residues . Interestingly , the specificity profile of serine peptidase activity in worms differs from serine peptidases identified in L1 larvae , which were described as trypsin-like enzyme ( s ) . The activity profiles of metallopeptidases in female and L1 lysates were also different . Metalloaminopeptidases and serine trypsin-like were characterized in excretory/secretory products of Anisakis simplex larvae using fluorogenic substrates . These enzymes were postulated to be involved in host tissue penetration [53] . Similarly , a metalloaminopeptidase from Brugia pahangi was found to participated in the moulting process [54] . Serine and metallo-peptidases have been identified in the secretome from the fourth-stage ( L4 ) larvae and adult worms of Trichostrongylus vitrines [55] . Analysis of transcriptomic data reveals the presence of these proteases in different developmental stages of Trichostrongylus colubriformis maintained in culture [56] . These peptidases may play key roles in several physiological processes , such as tissue penetration , immune evasion and feeding . Our data reveals previously unrecognized peptidase activity which are present in A . costaricensis at several developmental stages . These results are a first but critical step to our further understanding of the biology and pathogenesis of this nematode . | A . costaricensis is a poorly studied nematode that causes abdominal angiostrongyliasis , a human parasitic disease . Peptidases perform several functions in the life cycle of parasites , including nutrition , differentiation and host invasion . The present study characterized the repertoire of peptidases in A . costaricensis lysates using a combination of peptide degradation screening using mass spectrometry and validation of the activity using fluorescent substrates and class-specific peptidase inhibitors . The results improved our understanding of the role of these peptidases in parasite biology , shedding light on the underlying disease mechanisms . | [
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| 2018 | Activity profiling of peptidases in Angiostrongylus costaricensis first-stage larvae and adult worms |
Decades of study have revealed more than 100 ribonucleoside structures incorporated as post-transcriptional modifications mainly in tRNA and rRNA , yet the larger functional dynamics of this conserved system are unclear . To this end , we developed a highly precise mass spectrometric method to quantify tRNA modifications in Saccharomyces cerevisiae . Our approach revealed several novel biosynthetic pathways for RNA modifications and led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to mechanistically different toxicants . This is illustrated with the RNA modifications Cm , m5C , and m22G , which increase following hydrogen peroxide exposure but decrease or are unaffected by exposure to methylmethane sulfonate , arsenite , and hypochlorite . Cytotoxic hypersensitivity to hydrogen peroxide is conferred by loss of enzymes catalyzing the formation of Cm , m5C , and m22G , which demonstrates that tRNA modifications are critical features of the cellular stress response . The results of our study support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells .
The complexity of the transfer RNA ( tRNA ) system confers great potential for its use in cellular regulatory programs . There are hundreds of tRNA-encoding genes in S . cerevisiae and human genomes , with extensive post-transcriptional processing that includes enzyme-mediated ribonucleoside modifications [1] . Considering both tRNA and ribosomal RNA ( rRNA ) , there are more than 100 known ribonucleoside modifications across all organisms in addition to the canonical adenosine , guanosine , cytidine and uridine [2] , [3] . In general , tRNA modifications enhance ribosome binding affinity , reduce misreading and modulate frame-shifting , all of which affect the rate and fidelity of translation [4]–[7] . However , information about the higher-level biological function of ribonucleoside modifications has only recently begun to emerge . We have approached this problem with a systems-level analysis of changes in the spectrum of ribonucleosides in tRNA as a function of cell stress , which has revealed novel insights into the biosynthesis of tRNA modifications and their role in cellular responses . Emerging evidence points to a critical role for tRNA and rRNA modifications in cellular responses to stimuli , with evidence for a role in tRNA stability [8] , [9] , cellular stress responses [10]–[12] and cell growth [13] . We recently used high-throughput screens and targeted studies to show that the tRNA methyltransferase 9 ( Trm9 ) modulates the toxicity of methylmethanesulfonate ( MMS ) in S . cerevisiae [11] , [14] . This is similar to the observed role of Trm9 in modulating the toxicity of ionizing radiation [15] and of Trm4 in promoting viability after methylation damage [14] , [16] . Trm9 catalyzes the methyl esterification of the uracil-based cm5U and cm5s2U to mcm5U and mcm5s2U , respectively , at the wobble bases of tRNAUCU-ARG and tRNACCU-GLU , among others [17] . These wobble base modifications in the tRNA enhance binding of the anticodon with specific codons in mixed codon boxes [18] . Codon-specific reporter assays and genome-wide searches revealed that Trm9-catalyzed tRNA modifications enhanced the translation of AGA- and GAA-rich transcripts that functionally mapped to processes associated with protein synthesis , metabolism and stress signalling [11] . The resulting model proposes that specific codons will be more efficiently translated by anticodons containing the Trm9-modified nucleoside and that tRNA modifications can dynamically change in response to stress . To assess the dynamic nature of tRNA modifications proposed by this model , we developed a systems-oriented approach using liquid chromatography-coupled , tandem quadrupole mass spectrometry ( LC-MS/MS ) to quantify the full set of tRNA modifications in an organism . Mass spectrometry-based methods have recently emerged as powerful tools for identifying and quantifying RNA modifications [19] , [20] . We applied such an approach to quantify changes in the spectrum of tRNA modifications in yeast exposed to four mechanistically dissimilar toxicants . Multivariate statistical analysis of the data reveals dynamic shifts in the population of RNA modifications as part of the response to damage , with signature changes for each agent and dose . Further , analysis of yeast mutants lacking specific modification enzymes revealed novel biosynthetic pathways and compensatory or cooperative shifts in the levels of other modifications .
As shown in Figure 1 , we developed an LC-MS/MS method capable of quantifying 23 of the ∼25 known ribonucleoside modifications in cytoplasmic tRNA in S . cerevisiae [2] , [3] . The method begins with isolation of small RNA species ( <200 nt ) and quantification of the tRNA content ( ∼80–90% of small RNA species ) . Individual ribonucleosides in enzymatic hydrolysates of tRNA were resolved by HPLC and identified by high mass accuracy mass spectrometry , by fragmentation patterns with collision-induced dissociation ( CID ) and by comparison to chemical standards . Each ribonucleoside was subsequently quantified by pre-determined molecular transitions during CID in the LC-MS/MS system . We were able to quantify 23 of the 25 tRNA modifications in yeast , with 2′-O-ribosyladenosine phosphate ( Ar ( p ) ) not detected in positive ion mode , possibly due to the negatively charged phosphate , and only tentative identification of ncm5Um by CID due to weak signal intensities . A critical feature of our approach is quantitative rigor given the need for highly precise measurement of even small changes in the relative quantities of ribonucleosides . To this end , we used an Agilent Bioanalyzer ( microfluidics-based sizing and quantification against an internal standard ) for quantification of total tRNA species in the mixture of small RNA ( 85±5% , N = 39 ) and an internal standard ( [15N5]-2′-deoxyriboadenosine ) to minimize variation in the levels of the individual ribonucleosides . One caveat here is low-level contamination ( a few percent ) with 5S rRNA that also contains ribonucleoside modifications . We were able to obtain highly reproducible data for the signal intensity associated with each ribonucleoside ( see Figure S1 for linearity of signal intensity for the 23 ribonucleosides ) . Multiple reaction monitoring ( MRM ) mode yielded no detectable background signal in the absence of tRNA hydrolysates except for i6A ( 9±2% ) . The method proved to be highly precise: 3±1% intra-day variance in average signal intensity and 12±10% inter-day variance in average fold-change values for each ribonucleoside in treated and untreated cells ( 294 analyses in three biological replicates over several weeks ) . Analysis of tRNA from wild type cells revealed a three-log range of signal intensity , with I and ac4C producing the highest intensity and ncm5Um the lowest ( Figure 1 ) . In general , modifications can be categorized in high ( I , ac4C , m1A , m22G , Am , Y ) , medium ( Cm , m5C , Gm , m1G , t6A , m7G , m2G , m3C , i6A ) and low signal intensities ( m1I , D , m5U , ncm5Um , mcm5U , mcm5s2U , Um , yW , ncm5U ) , with signal intensity reflecting both the abundance and mass spectrometric sensitivity for each ribonucleoside . To quantify the dynamics of tRNA modifications in cellular responses , we selected four well studied chemicals that possess distinct mechanisms of toxicity: MMS , hydrogen peroxide ( H2O2 ) , sodium arsenite ( NaAsO2 ) , and sodium hypochlorite ( NaOCl , pKa 7 . 5; ref . [21] ) . The behavior of yeast upon exposure to MMS , NaAsO2 and H2O2 has been extensively studied in terms of transcriptional response and cytotoxicity phenotyping [11] , [22] , [23] . We also chose NaOCl since it produces an oxidative stress distinct from that of H2O2 and could thus affect the tRNA modification spectrum differently . We then performed cytotoxicity dose-response studies in S . cerevisiae exposed to agents ( Figure S2 ) , choosing concentrations ( Figure 2 ) that produced ∼20% , 50% and 80% cytotoxicity to ensure a common phenotypic endpoint for comparison . One important issue with the methylating agent , MMS , was the possibility that changes in methyl-based modifications in tRNA could be due to both enzymatic methylation and direct chemical methylation . Literature precedent indicates that MMS reacts with DNA to form adducts mainly at guanine N7 ( 68% ) , adenine N1 ( 18% ) and cytosine N3 ( 10% ) [24] , [25] . To address the extent of direct methylation of RNA by MMS , control studies were performed and revealed that direct alkylation by MMS contributes <25% to the cellular burden of m7G in small RNA , with the bulk of m7G arising by enzymatic methylation of tRNA ( Figure S3 ) . No other agent affected tRNA modifications in this manner , with changes in the relative quantities of the modifications resulting from alterations in biosynthesis , tRNA gene transcription or tRNA degradation . With exposure and analytical parameters established , we tested the hypothesis that the spectrum of tRNA modifications would dynamically change as a function of the S . cerevisiae stress response . In addition , we predicted that these changes would serve as biomarkers of each exposure . Cells were exposed to three concentrations of each chemical and 23 tRNA modifications were quantified by LC-MS/MS , with the results shown in Tables S1 and S2 , the latter as the ratio of treated to control signal intensities . A crude analysis of the data shows fold-changes ranging from 0 . 2 to 4 , with 25% and 36% of the exposure data significantly different from control values by Student's t-test at p<0 . 05 and p<0 . 1 , respectively ( Table S2 ) . These results point to the non-random and regulated nature of the exposure-induced changes in the levels of the tRNA modifications . Multivariate statistical analyses revealed important patterns or signatures in the toxicant-induced changes in tRNA modifications . As shown in Figure 2 , hierarchical clustering distinguished both agent- and dose-specific changes in the modification spectra , with unique patterns of increase and decrease apparent in all cases . H2O2 consistently increased the levels of m5C , Cm and m22G and , at the highest concentration , t6A , with dose-dependent decreases in m5U , m1G , m2G , mcm5s2U , i6A , yW and m1A . MMS consistently increased the level of m7G , and decreased Am , m5C , Cm , mcm5s2U , i6A , and yW . NaAsO2 caused only decreases in modification levels at the highest concentration , most notably for mcm5U , m3C , m7G , mcm5s2U , i6A , yW , m5C , and Cm . Interestingly , the dose-response for NaOCl showed an inverse correlation between concentration and increased levels of Am and Um and decreased levels of m5C . Given the reproducibility of the data , the changes in tRNA modification spectra can be considered signature biomarkers of exposure for these four classes of chemical stressor . Principal component analysis ( PCA ) creates a model that reduces the complexity of a data set by identifying hidden correlations ( the principal components ) comprised of weighted , linear combinations of the original variables , with the first principal component ( P1 ) accounting for the largest portion of the variation of the data and so on . The results of PCA of the dataset of nucleoside fold-change values ( Table S2 ) are shown in Figure 3 . With 88% of the variability expressed in the first 3 principal components ( 56% , 22% and 10% , respectively ) , individual agents contributed variance to each as shown in Table S3 , with H2O2 contributing 74% in P1 , MMS and NaOCl each contributing >40% in P2 and NaAsO2 contributing 53% in P3 . The scores plots ( Figure 3A , 3C ) clearly distinguish the four agents , with H2O2-induced changes as the major determinant of P1 and with MMS , NaOCl and NaAsO2 distinguished best in P2 . While H2O2 and NaOCl are negatively correlated in P1 , they are more closely grouped in P2 and P3 , which suggests that the changes in tRNA modifications reflect both common and unique facets of the toxic mechanism of each agent . For example , H2O2 and NaOCl are both oxidizing agents , but H2O2 generates hydroxyl radicals by Fenton chemistry while the protonated form of NaOCl yields hydroxyl radicals , chloramines and singlet oxygen [26]–[29] . Similarly , MMS and NaAsO2 are negatively correlated in P3 and more positively correlated in P2 , with the latter consistent with recent evidence for alkylation-like adduction of arsenic to DNA and proteins following its metabolism [30] , [31] . This would also explain the negative correlation of NaAsO2 and H2O2 in P1 , while the recognized oxidative stress caused by arsenite [32] is consistent with a positive correlation between NaAsO2 and H2O2 in P2 . Both PCA ( Figure 3B , 3D ) and cluster analysis ( Figure 2 ) revealed that m5C , m22G , Cm and t6A are major features of the H2O2 response , while m1A , m3C and m7G were associated with MMS . Increases in Gm , Um , I and Am were responsible for the variance induced by NaOCl , which is consistent with the inversely related doses and levels for Am and Um observed in cluster analysis . NaAsO2 was poorly distinguished in P2 , with only m2G accounting for variance only at the highest concentrations ( Figure 2 ) . The observation of toxicant- and dose-dependent changes in the levels of the 23 tRNA modifications is consistent with a model in which cells respond to toxicant exposure by modifying tRNA structure to enhance the synthesis of proteins critical to cell survival , as has been proposed in our earlier work with yeast exposure to MMS [11] . In this case , the conversion of cm5U to mcm5U by Trm9 was found to be critical for surviving MMS exposure [11] . To define the roles of specific tRNA modifications in the toxicant response , cytotoxicity phenotypic analyses were performed with yeast mutants lacking each of 13 trm tRNA methyltransferase genes and 3 other types of RNA modification biosynthetic genes . As shown in Figure 4 , heightened sensitivity to H2O2 was observed in mutants lacking Trm4 and Trm7 , which catalyze formation of two modifications elevated by H2O2 exposure: m5C and Cm , respectively [33] , [34] . The simple explanation is that the increase in a specific tRNA modification is needed to promote an efficient stress response . However , m22G was also elevated by H2O2 ( Figure 2 , Figure 3 ) , yet loss of an enzyme involved in its biosynthesis , Trm1 [35] , [36] , did not confer H2O2 sensitivity ( Figure 4 ) . This behavior draws a comparison to mRNA , as it has been reported that many of the transcripts induced in response to a stress are not essential for viability during a challenge from that stress [37] , [38] . MMS sensitivity was identified in trm1 , trm4 and trm9 mutants , the latter as shown previously [11] , whose corresponding proteins synthesize m22G , m5C and mcm5U/mcm5s2U , respectively . However , these modifications were not strongly associated with MMS exposure in PCA ( Figure 2 , Figure 3 ) . Somewhat surprisingly , loss of Trm1 , Trm4 , Trm7 and Trm9 conferred NaAsO2 sensitivity . These methyltransferases are responsible for m22G , m5C , m1G ( position 37 ) and mcm5u/mcm5s2U , respectively , of which only m2G was found to vary significantly in PCA ( Figure 3 ) . For NaOCl , only trm4 was sensitive to exposure and the m5C product of Trm4 was not associated with NaOCl exposure ( Figure 3 ) . Again , this behavior parallels that of mRNA transcripts the levels of which do not change after exposure but that encode proteins important for viability after exposure [37] , [38] . These results reveal a complex and dynamic control of tRNA modifications in cellular survival responses and suggest models for homeostasis of the modifications . One example involves modifications for which the biosynthetic mutant is sensitive to exposure but the modification level does not change in wild type cells following exposure ( e . g . , MMS exposure and trm1/m22G , trm4/m5C , trm9/mcm5U or mcm5s2U; Figure 2 , Figure 3 , Figure 4 ) . The simplest explanation here is that the modification change occurs in a single tRNA species and the change is masked by an inverse change in the level of the modification in the larger population of tRNA molecules . As noted in Table S4 , both m22g and m5C occur in multiple tRNAs . A second explanation parallels the idea of both pre-existing mRNA and stressor-induced transcription during a stress response . We have observed stress-induced increases in the levels of several modifications required for the survival response ( Figure 2 , Figure 3; ref . [11] ) . However , other modifications may already exist on tRNA molecules involved in selective translation of stress response messages . In both cases , the modifications are absolutely required for survival , but some are already present in unstressed cells and others are induced . Finally , it is possible that a modification , though its level may not change , is required for the subsequent synthesis of other modifications that are critical to the survival response . Such “cooperativity” is suggested by data from mod5-deficient cells , in which i6A decreases by ∼75-fold while D is reduced by ∼2-fold . The presence of i6A may signal downstream biosynthetic events , with deficiencies promoting a general reprogramming of tRNA . Similarly , cells deficient in Trm82 , a subunit of m7G methyltransferase , had a ∼7-fold reduction in m7G and a >1 . 5-fold increase in m3C , mcm5U , m1G , m2G , t6A , mcm5s2U and m22G ( Figure 5 ) , which raises the possibility that Trm82 itself or m7G inhibits other tRNA modifying enzymes . With the caveat of possible increases in tRNA copy number , the ∼50% increase in these modifications suggests a pool of unmodified tRNA molecules , an observation supported by increases in m3C after exposure to MMS , mcm5U after exposure to NaOCl , and both t6A and m22G after exposure to H2O2 ( Figure 2 , Figure 3 ) . Cooperativity could also explain the case in which the level of a modification changes significantly following exposure yet the mutant strain is not sensitive to the exposure . For example , loss of trm1 did not confer sensitivity to H2O2 but its product , m22G , rose significantly with H2O2 exposure ( Figure 2 , Figure 3 , Figure 4 ) . The stress-induced change in m22G may be a response to a change occurring with another modification for which the mutant strain might be sensitive to the exposure . In support of this argument , m5C modifications increase along with m22G after H2O2 exposure and deficiencies in the m5C-producing methyltransferase Trm4 confer sensitivity to H2O2 . Wohlgamuth-Benedum et al . have also demonstrated such cooperativity among RNA modifications in their observation of the negative regulation of wobble position C-to-U editing by thiolation of a U at position 33 outside the anticodon in T . brucei [39] . Finally , there is the case in which a modification decreases with exposure to a stressor and a deficiency in the enzyme responsible for that modification confers sensitivity , as in the case of m5C , trm4 and NaOCl ( Figure 2 , Figure 3 , Figure 4 ) . The population level of m5C may decrease with NaOCl exposure in spite of a protective increase in the level of m5C at some critical tRNA location . This may reflect a decrease in the transcription of tRNA substrates of Trm4 or the targeted degradation of specific tRNA species . It is important to note that biosynthetic redundancy , as in the case of Gm with Trm3 and Trm7 , could mask any major changes in tRNA modification levels that are associated with mutational loss of one enzyme ( Figure 5 ) , yet loss of one of the redundant enzymes can induce sensitivity , such as the case of H2O2 and trm7 ( Figure 2 , Figure 3 , Figure 4 ) . These observations lead to many questions that obviously require more mechanistic study to define the precise role of tRNA modifications in cellular responses to stress . One consistent feature that arose from our studies of modifications affected by or protecting against toxicant exposure was the frequent involvement of the wobble position , 34 ( Tables S4 , S6 ) . The correlation between the wobble modification and the importance of a corresponding enzyme after toxicant exposure is not surprising in light of recent observations of the critical role played by these modifications and anticodon loop ribonucleosides in translational fidelity and efficiency [4] . Controlled alteration of ribonucleoside structure at position 34 , and that at the conserved purine at position 37 , is proposed to allow reading of degenerate codons by modulating the structure of the anticodon domain to facilitate correct codon binding [4] . As the most frequently modified ribonucleosides , positions 34 and 37 also have the largest variety of modifications [40] , [41] , so it is reasonable that they would be extensively involved in translational control of the survival response . This is also consistent with our previous observation that mcm5U at the wobble position was critical to the translation of key protein synthesis and DNA damage response genes [11] . Perhaps more interesting is a potential role for putative non-anticodon loop ribonucleoside modifications in the survival response . For example , Trm44 is the 2′-O-methyltransferase in yeast responsible for formation of 2′-O-methyl-U ( Um ) , which occurs only at position 44 in yeast tRNA [42] , [43] . Loss of Trm44 conferred sensitivity to NaAsO2 exposure . This observation suggests three possibilities: ( 1 ) that Trm44 synthesizes or influences the synthesis of modifications at other positions in tRNA; ( 2 ) that Um occurs in positions other than 44 ( e . g . , anticodon loop ) ; or ( 3 ) that Um ( 44 ) plays a role in modulating translation in response to NaAsO2 exposure . Another example involves Trm1 and m22G at position 26 . Current evidence suggests that m22G occurs only at position 26 in yeast tRNA [43] and that Trm1 is the methyltransferase responsible for its formation [44] . The fact that loss of Trm1 conferred sensitivity to MMS and NaAsO2 exposure and that H2O2 exposure increased the level of m22G again suggest the three possibilities analogous to those for Trm44 and Um . Similar arguments can be made for Trm3 and Gm at position 18 with NaOCl exposure , for Trm11 and m2G at position 10 with NaOCl and NaAsO2 exposure , and for Trm8/82 and m7G at position 46 with MMS exposure . All of these observations point to participation of wobble and non-wobble RNA modifications in a complex and dynamic network of translational mechanisms in cellular responses . This expands the repertoire of translational control mechanisms , which includes recent discoveries about the effect of ribonucleoside modifications on tRNA stability [8] , [9] . In this model , cell stress leads to rapid degradation of specific tRNAs and subsequent effects on translational efficiency . Another similar stress response involves cleavage of cytoplasmic transfer RNAs by ribonucleases released during the stress [10] . One consequence of these degradation pathways would be to decrease the amount of modified ribonucleoside detected in our assay , which may explain some of our observations with the toxicant stresses . Our approach to quantifying tRNA modifications provides information only about population-level changes , so the observed changes could result from modification of existing tRNA molecules or changes in the number of tRNA copies . Of particular importance here is the observation by Phizicky and coworkers that loss of m7G at position 46 leads to degradation of specific tRNAs [9] , which suggests that our observation of changes in the levels of RNA modifications could be amplified by both reduction in the activity of modifying enzymes and by tRNA degradation . On the other hand , one argument against large increases in tRNA copy number arises from recent observations of repressed tRNA transcription during S-phase and , of direct relevance to the present studies , during replication stress induced by MMS , hydroxyurea and likely other toxicants [45] . Finally , our findings may also parallel recent work on tRNA charging . Reactive oxygen species have been implicated as a methionine misacylation trigger and modification status could help promote these programmed changes to the genetic code [12] . As we are beginning to appreciate the precision and coordinated nature by which cells mount a regulated stress-response , it is most likely the observed changes in tRNA modification levels promote multiple biological responses . As recognized by several groups [19] , [20] , the LC-MS/MS platform facilitates definition of biosynthetic pathways for RNA modifications . This is illustrated in Table S5 , which contains ratios of the basal levels of tRNA modifications in yeast mutants lacking various tRNA modification enzymes compared to wild type yeast , and in a heat map visual depiction of these ratios in Figure 5 . These data corroborate known substrate/enzyme pairs [43] and further demonstrate the highly quantitative nature of our approach . For example , the level of m1I drops to nearly undetectable levels with loss of Tad1 , the adenosine deaminase producing the inosine precursor to m1I [46] . That a diploid heterozygous mutant of trm5 , the product of which catalyzes N-methylation of I [47] , caused a ∼40% reduction in total m1I attests to the accuracy of our assay and demonstrate that gene dosage effects alter the level of tRNA modification . A similar ∼50% reduction in yW occurred in the trm5 mutant due to the absence of the m1G ( 37 ) precursor to yW [47] , while complete loss of Trm12 , which methylates the 4-demethylwyosine precursor of yW , made yW undetectable . Other pathways critical to yW are apparent in the smaller decreases in yW ( 0 . 3– to 0 . 5-fold ) occurred in cells deficient in other enzymes ( Trm8 , Trm82 , Tad1 , Mod5 , Tan1 , Trm11 , Trm5; Figure 5 , Table S5 ) . The data in Figure 5 also reveal several novel observations . Pintard et al . observed that Trm7 catalyzes 2′-O-methylation of G and C nucleosides at positions 32 and 34 , but they could not detect the ncm5Um product of 2′-O-methylation of ncm5U [34] . While we could only tentatively identify ncm5Um , we observed a quantifiable signal for a species with the correct molecular transition for ncm5Um and observed that loss of Trm7 led to a lowering of putative ncm5Um to undetectable levels ( Figure 5 , Table S5 ) . This supports their prediction that Trm7 catalyzes formation of ncm5Um in yeast . Another example involves the formation of Um . While Trm44 catalyzes synthesis of Um at position 44 in tRNA ( ser ) [42] , analysis of trm mutants in Figure 5 and Table S5 suggests a redundancy in methyltransferase activity capable of 2′-O-methylation of U ( 44 ) , including Trm7 , which methylates U at positions 32 and 34 [34] , and Trm13 methylation of C and A at position 4 in several yeast tRNAs . Cells lacking Trm44 , Trm7 or Trm13 have 53% , 50% and 76% of wild type levels of Um , respectively . More striking evidence for this redundancy arises in correlation analysis that revealed a strong covariance in the levels of tRNA modifications in cells lacking either Trm 44 or Trm 13 ( Table S7; C = 0 . 87 ) . This correlation ranks second highest in our analysis behind the two subunits of the m7G methyltransferase ( Trm8 and Trm82; C = 0 . 95 ) , which suggests possible functional redundancy for Trm44 and Trm13 , with broader substrate specificities for either or both enzymes . In summary , a quantitative bioanalytical approach to the study of tRNA modifications has revealed several novel biosynthetic pathways for RNA modifications and has led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to different toxicant exposures . The results support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells [8]–[10] , [13] . Further , these cellular response mechanisms almost certainly involve parallel changes in spectrum of ribonucleoside modifications in rRNA and perhaps other RNA species .
All chemicals and reagents were of the highest purity available and were used without further purification . 2′-O-Methyluridine ( Um ) , pseudouridine ( Y ) , N1-methyladenosine ( m1A ) , N2 , N2-dimethylguanosine ( m22G ) , and 2′-O-methylguanosine ( Gm ) were purchased from Berry and Associates ( Dexter , MI ) . N6-Threonylcarbamoyladenosine ( t6A ) was purchased from Biolog ( Bremen , Germany ) . N6-Isopentenyladenosine ( i6A ) was purchased from International Laboratory LLC ( San Bruno , CA ) . 2′-O-Methyladenosine ( Am ) , N4-acetylcytidine ( ac4C ) , 5-methyluridine ( m5U ) , inosine ( I ) , 2-methylguanosine ( m2G ) , N7-methylguanosine ( m7G ) , 2′-O-methylcytidine ( Cm ) , 3-methylcytidine ( m3C ) , 5-methylcytidine ( m5C ) , alkaline phosphatase , lyticase , RNase A , ammonium acetate , geneticine and desferrioxamine were purchased from Sigma Chemical Co . ( St . Louis , MO ) . Nuclease P1 was purchased from Roche Diagnostic Corp . ( Indianapolis , IN ) . Phosphodiesterase I was purchased from USB ( Cleveland , OH ) . PureLink miRNA Isolation Kits were purchased from Invitrogen ( Carlsbad , CA ) . Acetonitrile and HPLC-grade water were purchased from Mallinckrodt Baker ( Phillipsburg , NJ ) . All strains of S . cerevisiae BY4741 were purchased from American Type Culture Collections ( Manassas , VA ) . Cultures of S . cerevisiae BY4741 were grown to mid-log phase followed by addition of toxicants to the noted final concentrations ( cytotoxicity of ∼20% , 50% and 80% ) : H2O2 , 2 , 5 or 12 mM; MMS , 6 , 12 or 24 mM; NaAsO2 , 20 , 40 or 60 mM; NaOCl , 3 . 2 , 4 . 0 or 4 . 8 mM . The sensitivity of the following mutant strains to toxicant exposure was also determined ( doses producing ∼80% cytotoxicity in wild-type: 12 mM H2O2 , 24 mM MMS , 60 mM NaAsO2 , or 4 . 8 mM NaOCl ) : trm1 , trm2 , trm3 , trm4 , trm7 , trm8 , trm9 , trm10 , trm11 , trm12 , trm13 , trm44 , trm82 , tad1 , mod5 , and tan1 . Since trm5 is essential , a diploid strain ( GBY1 ) lacking one copy of trm5 was used . After a 1 h , cells were collected and viability determined by plating . Following lyticase treatment ( 50 units ) in the presence of deaminase inhibitors ( 5 µg/ml coformycin , 50 µg/ml tetrahydrouridine ) and antioxidants ( 0 . 1 mM desferrioxamine , 0 . 1 mM butylated hydroxytoluene ) , tRNA-containing small RNA species were isolated ( Invitrogen PureLink miRNA kit ) and the tRNA quantified ( Agilent Series 2100 Bioanalyzer ) . Following addition of deaminase inhibitors , antioxidants and [15N]5-2-deoxyadenosine internal standard ( 6 pmol ) , tRNA ( 6 µg ) in 30 mM sodium acetate and 2 mM ZnCl2 ( pH 6 . 8 ) was hydrolyzed with nuclease P1 ( 1 U ) and RNase A ( 5 U ) for 3 h at 37°C and dephosphorylated with alkaline phosphatase ( 10 U ) and phosphodiesterase I ( 0 . 5 U ) for 1 h at 37°C following addition of acetate buffer to 30 mM , pH 7 . 8 . Proteins were removed by filtration ( Microcon YM-10 ) . Ribonucleosides were resolved with a Thermo Scientific Hypersil GOLD aQ reverse-phase column ( 150×2 . 1 mm , 3 µm particle size ) eluted with the following gradient of acetonitrile in 8 mM ammonium acetate at a flow rate of 0 . 3 ml/min and 36°C: 0–18 min , 1–2%; 18–23 min , 2%; 23–28 min , 2–7%; 28–30 min , 7%; 30–31 min , 7–100%; 31–41 min , 100% . The HPLC column was coupled to an Agilent 6410 Triple Quadrupole LC/MS mass spectrometer with an electrospray ionization source where it was operated in positive ion mode with the following parameters for voltages and source gas: gas temperature , 350°C; gas flow , 10 l/min; nebulizer , 20 psi; and capillary voltage , 3500 V . The first and third quadrupoles ( Q1 and Q3 ) were fixed to unit resolution and the modifications were quantified by pre-determined molecular transitions . Q1 was set to transmit the parent ribonucleoside ions and Q3 was set to monitor the deglycosylated product ions , except for Y for which the stable C-C glycosidic bond led to fragmentation of the ribose ring; we used the m/z 125 ion for quantification [48] , [49] . The dwell time for each ribonucleoside was 200 ms . The retention time , m/z of the transmitted parent ion , m/z of the monitored product ion , fragmentor voltage , and collision energy of each modified nucleoside and 15N-labeled internal standard are as follow: D , 1 . 9 min , m/z 247→115 , 80 V , 5 V; Y , 2 . 5 min , m/z 245→125 , 80 V , 10 V; m5C , 3 . 3 min , m/z 258→126 , 80 V , 8 V; Cm , 3 . 6 min , m/z 258→112 , 80 V , 8 V; m5U , 4 . 2 min , m/z 259→127 , 80 V , 7 V; ncm5U , 4 . 3 min , m/z 302→170 , 90 V , 7 V; ac4C , 4 . 4 min , m/z 286→154 , 80 V , 6 V; m3C , 4 . 4 min , m/z 258→126 , 80 V , 8 V; ncm5Um , 5 . 5 min , m/z 316→170 , 90 V , 7 V; Um , 5 . 1 min , m/z 259→113 , 80 V , 7 V; m7G , 5 . 1 min , m/z 298→166 , 90 V , 10 V; m1A , 5 . 7 min , m/z 282→150 , 100 V , 16 V; mcm5U , 6 . 4 min , m/z 317→185 , 90 V , 7 V; m1I , 7 . 3 min , m/z 283→151 , 80 V , 10 V; Gm , 8 . 0 min , m/z 298→152 , 80 V , 7 V; m1G , 8 . 3 min , m/z 298→166 , 90 V , 10 V; m2G , 9 . 4 min , m/z 298→166 , 90 V , 10 V; I , 10 . 9 min , m/z 269→137 , 80 V , 10 V; mcm5s2U , 14 . 2 min , m/z 333→201 , 90 V , 7 V; [15N]5-dA , 14 . 4 min , m/z 257→141 , 90 V , 10 V; m22G , 15 . 9 min , m/z 312→180 , 100 V , 8 V; t6A , 17 . 2 min , m/z 413→281 , 100 V , 8 V; Am , 19 min , m/z 282→136 , 100 V , 15 V; yW , 34 . 2 min , m/z 509→377 , 80 V , 5 V , and i6A , 34 . 4 min , m/z 336→204 , 100 V , 17 V . The mass spectrometer monitored ions with the molecular transitions of D , Y , m5C , and Cm from 1 to 4 min; molecular transitions of m5U , ncm5U , ac4C , m3C , ncm5Um , Um , m7G , m1A , and mcm5U from 4 to 7 min; molecular transitions of m1I , Gm , m1G , and m2G from 7 to 10 min; molecular transitions of I , mcm5s2U , [15N]5-dA , m22G , t6A , and Am from 10 to 30 min; molecular transitions of yW and i6A from 30 to 40 min . The identities of individual ribonucleosides were established by comparison to commercially available synthetic standards , high mass accuracy mass spectrometry , fragmentation patterns generated by collision-induced dissociation ( CID ) in a quadrupole time-of-flight mass spectrometer ( QTOF ) or MSn analysis by ion trap mass spectrometry , with comparison to literature data ( e . g . , ref . [48] ) . To assess the direct and indirect effects of MMS on levels of methylated ribonucleosides , the absolute levels of m7G were quantified in small RNA hydrolysates isolated from MMS-exposed and unexposed mutant and wild type strains of yeast by the LC-MS/MS method described above . Calibration curves were generated by mixing variable amounts of m7G ( final concentrations of 0 , 5 , 50 , 300 , 600 , 1000 , and 2000 nM ) with a fixed concentration of [15N]5-dA ( 40 nM ) . A volume of 10 µl of each solution was analyzed with the LC-MS/MS system described earlier . Differences in the levels of ribonucleosides in exposed versus unexposed and in mutant versus wild-type yeast were analyzed by Student's t-test . Hierarchical clustering analyses were performed using Cluster 3 . 0 . Data were transformed to log2 ratios of modification levels in treated cells relative to unexposed controls . Clustering was carried out using the centroid linkage algorithm based on the distance between each dataset measured using the Pearson correlation , with heat map representations produced using Java Treeview . Principal component analysis was performed using XLStat ( Addinsoft SARL , Paris , France ) , with a Pearson correlation matrix consisting of data that were mean-centered and normalized to the standard deviation . Correlation analysis was used to assess the degree of covariance among the various sets of fold-change values for each mutant ( Table S5 ) , with correlation coefficients calculated using Excel ( Microsoft ) . | While the genetic code in DNA is read from four nucleobase structures , there are more than 100 ribonucleoside structures incorporated as post-transcriptional modifications mainly in tRNA and rRNA . These structures and their biosynthetic machinery are highly conserved , with 20–30 present in any one organism , yet the larger biological function of the modifications has eluded understanding . To this end , we developed a sensitive and precise mass spectrometric method to quantify 23 of the 25 ribonucleosides in the model eukaryotic yeast , Saccharomyces cerevisiae . We discovered that the spectrum of ribonucleosides shifts predictably when the cells are exposed to different toxic chemical stimulants , with these signature changes in the spectrum serving as part of the cellular survival response to these exposures . The method also revealed novel enzymatic pathways for the synthesis of several modified ribonucleosides . These results suggest a dynamic reprogramming of the tRNA and rRNA modifications during cellular responses to stimuli , with corresponding modifications working as part of a larger mechanism of translational control during the cellular stress response . | [
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| 2010 | A Quantitative Systems Approach Reveals Dynamic Control of tRNA Modifications during Cellular Stress |
The ribosome is an evolutionarily conserved organelle essential for cellular function . Ribosome construction requires assembly of approximately 80 different ribosomal proteins ( RPs ) and four different species of rRNA . As RPs co-assemble into one multi-subunit complex , mutation of the genes that encode RPs might be expected to give rise to phenocopies , in which the same phenotype is associated with loss-of-function of each individual gene . However , a more complex picture is emerging in which , in addition to a group of shared phenotypes , diverse RP gene-specific phenotypes are observed . Here we report the first two mouse mutations ( Rps7Mtu and Rps7Zma ) of ribosomal protein S7 ( Rps7 ) , a gene that has been implicated in Diamond-Blackfan anemia . Rps7 disruption results in decreased body size , abnormal skeletal morphology , mid-ventral white spotting , and eye malformations . These phenotypes are reported in other murine RP mutants and , as demonstrated for some other RP mutations , are ameliorated by Trp53 deficiency . Interestingly , Rps7 mutants have additional overt malformations of the developing central nervous system and deficits in working memory , phenotypes that are not reported in murine or human RP gene mutants . Conversely , Rps7 mouse mutants show no anemia or hyperpigmentation , phenotypes associated with mutation of human RPS7 and other murine RPs , respectively . We provide two novel RP mouse models and expand the repertoire of potential phenotypes that should be examined in RP mutants to further explore the concept of RP gene-specific phenotypes .
Ribosomes are responsible for constructing the myriad proteins required for the function of each individual cell . Ribosomes themselves , which consist of small ( 40S ) and large ( 60S ) subunits , are assembled from about 80 different ribosomal proteins ( RPs ) along with four species of rRNA synthesized in the nucleolus [1] , [2] . RPs fall broadly into two categories; the RPL proteins that make up the large ribosomal subunit , and the RPS proteins that constitute the small subunit . Mutations in both RPL and RPS genes have been implicated in a set of shared phenotypic characteristics in invertebrates . This is best illustrated by the approximately 50 Drosophila melanogaster Minute mutants , a collection of ribosomal gene mutations that are characterized by developmental delay , short thin bristles , growth retardation , reduced fertility and recessive lethality [3] . In vertebrates , a phenotypic overlap among RP mutants occurs similar to that seen in Drosophila Minute mutants , however additional phenotypic complexity is emerging , in which mutation or suppression of RPs results in some phenotypes that depend upon which gene is mutated . This is highlighted by a recent study in which 20 different RP genes were targeted using morpholino antisense oligos in the zebrafish Danio rerio [4] . Whereas some phenotypes were shared among knockdowns , such as hypoplasia of the yolk sac , others were gene-specific . For instance , knockdown of rps15 resulted in an enlarged 4th ventricle of the brain , whereas knockdown of rpl35a caused a sharply bent tail , and targeting of rps29 produced an enlarged lens [4] . A similar picture of phenotypic overlap paired with gene-specific phenotypes is emerging in mice . For example , mutations in Rps19 and Rps20 result in increased epithelial pigmentation , a ventral belly spot , small body size , and a reduction in red blood cell count [5] . Mice with mutations in Rpl24 also have ventral belly spots and body size reduction , but they present with additional retinal abnormalities and skeletal defects [6] . Mice harboring the Rpl27a sooty foot allele share an epidermal hyperpigmentation phenotype with Rps19 and Rps20 mutants , but display the additional feature of cerebellar ataxia [7] . These distinct features of individual RP mutant phenotypes suggest that vertebrate RPs may have unique , tissue-specific functions and/or tissue-specific expression levels . Indeed , the broad spectrum of distinct phenotypes that have been characterized in a relatively small number of mammalian RP mutants also hints at possible extra-ribosomal functions of RPs . In humans , mutations in RPs have been implicated in hematopoetic disorders , most notably Diamond-Blackfan anemia ( DBA ) , which has been attributed to mutations in RPS19 , RPS26 , RPS24 , RPS17 , RPS10 , RPS7 , RPL35a , RPL26 , RPL11 , and RPL5 [8]–[14] . While anemia is a shared phenotype among all patients carrying these various gene mutations , some specific attributes are associated with individual genes . For example , mutations in RPL5 are associated with cleft palate and anomalies of the thumb and heart , whereas isolated thumb malformations are predominant in patients carrying mutations in RPL11 [11] . In this paper , we further examine the role of ribosomes in mammalian development by investigating Rps7 . Through analysis of two new ENU-induced mouse mutants of Rps7 , montu ( Mtu ) and zuma ( Zma ) , we show that mutation of Rps7 impairs ribosomal biogenesis , resulting in variable lethality and reduced body size accompanied by abnormal skeletal , melanocyte , eye , and central nervous system ( CNS ) development . While many of these phenotypes have previously been associated with mutation of murine RP genes , the findings of overt brain malformations and behavioral abnormalities are novel . Similar to mutation of other RP genes in the mouse , the penetrance of the Rps7-associated phenotypes is affected by genetic background and the overt phenotypes are suppressed by TRP53 deficiency . These mutants provide the first mouse models of Rps7 disruption and increase our understanding of the phenotypic consequences of mammalian RP mutations .
The montu ( Mtu ) mouse was identified in a large-scale ENU mutagenesis program [15] , [16] exhibiting dominant inheritance of a ventral belly spot , kinked tail , and reduced body weight ( Figure 1A , 1B ) . Linkage analysis was performed using a cross of the Mtu founder ( BALB/c OlaHsd background ) with C3H/HeH . Analysis of genotypes of 104 offspring demonstrated linkage to a 74 Mb region of proximal chromosome 12 that was further refined to a 3 . 43 Mb critical region ( Figure S1A , S1C ) . DNA sequencing of exons and flanking regions of 17 candidate genes using genomic DNA from four affected animals and controls identified a single heterozygous sequence variant occurring only in affected mice within exon 6 of Rps7 , which encodes a 194 amino acid ribosomal protein , RPS7 ( or S7e , the eukaryotic specific homolog of the yeast S7A and S7B ) . The identified Rps7 variant , c . 574T>G ( NM_011300 ) , encodes a Gly substitution of a highly conserved Val residue ( p . V156G; NP_035430 ) ( Figure 1C , Figure S2A ) . Subsequent sequence analysis of 91 animals from a C3H/HeH congenic Mtu colony showed 100% concordance between the c . 574T>G mutation and the affected phenotypes . Analysis of Mtu animals demonstrated variable penetrance on a mixed background , however 100% penetrance of the tail kink , belly spot , and small body size phenotypes when assessed in the C3H/HeH congenic colony . We also observed a dominant lethality phenotype with incomplete penetrance , 72% viability in F1 BALB/c:C3H mice ( N = 251 ) and 26% viability in the C3H/HeH congenic colony ( N = 260 ) . The zuma ( Zma ) mouse was independently identified as part of a sensitized ENU screen designed to identify mutations that increased the severity of neural crest defects observed in Sox10 haploinsufficient mice ( Sox10LacZ/+ ) , a well characterized neural crest mutant which presents with a high frequency of small , white belly spots [17] . Affected backcross mice ( BALB/cJ×C57BL/6J ) from the Zma pedigree exhibited large white belly spots , tail kinks , and reduced body size . Linkage analysis of twelve affected N1 mice initially detected linkage of Zma to a region of chromosome 12 overlapping where the Mtu mutation was localized ( Figure S1B , S1C ) . Subsequent sequencing of Rps7 in Zma mice revealed a heterozygous A to C point mutation in exon 7 of Rps7 , predicted to cause substitution of a conserved amino acid ( p . Y177S; c . 637A>C ) ( Figure 1C , Figure S2A ) . Genotyping analysis of Zma mice outcrossed to C57BL/6J to establish a congenic colony showed that the c . 637A>C mutation was observed in 100% of affected mice . We initially observed incompletely penetrant phenotypes in N2 heterozygous Zma mice on a mixed BALB/cJ; C57Bl/6J background , where Zma/+ mice showed 74% viability , 76% belly spots , and 90% tail kinks ( N = 46 ) . However , heterozygosity for the Zma allele rapidly changed to a completely penetrant , lethal phenotype during outcrossing onto C57BL/6J , and no Zma/+ mice were observed at N4 . This contrasted with the phenotype we observed during establishment of a congenic C3H/HeJ Zma colony; on this genetic background , the N3 generation exhibited a low frequency of belly spotting and tail kinks ( 4% and 0% , respectively; N = 41 ) yet no lethality , as Zma heterozygotes were observed at the expected frequency through N6 . The comparably mild phenotype we observed in the congenic C3H/HeJ Zma colony suggests that the Mtu allele may exert more severe phenotypic effects than the Zma allele . This hypothesis is supported by the observations that Mtu/+ mice presented with reduced viability , fully penetrant belly spots , tail kinks , and small body size on a predominantly C3H/He genetic background , while Zma/+ mice on a similar background were observed at the expected frequency and were generally indistinguishable in phenotype from their+/+littermates . Sequencing of exons 6 and 7 of Rps7 in 9 inbred strains ( A/J , AKR/J , BALB/c , C57BL6/J , C3H/HeJ , CBA , DBA , LP/J , and 101 ) confirmed that the point mutations detected in Mtu and Zma are not natural variants . Additionally , we confirmed the presence of the mutations in Rps7 transcripts using Mtu/+ and Zma/+ cDNA for sequencing and real-time PCR , respectively . We also demonstrated a lack of complementation between the Mtu and Zma Rps7 alleles by performing an intercross of heterozygous mice from the two lines . Genotyping of newborn offspring revealed that no animals carried both the Mtu and Zma mutations ( N = 27 ) ( Figure S1D ) . The lack of viable double heterozygotes was consistent with homozygote lethality observed in each line , thus showing non-complementation of the two alleles . Collectively , the similar phenotypes , sequencing data , and genetic analyses provide strong evidence that the mutations identified in Rps7 are responsible for the observed Mtu and Zma phenotypes , hereafter referred to as Rps7Mtu and Rps7Zma . The high degree of evolutionary conservation of both mutated amino acids ( p . V156G and p . Y177S ) suggests that these alterations may disrupt normal function . The consequences of the RPS7Mtu and RPS7Zma mutant proteins were first assessed using the computational analyses PANTHER and SIFT . Results from the PANTHER coding SNP analysis tool [18] suggested that both mutations are likely to be deleterious ( PANTHER subPSEC score of −5 . 00 and −5 . 06 for RPS7Mtu and RPS7Zma , respectively ) . Similarly , the Sorting Tolerant From Intolerant ( SIFT ) algorithm [19] predicted both alleles to affect protein function , however , the high degree of conservation in the 72 database sequences at each position resulted in a low confidence level for the prediction ( SIFT prediction score 0 . 00 , median conservation 3 . 59 for both RPS7Mtu and RPS7Zma ) . Collectively , the computational analysis was consistent with the Rps7Mtu and Rps7Zma alleles both having functional consequences . The potential effects of p . V156G and p . Y177S on RPS7 secondary structure ( Figure 1D , Figure S2 ) were also examined . There is no three-dimensional protein structure currently available for metazoan RPS7 , therefore we used the available structures from Tetrahymena thermophila ( PDB ID 2XZM ) and Saccharomyces cerevisiae ( PDB ID 3U5C ) , whose RPS7 ortholog sequences share 37% and 55% residue identity with mouse RPS7 , respectively [20] , [21] . RPS7 structural elements appear to be highly conserved across species: the S . cerevisiae and T . thermophila proteins themselves share only 34% amino acid identity , yet their solved three-dimensional structures superimpose well , and are consistent with secondary structural predictions of mouse RPS7 generated using PSSpred ( http://zhanglab . ccmb . med . umich . edu/PSSpred ) and PROFsec [22] ( Figure S2B ) . Furthermore , the S . cerevisiae and T . thermophila sequences each contain a Tyr residue homologous to mouse p . Y177 ( Figure S2A ) , and a Val residue homologous to mouse p . V156 is present in S . cerevisiae and substituted conservatively with Ile in T . thermophila . The introduction of either p . V156G or p . Y177S into the mouse sequence did not alter predictions of the beta strand and alpha helix at the respective locations of the substitutions ( Figure S2B ) , suggesting that grossly altered secondary structure may not be responsible for the functional consequences of RPS7Mtu or RPS7Zma . We next used biochemical analyses to examine the effects of p . V156G and p . Y177S on stability and subcellular localization of the 22 kiloDalton ( kDa ) protein as well as ribosome assembly and biogenesis . Previous studies have shown that nonsense mutations in RPS19 can result in decreased protein levels , and missense mutations can alter the capacity of RPS19 to localize to the nucleolus [23] . C- and N-terminal FLAG-tagged wild-type , RPS7Mtu and RPS7Zma proteins were transiently expressed in human embryonic kidney ( HEK ) -293 cells . Western blot analysis revealed no differences in protein levels for any of the RPS7 mutant proteins ( Figure 2A , Figure S3A ) . The remaining in vitro studies were focused on the potentially more severe RPS7Mtu mutant protein . Analysis of the FLAG-tagged proteins revealed no notable differences in subcellular localization of the RPS7Mtu protein ( Figure 2B ) . To verify the capacity of RPS7Mtu to be incorporated into the ribosome , cytoplasmic extracts from transiently transfected HEK-293 cells were fractionated to separate ribosomes and ribosomal subunits ( in the pellet ) from free cytoplasmic proteins ( in the supernatant ) . A similar fraction of all transfected RPS7 proteins was observed in the ribosomal pellet ( Figure S3B ) indicating that the mutation does not alter RPS7 assembly into the ribosome . To confirm this finding , cytoplasmic extracts from Rps7Mtu/+ liver were analyzed by ultracentrifugation through sucrose gradients , and the ratio between the peaks of the ribosomal subunits in the absorbance profile was determined . This ratio can reveal defects in the synthesis of one of the two subunits , such as the net increase of the 60S/40S ratio seen in cultured human cells following depletion of single RPs [24] , [25] . However , the observed 60S/40S ratio in Rps7Mtu/+ liver was similar to control ( Figure S3C ) , indicating that RPS7Mtu does not drastically alter assembly of the ribosomal subunits . We further assessed whether RPS7Mtu affects ribosomal biogenesis by analyzing the pre-rRNA maturation pattern . Alterations are visualized by the accumulation of specific rRNA precursors: a defect in large subunit RPs affects cleavage of 28S precursors , whereas a defect in small subunit RPs alters that of 18S precursors . Northern blot analysis of total RNA from wild-type and Rps7Mtu/+ tissue , using a probe for the internal transcribed spacer ( ITS ) 1 of pre-rRNA that anneals to 18S rRNA precursors [26] , [27] , indicated that 18S rRNA pre-rRNA processing was altered both in liver and brain from Rps7Mtu/+ mutant mice ( Figure 2C ) . Quantitative measurement of the hybridization signals showed a significant accumulation of the 30S precursor in Rps7Mtu/+ ( indicated by an increased 30S/21S ratio; Figure 2D ) , confirming altered rRNA precursor processing and demonstrating that the RPS7Mtu mutation has functional consequences on ribosomal biogenesis .
In this paper we present genetic , functional , and phenotypic evidence that the montu ( Mtu ) and zuma ( Zma ) mouse lines isolated from independent ENU screens harbor distinct point mutations in Rps7 . These first-reported alleles of Rps7 in mice cause similar phenotypes including small body size , tail abnormalities , mid-ventral white spotting , eye defects , and an underdeveloped cerebral cortex . We provide functional evidence that the Rps7Mtu mutation ( p . V156G ) affects ribosome biogenesis . The altered Rps7Mtu 30S/21S ratio is consistent with the altered pre-rRNA maturation reported for a DBA patient harboring an RPS7 donor splice-site mutation ( c . 147+1G>A ) that results in accumulation of 30S and 45S precursors [11] . A requirement for RPS7 during rRNA maturation is supported by other studies showing a role for human RPS7 in early stages of 45S rRNA processing [24] or the nuclear stages of 40S maturation [38] . In yeast , depletion of the two RPS7 paralogs ( S7a and S7b ) results in a severe growth defect [39] , [40] and S7 has been hypothesized to play an early role as a component of the small subunit processome [41] as well as a later role in ribosome biogenesis during assembly of polysomes [42] . However , the mechanism underlying the altered rRNA maturation in these murine Rps7 mutants is not clear , as neither the Rps7Mtu nor the Rps7Zma mutation is predicted to grossly disrupt protein secondary structure , and RPS7Mtu protein is correctly localized and incorporated into ribosomes in cultured cells . Presumably , the mutant RPS7 proteins could be altered in their interactions with rRNA , other ribosomal proteins , or non-ribosomal proteins . The possibility of interaction with non-ribosomal proteins is intriguing given the position of RPS7 at the surface of the ribosome ( Figure S2C , S2D ) in close proximity to the binding site for eIF4G [21] , a eukaryotic initiation factor important for assembling the pre-initiation complex . Interestingly , most other known , viable mouse RP mutations also occur in eukaryotic-specific RPs located at the surface of the ribosome . Additional work is needed to determine the precise role RPS7 plays in mammalian ribosome biogenesis and how the Rps7Mtu and Rps7Zma mutations disrupt this function . While modeling the pre-rRNA processing defect of an RPS7 DBA patient , these Rps7 mutant mice do not replicate the characteristic DBA phenotype of severe anemia . This lack of an anemia phenotype is not entirely unexpected , since the analysis of many mouse mutants suggests that mice as a species are generally less sensitive than humans to any gene haploinsufficiency [43] , [44] . This is exemplified by the fact that , despite frequent mutation of RPS19 in DBA , Rps19 heterozygote knockout mice have been described with normal hematopoiesis [34] , [45] and an ENU allele of Rps19 displays only a mild erythrocyte phenotype , along with an elevated MCV that parallels the slightly elevated MCV in Rps7Mtu [5] . It is possible that further studies of Rps7 alleles on different genetic backgrounds or in mice with more severe alleles may reveal additional red blood cell phenotypes . In the future , comparison among mouse DBA models may provide insight into why the murine and human hematological phenotypes do not fully overlap , and may also reveal why mutation in RPS19 has a uniquely high frequency in DBA compared to other RPs that cause the disease . Both Rps7 mutants display a variety of skeletal phenotypes that collectively suggest that normal ribosome biogenesis is required for three distinct stages of somite development , which governs subsequent axial skeleton formation . First , the decreased number of tail vertebrae indicates an insufficient production of somite progenitors . Second , the mutant vertebral fusion leading to tail kinks is indicative of incorrect somite border formation . Third , the mutants exhibit an array of axial skeleton defects consistent with an anterior transformation , indicating that somite identity is perturbed . Widespread disturbances of the skeletal system encompassing each of these three defects are also found in Rpl38 ( tail short , Ts ) [46] , [47] and Rpl24 ( belly spot and tail , Bst ) mutants [6] , [48] . Indeed , there are striking similarities in the defects observed in Rpl38 and Rps7 mutants , including specific transformations within the cervical ( C7 to T1 ) , thoracic ( T8 to T7 ) , lumbar ( L1 to T13 ) and sacral ( fusion of additional transverse processes ) regions . Mutations in Rpl38 and Rpl24 cause additional disturbances of the appendicular skeleton not observed in Rps7 , however , given the similarities of these mutants to Rps7 it will be interesting to see if appendicular skeletal defects are observed with different Rps7 alleles and/or genetic backgrounds . Interestingly , severe skeletal malformations have not been reported for five other murine RP mutants ( Rps19 , Rps20 , Rpl22 , Rpl27a , Rpl29 ) [5] , . This suggests that either mutation of RP proteins do not universally affect skeletal development , or skeletal defects remain to be identified in these other RP mutants . In the case of Rps7 , the severity of skeletal defects was modified by both genetic background and by allele , suggesting that detailed characterization in existing and additional RP mutants is necessary to further explore the role of RP proteins in skeletal development . We show that Rps7Zma-mediated white spotting results from a severe developmental reduction in melanoblasts . A developmental reduction in melanoblasts has also been reported in Rps19 and Rps20 heterozygous mutants , however , these mutants subsequently develop epidermal melanocytosis [5] , a phenotype not seen in either Rps7 mutant . This difference is unlikely to be due to genetic background , as both Rps7 mutants and Rps19 and Rps20 mutants were examined on a predominantly C3H background . Alternatively , the difference could be a more severe developmental reduction in melanoblast numbers in Rps7 mutants , resulting in fewer melanoblasts available at later time points to generate epidermal melanocytosis . Another possibility is that loss of Rps7 may not have the same effect in keratinocytes as loss of Rps6 , Rps19 and Rps20 , which are thought to act in keratinocytes to produce TRP53-mediated dark skin [5] . Our in vitro experiments with Rps7 ( Figure 2 and Figure S3 ) suggest that these mutations are hypomorphic alleles , so a third possibility is that allele severity , rather than gene-specific function , could account for pigmentation phenotype differences among RP mutants . The mouse Rps7 mutants display additional developmental defects of reduced cortical and hippocampal size . We hypothesize that cell death within the developing CNS is sufficient to account for the neocortical thinning we observe in both Rps7 mutant lines . We also hypothesize that apoptosis within the developing telencephalon might account for the deficit in working memory observed in Rps7Mtu/+ heterozygotes . To our knowledge , this is a novel developmental brain abnormality in mouse arising from RP haploinsufficiency , differing substantially from the only other report of neuronal defects in RP mutant mice , that of cerebellar abnormalities and ataxia in Rpl27a sooty foot mutants [7] . Interestingly , the neuronal phenotypes in mouse Rps7 mutants correlate with the report of microcephaly as one of 11 congenital craniofacial anomalies that can be associated with DBA [51] , and are consistent with RP mutant phenotypes observed in different model organisms . In zebrafish , gene-specific neuronal phenotypes were seen in a subset of knocked-down RPs [4] and further studies of RPL11 revealed brain deformities and reduced neuronal progenitor cells along with apoptosis in the affected regions [32] . In the zebra finch , RPL17 and RPL37 both regulate the sexually dimorphic formation of the song control regions of the brain within the VZ [52] . Thus , the murine Rps7 mutant phenotypes add to a growing body of literature linking individual RPs to specific features of neural development and suggesting that other RP mouse mutants may have previously uncharacterized defects in brain development . Important questions remain regarding what underlying cellular mechanisms explain the similarities and differences among RP-associated phenotypes . A growing body of literature suggests a common mechanism where alterations in RP expression result in activation of TRP53 , suggesting ribosomal stress is one of the many cellular anomalies detected by TRP53 . For example , in their study of RP-mediated dark skin , McGowan and colleagues show that TRP53 acts as a sensor of ribosomal integrity , and that mutations in Rps19 and Rps20 can be phenotypically suppressed by Trp53 deficiency [5] . Trp53 haploinsufficiency also relieves the abnormal phenotypes of mice heterozygous for the sooty foot ataxia allele of Rpl27a [7] or the Bst allele of Rpl24 [6] . Similarly , our results show that loss of Rps7 increases TRP53 levels and that Trp53 haploinsufficiency suppresses all morphological aspects of the Rps7 phenotype . Our in vivo studies are supported by recent studies showing that Rps7 depletion in vitro as well as in vivo in zebrafish induces TRP53 expression [53] , [54] , and collectively suggest the mechanism underlying the Rps7 phenotype is TRP53 activation followed by TRP53-mediated apoptosis . While these data suggest that RP mutant phenotypes may all be attributed to alterations in RP—TRP53 interactions , the variability among RP phenotypes suggests more complexity , potentially as a result of RP gene-specific or cell-specific functions [55] . Tissue-specific effects of mutations in different RPs have been observed in zebrafish [4] , mouse [5] , [6] and human [11] . These apparently tissue-specific phenotypes could simply be due to incomplete phenotypic characterization , differing severity of hypomorphic alleles , and/or differing genetic backgrounds . However , if they truly reflect tissue-specific RP functions , one explanation could be differences in the relative expression levels of individual RPs , with a consequent differential sensitivity to RP insufficiency that could vary with tissue type , developmental stage or differentiation state . Indeed , remarkable heterogeneity in RP mRNA levels has been observed in a variety of tissues [56] including specifically during mouse embryonic development [47] and human neuronal differentiation [57] . A cell type-specific sensitivity to RP insufficiency is strongly supported by the observation that keratinocyte-specific Rps6 hemizygosity causes hyperpigmentation while melanocyte-specific Rps6 hemizygosity instead causes hypopigmentation [5] . Similarly , the severe melanoblast reduction in Rps7 mutants could suggest a melanocyte-specific sensitivity to RPS7 loss . Given that Sox10 and Rps7 pathways interact in melanocytes in vivo ( Figure S6 , Figure S7 ) , it will be interesting to determine if a melanocyte-specific transcriptional response to RPS7 deficiency is mediated by Sox10 , which is expressed in melanocytes but absent in keratinocytes . TRP53 activation in melanocytes has already been associated with downregulation of another white spotting gene , Kit , which is proposed as a transcriptional target of TRP53 [7] , [58] . Further work is needed in melanocytes and other cell types that display RP gene-specific phenotypes , to clarify if direct transcriptional regulation of lineage-specific genes plays a role in tissue-specific sensitivity to individual RP deficiencies . Another explanation of tissue-specific RP phenotypes could be a function for individual RPs in the translation of specific mRNAs . This is supported by studies of RPL13 [59] and by a recent study reporting patterning defects in Rpl38 mutant mouse embryos due to RPL38 translational regulation of a subset of Hox transcripts [47] . The tissue-specific phenotypes observed in Rps7 mutants would be consistent with a role for RPS7 in facilitating translational regulation of transcripts critical for development of the affected tissues . Alternatively , tissue-specific phenotypes observed in Rps7 mutants could be a consequence of a direct interaction between RPS7 and tissue-specific developmental regulators at the protein level . Interestingly , support for protein-protein interactions between RPS7 and SOX10 comes from recent evidence that RPS7 interacts directly with SRY , an HMG box protein related to SOX10 [60] . This mechanism of direct interaction between RPs and tissue-specific , non-ribosomal proteins could explain tissue-specific phenotypes in RP mutants , and is supported by a number of studies reporting extra-ribosomal functions of specific RPs in regulation of ribosome biosynthesis and binding of transcription factor complexes ( reviewed in ref . [55] ) . The relative contributions of these various mechanistic explanations for tissue-specific and gene-specific RP mutant phenotypes will be clarified with further detailed comparison of mutants on consistent genetic backgrounds and with additional information from tissue-specific conditional alleles . In summary , these novel alleles of Rps7 add to the growing collection of mammalian ribosomal mutants and provide two new mouse models of a DBA-associated gene . Importantly , the unique CNS apoptosis and behavioral phenotypes reported here suggest that RPs need to be considered as candidate genes for not only DBA but also a broad spectrum of neurodevelopmental human diseases .
All procedures performed in the UK were in accordance with the UK Animals ( Scientific Procedures ) Act 1986 , and those performed in the United States were approved by the IACUC in accordance with NIH guidelines . Montu mice were identified in a screen where BALB/c male mice were mutagenized using ENU and crossed to C3H/He for screening . Zuma mice were identified in an ENU screen sensitized by Sox10 haploinsufficiency ( Sox10LacZ/+ ) , where BALB/cJ mice were mutagenized using ENU and crossed to C57Bl/6J mice for further pedigree analysis . Trp53 null mice ( B6 . 129S2-Trp53tm1Tyj/J , stock #002101 ) were purchased from JAX Mice . For behavioral studies , mice were housed in facilities with a 12∶12 light∶dark cycle at a temperature of 22±1°C with a 60–70% humidity . Upon weaning , mice were separated into single-sex littermate groups and food was available ad libitum . For body weight analysis , each gender/genotype group represented in Figure 1B was weighed over 10 weeks ( average N = 9 for each Figure 1B data point , range N = 2–19 ) . A Student's t-test was used to compare wild-type to mutant within gender-matched groups at each age . Significance was assessed using the Bonferonni method to correct for multiple testing , with p<0 . 001 deemed significant . In females , all time points after 1 week were significantly different and in males all time points after 2 weeks were significantly different , with the exception of 8 . 5 weeks where only 2 mutant mice were weighed ( p<0 . 003 ) . Alizarin red and alcian blue staining of skeletons was conducted using standard techniques . Briefly , embryos were skinned and eviscerated , fixed 24 hours in ethanol , then fixed 24 hours in acetone . Staining was performed for 3–4 hours at 37°C and then 3–4 days at room temperature . After staining , embryos were rinsed in water and cleared in 1% KOH for 3 hours at room temperature and moved to fresh 1% KOH overnight . After clearing , embryos were serially transferred through 20%/1% KOH , 50% glycerol/1% KOH , and 80% glycerol/1% KOH . FACS analysis was completed using standard techniques . Briefly , fetal liver samples were collected in ice cold PBS and dispersed into a single cell suspension with passage through a 21 gauge needle . Samples were incubated for 20 minutes with the addition of 10 µl of each antibody ( BD Parmingen Ter119-APC and CD71-FITC ) , then washed once with PBS before FACS analysis . At each age ANOVA analysis was performed with a post-hoc test to compare selected pairs: ( Rps7+/+ versus Rps7Zma/+ ) and ( Rps7Zma/+ versus Rps7Zma/+; Trp53KO/+ ) . DNA was extracted from mice using standard techniques . For Mtu , the mutation was mapped using 11 affected animals and 51 polymorphic MIT makers that were amplified by PCR and visualized on a 4% agarose gel stained with ethidium bromide . Fine mapping was performed with 13 additional SNPs that distinguished between BALB/c and 101 . Seventeen known and predicted genes were identified in the Mtu critical interval from the Ensembl database ( ENSMUSG00000020633 , ENSMUSG00000066544 , ENSMUSG00000020636 , ENSMUSG00000036655 , ENSMUSG00000061477 , ENSMUSG00000020630 , ENSMUSG00000020629 , ENSMUSG00000020628 , ENSMUSG00000036613 , ENSMUSG00000061911 , ENSMUSG00000020674 , ENSMUSG00000020673 , ENSMUSG00000020672 , ENSMUSG00000043061 , ENSMUSG00000044573 , ENSMUSG00000020669 , ENSMUSG00000036136 ) . All exons and adjacent splice sites for these 17 genes were sequenced using a BigDye dideoxy-terminator system and analyzed on an ABI3700 sequencer ( Applied Biosystems ) . For Zma , DNA from 8 offspring was analyzed using the Illumina GoldenGate assay medium density linkage panel ( Illumina , San Diego , CA ) . The addition of 24 microsatellite markers , from 4 regions with suggestive linkage ( Chr 4 , 6 , 12 , 17 ) in 8 additional affected mice , allowed localization to chromosome 12 ( D12Mit182 -D12Mit60 ) . Rps7 was sequenced as a candidate gene using the same methods as for Mtu . mRNA was extracted from wild-type and Rps7Mtu/+ brains , and cDNA was prepared using reverse transcriptase ( Invitrogen ) . A high fidelity PCR polymerase ( KOD , Novagen ) was then employed to amplify the Rps7 transcript encoding a fusion protein tagged at either the C- or N-terminus with a FLAG epitope , prior to cloning into the pcDNA3 . 1 ( + ) vector . Constructs were transfected into HeLa cells grown on glass coverslips , before fixation and staining 48 hours later . The primary antibody employed was a rabbit anti-FLAG ( Abcam , ab21536 , 1∶1000 ) , followed by a fluorescent secondary ( 1∶200 , Molecular Probes ) . Five independent transfections were performed . Images were captured on a Zeiss LSM 510 confocal microscope . For Western blot experiments , DNA constructs encoding wild-type RPS7 , RPS7 p . V156G , and RPS7 p . Y177S fused to the FLAG epitope at the N- or C-terminus were used in transient transfection experiments in human embryonic kidney ( HEK ) 293 cells . Neomycin phosphotransferase II ( NPT2 ) expressed by the cloning vector pcDNA3 . 1 was used as a control for transfection efficiency . After transfection , proteins were separated on a 12% SDS-PAGE , transferred onto a nitrocellulose Protran membrane ( Schleicher and Schuell ) and incubated with a rabbit anti-FLAG or anti-neomycin phosphotransferase II ( NPT2 ) antibody for 1 hr . Following three consecutive washes in PBS/Tween , the membrane was incubated with horseradish peroxidase-conjugated goat anti-rabbit Ab ( Jackson Immunoresearch ) and visualized using a chemiluminescence detection kit ( Pierce ) and a LAS3000 Image System ( Fuji ) . Equal loading was checked by Ponceau-S red staining of the membranes before Western blot analysis . For Northern blots for rRNA processing , RNA was extracted from mouse tissues with Trizol ( Invitrogen ) according to the manufacturer's protocol . Total RNA was fractionated on formaldehyde-agarose gels and transferred to Gene Screen Plus membrane ( NEN ) . To detect rRNA precursor transcripts , ITS1 probe was prepared by 5′ end-labeling of a 28-mer oligonucleotide ( GCTCCTCCACAGTCTCCCGTTAATGATC ) with 25 µCi of γ [32P]-ATP and T4 polynucleotide kinase according to standard protocols . Hybridization was carried out overnight at 42°C in 6× SSPE , 1% SDS , 0 . 25 mg/ml ssDNA and 5× Denhardt's solution . After hybridization , the blot was washed twice with 1% SDS in 2× SSPE for 30 min at 37°C . Quantitation of signals was obtained by phosphor screen scanning with a STORM PhosphorImager and ImageQuant software analysis ( Molecular Dynamics ) . The experiments were performed on tissues from Rps7+/+ and Rps7Mtu/+ littermate mice ( 2 Rps7+/+ and 2 Rps7Mtu/+ for liver , 3 Rps7+/+ and 3 Rps7Mtu/+ for brain , each in triplicate ) . Quantitation is reported as the ratio between 30S and 21S rRNA precursors and was significantly different between genotypes in both brain ( p<0 . 01 ) and liver ( p<0 . 01 ) ( two-way ANOVA with post hoc t-test ) . For ribosome isolation , cytoplasmic extracts from RPS7 p . V156G transiently transfected HEK-293 cells were fractionated through ultracentrifugation on a sucrose cushion . After 2 hours of centrifugation at 100 , 000 g , ribosomes and ribosomal subunits were recovered in the pellet and free cytoplasmic proteins were recovered from the supernatant . For sucrose gradients separating polysomes and ribosomal subunits , cytoplasmic extracts from Mtu/+ liver were loaded onto a 10%–30% linear sucrose gradient containing 30 mM Tris-HCl ( pH 7 . 5 ) , 100 mM NaCl , and 10 mM MgCl2 . Gradients were centrifuged in a Beckman SW 41 rotor for 5 hr at 37 , 000 rpm and then the absorbance profile of the gradient was used to evaluate the ratio between the peaks of ribosomal subunits ( 60S and 40S ) . For whole mount , Pmel17-containing plasmid ( Riken cDNA clone G370069C13; GenBank Acc: BB766987 ) was digested with Kpn1 and transcribed with T3 polymerase to generate DIG-labeled in situ probes . Hybridizations were performed using published protocols [61] with the following modifications . After probe hybridization , Ribonuclease A digestion was omitted , and Tris-buffered saline was used in place of PBS . BM-purple substrate ( Roche , Molecular Biochemicals ) was used in place of 5-bromo-4-chloro-3-indolyl phosphate/nitroblue tetrazolium . Embryos were photographed using a Zeiss SteREO Discovery V12 microscope with a Zeiss AxioCam camera . Probes to detect Rps7 by in situ hybridization were amplified by PCR and cloned into the pCRII- TOPO vector ( Invitrogen ) using the following primer pairs: R7_IN_F AAGGAAATCGAAGTTGGTG and R7_IN_ R AATTAACATCCTTGCCTGTG . Mouse embryos were fixed in 2–4% ( w/v ) paraformaldehyde , cryoprotected with 30% ( w/v ) sucrose in phosphate buffer before sectioning ( 10–20 µm ) on a cryostat . In situ hybridization was carried out as previously described [62] . Briefly , hybridization of sections with 35S-UTP-labeled RNA probes was carried out in a 50% ( v/v ) formamide solution at 60°C . Sections were washed in 50% ( v/v ) formamide , an RNAse A treatment was performed for 30 min at 37°C , and then successively stringent SSC solution washes were performed , with a final wash at 0 . 1× SSC at 60°C . For Nissl staining , adult mice were perfused with 0 . 9% NaCl and 4% ( w/v ) paraformaldehyde before the brain was dehydrated in 30% sucrose , sectioned ( 40 µm ) on a freezing microtome , and stained . . For embryonic histology , embryos were fixed in either Bouin's fixative or 4% paraformaldehyde overnight , washed extensively in PBS , and dehydrated in 70% ethanol before sectioning ( 5–7 µm paraffin ) and hematoxylin and eosin ( H and E ) staining . For immunohistochemistry , embryos were fixed in 4% paraformaldehyde overnight , washed with PBS , dehydrated in 10% sucrose followed by 20% sucrose , then embedded in Neg-50 ( Thermo Scientific ) for cryosectioning ( 14 µm ) . Antibodies included anti-TRP53 ( Leica NCL-p53-CM5p , 1∶200 ) , anti-CC3 ( Cell Signaling #9661 , 1∶200 ) , and anti-PH3 ( Millipore #06-570 , 1∶200 ) and resulting stains were imaged using a Zeiss Observer . D1 microscope . For apoptosis and proliferation experiments , transverse cryo-sections collected at the level of the forelimb from 3 embryos of each genotype , 6 sections per embryo , were counted for CC3+ and PH3+ cells . InStat software ( GraphPad Software , Inc . ) was used for one-way ANOVA statistical analysis . No significant difference among the three genotypes was observed in PH3+ cell counts ( p = 0 . 902 ) . CC3 counts were significantly different among genotypes ( p = 0 . 0005 ) and a Tukey-Kramer multiple comparison post test was used for pairwise comparison among all genotypes with the following results: Rps7+/+ versus Rps7Zma/+ ( p<0 . 001 ) , Rps7+/+ versus Rps7Zma/+; Trp53KO/+ ( P>0 . 05 , no significant difference ) , Rps7Zma/+ versus Rps7Zma/+; Trp53KO/+ ( p<0 . 01 ) . Behavioral phenotyping was performed on mutant and unaffected sex-matched littermates between 8 and 12 weeks of age . Open-field tests were conducted for 5 minutes in a brightly lit , 60 cm diameter , enclosed white arena ( field ) , and monitored by an automated tracking system to measure the total distance travelled and the amount of time spent in the center versus the border of the field . The elevated plus maze test was conducted with an elevated platform consisting of two enclosed arms and two open arms . An automated tracking system was employed to measure the number of entries into each arm and distance traveled in open and closed arms of the elevated plus maze over 5 minutes . Spontaneous alternation was performed in an enclosed T-maze built from gray plastic ( arm dimensions 30 cm×10 cm×30 cm ) . Each mouse underwent 10 trials with an inter-trial interval of at least 20 minutes . For each trial the mouse was placed in the start arm facing the end wall and allowed to enter a goal arm of its own choice . The mouse was confined in the goal arm for 30 seconds and then put back in the start arm and allowed a free choice of either arm [31] . Late gestation ( E18 . 5 ) embryos were collected , fixed in 4% paraformaldehyde overnight , washed extensively in PBS , and stored in 70% ethanol . Prior to imaging , embryos were rehydrated in a solution of PBS and 0 . 5% ( v/v ) Magnevist ( Bayer Healthcare Pharmaceuticals , Wayne , NJ ) , a contrast agent . MRM was performed on specimens using a Bruker 14 . 1T MR imaging spectrometer ( Bruker Biospin , Billerica , MA ) using a multi-echo RARE technique [63] with TR/TE = 200/6 . 9 ms , 8-echoes , and 4 signal averages . The resulting 3D images were acquired in 53 min with an acquisition resolution of 50 microns isotropic . Manual segmentation of the whole brain and 10 major anatomical regions was performed using Amira visualization software ( v5 . 2 . 2 , Visage Imaging Inc . , Andover MA , USA ) with guidance from an anatomical reference atlas [64] . The volume of these regions was estimated and 3D rendering with smoothing was performed to generate 3D representations . Student's t-tests were used to compare volumes of each Rps7+/+ and Rps7Zma/+ brain region . Significance was assessed using the Bonferonni method to correct for multiple testing , with p<0 . 005 deemed significant . | Ribosomes are composed of two subunits that each consist of a large number of proteins , and their function of translating mRNA into protein is essential for cell viability . Naturally occurring or genetically engineered mutations within an individual ribosomal protein provide a valuable resource , since the resulting abnormal phenotypes reveal the function of each ribosomal protein . A number of mutations recently identified in mammalian ribosomal subunit genes have confirmed that homozygous loss of function consistently results in lethality; however , haploinsufficiency causes a variety of tissue-specific phenotypes . In this paper , we describe the first mutant alleles of the gene encoding ribosomal protein S7 ( Rps7 ) in mouse . Rps7 haploinsufficiency causes decreased size , abnormal skeletal morphology , mid-ventral white spotting , and eye malformations , phenotypes that also occur with haploinsufficiency for other ribosomal subunits . Additionally , significant apoptosis occurs within the developing central nervous system ( CNS ) along with subtle behavioral phenotypes , suggesting RPS7 is required for CNS development . Mutation of human RPS7 has been implicated in Diamond-Blackfan anemia ( DBA ) , yet the murine alleles do not present an analogous phenotype . The phenotypes we observe in the Rps7 mouse mutants indicate RPS7 should be considered as a candidate for a broader spectrum of human diseases . | [
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| 2013 | Mutation of the Diamond-Blackfan Anemia Gene Rps7 in Mouse Results in Morphological and Neuroanatomical Phenotypes |
One of the UN sustainable development goals is to achieve universal access to safe and affordable drinking water by 2030 . It is locations like Kathmandu , Nepal , a densely populated city in South Asia with endemic typhoid fever , where this goal is most pertinent . Aiming to understand the public health implications of water quality in Kathmandu we subjected weekly water samples from 10 sources for one year to a range of chemical and bacteriological analyses . We additionally aimed to detect the etiological agents of typhoid fever and longitudinally assess microbial diversity by 16S rRNA gene surveying . We found that the majority of water sources exhibited chemical and bacterial contamination exceeding WHO guidelines . Further analysis of the chemical and bacterial data indicated site-specific pollution , symptomatic of highly localized fecal contamination . Rainfall was found to be a key driver of this fecal contamination , correlating with nitrates and evidence of S . Typhi and S . Paratyphi A , for which DNA was detectable in 333 ( 77% ) and 303 ( 70% ) of 432 water samples , respectively . 16S rRNA gene surveying outlined a spectrum of fecal bacteria in the contaminated water , forming complex communities again displaying location-specific temporal signatures . Our data signify that the municipal water in Kathmandu is a predominant vehicle for the transmission of S . Typhi and S . Paratyphi A . This study represents the first extensive spatiotemporal investigation of water pollution in an endemic typhoid fever setting and implicates highly localized human waste as the major contributor to poor water quality in the Kathmandu Valley .
Enteric ( typhoid ) fever is a severe systemic infection and a common cause of community acquired febrile disease in many low-income countries in Asia and Africa [1] . The infection is triggered by the ingestion of the bacteria Salmonella Typhi ( S . Typhi ) and Salmonella Paratyphi A ( S . Paratyphi A ) . Both S . Typhi and S . Paratyphi A are human restricted pathogens ( they have no known animal reservoir ) and is it acknowledged that they are transmitted through contaminated food and water or via contact with fecal matter from acute or chronically infected individuals [1] . However , the predominant route of infection has never been rigorously investigated in an endemic setting outside a conventional case/control study design [2 , 3] . Typhoid fever is a common infection in Kathmandu ( the capital city of Nepal ) and our previously generated serological data implies that the local population has longitudinal exposure to both of these endemic pathogens [3 , 4] . Further studies , generated through investigating the spatiotemporal dynamics of typhoid fever in Kathmandu predicted that both S . Typhi and S . Paratyphi A are more likely to be transmitted through contaminated water than via human-to-human transmission in this setting [5 , 6] . Significantly , we found that typhoid fever cases cluster in areas with a high density of urban water sources , which are gravity driven and , therefore , rationally located at lower elevations . The various urban water sources in Kathmandu are most commonly in the form of sunken wells ( as found in many urban and rural settings in lower-income countries ) , piped supplies into large communal holding tanks or the more traditional stone waterspouts ( hitis/dhunge dharas ) [7] . The iconic stone spouts are common across the Nepalese capital and the water flow into these sacred locations is gravity-dependent ( Fig 1a ) , replenished by rainfall and snowmelt from the surrounding Himalayan Mountains . Natural soft-rock aquifers act as reservoirs for ground water and ultimately the untreated water enters the stone spouts from the aquifers through a series of ancient porous underground channels . Our previous analysis specifically identified locations around these stone spouts are hotspots for S . Typhi and S . Paratyphi A infections [5 , 6] . Hypothesizing that the local water , particularly the water accessed via the stone spouts , is a substantial public health risk for typhoid fever and other enteric infections in Kathmandu , we aimed to longitudinally assess bacterial contamination , the chemical composition and the ecological dynamics of enteric bacteria in the water supply in this location . To address this hypothesis , and focusing on water sources accessed by the local population , weekly water samples were collected over a one-year sampling period from ten locations and subjected to various physical , chemical , microbiological and molecular analyses .
The water sources for this study were the ten most commonly used water sources ( identified by questionnaire ) lying within a previously identified typhoid fever hotspot in Lalitpur , Kathmandu [6] . The selected locations were GPS located using an eTrex legend ( Garmin ) and consisted of five stone spouts , three sunken wells and two piped supplies . The location of these water sources are shown in Fig 1b and described in detail in Table 1 . Daily rainfall data from Kathmandu Airport was provided by the Nepalese Department of Hydrology and Meteorology ( http://www . dhm . gov . np/ ) and aggregated into weeks for the purposes of the analysis presented here . Water was collected ( when permitted by water flow ) , from all of the 10 locations once per week over one year from May 2009 to April 2010 . From each of the sources mid-flow water samples were collected in two sterile bottles in volumes of 1 L and 500 ml . From the stone spouts and the piped supply , the stopper was aseptically removed and free flowing water was allowed to flow directly into the sterile bottle . For wells , a sterilized steel bucket ( bleached and washed with autoclaved water prior to use ) was lowered into the well until it was partially submerged , the bucket was then removed and the water was poured into the bottles . All the bottles were labeled with the source code , date and time of collection of the samples . After recording the water temperature the bottles were transported to the laboratory at ambient temperature and were processed within one hour for physical , chemical and microbiological analysis . The Kathmandu Water Engineering Laboratory ( http://www . sodhpuch . com/water-engineering-training-centre-p . ) performed all chemical and physical analyses following their standard operating procedures for international water quality . The measured variables were pH ( Hanna pH meter , calibrated with pH4 , pH7 and pH9 buffers ) , temperature ( Hanna digital thermometer with probe ) , conductivity ( Hanna conductivity meter ) , color ( Perkin Elmer’s LAMDA 650 UV spectrophotometer at 270 nm ) turbidity ( NEPHELOstar Plus nephlometer ) , hardness ( EDTA titration ) , total alkalinity as CaCO3 ( methyl orange ) , chloride ( argentometric titration ) , ammonia ( nesslerisation ) , total nitrate ( Perkin Elmer’s LAMBDA 650 UV spectrophotometer at 275 nm ) , total nitrite ( Perkin Elmer’s LAMDA 650 UV spectrophotometer at 275 nm ) , and trace elements and heavy metals ( Atomic Absorption Spectrophotometric ( AAS ) method ) . All variables were recorded on the day of sampling and compared to WHO guidelines for water quality [8] . A modified most probable number ( MPN ) method was used to assess the microbiological quality of the water , specifically coliform contamination [9 , 10] . Briefly , five ten-fold serial dilutions were made from each water sample by inoculating 1 ml of undiluted water sample into 9 ml of MacConkey broth ( Oxoid , UK ) . This was continued until a dilution of 1 x 10−5 . A total of 30 tubes ( five tubes for each dilution ) were prepared for each sample . The inoculated broths were incubated at 44°C for 48 hours for the culture of thermotolerant coliforms . After incubation , each tube was examined and those that were positive ( production of acid and gas ) were counted . The number of positive and negative tubes in each of these three sets was noted in order and these data were used to estimate the coliform content using a five-tube MPN table . To detect the presence of enteric bacteria with pathogenic potential ( e . g . Salmonellae , Shigellae , Vibrionaceae , and E . coli ) 10 , 20 , 50 , 100 and 500 μl of undiluted water was directly plated onto Xylose lysine deoxycholate ( XLD ) and MacConkey agar plates . The plates were incubated at 37°C overnight and then observed for growth . To increase the likelihood of culturing Salmonellae and Shigellae , 100 ml of undiluted water was filtered through a membrane filter with a pore size 0 . 45 μm ( Whatman , GE Life Sciences , PA , USA ) using a sterile syringe . The filter paper was removed using sterile forceps and placed in 90 ml of typtic soya broth ( Oxoid , UK ) . The soya broth bottles were agitated using a vortex to displace the organisms on the membrane and incubated for 18 hours at 37°C . After overnight incubation 1 ml of the pre-enrichment culture was transferred to 10 ml of selenite broth . Further , 1 ml of the pre-enrichment culture was transferred to 10 ml of Rappaport-Vassiliadis Broth ( RVB ) . The incubated overnight broth was then plated onto XLD and MacConkey agar plates . The plates were incubated overnight at 37°C and then observed for growth . For the detection of Vibrionaceae , 1 ml of the undiluted water sample was diluted in 9 ml of alkaline peptone water . The suspension was then incubated overnight at 37°C and then plated on to MacConkey , XLD and thiosulphate-citrate-bile salts sucrose ( TCBS ) agar . The colony morphologies including the form , size , surface appearance , texture , color , elevation and margin of all individual colonies were recorded from MacConkey and XLD plates . Of special interest were colonies that were circular , with an entire margin and slightly raised elevation that were non-lactose fermenting on both plates , with or without the production of hydrogen sulphide on the XLD plate . Individual colonies with the aforementioned characteristics were isolated and plated on nutrient agar and incubated at 37°C for 24 hours . Isolated colonies obtained on the nutrient agar plates were then subject to API20E testing to identify Enterobacteriaceae and other non-fastidious Gram-negative rods . Total DNA from all water samples was extracted using the Metagenomic DNA Isolation Kit for Water ( Epicentre Biotechnologies , WI , USA ) . Water samples were centrifuged at 1 , 000 rpm ( Hettich Zentrifugen , EBA 21 , Germany ) for 5 minutes to remove large debris and then decanted into sterile containers . After centrifugation , 100 ml of the centrifuged water was filtered through a pre-sterilized filter with a pore size of 0 . 45 μm ( Whatman , GE Life Sciences , PA , USA ) . Using sterile forceps and scissors the membrane was removed from the filter apparatus and cut into four pieces . The cut filters were then placed in a 50 ml sterile conical tube with the upper surface of the filter facing inwards . One milliliter of filter wash buffer containing 0 . 2% Tween-20 was added to the filter pieces in the tubes to remove organisms on the filter surface . The tube was agitated at high speed for approximately 2 minutes with intermittent breaks . The cell suspension was transferred to a clean micro-centrifuge tube and centrifuged at 14 , 000 X g ( Thermo Fischer Scientific , IEC Micro CL17 , Germany ) for 2 minutes to pellet the cells . The supernatant was discarded . The cell pellet was re-suspended in 300 μl of TE buffer , and 2 μl of ready-lyse lysozyme solution and 1μl of RNAse A were added and mixed thoroughly . The tube was incubated at 37°C for 30 minutes and then 300 μl of 2 X meta-lysis solutions and 1 μl of Proteinase K were added to the tube and thoroughly mixed by vortexing . To ensure that all the solution was at the bottom of the tube , the tube was pulse centrifuged . The tubes were then incubated at 65°C for 15 minutes . The solution was cooled to ambient temperature and placed on ice for 5 minutes . 350 μl of MPC protein precipitation reagent was added to the tube and mixed thoroughly by vortexing vigorously for 10 seconds . The debris was pelleted by centrifugation for 10 minutes at 14 , 000 X g ( Thermo Fischer Scientific , IEC Micro CL17 , Germany ) at 4°C . The supernatant was transferred to a clean micro-centrifuge tube and the pellet was discarded . To the supernatant , 570 μl of isopropanol was added and mixed by inverting the tube multiple times . The DNA was pelleted by centrifugation for 10 minutes at 14 , 000 X g ( Thermo Fischer Scientific , IEC Micro CL17 , Germany ) at 4°C . The isopropanol was removed and the sample was briefly pulse centrifuged and any residual liquid was removed without disturbing the pellet . To the pellet 500 μl of 70% ethanol was added without disturbing the pellet . The tube was then centrifuged for 10 minutes at 14000 X g ( Thermo Fischer Scientific , IEC Micro CL17 , Germany ) at 4°C . Ethanol was removed without dislodging the DNA pellet and the sample was briefly pulse centrifuged and any residual fluid was removed without disturbing the pellet . The pellet was then air dried for 8 minutes at ambient temperature before being resuspended in 100 μl of nucleic acid free sterile water ( Epicentre Biotechnologies , WI , USA ) . Quantitative Real-time PCR was performed on all extracted DNA to detect DNA sequences specific for S . Typhi and S . Paratyphi A as previously described [11] . Primer and probe sequences were as follows; S . Typhi; ST-Frt 5' CGCGAAGTCAGAGTCGACATAG 3' , ST-Rrt 5' AAGACCTCAACGCCGATCAC 3' , ST- Probe 5' FAM-CATTTGTTCTGGAGCAGGCTGACGG-TAMRA 3'; S . Paratyphi A; Pa-Frt 5'ACGATGATGACTGATTTATCGAAC 3' , Pa-Rrt 5' TGAAAAGATATCTCTCAGAGCTGG 3' , Pa-Probe 5' Cy5-CCCATACAATTTCATTCTTATTGAGAATGCGC-BHQ5 3' . Briefly , 5 μl of environmental DNA extractions ( as above from 100 ml of water and resuspended in 100 μl of nucleic acid free sterile water ) was used as the template for each experiment , i . e . 5 μl equated to 5 ml of water sample . Quantification was performed using standard curves where plasmid DNA carrying the target sequences were diluted in 10-fold serial dilutions ranging from 100 to 105 plasmid copies per μl; standard curves for assessing S . Typhi and S . Paratyphi A copy number were constructed by plotting the Ct value against the plasmid DNA copy number . For the 16S rRNA gene surveying , variable regions 3 to 5 ( V3–V5 ) of the 16S rRNA gene were PCR amplified from the water DNA extractions . The primers used were as described previously [12] , see S1 Table for the full barcode and primer sequences ( 30 nucleotides for 454 adaptor , 12 nucleotides for unique recognition ( tag ) and 18 nucleotides to amplify the specific V3–V5 region ) used for each sample in the present study . The conditions of PCR were as follows: 1U of AccuPrime Taq DNA Polymerase High Fidelity ( Invitrogen , Carlsbad , CA USA ) , 200 mM of forward and reverse primer , 2 μl of template environmental DNA in a 20 μl reaction . The reaction was cycled for 1 x 94°C for 2 minutes , and then 20x ( 94°C for 30 seconds , 53°C for 30 seconds and 68°C for 2 minutes ) . Each sample was PCR amplified on four occasions , the resulting amplicons were pooled and then ethanol precipitated before resuspension in 20 μl of TE . The 16S rRNA gene amplicons were shipped to The Wellcome Trust Sanger Institute and pooled together into an equimolar mastermix , as measured by a Qubit fluorometer ( Invitrogen , Carlsbad , CA , USA ) , prior to sequencing on a GS FLX Titanium 454 machine ( Roche Diagnostics , Oakland , CA USA ) using the Lib-L kit . The resulting sequence data is available at the European Nucleotide Archive under Study Accession Number ERP004371/Sample Accession number ERS373486 . Sequence data was processed using the mothur software package ( http://www . mothur . org/ ) , following a previously described protocol [12] . This removed poor quality reads , and generated taxonomic classifications for each Operational Taxonomic Unit ( OTU ) . Following these filtering steps 326 , 155 sequences remained ( range of 1 to 7 , 396 sequences per sample ) . We first tested for geographic differences in bacterial assays , using the non-parametric MANOVA implemented in the package ade4 [13] for the R software suite [14] . 9 , 999 random permutations of the data were used to assess statistical significance of Pillai’s statistic [15] and compute the associated p-value . After ruling out the presence of geographic differences between samples , data were aggregated across locations by computing average weekly profiles , from which temporal trends were more straightforward to investigate . As bacterial assays may each capture different aspects of water contamination , these data were subjected to a centered Principal Component Analysis ( PCA ) [13] , which we used to derive a latent variable ( the first principal component , PC1 ) as correlated as possible to all the different assays [16] and therefore reflecting the extent of bacterial contamination . PCA is ideally suited to derive synthetic variables , which capture the essential trends of variation in quantitative or binary data , and is thus readily applicable to water quality data including physical chemical properties , bacterial assays and meta-genomic variation . The temporal trends in PC1 were visualized using ggplot2 [17] and modeled using a cubic spline of sample collection dates . Five breakpoints were used in the model as they gave the best visual fit , and no other number of breakpoints led to significantly better models . This model was compared to a model where PC1 was constant in time using a classical ANOVA comparing the residual variances of the two models . As for bacterial assays , the various chemical properties measured captured potentially different aspects of water pollution . PCA was used to identify the main trends of variation amongst water samples [18 , 19] . Because measurements were made using different units and had inherently different scales of variation , a centered and scaled PCA was used [20] . Missing data were replaced by the average of the corresponding variables , as is customary in PCA [20] . Results of this first PCA were driven by an outlier , which turned out to be a sample of exceptionally poor water quality . In such cases , because PCA finds linear combinations of variables with maximum variance , the presence of an outlier may conceal other interesting structures [20] . As a consequence , this sample was removed in a second PCA , the results of which are shown in Fig 2 . Differences in water chemistry of the water sources were tested using the same MANOVA procedure used in bacterial assays analysis . 16S rRNA gene survey data consisting of 93 samples and 11 , 212 OTUs were first transformed into compositional data [21] , so that each sample was transformed into a composition of OTUs frequencies summing to 1 . This transformation ensures that further analysis will only reflect differences in taxa composition , and not in absolute quantities of sequenced DNA . The Gini-Simpson index was computed for every sample as 1-∑ipi2 where pi is the relative frequency of OTU i in the sample . A centered PCA was used to analyze the metagenomic profiles , retaining the first five principal axes as they expressed most of the structured variation in the data ( Fig 3a ) . The proportion of the total variation represented by the jth principal axis was computed as λj/∑j λj ( Fig 3b ) . The contribution of an OTU i to a principal axis U defined by a vector of loadings [u1 , u2 , … , u11 , 212] was computed as ui2 , which is justified by the fact that U has a norm of 1 ( thus ∨U∨2 = ∑iui2 = 1 ) . The OTUs contributing most to the retained principal axes were defined as OTUs with contributions greater than a given threshold ( Fig 3c ) . Different sets of OTUs were defined using thresholds of 1% , 2% , 5% , 10% , 20% , 30% , 40% , and 50% , which resulted in retention of between 4 and 14 OTUs . A threshold of 10% was retained as it allowed for conserving essentially all of the variation of the 7 principal axes with only 10 OTUs , which still represented 80% of the total variation in the data . Discriminant Analysis of Principal Components ( DAPC , [22 , 23] ) was applied to the 16S rRNA gene data to identify combinations of OTUs that differed most between stone spouts and well . While originally developed for genetic markers data , this method has since been applied to various other types of data , including 16S rRNA ( e . g . [24 , 25] ) . Cross-validation was used to assess the optimal number of PCA axes to retain in the preliminary dimension-reduction step , using 100 independent replicates for each number of retained PCA axes and 70% of the samples as training set . The same analyses were repeated to investigate possible differences across the three locations .
A summary of the chemical and bacterial data generated for each of the 10 sampling locations ( 432 water samples ) is presented in Table 2 ( total data available in S1 Dataset ) . We firstly assessed the physical qualities and chemical composition of the water samples and compared these data to WHO guidelines ( Table 2 ) [8] . The chemical analyses of the water samples signified that several sources had concentrations of iron and ammonia in excess of WHO guidelines , but notably , only nitrate levels and turbidity consistently exceeded WHO recommendations in all locations . These finding , with respect to nitrate were broadly consistent with previous single time point observations in this location [26 , 27] . Further investigation identified significant differences in the chemical profiles of the water from the various locations ( non-parametric MANOVA: Λpillai = 0 . 250 , p = 1×10−4 with 9 , 999 permutations ) . These disparities in chemical compositions could be summarized using a PCA , with water from two of the sunken wells ( locations 4 and 6 ( Table 1 and Fig 1b ) ) forming distinct clusters with independent chemical signatures ( Fig 1c ) . These profiles were characterized by consistently high concentrations of iron and ammonia and greater turbidity at location 4 and greater conductivity , hardness , chlorides and nitrates at location 6 ( Fig 1c ) . Distinctively , one of the piped water supplies ( location 8 ) had consistently lower chemical contamination indices than all other sources ( Fig 1c and Table 2 ) . The differences in chemical composition between locations , confirmed by pairwise comparisons between the sampling locations ( Wilcoxon rank test , all p-values <0 . 05 with Bonferroni correction ) , suggest water mineralization and implicate contaminants such as vehicle exhaust gases [28 , 29] and poor waste handling systems as the key drivers of poor water quality in these locations [30] . Of the different chemical pollutants observed in Kathmandu drinking water , the sustained contamination of water by nitrites and nitrates was the most alarming . Nitrites and nitrates can be introduced into the water through a range of processes including surface water infiltration , industrial pollution , agricultural fertilizer run-off and the leakage of sewerage systems [26 , 31] . Sustained exposure to nitrates can lead to a range of non-communicable diseases including methemoglobinemia , gastrointestinal cancer , bladder and ovarian cancers , and may expedite type II diabetes , thyroid hypertrophy and respiratory tract infections [32] . Nitrate excess is also a notable marker of fecal contamination , and we found nitrites and nitrates correlated positively with coliform concentration ( see below ) and weekly rainfall ( p<0 . 001; Spearman’s rho ) . Consistently , the concentration of chloride , again a marker of sewage and manure contamination [33] , also increased with the onset of seasonal rainfall ( p<0 . 001; Spearman’s rho ) . More generally , several other chemical properties were also associated with weekly rainfall; positive correlations were observed for rainfall against turbidity , ammonia and hardness ( Spearman’s rho , p-values <0 . 05 in six locations ) , and negative correlations were observed with rainfall against pH and alkalinity ( Spearman’s rho , p-values <0 . 05 in four locations ) . Taken together , these results suggest that chemical pollution of drinking water in this setting is likely driven by a combination of rainfall runoff and localized contamination with human fecal waste . To determine the extent of potential fecal contamination in the water samples , we estimated the concentration of fecal indicator thermotolerant coliforms using the minimal probable number ( MPN ) method . The WHO guidelines state that no water directly intended for drinking , or in the distribution system should contain thermotolerant coliforms ( in a 100ml sample ) [8] . We found that majority of the cultured water samples were contaminated with thermotolerant coliforms , suggesting that all the sampled water sources are prone to fecal contamination ( Table 2 ) . The concentrations of cultured coliforms across the samples ranged from 0 . 1 to 2 . 5 x 108 CFU/100 ml , these figures are again largely consistent with prior investigations of drinking water quality in this setting [8 , 26 , 31] . The water from the stone spouts ( locations 1 , 2 , 3 , 9 and 10 ( Table 1 ) had higher coliform concentrations ( median 94 CFU/100 ml; IQR: 4 to 1 . 6 x 104 ) than that from the sunken wells ( locations 4 , 5 , 6 ) ( median 8 CFU/100 ml; IQR: 1 to 7 . 9 x 104 ) and from the piped supplies ( locations 7 , 8 ) ( Median 2 CFU/100 ml; IQR: 1 to 170 ) ( p<0 . 001; Kruskal Wallis ) ( Fig 2a ) . The highest coliform concentrations across the sampled water sources were in the months of June , July and August and , similar to the chemical contamination , positively correlated with the period of increased weekly rainfall ( Spearman’s rho = 0 . 27 , p<0 . 001 ) . The water sampled from source 2 ( stone spout ) consistently had the highest level of coliform contamination ( median coliform concentration; 1 . 3 x 104 CFU/100 ml; IQR: 200 to 1 . 68 x 105 ) and had the highest single coliform count of 2 . 5 x 108 CFU/100 ml in August . The alarming levels of coliform and chemical contamination highlight that water quality is a major public health issue in Kathmandu . The detection of thermotolerant coliforms is not suitable for the identification of specific waterborne pathogens , but is used as a general measure of bacterial contamination [34] . To address this methodological limitation , we concurrently performed membrane filtration and microbiological enrichment for a range of pathogenic bacterial species classified as high risk by the WHO , including Salmonella , Shigella , Vibrionaceae and E . coli [8] . Overgrowth on plates diminished the ability to accurately quantify these organisms but we repeatedly cultured and identified a wide range of organisms with pathogenic potential to humans including Vibrio cholerae 01 , Shigella dysenteriae type-1 , Pseudomonas spp . , and Plesiomonas shigelloides ( S2 Table ) . We additionally isolated multiple Salmonella spp . in the water through supplementary enrichment , yet , after serotyping , none were identified as S . Typhi or S . Paratyphi A . The lack of confirmative cultures for S . Typhi or S . Paratyphi A is not uncommon given previous efforts to culture these pathogens from water . S . Typhi has been cultured from environmental samples previously [35] , but is notoriously difficult to isolate from water in endemic locations . It has been suggested that S . Typhi is often present and viable , but in a non-culturable state [36] . To address this limitation we extracted and purified total nucleic acid from each of the water samples after filtration and performed appropriately controlled quantitative real-time PCR on all samples for chromosomal targets specific for S . Typhi and S . Paratyphi A . We found that 333 ( 77% ) and 303 ( 70% ) of 432 DNA extractions from the eater samples were PCR amplification positive for S . Typhi and S . Paratyphi A , respectively ( Fig 2b ) , with 266/432 ( 62% ) samples being PCR amplification positive for both . To confirm that the PCR amplicons were S . Typhi and S . Paratyphi A , a random cross section of 96 ( 48 for each serovar ) PCR amplicons were successfully cloned , sequenced and confirmed to originate from either S . Typhi or S . Paratyphi A . Further , through inference from standard curve , we identified a significant difference between the number of copies/reaction between S . Typhi ( median 208 copies/reaction; IQR: 72 to 603 ) and S . Paratyphi A ( median 11 copies/reaction; IQR: 4 . 6 to 28 ) , corresponding with inferred significantly different medians of 4 , 200 and 2 , 200 copies/100 ml , respectively ( p<0 . 001; Kruskal Wallis ) ( Fig 2b ) . The presence of S . Typhi and S . Paratyphi A DNA in the water samples did not differ significantly across locations ( non-parametric MANOVA: Λpillai = 0 . 020 , p = 0 . 66 with 9 , 999 permutations ) . Therefore , we propose that processes of fecal contamination of water with S . Typhi and S . Paratyphi A operate non-locally and are likely to equally contaminate water sources in this area . This observation confirms previous work predicting that infection with a specific S . Typhi genotype is a random process [6] . This lack of geographical structure allowed us to pool data from all locations , gaining additional power to investigate the temporal trends of the average weekly PCR amplification profiles through a PCA . The first principal component ( PC1 ) captured a gradient of S . Typhi and S . Paratyphi A DNA that exhibited a marked increase during weekly periods of rainfall in all water sources , and a subsequent decrease during the dry season ( May–October ) ( Fig 2c ) . A simple seasonal model of PC1 ( using a spline of the collection dates with five breakpoints ) showed that this temporal trend was a significantly better fit than a model where PC1 was constant over time ( ANOVA: F = 4 . 2184 , p = 0 . 0034 ) . Notably , the increased presence of S . Typhi and S . Paratyphi A in the water samples displayed a substantial temporal lag , with PC1 reaching a peak between one to two months after the end of the monsoon rains ( Fig 2c ) . This delay may reflect the time taken for the organisms to reach the water outlet from the source of contamination or a concentration effect reflecting lower ground water levels in drier periods . We speculate that a leaking sewerage system and fluctuations in the pressure in the water supply pipes ( negative pressure in the pipe results in an influx of sewage into the water pipe ) are a likely source of this contamination . While the trend of contamination by S . Typhi and S . Paratyphi A was similar across all sampled locations , these organisms likely represent a microscopic fraction of the diverse communities of microorganisms transiting through these water sources [37] . Assessing these complex microbial communities is relevant for investigating the extent of contaminating organisms present in these water sources and also aids tracing the likely sources of bacterial contamination . ( i . e . soil and/or fecal waste ) . 16S rRNA gene surveying provides a suitably broad approach in conducting this type of investigation [38] . Therefore , the longitudinal structures of the bacterial communities in the two water sources with the greatest coliform concentrations ( locations 2 and 5 ) were compared by 16S rRNA gene amplification and pyrosequencing ( data available in S2 Dataset ) [12] . The resulting 16S rRNA gene data showed that the bacterial communities in 93 tested water samples were composed of 11 , 212 OTUs , more than half of which were observed only once ( S1 Fig ) . The bacterial diversity , as measured by Gini-Simpson’s index [39] , was high in all analyzed samples ( S2 Fig ) . We observed an increase in the average number of taxa found during the dry season ( S3 Fig ) , suggesting that low rainfall induces a concentration effect and that exposure to a wider range of taxa likely increases during this period . For a more comprehensive analysis , we investigated the bacterial diversity using a PCA of the 16S rRNA gene survey data transformed into OTU frequency profiles . This analysis showed that >80% of the variation amongst the analyzed samples could be summarized in seven dimensions , each representing a different assemblage of the detected OTUs ( S4 Fig ) . Closer examination of the relative contributions of OTUs to each axis revealed that nearly identical principal components could be obtained through a combination of the 10 most common OTUs , which belonged to the genera Acinetobacter ( OTUs 00001 and 0019 ) , Acidovorax ( OTU00011 ) , Comamonas ( OTU00025 ) , Flavobacterium ( OTU00042 ) , Bacillus ( OTUs 00503 and 00155 ) , Chryseobacterium ( OTU00208 ) , Staphylococcus ( OTU00436 ) and Brevundimonas ( OTU00707 ) ( S4 Fig ) . The ten most commonly detected OTUs were comprised of bacterial genera that are typically found in the environment , and most are not recognized as enteric organisms . While many of these genera are known aquatic organisms , there is some overlap with genera that have recently been demonstrated to originate from sample preparation procedures [40] . To address this limitation , we specifically focused on the relative contributions of nine bacterial families that are common constituents of the gastrointestinal tract of mammals ( Bacteroidaceae , Clostridiaceae , Enterobacteriaceae , Erysipelotrichaceae , Lachnospiraceae , Lactobacillaceae , Prevotellaceae Ruminococcaceae and Veillonellaceae ) [41] . This sub-analysis ( 519 OTUs ) showed that 80% of the variation could be summarized in only five dimensions ( Fig 3a and 3b ) . Furthermore , almost identical principal components could be obtained through a combination of just six OTUs , which belonged to the Enterobacteriaceae ( OTUs 00024 , 00185 and 01479 ) , Bacteroidaceae ( OTU00979 ) , Clostridiaceae ( OTU00263 ) , and Prevotellaceae ( OTU 2149 ) ( Fig 3c ) . We additionally found that the OTU composition was not random , and varied substantially between the sunken well and the stone spout . A DAPC analysis of the 16S rRNA gene data was used to identify combinations of the 519 gastrointestinal OTUs showing the greatest difference between the well ( location 5 ) and the stone spout ( location 2 ) . The corresponding DAPC was able to recover the type of water source of the samples ( based on their OTU composition ) in 81% of cases ( Fig 3c ) . The OTUs exhibiting the greatest variation ( OTU00263; Clostridium , OTU00185; Enterobacteriaceae , OTU00979; Bacteroides and OTU00024; Enterobacteriaceae ) were additionally four of the six principal fecal OTUs driving the temporal trends ( Fig 3d ) . These results suggest the existence of local ecological factors , such as proximal sewage pipes , strongly affect the composition of bacterial communities in different types of water sources . The current daily demand for water in Kathmandu is estimated to be 200 , 000 m3/d , but is unable to be met by municipal supplies , resulting in deficits of 70 , 000 to 115 , 000 m3/d in the wet and dry seasons , respectively [26] . This water deficit means that drinking water distribution lines operate at low pressure as compared to the overused sewage pipes that are often co-located . This pressure differential between sewage and drinking water lines , their relative proximity and poor state of repair accounts for the ingress of sewage into drinking water lines and an overall deterioration in their quality and safety . Furthermore , the cost associated with municipal water has led to an upsurge in the use of stone spouts in the middle and low-income residents of Kathmandu . The users of stone spouts now represent >20% of the water usage in Kathmandu [26] . Bacterial contamination has previously been shown to be higher in stone spouts than sunken wells than piped supplies in Kathmandu , with similar trends seen with nitrate contamination [26] . Our data confirm these associations and for the first time we show seasonal variations in fecal contamination , chemical pollutants and , vitally , exposure to both S . Typhi and S . Paratyphi A . We suggest that stone spouts are the most contaminated type of water source in this location as they are maximally exposed to potentially contaminating sources of human sewage . Further , the shallow aquifers supplying the stone spouts are likely more contaminated than deep aquifers , as they are , again , closer to the sources of potential contamination . Our work shows that Kathmandu drinking water exhibits year round fecal contamination and is far from compliant with World Health Organization ( WHO ) standards [8] and it will be challenge to meet the United Nations sustainable development goal six ( http://www . un . org/sustainabledevelopment/water-and-sanitation/# ) . It has been known for >100 years that the provision of filtered water has a dramatic effect on communicable diseases . In a classic paper by Sedgwick and McNutt they describe the observed decrease in mortality from typhoid fever and other infections when clean water is supplied to an urban population [42] . Taking these historic findings in account , we surmise that stone spouts and sunken wells represent a major public health risk to those in Kathmandu , not only for typhoid but also for other communicable and possibly non-communicable diseases . The chemical composition of drinking water indicates localized , site-specific pollution profiles , consistent with complex populations of enteric bacteria , which show both temporal and location specific profiles . For the first time we provide a molecular proxy for S . Typhi and S . Paratyphi A persistently transiting through an urban water source , where they appear to reach a peak concentration one to two months after the end of the monsoon . Whilst these results are very specific to this setting , many of the mechanisms facilitating such a large degree of contamination ( water shortages , negative-pressure pipes , urban development , poor sanitation ) are likely to be common in other typhoid endemic settings in Asia and beyond . In conclusion , our work shows that municipal water in the capital city of Nepal exhibits evidence of substantial bacterial and chemical contamination . Further , we additionally show evidence of longitudinal contamination of both S . Typhi and S . Paratyphi A , demonstrating the impact of human fecal contamination and outlining that both these organisms are being continually transmitted through these water supply systems . Future research should focus on investigating the main routes of these bacterial and chemical pollutants into this water supply system . Further , the ability to culture and genotype organisms from the environment and human infection would close the circle on the role of the water delivery systems in urban settings for the transmission of typhoid fever . Nepal is amongst the poorest countries in Asia ( GDP per capita of USD 703 in 2013 [43] ) and substantial investment is required to improve the capacity and quality of the water supply in addition to the sewage handling systems in Kathmandu . As a rapid improvement of the water systems is unlikely to occur given the recent earthquake and ongoing political difficulties , we advocate the use of home water filters and sterilization systems alongside vaccination campaigns as the major public health interventions for typhoid fever prevention in this setting . | Aiming to understand the ecology of municipal drinking water and measure the potential exposure to pathogens that cause typhoid fever ( Salmonella Typhi and Salmonella Paratyphi A ) in Kathmandu , Nepal , we collected water samples from 10 water sources weekly for one year and subjected them to comprehensive chemical , bacteriological and molecular analyses . We found that Kathmandu drinking water exhibits longitudinal fecal contamination in excess of WHO guidelines . The chemical composition of water indicated site-specific pollution profiles , which were likely driven by localized contamination with human fecal material . We additionally found that Salmonella Typhi and Salmonella Paratyphi A could be detected throughout the year in every water sampling location , but specifically peaked after the monsoons . A microbiota analysis ( a method for studying bacterial diversity in biological samples ) revealed the water to be contaminated by complex populations of fecal bacteria , which again exhibited a unique profile by both location and time . This study shows that Salmonella Typhi and Salmonella Paratyphi A can be longitudinally detected in drinking water in Kathmandu and represents the first major investigation of the spatiotemporal dynamics of drinking water pollution in an endemic typhoid setting . | [
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| 2016 | The Ecological Dynamics of Fecal Contamination and Salmonella Typhi and Salmonella Paratyphi A in Municipal Kathmandu Drinking Water |
During the 2014–2016 West Africa Ebola Virus Disease ( EVD ) epidemic , the public health community had concerns that sexual transmission of the Ebola virus ( EBOV ) from EVD survivors was a risk , due to EBOV persistence in body fluids of EVD survivors , particularly semen . The Sierra Leone Ebola Virus Persistence Study was initiated to investigate this risk by assessing EBOV persistence in numerous body fluids of EVD survivors and providing risk reduction counseling based on test results for semen , vaginal fluid , menstrual blood , urine , rectal fluid , sweat , tears , saliva , and breast milk . This publication describes implementation of the counseling protocol and the key lessons learned . The Ebola Virus Persistence Risk Reduction Behavioral Counseling Protocol was developed from a framework used to prevent transmission of HIV and other sexually transmitted infections . The framework helped to identify barriers to risk reduction and facilitated the development of a personalized risk-reduction plan , particularly around condom use and abstinence . Pre-test and post-test counseling sessions included risk reduction guidance , and post-test counseling was based on the participants’ individual test results . The behavioral counseling protocol enabled study staff to translate the study’s body fluid test results into individualized information for study participants . The Ebola Virus Persistence Risk Reduction Behavioral Counseling Protocol provided guidance to mitigate the risk of EBOV transmission from EVD survivors . It has since been shared with and adapted by other EVD survivor body fluid testing programs and studies in Ebola-affected countries .
Prior to the EVD epidemic , the Demographic Health Survey conducted in 2013 in Sierra Leone showed that condom use was low , with 5% of female and 13% of male respondents reporting having used a condom in the past 12 months . Twenty-five percent of men and 6% of women reported having two or more sex partners in the last 12 months , and 23% of men and 5% of women reported concurrent sexual partnerships . Multiple and concurrent sexual partnerships were highest among older married men with low education [9] . Human Immunodeficiency Virus ( HIV ) prevalence was 1 . 5% among adults aged 15–49 years [9] . There is limited information regarding the impact of the EVD epidemic on sexual risk behavior , but adolescent girls in Sierra Leone reported more unplanned pregnancies and engagement in transactional sex in the nine months during the epidemic when public schools were closed [10] . In addition , EVD survivors reported stigma and feelings of bereavement similar to those experienced by people living with HIV , possibly negatively affecting both their quality of life and intimate relationships , and their motivation to seek healthcare and other services [11–13] . The impact of the EVD epidemic on the sexual behavior of EVD survivors has not yet been fully explored . In May 2015 , the Sierra Leone Ebola Virus Persistence Study was launched to investigate EBOV persistence in the body fluids of EVD survivors in Sierra Leone [5] . The study consisted of two phases . The first phase assessed EBOV persistence in semen of 100 adult male EVD survivors , and the second phase assessed EBOV persistence in semen and additional body fluids ( vaginal fluid , menstrual blood , urine , rectal fluid , sweat , saliva , tears , and breast milk as applicable by sex ) in 120 male and 120 female EVD survivors . Male and female EVD survivors living with HIV were also invited to participate in the study to characterize EBOV persistence among EVD survivors living with HIV . The study took place in two sites: Military Hospital 34 ( an urban facility in Freetown , Western District ) and Lungi Government Hospital ( a semi-rural facility in Lungi , Port Loko District ) . In this paper , we discuss the development and implementation of the Ebola Virus Persistence Risk Reduction Behavioral Counseling Protocol ( henceforth referred to as the behavioral counseling protocol ) used in the study . The test results from this study will be published separately and the overall study design has been described elsewhere [5] .
Although the authors note the importance of acquiring information on the persistence of EBOV in various body fluids within the pediatric population , for ethical reasons , this study was limited to adults aged 18 years or older . All participants provided written informed consent at the first study visit . The study was approved by the Sierra Leone Ethics and Scientific Review Committee and the WHO Ethical Review Committee ( No . RPC736 ) . We sought to reduce sexual exposure to EBOV by behavior change strategies such as abstinence , increased condom use , and choice of partners . To do this , we developed a behavioral counseling protocol adapted from the Project RESPECT Brief Counseling intervention , an individual face-to-face counseling model that has been proven effective at reducing both new sexually transmitted infections ( STIs ) and risky behaviors in randomized controlled trials [14] . RESPECT’s Brief Counseling was implemented in the context of Human Immunodeficiency Virus ( HIV ) testing , and included two 20-minute counseling sessions ( i . e . pretest/posttest ) in which the counselor supported risk reduction behaviors by increasing the client’s perception of personal risks , emphasizing self-efficacy and personalized goal setting through identifying concrete , incremental and achievable risk-reduction steps that limited sexual HIV/STI exposure . STI clinic patients were asked to describe their own sexual risk behaviors , and misconceptions about risk were clarified . Counselors supported clients in identifying personal barriers to risk reduction and possible ways to overcome them , and helped clients to identify and negotiate behavioral risk reduction steps relevant to their personal risk behaviors . RESPECT’s Brief Intervention model has been previously adapted in behavior change interventions aimed at reducing STIs and pregnancy in vulnerable populations residing in high HIV prevalence settings [15 , 16 , 17 , 18] . The intervention appears to be particularly effective among those with limited exposure to HIV/STI prevention guidance [15 , 16 , 17 , 18] . In sum , the rationale for using a RESPECT model for reducing sexual EBOV exposure was that the approach ( 1 ) has demonstrated efficacy at reducing behaviors relevant to sexual EBOV transmission ( e . g . , increasing abstinence , increasing condom use , reducing risky sexual partnerships ) , ( 2 ) uses a flexible approach that meets the client at his or her own understanding , ( 3 ) has been shown to be effective using counselors that are trained in adherence to the model but do not have advanced degrees in counseling , and ( 4 ) has been successfully adapted in many international settings with varying cultural contexts . The adaptation of Project RESPECT for male and female EVD survivors enrolled in the Sierra Leone Ebola Virus Persistence Study required several changes to HIV/STI prevention behavioral guidance . These changes were made in consultation with HIV service providers operating in Sierra Leone [e . g . Sierra Leone National AIDS Control Programme , National AIDS Secretariat , Dignity Association , Joint United Nations Programme on HIV/AIDS ( UNAIDS ) ] . The standard operating procedure for the behavioral counseling protocol included EBOV persistence pre-and post-test counseling as well as HIV pre-and post-test counseling . EBOV persistence pre-test counseling is an introduction to EBOV testing and risk reduction advice prior to testing , while post-test counseling is a delivery of tailored guidance based on qRT-PCR test results . Fig 1 shows the typical participant visit flow . Table 1 shows the behavioral risk reduction guidance corresponding to each body fluid tested . In the event of a positive qRT-PCR test result , relevant guidance was delivered to the participant in order to reduce transmission risk . All standard operating procedures associated with the delivery of guidance at pre-test and post-test counseling can be found in the full Ebola virus persistence study behavioral counseling protocol here: S1 Protocol . As prior evidence showed that EBOV could persist in semen [2] , male EVD survivors were encouraged at pre-test counseling to use condoms or engage in abstinence for the prevention of EBOV transmission as well as prevention of HIV/STI transmission and unwanted pregnancy . Given limited or no prior evidence for EBOV persistence in vaginal fluids , menstrual blood , urine , rectal fluids , sweat , saliva , tears , and breast milk , precautionary guidance was not provided at pre-test counseling for any of these body fluids . Women were advised at pre-test counseling to use condoms or abstain from sexual intercourse to prevent HIV/STI acquisition and unwanted pregnancy [2] . Female participants were given sexual risk reduction guidance to prevent EBOV transmission only in the event of a positive qRT-PCR test result . Sexual risk reduction behavioral guidance was tailored for each type of body fluid . Participants were given different guidance for fluids that posed a greater risk for sexual transmission of EBOV due to contact during sexual activity ( semen , vaginal fluids , menstrual blood , rectal fluid , urine , sweat , saliva ) as compared to fluids for which there was likely to be less contact during sexual activity ( breast milk , tears ) [2] . Sexual risk reduction guidance was tailored to participants’ religious and cultural practices , particularly around genital washing . For example , counselors reported that Muslim participants preferred to wash the genital area or entire body after sexual intercourse , while Christian participants preferred to wipe genital areas with tissues after sexual intercourse . For non-sexual risk reduction behavioral guidance , the study team reviewed general infection prevention and control ( IPC ) guidelines for other pathogens that are spread through contact with infectious body fluids such as viral meningitis and hepatitis B ( http://www . cdc . gov/meningitis/viral . html ) . This guidance was modified for a low resource setting , taking into consideration limited access to clean water and sustained power sources , and lack of flushing toilets , bleach , or other materials and practices used in standard IPC protocols . Special consideration was given to shaping guidance for female EVD survivors . A focus group with female EVD survivors was held prior to the enrollment of women in the second phase of the study . Participants discussed experience with stigma due to their status as an EVD survivor , fear of resuming sexual activity , and concerns that their partner would not accept condom use . Information from this focus group was shared with study counselors as preparation for discussions with participants regarding specimen collection , sexual activity , and condom use among female participants . Given high rates of sexual violence among women in Sierra Leone [9] , a referral pathway for intimate partner violence ( IPV ) was established in accordance with IPV service provision guidelines from the government of Sierra Leone . Additionally , if a breast milk specimen of a breastfeeding female survivor were to test positive for EBOV , she would be immediately asked to switch to replacement feeding using ready-to-use infant formula provided free of charge with support from UNICEF , and receive ongoing counseling and support from Sierra Leone MoHS Food and Nutrition Directorate staff or a trained nurse . Given the complex nature of the behavioral guidance for all body fluids , particularly for semen and other intimate fluids , counselors with a background in HIV/STI and/or mental health nursing with previous experience with the EVD epidemic were recruited for the study . At Military 34 Hospital , one female HIV/STI nurse and one male mental health nurse were chosen . At Lungi Government Hospital , one woman with a background in HIV/STI nursing and one woman with a background in midwifery were chosen . For the first phase of the study , we developed a four-day training package that focused on the science of EBOV transmission and diagnostic testing methods , how to effectively discuss sexual behavior and risk reduction , and common mental health/psychosocial issues reported by EVD survivors . We held an additional four day-training focused on HIV testing and counseling . For the second phase of the study , we added a four-day training to address behavioral guidance for additional body fluids , and to assess for IPV among female study participants in accordance with national and international guidelines [20] . These trainings were primarily geared towards study counselors but all study staff were invited to attend trainings relevant to their roles . The counselors used role-playing to practice using counseling scripts that had been translated into Krio language and then back-translated for the first phase of the study . The Sierra Leone National AIDS Control Programme , Dignity Association , and UNAIDS provided training on HIV testing and counseling , condom demonstrations , and stigma and discrimination . During and after the training sessions , suggestions from the counselors were incorporated in the counseling scripts and behavioral guidance .
We observed great receptivity to condom demonstrations , with many participants informing research staff that they had not participated in a condom demonstration prior to joining the study . Given the low national rates of condom use among men and women in Sierra Leone [9] , exposure to condoms via the behavioral counseling protocol may eventually be associated with gains in reproductive health and STI prevention in participants . Communicating the risk of sexual transmission of EBOV to study participants was challenging as there was limited scientific evidence regarding the length of persistence and the infectiousness of positive qRT-PCR body fluids . In this study , we observed that communicating uncertainty in a transparent way appeared to facilitate trust between participants and the research study . For example , following the report of an EVD survivor in the United Kingdom who relapsed with EVD meningitis [22] , the behavioral counseling protocol was adapted to ensure that participants were aware of possible severe relapses . Future development of similar counseling protocols may consider embracing scientific transparency as a trust-building communication tool . In the course of the study , we identified a need to clearly and simply explain the detection method of the qRT-PCR assay to participants . We developed a “mango tree” analogy comparing detection of EBOV-specific RNA primers ( short target sequences ) in the body fluids of Ebola survivors via qRT-PCR testing to trying to detect a whole mango tree ( the intact , viable Ebola virus ) by being able to detect only the mango fruits or the leaves ( the target RNA primers for EBOV ) . Detecting only fruits or leaves , one cannot determine whether the tree is indeed intact and alive just as qRT-PCR testing can only determine whether the target RNA primers are detected or undetected in a body fluid specimen , and not whether the virus is infectious . Detecting only one of the two target RNA sequences ( an indeterminate test result ) , could also be explained using this analogy , and also to explain possible variation in test results using different qRT-PCR assays with differing target sequences . This addition to the counseling script was very helpful for study participants to conceptualize the test , the different results they encountered , and the associated counseling messages . A flexible protocol was also instrumental in optimizing participant flow during the study . For example , some participants preferred to visit the counselor prior to specimen collection for more detailed instructions on specimen collection processes or for extra encouragement to continue with the testing process . Talking points were developed as reference materials for study counselors as the original scripts were lengthy . Because we believed that participants should receive their previous test results before deciding whether to donate specimens for testing at the current visit , there was a delay between delivery of test results and post-test counseling and explanation of guidance . Future iterations of similar counseling protocols may be able to concurrently deliver testing results and post-test counseling if a rapid diagnostic test for detection of Ebola RNA in the body fluids of Ebola survivors becomes available . The EVD epidemic had a devastating impact on the economic and social ties of communities across Sierra Leone [12 , 21 , 24] . EVD survivors commonly reported medical and mental health issues post-convalescence for which there was limited assistance in Sierra Leone [25] . These needs were far greater than the services the study could offer . This was particularly true for semen testing , as there were far more EVD survivors in Sierra Leone than could be enrolled in the EBOV persistence study . To this end , the Government of Sierra Leone initiated a comprehensive program for EVD survivors ( CPES ) in October 2015 that aimed to provide services for EVD survivors in Sierra Leone including counseling , semen testing , eye care , myalgia , and treatment for other EVD sequelae [26] . The mental health infrastructure of Sierra Leone is underdeveloped . There is limited capacity to identify and treat mental health disorders in the general public or EBOV-affected populations [12] . When the study was initiated , counseling staff were less familiar with behavioral counseling methods that emphasized dialogue regarding participants’ experiences with EVD survivorship , stigma , and intimate relationships . Learning these skills helped study counselors better assist participants who received multiple positive qRT-PCR test results to maintain participation in semen testing and openly discuss feelings of frustration and anxiety . After observing initial high rates of HIV testing uptake in the first phase of the study , acceptance of HIV testing declined among study participants during the first visit . We hypothesize that participants may have felt anxious about their EBOV qRT-PCR tests and did not want to learn their HIV test results at the same time . We re-trained study counselors on HIV testing and counseling procedures and encouraged them to offer HIV tests at follow-up study visits and observed a rise in uptake of HIV testing . More formative research is needed to determine how best to incorporate both HIV and EBOV testing and counseling in future EVD epidemics . One limitation of our behavioral counseling protocol is that , due to time constraints during study visits , we did not employ couples’ counseling . Future development of similar EBOV persistence behavioral counseling protocols may consider the inclusion of couples’ counseling to facilitate correct and consistent condom use , particularly for those participants who repeatedly test positive [27] . Consideration could also be given on how best to recruit , enroll , and counsel EVD survivors who are men who have sex with men ( MSM ) , and who may be reluctant to discuss their sexual behavior in an environment where same sex behavior is criminalized [9] , as well as survivors who may object to body fluid testing due to religious objections to masturbation or other specimen donation procedure . Another limitation of the protocol is that we developed and implemented risk reduction guidance when relatively little was known about the persistence of Ebola RNA in the body fluids of survivors , or how qRT-PCR results related to the risk of transmitting Ebola to others . Since the implementation of this behavioral counseling protocol , additional data on the persistence of Ebola RNA in semen has become available from multiple Ebola-affected countries [28–31] . These data show Ebola RNA persisting in semen for lengthy periods of time for some Ebola survivors , and further illustrate the critical role that semen testing and behavioral counseling can play in Ebola epidemic control [32] . Future development of similar behavioral counseling protocols should consider these additional data in adapting behavioral guidance for Ebola survivors , such as collecting body fluid specimens more frequently and increasing the number of consecutive negative tests needed before testing ceases following the receipt of positive test results . A behavioral counseling protocol that pairs test results with risk reduction behavioral guidance might help mitigate transmission risks associated with body fluids of EVD survivors in which EBOV has been detected . Risk reduction behavioral counseling is rapidly becoming an integral part of addressing the sexual transmission risk in this EVD epidemic and in filovirus outbreaks moving forward; its utility will likely also be swiftly demonstrated for other pathogens where virus persistence in body fluids may pose a risk for continued transmission from survivors [32] . A qualitative evaluation assessing the impact of the behavioral counseling protocol on study participants as well as staff perceptions of the protocol has been performed to add to lessons learned presented in this manuscript . As of July 2016 , the Ebola Virus Persistence Risk Reduction Behavioral Counseling Protocol has been used for more than 220 male and 120 female participants . The protocol has been adapted for use by other body fluid testing programs for EVD survivors , including the Government of Sierra Leone’s CPES ( Alpren , C . , personal communication ) and Liberia’s Men’s Health Screening Program [29] . Lessons learned from implementation of the behavioral counseling protocol in the Sierra Leone Ebola Virus Persistence Study have also been shared to advise the operations of these semen testing programs [29] . We hope that our experience implementing this behavioral counseling protocol in the midst of an EVD epidemic in Sierra Leone can inform similar future efforts so that robust and effective services can be provided to EVD survivors . | The 2014–2016 West Africa Ebola Virus Disease ( EVD ) epidemic was large and widespread , affecting thousands of people across Guinea , Liberia , and Sierra Leone . Prior to this epidemic , there were limited data on persistence of Ebola virus in body fluids of EVD survivors and the potential risk that viral persistence may pose for Ebola virus transmission , including possible sexual transmission . This paper documents the development and implementation of a behavioral counseling protocol to facilitate adoption of risk reduction behaviors among male and female EVD survivors enrolled in the Sierra Leone Ebola Virus Persistence Study . This behavioral counseling protocol , composed of pre-test , delivery of results , and post-test counseling , enabled study staff to translate the study’s body fluid test results into individualized information and preventive action for study participants . Risk reduction behavioral counseling became an important EVD epidemic control measure . | [
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| 2017 | Development of risk reduction behavioral counseling for Ebola virus disease survivors enrolled in the Sierra Leone Ebola Virus Persistence Study, 2015-2016 |
Advances in genomics in recent years have provided key insights into defining cancer subtypes “within-a-tissue”—that is , respecting traditional anatomically driven divisions of medicine . However , there remains a dearth of data regarding molecular profiles that are shared across tissues , an understanding of which could lead to the development of highly versatile , broadly applicable therapies . Using data acquired from The Cancer Genome Atlas ( TCGA ) , we performed a transcriptomics-centered analysis on 1494 patient samples , comparing the two major histological subtypes of solid tumors ( adenocarcinomas and squamous cell carcinomas ) across organs , with a focus on tissues in which both subtypes arise: esophagus , lung , and uterine cervix . Via principal component and hierarchical clustering analysis , we discovered that histology-driven differences accounted for a greater degree of inherent molecular variation in the tumors than did tissue of origin . We then analyzed differential gene expression , DNA methylation , and non-coding RNA expression between adenocarcinomas and squamous cell carcinomas and found 1733 genes , 346 CpG sites , and 42 microRNAs in common between organ sites , indicating specific adenocarcinoma-associated and squamous cell carcinoma-associated molecular patterns that were conserved across tissues . We then identified specific pathways that may be critical to the development of adenocarcinomas and squamous cell carcinomas , including Liver X receptor activation , which was upregulated in adenocarcinomas but downregulated in squamous cell carcinomas , possibly indicating important differences in cancer cell metabolism between these two histological subtypes of cancer . In addition , we highlighted genes that may be common drivers of adenocarcinomas specifically , such as IGF2BP1 , which suggests a possible link between embryonic development and tumor subtype . Altogether , we demonstrate the need to consider biological similarities that transcend anatomical boundaries to inform the development of novel therapeutic strategies . All data sets from our analysis are available as a resource for further investigation .
The classification of cancers for specific and tailored clinical management has been a topic of ongoing study . Historically , cancers have been classified primarily by the organs in which they originate—a convenient strategy that aligns with traditional anatomical divisions of medicine . Accordingly , many currently available treatments , such as surgery and radiation , are anatomically driven , and tumors arising in different sites are often managed in different ways ( and by different subspecialty divisions ) , even if they possess histological similarity ( i . e . squamous cell carcinomas arising in the head and neck , as opposed to the anogenital region ) . Meanwhile , systemic treatments such as chemotherapy largely do not discriminate between histologically distinct tumors arising within the same organ ( i . e . squamous cell carcinomas and adenocarcinomas of the esophagus ) . These tumors , on the whole , are commonly treated in the same manner . In fact , current guidelines suggest similar treatment of squamous cell carcinomas ( SCCs ) and adenocarcinomas ( ADCs ) in the esophagus as well as the cervix , despite observations of differences in prognosis , risk factors , patterns of recurrence , and even response to treatment [1–3] . Recent efforts to re-classify cancers for both clinical and research purposes have addressed this latter issue by utilizing “within-a-tissue” subtyping , identifying molecular signatures that can distinguish between tumors arising within the same organ [4–7] . This has facilitated the development of biologically relevant targeted therapies for particular cancer subtypes , such as molecular subtypes of lung cancer . However , a less common approach has been “across-tissues” analysis [8] , which focuses on more global molecular patterns that may transcend traditional anatomic boundaries . This latter approach may provide key insights into common cancer-associated genes and pathways that span several organ sites , which may facilitate drug development by allowing for the generation of therapeutics that can impact a wider population . For example , categorizing cancers based on mutational burden and immunogenicity has been critical to the broad application of immunotherapies , such as immune checkpoint inhibitors , across a wide array of solid malignancies . In addition , recent perspectives have called for a unified approach to the management and study of SCCs , based on observed similarities in risk factors , genomics , and functional biology [9] . Expanding on this concept , we sought to characterize further the molecular and functional similarities between SCCs and ADCs . ADCs represent the most common type of cancer as a whole , comprising over two-thirds of all cases ( of solid and hematologic malignancies combined ) in the United States from 2009 to 2013 [10] . Typically originating in organs containing native glandular tissue , adenocarcinomas are the predominant subtype of cancers arising in the breast , colon , prostate , lung , pancreas , stomach , ovary , kidney , and , in the western hemisphere , the esophagus [11] . To our knowledge , there is no literature explicitly comparing adenocarcinomas across tissues of origin at the genomic and transcriptomic levels . In this study , we utilized data generated by The Cancer Genome Atlas ( TCGA ) Research Network to study molecular and functional differences between SCCs and ADCs arising within the same organ site . By focusing on organs in which both these subtypes arise , namely the esophagus , lung and uterine cervix , we aimed to determine whether tissue of origin or another property , such as histology , contributes more significantly to observed variation in molecular signatures among tumors . Using a transcriptome-centered approach , we demonstrate here that differences in histology account for more variation than anatomic or tissue origin . Furthermore , via differential gene expression , DNA methylation , and microRNA analysis , we identify 1733 genes , 346 CpG sites , and 42 miRNAs that distinguish ADCs and SCCs between organs , and show that histology correlates with distinct transcriptional and epigenetic programs that are largely consistent across organ sites . Lastly , utilizing pathway and survival analysis , we highlight common markers of ADCs that not only have prognostic value but may also have functional roles in tumorigenesis , thus serving as potential targets for broad therapeutic intervention .
To identify the most significant drivers of variation in gene expression , we first performed a principal component analysis ( PCA ) of gene expression data from 80 esophageal adenocarcinomas ( EAC ) , 81 esophageal squamous cell carcinomas ( ESCC ) , 533 lung adenocarcinomas ( LUAD ) , and 502 lung squamous cell carcinomas ( LUSC ) ( Fig 1A ) . After ruling out significant batch effects ( S1 Fig ) , we discovered that the greatest component driving gene expression variation ( PC1 ) accounted for significantly more variation ( 34% of total ) than other components ( Fig 1B ) . Using multiple regression , we next determined the relative importance of histology and organ site to predicting PC1 . We found that of the variance that our regression model could explain , histology accounted for 90 . 7% , while organ site only accounted for 9 . 3% ( Fig 1C ) . We built on these findings using hierarchical clustering , which revealed two main clusters that appeared to be defined by histology: EAC tended to cluster with LUAD , while ESCC tended to cluster with LUSC ( Fig 1D ) . Interestingly , while the majority of EAC samples formed its own subcluster , this pattern was less apparent in ESCC . When we determined the relative contribution of histology or organ site to cluster number via multiple regression , we noted a much stronger contribution from histology ( Fig 1E ) , with histology accounting 98 . 9% of the variance explained by the regression model ( organ site accounted for 1 . 1% ) . These findings suggest that histology may be of greater importance to a tumor’s molecular profile than its tissue of origin . To understand how consistent histology-driven molecular patterns were across organs , we first compared mRNA expression between ADCs and SCCs in the esophagus and lung . We identified 3443 differentially expressed genes between EAC and ESCC , and 4198 differentially expressed genes between LUAD and LUSC , with 1733 genes that were common in both comparisons ( S2 Fig and S1 File ) . Among these were known markers of squamous cell-fate determination , TP63 and SOX2 , which have a role in both the development of normal squamous tissue in the esophagus as well as tumorigenesis along the squamous lineage in the lung [12 , 13] and esophagus [14 , 15] . Other notable genes relatively upregulated in SCCs were keratins KRT14 and KRT17; DSC3 , a member of the desmocollin family ( a component of intercellular desmosomes ) ; FAT2 , an atypical cadherin found to be induced by ΔNp63α ( isoform of TP63 ) and promote invasion in LUSC [16]; EGFR , a key growth factor receptor in many cancers; CCNA1 , which encodes for cyclin A1 , a cell cycle regulator; and WNT3A , a member of the Wnt family of signaling proteins , which also may have a role in SCCs [17] . Upregulated in ADCs were glandular markers such as mucins MUC3A and MUC13; claudins ( which modulate tight junction permeability ) CLDN2 and CLDN18 , which has recently been targeted therapeutically in advanced gastric and gastroesophageal junction adenocarcinomas [18]; drivers of cell differentiation such as GATA6; hepatic nuclear factors such as HNF1B , FOXA2 , FOXA3; and SPINK1 ( serine protease inhibitor Kazal-type 1 ) , which has been associated with a number of gastrointestinal and genitourinary cancers [19] . Next , we evaluated if global patterns of histology-driven gene expression were similar across organ sites by constructing heatmaps with clustering using the differentially expressed genes ( DEGs ) identified in the esophagus and lung , respectively ( Fig 2A and 2B ) . We found that histology drove a clear and consistent pattern of expression in both the esophagus and lung . Importantly , these DEGs were able to distinguish ADCs from SCCs across both organs after applying hierarchical clustering . These data indicate gene expression profiles determined by histology are largely consistent across different organ sites . To determine if the patterns observed in differential gene expression in ADCs versus SCCs were associated with epigenetic changes , we compared DNA methylation in each histological subtype . We identified 1734 differentially methylated CpG sites between EAC and ESCC , 1650 differentially methylated CpG sites between LUAD and LUSC , with 346 CpG sites in common between the comparisons ( S2B Fig and S2 File ) . When we observed patterns of DNA methylation and applied hierarchical clustering , we again found that the cancers grouped by histology and not by organ site ( Fig 3A and 3B ) . Interestingly , while EAC and LUAD appeared to form distinct subclusters within the ADC cluster , ESCC and LUSC were more homogeneous in DNA methylation profile and thus did not form separate subclusters . We then sought to identify particularly important genes by intersecting differentially expressed genes with those that were differentially methylated . We identified 174 such genes in the esophagus , 193 genes in the lung , and 33 common genes between them . Genes that were downregulated and hypermethylated in ADCs were squamous markers such as DSC3 and KRT5 , as well as transcription factors such as FOXE1 and TP73 , whose isoforms have demonstrated both tumor suppressor and oncogenic properties [20] . Interestingly , TP73 overexpression in SCCs coincided with TP63 overexpression in SCCs relative to ADCs ( S2C Fig ) . The predominant isoform of TP63 in squamous epithelia and SCCs is ΔNp63 [21] , which has been demonstrated previously to reversibly inhibit TP73-dependent transcription by direct promoter binding or physical interaction with the p73 protein , leading to decreased apoptosis [22] . We did not , however , detect a consistent downregulation of p73 target genes [23] , including those involved in apoptosis ( PMAIP1 and BBC3 ) , which may be due to selective inhibition of specific downstream targets or activation by alternative regulators ( S2C Fig ) . By contrast , genes that were upregulated and hypomethylated included mucins like MUC1; SERPINA1 , a serine protease inhibitor that acts in the liver and lung; HNF1B , a transcription factor that regulates in renal and pancreatic development; and ME3 , a mitochondrial malic enzyme whose deletion was shown recently to confer lethality in SMAD4-deleted pancreatic adenocarcinoma [24] . These data demonstrate that histological subtype-specific patterns of gene expression correspond to changes in epigenetic profiles . We then analyzed other potential influences on gene expression . MiRNAs have gained significant attention due to their post-transcriptional regulation of gene expression by destabilizing mRNA and silencing translation . Using miRNA expression data acquired from TCGA , we compared the levels of specific miRNAs in ADCs and SCCs of the esophagus and lung . We identified 118 differentially expressed miRNAs in the esophageal comparison , and 125 in the lung , with 42 overlapping miRNAs ( S2D Fig and S3 File ) . The miRNAs relatively overexpressed in SCCs included miR-994 , which has been found to be part of intronic sequence of ΔNp63 and is highly expressed keratinocytes [25] , as well as miR-224 , which has been implicated in the progression of colorectal cancer and non-small cell lung cancer ( NSCLC ) [26 , 27] . On the contrary , miRNAs upregulated in ADCs included miR-215 , which has been shown to modulate gastric tumor cell proliferation by targeting RB1 [28] , and miR-375 , which has been observed to be upregulated in lung adenocarcinoma but downregulated in lung squamous cell carcinoma , and promotes cell proliferation by decreasing levels of ITPKB , a putative tumor suppressor [29 , 30] . Interestingly , also downregulated in ADCs was miR-149 , which was demonstrated previously to have tumor suppressor capacity in breast cancer migration and invasion by targeting small GTPases Rap1a and Rap1b [31] . Furthermore , similar to mRNA expression , patterns of miRNA expression were largely conserved for ADC and SCC across both esophagus and lung ( Fig 4A and 4B ) . These findings suggest that similar patterns of miRNA expression also govern differences in histology , which can be observed across different primary organ sites . Given the histologically-driven patterns observed in transcriptome , epigenome and post-transcriptional profile , we next performed pathway analysis based on differentially expressed genes to identify functional pathways specific to ADCs or SCCs . Using DEGs from our previous ADC vs . SCC comparisons ( i . e . EAC vs . ESCC ) , we performed pathway analysis with Ingenuity software . As a reference , we also performed pathway analysis using DEGs from cancer vs . normal comparisons ( i . e . EAC vs . normal esophagus ) ( Fig 5A ) . This reference comparison allowed us to determine whether differential pathways identified in ADC vs . SCC were significantly altered in cancer biology ( relative to normal ) , and whether these differences were due to changes in ADC , SCC , or both . Importantly , the LUAD vs . normal lung ( both glandular ) and ESCC vs . normal esophagus ( both squamous ) comparisons provided insight into whether differential pathways were simply markers of a specific histological state , since pure histological markers would be unlikely to result in differences observed in these comparisons . In our analysis , there were several pathways relatively upregulated in ADCs compared to SCCs ( Fig 5B ) . Intriguingly , the only pathway that was also consistently upregulated in ADC versus normal tissue was liver X receptor / retinoid X receptor ( LXR/RXR ) activation ( S3A Fig ) . LXR/RXR activation was also downregulated in SCCs relative to normal tissue ( S3A Fig ) , suggesting a dichotomous function in these different histological subtypes . Furthermore , differences in LXR/RXR activation were maintained in the LUAD vs . normal lung and ESCC vs . normal esophagus comparisons , indicating that it is likely more than a simple marker for glandular histology ( S3A Fig ) . Other pathways relatively upregulated in ADCs included HIPPO signaling , Relaxin signaling , and Androgen Signaling; however , these pathways were mostly downregulated in all cancers relative to normal tissue ( S3A Fig ) . The differentially expressed genes corresponding to each pathway are listed in a supplementary table ( S1 Table ) . There were several pathways that were relatively downregulated in ADCs compared to SCCs . Some of these pathways were also downregulated in ADCs relative to normal tissue , including p38 MAPK Signaling and Paxillin Signaling . The remaining pathways were upregulated in SCCs relative to both ADCs and normal tissue , suggesting an important functional role in the carcinogenesis of SCCs . This included Basal Cell Carcinoma Signaling , the planar cell polarity ( PCP ) pathway , and Wnt signaling . Of note , Basal Cell Carcinoma Signaling is closely related to Sonic Hedgehog ( SHH ) Signaling , which has been associated with the carcinogenesis of SCCs of the oral mucosa , uterine cervix , esophagus and lung [32–35] , where it may have a role in mediating stemness via transcription factors like SOX2 [34 , 35] . Furthermore , aberrant Wnt signaling and non-canonical Wnt/PCP signaling , which normally regulates cell shape via the cytoskeleton [36] , have been hypothesized to have a unique role in SCCs [17] , and may represent a functional distinction between SCCs and ADCs [37] . Expanding on this pathway analysis , we investigated potential upstream regulators driving changes in gene expression ( Fig 5B ) . Notably upregulated in ADCs relative to SCCs was STK11 ( or LKB1 ) , loss of which has been previously shown to induce adeno-to-squamous differentiation of lung tumors in mice [38]; GATA4 and GATA6 , important for cell differentiation and commonly amplified in EAC and gastric cancers [4]; and NKX2-1 , a commonly used marker for lung adenocarcinomas . Furthermore , the estrogen receptor was found to be relatively upregulated in ADCs versus SCCs and normal tissue . Several genes were also found to be likely upstream regulators of SCC formation . For instance , CDH1 , which codes for E-cadherin , a membrane protein important for cellular adhesion , was relatively downregulated in SCCs , which aligns with reports of its methylated and suppressed expression in oral SCCs [39] . Other notable genes relatively upregulated in SCC were GLI1 , a downstream mediator of hedgehog signaling; SMAD4 , which is involved in TGF-β signaling and is commonly is deleted in EAC and LUAD [4 , 5]; CCND1 , a cyclin that is commonly amplified in ESCC [4]; and TWIST1 , thought to be important in epithelial-to-mesenchymal transition [40] . The activation of these upstream regulators in each cancer type relative to corresponding normal tissue is shown in a supporting figure ( S3B Fig ) . We next sought to highlight additional gene candidates that may serve as prognostic markers among ADCs , given the lack of unifying features previously described in the literature . Using the 500 genes showing the greatest variance in expression , we performed Cox proportional hazards survival analysis to find genes significantly correlated with survival outcomes in both EAC and LUAD . We discovered 109 such genes in EAC , and 691 genes in LUAD , with 32 genes in common between them ( Fig 6A and 6B ) . Genes whose increased expression was correlated with worse survival outcomes included TPX2 , KIF4A , IGF2BP1 , and HSPA6 . On the contrary , genes whose expression was correlated with a better prognosis included MS4A1 ( CD20 ) , SUSD2 , and CX3CL1 ( Fractalkine ) . As an example , we constructed Kaplan-Meier curves for IGF2BP1 , or IMP-1 , which we described previously as a modulator of tumor growth in colorectal cancer [41] . Not only did higher expression of IGF2BP1 correlate with poorer outcomes in EAC and LUAD ( Fig 6C and 6D ) , but the same trend was seen when we pooled several other ADCs from the TCGA Pan-Cancer dataset ( including cancers of the breast , prostate , endocervix , endometrium , ovary , pancreas , stomach , kidney , colon , rectum , and thyroid ) ( Fig 6E ) . Notably , the same trend was not observed in a pooled SCC dataset ( including cancers of head and neck , esophagus , lung , and ectocervix ) ( Fig 6F ) . Intriguingly , while ESCC and LUSC had 88 and 206 genes associated with survival , respectively , they shared no genes that had the same directional association with outcomes . As a validation , we attempted to reproduce our results in a third organ site in which both ADCs and SCCs arise: the uterine cervix . While the predominant subtype of cancer in the cervix is squamous cell carcinoma ( CESC ) associated with human papilloma virus ( HPV ) infection , several types of adenocarcinoma , specifically endocervical , endometroid , and mucinous adenocarcinoma , can also develop . Using data acquired from TCGA , we pooled 44 samples of these various types of cervical ADCs ( ECA ) and compared them with 254 CESC samples by using gene expression , DNA methylation , and miRNA expression data acquired from the esophagus and lung comparison . Upon clustering , we found that the same genes , methylated CpG sites , and miRNAs distinguishing histology in the esophagus and lung were also largely able to distinguish histology in the cervix ( Fig 7A–7C ) . The pathways we had identified previously to be important to ADC and SCC , respectively , also maintained similar patterns in the cervix , with deviations only in Regulation of Cellular Mechanics by Calpain Protease , Melatonin Signaling , and Calcium-induced T Lymphocyte Apoptosis in pathways favoring ADCs ( Fig 7D ) . LXR/RXR Activation was once again upregulated in ECA relative to CESC . In pathways that were relatively downregulated in ADCs , Notch Signaling and the PCP pathway maintained strong associations with SCC; however , there was a weaker relationship in the cervix between squamous histology and Basal Cell Carcinoma Signaling , Sonic Hedgehog Signaling , or TGF-β Signaling . We also performed upstream regulator analysis of ECA and CESC , again observing similar patterns to the histological subtypes of the esophagus and lung ( Fig 7E ) . Notable differences included ALDH2 , STK11 in genes relatively upregulated in ADCs , and CTNNB1 and GLI2 in genes relatively downregulated in ADCs . Altogether , histology-driven differences in transcriptome , epigenome , as well as pathways and upstream regulators , were largely consistent across all three organ sites studied .
Our analyses have revealed major differences in the transcriptomic and epigenomic profile of ADCs and SCCs , but major similarities among ADCs and SCCs spanning several anatomic sites . Importantly , histology was the greater determinant of molecular variation than organ site , which further emphasizes the need to re-classify tumors and re-structure current treatment paradigms . While genomic subtyping studies have demonstrated the need to consider molecular characteristics ( and biological relevance ) when managing tumors arising within the same organ , we also highlight the premise that some molecular features may be shared by tumors arising in different organs . Capitalizing on this property of cancer could lead to the development of widely applicable targeted therapies . Importantly , we highlighted several specific properties that may unify ADCs , including a large collection of genes , CpG methylation sites , and miRNAs . Specifically , we identified LXR/RXR activation as having a putative role in the development of ADC . Indeed , several recent reports have highlighted liver X receptors as potential therapeutic targets , as they appear to regulate aerobic glycolysis ( the Warburg effect ) and lipogenesis [42] , which malignant cells depend upon to sustain rapid proliferation , while also modulating antitumor immunity [43] . In fact , one study utilized an inverse agonist SR9243 to inhibit LXR activity , suppressing tumor growth in several colon , prostate , lung and pancreatic adenocarcinoma cell lines [42] . Curiously , our analyses also indicated that LXR/RXR activation was downregulated in SCCs relative to normal tissue . This is in line with a previous report that described the spontaneous development of peripheral squamous cell lung cancer in mice with ablated LXRα , β ( NR1H3-/- and NR1H2-/- ) [44] . Taken together , this evidence points to a possible dichotomous role of LXRs in ADCs versus SCCs and may indicate key differences in cell metabolism between these histological subtypes of solid malignancies . Despite these findings , however , other studies on LXRs have reported a downstream antiproliferative effect that can be harnessed by using LXR agonists ( oxysterols ) in several cancer types , including multiple ADCs [45] . It is clear , therefore , that further investigation on the precise role of LXRs in different contexts is required . In addition , we identified candidate genes that correlate with patient outcomes and may have a functional role in ADC carcinogenesis , such as IGF2BP1 . Intriguingly , increased IGF2BP1 expression was found to be associated with worse survival in EAC , LUAD , and a pooled dataset of other ADCs spanning breast to colon ( Fig 6C–6F ) . The same pattern was not observed in a pooled SCC dataset . IGF2BP1 is an mRNA binding oncofetal protein , not normally expressed in adult tissues , that we previously demonstrated to have a role in modulating colorectal tumor growth in vitro and in vivo , and may also play a role in early metastasis [41] . Similar findings were reported previously in ovarian and breast adenocarcinomas [46 , 47] . This association further emphasizes the importance of developmental pathways in tumorigenesis , and specifically indicates a possible link between embryonic drivers of differentiation and histological subtypes in cancer . Notably , there have been efforts to develop inhibitors of IGF2BP1 , with some recent success in the identification of a compound that selectively kills IGF2BP1 expressing cells [48] . Liver X receptors and IGF2BP1 are just two examples of therapeutic targets that may be particularly effective in ADCs . Our analysis has revealed an entire catalog of gene candidates for further study , including those that correlate with survival outcomes , though an association with survival is not a prerequisite for being an effective target ( i . e . estrogen receptor positivity in breast cancer ) . Notably , our findings also have implications for the use of currently available therapeutics in ADCs versus SCCs . For example , our upstream regulator analysis highlighted several putative regulators that have greater activity in SCCs than ADCs , such as GLI1 and the PI3K family . These results suggest that SCCs may have greater sensitivity to GLI1 or Hedgehog inhibitors , as well as PI3K inhibitors , than ADCs . Another finding with potential therapeutic relevance is TP73 methylation and low expression in ADCs . TP73 is a known tumor suppressor [20] , and its methylation could contribute to reduced apoptosis in response to chemo- or radiotherapy relative to SCCs [3] . One could hypothesize that the use of DNA methylation inhibitors , such as 5-azacitidine or decitabine , could restore sensitivity to cytotoxic therapy in ADCs , although this requires experimental validation . We then noted that although global histology-driven patterns were conserved in esophagus and lung , some of these similarities may have been due to a common embryonic precursor , as both the esophagus and lung are derived from the foregut endoderm [49] . To address this , we tested our findings in a third organ , the uterine cervix , which develops from the paramesonephric ducts and is mesodermal in origin . We found that histology-driven differences were largely maintained even in the cervix ( Fig 7A–7C ) . There were some notable exceptions , however , which could be related to different embryonic tissue origin or differences in other factors such as the local tumor microenvironment . For example , STK11 was discovered to be relatively downregulated in ESCC and LUSC , but relatively upregulated in CESC ( Fig 7E ) . These findings are consistent with other reports: while loss of STK11 has been associated with lung , skin , and head and neck SCCs [50] , inherited loss of STK11 , as in Peutz-Jeghers syndrome , has been associated with endometrial adenocarcinoma and a rare variant of endocervical carcinoma , minimal deviation adenocarcinoma of the endocervix [51] . Differences observed between the cervix and the esophagus and lung could also be due to distinct risk exposures . For instance , ALDH2 , which is important for acetaldehyde ( a metabolite of ethanol and an ingredient in tobacco smoke ) metabolism and whose deficiency increases the risk for esophageal and head and neck SCCs , was found to be relatively downregulated in ESCC and LUSC , but was not significantly different when comparing ECA and CESC ( Fig 7E ) . This could be due to direct exposure of the aerodigestive tract and respiratory mucosa to acetaldehyde via alcohol consumption and cigarette smoking , respectively , in ALDH2-/- individuals . Lastly , there were some technical limitations to our in silico analysis . First , TCGA datasets can be susceptible to batch effects , although when we assessed the data , variation due to batch was determined to be of relatively insignificant impact ( S1 Fig ) . Similarly , although TCGA datasets are generally large , they may not be representative of the general population , and critically , samples sent for sequencing are often not purely tumor cells and may contain associated stromal and immune components , as well as normal tissue . Additionally , because we were primarily interested in global molecular patterns , our analysis was not sensitive to relatively rare variants such as those described in genomic and mutational analyses . It should be noted that highlighted candidate pathways and genes need to be verified further for functional significance with additional in vitro or in vivo analyses .
These analyses further emphasize the need to re-categorize tumors in a biologically relevant manner—not only as a practice in pathology and research , but also in the clinic . Over the last decade , molecular subtyping efforts have already called for a shift in approach to viewing cancers—such that esophageal carcinoma , for instance , would not be viewed as a single entity [4–6] , but as multiple diseases with distinct characteristics and patterns of behavior . While our findings distinguishing between histological subtypes support this premise , we also propose that broader strategies of classification to direct research efforts and clinical management may be more aligned to the pragmatic concerns of drug development while still being biologically relevant . Consider the example of immune checkpoint inhibitors , which are being widely applied across cancers . In this report , we emphasize the call for a unified approach to the study , prevention , and treatment of SCCs , and on possible commonalities underlying ADCs . Despite apparent heterogeneity among ADCs , we have identified patterns of gene expression , functional pathways , and prognostic markers that may similarly unify our approach to ADCs . Furthermore , our analyses have provided a catalog of additional candidate genes and markers that can be further investigated . To our knowledge , this is the first study identifying common features among adenocarcinomas arising from different organ sites .
Publicly available gene expression , DNA methylation , and microRNA ( miRNA ) data from TCGA were downloaded from the Genomic Data Commons ( GDC ) Data Portal and pre-processed via the TCGAbiolinks R package [52] . Details regarding specific data type and subsequent analysis are described below . Survival outcomes data were acquired from the University of California Santa Cruz ( UCSC ) Xena at http://xena . ucsc . edu . Harmonized gene expression data ( HTSeq counts ) —to allow for easy comparison between cancer types—from GDC was downloaded for esophageal carcinoma ( TCGA-ESCA ) , lung adenocarcinoma ( TCGA-LUAD ) , lung squamous cell carcinoma ( TCGA-LUSC ) , and cervical and endocervical carcinoma ( TCGA-CESC ) , and assembled into a matrix using TCGAbiolinks [52] . Potential batch effects were visualized by constructing a PCA plot by batch and cancer type , using the R package DESeq2 [53] . Considering insignificant batch effects and to avoid masking biologically significant differences , no corrections for batch were applied . Using the edgeR package in R [54] , the counts data were filtered for low counts ( less than 1 count per million in greater than 50% of each group , i . e . esophageal adenocarcinoma vs . esophageal squamous cell carcinoma ) and normalized within samples using the trimmed means of M-values ( TMM ) method [55] . Differentially expressed genes ( DEGs ) were filtered using the glmTreat function in edgeR , which tests for significant differences relative to a set log2-fold change ( logFC ) cutoff of 0 . 5 . Results were then filtered by a false discovery rate ( FDR ) of 0 . 05 . Harmonized microRNA expression data for ESCA , LUAD , LUSC , and CESC were acquired from the GDC Data Portal and preprocessed via TCGAbiolinks [52] . Low counts were filtered as above differential expression analysis was performed using edgeR . A logFC cutoff of 0 . 5 and FDR cutoff of 0 . 05 , as above , were used . Legacy DNA methylation data ( Illumina Human Methylation 450 , aligned to hg19 ) from GDC was downloaded via TCGAbiolinks [52] . Using the TCGAanalyze_DMR function , we searched for differentially methylated CpG sites using beta-values , with a cutoff difference in beta-values of 0 . 25 and adjusted p-value of 10−20 . The results from these data were then integrated with the differential gene expression data . To identify global patterns in gene expression , raw counts were first quantile-normalized within and across samples , using the TCGAanalyze_Normalization function , then transformed using the varianceStabilizingTransformation function in DESeq2 to reduce heteroscedasticity ( chosen over log transformation to minimize variance at low count values ) [53] . Principal component analysis ( PCA ) was performed using the prcomp function in R . Hierarchical clustering was performed in R by Euclidean distance and the ward . D2 method . To determine the relative contribution of explanatory variables ( Histology and Organ ) to variance , we first constructed multiple linear regression models for response variables “PC1” and “Cluster” , respectively , using the lm function in R . We then utilized the relaimpo package [56] , which refers to the Lindeman , Merenda , Gold ( 1980 ) method of R2 partitioning by average over orders [57] , in order to determine the contribution to variance of each explanatory variable . For visualization , the PCA were plotted by cancer type using the plotPCA function in DESeq2 [53] . For heatmaps , normalized gene expression data ( as described above ) and miRNA expression data were further scaled to a mean of 0 and standard deviation of 1 , while for DNA methylation , β-values were used directly in visualization . All heatmaps were constructed using the ComplexHeatmap R package [58] . Bar charts were constructed in GraphPad Prism . Pathway analysis and identification of upstream regulators , using differentially expressed genes as input , were performed using QIAGEN Ingenuity Pathway Analysis software ( using the core analysis feature ) . The same cutoffs as described in the differential gene expression analysis were used to filter significant genes . To determine significant pathways across different organs , we used the comparison analysis function in Ingenuity . Significant pathways were filtered by activation z-score ( absolute value ≥ 1 . 0 ) and adjusted p-value ( ≤ 0 . 05 ) , in the ADC vs . SCC experimental comparisons . Specifically , the DEGs from seven comparisons were initially used as input for Ingenuity ( Fig 5A ) . These included three ADC vs . SCC comparisons ( esophagus , lung , and cervix ) , as well as four reference cancer vs . normal comparisons . As discussed in the text , these reference comparisons help to determine whether differential pathways identified in ADC vs . SCC were also significantly altered in cancer biology ( relative to normal ) , and whether these differences were due to changes in ADC , SCC , or both . Additionally , the LUAD vs . normal lung ( both glandular ) and ESCC vs . normal esophagus ( both squamous ) comparisons provided insight into whether differential pathways were simply markers of a specific histological state , since changes in these comparisons would not be expected for simple histological markers . Survival outcomes for TCGA samples were downloaded from UCSC Xena . To identify genes that have potential prognostic and functional significance , we only analyzed the top 500 genes with greatest variance in either ADCs or SCCs . Given the continuous nature of gene expression data , we used a cox proportional hazards model to identify genes correlated with survival . In order to improve sensitivity , no correction for multiple testing was applied . Survival was visualized by plotting Kaplan-Meier curves in GraphPad Prism 7 . 0 . Importantly , TCGA pan-cancer data was acquired from UCSC Xena . Pooled ADC data contained 14 different ADC types with 4934 samples . Pooled SCC data contained 4 SCC types with 1346 samples . For survival , high expression and low expression groups were determined by cutoff at the 75th percentile . Statistical analysis for principal component analysis , linear correlation , differential expression , and Cox proportional hazards were performed in R . Pathway statistical analysis was built-in to Ingenuity . Log-rank tests for Kaplan-Meier were performed using GraphPad Prism . | In clinical practice , the organ in which a cancer arises typically classifies it . However , developments in our understanding of cancer have revealed that this method overlooks key aspects of cancer biology relevant to both disease prevention and treatment . In fact , work characterizing the genetic make-up of cancers arising in a single organ has revealed that a shared organ of origin does not necessarily imply biological similarity ( i . e . not all lung cancers share similar biological and molecular properties ) . While this approach , known as “within-a-tissue subtyping , ” identifies key differences between cancers that arise in a single organ , a broader perspective may highlight important biological similarities between cancers across organs . Here we utilize this second approach , or “across-tissue subtyping , ” to gain insight into similarities between cancers ( of different organs ) that share the same histology—or appear similarly under a microscope . Using publicly available data from The Cancer Genome Atlas ( TCGA ) , we compare gene expression of two major classes of solid tumors—adenocarcinomas ( which arise from cells that form glands ) and squamous cell carcinomas ( which arise from flattened cells that form physical barriers ) . We identify several genes and biological pathways that may be common to adenocarcinomas and serve as targets for highly versatile therapies . | [
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| 2017 | Comparative transcriptomes of adenocarcinomas and squamous cell carcinomas reveal molecular similarities that span classical anatomic boundaries |
Chemotaxis is a dynamic cellular process , comprised of direction sensing , polarization and locomotion , that leads to the directed movement of eukaryotic cells along extracellular gradients . As a primary step in the response of an individual cell to a spatial stimulus , direction sensing has attracted numerous theoretical treatments aimed at explaining experimental observations in a variety of cell types . Here we propose a new model of direction sensing based on experiments using Dictyostelium discoideum ( Dicty ) . The model is built around a reaction-diffusion-translocation system that involves three main component processes: a signal detection step based on G-protein-coupled receptors ( GPCR ) for cyclic AMP ( cAMP ) , a transduction step based on a heterotrimetic G protein Gα2 βγ , and an activation step of a monomeric G-protein Ras . The model can predict the experimentally-observed response of cells treated with latrunculin A , which removes feedback from downstream processes , under a variety of stimulus protocols . We show that G α 2 β γ cycling modulated by Ric8 , a nonreceptor guanine exchange factor for G α 2 in Dicty , drives multiple phases of Ras activation and leads to direction sensing and signal amplification in cAMP gradients . The model predicts that both G α 2 and Gβγ are essential for direction sensing , in that membrane-localized G α 2 * , the activated GTP-bearing form of G α 2 , leads to asymmetrical recruitment of RasGEF and Ric8 , while globally-diffusing Gβγ mediates their activation . We show that the predicted response at the level of Ras activation encodes sufficient ‘memory’ to eliminate the ‘back-of-the wave’ problem , and the effects of diffusion and cell shape on direction sensing are also investigated . In contrast with existing LEGI models of chemotaxis , the results do not require a disparity between the diffusion coefficients of the Ras activator GEF and the Ras inhibitor GAP . Since the signal pathways we study are highly conserved between Dicty and mammalian leukocytes , the model can serve as a generic one for direction sensing .
In light of the restriction to LatA-treated cells , the backbone of the chemotactic pathway activated in response to changes in extracellular cAMP is Δ cAMP →Δ GPCR occupation →Δ Gαβγ activation →Δ Ras activity . We describe this pathway in terms of three modules: the GPCR surface receptors cAR1-4 , G α 2 β γ and Ras , as illustrated in Fig 1 .
Next we investigate how cells respond to a linear cAMP gradient along the x-axis , which we define as follows . C ( x , y , z ) = Δ C 10 · ( x - x r ) + C r where C ( x , y , z ) is the cAMP concentration on the membrane at ( x , y , z ) ∈ S 5 2 ( a sphere of radius 5 ) , ΔC ≡ Cf − Cr , and subscripts f and r denote the points ( 5 , 0 , 0 ) ( the ‘front’ ) and ( -5 , 0 , 0 ) ( the ‘rear’ ) . In the context of Dicty aggregation , the ‘back-of-the-wave’ problem refers to the fact that cells do not turn to follow the cAMP gradient after the wave has passed , despite the fact that the spatial gradient reverses as the wave passes over a cell [15 , 62] . This requires some level of persistence of ‘orientation’ of a cell , but there is as yet no agreed-upon mechanistic solution for this problem , since polarization and other factors may play a role [63] . Under uniform stimuli , cells are said to show rectification if there is an asymmetry in the amplitude and evolution of the response to a step increase in cAMP compared with the response following removal of the stimulus [21] . To test whether the proposed network exhibits rectification in this sense , we apply a uniform stimulus of various concentrations for 60 seconds and then remove it , as was done experimentally in fully aggregation-competent cells [21] . Fig 20 ( left and center ) show the simulation and the experimental results , resp . In both cases the concentration of cAMP is increased from 0 M to the concentrations indicated for 60 seconds ( green shaded area ) , followed by a decrease to 0 M , and in both cases one sees a much larger and faster change in RBD following application of the stimulus than on removal . We also applied the same stimuli as used above to gα-null cells and ric8-null cells . Results given in the Supporting Information show that Ric8 plays a significant role in the rectification , as will also be seen later in the traveling wave analysis . Some insight into this behavior can be gained from simple models of excitation and adaptation , such as the cartoon description defined by the system of equations d y 1 d t = S ( t ) - ( y 1 + y 2 ) t e , d y 2 d t = S ( t ) - y 2 t a . ( 1 ) Here S ( t ) represents the signal and the magnitudes of te and ta reflect the time scale for excitation and adaptation , resp . , and one see that y1 adapts perfectly to a constant stimulus whereas y2 compensates for the stimulus . However , the temporal responses to increasing and decreasing stimuli are symmetric , and therefore such a simple model cannot explain the observed response . Nakajima et al . [21] suggest that a single-layered incoherent feedforward circuit with zero-order ultrasensitivity [64] is necessary to generate rectification , but our model does not include an ultrasensitive circuit . Instead , rectification is induced solely by the balanced regulation of RasGEF and RasGAP activity . The ratio of RasGEF* to RasGAP* increases 2–4 fold very rapidly in response to a step increase in the cAMP concentration , but when the stimulus is removed this ratio does not drop significantly , as shown in the right panel of Fig 20 . Thus Ras activation persists because the ratio equilibrates rapidly while the absolute levels of the factors decrease more slowly . To study how cells would respond in wave-like spatially-graded stimuli , we first generate a simple trianglular wave that approximates a natural cAMP wave . Let W ( x , y , z , t ) denote the cAMP concentration at ( x , y , z ) of the cell at time t , and specify it as W ( x , y , z , t ) = 0 , 0 + 350 k ≤ t ≤ x + 5 v + 350 k 10 ( t - x + 5 v - 350 k ) , x + 5 v + 350 k < t ≤ x + 5 v + 100 + 350 k - 10 ( t - x + 5 v - 350 k ) + 2000 , x + 5 v + 100 + 350 k < t ≤ x + 5 v + 200 + 350 k 0 , x + 5 v + 200 + 350 k < t ≤ 350 ( 1 + k ) , where v is the wave speed and −5 ≤ x , y , z ≤ 5 , k = 0 , 1 , ⋯ . This wave resembles a natural wave when we choose the natural wave speed v = 5μm/s , as shown in Fig 21 . The wave length is 1000μm , and at the natural speed any point on a cell is subject to an increasing stimulus for 100 sec on the upstroke of the wave and a decreasing stimulus for 100 sec on the downstroke . As shown in Fig 22 , Ras is activated everywhere as the wave passes , but Ras activation is delayed about 1 sec in the rear half ( Fig 22 -right ) for a wave traveling at the natural wave speed . Ras activation is higher at the front of the cell than at the rear throughout passage of the wave , thereby providing persistent directionality in Ras activation and the potential for persistent orientation as the wave passes . It should be emphasized that we are simulating the rounded LatA-treated cells that have no intrinsic polarity , which suggests that polarity is not necessary for the persistence of direction sensing at the natural wave speed , even at the level of Ras activity . By comparing Figs 20 and 22 , one sees a similar pattern in Ras activation . In fact , due to the rectification characteristic observed in uniform stimuli , Ras* activity does not drop significantly in a wave , and therefore the front is able to maintain a higher Ras* . To determine whether the cell is able to respond after the first wave passes , we applied the same wave for three periods , and one sees in Fig 23 that the cell responses are almost identical for three successive passages of a wave . It is also known that wave speeds affect the spatial pattern of Ras activity over a cell [21] , in that Ras is activated uniformly for a fast wave , and activated at both the wavefront and waveback for slow waves . To test the effects of the wave speed , we apply a fast wave ( 50μm/s ) and a slow wave ( 0 . 5μm/s ) to the rounded LatA-treated cells . The results are shown in Fig 24 . At a wave speed of 50μm/s , Ras activation is uniform along the cell periphery , as is observed in the experiments , but at 0 . 5μm/s we see a significant Ras reactivation at the rear of the cell and the Ras* distribution reverses at the back of the wave . In order to demonstrate the effect of wave speed on rectification more clearly , we plot the time course of Ras activation at the front-most and rear-most points of the cell in Fig 25 . At a wave speed of 0 . 1μm/s , Ras is reactivated at the rear of the cell when the back of the wave passes over the rear . As the wave speed increases , the reactivation at the rear becomes weaker , and at the normal wave speed of 5μm/s persistent directionality is well-preserved . Of course , when a fast wave passes over the cell , Ras activation is almost spatially uniform . As was pointed out earlier , Ric8 plays an essential role in rectification under uniform stimuli , and to further emphasize that the back of the wave problem is closely connected with the disparity in the response to increasing vs . decreasing stimuli , we applied the same wave used previously to a ric8-null cell . The Ras* activity is shown in Fig 26 , where one sees that the persistence of directional information is essentially lost . It is not surprising to see that Ras* at the front becomes smaller than the rear , which indicates a reversal in the Ras* distribution , further reinforcing the importance of the asymmetric response to increasing vs decreasing stimuli in solving the back of the wave problem . Clearly there is a trade-off between the persistence of directionality in Ras activation and the ability of cells to respond to new gradients . To investigate whether the Ric8-induced rectification has an adverse effect on reorientation in response to a reversed gradient , we subject cells in a 0–100 nM gradient to reversals to increasingly weaker gradients . In each case we keep the mean concentration experienced by the cell fixed to eliminate the mean concentration effect ( see . Fig 14 ) . For an equally strong reverse gradient ( 100–0 nM ) , the directional persistence of Ras* is reversed within 100 seconds of gradient reversal , as shown in Fig 27 . The spatial profile also indicates that Ras* distribution is strongly reversed after switching to equally strong reversed gradients , ( Fig 27 –center and right ) . It is observed in Dicty that all cells ( 20/20 ) reversed their direction of migration under this protocol [22] . For intermediate gradients ( 75–25 nM ) , Ras* is slightly reversed ( Fig 28 –left ) in the same time window ( 0–200 s ) . The spatial plot of Ras* indicates spatial oscillations along the cell periphery at almost the end of the time window t = 180 s , ( see Fig . M in S1 Text ) suggesting spatio-temporal complexity in Ras* redistribution . Consistently , experiments show that a fraction of the cells ( 5/17 ) did not reverse their migration direction . For weak gradients ( 60–40 nM ) a difference in Ras activation is still maintained at the end of the time window ( t = 200 s ) ( Fig 28 ( right ) ) , consistent with the observation that that all cells continued moving in their original direction in this case [22] . These simulations suggest that Ric8-induced rectification does not harm cells’ reorientation in response to large amplitude reversals of the gradient , but it delays the reorientation in a weak reversed gradient .
Chemotaxis is a dynamic spatio-temporal process that involves direction sensing , polarization , and cell movement , and direction sensing is the first essential step in this process , becuase it defines the cell’s compass . A growing body of evidence suggests that Ras is an ideal candidate within the chemotactic signalling cascade to play an essential role in direction sensing [31 , 68] . In this article , we developed a novel modular model of direction sensing at the level of Ras activation . The model incorporates biochemical interactions in Dicty and captures many aspects of its response . The model consists of the cAMP receptor , the G-protein G α 2 β γ , and a Ras GTPase module in which both adaptation and amplification occur . Utilizing a rounded cell pretreated by LatA as was done in experiments , we investigated Ras activation patterns in various cAMP stimuli . Simulations of this model give insights into how the signal transduction network determines Ras activation characteristics in wild type cells , how an altered network in mutant cells changes Ras activation , and how the spatial profile and persistence of Ras activation can lead to directional persistence . We proposed an experimentally-based kinetic model of G α 2 β γ signaling in which the intact G α 2 β γ and the Gβγ subunit can cycle between the membrane and the cytosol , while the G α 2 subunit remains membrane-bound . Moreover , G α 2 can be reactivated by the only known ( to date ) GEF for G α 2 , Ric8 . The regulation of Ric8 is not well-defined , but we assume that it is also cycles between the cytosol and the membrane , and that its recruitment to the membrane is promoted by G α 2 * . The model replicates the persistent Gαβγ dissociation in the presence of cAMP , and also demonstrates that Gβγ and G α 2 * are produced in a dose-dependent manner . Interestingly , the model reveals that G α 2 exhibits dose-dependent kinetic diversities . The variety of G α 2 dynamics revealed here may have important implications in direction sensing because in neutrophils G α 2-GDP accumulates at the leading edge and is involved in regulating directionality [74] , although it has not been demonstrated that Ric8 is involved there . Adaptation of Ras activity is controlled by a balance between RasGEF and RasGAP , both of which can cycle between the membrane and the cytosol . This component of the network involves incoherent feed-forward , and becuase both can cycle betweenmembrane and cytosol , can give rise to spatial asymmetry in Ras activation . Both RasGEF and RasGAP are activated at the membrane by free Gβγ , but the translocation of RasGEF from the cytosol is enhanced by G α 2 * . The proposed translocation-activation topology is able to capture the dose-dependent Ras activation and various patterns such rectification and refractoriness under uniform stimuli . It also predicts that imperfect adaptation is inevitable in wild type cells due to the asymmetrical translocation of RasGEF . Takeda et al . [28] proposed an incoherent feedforward activation model to explain adaptation of Ras activity in which RasGEF is assumed to be confined to the membrane and RasGAP diffuses in the cytosol . In our model , both RasGEF and RasGAP can diffuse in the cytosol at equal rates , and both can be recruited to the membrane and activated by Gβγ . Direction sensing , biphasic Ras activation and signal amplification are achieved by complex interactions between the modules . The incoherent-feedforward-activation by globally-diffusing Gβγ contributes to a transient activation along the entire cell perimeter . The activation at the front of the cell ( facing the higher cAMP concentration ) is initially faster and stronger due to the cAMP gradient , but it provides no symmetry breaking or signal amplification since diffusion eliminates the initial Gβγ concentration gradient . This means that Gβγ does not reflect the external stimulus gradient and provides no basis for direction sensing in LatA-treated cells , although it is essential for RasGEF and RasGAP activation . It is the Ric8 regulated , membrane-bound G α 2 * that determines the symmetry breaking and signal amplification . G α 2 * creates an asymmetrical recruitment of RasGEF in a cAMP gradient , which in turn induces asymmetrical RasGEF activation , providing a basis for symmetry breaking . More importantly , Ric8 recruitment to the membrane is elevated by G α 2 * , while activated Ric8 reactivates G α 2 , forming a positive feedback loop . In addition , faster G α 2 β γ reassociation at the back of the cell due to less reactivation of G α 2 there induces faster G α 2 β γ cycling . Since G α 2 β γ diffuses in the cytosol , this provides a potential redistribution of G α 2 β γ from the back to the front , which in turn results in more G α 2 * at the front , thereby forming another positive feedback loop . These two positive feedback loops generate the symmetry breaking and signal amplification of Ras activation in a cAMP gradient . We also studied cell responses to gα2 and ric8 mutations extensively . It is predicted in numerical simulations that in the presence of uniform stimuli , adaptation of Ras activity is perfect and the maximum cytosolic RBD depletion is reduced in gα2-null cells . In a cAMP gradient , gα2-null cells fail to sense directions and there is only an initial transient Ras activation . Adaptation of Ras activity is still imperfect in ric8-null cells , but the magnitude of imperfectness is reduced as compared with wild type cells . Moreover , simulations suggest that ric8-null cells fail to sense direction when they are exposed to a shallow gradient or a steep gradient with high mean concentration , highlight the importance of Ric8 in regulating Ras activation . In contrast to LEGI-type models , the global diffusing Gβγ does not act as an inhibitor directly in our model—instead , it induces both activation and inhibition by activating RasGEF and RasGAP respectively . Gβγ also serves as a ‘global’ activator for the pool of RasGEF* and as a ‘global’ inhibitor by creating a uniform inhibition pool of RasGAP* . Asymmetry in their localization at the membrane arises from the fact that membrane-bound G α 2 * recruits RasGEF from the cytosol , thereby creating an asymmetrical pool of RasGEF* . Hence , our model can be regarded as a local-global transitions of both excitation and inhibition with a delayed local sequestrations of excitation model , in the sense that initially both activation and inhibition go through a local-global transition due to diffusion of Gβγ while a delayed localized translocation by G α 2 * contributes to a local excitation . Direction sensing is results from the Gβγ- mediated , G α 2-Ric8 dependent signal transduction network . Although the model is based on cAMP induced Ras activation in Dicty , GPCR-mediated Ras activation is highly conserved between Dicty and mammalian leukocytes [8] . GEF translocation via interaction with an upstream GTP-bound G protein is a principle conserved in evolution [47] and Gα’s role in GPCR-mediated signalling has been emphasized in other systems [50 , 75] and in drug discovery [76] . Therefore , our model could serve as a generic framework for GPCR mediated Ras activation in other systems and suggest new experiments in those systems .
We first formulate the reaction-diffusion system of signal transduction in general terms and then list the specific equations for the model . Consider a bounded three dimensional domain Ω ⊂ R3 representing a cell , and denote ∂Ω as the plasma membrane . Then the reaction diffusion equation for a cytosolic species A is ∂ C ∂ t = ∇ · ( D ∇ C ) + ∑ i s i r i , ( 2 ) in which C = C ( t , x ) represents the concentration of A at time t at x ∈ Ω and D is the diffusion coefficient of A . The summation is a reaction term indicating A participates in cytosolic reactions which either depletes it or produces it . The ith reaction produces si molecules of A , or consumes −si > 0 molecules of A with a reaction rate ri = ri ( t , x ) . In the signal transduction network considered in this article , si = 0 , 1 . The boundary conditions involve reactions on the boundary and binding and release of molecules at the membrane . We assume that the volume density C ( the concentration in the cytosol ) for A has the units μM and that the surface density ( the concentration on the membrane ) , Cm , has the units #/μm2 . We also assume that the binding reactions at the membrane take place within a layer of thickness δ ( nm ) at the membrane . Then the net flux to the boundary , which can be positive or negative , can be written as - n → · D ∇ C = - D ∂ C ∂ n = k + · δ · C - k - · C m ≡ j + - j - , ( 3 ) where n → is the exterior unit normal to ∂Ω , k± are the on and off rate of binding to the membrane , and κ = 602 relates the units of volume density and surface density scaled by Avogadro’s constant . For the membrane form of species A we have the translocation-reaction-diffusion equation , ∂ C m ∂ t = ∇ · ( D m ∇ C ) + κ ( j + - j - ) + ∑ s m i r m i , ( 4 ) where Cm = Cm ( t , x ) denotes the concentration on the membrane and Dm is the surface diffusion coefficient [77 , 78] . The first term represents the diffusion on the membrane , which we ignore throughout , and the second represents transolcation between cytosol and membrane , which could be absent if A is confined on the membrane , such as Ras , Ras* . There may also be conservation laws for certain substances . If the substances are confined to the membrane we write ∫ ∂ Ω ∑ i = 1 n A n d S = A t o t , ( 5 ) where Ais are the concentrations of different forms and Atot represents the total amount in the cell . If the substances are present both in the cytosol and on the membrane , we write ∫ Ω ∑ i = 1 k A i c d x + ∫ ∂ Ω ∑ j = 1 n A j m d S = A t o t , ( 6 ) where Ais are the concentrations of different forms in the cytosol and A i m s are the concentrations of different forms on the membrane . We are now ready to assemble the system of equations that constitute the full kinetic model in a given geometry Ω . We have to account for 6 cytosolic species in the system Gαβγ , c , Gβγ , c , RasGEFc , RasGAPc , Ric8c and RBDc . The evolution can be described by a system of diffusion-translocation equations ∂ G α β γ , c ∂ t = ∇ ⋅ ( D G α β γ , c ∇ G α β γ ) ∂ G β γ , c ∂ t = ∇ ⋅ ( D G β γ , c ∇ G β γ ) ∂ R a s G E F c ∂ t = ∇ ⋅ ( D R a s G E F c ∇ R a s G E F c ) ∂ R a s G A P c ∂ t = ∇ ⋅ ( D R a s G A P c ∇ R a s G A P c ) ∂ R i c 8 c ∂ t = ∇ ⋅ ( D R i c 8 c ∇ R i c 8 c ) ∂ R B D c ∂ t = ∇ ⋅ ( D R B D c ∇ R B D c ) with the following conditions on ∂Ω , D G α β γ , c ∂ G α β γ , c ∂ n = j 1 D G β γ , c ∂ G β γ , c ∂ n = j 2 D R a s G E F c ∂ R a s G E F c ∂ n = j 5 − j 6 D R a s G A P c ∂ R a s G A P c ∂ n = j 7 D R i c 8 c ∂ R i c 8 c ∂ n = j 3 − j 4 D R B D c ∂ R B D c ∂ n = j 8 − j 9 The species that evolve on the membrane are: R* , Gαβγ , m , Gβγ , m , G α * , Gα , Ric8m , Ric8* , RasGEFm , RasGAPm , RasGEF* , RasGAP* , Ras , Ras* and RBDm . The evolution equations for these are given by ∂ R * ∂ t = r 1 ∂ G α β γ , m ∂ t = − κ j 1 − r 2 + r 7 ∂ G β γ , m ∂ t = − κ j 2 + r 2 − r 7 ∂ G α * ∂ t = r 2 − r 3 + r 5 ∂ G α ∂ t = r 3 − r 5 − r 7 ∂ R i c 8 m ∂ t = − κ j 3 + κ j 4 − r 4 + r 6 ∂ R i c 8 * ∂ t = r 4 − r 6 ∂ R a s G E F m ∂ t = − κ j 5 + κ j 6 − r 8 + r 9 ∂ R a s G A P m ∂ t = − κ j 7 − r 10 + r 11 ∂ R a s G E F * ∂ t = r 8 − r 9 ∂ R a s G A P * ∂ t = r 10 − r 11 ∂ R a s * ∂ t = r 12 − r 13 + r 14 − r 15 ∂ R a s ∂ t = − r 12 + r 13 − r 14 − r 15 ∂ R B D m ∂ t = − κ j 8 + κ j 9 The following conservation laws are also imposed: ∫ ∂ Ω ( R + R * ) d s = R t , ( 7 ) where Rt is the total amount of receptors . ∫ Ω G α , c + G β γ , c + G α β γ , c + G α * d x + ∫ ∂ Ω G α + G β γ , m + G α β γ , m d s = G α β γ t , ( 8 ) where G α β γ t is the total amount of heterotrimetric G protein , indicating the cell does not produce additional heterotrimetric G protein . ∫ Ω R a s G E F c d x + ∫ ∂ Ω R a s G E F m + R a s G E F * d s = R a s G E F t . ( 9 ) Similarly , for RasGAP ∫ Ω R a s G A P c d x + ∫ ∂ Ω R a s G A P m + R a s G A P * d s = R a s G A P t . ( 10 ) For Ras , we have ∫ ∂ Ω R a s + R a s * d s = R a s t . ( 11 ) The parameters involved in the Receptor module are taken from the literature . We estimated the parameters in the heterotrimeric G protein module from steady state analysis ( SSA ) of the spatially lumped model averaged from the spatially distributed model . The parameters in the Ras module are also estimated from SSA and time dynamics of Ras activation . The detailed estimation scheme is described in the supporting information ( see section Parameter estimation in S1 text ) . We summarize the parameters in Table 3 . | Many eukaryotic cells , including Dictyostelium discoideum ( Dicty ) , neutrophils and other cells of the immune system , can detect and reliably orient themselves in chemoattractant gradients . In Dicty , signal detection and transduction involves a G-protein-coupled receptor ( GPCR ) through which extracellular cAMP signals are transduced into Ras activation via an intermediate heterotrimeric G-protein ( G α 2 β γ ) . Ras activation is the first polarized response to cAMP gradients in Dicty . Recent work has revealed mutiple new characteristics of Ras activation in Dicty , thereby providing new insights into direction sensing mechanisms and pointing to the need for new models of chemotaxis . Here we propose a novel reaction-diffusion model of Ras activation based on three major components: one involving the GPCR , one centered on G α 2 β γ , and one involving the monomeric G protein Ras . In contrast to existing local excitation , global inhibition ( LEGI ) models of direction sensing , in which a fast-responding but slowly-diffusing activator and a slow-acting rapidly diffusing inhibitor set up an internal gradient of activity , our model is based on equal diffusion coefficients for all cytosolic species , and the unbalanced local sequestration of some species leads to gradient sensing and amplification . We show that Ric8-modulated G α 2 β γ cycling between the cytosol and membrane can account for many of the observed responses in Dicty , including imperfect adaptation , multiple phases of Ras activity in a cAMP gradient , rectified directional sensing , and a solution to the back-of-the-wave problem . | [
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| 2016 | A Model for Direction Sensing in Dictyostelium discoideum: Ras Activity and Symmetry Breaking Driven by a Gβγ-Mediated, Gα2-Ric8 -- Dependent Signal Transduction Network |
Mutations in the SOD1 and TARDBP genes have been commonly identified in Amyotrophic Lateral Sclerosis ( ALS ) . Recently , mutations in the Fused in sarcoma gene ( FUS ) were identified in familial ( FALS ) ALS cases and sporadic ( SALS ) patients . Similarly to TDP-43 ( coded by TARDBP gene ) , FUS is an RNA binding protein . Using the zebrafish ( Danio rerio ) , we examined the consequences of expressing human wild-type ( WT ) FUS and three ALS–related mutations , as well as their interactions with TARDBP and SOD1 . Knockdown of zebrafish Fus yielded a motor phenotype that could be rescued upon co-expression of wild-type human FUS . In contrast , the two most frequent ALS–related FUS mutations , R521H and R521C , unlike S57Δ , failed to rescue the knockdown phenotype , indicating loss of function . The R521H mutation caused a toxic gain of function when expressed alone , similar to the phenotype observed upon knockdown of zebrafish Fus . This phenotype was not aggravated by co-expression of both mutant human TARDBP ( G348C ) and FUS ( R521H ) or by knockdown of both zebrafish Tardbp and Fus , consistent with a common pathogenic mechanism . We also observed that WT FUS rescued the Tardbp knockdown phenotype , but not vice versa , suggesting that TARDBP acts upstream of FUS in this pathway . In addition we observed that WT SOD1 failed to rescue the phenotype observed upon overexpression of mutant TARDBP or FUS or upon knockdown of Tardbp or Fus; similarly , WT TARDBP or FUS also failed to rescue the phenotype induced by mutant SOD1 ( G93A ) . Finally , overexpression of mutant SOD1 exacerbated the motor phenotype caused by overexpression of mutant FUS . Together our results indicate that TARDBP and FUS act in a pathogenic pathway that is independent of SOD1 .
ALS is the most common motor neuron disorder and is characterized by loss of upper and lower motor neurons . It is the third most common neurological disorder with an incidence of 1–2 people in 100 , 000 , a prevalence of 4–6 per 100 , 000 and with a lifetime risk of 1 in 1 , 000 [1] , [2] . ALS has a devastating course with disease onset generally first detected at 50–60 years of age followed by rapid muscle weakness , atrophy and eventual paralysis resulting in death due to respiratory failure within 1–5 years . Approximately 10% of ALS patients have a familial history for this disease ( FALS ) , whereas the majority ( 90% ) of cases appears to be of a sporadic nature ( SALS ) [3] , [4] . So far , three major genes have been implicated in ALS: SOD1 , TARDBP and FUS . However , it is not known whether these three genes interact in a common pathway or represent distinct ALS etiologies . Cu/Zn superoxide dismutase ( SOD1 ) mutations were the first to be identified predominantly in FALS patients in 1993 , with more than 130 mutations currently identified in approximately 20% of FALS patients [2] , [4]–[6] . A toxic gain of function of mutant SOD1 causes pronounced motor deficits in vivo correlated to motor neuron degeneration in a number of animal models [2] , [7] , [8] . Recently , mutations in TAR DNA binding protein ( TARDBP ) [9]–[12] and Fused in sarcoma ( FUS ) [13] , [14] genes were found in both FALS and SALS patients , opening novel possibilities of studying the predominant , sporadic form of this disease [11] , [15] . In 2008 , two concurrent reports , including ours , identified a dozen missense mutations in the TARDBP gene [9]–[11] . So far , 38 TARDBP mutations have been identified predominantly clustered in the C-terminus , glycine-rich region of the TDP-43 protein encoded by the TARDBP gene in approximately1% of SALS and 3% of FALS [11] , [16] . Two concurrent publications in 2009 identified FUS mutations that occur in about 5% of FALS and less than 1% of SALS [11] , [16] with 35 mutations so far identified in ALS cases . Similarly to TDP-43 ( encoded by TARDBP ) , FUS is an RNA binding protein mainly localized in the nucleus [11] . Further comparable to the TARDBP mutations identified in ALS cases , most of the FUS mutations cluster in the C-terminus of the FUS protein , including the most common mutations , R521C present in 22 FALS and 4 SALS and R521H present in 9 FALS and 4 SALS [11] , [13] , [14] , [17]–[28] . Interestingly , both proteins have been found to be major components of ubiquitinated inclusion bodies in autopsy tissue , not only in ALS patients , but in a number of commonly-related neurodegenerative disorders , such as frontotemporal lobar degeneration ( FTLD ) , Alzheimer's and Parkinson's diseases [11] , [16] . In rare cases , either TARDBP or FUS mutations have also been identified in FTLD cases with or without motor neuron involvement [29]–[32] . In line with this genetic evidence , pathological reports have shown that TDP-43 and FUS antibodies co-label protein aggregates consisting of inclusion bodies observed in both SALS and FALS cases [33] , distinguishing these aggregates from the SOD1 positive found in FALS patients [34] . Two recent studies in cell lines have shown that TDP-43 and FUS interact [35] with a report showing that both proteins are able to influence HDAC6 mRNA production [36] . Strong overexpression of both TARDBP and FUS ( either WT or carrying ALS mutations ) were shown to exacerbate a degenerative phenotype in the Drosophila eye as compared to either gene alone [37] . Further , recent studies have also demonstrated that depletion of TARDBP using RNAi in cell lines or tardbp in Drosophila leads to specific decrease in FUS mRNA levels [38] , [39] . Overall , this evidence suggests that these proteins are involved in similar pathogenic mechanisms leading to motor neuron degeneration . However , the relevance of TARDBP and FUS mutations to these pathogenic mechanisms is not well understood , since none of the known functions of TDP-43 and FUS , such as binding to RNA , DNA and heterogeneous nuclear ribonucleoproteins ( hnRNPs ) , have been shown to be perturbed by ALS-related mutations [36] , [40] , [41] . Recent insight in neurodegenerative diseases has revealed that several genes mutated in these disorders could participate in common molecular mechanisms , raising the possibility of a multigenic interaction at the root of the pathogenesis of neurodegeneration . One such example is the well-established genetic and functional interaction between PTEN-induced putative ( mitochondrial ) kinase 1 ( PINK1 ) and the E3 ubiquitin ligase parkin ( PRKN ) [42] , [43] , two genes mutated in Parkinson's disease [44] , [45] . Another recent example is the identification of interactions between TDP-43 and ataxin-2 in vivo , with ataxin-2 containing polyglutamine expansions being a potent modifier of TDP-43 toxicity [46] . This interaction was also found to have pathogenic implications since SCA1 triplet repeats are significantly increased in ALS patients as compared to controls [46] . In order to establish whether genetic interactions also exist in ALS pathogenesis we carried out a multigenic analysis of FUS , TARDBP and SOD1 in zebrafish . Zebrafish are proving to be a valuable vertebrate model to further our understanding of these multigenic interactions [47] . A number of recent studies in zebrafish with relevance to motor neuron diseases [8] , [48]–[52] suggest that this vertebrate organism is ideally suited as a model to rapidly and efficiently replicate certain aspects of these disorders [53] . This allows us to better understand genetic ( and eventually the cellular and molecular ) mechanisms of motor neuron degeneration during a much shorter time span ( in this case inside a few days ) . Further , these models allow us to define the crucial steps in disease development and to find ways to interfere with the development of early hallmarks of the disease , rather than to exactly replicate the ( likely later ) symptoms . We have previously demonstrated that mutations of TARDBP cause a pronounced motor phenotype characterized by aberrant motor neuron morphology and motor behavior in zebrafish through both toxic gain and loss of function [50] , with the toxic gain of function results being recently confirmed by another group [48] . Up to now , this represents the only vertebrate model of mutant TARDBP where a motor neuron disorder has been observed when compared to similar expression of WT TARDBP . Here we report that FUS mutations identified in ALS patients present a similar motor phenotype . Our results reveal a common genetic pathway for FUS and TARDBP in vivo , with FUS possibly acting downstream of TARDBP . These results also establish that SOD1 acts independently of FUS/TARDBP in causing a motor phenotype in these genetic models of ALS . The establishment of these new genetic models for ALS provides new tools to study the molecular mechanisms of neurodegeneration .
Three ALS-related mutations identified in our patient cohort at the University of Montréal Health Centre ( CHUM; individuals from Quebec and France ) were selected for this study . The R521C is the most common FUS mutation identified so far in 22 FALS and 4 SALS in a number of cohorts . The R521H mutation is also a very common mutation accounting for 9 FALS and 4 SALS [11] , [13] , [14] , [17]–[28] . Both of these mutations have been shown to mislocalize from the nucleus to the cytosol in a number of cell line experiments [14] , [54] , [55] . The S57Δ mutation was identified in one SALS patient from our cohort and is one of the only variants identified in ALS cases to be located in the N-terminus region of the FUS protein [19] . We first confirmed by in situ hybridization that Fus mRNA was indeed expressed as early as 24 hours post-fertilization ( hpf ) in zebrafish embryos ( Figure 1A ) mainly in the hindbrain , eye and intersomitic segments as well as the spinal cord ( Figure 1B ) . The antisense morpholino oligonucleotide ( AMO ) for Fus KD was designed to specifically bind near the ATG of zebrafish Fus but nowhere else in the zebrafish genome . As a control , a mismatch AMO was also designed that does not bind anywhere in the zebrafish genome . Western blot analysis using a FUS antibody demonstrated knockdown ( KD ) of Fus expression by approximately 60% solely in fish injected with an AMO designed to bind Fus , but not to the mismatch AMO control ( Figure 1C ) . Fus KD by AMO caused motor deficits consisting of a significantly abnormal motor behaviour measured as a deficient touch-evoked escape response ( TEER ) ( Figure 2Ai ) as well as reduced outgrowth of hyperbranched axons from motor neurons or unbranched axonal length ( UAL ) ( Figure 2Bi ) that we subsequently refer to as the motor phenotype . The TEER was deficient in 57% of fish injected but was negligible in zebrafish non-injected ( 2% ) , sham injected ( 3% ) or injected with mismatch AMO ( 7% ) . Only 26% of larvae injected with Fus AMO displayed a normal touch-evoked escape response ( Figure 2Ci ) as compared to 95% in non-injected , 87% in sham-injected and 82% in larvae injected with the mismatch AMO . Similarly , the length of the motor axons to the point of the first branch ( UAL ) was significantly reduced in larvae injected with Fus AMO when compared to non-injected zebrafish from 99 to 76 µm ( Figure 2B , 2Ci ) . These results are summarized in Table 1 row 1–4 . To determine if the human FUS and the zebrafish Fus genes are functionally similar , we co-injected human WT FUS mRNA alongside Fus AMO . The motor phenotype was rescued in these sets of injections when compared to injections of Fus AMO alone with significant increases of the percentages of zebrafish displaying a normal TEER from 26 to 70% as well as augmentation of the UAL of motor neurons from 76 to 91 µm ( Figure 2A , 2B , 2Ci versus 2A , 2B , 2Cii and Table 1 rows 4–5 ) . Next , we examined whether mutations in the FUS gene can cause loss of function and result in a motor phenotype similar to what we previously demonstrated for TARDBP mutations [50] . Contrary to WT FUS mRNA , co-injection of R521H FUS mRNA and Fus AMO ( Figure 2Ciii ) did not rescue the motor phenotype ( 21% of zebrafish with normal TEER and the UAL from motor neurons measured at 74 µm ) . Co-injection of the R521C FUS mRNA partially rescued this phenotype , from 26% with AMO ( see above ) to 48% of zebrafish displaying a normal TEER and UAL of 80 µm ( Figure 2Civ and Figure S1A , S1Biii ) since it was also found to be significantly different from the rescue of Fus KD by WT FUS mRNA injection with 70% of fish with normal TEER and UAL of 91 µm ( Figure 2A , 2B , 2Cii ) . However , co-injection of S57Δ FUS mRNA completely rescued the phenotype induced by Fus AMO ( Figure 2Cv and Figure S1A , S1Biv ) to a similar degree as the co-injection WT FUS mRNA injection ( Figure 2A , 2B , 2Cii ) with 64% versus 70% of zebrafish displaying a normal TEER and UAL at 92 versus 91 µm . These results are summarized in Table 1 rows 4–8 . Furthermore , we determined whether expression of WT and ALS-related mutant FUS mRNAs by themselves caused a motor deficit in zebrafish . Upon overexpression of R521H FUS mRNA in zebrafish embryos we observed a motor phenotype with only 43% of zebrafish larvae displaying a normal TEER ( Figure 2Aiv , 2Cvi ) as compared to 77% upon WT FUS injection ( Figure 2Aiii , 2Cix ) . Similarly , the UAL of motor neurons overexpressing R521H FUS ( Figure 2Biv , 2Cvi ) was reduced to 78 µm as compared to 94 µm upon WT FUS injection ( Figure 2Biii , 2Cix ) . Surprisingly , expression at similar levels of the R521C FUS mRNA ( Figure S1A , S1Bi and Figure 2Cvii ) and the S57Δ FUS mutations ( Figure S1A , S1Bii and Figure 2Cviii ) did not elicit a motor phenotype with similar percentages of embryos displaying a normal TEER ( R521C: 63% and S57Δ: 72% versus WT: 77%; Figure 2Cvii , viii and Figure S1Ai , ii ) as described above for WT FUS mRNA ( Figure 2Aiii , 2Cix ) . Similarly , the length of the UAL of motor neurons was not significantly altered when compared to WT FUS ( Figure 2Cix ) ( R521C: 93 µm and S57Δ: 98 µm versus WT: 91 µm; Figure 2Cvii , viii and Figure S1Bi , ii ) . These and subsequent sets of injections presented below did not significantly affect percentages of developmentally deficient ( ∼10% ) and of dead embryos ( ∼10% ) within our conditions ( data not shown ) . The differences observed upon mRNA injections were not due to different levels of protein expression , since Western blot analysis revealed no changes in expression between the WT protein and the three ALS-related FUS mutants described here ( Figure 1D ) . The results from this section are summarized in Table 1 rows 9–12 . These data indicate that the ALS-related R521H mutation of FUS can cause motor neuron deficits leading to a toxic gain of function . These in vivo results suggest that both toxic gain and loss of function ( R521H ) or solely loss of function ( R521C ) can render FUS mutations pathogenic . A lack of phenotype for the S57Δ FUS could indicate that this variant may be a rare polymorphism not causing disease . In accordance with this functional characterization performed in our zebrafish model , the R521H and R521C FUS mutations have been identified in a large number of FALS and SALS cases with both these mutations segregating with disease in large families , whereas the S57Δ variant was identified only in one SALS case [11] , [13] , [14] , [17]–[28] . We hypothesized that FUS and TARDBP operate through a common genetic pathway . To examine this possibility , we tested whether FUS or TARDBP could rescue the loss of function phenotype caused by knockdown of either of these genes in zebrafish . WT FUS mRNA was able to rescue the motor phenotype ( 49% of zebrafish with normal TEER and UAL of 83 µm ) ( Figure 3Aii and Figure 4A , 4Biii ) caused by Tardbp KD alone ( 27% of zebrafish with normal TEER and UAL of 71 µm ) ( Figure 3Ai ) , similarly to the rescue observed with WT TARDBP ( 59% of zebrafish with normal TEER and UAL of 86 µm ) ( Figure 3Aiii and Figure 4A , 4Bii ) . In contrast , co-injection of WT TARDBP mRNA with Fus AMO alone ( 28% of zebrafish with normal TEER and UAL of 77 µm ) ( Figure 3Biii and Figure 4A , 4Biv ) was unable to rescue the phenotype obtained by KD of Fus alone ( 26% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 2A , 2Bi and Figure 3Bi ) . As mentioned previously , co-injection of WT FUS mRNA with Fus AMO ( Figure 2A , 2Bii and Figure 3Bii ) was able to properly rescue the motor phenotype caused by Fus KD alone ( Figure 2A , 2Bi and Figure 3Bi ) . The results from this section are summarized in Table 1 row 4 , 13–14 and 16–17 . These results demonstrate that a genetic interaction between FUS and TARDBP exists , with FUS overexpression being able to rescue the Tardbp KD phenotype as a downstream effector . TAF15 has a high structural and functional homology to FUS , since both TAF15 and FUS belong to the class of TET family of multifunctional DNA/RNA-binding proteins [56] . TAF15 has been suggested by one study as a candidate gene in ALS with few variants identified solely in FALS cases , but not in controls [57] . We thus determined the motor phenotype upon Tardbp KD with or without overexpression of TAF15 mRNA . Overexpression of TAF15 mRNA did not cause an overt motor phenotype with 65% displaying a normal TEER . Co-injecting TAF15 mRNA with Tardbp AMO ( 33% of zebrafish with normal TEER ) failed to rescue the motor phenotype caused by Tardbp KD alone ( 27% of zebrafish with normal TEER ) . ( Figure S2 ) . These results further ascertain that the rescue of the phenotype induced by Tardbp KD by overexpressing WT FUS mRNA is specific to this gene and could be independent of the known functions of FUS and its homologue TAF15 , such as DNA and/or RNA binding . Conversely , we also determined whether the motor phenotype caused by the toxic gain of function of FUS could be rescued by overexpression of WT TARDBP and vice-versa ( a dozen combinations; Figure 3A–3D , i–iii ) , expecting that a toxic gain of function may be irreversible . We and others have previously demonstrated that overexpression of mutant TARDBP causes a similar motor phenotype to the one described above , most pronounced upon injection of the G348C mutation [48] , [50] . Overexpression of this mutant caused deficits in TEER in 54% of zebrafish with only 23% displaying a normal TEER and reduced UAL of motor axons ( 76 µm ) ( Figure 3Ci ) . Our results show that co-expression of mutant TARDBP with either WT FUS ( 23% of zebrafish with normal TEER and UAL of 73 µm ) ( Figure 3Cii ) or WT TARDBP ( 21% of zebrafish with normal TEER and UAL of 70 µm ) ( Figure 3Ciii ) failed to rescue the toxic gain of function phenotype caused by mutant TARDBP alone ( 23% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 3Ci ) . Similarly , we were unable to rescue the motor phenotype caused by expression of the R521H mutant FUS ( 43% of zebrafish with normal TEER and UAL of 78 µm ) ( Figure 2A , 2Biv and Figure 3Di ) with either WT TARDBP ( 41% of zebrafish with normal TEER and UAL of 78 µm ) ( Figure 3Diii ) or WT FUS ( 41% of zebrafish with normal TEER and UAL of 79 µm ) ( Figure 3Dii ) . The data from this section are summarized in Table 1 rows 9 , 19–24 . Thus , overexpression of WT FUS or TARDBP did not rescue the motor phenotype generated by ALS-related mutations in these two genes , consistent with their toxic gain of function . If TARDBP and FUS interact , then overexpression of both mutant TARDBP and FUS mRNAs or simultaneous KD of both genes should yield a similar motor phenotype , whereas an exaggerated , additive phenotype could be expected if these genes act in independent pathways ( see below for SOD1 ) . As predicted for interacting genes , a similar , non-exacerbated motor phenotype was observed upon injection of the R521H mutant FUS ( 43% of zebrafish with normal TEER and UAL of 78 µm ) ( Figure 2A , 2Biv and Figure 5Ai ) , G348C mutant TARDBP ( 23% of zebrafish with normal TEER and UAL of 76 µm ) , ( Figure 5Aii and Figure 6A , 6Biii ) or both mutant TARDBP and FUS ( 25% of zebrafish with normal TEER and UAL of 75 µm ) ( Figure 5Aiii and Figure 6A , 6Biv ) . We also determined that co-injection of Fus and Tardbp AMOs ( 21% of zebrafish with normal TEER and UAL of 69 µm ) ( Figure 5Avi and Figure 6A , 6Bii ) did not exacerbate the KD motor phenotype observed upon injection of either Fus AMO ( 26% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 5Aiv and Figure 2A , 2Bi ) or Tardbp AMO ( 27% of zebrafish with normal TEER and UAL of 71 µm ) ( Figure 5Av and Figure 6A , 6Bi ) . These data are summarized in Table 1 rows 4 , 9 , 13 and 18–20 . Since injections of two AMOs might harbor robust effects that would not allow the proper visualization of a possible exacerbation of the motor phenotype , we also injected suboptimal doses ( see Materials and Methods ) of both Fus and Tardbp AMOs and compared the motor phenotype with the one induced by single injection of Fus and/or Tardbp AMOs . The motor phenotype was not significantly altered when subdoses ( half ) of both Fus and Tardbp AMOs ( 61% of zebrafish with normal TEER versus 21% at the higher dose ) was compared to single AMO injection of either Tardbp ( 63% of zebrafish with normal TEER versus 26% at the higher dose ) and/or Fus ( 69% of zebrafish with normal TEER versus 27% at the higher dose ) ( Figure S3 ) . Similar results were thus obtained when using either full or subdoses of both Fus and Tardbp AMOs . Having provided evidence for an in vivo genetic interaction between TARDBP and FUS we sought to determine whether SOD1 interacts with these genes by performing further gain and loss of function genetic manipulations using a specific Sod1 AMO , as well as WT or mutant ( G93A ) SOD1 mRNAs . We then sought to determine whether SOD1 acted downstream , upstream or independently of TARDBP or FUS , comprising nineteen conditions ( Figure 3A–3D iv–v grey background and Figure 4A , 4Bv and Figure 4vi ) . We first tested if SOD1 acts downstream of TARDBP and FUS by examining whether WT SOD1 could rescue the motor phenotypes generated by loss or toxic gain of function of TARDBP or FUS . Overexpression of WT SOD1 did not yield a motor phenotype on its own ( motor phenotype consisting of 68% of zebrafish with normal TEER ) and failed to rescue the motor phenotype induced by KD of Tardbp ( 25% of zebrafish with normal TEER and UAL of 72 µm ) ( Figure 3Aiv and Figure 4A , 4Bvi ) , KD of Fus , ( 29% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 3Biv and Figure 4A , 4Bv ) as well as overexpression of the G348C mutant TARDBP ( 21% of zebrafish with normal TEER and UAL of 75 µm ) ( Figure 3Civ ) , or the R521H mutant FUS ( 41% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 3Div ) . Next , we tested whether KD of Sod1 could alleviate the motor phenotype caused by mutant TARDBP or FUS . Injection of an AMO to specifically KD Sod1 did not cause a motor phenotype on its own ( consisting of 68% of zebrafish with normal TEER ) , consistent with the lack of phenotype observed in SOD1 knockout mice [58] . In contrast , co-injection of AMOs to Sod1 and Tardbp ( 25% of zebrafish with normal TEER and UAL of 72 µm ) ( Figure 3Av ) or to Sod1 and Fus ( 25% of zebrafish with normal TEER and UAL of 75 µm ) ( Figure 3Bv ) yielded a similar motor phenotype to that observed upon KD of Tardbp ( motor phenotype consisting of 27% of zebrafish with normal TEER and UAL of 71 µm ) ( Figure 3Ai and Figure 6A , 6Bi ) or Fus ( 26% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 2A , 2Bi and Figure 3Bi ) alone . Co-injection of Sod1 AMO with mutant TARDBP ( 22% of zebrafish with normal TEER and UAL of 75 µm ) ( Figure 3Cv ) or mutant FUS ( 40% of zebrafish with normal TEER and UAL of 78 µm ) ( Figure 3Dv ) also failed to modify the motor phenotype obtained by injecting mutant TARDBP alone ( 21% of zebrafish with normal TEER and UAL of 75 µm ) ( Figure 3Ci and Figure 6A , 6Biii ) and mutant FUS alone ( motor phenotype consisting of 43% of zebrafish with normal TEER and UAL of 78 µm ) ( Figure 2A , 2Biv , Figure 3Di ) . From these combined results we can infer that SOD1 is not acting downstream of TARDBP or FUS . These results are summarized in Table 1 rows 4 , 9 , 13 , 19 and 25–34 . Alternatively , to determine if SOD1 acts upstream we tested for rescue of the mutant SOD1-induced motor phenotype by overexpressing WT TARDBP or FUS . Overexpression of mutant SOD1 mRNA in zebrafish embryos has been shown to cause shortening and premature branching of axonal projections from the motor neurons in the spinal cord [8] and is consistent with the toxic gain of function observed in ALS [2] , [7] , [8] . Consistent with these published results , we observed a motor phenotype consisting of 33% of zebrafish with swimming deficits , 44% of zebrafish with normal TEER and UAL of 81 µm when we overexpressed the G93A mutant SOD1 in our zebrafish model ( Figure 5Bi and Figure 6A , 6Bv ) . Similarly , co-expression with mutant SOD1 of WT TARDBP ( motor phenotype consisting of 44% of zebrafish with normal TEER and UAL of 80 µm ) ( Figure 5Biii ) or FUS ( 42% of zebrafish with normal TEER and UAL of 76 µm ) ( Figure 5Bii ) failed to rescue this motor deficit to any significant extent , indicating that mutant SOD1 does not act upstream of either of these two genes . These results are summarized in Table 1 rows 9 , 19 and 35–37 . The foregoing results strongly suggest that SOD1 acts independently of TARDBP and FUS in our models . If this is true , then the motor phenotype yielded by either mutant SOD1 or TARDBP/FUS alone should be less severe than the “additive” phenotype of mutant SOD1 and TARDBP/FUS . Indeed , co-injection of both the R521H mutant FUS and the G93A mutant SOD1 mRNAs ( motor phenotype consisting of 17% of zebrafish with normal TEER and UAL of 68 µm ) ( Figure 5Biv and Figure 6A , 6Bvi ) yielded an exaggerated motor phenotype with a higher percentage of embryos affected as well as an exacerbated axonal shortening from motor neurons when compared to injection of mutant FUS alone ( 42% of zebrafish with normal TEER and UAL of 78 µm ) ( Figure 2A , 2Biv and Figure 5Ai ) or mutant SOD1 ( 44% of zebrafish with normal TEER and UAL of 81 µm ) ( Figure 5Bi and Figure 6A , 6Bv ) alone . Similarly , co-injection of mutant SOD1 and of mutant TARDBP ( motor phenotype consisting of 19% of zebrafish with normal TEER and UAL of 68 µm ) ( Figure 5Bv ) led to a significantly exacerbated motor phenotype when compared to mutant SOD1 alone ( 44% of zebrafish with normal TEER and UAL of 81 µm ) ( Figure 5Bi and Figure 6A , 6Bv ) . The data in this section are summarized in Table 1 rows 9 , 19 and 38–39 . These results indicate that mutant SOD1 may yield a motor phenotype which is additive with that generated by ALS-related FUS and TARDBP mutations , suggesting that SOD1 may act independently of TARDBP and FUS .
As summarized in the matrix of Figure 7A , here we show that expression of WT FUS is able to rescue the motor phenotype induced by KD of Fus ( Figure 2 ) as well as the motor phenotype caused by KD of zebrafish Tardbp ( Figure 3 and Figure 4; summarized in Figure 7A , green cell ) . On the other hand , expression of WT TARDBP is unable to rescue the phenotype caused by KD of Fus ( Figure 3 and Figure 4; Figure 7A , red cell ) , whereas it does rescue the motor phenotype induced by KD of Tardbp . These results as well as in vitro reports of physical interactions [35] , [36] suggest that TARDBP and FUS share a common genetic pathway , with FUS being downstream of TARDBP . Alternatively , as certain studies have previously demonstrated , FUS could be a more general transcriptional regulator , potentially capable of compensating for certain of the functions of TARDBP [15] . This possibility is less likely since overexpression of TAF15 , a gene belonging to the class of TET family of multifunctional DNA/RNA-binding proteins with high functional and structural similarity to FUS was unable to rescue the motor phenotype caused by Tardbp KD ( Figure S2 ) . The lack of exacerbation of partial or more complete double knockdowns also indicates a non-additive effect of TARDBP and FUS ( Figure 5 and Figure S3 ) . Although we were unable to generate an increased phenotype upon double KD of Tardbp and Fus in this study , we cannot exclude the possibility of phenotype exacerbation at different doses of AMOs . Expression of SOD1 was unable to rescue the phenotypes caused by KD of Fus and/or Tardbp suggesting independent molecular pathways ( Figure 5 and Figure 6 ) . Further , mutant SOD1 does not appear to genetically converge with the pathogenic cascades elicited by mutant TARDBP or FUS , since expression of mutant SOD1 exacerbates the motor phenotype induced by mutant FUS ( as summarized in Figure 7A ) . Although the pathological mechanisms through which ALS-related TARDBP mutations cause motor neuron degeneration are not understood , protein misfolding and phosphorylation , nuclear to cytosolic shuttling and RNA imbalance are presumed to be involved [11] , [15] , [16] . Our results suggest that certain ALS-related FUS mutations , similarly to TARDBP [50] , can cause motor neuron deficits through both loss and toxic gain of function mechanisms . As illustrated in Figure 7B , the molecular mechanisms that cause motor neuron degeneration could be initiated with a loss of function of FUS or TARDBP due to mislocalization from the nucleus to the cytosol , whereas the toxic gain of function could be the result of abnormal accumulation of aggregate-prone proteins as reported for two of the FUS mutations described here , R521H and R521C [14] , [54] , [55] , as well as the mutations R495X , R522G and P525L [59]–[61] . A similar toxic gain of function has been described for the A315T , G348C and A382T TARDBP mutations [48] , [50] . In fact , several of these recent studies and others have shown that in neurons , expression of TDP-43 and/or FUS in the cytosol causes aggregation of these proteins with subsequent recruitment into stress granules , thus initiating pathogenic events [59]–[63] . While this article was under review , a study in Drosophila performed a functional characterization of several FUS mutations , including the R521H and R521C mutations described here [37] . When strongly overexpressed solely in neurons mutant FUS led to an increased severity of the “rough-eye” phenotype , widespread neurodegeneration and lethality as compared to WT FUS overexpression [37] . The authors also suggested genetic interactions between TARDBP and FUS since overexpression of both mutants together caused exacerbation of this phenotype as compared to expression of either mutant TARDBP or FUS . However , overexpression of WT TARDBP and WT FUS together caused similar increases in phenotype severity when compared to overexpression of either WT genes alone [37] . Thus , this exacerbation could be likely a result of excessive amounts of these proteins , which have been found as likely to aggregate in a number of models , including yeast [64]–[66] . On the other hand , we describe here a genetic interaction due to rescue ( as opposed to exacerbation ) by WT FUS of the motor phenotype upon Tardbp KD , suggesting that FUS is downstream to TARDBP in this pathway . Consistent with a possible action downstream from TARDBP , FUS is thought to have a more critical role in regulating neuronal morphology and connectivity [15] . In cell lines , TDP-43 was shown to form complexes with hnRNPs and a fraction of TDP-43 in these complexes does interact directly with FUS , with this in vitro interaction being enhanced in cell lines from ALS patients harboring TARDBP mutations [35] . Another study using cell lines demonstrated that a common biochemical pathway exists where FUS and TDP-43 interact by binding competitively HDAC6 mRNA , with TDP-43 being upstream in this pathway . Further , since FUS antibodies have been shown to co-label TDP-43 positive protein aggregates observed in both SALS and FALS cases , a similar pathogenic function for these mutant proteins has been suggested [33] . Finally , ubiquitinated aggregates observed in FALS cases with SOD1 mutations were not immunopositive against TDP-43 or FUS antibodies [33] , [34] , again consistent with independent pathogenic mechanisms for SOD1 and TDP-43/FUS . Interestingly , a number of in vivo studies have demonstrated that TARDBP has a number of genetic interactors such as Ataxin-2 [46] , progranulin ( GRN ) [48] , [67]–[69] , valosin-containing protein ( VCP ) [70] , [71] and histone deacetylase 6 ( HDAC6 ) [36] , [38] . Most of these interactors are mutated in other neurodegenerative disorders , such as dementia and expanded polyglutamine repeat disorders , whereas some may participate in generalized processes such as autophagy . Further , recent genetic studies have shown that mutations in Ataxin-2 and VCP are prevalent in ALS patients , with intermediate polyglutamine expansions significantly associated with ALS [46] and a study finding VCP mutations in 1–2% of FALS patients [72] . These combined results suggest that TARDBP plays a pivotal role in the pathogenic pathways leading to motor neuron degeneration culminating in ALS . These results also suggest that a multigenic pathway shared in a number of neurodegenerative disorders may exist . Unraveling the molecular and genetic components of this network of neurodegenerative interactions could have major implications in our understanding of the pathophysiology of these neurological disorders and could accelerate the discovery of future treatments for these increasingly prevalent diseases .
Zebrafish were raised from a colony maintained according to established procedures . All procedures described here were carried out in compliance with the Canadian Council for Animal Care . Injections were performed in 1–4 cell stage blastulae . FUS WT and mutants ( R521H , R521C , S57Δ ) , TARDBP WT and mutant ( G348C ) , SOD1 WT and mutant ( G93A ) mRNAs were transcribed from NotI-linearized pCS2+ using SP6 polymerase with the mMESSAGE Machine Kit ( Ambion ) . This was followed by a phenol-chloroform purification and ethanol precipitation , and diluted in nuclease-free water ( Ambion ) . The mRNAs were diluted in nuclease free water ( Ambion ) with 0 . 05% Fast Green vital dye ( Sigma ) at a concentration of 60 ng/µl ( FUS ) , 25 ng/µl ( TARDBP ) and 100 ng/µl ( SOD1 ) and were pulse-injected into early embryos using a Picospritzer III ( General Valve ) pressure ejector . The zebrafish TARDBP , FUS and SOD1 gene orthologues , Tardbp , Fus , and Sod1 ( NM_201476; NM_201083 . 2; and NM_131294 . 1 respectively ) were identified using the Ensembl's gene homology prediction program ( http://www . ensembl . org ) . AMOs were designed to bind and inhibit specifically the ATG of the following genes ( and no other genomic sequence ) : Fus ( GGCCATAATCATTTGACGCCATGTT ) , Tardbp ( GTACATCTCGGCCATCTTTCCTCAG ) and Sod1 ( GCACACAAACGGCCTTGTTCACCAT ) mRNA translation ( Gene Tools ) were designed complimentary to the region of translational initiation of the Fus CGAAGGCGACTGTACGTATAACACCTCAGAAATTGTTATTCTGCATCATTTCTAAAAGGATTTTAAGCCCAAAC [ ( ATG ) GCGTCAAATGATTATGGCC]AAA , Tardbp ( GGAAACAGTTAGCACAGCTCGCGCATTCGGTGTAATC [ ( ATG ) ACGGAGTGCTATATTCGTGTGG] ) , and Sod1 TCTTATCAAACACAGTCGGTTTCTTTCACTCTCTCACAACTTCTCAGTTTGCATAATCTACAGTCAGC [ ( ATG ) GTGAACAAGGCCGTTTGTGTGC] mRNAs to inhibit protein translation . These AMOs were designed to bind solely to the 1st ATG of the appropriate region of translational initiation for each gene and were confirmed by BLAST searches to not recognize any other sequences in the zebrafish genome ( or any human transcripts ) . An AMO having nucleotides that were mismatched represented as lowercase to disrupt specificity were also designed for Fus ( GGCgAaAATgATTTcACcCCATGTT ) . Dose-dependence curves of AMO and mRNA toxicity were performed and AMOs were injected at a concentration of 0 . 6 ( Fus ) , 0 . 1 ( Tardbp ) and 0 . 5 ( Sod1 ) mM to minimize morpholino-induced developmental delay and toxicity and to yield a consistent motor phenotype . Suboptimal doses were established for Fus ( 0 . 3 mM ) and Tardbp ( 0 . 05 mM ) . Morphology and behavioral touch responses were assessed with a stereomicroscope ( Zeiss , Oberkochen , Germany ) and only fish with no obvious developmental deficits were selected to determine the TEER . For escape swimming at 48 hpf , embryos were touched lightly at the level of the tail or head with a pair of blunt forceps . Fish that were unable to escape were touched several times ( 3–4 times ) in order to ascertain their failure to respond . Thus , for each injection set , larvae were separated in four groups; dead and developmentally deficient , fish with deficits in TEER and fish displaying a normal TEER . The percentages for the two last groups are described in Table 1 for each condition . Their responses were also recorded using a Photron ( San Diego , CA ) Fastcam PCI high-speed video camera at a rate of 125 frames/s . For immunohistochemical analysis of axonal projections of motor neurons , monoclonal antibodies anti-SV2 ( Developmental Studies Hybridoma ) were used to assess the motor neuron morphology at 48 and 72 hpf . Fluorescent images of fixed embryos were taken using a Quorum Technologies spinning-disk confocal microscope mounted on an upright Olympus BX61W1 fluorescence microscope equipped with an Hamamatsu ORCA-ER camera . Image acquisition was performed with Volocity software ( PerkinElmer ) . As previously described [50] , axonal projections from primary and secondary motor neurons at a defined location in the intersomitic segments were determined . Analysis of Z-stacks by confocal microscopy was performed in three to four axonal projections per animal . The axonal length to the first branching ( UAL ) was determined by tracing the labeled axon from the spinal cord to the point where it branches using Image J . These values were averaged for each of the animal analyzed ( 10–30 zebrafish per condition ) for the various conditions in our study . Sense and antisense probes of 500 bp length against FUS mRNA were designed . 24 hpf zebrafish embryos were processed for in situ hybridization using fluorescent FastRed as previously described [73] , with minor modifications . Zebrafish embryos were lysed in ice using cold SDS sample buffer ( 63 mM Tris-HCl pH 6 . 8 , 10% glycerol , 5% ß-mercaptoethanol , 3 . 5% sodium dodecyl sulfate ) and were maintained on ice and homogenized using a hand-held pestle . The lysates were centrifuged for 15 minutes at 13000 rpm and separated into soluble and insoluble fractions . SDS/PAGE Western blotting of both fractions were carried out as previously described [9] , using monoclonal antibodies against myc ( Invitrogen ) , actin ( Clone C4; ICN BIOMEDICALS , Inc . ) , a polyclonal antibodies against TDP-43 ( ProteinTech ) and a polyclonal antibody ( Bethyl Laboratories ) as well as a monoclonal antibody against FUS ( BD Transduction Laboratories ) . Statistical significance was determined by anova and post hoc analysis by Tukey's multiple comparison test using Prism software ( Prism Software Ltd . ) as well as a two-tailed distribution , two-sample equal variance t-test using Sigma Plot software ( Systat Software Inc . , San Jose , CA , USA ) . Significance was established at p<0 . 05 . | Mutations in the SOD1 , TARDBP , and FUS genes have been commonly identified in Amyotrophic Lateral Sclerosis ( ALS ) . However , possible interactions between these ALS–causative genetic mutations have not been examined . Here we expressed each of three human FUS mutations ( R521H , R521C , and S57Δ ) in zebrafish embryos , with or without knocking down the zebrafish homolog Fus , and observed a motor phenotype consisting of significant behavioral ( touch-evoked escape response ) and cellular ( shortened axonal projections from motor neurons ) deficits due to loss of function for the R521H and R521C mutations and/or toxic gain of function solely for the R521H mutation . Wild-type FUS could rescue the Tardbp knockdown phenotype , but not vice versa , suggesting that TARDBP is upstream of FUS in this pathway responsible for motor neuron disorder . Furthermore , neither TARDBP nor FUS were able to modify and/or rescue the motor phenotype caused by mutant SOD1 , and likewise SOD1 failed to rescue the phenotype of zebrafish expressing mutant TARDBP or FUS . Our results indicate that TARDBP acts upstream of FUS in a pathogenic pathway that is distinct from that of SOD1 . | [
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| 2011 | FUS and TARDBP but Not SOD1 Interact in Genetic Models of Amyotrophic Lateral Sclerosis |
Genomic imprinting is a normal process that causes genes to be expressed according to parental origin . The selective advantage conferred by imprinting is not understood but is hypothesised to act on dosage-critical genes . Here , we report a unique model in which the consequences of a single , double , and triple dosage of the imprinted Dlk1/Pref1 , normally repressed on the maternally inherited chromosome , can be assessed in the growing embryo . BAC-transgenic mice were generated that over-express Dlk1 from endogenous regulators at all sites of embryonic activity . Triple dosage causes lethality associated with major organ abnormalities . Embryos expressing a double dose of Dlk1 , recapitulating loss of imprinting , are growth enhanced but fail to thrive in early life , despite the early growth advantage . Thus , any benefit conferred by increased embryonic size is offset by postnatal lethality . We propose a negative correlation between gene dosage and survival that fixes an upper limit on growth promotion by Dlk1 , and we hypothesize that trade-off between growth and lethality might have driven imprinting at this locus .
Genomic imprinting , the process causing genes to be differentially expressed according to their parental origin acts on a subset of developmentally regulated genes [reviewed in 1] . Since imprinting results in functional haploidy of target genes , the evolution of such a mechanism must have conferred a significant advantage to its recipients to offset the cost . In addition , the maintenance of this method of gene dosage regulation must confer a continuing selective benefit to the individual . By creating genetic models that mimic the loss of imprinting of genes regulated in this way , we can study phenotype and consider what selective pressures act to maintain gene dosage , and begin to examine what may have driven the acquisition of mono-allelic expression . Dlk1 , Delta-like homologue 1 also known as Preadipocyte factor 1 ( Pref1 ) , on mouse chromosome 12 is part of an imprinted gene cluster [2] , [3] . It encodes a transmembrane glycoprotein that possesses six epidermal growth factor-like motifs in the extracellular domain similar to those present in the Delta/Notch/Serrate family of signalling molecules . In contrast to other NOTCH ligands , DLK1 does not have the Delta:Serrate:Lin-12 ( DSL ) domain believed to mediate the interaction and activation of the NOTCH receptor [4] . Nevertheless , DLK1 can interact with NOTCH through specific EGF-like repeats and can act as a Notch antagonist , both in culture and in vivo , binding to the receptor without activating it [5] , [6] , [7] . In vitro , Dlk1 maintains precursor cell populations and inhibits differentiation [4] , [8] , [9] . Dlk1 is expressed at high levels in a wide range of embryonic tissues [10] , [11] however its developmental functions in vivo are largely unknown . In more ancestral vertebrates such as the fish Oryzias latipes and Fugu rubripes , Dlk1 , and its neighbour Dio3 which encodes a negative regulator of thyroid hormone metabolism , are only 10–15 Kb apart . In eutherian mammals , the genes have become separated by the insertion of sequences including a retrotransposon-like gene ( Rtl1 ) and several non-coding RNAs , including a large microRNA cluster , and have acquired imprinting [12] . Dlk1 , Rtl1 and Dio3 are expressed from the paternally inherited chromosome and the non-coding RNAs from the maternally-inherited chromosome [13] . Imprinting on chromosome 12 is controlled by an intergenic differentially methylated region ( IG-DMR ) that is methylated on the paternally inherited chromosome [14] , [15] . The unmethylated maternal IG-DMR is necessary to repress protein-coding transcription and to activate the non-coding RNAs [14] . In terms of gene expression , the paternal chromosome 12 most likely resembles the ancestral ( pre-imprinted ) state as Dlk1 and Dio3 are expressed and the non-coding RNAs are silenced [12] . Furthermore , paternal deletion of the methylated IG-DMR has no effect on transcription [14] . Alteration of imprinting at chromosome 12 has considerable consequences for embryonic fitness . Uniparental disomy mice with two paternal copies and no maternal copy ( PatDi ( 12 ) /PatDp ( dist12 ) ) or mice with two maternal copies and no paternal copy ( MatDi ( 12 ) /MatDp ( dist12 ) ) of chromosome 12 , are lethal and show distinct phenotypes . These include growth abnormalities and developmental defects in muscle , cartilage/bone and placenta [16] , [17] , [18] . Animals die prenatally commencing at E16 . The embryonic uniparental disomic phenotypes can be ascribed to the Dlk1-Dio3 imprinted cluster because embryos with maternal deletion of the IG-DMR ( ΔIG-DMRMAT ) recapitulate the transcriptional profile and embryonic mutant phenotypes of the PatDi ( 12 ) /PatDp ( dist12 ) conceptuses [14] , [19] . Since loss of imprinting at chromosome 12 is lethal , there are clear advantages in maintaining the imprinted state . However since in PatDi ( 12 ) /PatDp ( dist12 ) and ΔIG-DMRMAT conceptuses multiple paternally expressed coding genes are up-regulated , and multiple maternally expressed non-coding genes are repressed , we are unable to determine if selection is acting to maintain the mono-allelic expression of one or all protein coding genes or to maintain expression of the non-coding RNAs . As Dlk1 is an ancestral gene at this imprinted locus and it is expressed at high levels in tissues where uniparental disomy conceptuses have the most pronounced abnormalities [10] , we consider Dlk1 as a strong candidate for the pathological defects associated with the Dlk1-Dio3 region . Therefore in this study , we specifically ask if imprinting might be maintained to prevent overdose of Dlk1 and moreover , if selection to control the dosage of this gene might have driven the evolution of imprinting of the chromosome 12 cluster . Manipulating imprinted gene dosage in vivo provides a powerful tool to study imprinted gene function and evolution . Here we describe a Dlk1 over-expression model in which the gene is driven by its own endogenous regulatory sequences . We generate mouse lines harbouring bacterial artificial chromosome ( BAC ) transgenes that encompass the entire unmanipulated Dlk1 gene with different lengths of flanking sequence . Of these , transgenic Dlk1 expression at sites where the endogenous gene is expressed , was achieved from a 70 kb BAC transgene in three independent lines and the outcome was the same for all three lines . This allowed us to compare the phenotypes of mice expressing a double ( transgene hemizygotes ) or triple ( transgene homozygotes ) dose of Dlk1 with normal animals expressing a single imprinted dose . This transgenic system of regulated Dlk1 over-dose allowed us to explore the developmental and physiological functions of this gene in comparison with other models , and infer evolutionary scenarios for Dlk1 imprinting .
BAC transgenes were generated that encompass the entire Dlk1 gene and endogenous flanking sequences but without other genes in the cluster . Transgenic mice were created by pronuclear injection of two BAC transgenes differing in the amounts of flanking regulatory sequences ( Figure 1A ) : TgDlk1-31 starts 8 kb upstream of the Dlk1 gene and ends approximately 18 kb downstream of the Dlk1 transcriptional start site . The TgDlk1-70 transgene shares its 3′ end with the TgDlk1-31 transgene but contains 49 . 4 kb of sequence upstream of Dlk1 ( Figure 1A ) . The four independent lines containing the TgDlk1-31 transgene failed to express Dlk1 and no Dlk1-associated phenotypes were observed ( data not shown ) regardless of transgene copy number ( Figure S1A ) . These mice were not analysed further . In contrast , the TgDlk1-70 transgenic animals successfully expressed Dlk1 ( Figure 1B and C and Table S1 ) , and were subjected to further analysis . The four independent TgDlk1-70 lines of mice , hitherto referred to as 70A , 70B , 70C and 70D were maintained as hemizygotes ( referred to as WT/TG ) and bred to C57BL/6 for more than ten generations to ensure stability of copy number and phenotype . Copy number was stable from the third generation onwards and estimated to be 4–5 ( 70A ) , 5–6 ( 70B ) , 7 ( 70C ) and 1 ( 70D ) ( Figure S1 and Table S1 ) . Once stable lines were established , analysis of the overall level of Dlk1 expression was performed by Northern Blotting for the different lines . In the single copy 70D line , no transgene-derived Dlk1 expression was observed and no phenotypic consequences were noted ( data not shown ) . This line was not analysed further . Importantly , for the 70A , 70B and 70C lines , WT/TG E16 embryos expressed approximately twice as much Dlk1 as their normal littermates regardless of copy number . Representative quantitative Northern blot expression data is illustrated in Figure 1B and 1C for 70C and 70B families . Detailed expression data ( both from Northern blots and RT-qPCR ) are shown independently for the three lines in Figure S2B and STable S1 ) . This expression analysis using two independent methods clearly shows that WT/TG E16 and E18 embryos for the three lines express approximately twice as much Dlk1 . Interestingly , expression of the transgene occurred independent of the parental-origin of the transgene in the three independent lines ( Figure 1B; Table S1 ) . Absence of transgene imprinting was confirmed using a single nucleotide polymorphism ( SNP ) located in the 3′UTR of Dlk1 ( Lin et al . , 2003 ) allowing the endogenous gene to be distinguished from the transgene ( Figure S2A; Table S1 ) . As the transgene is not imprinted , homozygous transgenic mice ( TG/TG ) were generated to further increase the dosage of Dlk1 in the three transgene expressing lines . As illustrated in Figure 1C for 70B and shown also for 70A and 70C ( Figure S2 and Table S1 ) levels of Dlk1 in the E16–E18 TG/TG embryos were approximately 3-fold higher than normal littermates . Dlk1 regulation is complex involving alternative splicing and extensive post-translational modifications , therefore we compared DLK1 protein isoforms between the genotypes ( for the 70B family ) . DLK1 protein levels in the different genotypes are consistent with the Dlk1 mRNA expression and we saw no change in isoform preference ( Figure 1D; Figure 2B ) . Furthermore , DLK1 protein levels in WT/TG embryos are comparable to that of PatDi ( 12 ) embryos , and are further increased in TG/TG conceptuses . Overall levels of Dlk1 expression were assessed in WT/WT and WT/TG embryonic tissues at E16 ( Table S1 ) and E18 by TaqMan RT-qPCR for 70B ( Figure 2A ) and 70A ( Table S1 ) . Results show that Dlk1 levels are over-expressed around 2–2 . 5 fold in embryonic tissues analysed in the WT/TG when compared to normal littermates ( Figure 2A ) . The placenta was the only tissue with no significant over-expression ( 109%×±15% compared to WT/WT ) ( Figure 2A and Table S1 ) . This suggests that key placenta specific regulators lie outside the region delineated by the transgene . Western Blot analysis of DLK1 protein expression was conducted in control , WT/TG and TG/TG tissues and compared with the RT-qPCR analysis . In all cases , using this semi-quantitative method , protein levels correlated with RNA levels for the tissues and stages analysed ( Figure 2B ) . Curiously , a slight increase in DLK1 protein levels was observed in the TG/TG placenta , suggesting that there is minimal expression of the transgene in this tissue . Tissue-specific expression of Dlk1 was compared between normal and transgenic conceptuses by in situ hybridisation and no ectopic Dlk1 activity was evident ( Figure 3 and Table S4 ) at E16 or E18 . This suggests that endogenous regulators are present on the transgene and are driving transgenic Dlk1 transcription . Tissue-specific expression of the endogenous neighbouring non-coding RNA gene , Gtl2 was unaffected in all genotypes ( data not shown ) allowing phenotypes of the transgenic mice to be attributed to Dlk1 over-expression . Phenotypic characterization was performed independently in all three lines and all the observed phenotypes were consistent so data was combined . Results generated from the individual lines are presented and also summarised in the Supplementary Data . Imprinted genes have long been known to function in controlling pre-natal growth and nutrient acquisition [20] . To address a growth function for Dlk1 , we performed extensive measurements of wet and dry embryonic masses , placental wet masses and crown-rump lengths . WT/TG fetuses are expressing a double dose of Dlk1 and showed consistent overgrowth from E16 to the day of birth . Wet and dry embryonic masses and crown-rump ( C–R ) length values increased by 6–10% compared to WT/WT littermates ( Figure 4A–B; Table S3 ) . TG/TG fetuses show growth enhancement at E16 ( wet mass and dry mass increases of 23–25% ) . In contrast , no differences are observed in E18 C–R length and dry mass compared to WT/WT littermates most likely due to the failure to thrive of these severely compromised fetuses at the later stages as has been observed in other models [16] . Differences in wet mass between TG/TG and WT/WT at late gestation are due to oedema ( Figure 6A–B ) . This late gestation data correlates with lethality of the TG/TG fetuses commencing around E16 ( Table 1 , Figure 4 and see below ) . To determine the contribution of individual tissues to the increase in fetal mass , E18–19 brains , livers , lungs , forelimbs , hindlimbs and brown adipose tissue ( BAT ) were weighed and analysed histologically ( Figure 4C , Figure 5 , Figure 6 ) . In the growth-enhanced WT/TG fetuses , lungs , forelimbs and hindlimbs were proportionally heavier . WT/TG brains , that significantly over-express Dlk1 , were spared . E18 liver growth was unaffected , suggesting that the milder Dlk1 over-expression in this tissue ( E18: 1 . 31×±0 . 21 of WT/WT Dlk1 liver expression and at E16: 1 . 67×±0 . 21 ) may not be sufficient to cause overgrowth . Overgrowth was observed in TG/TG livers evident from E16 . Histological examination revealed no obvious defects and portal triads , and hepatocyte size appeared unaffected ( Figure 4C , Figure 6 and data not shown ) . In general , this suggests that , with the exception of the brain , multi-organ overgrowth is associated with Dlk1 over-expression in an organ-autonomous manner . In the placentas , no significant differences between the genotypes were observed at any stage ( Figure 4A and Table S3 ) . Histological analyses indicated no obvious phenotypic abnormalities in the placenta at E16 and E19 for the three genotypes ( data not shown ) . This is consistent with the finding that Dlk1 is expressed at very low levels from the transgene in this tissue ( Figure 2 and Table S1 ) and suggests that embryonic growth enhancement occurs independently of the placenta . PatDi ( 12 ) /PatDp ( dist12 ) and ΔIG-DMRMAT exhibit costal cartilage defects and hypo-ossification of mesoderm-derived bones [16] , [18] , [19] . Since WT/TG embryos have increased crown-rump length , we examined the skeletal development of WT/TG and TG/TG mice . WT/TG skeletons show signs of growth enhancement and minor ossification delays in the sternum and the closure of the sagittal suture ( Figure 5 ) . Further increasing the dosage of Dlk1 in TG/TG animals led to more severe skeletal defects . Overall the TG/TG skeleton was smaller and the delay in the closure of the sagittal suture was more pronounced . In addition , we observed a bell-shaped thorax with a hypo-ossified sternum and thinner ribs and vertebrae . We can therefore relate the severity of hypo-ossification with increasing levels of Dlk1 . Another major defect associated with PatDi ( 12 ) /PatDp ( dist12 ) and ΔIG-DMRMAT is skeletal muscle immaturity [16] , [18] , [19] . Interestingly , in contrast to the defects in the skeleton , no muscle maturation abnormalities were observed even when Dlk1 dosage was tripled . Detailed morphometric analysis assessing myofiber diameter and % of myofibers with centrally located nuclei was performed on the E18 diaphragm and on a subset of muscles in the forearm and no differences were observed ( Figure S3 ) . Dlk1 over-expression of all isoforms was confirmed at the protein level ( Figure 2B ) . Therefore our data clearly show no prenatal muscle immaturity or hypertrophy induced by Dlk1 over-expression from endogenous regulators . Hemizygous transgenic embryos are viable and fertile and observed at Mendelian frequencies at birth indicating that a double dose of Dlk1 expression is compatible with embryonic viability ( Table 1 and . Table S2 ) . In contrast to WT/TG animals , TG/TG embryos have distinct morphological features from E16 including severe oedema , a small thoracic region and a protruding abdomen ( Figure 6A and B ) . This is associated with lethality from E16 with none of the TG/TG animals surviving more than a few hours after birth ( Table 1 and Table S2 ) . We also observed that the weight of the TG/TG lungs was significantly reduced ( Figure 4C ) with a denser cellular arrangement at E18 ( Figure 6C ) . Dlk1 is strongly expressed in the bronchioles of the lung ( Figure 3 and ref [10] ) and an intrinsic defect in lung development caused by over-expression of Dlk1 is likely to contribute to the lethality of these animals . Despite the absence of major embryonic abnormalities caused by doubling Dlk1 dosage in vivo , we observed a significant increase in early postnatal lethality in WT/TG animals . 32% of WT/TG pups died during the first three days after birth compared to 10% of WT/WT littermates , in crosses from wild type mothers ( Table 1 and Table S2 ) . In order to understand the cause of death , we studied processes essential to early postnatal survival in rodents such as temperature regulation , suckling ability and glucose homeostasis . Temperature regulation is required for adapting to cold exposure after birth and relies on the process of non-shivering thermogenesis ( NST ) mediated by brown adipose tissue ( BAT ) metabolism . Dlk1 is believed to inhibit adipogenesis of both brown and white adipose tissue [21] , [22] thus is a likely candidate for involvement in the NST process . We measured BAT per body weight and analysed markers for both fat differentiation ( such as Pparγ2 ) and thermogenesis ( such as Ucp1 ) before and after birth . At E19 , WT/TG BAT per body weight and levels of Pparγ2 and Ucp1 are comparable to WT/WT ( Figure S4 ) . At birth , we observed that Ucp1 and Pparγ2 expression was slightly elevated , perhaps to compensate for a slight decrease in BAT mass per body weight ( Figure 7A and B ) . Failure to thrive , therefore , cannot be ascribed to compromised NST . The suckling ability of Dlk1 WT/TG pups at birth was assessed by using stomach weight as a measure of milk content [23] . We observed that WT/WT animals were frequently found with stomach content in the range of 0 . 03–0 . 08 grams , whilst WT/TG animals never exceeded 0 . 03 grams ( Figure 7A ) . In situ data shows that at late gestation ( E18 ) , highest expression of Dlk1 is in the tongue and the upper and lower lips , which is consistent with a role of Dlk1 in suckling ( Table S4 and data not shown ) . Blood glucose levels were not different at birth between the two genotypes ( Figure 7C ) . We also decided to monitor the growth performance of WT/TG animals during the first three weeks of life , when juveniles are still dependent on the mother for nutrition . WT/TG animals are born bigger than their littermates ( Figure 4A and Figure 7A ) , but they fail to gain weight at the same rate as WT/WT neonates during the first two weeks ( Figure 7D ) . By 14 days , WT/TG animals are small and remain small thereafter ( ∼10% ) ( Figure 7D and data not shown ) . The differences in growth performance are most pronounced within the first week after birth ( Figure 7D ) , correlating with poor suckling and the increased lethality of these animals . In conclusion , despite an early growth advantage , animals expressing a double dose of Dlk1 fail to thrive in early life , and thus any benefit conferred by an increased embryonic size is offset by postnatal lethality .
We have developed a unique model in which the consequences of a single , double and triple dosage of one imprinted gene , Dlk1 , can be assessed in the growing embryo . The double dose is reminiscent of the situation where there is no imprinting of this gene . BAC and YAC transgenes have been widely used in mouse genetics and in imprinting studies as a molecular tool for the localisation of transcriptional or imprinting regulatory elements [24] , [25] , [26] . We generated transgenes differing in the extent of the sequences upstream of Dlk1 . The TgDlk1-31 did not express Dlk1 in four transgenic lines , regardless of integration site or copy number ( Figure S1A ) . In contrast , TgDlk1-70 , which extends from 49 kb upstream of the Dlk1 gene , is expressed . We have therefore determined that the majority of embryonic tissue-specific enhancer sequences for Dlk1 expression are located in the 8 kb to 49 kb interval upstream of the gene . This is consistent with a previous report showing that enhancers for Dlk1 are absent from 3 kb upstream to 175 kb downstream of the gene [26] . Minimal transgene expression in the placenta suggests the absence of the specific regulatory sequences for this organ in the 70 kb transgene . At E16 , we detected growth enhancement in a Dlk1 dose-dependent manner . TG/TG were more than 20% larger than WT/WT littermates , whereas WT/TG were growth-enhanced by 10% . At later stages , progressive morbidity and mortality of TG/TG embryos precluded a meaningful comparative assessment of their growth rate . This was not the case for viable WT/TG embryos which clearly maintained an increased growth trajectory from E16 up to birth . This is reciprocal to the 20% reduction in weight reported for Dlk1-null E19 fetuses [21] . In general , tissue-specific overgrowth correlated with levels of Dlk1 over-expression , except in the brain . This suggests Dlk1 acts locally on organ growth , though we cannot rule out an endocrine role [27] . With the exception of studies on placental specific Igf2 [28] , [29] , previous functional analyses of imprinted genes expressed in embryo and placenta have not been able to discern whether growth effects are intrinsic to the embryo or are secondary to placental function . This is because genetic manipulation of imprinted genes often results in altered gene dosage in both embryonic and extra-embryonic tissues [reviewed in 30] . In contrast , the WT/TG model reported here generates embryonic Dlk1 over-expression in the presence of a normal placenta with a normal Dlk1 dose , and the results indicate that Dlk1 can modulate embryonic growth independently of the placenta . To complement these studies , it will be important to determine the extent to which placental Dlk1 expressed in the fetal endothelium and some trophoblast cells of the labyrinthine zone [10] also contributes to embryonic growth . Importantly , this study shows that Dlk1 has a dual role on growth . WT/TG neonates are born larger but fail to thrive during the first week of life . The reduced postnatal growth rate is concurrent with reduced food intake in the neonatal period . Maternally repressed imprinted genes are expected to favour growth performance and resource acquisition from embryonic stages to weaning according to the kinship theory which posits that imprinting arose as a consequence of a conflict between males and females over the allocation of maternal resources to the offspring [31] , [32] . The embryonic overgrowth generated by Dlk1 double dosage follows the directionality predicted by this theory however the postnatal failure to thrive phenotype is contrary to it . Our model implicates Dlk1 in reduced postnatal nutrient acquisition . Indeed the results suggest that maternal repression of Dlk1 may have evolved to increase postnatal nutrient acquisition . Furthermore it has been hypothesized that maternally repressed imprinted genes , such as Dlk1 , would minimize heating contribution within huddles [33] , [34] . NST is not impaired in BAT over-expressing Dlk1 . In fact , slight over-expression of Pparγ2 and Ucp1 may suggest the opposite scenario , whereby a maternally repressed gene may contribute more to the communal heating . Our results are therefore inconsistent with the conflict hypothesis . It is possible that imprinting may have evolved due to more than one type of selective pressure , perhaps different for different domains . In TG/TG embryos over-growth occurs at E16 coincident with an increased frequency of embryonic mortality . Embryos expressing this triple dose did not survive past birth . A double dose of Dlk1 was compatible with embryonic viability but resulted in significant neonatal lethality . We propose a negative correlation between gene dosage and survival that may fix an upper limit on growth promotion by Dlk1 . One hypothesis for the lethality could be that Dlk1 dosage regulates the balance between proliferation and differentiation resulting in a trade-off between pre-natal size and developmental maturity . Increasing Dlk1 dosage incrementally shifts the embryo towards increased growth , perhaps at the expense of organ maturation as was suggested for PatDi ( 12 ) embryos [16] . The major abnormalities found in the liver , lungs and skeleton of the TG/TG fetuses may be signs of developmental immaturity of these organs . The inability of the lungs to support TG/TG animals surviving to term , as well as pre-natal oedema , is consistent with this . The Notch signalling pathway is required for branching morphogenesis and maturation of the lungs , and disruption of this pathway can lead to peri-natal lethality due to lung hypotrophy [reviewed in 35] , [36] , [37] . In contrast , except for subtle ossification delays in the WT/TG fetuses , no other clear signs of organ immaturity were identified , so we cannot conclude whether this contributes to the neonatal lethality of the hemizygous genotype . In order to explore the cause of lethality in the WT/TG neonates more closely , we measured several parameters related to rodent well-being . WT/TG animals were significantly less likely to have milk-filled stomachs on the day of birth . This may be a result of defects in suckling behaviour engendered by many causes , such as appetite or olfactory regulation , or motor function . Starvation may therefore be the cause of death of WT/TG neonates , and reduced feeding in the first week is also a likely cause of the reduced growth rate of surviving transgenic animals during this period . Further analysis of the physiological consequences of this interesting growth trajectory of prenatal growth enhancement followed by postnatal compromised growth is in progress . Imprinting of the Dlk1-Dio3 cluster is important . Disruption of imprinted expression in several models results in lethality therefore there is a clear selective pressure to maintain imprinting at this cluster [13] , [38] . The IG-DMR , like other imprinting control regions , controls the dosage of many linked genes . It has been postulated that in imprinted clusters , some genes are the target of dosage regulation while other are merely bystanders whose altered dosage do not have phenotypic consequences and therefore are not targets for selection [39] . We show that Dlk1 is a dose-dependent regulator of embryonic growth , as well as modulating processes necessary for postnatal survival . WT/TG animals model loss of imprinting of Dlk1 , and have significantly reduced fitness , suggesting that the maintenance of imprinting of solely Dlk1 in eutherians by the IG-DMR has been sufficient to selectively retain imprinting control of the entire chromosome 12 cluster . However , the phenotypes of PatDi ( 12 ) /PatDp ( dist12 ) and ΔIG-DMRMAT embryos are much more severe than that of a Dlk1 double-dose alone; therefore the dosage of at least one other gene in this cluster requires tight regulation . Rtl1 is a likely candidate for this as defects have been reported in both knockout and overexpression models [38] and this gene is found only in eutherian mammals that have imprinting [12] . We cannot know the selective pressures surrounding the acquisition of imprinting at chromosome 12 in eutherians however this mechanism must have evolved to regulate dosage-critical genes . We have shown Dlk1 to be such a dosage-critical gene through its significant consequences on animal fitness upon dosage modulation . It therefore could have been the target of selection for dosage control by imprinting at this locus . Current levels of Dlk1 represent an optimal balance of maximized growth with minimal neonatal lethality .
TgDlk1-31 and TgDlk1-70 transgenes were obtained by restriction endonuclease mapping of the BAC clone 163O05 , screened from a BAC library of the mouse strain 129/Sv . The two transgenes were microinjected individually into the male pronuclei of ( C57BL/6×CBA ) F1 zygotes and then implanted into foster ( C57BL/6×CBA ) F1 mothers . The first generation of animals was obtained by crossing the founder with a ( C57BL/6×CBA ) F1 animal . After the first generation , all transgenic animals were mated on a C57BL/6 background to maintain the lines . Animals were housed four per cage ( maximum ) in a temperature-controlled room ( 24°C ) with a 12-hr light/dark cycle . Food and water were available ad libitum . All experiments involving mice were carried out in accordance with UK Government Home Office licensing procedures . For the embryonic studies , the day of vaginal plug was considered day E1 . RNA was extracted from whole embryos using TRI Reagent ( Ambion ) , following the manufacturer's guidelines . mRNA was then isolated from 120 µg of total RNA using Dynalbeads Oligo ( dT ) 25 kit ( Dynal ) following the supplied protocol . We carried out northern-blot hybridization and used probes as described previously for Dlk1 and Gtl2 and Gapdh [3] . For RT-qPCR , total RNA ( 10 µg ) was DNase-treated with RQ1 RNase-free DNase ( Promega ) following the manufacturer's guidelines . All cDNA was synthesized using random hexamers and Superscript III RNase H− Reverse Transcriptase ( Invitrogen ) , following standard procedures . Taqman quantitative real-time PCR ( qRT-PCR ) was used to measure expression levels of Dlk1 and Gtl2 normalized to β-2-microglobulin ( β2m ) in different E16 and E18 embryonic tissues . All RT-qPCR reactions were performed in a 25 µl final volume using standard Taqman qPCR conditions ( Appplied Biosystems protocols ) and amplified on a DNA engine Opticon 2 thermocycler ( MJ Research ) . All reactions were conducted in triplicate . Dlk1 expression levels were measured using the TaqMan gene expression assay ID - Mm00494477_m1 ( Applied Biosystems ) . Gtl2 gene expression was measured using the forward primer 5′-GGGCGCCCACAGAAGAA-3′ , the reverse primer 5′- GGTGTGAGCCGATGATGTCA-3′ and the TaqMan MGB FAM probe FAM-5′-CTCTTACCTGGCTCTCT-3′-NFQ , spanning the Gtl2 exon 1-exon 2 boundary . Finally , for the β2m gene , the forward primer B2M-32 5′-CACCCCCACTGAGACTGATACA-3′ , the reverse primer B2M-38 5′- TGGGCTCGGCCATACTG-3′ and the TaqMan MGB VIC-labelled fluorogenic probe VIC-5′-CCTGCAGAGTTAAGC-3′- NFQ were used . Relative expression was calculated by normalisation to 100% using fetal E18 WT/WT tongue for the expression profile of the embryonic tissues and quantified using the comparative method ( 2−dCt ) [40] . Pparγ2 and Ucp1 analysis was performed as previously described [41] . The fluorescent signal emitted during PCR was detected using the DNA engine Opticon 2 sequence detection system ( MJ Research ) and post-PCR data analysis was performed using the Opticon Monitor analysis software version 2 . 02 ( MJ Research ) . In situ hybridisation was conducted on paraformaldehyde fixed , wax embedded embryo and placenta sections at E16 and E18 of gestation according to the procedures previously described [42] . Tissue lysates from whole embryos , whole placentas and multiple organs were used for SDS/PAGE analysis with a 10% polyacrylamide gel . Resolved proteins were transferred to a poly-vinylidene difluoride ( PVDF ) Western blotting membranes Immobilon-P ( Millipore ) which were incubated with anti-DLK1 H-118 rabbit polyclonal antibody ( 1∶500 ) ( Santa Cruz Biotechnology ) or anti-DLK1 ( 1∶500 ) for placenta ( Proteintech ) and anti-α-TUBULIN mouse monoclonal antibody ( Sigma ) ( 1∶5000 ) overnight at 4°C , followed by the secondary HRP-conjugated antibodies: polyclonal goat anti-rabbit ( 1∶5000 ) or polyclonal goat anti-mouse ( 1∶7500 ) ( DakoCytomotion ) , respectively , on the next day . Any signal was detected using ECL plus Western Detection System ( Amersham Biosciences ) . The intensity of the signal was measured by scanning densiometry using Image J software ( NIH ) . All embryonic dissections were recorded in terms of number and genotype of embryos/pups , dead embryos/pups , necrotic embryos and reabsorptions for all Dlk1 transgenic families . Wet masses were recorded for all fetuses/newborns/juveniles and placentas dissected at different developmental stages . Wet organ weights were also determined for E18 , E19 and P1 . For determination of the dry weight , pre-weighed embryos were dried by incubation at 60°C for 48 h plus 100°C for a further 24 h and , then , weighed . Crown-Rump length was accurately measured using callipers . Glycemia levels were measured using a One Touch Ultra glucometer ( Lifescan ) . For histology , freshly harvested embryos were fixed in 4% paraformaldehyde overnight at 4°C , dehydrated and embedded in paraffin wax using standard protocols . Sections of 7–10 µm were cut and 1 section in 10 was stained with Haematoxylin and Eosin ( H&E ) . For skeletal preparation , the skeleton of E19 embryos was stained with Alcian Blue and Alizarin Red as described previously [16] . Muscle was analysed after immunocytochemistry with MY32 antibody ( M4276 Sigma ) , a monoclonal antibody specific for skeletal muscle myosin heavy chain , performed according to the procedure described previously [16] . Comparative morphometrics were carried out on MY32-stained histological sections through the largest cross-sectional area of the forelimbs and diaphragm . Comparable sections were carefully selected for the TG/TG , WT/TG and WT/WT littermates and 3 sections ( 7 µm thick ) with 20 section intervals were selected for each embryo . Measurements were performed by randomly selecting the fields across the whole section . Details were as described previously [19] . All animal work in this study was conducted under a licence ( 80/2042 ) from the UK Government Home Office . | Genomic imprinting , the process that causes genes to be expressed from one of the two chromosome homologues according to parental origin , is likely to act on genes whose dosage is important for their correct function . To test this , we compared the phenotype of transgenic mice expressing a double and triple dose of the imprinted gene Dlk1/Pref1 with animals expressing the normal single dose expressed from the paternally inherited chromosome . Our results showed that a triple dose causes severe developmental abnormalities and death before or at birth . Embryos expressing a double dose , recapitulating absence of imprinting , are bigger at birth but then around one-third of them died within the first three days of life . Those that survived had poor early growth performance in the first week of life becoming small and remaining small , thus offsetting any benefit conferred by being born bigger . Therefore , imprinted levels of Dlk1/Pref1 represent the optimal balance of growth versus lethality . These findings lead to speculation about the evolutionary pressures acting to establish and maintain imprinting at this locus . | [
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| 2009 | Gene Dosage Effects of the Imprinted Delta-Like Homologue 1 (Dlk1/Pref1) in Development: Implications for the Evolution of Imprinting |
In order to promote infection , the blood-borne parasite Trypanosoma brucei releases factors that upregulate arginase expression and activity in myeloid cells . By screening a cDNA library of T . brucei with an antibody neutralizing the arginase-inducing activity of parasite released factors , we identified a Kinesin Heavy Chain isoform , termed TbKHC1 , as responsible for this effect . Following interaction with mouse myeloid cells , natural or recombinant TbKHC1 triggered SIGN-R1 receptor-dependent induction of IL-10 production , resulting in arginase-1 activation concomitant with reduction of nitric oxide ( NO ) synthase activity . This TbKHC1 activity was IL-4Rα-independent and did not mirror M2 activation of myeloid cells . As compared to wild-type T . brucei , infection by TbKHC1 KO parasites was characterized by strongly reduced parasitaemia and prolonged host survival time . By treating infected mice with ornithine or with NO synthase inhibitor , we observed that during the first wave of parasitaemia the parasite growth-promoting effect of TbKHC1-mediated arginase activation resulted more from increased polyamine production than from reduction of NO synthesis . In late stage infection , TbKHC1-mediated reduction of NO synthesis appeared to contribute to liver damage linked to shortening of host survival time . A kinesin heavy chain released by T . brucei induces IL-10 and arginase-1 through SIGN-R1 signaling in myeloid cells , which promotes early trypanosome growth and favors parasite settlement in the host . Moreover , in the late stage of infection , the inhibition of NO synthesis by TbKHC1 contributes to liver pathogenicity .
The protozoan flagellate parasite Trypanosoma brucei is responsible for the diseases human sleeping sickness and nagana in cattle . In experimental murine models , the host immune response to this blood-borne pathogen involves antibody production against the Variant Surface Glycoprotein ( VSG ) , as well as interferon-γ ( IFN-γ ) -mediated activation of macrophages/myeloid cells into cells of the M1 phenotype . These engulf opsonized parasites and synthesize factors that interfere with trypanosome growth including tumor necrosis factor-α ( TNF ) . However , uncontrolled IFN-γ-induced immune responses including TNF and NO production as the infection persists induce tissue pathogenicity and death of the host [1]–[5] . Induction of IL-10 can attenuate the IFN-γ/M1 response and hereby enables prolonged survival of T . brucei-infected mice [6]–[8] . Only few T . brucei-derived immunomodulatory factors have been identified . These include the glycosylphosphatidylinositol anchor of VSG , CpG oligodeoxynucleotides and the trypanosome suppressive immunomodulating factor ( TSIF ) which have been found to induce TNF- and NO-secreting M1 cells [9]–[12] . Factors inducing M1 cells favor the control of parasite development in the early stage of infection , but their continuous release in the late stage of infection can sustain inflammation responsible for tissue pathogenicity . Components found in the culture medium of T . brucei have also been shown to affect immune cells of the host . In particular , factors released by the parasite promote the degradation of L-arginine through increase of arginase activity in macrophages/myeloid cells , and antagonize NO synthases ( NOS ) -mediated conversion of L-arginine into NO in infected mice . Arginase induction appears to attenuate the innate response at the early stage of infection , and likely contributes to the synthesis of polyamines and the trypanosome anti-oxidant trypanothione , known to promote trypanosome growth and colonization of the host [5] , [13]–[15] . We have identified TbKHC1 , a kinesin heavy chain isoform , as a factor released by T . brucei to trigger host arginase-1 activity for promotion of its own growth in the host . Although arginase-1 expression is commonly associated with alternatively activated myeloid cell ( M2 ) functions , TbKHC1-induced arginase-1 was independent of IL-4Rα signaling but relied on a SIGN-R1 receptor-dependent IL-10 secretion .
T . brucei parasites were found to induce arginase activity in myeloid cells from non-infected mice ( Fig . 1A ) . This induction was maintained when myeloid cells and trypanosomes were separated by a cell-retaining insert , indicating that soluble components from trypanosomes were involved ( Fig . 1A ) . Parasite-released factors ( PRF ) were prepared under conditions leading to no detectable trypanosome death . PRF induced arginase activity , and this effect was abolished by heat-treatment ( Fig . 1A ) . Monoclonal antibodies raised against T . brucei PRF ( 2C12 ) inhibited arginase activity induced by PRF , whereas other antibodies from the same hybridoma fusion ( 2A3 , 2C6 , 4B5 ) or of irrelevant specificity ( 5F6 , 4J5 ) had no effect ( Fig . 1B ) . The PRF fraction eluted after binding to a 2C12 antibody-based affinity column retained full arginase-inducing activity , confirming that this activity was directly targeted by 2C12 ( Fig . 1C ) . The 2C12 antibody was used to screen a cDNA expression library and identify the T . brucei arginase-inducing protein . Among the 6 positive clones identified , 5 encoded fragments of the same putative kinesin heavy chain isoform ( geneDB accession number Tb927 . 6 . 4390 ) , which we termed TbKHC1 for T . brucei kinesin heavy chain 1 ( Fig . S1in Text S1 ) . The protein N-terminal domain contained the highly conserved kinesin motor domain with a typical ATP binding site . The region targeted by 2C12 antibody located downstream from the motor domain and was characterized by a predicted coiled structure ( Fig . S1 in Text S1 ) . TbKHC1 knock-out ( KO ) parasites were generated from wild-type ( WT ) T . brucei . In contrast to PRF from WT parasites , PRF from TbKHC1 KO parasites did not trigger arginase activity in myeloid cells from non-infected mice ( Fig . 1C ) . Pleomorphic trypanosomes , differentiating in the bloodstream from proliferative slender forms into quiescent stumpy forms , can induce long-standing infection in mammals and perform cyclical transmission in tsetse flies while differentiating into procyclic forms . In contrast , monomorphic trypanosomes , resulting from prolonged cultivation in vitro , are unable to develop long-lasting infection and cyclical transmission . The TbKHC1 gene was found to be expressed preferentially in slender bloodstream forms ( Fig . 2A , B ) . Parasite immunolabeling with both monoclonal 2C12 and polyclonal antibodies generated against recombinant TbKHC1 ( rTbKHC1 ) vanished in TbKHC1 KO parasites or RNAi-mediated TbKHC1 knock-down ( KD ) parasites , while it increased in trypanosomes over-expressing TbKHC1 ( Fig . 2A–D ) , confirming that TbKHC1 is the genuine target of the anti-PRF 2C12 antibody . Both antibodies revealed a scattered protein distribution primarily between the kinetoplast and the nucleus ( Fig . 2C , D ) . TbKHC1 partially co-localized with tomato lectin , which detects linear chains of poly-N-acetyllactosamine typical of the endocytic compartment [16] ( Fig . S2A in Text S1 ) . However , TbKHC1 did not bind to tomato lectin in affinity chromatography assay ( Fig . S2B in Text S1 ) , suggesting that TbKHC1 is not an endocytic component . Moreover , a fraction of TbKHC1 was reproducibly found in the culture medium of parasites , as revealed by immunoprecipitation of detergent-free supernatants from metabolically 35S-labelled T . brucei ( Fig . 2E ) . In these experiments , trypanosome lysis was unlikely as the supernatants did not contain any evidence of the cytoplasmic markers actin [17] or protein disulfide isomerase 2 [18] , but did contain enolase , a protein released from trypanosome-related parasites such as Leishmania [19] ( Fig . 2E ) . TbKHC1 immunoprecipitated from the trypanosome supernatant did not associate with other 35S-labelled proteins ( Fig . 2E ) , as could have been expected in case of release as a complex . We screened for pathways altering the levels of PRF-induced arginase activity in myeloid cells . Co-incident with the induction of arginase activity , PRF induced myeloid cells to express the regulatory cytokine IL-10 ( Fig . 3A ) . The arginase activity induced by PRF was inhibited by neutralizing anti-IL-10 antibody and by D-mannose , but not by D-galactose ( Fig . 3B ) . rTbKHC1 mimicked the arginase-inducing effect of PRF , including its inhibition by 2C12 , anti-IL-10R antibodies and D-mannose ( Fig . 3C ) . The increased arginase activity induced by rTbKHC1 associated with increased expression of the Arg1 , but not Arg2 gene , as well as impaired expression of the iNOS gene ( Fig . 3D ) . Activation of myeloid cells by rTbKHC1 also resulted in increased expression of the IL-10 gene ( Fig . 3D ) and protein ( Fig . 3C ) , the latter being inhibited by the 2C12 antibody and D-mannose . Thus , TbKHC1 interaction with myeloid cells involved a mannose-sensitive event triggering the synthesis of IL-10 , resulting in enhanced Arg1 gene expression and activity , and inhibition of iNOS expression . Although arginase-1 expression is commonly associated with alternatively activated myeloid cell ( M2 ) functions , rTbKHC1 did not trigger Il4 and Il13 gene expression in myeloid cells from WT mice ( Fig . 3D ) . Moreover , the induction of Arg1 and Il10 genes by rTbKHC1 was conserved in IL-4Rα KO mice ( Fig . 3E ) , excluding that IL-10 synergistically enhanced IL-4Rα-induced Arg1 expression as observed in M2 cells [20] , [21] . Accordingly , rTbKHC1 did not trigger the expression of M2-associated genes characteristic of African trypanosome infection [22] ( Fig . 3F ) . We also addressed whether the induction of Arg1 by rTbKHC1 required IL-6 and G-CSF in combination with IL-10 [23] . rTbKHC1 only induced Il6 gene expression ( Fig . 3D ) . This expression was not affected following addition of anti-IL-10R antibody that inhibited the induction of arginase activity by rTbKHC1 ( Fig . 3G ) , indicating that IL-10 induced by TbKHC1 is the main trigger of Arg1 expression in myeloid cells . In vitro , the parasite growth rate appeared to be identical between WT , TbKHC1 KD , TbKHC1 KO and TbKHC1-rescued parasites obtained by reinsertion of the TbKHC1 gene in one allele of its original locus in the TbKHC1 KO genome ( Fig . 4A , B ) , indicating that TbKHC1 is not required for parasite growth . Moreover , TbKHC1 KO parasites motility or flagellum movement addressed by microscopic examination and sedimentation analysis [24] appeared normal ( not shown ) . TbKHC1 KO parasites were able to perform the full parasite life-cycle , since procyclic transformants could cycle through tsetse flies to produce infective metacyclic forms . In C57Bl/6 mice , the cumulative parasite load in the first peak of parasitaemia by TbKHC1 KO trypanosomes was reduced by >70% as compared to WT parasites ( Fig . 4C ) . This reduced TbKHC1 KO parasite load was also observed under natural infection conditions in which infected tsetse flies were allowed to feed on mice ( Fig . 4D ) . Reinsertion of TbKHC1 in TbKHC1 KO parasites reverted early parasitaemia to WT levels ( Fig . S3 in Text S1 ) . As compared to infection with WT parasites , at day 7 post-infection ( p . i . ) with TbKHC1 KO parasites , spleen myeloid cells showed a reduced induction of arginase activity that coincided with increased NO2 accumulation , likely reflecting a shift towards NOS activity ( Fig . 4E , F ) . Moreover , the induction of arginase activity was not observed in infected mice treated with a neutralizing anti-IL-10R antibody ( Fig . 4E ) . Furthermore , mice infected with TbKHC1 KO parasites exhibited lower IL-10 serum levels than mice infected with WT parasites ( Fig . 4G ) . The reduction of IL-10 secretion in TbKHC1 KO-infected mice could account for the higher inhibition of arginase activity observed in the presence of anti-IL-10R antibodies in these mice ( Fig . 4E ) . In TbKHC1 KO-infected mice , it is unlikely that the reduction of the first peak of parasitaemia resulted from increased NO production . Indeed , inhibition of NOS activity by N- ( G ) -nitro-L-arginine methyl ester ( L-NAME ) or absence of iNOS activity in iNOS KO mice did not affect TbKHC1 KO parasitaemia , while it strongly reduced WT parasitaemia ( Fig . 5A , B ) . The lower parasitaemia in TbKHC1 KO-infected mice might rather result from reduced arginase activity , which would limit nutrient availability for the parasite . Indeed , this enzyme converts L-arginine to L-ornithine , a precursor of polyamines that are required for trypanosome growth [13] . Accordingly , in mice lacking arginase-1 in myeloid cells/macrophages following a cross between Arg1 loxP-targeted mice and LysMCre or Tie2Cre deleter mice [21] , WT parasitaemia dropped to that of TbKHC1 KO parasitaemia ( Fig . 5C , D ) . Moreover , treatment of mice with L-ornithine increased the cumulative parasite load to a greater extent in TbKHC1 KO- than in WT-infected mice ( ∼94% vs ∼43% , respectively; Fig . 5E ) . In addition , spermine levels tended to increase although without reaching statistical significance in spleen myeloid cells and blood from mice infected with WT but not TbKHC1 KO parasites ( Fig . S4A in Text S1 ) . Furthermore , an increase in L-ornithine production , coinciding with the consumption of L-arginine and the induction of N-acetylputrescine production , was observed in the supernatant of myeloid cells from non-infected mice activated in vitro with rTbKHC1 ( Fig . S4B in Text S1 ) . Since the in vitro induction of arginase activity by PRF and rTbKHC1 was inhibited by D-mannose ( Fig . 3B , C ) , we treated infected mice with D-mannose . This treatment reduced WT parasitaemia to the level of TbKHC1 KO parasitaemia , but did not significantly affect TbKHC1 KO parasitaemia ( Fig . 5F ) . To follow up on this in vivo finding and the in vitro observation that arginase activation by PRF and rTbKHC1 was inhibited by D-mannose as well as by an antibody recognizing coils of TbKHC1 ( Fig . 1B , 3C ) , we monitored the course of WT- and TbKHC1 KO-infection in mice deficient for myeloid cell receptors able to bind both mannose and peptidic coils , namely MMR ( CD206 ) and SIGN-R1 ( CD209b ) , using appropriate congenic control mice [25]–[27] . In MMR KO mice , WT and TbKHC1 KO parasitaemias were not affected as compared to infection in WT mice ( Fig . 5G ) . In SIGN-R1 KO animals the differential control of the first peak of parasitaemia between WT and TbKHC1 KO parasites vanished , due to the reduction of the WT parasite level to that of TbKHC1 KO parasites ( Fig . 5H ) . Thus , either the absence of SIGN-R1 in infected mice or the absence of TbKHC1 in the parasite decreased T . brucei parasitaemia similarly . These data suggested that TbKHC1 interacts with the SIGN-R1 receptor . To evaluate this hypothesis , we tested the in vitro activity of rTbKHC1 on myeloid cells from SIGN-R1 KO mice . In contrast to results obtained with myeloid cells from control mice , in myeloid cells from SIGN-RI KO mice rTbKHC1 did not stimulate IL-10 and Arg1 gene expression ( Fig . 6A ) and did not increase arginase activity ( Fig . 6B ) . Conversely , in myeloid cells from MMR KO mice rTbKHC1 induced expression of Il10 and Arg1 genes as in WT mouse myeloid cells ( Fig . S5 in Text S1 ) . Therefore , TbKHC1 appeared to trigger arginase activity through SIGN-R1-mediated signaling . The survival time of TbKHC1 KO-infected mice was significantly prolonged as compared to WT-infected animals ( Fig . S6 in Text S1 , Table 1 ) . Death of mice infected with African trypanosomes is related to systemic inflammatory response syndrome ( SIRS ) that causes multiple organ failure [28] . Liver myeloid cells are critical to the clearance of trypanosomes from the bloodstream [29] , hence maintaining liver integrity is fundamental to the host's ability to clear trypanosomes . At day 30 p . i . several indicators of liver damage differed between WT- and TbKHC1 KO-infected mice: ( i ) the serum levels of the liver damage marker alanine aminotransferase ( ALT ) were lower in TbKHC1 KO- than in WT-infected mice; ( ii ) anoxic infarcts were observed in none of the TbKHC1 KO-infected mice , but in 6 out of 8 WT-infected mice; ( iii ) lobular and portal mononuclear cell infiltrates were milder in all TbKHC1 KO- than in WT-infected mice ( Table 1 , Fig . 6C ) . Concomitantly , NO production was higher in TbKHC1 KO- than in WT-infected mice , while IL-10 production was not induced and similar in all infected experimental groups ( Fig . 6D ) . A moderate increase in iNOS mRNA expression level was also observed in myeloid cells from mice infected with TbKHC1 KO parasites ( 4 . 2±1 . 5 vs 1 . 32±0 . 33 fold in mice infected with WT parasites , n = 3; p = 0 . 08 ) . These data suggested that increased NO production protected liver integrity in TbKHC1 KO-infected mice . Accordingly , L-NAME treatment or knock-out of iNOS activity in TbKHC1 KO-infected mice reduced the survival time and increased ALT levels to those found during WT parasite infection ( Table 1 ) . L-NAME treatment or absence of iNOS activity in WT-infected mice did not affect the survival time , although it reduced the serum levels of ALT ( Table 1 ) . Therefore , a threshold of NOS activity , achieved in TbKHC1 KO- but not in WT-infected mice , might be required during T . brucei infection to protect liver integrity , thereby contributing to extended host survival . This hepatoprotective effect of NO was not due to difference in parasite load in mice infected with WT and TbKHC1 KO parasites ( Fig . S7 in Text S1 ) . The survival times and ALT levels in WT and TbKHC1 KO-infected mice were similar in Arg1-deficient mice and control mice ( Table 1 ) . However , treatment of WT-infected mice with L-ornithine slightly increased the serum level of ALT , while in TbKHC1 KO-infected mice L-ornithine treatment reduced the survival time and increased ALT levels to those found during WT parasite infection ( Table 1 ) . Therefore , L-ornithine as a polyamine precursor favored the development of liver injury and negatively affected the survival of T . brucei-infected mice . As in the liver , brain injury characteristic of African trypanosomiasis was also lower in TbKHC1 KO- than in WT-infected mice . Indeed , no parasites could be detected in the choroid plexus of TbKHC1 KO-infected mice , sharply contrasting with the presence of numerous parasites , associated with immune infiltrates , in the choroid plexus of WT-infected mice ( Fig . S8 in Text S1 ) . The survival times as well as ALT , NO and IL-10 levels in WT- and TbKHC1 KO-infected mice were similar in SIGN-R1 KO and control mice ( Table 1 , Fig . 6E ) , suggesting that in late infection TbKHC1 signaling no longer operates primarily through SIGN-R1 .
We report that a particular isoform of the large KHC family of T . brucei is released by the trypanosome and influences both parasite growth and host pathogenicity . T . brucei contains 51 sequences encoding kinesin-like proteins . The T . brucei isoform described here appears to be an orphan member without predicted function [30] . That a KHC isoform could be released in the extracellular environment is not unprecedented , since a KHC-like protein has been reported as secreted from the coagulating gland in rat [31] . TbKHC1 did not appear to be contained in high molecular weight complexes released from the parasite , for instance in exosomes as occurs in T . cruzi [32] and Leishmania [33] . Our immunoprecipitation data , rather , suggested that TbKHC1 is free in the medium , but the mechanism of release remains unclear . TbKHC1 did not appear to be essential for T . brucei cellular proliferation or differentiation . In vitro , TbKHC1 triggered arginase activity and , in contrast to the other documented T . brucei immunomodulators VSG , DNA or TSIF [9] , [10] , [12] , inhibited NO synthesis by myeloid cells . These effects involved a region rich in coils in the TbKHC1 C-terminal domain , since they were inhibited by an antibody specifically recognizing the coiled region of TbKHC1 . Arg1 is often recognized as a prototypic M2 myeloid cell marker induced following STAT6 activation by IL-4/IL-13/IL-4Rα signaling . However , Arg1 can be expressed by M1 cells in a STAT3-dependent pathway triggered by IL-10 , IL-6 and G-CSF [21] , [23] . In agreement , rTbKHC1 induced the expression of Il10 and Il6 genes in myeloid cells , and this neither involved IL-4Rα nor induced the expression of genes associated with M2 activation . Since Il6 expression was still induced in the presence of the Arg1 expression inhibitor anti-IL-10R antibody , we conclude that rTbKHC1-induced Arg1 expression primarily depended on IL-10 production . In mice , TbKHC1 activity appeared to promote parasite growth in the early stage of infection . Reduced parasitaemia induced by TbKHC1 KO trypanosomes was associated with increased NO production , but did not directly result from this increase . Indeed , inhibition of NO production by L-NAME treatment or in iNOS KO mice did not improve TbKHC1 KO growth . Rather , in agreement with previous studies [34] L-NAME treatment or absence of iNOS activity in mice actually increased the control of WT parasitaemia . Together , these experiments suggested that the negative/braking effect of NO on the control of WT parasitaemia requires the expression/activity of TbKHC1 . We propose that TbKHC1-mediated induction of host arginase activity fuels L-ornithine production and hereby the synthesis of polyamines , which are essential nutrients for growth of extracellular trypanosomes in the host . In bloodstream forms of T . brucei the arginase gene and activity are absent , but when exogenous L-ornithine is lacking in body niches these parasites can use L-arginine to produce L-ornithine through an arginase-independent mechanism [35] , [36] . However , the differential growth-promoting effect of exogenous L-ornithine during infection by WT or TbKHC1 KO parasites suggests that this arginase-independent mechanism is insufficient to provide all the amines necessary for the polyamine pathway . Similarly , the related but intracellular parasite Leishmania also requires host arginase induction for optimal growth , despite the presence of an arginase gene [37] . Therefore , optimal production of L-ornithine/polyamines for T . brucei growth would rely on host arginase activity induced by TbKHC1 . In accordance , in two models of mice deficient for arginase-1 in myeloid cells/macrophages , WT T . brucei growth was reduced to the level observed with TbKHC1 KO parasites . In T . brucei-infected mice , the TbKHC1-mediated effect on parasitaemia appeared to involve the myeloid cell receptor SIGN-R1 . Indeed , improved parasite growth control was observed after either removal of TbKHC1 from the parasite or removal of SIGN-R1 from the host . Moreover , rTbKHC1-mediated induction of arginase activity was lost in myeloid cells from SIGN-R1 KO mice . The involvement of SIGN-R1 in TbKHC1-mediated IL-10 and arginase production is in keeping with previous data demonstrating the anti-inflammatory role of SIGN-R1 in various diseases [38]–[40] . Although LPS and intracellular mycobacteria infection can induce Arg1 in a MyD88/IL-10 dependent manner [21] , [23] , we can exclude that TbKHC1 is simultaneously a ligand for SIGN-R1 and a trigger for MyD88 signaling in our extracellular infection model since the differential infection phenotype between WT and TbKHC1 KO T . brucei was conserved in Myd88 KO mice ( data not shown ) . D-mannose was found to decrease parasite growth during the first peak of parasitaemia by WT but not TbKHC1 KO parasites , and it inhibited rTbKHC1-mediated induction of IL-10 and arginase by myeloid cells . Similarly , the 2C12 antibody , which is specific to the coiled region of TbKHC1 , inhibited IL-10 production and arginase activity triggered by rTbKHC1 . Mannose-specific receptors such as SIGN-R1 and MMR are known to interact with coiled proteins , the complement protein C1q and collagen respectively [26] , [27] . Therefore , it is possible that mannose binding to SIGN-R1 could affect the binding of TbKHC1 peptidic coils to a second binding site of this receptor . After the parasite growth-promoting effect occurring during the first peak of parasitaemia , a distinct effect of TbKHC1 consisted in reducing the survival time of infected mice while contributing to liver injury . Given the similar in vitro growth properties between parasites expressing or not TbKHC1 , the mechanism by which this protein influenced the host survival time is likely to be independent from differences in parasite virulence . Parasite load throughout the infection , survival and pathogenicity are independent traits in experimental and natural infection with African trypanosomes [2] , [4] , [6]–[8] , [28] . Therefore , the survival time difference between WT- and TbKHC1 KO-infected mice is primarily due to differences in tissue damage inflicted by inflammatory immune responses , rather than differences of parasite load in the first peak of parasitaemia . NO contributes to peripheral pathogenicity induced by African trypanosomes , while IL-10 exerts counter-pathogenic activity [2] , [3] . In late stages of infection , IL-10 production was unaffected by the absence of TbKHC1 , while NO production ( and iNOS expression ) was lower when TbKHC1 was present . Moreover , the reduction of liver injury in TbKHC1 KO-infected mice was erased by inhibition of NO synthesis upon L-NAME treatment or by infecting iNOS KO mice . Thus , TbKHC1-mediated depletion of NO might contribute to the liver damage associated with WT parasite infection . Accordingly , NO plays a pivotal role in vascular tone , and decreased NO bioavailability contributes to pathogenesis in chronic infectious diseases like malaria , and in liver ischemia reperfusion injury [41] , [42] . Our observation that after the first peak of parasitaemia parasite load was similar in WT- and TbKHC1 KO -infected mice , even upon inhibition of NO production , supports the notion that blood parasite load is not a major contributor to liver pathogenicity . Besides NO , polyamines contribute to the pathogenicity of T . brucei infection [43] . Since our data suggest that induction of arginase activity by TbKHC1 could sustain the production of polyamines , these molecules could contribute to increase liver injury in WT- as well as in TbKHC1 KO-infected mice fed with L-ornithine . In the late stage of infection , the effects of TbKHC1 did not appear to depend on SIGN-R1 any longer , since the absence of SIGN-R1 did not affect the survival time and liver injury in WT- and TbKHC1 KO-infected mice . A major difference between early and late infection is the evolution of the immune response driven by the continuous release of immunomodulatory/inflammatory factors by the parasite , such as VSG and TSIF , which induce pathogenic TNF-α and IFN-γ [1] , [4] , [9] while switching off the IL-10 production [7] . The evolving context of host-parasite interaction could affect TbKHC1 signaling , and indeed the myeloid cell activation state and corresponding expression of receptors is known to vary during the time course of T . brucei infection [44] . Therefore , we cannot exclude that in infected SIGN-R1 KO mice , compensation by other myeloid cell receptors influences the immune response and outcome of pathogenicity in the late stage of the disease . In conclusion , we have identified an unexpected function for a KHC of extracellular T . brucei , which is SIGN-R1-mediated activation of myeloid cells to produce IL-10 leading to increase in arginase activity , resulting in promotion of parasite growth in the host .
Experiments , maintenance and care of mice complied with guidelines of the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes ( CETS n°123 ) and were approved by the Ethical Committee for Animal Experiments of the Université Libre de Bruxelles , Brussels , Belgium ( laboratory accreditation number LA2500482 ) . WT C57Bl/6 mice were from Harlan , iNOS KO C57Bl/6 mice from Jackson Laboratory . MMR KO , IL4Rα KO , control Arg1lox/lox , LysMcreArg1-/lox and Tie2creArg1-/lox mice ( C57BL/6 background ) , SIGN-R1 KO and congenic control mice ( BALB/c background ) were bred in-house . T . brucei bloodstream monomorphic “single-marker” [45] and pleomorphic AnTat 1 . 1E ( EATRO1125 strain ) were used . Mice were infected i . p . with 2 , 000 AnTat1 . 1E parasites [6] . Parasitaemia was monitored by tail blood puncture . When required , mice were injected i . p . 2 h before infection with 2 mg L-NAME , then 1 mg/ml L-NAME was added to drinking water . The less active enantiomer D-NAME was used as control . For L-ornithine treatment , mice received 2 mg i . p . 2 h before infection , and then were given 5 mg/ml in drinking water . For D-mannose or D-galactose treatment , mice received 50 mg i . p . 2 h before infection , then every 2 days . Sugar treatment caused massive inflammation and the animals were euthanized 12 days p . i . To neutralise IL-10 activity , mice were treated once with 200 µg control antibody ( rat IgG1 , BD Biosciences ) or 1B1 . 3a anti-IL-10R antibody at day 6 p . i . Injections of anti-IL-10R antibody at day 2 , 4 and 6 p . i . killed WT- and TbKHC1 KO- infected mice within 48 h , as reported [6] . PRF was prepared as described [15] . Monoclonal antibodies were obtained following an immunoenzymatic screening that selected antibodies directed against PRF , followed by a test based on inhibition of arginase activity induction . rTbKHC1 C-terminal fragment was used for production of rabbit polyclonal antibodies . PRF was incubated overnight with 2C12 monoclonal antibody coupled to Sepharose 4B CNBr resin at 4°C in carbonate buffer ( v/v ) . After loading on a column and washings , bound material was eluted with glycine-HCl ( pH 3 ) , then glycine-NaOH ( pH 10 ) , dialysed for 24 h against milliQ water , lyophilised and solubilized at 1 mg/ml in 50 mM phosphate buffer , 1 M NaCl ( pH 6 . 2 ) . The residual level of endotoxins was below 2 U/µg ( LAL assay , Biowhittaker ) . Approximately 105 plaques from a bloodstream T . b . gambiense LiTat1 . 3 cDNA expression library ( lambda ZAP express , Stratagene ) were screened by incubation with the mouse 2C12 monoclonal antibody . Clones targeted by an anti-mouse antibody/alkaline phosphatase conjugate were identified by incubation at pH 9 . 5 with the chromogenic substrate NBT/BCIP . Reactive clones were plaque purified by 2 additional rounds of screening . The positive clones were processed to obtain the circular pBK CMV plasmid version of the phage , according to the manufacturer instructions . For TbKHC1 KD , a 500 bp fragment from the TbKHC1 open reading frame ( ORF ) was amplified by polymerase chain reaction ( PCR ) using the KD primers ( Table S1 in Text S1 ) , digested with XhoI and BamHI and ligated into the p2T7-177TiTA plasmid [46] . The vector was linearized with NotI and transfected into monomorphic bloodstream forms . Stable transformants were selected with 1 µg/ml hygromycin . Double stranded RNA production was induced by addition of 1 µg/ml doxycyclin . For TbKHC1 KO parasites , both alleles of the complete TbKHC1 ORF were replaced in procyclic forms by genes encoding resistance to neomycin and bleomycin , following transfection of these genes flanked by 75 bp sequences from the TbKHC1 UTRs . Primers used for PCR amplification of the resistance genes were KOneo and KObleo ( Table S1 in Text S1 ) . Stable transformants were selected with 15 µg/ml geneticin and 2 . 5 µg/ml phleomycin . Pleomorphic TbKHC1 KO parasites were obtained by cyclical transmission of TbKHC1 KO procyclic forms in tsetse flies . TbKHC1 gene and UTRs were PCR-amplified ( Rescue primers , Table S1 in Text S1 ) and cloned into pCR-XL-TOPO vector ( Invitrogen ) . The neomycin resistance cassette was extracted from pLew114hyg5′ [45] with AflII and StuI restriction enzymes and cloned into the AflII and EcoRV digestion of the pCR-XL-TOPO-TbKHC1 clones presenting the 5′ sequence of TbKHC1 gene next to the pUC origin . pCR-XL-TOPO-TbKHC1-Neo was linearized at AvrII and transfected in vivo into TbKHC1 KO pleomorphic parasites as described [47] . TbKHC1 ORF was amplified by PCR from the initial pBK-CMV plasmid clone 4 ( primers OV , Table S1 in Text S1 ) digested with EcoRV and HindIII , and cloned in the pTSARib vector [48] . The construct was linearized with BglII before transfection . The full length TbKHC1 and a 2 , 235 bp TbKHC1 fragment were amplified by PCR from the initial pBK-CMV plasmid clone 4 using primers FL and C-term ( Table S1 in Text S1 ) . The products were digested with SalI and NotI and cloned in-frame in pET 21d ( Novagen ) . rTbKHC1 was extracted from IPTG-induced E . coli and purified on Ni-NTA agarose ( Qiagen ) using 60% isopropanol in the washing steps to remove endotoxins [49] . The protein was additionally purified by HPLC Superdex 200 ( 10/300 GL , Pharmacia ) in 50 mM phosphate buffer , 1 M NaCl ( pH 6 . 2 ) . The residual level of endotoxins was below 2 U/µg ( LAL assay , Biowhittaker ) . Fixed parasites ( 4% formaldehyde in PBS , 10 min , 20°C ) spread on poly ( L-lysine ) -coated slides were permeabilized ( 0 . 1% Triton X-100 in Tris-buffered saline 5 min , 20°C ) and incubated with 5% BSA . TbKHC1 was detected with the 2C12 monoclonal ( dilution 1/200 ) or rabbit polyclonal ( dilution 1/1000 ) antibodies and Alexa 488-conjugated anti-mouse or anti-rabbit IgG ( dilution 1/1000 ) . Washed trypanosomes were incubated 15 min at 37°C at a density of 2×107 cells/ml in IMDM methionine-free medium supplemented with 10% FCS , 0 . 07 mM L-arginine , 0 . 2 mM L-glutamine , 0 . 04 mM L-leucine , 0 . 1 µM inositol , 0 . 05 mM D-glucose , 30 mM Hepes , 1 mg/ml BSA , 0 . 5 mM adenosine and 5 µg/ml catalase . Then , a mix of 35S-labelled methionine and cysteine was added ( 100 µCi/ml ) . After 1 h labelling , cells were washed and incubated for 2 h at 37°C at a density of 2×106/ml . Trypanosomes were centrifuged and lysed in 50 mM Tris , 150 mM NaCl , 2 mM EDTA , 1% NP40 , complete protease inhibitors ( Roche ) . Fractionation of trypanosome extracts on tomato lectin affinity column was performed as described [16] . Non elicited peritoneal cells were collected in 0 . 34 M sucrose . Spleen homogenates were incubated 10 min in cold 0 . 83% NH4Cl/0 . 01 M Tris-HCl , pH 7 . 2 to lyse erythrocytes . Peritoneal and spleen cells were washed twice in complete medium ( RPMI 1640 supplemented with 10% heat-inactivated FCS , 5×10−5 M 2-mercaptoethanol , 2 mM L-glutamine , 100 IU/ml penicillin , 100 µg/ml streptomycin , and 0 . 1 mM non essential amino acids; all from Invitrogen Life Technologies ) and counted by trypan blue exclusion . To prepare myeloid cells , peritoneal cells adjusted to 5×106 cells/ml in complete medium were dispensed by 1 ml in 6-well plates and spleen cells adjusted to 107 cells/ml were dispensed by 10 ml in 10-cm tissue culture dishes . After 3 h at 37°C in 5% CO2 , cells were washed five times with warm complete medium to remove non adherent cells . Adherent cells were collected in cold complete medium using a scraper , washed and viability was determined by trypan blue exclusion ( usually >95% ) . Cells ( CD11b+ for >90% as determined by flow cytometry , data not shown ) were suspended in complete medium at 106 cells/ml . Peritoneal myeloid cells from non infected mice ( 5×105/ml ) were cultured in 24-well plates in presence of 5×105/ml trypanosomes , 80 µg/ml PRF , 3 µg/ml eluate of anti-PRF antibody affinity column or 3 µg/ml rTbKHC1 . When required , antibodies ( monoclonal anti-PRF , JES5-2A5 anti-IL-10 ( BD Biosciences ) , 1B1 . 3a anti-IL-10R or appropriate isotype-matched antibodies ( 3 µg/ml ) , D-mannose or D-galactose ( 50 mM ) were added to PRF- or rTbKHC1-stimulated cells . Unless specified , gene expression , arginase activity and NO2 and IL-10 secretions were determined after 24 h of culture . Alternatively , spleen myeloid cells from infected mice ( 106/ml ) were collected at day 7 or 30 p . i . and stored for arginase activity determination or cultured for 24 h before determining spontaneous NO2 and IL-10 production in culture supernatants . Spleen myeloid cells prepared as described above were cultured in modified complete medium ( 2% FCS , no 2-mercaptoethanol , no antibiotic ) in 24-well plates in presence of 3 µg/ml rTbKHC1 . After 12 h culture , supernatants were collected , rapidly cooled to 4°C in dry ice ethanol bath and centrifuged ( 3 min , 4°C , 13 , 000 g ) . Protein denaturation was achieved by adding 200 µl of 4°C chloroform∶methanol∶water ( 1∶3∶1 ) to 5 µl of cell supernatants . Samples were shaken for 1 h at 4°C and centrifuged again . The supernatants were extracted and frozen at −80°C until the samples were submitted to High Performance Liquid Chromatography-Mass Spectrometry ( HPLC-MS ) analysis at the Scottish Metabolomics Facility using High Performance Liquid Chromatography ( Dionex UltiMate 3000 RSLC system ) with a ZIC-HILIC 150×4 . 6 mm , 5 µm columns ( Merck Sequant ) . The mobile phase comprised 0 . 1% formic acid in water ( phase A ) and 0 . 08% formic acid in acetonitrile ( phase B ) . A linear gradient was applied to B from 80-20% over 30 minutes , followed by an 8 minute wash with 5% B and concluded with 8 minute equilibration with 80% B . The flow rate was 300 µL/min and 10 µL was injected into the column . The column temperature was 20°C and the autosampler temperature was 4°C . For data processing , LC-MS raw data were processed using IDEOM software ( version 17 ) [50] which is a composite of XCMS [51] , mzMatch [52] , R and Microsoft Excel packages . Firstly , XCMS detected peaks with a peak width between 10 and 100 seconds; signal to noise ratio of 5 and mass deviation between scans of 2 parts per million ( ppm ) . MzMatch then matched the peaks between the two samples , comparing their retention times ( RT ) and ion densities before calculating any deviations . Peaks were identified according to their mass-to-charge ratio and RT . Arginase activity was determined as described [53] . NO2 quantification ( reflecting NO production ) was assayed by a Griess reaction . IL-10 was quantified with a specific sandwich ELISA ( PharMingen ) in accordance to the manufacturers' protocols . Spermine levels ( Spermine ELISA Kit , Antibodies-online ) were determined in blood serum and myeloid cell homogenates following the manufacturers' protocols . Quantitative real time PCR was performed as described [6] . Results of the PCR analyses were normalized against the house-keeping gene S12 . Primers used are described in Table S2 in Text S1 . Brains and livers were fixed in 4% formaldehyde . Histological sections embedded in paraffin were stained with hematoxylin-eosin-saffron for microscopic evaluations . ALT was measured in serum samples using commercially available kits ( Boehringer Mannheim ) . GraphPad Prism 4 . 0 software was used to determine the cumulative parasitemia load ( Area Under Curve function ) and the statistical significance ( Two-way ANOVA ) . | From the first invasive step onwards , African trypanosomes can efficiently undermine the protective immune response of their mammalian host to favor their survival within the host and successful transmission by its vector , the tsetse fly . Identifying the parasite factors affecting the protective immune response is thus critical to detail the immune evasion mechanisms of these organisms . We report here that during acute infection , a Trypanosoma brucei protein named Kinesin Heavy Chain 1 ( TbKHC1 ) sustains the development of the first ( most prominent ) peak of parasitaemia in the blood and its control by the host . Mechanistically , TbKHC1 was found to interact with the SIGN-R1 molecule at the surface of immune cells . Hereby , TbKHC1 modulates arginine/NO metabolism in immune cells towards the production by the host of nutrients ( polyamines ) required for parasite growth via an IL-10-dependent induction of arginase 1 and down-regulation of iNOS activities . Consequently , IL-10/arginase 1 producing immune cells are impaired in their capacity to destroy the parasite , favouring parasite settlement . Moreover , in the late stage of infection , the inhibition of NO synthesis by TbKHC1 increases liver pathogenicity that contributes to compromised host survival . Thus , targeting TbKHC1 may benefit the host protective immunity against T . brucei parasite . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2013 | A Trypanosoma brucei Kinesin Heavy Chain Promotes Parasite Growth by Triggering Host Arginase Activity |
Elongator complex is required for formation of the side chains at position 5 of modified nucleosides 5-carbamoylmethyluridine ( ncm5U34 ) , 5-methoxycarbonylmethyluridine ( mcm5U34 ) , and 5-methoxycarbonylmethyl-2-thiouridine ( mcm5s2U34 ) at wobble position in tRNA . These modified nucleosides are important for efficient decoding during translation . In a recent publication , Elongator complex was implicated to participate in telomeric gene silencing and DNA damage response by interacting with proliferating cell nuclear antigen ( PCNA ) . Here we show that elevated levels of tRNALyss2UUU , tRNAGlns2UUG , and tRNAGlus2UUC , which in a wild-type background contain the mcm5s2U nucleoside at position 34 , suppress the defects in telomeric gene silencing and DNA damage response observed in the Elongator mutants . We also found that the reported differences in telomeric gene silencing and DNA damage response of various elp3 alleles correlated with the levels of modified nucleosides at U34 . Defects in telomeric gene silencing and DNA damage response are also observed in strains with the tuc2Δ mutation , which abolish the formation of the 2-thio group of the mcm5s2U nucleoside in tRNALysmcm5s2UUU , tRNAGlnmcm5s2UUG , and tRNAGlumcm5s2UUC . These observations show that Elongator complex does not directly participate in telomeric gene silencing and DNA damage response , but rather that modified nucleosides at U34 are important for efficient expression of gene products involved in these processes . Consistent with this notion , we found that expression of Sir4 , a silent information regulator required for assembly of silent chromatin at telomeres , was decreased in the elp3Δ mutants .
Elongator complex , first identified in Saccharomyces cerevisiae , consists of a core complex , Elp1–Elp3 and a sub-complex , Elp4–Elp6 [1]–[3] . Orthologs of Elp1 to Elp4 has been identified in higher eukaryotes and a six-subunit Elongator complex has been purified from humans [4]–[5] . In yeast , Elongator mutants display pleiotropic phenotypes in multiple cellular processes including RNA polymerase II transcription and exocytosis [1]–[3] , [6]–[9] . A crucial observation in understanding the role of the yeast Elongator complex was the discovery of its requirement for formation of 5-carbamoylmethyl ( ncm5 ) and 5-methoxycarbonylmethyl ( mcm5 ) side chains of wobble uridines [10] . In yeast Elongator mutants , the formation of ncm5 and mcm5 side chains were abolished in the 11 tRNA species that normally contain one of these two side chains [10]–[12] . Elongator complex in C . elegans and A . thaliana is also required for formation of ncm5 and mcm5 side chains at wobble uridines [13]–[14] . When the ncm5 and mcm5 side chains were eliminated , the corresponding tRNA species acted less efficiently in translation [12] . Although lack of modifications at position 5 affects the decoding properties of many tRNAs , it appears that the pleiotropic phenotypes of Elongator mutants are predominantly due to decreased translational decoding by hypomodified and [15] . Simultaneous over-expression of hypomodified and , which both have the mcm5s2U modification at wobble position U34 in wild type strains , compensated all phenotypes observed in Elongator mutants including those in RNA polymerase II transcription and exocytosis without restoring formation of ncm5 and mcm5 side chains in tRNA [15] . These observations not only argue against a direct involvement of Elongator complex in other cellular processes than tRNA modification , but they also suggest that the mcm5 side chain is important for efficient translation of mRNAs encoding gene products critical for the processes in which Elongator mutants generate phenotypes . In eukaryotes , the whole genome is packed into a nucleoprotein complex known as chromatin through which the genetic material is processed to regulate cellular processes including transcription , cell division , DNA replication and DNA repair [16]–[17] . Chromatin properties can be altered by the posttranscriptional modifications of histones including acetylation , methylation , phosphorylation and ubiquitination [16] . The Elp3 protein of Elongator complex contains a tentative histone acetyltransferase ( HAT ) domain in the C-terminal region and the histone acetylation levels are decreased in elp3 mutants [7] . However , the reduced histone acetylation levels in the elp3 mutant were restored by increased expression of and , indicating that the involvement of Elongator complex in chromatin remodeling is indirect [15] . In addition to the HAT domain , Elp3 contains an N-terminal region with sequence similarity to the radical S-adenosylmethionine ( SAM ) enzymes [18] . A recent report showed that Elongator mutants have a partial loss of telomeric gene silencing and are sensitive to DNA damage agents [19] . It was also observed that strains with different point mutations in the ELP3 gene , resulting in amino acid substitutions in the radical SAM and HAT domains , displayed differences in telomeric gene silencing and DNA damage response [19] . The participation of Elongator complex in telomeric gene silencing and DNA damage response was linked to its interaction with proliferating cell nuclear antigen ( PCNA ) , a protein involved in DNA replication and DNA repair [19] . In this report , we demonstrate that defects observed in DNA damage response and telomeric gene silencing of yeast Elongator mutants are caused by the absence of wobble uridine tRNA modifications . So far , all phenotypes observed in yeast Elongator mutants can be explained by their influence on tRNA modification . We conclude that the primary role of Elongator complex in yeast is in formation of ncm5 and mcm5 side chains at U34 of tRNAs .
In a recent report , Elongator mutants were shown to have decreased telomeric gene silencing , which was investigated by using an ura3-1 strain with a wild-type copy of the URA3 gene inserted near the left telomere of chromosome VII [19] . Cells with increased expression of Ura3 show reduced growth on plates containing 5-fluoroorotic acid ( 5-FOA ) since the nontoxic 5-FOA is converted to the toxic 5-flurouracil by the URA3 gene product . In such a strain , 30–50% of the cell population are resistant to 5-FOA [20] . The URA3 gene was expressed in a population of cells in both wild type and elp3Δ strains ( Figure 1A ) . However , the elp3Δ strain grew poorly on the 5-FOA containing plates compared to the wild type ( Figure 1A ) , suggesting that telomeric gene silencing was decreased in the elp3Δ strain . Since we earlier showed that the primary function of Elongator complex is in formation of wobble uridine tRNA modifications , we investigated whether increased levels of hypomodified , and could suppress the defects in telomeric gene silencing of an elp3Δ strain . Over-expression of these tRNA species significantly improved the growth of the elp3Δ strain on 5-FOA plates ( Figure 1B ) . The telomeric gene silencing defect of Elongator mutants was also investigated by using a color assay with the ADE2 marker inserted near the telomeric region . The elp3 mutant forms white color colonies due to loss of silencing of ADE2 , which could be rescued by increased expression of , and ( data not shown ) . This observation confirmed that Elongator mutants have a defect in telomeric gene silencing , which is caused by a translational dysfunction . The decreased telomeric silencing observed in other Elongator deletion mutants ( elp1Δ , elp2Δ , elp4Δ , elp5Δ and elp6Δ ) was also suppressed by elevated levels of , and ( Figure 1C ) . Elongator mutants are also sensitive to DNA damaging agents , especially hydroxyurea ( HU ) [19] ( Figure 2 ) . Similar to the defect in telomeric gene silencing , the HU sensitivity of Elongator mutants was suppressed by elevated levels of , and ( Figure 2 ) . Collectively , these observations indicate that the reduced gene silencing in telomeric regions and the defect in DNA damage response of Elongator mutants is caused by inefficient translation due to lack of wobble uridine tRNA modifications . To investigate which of the , and species most efficiently suppressed the defects in telomeric silencing and DNA damage response of the elp3Δ strain , we introduced plasmids encoding these tRNAs independently or in various combinations into the mutant . Increased expression of alone could efficiently suppress the telomeric silencing defect and the HU-sensitivity of an elp3Δ strain ( Figure S1 ) . Simultaneous over-expression of , and gave a minor improvement in suppression of the telomeric gene silencing defect compared to over-expression of alone ( Figure S1A ) . In the HU sensitivity assay , increased expression of together with improved the suppression compared to that of and was as good as elevated levels of , and ( Figure S1B ) . These results indicate that certain open reading frames , encoding gene products critical for telomeric gene silencing and DNA damage response , might be enriched in AAA , CAA and GAA codons . Of these three codons , translation of AAA codons by seems to be most affected by lack of the mcm5 side chain . Asf1 functions as a histone chaperone to direct the histone acetyltransferase Rtt109 in substrate selection and stimulate its acetyltransferase activity [21]–[23] . The combination of elp3Δ asf1Δ or elp3Δ rtt109Δ mutations causes synergistic phenotypes to the strains , such as a more pronounced reduction in growth and increased sensitivity to HU ( Figure 3 and Figure S2 ) , which was suggested to be caused by loss of histone acetylation in the elp3Δ strain [19] . GCN5 encodes a histone acetyltransferase that acetylate H2B and H3 [24]–[25] . Previously it was shown that the elp3Δ gcn5Δ mutations generate a synergistic growth reduction [26] . However , increased levels of hypomodified tRNAs suppressed the synergistic growth reduction caused by the elp3Δ gcn5Δ mutations , and restore the histone acetylation levels in the elp3Δ mutant but not in the gcn5Δ strain [15] . When we over-expressed , and from a high copy vector in the elp3Δ asf1Δ or elp3Δ rtt109Δ double mutants , the growth reduction and HU sensitivity of the double mutants were similar to the defects observed in an asf1Δ or rtt109Δ strain , respectively ( Figure 3 and Figure S2 ) . These observations support the earlier conclusion that Elp3 is not directly required for histone acetylation [15] . Elp3 contains two conserved domains , a radical S-adenosylmethionine ( SAM ) domain in the N-terminal region and a putative histone acetyltransferase ( HAT ) domain located in C-terminal end ( Figure 4A ) . Most strains expressing Elp3 proteins with amino acid substitutions in these two domains showed a reduction in telomeric gene silencing and HU resistance [19] ( Figure 4 ) . The elp3-C103A and elp3-G168R mutations did not influence telomeric gene silencing and HU sensitivity ( Figure 4B and 4C ) [19] . The elp3-Y540A and elp3-Y541A mutations partially reduced telomeric gene silencing and increased HU sensitivity but not as much as elp3Δ ( Figure 4B and 4C ) [19] . The remaining strains were similar as an elp3Δ null strain in telomeric gene silencing and HU sensitivity ( Figure 4B and 4C ) [19] . Moreover , all strains carrying individual mutations listed in Figure 4A except for elp3-C103A were resistant to Kluyveromyces lactis killer toxin ( data not shown ) , indicating that these mutants have a defect in formation of wobble uridines tRNA modification [11] . To examine the status of wobble uridine tRNA modification in these elp3 mutants , total tRNAs from these mutants were isolated and analyzed by HPLC . The elp3-C103A and elp3-G168R mutants , which did not have defects in telomeric silencing and DNA damage response , had 96% and 51% mcm5s2U left , respectively ( Figure 5 , Table 1 ) . Mutations in the HAT domain did not completely eliminate the formation of wobble uridine modifications , both elp3-Y540A and elp3-Y541A have 2 or 6% mcm5s2U left compared to the wild type ( Figure 5 , Table 1 ) . In the rest of mutants , the mcm5 side chain formation was entirely abolished ( Figure 5 , Table 1 ) . We conclude that phenotypes exhibited by elp3 mutants correlate with the levels of wobble uridine tRNA modification . Our observations suggest that phenotypes of Elongator mutants are caused by an inefficient translation due to lack of tRNA modification . If our model is correct , reduction in modification levels in elp3 mutants should result in decreased translation efficiency . To analyze whether the modification levels of different elp3 mutants listed in Table 1 influence translation efficiency , we used a dual-luciferase reporter system ( Figure 6A ) [27] to measure the ochre stop codon read through by a suppressor tRNA encoded by the SUP4 allele . The SUP4 allele encodes a suppressor with a G34 to U34 substitution in its anticodon . The U34 of this suppressor tRNA is modified at position 5 with a mcm side chain [10] . Presence of this modification improves the ability of the suppressor tRNA to read UAA ochre stop codons [10] , [12] . In the dual-luciferase construct , the Renilla and firefly luciferase genes are separated by an UAA ochre stop codon [27] . Read through of the ochre stop codon was determined by calculating the ratio of firefly luciferase activity to Renilla luciferase activity . This ratio was compared to the value obtained from a control construct in which a CAA codon replaces the UAA stop codon ( Figure 6A ) . Due to lack of mcm5 side chain in the SUP4 tRNA , the stop codon read through in the elp3Δ strain is reduced to 46% of wild type ( t-test , p = 0 . 001 ) , supporting that the mcm5 side chain is important for efficient decoding ( Figure 6B ) . In the elp3-G168R mutant , in which the mcm5 side chain is reduced to 51% , the level of read through was significantly decreased compared to that in wild type ( t-test , p = 0 . 008 ) , but is higher than that observed in strains carrying the elp3-Y540A , elp3-Y541A or elp3Δ alleles ( t-test , p = 0 . 04 and 0 . 03 respectively ) ( Figure 6B ) . In the elp3-Y540A and elp3-Y541A mutants , a small fraction of total tRNA was modified ( 2–6% ) ( Figure 5 , Table 1 ) , which contributed to an improvement of stop codon read through by the SUP4 suppressor tRNA compared to the elp3Δ strain ( t-test , p = 0 . 004 and 0 . 006 respectively ) ( Figure 6B ) . In mutant alleles eliminating formation of the mcm5 side chain , no differences were observed in stop codon read through by the SUP4-encoded suppressor tRNA compared to the elp3 null mutant ( Figure S3 ) . These data show that reduced mcm5 modification levels correlate with decreased translational efficiency . Our findings that the defects in telomeric silencing and DNA damage response in Elongator mutants were bypassed by elevated levels of , and indicated that the mcm5 side chain in tRNA is critical for the expression of gene products in these two processes ( Figure 1 and Figure 2 ) . In addition to the mcm side chain at position 5 of U34 , these three tRNAs also contain a 2-thio group forming mcm5s2U . Since the s2 group is also important for decoding [12] , [15] , [28] , we hypothesized that strains deficient in formation of the 2-thio group might also display defects in telomeric silencing and DNA damage response as Elongator mutants . Tuc2 in yeast is required for the formation of the 2-thio group of the mcm5s2U nucleoside [15] . In a tuc2Δ strain , the formation of s2 group is abolished . As expected , telomeric gene silencing was decreased in the tuc2Δ strain ( Figure 7A ) . This strain was also sensitive to 50 mM HU nearly to the same extent as observed in Elongator mutants ( Figure 2 and Figure 7B ) . The defects in telomeric gene silencing and DNA damage response were completely suppressed by increased levels of , and ( Figure 7 ) . The phenotypes of Elongator and tuc2Δ mutants demonstrates that a translational dysfunction due to lack of U34 modifications in , and causes the defects in telomeric gene silencing and DNA damage response . Among the three tRNA species responsible for the suppression of elp3Δ induced phenotypes , increased expression of gives the best suppression of the defect in telomeric gene silencing ( Figure S1 ) . Since decodes AAA codons , elimination of the mcm5 side chain from in the elp3Δ strain could influence the decoding efficiency of AAA codons . Therefore , we searched for open reading frames highly enriched in AAA codons ( unpublished results ) . This analysis lead to the identification of SIR4 , encoding a silent information regulator in yeast . Based on this observation , we hypothesized that the telomeric gene silencing defect of the elp3Δ mutant might be caused by decreased Sir4 expression . Accordingly , the Sir4 protein levels in the elp3Δ mutant were decreased to 34% of wild type ( Figure 8A ) . The decreased Sir4 levels were restored to 80% of wild-type by increased expression of , and , and to 74% of wild-type by elevated levels of alone ( Figure 8A and data not shown ) . We also observed that SIR4 mRNA levels were reduced to 76% of wild-type ( Figure 8B ) , which cannot account for the decreased Sir4 protein levels . In addition , introducing the SIR4 gene on a high copy vector significantly suppressed the telomeric gene silencing defect of the elp3Δ strain , confirming that this defect seems to be caused by decreased Sir4 expression ( Figure 8C ) . However , we do not exclude the possibility that there might be other open reading frames enriched in AAA codons whose translation is also affected and which might weaken silencing , directly or indirectly .
Elongator complex was initially identified by its apparent association with the elongating form of RNA polymerase II , implicating a role in PolII transcription [1] . However , its requirement in transcription was controversial based on its cytoplasmic localization and failure to detect this complex on actively transcribed genes [8] , [29]–[30] . We discovered that Elongator complex was required for formation of mcm5 and ncm5 side chains at wobble uridines of tRNA [10] . The participation of Elongator complex in PolII transcription and exocytosis was indirect as elevated expression of hypomodified and could suppress previously reported phenotypes of Elongator mutants without restoring tRNA modification [15] . Recently , it was reported that Elongator complex modulates telomeric gene silencing and DNA damage response by its interaction with PCNA and its requirement for histone acetylation [19] . Since the histone acetylation defect of the elp3Δ mutant could be completely suppressed by increased expression of and [15] , we assumed that Elongator complex indirectly participated in telomeric gene silencing and DNA damage response . In this report , we show that the defects in telomeric gene silencing and DNA damage response in Elongator mutants were also suppressed by increased expression of hypomodified , and ( Figure 1 , Figure 2 , and Figure S1 ) . Thus , all phenotypes exhibited by Elongator mutants except the tRNA modification defect are overcome by elevated tRNA levels , indicating that the major function of this complex , at least in yeast , is in the formation of mcm5 and ncm5 side chains of wobble uridines . When , and were over-expressed in Elongator mutants , the HU sensitivity phenotype , but not the defect in telomeric gene silencing , was fully suppressed ( Figure 1 and Figure 2 ) . Since Elongator mutants affect the mcm5 and ncm5 side chain formation in 11 tRNA species , it is possible that poor translation of codons decoded by any of the other 8 hypo-modified tRNA species contributes to the defect in telomeric gene silencing , but not the HU sensitivity . In addition to the mcm side chain at position 5 , U34 of , and are also thiolated at position 2 . If our model is correct that the phenotypes observed in Elongator mutants are a consequence of inefficient translation , strains lacking the 2-thio group in , and will have similar phenotypes as Elongator mutants . We observed that the failure to form the 2-thio group in the tuc2Δ mutant resulted in defects in telomeric gene silencing and DNA damage response ( Figure 7 ) . These defects of the tuc2Δ mutant were completely suppressed by increased expression of , and . In addition , lack of the methyl ester in mcm5 side chain at wobble uridines in a trm9Δ strain has been linked to the defect of DNA damage response [31] . Thus , both mcm5 and s2 side chains of mcm5s2U containing tRNAs are important for efficient expression of gene products required for telomeric gene silencing and DNA damage response . These observations strongly suggest that Elongator complex influence these two processes by promoting efficient translation . Since increased expression of gives the best suppression of the telomeric gene silencing defect in Elongator mutants , we assumed genes encoding products important for this process are enriched in AAA codons . One such gene is SIR4 . We demonstrate that Elongator mutants influence telomeric gene silencing by impairing efficient expression of SIR4 . Even though we observed a slight reduction in SIR4 mRNA levels in the elp3Δ mutant , it cannot fully explain the decrease in Sir4 protein levels , and it is unclear if this reduction is caused by reduced transcription or increased decay of the poorly translated mRNA . Recently , it was discovered that Elongator complex in C . elegans and A . thaliana is also required for formation of mcm5 and ncm5 side chains at wobble uridines of tRNA [13]–[14] , indicating that this function of Elongator complex might be conserved in eukaryotes . In multicellular organisms , Elongator complex has also been linked to multiple processes including transcription , cytoplasmic kinase signaling and development [32]–[34] . Two recent articles suggested that Elongator complex was also required for α-tubulin acetylation and played a role in neurological processes in both mice and C . elegans [35]–[36] . In early developmental stages , C . elegans Elongator mutants have a decreased α-tubulin acetylation [36] . However , in adult Elongator mutant worms , normal levels of α-tubulin acetylation were observed , suggesting that Elongator complex is not absolutely required for acetylation of α-tubulin [13] , [36] . Elongator mutants in C . elegans were also resistant to the acetylcholinesterase inhibitor aldicarb , indicating a reduced efficiency of synaptic exocytosis [13] , [36] . However , a mutant allele of mec-12 , which is completely missing α-tubulin acetylation , was not resistant to aldicarb , suggesting that the defect in synaptic exocytosis of Elongator mutants was not caused by reduced levels of α-tubulin acetylation [13] . Furthermore , mec-17 was discovered to be the α-tubulin acetylase in in Tetrahymena cells , C . elegans , zebrafish and mammalian cells , suggesting that Elongator might indirectly influence α-tubulin acetylation by modulating the expression of α-tubulin acetylase [37] . Based on these observations , it is tempting to speculate that the primary function of Elongator complex in multicellular organism is , as in yeast , in formation of wobble uridine tRNA modifications . The Elp3 subunit in yeast has an N-terminal radical S-adenosylmethionine ( SAM ) domain and a C-terminal histone acetyltransferase ( HAT ) domain . In Methanocaldococcus jannaschii , the radical SAM domain of mjElp3 contains an iron sulfur cluster region and a region that binds SAM [38] . Cysteine residues at positions 96 , 101 and 104 are critical for the FeS cluster formation in M . jannaschii [38] . When these corresponding cysteines at position 108 , 118 and 121 in the yeast Elp3 were substituted with alanines , it eliminated the activity of yeast Elongator in formation of modified nucleosides at U34 . In vitro , SAM can bind to M . jannaschii Elp3 , but the binding of SAM to Elp3 from S . cerevisiae has not been detected [38]–[39] . However , when the conserved SAM binding sites ( G180R G181R ) in the radical SAM domain were mutated in yeast ELP3 , a defect in formation of modified nucleosides was observed ( Figure 5 , Table 1 ) . This observation shows that the FeS cluster and the SAM binding regions of the radical SAM domain of Elp3 are critical for the tRNA modification reaction . Substitution of glycine at position 168 to arginine , another conserved site located in the SAM binding region , reduced the wobble uridine tRNA modification to 51% of wild type ( Figure 5 , Table 1 ) . In telomeric gene silencing and HU sensitivity assays , the elp3-G168R mutant displays the same phenotypes as a wild type strain suggesting that a 49% reduction in the levels of modified nucleosides do not cause phenotypes in telomeric gene silencing and DNA damage response . Two mutations in the HAT domain ( Y540A and Y541A ) of Elp3 did not entirely eliminate the formation of modified nucleosides at U34; 2 and 6% of mcm5s2U was detected in each mutant ( Table 1 ) . The residual level of modified nucleosides significantly improves the decoding capacity of the SUP4 encoded suppressor tRNA compared to the unmodified tRNA in the elp3 null mutant ( Figure 6 ) . This observation explains why the elp3-Y540A and elp3-Y541A mutants had increased telomeric silencing and reduced HU sensitivity compared to the elp3Δ strain ( Figure 4 ) . Among the elp3 mutants described in Table 1 , the elp3-G168R mutant , having 51% of modified nucleoside left ( Figure 5 and Table 1 ) , has the same phenotype as a wild type strain with respect to phenotypes in telomeric gene silencing and DNA damage response ( Figure 4 ) . However , this strain is resistant to killer toxin ( data not shown ) , a phenotype tightly connected to wobble uridine tRNA modification [11] . The γ subunit of killer toxin is a tRNA endonuclease which cleaves tRNA at the anticodon region [11] . The mcm5 side chain at U34 of tRNA is important for the substrate recognition by γ toxin . In the elp3-G168R mutant , a fraction of the U34 tRNAs are missing the mcm5 side chain and the mutant is resistant to γ toxin ( data not shown ) . However , the modified tRNAs in the elp3-G168R support the efficient expression of gene products required for telomeric gene silencing and DNA damage response . Thus , strains with tRNAs partially modified at U34 show weaker or no phenotypes compared to Elongator deficient strains . In summary , the major function of Elongator complex in yeast is in formation of wobble uridine tRNA modifications and this function is probably conserved in eukaryotes . We suggest that when new phenotypes of Elongator mutants are discovered in yeast , an important first step is to investigate whether the phenotypes can be suppressed by over-expressing , and .
All yeast strains used in this study are listed in Table S1 . Yeast transformation , media , and genetic procedures have been described previously [40] . To generate elp null mutants in different strain backgrounds , chromosomal DNA from KanMX deleted elp mutants UMY2911 ( elp1::KanMX4 ) , UMY2913 ( elp2::KanMX4 ) , UMY2915 ( elp3::KanMX4 ) , UMY2917 ( elp4::KanMX4 ) , UMY2919 ( elp5::KanMX6 ) and UMY2921 ( elp6::KanMX4 ) served as templates . Primers were designed to amplify DNA fragments containing the KanMX cassette and 300–500 nt flanking sequences of each ELP gene . PCR products were transformed into either W303-1A or UMY2584 , and the transformants were selected by using YEPD plates containing 200 µg/ml G418 . The deletion mutants were verified by PCR . To introduce asf1::KanMX4 and rtt109::KanMX4 into W303 background , chromosomal DNAs from the corresponding mutants in the deletion collection ( Open biosystems ) were used as templates . Primers were designed to amplify the KanMX4 cassette and 500 nt flanking sequences . PCR products were transformed into diploid strain UMY3104 and transformants were selected on G418 containing plates . The asf1::KanMX4 and rtt109::KanMX4 strains were obtained by tetrad dissection after sporulation . To construct asf1::KanMX4 elp3::KanMX4 and rtt109::KanMX4 elp3::KanMX4 , the elp3::KanMX4 strain was crossed with asf1::KanMX4 or rtt109::KanMX4 to generate the diploid and double mutants were obtained by tetrad dissection . To generate elp3::KanMX4 SIR4-13Myc-KanMX6 strain , the elp3::KanMX4 strain was crossed with SIR4-13Myc-KanMX6 strain . The diploid was sporulated and the elp3::KanMX4 SIR4-13Myc-KanMX6 strain was obtained by tetrad dissection . A two-step gene replacement procedure was used to obtain strains with different mutant alleles of ELP3 . Plasmids pABY1672 ( elp3-C103A ) , pABY1673 ( elp3-C108A ) , pABY1676 ( elp3-C118A ) , pABY1677 ( elp3-C121A ) , pABY1984 ( elp3-G168R ) and pABY1985 ( elp3-G180R G181R ) were digested with EcoRI and the linearized fragments were transformed into the UMY2894 . Transformants were selected on SC-Ura plates and streaked on YEPD plates . Eight independent colonies on YEPD plates were picked and streaked on 5-FOA containing plates . The strains with elp3 mutant alleles except for elp3-C103A were identified by their resistance to killer toxin and confirmed by sequencing . In order to identify the elp3-C103A mutant , DNA isolated from several candidates were sequenced . Plasmids used in this study are listed in Table S2 . The pRS306-ELP3 ( pABY1554 ) was constructed previously [10] and used as DNA template for mutagenesis . Plasmids pABY1672 ( elp3-C103A ) , pABY1673 ( elp3-C108A ) , pABY1676 ( elp3-C118A ) , pABY1677 ( elp3-C121A ) , pABY1984 ( elp3-G168R ) and pABY1985 ( elp3-G180R G181R ) were generated by using Quickchange Lightning Multi Site-Directed mutagenesis kit according to the instruction manual ( Agilent Technologies ) . Site-specific primers were designed by Agilent online service . To move mutant alleles of ELP3 to pRS315 , pRS306-elp3 derivatives were digested using restriction enzymes BamHI and XhoI , and the excised fragments were cloned into the corresponding sites of pRS315 . To generate pRS424-SIR4 , SIR4 gene was amplified by PCR using W303-1A genomic DNA as template with oligos AAAA GAATTC TGTGA GTACATATAT CCGCAG and AAAA CTCGAG TTG GTATTTGATG GGTTGCTC . The PCR product was digested with EcoRI and XhoI , and cloned to the corresponding sites on pRS424 . Cells were grown at 30°C in 100 ml YEPD and harvested at OD600 = 1 . 5∼2 . The cell pellet was resuspended in 3 ml 0 . 9% NaCl . The cell suspension was vortexed at room temperature for 30 minutes in the presence of 8 ml water-saturated phenol and vortexed for another 15 minutes after adding 0 . 4 ml chloroform . Centrifugation was carried out at 12000 g for 20 minutes . The water phase was collected and re-extracted with phenol . The final water phase was collected , mixed with 2 . 5 volume 99 . 5% ethanol and kept at −20°C for at least 3 hours . Total RNA was pelleted at 12000 g for 20 minutes . The RNA pellet was dissolved in 5 ml DE52 binding buffer ( 0 . 1 M Tris . HCl pH 7 . 4 and 0 . 1 M NaCl ) and loaded onto the DE52 cellulose column . The column was washed twice with 7 ml DE52 binding buffer and the tRNA was eluted with 7 ml elution buffer ( 0 . 1 M Tris . HCl pH 7 . 4 and 1 M NaCl ) . The tRNA was precipitated with 0 . 7 volume of isopropanol at −20°C for at least 3 hours and pelleted by centrifugation at 12000 g for 20 minutes . The pellet was washed once with 70% ethanol and dissolved in 50 µl MQ . Purified tRNA was digested with Nuclease P1 for 16 hrs at 37°C and treated with bacterial alkaline phosphatase for 2 hours at 37°C . The hydrolysate was analyzed by high pressure liquid chromatography with a Develosil C-30 reverse-phase column as described [41] . To investigate the defect in telomeric gene silencing of Elongator mutants , 10-fold dilutions of freshly cultivated yeast cells were spotted on 5-FOA containing plates and control plates . Plates were incubated at 30°C for 2 days . To analyze the DNA damage response , 10 fold dilutions of freshly cultivated yeast cells were spotted on the plates containing 50 mM HU and control plates . The results were scored after 2 days of incubation at 30°C . The luciferase activities were measured by GloMax 20/20 luminometer ( Promega ) and the dual-luciferase reporter assay system ( Promega ) . Cells were grown to 0 . 5 OD600 and diluted 10 fold before use . 20 µl of diluted cell culture was mixed with 100 µl passive lysis buffer , vortexed for 12 seconds and 20 µl of cell lysate was used to determine the luciferase activity . Each culture was measured 3 times and 3 independent experiments were performed . To determine the Sir4 protein levels , cells were grown at 30°C to OD600 = 0 . 5 before harvest . Cells were broken in breaking buffer ( 40 mM Hepes pH 7 . 3 , 50 mM NH4Ac , 10 mM MgCl2 and 1 mM DTT ) containing Complete Protease Inhibitor Cocktail Tablets ( Roche Applied Science ) by using FastPrep-24 homogenizer ( MP biomedicals ) . 60 µg proteins were loaded in each lane . Mouse anti-Myc antibody ( 9E10 ) with a dilution 1∶1000 was used to detect recombinant proteins . The actin levels , used as an internal control , were detected using mouse anti-Act1 antibody ( Thermo Scientific ) at a 1∶2000 dilution . RNA levels were determined as previously described [42] . | Elongator is a conserved protein complex in eukaryotes . Studies in yeast , worms , and plants have revealed that Elongator complex is required for formation of mcm5 and ncm5 side chains at wobble uridines in a subset of tRNA species . The primary function of Elongator complex in yeast is to modify U34 in tRNAs . Lack of these tRNA modifications causes pleiotropic phenotypes in yeast Elongator mutants due to inefficient translation . In this report , we demonstrate that the defects in telomeric silencing and DNA damage response observed in yeast Elongator mutants are a consequence of a tRNA modification defect . We suggest that the requirement of Elongator complex in tRNA modification is conserved in all eukaryotes , and diseases linked to human Elongator mutations may involve impaired translation due to lack of tRNA modifications . | [
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| 2011 | Elongator Complex Influences Telomeric Gene Silencing and DNA Damage Response by Its Role in Wobble Uridine tRNA Modification |
In the wet-dry tropics of Northern Australia , drinking water in remote communities is mostly sourced from bores accessing groundwater . Many aquifers contain naturally high levels of iron and some are shallow with surface water intrusion in the wet season . Therefore , environmental bacteria such as iron-cycling bacteria promoting biofilm formation in pipes or opportunistic pathogens can occur in these waters . An opportunistic pathogen endemic to northern Australia and Southeast Asia and emerging worldwide is Burkholderia pseudomallei . It causes the frequently fatal disease melioidosis in humans and animals . As we know very little about the microbial composition of drinking water in remote communities , this study aimed to provide a first snapshot of the microbiota and occurrence of opportunistic pathogens in bulk water and biofilms from the source and through the distribution system of three remote water supplies with varying iron levels . Using 16s-rRNA gene sequencing , we found that the geochemistry of the groundwater had a substantial impact on the untreated microbiota . Different iron-cycling bacteria reflected differences in redox status and nutrients . We cultured and sequenced B . pseudomallei from bores with elevated iron and from a multi-species biofilm which also contained iron-oxidizing Gallionella , nitrifying Nitrospira and amoebae . Gallionella are increasingly used in iron-removal filters in water supplies and more research is needed to examine these interactions . Similar to other opportunistic pathogens , B . pseudomallei occurred in water with low organic carbon levels and with low heterotrophic microbial growth . No B . pseudomallei were detected in treated water; however , abundant DNA of another opportunistic pathogen group , non-tuberculous mycobacteria was recovered from treated parts of one supply . Results from this study will inform future studies to ultimately improve management guidelines for water supplies in the wet-dry tropics .
Water providers in the wet-dry tropics of Northern Australia face significant challenges to keep drinking water safe and free of opportunistic pathogens . One such opportunistic pathogen is Burkholderia pseudomallei , an environmental saprophytic bacterium and causative agent of the severe disease melioidosis affecting humans and animals [1 , 2] . People most at risk are those suffering from diabetes , chronic lung or renal disease or hazardous alcohol use [3] . Until recently , melioidosis was thought to mainly affect people in Northern Australia and Southeast Asia where B . pseudomallei is endemic . However , a recent modelling study predicted 165 , 000 annual melioidosis cases worldwide of whom 89 , 000 were estimated to die [1] . B . pseudomallei is a natural component of the soil and surface water microbiota in rural Darwin , Northern Territory in northern Australia and 30% of tested unchlorinated residential water wells ( bores ) were positive for the bacteria [4 , 5] . B . pseudomallei has been isolated from aerator sprays and tank sludge from water treatment plants ( [6]; own observation ) and melioidosis cases and deaths due to contaminated drinking water have been documented in Northern Australia and Thailand [6–10] . These supplies were either not chlorinated or the disinfection process was interrupted . B . pseudomallei is successfully contained by free chlorine levels of 0 . 5 to 1 mg/L , although in laboratory experiments , some strains were more chlorine tolerant [11] . Groundwater in many areas of Northern Australia contains naturally high levels of iron and it is unclear to what degree this promotes B . pseudomallei survival . B . pseudomallei has a redundant system of siderophores allowing it to acquire non-bioavailable ferric iron [12 , 13]; a positive association between B . pseudomallei and total iron levels was found in bore water [14] while the association was negative in soil with high iron levels [15 , 16] suggesting a unimodal rather than linear relationship across the range of iron levels encountered in the environment . Water with high iron levels attracts naturally occurring iron bacteria which metabolize the iron and contribute to pipe corrosion and reduced bore yield . While some bacteria such as Geothrix fermentens or Shewanella sp . reduce iron in anoxic groundwater using organic carbon as electron donor , in niches with low oxygen iron oxidizers such as Gallionella ferruginea thrive and facilitate the production of abundant ferric oxide precipitates which block pipes , and contribute to biofilm formation reducing disinfection efficiency [17–19] . Most biofilms consist of a complex mix of bacterial taxa and can also be associated with fungi , viruses or protozoa [18] . Although biofilms are a known reservoir for opportunistic pathogens such as nontuberculous Mycobacteria , Legionella pneumophila or Pseudomonas aeruginosa [20] , we still do not know to what degree B . pseudomallei colonizes multi-species biofilms in water pipes . Water supplies of remote communities mainly depend on chlorination as disinfection treatment and are vulnerable to exposure to opportunistic pathogens in the event of a chlorination breakdown or if pathogens are chlorine-resistant . Indigenous people in remote communities often have higher rates of chronic diseases such as diabetes and thus , are more at risk of infection if exposed to opportunistic pathogens [21] . A multiple barrier approach to improve water quality is needed [22] . However , without full knowledge of what microbes occur in the source and distribution water , it can be difficult to design and apply barriers suitable for northern Australia . This scoping study aimed to provide a first snapshot of the microbiota in bulk water and biofilms from the source and through the distribution system in three water supplies of remote communities; one supply with naturally high iron levels , one with medium and one with low levels . There were three study objectives: A ) the detection and culture of opportunistic pathogens with a focus on endemic B . pseudomallei; B ) the detection of taxa with known iron bacteria using 16s rRNA gene amplicon sequencing for microbial profiling; and C ) the characterisation of the bacterial and archaeal microbiota and its association with nutrients and site characteristics . We hypothesized that water treatment would have the largest impact on the microbiota followed by the origin of the source water . We also hypothesized that water supplies fed from unconfined shallow aquifers would contain more bacteria also occurring in soil including B . pseudomallei . Results from this work will inform and guide future studies to ultimately improve management guidelines suitable for Northern Australia to minimize microbial risk in the drinking water distribution network .
Water and biofilms from the drinking water distribution system ( DWDS ) were sampled from three remote Indigenous communities in the Top End of the Northern Territory ( NT ) , Australia . The Top End has a tropical savannah climate , with a distinct dry and wet season and average annual rainfall of 1 , 727 mm between Oct and March ( www . bom . gov . au ) . The “HighFe” or HF community had a water supply with high iron ( Fe ) levels in the source water with median 0 . 80 mg/L total iron levels . This was above the aesthetic guideline value of 0 . 30 mg/L of the Australian drinking water guidelines [22] . The “MidFe” or MF community had a water supply with medium iron ( Fe ) levels with median 0 . 25 mg/L total iron and the “LowFe” or LF community had low iron levels with a median 0 . 05 mg/L total iron . All three communities had reported melioidosis cases in the past ( 1994–2017: HF 3 cases ( incidence rate IR 4 . 1 cases/1 , 000 population ) , MF 11 cases ( IR 9 . 9 ) and LF 4 cases ( IR 2 . 6 ) ) . It is not known where these patients acquired the melioidosis bacteria . Samples were collected in the late wet season i . e . in March 2017 for two of the three communities ( HF and LF ) while the third community ( MF ) was sampled in May 2017 as soon as waters receded sufficiently to allow access to the water bore fields . For each community , samples were collected from five points along the DWDS of which three were unchlorinated ( bores and tanks ) and two from the chlorinated reticulation system . Samples were collected from five sites from each of three water supplies . One litre of water was collected in duplicate for subsequent DNA extraction . An additional 500 mL were collected for B . pseudomallei culture ( Menzies School of Health Research ) , 200 mL in duplicate in 200 mL sodium thiosulphate dosed bottles for subsequent faecal indicator , heterotroph and amoebae culture , 100 mL into acid-washed 125 mL bottles for elemental analysis and 100 mL of in situ filtered water ( using 0 . 45 micron filters ) into acid-washed 125 mL bottles for nutrient analysis . All bores that were sampled were in operation for >6 hours and bores were purged for five minutes prior to water collection . The surface of biofilms in the bore head , pipes , tanks , and water meter walls was collected in duplicates using sterile swabs ( Interpath , Australia ) . All samples were kept on ice on the sampling day except water and biofilms for subsequent amoebae and B . pseudomallei culture which were kept at room temperature and protected from sunlight . A total of 60 water and biofilm samples were collected in duplicates from 15 water collection points of three water supplies . A YSI meter ( www . ysi . com ) was used to measure various physicochemical factors in water namely pH , salinity , temperature , turbidity and dissolved oxygen ( DO ) content . A colorimeter was used to measure free chlorine levels of the chlorinated water . A redox meter calibrated with Zobell’s solution ( YSI ) measured the oxidation redox potential ( ORP ) –redox measurements were conducted for all samples on the same day of collection upon return to the laboratory . E . coli , coliforms , P . aeruginosa , heterotrophs and free-living amoebae were cultured at the NATA accredited NT Government Dept . of Primary Industry and Resources laboratory and the Australian Water Quality Centre ( AWQC ) after overnight shipment of samples on ice ( room temperature for amoebae ) . Culture of E . coli and coliforms was based on the Most Probable Number ( MPN ) method and Colilert-18 Defined Substrate Technology ( DST ) ( AS/NZS 4276 . 21–2005 ) while culture of P . aeruginosa was by membrane filtration . Heterotrophic Colony Count was by pour plate method with incubation for 44 h at 36 C ( AS 4276 . 3 . 1–2007 ) . Culture for B . pseudomallei and near-neighbour Burkholderia was conducted at Menzies School of Health Research . Culture from 500 mL of water was based on membrane filtration ( 0 . 22 micron filters ) followed by culture in Ashdown broth and agar as previously described [4] . Similarly , biofilm swabs were incubated in Ashdown broth followed by plating on Ashdown agar . DNA extraction of six B . pseudomallei isolates was as previously described [23] and the genomes were sequenced on a Illumina HiSeq2500 platform ( Illumina , Inc . , San Diego , CA ) at the Australian Genome Research Facility ( AGRF ) . Orthologous core single nucleotide polymorphism ( SNP ) variants were identified among 89 B . pseudomallei genomes from the Northern Territory using the default settings of SPANDx v3 . 2 [24] and the closed Australian B . pseudomallei genome MSHR1153 [25] as reference ( N50 4 , 032 , 226 bp; 2 contigs; size 7 , 312 , 903 bp ) . A maximum parsimony phylogenetic tree was generated in PAUP* 4 . 0 . b5 [26] based on 174 , 905 SNPs and rooted using MSHR668 . Multi-locus sequence types ( MLST ) were assigned in silico using the BIGSdb tool which is accessible on the B . pseudomallei MLST website ( http://pubmlst . org/bpseudomallei/ ) . The following geographical and virulence genetic markers were extracted in silico using the Basic Local Alignment Search Tool ( BLAST ) [27] following previously published methods [28]: LPS A ( wbil to apaH in K96243 [GenBank ref: NC_006350] ) , LPS B ( BUC_3392 to apaH in B . pseudomallei 579 [GenBank ref: NZ_ACCE01000003] ) , LPS B2 ( BURP840_LPSb01 to BURP840_LPSb21 in B . pseudomallei MSHR840 [GenBank ref: GU574442] ) , BTFC ( lafU in B . pseudomallei MSHR668 [GenBank ref: NC_006350] ) , YLF ( BPSS0124 in B . pseudomallei K96243 [GenBank ref: CP009545 . 1] ) , bimABm ( BURPS668_A2118 in B . pseudomallei MSHR668 [GenBank ref: NZ_CP009545] ) , bimABp ( BPSS1492 in B . pseudomallei K96243 [GenBank ref: NC_006350] ) and fhaB3 ( BPSS2053 in B . pseudomallei K96243 [GenBank ref: NC_006350] ) . Elements ( total Fe Mn Mo Mg K Ca S Ni Cu Zn ) were measured at the Environmental Chemistry & Microbiology Unit ( ECMU ) ( CDU , Darwin , Australia ) by ICP-MS ( AGILENT 7700ce , www . agilent . com ) [29] . Dissolved nutrient analysis ( TDN , NOx , TDP and DOC ) of the filtered water was conducted at the laboratory of Queensland Health ( www . health . qld . gov . au ) . Within 24h of collection , water samples ( 1 L ) were filtered ( 0 . 45 micron filters , Sartorius ) and frozen until processed . DNA was extracted from filters and swabs using the FastDNA soil kit ( MPBio , Australia ) following the manufacturers’ instructions . Bacterial load was measured using a SYBR-based qPCR assay targeting the 16s rRNA gene with PCR primers 331-f and 797-r [30] and using the QuantiTect SYBR Green qPCR mix ( Qiagen , Australia ) resulting in a qPCR efficiency of 90% . The delta Ct method was used for relative quantification and a positive control was included in each run for inter-run comparisons . Five DNA extraction negative controls on filters ( #3 ) and swabs ( #2 ) with no water or biofilm added were also processed . The DNA was sent to the Australian Centre for Ecogenomics ( ACE , https://ecogenomic . org/ ) for 16s rRNA gene amplicon sequencing . Sixteen-s rRNA gene amplification and Illumina MiSeq sequencing was conducted at ACE using the Earth Microbiome Project 16s rRNA V4 515FB-806RB universal primers ( FWD:GTGYCAGCMGCCGCGGTAA; REV:GGACTACNVGGGTWTCTAAT ) targeting bacteria and archaea ( accessed June 2017: http://press . igsb . anl . gov/earthmicrobiome/protocols-and-standards/16s/ ) . These primers were extensively validated to minimize bias towards or against taxonomic groups; however , remaining preferential amplification of certain taxa cannot be excluded [31–33] . Sequences were processed to sequence variants ( SVs ) by ACE with the following pipeline . The software Trimmomatic was used for sequence quality trimming removing poor quality sequences with a sliding window of 4 bases and an average base quality above 15 . All reads were hard trimmed to 250 bases , and any with less excluded . Reads were processed to SVs using the QIIME-2 workflow with default parameters and the DADA-2 algorithm [34 , 35] . Taxonomic assignment of SVs was through BLAST+ using the reference database SILVA ( www . arb-silva . de ) . 15 , 590 SVs were further processed using the R library Phyloseq . Due to the low biomass of many samples , special care was taken to exclude potential contaminant SVs such as from lab reagents [36 , 37] . Seventeen SVs were excluded which consistently occurred in all five negative controls . A further five SVs were excluded which occurred in minimum two negative controls and showed a significant negative Spearman’s rank correlation with the bacterial DNA abundance based on 16s qPCR results ( P<0 . 05 ) . The R package “decontam” was used to exclude a further 93 SVs based on their occurrence in negative controls ( prevalence method ) . Thus , a total of 104 SVs were excluded due to contamination concerns . A further 9 , 787 SVs ( 63% ) were excluded as these only occurred in one sample ( with duplicate water and biofilm samples collected per site ) . As a last step , 38 SVs were excluded as these were not assigned to either Bacteria or Archaea . Nineteen of 60 samples ( 32% ) were excluded due to low sequence counts ( < 5 , 000 sequences ) ( 7 chlorinated water , 6 unchlorinated water , 4 chlorinated biofilms , 2 unchlorinated biofilms ) . Negative control samples had sequence counts ranging between 334 and 1 , 840 sequences . A hierarchical cluster analysis was performed in Primer-E v7 ( Plymouth , UK ) to test whether samples clustered with negative controls . This was only the case for samples with low sequence counts which were excluded from any downstream analyses . The final dataset contained 5 , 411 different SVs and 41 samples . All remaining samples were rarefied to the lowest common sequence number per sample ( 5 , 259 sequences ) . Rarefaction curves indicated that with this cut-off , the richness of all chlorinated samples plateaued i . e . was reached; however , four of 16 unchlorinated biofilm samples ( 25% ) and two of 12 unchlorinated water samples ( 17% ) only reached approximately 70–80% of their SV richness ( S1 Fig ) . To examine Mycobacteria counts and richness across primarily chlorinated samples , a lower rarefaction threshold of 2 , 000 sequences was adopted which allowed the inclusion of more chlorinated samples ( 22 of 24 chlorinated samples ) while their full SV richness was reached at this threshold , which was still higher than the sequencing depth of all negative controls ( S1 Fig ) . The weighted UniFrac and Bray Curtis distance matrices were trialled on the rarefied and square-root transformed SV data . Non-metric multidimensional scaling ordinations ( nMDS ) on the weighted UniFrac matrices showed a high stress value ( >0 . 2 ) and therefore , the Bray Curtis dissimilarity matrix on square-root transformed SV’s was chosen for subsequent analysis in Primer-E v7 . PERMANOVA was used to test whether the bacterial composition differed between communities , sample type ( water vs biofilm ) or chlorination status . Sample sites along the DWDS were included as random factor nested in community and chlorination status . A distance-based test for homogeneity of multivariate dispersions ( PermDISP ) was conducted to test for differences in data dispersion between sample groups . A canonical analysis of principal coordinates ( CAP ) was performed to assess the predictive ability of the microbiota for sample type and chlorination status . A distance linear model and distance-based redundancy analysis ( dbRDA ) were performed to associate abiotic factors ( water physicochemical factors , nutrients and metals ) with the bacterial composition . Model selection was based on the lowest AICc and a combination of forward and backward step elimination . A negative binomial model on the non-rarefied data was applied in Phyloseq ( DESeq2 library in R ) [38] to identify bacterial taxa whose abundance significantly differed between sample groups . The False Discovery Rate ( FDR ) method was used to account for multiple testing . To compare the occurrence of SVs across sample groups , the R package labdsv was used and those SVs considered which occurred at least twice in the sample group . The R package Vennerable was used to generate Venn diagrams . Multiple regressions on the log transformed bacterial DNA abundance and negative binomial models on taxa sequence counts and SV richness ( based on rarefied dataset ) were performed in Stata-14 ( www . stata . com ) with standard errors clustered for sites and model residual diagnostics conducted . A result was considered significant if P<0 . 05 unless otherwise stated . Sixteen-s rRNA gene sequencing data were submitted to the European Nucleotide Archive ( PRJEB29497 , ERR2882159 to ERR2882214 ) . Accession numbers of B . pseudomallei whole genome sequencing data are in S2 Table .
Water in the HF community had the highest levels of various metals , nutrients and salts ( 0 . 21–0 . 26 ppt ) ( Fig 1A–1C , S1 Table ) and a neutral to slightly alkaline pH ( 6 . 9 to 7 . 8 ) . The MF community water had lower iron levels of 0 . 03 to 0 . 78 mg/L , generally lower metal and nutrient levels and was more acidic ( pH 4 . 8 to 5 . 3 ) . Water in the LF community was the least buffered with the lowest metal and nutrient levels of the three water supplies and also the most acidic with a pH of below 5 for all five tested water samples ( S1 Table ) . For all three communities , total Fe levels were strongly correlated with total Mn levels ( Spearman’s rho 0 . 91 , P<0 . 001 ) . The DO and redox levels reflected the water origin with oxygen-deprived groundwater sampled from the bores showing the lowest DO and ORP levels . Redox levels overall were lowest at the HF community indicating a more reducing environment which was also reflected by the more neutral pH and higher DOC levels .
In this study we analysed a snapshot of the microbiota in bulk water and biofilms in the source and distribution system of three remote communities in Northern Australia . Changes in the microbiota were associated most with changes in redox levels and dissolved oxygen followed by various metals and nutrients such as TDN or DOC . These parameters not only differed along the water treatment train but also between the water supplies . Indeed , the geochemistry of the groundwater varied considerably between the three water supplies which was also reflected by significant differences in the source water microbiota . Remarkably , only 2% of SVs in untreated source samples occurred in all three water supplies as opposed to 23% SVs shared between the supplies for the treated parts . Significant microbial differences in source water of water supplies driven by geochemical differences have previously been reported [41] . The water supply of community LF was fed by the deepest aquifer , had the newest constructed bores and the water contained the least amount of nutrients and was the most acidic . There were fewer and less diverse bacteria in the water and while biofilm growth in the pipes was minimal , this was the only water supply with a significantly higher richness in the untreated biofilms compared to untreated bulk water . The scarce biofilms of all three tested LF bores were rich in chemo-organotrophic Acidobacteriaceae . Bacteria from these taxa typically have a growth optimum at lower pH and have adapted strategies to grow in low-carbon environments [41–43] . There were significantly more metals , bacteria and archaea in the bore water of the MF water supply . The MF bore heads were covered in thick loose iron flocs and biofilms and contained abundant Gallionella bacteria [44–46] . Ideal conditions for Gallionella growth have been reported to be at a neutral to slightly acidic pH , with a redox potential of 200–320 mV [47] matching conditions in the MF water supply . The bore field of the MF community is often inundated during the wet season during which contamination with surface water is possible . Two of the three tested bores were fed by a shallow aquifer and water from these bores grew heterotrophic bacteria . In contrast , the third bore accessed the Kombolgie sandstone aquifer at 20 meters depth , only had scarce heterotrophic growth but its water and biofilm samples were positive for B . pseudomallei . It is not known whether B . pseudomallei indeed occurred in the deeper aquifer . B . pseudomallei is a facultative anaerobe and hardy bacterium able to survive even in distilled water [16 , 48] . Alternatively , the bore could have been contaminated with surface water during the wet season although the scarce heterotrophic growth did not support the notion of a recent contamination . Previous research has shown that B . pseudomallei inhabits shallow unconfined aquifers [49–51] and B . pseudomallei has been found more often in residential bores with hard water , acidic pH , increased iron levels and turbid water also containing coliforms [14] . These are indicators for surface water influx or water from shallow seasonal inter-flow aquifers . More research is needed to establish the potential occurrence of B . pseudomallei also in deeper aquifers which would be more difficult to manage by water providers . Free-living Hartmannella amoebae were also recovered from the B . pseudomallei positive biofilm . Similar to other opportunistic pathogens , B . pseudomallei is able to survive within amoebae as shown in laboratory experiments [52 , 53] . Survival within amoebae increases the pathogens’ resistance to chlorination [53] . Community HF was built in a coastal swamp area with shallow unconfined aquifers . Accordingly , the groundwater was buffered with the highest levels of various nutrients and metals . Untreated samples had the largest microbial richness and the pipes were covered in biofilms and iron deposits of a firm and scaly nature . Water from HF had a lower redox potential indicating a more reducing environment and organic carbon levels were high . Consequently , despite the high iron levels no Gallionella were recovered but instead dissimilatory iron reducers or nitrate-dependent anaerobic iron oxidizers like Geobacter , Azospirillum or Ferrovibrio [54] . The sulphur oxidizing Thiobacillus or Thiothrix were also detected . Bacteria of these genera cause biogenic sulphuric acid corrosion of concrete and they produce sulphates used by sulphate reducing bacteria such as Desulfovibrio or Desulfobulbus , both of which were also found at HF and less so at MF [45] . Sulphate reducing bacteria are involved in anaerobic corrosion or pitting of iron or steel by producing hydrogen sulphide and promoting anaerobic iron oxidation [55] . The untreated tank and rising main of the HF water supply showed abundant microbial life which flourished in the warm nutrient-rich water with high heterotroph counts , coliforms and Hartmannella and Naegleria lovaniensis amoebae feeding on the bacteria . It was of interest that these samples were also rich in Sphingomonadaceae bacteria which have been identified as an abundant member of the intra-amoebal microbiota in drinking water [56] . They have also been described in biofilms of chlorinated parts of water supplies and may be a reservoir of antibiotic resistant genes [57] . Water and biofilms of the tank also grew P . aeruginosa , an opportunistic pathogen primarily known for its pathogenicity in nosocomial settings and potential spread of antibiotic resistant genes in water distribution systems [58 , 59] . In contrast to P . aeruginosa , there were no B . pseudomallei detected in the tank . Instead , B . pseudomallei was cultured from the shallow HF bore . Similar to the B . pseudomallei positive bore at MF , this bore only had scarce heterotrophic growth . Heterotrophic microbes require organic carbon for growth and HPC are routinely used by water providers to monitor the integrity of the supply and to indicate surface water contamination or presence of biofilms [22] . In this study , increased HPC did not match the presence of B . pseudomallei . Genome analysis of the B . pseudomallei isolates revealed the presence of the YLF gene cluster and fhaB3 gene in isolates from the MF bore . The YLF cluster is more common in B . pseudomallei from Southeast Asia and remote parts of the Northern Territory [60 , 61] while fhaB3 has been associated with B . pseudomallei positive blood culture as opposed to localized skin lesions [61] . LPS type B was found in B . pseudomallei from the HF supply together with the bimA-Bm gene . Both these genetic markers are more common in B . pseudomalllei from remote NT and bimA-Bm is also more widespread in Southeast Asia [62] . The bimA-Bm gene has been associated with neurological disease [61] . A phylogenetic tree with the water supply and other NT isolates showed no closely related B . pseudomallei isolates of clinical origin . Nitrifying Nitrospiraceae were abundant in the untreated biofilms . Their production of nitrates provides a source of nutrients increasing biofilm mass [63] . Most nitrifiers identified in this study belonged to the genus Nitrospira common in drinking water with a preference for low nutrient or low nitrite environments [41 , 64] . It was of interest that nitrate producing Nitrospiraceae were associated with B . pseudomallei positive samples . B . pseudomallei is a denitrifier under anaerobic conditions and in one study , B . pseudomallei load increased in sand upon nitrate treatment while in another study , B . pseudomallei was associated with soil containing elevated total nitrogen [65 , 66] . More research is needed to further explore this potential commensal relationship . Chlorination successfully contained B . pseudomallei and P . aeruginosa and reduced nuisance organisms . Similar to other studies , water treatment had the largest impact on the microbiota [67 , 68] . The largest reduction in bacterial richness was observed for the MF water supply . Water disinfection of this water supply also included UV treatment apart from chlorine gas . Gammaproteobacteria were more abundant in chlorinated samples across all water supplies and members of this taxa are more resilient to higher chlorine levels and oxidative stress compared to Alpha- and Betaproteobacteria [69 , 70] . One chlorinated site of the MF water supply had abundant DNA of several sequence variants of another group of opportunistic pathogens , called non-tuberculous mycobacteria . Further investigations are needed to establish whether these were from viable bacteria . Environmental mycobacteria are known to persist in water supplies and can cause disease in immunocompromised people or people with chronic lung disease [71] . Due to the low biomass of many samples in this study , the inclusion of several negative controls proved crucial . Various sequence variants in chlorinated samples were also detected in negative controls such as those of Ralstonia or Pseudomonas . This made it difficult to differentiate between hardy bacteria persisting in various environments including chlorinated water or mere contaminants of laboratory reagents and DNA extraction kits [36 , 72] . As outlined in the methods , utmost care was taken in excluding samples with low sequence numbers and/or similarity to microbial fingerprints of negative controls and excluding potential contaminant sequence variants . Subsequent studies will use larger water sample volumes and filters with smaller pore size to increase biomass and ensure capturing microbes of all sizes [70] . Overall , there were no significant differences in the microbiota between bulk water and biofilms; this was particularly the case for the turbid water of the MF supply with a high level of suspended solids . Swabs were used to collect biofilms which primarily captured the top layer of biofilms or microbes associated with suspended solids and loose deposits as opposed to other studies which scraped the biofilm off pipes or grew them on coupons inserted into pipes [73] . Nevertheless , we found untreated biofilms to be more heterogeneous than planktonic microbiota with a distinct microbial fingerprint for each water supply . Sequence variants of various nitrifying families were more common in untreated biofilms compared to untreated bulk water as previously reported [74] . Once the water was treated , the microbiota indeed differed between water and biofilms and the proportion of SVs unique to biofilms also increased while the proportion of SVs shared between the sample types decreased . This matches previous reports of an increase in differences between sample types upon water treatment [67] . In summary , we found that the geochemistry of the source water had a substantial impact on the untreated microbiota with largely different microbial communities in untreated parts of the three water supplies . Accordingly , a multiple barrier approach to improve water quality would have to account for the heterogeneous nature of the microbiota in different water supplies across Northern Australia . We detected three opportunistic pathogen groups; namely non-tuberculous mycobacteria , P . aeruginosa and B . pseudomallei . In contrast to our working hypothesis , B . pseudomallei was cultured from a bore accessing a deeper aquifer and future investigations across seasons will determine whether B . pseudomallei indeed occurs in deeper confined aquifers or is mainly linked to surface or shallow aquifer water intrusions during the wet season , with the latter easier to manage for a water provider . Similar to other opportunistic pathogens in water supplies [20] , B . pseudomallei was cultured from bulk water with low organic carbon and scarce heterotrophic growth . This matches its ability to thrive under nutritionally poor conditions [16 , 48] but also indicates that HPC routinely used by water providers to monitor the supply integrity is a poor indicator for B . pseudomallei presence . We also detected B . pseudomallei in a multi-species biofilm linked to iron bacteria . Further research is needed to examine these interactions as Gallionella is increasingly used in biological iron-removal filters . This study provided a first snapshot of the microbiota in a selection of remote water supplies informing future studies to ultimately improve management guidelines for water supplies in the wet-dry tropics . | Water providers in the wet-dry tropics of Northern Australia face additional challenges to keep drinking water microbiologically safe . The source water is often rich in iron-cycling bacteria leading to excessive biofilm formation in pipes and it can also contain the emerging opportunistic pathogen Burkholderia pseudomallei causing the severe disease melioidosis in humans and animals . We know very little about the ecology of microbes in remote community water supplies , so to start to fill this gap we assessed the microbial composition from the source to the distribution of three remote water supplies . We not only found that the geochemistry of the source water had a substantial impact on the composition of the iron-cycling bacteria but B . pseudomallei was cultured from source water with low organic carbon but elevated iron levels and from a multi-species biofilm linked to iron bacteria . No B . pseudomallei were detected in treated water; however , abundant DNA of another opportunistic pathogen group , non-tuberculous mycobacteria , was recovered from treated parts of one water supply . This work lays the foundation for future studies to ultimately improve management guidelines for water supplies in the wet-dry tropics . | [
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| 2019 | Opportunistic pathogens and large microbial diversity detected in source-to-distribution drinking water of three remote communities in Northern Australia |
Zaire ebolavirus ( ZEBOV ) infections are associated with high lethality in primates . ZEBOV primarily targets mononuclear phagocytes , which are activated upon infection and secrete mediators believed to trigger initial stages of pathogenesis . The characterization of the responses of target cells to ZEBOV infection may therefore not only further understanding of pathogenesis but also suggest possible points of therapeutic intervention . Gene expression profiles of primary human macrophages exposed to ZEBOV were determined using DNA microarrays and quantitative PCR to gain insight into the cellular response immediately after cell entry . Significant changes in mRNA concentrations encoding for 88 cellular proteins were observed . Most of these proteins have not yet been implicated in ZEBOV infection . Some , however , are inflammatory mediators known to be elevated during the acute phase of disease in the blood of ZEBOV-infected humans . Interestingly , the cellular response occurred within the first hour of Ebola virion exposure , i . e . prior to virus gene expression . This observation supports the hypothesis that virion binding or entry mediated by the spike glycoprotein ( GP1 , 2 ) is the primary stimulus for an initial response . Indeed , ZEBOV virions , LPS , and virus-like particles consisting of only the ZEBOV matrix protein VP40 and GP1 , 2 ( VLPVP40-GP ) triggered comparable responses in macrophages , including pro-inflammatory and pro-apoptotic signals . In contrast , VLPVP40 ( particles lacking GP1 , 2 ) caused an aberrant response . This suggests that GP1 , 2 binding to macrophages plays an important role in the immediate cellular response .
Zaire ebolavirus ( ZEBOV ) is a member of the family Filoviridae within the order Mononegavirales [1] . It was discovered in 1976 in what is now the Democratic Republic of the Congo [2] as the etiological agent of a severe human viral hemorrhagic fever known as Ebola Hemorrhagic Fever ( EHF ) . Infection with ZEBOV typically results in a rapidly fatal illness associated with high-level viremia , lack of an effective immune response , drastic lymphopenia , a severe coagulation disorder including disseminated intravascular coagulation and limited hemorrhages , widespread focal tissue necroses , systemic shock and multiorgan failure ( reviewed in detail in [3] ) . While the pathogenesis of ZEBOV infection has been relatively well described in experimental animals [4] , [5] , only a few studies were reported that shed light on the molecular events following infection in humans . Unfortunately , these studies are partially contradictory . For instance , higher serum cytokine concentrations ( IFN-α , IFN-γ , IL-2 , IL-6 , and TNF-α ) were measured in seven fatally infected patients compared to two survivors in one study , suggesting that a hyperactive immune responses may contribute to fatal outcome [6] . Other studies , describing the responses of eight fatally infected patients and four survivors , did not reveal significant concentration differences of IFN-α , IL-2 , and TNF-α . On the other hand , that study suggested that fatal infections are due to generalized immunosuppression , including decreased IFN-γ , IL-2 , and IL-4 concentrations , lymphocyte apoptosis , and diminished IgG synthesis [7] , [8] , [9] , [10] . The largest study to date included 42 fatally infected patients and 14 survivors . Hypersecretion of proinflammatory cytokines , chemokines and growth factors ( IL-1β , IL-1RA , IL-6 , IL-8 , IL-15 , IL-16 , CXCL1 ( GROα ) , CXCL10 ( IP-10 ) , eotaxin , M-CSF , MIP-1α , MIP-1β , MCP-1 , MIF ) and decreased concentrations of T lymphocyte-derived cytokines ( IL-2 , IL-3 , IL-4 , IL-5 , IL-9 , IL-13 ) concomitant to apoptotic loss of CD4 and CD8 T lymphocytes were typical for fatal cases [11] . Unfortunately , all these data reveal only the extent of homeostatic disarray in ZEBOV-infected individuals , but not its origin or genesis . It is therefore important to measure the responses of individual human cell types to infection , ideally in chronological order of their infection in vivo . Mononuclear phagocytes are very early , if not initial , targets of ZEBOV in humans and experimentally infected animals [12] , [13] , [14] , [15] . In vitro , human and nonhuman primate macrophages are highly susceptible to ZEBOV infection with subsequent robust virus production [16] , [17] , suggesting they may be the major source of the high viremia observed during the critical stages of infection . Studies performed in vitro are also strongly indicative that macrophages play a major role in inducing cytokine/chemokine dysregulation . For instance , human monocytes and macrophages infected with ZEBOV react with increased expression of MCP-1 , CXCL1 , IL-1β , IL-6 , IL-8 , MIP-1α , RANTES and TNF-α [16] , [18] . Previous studies revealed that similar increased levels of expression of some of these cytokines were triggered by incubation of human macrophages with Ebola virus-like particles ( VLPs ) consisting of the ZEBOV matrix protein VP40 and the spike glycoprotein ( GP1 , 2 ) [19] or with UV-inactivated ZEBOV [16] . These findings indicated that virus replication might not be required for the activation of macrophages . The aim of the current study was to determine the gene expression profiles of human macrophages exposed to infectious Ebola virions using DNA microarray technology ( reviewed in [20] , [21] , [22] , [23] , [24] ) and therefore elucidate virus-host interactions . Here , we determine the initial responses of human macrophages to Ebola virion exposure . In a first set of experiments , DNA microarray analysis was performed to determine gene expression profiles of human macrophages 1 h and 6 h after in vitro exposure to Ebola virions in comparison to mock-exposed cells . In parallel , macrophages were treated with LPS to assess the responsiveness of the cells and to compare the response to different stimuli as genes responding to both virions and LPS might highlight possible response pathways . In a second set of experiments , we distinguished between responses induced by virion binding/entry and responses that require virus gene expression , cellular signals occurring after virion entry by exposing macrophages to Ebola VLPs . We found that Ebola virions exposure , as well as exposure to VLPs can trigger most of the detected changes after 1 h of exposure , and thus independent of virus replication .
Primary human macrophages were obtained from two sources . For the first set of experiments , fresh elutriated primary human monocytes from three donors ( D1 , D2 , D3 ) were purchased from Advanced Biotechnologies , Columbia , MD . For the second set of experiments , primary human monocytes from three donors ( Poietics CD14+ , untreated 2W-400 series ) ( D4 , D5 , D6 ) were purchased from Cambrex Bio Science Walkersville ( Walkersville , MD ) . For differentiation of monocytes into macrophages , cells were cultivated in RPMI 1640 ( Invitrogen , Carlsbad , CA ) containing 20% heat-inactivated human AB serum ( Sigma-Aldrich , St . Louis , MO ) , penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml ) , and L-glutamine ( 2 mM ) . Human embryonic kidney ( HEK ) 293T epithelial cells and grivet ( Chlorocebus aethiops ) kidney epithelial Vero E6 cells ( ATCC , Rockville , MD ) were maintained in DMEM ( Invitrogen , Carlsbad , CA ) containing 10% heat-inactivated fetal bovine serum ( Invitrogen , Carlsbad , CA ) , penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml ) , and L-glutamine ( 2 mM ) . All cells were incubated at 37°C in a humidified 5% CO2 environment . The Mayinga strain of Zaire ebolavirus ( ZEBOV ) , isolated in 1976 [2] , was used for all infections , which were performed under biosafety level 4 conditions at the National Microbiology Laboratory of the Public Health Agency of Canada in Winnipeg , Manitoba . Prior to use , virus stocks were propagated in Vero E6 cells and clarified by centrifugation at 3 , 000 g for 10 min at 4°C . Supernatants were then layered on TNE buffer ( 20 mM Tris [pH 7 . 5] , 0 . 1 M NaCl , 0 . 1 mM EDTA ) containing 20% sucrose and spun at 28 , 000 rpm at 4°C for 2 h by using an SW28 rotor with a Beckman Optima L-70K ultracentrifuge . The virion pellet was resuspended in RPMI 1640 and titers were determined by plaque assay as previously described [25] . As a control , supernatant from mock-infected Vero E6 cells was purified and quantified by the same procedures . Generation and purification of virus-like particles ( VLPs ) were performed as described elsewhere [19] . In brief , ZEBOV VLPVP40-GP and VLPVP40 were generated by transient transfection of HEK 293T cells with plasmids encoding ZEBOV VP40 and/or ZEBOV GP1 , 2 [19] and quantitated by electron-microscopic particle counting [26] and a DC protein assay ( BioRad , Mississauga , Ontario ) . Electron-microscopic evaluation of VLPs was performed on a Phillips CM100 microscope with low dose software and Compustage attachments . Negative staining was performed on formvar carbon-coated copper grids ( Electron Microscopy Sciences , Hatfield , PA ) . Purified VLP solution ( 13 µl ) was exposed to a freshly glow-discharged grid for 2 min , and the grid then transferred to a drop of 1% sodium silicotungstate ( pH 7 . 5 ) for 1 min . The liquid was carefully removed by applying Whatman® paper at the edge of the grid . The grid was air dried for at least 1 h before electron-microscopic examination . Images were recorded at machine magnifications of 56 , 000× and 72 , 000× on Direct Positive Film 5302 ( Kodak , Rochester , NY ) . Prior to use , virion stocks , mock stocks , VLPs , and media were analyzed for endotoxin presence using the Limulus amebocyte lysate test ( BioWhittaker , Walkersville , MD ) . Six days after seeding , growth medium of human macrophages was replaced with fresh RPMI 1640 containing 2% human AB serum , penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml ) , and L-glutamine ( 2 mM ) . Cells were then incubated for one day at 37°C in a humidified , 5% CO2 environment . Cells from donors D1 , D2 , and D3 were infected with either mock virus preparation or ZEBOV at a multiplicity of infection ( MOI ) of 10 . Cells of donors D4 , D5 , and D6 were infected with ZEBOV at an MOI of 100 , mock virus preparation , ∼100 particles/cell VLPVP40-GP , ∼100 particles per cell VLPVP40 , mock VLP ( plasmid only ) , ∼100 latex particles/cell , or 10 ng/ml of lipopolysaccharide ( LPS ) . Cell supernatants were removed from the cells 1 h or 6 h post infection , and RNA from cells was purified using the RNeasy Mini Kit ( QIAGEN , Valencia , CA ) according to the manufacturer's instructions . The analysis of purified RNA from 10 MOI was analyzed by DNA microarrays as outlined below . The RNA obtained after infection with 100 MOI was subjected to quantitative real-time RT-PCR analysis only . Genechip technology from Affymetrix ( Santa Clara , CA ) was used to study the transcriptional activity of human genes using the human GeneChip® array HG-U95Av2 with a total of 12 , 626 probes representing approximately 10 , 000 full-length genes ( Affymetrix Technical Note to Human Genome U133 Genechip set ) . Normalized signal values were generated from image raw data and used to calculate p values indicating significance levels for signal strength ( absent or present call ) and log2 ratio values in comparison files ( change , increase , decrease calls ) using the Affymetrix Microarray Suite 5 . 0 ( MAS 5 . 0 ) . Analysis was performed on these normalized data with a reduced set of genes after removing all genes that were absent on all arrays . The remaining data included 8 , 861 probe sets . Additional data reduction was achieved by excluding all genes that were identified as ‘no change’ ( NC ) in all six experiments when comparing Ebola virion-exposed with mock-exposed cells , respectively . The remaining dataset included 2 , 025 upregulated or downregulated genes . An analysis of variance ( single factor ANOVA ) and determination of the correlation coefficient ( R2 ) of changes in gene expression levels between ZEBOV-treated and VLP-treated cells was performed using Microsoft Office Excel . Array data were displayed on MA plots ( Figure S1 ) , i . e . on a scatter plot showing the correlation between the average log2 intensity versus log2 ratio for a ZEBOV-treated versus mock-treated pair of cells . Log intensity was calculated as ½ ( log ( virion ) +log ( mock ) ) . Genes unchanged between test and control have a log2 value of zero; downregulated genes have negative values; and upregulated genes have positive values . Hierarchical clustering was performed using Stanford's GeneCluster and displayed with the TreeView program [27] . Clustering selection used average linkage clustering with the correlation uncentered . InnateDb ( www . innatedb . com ) is a publically available resource which , based on levels of either differential gene expression , predicts biological pathways based on experiment fold change datasets [28] . Pathways are assigned a probability value ( p ) based on the number of genes present for a particular pathway as well as the degree to which they are differentially expressed or modified relative to a control condition . For our investigation input data was limited to the subset of 2 , 025 genes identified above . Additionally , functional networks were created using Ingenuity Pathway Analysis ( IPA ) software ( Ingenuity Systems , Redwood City , CA ) . Those genes with known gene symbols and their corresponding expression values were uploaded and mapped to their corresponding gene objects in the IPA Knowledge Base . Networks of these genes were algorithmically generated based on their connectivity and assigned a score . Genes are represented as nodes , and the biological relationship between two nodes is represented as an edge ( line ) . The intensity of the node color indicated the degree of up- or down-regulation . Genes in uncolored notes were not identified as differentially expressed in our experiment and were integrated into the computationally generated networks on the basis of the evidence stored in the IPA knowledge memory indicating a relevance to this network . Reverse transcription and subsequent quantitative real-time polymerase chain reaction ( qPCR ) were performed as described previously [19] . Strand-specific qPCR was performed using strand-specific primers for the reverse transcriptase reaction . As controls for non-specific or self-priming events , control reverse transcriptase reactions lacking primer were performed in parallel . Relative amounts of different strands were determined by normalizing against the house-keeping gene GAPDH and by subtracting the amounts of PCR product resulting from self-priming from strand-specific products . Relative quantification was performed using the comparative CT method ( Applied Biosystems User Bulletin #2 , Dec . 11 , 1997 ) .
DNA microarray technology was used to determine the initial response of human macrophages exposed to Ebola virions . Total RNA isolated from primary human macrophages of three donors ( D1 , D2 , D3 ) at 1 h and 6 h after in vitro exposure to purified Ebola virions was compared to RNA from mock-exposed cells derived from the same donors . The time points were chosen as virus gene expression does not occur within one hour of cell-virion contact , whereas it will have commenced five hours later while ZEBOV replication is still absent ( see below ) . A total of 12 HG-U95Av2 GeneChip microarrays were analyzed to determine differences in cellular gene expression levels between Ebola virion-exposed and mock-exposed macrophages from donors D1–D3 and also compared to the responses of LPS-treated cells . Genes detected as absent ( p≤0 . 05 ) on all 12 arrays were removed from a total of 12 , 606 probe sets prior to ratio analysis resulting in a reduced number of 8 , 861 probe sets . The log2 ratio was plotted as a function of the average log2 intensity of Ebola virion-exposed versus mock-exposed samples to test for hybridization quality and for the extent of variation among biological replicates ( cells from the three donors ) . The MA plots for both the 1 h and 6 h time points demonstrate a low variability among donors and good overall reproducibility with the mean log2 being zero over the entire signal intensity range ( Figure S1 ) . The low overall variability among donors was corroborated quantitatively by an analysis of variance of the signal strength . A single factor analysis of variance ( ANOVA ) indicated no significant difference among the 12 arrays ( p = 0 . 83 ) , confirming that the expression of the majority of cellular genes was not affected by exposure to Ebola virions . Genes responsive to Ebola virion exposure were identified by data reduction , i . e . by exclusion of all genes that were identified as ‘no change’ ( NC , p≤0 . 05 ) for all conditions tested . The resulting 2 , 025 probe sets ( genes ) were characterized by at least one significant change in one donor at either 1 h or 6 h post infection . ANOVA indicated that differences in expression patterns among these probe sets were statistically significant ( p = 3×10−6 ) . The 2 , 025 genes identified were then screened for patterns of cellular expression changes that could be biologically relevant . Two types of selection criteria were designed to determine whether expression of a cellular gene was significantly affected by Ebola virion exposure , threshold-based criteria and trend-based criteria . First , thresholds for fold-change , p-values , and signal strength were established . Specifically , a minimum of a 2-fold-difference in cellular gene expression levels in infected macrophages from at least two of the three donors in at least one of the two time points and p-values<0 . 05 were defined as pertinent ( Figure 1 ) . Second , it was acknowledged that in the case of most genes the extent of changes in gene expression required for a biological impact are unknown . For instance , some genes might respond to virus infection with only minor changes in expression levels that may still be biologically relevant . Vice versa , rather dramatic changes may prove to be biologically irrelevant . Consequently , trend selection criteria were established to screen for a common tendency or direction of changes in cellular gene expression levels . Only changes with an associated p-value of ≤0 . 01 were considered , and no unique cutoff value for fold-change was specified . Accordingly , changes of less than 2-fold were accepted if the direction of change in macrophages from all donors was the same ( all increased or all decreased ) . The two resulting gene sets were analyzed by hierarchical clustering ( Stanford Cluster software ) and the resulting clusters were visualized using TreeView ( Figures 1 & 2 ) . Application of the threshold-based selection criteria identified 205 cellular genes whose expression changed upon Ebola virion exposure ( Figure 1 ) . Clustering of these 205 genes revealed two subclusters . Gene subcluster 1 was characterized by extensive expression variability across donors and time points . Inspection of transcript levels ( signal strength ) revealed very low signals , which may be a reason of the apparent variability among biological replicates . In contrast , many genes in subcluster 2 show consistent responses among all three donors in at least one time point . However , there are some genes that show similar responses in cluster 1 . Since the overall variability in subcluster 1 could be due to low signal strength the dataset of 205 genes was subjected to the additional requirement that of the two compared signal levels the higher one ( i . e . mock-signal for upregulated genes , Ebola-signal for downregulated genes ) has a minimum signal value of 100 ( signal threshold ) . Fifty-one genes remained when this added threshold criterion was applied . Most of them were located in subcluster 2 and are listed in Table S1 ( threshold selection ) . The majority of the 51 genes were upregulated at 1 h post infection , at 6 h , or at both time points . The second set of criteria , designed for selecting a trend of responses rather than assuming a threshold for fold-change , yielded a total of 66 probe sets ( 63 genes ) ( see Figure 2 and Table S1 , trend selection ) . Approximately half of them were characterized by a fold-change of ≥2 and were also identified after the first set of selection criteria was applied . A total of 88 genes were identified after either criterion was applied . Among those 88 genes from threshold and trend analysis ( Table S1 ) , 26 fulfilled both selection criteria . Twenty-one of these genes were characterized by altered expression levels following exposure to Ebola virions in the same direction as after treatment with LPS . Interestingly , the expression levels of cellular genes identified by applying the trend analysis ( Table S1 & Figure 2 ) , all changed at the 1 h time point . Importantly , this set includes genes whose elevated expression was previously associated with human Ebola virus disease , such as the genes encoding CXCL1 , IL-1β , IL-6 , IL-8 , and TNF-α [6] , [11] , as well as genes previously unknown to play a role in Ebola virus disease , such as those encoding CCL-20 , COX-2 , IL-15 receptor α , phosphodiesterase 4B , and t-Pa . Expression levels of almost all identified genes were upregulated , with the exception of genes encoding GNA13 , CREM , and LILRB2 at the 1 h time point and MRPS6 , GADD45 , and DUSP2 at the 6 h time point . Recently , the integration of bioinformatics to complex biological data sets has provided a network-based approach for delineating the host response . As cellular responses are mediated through the selective activation or repression of signaling pathways we sought to integrate our gene expression data into functional signaling networks . Functional networks were created from our biological data sets using Ingenuity Pathway Analysis ( IPA ) ( Figure 3 ) . Genes belonging to these functional networks were related to cell-to-cell signaling and interactions , hematological system development and function , immune cell trafficking , inflammatory response and cell movement . To expand on the biological significance of this analysis , and identify individual signaling pathways modulated following infection , we performed pathway over-representation analysis ( ORA ) with the online software tool InnateDB ( www . innatedb . com ) [28] and analyzed the 1 h and 6 h Ebola-infected vs . mock-infected comparative data sets . Integrated data was limited to the 2 , 025 genes identified above and fold-changes >1 . 5 and associated p-values>0 . 01 were chosen as parameters for pathway ORA . The resultant differentially regulated pathways with p-values<0 . 1 are presented in Tables 1 and 2 . Pathway ORA of the data set for the 1 h Ebola-infected vs . mock-infected treatments identified a number of pathways directly related to activation of the G-protein coupled receptor pathway ( Table 1 ) . As chemokines and inflammatory mediators activate GPCR signaling this correlates with previous investigations demonstrating increased secretion of cytokines and chemokines following Ebola virus insult [16] , [18] . It was also demonstrated that many of the genes identified as central nodes in the IPA functional networks ( IL-6 , IL-10 , IRF-7 , etc . ) also occupied central positions in the signaling pathways identified with InnateDB ( heterotrimeric GPCR signaling pathways ) . Indeed , the upregulation of pathways related to interleukin ( IL ) -2 , IL-12 , IL-23 and IL-27 signaling pathways were also identified as being differentially upregulated in Ebola-infected cells as compared to the mock-infected treatment ( Table S1 ) . Further , GPCR-related signaling pathways ( cytokine-cytokine receptor interaction pathway ) and the Jak-Stat signaling pathway were also upregulated during the immediate response to infection . Interestingly , pathways related to fibrinolysis ( dissolution of fibrin clot pathway; fibrinolysis pathway ) were also upregulated by Ebola infection as compared to the mock-infected control ( Table 1 ) . Previously , dysregulation of the fibrinolytic system was recognized in Ebola-infected macaques [13] . A limited number of downregulated pathways were identified in our analysis as being significantly downregulated at 1 h post-infection and were largely related to metabolic processes as well as inhibition of the negative regulation of GPCR signaling ( Table 1 ) . In contrast , differentially upregulated signaling responses at the 6 h time point were largely related to cell adhesion ( syndecan-1-mediated signaling events ) , metabolism ( prostaglandin and leukotriene metabolism; arachidonic acid metabolism; steroid hormone metabolism ) and cytokine/chemokine signaling ( cytokine-cytokine receptor interaction; chemokine signaling pathway; chemokine receptors bind chemokines ) ( Table 2 ) . Interestingly , there was a large increase in the number of downregulated pathways at the 6 h time point as compared to the 1 h time point . Multiple pathways belonging to transcription and nucleotide/nucleoside metabolism were identified as being significantly downregulated in Ebola-infected cells as compared to the mock-infected controls 6 h post-infection ( Table 2 ) . Comparison of the differentially regulated pathway ORA data sets between the 6 h Ebola-infected vs . mock-infected and LPS-stimulated vs . mock-stimulated treatments demonstrated minimal overlap of upregulated or downregulated pathways ( data not shown ) . Whereas LPS stimulation resulted in the upregulation of pathways largely related to TNF-α , Toll-like receptor ( TLR ) or apoptosis signaling pathways these were not identified in the Ebola-infected samples . Thus , this likely indicates that the pro-inflammatory response to Ebola may be largely repressed by a yet unidentified mechanism at early time points following infection . DNA microarrays are sensitive and specific in identifying regulated transcripts , but the technique commonly leads to underestimation of the degree of fold-change in gene expression . Comparison of the fold-changes in gene expression determined by DNA microarrays to a standardized quantitative real-time RT-PCR demonstrated that the underestimation error was larger when the fold-change differences were higher , corresponding to the finding that the quantitative range of real-time RT-PCR is larger than of DNA microarrays [29] . Consequently , real-time RT-PCR was performed for a subset of 16 selected genes that fulfilled at least one of the selection criteria to verify the results obtained by DNA microarray analysis . Some of the selected genes served as controls and are known to be influenced by ZEBOV infection , however , not necessarily at these early time points ( TNF-α , IL-1β , IL-6 , IL-8 , CXCL1 , RANTES , IL-10 ) . Some genes were selected according to their potential relevance in infection due to their known functions and roles in modulation of immune response , viral infections or signaling pathways ( hCOX-2 , CCL20 , 4-1BB , CSF-1 , G-CSF , INDO , RSAD2 , t-PA , DDX5 ) . Verification was achieved when the direction of gene expression levels was identical and when the expression level changes determined by real-time PCR were equal or stronger than those determined by DNA microarray analysis . Figure 4 illustrates the comparison of the results of microarray analysis and real-time PCR quantifications over both time points . Real-time PCR confirmed the direction of changes in gene expression levels determined by microarray analysis and verified that most changes in cellular gene expression already occurred at the 1 h time point . In particular , upregulation of genes known to play an important role in Ebola hemorrhagic fever ( CXCL-1 , IL-1β , IL-6 , IL-8 , and TNF-α [6] , [11] ) was verified , although some variation was seen among donors . The observation that most cellular responses occurred within 1 h after exposure to Ebola virions suggested that virion binding/entry , rather than expression of virus proteins , initiates them . This hypothesis is based on the absence of virus protein expression during the first hour of virion exposure . To investigate this assumption , real-time RT-PCR quantification , specific for either the negative-stranded ZEBOV genome ( vRNA ) or for the positive-stranded complementary ZEBOV cRNAs , was performed on RNA purified from macrophages at 1 h and 6 h after virion exposure . The obtained data ( Figure 5 ) confirmed that at 1 h cRNA amounts were below that of genomic RNA , whereas after 6 h cRNA was greatly increased . LPS served as a positive control for the responsiveness of the macrophages used in the virion exposure studies . A comparison of the effects of Ebola virion exposure versus LPS treatment revealed that the majority of genes responded similarly ( Supplemental Table 1 ) , further substantiating the hypothesis that the observed responses with Ebola virions were triggered by binding and/or entry . To further investigate which changes in expression levels in macrophages occur due to virion binding/entry , macrophage responses following exposure with infectious virions were directly compared to exposure to Ebola virus-like particles ( VLPs ) lacking viral genetic material and containing only the ZEBOV matrix protein VP40 and the ZEBOV spike glycoprotein GP1 , 2 ( VLPVP40-GP ) ( see also [19] ) . GP1 , 2 is the only known surface-exposed structural component of Ebola virions and contains the cell-surface receptor-binding domain [30] , [31] . We therefore hypothesized that if virion binding is responsible for the observed macrophage responses , then these effects would be mediated by GP1 , 2 . To evaluate the role of GP1 , 2 , VLPs consisting of VP40 , but lacking GP1 , 2 ( VLPVP40 ) , were tested in parallel with VLPVP40-GP . VLPVP40 particles were shown previously to be morphologically similar to the GP1 , 2-containing VLPs [32] , [33] . LPS was again used as a control for macrophage responsiveness and also to directly compare the LPS-induced responses to those of Ebola virion and VLP exposures . Additionally , cells were exposed to latex particles , which were used in approximate equal quantity to VLPs . Ebola VLP preparations were tested by negative-stain electron microscopy for authentic appearance and presence ( VLPVP40-GP ) or absence ( VLPVP40 ) of GP1 , 2 ( data not shown ) . Particles resembled those previously described [32] , [33] . In addition , Ebola virion , VLP , and latex preparations were tested for endotoxin-contamination before incubation with macrophages from donors D4 , D5 , and D6 . The endotoxin concentrations of all samples used in this study were not above those of the tissue culture media ( <0 . 5 U/ml ) . To determine whether the quantitative differences in gene expression levels between cells of donors D1–D3 observed in the first experiment were due to the MOI used , a higher MOI of Ebola virions ( ≈100 ) was used for this experiment . Cells were exposed to VLPs and latex particles at a concentration of 100 particles per cell . LPS was used at the same concentration as in the first experiment ( 10 ng/ml ) , which allowed for a direct comparison of the responsiveness of macrophages used in both experiments . The results revealed that while a higher MOI did increase the overall intensity of responses compared to the first experiments ( compare to Figure 4 ) , it did not eliminate or noticeably reduce the degree of variation of gene expression levels between donors ( Figure 6C & 6D ) . This suggests that genetic variation among identical cell types of different donors may have an influence on the effect of ZEBOV infection and therefore may influence chances of survival . Since all stimuli were given in parallel to macrophages from each donor , this experiment allowed for a direct qualitative comparison of the cellular responses to the different stimuli . Cellular gene expression levels were determined by real-time RT-PCR specific for 21 genes that were selected from the list of genes established using DNA microarray analysis in the first experiment . These 21 genes included the majority of genes previously analyzed by real time PCR ( Figure 4 ) , as well as additional genes that were suspected or known to play a role in ZEBOV infection in vivo . The results of the real-time PCR quantification were analyzed by two-dimensional cluster analysis . The first clustering was performed to group genes according to similar behavior in expression changes ( upregulation or downregulation ) following a stimulus ( Figure 6A ) . This first cluster was then used for clustering in a second dimension to group experimental stimuli according to similar effects they had on changes in gene expression ( Figure 6A , right cluster ) . Analysis revealed genes that were upregulated at the 1 h and then downregulated at the 6 h time point ( GADD45 , DUSP2 , IL-10 ) , genes that were first downregulated and then upregulated ( IDO , ISG 45K ) , a large group of genes induced at both time points , and finally genes that were downregulated at both time points ( TLR3 , GNA13 ) . The second clustering ordered the experimental groups according to similar responses in gene expression ( Figure 6B ) and revealed the following three major clusters: 1 ) 1 h exposure to Ebola virions , LPS , and VLPVP40-GP , 2 ) 6 h exposure to Ebola virions , LPS , and VLPVP40-GP , and 3 ) 1 h and 6 h exposure to VLPVP40 and latex particles . This result suggests that Ebola virions , LPS , and VLPVP40-GP caused strikingly similar responses in comparison to the responses to VLPVP40 and latex particles . This further supports the notion that binding/entry of virions mediated by GP1 , 2 plays a significant role in the host response to infection . Thirteen of the 21 genes tested responded to Ebola virions , VLPVP40-GP , and LPS at both time points . TNF-α , IL-1β , IL-6 and IL-8 were also found in a separate study on the effects of VLPs on macrophages [19] and CXCL1 represents a gene known to be affected by ZEBOV infection . However , COX-2 , GCH1 , GM-CSF , MIP-3α , t-Pa , GADD45A , and IDO ( 6 h ) are genes identified to play a role in Ebola hemorrhagic fever for the first time . Within the first two clusters , various subclusters were identified . One revealed that out of the 13 genes that were upregulated at both time points , 9 responded , albeit weakly , to VLPVP40 at the 1 h time point . This supports the notion that the matrix protein VP40 might also be involved in cellular interactions upon binding/entry , which may participate in triggering part of the detected responses in macrophages , mainly involved in proinflammatory signaling . Exposure to latex particles did not induce a response , indicating that the signals mentioned above were not due to non-specific cellular uptake of particles . Another subcluster differentiated macrophages treated with VLPVP40-GP versus Ebola virions or LPS and included IFN-inducible genes such as RSAD2 and IFIT2 , as well as G-CSF , TF , GADD45 , DUSP2 , IL-10 , IDO , and CD 137lig . Another sub-cluster revealed 3 genes , DUDP2 , IL-10 , and TLR3 , which were downregulated at the 6 h time point , and one gene , GNA13 , which was downregulated at both time points by Ebola virions or VLPVP40-GP but not by LPS . At the 1 h time point , cells responded to Ebola virions and LPS by upregulating GADD45A , DUSP2 , and IL-10 , whereas these genes were downregulated at the 6 h time point . Both stimuli ( virions and LPS ) resulted in upregulation at both time points of TF and 4-1BB . The IFN-inducible genes RSAD2 and IFIT2 , G-CSF ( CSF-3 ) , and GNA13 responded at the 1 h time point to both Ebola virions and VLPVP40-GP and at the 6 h time point to both Ebola virions and LPS . One possible explanation for this delayed response might be differences in the kinetics of the responses between the LPS and virions or VLPVP40-GP stimuli . Of the genes that responded to only two of the presented stimuli , more genes reacted similarly to virions and LPS than to virions and VLP . A direct comparison between responses to virion and VLPVP40-GP exposure is depicted in Figures 6C and D for the 1 h and 6 h time points , respectively . Overall , the cluster analysis and the graphs depicting the real-time RT-PCR quantifications indicate that there was , in general , a significant correlation between the effects of virions and effects of VLPVP40-GP with correlation coefficients of R2 of 0 . 746 and 0 . 9177 at the 1 h and 6 h time points , respectively . In fact , the difference consists of the expression patterns of G-CSF , 4-1BB , TF , RSAD2 ( at 6 h only ) , and DUSP2 , which are the genes identified in the sub-clusters . The real-time RT-PCR also revealed that the changes on gene expression of IL-10 , GNA13 and TLR3 are not significantly changed at these early time points even at 100 MOI infection .
The current study represents the first broad analysis of initial transcriptional responses to Ebola virions of human macrophages , the primary target cells of ZEBOV [12] , [13] , [14] , [15] . To assure accurate identification of cellular genes that responded significantly to virion exposure , DNA microarray data were scrutinized and stringently filtered by two different sets of selection criteria and statistical evaluations . In addition , the expression levels of selected genes were experimentally verified by real-time PCR analysis . Using these methods for statistical verification and assay evaluation , our study permitted the identification of a large number of genes not previously implicated in early cellular responses to ZEBOV infection . The detected changes of gene expression levels occurred within the first hours after primary human macrophages were exposed to Ebola virions in vitro . In addition , we identified genes whose expression is known to be upregulated during acute Ebola hemorrhagic fever . This not only validated the experimental approach and data screening criteria , it also demonstrated that elevated levels of gene products , such as cytokines , are already induced in primary target cells within the first hours of virion binding and entry . Lending further credence to these assertions , pathway ORA of the significantly differentially regulated genes identified from our gene expression studies demonstrated a strong upregulation of specific host signaling pathways related to cytokine/chemokine signaling during the immediate response to Ebola infection . Indeed , the upregulation of signaling pathways related to cytokine/chemokine signaling events suggests that specific innate immune responses are mounted during the acute phase of Ebola viral insult . Through multiple pathway ORA analyses we identified broad cellular functional networks that are modulated during the early course of Ebola infection and , importantly , have correlated this with specific cell signaling pathways . The identities of the individual signaling pathways modulated by Ebola infection will provide critical information regarding disease pathogenesis and as well information for the development of novel antiviral therapeutics . Some of the genes that had already been implicated in the pathogenesis of Ebola hemorrhagic fever such as IL-6 and TNF-α [6] , [11] were induced after 1 h , but returned towards basal levels of expression after 6 h . Indeed , whereas LPS stimulation resulted in the activation of a large subset of TNF-α related signaling pathways at the 6 h time point there were no common pathways identified in the Ebola-infected pathway ORA . Similar results were obtained in another study , during which primary human macrophages , exposed to Ebola VLPVP40-GP for 24 h , were characterized by IL-1β , IL-6 , IL-8 , RANTES , and TNF-α expression that also peaked at 1 h or 6 h before receding to base levels [19] . These results contradict observations in vivo , which demonstrated upregulation of cytokines for extended periods of time [6] , [7] , [8] , [9] , [10] , [11] . It is plausible that the early cytokine peak observed following high MOI infections in vitro is only achieved at a later time during in vivo infection . Another possible explanation for this difference is the duration of stimuli and thereby the duration of responses . For instance , in vivo , progeny virions are continuously produced by ZEBOV-infected cells and will therefore bind to and enter additional target cells , resulting in continuous stimuli that may maintain cytokine production and facilitate prolonged activation . Therefore , our in vitro approach most likely yielded results that are indicative of what continuously occurs in vivo . That increased cellular gene expression levels result in increased protein concentrations within 6 h was previously demonstrated in a study that used human macrophages exposed to VLPVP40-GP and ELISA measuring protein levels of IL-1β , IL-6 , IL-8 , TNF-α [19] . The identified cellular genes whose expression levels were altered after exposure to Ebola virions belonged to different functional categories ( Table S1 ) . These genes include inflammatory cytokines , molecules that regulate blood coagulation ( such as t-Pa , MMP-1 , and serpine 1 and 2 ) genes involved in stress-response , DNA repair , cell cycle arrest , and cell adhesion . In support of these functional categories , our pathway ORA also resulted in similar functional categorization of the differentially regulated signaling pathways following Ebola virus infection . The detection of a group of genes that responded to binding/entry of Ebola virions , VLPVP40-GP , as well as to LPS raises the question whether LPS and Ebola virions share receptors on the macrophage surface . Whereas receptors for Ebola virions on target cells remain elusive , it is known that LPS induces its effects by binding to TLR4 and MD2 and CD14 co-receptors . Recent studies demonstrated that Ebola VLPVP40-GP , but not VLPVP40 , induced cytokine and SOCS1 expression in a TLR4/MD2 dependent manner both in a human monocytic cell line ( THP-1 cells ) and in 293T cells expressing a functional TLR4/MD2 receptor [34] . The innate immune defense is achieved by activating NF-κB and type I IFN responses . It is already known that ZEBOV suppresses the host cell antiviral response by inhibiting interferon signaling via its VP35 and VP24 proteins [35] , [36] , [37] , [38] , [39] , [40] , [41] , and RNA silencing via VP35 [42] . Our studies revealed only limited IFN signaling in Ebola virion-exposed macrophages compared to those exposed to LPS , indicating that IFN signaling is inhibited early upon infection . The host response to virulent pathogens is likely to fall into two categories [21] . First , there are common responses to unrelated pathogens , such as the type 1 IFN innate immune response that renders uninfected neighboring cells resistant to virus infection . Second , there are responses specific for individual pathogens . On the other hand , pathogens have evolved to counter these responses to ensure their own survival and transmission . Examples are how viruses of disparate families overcome the antiviral action of apolipoprotein B mRNA editing enzyme , catalytic polypeptide-like 3G ( APOBEC3G ) [43] , tripartite motif containing 5 ( TRIM5α ) [44] , bone marrow stromal cell antigen 2 ( BST-2 ) /tetherin [45] , [46] , [47] , or interferon induced transmembrane proteins ( IFITM ) [46] , [48] , [49] . It is important to remember that this study partially characterizes the response of humans to ZEBOV infection and that humans are not natural hosts of this virus . Therefore , host responses and virus counteractions are not in a state of equilibrium . It is therefore a fundamentally interesting question whether infected humans succumb to Ebola hemorrhagic fever because of direct effects exerted by the virus on the body or because of overbearing immune responses by the individual . Recently , various frugivorous bats have been implicated as potential filovirus reservoirs that seemingly remain unaffected by infection [50] , [51] . It is therefore tempting to repeat our studies with cells from these animals to see whether their responses to Ebola virion exposure are fundamentally different . Taking together , our data indicate that the immediate responses of early cellular ZEBOV targets in the human organism derive from virion binding/entry mediated by the ZEBOV spike glycoprotein and do not require virus gene expression . The fact that many surveyed genes responded similarly to VLPVP40-GP , but not to VLPVP40 , treatment clearly supports this notion . However , the fact that some genes , such as those encoding TF or skin collagenase , were triggered only by Ebola virions but not VLPVP40-GP or LPS indicate that these genes must be influenced by factors other than virus binding or entry , such as other components packaged within Ebola virions in addition to GP1 , 2 and VP40 . In this regard , it is important to remember that infectious Ebola virions not only consist of seven structural proteins ( NP , VP35 , VP40 , GP1 , 2 , VP30 , VP24 , and L ) but also contain cellular transmembrane proteins that are usurped by budding virions [52] . | Ebola virus causes a severe hemorrhagic fever syndrome in man with high case-fatality rates . Following infection , monocytes and macrophages are among the first cells targeted by the virus . These cells respond by increasing expression of inflammatory cytokines and chemokines that contribute towards pathogenesis . In order to more thoroughly characterize the host response to Ebola infection , primary human macrophages were infected with Zaire ebolavirus and samples harvested for transcriptional changes after 1 or 6 hours post infection . Whereas previous studies have analyzed a relatively small subset of host genes , this study examined the transcriptional profile of over 10 , 000 genes and employed rigorous pathway analyses to the datasets . Ebola virus was found to significantly regulate the expression of over 88 host genes . These changes occurred within the first hours of infection . Subsequent experiments demonstrated that virus replication was not necessary for activation . Indeed , noninfectious virus-like particles expressing the ebolavirus glycoprotein and matrix proteins were sufficient stimuli to induce activation . | [
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| 2011 | Ebola Virion Attachment and Entry into Human Macrophages Profoundly Effects Early Cellular Gene Expression |
Accumulating evidence indicates high risk of bias in preclinical animal research , questioning the scientific validity and reproducibility of published research findings . Systematic reviews found low rates of reporting of measures against risks of bias in the published literature ( e . g . , randomization , blinding , sample size calculation ) and a correlation between low reporting rates and inflated treatment effects . That most animal research undergoes peer review or ethical review would offer the possibility to detect risks of bias at an earlier stage , before the research has been conducted . For example , in Switzerland , animal experiments are licensed based on a detailed description of the study protocol and a harm–benefit analysis . We therefore screened applications for animal experiments submitted to Swiss authorities ( n = 1 , 277 ) for the rates at which the use of seven basic measures against bias ( allocation concealment , blinding , randomization , sample size calculation , inclusion/exclusion criteria , primary outcome variable , and statistical analysis plan ) were described and compared them with the reporting rates of the same measures in a representative sub-sample of publications ( n = 50 ) resulting from studies described in these applications . Measures against bias were described at very low rates , ranging on average from 2 . 4% for statistical analysis plan to 19% for primary outcome variable in applications for animal experiments , and from 0 . 0% for sample size calculation to 34% for statistical analysis plan in publications from these experiments . Calculating an internal validity score ( IVS ) based on the proportion of the seven measures against bias , we found a weak positive correlation between the IVS of applications and that of publications ( Spearman’s rho = 0 . 34 , p = 0 . 014 ) , indicating that the rates of description of these measures in applications partly predict their rates of reporting in publications . These results indicate that the authorities licensing animal experiments are lacking important information about experimental conduct that determines the scientific validity of the findings , which may be critical for the weight attributed to the benefit of the research in the harm–benefit analysis . Similar to manuscripts getting accepted for publication despite poor reporting of measures against bias , applications for animal experiments may often be approved based on implicit confidence rather than explicit evidence of scientific rigor . Our findings shed serious doubt on the current authorization procedure for animal experiments , as well as the peer-review process for scientific publications , which in the long run may undermine the credibility of research . Developing existing authorization procedures that are already in place in many countries towards a preregistration system for animal research is one promising way to reform the system . This would not only benefit the scientific validity of findings from animal experiments but also help to avoid unnecessary harm to animals for inconclusive research .
Reproducibility is a fundamental principle of the scientific method and distinguishes scientific evidence from mere anecdote . The advancement of basic as well as applied research depends on the reproducibility of the findings , and can be seriously hampered if reproducibility is poor . However , accumulating evidence indicates that reproducibility is poor in many disciplines across the life sciences [1] . For example , in a study on microarray gene expression , only 8 out of 18 studies could be reproduced [2]; Prinz and colleagues [3] found large inconsistencies ( 65% ) between published and in-house data in the fields of oncology , women’s health , and cardiovascular diseases; oncologists from Amgen could confirm only 6 out of 53 published findings [4]; and , of more than 100 compounds that showed promising effects on amyotrophic lateral sclerosis ( ALS ) in preclinical trials , none displayed the same effect when retested by the ALS Therapy Development Institute in Cambridge [5] . Besides a waste of time and resources for inconclusive research [6–8] , however , poor reproducibility also entails serious ethical problems . In clinical research , irreproducibility of preclinical research may expose patients to unnecessary risks [9 , 10] , while in basic and preclinical animal research , it may cause unjustified harm to experimental animals [11] . Reproducibility critically depends on experimental design and conduct , which together account for the internal and external validity of experimental results [12] . External validity refers to how applicable results are to other environmental conditions , experimenters , study populations , and even to other strains or species of animals ( including humans ) [12] . Thus , it also determines reproducibility of the results across replicate studies ( i . e . , across different labs , different experimenters , different study populations , etc . ) [11 , 13 , 14] . Internal validity refers to the extent to which a causal relation between experimental treatment and outcome is warranted , and critically depends on scientific rigor , i . e . , the extent to which experimental design and conduct minimize systematic bias [12 , 15] . It has been suggested that poor internal validity due to a lack of scientific rigor may also be a major cause of poor reproducibility in animal research [16–18] . There are various sources of bias ( e . g . , selection bias , performance bias , detection bias ) , and specific measures exist to mitigate them ( e . g . , randomization , blinding , sample-size calculation; [12 , 15 , 19 , 20] ) . To assess the internal validity of studies , e . g . , in the peer review process , and to facilitate replication of studies , publications must contain sufficiently detailed information about experimental design and conduct , including measures taken against risks of bias [20 , 21] . However , systematic reviews generally found a low prevalence of reporting of measures against risks of bias ( further referred to as reporting ) in animal research publications . Thus , reporting ranged from 8% to 55 . 6% for allocation concealment , from 3% to 61% for blinded outcome assessment , from 7% to 55% for randomization , and from 0% to 3% for sample size calculation [19 , 22–29] . Low rates of reporting have been interpreted as evidence for a lack of scientific rigor ( e . g . , [20] ) . Indeed , several systematic reviews found correlations between poor reporting and overstated treatment effects [19 , 29–31] . Reporting guidelines have thus become a major weapon in the fight against risks of bias in animal research [32] . However , although the ARRIVE guidelines ( Animal Research: Reporting of In Vivo Experiments ) by the United Kingdom-based organization NC3Rs ( National Centre for the Replacement , Refinement & Reduction of Animals in Research ) have been endorsed by over 1 , 000 journals , this did not lead to a substantial improvement of reporting in animal studies [33] . Nevertheless , awareness seems to rise , as Macleod and colleagues [28] recently found that reporting increased over the past decades , although there is still considerable scope for improvement . Research on the internal validity of animal experiments has focused mainly on reporting in scientific publications . However , most published research has undergone peer review when submitted for funding , and in some countries ( e . g . , Switzerland , Germany ) , individual animal experiments are licensed by national or regional authorities . For example , in Switzerland , the licensing of animal experiments is based on an explicit harm–benefit analysis , whereby any harm imposed on the animals is gauged against the expected benefit ( gain of knowledge ) of the experiment . Because the gain of knowledge critically depends on the scientific validity of the findings , risks of bias may affect the weight attributed to the expected benefit of a study in the harm–benefit analysis . An accurate harm–benefit analysis thus depends on information regarding risks of bias and measures used to mitigate them . In the present study , we therefore screened applications for animal experiments submitted to the cantonal authorities in Switzerland ( n = 1 , 277 ) for evidence of the use of measures to avoid risks of bias , and compared the rates at which these measures were described in applications ( for reasons of simplicity hereafter also referred to as reporting ) with the rates of reporting of the same measures in a representative sub-sample of publications ( n = 50 ) resulting from experiments described in these applications . This allowed us , for the first time , to compare evidence of scientific rigor available to the authorities when licensing animal experiments with the evidence reported in scientific publications , and to assess whether poor reporting in the scientific literature is predicted by poor reporting in applications for experiments .
Reporting rates were generally very low ( Table 1 ) ; on average , less than one out of the seven items were reported in applications for animal experiments , with reporting rates varying among the seven items , ranging from 2 . 4% for the statistical analysis plan to 18 . 5% for the primary outcome variable ( Table 1 ) . However , reporting rates greatly differed between individual applications , with the IVS ranging from 0 ( i . e . , 0/7 items reported ) to 0 . 857 ( i . e . , 6/7 items reported ) , whereby 711 out of the 1 , 277 applications ( 55 . 68% ) scored 0 ( S1 Fig ) . We hypothesized that reporting rates and , thus , the IVS might depend on various characteristics of the studies , including the year of authorization ( Year ) , the types of animals used ( Species ) , the severity of the experimental procedures ( Severity ) , the institution conducting the study ( Institution ) , the canton authorizing the study ( Canton ) , and the language in which the application was written ( Language ) , as well as the AS of the application . Generalized linear models in a Bayesian information criterion selection process were used to identify which of the study descriptors best described our data , indicating that they were most likely to have influenced the IVS . The best fitting model included Year , Canton , Language , Institution , and the interaction between Species and AS ( see Eq 4 ) . According to the model output ( S1 Data ) , however , none of the individual descriptors had a significant effect on the IVS except Language , as applications written in German had a significantly higher IVS compared to applications written in English ( odds ratio [OR] = 0 . 79 , 95% confidence interval [CI] = 0 . 64–0 . 98 ) and applications written in French ( OR = 0 . 46 , CI = 0 . 32–0 . 65 ) , and the interaction between farm animals and AS ( OR = 168 . 24 , CI = 1 . 17–2 , 5571 . 31 ) . Thus , below we report trends that were observed regarding effects of the descriptors that were included in the final model on the IVS . The IVS was similar across all three years of authorization: 2012 ( median = 0 . 0 , range: 0–0 . 71 ) , 2010 ( 0 . 0 , 0–0 . 71 ) , and 2008 ( 0 . 0 , 0–0 . 85 ) . At the level of individual items , trends of improvement across years were observed in the reporting rates of blinding , sample size calculation , and statistical analysis plan ( Fig 1B–1E ) . While there was some variation in IVS across cantons , canton did not seem to have a strong effect ( Fig 2 ) . Among the different research institutions , academic institutions ( i . e . , universities , federal institutes of technology , or university hospitals ) accounted by far for the largest part of applications , with 972 ( 76% ) applications compared to 87 ( 7% ) from industry , 56 ( 4% ) from governmental institutions , and 162 ( 13% ) from other private institutions . Overall , academic institutions ( 0 . 0 , 0–0 . 86 ) tended to score lower on IVS than institutions from industry ( 0 . 14 , 0–0 . 57 ) , governmental institutions ( 0 . 14 , 0–0 . 71 ) , and other private institutions ( 0 . 14 , 0–0 . 57; Fig 3A ) . At the level of individual items , similar trends were observed in the reporting rates of randomization and sample size calculation ( Fig 3B–3E ) . There was also variation in IVS depending on the species of animals used ( Fig 4A ) . Thus , applications for experiments on “higher” mammals ( i . e . , cats , dogs , rabbits , and primates [CDRP] ) tended to score higher ( 0 . 17 , 0–0 . 71 ) compared to experiments on farm animals ( 0 . 14 , 0–0 . 86 ) , other mammals ( 0 . 15 , 0–0 . 29 ) , laboratory rodents ( 0 . 0 , 0–0 . 71 ) , and non-mammals ( 0 . 0 , 0–0 . 6 ) , respectively . A similar trend was observed in the reporting rates of blinding , randomization , sample size calculation , and statistical analysis ( Fig 4B–4E ) . Thus , applications for experiments on CDRP as well as farm animals scored higher compared to those involving laboratory rodents and non-mammals , while data from applications for experiments involving other mammals varied widely due to the small sample size ( n = 8 ) . In contrast to the IVS , the AS was generally high , with a median score of 0 . 8 , ranging from 0 . 11 to 1 . 00 . Despite the low IVS and more than half of the applications scoring 0 , there was a weak but positive correlation between AS and IVS ( Spearman’s rho = 0 . 17 , p < 0 . 001; Fig 5 ) . In order to ensure reliability of the data between the two investigators ( TSR , LV ) as well as across time , inter-rater and intra-rater reliability tests were conducted at regular intervals . Inter-rater reliability scores ( see Eq 3 ) of the IVS ranged from 91 . 4% to 97 . 1% , while the respective intra-rater reliability scores ranged from 87 . 1% to 95 . 7% for TSR and from 94 . 3% to 97 . 1% for LV . Similarly , inter-rater reliability scores of the AS ranged from 91 . 3% to 96 . 3% , while the respective intra-rater reliability scores ranged from 87 . 5% to 97 . 5% for TSR and from 92 . 5% to 98 . 8% for LV ( see S2 Data ) . In order to relate the reporting rates obtained from applications for animal experiments to reporting rates found in the scientific literature , we selected 50 publications originating from 50 independent applications in our sample , screened them for the same seven internal validity criteria , and calculated the IVS for each publication using the same method . Similar to what we found for applications , reporting rates in the 50 publications were generally low , albeit slightly higher than in the applications ( Fig 6 ) , resulting in a median IVS of 0 . 14 . Reporting rates for the seven items ranged from 0% for sample size calculation to 34% for the statistical analysis plan . Again , reporting rates differed greatly between individual publications , with IVS ranging from 0 to 0 . 6 , whereby 23 out of 50 publications ( 46% ) scored 0 . Except for sample size calculation and the primary outcome variable , reporting rates for individual items were higher in publications than in applications ( see Fig 6 ) . Whereas IVS of applications and publications were the same in 27 cases ( of which 21 scored 0 ) , it was higher in 18 pairs ( which was due to a statistical analysis plan in 12 cases ) and lower in five cases . This increase was corroborated by a weak positive correlation between the IVS of applications and that of publications ( Spearman’s rho = 0 . 34 , p = 0 . 014 ) . Due to the smaller sample size , not all descriptors assessed for their effects on the IVS of applications could be analyzed here . Instead , we analyzed publication-specific descriptors , namely whether or not the journal in which the study was published had endorsed the ARRIVE guidelines and the impact factor of the journal ( IF ) . There was no significant effect of ARRIVE on IVS ( yes: median = 0 . 14 , range: 0 to 0 . 57; no: median = 0 , range: 0 to 0 . 60; p = 0 . 69; Fig 7A ) . In contrast , IF had a significant negative effect on IVS ( Spearman’s rho = -0 . 49 , p < 0 . 001; Fig 7B ) .
We evaluated 1 , 277 applications for animal experiments and 50 publications derived thereof and found very low reporting rates in both applications and publications ( Fig 6 ) . Reporting rates in publications were within the range reported in previous studies ( e . g . , [19 , 20] ) . That reporting rates in applications were similar—even slightly lower—indicates that the authorities approving animal experiments are lacking important information about experimental conduct that may be critical for evaluating the expected benefit in a harm–benefit analysis . Risks of bias question the scientific validity of the results , which is a precondition for a study to achieve the expected benefit . Whether the authorities are unaware of risks of bias and measures to avoid them or whether they consider them as unimportant for the benefit of the research is unknown and warrants further study . As a result , however , animal experiments are authorized based on implicit confidence rather than explicit evidence of scientific rigor . Similarly , poor reporting in publications means that manuscripts are often accepted for publication in the absence of evidence of scientific rigor . This “trust me model” of science has been criticized before [1 , 35 , 36] . It sheds serious doubts on the current authorization procedure for animal experiments as well as the peer-review process for scientific publications , which in the long run may compromise the credibility of the research . We found a weak positive correlation between the IVS of applications and that of the corresponding publications . This suggests that the reporting of bias avoidance measures in applications predicted , at least to some extent , the reporting of such measures in publications . If this reflects a consistent relationship , asking for more detailed information on experimental conduct in applications for animal experiments might help to promote better experimental conduct as well as better reporting in publications . Asking for more detailed information at the planning stage of the research might also reduce the danger of normative responses , whereby scientists simply satisfy the guidelines ( e . g . , ARRIVE ) at a time when it is too late to take corrective actions on experimental conduct . The increase in the IVS of publications compared to applications was largely due to better reporting of the statistical analysis plan ( S2 Fig ) . This is likely due to the fact that journals ( and reviewers ) generally insist on a detailed description of the statistical analysis . It indicates that reporting guidelines ( such as ARRIVE ) could potentially increase scientific quality of animal research , if editors and reviewers helped to enforce them . However , as shown by Baker et al . [33] and confirmed by the present study ( Fig 7A ) , this has not been the case so far; publications in journals having endorsed the ARRIVE guidelines did not score higher than publications in other journals . We also found a weak positive correlation between the accuracy of completing the application forms ( AS ) and the IVS . Thus , applicants who answered questions in the application form more accurately had a higher IVS . As shown by Minnerup et al . [37] , this further confirms that enforcement of guidelines may be important in view of improving reporting standards . In the final statistical model , language was the only descriptor having a significant effect on IVS of applications for animal experiments . Applications written in German had significantly higher IVS than applications written in English or French . Several explanations may account for this result . For example , the proportion of German native speakers may have been higher among authors of German applications; German may have been mostly used by native German speakers , while English may have been used by many non-native English speakers . Similarly , French may have been used by many non-native French speakers because , apparently , authorities in French-speaking cantons of Switzerland strongly encourage submission of applications in French ( own observation ) . However , one might not necessarily expect language skills to affect such standardized terminology ( randomization , blinding , etc . ) , but because these items are not explicitly asked for , applicants writing in their native language might be more likely to provide unsolicited detail . Alternatively , differences in regional policies of authorities between French- and German-speaking cantons , as well as the fact that all French applications were scored by only one experimenter ( LV ) , may have contributed to this effect , but our data do not allow us to examine these explanations further . Apart from language , all other explanatory variables in the final model had only weak effects on IVS that did not reach statistical significance ( S1 Data ) . For example , there was a weak tendency for the reporting rates of blinding , sample size calculation , and statistical analysis to be higher in 2012 compared to those from previous years ( Fig 1 ) . This trend might reflect increasing awareness by both researchers and authorities of the importance of reporting , and it is consistent with recent evidence from a random sample of life sciences publications [28] . However , despite the many systematic reviews revealing flaws in experimental design and conduct since Ioannidis’ seminal opinion paper [38] , and the wealth of solutions that have since been proposed [2 , 5 , 32 , 39] , little progress has been made . Like Baker et al . in 2014 [33] , we did not find convincing evidence that reporting had increased from applications authorized before ( 2008 ) to those authorized after ( 2012 ) publication of the ARRIVE guidelines . Again , the main reason for this might be a lack of enforcement of these guidelines by authorities as well as journal editors . However , our sample was mostly based on studies designed and authorized before the ARRIVE guidelines became widely known . That the endorsement of the ARRIVE guidelines had no effect on the IVS of publications may thus reflect the delay in such a change taking effect . Recent evidence indicated that industry-sponsored research is less biased than academic research [40] . We therefore predicted higher rates of reporting of measures against risks of bias in applications from private compared to academic institutions . Although there was a weak tendency for applications from academic institutions to score lower on IVS compared to governmental and private institutions , we cannot exclude random variation as the source of this trend . If true , however , it might reflect the different incentives between institutions , favoring more conservative approaches in non-academic institutions [41] . An interesting tendency was found in relation to the type of animals being used . Thus , applications for experiments on CDRP , farm animals , and other mammals had slightly higher IVS than those for experiments on lab rodents and non-mammals . CDRP and , to a lesser extent , farm animals and other mammals may benefit from the attribution of a higher moral status , e . g . , because they are close relatives ( primates ) , social partners ( dogs , cats , rabbits ) , or otherwise elicit more compassion ( farm animals , other mammals ) than lab rodents ( that are also considered as “pest” species ) and non-mammals ( mostly fish; e . g . , [42 , 43 , 44] ) . On the one hand , this might indicate that applications are assessed more carefully when the stakes are perceived as morally high , although it would remain unclear whether this effect is due to the applicants providing more information or to the authorities asking for more . On the other hand , IVS was low throughout , and the difference between species categories was not significant . In addition , there was no such trend with increasing degree of severity of studies . Importantly , however , the Swiss Animal Welfare Act does not provide a legal basis for such “speciesism” among vertebrates , and both authors and authorities should treat all vertebrates equally . Finally , we found a weak but significant negative relationship between the IVS of publications and the IF of the journal in which it was published . That the journal IF does not necessarily reflect the quality of research has long been known ( e . g . , [45] ) , and a systematic review of a random sample of life sciences publications recently found no evidence for a positive relationship between IF and reporting [28] . Across the whole range of journal IF in our sample of publications , IVS of 0 clearly prevails , confirming that poor reporting of measures against risk of bias is common throughout the scientific literature . According to the Animal Protection Index ( API ) by World Animal Protection , Switzerland ( together with the United Kingdom , Austria , and New Zealand ) ranked top in an international comparison of animal protection policy among 50 countries ( http://api . worldanimalprotection . org/ ) . In particular , authorization of animal experiments is based on a harm–benefit analysis , and authorization is denied if , in relation to the anticipated gain in knowledge , they inflict disproportionate harm on the animals ( Article 19 ( 4 ) , [46] ) . Because the anticipated gain in knowledge critically depends on experimental design and conduct , the lack of information on measures against risks of bias in applications means that , in Switzerland , authorization of animal experiments is based on implicit confidence rather than explicit evidence of scientific rigor . Several arguments may be held against this interpretation of our results , namely ( i ) that the measures against risks of bias assessed here are not important determinants of scientific validity , ( ii ) that they are not explicitly asked for on the application form for animal experiments , ( iii ) that , as the system currently works , it is not the authorities’ duty to assess the scientific validity of the experiments , and ( iv ) that the authorities’ confidence in scientific rigor is well justified . First , it is certainly the case that the authorities assess the scientific rationale underlying the proposed studies , thereby assessing several important aspects of scientific validity , although these are not specified explicitly . Also , there may be other , even more important risks of bias ( e . g . , use of inappropriate control group ) that were not included in our evaluation . However , all seven items included here are considered as relevant measures against risks of bias that may compromise scientific validity in important ways; they have therefore been included in reporting guidelines such as the ARRIVE guidelines . Second , while it is also true that the application form does not explicitly ask for allocation concealment , randomization , blinding , and inclusion or exclusion criteria , it does ask explicitly for the primary and secondary outcome variables , sample size calculation , and a detailed statistical analysis plan . Moreover , the first example of how to describe procedures presented in the explanatory notes to the application form by the FSVO starts with “The dogs are divided randomly into 3 groups , ” indicating that randomization is also considered a relevant aspect of the description of procedures . Even if only those measures explicitly asked for on the application form were enforced , all applications would score IVS ≥ 0 . 42 ( i . e . , 3/7 ) . Third , authorities may argue that it is the peers’ duty to assess and guarantee scientific rigor , while the authorities’ duties ( and those of their advisory committees ) should be limited to assessing the scope for applying the 3Rs ( replacement of animal experiments , reduction of animal use , and refinement of procedures ) and whether the expected benefits ( as declared by the applicants ) outweigh the harms inflicted on the animals . However , it is important to note that not all experiments are based on project proposals that have undergone scientific peer review ( e . g . , most applications from the private sector ) , and that peer review does not seem to guarantee good scientific practice [47] . Finally , whether the authorities’ implicit confidence in the scientific validity of the results of licensed experiments is justified is an empirical question . Concerns that such confidence may not be warranted is largely based on studies showing a negative relationship between reporting of measures against risks of bias and inflation of treatment effect size in preclinical studies ( e . g . , [19 , 25] ) . Together with accumulating evidence of poor reproducibility of in vivo research , these findings have shed doubts on the quality of experimental design and conduct . However , there is clearly a need for more research on the actual implementation of measures against risks of bias in experimental animal research . We have recently conducted an online survey amongst all Swiss animal researchers to elucidate actual implementation of the same seven measures against risks of bias assessed here . Our findings suggest that although reporting rates found in the literature tend to underestimate actual implementation of these measures , there is considerable scope for improvement [48] . Lack of scientific rigor in experimental conduct is widely considered to be an important determinant of poor reproducibility of in vivo research [16 , 17 , 18] . However , this assumption is based on the indirect evidence outlined above , and has never been tested directly . Randomization , blinding , sample size calculation , and all the other measures against risks of bias assessed here mainly affect the internal validity of experiments . Although the reproducibility of results can be affected by the internal validity of studies , reproducibility depends more on the external validity of studies [11–13] . Reproducibility may thus be enhanced mainly by using design features aimed to increase the external validity of results , such as more heterogeneous study populations , independent replicate cohorts , or multicenter study designs [14 , 49 , 50] . Thus , there is also a need for more research on the relative contribution of experimental conduct and experimental design , respectively , to the reproducibility of results . Last , but not least , besides experimental design and experimental conduct , several other factors introduce bias into the scientific literature , in particular “hypothesizing after results are known” ( HARKing , [51] ) , p-hacking [52] , selective reporting [53] , and publication bias [54] . The most effective way of eliminating all of these biases would be prospective registration of preclinical animal experiments similar to preregistration of clinical trials [55] . Further research is certainly needed on how to facilitate practical implementation of preregistration in the face of several contentious issues such as confidentiality , property rights , and theft of ideas . However , the authorization procedure for animal experiments already in place in Switzerland ( and other countries , e . g . , Germany ) , provides an ideal basis for implementing preregistration of animal experiments , which would not only benefit the scientific validity of results from animal experiments but also minimize unnecessary harm to animals for inconclusive research . By this , Switzerland could consolidate its position as a leader in animal protection as well as extend its leadership to scientific rigor .
Applications for animal experiments ( Form A , S1 Text ) were selected from an anonymized database obtained from the FSVO , containing all applications submitted in Switzerland since 1983 . Access to applications archived by the FSVO was based on a contract between the FSVO and the authors of this study , which guaranteed confidentiality to the applicants . Applications were selected based on predefined inclusion and exclusion criteria . Thus , only new applications submitted during the years 2008 , 2010 , and 2012 were included , of which applications related to ( i ) diagnosis of disease , ( ii ) education and training , and ( iii ) the protection of humans , animals , and the environment by toxicological or other safety tests required by law were excluded a priori ( S3 Fig ) . A total of 1590 applications met these criteria and were subjected to formal screening . In order to assess risks of bias in the experiments described in the applications , a checklist was elaborated ( S2 Text ) based on checklists used in previous studies assessing the use of measures to reduce risks of biases as reported in the published literature [19 , 20 , 56] . We restricted our checklist to items that ( i ) are essentially applicable to all kinds of experimental studies and ( ii ) can be assessed objectively without specific expertise of the research topic , and included those seven items that we encountered most often in the literature: ( 1 ) allocation concealment , ( 2 ) blinded outcome assessment , ( 3 ) randomization , ( 4 ) formal sample size calculation , ( 5 ) inclusion and exclusion criteria , ( 6 ) a primary outcome variable , and ( 7 ) a statistical analysis plan . These seven items were also used to calculate an IVS based on the number of items that were reported in the application divided by the total number of items applicable to the study ( max = 7 ) . Additional items were assessed that were , however , not included in the IVS . These included additional aspects of study conduct ( blinded conduct of study , randomized conduct of study , termination criteria , references for the sample size , and general statements on statistical analysis; S2 Text ) . In addition , we assessed the accuracy with which the application forms ( Form A ) were filled out , using items that were explicitly asked for on Form A , and for which the content to be filled in was explicitly specified in the accompanying guidelines to Form A on the FSVO webpage ( https://www . blv . admin . ch/dam/blv/en/dokumente/tiere/publikationen-und-forschung/tierversuche/erlaeuterungen-form-a . pdf . download . pdf/erlaeuterungen-form-a . pdf ) . Furthermore , we chose items that are relevant for the harm–benefit analysis and could be determined with high reliability . The following six items were included: ( 1 ) description and justification of the methods used ( e . g . , by indicating references , previous results , or results from a pilot study ) ; ( 2 ) information about the identification of individual animals; ( 3 ) the total number of animals used , the number of treatment groups , and the number of animals per treatment group; ( 4 ) reference to a score sheet for the assessment of animal welfare; ( 5 ) the degrees of severity for all animals involved in the experiments; and ( 6 ) the fate of the animals at the end of the experiments . These six items were used to calculate an AS based on the number of items reported divided by the total number of items applicable to the study ( max = 6 ) . The AS was constructed as a control measure , to control for variation in IVS induced by variation in the accuracy with which the form was filled out . Both IVS and AS were assessed by scoring whether or not the respective items were reported in any of the experiments included in an application form . Thus , a “YES” was recorded if an item was reported in at least one of the described experiments and a “NO” if an item was either not reported at all or if it was unclear . If an item was not applicable to the experiment described in the application form , “NA” was recorded ( more details are given in the S3 Text ) . The 1590 applications were randomly allocated to two investigators ( LV , TSR ) for formal screening ( leading to two lists of 795 applications each , one for each investigator ) . During screening , 94 applications were excluded because they were either incomplete or not available in the archives of the FSVO . A further 36 applications were excluded because they met one or more of the exclusion criteria reported above . This left 1 , 460 applications that were deemed suitable for screening . Applications written in French ( n = 423 ) or Italian ( n = 5 ) were screened by the investigator with better knowledge of these languages ( LV ) , regardless of their assignment to the two investigators , while applications written in German ( n = 430 ) or English ( n = 602 ) were screened according to their assignments to the two investigators . Therefore , a total sample of n = 935 was screened by investigator LV while a total sample of n = 525 applications was screened by investigator TSR . To restrict analysis to experimental in vivo studies , a further 183 applications were excluded in the course of the screening process because they referred to in vitro studies ( if the animals were killed before the experimental treatment was applied; n = 106 ) , monitoring studies ( if the animals were observed in the wild; n = 28 ) , or other exceptions ( e . g . , breeding studies , post-mortem studies; n = 49 ) , resulting in a final sample size of n = 1 , 277 applications used for analysis ( see S3 Fig ) . Based on information provided by the applicants on Form A and used for the annual statistics of animal use by the FSVO , we also recorded several descriptors that might influence the reporting of internal validity items; these included ( i ) year of authorization ( 2008 , 2010 , 2012 ) , ( ii ) language ( English , German , French ) , ( iii ) canton ( the six largest cantons of Basel , Bern , Freiburg , Geneva , Vaud , Zurich , and the group of the remaining small cantons ) , ( iv ) type of institution ( academic institutions [i . e . , universities , federal institutes of technology , hospitals] , industry , governmental institutions [national and cantonal] , other [e . g . , private institutions , foundations] ) , ( v ) animal species ( laboratory rodents , higher mammals [CDRP] , farm animals , other mammals , non-mammals ) , ( vi ) genetically modified animals ( yes , no ) , and ( vii ) the prospective degree of severity of the planned procedures as defined by the FSVO ( 0 , 1 , 2 , 3 ) . Prior to the screening of the selected Form A , two pilot studies on separate applications ( i . e . , applications authorized in 2009 ) were conducted to ensure the applicability of the checklist and to ensure consistency of scoring within and between investigators . To ensure consistent scoring of applications between the two investigators , both investigators screened the same 10 applications , and discrepancies were checked at the end of the day . Inter-rater reliability ( Eq 3 ) was assessed at regular intervals ( on day 1 and then after the 100th , 300th , 500th , and 700th application on the investigators’ list , respectively ) by assessing the proportion of agreement between the two investigators . For this , the first five applications on each investigator’s list were screened by both investigators . Only applications written in either German or English were used for inter-rater reliability tests . Overall , 50 applications were screened twice in the course of these inter-rater reliability tests . Inter-rater reliability never dropped below 85% ( S2 Data ) . To ensure that both investigators scored applications consistently over time , samples of 10 applications were re-scored at regular intervals ( after 50 , 150 , 350 , and 550 listed applications , respectively ) . In addition , each investigator conducted a final intra-rater reliability test on 10 randomly chosen applications from the whole list after completing the screening procedure . If systematic discrepancies would have occurred , the applications previously scored would have been re-scored . However , as in the case of inter-rater reliability , intra-rater reliability never dropped below 85% ( S2 Data ) . No a priori sample size calculation was performed , as all applications were included in our sample that fulfilled the inclusion/exclusion criteria . However , once the sample size was determined , we verified that it was suitable for the planned statistical analysis ( see model description below ) . The screening data from the checklists were transferred to a tabulating program ( Microsoft Excel 2010 . Ink , Redmond , WA , USA ) and imported into the statistical software R [57] . We used descriptive statistics to represent reporting rates for individual criteria of internal validity ( allocation concealment , blinded outcome assessment , randomization , sample size calculation , inclusion and exclusion criteria , primary outcome , and statistical analysis ) . Furthermore , influences of relevant descriptors ( year , canton , institution , and animal species ) were represented graphically , with median and mean IVS of the group , and overall mean IVS . For the statistical analysis of the overall internal validity score of applications , we used generalized linear models to evaluate the influence of the a priori stated descriptors on the internal validity score . The analyses were performed in R [57] using the built in function glm with a binomial error distribution to account for the data structure ( primary outcome as proportions ) . As a first step , we compared univariate models ( model with one descriptor ) with an intercept-only model ( modelling the intercept of the internal validity score ) based on significant ( p < 0 . 05 ) likelihood ratio test of the package lmtest [58] in order to identify descriptors to be included in the further modelling process . The descriptors to be retained were language , canton , species category , institutions , authorization year , and accuracy of the application . In a second step , by means of an information theoretic approach to model selection using the Bayesian Information Criterion ( BIC ) , we identified the model that best fit our data . For an automated model selection procedure , the package MuMln [59] with the function dredge was used to compare all models with all possible combinations of the retained descriptors ( full model included also the interaction term for species category and accuracy; see Eq 4 ) . The dredge function ranks all descriptor combinations according to their BIC; the model with the lowest BIC was assumed to be the one representing our data best . The final model included the following main effects ( descriptors ) : language ( 3 levels ) , cantons ( 7 levels ) , species category ( 5 levels ) , accuracy ( continuous ) , institution ( 4 levels ) , and authorization year ( 3 levels ) . In addition to these main effects , the candidate model included the two-way interaction between species category and accuracy ( corresponds to full model , cf . Eq 4 ) . The model parameters were retrieved after correction for over dispersion ( see S1 Data ) . In order to relate the reporting rates of internal validity criteria assessed here by scoring applications for animal experiments with the reporting rates of such criteria in the published literature [19 , 22–28 , 60] , we also scored a sub-sample of publications originating from studies based on applications in our study sample . These were identified by searching through grant numbers mentioned in the applications and references listed as output in the annual reports to the FSVO . For 155 applications ( 12 . 1% ) we identified one or more corresponding publications . This number was reduced to 139 after excluding reviews and publications that were clearly unrelated to the study described in the applications ( mismatch in animal species , general topic , or methods ) . This low number can be explained by the fact that studies licensed in 2012 and also many of those licensed in 2010 were not yet published , and that the search for publications had to rely on grant numbers mentioned in both application and publication ( often grant numbers were not mentioned on applications ) or on publications listed in the final reports required by the authorities upon completion of licensed studies ( for most studies licensed in 2012 and also many of those licensed in 2010 , final reports were not yet available ) . For the comparison of the internal validity scores between applications and publications , we aimed to detect a medium effect size ( 0 . 3 ) with a statistical power of 0 . 8 at a significance level of p < 0 . 05 . Based on this , we chose a sample size of n = 50 , which allowed us to detect an effect size of 0 . 276 ( G*power for correlations , bivariate normal model ) [61] . A stratified random sampling procedure was used to select 50 publications from the 139 available publications , so as to select publications derived from a representative sample of all applications with respect to canton and type of animals used . Because this sample of publications was biased towards older applications , we compared the IVS of the sub-sample of 50 applications from which these 50 publications originated with the IVS of the entire sample of applications and found no significant difference; median IVS of the entire sample of applications ( n = 1 , 277 ) was 0 . 0 ( range 0 to 0 . 857 ) , compared to 0 . 0 ( range 0 to 0 . 714 ) for the sub-sample of applications ( n = 50 ) from which the 50 publications were derived . The publications were screened for reporting of internal validity criteria with a checklist containing the same seven internal validity criteria as were used for applications . The screening of all 50 publications was performed by one single investigator ( LV ) . Publications were randomly allocated to one of the 10 d of screening ( five publications per day ) . Days of screening were separated by two non-screening days . For the publications , descriptors were impact factor of the journal and endorsement of the ARRIVE guidelines by the journal . To determine the descriptors , the impact factor for the year of the publication as well as the ARRIVE status of the journal were assessed . If it was not possible to determine the ARRIVE status of a journal for the date of publication , given that all publications were published in 2012 or later , we used the ARRIVE status of the journal in 2015 . Whether or not the ARRIVE status affected the internal validity score of publications was tested with a univariate generalized linear model ( binomial error distribution ) , with IVS as dependent and the descriptor ( endorsement of ARRIVE yes or no ) as independent variables . Whether or not the internal validity score of publications was correlated with the impact factor of the journal was investigated using a spearman rank correlation test . To ensure that the investigator scored the publications constantly over time , an independent person randomly chose one publication per five publications screened ( i . e . , one per day of screening ) for an intra-rater reliability test . The chosen publication was re-screened on the second following day . The reliability ( Eq 3 ) never dropped below the threshold of 85% . | Scientific validity of research findings depends on scientific rigor , including measures to avoid bias , such as random allocation of animals to treatment groups ( randomization ) and assessing outcome measures without knowing to which treatment groups the animals belong ( blinding ) . However , measures against bias are rarely reported in publications , and systematic reviews found that poor reporting was associated with larger treatment effects , suggesting bias . Here we studied whether risk of bias could be predicted from study protocols submitted for ethical review . We assessed mention of seven basic measures against bias in study protocols submitted for approval in Switzerland and in publications resulting from these studies . Measures against bias were mentioned at very low rates both in study protocols ( 2%–19% ) and in publications ( 0%–34% ) . However , we found a weak positive correlation , indicating that the rates at which measures against bias were mentioned in study protocols predicted the rates at which they were reported in publications . Our results indicate that animal experiments are often licensed based on confidence rather than evidence of scientific rigor , which may compromise scientific validity and induce unnecessary harm to animals caused by inconclusive research . | [
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| 2016 | Authorization of Animal Experiments Is Based on Confidence Rather than Evidence of Scientific Rigor |
Core object recognition , the ability to rapidly recognize objects despite variations in their appearance , is largely solved through the feedforward processing of visual information . Deep neural networks are shown to achieve human-level performance in these tasks , and explain the primate brain representation . On the other hand , object recognition under more challenging conditions ( i . e . beyond the core recognition problem ) is less characterized . One such example is object recognition under occlusion . It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion . Furthermore , we do not know whether the conventional deep neural networks , such as AlexNet , which were shown to be successful in solving core object recognition , can perform similarly well in problems that go beyond the core recognition . Here , we characterize neural dynamics of object recognition under occlusion , using magnetoencephalography ( MEG ) , while participants were presented with images of objects with various levels of occlusion . We provide evidence from multivariate analysis of MEG data , behavioral data , and computational modelling , demonstrating an essential role for recurrent processes in object recognition under occlusion . Furthermore , the computational model with local recurrent connections , used here , suggests a mechanistic explanation of how the human brain might be solving this problem .
There is abundance of feedforward , and recurrent connections in the primate visual cortex [1 , 2] . The feedforward connections form a hierarchy of cortical areas along the visual pathway , playing a significant role in various aspects of visual object processing [3] . However , the role of recurrent connections in visual processing have remained poorly understood [1 , 4–7] . Several complementary behavioral , neuronal , and computational modeling studies have confirmed that a large class of object recognition tasks called “core recognition” are largely solved through a single sweep of feedforward visual information processing [8–13] . Object recognition is defined as the ability to differentiate an object’s identity or category from many other objects having a range of identity-preserving changes [8] . Core recognition refers to the ability of visual system to rapidly recognize objects despite variations in their appearance , e . g . position , scale , and rotation [8] . Object recognition under challenging conditions , such as high variations [14 , 15] , object deletion and occlusion [16–24] , and crowding [25–27] goes beyond the core recognition problem , which is thought to require more than the feedforward processes . Object recognition under occlusion is one of the key challenging conditions that occurs in many of the natural scenes we interact with every day . How our brain solves object recognition under such challenging condition is still an open question . Object deletion and object occlusion are shown to have different temporal dynamics [28] . While object deletion has been studied before in humans [17–20 , 24 , 29] , we do not know much about the dynamics of object processing under the challenging condition of occlusion; in particular there has not been any previous MEG study of occluded object processing , with multivariate pattern analyses approach , linking models with both brain and behavior . Furthermore , as opposed to the core object recognition problem , where the conventional feedforward CNNs are shown to explain brain representations [10–13] , we do not yet have computational models that successfully explain human brain representation and behavior under this challenging condition [Regarding feedforward CNNs , also see [30 , 31] where CNNs cannot fully explain patterns of human behavioral decisions] . Few fMRI studies have investigated how and where occluded objects are represented in the human brain [32–36] . Hulme and Zeki [33] found that faces and houses in fusiform face area ( FFA ) and lateral occipital cortex ( LOC ) are represented similary with and without occclusion . Ban et al . [35] used topographic mapping with simple geometric shapes ( e . g . triangles ) , finding that the occluded portion of the shape is represented topographically in human V1 and V2 , suggesting the involvement of early visual areas in object completion . A more recent study showed that the early visual areas may only code spatial information about occluded objects , but not their identity , and higher-order visual areas instead represent object-specific information , such as category or identity of occluded objects [36] . While these studies provide insights about object processing under occlusion , they do not provide any information about the temporal dynamics of these processes , and whether object recognition under occlusion requires recurrent processing . Our focus in this study is understanding the temporal dynamics of object recognition under occlusion; and whether recurrent connections are critical in processing occluded objects ? If yes , in what form are they engaged ( e . g . long range feedback or local recurrent ? ) , and how much is their contribution compared to the contribution of the feedforward visual information ? We constructed a controlled image set of occluded objects , and used the combination of multivariate pattern analyses ( MVPA ) of MEG signals , computational modeling , backward masking , and behavioral experiments to characterize representational dynamics of object processing under occlusion , and to determine unique contributions of feedforward and recurrent processes . Here , we provide five complementary evidence for the contribution of recurrent processes in recognizing occluded objects . First , MEG decoding time courses show that onset and peak for occluded objects—without backward masking—are significantly delayed compared to when the whole object is presented without occlusion . The timing of visual information plays an important role in discriminating the processing stages ( i . e . feedforward vs . recurrent ) with early brain responses reaching higher visual areas being dominantly feedforward and the delayed responses being mainly driven by recurrent processes [3 , 4 , 37–42] . Second , time-time decoding analysis ( i . e . temporal generalization ) suggests that occluded object processing goes through a relatively long sequence of stages that involve recurrent interaction—likely local recurrent . Third , the results of backward masking demonstrate that while the masking significantly impairs both human categorization performances and MEG decoding performances under occlusion , it has no significant effect on object recognition when objects are not occluded . Fourth , results from two computational models showed that a conventional feedforward CNN ( AlexNet ) that could achieve human-level performance in the no-occlusion condition , performed significantly worse than humans when objects were occluded . Additionally , the feedforward CNN could only explain the human MEG data when objects were presented without occlusion; but failed to explain the MEG data under the occlusion condition . In contrast , a hierarchical CNN with local recurrent connections ( recurrent ResNet ) achieved human-level performance and the representational geometry of the model was significantly correlated with that of the MEG neural data when objects were occluded . Finally , we quantified contributions of feedforward and recurrent processes in explaining the neural data , showing a significant unique contribution only for the recurrent processing under occlusion . These findings demonstrate significant involvement of recurrent processes in occluded object recognition , and improve our understand of object recognition beyond the core problem . To our knowledge this is the first MVPA study of MEG data linking feedforward and recurrent deep neural network architectures with both brain and behavior to investigate object recognition under occlusion in humans .
We used pairwise decoding analysis of MEG signals to measure how object information evolves over time ( Fig 1a ) . Significantly above-chance decoding accuracy means that objects can be discriminated using the information available from the brain data at that time-point . The decoding onset latency indicates the earliest time that the object-specific information becomes available and the peak decoding latency is the time-point wherein we have the highest object-discrimination performance . We found that object information emerges significantly later under occlusion compared to the no-occlusion condition . Object decoding under no-occlusion had an early onset latency at 79ms [±3 ms standard deviation ( SD ) ] and was followed by a sharp increase reaching its maximum accuracy ( i . e . peak latency ) at 139±1 ms ( Fig 1b ) . This early and rapidly evolving dynamic is well consistent with the known time-course of the feedforward visual object processing [see S2 Fig and [38 , 43 , 44]] . However , when objects were partially occluded ( i . e . 60% occlusion ) , decoding time-courses were significantly slower than the 0% occlusion condition: the onset for decoding accuracy was at 123±15 ms followed by a gradual increase in decoding accuracy until it reached its peak decoding accuracy at 199±3 ms ( Fig 1b ) . The difference between onset latencies and peak latencies were both statistically significant with p<10−4 ( two sided sign-rank test ) . Analysis of the behavioral response times was also consistent with the MEG object decoding curves , showing a significant delay in participants’ response times when increasing the occlusion level ( S3 Fig ) . The slow temporal dynamics of object recognition under occlusion and the observed significant temporal delay in processing occluded objects compared to un-occluded ( 0% occlusion ) objects do not match with a fully feedforward account of visual information processing . Previous studies have shown that first responses to visual stimuli that contain category-related information can reach higher visual areas in as little as 100 ms . [38 , 49 , 50] . Therefore , the observed late onset and the significant delay in peak and onset of the decoding accuracy for occluded objects , may be best explained by the engagement of recurrent processes . Under 80% occlusion , the MEG decoding results did not reach significance [right-sided signrank test , FDR corrected across time , p < 0 . 05] ( Fig 1b ) . However , behaviorally , human subjects still performed above-chance in object categorization even under 80% occlusion . This discrepancy might be due to MEG acquisition noise , whereas the behavioral categorization task is by definition free from that type of noise . While the MEG and behavioral data have different levels of noise , we showed that within the MEG data itself , presented images with different levels of occlusion ( 0% , 60% , 80% ) did not differ in terms of their level of MEG noise ( S4 Fig ) . Thus , the difference in decoding performance between different levels of occlusion cannot be simply explained by the difference in noise . Furthermore , patterns of cross-decoding analyses ( see the next section ) demonstrate that the observed delay in peak latency under occlusion cannot be simply explained by a difference in signal strength . We performed time-time decoding analysis measuring how information about object discrimination generalizes across time ( Fig 2a ) . Time-time decoding matrices are constructed by training a SVM classifier at a given time point and testing its generalization performance at all other time-points ( see Methods ) . The pattern of temporal generalization provides useful information about the underlying processing architecture [51] . We were interested to see if there are differences between temporal generalization patterns of occluded and un-occluded objects . Different processing dynamics may lead to distinct patterns of generalization in the time-time decoding matrix [see [51] for a review] . For example , a narrow diagonal pattern suggests a hierarchical sequence of processing stages wherein information is sequentially transferred between neural stages . This hierarchical architecture is well consistent with the feedforward account of neural information processing across the ventral visual pathway . On the other hand , a time-time decoding pattern with off-diagonal generalization suggests a neural architecture with recurrent interactions between processing stages [see Fig 5 in [52]] . The temporal generalization pattern under no-occlusion ( Fig 2b ) indicated a sequential architecture , without an off-diagonal generalization until its early peak latency at 140 ms . This is consistent with a dominantly feedforward account of visual information processing . There was some off-diagonal generalization after 140 ms , however that was not of interest here , because the ongoing recurrent activity after the peak latency ( as shown in Fig 1b ) did not carry any information that further improves pairwise decoding performance of 0% occluded objects . On the other hand , when objects were occluded , the temporal generalization matrix ( Fig 2c ) indicated a significantly delayed peak latency at 199ms with extensive off-diagonal generalization before reaching its peak . In other words , for occluded objects , we see a discernible pattern of temporal generalization , which is characterized by 1 ) a relatively weak early diagonal pattern of the decoding accuracy during [100 150]ms with limited temporal generalization , which is in contrast with the high accuracy decoding of 0% occluded objects in the same time period . 2 ) A relatively late peak decoding accuracy with a wide generalization pattern around 200ms . This pattern of temporal generalization can be simulated by a hierarchical neural architecture with local recurrent interactions within the network [Fig 5 of [52]] We also performed sensorwise decoding analysis to explore spatio-temporal dynamics of object information . To calculate sensorwise decoding , pairwise decoding analysis was conducted on 102 neighboring triplets of MEG sensors ( 2 gradiometers and 1 magnetometer in each location ) yielding a decoding map of brain activity at each time-point . The sensorwise decoding patterns indicated the approximate locus of neural activity: in particular , we see that for both 0% occlusion ( S2 Movie ) and 60% occlusion ( S1 Movie ) conditions , during the onset of decoding as well as the peak decoding time , the main source of object decoding is in the left posterior-temporal sensors . From [110ms to 200ms] , the peak of decoding accuracy remains locally around the same sensors , suggesting a sustained local recurrent activity . Visual backward masking has been used as a tool to disrupt the flow of recurrent information processing , while feedforward processes are left relatively intact [4 , 14 , 48 , 53–56] . Our time-time decoding results ( Fig 4d 0% occluded ) additionally supports the recurrent explanation of backward masking: off-diagonal generalization in time-time decoding matrices are representative of recurrent interactions; these off-diagonal components disappear when backward masking is present . Considering the recurrent explanation of the masking effect , we further examined how the recurrent processes contribute in object processing under occlusion . We found that backward masking significantly reduced both MEG decoding accuracy time-course ( Fig 4b ) and subjects’ behavioral performances ( Fig 5d ) , only when objects were occluded . When occluded objects are masked , the MEG decoding time-course from 185ms to 237ms is significantly lower than the decoding time-course when in no-mask condition ( Fig 4b , black horizontal lines; two-sided signrank test , FDR-corrected across time p < 0 . 05 ) . On the other hand , for un-occluded objects , we did not find any significant difference between decoding time-courses of the mask and no-mask conditions ( Fig 4a ) . Consistent with the MEG decoding results , while the masking significantly reduced behavioral categorization performances when objects were occluded , it had no significant effect on the categorization performance for the un-occluded objects ( Fig 5d ) [two-sided signrank test] . Particularly , the backward masking removed the late MEG decoding peak ( around 200ms ) under occlusion ( Fig 4f ) likely due to disruption of later recurrent interactions . Taken together , we demonstrated that visual backward masking , which is suggested to disrupt recurrent processes [4 , 48 , 53 , 55 , 57] , significantly impairs object recognition only under occlusion . On the other hand , masking did not affect object processing under no occlusion , when information from the feedforward sweep is shown to be sufficient for object recognition . Thus , providing further evidence for the essential role of recurrent processes in object recognition under occlusion . Recent studies have shown that convolutional neural networks ( CNNs ) achieve human-level performance and explain neural data under non-challenging conditions—also referred to as the core object recognition [8 , 10 , 11] . Here , we first examined whether such feedforward CNNs ( i . e . AlexNet ) can explain the observed human neuronal and behavioral data in a challenging object recognition task when objects are occluded . The model accuracy was evaluated by the same object recognition task used to measure human behavioral performance ( S6 Fig ) . A multiclass linear SVM classifier was trained on images from two occlusion levels and tested on the left-out occlusion level , using features from the penultimate layer of the model ( e . g . ‘fc7’ in AlexNet ) . The classification task was to categorize images into car , motor , deer , or camel . This procedure was repeated for 15 times , and the mean categorization performance is reported here . We , additionally , used representational similarity analysis ( RSA ) to assess model’s performance in explaining the human MEG data . RSA correlates time-resolved human MEG representations with that of the model , on the same set of stimuli . First , dissimilarity matrices ( RDMs ) were separately calculated for the MEG signals and the model . The model RDM were then correlated with MEG RDMs across time ( Fig 5a; also see Methods ) . We found that in the no-occlusion condition , the feedforward CNN achieved human-level performance and CNN representations were significantly correlated with the MEG data . Significant correlation between the model and MEG representational dissimilarity matrices ( RDMs ) started at ~90ms after the stimulus onset and remained significant for several hundred milliseconds with two peaks at 150ms and 220ms ( Fig 5b ) . However , the feedforward CNNs ( i . e . AlexNet and the purely feedforward version of HRRN ( HRRN with readout stage 0 ) ) failed to explain human MEG data when objects were occluded . And the model performance was significantly lower than that of human in the occluded object recognition task . We were wondering if a model with local recurrent connections could account for object recognition under occlusion . Inspired by recent advancements in deep convolutional neural networks [58–61] , we built a hierarchical recurrent ResNet ( HRRN ) that follows the hierarchy of the ventral visual pathway ( Fig 6 , also see Methods for more details about the model ) . The recurrent model ( HRRN ) could rival the human performance in the occluded object recognition task ( Fig 5d ) , performing strikingly better than AlexNet in 60% and 80% occlusion . We also compared confusion matrices ( patterns of errors ) between the models and human ( S7 Fig ) . Under the no-mask condition , HRNN had a significantly higher correlation with humans under 0% and 80% occlusion ( the difference was not significant in 60% occlusion , S7b Fig ) . Additionally , the HRRN model representation was significantly correlated with that of the human MEG data under occlusion [Fig 5c] ( onset = 138±2ms; peak = 182±19ms ) . It is worth noting that the recurrent model here may be considered functionally equivalent to an arbitrarily deep feedforward CNN . We think the key difference between AlexNet and HRRN , is indeed in the number of non-linear transformations applied to the input image ( please refer to section “Does a feedforward system with arbitrarily long depth work the same as a recurrent system with limited depth ? “for a discussion about this ) . The models here , the purely feedforward models ( i . e . HRRN readout stage 0 , and AlexNet ) and the model with local recurrent connections , were both trained on the same object image dataset [ImageNet [62]] and had equal number of feedforward convolutional layers . Both models performed similarly in object recognition under no-occlusion , and achieved human-level performance . However , under occlusion , only the HRRN ( i . e . the model with recurrent connections ) could partially explain the human MEG data and achieved human-level performance , whereas the purely feedforward models failed to achieve human-level performance under occlusion—in both MEG and behavior . To quantify the contribution of feedforward and recurrent processes in solving object recognition under occlusion , we first correlated the feedforward and recurrent model RDMs with the average MEG RDMs extracted from two time spans: 80 to 150 ms , which is dominantly feedforward [38 , 42 , 50] , and 151 to 300 ms ( significant involvement of recurrent processes ) . The results are shown in Fig 7a . HRRN and AlexNet both have a similar correlation with the MEG data at [80–150 ms] . However , the HRRN shows a significantly higher correlation with the MEG data at [151–300 ms] compared to the AlexNet . We were further interested to determine the unique contribution of the models in explaining the neural data under occlusion . To this end , we calculated semipartial correlations between the model RDMs and the MEG RDMs ( Fig 7b ) . We find that the HRRN and AlexNet perform similarly in explaining the mainly feedforward component ( i . e . 80-150ms ) of the MEG data and they do not have a significant unique contribution . On the other hand , for the later component ( 150–300 ms ) of the MEG data , only the HRRN model has a unique contribution .
Several recent findings have indicated that a large class of object recognition tasks referred to as ‘core object recognition’ are mainly solved in the human brain within the first ~100 ms after stimulus onset [38 , 43 , 45 , 49 , 64] , largely associated with the feedforward path of visual information processing [4 , 10–12] . More challenging tasks , such as object recognition under occlusion , go beyond the core recognition problem . So far it has been unclear whether the visual information from the feedforward sweep can fully account for this or otherwise recurrent information are essential to solve object recognition under occlusion . AlexNet ( i . e . feedforward model ) and HRRN ( i . e . recurrent model ) both equally explained the early component of the MEG data ( <150 ms ) with and without occlusion . Consistent with this , the semipartial correlation analyses further revealed no unique variance for these models in explaining the early component of the MEG data . These results suggest that the early component of the MEG data under both conditions ( with and without occlusion ) are mainly feedforward and both AlexNet and HRRN share a common feedforward component that is significantly correlated with dominantly feedforward MEG representations before 150 ms ( S8 Fig shows a plausible Venn diagram describing the relationship between the two models and the MEG data . ) . On the other hand , the later component of the MEG data ( >150 ms ) under occlusion was only correlated with the recurrent model , which had a significant unique contribution in explaining the MEG data under this condition . Under no occlusion , while the later component of the MEG data is significantly correlated with both AlexNet and HRRN , only the HRRN model showed a significant unique contribution in explaining the data ( S9 Fig ) . This shows that under no-occlusion the later component of the MEG data can still be partially explained by the common feedforward component of the two models , perhaps because object recognition under no-occlusion is primarily a feedforward process , however the recurrent model has some unique advantages in explaining later MEG components—even under no-occlusion . Object recognition when part of an object is removed without an occluder is one of the challenging conditions that has been previously studied [17–20 , 24 , 29] and may partly look similar to occlusion . However , as shown by Johnson and Olshausen [28] deleting part of an object is different from occluding it with another object; even under short stimulus presentation times , there are meaningful differences between occlusion and deletion at the level of behavior ( reaction time and accuracy of responses ) . Furthermore , Johnson and Olshausen report significant diffrences in ERP responses between occlusion and deletion , observed as early as ~130ms after simulus onset . See S10 Fig for sample images of occlusion and deletion . Occlusion occurs when an object or shape appears in front of another one [28] , in which case the occluding object might serve as an additional depth-base image cue for object completion . On the other hand , deletion occurs when part of an object is removed without additional cues about the shape or the place of the missing part . Occlusion is closely related with amodal completion which is an important visual process for perceptual filling-in of missing parts of an occluded object [28 , 77] . Given the difference between these two phenomena at the level of stimulus set , we expect the dynamics of object processing ( and the underlying computational mechanisms ) to be also different when part of an object is occluded compared to when it is deleted . Consistent with this , Johnson and Olshausen [28] demonstrated that ERP responses in occipital and parietal electrodes are signifcantly different between object occlusion and deletion . Furthermore , there were significant behavioural differences between object occlusion and deletion , including differneces in recogntion memory and response confidence . Object deletion has been previously studied in humans using a variety of techniques: Wyatte et . al . [18 , 20] used human behaviour ( in a backward masking paradigm ) , and computational modelling to show that object recogntion , when part of the object is deleted , requires recurrent processing . Tang et . al . [24 , 29] used intracranial field potential recording on epileptic patients to study temporal dynamics of object deletion; and proposed an attractor-based recurrent model that could explain the neural data . They found ~100 ms delay in processing objects when part of the object was deleted , compared to when the whole object was present . In comparison , in our study we found ~60 ms delay in object processing when objects were occluded . This suggests that while object recognition under both occlusion and deletion requires recurrent processes , temporal dynamics of object deletion is slower , potentially due to the absence of the occluder , which can make the recognition task more difficult . To summarize , while object deletion has been previously studied in humans , to our knowledge , temporal dynamics of object occlusion had not been studied before . In particular this was the first MVPA study in humans that charachaterized representational dynamics of object recognition under occlusion , and further provided a computational account of the underlying processes that explained both behavioral and neural data . While conventional CNNs could not account for object recognition beyond the core recognition problem , we do not rule out the possibility that much deeper CNNs could perform better under such challenging conditions . Computational studies have shown that very deep CNNs outperform shallow ones on a variety of object recognition tasks [78–80] . Specifically , residual learning allows for a much deeper neural network with hundreds [58] and even thousands [59] of the layers providing better performance . This is due to the fact that the complex functions that can be represented by deeper architectures cannot be represented by shallow architectures [81] . Recent computational modeling studies have tried to clarify why increasing the depth of a network can improve its performance [60 , 63] . These efforts have demonstrated that unfolding a recurrent architecture across time leads to a feedforward network with arbitrary depth , in which the weights are shared among the layers . Although such a recurrent network has far fewer parameters , Liao and Poggio [60] have empirically shown that it performs as well as a very deep feedforward network without shared weights . We also showed that a very deep ResNet ( e . g . with 150 layers ) can be reformulated into the form of a recurrent CNN with much fewer layers ( e . g . five layers ) ( Fig 6 ) . Thus , a compact architecture that resembles these very deep networks in terms of performance is a recurrent hierarchical network with much fewer layers . This compact architecture is probably what the human visual system has selected to be like [1 , 2] , given the biological constraints of having a very deep neural network inside the brain [82–86] . From a computational viewpoint , recognition of complex images might require more processing efforts; in other words , they might need to go through more layers of processing to be prepared for the final readout . Similarly , in a recurrent architecture , more processing means more iterations . Our modeling results supports this assumption , showing that under more challening recogntion tasks , more iterations are required to reach human-level perfomrance . For example , under 60% and 80% occlusion , the HRRN model reached human level performance , respectively after going through 13 recurrent stages , and 43 recurrent stages ( S11 Fig ) . With more iterations , the HRRN model tends to achieve a performance slightly better than the average categorization performance in humans . Our choice of a recurrent architecture , as opposed to an arbitrarily deep neural network , is mainly driven by the plausibly of such architecture with the hierarchy of vision , where there is only a limited number of processing layers . However , in terms of performance in real-world object recognition tasks ( e . g . object recognition under occlusion ) , the key in achieving a good performance is the number of non-linear operations , which can come either in the form of deeper networks in a feedforward architecture or otherwise more iterations in a recurrent architecture . Backward masking is a useful tool for studying temporal dynamics of visual object processing [48 , 53] . It can impair recognition of the target object and reduce or eliminate perceptual visibility through the presentation of a second stimulus ( mask ) immediately or with an interval after the target stimulus , e . g . 50 ms after the target’s onset . While the origin of masking effect was not the focus of the current study , our MEG results could provide some insights about the underlying mechanisms of backward masking . There are several accounts of backward masking in the literature: Breitmeyer and Ganz [87] provided a purely feedforward explanation ( two-channel model ) , arguing that the mask travels rapidly through the fast channel disrupting recognition of the target object traveling through the slow channel . A number of other studies , however , suggest that the masking mainly interferes with the top-down feedback processes [4 , 48 , 53 , 55] . And finally , Macknik and Martinez-Conde [57] explain the masking effect by the lateral inhibition mechanism of neural circuits within different levels of the visual hierarchy; arguing that the mask interferes with the recognition of the target object through lateral inhibition ( i . e . inhibitory interactions between target and mask ) . The last two accounts of masking , while being different , both argue for the disruption of recurrent processes by the mask: either the top-down recurrent processes , or the local recurrent processes ( e . g . lateral interactions ) . With a short interval between the target and mask , the mask may interfere with the fast recurrent processes ( i . e . local recurrent ) while with a relatively long interval it may interfere with the slow recurrent processes ( i . e . top-down feedback ) . Our results of MEG decoding time-courses , time-time decoding and behavioral performances under the no-occlusion condition does not support the purely feedforward account of visual backward masking . We showed that the backward masking did not have a significant effect on disrupting the fast feedforward processes of object recognition under no occlusion ( MEG: Fig 4a; behaviorally: Fig 5d ) . On the other hand , when objects were occluded the backward masking significantly impaired object recognition both behaviorally ( Fig 5d ) and neurally ( Fig 4b ) . Additionally , the time-time decoding results ( Fig 4c , 4d and 4f ) showed that backward masking , under no occlusion , had no significant effect on disrupting the diagonal component of the temporal generalization matrix that is mainly associated with the feedforward path of visual processing . On the other hand , the masking removed the off-diagonal components and the late peak ( >200ms ) observed in the temporal generalization matrix of the occluded objects . Taken together , our MEG and behavioral results are in favor of a recurrent account for backward masking . Particularly in our experiment with a short stimulus onset asynchrony ( SOA = time from stimulus onset to the mask onset ) , the mask seems to have affected mostly the local recurrent connections .
The study was conducted according to the Declaration of Helsinki . The study involved human participants . The experiment protocol was approved by the local ethics committee at Massachusetts institute of technology . Volunteers completed a consent form before participating in the experiment and were financially compensated after finishing the experiment . Images of four different object categories ( i . e . camel , deer , car , and motorcycle ) were used as the stimulus set ( see https://github . com/krajaei/Megocclusion/blob/master/Sample_occlusion_dataset . png for sample images of occlusion ) . Object images were transformed to be similar in terms of size and contrast level . To generate an occluded image , in an iterative process we added several black circles ( as artificial occluders ) of different sizes in random positions on the image . The configuration of black circles ( i . e . number , size , and their positions on the images ) were randomly selected as such that a V1-like model could not discriminate between images with 0% , 60% and 80% occlusion . To simulate the type of occlusion that occurs in natural scenes , the black circles are positioned in both front and back of the target object . The percent of object occlusion is defined as the percent of the target object covered by the black occluders . We defined three levels of occlusion: 0% ( no occlusion ) , 60% occlusion and 80% occlusion . Black circles also existed in the 0% occlusion , but not covering the target object; this was to make sure that the difference observed between occluded and un-occluded objects cannot be solely explained by the presence of these circles . The generated image set is comprised of 12 conditions: four objects × three occlusion levels . For each condition , we generated M = 64 sample images varying by the occlusion pattern and the target object position . To remove the potential effect of low-level visual features in object discrimination—objects positions were slightly changed around the center of the images ( by Δx ≤ 15 , Δy ≤ 15 pixels ) . Overall , we controlled for low-level image statistics , as such that images of different levels of occlusion could not be discriminated simply by using low-level visual features ( i . e . Gist and V1 model ) . Fifteen young volunteers ( 22–38 year-old , all right-handed; 7 female ) participated in the experiment . During the experiment , participants completed eight runs; each run consisted of 192 trials and lasted for approximately eight minutes ( total experiment time for each participant = ~70min ) . Each trial started with 1sec fixation followed by 34ms ( 2 × screen frame rate ( 17ms ) = 34ms ) presentation of an object image ( 6° visual angle ) . In half the trials , we employed backward masking in which a dynamic mask was presented for 102ms shortly after the stimulus offset—inter-stimulus-interval ( ISI ) of 17ms— ( S1 Fig ) . In each run , each object image ( i . e . camel , deer , car , motor ) was repeated 8 times under different levels of occlusions without backward masking; and another 8 repetitions with backward masking . In other words , each condition ( i . e . combination of object-image , occlusion-level , mask or no-mask ) was repeated 64 times over the duration of the whole experiment . Every 1–3 trials , a question mark appeared on the screen ( lasted for 1 . 5 sec ) prompting participants to choose animate if the last stimulus was deer/camel and inanimate if the last stimulus was car/motor ( S1 Fig; see S12 Fig for behavioral performance of animate/inanimate task ) . Participants were instructed to only respond and blink during the question trials to prevent contamination of MEG signals with motor activity and the eye-blink artifact . The question trials were excluded from further MEG analyses . The dynamic mask was a sequence of random images ( n = 6 images; each presented for 17ms ) selected from a pool of the synthesized mask images . They were generated by using a texture synthesis toolbox that is available at: http://www . cns . nyu . edu/~lcv/texture/ [88] . The synthesized images have low-level feature statistics similar to the original stimuli . To acquire brain signals with millisecond temporal resolution , we used 306-sensors MEG system ( Elekta Neuromag , Stockholm ) . The sampling rate was 1000Hz and band-pass filtered online between 0 . 03 and 330 Hz . To reduce noise and correct for head movements , raw data were cleaned by spatiotemporal filters [Maxfilter software , Elekta , Stockholm; [89]] . Further pre-processing was conducted by Brainstorm toolbox [90] . Trials were extracted -200ms to 700ms relative to the stimulus onset . The signals were then normalized by their baseline ( -200ms to 0ms ) , and were temporally smoothed by low-pass filtering at 20Hz . We ran a psychophysical experiment , outside MEG , to evaluate human performance on a multi-class occluded object recognition task . Sixteen subjects participated in a session lasting about 40 minutes . The experiment was a combination of mask and no-mask trials that were randomly distributed across the experiment . Each trial , started by a fixation point presented for 0 . 5s followed by a stimulus presentation of 34ms . In the masked trials , a dynamic mask of 102ms was presented after a short ISI of 17ms ( S5 Fig ) . Subjects were instructed to respond accurately and as soon as possible after detecting the target stimulus . They were asked to categorize the object images by pressing one of the pre-assigned four keys on a keyboard corresponding to the four object categories: camel , deer , car , and motorcycle . Overall , 16 human subjects ( 25 to 40 years-old ) participated in this experiment . Before the experiment , participants performed a short training phase on a totally different image-set to learn the task and reach a predefined performance level in the multi-class object recognition task . The main experiment consisted of 768 trials that were randomly distributed into four blocks of 192 trials ( 32 repetitions of object images with small variations in position and the pattern of occlusion × three occlusion levels × two masking conditions × four object categories = 768 ) . Images of 256x256 pixels size were presented at a distance of 70 cm at the center of a CRT monitor with the frame rate of 60 Hz and a resolution of 1024×768 . We used the MATLAB based psychophysics toolbox of [91] . We used the non-parametric Wilcoxon signrank test [98] for random effect analysis . To determine time-points with significantly above chance decoding accuracy ( or significant RDM correlations ) , we used a right-sided signrank test across n = 15 participants . To adjust p-values for multiple comparisons ( e . g . across time ) , we further applied the false discovery rate ( FDR ) correction [99] [RSA-Toolbox: is available from https://github . com/rsagroup/rsatoolbox [100]] . To determine whether two time-courses ( e . g . correlation or decoding ) are significantly different at any time interval , we used a two-sided signrank test , FDR corrected across time . Onset latency . We defined onset latency as the earliest time where performance became significantly above chance for at least ten consecutive milliseconds . Mean and standard deviation ( SD ) for onset latencies were calculated by leave-one-subject-out repeated for N = 15 times . Peak latency . The time for peak decoding accuracy was defined as the time where the decoding accuracy was the maximum value . The mean and SD for peak latencies were calculated similar to the onset latencies . | In recent years , deep-learning-based computer vision algorithms have been able to achieve human-level performance in several object recognition tasks . This has also contributed in our understanding of how our brain may be solving these recognition tasks . However , object recognition under more challenging conditions , such as occlusion , is less characterized . Temporal dynamics of object recognition under occlusion is largely unknown in the human brain . Furthermore , we do not know if the previously successful deep-learning algorithms can similarly achieve human-level performance in these more challenging object recognition tasks . By linking brain data with behavior , and computational modeling , we characterized temporal dynamics of object recognition under occlusion , and proposed a computational mechanism that explains both behavioral and the neural data in humans . This provides a plausible mechanistic explanation for how our brain might be solving object recognition under more challenging conditions . | [
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| 2019 | Beyond core object recognition: Recurrent processes account for object recognition under occlusion |
Candida albicans is a medically important pathogen , and recognition by innate immune cells is critical for its clearance . Although a number of pattern recognition receptors have been shown to be involved in recognition and phagocytosis of this fungus , the relative role of these receptors has not been formally examined . In this paper , we have investigated the contribution of the mannose receptor , Dectin-1 , and complement receptor 3; and we have demonstrated that Dectin-1 is the main non-opsonic receptor involved in fungal uptake . However , both Dectin-1 and complement receptor 3 were found to accumulate at the site of uptake , while mannose receptor accumulated on C . albicans phagosomes at later stages . These results suggest a potential role for MR in phagosome sampling; and , accordingly , MR deficiency led to a reduction in TNF-α and MCP-1 production in response to C . albicans uptake . Our data suggest that pattern recognition receptors sample the fungal phagosome in a sequential fashion .
C . albicans is an opportunistic pathogen , mostly confined to the gastrointestinal , genitourinary tracts and the skin . It can cause infections of mucosal tissues and is also able to invade systemically , especially in immunocompromised hosts [1] . Immune cells recognize C . albicans mainly through motifs on its cell wall which consists of approximately 90% carbohydrate , 5–10% protein and a small proportion of glycolipids [2] . The majority of the cell wall carbohydrates are β1 , 3- and β1 , 6-linked glucose polymers ( β-glucans ) ; it also contains a large amount of mannose polymers ( mannans ) covalently associated with protein , and a small amount of chitin [2] . The composition of the cell wall is dynamic and changes during cell growth and the transition of yeast to hyphal growth [3] . The cell wall is suggested to be layered and the inner layer adjacent to the plasma membrane is mainly composed of glucan and chitin that form a rigid structure giving the cell its morphology and protecting it from osmotic and other environmental factors [4] . The outer layer of the wall is primarily composed of mannans , the majority of which are covalently linked to the inner glucan layer [4] . The cell wall mannans and glucans can be recognised by pattern recognition receptors ( PPR ) of the immune system which then mediate subsequent binding , phagocytosis and stimulation of a pro-inflammatory response [5] , [6] . Cells of the innate immune system express a range of PRR for fungal recognition that mediate the first encounter with C . albicans . These include various Toll-like receptors ( TLR ) that have been suggested to play a role in the cytokine response to fungal cells including TLR2 , TLR4 , TLR6 and TLR9 [7]–[11] and MyD88-dependent signalling may be required for resistance to C . albicans [12] , [13] . A well characterised mechanism for yeast recognition is mediated via Dectin-1 where the subsequent inflammatory response involves collaboration between Dectin-1 and TLR2 [14] , [15] . Dectin-1 recognises 1–3 linked β-glucans , mediating recognition and phagocytosis of a range of fungi including C . albicans [14] , Aspergillus fumigatus [16] , Pneumocystis carinii [17] and Coccidioides posadasii [18] . Other fungi are able to escape Dectin-1 recognition; for example , immune responses against Histoplasma capsulatum are suppressed by the masking of β-glucans with α-glucans [19] . A study by Gantner et al . showed that C . albicans binds Dectin-1 in a restricted pattern , concentrated in discrete subdomains on the yeast surface and particularly strong in the region between the parent cell and the mature bud , so-called bud and birth scars . By contrast , C . albicans filaments failed to bind soluble Dectin-1 , not due to active inhibition , but rather a failure to provide access to the probe [20] . Besides mediating phagocytosis , dectin-1 also signals release of reactive oxygen , TNF-α , IL-2 , IL-6 , IL-10 , and IL-23 [21] , [22] . The mannan component of yeast cell walls has been suggested to be recognized by various receptors . One of these is the mannose receptor ( MR ) . COS-cells were able to phagocytose C . albicans upon transfection with MR [23] . Studies using mannosyl inhibitors and cells overexpressing MR support a role for this receptor as phagocytic receptor [24] , [25] . However , Le Cabec et al . suggested that whilst MR is able to bind ligands , it is unable to mediate phagocytosis upon binding [26] . Recognition of C . albicans N-linked mannosyl residues by the MR leads to TNF-α and IL-6 production [27] , DC-SIGN on DCs and a mouse homologue SIGN-R1 have also been shown to recognise mannan structures [28] , [29] . Dectin-2 has been shown to recognise mannan on C . albicans , primarily the hyphal form , inducing TNFα and IL-1Ra secretion upon recognition [30]–[32] . Signalling via Dectin-2 is dependent on an interaction with FcRγ [32] . The complement receptor 3 ( CR3 ) lectin binding domain has also been shown to recognise C . albicans [33] , [34] , and it could bind both mannans and glucans [35] . Recently , it was shown that 1–6 linked β-glucans can be opsonised by C3b which then mediates opsonic phagocytosis via CR3 [36] . The particles a professional phagocyte encounters are likely to engage more than one phagocytic receptor , resulting in a tailored downstream signalling response to regulate phagosome maturation , changes in phagosomal pH , oxygen-dependent and -independent killing and cytokine secretion . Phagocytosis of C . albicans is more efficient when opsonised with serum; then both FcγR and CR3 mediate recognition of exposed antibody or iC3b components respectively [37] , [38] . Furthermore , TLRs have also been shown to sample the phagosomal content and to regulate phagosome maturation and antigen presentation [39] , [40] . Together , this variety of receptors greatly increases the complexity of the response to phagocytic stimuli , due to cross-talk and synergy of downstream signalling pathways . The mechanisms that enable Mφ to produce a response that is appropriate to the ingested particle are poorly understood [41] , [42] . In this paper we determined the relative contribution of CR3 , Dectin-1 and MR to the recognition of non-opsonised C . albicans and assessed their localisation during different stages of phagocytosis by Mφ . We show that CR3 and MR are not required for phagocytosis of C . albicans and consistent with other reports show that Dectin-1 is the main receptor involved in uptake of these particles . However , CR3 and Dectin-1 accumulate at the site of particle binding , suggesting other roles for CR3 during fungal recognition . These receptors largely disappear from the phagosome within 20 minutes of phagosome formation . MR , which is mainly localised intracellularly in the endosomal compartment , is not readily detectable in association with the particles during nascent phagosome formation , but accumulates at the phagosomes 20 minutes after initiation of phagocytosis . Although MR does not seem to be involved in uptake of C . albicans we show that it is required for cytokine production upon recognition . This is the first report to describe sequential sampling by pattern recognition receptors during phagocytosis .
MR , Dectin-1 , and CR3 have been suggested to mediate fungal phagocytosis [14] , [23] , [33] , [34] , [43] . To evaluate their relative contribution to non-opsonised C . albicans phagocytosis by macrophages ( Mφ ) we assessed their involvement using uptake assays . We chose to use thioglycollate-elicited macrophages because we wanted to examine dectin-1 , MR and CR3 in the same system and these cells express all three receptors . In contrast , resident peritoneal do not express MR and alveolar macrophages do not express CR3 [44] . SIGN-R1 and Dectin-2 are not significantly expressed by thioglycollate elicited macrophages [29] , [45] . Soluble mannans and β-glucans were used to block recognition of the main known pathogen associated patterns exposed on the surface of C . albicans . To investigate the involvement of pattern recognition receptors , conditions were kept free of opsonins by use of serum free medium; cells were also washed to remove potential locally secreted opsonins . We found that addition of mannan did not influence uptake of these particles , suggesting that mannan recognising receptors like MR are not involved in this process . Laminarin , a soluble β-glucan known to inhibit Dectin-1 [46] , significantly reduced C . albicans uptake ( Figure 1A ) . We next assessed C . albicans association using thioglycollate-elicited peritoneal MΦ isolated from mice deficient in MR , CR3 or Dectin-1 . Mφ deficient in MR or CR3 did not show any impairment in phagocytosis of this yeast ( Figure 1B ) . The role of Dectin-1 in recognition of C . albicans has been demonstrated using Mφ from Dectin-1-deficient mice [47] and we confirmed that in the absence of this receptor , association of these particles was reduced by 80% ( Figure 1B ) . Residual association may be due to additional adhesion molecules known to be expressed by live C . albicans , or other receptors [48]–[50] . Together , these data suggest that Dectin-1 is the main pattern recognition receptor for uptake of non-opsonised C . albicans by thioglycollate-elicited peritoneal MΦ . To assess the role of these receptors in particle recognition , we used confocal microscopy to analyse their localisation during binding and uptake . Since no good anti-Dectin-1 antibodies were available for staining fixed cells , we developed a new monoclonal antibody 7G7 . 7G7 specifically recognised Dectin-1 ( Figure S1A ) and 7G7 staining of Dectin-1 on thioglycollate elicited MΦ showed that it was mainly expressed on the plasma membrane ( Figure S1B ) . To determine the localisation of MR , CR3 and Dectin-1 during phagocytosis , macrophages were stained for these receptors two minutes after onset of synchronised C . albicans phagocytosis . At this time phagocytic cups had formed , as could be seen from the accumulation of actin around the particles ( Figure 2A ) . Association of C . albicans , mainly mediated by Dectin-1 , was consistent with Dectin-1 localisation ( Figure 2A and 2B ) . Surprisingly , CR3 also became associated with these fungal particles even though it had no detectable role in uptake ( Figure 2B ) . MR which is mainly intracellular in location , did not show any association with the particles during this early stage of phagocytosis ( Figure 2B ) . Our results indicated that Dectin-1 and CR3 accumulate at the C . albicans phagosome , but this did not exclude the possibility that the receptors are enriched due to concentration of membrane around the particles . To address this further we measured receptor accumulation using ratiometric confocal microscopy . Besides receptor staining , PKH26 or Alexa 555-labelled cholera toxin B ( Figure 2B ) were used as general membrane dyes , to stain the plasma membrane uniformly . The fluorescence intensity ratio of the receptor and membrane staining at different cellular regions were used to assess receptor enrichment . The PKH26 dye contains a highly aliphatic reporter molecule that traps the probe once incorporated into the membrane , because of its inherent insolubility in an aqueous environment [51] , and Alexa 555-labelled cholera toxin B subunit binds ganglioside GM1 , localised to the Mφ plasma membrane . Ratiometric analysis showed that Dectin-1 ( Figure 2D ) and CR3 ( Figure 2E ) receptor levels at the C . albicans phagocytic cup increased significantly , with 33% and 42% respectively compared to the rest of the plasma membrane . Latex bead uptake , used as a negative control , did not show enrichment of CR3 during uptake ( Figure S2 ) . Taken together , these data confirm that both Dectin-1 and CR3 accumulate at the site of C . albicans phagocytosis , while MR does not . To assess the dynamics of phagosomal association of the receptors , we next determined receptor localisation at later timepoints after phagosome formation . We found that the CR3 and Dectin-1 started to disappear from the phagosomes around 10 minutes after onset of phagocytosis ( Figure 3A ) and 20 minutes after initial binding these receptors had for the most part disappeared from the phagosomes , leaving only a few phagosomes stained for these receptors ( Figure 3A ) . In contrast , the MR started to accumulate at the phagosomes at 10 minutes and this receptor had clearly localised at the phagosomes after 20 minutes ( Figure 3A ) . The localisation of MR was transient since at 40 minutes phagosomes were largely negative for MR ( Figure 3A ) . When cells were stained for both MR and CR3 we found that localisation of these receptors around the fungal particles was transient and stage specific with little colocalisation of these two receptors ( Figure 3B ) . Similar transient localisation of CR3 followed by transient localisation of MR was also found during C . albicans phagocytosis in human monocyte derived macrophages , demonstrating that this phenomenon is not specific for murine macrophages ( Figure 3C ) . Together these data show that C . albicans phagosomes associate with MR in a transitory manner while the phagosomes mature . We next assessed if the receptor involvement and localisation during C . albicans phagocytosis is specific to this fungus , since it has been shown that C . albicans phagosomes mature faster than other phagosomes [52] . To determine this we looked at zymosan uptake . Zymosan is a crude β-glucan- and mannan-rich cell wall extract of Saccharomyces cerevisiae routinely used as a model for fungal particles [53] . We found that addition of mannan did not influence uptake of these particles while laminarin significantly reduced zymosan uptake ( Figure S3A ) . Also macrophages deficient in MR or CR3 did not show any impairment in phagocytosis of this particle ( Figure S3B ) . Mφ from Dectin-1-deficient mice showed a reduction in uptake of more that 95% ( Figure S3B ) , suggesting that Dectin-1 is the major pattern recognition receptor for zymosan uptake by thioglycollate-elicited peritoneal Mφ . Next , we assessed localisation and accumulation of these receptors during uptake . As with C . albicans uptake , Dectin-1 and CR3 , but not the MR , accumulated at the phagocytic cup ( Figure 4A ) . As before , the levels of Dectin-1 and CR3 were increased at the zymosan phagocytic cup compared to the rest of the membrane ( Figure 4B and 4C ) . At later stages Dectin-1 and CR3 disappeared from the zymosan phagosomes while MR was clearly observed on phagosomes 20 minute post-uptake and at 40 minutes this receptor had also dissociated from the phagosomes ( Figure 4A ) . These data suggest that such receptor dynamics during phagocytosis are not specific for C . albicans , but are a common feature for fungal particles . Although MR does not seem to play a crucial role in C . albicans uptake , it has previously been reported that C . albicans recognition by MR influences cytokine responses [27] . We assessed MCP-1 and TNFα production by MR deficient thioglycollate elicited macrophages upon stimulation with C . albicans , and found that after 4 hours MCP-1 and TNF-α production was reduced by 88% and 60% , respectively , compared to wild type cells ( Figure 5A ) . We also assessed cytokine production upon stimulation with zymosan and found that while MCP-1 levels were reduced by 68% in MR-deficient cells , there was no significant difference in TNF-α production ( Figure 5B ) . Together these data suggest that MR plays a role in cytokine responses towards fungal particles .
The C . albicans cell wall is composed of layers containing different ligands [4] . Recognition of these ligands by PRR can mediate phagocytosis and regulate immune responses . In this study we analysed the role of the MR , Dectin-1 and CR3 in phagocytosis of non-opsonised C . albicans . Taken together , our observations suggest a model of sequential localisation of pattern recognition receptors during phagocytosis of fungal particles . We showed that Dectin-1 , but not CR3 or MR , is a major receptor for uptake of C . albicans under opsonin free conditions . However , during phagosome formation CR3 is also recruited to the particle , possibly contributing to phagosome maturation . Later during phagosome maturation these receptors disappear from the phagosome . Outer layers of the phagosomal content will be degraded while the phagosome matures and other pattern recognition receptors like TLRs and MR accumulate around the maturing phagosome where they can sample ligands exposed during particle degradation , thus modulating cytokine responses ( Figure 6 ) . Sequential localisation of proteins during endocytosis and endosome maturation was recently shown to provide access to distinct signalling pathways [54] . Before the discovery of Dectin-1 as a β-glucan receptor , β-glucan recognition was suggested to be mediated by CR3 . Studies using anti-CR3 antibodies and glucan containing carbohydrates to block opsonin independent recognition and phagocytosis of zymosan and C . albicans supported this hypothesis [33] , [35] , [43] , [55] , [56] . However , because of colocalisation of Dectin-1 and CR3 at the phagocytic synapse ( Figure 2 ) , it is possible that studies where anti-CR3 antibodies were used to block zymosan and C . albicans binding , Dectin-1 was sterically hindered by antibodies bound to CR3 . Using CR3-deficient Mφ , we have shown that CR3 does not play an essential role in the association and phagocytosis of zymosan or C . albicans ( Figure 1 and Figure S3 ) . Nevertheless , like Dectin-1 , CR3 did accumulate at the phagocytic synapse ( Figure 2 ) . Our ratiometric data assessed Dectin-1 and CR3 levels at the site of phagocytosis compared to the rest of the plasma membrane , showing that these receptors become enriched around the fungal particles ( Figure 2 ) . It is possible that CR3 and Dectin-1 interact with each other during the uptake of yeast particles , since CR3 has been shown to collaborate with other receptors for phagocytosis and adhesion [57]–[59] . Our experiments show that Dectin-1 is the main receptor involved in phagocytosis of unopsonised C . albicans , confirming several reports which show a role for Dectin-1 in C . albicans phagocytosis [14] , [46] , [47] . However , one paper has shown contradicting data on the recognition of C . albicans by macrophages from Dectin-1 deficient mice [60] , differences in fungal strain and mouse genetic background have been suggested as possible causes for this difference [61] . In this study we used the same C . albicans strain also with thioglycollate elicited macrophages as described by Saijo et al . [60] and in our hands Dectin-1 is the main receptor for phagocytosis of C . albicans ATCC18804 strain . This suggests that the difference in mouse strain cause differences in Dectin-1 involvement; we have previously shown that different mouse strains can express different isoforms which influence ligand binding and subsequent immune responses [62] . It is not clear if other differences in the methods used by Saijo et al . could account for their differences from the other published reports . Dectin-1 deficient macrophages show a residual uptake of 20% , this may be due to additional receptors . SIGN-R1 and Dectin-2 are not expressed by thioglycollate-elicited macrophages [29] , [45] . It is possible that TLR2 contribute to the phagocytic process . TLR2 is known to collaborate with Dectin-1 for inflammatory responses upon C . albicans recognition and has been shown to influence uptake of the fungus Aspergillus fumigatus [14] , [15] , [63] . Furthermore , Blander et al . showed that TLR2−/−TLR4−/− deficient cells have reduced bacterial uptake [39] . On the other hand , Gantner et al . showed that macrophages deficient in TLR2 or Myd88 have no reduced phagocytosis of zymosan [15] . Together , it is unclear which receptors or adhesins mediate the remaining 20% of dectin-1-independent C . albicans association with macrophages . Different experiments have led to the suggestion that MR could be involved in phagocytosis of mannan exposing particles; soluble MR was shown to bind C . albicans [25] and subsequent inhibition of Mφ recognition with mannan blocked the interaction with C . albicans [25] and other mannan exposing particles [64]–[68] . Also , COS cells over-expressing MR have been shown to phagocytose zymosan [23] . We were unable to demonstrate a role for MR in the binding and phagocytosis of C . albicans or zymosan by Mφ ( Figure 1 ) , which is consistent with previous reports [26] , [69] . We observed that MR was not located around the phagocytosed particles during binding or initial phagocytosis ( Figure 2 ) . Although MR is able to recognise yeast particles [25] , the level of its expression at the surface of primary Mφ may not be sufficient to initiate binding and/or phagocytosis . Other mannan recognising receptors have been suggested to be involved in phagocytosis; these include DC-SIGN [28] and the mouse homologue SIGN-R1 [29] . Interactions of these receptors are also inhibited with mannan . SIGN-R1 was not expressed on the thioglycollate elicited macrophages used in our experiments [29] . Previous work showed that MR is involved in initiating cytokine responses upon C . albicans mannan recognition [27] , [70] , and the receptor has also been implicated in phagocytosis of this yeast [23] , [25] , [64]–[68] . We showed that MR became enriched around the phagosome at later stages of phagosome maturation ( Figure 3 ) . This suggests that MR is appropriately positioned to recognise ligand intracellularly , directly or after further processing . Indeed , MR-deficient macrophages produce lower levels of TNF-α and MCP-1 in response to C . albicans ( Figure 5 ) . This is the first report showing a role for MR in MCP-1 production by macrophages . MR deficient macrophages did not show reduced TNF-α levels upon zymosan uptake , this could be explained by the large amount of TNF-α produced upon zymosan recognition which may mask MR influences on TNF-α levels . Intracellular compartments have been proposed to be the main site for TLR-mediated sampling of microbial components [71] , [72] , suggesting that the phagosome is a likely place for PRR to recognise ligands and mediate immune responses . This is the first report showing that MR also samples the maturing C . albicans phagosome , where it influences the cytokine response . In summary , we have observed sequential association of macrophage pattern recognition receptors with the phagosome during its maturation . The timing of this association was receptor specific and appeared to reflect the natural compartmentalisation of the receptor itself . These studies provide new insights into the processes by which surface and predominantly intracellular receptors sample the phagosome . Taken altogether this suggests that pattern recognition receptors sample ligands both during phagocytosis and phagosome maturation to tailor a response specific for the internalised particle .
Mice used in this study ( 6 BALB/c , 35 C57BL/6 , 10 C57BL/6 . MR−/− [73] , 6 C57BL/6 . CD11b−/−[74] and 7 129/Sv×C57BL/6 Dectin-1−/− and 6 129/Sv×C57BL/6 controls [47] ) were from the Sir William Dunn School of Pathology ( University of Oxford ) breeding colonies , sex-matched and between 8 and 12 weeks of age at the time of study . Animals were kept and handled in accordance with institutional guidelines . Thioglycollate-elicited peritoneal Mφ were isolated 4 days after intraperitoneal injection of 1 ml 4% ( w/v ) Brewer's thioglycollate medium ( BD ) . Primary Mφ were cultured overnight in serum-free defined medium , Optimem-I ( Invitrogen ) with 50 IU/ml Penicillin , 50 µl/ml streptomycin and 2 mM L-glutamine ( Optimem medium ) at 37°C in 5% CO2 . For binding studies , 2 . 5×105 cells were plated in 24 well tissue culture plates and cultured overnight . For confocal studies , 2 . 5×105 cells were cultured overnight on acid washed 13 mm diameter glass coverslips in 24 well tissue culture plates . After 1 to 2 hours , non-adherent cells were removed by washing three times with medium . Human monocytes were obtained from normal blood donor buffy coats by two step gradient centrifugation followed by an additional step using the Monocyte Isolation Kit II ( Miltenyi Biotec; Bergisch Gladbach , Germany ) as described previously [75] . Macrophages were obtained by culturing monocytes ( 98% CD14+ , 13% CD16+ ) for 7 days in X-VIVO 10 ( Cambrex ) supplemented with 1% autologous serum . Cells were cultured in Lumox hydrophobic dishes ( Sigma ) until day 7 , with a partial medium change at day 3 . On day 7 cells were detached with cold 10 mM EDTA and transferred into glass slides . The following primary antibodies were used for confocal microscopy experiments: rat IgG1 anti-Dectin-1 ( 7G7 ) , biotinylated rat IgG1 isotype control ( BD Pharmingen ) ) , rat IgG2a anti mannose receptor ( clone 5D3 , Serotec ) , rat IgG2b anti-CR3 ( clone 5C6 , Serotec ) , biotinylated anti-CR3 ( clone 5C6 ) , fluorescein isothiocyanate–conjugated 7/4 ( antibody to neutrophils and monocytes ) , phycoerythrin-conjugated anti-Ly-6G ( clone 1A8 ) and rat IgG2b and IgG2a isotype controls ( produced in house ) . Cy3 labelled streptavidin ( Jackson ImmunoResearch ) and Alexa 488 labelled goat anti-rat IgG ( Molecular Probes ) were used as secondary antibody . The antibody 7G7 ( rat IgG1 ) was produced as described [46] . NIH3T3 , a fibroblast cell-line overexpressing HA-tagged Dectin-1 , was used to confirm the specificity for Dectin-1 by confocal microscopy and FACS analysis . The NIH3T3 cells were cultured in RPMI1640 ( Gibco ) supplemented with 10% heat inactivated FCS , 100 IU/ml Penicillin G ( Gibco ) , 0 . 1 mg/ml streptomycin ( Gibco ) and 2 mM glutamine ( Gibco ) ( R10 medium ) . G418 ( Sigma ) , 250 µg/ml , was added as selective agent for the transfected cells . Stock cultures of C . albicans ( ATCC 18804 ) were maintained on Sabouraud's dextrose agar ( Difco ) at 4°C . For experiments , C . albicans was grown in 10 ml Sabouraud's dextrose broth ( Difco ) in a shaking incubator at 30°C for 24 hours , to obtain a stationary phase culture . FITC-labelled zymosan was from Molecular Probes . Live C . albicans were fluorescently labelled by incubation with 20 µM FUN1 ( Molecular Probes ) as described previously [76] . Binding experiments were performed as described [62] . Briefly , Mφ were plated at 2 . 5×105 cells/well in 24-well plates in the appropriate medium and cultured for 12 hours . Cells were cooled to 4°C and washed three times with pre-chilled medium to remove potential opsonins . To block receptor binding , 100 µg/ml mannan ( Sigma ) and/or laminarin ( Sigma ) were added in medium for 30 minutes at 4°C , prior to adding the particles . Fluorescently labelled particles were added to Mφ at a 20∶1 ratio and cells were incubated for 1 hour at 4°C or 30 minutes at 37°C . After incubation , unbound particles were removed by washing 4 times with pre-chilled PBS . The amount of C . albicans or zymosan associated with the cells was quantified after lysis with 3% w/v Triton X-100 solution , pH 8 , using a fluoroscan II fluorometer ( Titertek , Huntsville , Alabama , USA ) at excitation/emission of 485/538 nm . Data were analysed for statistically significant differences using One-way ANOVA with Bonferroni multiple comparison test . Non-fluorescent zymosan ( Invitrogen ) , 6 . 0 micron polystyrene polybead ( Polysciences Inc . , Warrington , Pennsylvania , United States of America ) and live C . albicans were used for these studies . Mφ were cooled to 4°C and washed three times with pre-chilled medium . Non-fluorescent particles were added to the Mφ at a Mφ∶particle ratio of 1∶5 , and incubated for 1 hour at 4°C . Unbound particles were removed by washing 3 times with pre-chilled medium . The cells were then incubated at 37°C for different periods . Cells were fixed with 4% paraformaldehyde ( Sigma ) in PBS or 1% formaldehyde ( Sigma ) in PBS for 15 minutes at 4°C . Cells were permeabilised in buffer containing 0 . 25% Saponin ( Sigma ) , 1% BSA ( Sigma ) , 1% heat inactivated goat serum ( Sigma ) and 1% heat inactivated rabbit serum ( Sigma ) in PBS ( permeabilisation buffer ) at room temperature for 30 minutes . Primary antibodies were added at a concentration of 10 µg/ml in permeabilisation buffer for 1 hour at room temperature . Cells were washed three times prior to incubation with the secondary antibody in the same buffer . After two washes with buffer and two washes with PBS , coverslips were mounted on glass microscopy slides with DakoCytomation Fluorescent Mounting Medium ( DakoCytomation ) . Double staining for CR3 and MR was done in the buffers described above , as follows: 5D3 was added first after which an anti-rat secondary reagent was used . This was followed with biotinylated 5C6 and incubation with Cy3 conjugated streptavidin . FITC-phalloidin ( Sigma ) was added at a concentration of 10 µg/ml in permeabilisation buffer for 1 hour at room temperature . Experiments were analysed using a confocal microscope ( Radiance 2000 , Biorad ) and representative images obtained using lasersharp software , with the pinhole set to the optimal size . Images were processed using Adobe Photoshop version 6 . For ratiometric analysis , Mφ plasma membranes were stained with 10 µg/ml Alexa fluor 555 conjugated cholera toxin B ( Invitrogen ) for 2–5 minutes at 37°C , or with PKH26 ( Sigma ) according to the manufacturer's protocols . Vybrant DiI ( Invitrogen ) and FM4-64FX membrane dye ( Invitrogen ) were also used according to the manufacturer's protocol , however the quality of staining was not adequate for our experiments . Following staining , preparations were treated as described above for confocal immunofluorescence studies . In brief , cells were cooled and washed , particles added and preparations kept at 4°C for 1 hour to allow binding of the particles . Unbound particles were removed by washing and cells incubated at 37°C for two minutes . Experiments were fixed on ice and stained as described above . The staining buffer for these experiments did not contain saponin , reducing membrane permeabilisation as much as possible to prevent loss of membrane staining . For each experiment , 25 representative images were collected on a Radiance 2000 ( Biorad ) confocal microscope , using a 60× PlanApo oil immersion objective ( NA 1 . 4 ) . The software used for collecting images was Bio-Rad Lasersharp 2000 ( v . 5 . 0 ) . Kalman-filtered images were collected with an optimal iris aperture and the minimum laser power that fitted the whole grey scale . To prevent bleedthrough between channels , sequential series were obtained by alternating the different channels . Fluorochrome intensity measurements were obtained for each image using MetaMorph . The mean intensity of each fluorochome was measured at three regions ( regions 1–3 ) around the particle and three regions elsewhere on the plasma membrane ( regions 4–6 ) . The regions were standard 15*15 pixel squares . These data were exported to Excel where the receptor to membrane ratio was calculated as described below and plotted using GraphPad Prism . The following calculation was used to assess the receptor intensity at the membrane and at the site of phagosome formation:Where 1 , 2 and 3 are regions from the phagosome and 4 , 5 and 6 are regions from the plasma membrane , Ijg is the intensity of green fluorescence ( receptor ) in region Rj and similarly Ijr for red fluorescence ( membrane ) in region Rj . Bg , Br are the background intensity of green and red respectively . The average receptor: membrane ratio was measured for three regions selected randomly at the Mφ plasma membrane and for another three regions selected randomly at the site of particle entry . Since both measured variables ( plasma and phagosomal membrane staining ) were derived from the same cell , receptor fluorescent intensity at the membrane and the phagosome was expressed for each cell as paired data . A paired t-test ( two tailed ) was used for statistical analysis . The sample mean was indicated in red . Mφ were plated at 2 . 5×105 cells/well in 24-well plates in the appropriate medium and cultured for 12 hours . Cells were cooled to 4°C and washed three times with pre-chilled medium . Live C . albicans were added to Mφ at a 20∶1 ratio and cells were incubated for 30 minutes at 37°C . Unbound particles were removed by washing 3 times with PBS . 300 ul medium were added to the cells , which were incubated for 3 . 5 hours at 37°C . TNF-α , IL-6 , MCP-1 and IL-10 were assessed in supernatants using BD Cytometric Bead Array ( BD Biosciences ) . IL-6 and IL-10 levels were below detection limit of 20 pg/ml . A t-test was used for statistical analysis . Mus musculus Dectin-1/Clec7a = Gene ID56644 MR/mrc1 = Gene ID17533 CD11b/Itgam = Gene ID16409 Homo sapiens Dectin-1/Clec7a = ID64581 MR/mrc1 = ID4360 CD11b/Itgam = ID120980 | Infection by Candida albicans has increased as a result of immunosuppression associated with AIDS and organ transplantation . We assessed the role of three pattern recognition receptors , namely Dectin-1 ( a beta glucan receptor ) , the type 3 complement receptor ( CR3 ) , and the mannose receptor , in mediating uptake of this fungus . These receptors are known to recognize structures on the C . albicans cell wall , but their exact contribution to binding and uptake is still unclear . We show that only Dectin-1 plays a major role in binding and uptake of C . albicans . Furthermore , we are the first to find that these receptors sample the internalized particle in a sequential manner; intracellular mannose receptor is recruited later and is involved in secretion of immune modulators . These findings provide a better understanding of the innate immune mechanisms involved in protection against this medically important fungal pathogen . | [
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| 2008 | Stage-Specific Sampling by Pattern Recognition Receptors during Candida albicans Phagocytosis |
More than 220 million people worldwide are chronically infected with schistosomes , causing severe disease or even death . The major pathological damage occurring in schistosomiasis is attributable to the granulomatous inflammatory response and liver fibrosis induced by schistosome eggs . The inflammatory response is tightly controlled and parallels immunosuppressive regulation , constantly maintaining immune homeostasis and limiting excessive immunopathologic damage in important host organs . It is well known that the activation of programmed death 1 ( PD-1 ) signaling causes a significant suppression of T cell function . However , the roles of PD-1 signaling in modulating CD4+ T cell responses and immunopathology during schistosome infection , have yet to be defined . Here , we show that PD-1 is upregulated in CD4+ T cells in Schistosoma japonicum ( S . japonicum ) -infected patients . We also show the upregulation of PD-1 expression in CD4+ T cells in the spleens , mesenteric lymph nodes , and livers of mice with S . japonicum infection . Finally , we found that the blockade of PD-1 signaling enhanced CD4+ T helper 2 ( Th2 ) cell responses and led to more severe liver immunopathology in mice with S . japonicum infection , without a reduction of egg production or deposition in the host liver . Overall , our study suggests that PD-1 signaling is specifically induced to control Th2-associated inflammatory responses during schistosome infection and is beneficial to the development of PD-1-based control of liver immunopathology .
Schistosomiasis is an infectious disease that affects at least 220 million people worldwide and causes serious morbidity and economic problems in developing countries [1 , 2] . During infection with Schistosoma japonicum ( S . japonicum ) or S . mansoni , granulomas form around eggs that are trapped in the host liver . This long-term immune-mediated granulomatous response results in severe fibrosis in the liver and eventually causes extensive tissue scarring , leading to irreversible impairment of affected organs , particularly the liver , and even death of the host [3–5] . The CD4+ T cell subsets play a critical role to develop hepatic granulomas and to maintain a balanced granulomatous response to prevent the growth of hepatic fibrosis during schistosomiasis [6 , 7] . Meanwhile , schistosomiasis also induces strong regulatory mechanisms , including T cell hyporesponsiveness , to prevent excessive immunopathology [8] . The inhibitory receptor programmed cell death 1 ( PD-1 ) is expressed in activated T cells and functions as a pivotal immune checkpoint protein that plays a critical role in the regulation of T cell function as well as its dysfunction in certain contexts [9–11] . Increasingly , studies in a number of murine and human infectious disease models and cancers have found an immunoregulatory function for PD-1 in T cells [12–17] . Recently , numerous studies have shown that exploiting the PD-1 pathway may be of interest for the treatment of chronic viral infections , cancers and autoimmune diseases [12–14] . PD-1 ligand 1 and 2 ( PD-L1/L2 ) have been shown to be significantly upregulated in macrophages and dendritic cells during schistosome infection , suggesting their involvement in T cell anergy [18 , 19] . However , very little is known about the regulation of PD-1 in CD4+ T cells or the impact of its signaling on the development of CD4+ T cell responses and egg-induced immunopathology during schistosome infections . In this study , we show that PD-1 expression is significantly up-regulated in CD4+ T cells from both humans and mice with schistosome infection . We further found that the inhibition of PD-1 signaling specifically enhanced T helper 2 ( Th2 ) cell responses and ultimately led to more severe liver immunopathology in mice with Schistosomiasis japonica .
All the animal experiments were conducted in strict accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals ( 1988 . 11 . 1 ) , and all efforts were made to minimize suffering . All the animals were used with approval by the Institutional Animal Care and Use Committee ( IACUC ) of Nanjing Medical University for the use of laboratory animals ( Permit Number: NJMU 14–0711 ) . Ethical approval for the human blood samples used in this study was obtained from the Institutional Review Board of Nanjing Medical University , Nanjing , China ( Permit Number: 2014NMUIEC001 ) . Written informed consent was obtained from each participant . Individuals with positive stool examination results were treated with a single oral dose of praziquantel ( 40 mg/kg ) . All personal identifiers of the study notes and tapes were kept confidential and destroyed after the study was completed . A total of 43 subjects were enrolled in the study . These subjects were from a village in Chizhou City , Anhui province . The subjects included 13 healthy adult controls and 26 patients with schistosomiasis japonica , diagnosed by the detection of parasite eggs using the Kato-Katz method with duplicate examination of three consecutive stool specimens obtained from each individual [20] . The healthy controls displayed no history , laboratory or clinical signs of schistosome infection . Participants who were positive for other intestinal helminth infections in the egg detection were excluded from this study . Furthermore , all of the participants were interviewed in person at enrollment . Participants who had been infected by hepatitis virus or had a history of influenza virus infection within 4 weeks were excluded from this study . Specific pathogen-free ( SPF ) 8-wk-old female C57BL/6 mice were purchased from the Model Animal Research Center of Nanjing University ( Nanjing , China ) . All the mice were housed and handled in accordance with the guidelines of Chinese animal protection laws with permission from the Institutional Review Board of Nanjing Medical University . Each C57BL/6 mouse was percutaneously infected with 12 cercariae of the Chinese mainland strain of S . japonicum from infected snails ( Oncomelania hupensis ) acquired from the Jiangsu Institute of Parasitic Diseases ( Wuxi , China ) . S . japonicum SEA and SWA were prepared as previously described [21 , 22] . The antigens were filter-sterilized and endotoxin was removed using Polymyxin B-Agarose ( Sigma-Aldrich , St . Louis , MO ) . The endotoxin activity ( <0 . 01 EU/μg ) was determined using the LAL assay kit ( BioWhittaker , Walkersville , MD ) . Protein concentrations were determined using the Lowry method ( DC Protein Assay Kit , Bio-Rad , Hercules , CA ) . Human peripheral blood mononuclear cells ( PBMCs ) were separated from whole blood by Ficoll-Paque PLUS ( GE healthcare , Uppsala , Sweden ) density gradient centrifugation . Cells were recovered from the gradient interface , washed twice and stained for 30 min at 4°C with the following antibodies: CD3-FITC ( clone HIT3a ) , CD4-PerCP-Cy5 . 5 ( clone RPA-T4 ) , PD-1-PE-Cy7 ( clone EH12 . 1 ) , all from BD Biosciences ( San Jose , CA ) . For measurement of Foxp3 expression , cells were further permeabilized at room temperature , incubated for 15 min at 4°C in permeabilization buffer containing anti-FcγR ( eBioscience , San Diego , CA ) to avoid nonspecific binding , and then stained for 30 min at 4°C with Foxp3-PE ( clone 259D/C7 , BD Biosciences ) . Spleens and mesenteric lymph nodes ( LNs ) were extracted from mice and pressed through nylon nets to prepare single-cell suspensions . Following red blood cell lysis , the remaining cells were washed and counted . Single cell suspensions of hepatic lymphocytes were prepared as previously described [23–25] . To analyze PD-1 expression in CD4+ T cells , the cells were incubated with CD3-APC ( clone 145-2C11 ) , CD4-FITC ( clone RM4-5 ) and PD-1-PE/PE-Cy7 ( clone J43 , all from eBioscience ) . To determine intracellular cytokine expression , T cells from each mouse were stimulated with 25 ng/ml of phorbol myristate acetate ( PMA; Sigma-Aldrich , St . Louis , MO ) and 1 μg/ml of ionomycin ( Sigma-Aldrich ) in complete RPMI 1640 medium ( Gibco , Grand Island , NY ) in the presence of 1 μl/ml of Golgistop ( BD PharMingen , San Diego , CA ) for 6 h at 37°C in 5% CO2 . After 6 h , the cells were collected and surface stained with CD3-APC ( clone 145-2C11 ) and CD4-FITC ( clone RM4-5 ) , and washed , fixed and permeabilized with Cytofix/Cytoperm buffer ( BD PharMingen ) . Next , the cells were intracellularly stained with PE-conjugated antibodies against IFN-γ ( clone XMG1 . 2 ) , IL-4 ( clone 11B11 ) , IL-17A ( clone eBio17B7 ) , or rat IgG1 isotype antibody ( all from eBioscience ) as a control . To analyze regulatory T cells , the Mouse Regulatory T Cell Staining Kit ( eBioscience ) was used , and the cells were surface stained with CD3-PerCP-Cy5 . 5 ( clone 145-2C11 ) , CD4-FITC ( clone RM4-5 ) , and CD25-APC ( clone PC61 . 5 ) . The cells were then permeabilized with cold Fix/Perm Buffer , and the Fc receptors were blocked with anti-mouse CD16/32 ( Fc Block ) for 15 min . A PE-labeled anti-mouse Foxp3 ( clone FJK16s ) or rat IgG2a isotype control antibody was then added . Following immunofluorescence staining , the cells were examined using a FACS Verse instrument ( BD Bioscience ) and analyzed using FlowJo ( Tree Star , version 10 . 0 . 7 ) . The cells were gated on CD3+CD4+ T cells . Single cell suspensions of splenocytes were prepared from the spleens of SPF C57BL/6 mice . The splenocytes were then stimulated in vitro with SWA ( 20 μg/ml ) , SEA ( 20 μg/ml ) , or PBS as a control . The cells were cultured in triplicate with complete RPMI 1640 medium ( Gibco , Grand Island , NY ) in 96-well round-bottom culture plates ( 1 . 5×106 cells/ml ) for 3 d and then collected for FCM analysis . To block PD-1 in vivo , 100 μg of rat anti-mouse PD-1 mAb ( clone 29F . 1A12; Biolegend , San Diego , CA ) , rat IgG2a isotype control ( Biolegend ) or PBS was injected intraperitoneally ( i . p . ) every three days , starting 24 d post-infection until 3 d before the mice were sacrificed [26] . At 42 d post-infection , all the mice were sacrificed . Serum samples were collected for ELISA detection of IL-4 levels , and splenocytes were prepared for FCM and qPCR analysis . In addition , livers were isolated for a pathological examination and an assessment of their egg burden . Total RNA from mouse splenocytes was prepared using TRIzol reagent ( Invitrogen , Carlsbad , CA ) . Any potentially contaminating DNA was removed using on-column DNAse treatment ( Qiagen , Hilden , Germany ) . An equivalent amount of total RNA from each sample was reverse transcribed with random hexamers using the SuperScript III First-Strand cDNA Synthesis System ( Invitrogen ) . The synthesized cDNA samples were then used as templates for qPCR and performed on a 7300 Real-Time PCR System ( Applied Biosystems , Foster City , CA ) with FastStart SYBR Green Master Mix ( Roche Applied Science , Meylan , France ) . Gene expression levels were normalized and shown as the fold increase over control . The following primers were used: IFN-γ , forward , 5’- TGCTGATGGGAGGAGATGTCT-3’ , and reverse , 5’- TGCTGTCTGGCCTGCTGTTA-3’; IL-12 p35 , forward , 5’-GATGCAGTCTCTCTGAATCATAATGG -3’ , and reverse , 5’-GGCACAAAAACAATAGCTTATCAGT -3’; IL-4 , forward , 5’-ACAGGAGAAGGGACGCCAT-3’ , and reverse , 5’-GAAGCCCTACAGACGAGCTCA-3’; IL-13 , forward , 5’- CCTGGCTCTTGCTTGCCTT-3’ , and reverse , 5’- GGTCTTGTGTGATGTTGCTCA-3’; IL-17A , forward , 5’-TCAGCGTGTCCAAACACTGAG-3’ , and reverse , 5’-CGCCAAGGGAGTTAAAGACTT-3’; IL-23 , forward , 5’- AATAATGTGCCCCGTATCCAGT-3’ , and reverse , 5’- GCTCCCCTTTGAAGATGTCAG-3’; IL-10 , forward , 5’-ACTTTAAGGGTTACTTGGGTTGC-3’ , and reverse , 5’-ATTTTCACAGGGGAGAAATCG-3’; TGF-β1 , forward , 5’-ATGCTAAAGAGGTCACCCGC-3’ , and reverse , 5’-CCAAGGTAACGCCAGGAATT-3’; GAPDH , forward , 5’-TGGTGAAGGTCGGTGTGAAC-3’ , and reverse , 5’-CCATGTAGTTGAGGTCAATGAAGG-3’ . The levels of IL-4 in serum were determined using a commercial ELISA kit ( Dakewe , Shenzhen , China ) according to the manufacturer’s instructions . The cytokine concentration in each sample was extrapolated from a standard curve . Liver tissue from infected mice was fixed in 4% buffered formalin , embedded in paraffin and sectioned ( 5–7 μm ) . The liver sections were stained with hematoxylin and eosin ( H&E ) to determine the size of granulomas . For each mouse , the sizes of 30 granulomas around individual eggs were quantified with the AxioVision Rel 4 . 7 Imaging System ( Zeiss , Oberkochen , Germany ) . The data are expressed in area units . All the images were captured at 100× magnification using an Axiovert 200M microscope and analyzed with Axiovision software ( Zeiss ) . The liver sections were stained with 0 . 1% Sirius red ( Sigma-Aldrich ) for semi-quantitative analysis of hepatic fibrosis [27] . Six to eight fields from each slide were randomly obtained using an optical microscope ( Zeiss ) coupled with a digital camera . The red-stained area per total area and the intensity of fibrosis were measured using Image-Pro Plus software ( version 6 . 0 for Windows; Media Cybernetics , Rockville , MD ) . A total fibrosis density score was determined by dividing the image intensity by the image area . Intensity exclusion parameters were identical for each of the images captured . Two grams of liver tissue from each infected mouse was digested with 5% KOH at 37°C overnight , and then the number of eggs per gram of liver was determined by microscopic examination . Significant differences were assessed using the SPSS program ( version 11 . 0 for Windows; SPSS , Inc . , Chicago , IL ) . The comparisons between two groups were analyzed by Student’s t-test . Comparisons between more than two groups were analyzed with one-way analysis of variance ( ANOVA ) using an LSD post hoc test . P values comparing human data were calculated using Chi-Square Test for categorical variables , and Mann-Whitney U test or Student’s t-test for continuous variables . P < 0 . 05 was considered to be statistically significant .
It is currently unknown if PD-1 is induced in CD4+ T cells in schistosomiasis patients . Therefore , we assessed the expression of PD-1 in CD4+ T cells from the peripheral blood of S . japonicum-infected patients . Total of 26 patients and 13 healthy controls were recruited and there was no statistically significant difference in the distribution of age or gender between groups ( Table 1 ) . The gating scheme for the identification of the human CD4+PD-1+ T cell population is shown in Fig 1A . Overall , a greater number of CD4+ T cells in S . japonicum-infected patients expressed PD-1 than in healthy controls ( Fig 1A–1C ) . As shown in Fig 1D , similar patterns were observed when PD-1 expression was analyzed by mean fluorescence intensity ( MFI ) . In addition , results showed that PD-1 expression increased in Foxp3-CD4+ T cells rather than in Foxp3+CD4+ T cells ( Fig 1E–1G ) . Collectively , our results demonstrate that PD-1 expression is upregulated in CD4+ T cells from S . japonicum-infected patients . Next , we observed the kinetics of PD-1-expressing CD4+ T cells , as well as the relative PD-1 MFI , at different time points following S . japonicum infection in mice . Both the frequency and MFI of PD-1 expression in splenic and mesenteric CD4+ T cells showed a continuous increase after infection ( Fig 2A–2C ) . Specifically , PD-1 expression in splenic CD4+ T cells barely increased during the first three weeks post-infection and then rapidly increased ( average fold-increase of 4 . 1 in frequency , 5 . 6 in total number , and 2 in MFI ) and reached a plateau at five weeks post-infection , remaining at a high level thereafter ( Fig 2B and 2C and S1 Fig ) . The mesenteric CD4+ T cells showed a similar , but slightly slower , increase in PD-1 expression ( average fold-increase of 2 . 9 in frequency , 1 . 8 in total number , and 1 . 2 in MFI ) during the first five weeks post-infection , reaching a plateau at eight weeks post-infection ( average fold-increase of about 6 in frequency , 5 . 6 in total number , and 1 . 7 in MFI ) ( Fig 2B and 2C and S1 Fig ) . Meanwhile , the CD4+ T cells in liver showed a continuous increase in PD-1 expression ( frequency and MFI ) since three weeks post-infection and reached a plateau at eight weeks post-infection ( Fig 2A–2C ) . These results demonstrate that the expression of PD-1 increases in CD4+ T cells after S . japonicum infection . In addition , Foxp3-CD4+ T cells showed a continuous increase in PD-1 expression till eight weeks post-infection . However , PD-1 expression in Foxp3+CD4+ T cells was significantly decreased at three weeks post-infection and increased since eight weeks post-infection ( Fig 2D and 2E ) . We also detected PD-1 expression on non-CD4+ T cells in S . japonicum-infected mice and found that the frequency of PD-1 expression on splenic CD8+ T cells or non-T cells ( CD3- cells ) ( S2 Fig ) was much lower than that on splenic CD4+ T cells ( Fig 2A and 2B ) . To determine whether CD4+ T cells are liable to be anergic , we analyzed Fas and PD-L1 expression by FCM . Compared with normal uninfected control mice , significantly higher levels of Fas and PD-L1 were detected on splenic and mesenteric CD4+ T cells of S . japonicum-infected mice eight weeks post-infection , suggesting that CD4+ T cells tend to be anergic in S . japonicum infection ( S3 Fig ) . The CD4+ T cell subsets are involved in the regulation of schistosomiasis progression [3] . FCM analyses revealed significantly increased frequencies and numbers of IL-4-producing splenic and mesenteric CD4+ T cells in S . japonicum-infected mice treated with a blocking anti-PD-1 mAb ( Fig 3A and S4A Fig ) . However , the frequencies or numbers of IFN-γ+ population ( Fig 3B and S4B Fig ) , IL-17A+ population ( Fig 3C and S4C Fig ) , and Treg cells ( Fig 3D and S4D Fig ) in CD4+ T cells did not show any significant increase after the PD-1 blockade . Similar results were also obtained in liver ( S5 Fig ) . Additionally , there were also no significant differences in the proportions of activated ( CD62LlowCD44hi ) or resting Treg cells ( CD62LhiCD44low ) from either spleens or LNs among groups ( S6 Fig ) . To investigate whether PD-1 restricts Th2 effector function or Th2 differentiation , we detected GATA-3 level in CD4+ T cells . As shown in S7A and S7B Fig , PD-1 blockade did not affect GATA-3 expression in splenic or mesenteric CD4+ T cells from S . japonicum-infected mice , suggesting PD-1 does not affect Th2 differentiation but regulates Th2 effector function . On the other hand , no significant change of PD-1 expression was detected in GATA-3+CD4+ T cells after PD-1 blockade ( S7A and S7C Fig ) . Consistently , PD-1 blockade in infected mice resulted in significantly increased mRNA expression of the Th2 ( IL-4 and IL-13 ) but not Th1 ( IFN-γ and IL-12 ) , Th17 ( IL-17 and IL-23 ) or Treg ( TGF-β and IL-10 ) -associated cytokines in splenocytes from S . japonicum-infected mice ( Fig 3E ) . We next examined the systemic levels of IL-4 in the serum of infected mice with or without PD-1 blockade . We found that the levels of serum IL-4 were significantly greater in mice that received PD-1 blockade than in control mice ( Fig 3F ) . Consistently , PD-1 blockade in infected mice significantly increased the frequency of M2 macrophages in liver ( S8 Fig ) . Thus , PD-1 blockade promoted Th2 cell responses , suggesting that PD-1 may restrict Th2 cell responses during S . japonicum infection . Previous studies have shown that stronger Th2 cell responses during S . japonicum infection result in more severe hepatic immunopathology [6 , 7] . The results in Fig 4A and 4B show that the average liver granuloma size in infected mice receiving anti-PD-1 mAb treatment was significantly increased compared to the granulomas in control mice . In addition , PD-1 blockade enhanced the severity of liver fibrosis in infected mice ( Fig 4C–4E ) . In addition , compared to the control group , no reduction of egg burden was observed in the livers of infected mice receiving anti-PD-1 mAb treatment ( Fig 4F ) . Thus , PD-1 blockade results in enhanced immunopathology in S . japonicum-infected mice .
Multiple immunoregulatory mechanisms are triggered by schistosomes to protect the host from severe immunopathology [3 , 7 , 8] . PD-1 signaling plays a critical role in the regulation of T cell function , as well as its dysfunction in certain contexts [9–11] . However , the role of PD-1 in schistosome infections remains elusive . Here , we uncovered that the PD-1 pathway specifically enhances Th2 cell responses and is critical to control liver immunopathology in mice with Schistosomiasis japonica . Previous studies have demonstrated that along with T cell suppression during schistosomal infection , the expression of PD-L1 and PD-L2 are selectively up-regulated in macrophages and dendritic cells respectively [18 , 19] , suggesting critical roles for both PD-L1 and PD-L2 in regulating T cell responses during schistosomal infection . However , the role of PD-L1/L2 in the regulation of CD4+ T cell responses and egg-induced immunopathology during schistosomal infections have not yet been investigated . To our knowledge , the present study is the first to report a significantly higher expression of PD-1 in CD4+ T cells from chronic schistosomiasis patients . In consistence with previous report [19] , we also observed a gradual increase in PD-1 expression in CD4+ T cells in vivo . Additionally , both splenic and mesenteric CD4+ T cells had a high expression level of PD-1 , even 8 weeks post-infection . This may , in part , account for the hyposensitive phenotype of CD4+ T cells observed in the later stages of chronic schistosomiasis [8] . Together , these observations suggest that increased PD-1 expression may be instrumental in the modulation of CD4+ T cell immune responses during chronic infection . To support this hypothesis , blocking antibodies against PD-1 were examined in S . japonicum-infected mice . The blockade of the PD-1 pathway in S . japonicum-infected mice selectively enhanced Th2 cell responses by increasing Th2 cells and the levels of Th2-type cytokines ( IL-4 and IL-13 ) , suggesting that the PD-1 pathway controls the Th2 cell responses during schistosome infection . Although PD-1/PD-L1 signaling has been reported to be involved in the development or proliferation of regulatory T cells in PD-L1-/- mice models or patients with chronic virus infection [28 , 29] , similar numbers of Tregs were observed in the spleens and lymph nodes of S . japonicum-infected mice receiving PD-1 blocking antibodies . Thus , this finding is inconsistent with prior studies [28 , 29] and suggests that the PD-1 pathway may be redundant for the peripheral induction of Treg cells during schistosome infection . Overall , our present study is the first to suggest that PD-1 blockade selectively augments Th2 cell responses in the spleens , mesenteric lymph nodes , or livers of mice with schistosomal infection , though the mechanism by which this occurs remains unclear . Further studies will be important to better understand how the PD-1 pathway regulates Th2 cell responses during chronic helminthic infections . It has been previously reported that the development of pathology during schistosome infections is typically driven by Th2 immune responses [6 , 7] , suggesting that PD-1 may limit this immunopathology by inhibiting Th2 cell responses . Indeed , we blocked PD-1 signaling and observed that mice infected with S . japonicum suffered more severe liver pathology , demonstrating the importance of the PD-1 pathway to reduce liver immunopathology during chronic schistosome infections . The PD-1 pathway has also been shown to be associated with long-term exposure to schistosome eggs and elevated Th2 responsiveness to SEA [30] . However , considering PD-1 blocking antibodies may target all populations of PD-1-expressing cells , it is definitely possible that some other PD-1-expressing cells , except for CD4+ T cells , may also be involved in the regulation of liver immunopathology after schistosome infection . However , in contrast to many studies that support a dominant role for PD-1 blockade in protecting against infection [15–17 , 28] , here , we found that PD-1 blockade failed to elicit protection against schistosomes in mice , with no reduction of the schistosome egg burden . One possible reason is that immune protection against schistosomes is associated with the induction of Th1-biased immune responses [30–32] . However , in our study , PD-1 blockade had no effect on Th1 immune responses . Overall , our results suggest that the PD-1-mediated reduction of hepatic immunopathology during schistosome infection is due to its immune regulation , not a reduction in egg burden . Taken together , our study is the first to demonstrate that egg antigens are likely responsible for the upregulation of PD-1 in CD4+ T cells in mice with S . japonicum infection . This results in a specific suppression of the Th2 cell response and leads to reduced liver immunopathology in mice during schistosome infection . It will be of interest to further explore therapeutic possibilities that target this inhibitory PD-1/Th2 axis for preventing the excessive immunopathology caused by an overactive immune response to schistosome infection . | Schistosomiasis is a parasitic disease that affects approximately 220 million people and causes serious morbidity and economic problems mainly in ( sub ) tropical regions . After Schistosoma japonicum or Schistosoma mansoni infection , parasite eggs are trapped in host liver and induce liver inflammation and fibrosis , leading to irreversible impairment of the liver , and even death of the host . Meanwhile , schistosomes also induce strong regulatory mechanisms to suppress inflammation and prevent excessive immunopathology . Considering it is well known that PD-1 plays a critical role in suppressing T cell function , understanding the role of PD-1 in modulating immune responses during schistosome infection is necessary for the development of PD-1-based control of liver damage in schistosomiasis . Here , increased PD-1 expression in CD4+ T cells from both humans and mice with schistosome infection was shown . We further showed that PD-1 blockade preferentially augmented Th2 cell responses and ultimately resulted in more severe liver immunopathology in mice with Schistosomiasis japonica , suggesting that PD-1 signaling is beneficial to further explore therapeutic possibilities for preventing the excessive liver immunopathology . | [
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| 2016 | Blockade of PD-1 Signaling Enhances Th2 Cell Responses and Aggravates Liver Immunopathology in Mice with Schistosomiasis japonica |
Despite their high degree of genomic similarity , different Salmonella enterica serovars are often associated with very different clinical presentations . In humans , for example , the typhoidal S . enterica serovar Typhi causes typhoid fever , a life-threatening systemic disease . In contrast , the non-typhoidal S . enterica serovar Typhimurium causes self-limiting gastroenteritis . The molecular bases for these different clinical presentations are incompletely understood . The ability to re-program gene expression in host cells is an essential virulence factor for typhoidal and non-typhoidal S . enterica serovars . Here , we have compared the transcriptional profile of cultured epithelial cells infected with S . Typhimurium or S . Typhi . We found that both serovars stimulated distinct transcriptional responses in infected cells that are associated with the stimulation of specific signal transduction pathways . These specific responses were associated with the presence of a distinct repertoire of type III secretion effector proteins . These observations provide major insight into the molecular bases for potential differences in the pathogenic mechanisms of typhoidal and non-typhoidal S . enterica serovars .
Salmonella enterica encompasses multiple serovars that are associated with distinct pathogenic features and host specificities [1 , 2] . Salmonella enterica serovar Typhi ( S . Typhi ) , for example , is the cause of typhoid fever , a systemic disease of humans that leads to an estimated 200 , 000 deaths worldwide [3–6] . In contrast , the broad host Salmonella Typhimurium causes limited gastroenteritis ( i . e . “food poisoning” ) and is one of the most common causes of food-borne illnesses in the industrialized world [1 , 2] . The molecular bases for these differences are incompletely understood but they are expected to involve multiple virulence factors that are unique to each serovar . Unique factors to S . Typhi include typhoid toxin , which is thought to be responsible for much of the acute specific symptoms associated with typhoid fever [7] , and the Vi capsular polysaccharide , which is thought to modulate the inflammatory response to this pathogen [8] . Furthermore , through the process of host adaptation , S . Typhi has lost a number of genes that are present in non-typhoidal Salmonella serovars [9] . Despite the different clinical presentations , however , S . Typhi and S . Typhimurium share a substantial portion of their genomes and consequently many pathogenic traits [10–12] . For example , both serovars encode two type III protein secretion systems ( TTSSs ) within their pathogenicity islands 1 ( SPI-1 ) and 2 ( SPI-2 ) , which mediate their close interactions with host cells [13 , 14] . Through the activity of the several effector proteins they deliver , these T3SSs mediate bacterial entry , intracellular replication , and the transcriptional reprogramming of the target cells by subverting the cellular machineries that control actin cytoskeleton dynamics , vesicle trafficking , and signal transduction . Despite the highly conserved nature of these T3SSs , the composite of effector protein that they deliver differs among S . enterica serovars [12] . We have recently shown that even small differences in the T3SS effector protein repertoire translate into profound differences in the biology of different Salmonella enterica serovars . For example , the absence of two effector proteins , GtgE and SopD2 , which target Rab32 , an essential component of a cell-autonomous pathogen restriction pathway , prevents S . Typhi replication in non-human hosts [15 , 16] . The ability to stimulate transcriptional responses in infected cells is emerging as a central strategy in the pathogenesis of S . enterica serovars [17–21] . S . Typhimurium , for example , stimulates transcriptional responses in intestinal epithelial cells that lead to the production of pro-inflammatory cytokines that initiate the inflammatory response that is central for its pathogenesis [17–21] . Furthermore , this transcriptional re-programming renders the infected cells more permissive for bacterial replication [19] . S . Typhi has also been shown to stimulate transcriptional responses in infected cells [22] and infected individuals [20 , 23] . Here we have compared the transcriptional responses of cultured epithelial cells infected with S . Typhi and S . Typhimurium . We have identified serovar specific transcriptional fingerprints that were correlated with the stimulation of specific signal transduction pathways and with the presence or absence of serovar-specific TTSS effector proteins . These findings provide major insight into the molecular bases for the differences in the pathogenic mechanisms of these S . enterica serovars .
Using RNAseq , we compared the transcriptional responses of cultured epithelial cells infected with wild-type S . Typhi , S . Typhimurium , or their specific isogenic mutants carrying a deletion in invA , which encodes an essential component of the SPI-1 TTSS [24] . We found that cells infected with the wild type strains exhibited distinct transcriptional responses relative to the responses to infection with their respective invA mutant strains , which have been previously shown to be similar to mock-infected cells [18 , 19] . Four hours after infection , cells infected with S . Typhi showed a significant increase ( 3 fold or higher ) in the expression of 107 genes , while 61 genes showed increased expression in cells infected with S . Typhimurium . The expression pattern was distinct for each serovar in that only 22 upregulated genes were common to both serovars ( Fig 1A and 1B , and S1 Table ) . The pattern of gene expression in infected cells 10 hs after infection was also distinct for each serovar . At this time point , cells infected with S . Typhimurium showed a larger number ( 264 ) of upregulated genes than cells infected with S . Typhi ( 107 ) ( Fig 1A and 1B , and S1 Table ) , with 61 of the upregulated genes being common to both serovars . We verified these findings by two alternative approaches . We selected genes whose expression varied in a serotype-specific manner and that previous studies have shown them to be consistently and significantly upregulated after S . Typhimurium infection [18 , 19] and examined their expression by real time PCR at different times after infection with S . Typhimurium or S . Typhi . In agreement with the RNA-Seq results , we observed serotype-specific induction of expression of the different genes in infected cells ( Fig 1C ) . For example , genes encoding SerpinB3 or TTP were more highly expressed in S . Typhimurium- than in S . Typhi-infected cells , while the mRNA levels of IL-8 and EGR1 showed the reverse pattern ( Fig 1C ) . We also examined whether the observed increase in gene expression was reflected in an increase in the respective protein levels by examining the levels of SerpinB3 or TTP in whole cell lysates of S . Typhi- or S . Typhimruium-infected cells . In line with the expression at the mRNA level , we observed increased levels of TTP during the early course of infection with both serovars ( Fig 1D ) . However , late in infection the TTP levels dropped in S . Typhi-infected cells but were maintained in cells infected with S . Typhimurium ( Fig 1D ) . Also , consistent with mRNA measurements , SerpinB3 was detectable in S . Typhimurium-infected cells late ( 20 hs ) in infection , but was undetectable in cells infected with S . Typhi at any time after infection ( Fig 1D ) . These findings were confirmed using immunofluorescence microscopy ( Fig 1E ) . Taken together , these results describe a serovar-specific reprogramming of gene expression in culture epithelial cells after infection with typhoidal ( S . Typhi ) or non-typhoidal ( S . Typhimurium ) Salmonella enterica serovars . The analysis of the pattern of gene expression in cells infected with S . Typhi or S . Typhimurium for functional associations using the Search Tool for the Retrieval of Interacting Genes/Proteins ( STRING ) ( http://string-db . org ) detected serovar-specific networks . Early ( 4 hs ) in infection , the STRING analysis revealed that both S . Typhimurium and S . Typhi stimulated transcriptional responses associated with the transcription factor NF-κB , as well as those associated with AP1 , which is linked to the activation of mitogen-activated protein kinase ( MAPK ) Erk , Jnk , and p38 signaling ( Fig 2 ) [25–27] . Notably , these responses were more robust in the case of S . Typhi-infected cells , resulting in the increased expression of a larger number of host-cell genes associated with these pathways . Later in infection , the STRING analysis indicated a shift in the response to S . Typhimurium infection to gene expression patterns associated with STAT3 activation ( Fig 2 ) . Importantly , such shift was not apparent in S . Typhi-infected cells , which exhibited a more restricted response with a pattern of gene expression more consistent with the activation of NF-κB signaling pathways ( Fig 2 ) . The distinct pattern of gene expression observed in cells infected with the different S . enterica serovars suggested that S . Typhi and S . Typhimruium stimulate distinct signaling pathways . Previous studies in our laboratory have shown that S . Typhimurium stimulates MAPK , NF-κB , and STAT3 signaling pathways in cultured epithelial cells [17–19 , 28] . However , equivalent studies have not been carried out with S . Typhi . To gain insight into the mechanisms underlying the distinct transcriptional responses observed after infection with S . Typhimurium and S . Typhi , we examined the stimulation of MAPK , NF-κB , and STAT3 signaling pathways in cultured epithelial cells . Consistent with previous studies [17 , 18 , 28] , infection with S . Typhimurium resulted in increased phosphorylation of the MAPKs Erk , Jnk , and p38 as well as in the activation of NF-κB as indicated by the degradation of I-κB , an inhibitor of NF-κB , and the activation of a luciferase reporter ( Fig 3A–3G ) . In addition and also consistent with previous observations [19] , S . Typhimurium infected cells , particularly later in infection , showed potent activation of STAT3 as monitored by the phosphorylation of STAT3Y705 ( Fig 3H and 3I ) . In contrast , STAT3 phosphorylation was not detected in S . Typhi-infected cells throughout infection ( Fig 3H and 3I ) . Furthermore , relative to S . Typhimurium-infected cells , S . Typhi-infected cells showed a more robust activation of NF-κB and MAPK signaling ( Fig 3A–3G ) . These results are consistent with the STRING analysis of the pattern of gene expression , which showed an enrichment of genes associated with the stimulation of these signaling pathways in S . Typhi-infected cells . Taken together these findings indicate that S . Typhi and S . Typhimurium stimulate distinct signaling pathways that lead to a distinct pattern of host cell gene expression . As shown above , cells infected with different Salmonella enterica serovars exhibited distinct signaling responses that resulted in distinct patterns of gene expression . To gain insight into the molecular bases for the observed differences , we sought to identify serovar-specific genes that might be correlated with the different responses . It is well established that the ability of Salmonella to interact with host cells is strictly dependent on the activity of effector proteins delivered through its SPI-1 and SPI-2-encoded type III secretion systems [13] . Furthermore , our analysis revealed that the transcriptional and signaling responses to both , S . Typhi and S . Typhimurium were dependent on a functional type III secretion system ( Figs 1 and 3 ) . Although the components of these systems are highly conserved across all Salmonella serovars , the effector proteins they deliver are not , and different Salmonella serovars encode a specific composite of effectors [12] . Notably , in comparison to S . Typhimurium , S . Typhi encodes a reduced number of effectors , which is consistent with the genome reduction driven by its adaptation to the human host [29] . We therefore reasoned that at least some of the differences observed in the signaling capacity of these two Salmonella serovars might be due to differences in the effector protein repertoire . To test this hypothesis , we constructed a series of S . Typhi strains encoding each one of the specific effectors that are present in S . Typhimurium but that are absent from S . Typhi . We then examined the capacity of these different S . Typhi strains to activate Jnk , Erk , or STAT3 signaling . We found that expression of the S . Typhimurium effector proteins AvrA or SpvC reduced the ability of S . Typhi to activate the MAPKs Jnk and Erk to levels that were equivalent with those observed in S . Typhimurium-infected cells ( Fig 4A ) . These findings are consistent with previous studies that have shown that AvrA negatively modulates the ability of S . Typhimurium to activate Jnk by inhibiting its activating kinase MKK7 [30] . Likewise , these findings are also consistent with previous reports indicating that SpvC is a threonine lyase that directly targets and inhibits Erk kinases [31] . These results therefore indicate that the absence of AvrA and SpvC in S . Typhi results in a heightened ability to stimulate MAPK signaling . To gain insight into the mechanisms underlying the increased ability of S . Typhi to stimulate NF-κB activation , we investigated the effect of expressing S . Typhimurium T3SS-effector proteins that have been previously shown to have the capacity to modulate this signaling pathway . Expression of spvD , which encodes a cysteine protease that has been reported to negatively regulate nuclear transport of NF-κB components [32 , 33] , did not affect the ability of S . Typhi to stimulate NF-κB signaling ( Fig 4B ) . Similarly , expression of ssek3 , sseL , or sspH1 , which have been reported to modulate NF-κB signaling [34–36] , had no effect on the ability of S . Typhi to stimulate the expression of a NF-κB dependent reporter ( Fig 4B ) . Recent studies from our laboratory identified a family of highly related effector proteins , GtgA , GogA and PipA , which redundantly inhibit NF-κB by proteolytically targeting the RelA and RelB transcription factors [37] . S . Typhi does not encode homologs of GtgA or GogA although it encodes a homolog of PipA . However , we were unable to detect expression of pipA suggesting that at least under the experimental conditions used here , this gene is not expressed in S . Typhi . We expressed GtgA , one of the members of this family of effectors , and examined its effect on the ability of S . Typhi to stimulate NF-κB signaling . Consistent with previous studies in S . Typhimurium , expression of GtgA markedly diminished the ability of S . Typhi to stimulate the expression of an NF-κB reporter ( Fig 4B ) . These results therefore indicate that , similar to MAPK signaling , the absence in S . Typhi of specific effectors with inhibitory activity results in a heightened ability to stimulate NF-κB . Previous studies have shown that the ability of S . Typhimurium to stimulate MAPK , NF-κB , and STAT3 signaling pathways is dependent on the functionally redundant activities of the SPI-1 T3SS effectors proteins SopE , SopE2 and SopB [18 , 19 , 38] . While sopE and sopB are highly conserved , in S . Typhi sopE2 has a frame-shifting mutation that leads to a non-functional polypeptide [29] . The absence of one of the effectors responsible for the activation of STAT3 suggested the possibility that such a loss could account for the inability of S . Typhi to activate this signaling pathway . However we found that the expression of sopE2 in S . Typhi did not confer the ability to stimulate STAT3 activation ( Fig 4A ) . In fact , there was no detectable effect on the ability of S . Typhi to stimulate STAT3 phosphorylation after heterologous expression of any of the S . Typhimurium effectors that are absent from this serovar ( Fig 4A ) . Given the conservation of the effectors responsible for STAT3 activation , we hypothesized that S . Typhi may encode an as yet unidentified effector protein , absent from S . Typhimurium , that inhibits STAT3 activation . We therefore searched the S . Typhi genome for genes encoding putative TTSS effector proteins that are absent from S . Typhimurium . We identified three open reading frames , sty1423 , sty1360 , and sty1076 , which encode homologs of the E . coli type III secreted effectors EspN [39] , OspB [40] , and NleG [39] , respectively . We found that the deletion of any of these genes had no impact in the ability of S . Typhi to stimulate signal transduction pathways , and more specifically , none of the S . Typhi deletion mutants were able to stimulate STAT3 activation ( Fig 4C ) . While S . Typhimurium and S . Typhi share their core genome , there are a number of virulence factors that are uniquely present in S . Typhi [11] . One of the virulence factors unique to S . Typhi is the Vi antigen , a capsular polysaccharide encoded within its SPI-7 pathogenicity island that has been proposed to interfere with Toll receptor agonists [8] . However , deletion of viaA and viaB , which encode essential enzymes for the synthesis of Vi antigen , had no effect on the ability of S . Typhi to stimulate STAT3 signaling although it had a small but measurable enhancement of its ability to stimulate NF-κB signaling ( Fig 4C ) . Similarly , removal of typhoid toxin , a critical S . Typhi virulence factor [7] , had no effect on its ability to stimulate any of these signaling pathways including STAT3 ( Fig 4C ) . These results indicate that an as yet unidentified factor may inhibit the ability of S . Typhi to stimulate STAT3 signaling . In summary , we have identified specific effector proteins that provide mechanistic explanations for some of the differences in the signalling pathways stimulated by S . Typhimurium and S . Typhi , which lead to distinct host cell transcriptional responses to these two pathogens . The absence of these effector proteins from S . Typhi suggest that the process of host-adaptation may have driven differences in the ability of these serovars to stimulate specific transcriptional responses , which may impact disease .
The ability to stimulate transcriptional responses is thought to be central for the pathogenesis of many microbial pathogens [26] . In the case of S . Typhimurium , transcriptional responses in intestinal epithelial cells are central to its capacity to stimulate inflammation in the gut epithelium [17 , 18 , 28 , 41 , 42] , which is instrumental for its ability to acquire essential nutrients and compete with the resident microbiota [43 , 44] . Furthermore , previous work from our laboratory has shown that the ability of S . Typhimurium to re-program gene expression is also important for its replication within epithelial cells [19] . S . Typhi has also been shown to re-program gene expression in cultured cells [22] and in infected individuals [20 , 23] , although the significance of these observations for its pathogenic mechanisms remains to be established . The stimulation of transcriptional responses in infected cells by Salmonella is strictly dependent on the presence of the SPI-1-encoded TTSS , which through the delivery of specific effectors stimulate signal transduction pathways leading to re-programming of gene expression [17 , 28] . More specifically , the delivery of the effector proteins SopE , SopE2 , and SopB , results in the direct activation of Rho-family GTPases and the subsequent stimulation of downstream signaling , which ultimately results in host-cell transcriptional reprogramming [18 , 38] ( Fig 5 ) . In this study , we have compared the transcriptional response of cultured epithelial cells after infection with S . Typhimurium and S . Typhi . We found that each serovar stimulates a distinct pattern of gene expression associated with specific signaling events . The transcriptional profile of cells infected with S . Typhi is consistent with the activation of MAPK and NF-κB signaling pathways , both early and late in infection . The transcriptional profile of cells infected with S . Typhimurium was also consistent with the activation of MAPK and NF-κB signaling pathways , particularly early in infection . However , late in infection and in sharp contrast to cells infected with S . Typhi , cells infected with S . Typhimurium showed a pattern of gene expression associated with the activation of STAT3 signaling . We found that these transcriptional responses were entirely consistent with the signaling pathways stimulated by these pathogens . Both S . Typhi and S . Typhimurium-infected cells showed activation of NF-κB and MAPK signaling pathways , particularly early in infection . However , the level of activation was clearly more robust in the case of cells infected with S . Typhi . Cells infected with S . Typhimurium showed a marked activation of STAT3 , particularly late in infection . In contrast , cells infected with S . Typhi showed no detectable activation of this signaling pathway . The pattern of signaling responses in S . Typhi-infected cells with a heightened activation of NF-κB and MAPK signaling pathways , traditionally associated with inflammatory responses , is surprising considering that S . Typhi infections are known to result in less intestinal inflammation that infections with S . Typhimurium [1–6] . However , S . Typhi infections ( i . e . typhoid fever ) do results in higher fever , another marker of an inflammatory response . Furthermore , it is well established that transcriptional responses are profoundly affected not only by the nature of the signaling pathways but also by the strength and duration of signaling [45 , 46] . In this context , the substantially different pattern of gene expression observed in cells infected with S . Typhi and S . Typhimurium is entirely consistent with the difference in the nature , strength , and duration of the signaling responses stimulated by these pathogens . Nevertheless , more studies will be required to determine how and if the different transcriptional and signaling responses stimulated by these pathogens contribute to the differences observed in their clinical presentations . The ability of Salmonella to stimulate signaling pathways is dependent on the functionally redundant activity of the TTSS effector proteins SopE , SopE2 , and SopB . Given the conservation of these effector proteins across Salmonella serovars , it was surprising to observe differences in the signaling responses to S . Typhi and S . Typhimurium in infected cells . Our results indicate that the heightened MAPK and NF-κB activation observed in S . Typhi infected cells is most likely not due to differences in the agonistic capacity of these two Salmonella serovars but rather to differences in their ability to down-modulate those responses after their stimulation ( Fig 5 ) . Indeed , we found that the absence in S . Typhi of effector proteins that in S . Typhimurium antagonize MAPK and NF-κB signaling pathways account at least in part for the unique responses that follow infection with each one of these pathogens . More specifically , we found that expression of AvrA , SpvC , and GtgA , which target the MAPK and NF-κB signaling pathways , reduced the level of activation of these pathways to a level comparable to that observed in cells infected with S . Typhimurium . In contrast , no S . Typhimurium effector protein was able to confer upon S . Typhi the ability to stimulate STAT3 signaling . Since the stimulation of STAT3 signaling by S . Typhimurium is strictly dependent on the effectors proteins SopE and SopB [19] , which are highly conserved in S . Typhi , these observations suggest the presence of an as yet unidentified factor in S . Typhi that may inhibit STAT3 signaling . It is becoming increasingly clear that even small differences in the repertoire of TTSS effector proteins in different Salmonella enterica serovars can have a profound effect in the manner in which these pathogens interact with their hosts . For example , the absence of just two effector proteins , GtgE and SopD2 , can have a very significant impact on the ability of the human-adapted S . Typhi to explore other hosts [15 , 16] . These two effectors target Rab32 , which coordinates the activity of a cell-autonomous pathogen restriction mechanism . We have shown here another example in which differences in the composite of T3SS effector proteins in S . Typhi and S . Typhimurium can have a significant impact in their ability to reprogram gene expression in infected epithelial cells . How these differences may affect disease is not clear but it is known that S . Typhi and S . Typhimurium interaction with the gut epithelium leads to markedly different outcomes . While S . Typhimurium stimulates an acute , though self-limiting , inflammatory response leading to diarrhea , S . Typhi intestinal infection leads to the invasion of deeper tissue with little to no diarrhea . It is possible that the differences in the ability of these pathogens to stimulate signaling pathways leading to different patterns of gene expression in intestinal epithelial cells may account for some of the observed differences in the pathogenesis of these two Salmonella serovars .
All bacterial strains used in this study were derived from the Salmonella enterica serovar Typhimurium strain SL1344 [47] or Salmonella enterica serovar Typhi strain ISP2825 [48] and are listed in S2 Table . Plasmids used in this study are listed in S3 Table and have either been described before or were constructed as part of this study using standard recombinant DNA techniques . S . Typhimurium and S . Typhi were grown under conditions that increase expression of the SPI-1 T3SS [49] . Briefly , a 1:20 dilution of a bacterial overnight cultures were grown at 37°C in L-broth containing 0 . 3 M NaCl until an OD600 = 0 . 9 prior to their use in infections . Antibodies and other reagents were purchased from the indicated companies: rabbit-anti-TTP ( Abcam ) ; rabbit-anti-Salmonella O Group B Antiserum ( Becton Dickson ) ; rabbit-anti- Phospho-STAT3 ( Ser727 ) , rabbit-anti-Phospho-STAT3 ( Tyr705 ) , Erk ( Thr202 , Tyr204 ) , JNK ( Thr183 , Tyr185 ) , P38 ( Thr202 , Tyr204 ) and I-kBα ( Cell Signaling Technology ) ; mouse-anti-SerpinB3 ( Santa Cruz Biotechnology ) ; mouse-anti-tubulin ( Sigma-Aldrich ) ; secondary antibodies ( Molecular Probes ) ; 49 , 6-diamidino-2-phenylindole ( DAPI ) ( Sigma-Aldrich ) . The human embryonic kidney epithelial HEK 293T ( ATCC ) and and epithelial Henle-407 ( Roy Curtiss laboratory collection ) cell lines were cultured in antibiotic free Dulbecco’s Modified Eagle Medium ( DMEM , Gibco ) supplemented with 10% bovine calf ( Henle-407 ) or bovine fetal ( HEK-293T ) sera . For bacterial infections , serum-starved ( DMEM without serum ) Henle-407 cells at a confluency of 80% were washed with pre-warmed Hank’s buffered salt solution ( HBSS ) and allowed to equilibrate in HBSS for 15 min at 37°C . Cells were then infected for 1 h with S . Typhimurium or S . Typhi strains at an adjusted multiplicities of infection ( MOI ) so as to insure equal number of internalized bacteria as indicated in the figure legends . Thus due to the slightly reduced infection rate of S . Typhi , infections were done with a 5-fold excess relative to S . Typhimurium ( S1 Fig ) . In all S . Typhimurium infections an MOI of 20 was used except for experiments involving measurement of gene expression by PCR ( MOI of 30 ) , immunofluorescence ( MOI = 5 ) or Luciferase measurements ( MOI of 10 ) . Infected cells were washed once with pre-warmed PBS and incubated for 1 h in pre-warmed DMEM containing 50 μg/ml gentamicin . Cells were washed again with PBS and further incubated in pre-warmed DMEM containing 10 μg/ml gentamicin for the indicated times . Similarly , HEK-293T cells were seeded in a 24-well plate and transfected with the NFκB reporter plasmid as previously described [37] . Transfected cells were serum-starved 18 hs prior to infection with the different S . Typhimurium or S . Typhi strains as described above and harvested 7 hs post infection for measurement of luciferase activity using a Dual luciferase reporter assay ( Promega ) . Statistical significance was calculated by a two-tailed distributed paired Student’s t-test with equal variance . Resulting p values of less than 0 . 05 were considered significantly different . Cells were washed once with PBS , lysed in 2 x SDS Laemmli buffer and boiled for 10 min . Proteins in cell lysates were separated by SDS-PAGE , transferred to nitrocellulose membranes . Membranes were washed once with Tris buffered saline ( TBS ) , blocked in a buffer containing 3% BSA or 5% milk in TBS for 30 min at room temperature and probed with the respective primary and secondary antibodies in blocking solution supplemented with 0 . 02% SDS and 0 . 1% Tween 20 . Blots were visualized and analyzed using the Odyssey LI-COR system and the LI-COR Odyssey application software . Alternatively , blots were visualized by enhanced chemiluminescence ( ECL ) . Human epithelial cells grown on glass coverslips were infected with S . Typhimurium or S . Typhi strains as described above , washed once with HBSS and fixed in 4% PFA/PBS for 15 min at RT . Cells were treated with blocking solution ( 3% BSA , 0 . 1% Saponin , 50 mM NH4Cl in PBS ) for 20 min at RT , probed with primary antibody overnight at 4°C in a wet chamber , washed three times in blocking solution and subsequently probed with secondary antibody in combination with 4 , 6-Diamidino-2-phenylindole , dihydrochloride ( DAPI ) for 30 min at RT . Finally , glass coverslips were washed twice with blocking solution , PBS and water before they were mounted on glass slides and examined by epifluorescence microscopy ( Nikon Diaphot ) and the Micro-Manager software [50] . RNA isolation , in vitro transcription and quantitative real-time PCR were carried out as described elsewhere [18] . Briefly , total RNA from serum starved and infected Henle-407 cells was isolated using the ‘‘RNeasy Mini Kit” ( QIAGEN ) . Following DNAse treatment , RNA was transcribed using the iScript reverse transcriptase ( BIO RAD ) . Transcript levels were determined in an iCycler real time PCR machine ( BIO RAD ) using gene specific primer sets ( S4 Table ) , which have been designed by PrimerBank ( http://pga . mgh . harvard . edu/primerbank/ ) . Total RNA was isolated from infected , serum starved Henle-407 cells at the indicated time points as described for quantitative real-time PCR . Samples were submitted to the Yale University’s Center for Genomic Analsysis on an Illumina HiSeq 2500 system . The sequencing data was analyzed using the Galaxy platform [51] ( http://www . usegalaxy . org ) with the TopHat package for alignment of cDNA fragments in combination with mapping to the human Hg19 reference genome and the Cufflinks package to estimate differential transcript abundance applying a false discovery rate ( FDR ) of 0 . 05 [http://www . nature . com/nprot/journal/v7/n3/full/nprot . 2012 . 016 . html] . For further analysis , only genes above 150 nucleotides were considered and the fold differences in transcript abundance calculated . | Salmonella Typhimurium and Salmonella Typhi are associated with very different clinical presentations . While S . Typhimurium causes self-limiting gastroenteritis ( i . e . “food poisoning” ) , S . Typhi causes typhoid fever , a systemic , life-threatening disease . The bases for these major differences are not fully understood but are likely to involve many factors . We have compared the transcriptional responses of cultured cells infected with S . Typhimurium or S . Typhi . We found that these Salmonella serovars stimulated distinct transcriptional responses , which could be correlated with their ability to stimulate serovar-specific signal transduction pathways . Importantly , the ability to stimulate these cellular responses was correlated with the presence or absence of specific type III secretion effector proteins . These observations provide major insight into the molecular bases for the differences in the pathogenic mechanisms of typhoidal and non-typhoidal S . enterica serovars . | [
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| 2017 | Salmonella enterica serovar-specific transcriptional reprogramming of infected cells |
Neuroinvasion and subsequent destruction of the central nervous system by prions are typically preceded by a colonization phase in lymphoid organs . An important compartment harboring prions in lymphoid tissue is the follicular dendritic cell ( FDC ) , which requires both tumor necrosis factor receptor 1 ( TNFR1 ) and lymphotoxin β receptor ( LTβR ) signaling for maintenance . However , prions are still detected in TNFR1−/− lymph nodes despite the absence of mature FDCs . Here we show that TNFR1-independent prion accumulation in lymph nodes depends on LTβR signaling . Loss of LTβR signaling , but not of TNFR1 , was concurrent with the dedifferentiation of high endothelial venules ( HEVs ) required for lymphocyte entry into lymph nodes . Using luminescent conjugated polymers for histochemical PrPSc detection , we identified PrPSc deposits associated with HEVs in TNFR1−/− lymph nodes . Hence , prions may enter lymph nodes by HEVs and accumulate or replicate in the absence of mature FDCs .
Prions are unusual infectious agents thought to be comprised solely of an abnormally folded , aggregated isoform ( PrPSc ) of the endogenous cellular prion protein ( PrPC ) [1] . Deposition of PrPSc aggregates , vacuolation , and neuronal loss in brain tissue are histopathological hallmarks of a group of neurological disorders collectively known as prion diseases or transmissible spongiform encephalopathies ( TSEs ) , including scrapie in sheep , bovine spongiform encephalopathy ( BSE ) in bovids , chronic wasting disease ( CWD ) in cervids , and Creutzfeldt-Jakob disease ( CJD ) in humans [2] . Although TSEs seem to selectively damage the central nervous system ( CNS ) , peripheral prion exposure leads to the accumulation of prions and PrPSc in secondary lymphoid organs ( SLOs ) long before neurological symptoms appear [3] , [4] , [5] , [6] , [7] , [8] , and it is largely from these extraneural sites that prions transmigrate to the peripheral nervous system ( PNS ) and finally the CNS [9] , [10] , [11] . Extraneural prion accumulation is thought to occur primarily within stromal cells found in germinal centers of lymphoid follicles known as follicular dendritic cells ( FDCs ) [4] , [12] , [13] , [14] , [15] , [16] , [17] . Maintenance of mature FDC networks depends on signaling through FDC-expressed lymphotoxin β receptor ( LTβR ) and tumor necrosis factor receptor 1 ( TNFR1 ) by B cell-derived tumor necrosis factor ( TNF ) and lymphotoxins ( LT ) α and β [18] , [19] , [20] , [21] , [22] , [23] . Accordingly , ablation of B cells , and hence loss of LTα/β and TNFα ligands , antagonizes prion deposition in secondary lymphoid organs [24] , [25] , and intraperitoneal ( i . p . ) injection of mice with TNFR1 or LTβR blocking antibodies prior to peripheral prion inoculation causes transient de-differentiation of FDCs , reduces splenic prion accumulation , and delays prion neuroinvasion [26] , [27] , [28] , [29] . However , extraneural prion accumulation in SLOs is not strictly dependent on the presence of mature FDCs . Although prion titers remain below detection in spleens of i . p . -inoculated TNFR1−/− and TNFα−/− mice , PrPSc levels and prion infectivity in TNFR1−/− and TNFα−/− lymph nodes are only marginally reduced compared to TNFR1−/− , LTβR−/− , LTα−/− , or LTβ−/− spleens [30] , [31] . Furthermore , TNFR1−/− and TNFα−/− mice succumb to terminal disease upon i . p . prion inoculation at a noticeably higher rate than lymphotoxin signaling-deficient mice , indicating that prions are still effectively transmitted to the CNS in the absence of TNFR1 signaling . Since TNFR1−/− lymph nodes are devoid of detectable mature FDCs , this implies that other undefined cell types may also be required for prions to colonize SLOs . However , lymph nodes are either absent or profoundly disrupted in LTβR−/− , LTα−/− , and LTβ−/− mice compared to TNFR1−/− and TNFα−/− , making it difficult to formally conclude that LTβR signaling is specifically required for prion accumulation in lymph nodes while TNFR1 is not . To determine whether continuous LTβR signaling is required for prions to accumulate in TNFR1−/− lymph nodes , we investigated the ability of prions to colonize SLOs of prion-infected TNFR1−/− mice treated with an LTβR-Ig blocking antibody .
We previously showed that mice devoid of TNF signaling accumulate prion infectivity and PrPSc in lymph nodes but not in spleen , in contrast to LT signaling-deficient mice which do not accumulate prions in either spleen or lymph nodes [31] . To determine whether prion accumulation in TNFR1−/− lymph nodes was dependent on continuous LTβR signaling , we administered weekly 100 µg intraperitoneal ( i . p . ) injections of an LTβR immunoglobulin fusion protein ( LTβR-Ig ) or control pooled human immunogloblulin ( Ig ) to wild-type ( WT ) and TNFR1−/− mice to achieve sustained inhibition of LTβR signaling [32] . One week following the initial LTβR-Ig or control Ig injection , mice were inoculated intraperitoneally ( i . p . ) with 6 log LD50 RML6 prions . At 60 days post-injection ( d . p . i . ) , spleens and mesenteric lymph nodes ( mLNs ) from these mice were assessed for accumulation of prion infectivity and PrPSc . To confirm that LTβR-Ig treatment effectively inhibited LTβR signaling , we analyzed follicular dendritic cell marker 1 ( FDCM1 ) immunoreactivity in spleens from WT or TNFR1−/− mice treated with either control Ig or LTβR-Ig . FDCM1 immunoreactivity was absent in spleens and mLNs from TNFR1−/−-Ig , WT-LTβR-Ig , and TNFR1−/−-LTβR-Ig spleens in contrast to WT-Ig , indicating that LTβR-dependent FDCs had de-differentiated in response to LTβR-Ig treatment ( Fig . 1A–J ) . In addition , we analyzed transcriptional targets of the LTβR pathway in spleens from WT or TNFR1−/− mice treated with either control Ig or LTβR-Ig . As expected , levels of both NFκB2 ( p100 ) and CXCL13 mRNA were reduced in TNFR1−/−-Ig , WT-LTβR-Ig , and TNFR1−/−-LTβR-Ig spleens compared to WT-Ig ( Fig . 1K & L ) . Next , to determine the effect of inhibited LTβR signaling on prion accumulation in SLOs , we compared the pattern of PrPSc deposition in spleens and mLNs from prion-infected TNFR1−/−-Ig , WT-LTβR-Ig , and TNFR1−/−-LTβR-Ig mice to WT-Ig mice by histoblotting . As expected , TNFR1−/−-Ig , WT-LTβR-Ig , and TNFR1−/−-LTβR-Ig spleens accumulated less PrPSc than WT-Ig spleens ( Fig . 2A–D ) , though chronic LTβR-Ig administration seemed less effective at preventing PrPSc deposition in WT spleens than genetic ablation of TNFR1 ( compare Fig . 2B with 2C ) . Since the 60 day treatment period approaches the limit of effective LTβR-Ig inhibition , this most likely reflects partial FDC re-maturation in WT SLOs near the end of the experiment ( J . Browning; personal communication ) . Regardless , the ability of LTβR-Ig to block prion replication in WT SLOs is well-established [26] , [27] . Consistent with our previous studies , mLNs from WT and TNFR1−/−-Ig-treated mice contained similar numbers of PrPSc deposits ( Fig . 2E & F ) , whereas PK-resistant PrP deposits in WT-LTβR-Ig mLNs were less numerous than in WT-Ig or TNFR1−/−-Ig mLNs ( Compare Fig . 2G with Fig . 2E & F ) . Background PrP immunoreactivity in PK-digested histoblots from non-infected wild-type spleens was negligible ( Supp . Fig . S1 ) . Of note , TNFR1−/−-LTβR-Ig mLNs were devoid of PrPSc immunoreactivity ( Fig . 2H ) , demonstrating that prion accumulation in lymph nodes in the absence of TNFR1 is dependent on LTβR signaling . To confirm this result quantitatively , we analyzed prion infectivity in mLNs from Ig-treated versus LTβR-Ig-treated WT and TNFR1−/− mice using the scrapie cell assay ( SCA; [33] , [34] , [35] ) . Consistent with the corresponding histoblots from these mice , LTβR-Ig treatment decreased prion infectivity in WT and TNFR1−/− mLNs by 2–3 log tissue culture infectious ( TCI ) units per gram of tissue compared to Ig treatment ( Fig . 2I ) . To determine the effect of LTβR-Ig treatment on PrPC expression in SLOs , which might explain the differential ability of TNFR1−/−-Ig versus TNFR1−/−-LTβR-Ig mLNs to accumulate prions , we measured Prnp mRNA levels in spleens and lymph nodes from all treatment groups by quantitative PCR . Prnp mRNA levels were reduced in WT-LTβR-Ig , TNFR1−/−-Ig , and TNFR1−/−-LTβR-Ig spleens compared to WT-Ig ( Fig . 2J ) , most likely reflecting de-differentiation of FDCs , the primary PrPC-expressing cell type in spleen . In contrast , no differences in Prnp mRNA expression were found in mLNs from mice of different treatment groups . Of particular note , no difference in Prnp mRNA expression was found between TNFR1−/−-Ig and TNFR1−/−-LTβR-Ig mLNs ( Fig . 2K ) . Taken together , our data indicate that TNFR1−/− lymph nodes accumulate prions in the absence of mature FDCs yet in an LTβR-dependent manner . Moreover , inhibited prion deposition in mLNs upon loss of LTβR signaling seems to be unrelated to local Prnp levels . We reasoned that prion accumulation in TNFR1−/− lymph nodes might rely on a putative LTβR signaling-dependent , TNFR1 signaling-independent cell present in lymph nodes but not in spleens . In order to identify such a cell , we screened spleens and mLNs from WT-Ig , WT-LTβR-Ig , TNFR1−/−-Ig , and TNFR1−/−-LTβR-Ig mice for a variety of hematopoietic and stromal cell markers by immunohistochemistry ( IHC ) whose expression correlated with prion replication ability . As previously noted , the pattern of FDCM1 immunoreactivity was consistent with PrPSc deposition in spleen , but not in mLNs ( Fig . 1A–J ) . Likewise , several other markers for B cells ( B220 ) , T cells ( CD3 ) , marginal zone macrophages ( MOMA-1 ) , monocytes ( F4/80 ) , and stromal cells ( CD21/35 , C4 , VCAM1 , ICAM1 ) revealed no staining patterns consistent with an involvement in splenic or lymph nodal prion accumulation ( Supp . Figs S2A–H & S3A–H ) . However , we identified one stromal cell marker that exhibited a staining pattern consistent with prion deposition in both spleen and mLNs: mucosal addressin cell adhesion molecule 1 ( MadCam1 [36] ) . MadCam1 weakly stained FDC networks of both WT-Ig spleen ( Fig . 3A & E ) and lymph nodes ( Fig . 4A ) , as well as the marginal sinus ( MS ) of spleens from WT-Ig mice ( Fig . 3A ) . Although FDC and MS-associated MadCam1 immunoreactivity was absent upon loss of TNFR1 or LTβR signaling in both spleen ( Fig . 3B–E ) and lymph nodes ( Fig . 4 B–D ) , MadCam1 immunoreactivity persisted in TNFR1−/−-Ig mLNs ( Fig . 3G & J; 4B ) . Of note , MadCam1 immunoreactivity was completely absent in mLNs from LTβR-Ig-treated WT and TNFR1−/− mice ( Fig . 3I–J & 4C–D ) . Furthermore , analysis of MadCam1 mRNA expression by Real Time PCR in mLNs quantitatively corroborated the MadCam1 IHC findings: MadCam1 mRNA levels were equally reduced in spleens from WT-LTβR-Ig , TNFR1−/−-Ig , and TNFR1−/−-LTβR-Ig mice , compared to WT-Ig ( Fig . 4E ) . In contrast , MadCam1 mRNA levels were intermediately reduced in TNFR1−/−-Ig mLNs compared to WT-Ig , and a further reduction in MadCam1 mRNA levels was observed in TNFR1−/−-LTβR-Ig mLNs compared to TNFR1−/−-Ig ( Fig . 4F ) . Thus far , our data suggested that the presence of a MadCam1-expressing cell was associated with accumulation of prions in lymph nodes but not in spleen . MadCam1 immunoreactivity in TNFR1−/−-Ig mLNs was restricted to thick vessels ( Fig . 3G & 4B ) that were absent from TNFR1−/−-LTβR-Ig mLNs ( Fig . 3I & Fig . 4D ) , as well as isotype controls ( Supp . Fig . S4B ) , and were morphologically distinct from MadCam1-positive FDC networks found in WT-Ig spleens ( Fig . 3A ) and mLNs ( Fig . 4A ) . These MadCam1-positive structures were morphologically consistent with high endothelial venules ( HEVs ) responsible for controlling lymphocyte entry into LNs , Peyer's patches ( PPs ) , and other SLOs , with the exception of spleen [37] . Co-localization of Madcam1-positive vessels with peripheral node addressin ( PNAd; a . k . a . MECA-79 ) , a specific marker of HEVs [38] , confirmed that these structures in TNFR1−/−-Ig mLNs were HEVs ( Fig . 5A–F ) , whereas MadCam1-positive FDC networks in WT-Ig mLNs ( Fig . 5A–C ) and isotype controls ( Supp . Fig . S4A ) were PNAd-negative . To gather evidence that HEVs could be potential sites of prion replication , we analyzed PrPC immunoreactivity in HEVs of WT-Ig mLNs . Co-immunofluorescent ( co-IF ) staining of WT-Ig mLNs with MadCam1 and PrPC antibodies confirmed that PrPC immunoreactivity was present in HEVs ( Fig . 5G–I ) and absent from isotype controls ( Supp . Fig . S4C ) . Thus , HEVs fulfill at least one requirement of a prion replicating tissue - PrPC expression . To determine whether prions were localized to HEVs in mLNs , we performed co-IF with PrPC and MadCam1 antibodies in prion-infected TNFR1−/−-Ig mLNs . Prion-infected TNFR1−/−-Ig mLNs contained areas of intense PrP-positive deposits which were not detectable in non-infected TNFR1−/−-Ig mLNs ( data not shown; [31] ) . Of note , many of these PrPC-positive areas localized to MadCam1-postive vessels in prion-infected TNFR1−/−-Ig mLNs , indicating that HEVs are probable sites of PrPSc localization in infected lymph nodes ( Fig . 6A–C ) . However , PrP immunostaining of prion-infected tissue cannot reliably distinguish between PrPSc deposits and sites of high PrPC expression , and HEVs expressed relatively high levels of PrPC in uninfected mLNs ( Fig . 5G–I ) . To develop an independent method of distinguishing PrPSc from PrPC in lymphoid organs , we tested the ability of a series of fluorescent amyloid-binding dyes known as luminescent conjugated polymers ( LCPs [39] , [40] , [41] , [42] ) to stain PrPSc deposits in spleen and lymph node . LCPs were previously shown to recognize PrPSc in brain [43] . One LCP , pentameric formic thiophene acetic acid ( p-FTAA; [44] ) , stained PrP-positive FDC networks of prion-infected spleens from WT mice ( Supp . Fig . S5 ) . In contrast , no immunofluorescence could be detected with p-FTAA in spleens and lymph nodes from uninfected mice ( Supp . Fig . S6 ) . Using this method , points of PrPSc/MadCam1 co-localization could be reliably identified in prion-infected TNFR1−/− lymph nodes , which again indicated that a proportion of PrPSc localizes to HEVs in TNFR1−/− mLNs ( Fig . 6D–F ) . However , at lower magnification we also noted that a number of PrPSc deposits were located outside of HEVs ( Fig . 6G–I ) . To confirm this observation and to analyze the tissue-wide distribution of PrPSc deposits relative to HEVs in prion-infected TNFR1−/−-Ig mLNs , we performed histoblot co-stains with PNAd antibody , which is highly immunoreactive to HEVs and can be visualized even after proteinase K digestion ( Supp . Fig . S7 ) . This analysis confirmed that some PrPSc deposits were localized to HEVs in TNFR1−/−-Ig mLNs . However , PrPSc deposits were also present outside of HEVs ( Fig . 6J–K ) , which is consistent with our previous study showing that strong PrP immunoreactivity in prion-infected TNFR1−/− mLNs can also be found in other cell types ( i . e . macrophages [31] ) . Since HEVs could potentially serve as entry portals for prion-harboring lymphocytes , and it was recently reported that neighboring dendritic cells ( DCs ) are responsible for HEV differentiation [45] , we also performed PrP co-immunofluorescence on prion-infected TNFR1−/−-Ig mLNs using the DC marker , CD11c , to determine whether DCs in the vicinity of HEVs might also contain PrPSc . However , no overlap of PrP and CD11c immunoreactivity in prion-infected TNFR1−/−-Ig mLNs was identified ( Supp Fig . S8 ) .
The means by which prions evade the immune system's numerous defense mechanisms and finally transmigrate to , and selectively damage , neurons of the CNS has been the subject of scientific scrutiny for two decades . A number of studies have implicated FDCs in the germinal centers of SLOs as the primary reservoirs of prions prior to neuroinvasion [4] , [12] , [13] , [14] , [15] , [16] , [17] . Yet the ability of TNFR1−/− mLNs to accumulate prions with a minimal loss of infectivity compared to WT mLNs presents an apparent paradox , since FDC maintenance depends on TNFR1 signaling [21] , [22] , [23] . Here we have established that lymph nodal prion accumulation in the absence of TNFR1 signaling is LTβR signaling-dependent . Crucially , transient loss of LTβR signaling was sufficient to block TNFR1-independent prion accumulation in lymph nodes , indicating that prion accumulation in lymph nodes specifically requires LTβR signaling and is not simply prevented by general developmental defects or architectural disruptions caused by lack of LTβR signaling in LTβ−/− lymph nodes . We previously showed that intense PrP immunoreactivity was localized to macrophages in prion-infected TNFR1−/− mLNs [31] , and others have reported that PK-resistant PrP is localized to macrophages in spleens with PrPC-deficient FDCs [46] , indicating that macrophages serve as alternative sites of prion accumulation in the absence of PrPC-expressing FDCs . How this phenomenon was mechanistically linked to LTβR signaling and the pattern of prion accumulation in lymph nodes was initially unclear , since most macrophage populations were preserved in the absence of both TNFR1 and LTβR signaling [31] . Here , we discovered that loss of LTβR signaling in mLNs was also correlated with the dedifferentiation of HEVs – the primary point of entry for lymphocytes into lymph nodes and a likely determinant in the ability of prions to colonize lymph nodes . Consistent with the pattern of prion accumulation in spleens and lymph nodes of mice lacking TNF and/or LT signaling components , HEVs exist in lymph nodes and other SLOs but not spleens [37] , and the maintenance of HEV architecture is TNFR1 signaling-independent yet LTβR signaling-dependent [47] . Moreover , we identified sites of PrPSc-HEV overlap in TNFR1−/−-Ig mLNs , indicating that HEVs might replicate prions and/or serve as points of entry for prions or prion-harboring lymphocytes . FDC-deficient mice can succumb to scrapie even in the absence of detectable splenic prion titers [24] . In light of our current results , this phenomenon is most likely explained by HEV-dependent entry and accumulation of prions in lymph nodes and other HEV-containing SLOs , such as Peyer's patches . HEVs can also form ectopically in certain chronic inflammatory conditions . Prion accumulation at sites of chronic inflammation has previously been observed , but in most cases this could be attributed to local formation of FDC networks [48] , [49] , [50] . However , we also previously reported LTβR-dependent prion colonization of granulomas in the absence of FDC markers [51] . Since ectopic HEV formation without any evidence of FDC networks has previously been reported in certain inflammatory conditions [52] , ectopic HEV formation might be a source of prion replication and/or uptake in granulomas , as well . Of note , LTα/β signaling to HEVs differs from LTα/β signaling to FDCs . Though HEVs express LTβR [53] , HEVs persist in the absence of B cells [47] , in contrast to FDCs which rely on B cells to provide LTα/β ligand [19] , [20] . This indicates that the LTα/β signal to HEVs emanates from another cell type . Until recently the cell type providing LTα/β to HEVs was not known; however a recent publication reports that dendritic cells ( DCs ) may be the source of LTα/β signaling to HEVs [45] . If HEV differentiation is indeed B cell-independent , this may explain why prion neuroinvasion can occur in the absence of B cells in some cases , since LTβR signaling to HEVs and hence the ability of prions to enter and accumulate in lymph nodes would be preserved [24] , [54] . Do prions actually replicate in TNFR1−/− lymph nodes , or do they simply accumulate ? Accumulation seems likely , as the only cell type in SLOs known to replicate prions are FDCs , and prion replication in macrophage populations was not observed [46] . In any case , experimental evidence suggests that prion accumulation in SLOs is sufficient for prions to invade the CNS [55] , [56] , [57] . Moreover , deletion of complement factors dramatically impairs prion accumulation in SLOs and subsequent neuroinvasion without affecting PrPC levels [58] , indicating that the ability of SLOs to physically capture prions is indeed critical for the development of downstream pathology . Curiously , we previously showed that PrPC expression is required in either the stromal or the hematopoietic compartment for lymph nodes to accumulate prion infectivity [31] , which implies that prion replication can occur in both compartments in lymph nodes . However , an alternative explanation is that a hematopoietic cell is required for delivery of prions to a “trapping” cell within the lymph node , and PrPC expression on the hematopoietic cell mediates efficient uptake of PrPSc in the bloodstream . Hence , HEVs may facilitate the selective uptake and accumulation of prions or prion-containing lymphocytes into lymph nodes , rather than serving as sites of bona fide prion replication . Based on both current and past findings , it is likely that the “trapping” cells in lymph nodes are endothelial cells in HEVs ( HEVECs ) , and the hematopoietic delivery cells are macrophages . Once prions or prion-harboring cells have successfully invaded mLNs through HEVs , they may be transported via conduits to FDCs [59] under normal conditions . However , in the absence of mature FDCs , prions may remain in or be transferred to macrophages .
TNFR1−/− mice carrying a targeted deletion of exons 2 , 3 , and part of exon 4 of the tumor necrosis factor receptor 1 ( TNFR1 ) open reading frame [60] were maintained on a C57BL/6 background in-house . C57BL/6 wild-type control mice were purchased from Harlan Laboratories and bred in-house . Murine LTβR-murine IgG1 ( LTβR-Ig ) and MOPC21 mouse immunoglobulin control were obtained from Biogen Idec . For analysis of prion-infected tissues ( Figs 2A–I , 6 & Supp . Figs S5 , S7 & S8 ) , TNFR1−/− or C57BL/6 mice were injected intraperitoneally ( i . p . ) with 100 µg LTβR-Ig or MOPC21 ( n = 3–4/group ) . One week later , mice were inoculated i . p . with 100 µL 6 log LD50 Rocky Mountain Laboratory mouse-adapted prion strain [61] , passage 6 ( RML6 ) . Mice were then boostered weekly with 100 µg LTβR-Ig or MOPC21 until 60 days post-inoculation ( d . p . i ) , at which point mice were sacrificed . For analysis of uninfected tissue ( Figs 1 , 2J–K , 3 , 4 , 5 & Supp . Figs S1 , S2 , S3 , S4 & S6 ) , TNFR1−/− or C57BL/6 mice received 2 weekly injections of LTβR-Ig or MOPC21 and were sacrificed 3 weeks following the initial injection ( n = 3–4/group ) . Spleens and lymph nodes from prion-infected and uninfected mice were either flash frozen for scrapie cell assays and gene expression analysis or frozen in 1x Hank's balanced salt solution for immunohistochemistry . Tissues were stored at −80°C until analysis . All animal experiments were carried out in strict accordance with the rules and regulations for the Protection of Animal Rights ( Tierschutzverordnung ) of the Swiss Bundesamt für Veterinärwesen and were pre-approved by the Animal Welfare Committee of the Canton of Zürich . Animal permit # 130/2008 . CD1 wild-type mice were inoculated i . c . with 30 µL 1% ( w/v ) RML-5-infected brain homogenate and sacrificed at terminal stage disease . Brains were flash frozen in liquid nitrogen and stored at −80°C . Brains were then homogenized in sterile 0 . 32 M sucrose in 1x PBS ( 20% w/v ) using a Ribolyser ( Hybaid , Catalys ) . Subsequent dilutions of inoculum were performed in sterile 5% ( w/v ) bovine serum albumin ( BSA ) in 1x PBS . For titer determinations , serial dilutions of RML6 were inoculated i . c . into indicator mice , and LD50 was defined as the dilution that induced a 50% attack rate . Frozen spleens and lymph nodes were homogenized in Qiazol ( 10% w/v ) using a TissueLyser , and total mRNA was isolated using an RNeasy Mini kit , according to the manufacturer's instructions ( Qiagen ) . 1 µg mRNA from each sample was used for first-strand cDNA synthesis using random hexamers from a Superscript II kit ( Invitrogen ) . ∼100 ng cDNA , 500 nM primers , and 1x Faststart SYBR Green reaction mixture ( Roche ) in 25 µL reaction volumes were used for Real Time PCR amplification of target sequences from spleen and lymph nodes normalized to GAPDH using an ABI 7900HT ( Applied Biosystems ) . ( See Table 1 for list of primer sequences . ) Reactions from each tissue were performed in triplicate and averaged . Samples with a triplicate variability exceeding 10% were eliminated from further analysis . Amplification data was analyzed using the relative quantification method ( RQ ) with WT-Ig samples serving as the calibrator values . RQ values were calculated and averaged for each sample . Average values depicted in the graphs represent the mean value of single spleens or lymph nodes from each individual mouse from each treatment group . N = 2–4 mice per group , depending on the specific tissue and treatment group . 10 µM frozen sections were transferred to nitrocellulose pre-soaked in 1x Tris-buffered saline with Tween 20 ( TBST; 50 mM Tris-HCl , pH = 7 . 8 , 150 mM NaCl , 0 . 1% Tween 20 ) and air-dried . Membranes were washed in 1x TBST for 1 hr . and then digested with proteinase K ( Roche , 20 µg/mL ) diluted in digestion buffer ( 10 mM Tris-HCl , pH = 7 . 8 , 100 mM NaCl , 0 . 1% ( v/v ) Brij 35 ) at 37°C for 4 hrs . Membranes were then washed in TBST , incubated in denaturing solution ( 10 mM Tris-HCl , pH = 7 . 8 , 3 M guanidine thiocyanate ) for 10 min , washed in 1x TBST , blocked in 5% dried milk in TBST for 1 hr , and then probed with 0 . 1 µg/mL POM1 [62] diluted in 1% milk in 1x TBST overnight at 4°C . Membranes were then washed in 1x TBST , blocked in 1% milk in 1x TBST , and then incubated with 1 µg/mL alkaline phosphatase ( AP ) -conjugated goat anti-mouse secondary antibody ( Dako ) for 1 hr . Membranes were then washed in 1x TBST , 10 min . in B3 ( 100 mM Tris , 100 mM NaCl , 100 mM MgCl2 , pH = 9 . 5 ) , and then developed for 40 min . with BCIP/NBT ( Roche ) . Histoblot sections were then washed in distilled water , dried , and imaged using an Olympus SZX12 stereomicroscope . Uninfected spleens from wild-type mice served as negative controls for background PrP immunoreactivity ( Supp . Fig . S1 ) . Scrapie-susceptible neuroblastoma cells ( subclone N2aPK1 , [33] ) were incubated with uninfected brain homogenate , defined titers of RML6-infected brain homogenate , or 10−3 to 10−6 dilutions of mesenteric lymph node homogenate from WT-Ig , WT-LTβR-Ig , TNFR1−/−-Ig , or TNFR1−/−-LTβR-Ig for 3 days . Infected N2aPK1 cells were passaged 1∶3 three times every 2 days , and then 1∶10 four times every 3 days . After reaching confluence , 2×104 cells from each well were filtered onto the membrane of an ELISPOT plate ( Millipore; MultiScreenHTS filter plates with Immobilon-P PVDF membrane ) and denatured with 0 . 5 µg/mL proteinase K ( PK ) . Individual prion-infected cells were immunodetected with POM1 . Wells were scored positive if the spot number exceeded mean background values , determined as three times the standard deviation of the uninfected control . In this experiment , an ELISPOT membrane with ≥3 PrPSc+ colonies was regarded as infected . From the proportion of negative to total wells , the number of tissue culture infectious units per mL was calculated with the Poisson equation . In two independent experiments , a 10−8 dilution of a standard inoculum ( brain homogenate from a terminally scrapie-sick mouse ) yielded 11/24 or 12/24 positive wells , corresponding to a titer of ∼8 . 3 log tissue culture infectivity ( TCI ) units/g of brain tissue for the initial inoculum . The sensitivity threshold was calculated to be 2 . 8 log TCI units/g of brain tissue . 7 µM frozen sections on glass coverslips ( Thermofisher ) were dried for several hours at room temp , fixed in 4% formalin for 2 min , 50% acetone for 2 min , 100% acetone for 2 min , and 50% acetone for 2 min . Sections were then washed in 1x PBS , then 1x PBS+ 0 . 05–0 . 1% Tween 20 , and blocked for 1 hr . in SuperBlock ( Pierce ) . Sections were then incubated overnight at 4°C in primary antibody ( see Table 2 for antibodies and dilutions ) diluted in 1∶10 SuperBlock . For vessel stains , isotype controls ( rat IgM for PNAd , Pharmingen # 553941; rat IgG2a for MadCam1 , eBioscience # 14-4321; and mouse IgG1 for PrP , Sigma # 15381 ) were performed using equivalent concentrations to the corresponding primary antibodies ( Supp . Fig . S4 ) . Sections were then washed in 1x PBS , followed by 1x PBST , then incubated with 0 . 2–0 . 4 µg/mL Alexa Fluor secondary antibody ( Invitrogen ) diluted in 1x PBST ( for immunofluorescence [IF] ) or 5 . 3 µg/mL unconjugated goat anti-rat secondary antibody ( Caltag Laboratories ) or goat anti-mouse ( Jackson Immunoresearch ) diluted in 1∶10 SuperBlock for 1 hr . at room temperature ( for light microscopy [LM] ) . Sections were then washed in 1x PBS followed by 1x PBST and incubated in 7 . 5 µg/mL AP-conjugated donkey anti-goat tertiary antibody ( Jackson Immunoresearch ) in 1x PBST for 1 hr for LM . Sections were then washed and developed with Fast Red ( Sigma ) staining kit and counter-stained with hematoxylin & eosin ( H&E ) for LM . Sections were then mounted ( fluorescent mounting medium for IF or aqueous mounting medium for LM; Dako ) and coverslipped . Sections were imaged using an Olympus BX61TRF fluorescent microscope , a Zeiss Axiophot light microscope , or a Leica SP5 confocal microscope ( where indicated ) . FDCM1: Boundaries of lymphoid follicles were identified by H&E counterstaining in LM images of AP-developed spleens and lymph nodes immunostained with FDCM1 . Follicles containing FDCM1-stained germinal centers were classified as “FDCM1-positive , ” whereas follicles devoid of FDCM1 staining were classified as “FDCM1-negative . ” The total number of FDCM1-postive or negative follicles per treatment group was expressed as a percentage of the total follicles . A total of 55 follicles were scored for WT-Ig spleens , 39 for WT-LTβR-Ig spleens , 111 for TNFR1−/−-Ig spleens , 27 for TNFR1−/−-LTβR-Ig spleens , 19 for WT-Ig mLNs , 10 for WT-LTβR-Ig mLNs , 13 for TNFR1−/−-Ig mLNs , and 10 for TNFR1−/−-LTβR-Ig mLNs . MadCam1: Boundaries of lymphoid follicles were identified by H&E counterstaining in LM images of AP-developed spleens immunostained with MadCam1 . Follicles containing MadCam1-stained germinal centers were classified as “MadCam1-positive , ” whereas follicles devoid of MadCam1 staining were classified as “MadCam1-negative . ” The total number of MadCam1-postive or negative follicles per treatment group was expressed as a percentage of the total follicles . For mLNs , the total number of MadCam1-positive vessels was scored per organ and averaged . A total of 80 follicles were scored for WT-Ig spleens , 37 for WT-LTβR-Ig spleens , 137 for TNFR1−/−-Ig spleens , 39 for TNFR1−/−-LTβR-Ig spleens , n = 2 mLNs for all treatment groups . PNAd/PrPSc co-stains: Total numbers of HEVs and PrPSc deposits were counted on PNAd pre-stained histoblots from TNFR1−/−-Ig mLNs . HEVs that overlapped with PrPSc deposits were scored as “PrPSc-postive , ” whereas HEVs with no PrPSc were scored as “PrPSc-negative . ” Likewise , PrPSc deposits that overlapped with HEVs were scored as “PNAd-positive , ” whereas PrPSc deposits that were not associated with HEVs were scored as “PNAd-negative . ” For HEVs , values represent the percentage of total HEVs that were either PrPSc-positive ( black ) or negative ( white ) . For PrPSc deposits , values represent the percentage of total PrPSc deposits that were associated with HEVs ( black ) or not ( white ) . A total of 82 HEVs and 112 PrPSc deposits were scored . 7 µM frozen sections on glass coverslips were dried and then fixed in pre-chilled 100% acetone or ethanol at −20°C for 10 min . Sections were dried for 1 min . , re-hydrated in 1x PBS for 10 min . , blocked in SuperBlock , and then incubated in 20 µg/mL MadCam1 or 6 . 25 µg/mL FDCM1 in 1x PBS overnight at 4°C . Sections were then washed in 1x PBS and incubated with 0 . 2–2 µg/mL Alexa Fluor 594-conjugated goat anti-rat secondary antibody for 1 hr . Sections were then washed in 1x PBS and incubated with 30 µM pentameric formic thiophene acetic acid ( p-FTAA; [44] ) in 1x PBS for 30 min . Sections were then washed in 1x PBS , mounted with fluorescent mounting medium ( Dako ) , coverslipped , and imaged using an Olympus BX61TRF fluorescent microscope . Sections from uninfected WT spleens were used as negative controls for non-specific pFTAA staining ( Supp . Fig . S6 ) . TNFR1−/−-Ig mesenteric lymph node sections ( 10 µm ) on nitrocellulose were soaked in 1x TBST for 1 hr . , blocked in 5% ( w/v ) milk in 1x TBST for 1 hr . , and then probed with 0 . 5 µg/mL PNAd antibody diluted in 1% ( w/v ) milk in 1x TBST overnight at 4°C . Histoblots were then washed in 1x TBST and probed with 0 . 5 µg/mL AP-conjugated goat anti-rat ( Biosource # ARI3405 ) secondary antibody in 1% ( w/v ) milk in 1x TBST . Histoblots were then washed in 1x TBST and developed with Fast Red ( Sigma ) for 20 min . Histoblots were then washed in 1x TBST , digested with 20 µg/mL PK for 4 hrs at 37°C , washed and further processed as described above for standard histoblots . | Prions are unique infectious agents thought to be composed entirely of an abnormal conformer of the endogenous prion protein . Prions cause a severe neurological disorder in humans and other animals known as prion disease . Though prion disease can arise spontaneously or from genetic mutations in the gene encoding the prion protein , many cases of prion disease arise due to peripheral exposure to the infectious agent . In these cases , prions must journey from the gastrointestinal tract and/or the bloodstream to the brain . Prions often colonize secondary lymphoid organs prior to invading the nervous system via neighboring peripheral nerves . Prion accumulation in follicular dendritic cells found in germinal centers of lymphoid organs is thought to be a crucial step in this process . However , prion colonization of lymph nodes , in contrast to spleen , does not depend on follicular dendritic cells , indicating that other mechanisms must exist . Here , we identify the signaling pathway required for follicular dendritic cell-independent prion colonization of lymph nodes , which also controls the differentiation of high endothelial venules , the primary entry point for lymphocytes into lymph nodes . Importantly , prions could be found within these structures , indicating that high endothelial venules are required for prion entry and accumulation in lymph nodes . | [
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]
| 2012 | Lymphotoxin, but Not TNF, Is Required for Prion Invasion of Lymph Nodes |
Sequence-specific transcription factors ( TFs ) represent one of the largest groups of proteins that is targeted for SUMO post-translational modification , in both yeast and humans . SUMO modification can have diverse effects , but recent studies showed that sumoylation reduces the interaction of multiple TFs with DNA in living cells . Whether this relates to a general role for sumoylation in TF binding site selection , however , has not been fully explored because few genome-wide studies aimed at studying such a role have been reported . To address this , we used genome-wide analysis to examine how sumoylation regulates Sko1 , a yeast bZIP TF with hundreds of known binding sites . We find that Sko1 is sumoylated at Lys 567 and , although many of its targets are osmoresponse genes , the level of Sko1 sumoylation is not stress-regulated and the modification does not depend or impinge on its phosphorylation by the osmostress kinase Hog1 . We show that Sko1 mutants that cannot bind DNA are not sumoylated , but attaching a heterologous DNA binding domain restores the modification , implicating DNA binding as a major determinant for Sko1 sumoylation . Genome-wide chromatin immunoprecipitation ( ChIP-seq ) analysis shows that a sumoylation-deficient Sko1 mutant displays increased occupancy levels at its numerous binding sites , which inhibits the recruitment of the Hog1 kinase to some induced osmostress genes . This strongly supports a general role for sumoylation in reducing the association of TFs with chromatin . Extending this result , remarkably , sumoylation-deficient Sko1 binds numerous additional promoters that are not normally regulated by Sko1 but contain sequences that resemble the Sko1 binding motif . Our study points to an important role for sumoylation in modulating the interaction of a DNA-bound TF with chromatin to increase the specificity of TF-DNA interactions .
Sumoylation is an essential eukaryotic post-translational modification that functions in many , predominantly nuclear , cellular processes , such as DNA repair and transcription , by regulating target protein localization , stability , or interactions with other proteins or with chromatin [1–5] . The modification involves the covalent attachment of a ~12-kDa SUMO ( Small Ubiquitin-like Modifier ) peptide to specific lysine residues on substrate proteins through a three-enzyme cascade that is analogous to ubiquitination [1] . In contrast to ubiquitination , however , the sole SUMO E2 conjugating enzyme Ubc9 can recognize its substrates via the consensus sequence ΨKxD/E , where Ψ is a hydrophobic residue , K is the modified lysine and x is any amino acid [6] . Although thousands of proteins have been identified as potential SUMO targets , modification levels of individual proteins are typically low and can be controlled by the desumoylating action of SUMO-specific isopeptidases ( SUMO proteases ) , including the SENP family in mammals , and Ulp1 and Ulp2 in budding yeast [3 , 7 , 8] . On a global level , cellular sumoylation can be coordinately regulated , which is exemplified by the SUMO stress response , a rapid increase in overall SUMO conjugation that is observed in both yeast and mammalian cells in response to various stress conditions , including temperature , oxidative , and osmotic stress [4 , 9–12] . Chromatin immunoprecipitation ( ChIP ) analyses , both genome-wide and on individual genes , have demonstrated that sumoylated proteins are detected specifically at promoter regions of constitutively active and induced genes , suggesting that the modification is important for regulating early steps of transcription [13–17] . Supporting this , proteomics studies have identified subunits of the general transcription factors ( GTFs ) , RNA Polymerase II ( RNAP II ) , and Mediator as SUMO conjugates in yeast , Drosophila , and human cells [18] . Moreover , one of the largest groups of SUMO substrates , with over 300 substrates identified in human SUMOylome analyses , is sequence/gene-specific transcription factors ( TFs ) [3 , 19] . Numerous studies have characterized the role of sumoylation on individual TFs , and in most cases the modification is associated with a repressive effect on transcription of target genes [5 , 19–21] . For example , upon sumoylation , NFAT , Elk-1 and MafG interact with histone deacetylases ( HDACs ) to condense DNA and restrict access of the transcription machinery , whereas sumoylation of the Forkhead Box TF FoxM1b promotes its cytoplasmic retention and ubiquitin-mediated degradation , thereby limiting its access to target genes [22–25] . Why a transcriptionally repressive mark like SUMO is enriched at promoter regions of transcriptionally active genes remains unknown . Providing a possible explanation , studies on two basic leucine zipper ( bZIP ) motif-containing TFs , yeast Gcn4 and human FOS ( c-Fos ) , showed that promoter-associated sumoylation is important for regulating their occupancy levels [26–28] . DNA-bound Gcn4 and FOS are sumoylated during active transcription , and the modification promotes their clearance from DNA . This limits the association of these TFs with their target genes once they are activated , which likely serves to prevent excessive gene expression . Consistent with this , ChIP studies have shown that sumoylation modulates the chromatin occupancy of multiple other TFs , including human TFs FOXA1 , MITF , c-Maf , and the androgen and glucocorticoid nuclear receptors [29–33] . How sumoylation affects TF occupancy is not always known , but in some cases SUMO modifications directly affect DNA binding . For example , modification of SP1 , specifically by the SUMO2 isoform , reduces its DNA binding activity in an in vitro assay [34] . These studies have led to speculation that sumoylation has a general role in controlling the interaction of TFs with their target sites on chromatin [19] . This can serve to regulate the expression levels of target genes , but another possible function for SUMO-mediated modulation of TF-chromatin interactions is in binding site selection . Multiple factors , in addition to DNA sequence , play a role in determining what genomic sites are recognized and bound by TFs , including genomic context , DNA modifications , the nature of flanking DNA sequences , and interaction with cofactors [35 , 36] . However , it is not known whether sumoylation plays a role in regulating binding site selection for TFs across eukaryotes . To address this , we examined how sumoylation regulates the bZIP TF Sko1 ( Suppressor of Kinase Overexpression-1 ) from the budding yeast , Saccharomyces cerevisiae . Sko1 was identified as a putative SUMO target in multiple proteomics screens , suggesting that its activity or association with chromatin is regulated by sumoylation [37–39] . It was first characterized as a repressor of cAMP response element ( CRE ) -containing genes , such as HIS3 and ENA1 , that are induced after exposure to osmotic stress [40–43] . Repression by Sko1 involves recruitment of the Tup1/Cyc8 corepressor complex , and relief of repression occurs during osmotic stress as a result of phosphorylation by the activated MAP kinase Hog1 [40 , 41 , 44–47] . In addition to functioning as a repressor , Sko1 was subsequently shown to be involved in the induction of some stress response genes , through the Hog1-dependent recruitment of the SAGA and SWI/SNF nucleosome remodelers to promoter regions bound by Sko1/Tup1/Cyc8 complexes [45 , 47 , 48] . Genome-wide analyses showed that Sko1 is constitutively associated with >250 gene promoters and that osmotic stress results in a general redistribution of Sko1 , with some genomic sites showing stress-dependent gain or loss of Sko1 , while occupancy at other sites remains unchanged [49–51] . Nonetheless , many Sko1 binding sites are not associated with genes specifically involved in the osmotic stress response , and sko1Δ cells show no growth defect in high osmolarity medium , suggesting that the TF plays a wider role in gene regulation [45 , 49] . Here , we demonstrate that Sko1 is indeed a SUMO target and that the modification functions primarily in preventing Sko1 from associating with nonspecific sites on the genome . We identify Lys 567 as the major SUMO modification site on the TF and show that Sko1 sumoylation is a constitutive modification whose levels do not significantly change under different growth conditions , including osmotic stress . DNA binding activity is necessary for Sko1 sumoylation in vivo , implying that the modification takes place after Sko1 associates with chromatin . Significantly , our genome-wide analysis shows that a sumoylation-deficient form of Sko1 shows increased occupancy levels at its binding sites and is found at numerous additional promoter regions across the genome , when compared with sumoylatable Sko1 . Intriguingly , although the consensus Sko1 binding motif is largely absent from these additional sites , most contain sequences that resemble the motif . Taken together , our results imply that a key function for sumoylation is in controlling the association of a DNA-bound TF with chromatin to increase its binding site specificity .
Previous studies demonstrated that for two different bZIP TFs , Gcn4 in budding yeast and human FOS , sumoylation acts to restrict their association with DNA thereby preventing excessive expression of target genes [26–28] . We examined published lists of sumoylated proteins that were generated through proteomics analyses and identified 28 additional bZIP motif-containing TFs that are probable SUMO targets , four in S . cerevisiae and 24 human proteins ( Fig 1A ) [4 , 8] . This represents almost half of known bZIP TFs in these species and suggests that sumoylation is a common mechanism of regulating their functions . To explore this , we selected for further study the yeast bZIP TF Sko1 which was identified as a SUMO target in large-scale studies , but the effects of its sumoylation have not yet been reported [37–39] . A yeast strain was generated that expresses a C-terminal 6xHA-tagged form of Sko1 ( Sko1 . HA ) from its natural locus and we confirmed that the presence of the tag does not affect cell growth under normal and osmotic stress conditions ( S1A Fig ) . Cell lysates were analyzed using the same procedures that were previously used for examining sumoylated proteins in yeast , i . e . HA immunoprecipitation ( IP ) followed by HA and SUMO immunoblotting ( see Materials and Methods ) [27 , 28 , 52] . Protein bands of the molecular weights expected for sumoylated Sko1 were detected in the SUMO immunoblot , including a “ladder” of bands that is typical for proteins that are multi- or poly-sumoylated , confirming that Sko1 is sumoylated ( Fig 1B ) . Supporting that the sumoylated species detected in the immunoblots correspond to modified Sko1 specifically , we repeated the analysis using protein samples that were prepared under denaturing conditions ( i . e . precipitation with trichloroacetic acid , TCA ) , which again showed bands of the expected size in the SUMO immunoblot ( S1B Fig ) . These results confirm the observation made in multiple proteomics studies that Sko1 is sumoylated in yeast grown in normal conditions [37–39] . We analyzed the polypeptide sequence of Sko1 using a SUMO-site prediction tool , GPS-SUMO [53] , and identified Lys 567 , which is within a SUMO consensus motif , as the most likely site of SUMO modification . To test this , we generated a yeast strain expressing only a K567R-mutant form of Sko1 . HA from the SKO1 locus ( hereafter referred to as Sko1-MT ) , and examined its sumoylation by IP-immunoblot . As shown in Fig 1C , Sko1-MT is expressed at normal levels , but the mutation virtually abolishes its sumoylation , indicating that Lys 567 is the major site of sumoylation on the protein . This residue lies in the C-terminal part of the protein , significantly downstream of the bZIP DNA binding motif , the Hog1 and PKA phosphorylation sites , and the region of the protein required for nuclear entry , and therefore defines a novel regulatory region on Sko1 ( Fig 1D ) [54] . A higher molecular weight form of Sko1 that is detectable in HA immunoblots of Sko1-WT ( asterisks in Figs 1B , 1C , 1E and S1B ) , but is not present in the Sko1-MT IP lane in Fig 1C , corresponds in size to monosumoylated Sko1 , implying that both unmodified Sko1 and its major sumoylated form can be detected on the same HA immunoblot . Taking advantage of this , we used densitometry to measure the intensity of bands corresponding to unmodified and monosumoylated Sko1 and determined that at least 13% of Sko1 is sumoylated in normally growing yeast . Nonetheless , sumoylation of Sko1 is not required for cell viability as the sko1-MT strain grew as well as strains expressing Sko1-WT on rich ( YPD ) or synthetic complete ( SC ) media ( S1C Fig ) . This result is not surprising since SKO1 itself is not required for normal yeast growth ( e . g . sko1Δ in S1C Fig and [44] ) , but our data indicates that , at any given time , a significant fraction of Sko1 polypeptides are modified by sumoylation . To examine whether sumoylation might regulate Sko1 in response to high osmolarity or other types of stress , the SKO1-WT strain was grown in different conditions , and the level of Sko1 sumoylation was determined in each condition by IP-immunoblot analysis . As shown in Figs 1E and S1D , compared to growth in normal medium ( “untreated” ) , Sko1 sumoylation levels were essentially unchanged after exposure to osmotic , oxidative , or temperature stress , or during amino acid starvation ( through addition of sulfometuron methyl , SM , to growth medium ) [55] . This indicates that sumoylation of Sko1 does not occur as part of a stress response but that the modification regulates Sko1 constitutively . Cellular levels of Sko1 in SKO1-WT and sko1-MT strains were approximately the same under normal growth conditions , and remained constant during osmotic stress , implying that sumoylation does not act to regulate Sko1 stability or abundance ( S1E Fig ) . Moreover , the sko1-MT strain grew as well as the strain expressing Sko1-WT on a variety of osmotic stress media , indicating that Sko1 sumoylation is not required for cell survival during osmotic stress ( Fig 1F ) . However , Sko1 itself is not required for survival during osmotic stress ( sko1Δ in Fig 1F and [45] ) , reflecting that its role in regulating the transcription of target stress response genes has more subtle consequences , and that the effects of sumoylation on Sko1 function are likely intricate ( see below ) [48] . Many SUMO modifications show codependence or interference with other types of post-translational modifications , including phosphorylation , on the same protein [19 , 56] . To determine whether prior phosphorylation by Hog1 or PKA is required for Sko1 sumoylation , we generated yeast strains expressing mutant forms of Sko1 . HA , including Sko1 ( pMT . Hog1 ) , which contains Ala substitutions at the three Hog1 target residues ( Ser 108 , Ser 126 , and Thr 113 ) , and Sko1 ( pMT . PKA ) , which has Ala substitutions at the three PKA target Ser residues ( 380 , 393 , and 399 ) [44] . The strains were then grown untreated or treated with NaCl for 10 min prior to the preparation of lysates and subsequent examination by IP-immunoblot . As shown in Fig 2A , normal levels and patterns of Sko1 sumoylation were observed in the SUMO immunoblot analysis of both the Sko1 ( pMT . Hog1 ) and Sko1 ( pMT . PKA ) forms of Sko1 . HA . This indicates that sumoylation of Sko1 occurs independently of its prior phosphorylation by Hog1 or PKA . Next we examined whether sumoylation can influence subsequent phosphorylation of Sko1 by Hog1 , which occurs in response to osmotic stress [44] . HA immunoblot analysis of Sko1-WT from cells grown in osmotic stress showed a mobility shift that is consistent with its phosphorylation and similar to a mobility shift that was previously reported ( Fig 2B ) [44] . The NaCl-dependent shift occurred for both the prominent band in the immunoblot , which corresponds to unsumoylated Sko1 , as well as for the monosumoylated form of Sko1-WT ( compare lanes 1 and 2 in upper and lower panels of Fig 2B ) . Sko1-MT also showed a NaCl-dependent shift in migration during osmotic stress ( compare lanes 3 and 4 ) . To examine this further , we repeated the HA immunoblot analysis using a phosphate-binding compound present in the acrylamide mix that enhances detection of phosphorylated protein isoforms during SDS-PAGE ( “Phos-tag” ) [57] . As shown in S1F Fig , prominent higher-molecular weight bands appeared after treatment of either SKO1-WT or sko1-MT strains with NaCl ( “Sko1-P” ) , which we attribute to Hog1-mediated phosphorylation since this shift was not observed in a hog1Δ strain . Together , these analyses indicate that sumoylation is not a pre-requisite for Sko1 phosphorylation during osmotic stress but suggests that both unsumoylated and sumoylated Sko1 can be phosphorylated by Hog1 . To investigate whether Sko1 becomes sumoylated prior to , or after binding target DNA sequences , we tested if its DNA binding activity is necessary for the modification to take place . Lys 450 of Sko1 is at a conserved position in the bZIP motif , and a Lys-to-Glu mutation of the corresponding Lys in the human bZIP TF CREB1 abolished its ability to bind DNA [43 , 58] . We constructed yeast strains that expresses an analogous K450E mutant form of Sko1 . HA , or Sko1 . HA with a 12-amino acid residue deletion surrounding Lys 450 , Δ ( 445–456 ) . To confirm that these mutant forms of Sko1 are indeed defective in DNA binding , we performed ChIP analysis at the promoter regions of the RTC3 and ENA1 genes , both of which are bound by Sko1 during osmotic stress [49 , 50] , and PMA1 , which is not targeted by Sko1 [48] . As expected , treatment of the SKO1-WT strain with NaCl led to rapid recruitment of Sko1 to the RTC3 and ENA1 promoters , but not to the promoter of PMA1 ( Fig 2C ) . For both Sko1-K450E and Sko1-Δ ( 445–456 ) , however , no recruitment was detected , indicating that both forms of Sko1 are indeed defective in DNA binding . WT , MT , and the DNA-binding-deficient forms of Sko1 . HA were then examined by IP-immunoblot , and , as shown in Fig 2D , even though the K450E and Δ ( 445–456 ) forms were expressed and IPed at levels comparable to Sko1-WT , neither showed a signal on the SUMO immunoblot . This indicates that Sko1 that is not able to bind DNA is not sumoylated and suggests that the modification takes place only after the TF binds to its target DNA sites . To determine whether DNA binding itself can trigger Sko1 sumoylation , we constructed an expression plasmid that generates the DNA binding-deficient form of Sko1 . HA ( K450E ) fused to the DNA binding domain of the transcription activator Gal4 ( construct Gal4DB-K450E ) . Strikingly , when introduced into a yeast strain that contains multiple Gal4 binding sites , the fusion protein showed high levels of sumoylation ( Fig 2E ) . This indicates that DNA binding itself can restore SUMO modification to the Sko1-K450E mutant , and strongly implies that DNA binding is a major determinant for Sko1 sumoylation . To explore whether sumoylation regulates the association of Sko1 with chromatin at its binding sites genome-wide , we performed HA ChIP followed by next-generation sequencing ( ChIP-seq ) for both SKO1-WT and sko1-MT strains , under normal growth and after exposure to osmotic stress ( 0 . 4 M NaCl for 5 min ) . Sko1 binding sites ( peaks ) were identified , using the MACS2 software tool , through detection of genomic regions with statistically significant Sko1 occupancy in IPed samples relative to background levels from input samples ( statistical significance cut-off of q < 0 . 05 ) . Numerous peaks were identified for both Sko1-WT and Sko1-MT in both untreated and osmotic stress-treated ( +NaCl ) samples ( Fig 3A and 3B and S3 Table ) , including peaks near many known Sko1-regulated genes ( Fig 3C and see S5 Table ) . NaCl-treated samples had somewhat fewer identified peaks than untreated samples for both Sko1-WT and Sko1-MT , which likely reflects the osmotic stress-associated binding dynamics of Sko1 in which its levels are reduced on some targets [49–51] . Consistent with previous genome-scale binding studies , occupancy levels of Sko1-WT range widely among its binding sites , with about half showing relatively high occupancy levels ( at least a two-fold enrichment in read density compared to input , FE > 2; Fig 3A and 3C ) , including binding sites for some known Sko1 target genes , as indicated in Fig 3C [48–51] . Supporting the effectiveness of this ChIP-seq experiment , we compared it with a previous Sko1 ChIP-seq study using the ChIPPeakAnno analysis toolkit and found that 80% of the peaks in our Sko1-WT set were also identified in that study [50] . Furthermore , a search for recurring sequences in the Sko1-WT peak set from our analysis produced the previously reported Sko1 binding motif , ATGACGT , with very high confidence ( Fig 3D ) [50 , 59] . Intriguingly , the ChIP-seq analysis showed that Sko1-MT binds dramatically more sites than Sko1-WT , in both untreated and +NaCl samples ( 66% and 47% more peaks , respectively; Fig 3A and 3B , and S3 Table ) . To confirm this observation , we performed an independent ChIP-seq replicate experiment ( Replicate 2 ) , which again showed more binding sites for Sko1-MT than Sko1-WT in untreated and +NaCl conditions ( S2A and S2B Fig ) . Overall , Replicate 2 showed consistently lower normalized read counts ( i . e . occupancy levels ) than Replicate 1 and it consisted of many peaks of low fold enrichment that were largely absent from Replicate 1 ( S2A and S2C Fig ) . As we did not produce an input control set for Replicate 2 , this might reflect that peak assignment normalization was performed by other methods for this replicate ( see Materials and Methods ) . Nonetheless , a large number of overlapping peaks were identified in each analysis between the two replicates ( S2C Fig , left and S3 Table ) , and , when only peaks from Replicate 2 that have high fold enrichment ( FE > 2 ) were considered , 75% to 91% were found to overlap with peaks from Replicate 1 ( S2C Fig , right ) . Importantly , each replicate identified more peaks bound by Sko1-MT compared with Sko1-WT , regardless of whether yeast were grown in normal or osmotic conditions ( compare Fig 3A with S2A Fig ) . To increase confidence in our results , the peak analyses described below were performed using only binding sites that were identified in both replicates for each sample ( “overlapping peak sets” ) . Notably , the overlapping peak sets also showed dramatically higher numbers of peaks for Sko1-MT than for Sko1-WT in untreated and +NaCl conditions ( S2D and S2E Fig ) . Lists of peaks identified in each replicate and in the overlapping peak sets are presented in S3 and S4 Tables , respectively . We next performed a detailed analysis of the Sko1-WT and Sko1-MT peaks derived from normally growing yeast ( untreated ) from the overlapping peak sets . Nearly all the 207 Sko1-WT binding sites were also bound by Sko1-MT , but Sko1-MT was found at an additional 277 sites ( Fig 4A ) . This is not the result of random binding of Sko1-MT at positions across the genome because ~90% of the peaks that are unique to Sko1-MT ( “MT only” ) are found near promoter regions , which is only slightly higher than the ratio of promoter-associated peaks in the Sko1-WT set ( Fig 4B ) . Promoter regions include 2000 nt upstream , and 200 nt downstream , of transcriptional start sites ( TSSs ) , which encompasses the upstream activation sequences ( UASs ) to which TFs typically bind [60] . Peaks that are unique to Sko1-WT ( “WT only” ) showed a somewhat different distribution , with about one third appearing in regions immediately downstream of gene ends , but this might be skewed by the small number of peaks ( 20 ) , and its significance is not known ( Fig 4B ) . For a more detailed analysis of the position of peaks , we plotted their distribution around TSSs ( Figs 4C and S3A ) . MT-only peaks show a slightly more focused distribution than peaks that are common to both the Sko1-WT and MT sets ( “WT & MT” ) , but they are predominantly situated around 400 to 500 bp upstream of TSSs , which is similar to the distribution of WT & MT peaks . These results indicate that Sko1 that cannot be sumoylated binds to numerous additional promoter regions in normally growing yeast , thereby implicating sumoylation in binding site specificity . We then examined the nature of the Sko1-WT and Sko1-MT peaks to determine whether binding sites unique to Sko1-MT have common or distinguishing features . Genes situated nearest the Sko1-WT peaks are involved in diverse pathways , but Gene Ontology ( GO ) term analysis indicates that glucose , hexose , and ethanol metabolic processes are significantly enriched among these ( P < 1 . 0 x 10−4 ) , which matches the results of a previous examination of the Sko1 regulon [49] . GO term analysis for peaks unique to Sko1-MT , however , showed no GO term enrichment , indicating that there is no apparent bias in the distribution of MT-only binding sites with respect to target gene function . To explore whether the SUMO-site mutation might alter the Sko1 binding sequence specificity , we determined the frequency at which the consensus Sko1 binding motif appears in Sko1-WT and Sko1-MT peak sets . About 60% of Sko1-WT peaks contain the sequence ATGACGT , whereas it is found in only ~12% of MT-only peaks ( Fig 4D ) , strongly suggesting that blocking sumoylation alters Sko1 binding specificity . When we repeated this analysis with a less-specific version of the Sko1 binding site , TKACG ( where K is G or T; based on the logo in Fig 3D ) , we found that this sequence is present in >80% of both Sko1-WT and MT-only peaks ( Fig 4D ) . This implies that Sko1-MT recognizes Sko1 binding motif-like sequences , but with less stringency than Sko1-WT . In support of this , in a de novo motif discovery analysis , the only significant recurring motif identified in the MT-only peak set is a slightly weaker match to the consensus Sko1 motif , and it is found in a smaller fraction of peaks , when compared to the most recurring motif in the WT & MT set ( S3B Fig ) . Taken together , our analysis suggests that sumoylation functions in preventing the association of Sko1 with non-specific binding sites that show some sequence similarity to its consensus binding motif . In both SKO1-WT and sko1-MT strains , osmotic stress resulted in a partial redistribution of Sko1 with many binding sites gained and several lost , which demonstrates that high osmolarity influences Sko1 binding site selection ( Figs 3B , S2B and S2E ) . We analyzed the overlapping peak sets obtained by ChIP-seq after treatment with NaCl , which again indicated that Sko1-MT binds significantly more sites than Sko1-WT ( 240 peaks for MT versus 122 for WT; Fig 5A ) . Approximately 80% of all +NaCl Sko1-bound sites , including the 129 bound only by Sko1-MT and the 111 bound by both WT and MT , are near promoters ( Fig 5B ) . De novo motif discovery indicates that the consensus Sko1 binding site is the most significantly recurring motif for both the WT & MT and MT-only +NaCl peak sets ( S3B Fig ) , with the consensus motif appearing in ~45% of MT-only peaks and in ~60% of WT & MT peaks ( Fig 5C ) . This is a greater fraction of MT-only peaks that contain the Sko1 binding site than in the untreated set ( ~45% vs ~12%; compare Figs 4D with 5C ) , which suggests that the effect of osmotic stress on Sko1 binding site selection outweighs the tendency for Sko1-MT to interact with degenerate binding sites . Consistent with this , high osmolarity likely influences Sko1 redistribution through phosphorylation of Sko1 by Hog1 [50] , which we have shown occurs independently of its sumoylation ( see above ) . Our results indicate that Sko1-MT binds many more sites than Sko1-WT in both untreated and +NaCl conditions , but binding appears to be more stringent for Sko1-MT under osmotic conditions , as more of the additional sites match the Sko1 consensus motif than under normal conditions . To explore the effects of osmotic stress on Sko1 binding site redistribution , we examined the status of peaks from the untreated WT & MT and MT-only samples in the +NaCl peak sets ( Fig 5D ) . About 75% of sites that were common to both Sko1-WT and Sko1-MT sets during normal growth remained bound during osmotic stress ( Fig 5D ) . Strikingly , however , 83% of sites bound only by Sko1-MT during normal growth were unbound after treatment with NaCl . This indicates that Sko1-MT is dissociated from most of its nonspecific binding sites after exposure to NaCl , which suggests that binding to these sites can be less stable than to actual Sko1 binding sites . Altogether , our analysis supports the notion that sumoylation acts to restrict Sko1 to appropriate stable binding sites , even during osmotic stress . To investigate whether sumoylation can influence the occupancy level of Sko1 , we next compared Sko1-WT and Sko1-MT occupancy levels at common binding sites across the genome . For this analysis , the DiffBind analysis tool was applied , which used a normalized number of sequence reads as a measure of occupancy across a consensus set of 630 peaks . The consensus peak set consists of peaks that were found in at least two of the four independent ChIP-seq analyses from both replicates , as listed in S5 Table . Significantly , as shown in the boxplots in Fig 6A and in S5 Table , Sko1-MT had overall higher occupancy levels than Sko1-WT in both untreated and +NaCl sets . To be sure that the effect is not only a result of the high number of MT-only peaks in the consensus peak set , two additional peak sets were also examined: ( i ) the set of 52 peaks that are present in both replicates of all four ChIP-seq analyses and , ( ii ) the 212 peaks that are present in both replicates of the Sko1-WT analyses in either the untreated or +NaCl sets , which can be considered normal Sko1 binding sites . In all cases , Sko1-MT showed significantly higher occupancy levels than Sko1-WT ( S4A Fig ) . To visualize the differences in occupancy levels at each of the 630 consensus peaks , heatmaps were generated ( Fig 6B ) . For both Sko1-WT and Sko1-MT , osmotic stress resulted in an overall similar redistribution , with some sites showing increased binding ( shown in yellow ) , but slightly more sites showing reduced binding ( blue; Fig 6B; left pair of heatmaps ) . When comparing occupancy levels of Sko1 in SKO1-WT versus sko1-MT strains , however , most sites showed higher levels of Sko1-MT , and the effect was similar but more pronounced after treatment with NaCl ( yellow; Fig 6B; right pair of heatmaps ) . Increased occupancy levels can also be seen in the unnormalized peak alignments shown in Fig 6C near eight selected representative Sko1 target genes , including those that show Sko1 recruitment ( GPD1 , STL1 , MPC3 , and ALD3 ) , release of Sko1 ( GRE2 , FSH1 ) , or no observable change in Sko1 occupancy in response to NaCl treatment ( PRR2 , SED1 ) . To validate these results , we performed independent ChIP experiments followed by qPCR analysis of the promoter regions of the eight representative Sko1 target genes . Indeed , most genes showed statistically significantly higher levels of Sko1-MT compared with Sko1-WT in untreated and NaCl-treated samples ( S4B Fig ) . When averaged across all eight genes , Sko1-WT occupancy was ~1 . 4 times greater than Sko1-MT for untreated samples , and ~2 . 6 times greater in the +NaCl samples ( Fig 6D ) . Taken together , both our ChIP-seq and ChIP-qPCR experiments strongly indicate that sumoylation-deficient Sko1 shows increased occupancy at the majority of its binding sites and suggest that sumoylation decreases the binding affinity of Sko1 . To explore the consequences of increased occupancy of Sko1-MT compared to Sko1-WT , we examined expression levels of some Sko1 target genes in SKO1-WT and sko1-MT cells , in both normal growth conditions and at various time points after treatment with NaCl . Blocking Sko1 sumoylation ( sko1-MT ) resulted in reduced RNA levels of one gene , FSH1 , both prior to and after NaCl treatment , but had no significant effect on expression of the inducible genes STL1 , PRR2 , or MPC3 ( S5A Fig ) , suggesting that the consequences of Sko1 sumoylation on target gene expression are gene-specific . We also determined the mRNA levels of a selection of genes that were not bound by Sko1-WT but were bound by Sko1-MT in our ChIP-seq analyses . For all eight of these genes , there was no significant difference in expression level in the SKO1-WT and sko1-MT strains , both under normal conditions and 10 min after treatment with NaCl ( S5B Fig ) . This indicates that binding of Sko1-MT alone is not sufficient to significantly alter the expression of genes that it does not normally regulate . We then examined whether Sko1 sumoylation can influence the recruitment of Hog1 , which associates with many Sko1 target genes during osmotic stress [47 , 50] . Strains expressing a 3xMyc-tagged version of Hog1 were generated in the SKO1-WT or sko1-MT backgrounds , and we performed Myc ChIP analysis over a NaCl-treatment time-course . The analysis showed robust but transient recruitment of Hog1 to seven of its known target genes in SKO1-WT cells after addition of NaCl , but not to FSH1 , which is not regulated by Hog1 ( S6 Fig ) . Two genes , STL1 and PRR2 , showed significantly reduced levels of Hog1 occupancy in the sko1-MT strain at 5 min after addition of NaCl . More significantly , however , in the sko1-MT strain , recruitment of Hog1 , which we calculated as the ratio of occupancy at 5 min to 0 min post NaCl treatment , showed a statistically significant reduction at four of the seven genes ( Figs 6E and S6 ) . Indeed , when the average recruitment level was calculated across all seven genes , sko1-MT cells showed only 50% as much Hog1 recruitment as in SKO1-WT cells ( Fig 6F ) . These observations suggest that sumoylation prevents excessive binding of Sko1 at target sites , and that elevated Sko1 binding affects expression of some genes and can significantly impair the recruitment of Hog1 .
SUMO is a predominantly nuclear modifier that targets a large number of chromatin-associated proteins , including chromatin remodelers , general and sequence-specific transcription factors , and proteins involved in mRNA transport and processing [3 , 4 , 19 , 61] . Potentially then , the expression of numerous genes can be controlled by the cumulative effects of sumoylation of multiple substrates . Unlike ubiquitination , sumoylation in yeast and mammals involves a single E2 conjugating enzyme and a small number of E3 ligases and proteases [1] . This suggests that regulating the activity of only one enzyme in the SUMO pathway , such as Ubc9 , can have widespread consequences for the cell since so many substrates may be coordinately affected . This scale of vast protein regulation , particularly on chromatin , might be necessary for achieving a specific goal , such as the dramatic refocusing of the transcription machinery to specific genes after exposure to heat stress [62] . However , unstressed cells show substantial levels of sumoylation and SUMO modification of numerous chromatin-associated proteins is constitutive , implying that this modification functions primarily in the mechanisms of gene expression under normal growth conditions [3 , 8] . Our analysis of Sko1 sumoylation supports this . Although osmotic stress resulted in the redistribution of Sko1 across the genome , its sumoylation level did not change , and blocking its sumoylation increased the number of its binding sites both in the presence or absence of NaCl stress . Altogether , our data point to a role for SUMO modification in preventing Sko1 from binding to nontarget sites , genome-wide , which supports a novel function for the modification . Intriguingly , nonspecific sites bound by sumoylation-deficient Sko1 are not randomly distributed across the genome but are mostly situated near gene promoters that contain Sko1 binding site-like sequences . This might reflect the intrinsic accessibility of chromatin around promoters and the frequent occurrence of such sequences in many promoter regions [63] . Indeed , binding motifs for Sko1 and the related TFs Aca1 and Cst6 are highly similar , and it has been proposed that these TFs compete with each other for binding to promoter elements [40 , 45 , 64] . Based on the results of our study , we propose a model in which unbound Sko1 has high affinity for sequences that generally resemble the Sko1 motif . This might be necessary to ensure that all functional Sko1 target sites become occupied by the TF , even though nonspecific sites are initially bound , as well . Our model then posits that subsequent sumoylation of Sko1 relaxes its interaction with bound DNA , resulting in release from inoptimally bound nonspecific sites but allows sustained association with actual Sko1-regulated genes . Key to this model is that sumoylation takes place only after Sko1 has bound DNA , which is supported by our finding that DNA binding mutations eliminate Sko1 sumoylation . Similarly , DNA binding mutations have been shown to prevent sumoylation of other TFs such as Gcn4 and Ikaros , as well as non-TF DNA-binding proteins , including yeast Yku70 , human TDG and viral ULFF , which are involved in DNA damage repair [28 , 65 , 66] . This suggests that the sumoylation apparatus can distinguish between DNA-bound and unbound forms of many chromatin-associated proteins , possibly as a result of conformational changes that may occur after binding DNA , or due to the proximity of the sumoylation machinery with chromatin . Supporting this , ChIP analyses have shown that Ubc9 is associated with chromatin , including promoter regions of transcriptionally activated genes [14 , 15] . Further studies are necessary to test our model and explore the consequences of Sko1 sumoylation on its structure and DNA binding affinity . Consistent with the idea that sumoylation reduces Sko1 DNA binding affinity , blocking Sko1 sumoylation also resulted in increased occupancy levels at most of its target genes . However , not all target genes showed altered expression in the sko1-MT strain . The increase in Sko1 levels at these sites might not have been sufficiently dramatic to noticeably alter their gene expression levels , but this supports the previous finding that Sko1 occupancy levels on target genes generally do not correlate with their expression levels [49] . Instead , its context-dependent roles involve regulating the recruitment or release of repressors , such as Tup1/Ssn6 , chromatin remodelers SAGA and SWI/SNF , and the kinase Hog1 [44 , 47 , 50] . Hog1 is activated in response to osmotic stress which triggers its nuclear localization and association with promoters of osmo-regulated genes where it phosphorylates a number of transcription-related substrates , including Sko1 and RNAP II , resulting in efficient induction of the genes [67 , 68] . Whereas Sko1 itself is required for the recruitment of Hog1 to at least some target genes during osmotic stress [47 , 50] , our results suggest that increased binding of Sko1 in sko1-mt cells generally hinders the recruitment of Hog1 to its targets . Increased Sko1 binding in these cells might inhibit promoter-associated rearrangements that are necessary for induction of osmoregulated genes [47] , or Sko1 sumoylation itself might stimulate the recruitment of Hog1 through unknown mechanisms . In any case , our results point to a role for Sko1 sumoylation in controlling its association with chromatin not only to ensure binding site specificity , but also to prevent excessive binding at its authentic target sites . Few studies to date have examined the role of sumoylation on the genome-wide occupancy TFs , and , when considered with our results , the conclusions of these studies strongly point to a conserved role for sumoylation in regulating TF binding specificity . In the first such study , it was reported that a germline mutation in the human TF MITF that is associated with increased coincidence of melanoma and renal cancer also significantly reduces sumoylation of the protein [30] . ChIP-seq analysis was performed in human melanoma cell lines expressing either WT MITF or the sumoylation-deficient form , MITF-E318K . As in our analysis with Sko1 , dramatically more sites across the genome were bound by the sumoylation-deficient form of MITF , and occupancy levels were higher compared with WT MITF at sites that were bound by both the mutant and WT proteins . Recent ChIP-seq studies of the glucocorticoid and androgen nuclear receptors again showed that SUMO-site mutation led to altered genome occupancy patterns for both TFs , with the mutant proteins binding to significantly more sites than the wild-type counterparts [32 , 33] . The parallel observations of these analyses of different TFs in evolutionarily distant organisms strongly suggests a common function for sumoylation across eukaryotes . Considering the vast number of eukaryotic TFs that have been reported as SUMO targets and the close association of the modification with chromatin , we anticipate that future additional genome-wide studies will reveal that , indeed , a major role for sumoylation is in regulating the association of DNA-bound TFs with chromatin in order to restrict their activity to appropriate target genes .
Yeast strains used in this study are listed in S1 Table . Sko1- and Hog1-tagged strains were epitope tagged using homologous recombination as previously described [69] . Strains with a SKO1 deletion were generated using a KanMX deletion cassette by homologous recombination . Plasmid Gal4DB-K450E was generated by PCR-based sub-cloning of the sko1-K450E coding sequence into vector pGBT9 at the SmaI restriction site . It was then transformed into the HF7c yeast strain , which contains two genomic reporter genes with Gal4 binding sites . Yeast cultures were grown overnight in appropriate liquid medium and diluted to an optical density ( OD at 595 nm ) of 0 . 2 . Then , cells of each strain were spotted side-by-side in five-fold serial dilutions on solid-media plates with or without indicated osmotic stress conditions . Plates were incubated at 30°C and images were taken after the indicated durations . Yeast cultures ( 40–50 mL ) were grown in appropriate liquid medium to mid-log phase ( OD595 nm of 0 . 5 to 0 . 7 ) . Osmotic stress was induced by adding NaCl to a final concentration of 0 . 4 M for the indicated time points . Cells were then harvested by centrifugation at 3000 g for 5 min , followed by a wash with IP buffer ( 50 mM Tris-HCl , pH 8 , 150 mM NaCl , 0 . 1% Nonidet P-40 ( NP40 ) , 1X yeast protease inhibitor cocktail ( BioShop ) , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , and 2 . 5 mg/mL N-ethylmaleimide ( NEM ) ) . Cells were lysed with glass beads in IP buffer containing 0 . 1 mM dithiothreitol for 30 min at 4˚C . The lysed material was isolated from the beads and centrifuged twice to remove insoluble materials . Samples were either analyzed by immunoblot or used for IP . For IP experiments , an aliquot of yeast lysate was retained as input sample , and the remainder was incubated overnight with anti-HA agarose beads or Protein G agarose with HA antibody at 4˚C . IPs were washed three times with ice-cold IP buffer plus 0 . 1% NP40 , and twice with IP buffer alone . Beads were then resuspended and boiled with SDS loading buffer for 3 min , prior to analysis by the indicated immunoblots . Yeast cultures ( 50 mL ) were grown in YPD medium to an OD595 nm of ~0 . 65 . Cells were harvested by centrifugation at 3000 g for 5 min and washed with 20% trichloroacetic acid ( TCA ) . Washed cells were then lysed with glass beads in 20% TCA . Lysates were precipitated and washed with 5% TCA and resuspended with modified SDS buffer ( 60 mM Tris pH 6 . 7 , 5% 2-mercaptoethanol , 1% SDS , few drops of a bromophenol blue solution ) prior to boiling for 5 min . Boiled samples were centrifuged at room temperature for 10 min . An aliquot ( 40 μL ) of the supernatant was retained as input sample and the remainder was diluted with denaturing IP buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 0 . 5% NP40 ) containing 0 . 5 mg/mL of bovine serum albumin . Diluted samples were then incubated overnight with anti-HA agarose beads at 4˚C . IPs were washed three times with ice-cold denaturing IP buffer . Beads were then resuspended and boiled with SDS loading buffer for 3 min prior to analysis by the indicated immunoblots . Yeast cultures ( 50 mL ) were grown in YPD medium and induced by osmotic stress as indicated for IP procedure . Cells were then cross-linked with 1% formaldehyde for 20 min , followed by 5 min of quenching with 282 mM of glycine . Cells were pelleted by centrifugation and washed twice with ice-cold TBS ( 20 mM Tris-HCl , pH 7 . 5 and 150 mM NaCl ) , then in ChIP buffer ( 50 mM HEPES-KOH , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate and 0 . 1% SDS ) . Washed samples were resuspended in ChIP buffer and lysed with glass beads using a mini bead beater . The lysed materials were isolated from glass beads and sonicated to yield an average DNA fragment size of 300 to 500 bp in length . Samples were then centrifuged at 14 , 000 g for 5 min . To the isolated supernatants , additional NaCl was added to a final concentration of 225 mM . For IP , salt-adjusted supernatants were incubated with 15 μL magnetic Protein G beads ( Dynabeads , Thermo Fisher Scientific ) pre-bound with 2 μg rabbit anti-HA ( Novus ) or mouse anti-MYC ( NEB ) antibodies . IPs were washed with four different buffers for 4 min in the following order: ( 1 ) ChIP buffer with 275 mM NaCl; ( 2 ) ChIP buffer with 400 mM NaCl; ( 3 ) A buffer containing 10 mM Tris-HCl , pH 8 , 0 . 25 M LiCl , 1 mM EDTA , 0 . 5% NP-40 and 0 . 5% sodium deoxycholate; ( 4 ) Tris-EDTA buffer ( 10 mM Tris-HCl , pH 8 and 1 mM EDTA ) . Washed beads were then incubated in ChIP elution buffer ( 50 mM Tris-HCl , pH 7 . 5 , 10 mM EDTA and 1% SDS ) for 10 min at 65°C . Bound material was isolated on a magnet , treated with RNase for 30 min at 37°C , then with proteinase K at 42°C for 1 h . To reverse crosslinking , samples were incubated overnight at 65°C . DNA was purified and recovered using GeneJet Gel Extraction Kit ( Thermo Fisher ) followed by a quantitative PCR using primers listed in S2 Table . Results from qPCR were normalized to an untranscribed region of Chromosome V for Fig 2C , then an internal control gene , PMA1 , for other ChIP analyses . ChIP experiments were repeated at least three times and the averages were plotted with the standard deviations shown as error bars . Pair-wise statistical analyses were performed by Student’s t-test ( Figs 2C , 6A , 6D , 6E , 6F and S6 ) , whereas statistical comparison of sets of data in S4B and S5 Figs were performed by ANOVA . Asterisks indicate a significant difference with P-values less than 0 . 05 for ChIP and RT qPCR analyses , whereas pairs and sets with P-values greater than 0 . 05 are unmarked or marked as “n/s . ” RNA was isolated as previously reported from 10 mL yeast culture and subjected to DNase treatment [70] . Reverse transcription was performed on 1 μg of DNA-free-RNA using iScript cDNA synthesis ( Bio-Rad ) followed by qPCR . mRNA levels were normalized to an internal loading control , 25S rRNA . Primer sequences used for these experiments are listed in S2 Table . Error bar represents the standard deviation of three replicates . For ChIP-seq analysis , ChIP was performed as described above , but scaled up as appropriate . Namely , culture volumes were 200 mL in rich ( YPD ) medium , and 6 μg of HA antibody was pre-bound to 40 μL of Dynabeads . Two replicates of the ChIP-seq experiment described above were performed , with sequencing libraries generated for both IP and input samples for Replicate 1 , but only IP samples were sequenced for Replicate 2 . Libraries were prepared using the NEBNext Ultra II DNA library prep kit ( New England Biolabs ) , and paired-end sequencing ( two times 126 bases ) was performed using an Illumina HiSeq 2500 instrument at The Centre for Applied Genomics ( TACG ) at the Hospital for Sick Children ( Toronto ) . Raw and processed sequencing data files have been uploaded to the NCBI Gene Expression Omnibus ( GEO ) with accession number GSE118655 . For differential binding analysis , which was performed at TACG , sequencing reads were aligned , and peaks assigned as follows . Sequencing adaptors were trimmed using Trim Galore ! ( version 0 . 0 . 4 ) running Cutadapt ( version 1 . 10 ) with the following parameters: quality score cut-off of 25 , six nucleotides were removed from 5′ ends , Illumina universal adapter sequences were removed , stringency setting of 5 , sequences shorter than 40 nt after trimming were discarded , and only pairs of reads were retained . Trimmed forward and reverse reads were then aligned to the sacCer3 reference genome using Bowtie2 ( version 2 . 3 . 2 ) [71] . Peak calling was performed using the MACS2 peak assignment tool ( version 2 . 1 . 1 ) [72] in paired-end mode , with a genome size of 1 . 2e7 , and using corresponding input samples derived from Replicate 1 as controls for peak calling for both Replicates 1 and 2 . The default statistical significance cut-off was applied: 0 . 05 for the q-value , which is the false-discovery rate ( FDR ) -adjusted p-value , calculated using the Benjamini-Hochberg correction . Fold enrichment ( FE ) is calculated as the fold enrichment for each peak summit against a random Poisson distribution with local lambda . A set of consensus peaks was assembled using DiffBind ( version 2 . 2 . 12 ) , such that each peak was identified in at least two independent samples from both replicates , or as otherwise noted . Binding affinities were determined and compared using DiffBind based on the number of ChIP read counts ( log2 of normalized ChIP read counts with input read counts subtracted ) ( S5 Table ) . Because input samples were derived only for Replicate 1 , and Replicate 2 had consistently lower read counts , the replicate number was used as a blocking factor for batch correction in the statistical model used by DiffBind when calculating the affinity scores . Peaks were annotated with all genomic features within 5 kb of consensus peaks using ChIPpeakAnno ( version 3 . 12 . 7; Bioconductor Package ) [73] , with the reference genome annotation package TxDb . Scerevisiae . UCSC . sacCer3 . sgdGene . Differential binding data provided by TACG was then used to generate heatmaps to display pair-wise fold changes in binding occupancy levels ( Fig 6B ) using the Heatmapper web tool [74] . For peak number analysis ( Figs 3 , 4 , 5 and S2 ) , sequence read alignments and peak calling were performed as described above , with the following notes and exceptions . Peak assignment normalization for Replicate 2 was performed using either the input controls from Replicate 1 , or using local genomic bias from the ChIP samples , themselves ( i . e . without a control ) [72] . Both methods produced similar results , with the latter used to produce the data shown in Figs 4 , 5 and S2 . Some computations were performed using the Niagara supercomputer at the SciNet HPC Consortium . Lists of identified peaks , excluding peaks found with the mitochondrial genome , are presented in S3 Table . Peak analysis , including identifying overlapping peaks among data sets ( findOverlapsOfPeaks function; S4 Table ) and the distribution of peaks across gene features ( assignChromosomeRegion function ) were performed using ChIPpeakAnno . Frequency of motif occurrences within peak sets ( Figs 4D and 5C ) was determined using the summarizePatternInPeaks function within the ChIPpeakAnno toolset . De novo motif discovery was performed using the MEME analysis tool for indicated peak sets using 41 nt surrounding peak summits and restricted to motifs of 6 to 12 nt in length [75] . An analysis with 81 nt surrounding peak summits was also performed , which resulted in similar results . Visualization of peak alignments ( Fig 6C ) was performed using the Integrative Genomics Viewer ( Broad Institute ) [76] . | Transcription factors bind the genome at specific sites to control the expression of target genes . Although the DNA sequence of these sites is critical for determining where they bind , additional factors must act to ensure that only appropriate sites remain bound and regulated by transcription factors . Here we demonstrate that SUMO post-translational modification functions in transcription factor binding site selection . We show that a yeast transcription factor , Sko1 , becomes sumoylated after it binds DNA , and that the modification reduces the interaction of Sko1 with DNA . We propose that this is important to ensure that Sko1 remains associated only with actual Sko1 genomic binding sites . Indeed , we find that a mutant form of Sko1 that cannot be sumoylated binds hundreds of sites on the genome that are not normally bound by Sko1 . Most of these additional binding sites contain sequences that somewhat resemble the normal Sko1 binding site sequence , suggesting that sumoylation increases the stringency of Sko1 binding site selection . As numerous transcription factors are known to be targets of SUMO modification , our work suggests a general role for sumoylation in promoting specificity of binding to genomic sites in eukaryotic cells . | [
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| 2019 | Sumoylation of DNA-bound transcription factor Sko1 prevents its association with nontarget promoters |
Aedes aegypti is the primary vector of the four serotypes of dengue virus ( DENV1-4 ) , Chikungunya and yellow fever virus to humans . Previous population genetic studies have revealed a particular genetic structure among the vector populations in the Americas that suggests differences in the ability to transmit DENV . In Colombia , despite its high epidemiologic importance , the genetic population structure and the phylogeographic depiction of Ae . aegypti , as well as its relationship with the epidemiologic landscapes in cities with heterogeneous incidence levels , remains unknown . We conducted a spatiotemporal analysis with the aim of determining the genetic structure and phylogeography of Colombian populations of Ae . aegypti among cities with different eco-epidemiologic characteristics with regard to DENV . Mitochondrial cytochrome oxidase C subunit 1 ( COI ) - NADH dehydrogenase subunit 4 ( ND4 ) genes were sequenced and analyzed from 341 adult mosquitoes collected during 2012 and 2013 in the Colombian cities of Bello , Riohacha and Villavicencio , which exhibit low , medium and high levels of incidence of DENV , respectively . The results demonstrated a low genetic differentiation over time and a high genetic structure between the cities due to changes in the frequency of two highly supported genetic groups . The phylogeographic analyses indicated that one group ( associated with West African populations ) was found in all the cities throughout the sampling while the second group ( associated with East African populations ) was found in all the samples from Bello and in only one sampling from Riohacha . Environmental factors such as the use of chemical insecticides showed a significant correlation with decreasing genetic diversity , indicating that environmental factors affect the population structure of Ae . aegypti across time and space in these cities . Our results suggest that two Ae . aegypti lineages are present in Colombia; one that is widespread and related to a West African conspecific , and a second that may have been recently introduced and is related to an East African conspecific . The first lineage can be found in cities showing a high incidence of dengue fever and the use of chemical insecticides , whereas the second is present in cities showing a low incidence of dengue fever where the use of chemical insecticides is not constant . This study helps to improve our knowledge of the population structure of Ae . aegypti involved in the diversity of dengue fever epidemiology in Colombia .
Dengue is one of the major public health problems in the tropics and is the second-most deadly vector-borne disease in the world after malaria [1] . The mosquito Aedes aegypti is the main vector of the four serotypes of dengue flaviviruses ( DENV1-4 ) and yellow fever virus ( YFV ) , and is a known vector of Chikungunya virus [2] . Around the world , approximately 2 . 5 billion people are at risk of infection with dengue . Moreover , 50 to 100 million new cases of dengue fever ( DF ) are estimated to occur each year , including up to 500 , 000 cases of the more severe form of the disease known as dengue hemorrhagic fever ( DHF ) , which has a fatality rate of up to 5% [3] . Thus far , because no effective vaccine is available for DF prevention and no specific drugs are available to treat DF , vector control and entomologic surveillance remain the principal strategies against dengue infection . Two recognized subspecies of Ae . aegypti sensu lato ( s . l ) have been described according to several molecular and ecological studies [4 , 5] . The presumed ancient form is Ae . aegypti formosus ( Aaf ) , a sylvan mosquito restricted to the sub-Saharan region of Africa and the Ae . aegypti aegypti ( Aaa ) form referred to as Ae . aegypti sensu stricto ( s . s ) , which is widespread across most of the tropical and subtropical regions of the world in association with humans and is considered the main epidemiologically relevant subspecies [6 , 7] . Recent evolutionary studies based on the molecular analyses of the NADH dehydrogenase subunit 4 ( ND4 ) and cytochrome oxidase I ( COI ) mitochondrial genes suggest that populations of Aaa outside of Africa consist of mosquitoes derived from one of two ancestral clades . One clade is basal and is primarily associated with the mosquito population from Western Africa while the second arises from the first and contains primarily mosquitoes from Eastern Africa [8] . This differentiation is epidemiologically important because certain characteristics such as vector competence for yellow fever and dengue viruses as well as insecticide resistance have been found to vary in populations from different origins [7 , 9–11] . The sympatric distribution of both Aaa clades has been extensively reported in several countries of Central and South America , including Mexico [12] , Brazil [13 , 14] , Peru [15] , Venezuela [16] and Bolivia [17] where they have been recognized as distinct genetic lineages . Moreover , the presence of only one lineage in some locations is less common and suggests that the total absence of one lineage , or its incomplete colonization , is due to micro-evolutionary forces acting against one lineage to prevent its emergence ( i . e . , the process of selection to a particular lineage ) [13] . In this regard , the seasonal variations in the natural populations of Aaa may also explain this occurrence , thereby generating misconceptions about the true absence of a lineage in a particular location; however , clear evidence for this has not emerged thus far . Therefore , the genetic characterization of natural populations over time may help to further elucidate the behavior of these two lineages over time and may explain any possible effects on the epidemiology of dengue . Colombia is a hyperendemic country for DENV , and the number of cases of DHF and deaths due to dengue have increased dramatically during the last few years , mainly in the Central and Eastern regions [18 , 19] . For example , in 2010 the number of confirmed deaths due to dengue was 217 , the highest reported thus far [19] . Despite the importance of Aaa in several cities of Colombia , only two studies were conducted locally in populations in the north and the south of the country , suggesting that the Colombian populations are genetically diverse and are affected by the continued use of chemical insecticides [20 , 21] . However , many attributes regarding the origin , phylogenetic relationship with previously reported lineages , population structure and population dynamics ( i . e . , the movement among cities or dispersion among zones having distinct dengue incidence rates ) as well as vector competence within populations remain unknown . Knowledge of these biologic attributes is essential to improving mosquito control strategies and in predicting the progressive dispersion and reinvasion across the country . Here we conducted a genetic and phylogeographic analysis using the COI and ND4 mitochondrial genes of 341 Aaa individuals from three Colombian cities showing distinct dengue incidence rates that were collected during three samplings during 2012 and 2013 . The results indicate two maternal lineages of Aaa in Colombia that are differentially distributed across time and space in cities with different eco-epidemiologic characteristics , suggesting distinct colonization events and microclimate variables that modulate the frequency and distribution of each lineage .
A total of 343 adult mosquitoes were collected in three cities in Colombia between August 2012 and September 2013 with the assistance of the staff of the Vector-Borne Diseases program of the Instituto Nacional de Salud ( INS de Colombia ) . Three samplings were performed in each city , including at least one during the rainy and dry seasons ( Table 1 ) . To minimize inbreeding bias in the sample , insect captures using entomologic networks were performed in 20 randomized houses from two neighborhoods separated by more than 1 km and less than 3 specimens from each house were used in the genetic analyses . The cities sampled are localized in three eco-geographic regions with distinct climates and dengue incidence ( Fig . 1 ) . Bello ( BE ) is located in the central Andean region of Colombia in the inter-Andean valleys ( Valle de Aburrá ) at an altitude of 1250 meters [22] where , because of social control programs and the continuous removal of potential breeding sites of Ae . aegypti , the city manifests low incidence rates of between 25 . 9 to 52 . 3 cases ( per 100 , 000 inhabitants ) and a reduced use of chemical insecticides [19] . Riohacha ( RI ) is located in northern Colombia’s Caribbean Coast region at an altitude of 2 meters [22] , a desert region with a comparatively moderate dengue fever incidence of between 45 . 3 to 206 . 8 cases ( per 100 , 000 inhabitants ) [19] . Villavicencio ( VI ) is located in the east in the wide plains of the Orinoquia region at an altitude of 467 meters [22] and is the city that shows the highest incidence rates of dengue in Colombia , between 118 . 2 to 974 . 4 cases ( per 100 , 000 inhabitants ) [19] . In the last two cities , the strategic plans against dengue fever include entomologic surveillance and the continuous use of chemical insecticides . The geographic origin and the dates of sample collections are detailed in Table 1 and Fig . 1 . To avoid possible Numts amplifications [23–25] , and therefore obtaining incorrect phylogenetic and population genetic inferences , enriched mtDNA was obtained for all samples before the PCR amplifications . To achieve this , organelles were obtained according to the method described by Tamura and Aotsuka [26] , omitting the alkaline lysis procedure . Each individual adult was homogenized in 500 μl of chilled buffer containing 0 . 25 M sucrose , 10 mM EDTA and 30 mM Tris-HCl ( pH 7 . 5 ) . The homogenate was centrifuged at 1000 g for 5 min at 4°C and the supernatant ( mitochondria , lysosomes and peroxisomes sediment ) was retained . The centrifugation process was repeated four times , with the aim of eliminating the greatest amount of nuclear sediment . The resulting supernatant was centrifuged at 12 , 000 g for 10 min at 4°C and the pellet was dried and stored at -20°C . DNA was extracted from the final pellet using the mosquito standard protocol developed by Collins et al . [27] . To test the removal of nuclear DNA , a fragment of variable region D2 of the ribosomal 28S gene was amplified in samples of total DNA of all individuals using the primers D2F 5'- GCGAGTCGTGTTGCTTGATAGTGCAG-3' and D2R 5'-TTGGTCCGTGTTTCAAGACGGG-3' [28] . PCR amplification was performed using a T-1 Thermocycler ( Biometra GMBH , D-37079 Goettingen , Germany ) with 35 μl reaction mixes/total volume containing 2 μl of 1/16 template DNA dilution , 3 . 5 μl of 10X reaction buffer ( Fermentas ) , 0 . 2 mM dNTP , 10 pmol of each primer and 1 UI of Taq polymerase ( Fermentas ) . The amplification reaction was conducted at 95°C for 5 min; 25 cycles at 94°C for 1 min , 50°C for 2 min , 72°C for 2 min; and with a final extension at 72°C for 5 min . PCR products were detected by agarose gel electrophoresis in Tris-borate-EDTA buffer ( TBE ) , stained with GelRed 10 , 000X diluted 1:10 , 000 in agarose gel , and visualized under UV light . Only samples showing negative D2-28S amplifications were used for mtDNA amplifications . The 400 bp and 900 bp fragments of the ND4 and COI genes , respectively , were amplified individually using primers FOR-ND4 5ʹ—ATTGCCTAAGGCTCATGTAG-3’/REV-ND4 5ʹ-TCGGCTTCCTAGTCGTTCAT-3’ [15] , and COI-FOR 5ʹ-GTAATTGTAACAGCTCATGCA-3’/COI-REV 5ʹ-AATGATCATAGAAGGGCTGGAC-3’ , respectively [17] . For both genes , PCR amplification was performed using a T-1 Thermocycler ( Biometra GMBH , D-37079 Goettingen , Germany ) with 50 μl reaction mixes/total volume containing 4 μl of 1/16 template DNA dilution , 5 μl of 10X reaction buffer ( Fermentas ) , 0 . 2 mM dNTP , 20 pmol of each primer and 1 UI of Taq polymerase ( Fermentas ) . For the ND4 gene , reaction conditions were conducted at 94°C for 5 min , 35 cycles at 94°C for 30 s , 50°C for 30 s and 72°C for 30 s with a final extension at 72°C for 5 min . For the COI gene , 40 cycles were conducted at 94°C for 30 s , 48°C for 30 s and 72°C for 1 min followed by 10 min at 72°C . PCR products were detected by agarose gel electrophoresis in Tris-borate-EDTA buffer ( TBE ) , stained with GelRed 10 , 000X diluted 1:10 , 000 in agarose gel , and visualized under UV light . All positive PCR products were purified and sequenced using the Sanger methodology at Macrogen sequencing service , Seoul , South Korea . Sequences were aligned using CLUSTAL W [29] as implemented in BioEdit 7 . 1 . 9 [30] . Genetic variability for each marker as well as for the combined COI-ND4 dataset ( 1178 bp ) was evaluated by the number of haplotypes ( h ) , haplotype diversity ( Hd ) and nucleotide diversity ( π ) using DnaSP v . 5 . 0 [31] . Significant differences in the π values between different isolated samples were evaluated by analysis of variance ( ANOVA ) using GraphPad Prism® v . 5 . 1 software . The genetic relationships between individuals were analyzed using a principal coordinate analysis ( PCoA ) using GenAlEx v . 6 . 4 [32] . The Tajima’s D [33] tests was performed to test the hypothesis that all mutations were selectively neutral [34] using DnaSP v . 5 . 0 [31] . Mismatch distribution analysis was performed to identify the pairwise differences spectrum between haplotypes in the different isolates of time and space using DnaSP v . 5 . 0 [31] . A Median-joining haplotype network was built to examine the inter-haplotype relationship among individuals using Network v . 4 . 6 . 1 . 1 software [35] . To build the haplotype network , default parameters were used ( equal character weight = 10; epsilon value = 10; transversions/transitions weight = 1:1 , and connection cost as a criterion ) . Genetic differentiation in Colombian Ae . aegypti was assessed by ΦST pairwise comparison , and the genetic structure of isolates in time and space was also assessed using a hierarchical analysis of molecular variance ( AMOVA ) in GenAlEx v . 6 . 4 [32] . The statistical significance for the population structure analyses was assessed by permutation tests with 1000 iterations . Nucleotide ( π ) and haplotype diversities ( Hd ) values were compared with the quartiles of relative humidity ( HR-Q ) , in situ temperature and number of chemicals interventions with adulticides and larvicides performed in each city during the study using non-parametric Spearman`s correlation coefficients ( ρ ) with post-hoc Holm’s method for multiple comparisons in Hmisc and Rcmdr packages in R software . The spatiotemporal distribution of genetic groups was first evaluated through the frequency of individuals in the positive houses . Also , due to any particular spatial arrangement was observed in geographical distribution of Ae . aegypti groups , an analysis of Inverse Distance Weighting ( IDW ) interpolation between the number of positive and negative houses , and the geographic area of each neighborhood studied was performed . The search classes in the IDW interpolation analysis ranked between 0 ( absence ) to 1 ( presence ) individuals for each group , with a maximum search area of 2 . 5° without anisotropy ( i . e . , circular search area ) using ArcGis software ( v . 9 . 3; ESRI , Redlands , CA ) [36] . This analysis is based on the assumption that the interpolating surface should be influenced most by the nearby points and less by the more distant points [36] . Phylogeographic analysis was performed on the combined COI-ND4 dataset because congruent polymorphism and diversity proportions were observed for separated COI and ND4 genes ( S1 Table ) . The phylogeographic analysis of the Colombian haplotypes included 18 additional haplotypes from Africa , Asia , North America , South America and the Caribbean region ( S2 Table ) . The analysis was executed through a Median-joining haplotype network using Network v . 4 . 6 . 1 . 1 software [35] with a preprocessing step to reduce star-shaped haplotypes with a contraction within a radius of five mutational steps . Additionally , a phylogenetic tree including representative most frequent haplotypes ( frequency > 0 . 003 ) was performed through the neighbor-joining ( NJ ) method using Mega v . 5 . 1 software [37] . The model parameters HYK + G + I obtained for separated COI and ND4 genes as well as combined COI-ND4 was derived empirically from the best-fitting model estimated from the Akaike information criterion [38 , 39] . The bootstrap node support was estimated with 1000 replicates and the resulting tree was edited in FigTree v . 1 . 3 . 1 [40] . The overall topological match score and a well-supported node match score between topologies obtained for separated COI and ND4 genes as well as combined COI-ND4 were calculated using Compare2Trees software [41] . All nucleotide sequences are available with GenBank accession codes for COI: KM203140—KM203248 and ND4: KM203249—KM203336 .
For the combined COI-ND4 dataset ( 1178 bp ) , we identified 160 haplotypes harboring 147 variable sites ( 12 . 47% ) of which 79 ( 53 . 74% ) were parsimony informative and 68 ( 46 . 25% ) were singleton sites ( Table 2 ) . The overall Hd was 0 . 914 ± 0 . 014 and π was 0 . 008 ± 0 . 001 , and both haplotype and nucleotide diversities were comparatively higher in BE ( 0 . 961 ± 0 . 013 , 0 . 0113 ± 0 . 0002 ) than in RI ( 0 . 901 ± 0 . 026 , 0 . 0044 ± 0 . 0006 ) and VI ( 0 . 857 ± 0 . 036 , 0 . 0022 ± 0 . 0002 ) , respectively ( Table 2 ) . As expected for mitochondrial genes showing congruent evolutionary rates with each other , the observed values for Hd and π for both genes were higher in BE than in RI and VI , respectively ( S1 Table ) . A significant difference of π values was observed between the cities , and only RI showed significant differences of these values between samplings ( Table 2 ) . The first two coordinates of the PCoA , harboring 87 . 2% of the genetic variability in the dataset , roughly indicated that the two groups of haplotypes are inferred ( Fig . 2A ) , as supported by observed PC1-eigen values ( Fig . 2B ) . A first group ( group 1 ) was composed of most haplotypes from all locations and samplings , whereas a more dispersed group ( group 2 ) of haplotypes from BE individuals were collected in all samplings , and a few haplotypes from RI individuals were collected only in the first sampling ( sampling A details in Table 1 ) ( Fig . 2A ) . Nucleotide differentiation between the suggested groups was clearly observed in the mismatch distribution where a bimodal-shaped curve for the entire dataset was observed ( Fig . 3A ) . Furthermore , mismatch distribution performed in each of the cities showed a bimodal trend in BE that harbored haplotypes of insects collected during all samplings ( Fig . 3B ) , whereas in RI and VI a unimodal curve was mostly observed ( Fig . 3C , D ) . For the suggested groups , estimates of genetic diversity ( Hd and π ) were comparatively lower for group 1 than were observed within group 2 ( Table 2 ) . Moreover , only within group 1 were significant differences in π values observed between the cities , with the highest π values occurring in BE and RI compared with VI ( Table 2 ) . The haplotype network inferred for combined CO1-ND4 ( and those obtained for each gene separately , see S1 Fig . ) showed a high number of low-frequency haplotypes belonging to two main groups separated by a significant number of mutational steps ( Fig . 4 ) . For group 1 , a clear star-shaped network was observed , and no apparent differentiation among samples from the cities or samplings was identified . Within this topology , most of the haplotypes found in all of the localities during the three samplings were grouped , and the central and the most frequent haplotype found in all localities/sampling was H4 ( frequency = 0 . 288 ) ( Fig . 4 , S3 Table ) . Moreover , for group 2 a more dispersed haplotype network was observed . Here , only haplotypes from BE plus six haplotypes from RI were grouped ( Fig . 4 ) , and the most frequent haplotypes were H3 and H8 ( frequencies = 0 . 020 and 0 . 016 , respectively ) , which were present only in BE ( Fig . 4 , S3 Table ) . The Tajima’s tests showed negative and significant values ( P <0 . 05 ) in all samplings from VI , and in the second sampling in RI , indicating that an excess of rare haplotypes ( i . e . , a high number of low-frequency haplotypes ) is remained across the studied time in VI and eventually in RI ( Table 2 ) . However , for group 1 negative and significant values were obtained in all cities in different samplings , except for group 2 , wherein significant values were not observed between cities ( Table 2 ) . Aiming to identify some level of genetic structure among or within the cities as well as among samplings , hierarchical spatiotemporal AMOVA was performed by using HierFstat package in R sofware [42 , 43] . Hierarchical spatio-temporal AMOVA revealed that the majority of genetic variance was observed among samplings within cities ( 66% ) , but having no significant genetic differentiation between them ( FST = 0 . 06 ) . Moreover , similar significant values of genetic differentiation was observed among cities and among samplings ( Fst = 0 . 29 and 0 . 33 , respectively ) , but higher variance component was attributed to among cities ( 29% ) ( Table 3 ) . Because those significant spatial and temporal structure observed between cities and samplings may be caused by the presence of the two identified groups in BE and RI , an additional AMOVA was performed comparing the genetic variation and structure between and within groups . The results showed that the highest values of genetic variation could be attributed to comparisons between groups ( FST = 0 . 79 ) , and between groups within cities ( FST = 0 . 81 ) ( Table 3 ) . These results indicate that Colombian Ae . aegypti genetic differentiation observed is caused by the differential presence of the two genetic groups across of the cities and samplings rather than by any spatio-temporal level . After post-hoc Holm`s test , significant ( P < 0 . 05 ) negative Spearman correlation coefficients ( ρ ) was observed only for nucleotide diversity ( π ) and number of chemical interventions with adulticides ( ρ = -0 . 73 ) , indicating possible selective pressure is drown in the studied sample ( Table 4 ) . The spatiotemporal distribution of the frequency of each group across the studied cities was different among them ( Fig . 5 ) . Thus , whereas in BE both groups were present with similar frequencies during all samplings , with frequencies ranging from 45%–62% for group 1 , and 38%–55% for group 2 ( Fig . 5 ) , in RI , group 2 was observed only in sampling A ( frequency = 10% ) ( Fig . 5 ) and absent in all samplings in VI . Similarly , the distribution of each group in the positive houses indicates that whereas in BE the 66 . 6% of the positive houses presented both groups , 27 . 7% were only group 2 and 5 . 5% were only group 1; in RI , 10 . 3% of houses presented both groups and in the remaining 89 . 7% were only group 1; and in VI , 100% of the positive houses showed only group 1 ( Fig . 5 ) . Moreover , the interpolation analysis ( IWD ) performed in the geographic area of neighborhoods from BE indicates that approximately 32 . 6% of the Cumbre area ( Fig . 6A ) and 67 . 3% of Granjas ( Fig . 6D ) had at least one mosquito of group 1; whereas approximately 32 . 1% of Cumbre ( Fig . 6B ) and 80 . 4% of Granjas ( Fig . 6E ) had at least one mosquito belonging to group 2 . Therefore , a wide potential distribution across the two neighborhoods from BE for both groups was observed , indicating sympatric distribution could be sustained across this city ( Fig . 6 ) . The phylogeographic network build for the entire dataset ( COI-ND4 ) showed two groups of haplotypes that were associated with the haplotypes of America , Asia and Africa belonging to previously suggested mitochondrial lineages I and II [13–17 , 44] ( S2 Fig . ) . Furthermore , whereas the Colombian haplotypes analyzed here that suggested that group 1 was grouped with haplotypes from West Africa ( H1 Cameroon , H1 Guinea and H1 and H3 of Republic of Côte d'Ivoire ) , and Central ( H1 Venezuela ) and North America ( H1 USA ) ( reported as lineage I ) , group 2 was grouped with haplotypes from Eastern Africa ( H1 Tanzania ) , Central America ( H1 Mexico ) , South America ( H1 Brazil and H1 , H2 , H3 and H4 of Bolivia ) , and the Caribbean region ( H2 Martinique ) ( reported as lineage II ) , along with the Liverpool strain of Ae . Aegypti ( S2 Fig . ) . In the group 1 , haplotype 13 ( H13 ) was shared by specimens from Colombia ( BE and RI ) , Venezuela and the USA as haplotype 1 ( H1 ) , and in the group 2 , the haplotype 3 ( H3 ) of Colombia ( BE ) was shared with specimens from Bolivia as haplotype ( H1 ) ( S2 Fig . , S4 Table ) . In accordance with the phylogeographic network , the phylogenetic tree using the entire dataset ( COI-ND4 ) , showed two main monophyletic clades of Ae . aegypti supported by a Bootstrap of 100% ( Fig . 7 ) . One clade ( clade I ) included haplotypes that corresponded to the haplotypes suggested here as harboring group 1 , whereas the second ( clade II ) included haplotypes that corresponded to the haplotypes suggested as group 2 ( Fig . 7 ) . The overall topological match score between both separated genes ( COI and ND4 ) was of 29 . 7% , but a high match node score ( 75% ) was observed supporting monophyletic clades corresponding both genetic groups . Similarly , an overall topological match score of 43 . 4% , and match node score of 87% was observed between COI and combined COI-ND4 , and overall topological match score of 51 . 9% , and match node score of 77 . 4% for ND4 and combined COI-ND4 . These results suggest the suitability of the supermatrix approach supporting Ae . aegypti clades reported .
In this study we observed that values of nucleotide diversity ( π ) in Colombia are similar to those detected across several Brazilian localities [13 , 14] , Venezuela [45] and Mexico [12] where both lineages have been found , but are comparatively higher than the values detected in 21 localities in Bolivia , where according to the authors , the low genetic diversity in this country is explained by its geographic isolation due to the poor terrestrial access with other populations of the continent [17] . Similar genetic composition between the Colombian and most of the American populations might suggest common processes of reinvasion and gene flow occurring across those countries . Since the introduction of different genetic groups of Ae . aegypti in the same locality may significantly increase the genetic variability of the vector populations [46] . We suggest the high frequency of group 1 and group 2 in BE , the low frequency of group 2 in RI and the absence of this latter group in VI may explain the high genetic diversity observed in BE , followed by RI and VI , respectively as well as genetic structure observed between cities ( Table 3 ) . Furthermore , this differentiation may also be related to the different vector control measures implemented in each city because , in BE , the social control campaigns and the elimination of potential breeding sites of Ae . aegypti are the main control measures against dengue [19] , whereas in RI and VI , the use of insecticide chemicals is constant throughout the year [19] . These findings suggest that in these latter cities , the constant use of insecticides has had a marked effect in the effective size of the mosquito population , as evidenced in the neutrality test and negative correlation between nucleotide diversity and number of chemical interventions with adulticides . Similar to observations in Brazilian populations [46] , higher values of genetic diversity in group 2 than those of group 1 , which was the most widespread in the three cities , were obtained for the Colombian Ae . aegypti studied . This attribute , as well as the star-shape of the haplotype network exhibited by group 1 suggests that this group is most likely more ancient in Colombia because it presents as central ( or basal ) and presents the most widespread haplotypes in the country . Additionally , for this group negative and significant values of neutrality testing were observed in the three cities , indicating that it has been subjected to selection pressures across the country . Thus , for example in VI ( where only group 1 was found ) , recent studies demonstrated that mosquito populations have high levels of physiologic resistance to DDT , lambda cyhalothrin and deltamethrin [47] , as well as biochemical resistance to organophosphates [48 , 49] . These findings also suggest that the use of chemical insecticides exerts a strong bottleneck effect on the populations of group 1 in Colombia , although other processes that might reduce the genetic diversity ( i . e . , founder effects and genetic drift ) cannot be discounted [50] . Furthermore , the high frequency of group 2 in BE , where no chemical control is conducted , might suggest that this group is not under strong pressure in this city , contrary to the observations in RI , where this group occurred in lower frequencies and only in one of the samplings . As relates to the local distribution of Ae . aegypti groups , in BE the spatial analyses indicate that both groups are widespread across complete areas in the neighborhoods studied , but group 2 might have higher geographic dispersion ( 12 , 6% ) compared with group 1 in this city , where the use of chemical insecticides was not reported for sampling periods . These results support the conclusions of some studies , which have shown that mosquito populations with lower levels of resistance to chemical insecticides ( in our case , possibly group 2 ) have high fitness compared to those with a higher degree of resistance ( in our case , group 1 ) without this selection pressure [51] . However , further micro-environmental features that favoring the potential dispersion of group 2 cannot be discarded . Aedes aegypti is a poikilothermic species; therefore , changes in response to environmental factors such as humidity , temperature and rainfall can affect the genetic structure of its populations [52 , 53] . In the present study , it was observed that number of chemical interventions with chemical adulticides was negatively correlated with nucleotide diversity indicating a possible selective process can be drawn in cities where these types of insecticides are used . Several studies have shown that their exacerbated use generates selection pressures on resistant haplotypes , reducing the genetic variability in natural Ae . aegypti populations [51 , 54 , 55] . We observed this finding in RI and VI , where the number of reported interventions was the highest and the genetic variability was lower compared with BE where the use of chemical insecticides is uncommon . These results , coupled with the high levels of insecticide resistance reported in VI , support the idea that group 1 in this locality is often under environmental pressures that reduce genetic diversity and suggest a possible directional selection of insecticide-resistant haplotypes . In Colombia , Ae . aegypti was considered eradicated between 1952 and 1960 as a result of the eradication program initiated in the 1940s by the Pan American Health Organization ( PAHO ) [56] . Unfortunately , this program was discontinued in 1960 , producing a progressive re-invasion of the vector in all regions of the country [56] . Based on the phylogeographic analysis , our data indicate the presence of two genetic groups of Ae . aegypti , which is in accordance with mitochondrial lineages previously suggested in the Americas [13–17 , 44] . These lineages likely evolved from the ancestral population of North Africa and later on dispersed around the world . In this study , the haplotypes belonging to Colombian group 1 exhibited a close relationship with the populations of West Africa ( Cameroon , Guinea and the Republic of Côte d’Ivoire ) [17] , whereas the haplotypes of group 2 were grouped with the populations of East Africa ( Tanzania ) [17] . Since the work conducted by Tabachnick and Powell [57] in analyzing populations of Ae . aegypti s . l . worldwide , it has been suggested that the populations that invaded North America belong to the ancestral clade associated with West African populations , whereas those that invaded South America and the Caribbean region derive from a clade belonging to East African populations [6] . According to this hypothesis , our results indicate that group 1 , which might be considered ancestral in Colombia , is related to the West Africa clade . However , group 2 , associated with populations of East Africa , may be considered as being recently introduced in some regions of the country ( BE and RI ) due to the gene flow reported among American populations [50] . A different landscape is observed in the Amazon region of Brazil and Bolivia where the ancestral group is related to East Africa and the West Africa group is less frequent and dispersed [17 , 46] . These hypotheses indicate that distinct colonization routes might have occurred in northern South America and that multiple introductions of populations derived from ancestral lineages might have occurred in Colombia . According to the colonization routes of Ae . aegypti in the northern regions South America , we suggest that group 1 might have been introduced into Colombia through the land border with Venezuela or by the increase in imports from the USA [50] , since H13 , belonging to group 1 , has been found in BE and RI as well as in Venezuela and USA ( in these areas group 1 is termed haplotype 1 ) [17] . However , it is also possible that the haplotypes of group 1 represent relics of Colombian populations that escaped the DDT-based eradication . Alternatively , the high genetic diversity observed in group 2 suggests that this group was the result of multiple introductions from several American countries such as Bolivia , Mexico or Brazil , as inferred by the close relationship with the haplotypes reported in these countries [17] . These hypotheses indicate that distinct colonization routes might have occurred in northern South America and that multiple introductions of populations derived from ancestral lineages might have occurred in Colombia . Genetic structure of Ae aegypti populations is thought influencing their ability to transmit arbovirus as YFV , Chikungunya and dengue in endemic areas [11] . According to Powell and Tabachinick [58] , the vector competence is likely the result of the effects of adaptation for other functions not having anything to do with vector competence ( i . e . , adaptations accompanying domestication and adaptation of the virus to the mosquito ) [58] . Although no vector competence studies have been performed for both genetic lineages so far , it is known that populations of Ae . aegypti from West Africa historically are involved in the transmission of YFV , while East Africa population has not been implicated in outbreaks [59] . Based on these features and considering that YFV and dengue virus belong to same family ( Flaviviride ) , DENV vectorial capacity of the American mitochondrial genetic groups should be still addressed . Finally , whereas some works suggest that the presence of two mitochondrial lineages of Ae . aegypti is due to a genetically linked faulty production during Numts amplification [24 , 25] , our work confirms that those can be considered as true mtDNA lineages . First , we used a protocol for the extraction of DNA and the amplification of mtDNA that ensured that most of DNA obtained was mitochondrial [26] . Second , the congruent results of both markers used ( COI and ND4 ) , showing similar rates of mutation in mtDNA , is contrary to those expected in their respective Numts [24] . Finally , none of the nucleotide sequences of the Colombian haplotypes contained stop codons or insertions/deletions . The results demonstrated in this study indicate multiple introductions of Ae . aegypti in Colombia , inferring that a distinct ancient origin is involved . Cluster analysis clearly showed two genetic groups , each of which share haplotypes with populations of West Africa and East Africa , respectively . Our results suggest that the group 1 specimens related to West Africa might be present in cities where the use of insecticides is constant , whereas the group 2 specimens related to East Africa are associated with cities exhibiting lower incidence rates , without any selection pressure due to insecticides . The presence of genetically distinct groups in Colombia might imply differences in vector competence for transmitting dengue and urban yellow fever viruses , as well as a differential response to vector control strategies [7 , 11 , 60] , although this hypothesis must be further supported with more accurate evidence as to how endogenous factors relate with dengue incidence . These findings may contribute to a better understanding of the epidemiologic aspects of dengue fever , and possibly help to improve vector control measures , although the biologic characterization of these lineages remains necessary to conduct a more efficient strategy against dengue fever in Colombia . | Knowledge on the population structure of Aedes aegypti , the main vector of the dengue virus ( DENV ) , is essential to improving dengue fever prevention strategies in endemic countries . In Colombia , despite the epidemiological relevance of dengue fever , the genetic population structure and phylogeography of the vector Ae . aegypti is little known . In this study , we evaluated the spatio-temporal structure and phylogeography of Colombian Ae . aegypti populations from cities showing different eco-epidemiologic attributes related to dengue fever . Our results indicated that Colombian Ae . aegypti populations harbor two mitochondrial lineages related to West and East African ancestors . The lineage related to West African populations is the most frequent and widely distributed in Colombia , and it was found in cities with a high incidence of the dengue fever . A second lineage related to East African populations , which may have been recently introduced in some regions , was found in cities showing a low incidence of dengue . These findings suggest complex population dynamic is involved in dengue fever epidemiology in Colombia , and indicate further studies about biological attributes of the Ae . aegypti lineages should be performed . | [
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| 2015 | Spatio-Temporal Distribution of Aedes aegypti (Diptera: Culicidae) Mitochondrial Lineages in Cities with Distinct Dengue Incidence Rates Suggests Complex Population Dynamics of the Dengue Vector in Colombia |
The spindle checkpoint ensures that newly born cells receive one copy of each chromosome by preventing chromosomes from segregating until they are all correctly attached to the spindle . The checkpoint monitors tension to distinguish between correctly aligned chromosomes and those with both sisters attached to the same spindle pole . Tension arises when sister kinetochores attach to and are pulled toward opposite poles , stretching the chromatin around centromeres and elongating kinetochores . We distinguished between two hypotheses for where the checkpoint monitors tension: between the kinetochores , by detecting alterations in the distance between them , or by responding to changes in the structure of the kinetochore itself . To distinguish these models , we inhibited chromatin stretch by tethering sister chromatids together by binding a tetrameric form of the Lac repressor to arrays of the Lac operator located on either side of a centromere . Inhibiting chromatin stretch did not activate the spindle checkpoint; these cells entered anaphase at the same time as control cells that express a dimeric version of the Lac repressor , which cannot cross link chromatids , and cells whose checkpoint has been inactivated . There is no dominant checkpoint inhibition when sister kinetochores are held together: cells expressing the tetrameric Lac repressor still arrest in response to microtubule-depolymerizing drugs . Tethering chromatids together does not disrupt kinetochore function; chromosomes are successfully segregated to opposite poles of the spindle . Our results indicate that the spindle checkpoint does not monitor inter-kinetochore separation , thus supporting the hypothesis that tension is measured within the kinetochore .
Faithful chromosome segregation is essential . Mistakes lead to aneuploidy [1] , cancer progression [2] , and birth defects [3] . To ensure proper division of chromosomes , eukaryotes have evolved the spindle checkpoint , which monitors the kinetochore , a large multi-protein complex that assembles on centromeric DNA and attaches microtubules to chromosomes . In Saccharomyces cerevisiae , the budding yeast , the kinetochore consists of over 65 proteins that are assembled on the conserved 125 bp centromere [4] . The spindle checkpoint delays the onset of chromosome segregation until all chromosomes have attached their two sister kinetochores to microtubules emanating from opposite poles ( bi-orientation ) [5] , [6]; it is activated by unattached kinetochores [5] , [7] and lack of tension at the kinetochore [8] , [9] . Morphologically , the checkpoint regulates the transition between metaphase , when the pairs of sister chromatids are aligned equidistant from the two poles , and anaphase , when the sisters split apart and are pulled to opposite poles . Bi-oriented kinetochores are under tension: microtubules pull them towards the poles , but the chromosomes they lie on are held together by cohesin . In metaphase , this tension can be seen as separation of GFP-labeled centromeres [10] , [11] and by elongation of the kinetochores , detected by measuring the separation between different kinetochore proteins [12]–[14] . In budding yeast , removing tension ( by preventing replication or uncoupling sister chromatids ) activates the spindle checkpoint and arrests cells in mitosis [9] . An unpaired , tensionless chromosome in praying mantid spermatocytes delays cell division , and applying tension to this chromosome allows cells to enter anaphase [8] . Although there is debate about whether the release of tension , or the subsequent release of microtubules from the kinetochore , generates the molecular signal that arrests cells in mitosis , it is clear that kinetochores can monitor tension , thus controlling the stability of microtubule attachment and progress through mitosis . The release of chromosomes and subsequent cell cycle arrest by the spindle checkpoint requires the activity of Sgo1 and the protein kinase , Ipl1/Aurora B [15]–[19] . Where does the spindle checkpoint measure tension ? There are two possible locations: between the two sister kinetochores ( inter-kinetochore , L1 in Figure 1A ) or within an individual kinetochore ( intra-kinetochore , L2 in Figure 1A ) . Inter-kinetochore tension could be measured by the stretching of pericentric chromatin [11] , or by a protein spring that spans the distance between kinetochores , such as PICH [20] , a protein seen to span the inter-kinetochore gap in HeLa cells [21] , [22] ( Figure 1B ) . Intra-kinetochore stretch could be detected by monitoring changes in the distance between different parts of the kinetochore or conformational change in a single molecule . For either model , stretch stabilizes microtubule attachment to the kinetochore and relaxation destabilizes attachment and activates the checkpoint [12] , [13] , [23] ( Figure 1B ) . We manipulated budding yeast chromosomes to determine whether inter- or intra-kinetochore stretch regulates the spindle checkpoint ( Figure 1C ) . By binding the tetrameric form of the GFP-labeled Lac repressor to an array of Lac operators , we held sister centromeres together ( and measured their separation ) , inhibiting inter-kinetochore separation as cells entered mitosis . Despite the inhibited inter-kinetochore stretch , sister chromatids still separated on schedule , even though our manipulation left cells capable of assembling functional kinetochores and activating the spindle checkpoint . Because inhibiting inter-kinetochore separation does not slow mitosis , we believe that the spindle checkpoint senses tension by monitoring events within the kinetochore .
Tension on bi-oriented chromosomes allows the spindle checkpoint to distinguish between correct and incorrect attachments . Tension increases the separation between the centromeres and the kinetochores that have been assembled on them [10] , [11] ( L1 in Figure 1A ) and the separation between components in a single kinetochore [12]–[14] ( L2 in Figure 1A ) , but we do not know which distance the checkpoint monitors . To reduce the inter-kinetochore distance ( L1 ) , we tethered the sister chromatids of Chromosome III to each other by placing Lac operator ( LacO ) arrays on either side of the centromere and expressing two alternative versions of the Lac repressor . The tetrameric Lac repressor ( LacI4 ) can bind simultaneously to two chromatids thus holding them together . The dimeric form of the repressor ( LacI2 ) [24] is a control; it binds the Lac operator , but the two DNA binding domains must bind to the same operator , preventing the dimer from holding two DNA molecules together . It has been previously demonstrated that the tetrameric Lac repressor can hold homologous sister chromosomes together during meiosis in budding yeast while the dimeric Lac repressor cannot [25] . Both repressors were fused to GFP to see the centromeric DNA . Centromeric separation gives rise to two GFP dots [10] , [11] , and one GFP dot indicates two centromeres separated by less than the resolution of light microscopy , which is theoretically 200 nm , but is probably closer to 350 nm in our hands ( Figure 2B ) . Both repressors contained two point mutations ( P3Y and S61L ) in the DNA-binding domain to produce the tightest binding affinity of all characterized Lac repressors ( Kd≈10−15 M ) [26] . We asked if the tetrameric Lac repressor inhibits centromere separation in metaphase . Cells were synchronized in G1 by treating them with the mating pheromone , alpha factor , released from this arrest , and allowed to proceed to a metaphase arrest , caused by removal of Cdc20 , an essential activator of the anaphase promoting complex ( APC , Figure 2A ) . Cells expressing GFP-LacI2 or GFP-LacI4 were sampled every 30 minutes for 3 hours and examined by fluorescence microscopy . Their centromeres were scored as stretched apart ( 2 GFP dots ) or unstretched ( 1 GFP dot ) ( Figure 2B ) . We initially placed a Lac operator array on only one side of the centromere , but we found that a single array did not inhibit the separation of the centromeres ( Figure 2C ) . Both dimer- and tetramer-expressing cells containing an array on one side of the centromere had equivalent percentage of visibly separated centromeres at all time points; there was no statistical difference between the two populations ( p>0 . 35 at all time points , Student's t-test ) . To better tether the two chromatids together , we placed Lac operator arrays on both sides of the centromere ( Figure 2D ) . For the first 30 minutes after their release from alpha factor , control ( GFP-LacI2 ) or tethered ( GFP-LacI4 ) cells both showed little centromere separation ( <10% stretched ) consistent with cells being in S phase and lacking a spindle . At 60 minutes , cells were entering mitosis: 50±3% of control , GFP-LacI2 cells ( n>100 ) had 2 GFP dots whereas only 24±2% of tethered GFP-LacI4 cells ( n>100 ) had 2 dots ( p<0 . 005 , Student's t-test , Figure 2D ) . Throughout the remaining time points , approximately 50% of control GFP-LacI2 cells had 2 dots , similar to previous studies [10] , [11] . Cells expressing GFP-LacI4 had significantly lower percentage of visible inter-kinetochore separation at all time points ( p<0 . 005 , Student's t-test ) , but the fraction rose during the metaphase arrest from 24±2% at 60 minutes to 42±2% at 180 minutes ( p<0 . 005 , Student's t-test ) . This experiment shows that the tetrameric Lac repressor can reduce inter-kinetochore separation only if Lac operator arrays are placed on both sides of the centromere , and reveals that this effect is primarily kinetic: the fraction of cells with visibly separated centromeres rises slowly during a prolonged metaphase arrest . We interpret the reduction in the fraction of cells with 2 GFP dots as evidence that the tetrameric Lac repressor is tethering the chromatids together , inhibiting the stretch of a correctly bi-oriented chromosome whose sister chromatids have attached to both poles . However , it is possible that the tetrameric Lac repressor generates fewer cells containing 2 GFP dots because it disrupts kinetochore assembly or slows error correction mechanisms in a way that the dimeric Lac repressor does not . If tetrameric Lac repressor disrupts kinetochores or inhibits error correction , a higher frequency of GFP-LacI4 bound chromosomes should be mis-segregated compared to GFP-LacI2 bound chromosomes . To test the segregation of GFP-LacI2 and GFP-LacI4 bound chromosomes , cells were arrested in anaphase using a temperature sensitive cdc15-2 allele that inhibits mitotic exit [27] . Cells were synchronized in G1 with alpha factor , raised to the restrictive temperature , washed and released at the restrictive temperature to arrest cells in anaphase ( Figure 3A ) . Cells were collected for scoring three hours after release from their G1 arrest , allowing cells to proceed to and arrest in anaphase as previously described [9] , [27] , [28] . Cells were stained with DAPI to confirm their arrest . Anaphase cells are large-budded and have DNA masses in each cell ( Figure 3B ) ; 99±1 . 5% of cells scored displayed this morphology . Correct segregation of the GFP-LacI bound chromosome was scored by the presence of one GFP dot in each mother and daughter cell , and mis-segregation was scored by one cell possessing both copies of the chromosome ( two GFP dots in one cell ) ( Figure 3C ) . As a control , the segregation of GFP-labeled Chromosome III was also measured in cells with a conditional centromere . The GAL1 promoter was placed upstream of CEN3; when cells are grown in glucose , the promoter is silent and the centromere functions normally ( Figure 3D ) . When cells are grown in galactose , transcription initiated from the GAL1 promoter disrupts centromere function and the chromosome is mis-segregated a high frequency [29] . Similar to previous studies using the conditional centromere [28] , we found that 96±1% of cells grown in glucose correctly segregated the chromosome , but correct segregation occurred in only 41±6% of cells grown in galactose ( Figure 3E ) . The presence of tetrameric Lac repressor did not disrupt chromosome segregation; both GFP-LacI2 and GFP-LacI4 bound chromosomes segregated correctly in 92±3% of cells . There was no statistical difference between cells grown in glucose , cells with GFP-LacI2 , and cells with GFP-LacI4 , but all were significantly different from cells grown in galactose ( p≤0 . 003 , Student's t-test ) . These results indicate that the presence of tetrameric Lac repressor does not disrupt kinetochore assembly or interfere with the correction of erroneous attachments , suggesting that the reduction in the fraction of metaphase-arrested cells with 2 GFP dots ( Figure 2D ) represents chromosomes that are correctly attached to opposite poles but cannot stretch apart due to the tethering effect of the tetrameric Lac repressor . Does reduced inter-kinetochore separation produced by binding the tetrameric Lac repressor near the centromere activate the spindle checkpoint and thus delay the onset of anaphase ? Cells were synchronized in G1 with alpha factor , washed and released to proceed through the cell cycle under conditions where they produce Cdc20 , activate the APC , enter anaphase , and divide . Samples were taken every 10 minutes , fixed , and visualized to score mitotic progression ( Figure 4A ) . Cells were scored for anaphase by the segregation of their GFP-labeled chromosome ( Figure 4B ) . The separation of sister centromeres that indicates bi-orientation is always less than 1 µm , whereas the separation associated with anaphase is always greater than 2 µm , making it easy to rigorously distinguish the centromere separation associated with metaphase bi-orientation from the chromosome segregation of anaphase . The fraction of anaphase cells falls at the end of the experiment because cells divide , producing two daughter cells , each containing a single GFP dot . Control cells expressing GFP-LacI2 began to enter anaphase 40–50 minutes post-release from G1 and peaked with approximately 80% of cells in anaphase between 60 and 70 minutes . By 100 minutes , nearly every cell had exited mitosis ( Figure 4C ) . Cells expressing GFP-LacI4 showed the same pattern of mitotic progression as control cells; they entered anaphase , reached a peak fraction of anaphase cells , and had fully exited mitosis at the same time as the GFP-LacI2 control ( Figure 4C ) . At each time point , there was no statistically significant difference between control and tethered cells , suggesting that inhibition of chromatin stretch does not activate the spindle checkpoint . Since some of the cells that express GFP-LacI4 have not achieved the metaphase separation of sister centromeres after two hours in metaphase-arrested cells ( Figure 2D ) , but cells that are allowed to pass through mitosis all complete anaphase within 90 minutes , we conclude that the failure to achieve metaphase centromere separation does not prevent entry into anaphase . It is possible , however , that both GFP-LacI2 and GFP-LacI4 cells activated the spindle checkpoint and experienced mitotic delay . To rule out this possibility , we removed Mad2 , an essential component of the spindle checkpoint , from both dimeric and tetrameric Lac repressor strains . All four strains ( GFP-LacI2 , GFP-LacI4 , GFP-LacI2 mad2Δ , and GFP-LacI4 mad2Δ ) moved through mitosis on the same time scale , with the peak of anaphase 60–70 minutes after release from G1 and with no statistically significant difference between any of the four strains ( Figure 4C ) . These results show that neither the dimeric or tetrameric Lac repressor cause a mitotic delay by activating the spindle checkpoint . We wanted to eliminate the possibility that our manipulations had interfered with the checkpoint in either of two ways . The first is that introduction of the tethering components ( Lac operator and either form of the Lac repressor ) might disrupt the spindle checkpoint . The second is that tethering sister centromeres might activate the checkpoint and , as a result , strains containing the tetrameric Lac repressor could only be produced by selecting cells that have mutationally or epigenetically inactivated the checkpoint . To confirm that strains expressing either form of the Lac repressor can still activate the spindle checkpoint , cells were synchronized in G1 with alpha factor and released into the microtubule-depolymerizing drugs benomyl and nocodazole ( Figure 5A ) . Treatment with these drugs activates the spindle checkpoint , preventing cells from going through mitosis and causing them to arrest as large-budded cells [5] . Approximately 90% of dimeric and tetrameric repressor-containing cells reached the large-budded stage 120 minutes after being released from G1 into microtubule poisons and remained arrested at this stage for the duration of the experiment ( Figure 5B ) . Cells that lacked Mad2 ( GFP-LacI2 mad2Δ and GFP-LacI4 mad2Δ ) did not arrest; after peaking at a value of 90% at 120 minutes , the fraction of large-budded mad2Δ cells declined to 55% at 180 minutes and 20% at 240 minutes ( p≤0 . 002 for all time points , Student's t-test ) , compared to the MAD2 cells , 90% of which remained large-budded in the presence of the drugs . The difference between mad2Δ and MAD2 cells was statistically significant at 180 and 240 minutes post-release ( p≤0 . 005 , Student's t-test ) . These results show that cells expressing the dimeric and tetrameric forms of the Lac repressor remain capable of activating the spindle checkpoint and arresting the cell cycle . To demonstrate that Lac repressor-containing cells can inactivate the spindle checkpoint and resume mitosis , cells expressing the dimeric or tetrameric Lac repressor were synchronized in G1 with alpha factor , released into benomyl and nocodazole . After 90 minutes of drug treatment , the cells were washed and transferred to drug-free media ( Figure 5C ) . During drug treatment , no dimeric or tetrameric-expressing cells entered anaphase ( 0% anaphase cells through T = 90 minutes ) , but after drug wash-out ( marked by red arrow ) both dimeric and tetrameric cells recovered from the mitotic arrest and began entering anaphase ( Figure 5D ) . By 150 minutes after their release from G1 arrest , approximately 30% of both GFP-LacI2 and GFP-LacI4 cells had entered anaphase . This result shows that both strains have functional spindle checkpoints that can be inactivated to allow cells to resume mitosis .
If the checkpoint does not monitor events between sister centromeres , it must respond to changes within the kinetochore . Maresca and Salmon [12] showed that treating Drosophila melanogaster tissue culture cells with taxol reduces inter-kinetochore but not intra-kinetochore stretch and does not activate the spindle checkpoint . Uchida et al . [13] showed that treating HeLa cells with low nocodazole concentrations reduces intra-kinetochore but not inter-kinetochore stretch and does activate the checkpoint . Our studies agree with the conclusion that the checkpoint responds to events within kinetochores rather than between them: we find that inhibiting chromatin stretch does not activate the checkpoint , and our approach avoids the potential side effects of altering microtubule dynamics with drugs , and isolates chromatin stretch from other effects on spindle structure and dynamics . Kinetochores can elongate under tension [12]–[14] . In Drosophila S2 cells , unattached kinetochores measure 65±31 nm from the inner centromere protein , CENP-A , to the outer kinetochore protein , Ndc80 . When attached and bi-oriented , this distance increases by an average of 37 nm [12] . Kinetochores could elongate by two mechanisms: altering their composition [34] or changing the conformations and contacts of individual proteins . Studies using immuno-electron and fluorescent microscopy showed that inner kinetochore proteins CENP-A , -C , and -R deform under tension , and CENP-T elongates , separating its N- and C-termini [35] . The outer domains of the microtubule-binding Ndc80 complex has also been shown to move 15 nm further away from the inner kinetochore upon bi-orientation [14] , perhaps by straightening of a long coiled-coil domain broken by a flexible , elbow-like hinge [36] . Two different mechanisms have been proposed for the link between kinetochore elongation and the activity of Ipl1: relaxing the kinetochore activates Ipl1 , or it allows an already activated kinase better access to its substrates . In budding yeast , Bir1 and Sli15 ( Survivin and INCENP in higher eukaryotes ) , members of the chromosomal passenger complex that localize and activate Ipl1 , help link centromeres and microtubules [37] , [38] . Studies on SLI15 and BIR1 mutants have led to the proposal that these proteins activate Ipl1 on relaxed kinetochores [37] . Recently , it has been shown that Sli15's ability to cluster Ipl1 together rather than its ability to localize the kinase to the centromere may be sufficient for distinguishing between correct and incorrect attachments [39] . There is also evidence supporting a constitutively active kinase that is separated from its substrates when the kinetochore is stretched: the phosphorylation of an Ipl1/Aurora B target depends on its distance from the kinase , located in the inner kinetochore , and repositioning the kinase closer to the outer kinetochore destabilizes microtubule attachments and activates the checkpoint [40] . Our results in yeast corroborate other work arguing that the spindle checkpoint measures the effects of tension within kinetochores . Monitoring the kinetochore means that the checkpoint would not activate in response to the observed variations in the distance between sister chromatids , but would detect mono-oriented chromosomes . Preventing false alarms from a tensiometer at the kinetochore would requires it to have one of two properties to keep the checkpoint from activating as the distance between sister centromeres fluctuates: 1 ) the extensible element within the kinetochore would have to have a lower spring constant than the linkage between the centromeres to make sure the tensiometer remained stretched , or 2 ) the conformational change that activated the checkpoint would have to be slower than the variations in the overall force separating the sister centromeres . Distinguishing between these possibilities will require further investigation of kinetochore dynamics and biochemistry .
Strains used in this study are listed in Table 1; all were constructed in W303 ( ade2-1 his3-11 , 15 leu2-3 , 112 trp1-1 ura3-1 can1-100 ) using standard genetic techniques . Lactose operator arrays containing 256 repeats of the operator were integrated either upstream of the centromere or on either side of the centromere on Chromosome III . Both arrays were integrated approximately 1500 bp from the centromere . Dimeric control strains contained a C-terminal truncation mutant of the Lac repressor ( LacI2 ) that cannot cross-link two arrays; experimental cells contained the wild-type version of the Lac repressor capable of tetramerizing and cross-linking two arrays ( LacI4 ) [24] . Both versions of the repressor were placed under the HIS3 promoter and were fused via their N-terminus to monomeric yeast optimized GFP . Cells were either grown in Synthetic Complete media ( 2% glucose ) lacking histidine ( SC-HIS ) or Synthetic Complete media ( 2% glucose ) lacking histidine and methionine ( SC-HIS-MET ) at 30°C to promote expression of the Lac repressor under the HIS3 promoter . YPD containing 1- ( butylcarbamoyl ) -2-benzimidazolecarbamate ( benomyl ) and nocodazole was prepared by heating YPD to 65°C and adding dimethyl sulfoxide ( DMSO ) 10 mg/ml stocks of benomyl drop-wise to a final concentration of 30 µg/ml; media was cooled to 37°C for drop-wise addition of DMSO 10 mg/ml stock of nocodazole to a final concentration of 30 µg/ml . All drugs and chemicals were purchased from Sigma Aldrich . Strains were grown in SC-HIS-MET at 30°C and maintained in log phase for 24 hours before the experiment . Log phase cells ( ∼5×106 cells/ml ) were arrested in G1 with 10 µg/ml alpha factor ( Bio-Synthesis ) for 3 hours . After confirmation of arrest by light microscopy , cells were washed three times with YPD to remove alpha factor and released into SC-HIS media containing methionine ( 250 µg/ml ) . Media lacking methionine allows cells to grow , but media containing methionine inhibits expression of Cdc20 from the MET promoter and induces metaphase arrest . Cells were grown at 30°C for 3 hours , and samples were collected every 30 minutes ( see Figure 2A ) . Samples were fixed with formalin ( see below ) and stored at 4°C for imaging . Using fluorescence microscopy to visualize GFP-tagged chromatids , samples were scored for the presence of one or two GFP dots; two dots indicates stretched chromatids . Strains were grown in SC-HIS plus 2% raffinose at 23°C and maintained in log phase for 24 hours before the experiment . Log phase cells ( ∼5×106 cells/ml ) were arrested in G1 with 10 µg/ml alpha factor ( Bio-Synthesis ) for 3 hours at 23°C . Cells were transferred to either SC -HIS+2% galactose+10 µg/ml alpha factor to induce the GAL1 promoter or to SC-HIS+2% glucose+10 µg/ml alpha factor to repress the promoter , and G1 synchronization continued an additional hour at the restrictive temperature ( 37°C ) . After confirming the arrest by light microscopy , cells were then washed three times in YEP , and incubated for a further three hours in either SC-HIS+2% glucose or 2% galactose at 37°C . Under these conditions , cells proceed through the cell cycle and arrest at anaphase , as large-budded cells because of the cdc15 mutation ( see Figure 3A ) . Samples were sonicated , fixed with formalin ( see below ) , and stored at 4°C for imaging . Cells were scored for chromosome segregation based the position of the two chromatid copies of GFP-labeled chromosome III . Correct chromosome segregation produces one copy of the chromosome ( one GFP dot ) in both the mother and daughter cells , whereas incorrect chromosome segregation leads to two GFP dots in a single cell . Anaphase arrest was confirmed by staining fixed cells with ProLong Gold antifade reagent with DAPI ( Life Technologies ) ; 100 cells were scored in three independent trials for DNA masses in both mother and daughter cells . Strains were grown in SC-HIS at 30°C and maintained in log phase for 24 hours before the experiment . Log phase cells ( ∼5×106 cells/ml ) were arrested in G1 with 10 µg/ml alpha factor ( Bio-Synthesis ) for 3 hours . After confirmation of arrest by light microscopy , cells were washed three times with YPD to remove alpha factor and released into SC-HIS media . Cells were grown at 30°C for 3 hours , and samples were collected every 10 minutes ( see Figure 4A ) . Samples were sonicated , fixed with formalin ( see below ) , and stored at 4°C for imaging . After 60 minutes , 10 µg/ml alpha factor was added to prevent additional entry into a second mitosis during the experiment . Samples were scored for mitotic progression by cell morphology and position of GFP-tagged chromatids . Anaphase was scored as large-budded cells with GFP-tagged chromatids separated into mother and daughter cells . Strains were grown in SC-HIS at 30°C and maintained in log phase for 24 hours before the experiment . Log phase cells ( ∼5×106 cells/ml ) were arrested in G1 with 10 µg/ml alpha factor ( Bio-Synthesis ) for 3 hours . After confirming the arrest by light microscopy , cells were washed three times with YPD to remove alpha factor and released into YPD containing 30 µg/mL 1- ( butylcarbamoyl ) -2-benzimidazolecarbamate ( benomyl ) and 30 µg/mL nocodazole prepared as described above . In Figure 5B , cells were grown in the drugs at 30°C for 4 hours with samples collected every 60 minutes and scored for the percentage of large-budded cells . In Figure 5D , cells were grown in the drugs at 30°C for 90 minutes then washed three times with YPD and released into drug-free YPD for an additional 60 minutes of growth at 30°C . Samples were taken every 10 minutes post-release from G1 , fixed with formalin ( see below ) and scored for anaphase , identified as large-budded cells with GFP-tagged chromatids separated into mother and daughter cells . Samples for imaging were fixed with 10% formalin added directly to growth media containing cells ( final concentration of 1% ) , incubated for 10 minutes at room temperature , washed with 0 . 1M KH2PO4 pH 8 . 5 , washed with 1 . 2M Sorbitol+0 . 1M KH2PO4 pH 8 . 5 , resuspended in 1 . 2M Sorbitol+0 . 1M KH2PO4 pH 8 . 5 , and stored at 4°C . Images were acquired at room temperature ( 25°C ) using a Nikon Eclipse Ti-E inverted microscope with a 60× Plan Apo VC , 1 . 4 NA oil objective lens with a Photometrics CoolSNAP HQ camera ( Roper Scientific ) . Metamorph 7 . 7 ( Molecular Devices ) was used to acquire images . Fixed samples were imaged in 1 . 2M Sorbitol+0 . 1M KH2PO4 pH 8 . 5 buffer on Concanavalin A-coated coverslips ( VWR ) adhered to glass slides ( Corning ) . Exposure times were 10 ms for differential interference contrast and 300 ms for fluorescence . | The spindle checkpoint monitors tension on chromosomes to distinguish between chromosomes that are correctly and incorrectly attached to the spindle . Tension is generated across a correctly attached chromosome as microtubules from opposite poles attach to and pull kinetochores apart , but are resisted by the cohesin that holds sister chromatids together . This tension generates separation between kinetochores as pericentric chromatin stretches and it also elongates the kinetochores . To monitor tension , the checkpoint could measure the separation between kinetochores or the stretch within them . We inhibited the ability of pericentric chromatin to stretch by tethering sister centromeres to each other , and we asked whether the resulting reduction in inter-kinetochore separation artificially activated the spindle checkpoint . Inhibiting inter-kinetochore separation does not delay anaphase , and the timing of mitosis was the same in cells with or without the spindle checkpoint , showing that the checkpoint is not activated . Inhibiting chromatin stretch does not alter the function of kinetochores as chromosomes are still segregated correctly , nor does it hinder the checkpoint . Cells whose sister kinetochores are held together can still activate the checkpoint in response to microtubule depolymerization . Our results indicate the spindle checkpoint does not monitor inter-kinetochore separation and likely monitors tension within kinetochores . | [
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| 2014 | Tethering Sister Centromeres to Each Other Suggests the Spindle Checkpoint Detects Stretch within the Kinetochore |
Usutu virus ( USUV ) is an African mosquito-borne flavivirus closely related to West Nile virus and Japanese encephalitis virus , which host range includes mainly mosquitoes and birds , although infections in humans have been also documented , thus warning about USUV as a potential health threat . Circulation of USUV in Africa was documented more than 50 years ago , but it was not until the last decade that it emerged in Europe causing episodes of avian mortality and some human severe cases . Since autophagy is a cellular pathway that can play important roles on different aspects of viral infections and pathogenesis , the possible implication of this pathway in USUV infection has been examined using Vero cells and two viral strains of different origin . USUV infection induced the unfolded protein response , revealed by the splicing of Xbp-1 mRNA . Infection with USUV also stimulated the autophagic process , which was demonstrated by an increase in the cytoplasmic aggregation of microtubule-associated protein 1 light chain 3 ( LC3 ) , a marker of autophagosome formation . In addition to this , an increase in the lipidated form of LC3 , that is associated with autophagosome formation , was noticed following infection . Pharmacological modulation of the autophagic pathway with the inductor of autophagy rapamycin resulted in an increase in virus yield . On the other hand , treatment with 3-methyladenine or wortmannin , two distinct inhibitors of phosphatidylinositol 3-kinases involved in autophagy , resulted in a decrease in virus yield . These results indicate that USUV virus infection upregulates the cellular autophagic pathway and that drugs that target this pathway can modulate the infection of this virus , thus identifying a potential druggable pathway in USUV-infection .
The variety of factors that have contributed to the emergence of the flavivirus West Nile virus ( WNV ) in the Americas and its re-emergence in other parts of the world could also provide a suitable scenario for the emergence of other arboviruses [1] , [2] , [3] . These potential threats for human and animal health include other related mosquito-borne viruses such as Usutu virus ( USUV ) [4] . USUV is an enveloped single-stranded positive polarity RNA virus that belongs to the Flavivirus genus in the Flaviviridae family . USUV was first described in South Africa in 1959 , and since then , it has been reported in several African countries including Senegal , Central African Republic , Nigeria , Uganda , Burkina Faso , Cote d'Ivore , and Morocco [5] . The host range of USUV in Africa mainly comprises ornitophilic Culex mosquitoes and birds , although two isolations of USUV from human serum , including one severe case , have been documented [5] . USUV was reported to be circulating only in Africa until 2001 , when it emerged in Central Europe [6] . From that time-point , USUV has been detected in several European countries often associated to episodes of avian mortality [4] , [6] . There is also increasing evidence of virus circulation among horses and humans in Europe [7] , [8] , [9] , [10] and recently two cases of neuroinvasive disease in humans have been documented [11] , [12] . This current scenario reinforces the notion that USUV can infect humans and play a role as a pathogen capable to induce a broad spectrum of symptoms that range from fever , rash or jaundice to meningoencephalitis [5] , [11] , [12] . Albeit the number of cases of human USUV infections is rather limited , the similarities of USUV ecology with that of WNV emphasize the need to be cautious about its potential threat to human health [4] , [5] . Even more , the observed symptoms of human USUV infections are not very specific , which could have probably led to an underestimation of the infections in endemic areas , which mainly comprise developing countries . A detailed knowledge of the cellular processes involved in pathogen and host cell interactions is desirable to design effective strategies to combat arboviral diseases . In the case of USUV , the role of many aspects of the interaction between the virus and the host cell , for instance its relationship with the autophagic pathway , remains to be explored . Macroautophagy ( thereafter referred as ‘autophagy’ ) is a cellular process by which cytoplasmic components are sequestered into double-membrane vesicles and degraded to maintain cellular homeostasis . In addition to this , autophagy constitutes an evolutionarily ancient process for survival during different forms of cellular stress , including infection with viruses [13] , [14] . As a first line of defence against intracellular pathogens , autophagy can contribute to viral clearance through the degradation of viral components located in the cell cytoplasm [13] . But antiviral aspects of autophagy go beyond , and this catabolic route has been also implicated in both innate and adaptive immunity , i . e . by promoting the delivery of Toll-like receptor ( TLR ) ligands to endosomes , or by feeding antigens to MHC class II pathway [13] , [15] , [16] . Autophagy can also extend the survival of infected cells by limitation of apoptosis [17] . Conversely , some viruses can take advantage on the induction of autophagy by co-opting components from the autophagic machinery in their own benefit to provide the adequate cellular platforms for replication [18] , [19] , [20] or by rearrangement of cellular lipid metabolism in order to support strong viral replication [21] . All these features make of autophagy a relevant druggable metabolic pathway during multiple human disorders , including viral infections , so interventions on this route could constitute potential therapies [14] , [16] , [22] . Regarding the Flaviviridae , autophagy has been associated to different aspects of the replication and pathogenicity of some members of this virus family , including Dengue virus ( DENV ) [21] , [23] , [24] , [25] , Modoc virus [24] , Japanese encephalitis virus ( JEV ) [26] , and hepatitis C virus ( HCV ) [27] , [28] . In the case of WNV , the induction or not of an autophagy response remains contentious . One recent report pointed that WNV infection induced an autophagic response [29] , whereas another suggested that the autophagic pathway was not upregulated in WNV-infected cells [30] . Relative to USUV , to our knowledge , the involvement of the autophagic machinery during its replication has not been previously documented . In this study we have analyzed the induction of autophagy following infection with a prototypic African strain of USUV and a recent European isolate [31] . The ability of both strains to provoke an autophagic response on infected cells was documented . Even more , pharmacological intervention at the autophagic pathway modulated USUV infection , thus identifying a cellular pathway for potential interventions on USUV infection .
All animals were handled in strict accordance with the guidelines of the European Community 86/609/CEE at the biosafety animal facilities of the Centro de Investigación en Sanidad Animal of the Instituto Nacional de Investigación Agraria y Alimentaria ( CISA-INIA ) . The protocols were approved by the Committee on Ethics of Animal Experimentation of INIA ( permit number 2013–015 ) . Mouse monoclonal antibody J2 against double-stranded RNA ( dsRNA ) was purchased from English & Scientific Consulting ( Hungary ) . Rabbit monoclonal anti-LC3B , rabbit anti-p62/SQSTM1 and mouse monoclonal anti-β-actin antibodies were from Sigma-Aldrich ( St . Louis , MO ) . Rabbit anti-calnexin antibody was from ECM Biosciences ( Versailles , KY ) . Polyclonal serum from a mice experimentally infected with USUV SAAR 1776 ( INIA permit number 2013–015 ) was also used to detect USUV proteins . Secondary antibodies against Mouse or Rabbit IgGs coupled to Alexa Fluor-488 , -594 or -647 were purchased from Life Technologies ( Molecular Probes , Eugene , O ) . Anti-rabbit and anti-mouse secondary antibodies coupled to horseradish peroxidase were from Dako and Sigma , respectively . All manipulations of infectious virus were carried out in Biosafety level 3 ( BSL-3 ) containment facilities . USUV strain SAAR 1776 ( GenBank acc: AY453412 . 1 ) , the reference South African strain of USUV , and the Austrian strain of USUV Vienna 2001-blackbird ( USUV 939/01 , GenBank acc: AY453411 . 1 ) , a recent European isolate of USUV [31] were propagated five and three times , respectively , in Vero cells [32] . Viruses were used at a multiplicity of infection ( MOI ) of 5 PFU/cell in microscopy experiments and of 0 . 5 PFU/cell in the rest of experiments . Infections and virus titrations on semisolid agar medium were performed as previously described [33] . Cells were routinely tested for mycoplasma with Mycoalert Mycoplasma Detection Kit ( Lonza , Rockland , ME ) . Autophagy inhibitors 3-methyladenine ( 3-MA ) and wortmannin , and autophagy inducer rapamycin , were purchased from Sigma and used at the concentrations of 2 . 5 mM , 0 . 5 µm and 100 ng/ml , respectively . Amonium chloride ( NH4Cl , Merck ) was used at 25 mM . Cells were infected , or mock-infected , and drugs were added to the medium after the first hour of infection . Stock solutions of wortmannin and rapamycin were prepared in dimethyl sulfoxide ( DMSO ) , and DMSO was also used as control in non-treated cells ( drug vehicle ) . Tunicamycin ( Sigma ) , an inducer of unfolded protein response , was also dissolved in DMSO and used at 10 µg/ml . The viability of cells with or without treatment was tested with CellTiter-Glo Luminiscent Cell Viability Assay ( Promega ) . A plasmid encoding GFP-LC3 [34] was transfected to visualize autophagosome formation . Plasmid encoding mCherry-GFP-LC3 was used to detect acidified autophagosomal structures [35] . Fugene HD ( Promega , Madison , WI ) was used as transfection reagent according to the instructions provided by the manufacturer . Cells were infected or treated with the drugs 24 h post-transfection . Assays were carried out as described [32] . Briefly , cells grown on glass cover slips were washed with PBS and fixed with 4% paraformaldehyde in PBS for 15 min at room temperature . Fixed cells were washed with PBS and permeabilized with BPTG ( 1% BSA , 0 . 1% TritonX-100 , 1M glycine in PBS ) for 15 min . Cells were incubated with primary antibody diluted in 1% BSA in PBS for 1 hour . After washing , cells were incubated with fluorescently conjugated secondary antibody for 45 min at room temperature . Samples were mounted with Fluoromount-G ( SouthernBiotech , Birmingham , AL ) and observed using a Leica TCS SPE confocal laser-scanning microscope . Images were acquired using Leica Advanced Fluorescence Software . Images were processed using ImageJ ( http://rsbweb . nih . gov/ij/ ) and Adobe Photoshop CS2 . Vero cells infected with USUV ( MOI of 5 PFU/cell ) were washed and fixed 24 h p . i . ( 30 min at 37°C ) in 4% paraformaldehyde-2% glutaraldehyde in 0 . 1 M phosphate buffer pH 7 . 4 plus 5 mM CaCl2 . Cells were scrapped from the flasks and post-fixed in 1% osmium tetroxide-1% potassium ferricyanide ( 1 h at 4°C ) , washed three times with bidistilled water and treated with 0 . 15% tanic acid ( 1 min ) . Cells were washed with the buffer and with bidistilled water and stained with 2% uranyl acetate ( 1 h ) . Samples were washed and then dehydrated in ethanol and embedded in the resin . Samples were examined using a Jeol JEM-1010 electron microscope ( Jeol , Japan ) operated at 80 kV and images were acquired using a digital camera 4K×4K TemCam-F416 ( Tietz Video and Image Processing Systems GmbH , Gauting , Germany ) . Western blot were performed as reported [32] . Cells were lysed on ice in RIPA buffer ( 150 mM NaCl , 5 mM β-mercaptoethanol , 1% NP-40 , 0 . 1% sodium dodecyl sulfate [SDS] , 50 mM Tris-HCl pH 8 ) supplemented with cOmplete protease inhibitor cocktail tablets ( Roche , Indianapolis , IN ) and Benzonase Nuclease ( Novagen , EMD Chemicals , San Diego , CA ) . Protein concentration was determined by Bradford assay . Equal amounts of proteins were mixed with Laemmli sample buffer , subjected to SDS-PAGE and electrotransferred onto a nitrocellulose or a PVDF membrane . Membrane was blocked with 5% skimmed milk in PBS 0 . 05% Tween-20 , incubated with primary antibodies ( overnight at 4°C ) , washed three times with PBS-Tween , and subsequently incubated with secondary antibodies coupled to horseradish peroxidase ( 1 h at RT ) diluted in 1% skimmed milk in PBS-Tween . Membrane was washed three times and proteins were detected by chemiluminiscence using a ChemiDocTM XRS+ System ( Bio-Rad , Hercules , CA ) . Intensity of protein bands was quantified using ImageLab software 2 . 0 . 1 ( Bio-Rad ) . RNA was extracted from control and infected cells with TriPure Isolation Reagent ( Roche , Mannheim , Germany ) as indicated by the manufacturer . Reverse transcriptions PCR reactions ( RT-PCR ) were carried out using the SuperScript One Step RT-PCR ( Life Technologies ) . Unspliced or spliced Xbp-1 mRNA was amplified as described [36] . Amplification of GAPDH mRNA was carried as a control for RNA extraction . PCR products were resolved by electrophoresis in a 2% agarose gel . Data are presented as mean ± standard error of the mean ( SEM ) . To test the significance of the differences , analysis of the variance ( ANOVA ) was performed with statistical package SPSS 15 ( SPSS Inc , Chicago IL ) applying Bonferroni's correction for multiple comparisons . Statistically significant differences were considered at P<0 . 05 .
As a first approach , cells infected with USUV strain SAAR 1776 were analyzed by transmission electron microscopy . Infected cells exhibited morphological characteristics associated to flavivirus infection . These included electron dense virions located inside endoplasmic reticulum cisternae ( Fig . 1A ) and membrane enclosed groups of spherical vesicle-like structures ( Fig . 1B ) . These clusters of vesicles correspond to vesicle packets ( VPs ) observed for WNV [32] , [37] , which have been also named double membrane vesicles ( DMVs ) in the case of DENV [38] . In addition to these classical features associated to flavivirus replication , an accumulation of cellular organelles morphologically related to those associated to degradative processes was patent in USUV-infected cells ( Fig . 1C–F ) . Rearrangement of endoplasmic reticulum-derived structures wrapping around cytoplasmic material was observed ( Fig . 1C ) , providing images typical of autophagic processes . Notably these membranes could be also observed continuous with the membrane of VPs . Membrane rearrangements included double membrane vacuoles ( Fig . 1D , arrowheads ) compatible with the morphology of autophagosomes . Double membranes engulfing cytoplasmic portions that resemble phagophore-like structures , which constitute the first stages of the formation of autophagosomes , were also observed ( Fig . 1D , arrows ) . In addition to these , multi-lamellar structures constituted by smooth stacked membranes , which have been also associated to autophagic processes [39] , were accumulated in the cytoplasm of USUV-infected cells ( Fig . 1E ) , and even observed in association with double membrane vesicles ( Fig . 1F , arrowheads ) . Taken together , the ultrastructural alterations observed in USUV-infected cells are compatible with the activation of an autophagic response . Following induction of autophagy , microtubule-associated protein 1 light chain 3 ( LC3 ) , a mammalian homolog of yeast Atg8 ( autophagy-related protein 8 ) is conjugated to phosphatidylethanolamine and targeted to autophagic membranes labelling autophagic vacuoles [34] , [40] . This prompted us to analyze LC3 modification following infection with USUV . As controls , Vero cells treated in parallel with the inhibitor of autophagy 3-methyladenine ( 3-MA ) or with the inductor of autophagy rapamycin [40] were analyzed by western blot ( Fig . 2A ) . A significant increase in LC3-II was noticed on cells treated with rapamycin , whereas a significant decrease of LC3-II was observed in cells treated with 3-MA ( Fig . 2A ) . Quantification of the LC3-II/actin ratio confirmed these observations ( Fig . 2B ) . Interestingly , a significant increase in LC3-I was also noticed in cells treated with rapamycin , suggesting that rapamycin could not only promote LC3-I/II turnover whereas it could also result in accumulation of both LC3 species under our experimental conditions . As these results confirmed the reliability of western blot assays to detect alterations of the autophagic flux , the modification of LC3 was analyzed on cells infected with USUV ( Fig . 2C ) . An increase in the amount of LC3-II was observed following infection with USUV when compared to mock-infected cells . This finding is compatible with alterations of the autophagic pathway in cells infected with USUV . Since PVDF membranes can result more sensitive to detect LC3-II than those of nitrocellulose [41] , we performed similar analyses using these membranes . An increase in both LC3-I and LC3-II along time was observed in samples infected with USUV ( Fig . 2D ) , which was confirmed by densitometry of protein bands ( Fig . 2E ) , as commented above for cells treated with rapamycin . The accumulation of total LC3 ( LC3-I and LC3-II ) suggests that the formation of autophagosomes could be upregulated in USUV infected cells . Indeed , accumulation of total LC3 has been documented during DENV-induced autophagy [24] . As the increase in LC3-II was not accompanied by a decrease in LC3-I , this could indicate that the autophagic flux , and hence LC3-I/II turnover , is not upregulated in cells infected with USUV , or that expression of LC3 is stimulated by USUV infection . In fact , transcription of LC3 is increased in certain systems upon induction of autophagy [41] . To analyze if USUV infection altered the normal autophagic flux that involves degradation or turnover of autophagosomal proteins , the levels of other autophagy-related protein , p62/SQSTM1 , were analyzed in USUV infected cells . The degradation of p62/SQSTM1 , a polyubiquitin-binding protein that interacts with LC3 [35] , has been described following upregulation of the autophagic flux under certain conditions [40] . However , no change of p62/SQSTM1 levels was found in cells infected with USUV ( Fig . 2F ) , These results indicate that Vero cells infected with USUV presented an accumulation of both LC3-II and LC3-I , a finding that could suggest an accumulation of autophagosomes , apparently not associated to alteration of p62/SQSTM1 level . As commented above , following induction of autophagy the lipidated form of LC3 is targeted to autophagic membranes labelling autophagic vacuoles [34] , [40] . This feature can be monitored by fluorescence microscopy as an increase in LC3 puncta in the cell cytoplasm [34] , [40] . Vero cells were transfected with a plasmid encoding GFP-LC3 for 24 h , and then infected with USUV ( Fig . 3A ) . Cells were fixed at 24 h p . i . and samples were processed for immunofluorescence using an antibody specific for double-stranded RNA ( dsRNA ) -a well characterized marker of the flavivirus replication complex [32] , [37] , [38]- to verify that transfected cells were infected . As expected , no positive signal corresponding dsRNA was observed on mock-infected cells , whereas fluorescent spots could be detected in the cytoplasm of USUV-infected cells ( Fig . 3A ) . As controls , transfected cells were also treated in parallel with 3-MA or with rapamycin . Cells treated with rapamycin displayed a spotted cytoplasm compatible with the formation of LC3 aggregates as a result of the upregulation of autophagy , whereas cells treated with 3-MA were apparently undistinguishable from control cells ( Fig . 3A ) . The cytoplasmic aggregates displayed by cells infected with USUV suggest an upregulation of the autophagic pathway during USUV-infection . When the number of puncta corresponding to GFP-LC3 per cell was determined by fluorescence microscopy [40] ( Fig . 3B ) , it was found that cells treated with 3-MA displayed a tendency to a reduction on the number of LC3 aggregates , although not statistically significant , whereas a statistically significant increase was observed in rapamycin treated cells , thus validating the reliability of this method to detect induction of autophagy . A statistically significant increase in the mean number LC3 aggregates in cells infected with USUV was also noticed . The extent of this increase was similar to that found for cells treated with rapamycin . Therefore , this analysis confirmed the accumulation of autophagosomes in cells infected with USUV . It has been proposed that replication of several members of the Flaviviridae family may be based on autophagosome membranes labelled with LC3 [28] , [42] , although other studies do not support this view [21] , [43] . Detailed observation of USUV-infected cells revealed that GFP-LC3 puncta did not colocalize with dsRNA positive spots that labelled USUV RNA replication sites ( Fig . 4A ) , thus indicating that although USUV infection increased the formation of autophagosomes the viral replication did not take place directly on autophagic membranes . The localization of viral proteins in infected cells was analyzed using a specific polyclonal serum raised against USUV ( Fig . S1 ) . As commented for dsRNA , USUV proteins did not colocalize with GFP-LC3 aggregates ( Fig . 4B ) , pointing that viral proteins were not associated with autophagic membranes . In contrast to this , dsRNA colocalized with calnexin ( Fig . 4C ) , a maker from the endoplasmic reticulum , a finding consistent with previous observations pointing that replication of USUV is based on membrane structures derived from the endoplasmic reticulum [32] . The viral proteins stained with the polyclonal serum also partially colocalized with calreticulin ( Fig . 4D ) , thus confirming the interaction of the endoplasmic reticulum and viral components during USUV infection . Since GFP is acid-labile , it makes difficult to detect autophagosomal structures by fluorescence microscopy using GFP-LC3 once they have fused with endosomes or lysosomes [35] . To analyze if USUV-infected cells were enriched only on autophagosomal compartments that had not already fused with acidic compartments we used a tandem mCherry-GFP-tagged LC3 expression vector [35] . The mechanism of action of this construction is based on that GFP signal is reduced in an acidic environment , whereas mCherry is more stable [35] , [40] , [41] . In this way , colocalization of GFP and mCherry indicates a cellular compartment that has not fused with an acidic compartment ( phagophore or autophagosome ) whereas mCherry signal without GFP corresponds to an autophagosomal compartment that has fused with an endosome or lysosome ( amphisome or autolysosome ) [40] . To verify that this construction worked properly under the experimental settings , cells were transfected with mCherry-GFP-tagged LC3 plasmid and then treated with rapamycin ( to promote autophagosome maturation ) or with NH4Cl ( to block the normal autophagic flux ) ( Fig . 5A ) [41] , [44] . When compared to control cells , an increase in the number of GFP puncta following rapamycin treatment was observed , however the increase in mCherry puncta was more marked ( Fig . 5B ) , indicating that rapamycin promoted maturation of autophagosomal structures towards acidic organelles in which GFP fluorescence was lost although they retained mCherry fluorescence . In contrast , the impairment of organelle acidification and the blockage of the normal autophagic flux exerted by NH4Cl induced an accumulation of LC3 puncta positive for both GFP and mCherry ( Fig . 5A and B ) that indicated a reduction in the number of acidified autophagic structures ( Fig . 5C ) . Cells infected with USUV displayed an accumulation of mCherry puncta that did not colocalize with GFP puncta ( Fig . 5A , B and C ) . This indicates that at this time postinfection there is an accumulation of acidified autophagosomal structures in USUV-infected cells that correspond to amphisomes or autolysosomes . In addition to this , no colocalization between dsRNA and mCherry was found in these experiments indicating that neither autophagosomes nor autolysosomes are the places for replication of USUV ( Fig . 5A ) . This is again consistent with the notion that replication of USUV is associated to the endoplasmic reticulum and not to structures of an autophagic origin . The interaction of flavivirus with endoplasmic reticulum during viral replication can result in induction of endoplasmic reticulum stress [45] , [46] , [47] . To cope with this problem , flavivirus-infected cells can undergo a coordinated change in gene expression collectively known as unfolded protein response [45] , [46] , [47] . Since induction of the unfolded protein response can trigger an autophagic response [48] , we analyzed if infection by USUV also activated the unfolded protein response . To this end , we monitored the splicing of Xbp-1 ( X box binding protein 1 ) mRNA ( Fig . 6A ) , which allows expression of the full length transcription factor Xbp-1 that upregulates transcription of multiple genes aimed to cope with endoplasmic reticulum stress and that has been detected as a common feature of unfolded protein response in flavivirus-infected cells [45] . Cells treated with tunicamycin , to pharmacologically induce the unfolded protein response , displayed an increase in the amount of spliced Xbp-1 not observed in control cells . Cells infected with USUV also displayed an increase in the amount of spliced Xbp-1 that was observed between 18 and 24 h p . i . The amount of spliced Xbp-1 detected in USUV-infected cells was comparable to that observed in tunicamycin treated cells ( Fig . 6B ) . These results evidenced that infection with USUV shared in common with other flaviviruses the activation of the unfolded protein response . Overall , our results pointed to an upregulation of the autophagic pathway in USUV-infected cells and , thus , the possibility of manipulating the autophagic pathway to modulate infection with USUV was addressed . Hence , to evaluate the potential of pharmacological modulation of autophagy as a candidate for antiviral approach design against USUV , the effect of two inhibitors of autophagy , 3-MA and wortmannin [49] , and that of the inductor of autophagy rapamycin were analyzed [40] . First of all , the cellular viability under drug-treatments was determined ( Fig . 7A ) . After 24 h of treatment with 3-MA , wortmannin , or rapamycin no major toxic effects on Vero cells were noticed , confirming the adequacy of these conditions for subsequent analyses . Treatment with either 3-MA or wortmannin resulted in a significant reduction of the virus yield of USUV virus ( Fig . 7B ) . On the other hand , rapamycin induced a significant increase in the viral production of USUV . Taken together , these observations support an implication of the autophagic machinery on the replication of USUV and confirm that pharmacological intervention on the autophagic pathway modulates USUV infection . The findings reported above were obtained using the prototypic USUV strain SAAR 1776 whose pathogenic capability has not been conclusively proven even in the birds . To analyze if the USUV circulating in Europe displayed similar interactions with the autophagic pathway , the USUV strain Vienna 2001 ( a recent isolate of USUV which pathogenicity has been extensively proven at least in birds ) was included in the study [6] , [50] . Infection with USUV Vienna 2001 induced an increase in the levels of LC3-II and also LC3-I , as described for USUV SAAR 1776 ( Fig . 8A and B ) . In addition to this , cells transfected with the plasmid encoding GFP-LC3 and infected with USUV Vienna 2001 also displayed a significant accumulation of GFP-LC3 aggregates throughout the cytoplasm compared to mock-infected cells ( Fig . 8C and D ) . These fluorescent GFP-LC3 aggregates did not colocalize with dsRNA ( Fig . 8C ) as commented for cells infected with USUV SAAR 1776 ( Fig . 4A ) . However , dsRNA colocalized with the endoplasmic reticulum marker calnexin ( Fig . 8E ) as described for USUV SAAR 1776 ( Fig . 4C ) . Even more , USUV proteins detected using a specific mouse serum colocalized with calnexin ( Fig . 8F ) confirming the association of viral antigens of USUV Vienna 2001 with endoplasmic reticulum , as described for USUV SAAR 1776 ( Fig . 4D ) . The effect of the inhibitors of autophagy 3-MA and wortmannin , and that of the inductor of autophagy rapamycin was also analyzed in parallel for USUV Vienna 2001 and SAAR 1776 ( Fig . 8G ) . Treatment with 3-MA or wortmannin resulted in a significant reduction of the virus yield of USUV Vienna 2001 as well as USUV SAAR 1776 . The extent of inhibition exerted by 3-MA was similar for both viral strains , whereas USUV Vienna 2001 was slightly less inhibited by wortmannin than USUV SAAR 1776 . In contrast , rapamycin induced a significant increase in the viral production of both USUV strains . Taken together , these results indicate that the findings observed for USUV SAAR 1776 were shared by USUV Vienna 2001 , a strain of USUV that is currently circulating in Europe with documented pathogenecity in birds .
The Flavivirus genus comprises more than 50 viral species that include well long known arthropod-borne pathogens as DENV , WNV , JEV , St . Louis encephalitis virus , Murray Valley encephalitis virus , Yellow fever virus or tick-borne encephalitis virus ( http://www . ictvonline . org/virusTaxonomy . asp ? version=2012 ) . But this viral genus also contains other neglected viral pathogens of currently increasing interest . Among these recently considered potential threats is USUV , a flavivirus endemic from Africa that emerged in Europe during the last decade ( see Introduction ) . In addition to basic knowledge , characterization of cellular pathways involved in virus replication could help to identify novel therapeutic targets . In this regard , the interaction of USUV with the host cell almost remains as an unexplored field , so at this point the identification of cellular pathways that regulate USUV infection is desirable . In this study we have explored the possible interaction of USUV with the autophagic pathway during infection . Due to the availability of more suitable reagents to analyze the autophagic pathway in mammalian cells , we selected Vero cells for the analysis . In fact , Vero cells constitute a cell line widely used for the cultivation and titration of USUV . In this way , the interaction of USUV with the autophagic pathway in cells derived from bird or mosquito , the main natural hosts for USUV , remains to be further evaluated . Our results showed that infection in mammalian cells by either the reference South African strain of USUV ( SAAR 1776 ) or a recent European strain ( Vienna 2001 ) triggered an autophagic response in the host cell . This is consistent with findings obtained for other flaviviruses [21] , [23] , [24] , [25] , [26] . The autophagic response was characterized by an increase in the levels of both LC3-II and LC3-I , which correlated with the accumulation of autophagic structures in the cytoplasm of infected cells . Our results also showed an induction of Xbp-1 mRNA splicing following USUV infection . Xbp-1 mRNA splicing constitutes a marker of the induction of the unfolded protein response , a finding that has been related to autophagic process during the viral infection [51] . Regarding the characteristics of the autophagic response induced by USUV , no p62/SQSTM1 degradation was found in infected cells , a feature that has been also described for other flaviviruses [26] , [29] . This finding together with the accumulation of both LC3-I and LC3-II could suggest an incomplete autophagic response that takes place without autophagosome maturation . However , the experiments performed with mCherry-GFP-LC3 plasmid revealed that USUV-infected cells were enriched in acidified autophagosomal structures , suggesting that at least a significant proportion of autophagic structures can maturate and fuse with acidified organelles in USUV-infected cells . These structures could include amphisomes or autolysosomes , whose morphology is compatible with those of multi-lamellar organelles observed by transmission electron microscopy [39] . Regarding the accumulation of both LC3-I and LC3-II , this has been described as a feature of DENV-induced autophagy [24] , and was also observed for cells treated with rapamycin . Although this could result of reduced autophagosomal degradation , the detection of acidified autophagosomal structures in USUV-infected suggests that autophagosomes can maturate in USUV-infected cells . However , another non-excluding possibility is an increase of expression of LC3 following infection of USUV , since such increase by a mechanism involving the unfolded protein response has been documented [41] , and this response is also activated following USUV infection . According with this possibility , the increase in other cellular proteins involved in autophagy , as p62/SQSTM1 , during infection with the related flavivirus WNV has also been proposed [29] . There is a controversy related to the autophagic origin or not of the structures that provide the platform for replication of distinct members of the Flaviviridae family as DENV or HCV . While several studies have been pointed that viral replication may be based on membranes of autophagosomal origin that contain LC3 [28] , [42] other studies clearly contradict these results [21] , [43] . In USUV-infected cells , no major colocalization between LC3 containing structures and dsRNA ( a well characterized marker of the flavivirus replication complex ) was found . In addition to this , no colocalization was found between USUV proteins and LC3 , indicating that viral proteins were not associated with autophagic structures , and suggesting that these structures did not provide the main platform for viral replication . In fact , these results agree with data pointing that USUV replication , as well as those of WNV or DENV [37] , [38] , is mainly based on modified endoplasmic reticulum structures [37] , [38] . Even more , dsRNA in USUV infected cells colocalized with calnexin , a marker of the endoplasmic reticulum . In this way , USUV replication most probably would take place associated to the vesicle packets ( VPs ) , which in other viral models have been shown to contain the dsRNA intermediates and have been probed to be constituted by invaginations of endoplasmic reticulum-derived membranes [37] , [38] . Even more , the observed colocalization between calnexin and USUV proteins confirms the interaction of viral components with the endoplasmic reticulum . Autophagy constitutes a major metabolic pathway that is currently being explored for treatment of multiple human disorders that include certain types of cancer and metabolic diseases , neurological disorders or viral infections [14] , [16] , [22] . In this regard , drugs that interfere with the autophagic pathway were assayed . The inductor of autophagy rapamycin increased virus yield of both USUV strains analyzed . In contrast to this , two structurally unrelated inhibitors of phosphatidylinositol 3-kinases ( PI3Ks ) involved in the induction of autophagy ( 3-MA and wortmannin ) decreased virus yield of both USUV strains here analyzed , including the European USUV isolate Vienna 2001 , representative of the USUV that is currently circulating in Europe . Interestingly , whereas 3-MA inhibited both viral strains in a similar manner , infection by USUV Vienna 2001 was less inhibited by wortmannin than that of USUV SAAR 1776 . These observations point to the autophagic pathway as a novel partner of USUV infection and specifically point to PI3Ks as valid antiviral targets . Having in mind that different PI3Ks are under strict consideration as cellular targets for treatment of human disorders [52] , the results here presented set a starting point for antiviral development to combat USUV based on inhibition of these cellular enzymes . Other previously identified cellular pathways as regulators of USUV infection have been the synthesis of fatty acids [32] , the innate immune response induced in infected cells [53] , and preliminary data also point to the induction of apoptosis in infected cells [32] . In fact , connections between autophagy and these other metabolic pathways involved on USUV infection have been also documented for other members of the Flaviviridae family [21] , [24] , [54] . In this way , the identification of the involvement of autophagy during USUV infection will help to decipher the puzzle of the interaction of USUV with host cells . Overall this study provides the first evidence for a role of autophagy during the infection of the mosquito-borne USUV . Our results indicate that pharmacological inhibition of the autophagic pathway can reduce infection by this virus in cultured cells . These observations identify autophagy as a metabolic pathway involved on USUV-infection , thus opening a potential new research line for the design of antiviral therapies against this pathogen . | The identification of cellular components and metabolic pathways involved in virus replication provides valuable information for the development of new antiviral strategies . Autophagy is one of these metabolic pathways with multiple implications during viral replication . Autophagy literally means self-digestion and constitutes a cellular process by which intracellular components are enclosed by membrane structures and degraded . Interestingly autophagy can contribute either positively or negatively to viral infections . For instance , several viruses hijack these autophagic membranes to build their replication complexes or take advantage on metabolic rearrangements induced following autophagy , while in other cases autophagy contributes to viral clearance and innate immunity . In this study , we explored the possible implication of the autophagic pathway during Usutu virus infection ( USUV ) . USUV is an African mosquito-borne flavivirus that mainly infects mosquitoes and birds , although infections in humans have been also documented , thus warning about USUV as a potential health threat . Our results indicate that infection by USUV of different origins triggers an autophagic response within infected cells . Even more , drugs that target components from the autophagic pathway modulate USUV-infection . These results provide the basis for the design of new antiviral research lines against this pathogen . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| []
| 2013 | Infection with Usutu Virus Induces an Autophagic Response in Mammalian Cells |
Mutation is the engine that drives evolution and adaptation forward in that it generates the variation on which natural selection acts . Mutation is a random process that nevertheless occurs according to certain biases . Elucidating mutational biases and the way they vary across species and within genomes is crucial to understanding evolution and adaptation . Here we demonstrate that clonal pathogens that evolve under severely relaxed selection are uniquely suitable for studying mutational biases in bacteria . We estimate mutational patterns using sequence datasets from five such clonal pathogens belonging to four diverse bacterial clades that span most of the range of genomic nucleotide content . We demonstrate that across different types of sites and in all four clades mutation is consistently biased towards AT . This is true even in clades that have high genomic GC content . In all studied cases the mutational bias towards AT is primarily due to the high rate of C/G to T/A transitions . These results suggest that bacterial mutational biases are far less variable than previously thought . They further demonstrate that variation in nucleotide content cannot stem entirely from variation in mutational biases and that natural selection and/or a natural selection-like process such as biased gene conversion strongly affect nucleotide content .
Mutation generates the variability on which natural selection acts . Mutation is not an entirely stochastic process , as it acts according to certain deterministic biases . Because of this , biases in the outcome of the evolutionary process result not only from selection but also from the biases of mutation . In order to understand evolution it is therefore necessary to elucidate mutational biases and the ways in which these biases themselves change in evolution . Nucleotide content variation is much more pronounced in bacteria compared to multi-cellular eukaryotes [1] . GC contents in bacteria vary from less than 25% to over 75% [1]–[3] . Related bacteria even from relatively broad phylogenetic groupings tend to show similar genomic nucleotide content [3] . For example bacterial genomes from the order Bacillales tend to be GC-poor , from the order Enterobacteriales to have intermediate GC contents , and from the phylum Actinobacteria to be GC-rich ( Figure 1A ) . In addition , GC content values measured at different functional site categories ( intergenic , synonymous , and non-synonymous ) show highly correlated patterns of variation across bacteria [4] ( Figure 1B ) . These observations suggest that forces determining GC content in bacteria operate both genome-wide and consistently over long periods of time . One possibility is that the main force driving nucleotide content variation in bacteria is mutation . This possibility has often been assumed true ( for example see [2] , [4]–[7] ) . Under this assumption , clades that are GC rich are clades in which mutation has been consistently biased towards GC while clades that are AT rich are clades in which mutation has been consistently biased towards AT . If true , bacterial mutational biases must to be extremely variable to be able to generate the extreme variation observed in bacterial nucleotide content . A second possibility is that it is not variation in mutational biases that leads to variation in nucleotide content , but rather variation in the relative probabilities of fixation of A/T to G/C and G/C to A/T mutations [1] , [3] , [8] . When considering changes to nucleotide content , differences in fixation probabilities can stem both from differences in the strength and direction of natural selection and differences in the rates of biased gene conversion ( BGC ) [1] , [9] , [10] . Natural selection affects the probability of fixation of an allele based on the alleles fitness advantage or disadvantage and the effective population size ( Ne ) of the organism in question . Similarly BGC is also dependent on Ne and on the advantage or disadvantage the allele has . Here however , this advantage or disadvantage is not in fitness but rather in the increased or decreased probability of the allele to be passed on to the next generation through gene conversion [1] , [10] . This increase or decrease is determined by recombination rates and by the conversion bias , which has been shown in many eukaryotes to be in favor of GC nucleotides compared to AT nucleotides [1] , [9] . A recent study that showed that in Escherichia coli regions of low recombination tend to be more AT rich demonstrates that BGC may affect nucleotide content in bacteria in a similar manner [11] . In order to gain insight into mutational biases it is necessary to investigate the results of mutation in isolation from those of selection and BGC . When effective population sizes are small , the efficacy of both natural selection and BGC is severely reduced relative to stochastic processes and therefore sequence evolution is affected strongly by mutational biases . Mutation-accumulation experiments artificially reduce Ne of evolving laboratory cultures [12] and can thus be used to assess mutational biases in culturable bacteria . Similarly , reporter constructs have also been used to estimate mutational biases [13] , [14] . However , without knowing the relative amount of time bacteria spend in different growth phases ( logarithmic vs . stationary ) and given that mutational rates and patterns vary between growth phases [15] , [16] it could be difficult to estimate the true mutational biases operating in nature using such experimental approaches . An additional approach is to examine nucleotide substitutions at sites that are expected not to be subject to selection due to protein functionality , such as pseudogenes [17] , or fourfold degenerate sites [18] . This approach is also problematic because while pseudogenes and fourfold degenerate sites are expected to be under no , or low selection for protein functionality , they should be subject to the same levels of selection on nucleotide content as the rest of the genome . A good way to estimate mutational biases is to analyze the patterns of single nucleotide polymorphisms ( SNPs ) within species . Population genetic studies have shown that natural selection and other selection-like processes are less efficient in affecting patterns of nucleotide polymorphisms among very closely related strains compared to nucleotide differences between distantly related strains or species [19] , [20] . Thus SNPs should better reflect the mutational patterns compared to substitutions between species . The analysis of SNPs has been used to investigate the mutational biases of a number of AT-rich eukaryotic genomes , such as Drosophila [21]–[23] . However , using this methodology in bacteria has been problematic due to a severe blurriness in species boundaries among prokaryotes [24] . As a result of such blurriness quite often strains sharing a “species” name ( such as E . coli ) are in fact quite diverged . While it is difficult to define species in bacteria , severely reduced selection has been observed among very closely related bacterial strains of some strictly clonal pathogens , such as strains belonging to the Mycobacterium tuberculosis cluster ( MTBC ) [25] . This can be explained by the fact that strains belonging to such lineages of pathogens are extremely closely related and thus these lineages may be good proxies of “species” and nucleotide differences between them can be viewed as polymorphisms . In addition , the lifestyle and clonality of such pathogens is likely to lead to small Ne , further reducing efficacy of natural selection [25] . The patterns of SNPs among closely related strains of such clonal pathogens should thus reflect directly the predominant mutational biases . Here we estimate mutational biases by analyzing SNPs extracted from large sequence datasets of five lineages of clonal pathogens ( including MTBC ) from four broad clades of bacteria that span virtually the whole bacterial phylogeny and the range of bacterial nucleotide contents ( Figure 1A ) : Bacillales ( AT rich ) , Enterobacteriales ( intermediate GC ) , Actinobacteria ( high GC ) , and Burkholderiales ( high GC ) . We find that in all lineages mutation is biased towards AT , and that G/C to A/T transitions are always predominant . Previous studies indicate that mutation may be universally AT-biased in eukaryotes [21] , [26]–[31] . Our results together with additional studies that have focused on Enterobacteriales ( E . coli , Shigella , and Salmonella typhimurium ) [32] , [33] demonstrate that mutation may be universally AT-biased in bacteria as well . These findings contradict the long-held view that mutational biases are the main contributors to variation in bacterial nucleotide content and are therefore highly variable among bacteria . Rather they suggest that nucleotide content in bacteria is strongly affected by variation in the relative rates of fixation of AT to GC and GC to AT mutations and that mutational biases are far less variable than previously thought .
We focused on five lineages of clonal pathogens from four diverse bacterial clades ( Table 1 ) . The five lineages we investigated are unique in their suitability for this type of analysis because they provide us with sufficient amounts of available sequence data for sufficiently closely related strains in which we can demonstrate a genome-wide relaxation in the efficacy of natural selection . The chosen strains are indeed very closely related with each lineage exhibiting less than 0 . 5 pairwise differences per gene ( Table 1 ) . However , because of the availability of multiple whole genome sequences in each lineage the total number of SNPs is substantial ( Table 1 ) , ranging from 165 to 1877 . In addition , these lineages are thought to be clonal [34] . Thus , horizontal gene transfer should occur only rarely , if at all and should not strongly influence our ability to infer the ancestral and derived states of mutations . Finally , the inference of SNPs from such closely related sequences is almost trivial , as alignment programs do much better when sequences are highly similar . Therefore , we expect to have no biases introduced through misalignment of sequences . We assessed whether the patterns of SNPs in these data are indeed weakly affected by natural selection by estimating the ratio of non-synonymous and synonymous differences per non-synonymous and synonymous site ( dN/dS ) [35] , [36] across all alignable proteins within each dataset ( Materials and Methods ) . If selection is strong , dN/dS should be much smaller than 1 as it would efficiently remove most non-synonymous mutations [35] , [36] . For example , comparisons of E . coli strains yields dN/dS values of approximately 0 . 05 [37] . In contrast , for MTBC where multiple lines of evidence suggest that natural selection is severely reduced [25] , dN/dS goes up to 0 . 59 . In our dataset , dN/dS values for the other four lineages range between 0 . 45 and 0 . 64 ( Table 1 , Materials and Methods ) . This suggests that selection is indeed relaxed in a genome-wide manner in these genomes and thus that the pattern of SNPs should be reflective of the mutational biases in these lineages . Relaxed selection over long evolutionary timescales can lead to extreme genome reduction and the loss of many repair pathways , which could affect mutational patterns [1] , [38] . The pathogens we use in this study have suffered only a short-term relaxation in the efficiency of selection and there is no indication that any of them have lost repair functions . To further substantiate that these pathogens are not likely to have suffered loss of repair functions we examined whether any of the repair genes annotated in close relatives of the examined pathogens that are not evolving under inefficient selection , have been lost in the pathogens . We found that in B . anthracis , B . mallei , S . typhi and Y . pestis there has been no loss of repair genes and that in all cases the repair genes are highly similar to those found in the outgroups ( Materials and Methods , Table S1 ) . In the case of MTBC , since the closely related outgroup strain M . canettii is not fully sequenced we could only compare the genes present in fully sequenced strains of MTBC to those present in a more distantly related outgroup , M . marinum . All but one of the repair genes found in M . marinum are also found in MTBC ( Table S1 ) . The M . marinum gene that is not found in MTBC is an un-named gene of unclear function . These results together with a lack of previous evidence for loss of repair functions in these well studied pathogens makes it unlikely that these pathogens have lost repair functions . It is even less likely that all of them suffered similar losses of repair functions . We polarized the SNPs ( Materials and Methods ) and classified changes into six possible types of mutations ( G/C to A/T , G/C to T/A , G/C to C/G , A/T to G/C , A/T to C/G , and A/T to T/A ) . The relative rate of each of the six mutation types was calculated after normalizing for the current GC content at the studied positions ( Materials and Methods ) . For all five lineages , irrespective of current genomewide nucleotide content , the predominant mutation is G/C to A/T transition ( Figure 2 ) . It is important to remember that relaxation of selection in the studied lineages is fairly recent and that nucleotide content is a slowly evolving trait . Therefore , if driven by selection or BGC , nucleotide content should not have had time to reach a new mutational equilibrium . However , if nucleotide content is driven predominantly by mutation , and selection and BGC do not strongly affect nucleotide content the genomic nucleotide contents should already be at the mutational equilibrium . We test whether nucleotide content is at equilibrium by comparing the number of GC→AT and AT→GC changes observed in each dataset . Under equilibrium these numbers will be equal . The results of such comparisons ( Table 2 ) clearly show that the lineages with intermediate ( Salmonella typhi & Yersinia pestis ) and high ( Burkholderia mallei & MTBC ) nucleotide contents are currently far from equilibrium and that GC→AT changes are much more frequent . These results are statistically significant for all but the intergenic dataset of B . mallei , in which a small number of SNPs leads to very low statistical power ( Table 2 ) . The above results indicate that under continued relaxed selection the lineages with intermediate or high GC content will evolve to become more AT rich . It is easy to calculate the expected equilibrium GC content based on the mutational rates we found ( GCeq , Materials and Methods ) [1] , [8] , [27] . Such a calculation shows that for all lineages GCeq is lower than 50% ( Figure 3 , Table 3 ) . In other words , mutational biases by themselves should lead to AT-rich genomes . Furthermore , if nucleotide content is primarily determined by the mutational biases of the genome , GCeq should approximate the observed GC content genomewide , However , for the four clonal pathogen lineages with either intermediate or high GC contents , GCeq is always significantly lower than the current genomewide GC at all types of sites ( Figure 3 , Table 3 ) . Finally , no significant correlation was observed between GCeq and current GC across all site categories in all lineages ( r = 0 . 09 , Spearman correlation , n = 14 , P≤1 . Note that the 14 data points examined are not entirely independent . The calculated P-value should therefore be taken with a grain of salt , and is only provided to demonstrate that GCeq does not appear to be correlated to current GC ) . In order to show that these results are not an artifact of sequencing errors , we recalculated GCeq after removing all cases in which the derived allele appears only in a single genome ( singletons ) . While this reduces the number of SNPs per dataset and increases the error of the estimate , GCeq values remain lower than the current GC for all datasets from lineages with intermediate or high GC contents and the results remain statistically significant in all the datasets except for the B . mallei intergenic sites ( Table S2 ) . These results together show that in five lineages of clonal pathogens , belonging to four broad clades that span a large portion of bacterial phylogeny ( Figure 1A ) mutation is consistently biased towards AT . Furthermore , these results suggest that in bacteria that are GC rich or have intermediate GC contents , it is an elevated fixation of GC-enriching mutations rather than a change in mutation bias that drives the elevated GC content . Differences between genomes that are more distantly related to each other should reflect the effects of natural selection and/or BGC better . The GCeq calculated based on such differences should be more similar to the observed GC content than that calculated based on SNPs from more closely related strains . To examine these predictions we analyzed two additional datasets in which sequences are still closely related enough to create reliable multiple sequence alignments for a relatively large number of their genes and intergenic regions , yet show higher divergence than the five lineages in Table 1 ( Table 4 ) . The first dataset was created by aligning sequences from Y . pestis and Yersinia pseudotuberculosis and examining the differences between these two lineages . The second dataset was created by aligning sequences from 10 strains of Burkholderia pseudomallei . These two datasets were selected because they can be naturally paired to two of the five datasets in Table 1 ( B . mallei and Y . pestis ) . dN/dS values for these comparisons are lower than in the other five datasets ( χ2 test , P<0 . 00001 , Table 4 ) , suggesting that the effects of selection are indeed more evident in these data . As predicted , the GCeq values derived from differences between Y . pestis and Y . pseudotuberculosis and from B . pseudomallei comparisons are more similar to the current GC content at all types of sites compared to GCeq calculated from the paired datasets of clonal pathogens ( Figure 4 ) . These results are statistically significant for the non-synonymous and synonymous site comparisons ( Figure 4 , Table 4 ) . For intergenic comparisons a small number of identified B . mallei SNPs , and Y . pestis/Y . pseudotuberculosis differences makes it difficult to demonstrate statistical significance . Furthermore , the GCeq values calculated based on the more diverged datasets are significantly correlated with the current genomewide GC content in Y . pestis and B . pseudomallei ( r = 0 . 99 , Spearman correlation , n = 6 , P≤0 . 03 ) . It is important to note that the GCeq values derived from differences between Y . pestis and Y . pseudotuberculosis and from B . pseudomallei are still lower than current GC content for all comparisons ( Figure 4 ) . This indicates that even a relatively slight reduction in selection can lead to a certain reduction in GC content . A similar result was recently found for E . coli and Shigella , where a relatively slight reduction in the efficiency of selection [37] was shown to lead to an excess of GC→AT changes in Shigella compared to E . coli [32] . Obligate intracellular bacteria are known to be evolving under extremely prolonged relaxed selection [38] . It is therefore possible that in these organisms nucleotide content will be strongly affected by mutation . The genomes of obligatory intracellular bacteria tend to be AT rich [38] , indicating that mutation may be biased towards AT in these organisms . However , it is unknown whether the mutational biases of obligate intracellular bacteria reflect those of their clade members that are not living this lifestyle , since these organisms lack many of the repair genes which are present in their other clade members that are not evolving under such relaxed selection [38] . It is unclear how the absence of repair pathways affects the mutational biases of these bacteria . Lind and Anderson [33] have carried out mutation accumulation experiments in Salmonella typhimurium strains lacking the major DNA repair systems involved in repairing common spontaneous mutations caused by deaminated and oxidized DNA bases . They found that in such strains of S . typhimurium mutation is strongly AT-biased but that unlike what we find for our pathogens , when these repair pathways are absent transversions become much more frequent than transitions [33] . The question however remains of whether loss of repair pathways changes the AT bias of mutation in obligate intracellular bacteria . It is also unclear whether the nucleotide contents of obligate intracellular bacteria are currently at mutational equilibrium . Even though these organisms have been subject to prolonged , severe relaxations of selection it is possible that they have not yet reached nucleotide content equilibrium . It is also theoretically possible that the strength of selection in favor of GC nucleotides increases as genomes become more AT rich ( a from of synergistic epistasis [26] , [39] , [40] ) . If this is the case it is possible that nucleotide content in obligate intracellular bacteria may be at a new selection-mutation equilibrium . The questions of whether the mutational biases of obligate intracellular bacteria resemble those of their other clade members and of whether their nucleotide content is at mutational equilibrium can be addressed by comparing our estimates of GCeq to the GC content of obligatory intracellular bacteria from the same clades . Among the four analyzed clades , two ( Enterobacteriales , and Actinobacteria ) include sequenced obligatory intracellular bacteria . Intriguingly , the GC contents of the obligate intracellular bacteria within these two clades are similar to the values of GCeq which we calculated using clonal pathogen SNP data from their corresponding clades: GCeq values calculated based on SNPs from the Enterobacteriales S . typhi , and Y . pestis were ∼22% , and ∼25% respectively . While other members of the order Enterobacteriales have a current GC content of 43–57% , obligate intracellular bacteria within this order have GC contents of 23–30% . Within the phylum Actinobacteria all genomes have GC content of over 50% except for the only sequenced obligate intracellular bacteria , Tropheryma whipplei that has a GC content of 46% . This is very close to the GCeq calculated for the Actinobacteria MTBC ( ∼42% ) . These results suggest that the GC content of obligate intracellular bacteria corresponds to what we would expect to find in their non-obligate intracellular clade members at equilibrium if nucleotide content had been determined by mutation alone . This anecdotal evidence suggests that even though obligate intracellular bacteria do tend to lose many repair functions the extent of their mutational AT bias resembles that of their other clade members . Additionally , it appears that nucleotide contents of these bacteria are close to mutational equilibrium . The observation that obligate intracellular bacteria across additional clades tend to have high AT contents relative to their non-obligate intracellular clade members , and to have GC contents lower than 50% ( Table S3 , Figure S1 ) may therefore support the generality of AT-biased mutation in bacteria beyond the four phylogenetically diverse broad clades examined in this study .
In this study we used data from five strictly clonal pathogens to analyze the variation in point mutation biases in bacteria . These pathogens are uniquely suitable for such analyses as they can be shown to be evolving under selection that is severely inefficient relative to stochastic processes . Unlike obligate intracellular bacteria that have been evolving under inefficient selection for long evolutionary times and have lost much of their repair pathways these clonal pathogens have experienced only a short-term relaxation in selection efficiency and are likely to have intact repair mechanisms . Their mutational biases should therefore reflect those of their other clade members that are not subject to inefficient selection . We demonstrated that even though these five pathogens belong to four very diverse clades with very different nucleotide contents mutation in all of them is biased towards AT , and that the most frequent mutations are always G/C to A/T transitions . Our results show that variation in nucleotide content in bacteria cannot be explained by variation in mutational biases and that biases in point-mutation appear to be far less diverse among bacteria than was previously assumed .
Salmonella typhi SNPs were taken from the study of Holt et al [52] . MTBC sequences of 89 genes from 107 MTBC strains and one outgroup strain ( M . canettii ) were taken from our previous study [25] . 18 fully and partially sequenced genomes of B . anthracis , 11 of B . mallei and 10 of B . pseudomallei were taken from the Pathema database [53] , together with the completed sequences of the outgroup strains Bacillus thuringiensis and Burkholderia thailandensis . Seven fully sequenced strains of Y . pestis , four fully sequenced strains of Y . pseudotuberculosis and the fully sequenced outgroup strain Y . enterocolitica were downloaded from the NCBI FTP server ( ftp . ncbi . nih . gov ) . Within each dataset one strain was randomly selected to be the reference genome . The protein sequences of the reference genome were compared using the FASTA algorithm [54] to the protein sequences of all the other strains within the dataset including the outgroup strain . In such a way orthologs were identified as the best reciprocal hits . To prevent false identification of orthologs conservation of gene order along the chromosome was also required . More specifically if in one genome gene X is adjacent to genes Y and Z along the chromosome , and in another genome gene X′ is adjacent to genes Y′ and Z′ and if the best reciprocal hit of gene X in the other genome is gene X′ , gene X′ will be considered the ortholog of gene X only if genes Y′ and Z′ are also gene Y and Z's reciprocal best hits . Multiple sequence alignments ( MSAs ) were created at the protein level for genes for which orthologs could be identified in all of the strains within the dataset and in the outgroup strain . The MSAs were created using the clustalW alignment program . DNA/codon level MSAs were then created based on the protein level alignments by threading the DNA sequences unto the protein alignment . SNPs were extracted from these MSAs and the identities of the ancestral and derived alleles ( polarization ) were determined according to the outgroup strain sequence . To prevent false identification of SNPs due to misalignments we excluded SNPs from genes with more than 10 SNPs from further analyses . In a similar manner the intergenic regions of the reference genome were compared at the DNA level to the intergenic regions of all other strains and of the outgroup . Intergenic sequences were considered orthologous if they were reciprocal best hits and if they could be aligned across their entire sequence . MSAs were created at the DNA level for intergenic sequences for which orthologs could be identified in all strains . To prevent false identification of SNPs due to misalignments ( a problem that seemed to affect intergenic regions in particular ) we excluded SNPs from intergenic sequences with more than 10 SNPs from further analyses . In the cases of B . anthracis and B . mallei this left us with very few SNPs when an outgroup strain was used . Therefore in these two datasets the intergenic SNPs were not polarized using the outgroup . Instead we assumed that the most frequent allele within the SNP is the ancestral allele , while the less frequent one is derived . An outgroup strain was used to polarize the intergenic SNPs in the remaining datasets . In order to account for the unequal nucleotide content of the five different lineages we normalized the counts of the mutations from A/T to G/C , C/G , or T/A by multiplying them by , where #GCsites and #ATsites are the current genome wide number of GC or AT sites at the considered site category . In this way we determine the expected number of such mutations under equal GC and AT contents . In order to calculate the relative rates of each possible pairwise mutation each of the resulting counts ( unaltered in the case of mutations from G/C , and normalized in the case of mutations from A/T ) was multiplied by 100 and divided by the sum of these counts . From the polarized SNPs calculating the number GC→AT and AT→GC changes ( #GC→AT and #AT→GC ) is straightforward . The rates of the two types of changes were calculated separately for intergenic , synonymous and non-synonymous sites as:Where #GCsites and #ATsites are the current genome wide number of GC or AT sites at this site category in a randomly selected strain of the considered lineage . In order to calculate the current genome wide number of GC and AT sites for non-synonymous and synonymous sites for each genome we classified sites into synonymous and non-synonymous based on the method suggested by Nei and Gojobori [35] . According to this method sites will be considered entirely synonymous if no changes in them can lead to an amino acid change and will be considered entirely non-synonymous if all changes in them will cause an amino acid change . For sites in which some changes may change the amino acid while others will not the site is considered partially synonymous and partially non-synonymous according to the proportion of the changes that will lead to an amino acid change . We added to the relative GC or AT count of the sites category the proportion of the site which is attributable to that category . For example , if a site is 1/3 synonymous and 2/3 non-synonymous and the current base in this site is a C we added 1/3 of a count to #GCsites of the synonymous sites and 2/3 of a count to #GCsites of the non-synonymous sites . The GC contents we calculated for non-synonymous and synonymous sites were also used to draw Figure 1B . Next , the expected equilibrium GC content based on these mutational rates ( GCeq ) was calculated as [1] , [8]: 1000 values were sampled from the Poisson distribution once with a mean of #GC→AT and once with a mean #AT→GC . This was done using the R program , rpois . The resulting values were sorted and used to estimate 95% confidence intervals for #GC→AT and #AT→GC . They were also used to recalculate GCeq 1000 times and the resulting GCeq values were sorted and used to estimate the 95% confidence intervals for GCeq . dN/dS calculations were performed using the method of Nei and Gojobori [35] . dN/dS estimates were calculated for the entire genome rather than per gene . If in all considered genes we found ns non-synonymous SNPs and s synonymous SNPs and in the genome there are N nonsynonymous sites and S synonymous sites: Repair genes were identified based on genome annotation for five close relatives of the five pathogens , for which selection is efficient . Genes annotated as putative or hypothetical were excluded . For Y . pestis the close relative used was Y . pseudotuberculosis . For B . mallei the close relative used was B . pseudomallei . For S . typhi the close relative used was S . typhimurium , for B . anthracis the close relative used was B . thuringiensis . For MTBC this analysis was problematic as M . canettii which was used as an outgroup for MTBC strains in this study is not yet fully sequenced . We were therefore forced to use a more distantly related outgroup , M . marinum , which is far more diverged from MTBC than the other outgroups are from their pathogens . The orthologs of the repair genes from the outgroups were identified in the pathogens and the sequences of the pathogen versions of the genes were compared to those of the outgroups . The results of this analysis are summarized in Table S1 . | Natural selection sorts through the variability generated by mutation and biases evolution toward fitter outcomes . However , because mutation is itself not entirely random it can also bias the direction of evolution independently of selection . For instance , it is often assumed that the extreme variation observed in nucleotide content among bacteria ( from ∼20% to ∼80% GC ) is predominantly driven by extreme differences in mutational biases towards or away from GC . Here , we show that bacterial lineages that recently developed clonal , pathogenic lifestyles evolve under weak selection and that polymorphisms in these bacteria can be used as a fair proxy for mutational spectra . We analyze large sequence datasets from five clonal pathogens in four diverse bacterial clades spanning most of the range of genomic nucleotide content . We find that , surprisingly , mutation is AT-biased in every case to a very similar degree and in each case it is dominated by transitions from C/G to T/A . This demonstrates that mutational biases are far les variable than previously assumed and that variation in bacterial nucleotide content is not due entirely to mutational biases . Rather natural selection or a selection like process such as biased gene conversion strongly affect nucleotide content in bacteria . | [
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| 2010 | Evidence That Mutation Is Universally Biased towards AT in Bacteria |
Gastro-intestinal nematodes in ruminants , especially Haemonchus contortus , are a global threat to sheep and cattle farming . The emergence of drug resistance , and even multi-drug resistance to the currently available classes of broad spectrum anthelmintics , further stresses the need for new drugs active against gastro-intestinal nematodes . A novel chemical class of synthetic anthelmintics , the Amino-Acetonitrile Derivatives ( AADs ) , was recently discovered and the drug candidate AAD-1566 ( monepantel ) was chosen for further development . Studies with Caenorhabditis elegans suggested that the AADs act via nicotinic acetylcholine receptors ( nAChR ) of the nematode-specific DEG-3 subfamily . Here we identify nAChR genes of the DEG-3 subfamily from H . contortus and investigate their role in AAD sensitivity . Using a novel in vitro selection procedure , mutant H . contortus populations of reduced sensitivity to AAD-1566 were obtained . Sequencing of full-length nAChR coding sequences from AAD-susceptible H . contortus and their AAD-1566-mutant progeny revealed 2 genes to be affected . In the gene monepantel-1 ( Hco-mptl-1 , formerly named Hc-acr-23H ) , a panel of mutations was observed exclusively in the AAD-mutant nematodes , including deletions at intron-exon boundaries that result in mis-spliced transcripts and premature stop codons . In the gene Hco-des-2H , the same 135 bp insertion in the 5′ UTR created additional , out of frame start codons in 2 independent H . contortus AAD-mutants . Furthermore , the AAD mutants exhibited altered expression levels of the DEG-3 subfamily nAChR genes Hco-mptl-1 , Hco-des-2H and Hco-deg-3H as quantified by real-time PCR . These results indicate that Hco-MPTL-1 and other nAChR subunits of the DEG-3 subfamily constitute a target for AAD action against H . contortus and that loss-of-function mutations in the corresponding genes may reduce the sensitivity to AADs .
Throughout the world , successful livestock production of ruminants is hampered by gastro-intestinal nematodes . Haemonchus contortus in particular is responsible for substantial losses to the global sheep industry [1] . Haemonchus contortus is a blood-feeding nematode that inhabits the abomasum of sheep , producing in acute infections , severe anemia that can lead to the death of infected animals . Broad spectrum chemotherapy against gastro-intestinal nematodes is restricted to 3 anthelmintic classes: the benzimidazoles , such as albendazole and oxfendazole , the imidazothiazoles , including levamisole and tetramisole and the macrocyclic lactones ( e . g . ivermectin , moxidectin , abamectin and doramectin ) . The increased usage of anthelmintics has contributed to the spread of resistant nematodes with increasing reports of nematodes insensitive to most if not all of the available classes of anthelmintics [2]–[10] . In some countries in the southern hemisphere , sheep farming is severely endangered by such populations [4] , further increasing the need for a new class of anthelmintic [11] . Recently , a new class of compounds , the Amino-Acetonitrile Derivatives ( AADs ) was discovered [12] with good tolerability in mammals and promising activity against drug-resistant nematodes . The AADs are low molecular mass compounds bearing different aryloxy and aroyl moieties on an amino-acetonitrile core [13] . Further studies [14] have allowed the selection of a drug candidate , AAD-1566 ( monepantel ) . In order to investigate the mode of action of this new class of compounds , AAD-resistant Caenorhabditis elegans mutants were generated by EMS mutagenesis . Classical forward genetics revealed that the majority of recuperated AAD-resistant mutants carried mutations in the gene acr-23 , a member of the nematode-specific DEG-3 subfamily of nicotinic acetylcholine receptor ( nAChR ) alpha subunits [12] . Preliminary data had already indicated an involvement of similar acetylcholine receptors in AAD action against H . contortus [12] . Here we report the identification of the gene monepantel-1 ( Hco-mptl-1 , formerly named Hc-acr-23H ) and other members of the DEG-3 subfamily of ACR genes from H . contortus . A panel of different mutations , mis-splicing in particular , in Hco-mptl-1 transcripts from AAD-resistant worms indicates that Hco-MPTL-1 is a target for monepantel action against H . contortus .
The drug-susceptible H . contortus CRA ( Hc-CRA ) was received in 1984 from the Veterinary Institute of Onderstepoort , Republic of South Africa and has since been passaged in sheep 75 times . The H . contortus Howick isolate ( Hc-Howick ) was received from the same institute in 2001 . This is a multidrug-resistant isolate that is completely resistant to albendazole , rafoxanide , morantel , ivermectin and trichlorfon [6] , [15] . The isolate has been passaged in sheep 9 times since being received . The mutant lines Hc-CRA AADM and Hc-Howick AADM were selected from Hc-CRA and Hc-Howick , respectively , by in vitro exposure to increasing doses of AAD-1566 alternatively with propagation in sheep [12] . Haemonchus contortus isolates were propagated in 3–6 month old sheep ( ‘Blanc des Alpes’ ) , which had been experimentally infected with the nematode . The sheep were kept in groups of 4 and housed indoors off pasture to prevent natural infection . After 14 days , they were transferred to individual cages . Starting on day 21 after infection , eggs were collected from homogenized feces and filtered several times through a 32 µm sieve . Eggs were further purified by floating on 50% sucrose solution , rinsed with water and counted microscopically . Sheep studies were performed with approval of a Cantonal animal welfare committee ( permit number FR 25A/05 ) . Anthelmintic efficacy tests in sheep were performed according to the guidelines of the World Association for the Advancement of Veterinary Parasitology [16] . Each animal was infected intraruminally on study day −21 with 3000 L3-larvae of H . contortus ( cultivated in coprocultures ) . On study day 0 , the sheep were treated with single anthelmintics or combinations thereof as an oral drench at the recommended dose . A sheep was classified as ‘cured’ when no more eggs were counted in the feces and no adults were found in the abomasum at necropsy . Adult worms were recovered from the abomasum of freshly euthanized sheep , washed in Hank's Buffered Salt Solution ( HBSS; Invitrogen ) and immediately shock-frozen in liquid nitrogen . While frozen , the worms were crushed with a Kontes pellet pestle ( Fisher Scientific ) . The powder was resuspended in 600 µl of lysis buffer ( 10 mM Tris pH 7 . 5 , 1 mM EDTA , 100 mM NaCl , 0 . 5% SDS , 100 µg/ml RNase A ) and incubated at 37°C for 1 hour . Pronase ( 100 µg/ml ) was added to the mixture and the tubes were incubated at 37°C until the solution became clear . The samples were extracted with equal volumes of phenol∶chloroform ( 1∶1 ) and chloroform . The DNA was ethanol precipitated , washed and resuspended in 50 µl of Tris-Cl ( pH 7 . 5 ) . For RNA extraction , worms were homogenized in TRIzol and processed according to the instructions of the supplier ( Invitrogen ) . To remove DNA contamination , the RNA samples were treated with a TURBO DNA-free kit ( Ambion ) . To generate cDNA , 1 µg of total RNA was reverse transcribed to cDNA using a d ( T ) 30 primer and a Moloney Murine Leukemia Virus Reverse Transcriptase ( MMLV RT; SMART cDNA library construction kit from Clontech ) . A total of 4 µg of mRNA was isolated from a mixture of male and female Hc-CRA using a Oligotex kit from Qiagen . A cDNA library was constructed with the ZAP-cDNA Cloning kit and Gigapack III Gold packaging kit . The library was screened at high stringency ( hybridization at 65°C in 5×SSC , 5× Denhardt's solution , 0 . 1% SDS , 0 . 1% sodium pyrophosphate , 100 µg/ml salmon sperm DNA; final wash at 60°C in 0 . 2×SSC , 0 . 1% SDS ) with a 32P-labeled 456 bp fragment of Hco-mptl-1 . This fragment had been amplified from cDNA with the primers Hco-mptl-1_frw3 and Hco-mptl-1_rev1 and cloned into pCR®2 . 1-TOPO® ( Invitrogen ) . Positive phages were taken through 3 rounds of plaque purification with this probe and the phagemid ( pBluescript SK+ ) was excised using the ExAssist helper phage in the E . coli SOLR strain . Inserts were sequenced in both directions with standard M13 forward and reverse primers and the internal primers Hco-mptl-1_frw4 and Hco-mptl-1_rev3 . The sequences were read and assembled using 4Peaks ( by A . Griekspoor and T . Groothuis; http://mekentosj . com ) . The primers used for PCR-amplification , real-time PCR or for cDNA first strand synthesis of H . contortus nAChR genes are summarized in Table S1 . For nested PCR on cDNA with spliced leader ( SL ) primers , the primary products were diluted 50-fold and 2 µl were used for the second PCR with nested primers . The annealing temperature was fixed at 55°C for cDNA and 58°C for genomic DNA template . PCR products were gel purified using the NucleoSpin® ExtactII kit ( Macherey-Nagel ) and cloned into either pGEM-T easy ( Promega ) or pCR®2 . 1-TOPO® ( Invitrogen ) . Plasmid DNA was purified using the QIAprep Spin Miniprep Kit ( Qiagen ) and sequenced using the standard primers M13 forward and reverse and , if necessary , an additional internal primer to cover long products . For rapid amplification of cDNA ends by PCR ( RACE-PCR ) , an internal reverse primer ( Table S1 ) was combined with splice leader sequence ( 1 or 2 ) to obtain the 5′ UTR , or an internal forward primer combined with a poly-dT primer for the 3′ UTR of the transcript . For real-time PCR , 1 µg of total RNA from adult H . contortus was used to synthesize first-strand cDNA by random priming using Superscript II reverse transcriptase ( Invitrogen ) in a final volume of 20 µl following the manufacturer's instructions . Reverse-transcribed material corresponding to 40 ng RNA was amplified in 25 µl MESA GREEN qPCR MasterMix Plus for SYBR Assay ( Eurogentec ) by using the ABI SDS7000 Sequence Detection System under the following conditions: 1 cycle of 95°C for 15 minutes followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute . The primer pairs used for the amplification are listed in Table S1 and target the following genes: β-tubulin , Hco-mptl-1 , Hco-des-2H and Hco-deg-3H . Three independent total RNA extractions were performed and each was tested in duplicate . Relative expression values were calculated according to Livak and Schmittgen [17]; a 136 bp region within the phosphoglucose isomerase gene was used for normalization , a 122 bp region within the β-tubulin gene was used as a ( presumably ) non-affected control , and no reverse transcriptase and no template reactions as negative controls . The specificity and identity of individual amplicons were verified by melt curve analysis and visualized on a 2% agarose gel .
In order to study the mode of action of the AADs , we used 2 mutant isolates , Hc-CRA AADM and Hc-Howick AADM selected from parent Hc-CRA and Hc-Howick isolates , respectively . Both mutant isolates showed reduced sensitivity to AAD-1566 ( monepantel ) in vitro [12] . To test whether the observed loss of susceptibility to AAD-1566 in vitro was relevant for the situation in vivo , Hc-CRA , Hc-Howick and their AADM derivatives were challenged in vivo with single compounds or combinations thereof; AAD-1566 and the commercial compounds were applied at their recommended doses to sheep . Sheep were infected intraruminally with Hc-CRA AADM . Following treatment with AAD-1566 at the proposed minimum dose rate of 2 . 5 mg/kg body weight [18] eggs were found in the feces and adults seen at necropsy ( Table 1 ) . Likewise , nematode eggs and adults were also found in sheep infected with Hc-Howick AADM larvae when treated either with AAD-1566 or albendazole or a combination of AAD-1566 and ivermectin ( Table 1 ) . The offspring from the Hc-Howick AADM isolate that survived the AAD-1566 and ivermectin treatment were cultured and challenged with albendazole and levamisole over the following generations ( data not shown ) . Finally , Hc-Howick AADM was able to survive a full simultaneous in vivo treatment with albendazole , levamisole , ivermectin and AAD-1566 , administered at their recommended doses ( Table 1 ) . Thus the reduction of sensitivity to AAD-1566 induced in vitro was also relevant in vivo for the mutant lines . The AAD-mutant H . contortus apparently did not show any alterations in motility , infectivity to sheep ( determined by the numbers of adult H . contortus recovered at necropsy ) or egg production , and did not exhibit any phenotype with respect to the ultrastructure ( by electron microscopy ) of the cuticle , head or tail . The putative target of the AADs in C . elegans , ACR-23 , is a member of the nematode-specific DEG-3 family of nAChR alpha subunits . A tblastn search [19] with DEG-3 members against the ( incomplete ) H . contortus genome database ( http://www . sanger . ac . uk/Projects/H_contortus ) returned strong hits from different contigs , coding for a total of 6 different DEG-3 subfamily nAChR subunit homologues . However , the lack of overlap between the different contigs precluded the assembly of full length coding sequences . The predicted H . contortus proteins were named Hco-MPTL-1 ( accession number: contig_0024907; contig_0033952; contig_0079482; haem-240m02 . q1k; contig_0053297; contig_069357 ) , Hco-DES-2H ( contig_0064641 ) , Hco-DEG-3H ( contig_0075200; contig_0075201 ) , Hco-ACR-24H ( contig_0003482; contig_0064300 ) , Hco-ACR-5H ( contig_0106281; contig_0023143 ) and Hco-ACR-17H ( contig_0101516; contig_0101514 ) . For Hco-MPTL-1 , Hco-DES-2H and Hco-DEG-3H , full-length coding sequences were obtained by cDNA library screening or RACE-PCR , respectively ( see below ) . Figure 1 shows the position of the H . contortus sequences in a phylogenetic tree of the DEG-3 subfamily nAChR from C . elegans , C . briggsae and Brugia malayi . Note that an incomplete sequence of Hco-MPTL-1 was previously named Hc-ACR-23H [12] . To obtain the full length coding sequence of the Hco-mptl-1 gene , a lambda phage cDNA library from mRNA of adult H . contortus was constructed and screened at high stringency with a radioactive probe from a partial Hco-mptl-1 sequence . After 3 rounds of selection , a clone with the full-length coding sequence , Hco-mptl-1 , was isolated and sequenced . The Hco-mptl-1 mRNA is composed of at least 17 exons and 16 introns ( 1992 bp ) with a short 5′ UTR and 3′ UTR ( 21 bases and 42 bases , respectively ) . The transcript is trans-spliced as the splice leader 1 ( SL1 ) is present at its 5′ end . Interestingly , a start codon ( AUG ) is present in exon 1 but is followed after 8 amino acids by a stop codon in frame ( UGA ) . This is a feature found in many other organisms [20]–[22] and it is assumed to play a role in the regulation of translation efficiency . In most cases , upstream AUGs decrease mRNA translation efficiency and have a strong , negative regulatory effect [23] . The longest open reading frame ( ORF ) in the Hco-mptl-1 gene is obtained when the translation is initiated at the second AUG codon in exon 3 and extends over 1695 bases . Overlapping long range PCR was performed in order to estimate the total size of Hco-mptl-1 . The gene was found to be approximately 18 . 5 kb long with a large intron ( about 7 kb ) between exons 3 and 4 ( see below ) . The predicted Hco-MPTL-1 protein consists of 564 amino acids and possesses motifs typical for Cys-loop ligand-gated ion channels , including an N-terminal signal peptide of 18 amino acids [24] , 4 transmembrane domains and the Cys-loop ( 2 cysteines separated by 13 amino acids ) . Loops A to F , which are involved in ligand binding [25] are also present in the protein ( Figure S1 ) . In loop C , there are 2 adjacent cysteines , defining Hco-MPTL-1 as a nAChR alpha subunit . As determined by PCR with gene-specific primers on genomic DNA , Hco-mptl-1 ( Hco-mptl-1_frw6 and Hco-mptl-1_rev6 ) , Hco-des-2H ( Hco-des2_frw8 and Hco-des2_rev8 ) and Hco-deg-3H ( Hco-deg3_frw1 and Hco-deg3_rev1 ) are present in the Hc-CRA and Hc-Howick parental isolates ( Figure 2 ) . Of the 3 products obtained for the Hco-mptl-1 gene , the smallest one ( 1478 bp ) corresponded to Hco-mptl-1 . The same primers were used for reverse transcriptase PCR on total RNA , showing that all 3 genes were expressed and spliced in L3-larvae as well as in adult nematodes ( Figure 2 ) . The predicted Hco-MPTL-1 protein shares 48 . 5% identity and 66 . 8% similarity with C . elegans ACR-23 and 60 . 2% identity and 70 . 7% similarity with C . elegans ACR-20 . The novel H . contortus nAChR was originally named Hc-ACR-23H based on a partial sequence that was most closely related to C . elegans ACR-23 [12] . In the light of the full-length sequence , this nomenclature seems to have been premature since the Haemonchus nAChR turned out to be more closely related to C . elegans ACR-20 ( Figure 1 ) . In the absence of a complete record of ACR paralogues from H . contortus , and in analogy to levamisole-insensitive ( lev- ) mutants in C . elegans [26] , we propose to name the gene monepantel-1 ( Hco-mptl-1 ) due to its apparent involvement in monepantel sensitivity . In order to compare the Hco-mptl-1 sequences from the AAD-susceptible isolates and their AAD-mutant progeny , primers were designed at each extremity of the ORF ( Hco-mptl-1_5′_frw3 and Hco-mptl-1_3′end_rev1 ) and the full length Hco-mptl-1 coding sequences amplified from cDNA from adults . A product of about 1800 bp was obtained for all isolates apart from the Hc-CRA AADM , which produced a shorter product of 1650 bp ( Figure 3B ) . Sequencing clones of the latter revealed that they lacked either exon 4 or exon 15 ( Figure 4 , Hco-MPTL-1-m2 and m3 ) . This was confirmed with primers flanking either exon 4 ( Hco-mptl-1_5′_frw2 and Hco-mptl-1_rev8; Figure 3C ) or exon 15 ( Hco-mptl-1_frw6 and Hco-mptl-1_rev6; Figure 3D ) . PCR with a SL1 forward primer and a reverse primer in the Hco-mptl-1 coding sequence ( Hco-mptl-1_rev1 , product of about 1200 bp; Figure 3A ) also produced shorter products ( 1000 bp and 850 bp; Figure 3A ) from Hc-CRA AADM . The 850 bp product turned out to lack both exon 2 and exon 3 while the 1 kb product lacked exon 4 ( Figure 4 , Hco-MPTL-1-m1 and m2 ) . The 1200 bp product was cloned from Hc-CRA AADM but contained only silent mutations compared to Hc-CRA . Loss of exon 4 caused a frame-shift leading to a premature stop of translation and a predicted Hco-MPTL-1 protein truncated at amino acid 19 ( Figure 4 ) . Loss of exon 15 also led to a premature stop codon that truncated the Hco-MPTL-1 protein at amino acid 448 ( Figure 4 ) . The mutation Hco-MPTL-1-m1 ( loss of exon 2 and 3 ) did not cause a frame-shift but the loss of the signal peptide and the first 39 amino acids of the extracellular loop . To understand the molecular basis of exon loss in the Hc-CRA AADM isolate , PCR primers Hco-mptl-1_frw8 and Hco-mptl-1_rev6 ( Table S1 ) were designed to flank the mis-spliced exon 15 . PCR was performed using genomic DNA as a template . Sequencing of cloned PCR products revealed a 10 bp deletion upstream of exon 15 in the Hc-CRA AADM mutant that encompasses the predicted splice acceptor site ( UUUCAG; Figure 5 ) . Presumably , the splicing machinery is not able to identify the end of intron 14 and uses the next splice acceptor site ( intron 15 ) . This would explain why exon 15 is skipped ( Figure 4 , Hco-MPTL-1-m3 ) . Joining of exon 14 to exon 16 causes a frame-shift leading to a premature stop codon . With primers flanking exon 4 ( Hco-mptl-1_frw10/gDNA and Hco-mptl-1_rev8; Table S1 ) , a 323 bp deletion was detected consisting of the end of intron 3 ( 206 bp ) and most of exon 4 ( 117 bp ) . Again , loss of the predicted splice acceptor site at the end of intron 3 may explain the observed loss of exon 4 ( Figure 4 , Hco-MPTL-1-m2 ) , since the splicing machinery will use the next available splice acceptor site ( intron 4 ) , joining exon 3 and exon 5 . The resulting frame-shift causes a premature stop at codon 19 ( TGA ) , terminating translation after the signal peptide ( Figure 4 , Hco-MPTL-1-m2 ) . No obvious mutations such as mis-spliced exons were detected in the Hc-Howick AADM isolates . When sequencing the Hco-mptl-1 coding regions ( SL1 and Hco-mptl-1_rev6 ) from both susceptible and AAD-1566-mutant Howick isolates , a transversion from G277 to T in exon 6 of the Hco-mptl-1 gene was observed that led to a premature stop codon ( E93*; Figure 6 ) . Direct sequencing of RT-PCR products ( using Hco-mptl-1_frw4 and Hco-mptl-1_rev1 primers ) revealed that about 80% of the Hc-Howick AADM cDNAs , as estimated from the electropherogram [27] , carried a T at position 277 ( Figure 6A ) . The point mutation underlying E93* creates a restriction site for the endonuclease BfrI ( recognition site: CTTAAG ) that lent itself for RFLP analysis . Only the PCR product amplified from cDNA of Hc-Howick AADM was digested by BfrI ( Figure 6B ) . As expected from the sequencing , a small proportion ( about 20% ) of the product was not cut , indicating that not all of the Hco-mptl-1 genes from Hc-Howick AADM population carried the G277T mutation . When this BfrI-unrestricted product from Hc-Howick AADM was excised from an agarose gel , cloned and sequenced , a further polymorphism was detected that led to skipping of exon 8 ( Figure 4 , Hco-MPTL-1-m6 ) . As this exon is very short ( 22 bases ) , it was impossible to discriminate between mutant and parental wild type PCR products ( Figure 3 ) . Loss of exon 8 causes a frame-shift leading to a premature stop codon and a predicted Hco-MPTL-1 protein truncated at amino acid 166 ( Figure 4 ) . A minority of the Hco-mptl-1 PCR products obtained from Hc-Howick AADM did not contain any major mutations . These sequences could come from AAD-susceptible individuals within the H . contortus Howick AADM populations or from AAD-mutant individuals that carry other , yet to be identified mutations . As the DEG-3 subfamily gene Hco-des-2H has also been implicated in AAD action in H . contortus [12] , we cloned and sequenced the full-length Hco-des-2H coding sequence from H . contortus cDNA by RACE-PCR . Using primers NheI_des2_frw1 and XhoI_des2_rev1 ( Table S1 ) , 2 products were obtained from the four H . contortus isolates . Cloning and sequencing revealed the smaller transcript to lack 168 bases coding for part of the internal loop between TM3 and TM4 , possibly indicating alternative splicing of the Hco-des-2H gene . The predicted protein ( full version ) consists of 534 amino acids and shows 69% identity and 80% similarity with C . elegans DES-2 . Hco-DES-2H possesses motifs typical for Cys-loop ligand-gated ion channels ( 4 transmembrane domains , a Cys-loop and loops A to F ) and the 2 adjacent cysteines in the C-loop , defining Hco-DES-2H as a nAChR alpha subunit ( Figure S2 ) . When comparing Hco-des-2H coding sequences ( Table 2 ) obtained from Hc-CRA and Hc-CRA-AADM , respectively Hc-Howick and Hc-Howick-AADM , no mutation was found to correlate perfectly with AAD-susceptibility . Nevertheless , using the SL1 primer and 2 internal reverse primers ( Hco-AcRa_rev3 and Hco-AcRa_rev2 ) in a nested PCR experiment , an insertion of 135 bp was detected in the 5′ UTR of the Hco-des-2H gene from the Hc-CRA AADM and Hc-Howick AADM isolates , creating 2 additional start codons . Both start codons are followed by an early stop codon in frame . In the C . elegans genome , DES-2 and DEG-3 are encoded on the same operon and both subunits are co-expressed to form a functional channel [28] , [29] . Performing RACE-PCR on H . contortus ( adults ) cDNA we identified Hco-deg-3H encoding a protein of 569 amino acids that shows 68 . 4% identity and 78% similarity to C . elegans DEG-3 . Again , Hco-DEG-3H carried all the hallmarks of nAChR alpha subunits ( Figure S3 ) . No mutations were detected for Hco-deg-3H in the AAD-mutant H . contortus isolates compared to the parental isolates . The Hco-deg-3H mRNA carries a spliced leader type 2 ( SL2 ) sequence at its 5′ end . To test whether Hco-des-2H and Hco-deg-3H are also on an operon in H . contortus , a long range PCR was performed using a forward primer designed at the end of Hco-des-2H ( Hco-des2_frw11 ) and a reverse primer at the beginning of Hco-deg-3H ( Hco-deg3_2r ) . A band of approximately 6 kb was obtained for the 4 isolates confirming that Hco-des-2H and Hco-deg-3H are encoded on a single operon . However , the distance between the 2 genes is 10 times larger in H . contortus than in C . elegans . The steady-state mRNA levels of the DEG-3 subfamily acetylcholine receptor genes Hco-mptl-1 , Hco-des-2H and Hco-deg-3H were quantified by real-time PCR ( Figure 7 ) . For the Hc-CRA AADM isolate , a small , statistically not significant ( p>0 . 05 ) decrease in the mRNA level was observed for Hco-mptl-1 ( −21% ) and Hco-des-2H ( −16% ) . In contrast , the relative mRNA level of the Hco-deg-3H gene was higher ( 69%; p<0 . 01 ) in this mutant . For Hc-Howick AADM , a significant ( p<0 . 01 ) down-regulation of the 3 measured DEG-3 subfamily members was observed: −70% for Hco-mptl-1 , −77% for Hco-des-2H and −92% for Hco-deg-3H . The relative expression level of the β-tubulin gene was measured in both mutant isolates as a ( presumably ) non-affected control . No statistically significant changes were observed .
A new chemical class of synthetic anthelmintics , the AADs , was recently discovered [12] . The AADs exhibit excellent efficacy against various species of livestock-pathogenic nematodes and more importantly , can control nematodes resistant to the currently available anthelmintics [30] , [31] . To get insights into the mode of action of the new AADs , a classical ‘forward genetic’ screen for AAD-resistant C . elegans mutants was performed previously [12] . As a result , AADs were proposed to act through the nAChR ACR-23 , a member of the nematode-specific DEG-3 subfamily [32] . By screening the currently available ( but incomplete ) H . contortus genome sequence for DEG-3 nAChR homologues , it was found that this subfamily is conserved between C . elegans and H . contortus . Six paralogous proteins out of 8 in C . elegans or C . briggsae were identified ( Figure 1 ) , in contrast to only 2 in the genome of B . malayi [33] . The AADs possess a unique mode of action: the nAChR subunits involved in AAD action are different from those targeted by imidazothiazoles [34] , [35] and there is no cross-resistance between the 2 chemical classes [12] . Two independent AAD-mutant H . contortus lines were used to screen for mutations in ACR genes of the DEG-3 subfamily . Two genes were found to be affected: The H . contortus des-2 homologue Hco-des-2H , where all AAD-mutant H . contortus carried an insertion in the 5′ UTR introducing 2 additional , out-of-frame start codons , and the gene monepantel-1 ( Hco-mptl-1 ) , for which a panel of different mutations were detected in AAD-mutant ( AADM ) H . contortus . Apart from 1 nonsense mutation discovered in Hc-Howick AADM nematodes ( Hco-MPTL-1-m5; Figure 4 ) , the detected mutations all involved mis-splicing resulting in loss of exon ( s ) from the mRNA as indicated by shortened reverse transcriptase-PCR products ( Figure 3 ) . This unusual mechanism has not been described before in H . contortus . In the genetic screen performed on AAD-resistant C . elegans [12] , 2 mutants bearing a G-to-A transition of the conserved G nucleotide in the 3′ splice acceptor sites of either the second or third introns were described; these mutations are predicted to cause an increase in the size of the mRNA due to the lack of splicing of the affected intron . In another study [36] , a single base pair change in the first intron of the lev-8 subunit gene was identified in a partially levamisole-resistant C . elegans mutant . This mutation leads to alternative splicing and introduction of a premature stop codon . In the case of mutations Hco-MPTL-1-m2 ( loss of exon 4 ) , Hco-MPTL-1-m3 ( loss of exon 15 ) or Hco-MPTL-1-m6 ( loss of exon 8 ) , exon skipping creates a frame-shift that leads to a premature stop codon ( Figure 4 ) . These mutations , including the Hco-MPTL-1-m5 ( stop codon ) are predicted to result in a truncated , non-functional Hco-MPTL-1 protein and/or , if the mutant mRNA is recognized by the nonsense-mediated mRNA decay ( NMD ) machinery [37] , degradation of the mRNA ( some known genes of the NMD machinery in C . elegans have orthologues in the H . contortus genome; Rufener and Mäser , unpublished ) . Measuring the expression levels of the 3 DEG-3 subfamily genes Hco-mptl-1 , Hco-des-2H and Hco-deg-3H in adult H . contortus , we found statistically significant differences in the steady state level of mRNA in AAD mutant worms . In the Hc-CRA AADM isolate , a significant increase of the Hco-deg-3H transcript was observed . A possible explanation may be that this compensates for the loss of the Hco-MPTL-1 subunit since no more full-length Hco-mptl-1 transcript was detectable in Hc-CRA AADM . In the case of Hc-Howick AADM , all 3 nAchR genes were down-regulated compared to Hc-Howick . Although we cannot give a result-based explanation , we interpret the finding that the expression of DEG-3 subfamily nAChR genes is affected in H . contortus as further evidence for the involvement of these genes in AAD susceptibility . The mutations Hco-MPTL-1-m1 ( loss of exon 2 and 3 ) and Hco-MPTL-1-m4 ( partial loss of exon 4 and 15 ) did not cause a frame-shift , but the loss of the signal peptide and the first 39 amino acids of the extracellular loop for the first mutation , and a truncated protein for the second mutation . Interestingly , 1 of the previously identified AAD-resistant C . elegans mutants also carried a mutation in the signal peptide of the Cel-ACR-23 protein [12] . Receptors are assembled in the endoplasmic reticulum ( ER ) [38] and interference with the signal peptide could result in mis-localization of the protein or in improper interactions with ER-resident , ACR-specific chaperones [25] , [39]–[41] . Furthermore , it is known that the expression , assembly and transport to the surface of ACR subunits is subject to stringent quality controls that guarantee the functional competence of the final product [42]–[44] . Truncated nAChR proteins are likely to be targeted to the lysozyme and degraded . In summary , we have detected a large number of different mutations affecting the Hco-mptl-1 gene and transcript , respectively , from AAD mutant H . contortus ( Table 2 ) . For the benzimidazoles , a variety of different mutations in the target protein ß-tubulin are associated with drug resistance , 3 so far from H . contortus [15] , [45] , [46] and many more from phytopathogenic fungi [47] . These are point mutations , that are thought to interfere with benzimidazole binding while preserving microtubular function . The mutations have less drastic effects on the predicted protein than those described here for Hco-mptl-1 of H . contortus . At present , we do not know whether Hco-mptl-1 is an essential gene in H . contortus , but our findings indicate that it may not be . There were no mutations in common between H . contortus CRA-AADM and Howick-AADM , indicating that the genetic screen was not saturated . However , for Hco-des-2H , an insertion of 135 bp creating 2 additional start codons was present in the 5′ UTR from both AADM isolates . While Hco-des-2H mRNA levels were significantly lower in Hc-Howick AADM ( compared to Hc-Howick ) , no effect was observed on Hco-des-2H expression in Hc-CRA AADM . It is interesting to note that in C . elegans , mutant worms lacking a functional DES-2 did not exhibit any AAD resistance [12] . The in vitro protocol used to breed AAD-mutant H . contortus is very focused using a large number of individuals and a border line subcurative exposure concentrations over extended time period . This protocol is different from the situation in the field , e . g . after multiple generations exposed to subcurative treatment in sheep , we have so far not been able to obtain AAD-resistant H . contortus ( Pradervand and Kaminsky , unpublished data ) . In conclusion , several independent mutations were found in the Hco-mptl-1 gene from H . contortus mutants with reduced sensitivity for monepantel , implicating Hco-MPTL-1 as a likely target for AAD action against H . contortus . However , this hypothesis is difficult to test since H . contortus is not readily amenable to genetic manipulation [48] . The AADs are very well tolerated by sheep or cattle [14] . The absence of DEG-3 subfamily acetylcholine receptors in mammals might explain the selective toxicity of AADs to nematodes . | Worldwide , sheep and cattle farming are threatened by anthelmintic-resistant gastro-intestinal nematodes . A novel chemical class of synthetic anthelmintics was recently discovered , the Amino-Acetonitrile Derivatives ( AADs ) , which exhibit excellent efficacy against various species of livestock-pathogenic nematodes and , more importantly , overcome existing resistances to the currently available anthelmintics . Haemonchus contortus , the largest nematode found in the abomasum of sheep and cattle , is a blood-feeding parasite that causes severe anemia that can lead to the sudden death of the infected animal; H . contortus is highly susceptible to AADs . In order to elucidate the mode of action of the AADs , we have developed 2 independent H . contortus mutants with reduced sensitivity to monepantel ( AAD-1566 ) . Both mutants were affected in their acetylcholine receptor ( ACR ) genes of the DEG-3 subfamily . In particular , we discovered a panel of mutations in the gene monepantel-1 ( Hco-mptl-1 ) including deletions leading to mis-splicing , insertions and point mutations leading to premature termination of translation of the protein . These findings support the notion that Hco-MPTL-1 and other nAChR subunits of the DEG-3 subfamily are targets of the AADs . The fact that the DEG-3 subfamily of acetylcholine receptors is nematode-specific may explain the good therapeutic index of AADs in mammals . | [
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| 2009 | Haemonchus contortus Acetylcholine Receptors of the DEG-3 Subfamily and Their Role in Sensitivity to Monepantel |
Mycetoma is a devastating , neglected tropical disease characterised by extensive tissue involvement resulting in destruction , deformities and disabilities in the affected patients . The hand is commonly affected by mycetoma thus compromises its functionality and hinder the patient’s daily activities of living . In this communication , we report on 533 patients with hand mycetoma managed over a period of 24 years at the Mycetoma Research Centre , University of Khartoum , Khartoum , Sudan . Eumycetoma was the commonest type of mycetoma ( 83 . 3% ) encountered . Males were predominately affected ( 69 . 2% ) with a sex ratio of 2 . 2:1 . The majority of the patients ( 84% ) were young adult below the age of 40 years old at presentation . The generality of patients ( 86 . 4% ) were from the Sudan mycetoma belt . Children and adolescents ( 28 . 1% ) , farmers ( 18 . 2% ) and workers ( 17 . 4% ) were more frequently affected . The majority of patients ( 67 . 4% ) had disease duration of less than 5 years at presentation . The study , did not document significant history of local trauma , familial tendency , concomitant medical diseases or other predisposing cause for mycetoma in this population . Pain ( 23 . 1% ) was not a disease feature in this series and 52% of patients had past surgery for mycetoma and recurrence . The right hand was affected most ( 60 . 4% ) , and 64% of them had small lesion at presentation . Conventional x-ray was only helpful in patients with advanced disease and the MRI accurately determined the disease extension . Cytological smears , surgical biopsies histopathological examination and grains culture were the principal diagnostic tools for causative organisms’ identification . In the present series it was difficult to determine the treatment outcome due to high patients follow up dropout .
Mycetoma is a highly devastating and distressing neglected tropical disease characterized by painless subcutaneous masses with multiple sinuses draining pus and grains [1 , 2] . The disease leads to massive deformities , disabilities with severe adverse bearings on the affected patients [3 , 4 , 5] . The aetiological causative agents are either fungi or actinomycetes and thus it is classified as eumycetoma and actinomycetoma . Madurella mycetomatis is the commonest eumycetoma causative agent , while Streptomyces somaliensis and Nocardia spp . are the common actinomycetes causing actinomycetoma [6 , 7 , 8] . This chronic infection is believed to be caused by subcutaneous inoculation of the causative organisms through minor injuries which remain inactive for years . The infection eventually spread to involve the skin and the deep structures , resulting in destruction , deformity and loss of function , occasionally it can be fatal [9 , 10] . It has enormous socioeconomic effects on the affected communities [11 , 12] . The foot and hand are the most frequently affected sites accounting for 82% of cases [13 , 14] , other parts of the body may be involved such as the knee , arm , leg , head and neck , thigh and perineum . No age is exempted in mycetoma; however , it occurs more frequently in young adult men in the age range 15–30 years and almost 30% of reported patients are children [15] . In order to provide proper treatment , it is important to identify the causative agent and the disease extension and this can be achieved by a battery of investigations [16 , 17 , 18 , 19 , 20] . Treatment of mycetoma is unsatisfactory and recurrence is common [21 , 22] . A combination of surgical excisions and antifungal agents for eumycetoma and antibiotics combination for actinomycetoma are currently the recommended standard treatment [23 , 24 , 25 , 26 , 27] . Mycetoma patients need close and continuous clinical follow up to detect recurrence which is common problem .
The study ethical clearance was obtained from Soba University Hospital Ethical Committee , it waived the need for informed patients consent . The data was managed by SPSS computer programme . Data was summarized as percentages for categorical variables and mean for continuous variables .
The study included 533 patients with confirmed hand mycetoma seen in the period 1991 and 2015 , comprise 7 . 4% of the total patients seen during that study period . There were 369 males ( 69 . 2% ) and 164 females ( 30 . 8% ) with a sex ratio of 2 . 2:1 . Most of the patients had eumycetoma 444 ( 83 . 3% ) and actinomycetoma was seen in 89 patients ( 16 . 7% ) ( Table 1 ) . The majority of the studied patients , 449 ( 84 . 2% ) were below 40 years old at presentation , 158 patients ( 23 . 8% ) were below 20 years old and only 22 patients ( 4 . 1% ) were above 60 years old . Children and adolescents were affected most; 150 ( 28 . 1% ) , followed by farmers 97 ( 18 . 2% ) and workers 93 ( 17 . 4% ) . Fifty four patients ( 10 . 1% ) were unemployed while 93 patients ( 17 . 4% ) were homemakers ( Table 1 ) . The majority of the affected population were from the Sudan mycetoma belt . There were 227 patients ( 42 . 6% ) from Geizira State , 80 patients ( 15% ) from Khartoum State , 62 patients ( 11 . 6% ) from the White Nile State , 48 patients ( 9% ) from the Sinner State and 44 patients ( 8 . 3% ) were from Northern Kordofan State . Most of the patients 390 ( 73 . 2% ) had painless lesions . A history of trauma at the mycetoma site was recalled by only 104 patients ( 19 . 5% ) , while the majority 360 ( 67 . 5% ) had no history of trauma and 61 patients ( 11 . 4% ) were not sure of that . Concomitant medical problems were documented in only 18 patients ( 3 . 4% ) . Only 59 patients ( 11 . 1% ) had a family history of mycetoma . The study showed that , 359 patients ( 67 . 4% ) had mycetoma of less than 5 years duration at presentation while only 48 patients had mycetoma for more than ten years . Most of the patients 277 ( 52% ) had past surgery for mycetoma and recurrence and that ranged between once; 190 ( 35 . 6% ) , twice; 51 ( 9 . 6% ) , thrice; 16 ( 3 . 0% ) and more than three times; 19 ( 3 . 9% ) . The right hand was affected more frequently in this series , which was documented in 322 patients ( 60 . 4% ) while the left one was affected in 211 patients ( 39 . 6% ) ( Table 1 ) . The hand mycetoma lesions size at presentation were variables , they were classified as small lesions less 5 cm , moderate sized between 5–10 cm and massive lesions which were more than 10 cm in diameter . The study showed that , 245 patients ( 46% ) had small lesions , 140 patients ( 26 . 1% ) had moderate lesions and 122 patients ( 22 . 9% ) had massive lesions , while 16 patients ( 3 . 0% ) were operated upon recently elsewhere . All of the patients had mycetoma clinical triad , all had subcutaneous swelling , 80 . 7% had active or closed sinuses and 42 . 2% had discharge with grains ( Figs 1 and 2 ) . The final diagnosis of mycetoma was achieved by a combination of at least two techniques and these techniques included X-ray examination , ultrasonic , cytological , histological techniques and grain culture . X-ray examination of the affected series in two views showed , soft tissue swelling in 154 patients ( 28 . 9% ) , bone cavitation in 55 patients ( 10 . 3% ) , periosteal reaction in 11 patients ( 2 . 1% ) and a combination of these findings in 41 patients ( 7 . 7% ) and normal in 85 patients ( 15 . 9% ) ( Fig 3 ) . In the present study , 199 patients underwent ultrasonography examination to establish the diagnosis of mycetoma and to differentiate between its two types . 165 patients ( 82 . 9% ) had eumycetoma , 15 patients ( 7 . 5% ) had actinomycetoma and in 19 patients ( 9 . 5% ) no grains were detected and no definite diagnosis was established ( Fig 4 ) . Few patients had MRI examination which showed the typical mycetoma features ( Fig 5 ) . In this series , 200 patients ( 37 . 5% ) had histopathological examinations of surgical biopsies . The diagnosis of M . mycetomatis was established in 130 patients ( 65% ) , Streptomyces somaliensis in 29 patients ( 14 . 5% ) , Actinomadura pelletierii in five ( 2 . 5% ) and Actinomadura madurae in four patients ( 2% ) . In seven patients ( 3 . 5% ) grains were absent and the diagnosis was established by other modalities . FNAC was performed in 258 patients ( 48 . 4% ) ; 216 patients ( 83 . 7% ) had M . mycetomatis , 20 patients ( 7 . 7% ) had Actinomadura madurae , eight patients ( 3 . 1 ) had Streptomyces somaliensis and 14 patient ( 5 . 4% ) no grains were detected . Combination of antimicrobial agents was given for actinomycetoma which included streptomycin sulphate one gram daily and dapsone 100mg daily , or streptomycin and trimethoprim-sulfamethoxazole . More recently , trimethoprim-sulfamethoxazole 8/40 mg/kg/day combined with amikacin 15 mg/kg/day was given in the form of cycles ( Fig 6 ) [16 , 17] . A combination of various antifungal agents and different surgical procedures were offered to these patients . The antifungal agents prescribed were ketoconazole in a dose of 400–800 mg daily and 200–400 mg of itraconazole daily ( Fig 7 ) [19 , 20] . The follow up and treatment dropout was 55% of patients which is rather high and cure was 25% of the regularly followed up patients . The reasons for this high dropout rate are multifactorial and that include the high treatment cost which is not affordable by the patients and their families , the treatment is prolong more than one year in most patients , the drugs in use are commonly toxic and have several side effects and the patients low health education and socio-economic status that made it difficult for them to travel to the center from their remote villages for treatment . All these can contribute to the poor treatment outcome .
This communication reports on 533 patients with hand mycetoma . It is considered the largest series ever reported and thus provides a significant addition to the literature . It confirms the previous reports that , the hand is the second frequent site for mycetoma [4 , 9 , 28] . The incidence of hand mycetoma reported here is in accordance with previous reports that ranged between 1 . 8% and 7 . 4% . [4 , 13 , 9 , 28] . A report on mycetoma from a single centre in Mexico showed out of 482 patients , 36 patients ( 7 . 46% ) had hand mycetoma [4] . In another series of 3933 patients from Mexico , most of the studied patients ( 60 . 29% ) had extremities mycetoma [28] . Males were mostly affected in our series and this is in agreement with preceding reports from the Sudan and globally , however , the sex ratio reported in this series is smaller [1 , 2 , 4 , 22] . Research suggested sex hormones may play a role in this male predominance , but further studies are need to verify it [29] . The young adults were most frequent affected cohort in the present study which is in agreement with other series [1 , 2 , 4 , 22] . The affection of these young group is serious as they are the most active group in developing countries . It leads to serious socio-economic consequences and many of them drop out their education . The study showed that , 76 . 4% of the studied population had no family history of mycetoma . The susceptibility to contract the disease can be due to environmental , genetic or immunogenic factors . However , susceptibility and resistance to mycetoma demands further in depth study . Children and adolescents were the common affected group reported in this study , most of previous reports showed farmers and workers were affected most [1 , 2 , 4 , 28] . The explanation is indistinct but this may be due to fact that mycetoma is common in this age group , and children are commonly in contact with the soil during playing or helping their families in farming activities . As the soil may harbour the causative organisms and they are more prone to minor injuries which facilitate the inoculation of causative organism make them more liable to develop mycetoma . Most of the affected patients 359 ( 67 . 4% ) had disease duration of less than five years , which is rather short duration compared to previous reports [1 , 2 , 4 , 9 , 28] . This may be due to improvement in health education among the affected population and hand swelling may be recognised more frequent and earlier than other parts of the body . This contrast the long disease duration in patients with scrotum , gluteal region , vulva or perineum who are commonly reluctant to seek medical advice . This explanation may be supported be the fact that , 46% of the hand mycetoma patients reported with a small sized lesions at presentation . The study showed that 52% of the studied population had past history of post- operative recurrence . This can be multifactorial and to mention but a few , surgery was performed in rural health centres , under local anaesthesia by inexperienced health assistants and in a poor surgical setting . Some patients had massive lesion with long disease duration that makes it difficult to completely excise all the affected tissues and hence the recurrence . In agreement with the previously reported series [12] , Madurella mycetomatis eumycetoma was the prevalent type in our series and this is due to the predominance of this organism in the Sudan . The other common causative organisms encountered were Streptomyces somaliensis , Actinomadura madurae and Actinomadura pelletierii . Reviewing the medical literature revealed several hand mycetoma cases caused by other causative organisms . Pupaibul in 1982 , reported hand mycetoma caused by Phialophora Jeanselmei [30] while Leptosphaeria tompkinsii , black grain causative organisms , was reported by Cartwright and colleagues [31] and by Machmachi and associates [32] . Hand mycetoma caused by Nocardia caviae was reported in few patients as well [33] . Scedosporium boydii ( Scedosporium apiospermum ) is a frequent cause of hand mycetoma . It was reported in a 75-year-old immunocompromised male patient who received long-term corticosteroid and immunosuppressant therapy for the treatment of nephrotic syndrome , in a patient with adult Still's disease and in a 52-year-old male heart transplant recipient [34 , 35 , 36] . Scedosporium boydii of the hand and forearm was reported in a patient with Behçet's disease treated with infliximab and chronic prednisone therapy [37] . A case of mycetoma caused by Fusarium solani with osteolytic lesions on the hand in a Brazilian farmer aged 71 years was reported [38] . The first case of hand Arthrographis kalrae eumycetoma which was cured by a 4-month course of itraconazole was reported in 1997 [39] . Acremonium recifei was reported to cause hand eumycetoma with white-yellowish grains mycetoma in immunocompetent patients [40 , 41] . These organisms were not documented in this study and that may be due to the rarity of these organisms in the Sudan or they are under diagnosed . Hence , the use of PCR for organisms’ identification is required to identify such rare organisms and for planning the appropriate treatment . The right hand was affected most ( 60 . 4% ) and been the dominant hand in most of the patients , some researchers incline to attribute that to the causative organisms traumatic inoculation theory [1 , 2] . The clinical presentations of the studied patients were in line with previously reported . All had subcutaneous swelling , 80 . 7% had active or closed sinuses and 42 . 2% had discharge with grains , the mycetoma characteristic triad . However , clinically hand mycetoma is to be differentiated from two other mycotic infections caused by a group of black fungi and these are phaeohyphomycosis and chromoblastomycosis [42 , 43] . Phaeohyphomycosis , a rare disease and sporadically reported is distinguished from mycetoma by the absence of grains formation while chromoblastomycosis is differentiated by the absence of sclerotic bodies . The black fungi are a group of fungi that are characterized by the development of a pale brown to black colour in the cell walls of their vegetative cells , conidia , or both . The differentiation between these three mycotic infections can be established by surgical biopsies and histopathological examination using special staining and by PCR identification . Several radiological signs can be detected on conventional x-ray of the hand mycetoma [44] . In this study , fanning of the metacarpals bones , bone erosions , sclerosis , periostitis and soft tissue swelling were reported and the most common signs were soft tissue swelling ( 88% ) and bones involvement ( 45% ) . These findings were seen in patients with advanced mycetoma . For this reason , more MRI and CT scans are currently in use to determine the disease extend along the different tissues planes . Mycetoma has distinctive MRI appearance , the “in-dot sign” is diagnostic . It can outline the skin , subcutaneous , muscles and bones involvement accurately and can grade the disease which help in patients’ management [45] . In mycetoma , the bone affection can accurately be assessed by CT scan . In the present series it was difficult to determine the treatment outcome accurately and precisely due to high patients follow up dropout . The reasons for the high dropout rate are multifactorial and that include the patients’ dissatisfaction with the high cost and the prolonged treatment duration which is commonly more than one year , the drug side effects and complications , the patients low socio-economic status , the lack of health education and difficulty to reach the MRC , particularly during rainy seasons . All these had contributed to the poor treatment outcome also . To overcome these constrains and shortcomings a new treatment model was adopted by the MRC . The model consists of regular visits to endemic remote areas in the Sudan for early case finding and management . Through a mobile surgical teams , surgical treatment was carried at the local villages and recently free medicines were provided [47] . In conclusion , the hand is a precious human organ with complex functions and losing it through destruction , distortion , deformities , or amputation is unacceptable particularly due to benign inflammatory disease like mycetoma . Knowledge gaps in mycetoma epidemiology and pathogenesis will need to be addressed careful by further research , likewise novel diagnostic tools and treatment need be researched [46] . Until that time , health education and awareness are the cornerstone in prevention and reducing disease morbidity and mortality . | Hand mycetoma is a serious condition due to the complexity of the hand anatomy and functionality . The disease and the surgical intervention can both produce distortions , deformities and disabilities which interfere with patient daily activities of living . Thus producing huge burden on the patients , the families and inflicts numerous socio-economic bearings on the communities in endemic regions . The present study , identified that , most patients were young adult males , of poor socioeconomic status and from rural areas of the Sudan . Compared with patients with mycetoma in other parts of the body , they presented rather early having noticed the lesion earlier because they frequently wash and examine their hands . No predisposing factors were evidenced in the study . Several diagnostic tools and techniques were required to establish the diagnosis of mycetoma in these patients and hence there is a need for a simple , affordable , field friendly diagnostic test which is of reasonable sensitivity and specificity . The treatment is prolonged and in most cases the outcome was unsatisfactory , thus new novel treatment is desirable . Until that time , a well-structured health education programmes are required to encourage early presentation and treatment compliance . | [
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| 2016 | Hand Mycetoma: The Mycetoma Research Centre Experience and Literature Review |
The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation . Recent studies have shown a strong correlation between chromatin interactions and gene co-expression . However , predicting gene co-expression from frequent long-range chromatin interactions remains challenging . We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures . We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex . Consistent with previous findings , we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression . However , the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network . We conclude that , for co-expression prediction , it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes ( i . e . small-scale ) to chromatin compartment interactions ( i . e . large-scale ) .
The three dimensional ( 3D ) conformation of chromosomes in the cell nucleus plays an important role in determining which genes are expressed in a cell [1–6] . In particular , it has been shown that genes are often regulated by elements that are located far away in terms of the linear genome sequence [7 , 8] . In fact , transcribed genes tend to spatially associate with their regulatory elements which results in 3D clustering of co-regulated genes [7 , 9] . Moreover , there is increasing evidence that transcription occurs at specific nuclear sites , sometimes called transcription factories [7 , 10] . Chromosome conformation capture techniques , such as 3C , 4C , 5C , and Hi-C , allow direct measurement of chromatin interactions and thereby the study of the role of these interactions in gene regulation [11–13] . Using 4C , for instance , it was demonstrated that the 3D structure of the yeast genome correlates with gene co-expression [3] . Dong et al . [2] used Hi-C data from two human cell lines to demonstrate that chromatin interactions associate with co-expression [2] . Both studies , however , have shown that it is difficult to explain the relationship between co-expression and the 3D structure of the genome by considering direct chromatin interactions only . Thus , while a clear relation between chromatin interaction and co-expression exists [2–4] , this relation may be better understood if more comprehensive characterizations of long-range chromatin interactions , i . e . those involving also indirect interactions , are taken into account [14] . A more comprehensive characterization of long-range chromatin interactions can be obtained by considering the chromatin conformation data as a network [15 , 16] . In such network , termed Chromatin Interaction Network ( CIN ) , a genomic locus is represented by a node while links between the nodes denote chromatin interactions . Investigation of the CIN topology may reveal properties of the 3D genome organization that are important for understanding its function , such as co-expression of genes . Characterizing the topology in biological networks has been extensively explored , for instance to gain insight into the functional relationships encoded in such networks [17 , 18] . Standard network topological measures , such as shortest path , betweenness centrality and clustering coefficient , have been used to capture either the topology around a single node or the global topology of the whole network [19 , 20] . As a result , these measures of network topology operate at a fixed zoom-level . Recently , scale-aware topological measures have been shown to superiorly predict gene function and interactions by characterizing the topology of protein interaction networks at different scales [18 , 21] . In this work , we explore the use of scale-aware topological measures ( STMs ) , proposed in [21] , to describe the CIN topology . Analyzing the CIN topology enables us to study the relation between long-range chromatin interactions and co-expression . The CIN constructed in this study is based on Hi-C measurements from the mouse cortical cells [8] . In the brain , genes with a common expression pattern across the brain may have a common role in influencing the function of the brain region in which they are co-expressed [22] . In order to study spatial co-expression in the mouse brain , and mammals in general , it is necessary to map the expression at sufficient resolution to decode the high complexity [23] . The Allen Mouse Brain Atlas ( ABA ) [24] , a genome-wide map of gene expression across the brain , provides sampled cellular-resolution insitu hybridization sections at a 25μm interval across the entire brain . We use this high-resolution dataset to obtain spatial co-expression relationships between genes at the cellular level ( Fig 1 ) , i . e . two genes will be co-expressed if they are expressed in the same set of cells across the brain . To test the hypothesis that co-expression in the cortex is encoded in the CIN , we employ a supervised learning procedure . More specifically , we aim to predict the spatial co-expression between gene-pairs based on a set of features that describe the topology of the connection between the two genes in the CIN . We show that the resolution at which the chromatin interactions are captured affects the prediction of co-expression from genomic organization . In particular , our results reveal that the accuracy of the prediction is increased when measures from different Hi-C resolutions are integrated . Finally , we clearly demonstrate the importance of using descriptions of the CIN topology at different scales , ranging from specific interactions between transcription start sites of genes ( small-scale ) through interactions between whole genes ( medium-scale ) and interaction between chromatin compartments ( large-scale ) .
We collected the intra-chromosomal Hi-C data from Shen et al . [8] . They obtained Hi-C measurements in the mouse cortex following the methods proposed in Lieberman-Aiden et al . [12] . About 20–30 million cortex cells from 8-week old male C57Bl/6 mice were used to generate Hi-C contact matrices [8] . The resulting Hi-C matrices contain pair-wise chromatin contact frequencies between pairs of 40kb genomic segments ( i . e . bins ) . Experimental biases , such as GC content of trimmed ligation junctions and distance between restriction sites , were eliminated by an integrated probabilistic background model as described by Yaffe et al . [25] . Hi-C technology measures only steady-state chromosome conformations across a population of cells . So , the resulting genome-wide interactions are averaged across the cells and are not exactly the same in any given cell [8 , 26] . Yet , the variability of chromatin interactions is mostly confined to local interactions , while long-range interactions are relatively well conserved and stable [27] . This demonstrates that different cell types share a common global architecture of their chromosomes which can be well described by the chromatin contact matrix . Two regions that are close-by in the linear genome are expected to have higher chromatin interaction frequency , irrespective of the actual 3D organization of the genome ( S1 Fig ) . To account for this , several studies [12 , 28 , 29] have defined normalized Hi-C contact matrices assuming that the Hi-C interactions are normally distributed [12 , 28] or independent [29] . Alternatively , we used a non-parametric rank based normalization method [30] to describe the Hi-C score distributions for a certain distance , which we found to be more powerful for detecting variations across the genomic distance . Since we are interested in predicting co-expression patterns of genes , each bin-based Hi-C matrix is converted to a gene-based Hi-C matrix based on the Hi-C interaction between the corresponding bins in which the genes reside ( see Methods ) . While assigning Hi-C interactions between genes , the bin size of the Hi-C data controls the genomic neighborhood considered around genes . In order to capture interactions between genes at variable linear genomic distances we varied the resolution of the Hi-C data matrices , before constructing gene-based matrices . This was achieved by considering different bin sizes between 40kb ( high-resolution ) and 1Mb ( low-resolution ) . The lower resolution matrices were obtained by summing the contact frequencies of consecutive bins in the higher resolution matrices . To determine the Hi-C interactions between each gene-pair we take the Hi-C interaction between the corresponding bins in which the genes reside . However , some genes might span multiple bins , depending on gene size and bin size . In this case , we determine the Hi-C interaction for a gene-pair ( x , y ) by one of two approaches . In the first approach , referred to as MAX-mapping , we define a link as the maximum Hi-C value among all possible interactions , i . e . h ^ x y = m a x i ∈ x , j ∈ y ( h ^ i j ) . In the second approach , referred to as TSS-mapping , we define a link as the Hi-C score between the bin-pair which contains the transcription start sites ( TSS ) of the two genes , i . e . h ^ x y = h ^ i j ; where: TSS ( x ) ∈ i and TSS ( y ) ∈ j . We applied a threshold to convert the weighted gene-based Hi-C matrix to an un-weighted matrix by retaining only interactions that exceed the 90th-percentile of all Hi-C score across all chromosomes at the corresponding bin size . We constructed one CIN per chromosome per resolution because the employed Hi-C data contains only intra-chromosomal interactions . For each CIN H c h r R = ( G , I H ) , G represents the set of nodes corresponding to genes and IH represents the set of links corresponding to Hi-C interactions between genes that exceed the 90th-percentile of all Hi-C scores across all chromosomes at a resolution R . There are several topological measures which capture graph structure for nodes and/or links in a network [17 , 19] . In this work , we calculated five graph-topological measures of the chromatin interaction network: shortest path length , Jaccard index , degree ( and closeness ) centrality , betweenness centrality , and clustering coefficient ( Table 1 ) . Since our goal is to predict co-expression between gene-pairs , all features used by the classifier should be link-based . Therefore , we converted all the node-based topological measures ( degree-closeness centrality , betweenness centrality and clustering coefficient ) to link-based measures by taking the average and the difference between the values of the gene-based measure for each gene-pair . For example , for a gene-pair ( x , y ) , the clustering coefficient of the link between x and y is described by { | ( c c ( x ) − c c ( y ) | , 1 2 ( c c ( x ) + c c ( y ) ) } . As a result , each link in the interaction network is represented by eight link-based topological features . In addition to the standard topological measures , we used the scale-aware topological measures ( STMs ) described by Hulsman et al . [21] to capture the network characteristics across different scales . STMs are based on diffusion kernels [30] , a network smoothing process in which the diffusion strength β parameter determines the scale at which the network is considered [31] . By varying the scale at which we consider the CIN , different types of interactions are taken into account . For example , specific interactions between transcription start sites of genes are more pronounced at the small-scale while interactions between chromatin compartments are more pronounced at the large-scale . The Allen Mouse Brain Atlas ( ABA ) [24]; ( http://mouse . brain-map . org/ ) provides a genome-wide cellular-resolution , insitu hybridization ( ISH ) -based , gene expression map of the 8–week old adult C57BL/6J male mouse brain . A spatial co-expression map was constructed based on the similarity of the spatial expression profiles of each pair of genes across the cortex ( see Methods ) . The employed Hi-C data contains only intra-chromosomal interactions . Therefore , one co-expression network was constructed per chromosome and is denoted by Echr = ( G , IE ) , where G indicates a set of nodes representing genes and IE indicates set of links representing intra-chromosomal co-expressions between gene-pairs . The largest and smallest networks E2 and E18 ( S2 Fig ) consisted of 338 and 119 genes ( i . e . nodes ) , respectively . To focus our predictions on reliable interactions , we included only strongly co-expressed genes and gene-pairs without strong co-expression ( see Methods ) . To examine whether gene-pairs with high spatial co-expression frequently interact in the 3D conformation of chromosomes , we defined two sets of gene-pairs: strongly co-expressed genes and gene-pairs without strong co-expression ( see Methods ) . We used a Wilcoxon rank-sum test to determine if strongly co-expressed gene-pairs have stronger Hi-C interactions , and hence are closer to each other in the 3D conformation of the chromosome , compared to gene-pairs without strong co-expression . Fig 2A ( and S3 Fig ) shows that co-expressed genes are significantly co-localized in the nucleus in most of the chromosomes and most CIN-resolutions ( Wilcoxon rank-sum test; p-value < 0 . 0002 , Bonferroni corrected for 260 tests: 20 chromosomes × 13 resolutions ) . Strikingly , we observe that the resolution for which the strongest co-localization is attained is different for different chromosomes ( Fig 2B ) . This observation underscores the importance of a multi-resolution approach to characterize chromatin interactions which apparently can occur between loci in the direct vicinity of genes as well as between broader regions ( domains ) in which these genes reside . To determine whether strong co-expression can be predicted from chromatin interactions , we calculated the correlation between the Hi-C matrix and the co-expression matrix for each chromosome at different resolutions . S4 Fig shows that the correlation is very low across different chromosomes and Hi-C resolutions ( −0 . 4 to +0 . 1 ) . Additionally , training a classifier on the presence or absence of links in the CIN results in a poor classification performance ( 0 . 55 median AUC across chromosomes at 40kb resolution ) . S5 Fig shows that only 2% ( average across all chromosomes ) of all gene-pairs are co-expressed and connected in the CIN of each chromosome . This observation further highlights the importance of indirect chromatin interactions in explaining co-expression . Taken together , these results indicate that chromatin interaction and co-expression do not have an injective ( one-to-one ) relation . The relation between chromatin interaction and co-expression would be better described by a more comprehensive characterization of long-range interactions , i . e . indirect interactions . A compelling example is given in Fig 3A . In Chromosome 16 , Synj1 and Dyrk1a genes are co-expressed ( dashed red line ) while their corresponding genomic loci do not frequently interact , i . e . there is no link ( solid blue line ) between them in the CIN at 200kb resolution . A classifier only taking direct chromatin interactions into account will mistakenly predict that the two genes are not co-expressed . However , both Synj1 and Dyrk1a genes have strong chromatin interactions with Pam16 , Fstl1 , Hmox2 , Sidt1 and their strong co-expression can be correctly predicted if these indirect interactions are considered . For this particular example , the indirect interactions between the two genes can be characterized by the Jaccard index which captures to what extent the two genes have common direct neighbors . Another example is the interaction between Kcnc4 and Tspan5 in Chromosome 3 ( Fig 3B ) . Kcnc4 and Tspan5 directly interact in the 200kb-CIN ( solid blue line ) but they are not strongly co-expressed ( no dashed red line ) . Nevertheless , this direct chromatin interaction may explain the strong co-expression between gene-pairs in the CIN neighborhood that lack a direct chromatin interaction themselves . For example , Wdr47 and Lphn2 are co-expressed although they are not directly connected in the CIN ( no solid blue line ) but their co-expression can be explained by the chromatin interaction path through the Kcnc4 , Tspan5 and Tspan5 genes . Similarly , the co-expression of Wdr47 and Rap1gds1 can be explained by the chromatin interaction path through Kcnc4 and Tspan5 . For this example , the importance of the Hi-C link between Kcnc4 and Tspan5 to describe strong co-expression between their neighboring genes in the CIN can be captured using the betweenness centrality of both genes . Both examples illustrate that strong co-expression between gene-pairs can be better explained by their chromatin interaction profile , defined as the path connecting two genes in the context of the CIN . For each CIN of a certain resolution , we calculated the standard graph-topological measures and trained a random neural network ( RNN ) classifier using the resulting topological features ( see Methods ) . The classification results are summarized in Fig 4 ( Box 1–4 and 7 ) . The figure shows that an increased—yet moderate—classification performance is obtained when standard topological measures of the CIN at a single resolution ( median AUC of 0 . 72 for 200kb and 0 . 73 for 40kb , Fig 4 ( Box 1 , 2 ) ) are used as features ( compared to 0 . 55 AUC when using only direct interactions ) . To evaluate the effect of Hi-C resolution on co-expression prediction , we applied the RNN classifier to a concatenated set of standard topological measures obtained from CINs at different Hi-C resolutions ( 40 , 80 , 120 , 160 , and 200kb ) , i . e . the topological descriptions of each resolution are concatenated in one feature representation . At a high Hi-C resolution ( 40kb ) we mainly focus on chromatin interactions between pairs of genes . On the other hand , at a low Hi-C resolution ( 200kb ) we consider interactions between larger genomic domains . Our multi-resolution approach increased the power of the interaction data to predict co-expression ( Fig 4 , Box 3 , 4 , 7 ) , supporting our earlier observation that gene regulation occurs at different regional scales of chromatin interaction , such as the gene-level or the level of broad regions . So far , the best prediction performance is obtained by concatenating standard topological measures of CINs built using both TSS- and MAX-mapping methods ( 0 . 77 median AUC , Fig 4 , Box 7 ) . To examine the effect of indirect chromatin interactions on the prediction of co-expression , we described the CIN topology at multiple topological scales using STMs ( see Methods ) . We calculated STMs of the CIN at each Hi-C resolution separately and then concatenated all STMs , resulting in 800 features; 8 STMs at 10 scales applied to 10 CINs; 5 different resolutions and two mapping methods ( see Methods for more details ) . We then followed the same procedure as before and trained a RNN classifier on this combined feature set . Fig 4 ( Box 5–6 and 8–10 ) summarizes the results obtained when using STMs rather than the standard topological measures . The performance obtained using STMs calculated at a single resolution CIN ( Fig 4 , Box 5 , 6 ) is comparable to the performance obtained by concatenating standard topological measures from multi-resolution networks ( Fig 4 , Box 7 ) . However , by combining features from STMs applied to multi-resolution CINs , the power to predict co-expression improves significantly ( Wilcoxon rank-sum test; p-value < 0 . 00001 ) ( 0 . 82 AUC , Fig 4 , Box 10 ) . The best performances are obtained for Chromosome 16 ( 0 . 86 AUC ) and Chromosome X ( 0 . 85 AUC ) . The observed performance improvement demonstrates that it is important to use a scale-aware topological description of the CIN to capture the complex 3D organizational features of the genome that determine gene co-expression . In order to analyze the effect of considering only strongly co-expressed genes on the classification performance , we assessed the performance when all co-expression links are included . In this analysis , a gene-pair is labeled co-expressed ( i . e . positive class ) or not co-expressed ( i . e . negative class ) if their correlation is above or below the median ( i . e . 50th-percentile ) of all correlations across all chromosomes , respectively . The resulting AUCs across all chromosomes show that STMs performs better than standard measures to distinguish between co-expressed and no co-expressed gene-pairs ( S6 Fig ) . As expected , the classification performance is lower with respect to the case where we excluded weakly co-expressed gene-pairs ( i . e . gene-pairs that have a co-expression that is in between the 50th and 90th-percentile of all correlations across all chromosomes ) ( Fig 4 ) . Most likely this is caused by a noisy class assignment for weakly correlated gene-pairs which confuses the classifier during training . We also performed the classification procedure by including Hi-C scores above the median of all Hi-C scores across all chromosomes . The resulting AUCs across all chromosomes show that STMs perform better than standard measures to distinguish between co-expressed and non-co-expressed gene-pairs ( S6 Fig ) . The classification performance is , however , less than the AUC when we defined strong Hi-C interactions as Hi-C scores above the 90th-percentile of all Hi-C scores across all chromosomes ( Fig 4 ) . To compare the rank-based normalization method [32] with the average-based method proposed by Lieberman et al . [12] , we trained the classifier on the standard and scale-aware topological measures of the CIN that was built using the average-based normalized Hi-C matrices . The performance of these classifiers is lower than when constructing the CIN on using the rank-based normalized Hi-C data ( S7 Fig ) , underscoring the usefulness of the rank-based normalization for predicting co-expression from chromatin interaction data . Nevertheless , STMs perform better than standard measures for both normalization methods , indicating that the classifier is not biased towards the normalization method . To investigate the effect of chromatin interactions between non-genic and genic regions on the co-expression prediction we built a bin-based CIN ( instead of a gene-based CIN ) . In the bin-based CIN , nodes represent non-overlapping bins with size of 200kb and links represent Hi-C interactions between bins that exceed the 90th-percentile of all Hi-C scores across all chromosomes at a 200kb resolution . We calculated standard and scale-aware topological measures ( 8 measures ) for all links in the bin-based CIN . The classifier was trained on topological measures of the portion of links that connect two gene-loci . In this strategy , the interaction profile between two gene-loci is characterized by chromatin interactions of all genomic regions across the scales . The resulting AUCs across all chromosomes show that STMs performs better than standard measures to distinguish between co-expressed and non-co-expressed gene-pairs ( S8 Fig ) . It is interesting to observe that the classification performance is approximately similar to that obtained when gene-based CINs were used . This suggests that the STMs can capture all the necessary information from the genic Hi-C links . To investigate the variation in topological properties of the CIN of different chromosomes , we performed a leave-one-chromosome-out experiment . If the CINs of all 20 mouse chromosomes share the same topological properties , then it would be possible for a classifier trained on all but one chromosome to accurately predict the co-expression labels of the left-out chromosome . To test this hypothesis , we trained the RNN classifier on the STMs ( 800 features ) extracted from 19 chromosomes and then tested the performance on the left-out chromosome . We repeated the procedure 20 times and each time , a different chromosome was left out of training and used for testing . The maximum AUC obtained was 0 . 54 , which indicates that the CIN of each chromosome has a unique topology , to which the high-scale STM feature values are sensitive . The variation in topological properties of CINs across chromosomes is also observed when we trained an RNN classifier on individual topological measures . The classification performance using individual standard measures ( S9 Fig ) and individual STMs ( S10 Fig ) is highly variable across chromosomes , which explains the poor performance obtained in the leave-one-chromosome-out experiment . For instance , the clustering coefficient STM is a good descriptor of the CIN of Chromosome 3 at medium-resolution and low-scale , while it is a good descriptor of the CIN of Chromosome 10 at high-resolution across the scales ( S10 Fig ) . To analyze the topological properties that are most predictive we trained the classifier on individual topological measures . The classification performance using individual standard measures ( S9 Fig ) and individual STMs ( S10 Fig ) shows that none of the topological measures has dominant power to predict co-expression . Therefore , the classifier requires more than a single topological descriptor to describe chromatin interaction profile between two gene-loci . In order to determine the set of STMs that characterizes the CIN of each chromosome the best , we performed forward feature selection in combination with the RNN classifier . To facilitate this computationally , we reduced the number of nodes in the hidden layer to 100 and applied 5-fold cross validation . To ease interpretation , we used the STMs derived from multi-resolution CINs using the MAX-mapping method only ( 400 STMs , 8 measures × 5 resolutions × 10 scales ) . S11 Fig shows that the classification performance achieved using feature selection ( 0 . 8 AUC ) is higher than the performance achieved using all features ( 0 . 72 AUC ) . For most chromosomes , the top 5 selected features in all 5 folds are clustering coefficient ( at small-scale , β < 0 . 5 ) , closeness centrality ( at medium-scale ( 0 . 5 < β < 3 ) and Jaccard index ( at large-scale , β > 3 ) STMs ( S2 Table ) . The clustering coefficient measures to what extent a gene is embedded in a well-connected component of the CIN . Selecting the small-scale clustering coefficient implies that co-expressed genes are usually embedded in a locally well-connected component in the CIN ( e . g . chromatin compartment ) . The Jaccard index determines the fraction of common interacting genes between gene-pairs in the CIN . At a large scale it takes more indirect neighboring nodes ( e . g . genes located in different chromatin compartments ) into account . The closeness centrality reflects the farness of a gene by summing the shortest path distances to all other genes and at a medium scale it thus takes somewhat longer paths into account . Both Jaccard index and closeness centrality explain that common indirect interacting genes ( e . g . interaction between chromatin compartments ) are important to describe the co-expression pattern of a pair of genes . Additionally , we observed that all scale-levels ( small , medium and large ) were selected reflecting the importance of characterizing CINs at different scales . The selection of various scale-levels could be explained by the hierarchical structure of the chromatin folding in the cell nucleus ranging from looping between the promoter regions of genes to larger chromatin compartments [11 , 15] . This is corroborated in the work by Sandhu et al . [15] who have shown that genomic regions are organized into a hierarchical chromatin interaction network . We analyzed the top selected STMs of the 200kb-CIN of Chromosome 16 , for which the highest prediction performance is achieved , to gain insight into the topological measures and scales that best describe the network . The best classification performance ( AUC = 0 . 84 ) is obtained using 206 of the 400 STMs ( S2 Table ) which are selected by forward feature selection . We mapped these 206 features to a 2D space using t-Distributed Stochastic Neighbor Embedding ( t-SNE ) [33 , 34] ( see Methods ) . The 2D map of all gene pairs in Chromosome 16 ( Fig 5 ) shows that there are few distinct clusters of co-expressed and not co-expressed gene-pairs , i . e . clustering of red and blue dots in Fig 5B respectively . However , it is difficult to discriminate between the majority of gene-pairs ( big cluster in the middle of Fig 5B ) , further supporting our observation of complex organization of chromatin interactions . Coloring the t-SNE with two of the top selected features , the clustering coefficient at small-scale ( Fig 5A ) and the Jaccard index at the medium-scale ( Fig 5C ) , shows that gene pairs are characterized by different values of those two features , indicating their importance for the classification performance . Since the clustering coefficient at small-scale is one of the top selected features for Chromosome 16 , we used the t-SNE map to select a co-expressed gene-pair with a high clustering coefficient at a small-scale ( Fig 5A ) . We constructed a sub-network of the selected gene-pair by retrieving all the Hi-C and co-expression interactions surrounding the gene-pair ( Fig 5D ) . B3galt5 and Carhsp1 are co-expressed ( dashed red link in Fig 5D and red dot in Fig 5B ) although there is no direct Hi-C interaction between them ( no blue link ) . However , it is possible to predict their co-expression because they are both part of a very well connected cluster , which is captured by a high average clustering coefficient at small scale . Similarly , we select a co-expressed gene-pair with a high Jaccard index at the medium-scale , another top selected STM of the CIN of Chromosome 16 ( Fig 5C ) . The sub-network including the selected gene-pair Masp1 and Abat ( Fig 5E ) , shows that they are co-expressed although no direct Hi-C interaction exists between them ( no blue link in Fig 5E ) . The two genes also do not share many direct neighbors . At a medium scale , however , the Jaccard STM takes indirect neighbors into account , resulting in a high Jaccard index based on the Hi-C links between the neighbors of Masp1 and Abat .
We proposed a network-based approach to better understand the 3D structure of the genome based on scale-aware topological measures of the chromatin interaction network . Previous studies have shown a strong correlation between co-expression and chromatin interaction , for example in model organisms ( e . g . yeast ) [3] or cell lines ( human gm06990 and K562 cells ) [2] . Our results demonstrate that the co-expression relationship between a pair of genes in the mouse cortex could be accurately predicted from their chromatin interaction profile , extending previous observations in [2 , 3] . Furthermore , the predictive power of our model depends greatly on the resolution at which the interactions are observed as well as the scale at which the topological properties on the interaction network are calculated . By integrating scale-aware topological measures at multiple Hi-C resolutions , we were able to predict spatial co-expression between gene-pairs with an AUC performance of 0 . 82 . To our knowledge , this is the first attempt to predict co-expression based on genome-wide chromatin interactions . The results also showed a general trend of the prediction performance ( Fig 4 ) suggesting that STMs across multiple Hi-C resolutions are necessary to accurately capture the 3D structural features in the genome that determine spatial co-expression between genes in the mouse cortex . While the multi-resolution approach captures direct chromatin interactions between genes at variable linear genomic distances , standard topological measures extracted from a single-resolution CIN fail to represent the complex 3D structure of genome . By using STMs [21] to describe each single-resolution CIN , we were able to capture both direct and indirect interactions between genes , and hence correctly predict their co-expression status . The 2D t-SNE maps of the CINs using 80 standard topological measures ( S12 Fig ) and 800 STMs ( S13 Fig ) reveal a complex organization of chromatin interactions , indicating that the discrimination between co-expression labels ( blue and red points in S12 Fig and S13 Fig ) is a difficult task . These observations may also explain the poor classification performance obtained using a simple classifier such as nearest mean ( NM ) . The RNN classifier , however , is able to capture the complex chromatin interaction profile of a gene-pair and their co-expression status . Comparing the t-SNE map of standard topological measures and STMs of Chromosome 16’s CIN shows that STMs are indeed more powerful in discriminating co-expression labels ( S14 Fig ) . For example , the t-SNE map of standard topological measures shows that most of the interactions in the CIN of Chromosome 16 are characterized by a low Jaccard index value and consequently , the contribution of the Jaccard index to the classification performance is very low ( S14 Fig ) . The scale-aware Jaccard index , however , captures indirect neighbors between a gene-pair which improves the classification performance . Furthermore , we showed that each STM characterizes the CIN differently across scales and resolutions . For instance , the t-SNE map of STMs shows that the chromatin interaction profiles between gene-pairs in a well-connected component , indicated by a high clustering coefficient , are better captured at low resolution , whereas other well-connected components are better characterized at the high-resolution ( different color pattern in S14 Fig ) . Additionally , some interactions are well discriminated using the clustering coefficient ( a node-based STM ) while other interactions are better discriminated using the Jaccard index ( a link-based STM ) ( S14 Fig ) . This highlights the importance of both link- and node-based STMs in characterizing the topology of connectivity and neighborhood , respectively , of gene-pairs in the CIN to predict co-expression . Our observations are in line with the two complementary models of how regulatory elements , such as enhancers and insulators , act to regulate the expression of distant genes [35] . The looping model assumes that loops along the genome are formed to bring distal regulatory sequences in direct contact with the promoters of target genes . Alternatively , genes undergoing transcription might co-localize in the nucleus in transcription factories , and enhancers facilitate the movement of genes into or out of these factories . Our finding that a multi-resolution scale-aware encoding of the CIN topology better predicts co-expression indeed shows that chromatin interactions occur at different levels , ranging from direct interactions between the transcription start sites of genes ( small-scale ) through interactions between genes ( medium-scale ) up to interaction between chromatin compartments ( large-scale ) . The topology of different chromosomes might be radically different , due to both chromosome length and different fractions of chromatin types . High-scale STM values are in particular sensitive to such a change in topology , and are likely to be one of the causes for the differences in performance . Indeed , a classifier , such as the one proposed here , might also be used to characterize chromatin conformation . In the current study , we used only intra-chromosomal interactions . Nevertheless , our proposed methods could principally be applied to inter-chromosomal interactions given that the data is normalized properly across chromosomes [25 , 36] . Furthermore , the method is not tissue- or organism-specific and can be generalized to predict any functional relationships ( not only co-expression ) between genomic loci ( bins or genes ) based on the characterization of the CIN . The brain is a very complex structure with large variability in gene expression patterns across different regions . Using the high-resolution maps of the ABA , this variability could be used to identify distinct groups of genes with a similar expression pattern indicating their functional similarity [37 , 38] . For example , several studies analyzed the relationship between spatial-co-expression and connectivity in the mouse brain [39–42] . Menashe et al . [23] used a spatial co-expression network of the mouse brain to identify common neuro-functional properties of autism-related genes . We expect that within the brain , and especially the cortex , many genes vary and that their biologically meaningful spatial correlation patterns are reflected by long-range chromatin interactions . With the recent association of dozens of mutations in chromatin regulators to neuropsychiatric disorders [43] , our method provides a promising approach to investigate the effect of those regulators on the cortical regulatory network . A good characterization of interactions in the CIN and their relationship to co-expression can add to our understanding of the genetic etiology of these diseases .
In order to eliminate genomic distance bias in a Hi-C matrix , each Hi-C contact value is replaced by its relative rank compared to Hi-C contacts between bins with a similar genomic distance , measured in base-pairs [32] . The normalized Hi-C score c ^ i j is defined as the rank of cij in the vector Cd , where cij is the Hi-C contact between bin i and j with genomic distance of d base pairs ( bp ) . The vector Cd is the mth super-diagonal of the Hi-C contact matrix with m = d b i n s i z e which contains Hi-C scores between all bin pairs that have the same genomic distance d . Ranks are adjusted for ties by using the average rank whenever values in Cd are tied . Note that by increasing the genomic distance , the length of Cd decreases . Therefore , Cd s are extended to have an equal length L . The extension is done by adding elements from n neighboring super-diagonals around mth super-diagonal to reach the constant length L . As we move further from the main diagonal , the number of elements on the mth super-diagonal becomes very small . Therefore , a substantial number of elements from neighboring super-diagonals are included . This is acceptable since the distributions of Cd are more similar for large d , and can thus be pooled . We set L equal for all chromosomes to determine a genome-wide threshold of strong Hi-C scores between gene-loci . So , the normalized Hi-C scores ( i . e . ranks ) are set to be in the same range across all chromosomes . We set L to be equal to twice the number of bins on Chromosome 1 , the largest chromosome in the mouse genome . STMs were acquired by calculating the five topological measures described in Table 1 on a diffused network , across a range of scales ( β ) . We empirically choose 10 values for beta in range of [0 , 10] according to: β = 2 6 b - 1 2 6 - 1 × ( 10 - 0 . 0001 ) + 0 . 0001 with b = 0 . 0 , … , 1 . 0 in 10 steps resulting β:[0 . 0001 , 0 . 09 , 0 . 24 , 0 . 47 , 0 . 8 , 1 . 4 , 2 . 3 , 3 . 8 , 6 . 2 , 10] . As a result , for the scale-aware classification , 80 features ( 8 measures × 10 scales ) were extracted from the chromatin interaction network . We downloaded all the expression energy volumes of the 4 , 345 genes with coronal experiments from ( http://mouse . brain-map . org/ ) [24] , using the ABA Application Programming Interface ( API ) . Expression energy is a measurement combining the expression level , defined as the integrated amount of signal within each voxel , and the expression density , defined as the amount of expressing cells within each voxel . We selected all voxels belonging to the cortex , defined as Isocortex in the ABA , and all the RefSeq genes , resulting in an expression matrix of 15 , 410 rows ( voxels ) and 4 , 230 columns ( genes ) . We used SpearmanÍs Rank correlation as a measure of similarity between the spatial expression profiles of each pair of genes , resulting in a 4 , 230 × 4 , 230 spatial co-expression matrix . Gene entries from the spatial co-expression matrix were mapped to their genomic locations to determine the Hi-C contact frequency between gene-pairs based on the mouse reference genome ( mm9: NCBI m37 , GCA000001635 . 18 ) . We considered a gene-pair to be strongly co-expressed ( i . e . positive label ) if their correlation exceeds the 90th-percentile of all correlations across all chromosomes . Conversely , gene-pairs are considered to be without strong co-expression ( i . e . negative label ) when their correlation falls below the median of all correlations across all chromosomes . We used a random neural network ( RNN ) classifier from the PRTools toolbox [44] ( Matlab 2012b ) to predict the co-expression label of gene pairs using the topological measures of link connecting them in the CIN as features . RNN is a feed-forward neural network with one hidden layer . We set the number of hidden nodes to 800 , the maximum number of input features ( 8 STMs at 10 scales applied to 10 CINs; 5 different resolutions and two mapping methods ) . The performance of the classifier was determined using 10-fold cross validation and reported in terms of the area under the ROC ( receiver operating characteristic ) curve ( AUC ) . The ROC curve represents the true positive rate ( sensitivity ) as a function of the false positive rate ( 1—specificity ) for different discrimination thresholds of the classifier ( S15 Fig ) . An AUC of 1 represents a perfect classification and 0 . 5 represent a random classification . t-Distributed Stochastic Neighbor Embedding ( t-SNE ) [33 , 34] was used to map the links of each chromosome’s CIN to a 2D space by reducing the dimensionality of the N × M data , where N is the number of gene-pairs in each chromosome and M is the number of topological features . In the resulting map , each Hi-C link is represented by a point in the 2D space where the distance between points reflect the similarity between their corresponding topological profiles . We applied t-SNE with perplexity of 30 and initial dimensionality reduction using 50 principal components . | Regulatory elements can target genes over large genomic distances through long-range chromatin interactions . These interactions arise as a result of the three-dimensional ( 3D ) conformation of chromosomes in the cell nucleus . This 3D conformation can also result in the co-localization of co-regulated genes . To investigate this , we asked whether genome-wide chromatin interactions can predict co-expression patterns of genes . To address this question , we characterized 3D interactions between genes , captured by Hi-C measurements , by a network , termed chromatin interaction network ( CIN ) . We applied scale-aware topological measures to the network to comprehensively characterize the chromatin interactions at different scales , ranging from direct interaction between gene pairs to chromatin compartment interactions . We then used multi-scale chromatin interactions to predict spatial co-expression patterns in the mouse cortex . The results show that the prediction performance improves when scale-aware topological measures of the multi-resolution chromatin interaction network are used . | [
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| 2015 | Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex |
Epithelial renewal in the Drosophila intestine is orchestrated by Intestinal Stem Cells ( ISCs ) . Following damage or stress the intestinal epithelium produces ligands that activate the epidermal growth factor receptor ( EGFR ) in ISCs . This promotes their growth and division and , thereby , epithelial regeneration . Here we demonstrate that the HMG-box transcriptional repressor , Capicua ( Cic ) , mediates these functions of EGFR signaling . Depleting Cic in ISCs activated them for division , whereas overexpressed Cic inhibited ISC proliferation and midgut regeneration . Epistasis tests showed that Cic acted as an essential downstream effector of EGFR/Ras signaling , and immunofluorescence showed that Cic’s nuclear localization was regulated by EGFR signaling . ISC-specific mRNA expression profiling and DNA binding mapping using DamID indicated that Cic represses cell proliferation via direct targets including string ( Cdc25 ) , Cyclin E , and the ETS domain transcription factors Ets21C and Pointed ( pnt ) . pnt was required for ISC over-proliferation following Cic depletion , and ectopic pnt restored ISC proliferation even in the presence of overexpressed dominant-active Cic . These studies identify Cic , Pnt , and Ets21C as critical downstream effectors of EGFR signaling in Drosophila ISCs .
EGFR/Ras/MAPK signaling has diverse functions in regulating cell proliferation , growth , differentiation and survival in most animal cells [1] . Abundant studies also indicate that epidermal growth factor receptor ( EGFR ) activation is a causal driver of many cancers , including breast , lung , brain , and colorectal cancer [2] . Similarly , activating mutations in KRAS and BRAF , which are essential downstream effectors of the EGFR , are among the most common mutations found in a very wide range of human cancers [3 , 4] . However , despite much study , many questions remain to be answered to fully understand the impact of EGFR and its downstream effectors during normal cell function and in carcinogenesis . As many epithelial cancers arise through dysregulation of the stem cell self-renewal and homeostatic maintenance of the epithelium [5] , understanding the precise functions of EGFR signaling in epithelial homeostasis is very important . The Drosophila midgut is an outstanding model system to study the basis of epithelial homeostasis due to its simple structure , similarity to the mammalian intestine , and powerful genetics . As in the mammalian intestine , epithelial turnover in the fly midgut is carried out through a dynamic process mediated by intestinal stem cells ( ISCs ) . ISCs undergo cell division to renew themselves and give rise to transient cells called enteroblasts ( EBs ) , which can further differentiate into either absorptive enterocytes ( ECs ) or secretory enteroendocrine ( EE ) cells . When damaged or aged cells are lost from the fly’s gut epithelium , ISCs respond by dividing to replenish the epithelium [6 , 7 , 8] . During this response multiple Drosophila EGFR ligands , namely spitz ( spi ) , vein ( vn ) , and keren ( krn ) are induced in progenitor cells ( EBs and ISCs ) , visceral muscle ( VM ) and ECs respectively . Thereby , the EGFR signaling pathway is activated in ISCs . This promotes ISC growth , division and midgut epithelial regeneration [9 , 10 , 11] . ISCs defective in EGFR signaling cannot grow or divide , are poorly maintained , and are unable to support midgut epithelial replenishment after enteric infection by the bacteria Pseudomonas entomophila ( P . e . ) [11] or Erwinia carotovora carotovora 15 ( ECC15 ) [12] . Interestingly , the critical role of EGFR signaling in the Drosophila intestine is consistent with its role during mammalian gut homeostasis and colorectal cancer development [10 , 11 , 12 , 13] . EGFR signaling is required for murine ISC growth [14 , 15] , and the deletion of Lrig1 , a negative feedback regulator of EGFR signaling , causes excessive ISC proliferation [16] . Furthermore , adenoma formation in Apcmin/+ mice was severely impaired in a genetic background with partial loss of function of EGFR ( Egfrwa2 ) [17] . Despites its importance , the mechanism by which EGFR/Ras/MAPK signaling promotes ISC proliferation is poorly understood in this cell type . Indeed , despite decades of intensive study , the precise linkage between EGFR/Ras/MAPK signaling and cell growth and division is surprisingly obscure for animal cells in general [3] . Textbook models highlight a prevailing model in which EGFR/Ras signaling controls cell proliferation via a Ras-Myc-CyclinD-Rb pathway [18 , 19] . While this may have relevance in some human cancers it is clearly not the case in normal Drosophila cells , and so other mechanisms should be sought and characterized . One potentially important downstream effector of EGFR signaling is the HMG-box transcriptional repressor Capicua ( Cic ) . This highly conserved DNA binding factor has been shown to act downstream of receptor tyrosine kinase ( RTK ) /Ras/MAPK signaling in Drosophila eye and wing imaginal discs , embryos , and ovaries [20 , 21 , 22 , 23] where it regulates diverse RTK-dependent processes including cell proliferation , specification , and pattern formation . Cic orthologs from invertebrate and vertebrate species share two well-conserved regions: the HMG-box , presumed to mediate DNA binding at target promoters [21] and a C-terminal domain [24] . The C-terminal region of Drosophila Cic contains a “C1” motif important for repressor activity , and a “C2” motif that functions as a MAPK docking site responsible for downregulation of Cic following the activation of RTK signaling [25] . It has been proposed that MAPK phosphorylates Cic in its C2 motif , and that phosphorylated Cic is either degraded or re-localized to the cytoplasm [25] . Cic downregulation controlled by Torso and EGFR signaling varies in different Drosophila tissues [24] . For example , Torso RTK signaling , which also works via the Ras/Raf/MAPK pathway , apparently increases the rate of Capicua degradation by promoting its accumulation in the cytoplasm [26] . EGFR signaling has been reported to regulate Cic protein in distinct ways in different tissues . Wing and eye discs cell clones mutant for Egfr or Ras showed elevated levels of Cic protein [20 , 27] . In the ovary , in contrast , Cic protein localized to the cytoplasm in cells in which EGFR signaling was active , but in nuclei in cells in which EGFR signaling was inactive [25] . A recent study suggested that Cic actually undergoes a two-step process in releasing its target gene repression: slower changes in nuclear localization occur after a faster reduction of Cic repressor activity [28] . In cultured human cells , EGF stimulated dissociation of human CIC from importin-α4 ( also known as KPNA3 ) , an adaptor required for the nuclear import of many proteins . But full length GFP-CIC was nuclear even after EGF stimulation , and the N-terminal half of the CIC protein was found to be nuclear , even though it does not bind to importin-α4 . Hence the biological significance of the CIC:importin association remains unclear [29] . CIC , the human homolog of Drosophila Cic , has been implicated in several human diseases including spinocerebellar ataxia type 1 ( SCA1 ) neuropathology , oligodendroglioma ( OD ) [30] and Ewing-like sarcoma [31] . Human CIC is frequently mutated in samples from cancer genome studies such as The Cancer Genomic Atlas ( TCGA ) ( S1 Fig ) [32 , 33] . For instance CIC mutation was reported in 6 out of 7 brain tumors [30] , 3 out of 11 breast cancers [34] and 6 out of 72 colorectal cancers [35] . The Drosophila work suggests that in these cases CIC loss might have the same downstream consequences ( e . g . cell transformation ) as oncogenic activation of the EGFR , RAS or BRAF , but this has not been rigorously evaluated . During RNAi screening we discovered that depletion of Cic in Drosophila’s intestinal stem cells ( ISCs ) activates these cells for rampant proliferation [11] . Based on previous studies in other fly organs we hypothesized that Cic might act as an obligate repressor downstream of EGFR signaling , itself a central driver of normal ISC proliferation in both flies and mice , as well as in many human colorectal cancers , which are frequently mutant for RAS , BRAF , or CIC . However , until now this hypothesis had not been tested and the underlying mechanisms via which Cic might control ISC proliferation remained undefined . In this report we demonstrate that Cic acts as a critical negative downstream regulator of EGFR signaling to control ISC proliferation . We show that EGFR/Ras activity controls Cic nuclear localization , and we present RNA-Seq and DamID-Seq datasets that together constitute a genome-wide survey of potential Cic target genes in Drosophila ISCs . Our analysis indicated that Cic not only directly regulates cell cycle regulators such as string ( cdc25 ) and Cyclin E , but also the ETS transcription factors pnt and Ets21C , all of which must be de-repressed to activate ISCs for growth and division .
To investigate a potential role for Cic in regulating ISC proliferation , we used the esg-Gal4-UAS-2XEYFP; Su ( H ) GBE-Gal80 , tub-Gal80ts system ( henceforth referred as esgts; Su ( H ) -Gal80 ) to express UAS-cic-RNAi specifically in ISCs . After 4 days of cic-RNAi induction , a dramatic increase in the number of YFP positive cells ( Fig 1A and 1B ) and a large increase in ISC mitoses were observed ( Fig 1C ) . Most of the PH3+ cells were YFP+ [YFP+ , PH3+ cells = 99 . 37% ( nmidguts = 10 midguts , ncells = 994 ) , YFP- , PH3+ cells = 0 . 63% ( n = 10 , ncells = 7 ) ] , indicating that Cic regulates ISC proliferation cell autonomously . When we used another ISC-specific driver Dlts ( tub-Gal80ts UAS-GFP; Dl-Gal4 ) to knock down cic in ISCs specifically , we not only detected the same overporoliferation phenotype ( S3A , S3B and S3E Fig ) but also found that most of mitotic cells were GFP+ ( S3F Fig ) . Increased GFP+ cells and mitoses were also noticed when the esgGal4 UAS-GFP tub-Gal80ts system ( henceforth referred as esgts ) was used to express UAS-cic-RNAi in ISCs and their undifferentiated daughters , the EBs ( S2A–S2B and S3 Figs ) . To further validate the specificity of this RNAi experiment , GFP-marked ISC clones homozygous for the loss-of-function allele cicfetU6 [22] were generated using the MARCM system [36] ( S2C–S2H Fig ) . The size of marked ISC clones was quantified at intervals after clone induction by measuring GFP-labeled clone areas . cic mutant clones were larger than control clones at all time points assayed ( Fig 1D ) . In addition , the numbers of cells per clone were increased in the cic mutant clones ( Fig 1E ) . To further confirm Cic’s function in the midgut , we generated viable transheterozygotes using three different loss-of-function alleles of cic . cicfetE11 is a P-element insertion mutant , while both cicfetT6 and cicfetU6 are homozygous lethal EMS alleles [22] . In addition to the EGFR-related extra wing vein phenotype reported previously [27] , these transheterozygote mutants showed increased mitoses in their midguts ( Fig 1I ) . As the ISCs are the predominant dividing cell type in Drosophila midguts , these data further indicate a role for Cic as an obligate repressor of ISC proliferation . To investigate the respective requirements of Cic in the ISC and EB cell types , the EB-specific driver Su ( H ) ts [Su ( H ) -Gal4 , UAS-CD8-GFP; tub-Gal80ts] was used to knock down cic in EBs . Increased mitoses were observed after depleting cic in EBs ( S3C–S3E Fig ) . However , in this case only a few GFP+ EBs were observed in mitosis , while most of the dividing cells marked by PH3 were GFP-negative ( S3F Fig ) . These GFP-negative mitotic cells are likely ISCs . These data indicated that Cic has both cell autonomous and non-cell autonomous functions in regulating ISC proliferation . In this study we followed up on Cic’s cell autonomous effects on ISC proliferation , and the non-cell autonomous effect was not investigated further . To determine whether increased Cic function yields a phenotype similar to that of EGFR loss-of-function , we generated transgenic flies harboring UAS-cicΔC2-HA or UAS-cic-HA . CicΔC2 is a Cic derivative carrying a deletion of the MAPK docking site-C2 motif , and has been shown to be a dominant repressor that escapes inactivation by MAPK [25] . Either cic or cicΔC2 were over-expressed in progenitor cells using esgts , and then the flies were fed Pseudomonas entomophila ( P . e . ) for 12 hours to generate an enteric infection . ISCs from control midguts , which expressed GFP only , showed regeneration-associated proliferation [8] . In contrast both cic and cicΔC2 overexpressing midguts displayed an inhibition of regeneration after 12 hours P . e . infection ( Fig 1J ) . To test if cic or cicΔC2 overexpression could influence turnover of the midgut epithelium we used the esgts F/O system ( esg-Gal4; tubGal80ts Act>Cd2>Gal4 UAS-flp UAS-GFP ) [11] to mark all the ISC progeny produced during 12 days of cic overexpression . Normally , the posterior midgut epithelium renews it self within about 12 days [8] . Therefore , control midgut epithelia were almost completely replaced by large GFP+ clones that formed during 12 days . However , in the gain-of-function Cic conditions , growth of GFP-marked clones was significantly decreased , indicating that gut epithelial renewal was greatly suppressed ( Fig 1H–1J ) . EGFR activates ISCs for growth and division via Ras/Raf/MAPK signaling . When an activated form of the EGFR ( λTOP ) [37] or activated Ras ( RasV12G ) [38] is ectopically expressed in progenitor cells , ISC division is dramatically induced . Conversely , EGFR suppression by inducing Egfr-RNAi , Ras-RNAi , or MEK-RNAi in progenitor cells almost completely inhibits ISC division and growth [11 , 12] . Furthermore , inhibition of EGFR signaling suppresses the activation of ISC divisions after P . e . infection [10 , 11] . As demonstrated above , Cic knockdown and overexpression phenocopy these EGFR overexpression or knockdown phenotypes , respectively , suggesting that Cic may act as a downstream effector in the EGFR signaling in ISCs . To test the function of Cic in EGFR signaling we performed epistasis tests . After 2 days of clone induction with the esgts F/O system , control midguts generated only 2-cell clones , whereas clones overexpressing an activated variant of the EGFR , ( λtop ) , grew very large and showed increased ISC division . However , when cic or cicΔC2 was co-overexpressed along with λtop , clone sizes and ISC mitoses were significantly reduced ( Fig 2A–2C and 2I ) . Overexpression of Cic or CicΔC2 could also partially inhibit the ISC growth effects of RasV12S35 , an activated allele that can activate RAF/MAPK signaling but not PI3K signaling [38] ( Fig 2D , 2H , and 2J ) . Furthermore , we used esgts to induce Egfr-RNAi , or Ras-RNAi in combination with cic-RNAi . The cic , Egfr or cic , Ras double RNAi animals exhibited increased ISC mitosis relative to controls expressing Ras-RNAi or Egfr-RNAi only ( Fig 2E–2G and 2K ) , indicating that reduced ISC proliferation caused by the inactivation of EGFR signaling can be restored by Cic knock-down . These epistasis data further support the hypothesis that Cic acts as a negative downstream effector of EGFR to regulate ISC proliferation . To understand how EGFR signaling controls Cic in ISCs , we expressed HA-tagged Cic or CicΔC2 protein in midgut progenitor cells ( ISCs and EBs ) . As expected , HA-tagged Cic or CicΔC2 proteins were only detected in nuclei under normal conditions ( Fig 3A–3A’ and 3B–3B’ ) . However , HA-tagged Cic protein accumulated nearly exclusively in the cytoplasm when RasV12S35 was co-expressed with it ( Fig 3E–3E’ ) . In contrast , CicΔC2 remained in the nucleus even following ectopic RasV12S35 expression ( Fig 3F–3F’ ) . A similar but milder re-localization of Cic protein from the nucleus to the cytoplasm was observed following P . e . infection ( Fig 3C and 3C’ ) , a treatment known to increase MAPK signaling in the gut [11] . It is interesting to note that CicΔC2 did not completely suppress RasV12S35 induced ISC proliferation , even though it remained localized to nuclei in RasV12S35 expressing cells ( Fig 2H and 2J ) . However , nuclear CicΔC2 lost its characteristic punctate localization in the presence of RasV12S35 expression , and became more diffusely localized in the nucleoplasm ( Fig 3F–3F’ ) . These results suggest that , although EGFR signaling controls Cic nucleo-cytoplamic localization via the C2 motif , there may be a second MAPK-dependent mechanism to regulate Cic repressor activity , involving dissociation from chromatin , that is C2-independent . Cic has been studied in several cell types from both Drosophila and humans . In human melanoma cells , CIC represses mRNA expression of the PEA3 subfamily of ETS transcription factors , namely ETV1 , ETV4 and ETV5 [29] . In early Drosophila development post-transcriptional down-regulation of Cic by the Torso and EGFR pathways regulates terminal and dorsal-ventral patterning , respectively , by allowing expression of Cic target genes such as huckebein ( hkb ) , intermediate neuroblasts defective ( ind ) , and argos ( aos ) [39] . However , a genome-wide mapping of Cic target genes has not yet been reported . To identify Cic target genes involved in ISC growth and proliferation we profiled Cic binding throughout the genome using the “TaDa” ( Targeted DamID ) ” technique . The TaDa method involves low-level expression of a GAL4-inducible Dam methylase-fusion protein in a specific cell type , enabling cell-specific profiling without cell isolation [40 , 41] . Here , we induced a low level of Dam-only or Dam-Cic fusion protein in progenitor cells ( ISC & EB ) using the esgts system and a 24-hour induction . Genomic DNA was extracted from isolated midguts , digested with Dpn I , which cuts only methylated GATCs , and amplified . The amplified gDNA fragments were subjected to high-throughput sequencing , rather than tiling microarrays as previously reported [40] . We identified 2279 binding sites that were highly enriched ( log2 fold change > 3 , false discovery rate<0 . 01% ) when comparing Dam-Cic to Dam alone samples ( S1 Table ) . These sites were non-randomly distributed in the genome , and were significantly over-represented ~500 bp 5’ to Transcription Start Sites ( TSS; Fig 4A ) . Cic DamID was also performed on progenitor cells from P . e . infected midguts . After a 24 hours induction of Dam or Dam-Cic transgenes via the esgts system , flies were fed P . e . bacteria for 16 hours . The number of highly enriched ( log2 fold change > 3 , FDR < 0 . 1% ) peaks was reduced to 1903 . In addition , the fold change of peaks ( Dam-Cic vs Dam-alone ) after P . e . infection was significantly decreased ( Figs 4B–4C and S4A ) . The frequency of peaks adjacent to TSS was also significantly reduced in the P . e . -infected midgut sample ( Fig 4A ) . We believe that this decrease was due to the change of Cic localization from the nucleus to cytoplasm , which was caused by the activation of EGFR/Ras/MAPK signaling after infection . To further understand how Cic regulates ISC proliferation we performed gene expression profiling using amplified mRNA from FACS-sorted esg+ progenitor cells that expressed cic-RNAi , and controls . As a way to identify potentially direct target genes of Cic , the RNA-Seq and DamID-Seq data sets were cross-compared . Amongst 439 transcriptionally up-regulated genes ( >1 . 5 fold change , 90% CI ) ( S2 Table ) , a large fraction [134 genes , ( S3 Table ) ] had Cic binding sites as defined by DamID ( Fig 4E ) . We next examined the enrichment of the DamID peaks in the transcriptionally induced genes , ranked by absolute expression change in cic knockdown progenitor cells ( see Materials and Methods ) . Cic binding peaks that were significantly reduced upon P . e . infection ( < 2 fold change ) were enriched in up-regulated genes from the RNA-Seq dataset ( Fig 4F ) . Hence , the set of genes present in the overlapping set are likely to be direct target genes of Cic . Many cell cycle regulators and genes involved in DNA replication were upregulated in Cic-depleted progenitor cells ( Fig 4D ) . In addition , a large portion of cell cycle control genes that were upregulated upon cic-RNAi , including string ( stg , Cdc25 ) and Cyclin E ( CycE ) , had Cic binding sites ( Fig 4D , 4G , and 4H ) . To further assess the reliability of this approach we examined the occupancy of Cic on its previously characterized direct target gene-aos [39] . Our DamID-Seq data showed that aos contained two Cic binding sites within its enhancer , and that their occupancy was significantly reduced after P . e . infection ( S4B Fig ) . The significant induction of aos transcription was verified both by RNA-Seq and qRT-PCR data from FACS-sorted progenitor cells expressing cic-RNAi ( S4C Fig ) . Having confirmed the reliability of our approach for identifying genes that are repressed by Cic in ISCs , we focused on genes likely to contribute to ISC proliferation . We were interested in stg and CycE because they are transcriptionally induced in proliferating ISCs [42] , required for ISC divisions , and also sufficient to induce sustained ISC division when co-overexpresssed [42] . To further test whether Cic regulates the transcription of stg and CycE we measured their normalized expression ratios in gain- or loss-of-function Cic midguts via RT-qPCR ( Fig 4I–4K ) . The stg and CycE mRNAs were significantly increased in Cic-depleted midguts , and decreased in midguts expressing the dominant active CicΔC2 . Strong inductions of stg and CycE were also observed in Cic-depleted progenitor cells or ISCs purified using FACS ( Fig 4J ) . Moreover , both the stg and CycE loci had multiple strong Cic-Dam-ID binding peaks containing TGAATG ( G/A ) A motifs , and binding these peaks were reduced by P . e . infection ( Fig 4G and 4H ) . Consistently , the induction of stg and CycE transcription upon P . e . infection was significantly repressed by CicΔC2 overexpression ( Fig 4K ) . These data support the notion that Cic controls ISC cell cycle progression by directly repressing transcription of stg and CycE via binding sites in their regulatory regions . It has been suggested that Cic might regulate the transcription of certain members in a subfamily of ETS transcription factors [29 , 31] . Consistent with this , we identified the Drosophila ETS transcription factors pnt and Ets21C as potential Cic direct target genes by both RNA-Seq and DamID-Seq ( Figs 5 and S5 ) . These genes contain Cic binding sites , were highly expressed in midgut progenitor cells , and were significantly induced upon infection or cic depletion or mutation . Notably , induction of pnt and Ets21C was detected in FACS-sorted ISCs depleted of Cic ( Fig 5B ) . Moreover , the induction of pnt and Ets21C expression by P . e . infection was suppressed when the dominant active form , CicΔC2 was overexpressed ( Fig 5D ) . Similar effects were observed when Cic was either depleted or overexpressed in whole midgut samples ( Figs 5C and S5C ) . These data suggest that Cic also regulates pnt and Ets21C transcription in Drosophila midgut ISCs , by directly binding to these loci . As in the case of stg and CycE , this regulation appeared to be modulated by P . e . infection , most likely in a MAPK-dependent manner . The HMG box of Human Cic binds to TGAATG ( G/A ) A octamers in vitro [31] . This motif was also verified as a Cic binding sequence in several Cic target genes in Drosophila embryos and wing discs [39] . Notably , the TGAATG ( G/A ) A motif was observed in 692/2279 Cic binding sites in our DamID-Seq dataset ( p-value = 3 . 045475× 10−11 ) . Each of the four Cic target genes discussed above contained more than one TGAATG ( G/A ) A motifs in its Cic binding sites . Moreover , TGAATGAA motifs found in the pnt locus also mapped to Cic binding sites that we determined from Drosophila embryo ChIP-Seq ( Fig 5E ) . This suggests that Cic may bind to the pnt locus via TGAATGAA octamers , and that the occupancy of Cic at the pnt locus may also be conserved in different Drosophila cell types . To further evaluate this hypothesis we performed electrophoretic mobility shift assay ( EMSA ) . Cic showed specific binding to two DNA fragments from the pnt locus that were identified as prominent in vivo Cic binding peaks by DamID-Seq and ChiP-Seq ( Fig 5F and 5G ) . Importantly , the EMSA interaction was lost when the HMG-box in Cic was mutated , or when the TGAATGAA motifs were mutated . These data strongly support the idea that Cic directly regulates pnt transcription by directly binding to TGAATGAA motif in pnt locus . Pnt is believed to be a downstream effector of EGFR signaling in developing Drosophila eyes [43 , 44 , 45] . The pnt locus produces two alternative transcripts that encode two different protein isoforms: PNTP1 and PNTP2 [44] . PNTP1 was proposed to be a constitutive activator of transcription , whereas PNTP2 has a PNT ( pointed ) domain that was reported to be phosphorylated by MAP kinase in vitro [45] . The mutant protein , PNTP2T151A , which cannot be phosphorylated in vitro , was unable to rescue pnt phenotype in eyes but instead enhanced the mutant phenotype , suggesting that the PNT domain is an auto-inhibitory domain that can be inactivated by MAPK-dependent phosphorylation [45] . Furthermore PNTP2 is thought to induce transcription of PNTP1 , which might thereby encode the final nuclear effector of the EGFR pathway in eye discs [43] . In the midgut , we found an interesting interaction between Pnt and Cic: pntP1 and pntP2 were both induced when Cic was depleted , and both decreased when Cic was overexpressed ( Figs 5B , 5C , and S5A ) . The expression of transcripts encoding both isoforms was also increased in P . e . infected guts ( Figs 5D and S5B ) . This raises the possibility that pnt might be an important downstream effector of Cic in controlling ISC proliferation . To test this we over-expressed either pntP1 or pntP2 in progenitor cells using the esgts or Dlts driver systems . After 4 days of transgene induction a dramatic increase in ISC division was evident in response to either pntP1 or pntP2 ( Figs 6A–6B , 6I , and S6A–S6B ) . Conversely , mutant clones that were generated using a pnt null allele ( pnt Δ88 ) [46] did not grow past the 2-cell stage ( S6D Fig ) . Moreover , when we depleted pnt in progenitor cells by expressing a pnt-RNAi that recognizes both isoforms , or generated homozygous pnt null mutant ISCs via MARCM , ISC proliferation after P . e . infection was suppressed ( Figs 6C–6D , 6J , and S6E ) . Next , we investigated the functional significance of the inhibition of pnt expression by Cic . Whereas loss of Cic function induced massive ISC proliferation , inhibiting both isoforms of pnt in this context suppressed this over-proliferation ( Figs 6G–6H , 6K and S6F–S6G ) . Conversely , when we over-expressed either pntP1 or pntP2 in ISCs that also overexpressed CicΔC2 , the inhibitory effect of CicΔC2 on proliferation was bypassed and the cells divided ( Figs 6F , 6L and S6C ) . Hence , a significant fraction of the ISC over proliferation caused by Cic knockdown can be attributed to Cic’s effects on pntP1 and pntP2 Interestingly , mutant clones generated using a pntP1 specific mutant allele , pnt Δ33 [45 , 47] , or a pntP2 specific mutant allele , pnt Δ78 [45 , 47] , grew normally . However ISCs mutant for the pnt null allele pnt Δ88 did not expand ( S6D Fig ) . In addition , pnt Δ33 and pnt Δ78 homozygous clones in which cic was depleted by RNAi had similar numbers of cells to cic-depleted control clones ( i . e . they overgrew ) , whereas pnt Δ88 null mutant clones contained significantly fewer cells ( S6G Fig ) . These data not only support our conclusion that pnt is required for ISC proliferation as a target of Cic , but show that PNTP1 and PNTP2 have redundant function in regulating ISC proliferation . Furthermore , pntP2 homozygous mutant ISCs did not appear to have any defect in proliferation upon P . e . infection ( S6E Fig ) . Overall these results indicate that pntP2 , the isoform proposed to be activated directly by MAKP phosphorylation [45] , is not specifically required in ISC proliferation . Pnt is the Drosophila ortholog of the human ETS2 transcription factor and has a conserved ETS-type DNA binding domain , while Ets21C is the Drosophila ortholog of the human proto-oncogene ERG . In addition to having Cic binding sites , RT-PCR and RNA-Seq data showed that Ets21C was highly induced upon P . e . infection ( Figs 5A and S5C ) . Moreover RNAi mediated depletion experiments indicated that Ets21C was also required for ISC proliferation in response to P . e . infection ( Fig 6J ) . Over-expression of Ets21C caused a strong increase of ISC division ( Fig 6E and 6I ) suggesting that transcriptional induction of Ets21C could promote ISC proliferation . Furthermore , ectopic expression of Ets21C in progenitor cells could bypass the strong growth-suppressive effect of depleting MEK ( Fig 6M ) . These data indicated that Cic controls ISC proliferation in part by regulating Ets21C transcription . Finally , we tested whether Yan , an inhibitory ETS type transcription factor , reported to be MAPK responsive and to compete with Pointed for binding to common sites on the DNA [45 , 48 , 49] , had an opposite function in ISCs . Although yan mRNA is expressed in the midgut ( Fig 5A ) , yan depletion from ISCs did not produce a detectable effect ( S6F Fig ) . Two independent yan-RNAi lines were used , both of which were proven to be effective by qRT-PCR ( S6H Fig ) . In summary these observations suggest that EGFR signaling controls ISC growth and division by regulating the activity of Cic , Pnt and Ets21C but not Yan , and that Cic directly represses pntP1 , pntP2 and Ets21C in this context .
It is well established that EGFR signaling is essential to drive ISC growth and division in the fly midgut [10 , 11 , 12] . However , the precise mechanism via which this signal transduction pathway activates ISCs has remained a matter of inference from experiments with other cell types . Moreover , despite a vast literature on the pathway and ubiquitous coverage in molecular biology textbooks , the mechanisms of action of the pathway downstream of the MAPK are not well understood for any cell type . From this study , we propose a model summarized in Fig 7 . Multiple EGFR ligands and Rhomboid proteases are induced in the midgut upon epithelial damage , which results in the activation of the EGFR , RAS , RAF , MEK , and MAPK in ISCs . MAPK phosphorylates Cic in the nucleus , which causes it to dissociate from regulatory sites on its target genes and also translocate to the cytoplasm . This allows the de-repression of target genes , which may then be activated for transcription by factors already present in the ISCs . The critical Cic target genes we identified include the cell cycle regulators stg ( Cdc25 ) and Cyclin E , which in combination are sufficient to drive dormant ISCs through S and M phases , and pnt and Ets21C , ETS-type transcriptional activators that are required and sufficient for ISC activation . Although we found more than 2000 Cic binding sites in the ISC genome , not all of the genes associated with these sites were significantly upregulated , as assayed by RNA-Seq , upon Cic depletion . One possible explanation for this is that Cic binding sites from DamID-Seq were also associated with other types of transcription units ( miRNAs , snRNAs , tRNAs , rRNAs , lncRNAs ) that were not scored for activation by our RNA-Seq analysis . Indeed a survey of the Cic binding site distributions suggests this ( S5 Table ) . This might classify some binding sites as non-mRNA-associated . However , it is also possible that many Cic target genes may require activating transcription factors that are not expressed in ISCs . Such genes might not be strongly de-repressed in the gut upon Cic depletion . In other Drosophila cells MAPK phosphorylation is thought to directly inactivate the ETS domain repressor Yan , and to directly activate the ETS domain transcriptional activator Pointed P2 ( PNTP2 ) [45 , 50] . In fact Pnt and Yan have been shown to compete for common DNA binding sites on their target genes [45 , 48 , 49] . Thus , previous studies proposed a model of transcriptional control by MAPK based solely on post-translational control of the activity of these ETS factors . However , we found that Pnt and Ets21C were transcriptionally induced by MAPK signaling , and could activate ISCs if overexpressed , and that depleting yan or pntP2 had no detectable proliferation phenotype . In addition , overexpression of PNTP2 was sufficient to trigger ISC proliferation , suggesting either that basal MAPK activity is sufficient for its post-translational activation , or that PNTP2 phosphorylation is not obligatory for activity . Moreover , pntP2 loss of function mutant ISC clones had no deficiency in growth ( S6D Fig ) even after inducing proliferation by P . e . infection , which increases MAPK signaling ( S6E Fig ) . These observations indicate that the direct MAPK→PNTP2 phospho-activation pathway is not uniquely or specifically required for ISC proliferation . Or results suggest instead that transcriptional activation of pnt and Ets21c via MAPK-dependent loss of Cic-mediated repression is the predominant mode of downstream regulation by MAPK in midgut ISCs . In addition to activating ISCs for division , EGFR signaling activates them for growth . Previous studies showed loss of EGFR signaling prevented ISC growth and division , and that ectopic RasV12 expression could accelerate the growth not only of ISCs but also post-mitotic enteroblasts [11] . Similarly , our study shows that loss of cic caused ISC clones to grow faster than controls , by increasing cell number as well as cell size ( Figs 1H and S2C–S2H ) . For instance , increased size of GFP+ ISCs and EBs was observed when cic-RNAi was induced by the esgts or esgtsF/O systems ( Figs 1B , 6G and S2B ) . Therefore , in our search for Cic target genes we specifically checked probable growth regulatory genes such as Myc , Cyclin D , the Insulin/TOR components InR , PI3K , S6K and Rheb , Hpo pathway components , and the loci encoding rRNA , tRNAs and snRNAs . We found that Cic bound to the InR , Akt1 , Rheb , Src42A and Yki loci . However , of these only InR mRNA was significantly upregulated in Cic-depleted progenitor cells ( S4 Table ) . In surveying the non-protein coding genome , we found that Cic had binding sites in many loci encoding tRNA , snRNA , snoRNA and other non-coding RNAs ( S5 Table ) , though not in the 28S rRNA or 5S rRNA genes ( S4 Table ) . Due to the method we used for RNA-Seq library production , our RNA expression profiling experiments could not detect expression of these loci , and so it remains to be tested whether Cic may regulate some of those non-coding RNA’s transcription to control cell growth . It is also possible that Cic controls cell growth regulatory target genes indirectly , for instance via its targets Ets21C and Pnt , which are also strong growth promoters in the midgut ( Figs 6A–6B , 6E and S6A–S6B ) . But given that no conclusive model can be drawn from our data regarding how Cic restrains growth , one must consider the possibility that ERK signaling stimulates cell growth via non-transcriptional mechanisms , as proposed by several studies [51 , 52 , 53 , 54] . The critical role of Cic as a negative regulator of cell proliferation in the fly midgut is consistent with its tumor suppressor function in mammalian cancer development ( S1 Fig ) . Also consistent with our findings are the observations that the ETS transcription factors ETV1 and ETV5 are upregulated in sarcomas that express CIC-DUX , an oncogenic fusion protein that functions as a transcriptional activator [31] , and that knockdown of CIC induces the transcription of ETV1 , ETV4 and ETV5 in melanoma cells [29] . Moreover the transcriptional regulation by ETS transcription factors is important in human cancer development ( S7 Fig ) . Their expression is induced in many tumors and cancer cell lines . For example , ERG , ETV1 , and ETV4 can be upregulated in prostrate cancers [55] , and ETV1 is upregulated in post gastrointestinal stromal tumors [56] and in more than 40% of melanomas [57] . In addition , the mRNA expression of these ETS genes was correlated with ERK activity in melanoma and colon cancer cell lines with activating mutations in BRAF ( V600E ) , such that their expression decreased upon MEK inhibitor treatment [58] . Furthermore , overexpression of the oncogenic ETS proteins ERG or ETV1 in normal prostate cells can activate a Ras/MAPK-dependent gene expression program in the absence of ERK activation [59] . These cancer studies imply that there is an unknown factor that links Ras/Mapk activity to the expression of ETS factors , and that some of the human ETS factors might act without MAPK phosphorylation , as does Drosophila PntP1 . Combining our knowledge of Cic with what was previously known about CIC in tumor development , we propose that CIC is the unknown factor that regulates ETS transcription factors in Ras/MAKP-activated human tumors . In summary , our study has elucidated a mechanism wherein Cic controls the expression of the cell cycle regulators stg ( Cdc25 ) and Cyclin E , along with the Ets transcription factor Pnt , and perhaps also Ets21C , by directly binding to regulatory sites in their promoters and introns . Using genetic tests we show that these interactions are meaningful for regulating stem cell proliferation . Therefore , we suggest that human CIC may also lead to the transcriptional induction of cell cycle genes and ETS transcription factors in RAS/MAPK activated- or loss-of-function-CIC tumors such as brain or colorectal cancers .
esgts: esg-Gal4/Cyo; tubGal80ts UAS-GFP/TM6B [60] esgts F/O: esg-Gal4 tubGal80ts UAS-GFP/Cyo;UASflp>CD2>Gal4/TM6B [8] Tubts: tub-Gal4; tubGal80ts/TM3 , ser [61] ( provided from Valeria Cavaliere lab ) esgts; Su ( H ) -Gal80: esg-Gal4-UAS-2XEYFP; Su ( H ) GBE- Gal80 , tub-Gal80ts ( Gift from Steven Hou’s lab ) UAS-λTOP/FM7 [37] UAS-RASv12s35 [38] UAS-Ras RNAi [11] UAS-Egfr RNAi [11] UAS-cic-RNAi/Cyo ( VDRC KK103805 ) UAS-cic-RNAi/Cyo ( VDRC KK103012 ) UAS-pnt . P1 ( Bloomington Drosophila Stock Center 869 ) UAS-pnt . P2 ( Bloomington Drosophila Stock Center 399 ) UAS-pnt-RNAi ( Bloomington Drosophila Stock Center 31936 ) UAS-pnt-RNAi ( Bloomington Drosophila Stock Center 35808 ) UAS-yan-RNAi ( Bloomington Drosophila Stock Center 26759 ) UAS-yan-RNAi ( Bloomington Drosophila Stock Center 34909 ) UAS-yan-RNAi ( Bloomington Drosophila Stock Center 35404 ) UAS-Ets21C-RNAi ( VDRC KK103211 ) FRT82B cicfetu6 / TM3 , Sb , Se ( gift from Jimenez lab , Barcelona ) w; cicfetT6 /TM3 , Ser ( gift from Nilson lab , Canada ) w; cicfetE11 / TM6b ( gift from Nilson lab , Canada ) w; +; UAS-cic-HA w; UAS-cic-HA; + w; +;UAS-cic ΔC2-HA w; UAS-cic ΔC2-HA; + FRT82B pnt Δ33 [45 , 47] ( gift from Joseph Bateman lab , Wolfson Centre for Age-Related Diseases ) FRT82B pnt Δ78 [45 , 47] ( gift from Joseph Bateman lab , Wolfson Centre for Age-Related Diseases ) FRT82B pnt Δ88[45 , 47] ( gift from Joseph Bateman lab , Wolfson Centre for Age-Related Diseases ) The cic ΔC2 was amplified from the pCasper4—cic ΔC2 plasmid . The cic or cic ΔC2 cDNAs were inserted into pUASg-attB-HA [62] vector and used to generate transgenic flies . To generate UAS-cicDam transgenic flies , Cic was amplified from a cDNA library prepared from midgut . This cic cDNA was inserted into the pUASTattB-LT3-NDam plasmid ( from Andrea brand lab ) , and transgenics were produced . Ectopic expression of transgenes in the adult midgut was achieved using the temperature sensitive inducible UAS-Gal4 system [63] , TARGET . Crosses were set up and maintained at 18°C , the permissive temperature . 3–7 day old flies were shifted to 29°C for different times as indicated . Gut infections were performed by feeding flies live P . e . in 5% sucrose on Whatman filter paper and yeast paste at 29°C . The MARCM system was used to generate ISC clones . In order to reduce heat shock dependent stress , the clones were induced by heat shocking 3–5 days old flies at 34°C for 20 minutes . The heat shocked flies were then kept at 25°C . Clone size was measured after 10 , 20 , 30 days of clone induction . The size of the clones was quantified by Fiji software measuring GFP+ area from z-projected confocal microscopy images . Female adult flies were dissected in 1×PBS . Midguts were fixed in 1×PBS with 4% paraformaldehyde for 30 minutes at room temperature . Samples were washed in PBS with 0 . 1% X-100 ( PBST ) for 3x10 minutes each . Then the tissues were blocked in PBS with 0 . 1% X-100 , 2 . 5%BSA , 10% NGS for at least 30 min at room temperature . All samples were incubated with primary antibody overnight at the following dilutions: rat anti-HA ( 1:200; Roche ) , guinea pig anti-Cic ( 1:1000 , generated by author ) , rabbit anti-PH3 ( 1:1000 , Millipore ) . After washing 3 times 10 minutes each in PBST , samples were incubated with secondary antibodies for at least 2 hours at room temperature at a dilution of 1:1000 . DNA was visualized with DAPI ( 0 . 1mg/ml , Sigma ) , diluted 1:200 . Images of Figs 1A–1B and 2E–2H were acquired by Delta vision microscope and the rest of the fluorescence images were taken by Leica SP5 confocal microscope . Images were then processed using Fiiji and Adobe Photoshop software . RNA was extracted from 10–12 female midguts using the RNAeasy kit ( QIAGEN ) . RNA isolation from sorted cells was performed as previously described [64] and 100ng RNA ( non-amplifed ) used for reverse transcription . cDNA was synthesized by QuantiTect reverse transcription kit ( QIAGEN ) . RT-qPCR was performed on a Light Cycler 480 II using SYBR Green I ( Roche ) . Experiments were performed in biological triplicate . Relative fold differences in expression level of target genes were calculated as ratios to the mean of the reference genes rp49 [65] and tubulin [23] . Primer sequences are given in Supplementary Material and Methods . RNA isolation and amplification from sorted cells was performed as previously described [64] . Four independent biological replicates were used for sequencing . Raw reads were checked for quality using Fastqc and subsequently aligned using Tophat2 , version 2 . 0 . 9 , against the Flybase genome version 6 . Mapped reads were counted using HTSeq-count version 0 . 5 . 4p5 [66] with mode „union“ . Genes showing a cpm value below 1 in four samples per treatment were considered as poorly expressed and filtered out before conducting differential expression analysis using edgeR , version 3 . 2 . 4 [67] . Since our replicates were generated independently , we used a paired design and corrected the resulting p-values by the Benjamini-Hochberg method [68] . Subsequently , genes with a fold change of 1 . 5 and an adjusted p-value lower than 0 . 1 were considered as significantly deregulated . Rp49 –Forward: TCGATATGCTAAGCTGTC Rp49 –Reverse: GGCATCAGATACTGTCCCTTG β-tubulin-Forward: ACATCCCGCCCCGTGGTC β-tubulin-Reverse: AGAAAGCCTTGCGCCTGAACATAG pnt-Forward: ACGCCCTATGATGCTCAATC pnt-Reverse: TATCCAGACCCAAGGTGCTC pntP1-Forward: CGACTGCGAACAATCTGGT pntP1-Reverse: TTGCTGGTGTTGTAGCCTGT pntP2-Forward: TTAGCCAATTGAACGGCATC pntP2-Reverse: GCACAGATCCTTGCATCCAT Ets21C-Forward: CCGGGCACTCAGGTACTACT Ets21C-Reverse: CATACTGGAGGCCGGATCT aos-Forward: AGAACCCATGGCTTACATGC aos-Reverse: CGTCGCGGGTGTTAAGTTAC yan-Forward: CTGCTGGTCATCGTGCTTAG yan-Reverse: GACCTCAGTGTGAGCAGCAA stg-Forward: CAGCATGGATTGCAATATCAGTA stg-Reverse: CAACGTCGTCGTCGTAGAAC CycE-Forward: ACAAATTTGGCCTGGGACTA CycE-Reverse: GGCCATAAGCACTTCGTC | Studies suggest that epidermal growth factor receptor ( EGFR ) signaling activation is a causal driver of many stem cell-derived epithelial cancers , including colorectal cancer . As in the human intestine , epithelial renewal in Drosophila intestine is orchestrated by intestinal stem cells ( ISCs ) . EGFR signaling also plays an important role in regulating ISC proliferation in flies . However , the mechanism by which EGFR/Ras/MAPK signaling promotes ISC proliferation is poorly understood . Here we demonstrate that the transcriptional repressor , Capicua ( Cic ) , mediates these functions of EGFR signaling . We found that the critical role of Cic as a negative regulator of cell proliferation in the fly midgut is consistent with its tumor suppressor function in mammalian cancer development . The direct target genes of Cic were identified by ISC-specific mRNA expression profiling and DNA binding mapping ( DamID ) method . Cic represses cell proliferation via regulating string ( stg ) , Cyclin E ( CycE ) , and the ETS domain transcription factors Ets21C and pointed ( pnt ) . Using genetic tests we show that these interactions are meaningful for regulating stem cell proliferation . Combining our knowledge of Cic with what was previously known about CIC in tumor development , we propose that human CIC may regulate Ets transcription factors and cell cycle genes in Ras/MAKP-activated tumors . | [
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| 2015 | EGFR/Ras Signaling Controls Drosophila Intestinal Stem Cell Proliferation via Capicua-Regulated Genes |
Neisseria meningitidis is a commensal of human nasopharynx . In some circumstances , this bacteria can invade the bloodstream and , after crossing the blood brain barrier , the meninges . A filamentous phage , designated MDAΦ for Meningococcal Disease Associated , has been associated with invasive disease . In this work we show that the prophage is not associated with a higher virulence during the bloodstream phase of the disease . However , looking at the interaction of N . meningitidis with epithelial cells , a step essential for colonization of the nasopharynx , we demonstrate that the presence of the prophage , via the production of viruses , increases colonization of encapsulated meningococci onto monolayers of epithelial cells . The analysis of the biomass covering the epithelial cells revealed that meningococci are bound to the apical surface of host cells by few layers of heavily piliated bacteria , whereas , in the upper layers , bacteria are non-piliated but surrounded by phage particles which ( i ) form bundles of filaments , and/or ( ii ) are in some places associated with bacteria . The latter are likely to correspond to growing bacteriophages during their extrusion through the outer membrane . These data suggest that , as the biomass increases , the loss of piliation in the upper layers of the biomass does not allow type IV pilus bacterial aggregation , but is compensated by a large production of phage particles that promote bacterial aggregation via the formation of bundles of phage filaments linked to the bacterial cell walls . We propose that MDAΦ by increasing bacterial colonization in the mucosa at the site-of-entry , increase the occurrence of diseases .
Neisseria meningitidis ( Nm ) is a commensal bacterium commonly carried asymptomatically in the human nasopharynx . In a small proportion of colonized people , the bacteria invade the bloodstream from where they cause septicaemia and/or meningitis after crossing the blood brain barrier . Most meningococcal diseases are caused by bacteria belonging to only a few of the phylogenetic groups that constitute the population structure of this genetically variable organism [1] . Numerous virulence factors are expressed by meningococci . The capsular polysaccharide , the iron chelation systems [2] and the factor H binding protein are required by the bacteria to survive in the extra cellular fluids [3] . The type IV pili and Opa proteins are important for bacterial host cell interaction and allow nasopharyngeal colonization [4] . When bacteria are encapsulated , type IV pili are the sole bacterial attribute able to aggregate bacteria and to initiate the interaction with host cells . None of these virulence factors is specific of disease isolates and these bacterial attributes are also found in bacteria belonging to clonal complexes associated with a carrier state . In order to get insights into the genetic basis responsible for the differences in pathogenic potential , a whole genome comparison using a collection of meningococci of defined pathogenic potential was performed . This study brought to light a sequence of 8 kb , designated MDA for Meningococcal Disease Associated island , which is associated with an increase ability of invasive disease [5 , 6] . Subsequent studies have demonstrated that the MDA island encodes a functional filamentous prophage , designated MDAΦ , able to produce infectious filamentous phage particles [7] ( S1 Fig highlights the organization of the MDAΦ genome ) . However the mechanism by which the MDAΦ prophage increases bacterial invasiveness remains unknown . Horizontally transferable mobile elements ( plasmids , transposons , genetics islands and bacteriophages ) are responsible for the acquisition of novel properties by bacteria , such as antibiotic resistances , detoxification of heavy metals , or virulence factors [8 , 9] . Filamentous bacteriophages are part of these horizontally mobile elements [10] . CTXΦ of Vibrio cholerae , which encodes the cholera toxin , can transduce non-toxigenic strains into toxigenic strains , contributing to the emergence of new pathogenic V . cholera clones . The Pf bacteriophages of Pseudomonas aeruginosa are involved in the formation of biofilm by inducing cell death and the subsequent release of bacterial DNA [11] . Moreover , the Pf bacteriophages inside the Pseudomonas biofilm on acellular surfaces interact with the extracellular matrix and enhance biofilm formation by increasing adhesion and tolerance to desiccation and antibiotics [12] . Recently , Secor and colleagues have shown that Pf4 bacteriophages of P . aeruginosa promote bacterial adhesion to mucine and reduce the inflammatory response [13] . Other effects of filamentous bacteriophages include horizontal gene transfer ( VPIΦ of V . cholerae ) , increase of motility ( RSS1Φ of Ralstonia solanacearum , SW1Φ of Shewanella piezotolerans ) and formation of host morphotypic variants ( Cf1tΦ of Xanthomonas campestris , Pf4Φ and Pf6Φ of P . aeruginosa ) [10] . In this work we demonstrate that the presence of the MDAΦ prophage in meningococci is not associated with virulence during the septicemic phase of the disease . On the other hand , we show that phage particles increase colonization of encapsulated bacteria onto epithelial cells . Our data suggest that this effect is mediated by a large production of phage particles within the biomass of colonizing bacteria that promote bacterial aggregation via the formation of bundles of phage filaments . We propose that the production of MDAΦ phage particles increases the occurrence of disease by promoting bacterial colonization in the nasopharynx .
As mentioned above , the presence of the MDAΦ prophage in the genome of a Nm strain is associated with increased invasiveness [5 , 6] . We aimed at determining whether its presence could increase the virulence during the septicemic phase of meningococcemia . We used a previously described experimental model of meningococcemia [14] and compared the course of infection of a wild type ( WT ) strain with that of an isogenic MDAΦ deleted variant . This model uses SCID mice grafted with human skin . The vascularisation inside the human skin remains of human origin even though it connects with the mice vessels . This model addresses the two events associated with the clinical presentation of meningococcemia , i . e . ( i ) the growth in the bloodstream and the extra cellular fluids , and ( ii ) the interaction with the microvessels , responsible for the thrombotic/leakage syndrome and the meningeal invasion . Grafted-mice were injected IV with either the WT strain or an isogenic derivative deleted of the MDAΦ prophage ( ΔMDA ) , as described in the material and methods section . Results , reported S2A Fig , did not show any significant difference in the course of infection induced by the two strains . We then performed competition experiments by infecting intravenously three grafted-mice with an equal quantity of the WT strain and the ΔMDA strain ( S2B Fig ) . The number of bacteria in the bloodstream was determined at 1 and 18 hours after infection and the number of bacteria colonizing the graft at 18 hours [14] . The latter is directly correlated with the ability of the bacteria to interact in vivo with endothelial cells . The competitive index was calculated as described in the material and methods section . In all cases , the competitive index was close to one and no statistical difference was observed . Since the ability to resist human complement is not addressed in the above mouse model and considering that one of the phage encoded protein , MDAORF6 , has recently been implicated in the resistance to normal human serum when expressed simultaneously with other homologous proteins [15] , we compared the ability of the WT strain and that of the ΔMDA strain to resist to complement containing human serum . The number of surviving bacteria after 30 min of contact with 60% of human serum was determined . Control experiments using heat-inactivated human serum and an isogenic non-capsulated strain were performed . The deleted MDA mutant was as resistant as the WT strain to complement containing human serum ( S3 Fig ) . This result is consistent with the previously published results [15] which showed that an effect on the complement resistance was observed only when all homologous proteins of MDAORF6 were simultaneously deleted . Altogether these results ruled out a role of the MDAΦ prophage in the virulence of strain Z5463 during the septicemic phase of meningococcal infection . Considering the above results , we hypothesized that the presence of the phage does not confer an advantage to bacteria during the septicemic phase of the disease but in the nasopharynx . A large number of meningococci in this location may be responsible for a higher translocation rate of bacteria in the bloodstream and/or a better dissemination of the bacteria among a population , which in turn increases the number of meningococcal diseases by amplifying the number of carriers . To test this hypothesis , we assessed the ability of the WT strain and that of the MDA deleted isogenic strain to interact with a monolayer an epithelial cell line derived from a pharyngeal tumor , the FaDu cells . Initial experiments were performed during a short period of time , and results , reported S4 Fig , did not show any difference between two isogenic isolates , carrying or not a MDAΦ prophage . Considering that , in the nasopharynx , the site-of-entry of meningococci , adherent bacteria are subject to a flow , due to the presence of ciliated cells [16] , we assessed the ability of bacteria to colonize a monolayer during a long period of time ( 18 hours ) under constant flow in order to be closer to the in vivo situation [17] . Two isogenic fluorescent strains , Z5463gfp and Z5463gfpΔMDA were used to quantify the biomass of bacteria adhering onto the monolayer ( see Material and methods ) . Results are shown Fig 1A and 1B . The biomass covering the monolayers formed by the MDAΦ deleted strain was constantly reduced by 40 to 50% when compared to that of the parental strain . It should be pointed out that this difference was not explained by a difference in growth rate of the two strains as the doubling time of these strains was identical ( S5 Fig ) . It should be pointed out that a similar phenotype was observed using another epithelial cell line , a monolayer of Calu-3 cells ( cells from a lung adenocarcinoma ) ( S6 Fig ) . The above results showing an increased colonization onto epithelial cells of the isolate containing an MDAΦ prophage were surprising in light of the in vivo data that did not show a selective advantage of the MDAΦ producing strain inside the skin graft where bacteria interact with endothelial cells . We subsequently determined the ability of the phage to promote bacterial colonization onto a monolayer of endothelial cells using the same conditions as above for epithelial cells . As shown Fig 1C and 1D , the WT and MDAΦ deleted strains showed the same level of colonization onto endothelial cells . A possible explanation for this discrepancy observed with the two cell types was the different cross talk observed when meningococci infect endothelial and epithelial cells , as this has been previously suggested [18] . To test this hypothesis , experiments were also performed using fixed monolayers ( i . e . pretreated with a solution of 4% paraformaldehyde ) . As shown Fig 1C , the WT strain colonized significantly less a monolayer of fixed endothelial cells when compared to that of living cells . On the other hand , colonization of the MDAΦ deleted strain was dramatically reduced onto fixed cells when compared to that of the WT strain . In contrast , data obtained on a monolayer of fixed FaDu epithelial cells were similar to those obtained on living cells ( Fig 1A and 1B ) . Altogether these results are consistent with the in vivo data which did not show any advantage to prophage containing strains when adhering onto the microvessels of the skin graft and clearly showed that the ability of the prophage to increase bacterial colonization is specific of epithelial cells and depends upon the bacteria host cell cross talk . We next assessed whether the above phenotype observed onto FaDu cells was a consequence of the production of phage particles or a consequence of the presence of prophage encoded genes . Experiments were performed using strains deleted in two genes that have been shown to be important for phage replication and the production of viruses , MDAorf1 and MDAorf9 ( S1 Fig ) [5 , 7] . Strains Z5463gfpΔorf1 and Z5463gfpΔorf9 described in the material and methods section are unable to produce replicative cytoplasmic forms of the phage , and infectious particle , even though they carry a prophage in their genome . As shown S7 Fig , these strains colonized a monolayer of FaDu cells at a level identical to that of a prophage deleted strain . Altogether these results are in favour of a direct role of the virus particles in the observed phenotype . Consistent with the above results , was the intense labelling of the major capsid protein , MDAORF4 , inside the biomass covering the monolayer of cells ( Fig 2A ) . This suggested that phage production occurred during bacterial colonization of the epithelial cells . To confirm this point , phage DNA and phage proteins were monitored during bacterial colonization of the epithelial monolayer . The quantity of circular MDAΦ DNA per chromosome increases as a function of time ( Table 1 ) , and consistently the amount of MDAORF10 and MDAORF5 proteins ( S8 Fig ) increased in a proportion higher than expected from the growth of the biomass ( see panel C S8 Fig ) . Considering the above results showing the absence of phenotype onto endothelial cells , we aimed at precising the phage production onto this cell type . The quantification of the circular MDAΦ DNA per chromosome in the biomass covering endothelial cells reveals the absence of production of MDAΦ during the formation of the biomass under flow on endothelial cells ( Table 1 ) . On the other hand onto fixed endothelial cells , the quantity of circular MDAΦ DNA per chromosome increased significantly ( Table 1 ) . Altogether , these results are consistent with the above reported data showing that the prophage does not provide a selective advantage to bacteria colonizing endothelial cells . Altogether these results suggest that the increased colonization observed during interaction onto epithelial cells was associated with the production of viruses . We next aimed at determining the mechanism by which the production of MDAΦ increases the biomass of bacteria onto epithelial cells . We first tested the hypothesis that the addition of exogenous bacteriophages during bacterial adhesion to a culture of a phage-deleted strain could mimic the phenotype observed . Bacteriophages were prepared as described in the material and methods section , and the ability of strain Z5463gfpΔMDA to form a biomass on cells was determined in the presence of 1012 bacteriophages per mL in the supernatant . Results are presented Fig 3 . No significant difference was observed with or without the presence of exogenous bacteriophages , this strongly suggests that the phage has to be produced locally by the bacteria to increase the formation of the biomass . The production of a phage by a bacterial population is possibly associated with bacterial lysis and subsequent release of extra cellular DNA ( eDNA ) that in turn can participate in the formation of a biofilm [19] . Even though this hypothesis was unlikely considering that filamentous phages such as MDAΦ do not have a lytic cycle , we ruled out a possible role of extra cellular DNA ( eDNA ) by quantifying the biomass covering the monolayer after having added DNAse in the culture media to degrade possible eDNA in the extra cellular matrix . The final concentration of DNAse was 1 μg/mL corresponding to that routinely used to degrade eDNA in extra cellular matrix [20] . As shown Fig 4 , the biomass of colonizing bacteria was identical regardless of the presence of DNAse . Type IV pili have long been identified as being the main bacterial attribute promoting both the formation of bacterial aggregates and the initial interaction of encapsulated meningococci onto host cells [21] . Considering the above results suggesting a direct role of viral particles , we determined the localisation of both pili and bacteriophages inside the biomass . To be able to visualize both , MDAΦ and pili , we used a derivative of strain Z5463 which was modified in order to express a pilin variant from strain 2C4 . 3 , designated SB . The pili encoded by this variant can be labelled by a monoclonal antibody , 20D9 . The construction of this strain designated Z5463 ( SB-aph3’ ) has been previously reported [7] . Bacteria colonizing a FaDu epithelial cell monolayer in a laminar flow chamber were harvested by aspiration as described in the material and methods section . This step removed most of the bacteria colonizing the epithelial cells , leaving only bacteria strongly adhering to the epithelial monolayer . Both adhering bacteria and bacteria peeled off the epithelial cells were labelled using the 20D9 monoclonal antibody and a polyclonal antibody directed against the MDAORF4 , the major capsid protein [7] . As shown Fig 5A , bacteria obtained from the aspiration were heavily labelled by the polyclonal antibody against the major phage capsid protein , but almost no labelling was obtained using the 20D9 anti-pili monoclonal antibody . In contrast , bacteria still interacting with the epithelial monolayer were piliated but were not associated with bacteriophages . A similar experiment was performed using a prophage-deleted isogenic derivative . Only bacteria adhering strongly to epithelial cells were piliated whereas those peeled off the monolayer were not piliated ( Fig 5B ) . These results suggested that bacteria close to the apical surface of the epithelial monolayer were piliated but not surrounded by bacteriophages whereas those located in the upper layers of the biomass produced large amount of bacteriophages but were not piliated . This finding was confirmed by performing XZ sections of an infected epithelial monolayer . Results are shown Fig 5C . After 18 hours of interaction onto a monolayer of epithelial cells ( i ) piliated bacteria are found close to the apical surface of the epithelial cells , in these layers very little labelling of bacteriophages is visible , and ( ii ) in the upper layers , bacteria are non piliated but surrounded by large amount of bacteriophages . It should be pointed out that similar XZ sections performed on infected living endothelial cells revealed the absence of MDAΦ particles at the surface of the biomass ( Fig 5C ) . On the other hand , onto fixed endothelial cells , the XZ sections confirmed a large production of MDAΦ particles in the upper layers of the biomass ( Fig 5C ) . Regardless of the cell type studied , the presence of type 4 pili is always restricted to bacteria associated with the apical surface of cells . These results are consistent with the data reported above showing that the prophage does not provide a selective advantage to bacteria colonizing endothelial cells . Electron microscopy combined with immunogold labelling using both the anti-pilin 20D9 monoclonal antibody and anti-MDAORF4 polyclonal antibody were performed on bacteria obtained as above by aspiration . Results are reported Fig 6 . Images shown Fig 6A confirmed that , using these labellings , we could visualise independently type IV pili and bacteriophages surrounding bacteria . It should be pointed out that the morphology of the bacteriophages is indistinguishable from that of pili . Numerous bacteriophage filaments remained linked to the cell wall . This is consistent with the fact that being a filamentous phage , the release of MDAΦ is not a consequence of bacterial lysis but the consequence of a secretion through the outer-membrane . The labelling of the aspirated biomass confirmed that in the upper part of the biofilm most of the meningococci were producing bacterial bacteriophages and not type IV pili ( Fig 6B ) . In addition they showed ( i ) that numerous viruses remained associated with the bacterial cell wall , and ( ii ) that phage-phage interactions occurred leading to the formation of bundles of viruses that are able to connect bacteria ( Fig 6B and 6C ) . The ability of this phage to bundle was confirmed on an immunofluorescence staining of a phage preparation using the anti MDAORF4 polyclonal antibody that showed that MDAΦ phage particles are able to form large bundles of filaments ( Fig 3A ) . Altogether these data suggest that , in the upper layers of the biomass colonizing the biofilm , numerous bundles of viruses embed bacteria ( Figs 5C , 6B and 6C ) and that these bundles by interacting with phage particles still associated with the cell wall increase bacteria/bacteria connections ( Fig 6C ) . The lack of complementation by exogenous phage suggests that the phenotype observed is specific of phage producing bacteria . Similar experiments were performed , as described in the material and methods section , with pilE derivatives of strains Z5463gfp and Z5463gfpΔMDA . Type IV pili is the main bacterial attribute allowing the interaction of encapsulated meningococci with host cells , and as expected , very few non-piliated derivatives of strain Z5463gfpΔMDA were interacting with the monolayer . On the other hand a larger biomass of the non piliated derivative of strain Z5463gfp which carries the prophage , was colonizing the monolayer of host cells , even though the thickness of the biomass was reduced compared to that of a wild type piliated strain ( Fig 7A and 7B ) . Electron microscopy performed on the harvested biofilm confirmed that the prophage containing strain was able to produce large amount of phage particles that can remain associated to the bacterial cell wall , and provide interbacterial connection via phage/phage interactions ( Fig 7C ) .
The presence of the bacteriophage MDAΦ has been associated with hypervirulent clonal complexes of N . meningitidis [5] , [6] . The initial hypothesis suggested the presence of virulence factors encoded by the prophage giving a selective advantage during the bloodstream phase of the disease . Our data obtained with our animal model mimicking the septicemic phase of the neisserial invasive diseases do not support this hypothesis . On the other hand , our results suggest that the virulence factor encoded by the prophage is the phage particle itself promoting bacterial aggregation when the bacteria interact with epithelial cells . The production of phage in the nasopharynx is therefore likely to increase the biomass of bacteria at the site of entry . This increased biomass could in turn increase the frequency of bacterial dissemination in the bloodstream and/or the dissemination of the bacteria inside a population , which , by increasing the number of carriers , is responsible for a higher rate of diseases . These hypothesis are consistent with previous results reported for Streptococcus pneumoniae , where an association between an increased density of nasopharyngeal colonization has been associated with a higher rate of invasive pneumococcal pneumonia [22] . Capsulated meningococci interact with epithelial cells via their type IV pili . Following this initial interaction , bacteria divide and , as previously reported , repress the transcription of the major pilin subunit PilE [23] , retract their pili , and loose piliation [24] , leading to the formation of layers of piliated bacteria directly in contact with the apical surface of the host cells , and bacteria loosing their pili are washed away from the monolayers by the shear stress . These previous reports are consistent with the data shown with the MDAΦ deleted strain . On the other hand , with the WT parental strain the biomass is thicker than expected , as bacteria in the upper layers are non piliated and included in a network of MDA phage particles which form large bundles able to connect bacteria most likely via interactions with filamentous bacteriophages remaining associated with the outer membrane of bacterial cells as they extrude through the cell wall ( Fig 8 ) . The mechanism responsible for the segregation of phage producing strains could resemble the one identified in Neisseria gonorrhoeae for mixed population of piliated and unpiliated bacteria [25] , where non-piliated bacteria segregate from those that are piliated . The authors suggested that this cell-sorting was under the control of active forces which rely on similar physical principles as those observed in developing embryos . A striking observation is the fact that this effect of the phage on bacteria colonization is specific of epithelial cells . Indeed , the prophage does not provide a selective advantage for bacterial colonization onto endothelial cells . Meningococci induce signalling pathways on epithelial and endothelial cells that are known to be different [18] . On endothelial cells , adhesion of N . meningitidis leads to the recruitment of the junctional components [26] and to the formation of microvilli like structures which allow bacterial protection from shear stress [27] . We hypothesized that the cross talk between bacteria and endothelial cells could be responsible for our observation , the fact that fixed endothelial cells behave like epithelial cells support this hypothesis . The molecular mechanisms responsible for this are unknown , a possible explanation is to consider that the formation of microvilli on the apical surface of endothelial cells by protecting the bacteria from shear stress prevent the production of phage particles , which may not be the case onto epithelial cells . Interestingly , Sigurlàsdòttir and colleagues have shown that , after initial adhesion on epithelial cells , lactates , produced by host cells , initiate rapid dispersal of microcolonies of N . meningitidis [28] . In our model of adhesion under flow conditions , the permanent renewal of the medium prevents the increase of lactates concentration and the subsequent dispersal of bacteria . Another striking result is the fact that meningococci seem to produce either pili or bacteriophages . Indeed , type IV pili and bacteriophages labelled very rarely the same bacterium . This is somewhat consistent with previous results showing that some of the type IV pilus machinery is used by the phage for its secretion , especially the secretin PilQ which is used to extrude the pilus fiber and the phage filament through the outer membrane [5] . This suggests that production of pili and bacteriophages are co-regulated and mutually exclusive . Indeed as already mentioned following initial adhesion , piliation is down regulated by the inhibition of transcription of the major pilin subunit and pilus retraction [23 , 24 , 29] . It is likely that these regulatory pathways control the induction of bacteriophage production . Altogether our data by demonstrating that MDAΦ increase the colonization of bacteria specifically onto epithelial cells suggest that the increase invasiveness observed by strain carrier this prophage may be a consequence of a high bacterial load at the site-of-entry which in turn increase the chance of translocation in the bloodstream and/or the dissemination of the bacteria in a population and the number of carriers .
Z5463 , formerly designated C396 , is a serogroup A strain isolated from the throat of a patient with meningitis in The Gambia in 1983 ( gracefully provided by J . Parkhill [30] ) . The genomic sequence of this strain is deposited in the PubMLST website ( [Neisseria PubMLST:17882] [31] ) . Neisseria were grown at 37°C in 5% CO2 on GC medium base ( Difco ) containing Kellogg’s supplements [32] , or in GC-liquid medium [1 . 5% proteose peptone ( Difco ) ; 0 . 4% K2HPO4 , 0 . 1% KH2PO4 , 0 . 1% NaCl , with 12 μM FeSO4 and Kellog’s supplements] . Kanamycin ( Km ) was used at a concentration of 200 μg/mL , spectinomycin ( Sp ) at 75 μg/mL , erythromycin ( Em ) at 3 μg/mL and chloramphenicol ( Cm ) at 5 μg/mL . Serum survival was performed as previously described [15 , 33] with minor modifications . Bacterial strains were grown overnight on GC medium base plates and then cultures in chemically defined medium ( CDM ) supplemented with 1 mg/mL of Cohn fraction IV from human serum ( Sigma-Aldrich ) . The CDM was Catlin 6 medium modified to contain 5 . 5 mM glucose , 4 mM D , L-lactate , 50 μM cysteine and 150 μM cystine [15] . Cultures were grown until an optical density at 600nm ( OD600nm ) of 0 . 6 . A 1/600 dilution of this broth was realized in 60% of NHS ( Normal Human Serum ) or hiNHS ( heat-inactivated Normal Human Serum ) and incubated at 37°C . Normal Human Serum AB-type ( PAA Laboratories ) used was handled in a manner to preserve complement activity [34] or heat-inactivated at 56°C during 30 min . The percentage of bacteria surviving at 30 min was determined . Each assay was performed in triplicate . The list of strains and mutants used in this study is reported in S1 Table . Mutants Z5463ΔMDA and orf1 were previously described [5 , 7] . Z5463 ( SB-aph3’ ) was obtained following transformation of DNA of strain Nm 8013 SB-aph3’ [35] into Z5463 and selecting for Km resistance [7] . Z5463 expressing a green fluorescent protein ( GFP ) under IPTG-inducible promoter was obtained by transformation of plasmid pAM239 [36] , the resulting strain was designated Z5463gfp . Z5463gfpΔMDA , Z5463gfpΔorf1 and Z5463gfpΔorf9 were obtained following transformation of Z5463gfp with DNA of strains Z5463ΔMDA , Z5463Δorf1 and Z5463Δorf9 respectively [7] . Z5463gfpΔpilE and Z5463gfpΔMDAΔpilE were obtained following transformation of the strains Z5463gfp and Z5463gfpΔMDA by the DNA of Z5463ΔpilE ( erythromycin resistant ) described by Meyer and collaborators [7] . To engineer a non-capsulated strain , a mutant of the gene lipA was generated . A previously described mutation [37] was amplified by PCR and introduced into Z5463 by transformation . The experimental procedures described in this paper were conformed to the European ethical legislation ( Directive 2010/63/EU ) and with the guidelines established by the French regulations ( Décrets 87–848 , 2001–464 , 2001–486 and 2001–131 ) . The experimental protocol was approved by the Comité d'Expérimentation Animale de l'Université Paris Descartes ( France , project number CEEA 12–030 ) . Six weeks old CB17/Icr-Prkdcscid ( Severe Combined Immunodeficiency: SCID ) female mice were obtained from Charles River Laboratories ( Saint Germain sur l'Arbresle , France ) . Human skin grafts were obtained anonymously from surgical waste from patient undergoing plastic surgery at Groupe Hospitalier Paris Saint-Joseph ( Paris , France ) . According to the French legislation , the patients were informed of the research finality and their non-opposition was orally received . CB17/Icr-Prkdcscid/IcrIcoCrl female mice were grafted with human skin as described by Join-Lambert et al . [14] . Nm strains were grown overnight at 37°C on GCB agar plates prepared without iron and supplemented with deferoxamine ( Desferal , Novartis ) at a final concentration of 15 μM . Bacterial colonies were harvested and cultured in RPMI with 1% bovine serum albumin medium and 0 . 06 μM deferoxamine with gentle agitation to reach the exponential phase of growth . Bacteria were then resuspended in physiological saline . All mice received 10 mg of human holotransferrin ( R&D Systems ) administered intraperitoneally just before infection . For the single infection model , six grafted mice were infected IV with 107 CFU of WT strain or the ΔMDA isogenic strain . The injected dose is just below the LD50 . The numbers of CFU in blood and in the graft were determined at 1 and 18 hours . To obtain a competitive index , three grafted mice were infected intravenously with a mix of 5 . 106 CFU of each strain . The numbers of CFU in the blood and in the graft were determined . The number of CFU corresponding to the ΔMDA strain was obtained by determining the number of spectinomycin resistant colonies . The competitive index was calculated by the ratio of [log ( UFCZ5463ΔMDA ) /log ( UFCWT ) in the blood or in the graft] on [log ( UFCZ5463ΔMDA ) /log ( UFCWT ) of the inoculum] . The pharynx carcinoma-derived FaDu epithelial cell line and the lung adenocarcinoma-derived Calu-3 epithelial cell line were obtained from the American Type Culture Collection . HDMEC ( Primary Human Dermal Microvascular Endothelial Cells ) were purchased from Promocell . FaDu cell lines were grown in Ham F-12 medium ( PAA Laboratories ) supplemented with 10% fetal calf serum ( FCS; PAA Laboratories ) , 20 mM HEPES ( PAA Laboratories ) and 1% penicillin-streptomycin-amphotericin ( PSA; PAA Laboratories ) . Calu-3 cell lines were grown in Opti-MEM ( Gibco ) supplemented with 5% fetal calf serum ( FCS; PAA Laboratories ) . HDMEC were grown in ECM ( Endothelial Cell Medium with supplements provided by the manufacturer , Promocell ) , 20 mM HEPES ( PAA Laboratories ) and 1% penicillin-streptomycin-amphotericin ( PSA; PAA Laboratories ) . Cells were grown at 37°C in a humidified incubator under 5% CO2 . The cells were fixed using a solution of PBS-4% paraformaldehyde ( PFA ) during 20 min . Phage preparation was performed as previously described [7] . Bacteria were pelleted from 200 mL of an overnight culture in GC-liquid medium . After filtration at 0 . 45 μm , the supernatant was treated for 3 h at 20°C with DNase I and RNase A , 25 μg/mL each . Phage was precipitated by addition of 10% NaCl and 20% polyethylene glycol 6000 , and overnight incubation at 4°C . The phage was then pelleted by centrifugation at 11 , 000 g for 30 min , and resuspended in PBS 1X and added directly to the cell medium at a final concentration of 10% during the bacterial colonization onto epithelial monolayers . The concentration of phage was determined by real time PCR using a preparation of DNA of strain Z5463Δorf1 as standard . Short-term adhesion of meningococci to FaDu cells was performed as described previously [38] , with minor modifications . The 24 well plates were seeded with 105 cells per well . Before the assay , bacteria grown on GCB agar plates were adjusted to a specific OD600nm and then incubated for 2 h at 37°C in prewarmed culture cell specific medium . The number of CFU in the inoculum was determined . Cells were infected with 1 mL of bacterial suspension in cell culture specific medium . After 30 min of contact , unbound bacteria were removed by three washes with 1 mL of cell culture medium and the infection was pursued for 6 h . The number of adherent bacteria was determined at 30 min , 3 h and 6 h . Long-term colonization of bacteria was performed under flow conditions using FaDu , Calu-3 or HDMEC as previously described [17] . Laminar flow chamber experiments were performed on disposable flow chambers composed of six independent flow channels ( μ-Slide VI 0 . 4 purchased from Ibidi , surface area 0 . 6 cm2 per channel ) coated with 5 μg of rat tail collagen type I/cm2 . Cells were seeded in the six channels at a density of 0 . 3x105/cm2 and incubated for 7 days at 37°C in 5% CO2 until confluent . A microscopic examination of the cell layers was performed before each flow assay and only channels with a uniformly confluent layer were used . Prior to infection , cell monolayers grown in μ-Slide were stained with cytoplasmic Cell Tracker Orange CMTMR ( Life Technologies ) according to the manufacturer’s instructions . The GFP-expressing strains were grown during 2 hours with agitation . The OD600nm was then determined and each strain was adjusted to an OD600nm of 0 . 1 for the piliated strains and 0 . 3 for the non-piliated strains . 60 μL of this suspension was used to inoculate triplicate channels of a μ-Slide . Bacteria were allowed to adhere to the monolayers for 1 . 5 hours without flow for the piliated strains and for 2 hours for the non-piliated strains . At 1 . 5 or 2 hours postinfection , a continuous flow of cell medium containing 3 μg of vancomycin/mL and , when necessary , 1 mM IPTG ( isopropyl-β-D-thiogalactopyranoside ) was applied for 18 hours at a constant flow rate of 0 . 04 mL/min for the piliated strains and 0 . 02 mL/min for the non-piliated strains using a syringe pump ( Harvard Apparatus ) . The flow chamber was placed in an incubator at 37°C with 5% CO2 throughout the experiment . When indicated , DNAse I ( Roche ) used at a final concentration of 1 μg/mL in the cell medium [20] . The Ibidi μ-Slide flow chambers allow direct observation with inverse microscopy through its transparent plastic bottom . All microscopic observations and image acquisitions were performed on a Leica SP5 confocal microscope . Images were obtained using a x40/1 . 3 Plan Apo oil objective lens . At the time of confocal acquisition , the cells were examined using red channel to assess the integrity of the monolayer . Three-dimensional biofilm structures reconstructions were generated using the IMARIS software package ( Bitplane AG ) . Biofilm development was quantified with the COMSTAT computer program using biomass and average thickness parameters [39] . The results are expressed as a percentage of the biofilm produced by the WT strain , which is set to 100% . Values represent the means of three independent experiments , with the acquisition of at least six image stacks per each channel . In some experiments , after 18 hours of incubation , biofilms were aspirated from the flow chamber using a large gauge needle syringe and used for immunofluorescence or immunogold labelling . In this case most of the biofilm was removed for analysis from the surface of the monolayers , leaving a single layer of bacteria which remained adherent to the apical surface of the cells . The N-terminal domains of MDAORF4 were detected using purified rabbit polyclonal antibodies raised against peptides H2N-DGFDAAAIGTQVANV-COOH [7] . Type 4 pili was detected using the 20D9 monoclonal antibody that is specific for the SB pilin variant of strain 2C4 . 3 [40] . MDAORF5 was detected using rabbit polyclonal antibodies against peptides H2N-CINFLKDMGKVGTD-COOH and H2N-CVTEEGKIIRPERVGD-CONH2 [7] . MDAORF10 was detected using rabbit polyclonal antibodies against peptide H2N-FYQFRHGEPHKLINQE-COOH . MDAΦ was detected using IF staining as previously described [38] . Briefly , aspirated biofilms or biofilms on Ibidi chamber were fixed on coverslips for 20 min with a solution of PBS-4% paraformaldehyde ( PFA ) . After 2 washes in PBS , samples were incubated with PBS-NH4Cl during 5 min . Then samples were washed twice with PBS-0 . 1%Triton-1%BSA and incubated for 1 hour with the same solution . MDAΦ were stained using the anti-ORF4 N-ter antibody , used at 1/50 dilution and type IV pilin using the 20D9 antibody used at 1/1 , 000 dilution . While the bacteria were stained with DAPI ( 4′ , 6-Diamidine-2′-phenylindole dihydrochloride ) solution at 100 ng/mL , the secondary antibodies , used at 1/400 dilution in PBS-0 . 1%Triton- 1% BSA , were a goat anti-rabbit antibody labelled with Alexa Fluor 488 ( Molecular Probes Life tech ) and a goat anti-mouse antibody labelled with Alexa Fluor 546 ( Molecular Probes Life tech ) . Oligonucleotides were designed using the Primer Express software ( PE Applied biosystems ) to obtain amplicons of the same size ( S2 Table ) . Real-time PCR was run on an ABI Prism 7700 sequence detection system ( Perkin-Elmer Biosystems ) using SYBR Green PCR Master Mix ( PE Biosystems ) , according to the manufacturer’s instructions . Data analyses for a relative quantification of gene DNA were performed by the comparative Ct ( threshold cycle ) method according to the manufacturer’s instructions ( user bulletin 2 for the ABI PRISM sequence detection system ) and published data [41] . The parameter Ct is defined as the cycle number at which fluorescence ( which is proportional to the quantity of DNA in the tube during the exponential phase of the PCR ) passes the fixed threshold . The relative amount of target after normalization to a chromosomal gene pgm , is obtained by 2 ( Ctorf5—Ctpgm ) . Preparation of protein samples , SDS-PAGE separation , transfer to membranes and immunoblotting were performed using standard molecular biology techniques [42] . Detection of immobilized antigens was performed by chemiluminescence using ECL Plus detection reagents ( Amersham ) . For quantification , we normalized the signal of each western-blot on the number of bacteria used for the protein preparation . All values were then normalized on the corresponding signal of NADP glutamate dehydrogenase . Immunogold labelling of the MDAΦ and the pili were performed as previously described [7] . After aspiration from the Ibidi chambers of the biomass using a syringe with a large gauge needle , biofilms were resuspended in PBS-4% PFA and adsorbed to the grids for 15 min . The grids were then rinsed twice in PBS and placed sequentially onto drops of the following reagents at room temperature: PBS-50 mM NH4Cl ( 5 min ) , PBS-5% normal goat serum ( 5 min ) , and then the anti-ORF4 N-ter antibody diluted 1/50 in PBS-0 . 2% gelatine for the MDA or the anti-PilE 20D9 monoclonal antibody diluted 1/100 in PBS-0 . 2% gelatine for the pili ( for 60 min ) . After five washes in PBS-0 . 2% gelatine , the grid was placed for 60 min on a drop of goat IgG anti-rabbit IgG conjugated to 8-nm-diameter gold particles and donkey IgG anti-mouse IgG conjugated to 12-nm-diameter gold particles diluted 1/60 in PBS-0 . 2% gelatine . The grids were then subjected to five washes in PBS-0 . 2% gelatine , fixed in PBS-1% glutaraldehyde ( 15 min ) , and washed twice in distilled water . The grids were then treated with phosphotungstic acid , air-dried and viewed . Image acquisition was performed with a JEOL 1011 transmission electron microscope . For the immunogold labelling of the biofilm of the Z5463gfpΔMDAΔpilE mutant , the goat IgG anti-rabbit IgG was conjugated to 18-nm-diameter gold particles . | Bacteriophages are bacterial viruses , which in some cases encode for virulence factors and increase bacterial virulence . Comparative genomic of several strains of Neisseria meningitidis , a major human pathogen , identified the presence of an 8kb prophage in strains belonging to invasive clonal complexes . The analysis of this filamentous bacteriophage , designated MDA for Meningococcal Disease Associated ( MDAΦ ) did not reveal any obvious virulence factors responsible for an increase invasiveness of strains carrying this prophage . Using our animal model mimicking the septicemic phase of the neisserial invasive diseases , we demonstrate that the presence of the MDAΦ is not associated with a higher virulence , but we show that the bacteriophage particles , by promoting bacteria-bacteria interactions , increase the biomass of bacteria colonizing a monolayer of epithelial cells . These data suggest that the increased invasiveness mediated by the MDAΦ bacteriophage is likely to be due to a better ability of the bacteria to colonize the nasopharyngeal mucosa . | [
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| 2017 | A virulence-associated filamentous bacteriophage of Neisseria meningitidis increases host-cell colonisation |
Fungal infections have skyrocketed in immune-compromised patients lacking CD4+ T cells , underscoring the need for vaccine prevention . An understanding of the elements that promote vaccine immunity in this setting is essential . We previously demonstrated that vaccine-induced IL-17A+ CD8+ T cells ( Tc17 ) are required for resistance against lethal fungal pneumonia in CD4+ T cell-deficient hosts , whereas the individual type I cytokines IFN-γ , TNF-α and GM-CSF , are dispensable . Here , we report that T cell-intrinsic MyD88 signals are crucial for these Tc17 cell responses and vaccine immunity against lethal fungal pneumonia in mice . In contrast , IFN-γ+ CD8+ cell ( Tc1 ) responses are largely normal in the absence of intrinsic MyD88 signaling in CD8+ T cells . The poor accumulation of MyD88-deficient Tc17 cells was not linked to an early onset of contraction , nor to accelerated cell death or diminished expression of anti-apoptotic molecules Bcl-2 or Bcl-xL . Instead , intrinsic MyD88 was required to sustain the proliferation of Tc17 cells through the activation of mTOR via Akt1 . Moreover , intrinsic IL-1R and TLR2 , but not IL-18R , were required for MyD88 dependent Tc17 responses . Our data identify unappreciated targets for augmenting adaptive immunity against fungi . Our findings have implications for designing fungal vaccines and immune-based therapies in immune-compromised patients .
The rising incidence rate of life threatening fungal infections in immune-deficient hosts requires preventive measure in at risk individuals . CD4+ T cells are the primary effector cells that control fungal infections in healthy hosts , and their loss in lymphopenic patients necessitates targeting residual immune subsets to elicit antifungal immunity . We previously showed in a mouse model of lethal fungal pneumonia that , even in the absence of CD4+ T cell help , vaccine-induced CD8+ T cells could differentiate and expand into cytokine producing cells , persist as long-lasting memory cells , and mediate sterilizing immunity [1] . Antifungal CD8+ T cells that produce IL-17A are indispensable in this model . In contrast , CD8+ T cells that produce type I cytokines ( IFNγ , TNFα or GM-CSF ) contribute to vaccine immunity , but are expendable [2 , 3] . A deeper understanding of the elements required to elicit CD8+ T cell responses will be required to catalyze the development of rationally designed anti-fungal vaccines . T cell respond to antigen in three distinct phases: in the expansion phase , upon recognition of cognate antigen , T cells undergo rapid proliferation and differentiation into effectors; in the contraction phase , ~90% of effectors T cells die by apoptosis; and in the memory phase , the remaining 10% of effector T cells differentiate into long-lasting memory cells . Hence , in general , the magnitude of expansion and survival of effector cells will dictate protective immunity [4] . The inflammatory milieu influences the quality and quantity of effector T cells . For example , a lack of type I interferon signaling abrogates clonal expansion of CD8+ T cells due to reduced survival , whereas enhanced inflammation exaggerates terminal differentiation and apoptosis [5 , 6] . Among other factors , cytokines regulate differentiation of T cells into distinct subsets that express prototypic transcription factors and signature cytokines . For Th17 cell responses , different combinations of cytokines including IL-6 , TGFβ , IL-1 , IL-21 and IL-23 have been implicated in differentiation in vitro and in vivo [7 , 8] . CD8+ T cell responses are typically associated with defense against intracellular pathogens and tumors by mechanisms that are largely dependent on IFNγ , granzyme , and perforin . CD8+ T cells control fungal infections chiefly by secretion of proinflammatory cytokines such as IFN-γ , TNF-α , and GM-CSF that activate phagocytes to kill fungi [9] . A distinct subset of IL-17A producing CD8+ T cells , Tc17 cells , also play a role in defense against infections and tumors . Elimination of Tc17 cells is associated with progressive SIV/HIV infection [10–12] and Tc17 cells are protective against vaccinia and influenza virus infections [13 , 14] and tumors [15 , 16] . Likewise , we have found that Tc17 cells are indispensable for vaccine-induced protection against fungal pneumonia [2] . Differentiation of Tc17 cells requires TGFβ and IL-6 or IL-21 [17]; IL-23 signaling has been shown to promote pathogenic Tc17 cells [18] . IRF4 facilitates Tc17 responses by transcriptionally activating RORγt and RORα and repressing EOMES and FOXP3 , while IRF3 inhibits Tc17 programming by altering RORγt promoter binding [19 , 20] . The molecular switch that regulates initial programming of Tc1 and Tc17 responses under similar in vivo ‘inflammatory milieu’ is poorly understood . MyD88 , a signaling adaptor for TLRs and IL-1R family members in myeloid cells , is critical for innate and adaptive immunity [21] . MyD88 signaling activates macrophages and DCs , elicits production of proinflammatory cytokines and promotes antigen presentation to initiate adaptive immune responses during viral , bacterial and parasitic infections [22] . Impaired MyD88 signaling increases susceptibility to fungal infections such as candidiasis , cryptococcosis , aspergillosis , paracoccidioidosis , pneumocystis and coccidioidomycosis [23–25] . Conversely , bolstering MyD88 signaling in dendritic cells improves resistance to aspergillosis [26 , 27] . Thus , MyD88 signaling in myeloid cells plays an integral role in immunity against fungal infections . However , the T cell-intrinsic role of MyD88 in adaptive immune responses to fungal infections has not been defined . In experimental Toxoplasma gondii infection , T cell expression of MyD88 is required for Th1 mediated resistance [28] . This Th1 response is independent of IL-1R and IL-18R , implying a role for TLRs in orchestrating MyD88-mediated T cell responses to T . gondii . Toll-like Receptor 2 signaling in CD4+ T cells is known to promote Th17 responses in vitro [29] and regulate the pathogenesis of autoimmunity in a model of experimental autoimmune encephalitis ( EAE ) . During LCMV infection , IFNγ-producing CD8+ T cells ( Tc1 cells ) require intrinsic MyD88 signals for differentiation and survival [30 , 31] . The importance of intrinsic MyD88 signals for the development of Tc17 cells that confer resistance against microbes including fungi remains poorly understood . We have reported that IL-17-producing CD8+ T cells are indispensible in mediating vaccine immunity against fungal pneumonia in CD4+ T cell deficient mice [2] . In the current study , we investigated the underlying mechanisms that enable the priming and development of these potent vaccine effectors . Here , using a mouse model of vaccination against lethal fungal pneumonia caused by Blastomyces dermatitidis , we show that T cell-intrinsic MyD88 signals are required for Tc17 cell responses and immunity . In contrast , Tc1 responses are relatively spared in the absence of such signals . Unlike the situation for anti-viral CD8+ T cells , poor accumulation of anti-fungal Tc17 cells is not linked to accelerated death or reduced expression of anti-apoptotic molecules Bcl-2/Bcl-xL . Instead , the poor accumulation is due to impaired proliferation that is mediated via Akt1 through the mTOR pathway . Moreover , we show that IL-1R and TLR2 , and not IL-18R , are the upstream sensors and signaling receptors that initiate these anti-fungal Tc17 cell responses . Thus , we describe the novel contribution of intrinsic MyD88 signals in Tc17 cells during the development of anti-fungal immunity , and the role of the AkT1-mTOR axis in fostering sustained proliferation of these cells and establishment of Tc17 memory and immunity in CD4+ T cell deficient hosts .
We initially investigated the general requirement of MyD88 signaling for Tc17 responses following fungal vaccination . We adoptively transferred OT-I cells into naïve congenic wild-type and MyD88-/- mice , and vaccinated the animals with attenuated recombinant Blasomyces yeast expressing the OVA epitope SIINFEKL . On day 18 post-vaccination , following ex vivo restimulation with anti-CD3/CD28 antibodies , we first analyzed the percentage and total number of endogenous Tc17 and Tc1 cells that lack MyD88 by gating on activated Thy1 . 2+ve CD8+ T cells ( CD44hi ) ( Fig 1A ) . The endogenous , IL-17 producing CD8+ T cells in MyD88-/- mice were severely blunted in the draining lymph nodes ( dLNs ) and spleen , whereas IFN-γ producing cells were largely spared ( 8 . 8 fold vs . 2 . 2 fold reduction , respectively ) . MyD88 signals therefore are required to promote the generation of Tc17 cell responses after fungal vaccination . To dissect the intrinsic vs . extrinsic requirement for MyD88 , we analyzed the transferred , wild-type , OT-I cells bearing a distinct , congenic Thy1 . 1 marker . Surprisingly , IL-17A+ and IFNγ+OT-I responses were largely intact in both the wild-type and MyD88-/- recipients ( Fig 1B ) . Thus , intrinsic MyD88 signaling is involved in CD8+ T cell responses , especially for the Tc17 subset . To study the intrinsic role of MyD88 in Tc17 cells , we pursued further approaches . First , we purified CD8+ T cells from naïve wild-type and MyD88-/- mice and transferred them into naïve TCRα-/- mice ( S1A Fig ) . Recipients were vaccinated and challenged by the pulmonary route to assess recall responses in the lung , which are reminiscent of vaccine responses in the dLNs . Vaccinated TCRα-/- hosts that received wild-type CD8+ T-cells had pronounced Tc17 cell responses compared to unvaccinated recipients ( S1B and S1C Fig ) . Vaccinated TCRα-/- hosts that received MyD88-/- CD8+ T-cells had significantly lower Tc17 responses vs . the recipients of wild-type cells ( ~10 fold , p≤0 . 05 ) . These data support the hypothesis that intrinsic MyD88 signaling is required for Tc17 more than Tc1 responses to a fungal infection ( Fig 1A and 1B and S1 Fig ) . In an alternative approach , we confirmed an intrinsic role of MyD88 for CD8+ T cell responses by using MyD88ΔT mice in which only T cells lack MyD88 . After vaccination and analysis of the dLNs , Tc17 cells were significantly impaired in MyD88ΔT mice vs . wild-type mice , whereas Tc1 cells were relatively spared ( Fig 1C; p≤0 . 05 ) . In yet another approach , to assess antigenic specificity and exclude possible developmental T-cell repertoire anomalies in MyD88ΔT mice , we tested OT-Imyd88-/- mice . We transferred OT-I cells into congenic recipients , vaccinated with recombinant OVA yeast and analyzed SIINFEKL-specific Tc17 and Tc1 responses . OT-I cells lacking MyD88 produced significantly less IL-17A compared to wild-type OT-I cells in the dLNs and spleen ( Fig 1D; p≤0 . 05 ) . MyD88 signaling was relatively dispensable for IFN-γ responses . In vitro studies with OT-I cells and OVA-vaccine yeast illustrated the non-redundant role of TCR signaling in fungal-induced Tc17 responses , and studies with naïve CD8+ T cells illustrated the cell intrinsic role of MyD88 for Tc17 cell responses ( S2A and S2B Fig ) . Thus , intrinsic MyD88 signals preferentially affect Tc17 over Tc1 responses after fungal vaccination . Previously , we showed that Tc17 cells were necessary for vaccine immunity in the absence of CD4+ T cells [2] . Here , we explored the functional role of MyD88 signaling in vaccine resistance of CD4+ T cell depleted mice . Unvaccinated wild-type mice failed to control pulmonary infection and harbored ~4 log cfu of yeast in their lungs , whereas vaccinated mice acquired sterilizing immunity ( Fig 2A ) . Unvaccinated MyD88-/- mice have a slightly higher fungal burden than unvaccinated wild-type mice indicating MyD88 promotes innate resistance in the lung . However , vaccinated MyD88-/- mice failed to acquire immunity and exhibited a fungal burden similar to unvaccinated wild-type mice ( Fig 2A ) . Thus , MyD88 signaling is essential for vaccine immunity . To assess a cell-intrinsic role of MyD88 for vaccine-induced CD8+ T cell immunity , we vaccinated MyD88ΔT mice . Vaccinated MyD88ΔT mice had a significantly higher fungal burden than vaccinated control mice ( Fig 2B ) . Vaccinated MyD88ΔT mice did have a lower fungal burden ( ~1 log ) than unvaccinated controls , suggesting contributions to vaccine resistance by IFN-γ , TNFα , GM-CSF and IL-17A that are MyD88 independent . To correlate the resistance phenotype with cellular infiltration of cytokine producing CD8+ T cells , we harvested lungs 4 days after challenge ( peak of cell influx ) and analyzed cells by flow cytometry . The percentage of IL-17A+ CD8+ T cells in the lungs was significantly lower in vaccinated MyD88ΔT mice than controls ( Fig 2C; p≤0 . 05 ) . The total numbers of IL-17A+ , IFNγ+ and GM-CSF+ CD8+ T cells also were significantly lower in vaccinated MyD88ΔT mice than vaccinated controls , with a greater impact on Tc17 cells than Tc1 cells ( Fig 2D; ~8-fold vs . 2-fold , respectively ) . Thus , impaired immunity in vaccinated MyD88ΔT mice was correlated with poor influx and/or accumulation of cytokine-producing CD8+ T cells in lungs , reflecting impaired vaccine responses in dLNs and spleens ( Figs 1C and 2 ) . Collectively , these data suggest that intrinsic expression of MyD88 is required for vaccine-induced CD8+ T cell immunity and protective Tc17 cell responses . In Fig 1 , we investigated the intrinsic role of MyD88 signaling by analyzing CD8+ T cell responses approximately 3 weeks after vaccination . Here , we asked whether intrinsic MyD88 signaling affects CD8+ T cell responses during the early or late stages of expansion . These phases of expansion include priming , differentiation and proliferation of effector CD8+ T cells . To analyze the kinetics of CD8+ T cell responses during these phases , we vaccinated mice and assessed responses in the dLNs and spleens on days 0 ( naïve ) , 10 , 15 and 23 . As early as day 10 , the percentage and total numbers of IL-17A+ CD8+ T cells were significantly blunted in the spleens of MyD88ΔT vs . wild-type mice ( Fig 3A and 3B ) . These impairments were evident in the dLNs only later , by 15 to 23 days post-vaccination , suggesting that differentiated Tc17 cells become effector cells and emigrate from dLNs . Tc1 cell responses also were reduced in the MyD88ΔT mice , but these impairments appeared later and were less pronounced than blunted Tc17 responses . Of note , in the early stages of expansion , the activation ( CD44hi ) of total CD8+ T cells was less impaired in MyD88ΔT mice , suggesting that intrinsic MyD88 signaling preferentially affects Tc17 cell responses ( S3A Fig ) . Tc17 cells produced significantly less IL-17 on a per cell basis in MyD88ΔT vs . wild-type mice ( mean fluorescence intensity: 9986±403 vs 6153±398; S3B Fig ) , suggesting that intrinsic MyD88 signaling shapes not only the quantity , but also the quality of Tc17 cells . We stained for RORγt , but found an insignificant difference between the two groups . Thus , MyD88 signaling in CD8+ T cells is required for optimal Tc17 cell expansion following fungal vaccination . The net number of T cells during the expansion phase is governed by proliferation and apoptosis of effector cells . Bcl-2 and Bcl-xL play an important role in survival of effector CD8+ T cells [32] . We asked whether reduced expansion of Tc17 cells in MyD88ΔT mice is linked to the reduced expression of anti-apoptotic molecules Bcl-2 and Bcl-xL . The expression levels of Bcl-2 and Bcl-xL in Tc17 ( and Tc1 ) cells were comparable in vaccinated wild-type and MyD88ΔT mice ( Fig 4A ) . We also assessed active caspase 3 following ex vivo restimulation , and found no significant differences between the groups in either cytokine-producing cells or in total CD8+ T cells ( Fig 4B ) . Finally , we stained effector CD8+ T cells with Annexin V to detect signs of early apoptosis and again found no difference . Thus , reduced Tc17 cell responses in the absence of MyD88 signaling are not due to either reduced survival or augmented death of effector CD8+ T cells . Our previous work showed that CD43 expression is higher in Tc17 than in Tc1 cells [2] . CD43 signaling has a dichotomous role in effector CD8+ T cells; CD43 promotes expansion during the early phase of the T cell response , but augments apoptosis in the later phase [33] . Similarly , CD27 signaling is necessary for survival and/or proliferation of effector CD8+ T cells [34] . Therefore , we explored whether reduced accumulation of Tc17 cells induced by deficient MyD88 signaling was associated with decreased CD43 and CD27 expression . Fig 4C shows the frequency of CD43+ and CD27+ expression on Tc17 and Tc1 cells . As before , CD43 expression was higher on Tc17 than Tc1 cells , but there was no significant difference between cells from wild-type and MyD88ΔT mice . Likewise , expression levels of CD27 on Tc17 and Tc1 cells were comparable between the groups ( Fig 4C ) . Thus , poor Tc17 cell accumulation in the absence of MyD88 is neither due to augmented apoptosis nor to blunted CD43 or CD27 receptor expression . We evaluated whether MyD88 signaling regulated the proliferation of effector CD8+ T cells during the expansion and contraction phases of the T cell response . To evaluate CD8+ T cell proliferation , mice received BrdU for three intervals after vaccination ( Fig 5 ) . BrdU+ Tc17 and Tc1 cells were analyzed at the end of each period . Wild-type Tc17 cells in dLNs exhibited rapid proliferation by day 14 ( 80% ) , which peaked by day 21 ( ~93% ) and showed contraction or memory transition by day 30 ( ~82%; Fig 5 ) . Similar results were found in spleens , especially at day 30 , where proliferation of Tc17 cells was dramatically reduced ( S4 Fig ) . The proliferation of Tc17 cells in MyD88ΔT mice followed similar kinetics , however the percentage of BrdU+ cells was significantly lower on day 14 and remained lower at subsequent time points . Unlike the proliferation defect in Tc17 cell , the absence of MyD88 signaling did not significantly affect Tc1 proliferation ( Fig 5 ) . We also did not detect proliferation defects in MyD88-deficient , activated CD8+ ( CD44hi ) T cells during the early phases of expansion . Thus , MyD88 signaling sustained the proliferation of Tc17 cells , but not Tc1 cells , throughout the expansion phase , without exhibiting delayed expansion following fungal vaccination . mTOR has an important role in the metabolism and functions of both innate and adaptive immune cells [35] . Under Th17 polarization conditions in vitro , rapamycin treatment inhibited mTOR , blunting the expression of IL-17a transcript and proliferation of CD4+ T cells [36] . We postulated that mTOR mediates the proliferation and/or survival of Tc17 cells in MyD88-sufficient mice , and evaluated the effect of rapamycin treatment on Tc17 and Tc1 responses . Rapamycin treatment significantly blunted the total number and percentage of vaccine-induced Tc17 cells in the dLNs and spleens of wild-type mice , but had no effect in MyD88ΔT mice ( Figs 6A and S5 ) . The number of Tc17 cells was similar in rapamycin-treated wild-type mice and MyD88ΔT mice , supporting the requisite role of mTOR for MyD88-dependent Tc17 responses . Rapamycin treatment inconsistently affected Tc1 cell responses in vaccinated wild-type mice , blunting the numbers of Tc1 cells in dLNs , but not in spleen , and leaving the percentage of Tc1 cells unaffected in these organs . We next asked whether blunted Tc17 responses after rapamycin treatment are due to inhibition of proliferation . Vaccinated mice were pulsed with BrdU and treated with either rapamycin or PBS control . Treatment with rapamycin significantly reduced BrdU+ Tc17 cells in wild-type mice , but not MyD88ΔT mice ( Fig 6B ) . In contrast , rapamycin treatment did not significantly affect the proliferation of Tc1 cells in either group of mice ( Fig 6B ) . Thus , MyD88 signaling enhances antifungal Tc17 cell responses by augmenting proliferation of these cells via an mTOR dependent pathway . Many kinases can phosphorylate and activate mTOR , including Akt [37] . TLR2 ligation enhanced T-bet expression in CD8+ T cells and increased their cytotoxic functions , which were dependent on Akt and mTOR activation [38] . As shown above , mTOR activity is likely modulated by MyD88 signaling . Here , we asked if Akt signaling is required for MyD88-mediated Tc17 responses and mTOR phosphorylation . We first assessed phosphorylation of Akt in CD8+ T cells stimulated by yeast in vitro . We observed a pronounced increase in phosphorylation of Akt at T308 and S473 sites in the presence of DC supernatant from yeast-stimulated cultures ( S6A Fig ) . We next assessed whether Akt1 is phosphorylated in a MyD88-dependent manner in CD8+ T cells activated in vitro . We saw higher phosphorylation of Akt1 in wild-type CD8+ T cells compared to MyD88ΔT cells consistently at 20 , 40 and 60 minutes after activation ( S6B Fig ) . To test the functional role of Akt signaling in vivo during vaccination , we inhibited Akt with compound A-443654 [39] . Akt inhibition blunted Tc17 responses in the dLNs and spleen of wild-type mice , but not MyD88ΔT mice ( Fig 7A ) . Conversely , Akt inhibition did not affect Tc1 cell responses in wild-type mice . Instead , Akt inhibition actually increased the numbers of IFN-γ producing cells in MyD88ΔT mice . The effects of Akt inhibition resembled those of rapamycin treatment , suggesting a possible link between MyD88 activation of mTOR and Akt1 function in regulating downstream Tc17 responses . To test a direct link between them , we analyzed the influence of Akt1 inhibitor on mTOR ( S2448 ) phosphorylation in CD8+ T cells . Akt1 inhibited mTOR phosphorylation only in the presence of MyD88; that is , in wild-type CD8+ T cells , but in not MyD88ΔTcells , which confirmed that MyD88-dependent activation of mTOR occurred through Akt1 signaling ( Fig 7B ) . Collectively , these results suggest that Akt1 signaling is required for MyD88 dependent Tc17 responses that are mediated through mTOR upon fungal antigen engagement and that these signals are promoted via IL-1 ( see section below ) . We previously reported that Tc17 cells are reduced in IL-1R-/- mice [2] . Others have documented TLR2 and IL-18R signaling in CD8+ T cells [38 , 40] , although the intrinsic role of IL-1R , TLR2 and IL-18R for Tc17 responses has not been described . We assessed the function of these receptors in vitro and in vivo . For in vitro studies , we purified CD8+ T cells and incubated them with wild-type or MyD88-/- BMDCs loaded with yeasts . IL-17A levels were significantly lower in the supernatants from IL-1R1-/- , MyD88-/- , TLR2-/- and MyD88ΔT vs . wild-type CD8+ T cells , but were unaffected for IL-18R-/- CD8+ T cells ( Fig 8A ) . The deficit in IL-1R-/- CD8+ T cells was similar to the MyD88-deficient groups , and more pronounced than for TLR2 deficient CD8+ T cells . Phosphorylation of Akt at T308 and p-mTOR levels in CD8+ T cells were enhanced by the addition of either IL-1α or IL-1β or both , but only in the presence of MyD88 ( S6C Fig ) . CD8+ T cells incubated with MyD88-/- BMDCs also produced significantly less IL-17A ( S7 Fig ) , which is consistent with an independent , extrinsic contribution . For in vivo studies , we created mixed bone-marrow chimeras using irradiated TCRα-/- mice as a recipient for different donors ( Fig 8B ) , or administered blocking antibody against IL-18R throughout the study . Tc17 and Tc1 cells were both reduced in the dLNs in the absence of T-cell specific MyD88 , IL-1R and TLR2 ( Fig 8C ) , but Tc17 cells were unaffected by blockade of IL-18R ( Fig 8D ) . The spleens of chimeric mice revealed more pronounced impairments in Tc17 vs . Tc1 cells ( S7 Fig ) , however TLR2-/- Tc17 cell numbers were not significantly different from wild-type . Thus , IL-1R and TLR2 exert hierarchical contributions to intrinsic MyD88 signaling in Tc17 cells , with the former being most important , and IL-18R signals appears to be dispensable in this model of vaccine-induced anti-fungal Tc17 cells .
Th17 cell responses are essential for immunity against infections including those caused by fungi [9] . AIDS and other immune compromising disorders are associated with increased rates of opportunistic fungal infections due to CD4+ T cell lymphopenia [41] . Hence , uncovering residual protective immune cells against fungi is essential for vaccination of at risk individuals . We previously showed that in the absence of CD4+ T-cell help protective anti-fungal CD8+ T cell responses are elicited and maintained as long-lasting memory cells . Tc17 cells are indispensible for this vaccine-induced fungal immunity [2] . The extrinsic cytokine signals required for differentiation of Tc17 ( and Th17 ) cells have been characterized , and include TGFβ , IL-6 , IL-21 , and IL-23 . However , the role of T cell intrinsic signals including MyD88 and upstream TLRs and IL-1R family members for Tc17 ( and Tc1 ) responses during immunity to infection has been less clear . Here , we document a requisite role for intrinsic MyD88 in Tc17 cell responses and fungal vaccine immunity . We also show that under the same ‘inflammatory milieu’ , intrinsic MyD88 signals are indispensable for Tc17 cell responses , whereas Tc1 cells are less affected in the absence of these signals . MyD88 deficiency enhances susceptibility to infections caused by viruses , bacteria , parasites and fungi , but its contribution to resistance varies depending upon the pathogen . MyD88 is essential for innate immunity and resistance without affecting CD8+ T cell responses during Trypanosoma infection [42] . In contrast , MyD88 has a cell-intrinsic role for CD8+ T cell responses during lymphocytic choriomeningitis and vaccinia infections , where Tc1 responses are compromised in its absence [31 , 43 , 44] . During experimental toxoplasmosis , T-cell-intrinsic MyD88 deficiency severely affects Th1 responses and impairs resistance [28] . Our study unveils a critical role for intrinsic MyD88 function in CD8+ T cells during vaccine immunity against fungal pneumonia; its absence leads to a profound deficit of Tc17 responses in the lung . We explored mechanisms underpinning MyD88 dependent , intrinsic control of Tc17 responses . After antigen engagement , T cell expansion is the net result of effector T cell proliferation and death . Several modes of cell intrinsic MyD88 action are possible . Lack of intrinsic MyD88 signaling during viral infection enhanced apoptosis of effector CD8+ T cells ( despite normal Bcl-xL expression ) without affecting proliferation [31] . In contrast , intrinsic MyD88 was required to sustain proliferation of effector Tc1 cells in a model of protracted viral infection [30] . Our findings suggest that intrinsic MyD88 is required for sustained proliferation of Tc17 cells , but not Tc1 cells . Our studies also show that MyD88-/- CD8+ T cells were not prone to apoptosis and that both Tc17 and Tc1 cells displayed similar levels of active-caspase3 , Annexin V and Bcl-xL expression . Bcl-2 levels were also not influenced by MyD88 expression in Tc17 cells , however Bcl-2 levels were lower in Tc17 cells than in Tc1 cells . The relevance of this finding is unclear , but our prior work showed that Tc17 cells portend long-term memory and display stem-cell like features [2] . Akt signaling is integral for T cell activation and expansion [45] . T cell differentiation may involve combinatorial signals that naïve T cells receive under a ‘micro-inflammatory milieu’ , but the role of TCR signaling in regulating T cell responses via MyD88-Akt for Tc17 cell responses is poorly understood . Intrinsic MyD88 signals are known to boost functional avidity of IFNγ+ CD8+ T cell responses during vaccinia vaccination by reducing the activation threshold [46] , whereas our data show that these signals augment Tc17 responses more than Tc1 responses . We also found that Akt1 signals were critical for boosting Tc17 , but not Tc1 responses , suggesting the hypothesis that low avidity CD8+ T cells require MyD88 signals to augment Tc17 responses whereas high avidity Tc1 cells may not require augmented Akt signaling . This idea is in line with data from Th17 cells where low-strength T cell activation promotes their phenotype [47] . Our in vivo results support this premise since Tc1 cells were unimpaired or even augmented in the presence of Akt1 inhibitor . mTOR , a key metabolic sensor , is chiefly activated by PI3K-Akt pathway in T cells [35] . To our knowledge the role of MyD88 in Akt-mTOR regulation of Tc17 cell responses has not been defined , although ligation of TLR2 has been shown to enhance T-bet and Tc1 cell responses in a manner dependent on Akt and mTOR [38] . mTOR activity has been linked to Th17 cell responses by enhancing HIF-1α expression , Stat3 phosphorylation , RORγt translocation , and cell proliferation [48] . We show here that pharmacological inhibition of mTORC1 reduced Tc17 cell , but not Tc1 cell proliferation in a MyD88-dependent manner . We also found that MyD88 deficiency did not affect RORγt levels , similar to a report on Th17 cell polarization [36] . That study , which involved in vitro Th17 polarization , showed that IL-1 signaling was required for the expression of IL-23R and together they enhanced mTOR activity to promote Th17 cell responses . Other cytokines including IL-6 , IL-21 and Il-23 also can augment the expression of IL-23R [49] . Our studies suggest that MyD88 signals influence mTOR through Akt to enhance Tc17 cell responses , and that MyD88 signaling may function independent of IL-23 signaling through Akt . Nevertheless , IL-23 signaling in Th17/Tc17 cells may enhance Akt-mTOR signaling via Jak2 [50] . Further studies are needed to address whether IL-23 integrates MyD88 signaling downstream of mTOR . One possible mechanism is that MyD88-Akt-mTOR may bolster stat3 function [48 , 51] that is activated by IL-6 , IL-21 and IL-23 . Rapamycin treatment can also affect innate immunity by mechanisms that may , in turn , affect Tc17 cell responses [52] . Our in vivo work suggested that Rapamycin treatment of vaccinated mice chiefly and selectively affected intrinsic MyD88 signaling for Tc17 cell responses , in view of the insignificant effect on Tc17 responses in MyD88ΔT mice . Accumulating evidence suggests that IL-1 is required for both systemic and mucosal Th17 responses [53] . IL-1 has pleotropic effects on both innate and adaptive immunity and the lack of IL-1 enhances the susceptibility to bacterial , viral and fungal infections [54] . IL-1R-/- mice are vulnerable to coccidioidomycosis and blastomycosis , and IL-1 administration was shown to enhance fungal vaccine immunity in a manner that required IL-17R signaling [51 , 55] . Administration of IL-1 enhanced the expansion and function of CD8+ T cells [55] and IL-1R-/- mice had reduced CD8+ T cell responses , IFN-γ production and viral clearance [56] , although the cell-intrinsic role of IL-1R signaling for CD8+ T cell responses was not explored . Our study here shows that intrinsic IL-1 signaling sharply affects Tc17 cell responses . This differing impact of IL-1 on Tc17 vs . Tc1 responses in the two studies may be due to the model system where differentiation towards Tc1 cell responses is favored and exogenous IL-1 just augmented the responses by increasing T-bet and activating mTOR [38] . Ligands for TLR2 influence the polarization of Th17 cells in vitro and development of EAE in mice [29] . Among the many ligands known for TLR signaling , fungi display zymosan , phospholipomannan , O-linked mannans , and DNA , which are recognized by TLR2 , TLR4 and TLR9 , respectively . While impaired MyD88 signaling reliably enhances susceptibility to numerous fungal infections , the absence of individual TLRs shows varying results , perhaps due to impaired IL-1R family signaling in MyD88-/- mice or due to compensation by other TLRs [9] . While a T-cell intrinsic role was not explored , IL-1R but not TLR2 signaling was essential for Th17 responses during Coccidioides infection [51] . In an in vitro model , a TLR2 agonist was shown to enhance T-bet expression in CD8+ T cells by activating Akt and mTOR [38] . Here , we observed that intrinsic TLR2 signaling was essential for Tc17 as well as Tc1 responses . Differences among these studies may be due to the model , fungal strain , T cell type or compensation by other receptors . Our data also suggested that TLR2 was only essential for initial priming in the skin dLNs , but not in the spleen , where exuberant circulating IL-1 may compensate the defect for Tc17 responses . Collectively , we show that intrinsic MyD88 signals are required for anti-fungal vaccine immune responses in vulnerable CD4+ T cell deficient hosts through sustained proliferation and preferential expansion of Tc17 cells , which is dependent on Akt and mTOR . Our study therefore identifies unappreciated targets for augmenting adaptive immunity against pathogenic fungi . Our findings are important for designing vaccines against fungal infections in at risk individuals with CD4+ T cell defects and for immunotherapeutic intervention during infection and possibly autoimmune disorders .
Animal procedures were done in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Care was taken to minimize animal suffering . The work was done with the approval of the IACUC of the University of Wisconsin-Madison . The IACUC protocol number for the study is M00969 . Seven- to eight-week-old C57BL/6 ( WT ) were purchased from the National Cancer Institute . Inbred strains of mice on the C57BL/6 background were purchased from Jackson Laboratories and included Thy1 . 1 allele carrying congenic B6 mice strain B6 . PL-Thy1a/Cy ( Stock 000406 ) , Ly5 . 1 allele carrying congenic B6 mice strain B6 . SJL-Ptprca Pep3b /BoyJ ( stock 002014 ) , Il1r1-/- B6 . 129S7-Il1r1tm1Imx/J mice ( stock 003245 ) , B6 . 129-Tlr2tm1Kir /J ( stock004650 ) , Il18r1-/- B6 . 129P2-Il18r1tm1Aki/J , B6-Tg ( TcraTcrb ) 1100Mjb/J ( 003831 ) , B6 . 129P2 ( SJL ) -Myd88tm1Defr /J ( Stock 008888 ) , and B6 . 129P2 ( SJL ) -Myd88tm1 . 1Defr /J ( Stock 009088 ) . Thy1 . 1+ OT-I Tg mice were generated by crossing the Thy1 . 1 allele carrying strain with the OT-I Tg strain . Breeding pairs of T-cell specific MyD88-/- ( MyD88ΔT ) mice were a kind gift from Laurence Turka . Congenic OT-I Tg-MyD88ΔT ( Thy1 . 1 ) mice were generated by crossing OT-I Tg ( Thy1 . 1 ) and MyD88ΔT mice . Mice were housed and cared for according to guidelines of the University of Wisconsin Animal Care Committee , who approved all aspects of this work . Wild-type virulent B . dermatitidis strain 26199 was purchased from ATCC . An isogenic attenuated mutant lacking BAD-I ( strain #55 ) and recombinant #55 strain carrying the OT-I epitope SIINFEKL were also used for vaccination ( below ) . Isolates were maintained as yeast on Middlebrook 7H10 agar with oleic acid-albumin complex ( Sigma-Aldrich , St . Louis , MO ) at 39°C . Vaccination of mice with specific strains of fungus has been described elsewhere [1] . Briefly , ~105 cfu of attenuated strain #55 was inoculated subcutaneously ( s . c . ) at each of two sites , dorsally and at the base of tail . For vaccination with recombinant OT-I #55 strain , a total of 107 yeast was used after heat killing at 65°C for 30 min . Pulmonary challenge studies were done with 2x103 yeast of wild-type strain #26199 . CD4+ T cell depletion was performed by using a weekly dose of 100 μg of GK1 . 5 mAb ( Biovest International Inc . Minneapolis , MN/BioXCell , West Lebanon , NH ) given intravenously ( i . v . ) , with an efficiency of >99% depletion [1] . All adoptive transfers of enriched CD8+ T cells were done using BD Biosciences ( Palo Alto , CA ) or Miltenyi kits ( Auburn , CA ) . Equal numbers of CD8+ T cells were used for transfer into cohorts of recipients by the i . v . route . For blocking IL-18R , we purchased anti-IL-18R ( R&D Systems ) and administered 400 μg/mouse on day -1 of vaccination and 300 μg/mouse subsequently every 3 days by the i . v . route . Organs were harvested and single cell suspension cells prepared using BD biosciences cell strainers and plunger . Cells were subjected to Fc block ( BD Biosciences ) for 20 min before staining for surface markers with antibodies for 30 min at 4°C . Fluorochrome-labeled anti-mouse antibodies against CD8 ( clone 53–6 . 7 ) , Thy1 . 1 ( clone OX-7 ) , CD45 . 1 ( clone A20 ) , CD27 ( clone LG . 3A10 ) , IFNγ ( clone XMG1 . 2 ) , TNFα ( clone MP6-XT22 ) , IL-17A ( clone TC11-18H10 ) , IL-2 ( clone JES6-5H4 ) , BrdU Flow kit ( Cat 51-2354AK ) and Bcl-2 set ( Cat 554221 ) were purchased from BD Biosciences/Pharmingen , whereas , CD44 ( clone IM7 ) , GM-CSF ( clone MP1-22E9 ) and ROR gamma ( t ) ( clone AFKJS9 ) were purchased from eBioscience ( San Diego , CA ) . Rabbit anti-mouse antibodies phospho-Akt ( T308 ) ( C31E5E ) , phospho-mTOR ( S2448 ) ( D9C2 ) , phospho-Akt ( S473 ) ( D9E ) and Bcl-xL ( 54H6 ) were obtained from Cell Signaling . Anti-mouse CD43 ( clone 1B11 ) was purchased from Biolegend ( San Diego , CA ) . Single cell suspensions were restimulated with anti-CD3 {clone 145 . -2C11; 0 . 1μg/ml ) and anti-CD-28 ( clone 37 . 51; 1μg/ml ) in the presence of Golgi-stop ( BD Biosciences ) for 5 hours at 37°C . Cells were first stained for surface markers followed by intracellular cytokines and/or Bcl-2 and Bcl-xL staining using BD Perm/Fix kit . Cells were analyzed by flow cytometry using an LSR II instrument . Cells were surface stained prior to fixing and permeabilizing with Phosflow Lyse/Fix buffer and Phosflow Perm/Wash buffer II ( BD Biosciences ) . Cells were then stained with phospho-specific antibodies ( Cell Signaling , Danvers , MA ) and analyzed by flow cytometry and mean fluorescence intensity was determined . BrdU ( MP Biomedicals , Santa Ana , CA ) was fed through drinking water at the concentration of 0 . 8 mg/ml daily on indicated days . At the end of the experiment , the cells were restimulated , stained for surface markers and intracellular cytokines as above . Cells were washed and subjected to a BrdU staining protocol as per the manufacturer’s protocol ( BD Biosciences ) . Stock solutions of rapamycin were diluted in PBS and mice were given 2 μg daily by the intraperitoneal route ( i . p . ) . Akt1 inhibitor , A-443654 , was suspended in 0 . 2% HPMC solution and mice were given 7 . 5mg/kg/d divided twice daily by the subcutaneous route . A-443654 and Rapamycin were kind gifts from Dr . M . Suresh , University of Wisconsin-Madison . TCRα-/- recipient mice were lethally irradiated ( a total of Gy 1100 ) and transfused with mixed 3x106 bone marrow cells in the ratio of 70% TCRα-/- and 30% of respective donor cells . After a 3-month rest period , mice were used for the experiment . Bone marrow cells were obtained and differentiated into dendritic cells ( BMDCs ) in the presence of GM-CSF and IL-4 for 6 days . 105 BMDCs were incubated with heat killed yeast of strain #55 at a 1:1 ratio and 106 enriched CD8+ T cells were added to the well and incubated for 5 days . Culture supernatants were harvested to quantify IL-17A levels by ELISA . In some experiments , enriched CD8+ T cells were cultured for 4 days in the presence of anti-CD3 antibody and BMDCs supernatant that was collected after culturing BMDCs with heat killed yeast for 48 hrs . In some experiments , we used supernatant of BMDCs stimulated with yeast for 48 hours as a source of cytokines for in vitro stimulation of naïve CD8+ T cells , along with the addition of anti-CD3 antibody . Statistical analysis was performed using a two-tailed , unpaired Student t test . For statistical analysis for fungal CFUs , a nonparametric Kruskal-Wallis test with Dunns post-test was used to compare unvaccinated vs vaccinated groups and among vaccinated groups . For comparing more than 2 groups , one-way ANOVA was used with Bonferroni post-test correction . A 2-tailed P value of ≤0 . 05 was considered statistically significant . | Patients with AIDS , cancer or immune suppressive treatments are vulnerable to infection with invasive fungi . We have found that even when helper CD4 T cells are profoundly reduced in a mouse model that mimics this defect in AIDS , other remaining T cells are capable of mounting vaccine immunity against a deadly fungal infection , and they do so by producing the powerful , soluble product , IL-17 . It has been widely believed that the activation and instruction of such cells , called Tc17 cells , is governed by another population of immune cells in the body , but we have found here that pathways within these Tc17 cells themselves mediate their activation and ability to produce the IL-17 needed for resistance to infection . We have also identified elements of the circuitry controlling this pathway—elements called MyD88 , Akt1 and mTOR—and found that they control the production of IL-17 and not other products such as IFN-γ often produced by these cells . Further , we determined that this circuitry controls the development of Tc17 cells by regulating their ability to divide and expand . Thus , in a mouse model of vaccination against lethal fungal pneumonia caused by Blastomyces dermatitidis , we uncovered an important cellular arsenal that can be recruited to bolster resistance against a fungal infection , and identified novel ways in which the cells develop and expand into potent killers of fungi . | [
"Abstract",
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"Results",
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"Methods"
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| 2015 | Intrinsic MyD88-Akt1-mTOR Signaling Coordinates Disparate Tc17 and Tc1 Responses during Vaccine Immunity against Fungal Pneumonia |
As small regulatory transcripts , microRNAs ( miRs ) act as genetic ‘fine tuners’ of posttranscriptional events , and as genetic switches to promote phenotypic switching . The miR miR26a targets the BMP signalling effector , smad1 . We show that loss of miR26a leads to hemorrhage ( a loss of vascular stability ) in vivo , suggesting altered vascular differentiation . Reduction in miR26a levels increases smad1 mRNA and phospho-Smad1 ( pSmad1 ) levels . We show that increasing BMP signalling by overexpression of smad1 also leads to hemorrhage . Normalization of Smad1 levels through double knockdown of miR26a and smad1 rescues hemorrhage , suggesting a direct relationship between miR26a , smad1 and vascular stability . Using an in vivo BMP genetic reporter and pSmad1 staining , we show that the effect of miR26a on smooth muscle differentiation is non-autonomous; BMP signalling is active in embryonic endothelial cells , but not in smooth muscle cells . Nonetheless , increased BMP signalling due to loss of miR26a results in an increase in acta2-expressing smooth muscle cell numbers and promotes a differentiated smooth muscle morphology . Similarly , forced expression of smad1 in endothelial cells leads to an increase in smooth muscle cell number and coverage . Furthermore , smooth muscle phenotypes caused by inhibition of the BMP pathway are rescued by loss of miR26a . Taken together , our data suggest that miR26a modulates BMP signalling in endothelial cells and indirectly promotes a differentiated smooth muscle phenotype . Our data highlights how crosstalk from BMP-responsive endothelium to smooth muscle is important for smooth muscle differentiation .
Vascular smooth muscle cells ( vSMCs ) provide structural integrity to the vessel wall . Guided control of signalling cascades , including Platelet derived growth factor ( Pdgf ) , Notch , and Transforming Growth Factor-β/Bone morphogenic Protein ( TGF-β/BMP ) recruits and induces differentiation of perivascular mural cells ( vSMCs and pericytes ) to create a two-layered vessel wall with an internal endothelial cell lining and a muscle cell covering [1–3] . Once the vSMCs surround the vessel , they begin depositing extracellular matrix ( ECM ) proteins Laminin , Collagen IV and Fibulins to support the vessel wall [4] . vSMCs then take on a mature phenotype that stabilizes the underlying endothelial cells through induction of quiescence , expression of junctional and attachment proteins , and expression of contractile proteins to provide myogenic tone [2 , 5–7] . vSMCs maintain phenotypic plasticity and can undergo a phenotypic switch from a quiescent contractile state to a proliferative synthetic state in response to cellular stimuli [4 , 8] . Contractile vSMCs are defined by an elongated and thin ‘spindle-shaped’ morphology and low rates of proliferation . The expression of key differentiation markers such as smooth muscle ( α ) -actin ( Acta2 ) , smooth muscle β-myosin heavy chain ( Myh11 ) , and transgelin ( Sm22α ) allows vSMCs to perform their contractile function and provide vascular tone . In contrast , the immature synthetic vSMCs have reduced expression of contractile genes , produce ECM proteins , are highly proliferative , and have a rhomboid or rounded morphology [9–12] . Numerous studies have demonstrated that BMP signaling through Smad1 modulate vSMC plasticity ( reviewed by [13] ) . Defective BMP signalling can affect both endothelial and vSMC cells [14–21] . Aberrant vSMCs phenotype switching plays a critical role in the pathogenesis of vascular diseases such as hereditary hemorrhagic telangiectasia ( HHT ) and pulmonary arterial hypertension ( PAH ) . In canonical Smad-mediated BMP signaling , Smad1 is phosphorylated by the serine-threonine kinase activity of a type 1 BMP receptor ( ACVRL1 ( ALK1 ) / BMPR1A , BMPR1B ) allowing it to associate and dimerize with the co-mediator Smad4 and translocate to the nucleus to control gene transcription . Murine homozygous null mutants for BMPR-1a ( Activin like kinase 3 , ALK3 ) or the type II receptor BMPR-2 ( which is mutated in human patients with PAH ) [22 , 23] and their ligand Bmp4 or downstream co-Smad4 are embryonic lethal , and present with vascular deformities attributable to a loss of Smad1 mediated signalling [24] . Mutations in ALK1 lead to HHT2 , a disease characterized by arteriovenous malformations ( AVMs ) [25] . Deletion of Alk1 in mice leads to cranial hemorrhages , AVM-like fusion of micro-vessel plexi , dilation of large vessels and reduced coverage of vessels by vSMCs [26] . In zebrafish , disruption of Alk1 signalling results in pathological arterial enlargement and maladaptive responses to blood flow that generate AVMs . Potential vSMCs defects in this model have not been assessed [27] . As small noncoding RNAs , microRNAs ( miRs ) regulate gene expression of key vSMC marker genes to control vSMC dynamics . [28–31] . A number of miRs have been identified as modulators of the vSMC phenotype in vitro and in vivo , including miR-145 , miR-21 , miR-221 , miR-222 and miR-146a [32–40] . We previously showed that miR-145 promotes visceral smooth muscle differentiation via controlling cross-talk between epithelial cells and smooth muscle [32 , 41] . Here , we investigate the role of microRNA26a ( miR26a ) in regulating vSMC dynamics using the zebrafish model of vessel stabilization . miR26a regulates proliferation , migration and differentiation of vSMCs and has been shown to target smad1 , a key intracellular mediator of BMP signalling , in cultured vSMCs in vitro [42–45] . miR26a expression is altered during abdominal aortic aneurysm ( AAA ) and neointimal lesion formation [43 , 45] . However , the role of miR26a in vivo in an intact animal in the context of developing vSMCs are largely unknown . Using a combination of genetic gain and loss of function methods to understand the role of miR26a in vivo , we show that miR26a acts within a BMP responsive pathway to fine tune vSMC maturation via targeting smad1 . Interestingly , we find that active BMP signalling and changes in Smad1 activation are observed within endothelium in vivo , and not in smooth muscle cells . Together the evidence suggests that miR26a plays a role in regulating blood vessel stabilization via a non-autonomous mechanism .
miR26a targets smad1 and thereby directly regulates BMP signalling ( [36 , 43] Fig 1A ) . To observe the spatial gene expression pattern of miR26a in developing embryos we used in situ hybridization . At 48 hpf , miR26a has a ubiquitous expression pattern ( Fig 1B and 1B’ ) , however by 4 dpf expression becomes enriched in the ventral head of the embryo , with strong expression in the pharyngeal region , bulbus arteriosus and ventral aorta ( Fig 1C ) . miR26a is expressed in and around the blood vessel endothelium where it could potentially play a role modulating BMP signalling in blood vessels ( compare to kdrl: GFP stain; Fig 1C’ , inset ) . In order to further analyze the cell specific expression of miR26a , we used fluorescent-activated cell sorting ( FACS ) to isolate EGFP+ve vSMCs and mCherry+ve endothelial cells from 4 dpf Tg ( acta2:EGFP;kdrl:mCherry ) embryos . In keeping with the in-situ hybridization data , RT-qPCR showed that miR26a is indeed expressed in both cell types , although it is not significantly enriched in endothelial cells ( S1 Fig ) . FACS sorting efficiently separated vSMCs and endothelial cells; we find that acta2: EGFP+ve vSMCs cells have an average 37 . 4-fold enrichment in acta2 expression , and minimal expression of alk1 or smad1 when compared to kdrl: mCherry endothelial cells . However , smad1 is 14-fold enriched and acvrl1 is 3 . 5-fold enriched in mCherry+ve endothelial cells while there is nearly no expression of acta2 ( S1 Fig ) . Thus , a miR26a target , smad1 is enriched in endothelial cells . We next tested the relationship between mir26a expression and activated BMP signaling using an in vivo reporter of Smad1/5 activity . Tg ( BRE:EGFP] transgenic fish encode EGFP driven by an upstream Bmp Response Element ( BRE ) that contains multiple short Smad-binding sites from the id1 promoter , a major transcriptional target of canonical Bmp/Smad1 signaling [46 , 47] . We crossed Tg ( BRE:EGFP] to endothelial Tg ( kdrl:mCherry ) or vSMC Tg ( acta2:mCherry ) lines to observe BMP activation in endothelial and vSMCs , respectively ( Fig 1D and 1G ) . We use the 4 dpf time point as vSMC cells first differentiate and begin to express the mature marker acta2 between 3 and 4 dpf [7 , 48] . Surprisingly , although miR26a has been implicated in controlling Smad1 regulated vSMC dynamics directly , we found that transgenic BRE:EGFP signals are restricted to the endothelium of the vessel wall and have co-localized expression with kdrl:mCherry ( Fig 1E , 1E’ , 1F and 1F’ ) . The acta2:mCherry-positive vSMCs lie directly adjacent to BRE:EGFP-expressing cells , with no detectable expression of BRE:EGFP in vSMCs on the ventral aorta or in pharyngeal aortic arch arteries ( Fig 1G , 1G” and 1H , both ventral and lateral projections are shown ) . Similarly , acta2:mCherry-positive cells are closely associated with pSmad1-positive endothelial cells but do not show pSmad1 staining ( S1D Fig ) . Together , our data suggest that in early development , miR26a and smad1 are expressed within endothelial cells where BMP signaling is also active , as visualized by two methods of detection . The highly conserved miR-26 family constitutes miR26a-1 , miR26a-2 , miR26a-3 and miR26b [44] as identified by their seed sequences and accessory sequence . In zebrafish and humans , miR26a-1 , miR26a-2 and miR26a-3 have the same mature sequence , and only differ from the mature miR-26b sequence by two nucleotides [42 , 49] . To investigate the role of miR26a in vascular development in vivo , we knocked down miR26a using an antisense morpholino that targets the mature miRNA seed sequence of all three miR26a isoforms . A 6bp mismatch scrambled control morpholino was used as a control . 1 ng doses of morpholino were used , as suggested by current guidelines [50 , 51] . In parallel , we designed a second genetic knockdown approach using CRISPR interference ( CRISPRi ) [52] to target the pri-miR hairpin structure using the complementary sequence to the mature miRNA ( Fig 2A ) . RT-qPCR shows a 26% ( 0 . 74±0 . 65 ) reduction in miR26a following miR26a MO knockdown and 34% ( 0 . 65±0 . 10 ) reduction of miR26a using CRISPRi ( Fig 2B and 2C ) confirming that both knockdown methods result in decreased miR26a expression . smad1 is a demonstrated target of miR26a in vitro [36 , 43] . In support of smad1 being a miR26a target in vivo , miR26a knockdown results in increased smad1 expression in 48 hpf and 4 dpf injected embryos as compared to controls by in situ hybridization ( Fig 2D ) and resulted in an average 1 . 6-fold and 1 . 8-fold increase by RT-qPCR , respectively ( Fig 2E and 2F ) . To further determine whether miR26a can regulate smad1 expression in vivo , we designed a sensor assay and fused the smad1 3′UTR to EGFP ( EGFP: smad1pA ) . This was co-injected with an internal mCherry control into single-cell zebrafish embryos in the presence or absence of a miR26a morpholino . When fluorescence levels were examined at 24 hpf , injections of EGFP: smad1pA sensor mRNA alone resulted in EGFP expression; however , this fluorescence was enhanced by over 65% by co-injection of miR26a morpholino ( S2A and S2B Fig ) . At 4 dpf , upregulation of smad1 in miR26a morphants and CRISPRi knockdown embryos is more prominent in the ventral pharyngeal region , with staining in the ventral aorta , aortic arches and bulbous arteriosus ( Fig 2D , highlighted areas ) , similar to where miR26a is expressed most strongly ( Fig 1B and 1C ) . In a complementary approach , we injected a miR26a mimic to overexpress miR26a and observed an increase in miR26a expression by RT-qPCR ( S2C Fig ) , as well as a marked reduction of smad1 expression in the ventral pharyngeal region by in situ hybridization ( S2E Fig ) . Overexpression of miR26a results in mildly dorsalized embryos by 48 hpf with pericardial edema , dorsal axis defects and poor circulation ( S2D Fig ) , suggesting overexpression of miR26a disrupts the BMP pathway that patterns early embryonic axes . We next tested whether the increased expression of smad1 mRNA in miR26a knockdown embryos leads to enhanced Smad1 phosphorylation . Wildtype immunostaining showed pSmad1/5/9 is high in endothelium but not in vSMCs ( S1D–S1D” Fig ) . miR26a knockdown embryos do not show any significant difference in endothelial cell number as compared to controls , using endothelial nuclear transgenic lines ( Tg ( fli1a:nEGFP; kdrl:mCherry ) ; Fig 3A–3E , and S3 Fig ) . However , there is a significant 20% increase in pSmad1 positive/ fli1a:nEGFP nuclei in miR26a knockdown embryos as compared to controls , with an average of 60±3 . 1% and 64 . 9±3 . 9% in miR26a morphants ( Fig 3B–3B” and 3F ) and CRISPRi embryos ( Fig 3D , 3D” and 3G ) , respectively as compared to 41±2 . 9 and 46±6 . 5% in controls . Loss of miR26a leads to compromised vessel integrity at 48 hpf . miR26a morphants have an average 13±2% hemorrhage ( Fig 4B and 4D ) and CRISPRi embryos have an average 15±1% hemorrhage ( Fig 4C and 4D ) as compared to 2–3% rate of the controls . The phenotype is dose-dependent as higher doses of morpholino lead to an increase in hemorrhage to 40% and a 1 . 8-fold reduction in miR26a expression ( S4C and S4D Fig ) . As smad1 overexpression has not been previously connected to vascular stability defects , we next tested whether overexpressed smad1 could lead to hemorrhage . Injection of smad1 mRNA into single cell stage embryos resulted in significantly higher hemorrhage rate of 12±0 . 9% in injected embryos as compared to uninjected controls ( Fig 4E , 4F and 4I ) . Further , as miR26a knockdown leads to increased smad1 levels , we predicted that reduction in smad1 would rescue hemorrhage in miR26a knockdown embryos . Double knockdown by co-injection of smad1 [53] and miR26a morpholinos reduced hemorrhage rates to below 5±0 . 8% ( Fig 4H , top embryo and Fig 4I ) . Of note , smad1 MO alone did not result in hemorrhage; however it did result in a range of phenotypes associated with smad1 knockdown including dorsal-ventral axis defects and hydrocephalus as previously reported [53] . smad1 knockdown led to an average a 77±8 . 6% of embryos with a mild ( V1 ) ventralized defect and 12±17% with a more severe ( V2 ) phenotype ( Fig 4G–4I ) . Both defects were reduced in double knockdown embryos ( Fig 4G and 4H , bottom ) . Thus , reducing miR26a or increasing smad1 in vivo leads to leads to a loss of vascular stability and hemorrhage . To demonstrate functional consequences of increased endothelial BMP signalling on vSMCs , we next investigated vSMC investment on the ventral aorta and pharyngeal arch arteries of Tg ( BRE:EGFP;acta2:mCherry ) embryos in miR26a knockdown embryos . This assay allowed us to make three key observations . Firstly , BRE:EGFP signal intensity is enhanced in miR26a morphants ( Fig 5A’ , 5B’ and 5C ) , which correlates with the increased pSmad1 staining we observed in endothelial nuclei of knockdown embryos ( Fig 3 ) . Secondly , the number of acta2:mCherry positive cells along the ventral aorta and pharyngeal arch arteries ( PAA ) is increased in miR26a knockdown embryos ( 33 . 8±1 . 6 in controls vs 47 . 2 ±2 . 2 in miR26a knockdown , Fig 5A” , 5B” and 5D ) . Thirdly , the increase in acta2 positive cell number is accompanied by a change in cell morphology in miR26a knockdown embryos ( Fig 5E–5G ) . In control embryos , acta2 positive vSMCs have a rounded , punctate morphology , and ‘sit’ high on the vessel wall with an average height of 4 . 5±0 . 4 μm , above the underlying endothelium . In miR26a morphants , vSMCs are have a significantly reduced vSMC height of 3 . 4 ± 0 . 1μm , and appear flatter and more closely apposed to the endothelium when compared to control embryo vSMCs . These data suggest that loss of miR26a results in increased vSMC coverage along blood vessels and a shift to a differentiated morphology . In parallel , we quantitated gene expression for vSMC differentiation genes . RT-qPCR using isolated embryonic head mRNA at 4 dpf showed a 1 . 7-fold increase in acta2 and 1 . 8-fold increase in myh11a mRNA in miR26a morphants ( Fig 5H ) . Further , using in situ hybridization , we found that miR26a morphants have increased expression of acta2 and myh11a in the pharyngeal region ( S5 Fig ) , similar to the location of increased smad1 staining ( Fig 2D ) . Conversely by 4 dpf , miR26a mimic injected embryos had reduction in acta2 and sm22 expression by in situ hybridization ( S5 Fig ) . The Bmp/ Notch3/ Pdgf signalling axis is an important regulator of vSMC proliferation and subsequent differentiation . We found that the vSMC notch receptor notch3 has a 2 . 0-fold increase in miR26a morphants ( Fig 5H ) . Furthermore , the endothelial expressed ligand pdgfba and its mural cell receptor pdgfrβ , had a 3 . 6 and 2 . 0-fold increase , respectively , in miR26a morphants as compared to controls . This suggests that increased vSMC numbers could potentially arise from enhanced proliferation via activation of the Pdgfrβ pathway downstream of Smad1 activation . Increases in acta2 , myh11 and notch3 may therefore reflect increased cell numbers in addition to increased vSMC differentiation . To demonstrate that smad1 expression in endothelial cells promotes vSMC differentiation , we expressed smad1 under an endothelial promoter in a transposon vector ( TolCG2:kdrl:smad1 , hereafter smad1ECOE; Fig 6A ) . The vector and transposase or transposase alone control were injected into Tg ( BRE:EGFP;acta2:mCherry ) embryos and scored at 48 hpf and 4 dpf . At 48 hpf , 10% of smad1ECOE embryos hemorrhage , similar to the increased hemorrhage observed in mirR26 knockdown and global smad1 mRNA overexpression ( Fig 4 ) . Higher doses of the vector result in significant cranial and pericardial edemas ( S6A and S6B Fig ) . At 4 dpf , RT-qPCR of smad1ECOE embryos shows a 1 . 9-fold increase in smad1 and 1 . 4-fold increase in the BMP responsive gene id1 expression as compared to control ( Fig 6B ) . Similarly , BRE:EGFP fluorescence is also increased in smad1ECOE embryos by 30% as compared to controls ( Fig 6C” , 6D” and 6F ) . Together , the data show that activation of Smad1 was significantly increased in smad1 injected embryos . smad1ECOE embryos do not show a change in the length of the ventral aorta , however the BRE:EGFP signal extends further along the ventral aorta ( Fig 6C” , 6D” and 6G ) . The total number of acta2:mCherry positive vSMCs along the ventral aorta in smad1ECOE embryos was significantly increased from 15±0 . 6 in controls to an average 24±1 . 4 cells ( Fig 6C’ , 6D’ and 6E ) . The percent vSMC coverage of the ventral aorta is also increased by 20% with an average of 80 ± 2 . 05% in smad1ECOE as opposed to 65 . 3±2 . 1% in controls ( Fig 6C’ , 6D’ and 6H ) . Our data suggests that upregulation of smad1 in endothelial cells is sufficient to increase vSMC number and coverage of the ventral aorta of 4 dpf embryos . Our results showed that miR26a knockdown leads to an increased number of acta2 positive vSMCs on the ventral aorta and upregulation of Smad1 activation in the endothelium . To further investigate the interplay between BMP signalling in endothelial cells and vSMC differentiation , we tested whether the increase in vSMC number and differentiation after loss of miR26a could be reversed by blocking endothelial BMP signalling . K02288 is a selective and potent small molecule inhibitor of BMP signalling that blocks Smad1 phosphorylation by type I receptor Activin like kinase 1 ( Alk1 ) and Alk2 [54 , 55] . We show that alk1 expression is enriched in endothelial cells at this developmental stage , but not vSMCs ( S1C Fig ) . We selected a time point for drug application when the endothelium of the major blood vessels is patterned [56] , but when vSMC coverage of the ventral aorta and PAA is only starting [7] . Tg ( acta2:EGFP;kdrl:mCherry ) embryos were treated with 15μM K02288 from 52 hpf to 4 dpf . As expected , miR26a morphant and miR26a CRISPRi treated embryos have significantly more vSMCs than wildtype embryos ( Fig 7B , 7B’ , 7C , 7C’ and 7G ) . Wildtype embryos treated with K02288 show a 62% reduction in the total average number acta2:EGFP positive cells compared to vehicle control alone ( Fig 7A’ , 7D’ and 7G . 29±1 to 11±3 ) . In miR26a knockdown embryos ( Fig 7B’ and 7C’ ) , the effects of K02288 were rescued; miR26a morphants had a non-significant 17% reduction in vSMC numbers ( 35±5 to 29±5 ) and miR26a CRISPRi embryos had a non-significant reduction from 39±1 to 34±3 ( Fig 7E’ , 7F’ and 7G ) . We also found that BMP inhibition not only affects vSMC number , but also reduces ventral aorta length by 55% in K02288 treated wildtype embryos , from an average 159±2 . 08 μm to 77±19 . 5 μm ( Fig 7A , 7D and 7H ) . However , miR26a morphants treated with K02288 are rescued and have a ventral aorta length not significantly different than wildtype . miR26a CRISPRi embryos showed a smaller rescue and had a 20% decrease in length when treated ( Fig 7B , 7C , 7E and 7F , 177±8 . 6 to 136±5 . 6 ) . Of note , there was no statistical difference in ventral aorta length between miR26a knockdown and control embryos , which supports our finding that endothelial cell number is not affected by loss of miR26a . We next tested whether pSmad1 levels are rescued in K02288 treated miR26a knockdown embryos as compared to controls ( Fig 7I–7L ) . Using the endothelial nuclear marker fli1a:nEGFP , we confirmed that there was no significant difference in endothelial cell number between untreated control and miR26a morphants ( Fig 7M ) . In control embryos , treatment with K02288 ( Fig 7I , 7I’ , 7J and 7J’ ) significantly reduced the number fli1a:nEGFP positive cells to 16± 0 . 2 , which is 20% less than controls . Similarly , the proportion of pSmad1 positive/nEGFP nuclei also decreased from 43±1% to 28±2% ( Fig 7N ) . Although K02288 treated miR26a morphants have a slight reduction in the total number fli1a:nEGFP positive cells ( Fig 7M ) , there is no significant decrease in the proportion of pSmad1 positive/fli1a:nEGFP nuclei ( Fig 7K , 7K’ , 7L , 7L’ and 7N ) , and they remain similar to untreated controls . Taken together , our results further suggest that the endothelial cell is a critical site of Smad1-mediated BMP signalling and blocking its activation can significantly affect vSMC coverage . Loss of miR26a is able to rescue these defects to maintain both endothelial signalling and vSMC coverage .
Compromised structural vascular integrity , vessel weakening and rupture ( hemorrhage ) can result from aberrant BMP signalling [57–59] . Hemorrhage ultimately results from weak endothelial junctions , however defects in mural cell coverage ( attachment and ECM secretion ) are implicated in the pathological progression of vascular diseases . We show that the endothelium of the ventral aorta in zebrafish has activated pSmad1 at 4 dpf , but that pSmad1 is not detectable in mural cells . At a stage when mature vSMC are normally present , embryos with loss of miR26a have upregulation of pSmad1 , increased vSMC coverage and a change in vSMC morphology , with no observable changes in the number or morphology of the pSmad1-expressing endothelial cells . We show that inhibition of BMP signalling reduces both vSMC coverage and the length of the ventral aorta while dual miR26a knockdown and BMP receptor inhibition leads to a rescue such that animals maintain normal vSMC number , length of the ventral aorta , and vSMC coverage . We therefore suggest that miR26a modulates BMP signalling in endothelial cells to control vSMC differentiation via a paracrine mechanism potentially involving Notch and/or Pdgfrβ signalling . We propose that miR26a therefore functions in vivo to fine tune endothelial signals to the vSMCs ( Fig 8 ) . Studies in cultured vSMC have suggested that miR26a controls Smad1-mediated BMP signalling within vSMCs to modulate their phenotype [28] . However , these studies do not address whether the levels of pathway activation in vitro are relevant to tissues in vivo . Additionally , data collected from in vitro culture systems do not address the role of cell to cell communication ( autonomous and non-autonomous signalling ) that is critical in vivo [1 , 9] . We therefore sought to use an in vivo model of vascular development with intact tissue and cellular contexts to assess how loss of miR26a and subsequent increases can affect vSMC coverage . We show multiple lines of evidence that suggest that endothelial pSmad1 levels correlate with increased vSMC coverage of blood vessels . Our use of BMP-reporter transgenic fish reveals that during normal development , and under physiological conditions , vSMCs directly contact BRE and pSmad1 positive endothelial cells but have undetectable BRE or pSmad1 signal themselves . In parallel to loss of miR26a resulting in a subsequent increase in smad1 and vSMC coverage , we also demonstrate that endothelial specific overexpression of smad1 ( smad1ECOE ) results in increased vSMC coverage . Our data therefore inversely complement the murine knockout models of HHT that have noted reduced vSMC coverage when endothelial Smad1 signalling is reduced . Of note , endothelial specific knockdown of Alk1 or Smad4 leads to a reduction of αSMA/Acta2 coverage on larger arterial vessels . Interestingly , there is a context-dependant shift in vSMC coverage in these studies , as ectopic expression of vSMCs is seen on venous and capillary vessel beds [60] . This hypervascularization was presumed to be in response to increased flow from AVM affected vessels into finer retinal vessels . Similar shifts are seen when BMP9/10 blocking antibodies are used [61] . Our study did not address changes in vSMC coverage in venous beds , but it would be interesting to see if overexpression of smad1 leads to increased vSMC coverage across both arterial and venous vessel beds . Our data suggest that the normal function of miR26a is to reduce Smad1 protein activation within the endothelium , and indirectly inhibit vSMC differentiation in early development . Treatment with K02288 , a potent ALK1/2 inhibitor , significantly reduced both acta2-positive vSMC coverage and reduced the length of the ventral aorta . These effects could be rescued by loss of miR26a . Thus , we suggest that enhanced Smad1 activation in these embryos compensates for receptor inhibition . ALK1 , ALK2 and ALK3 are expressed in both endothelial and vSMCs [17–21] , however in zebrafish alk1 is highly expressed only in the endothelium at 36 hpf [47] . Violet beauregarde ( vbgft09e ) alk1 loss of function zebrafish mutants develop striking cranial vessel abnormalities by 48 hpf due to increased endothelial cell proliferation [19] . vbgft09e are also unable to limit the diameter of arteries carrying increasing flow from the heart [27] . Based on our data involving indirect control by endothelial signalling , we would predict there is an additional defect in vSMC recruitment in alk1 mutants , although this remains to be tested . Endothelial and mural cells signal through several paracrine pathways to stabilize vessels [62 , 63] . BMP signalling in endothelial cells activates an axis of BMP/ Notch3/ Pdgf signalling to promote the expression of contractile vSMCs genes such as Acta2 and Myh11a in in vitro co-culture systems [64] . Specifically , BMP9 signalling via endothelial cells induces NOTCH3 in vSMCs , which in turn induces expression of Pdgfrβ and maintains the proper response to Pdgf ligands [17 , 65] . There is evidence that mouse MiR26a is modulated by Pdgf-BB signalling [45] . For instance , neointimal hyperplasia results in elevated levels of Pdgfbb associated with upregulation of MiR26a and accumulation and proliferation of vSMC at sites of injury . Furthermore , treatment of primary mouse aortic vSMCs with MiR26a mimic drives cells to a synthetic vSMC state [45] . We found that notch3 , pdgfrβ and contractile vSMC markers were significantly increased in miR26a knockdown embryos , suggesting that increases in endothelial Smad1 in zebrafish may be transmitted to vSMCs through a BMP/ Notch3/ Pdgf signalling axis . Pdgf ligands are primarily released by endothelial cells , and we observe an increase in pdgfba in miR26a morphants , providing a potential mechanism by which active BMP signaling in endothelium can recruit and induce vSMC differentiation via paracrine non-autonomous signalling pathways . While we found increased differentiation of vSMCs at the later stage 4 dpf time point , at 48 hpf loss of miR26a results in hemorrhage . The 48 hpf to 4 dpf window is a common window for vascular instability phenotypes to emerge in zebrafish [3 , 66–68] . BMP signalling is initiated in endothelium at this time point and perturbations can affect endothelial cell junction development [63] . We have previously shown mural cells present around vessels by 48 hpf , although they are mesenchymal and immature [3] . These cells express pdgfrβ but have no expression of mature vSMC markers [69] , suggesting the 48 hpf time point represents a critical window for vascular mural cell attachment to endothelium and differentiation to a mature phenotype . It is paradoxical then that we see increased maturation of vSMCs at 4 dpf when mir26a is reduced . We suggest that the altered receptor and ligand expression in miR26a morphants may promote morphological change towards maturation , but may not regulate all aspects of maturation , leading to destabilization . For instance aberrant ECM deposition would not be visible in our assays and could lead to vascular instability at the earlier time points [63] . As critical modulators of vascular cell function and with roles in cell differentiation , contraction , migration , proliferation and apoptosis , miRs are attractive targets of therapeutic treatments aimed at modulating the vSMC phenotypic switch . Specific to TGF-β/BMP signalling , the miR-145/143 family has direct involvement in SMC differentiation by repressing the Klf4 to induce a contractile morphology and reduced rates of proliferation [40] . miR-21 controls vSMC differentiation through cross-talk with miR-143/-145 [35] and by mediating TGF-β/BMP induction to promote miR-21 cleavage to its mature form and a more contractile phenotype ( Fig 5 ) . miR26a is unique in this group in that it represses smooth muscle differentiation , likely via a paracrine signalling from endothelial cells . As drug delivery to the endothelium is relatively straightforward , modulation of miR26a might be therapeutically useful for post-transcriptional control of key genes involved in vSMC phenotypic switching .
All animal procedures were approved by the University of Calgary Animal Care Committee ( AC17-0189 ) . Anesthesia and euthanasia used MS-222 ( Tricaine ) at 10–40 mg/L . Zebrafish ( Danio rerio ) embryos were collected and incubated at 28 . 5°C in E3 embryo medium and staged in hours post-fertilization ( hpf ) or days post fertilization ( dpf ) . Endogenous pigmentation was inhibited from 24 hpf by the addition of 0 . 003% 1-phenyl-2-thiourea ( PTU , Sigma-Aldrich , St . Louis , MO ) in E3 embryo medium . The fluorescent transgenic endothelial mCherry-expressing Tg ( kdrl:mCherry ) ci5 , GFP-expressing Tg ( kdrl:EGFP ) la116 report endothelial expression and Tg ( fli1a:nEGFP ) y7 [19] reports EGFP cDNA fused to a nuclear localization sequence in endothelial nuclei . Tg ( acta2:GFP ) ca7 and Tg ( acta2:mCherry ) ca8 report smooth muscle expression [7] . BMP-reporter fish Tg ( BRE-AAVmlp:EGFP ) mw29 [BRE:EGFP] report active BMP signaling [46] . Both MO and mimic were injected into one- to four-cell stage embryos within recommended dosage guidelines [50 , 70] . Injected doses were 1ng/ embryo for miR26a MO , Scrambled ( Scr . ) control , miR26a , and smad1 MO . Morpholinos ( MO ) were obtained from Gene Tools LLC ( Corvallis , OR , USA ) . mir-26a MO blocks the mature microRNA ( 5 AGCCTATCCTGGATTACTTGAAC-3’ ) , miR26a Scrambled control has 6bp mismatch ( 5’-ACCGTATCGTGCATTACTTCAAC-3’ ) , and smad1 MO blocks Smad1 translation ( 5’-AGGAAAAGAGTGAGGTGACATTCAT-3’ ) [53] . For rescue experiments , embryos were first injected with miR26a MO and then smad1 MO . To control for non-specific neural cell death that occurs from nonspecific activation of p53 with morpholinos , a standard p53 MO was co-injected with high dose morpholino to establish dosage curve . Hsa miR26a miRIDIAN mimic was obtained from Dharmacon ( Chicago , IL ) and injected in a dose of 3ng/ embryo . For CRISPRi mediated knockdown of miR26a , sgRNA were designed using CHOPCHOP [71 , 72] to target the seed sequence of miR26a family members , to reduce miR26a processing . MiR26a-1 , miR26a-2 , miR26a-3 , are independent genes located on different chromosomes . miR26b differs by one nucleotide . To generate sgRNA we followed a method established by [73] . 10 μmol of forward primer ( 5’ TAATACGACTCACTATAGGATCCT GGATTACTTGAACCAGTTTTAGAGCTAGAA-3′ ) and 50 μmol of a universal reverse primer ( 5′AAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAAC-3′ ) , ( IDT Oligos , Coralville , Iowa were annealed and filled in [72] , purified ( Qiagen PCR purification kit ) and in vitro transcribed ( T7 mMESSAGE mMACHINE kit , Ambion , Austin , TX . Zebrafish codon optimized dCas9 plasmid [74] was linearized with XbaI and in vitro transcribed using Ambion Maxi Kit ( Life Technologies Inc . , Burlington , ON ) , and RNA purified using an RNeasy Mini Kit ( Qiagen , Hilden , Germany ) . Zebrafish embryos at the one-cell stage were injected with 200pg of a solution containing 75 ng/ μl of sgRNA with 150 ng/ μl of Cas9 mRNA . For overexpression of smad1 , mRNA was in vitro transcribed as described ( McReynolds et al . 2007; gift from Todd Evans Lab ) using mMessage mMachine ( Life Technologies Inc . , Burlington , ON ) . 40 pg of mRNA was injected per embryo at the 1 cell stage . For endothelial specific overexpression , smad1 was amplified from zebrafish cDNA using primers that incorporate attb1/b2 recombination sites ( 5’-GGGGACAAGTTTGTACAAAAAAGCAGGCTTCACCATGAATGTCACCTCACTCTTTTCC-3’ and 3’- GGGGACCACTTTGTACAAGAAAGCTGGGTGCTAGGACACTGAAGAAATGGGGT-5’ and inserted into pDONR221 to create a pME-smad1 vector . Three way Tol2 gateway cloning [75] was used to insert smad1 downstream of the kdrla promoter to achieve a TolCG2:kdrla:smad1 vector . One-cell stage zebrafish embryos were injected with a solution consisting of 5–20 ng/ μl kdrla-smad1 plasmid and 50 ng/ μl transposase mRNA . For the in vivo sensor test , smad1 3′UTR forward and reverse oligos ( IDT ) were designed incorporating BamHI and Bsrg1 sites using the prediction software TargetScan [76] for miR26 targets within the 3’UTR of zebrafish smad1 ( underlined ) . ( 5’GCGTGTACACCGGATGACTAGAGGGTTAGGTTGTGTACTACTTGAAGGCAGTTTGTTAGGGTGGGGGTCATCGAATCTGGCTGAAGAGTCCTCAGTTTTCAGCCCGTGAGAATCTGGAAGATACTTGACAACTCTGTGGCCGGATCCATA-3’ and 3’- TATGGATCCGGCCACAGAGTTGTCAAGTATCTTCCAGATTCTCACGGGCTGAAAACTGAGGACTCTTCAGCCAGATTCGATGACCCCCACCCTAACAAACTGCCTTCAAGTAGTACACAACCTAACCCTCTAGTCATCCGGTGTACACGC-5’ ) . Oligos were digested and ligated into the p3E-polyA vector . This construct was then recombined into pDestTol2pA2 by Gateway cloning to achieve a CMV-SP6 promoter upstream of EGFP: smad1 3′UTR:EGFP or a control EGFP: p3E-polyA 3′UTR . Sensor mRNA and mCherry mRNA were in vitro transcribed from the pCS2 Gateway compatible vector ( 39 ) by using the mMessage Machine SP6 kit ( Ambion ) . One-cell zebrafish embryos were injected with 150 pg sensor mRNA and 100 pg mCherry mRNA . When applicable , miR26a MO or miRNA mimic were added . Live embryos were imaged with an identical exposure time at 24 hpf ( n = 10/group ) . The average pixel intensity for fluorescence was measured as described ( 17 ) For FACS analysis ~200 embryos were collected from 4 dpf Tg ( acta2:EGFP;kdrl:mCherry ) fish . Embryos were anesthetized with 0 . 4% Tricaine ( Sigma ) and heads dissected and pooled . Single cell dissociation was performed according to Rougeot et al . 2014 . Briefly dissected embryo heads were washed once with calcium-free Ringers Solution and gently triturated 5–10 times before dissociation solution was added and incubated in a 28 . 5°C water bath with shaking and periodic trituration for 45 min . The reaction was stopped , centrifuged and resuspended in Dulbecco’s Phosphate-Buffered Saline ( GE Healthcare Life Sciences , Logan , Utah , USA , centrifuged and resuspended in fresh resuspension solution . The single cell suspension was filtered with 75 μm , followed by 35 μm filters . Cells were then sorted with a BD FACSAria III ( BD Bioscience , San Jose , USA ) and collected . Total RNA from 48 hpf whole embryos , 4 dpf dissected embryo heads or FACS sorted cells was isolated using the miRNeasy Mini Kit ( Qiagen ) . For microRNA RT-qPCR , 5 ng of total RNA from each sample was reverse transcribed using the miRCURY LNA Universal RT cDNA Synthesis Kit and expression assayed using the miRCURY LNA Universal RT microRNA PCR System ( Qiagen ) . Primers were ordered for miR26a-5p ( MIMAT0000082 , Target sequence: UUCAAGUAAUCCAGGAUAGGCU ) , and , expression levels normalized to that of miR-103a-3p ( MIMAT0000425 , Target sequence: CAGUGCAAUGUUAAAAGGGCAU ) or miR122 ( MIMAT0000421 , Target sequence: AGCUACAUUGUCUGCUGGGUUUC for miRNA expression ) For gene expression , zebrafish specific Taqman assays ( Thermo Fisher Scientific , Waltham , Massachusetts , USA ) were used: smad1 ( Cat# 4351372 , Clone ID: Dr03144278_m1 ) , acta2 ( 4331182 , Dr03088509_mH ) , myh11a ( 444889 , Dr03141711_m1 ) , pdgfrβ ( 4441114 , ARKA4GC ) , pdgfba ( 4441114 , ARWCXGT ) , nothch3 ( 4448892 , Dr03432970_m1 ) and normalized to β-actin ( 4448489 , Dr03432610_m1 ) . 500 ng of total RNA from each sample were reverse transcribed into cDNA using and assayed using according to manufacturer’s protocols in a 5ng/ 10ul final reaction using TaqMan Fast Advanced Master Mix ( Thermo Fisher ) . Reactions were assayed using a QuantStudio6 Real-time system ( Thermo Fisher ) . The ΔΔCt method was used to calculate the normalized relative expression level of a target gene from triplicate measurements . Experiments were repeated independently at least three times , unless stated otherwise . K02288 was used at a dose of 15μM ( SML1307 , Sigma ) . DSMO ( D8418 , Sigma ) was used as a vehicle and control . Drug stocks were heated for 20 min at 65C and then diluted in E3 embryo medium . Drug or control was applied to the media from 52 hpf until 4 dpf . Embryos were grown at 28 . 5C in the dark until imaging , and drug changed once . All embryos were fixed in 4% paraformaldehyde in PBS with 0 . 1% Tween-20 at 4°C overnight , followed by 100% methanol at −20°C . Digoxigenin ( DIG ) -labeled antisense RNA probes were used for in situ hybridization . Probes for smad1 ( construct described by [53] ) sm22a , acta2 , myh11a were synthesized from PCR fragments previously described [7 , 48] . Probes were synthesized by using SP6 or T7 RNA polymerase ( Roche , Basel , Switzerland ) . miR26a double-DIG-labeled LNA probe was obtained from Exiqon , ( Copenhagen , Denmark . In situ hybridization was performed as described [32]using a Biolane HTI robot ( Holle and Huttner AG , Tubingen , Germany ) . For microRNA in situ hybridization , a double-DIG-labeled Locked Nucleic Acid ( LNA ) probe ( Exiqon ) was used to detect the mature miR26a in whole-mount embryos as recommended by the manufacturer with the modification that hybridization was at 54°C . For wholemount immunostaining an antigen retrieval protocol optimized from [77] was used . Briefly embryos are hydrated into PBST , washed twice with 15 mM Tris–HCl pH 9 . 5 , 150 mM EDTA and then heated in 15 M Tris–HCl pH 9 . 5 , 150 mM EDTA at 70°C for 15 min . Embryos are then washed 3 times in PBST at room temperature and incubated in 10% normal sheep serum in PBST with 1% triton block and incubated for at least 48 hours at 4°C in primary antibody . Phospho-SMAD1/5/9 ( pSMAD1/5/9 ) was detected with Rabbit anti-Phospho-Smad1 ( Ser463/465 ) /Smad5 ( Ser463/465 ) /Smad9 ( Ser426/428 ) ( 1:400; Cell Signaling Technology , Danvers , Massachusetts , USA ) , GFP was detected with mouse anti-GFP antibody , JL8 ( 1:500 , Clontech , Mountain View , California , USA ) and detected with Alexafluor 647 or 488 secondary antibodies for 1 hour at room temp in 5% normal sheep serum in PBST with 0 . 1% triton ( 1:500; Invitrogen Molecular Probes ) . For imaging , embryos were immobilized in 0 . 004% Tricaine ( Sigma ) and mounted in 0 . 8% low melt agarose on glass bottom dishes ( MatTek , Ashland , MA ) . Confocal images were collected on a Zeiss LSM 700 inverted microscope . Image stacks were processed in Zen Blue and are presented as maximal intensity projections and analyzed using FIJI/ImageJ [78] For cell counts images were converted to 16-bit using ImageJ and the threshold adjusted to allow counting of cells over a region of the VA from the anterior bulbous arteriosus to the most anterior PAA . To measure intensity , total cell fluorescence ( CTCF ) was calculated using the formula: CTCF = Integrated Density— ( Area of selected cell X Mean fluorescence of background readings ) . The area for measurement was gated by tracing the aorta from bulbous where the bulbous arteriosus merges with the ventral aorta to the distal tip of the ventral aorta or to the bifurcation point of the ventral aorta using the free form drawing tool , whichever was shorter [56] . For measurement of vSMC cell heights , measurements were made from the endothelial kdrla:EGFP expression to the highest point of the vSMC . 8 measurements were taken for each sample where possible . Ventral head measurements were taken from the ventral aorta and the aortic arch arteries . Measurements represent mean vessel diameter ± standard deviation in micrometers . Distribution of data points are expressed as mean ± standard error of the mean ( S . E . M . ) , or as relative proportion of 100% as mentioned in the appropriate legends . Depending on the number of the groups and independent factors , student's t-tests , one-way or two-way analyses of variance ( ANOVA ) with non-parametric tests were used as indicated in the figures . Two treatment groups were compared using Student’s t-test , using Welch’s correction . Three or more treatment groups were compared by one- or two-way ANOVA followed by post hoc analysis adjusted with a least significant-difference correction for multiple comparisons using GraphPad Prism version 7 . 00 ( La Jolla California USA ) . Results were classed as significant as follows: *P < 0 . 05 , **P < 0 . 01 , and ***P < 0 . 001 . | The structural integrity of a blood vessel is critical to ensure proper vessel support and vascular tone . Vascular smooth cells ( vSMCs ) are a key component of the vessel wall and , in their mature state , express contractile proteins that help to constrict and relax the vessel in response to blood flow changes . vSMCs differentiate from immature vascular mural cells that lack contractile function . Here , we use a zebrafish model to identify a small microRNA that regulates vascular stabilization . We show that a small regulatory RNA , microRNA26a is enriched in the endothelial lining of the blood vessel wall and , through signalling , communicates to the smooth muscle cell to control its maturation . Providing a mechanistic insight into vSMC differentiation may help develop and produce feasible miR-based pharmaceutical to promote SMC differentiation . | [
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| 2019 | MicroRNA26 attenuates vascular smooth muscle maturation via endothelial BMP signalling |
The natural history of infections with many human papillomavirus ( HPV ) types is poorly understood . Here , we describe for the first time the age- and sex-dependent antibody prevalence for 29 cutaneous and five mucosal HPV types from 15 species within five phylogenetic genera ( alpha , beta , gamma , mu , nu ) in a general population . Sera from 1 , 797 German adults and children ( 758 males and 1 , 039 females ) between 1 and 82 years ( median 37 years ) were analysed for antibodies to the major capsid protein L1 by Luminex-based multiplex serology . The first substantial HPV antibody reactions observed already in children and young adults are those to cutaneous types of the genera nu ( HPV 41 ) and mu ( HPV 1 , 63 ) . The antibody prevalence to mucosal high-risk types , most prominently HPV 16 , was elevated after puberty in women but not in men and peaked between 25 and 34 years . Antibodies to beta and gamma papillomaviruses ( PV ) were rare in children and increased homogeneously with age , with prevalence peaks at 40 and 60 years in women and 50 and 70 years in men . Antibodies to cutaneous alpha PV showed a heterogeneous age distribution . In summary , these data suggest three major seroprevalence patterns for HPV of phylogenetically distinct genera: antibodies to mu and nu skin PV appear early in life , those to mucosal alpha PV in women after puberty , and antibodies to beta as well as to gamma skin PV accumulate later in life .
Papillomaviruses ( PV ) are non-enveloped DNA viruses infecting cutaneous or mucosal epithelia of warm-blooded vertebrates . So far at least 118 distinct PV types , more than 100 of them isolated from humans , have been completely described [1] . In addition , about 130 L1 sequence fragments have been isolated by means of a broad spectrum polymerase chain reaction ( PCR ) representing putatively new cutaneous human papillomavirus ( HPV ) types [2] . Based on the nucleotide sequence encoding the major capsid protein L1 , PV systematics defines 16 genera ( sharing less than 60% sequence identity ) which encompass 44 species ( sharing 60–70% sequence identity ) . PV within the same genus may or may not show similar biological and pathological characteristics . Thus , cutaneous HPV are found among the five genera alpha ( species 2 , 4 and 8 ) , beta ( β ) , gamma ( γ ) , mu ( μ ) , and nu ( ν ) , whereas the 48 HPV types infecting the mucosa belong exclusively to the genus alpha ( α ) . HPV infections are widespread and can cause a variety of mostly benign tumours such as warts and condylomata . However , the infection with certain mucosal HPV types leads to malignant cell proliferation [3] . Fifteen so-called high-risk ( HR ) and three putative HR mucosal HPV of genus α , most notably the two most prevalent HR types 16 and 18 , are found in more than 90% of cervical tumours [4] and with lower frequency in other anogenital and oro-pharyngeal carcinomas [3] . Thirteen of these HPV types have recently been classified as human carcinogens [5] . The mucosal low-risk ( LR ) HPV types 6 and 11 cause benign genital lesions like condylomata acuminata and low-grade squamous intraepithelial lesions of the cervix . The cutaneous HPV types 1 ( genus μ ) , 2 , 3 , 10 , 57 ( genus α ) and HPV 4 ( genus γ ) , although belonging to three different genera , are associated with benign plantar , common , and flat skin warts both in the general population and in renal transplant recipients [6] , [7] . β PV are found in high copy numbers in benign macular skin lesions of patients with the rare hereditary disease Epidermodysplasia verruciformis ( EV ) [8] . The same types are also found in non-melanoma skin cancer ( NMSC ) , namely squamous cell carcinoma ( SCC ) and less frequently basal cell carcinoma ( BCC ) of the skin , but also in normal skin and plucked hairs of EV patients , immunosuppressed patients , e . g . transplant recipients , and less frequently immunocompetent patients [9] , [10] . γ PV and HPV 41 ( genus ν ) cause benign skin lesions but have been also found in NMSC [1] , [3] , [11] , [12] . Thus , infections with cutaneous HPV are discussed to play a role in the development of NMSC [9] , [10] . Besides the extensively studied HPV 16 and some closely related mucosal HR types , only little is known about the natural history of infections by other types . For both HR ( e . g . HPV 16 , 18 , 31 , 33 , 35 , 45 , 52 , 58 ) and LR ( e . g . HPV 6 and 11 ) mucosal HPV , transmission occurs mainly via sexual intercourse [13] . However , non-sexual routes of transmission , e . g . oral and perinatal transmission have been reported and cannot be entirely disregarded [14] , [15] . The rare disease recurrent respiratory papillomatosis apparently is caused by perinatal transmission of the LR HPV types 6 and 11 [16] . Infections with cutaneous HPV are assumed to occur via skin contact with contaminated material . For mucosal HPV types , antibodies to the viral major capsid protein L1 have been shown to be markers for present and past infection . Thus serology provides a powerful epidemiological tool to investigate the wide variety of mucosal and cutaneous HPV types and their distribution in the population [17] . DNA detection methods identify only current infections and are restricted to small samples of certain bodily sites . In contrast , for HPV 16 it has been shown that serology may be used as a proxy for lifetime cumulative exposure [17] . However , only 50–60% of HPV 16 DNA positive women develop HPV antibodies and seroconversion can occur even several months after the infection [17] , [18] . HPV 16 L1 antibodies have low but stable titres and can be detected decades after the infection [17] . Antibodies to HPV 16 L1 have been shown to be type-specific [17] , [18] , although some cross-reactivity with closely related types was reported [18] , [19] . For the other HPV types , similar characteristics of L1 antibodies are assumed . Seroepidemiological studies especially on cutaneous HPV infection are scarce and those performed in the past were restricted to a single or few HPV types . To our knowledge , mucosal HPV L1 antibody analyses have been performed so far for HR HPV types 16 , 18 , 31 , 33 , 39 , 45 , 52 , 58 , 59 , 73 , for LR HPV types 6 and 11 and for HPV types 13 and 32 . The serologically best studied cutaneous types are HPV 1 , 5 , and 8 [20]–[37] , while little or nothing is known about the antibody response to other cutaneous types [38]–[45] . HPV antibody analyses are complex because of the large number of HPV types , and conventional enzyme-linked immunosorbent assay ( ELISA ) methods allow analysing the reactivity of a serum sample to only one antigen per reaction . We recently developed multiplex serology [46] , [47] that allows the simultaneous determination of antibodies to a large number of HPV types and the analysis of more than 1 , 000 sera per day . Multiplex serology uses viral L1 proteins expressed in bacteria as glutathione S-transferase ( GST ) fusion proteins as antigens [48] , [49] . Rizk et al . [50] demonstrated that these recombinant L1 proteins display conformational epitopes . Three first case-control studies on NMSC investigating the antibody response to a total of 16 [39] , 31 [45] , and 38 [44] different HPV types have been reported . However , our knowledge about the natural history of HPV infections is still limited . Here we describe for the first time the age- and sex-dependent antibody prevalence for 29 cutaneous and five mucosal HPV types representing 15 species within five phylogenetic genera ( α , β , γ , μ , ν ) in a general population .
To obtain an unbiased ( cut-off free ) impression of HPV antibodies , the strength of the antibody reactions was plotted against the percentile for each HPV type and age and sex group . Figure 1 shows the plots for HPV types 1 , 16 and 8 , which are representative for the three major antibody patterns found among the 34 types analysed . For all HPV types and in both sexes , antibody reactivity was lowest in children . The biggest difference in antibody reactivity was observed between children and young adults ( 15–24 years ) against HPV 1 , which is representative for the HPV types within the μ and ν genera . HPV 1 antibody reactivities were similar in adults aged 25–54 years and gradually declined thereafter . Antibody levels against HPV 16 , which is representative for genital/mucosal HPV types within the α PV , changed with age in women but hardly in men . In comparison to children , antibody reactivities were substantially higher in young women and peaked in the 25–34 years age group . Antibody reactivity to HPV 8 , which is representative for HPV types within the β and γ PV , became stronger with age in both genders . A first peak in antibody reactivity was observed in women at about 40 years and in men at about 50 years . Especially among men , a second peak was found in the oldest age group . These age-dependent antibody patterns were present for both strong and weak responses , down to reactions as low as 50 median fluorescence intensities ( MFI ) . The analysis of the complex antibody patterns was simplified by the generation of seroprevalence values . HPV seroprevalence is defined here for all HPV types and age and sex strata as percent of sera reacting with a given HPV type above a cut-off value of 200 MFI . In all 1797 sera , the overall seroprevalence for any of the 34 HPV types analysed was 59 . 7% ( Table 1 ) . However , the overall type-specific seroprevalences strongly depended on age and sex . HPV types 1 and 4 showed the highest type-specific seroprevalences ( 17 . 0 and 16 . 9% , respectively ) ( Table 1 ) . Among children , HPV antibody prevalences were mostly low , with the highest seroprevalences for types 3 ( 8 . 6% ) , 1 ( 6 . 4% ) , and 4 ( 4 . 8% ) . Among adults ( individuals post-puberty , ≥15 years ) , seroprevalence was highest again for HPV types 4 ( 18 . 3% ) and 1 ( 18 . 2% ) , followed by types 8 ( 12 . 4% ) , 63 ( 11 . 5% ) , 65 ( 11 . 0% ) , and 49 ( 10 . 1% ) , and lowest ( <1 . 5% ) for types 52 , 60 and 93 . Seroprevalence patterns were similar for μ and ν PV , for high-risk mucosal α PV in females , and for β and γ PV , respectively ( Figure 2 ) . Most HPV showed two seroprevalence peaks . Age at the first peak varied from about 20 to 50 years for the different genera , but the second prevalence peak rather uniformly occurred at old age , in females at about 60 years and in males at above 64 years . For statistical analysis of the age- and sex-dependent seroprevalence patterns , age groups were combined to increase statistical power ( Table 1 ) . Highly significant seroprevalence increases ( p<0 . 0001 ) from children to younger adults ( 15–34 years ) ( but not from younger to older adults >34 years ) were seen for mucosal α ( HPV 16 ) , γ ( HPV 4 , 65 ) and both μ PV ( HPV 1 , 63 ) . In contrast , none of the 15 β PV types showed highly significant seroprevalence increases from children to younger adults , but 6 types from younger to older adults ( HPV 49 , 76 , 75 , 38 , 17 and 8 ) . Multiple seropositivity ( >3 , >8 types ) increased significantly ( at least p<0 . 05 ) from children to younger adults but also from younger to older adults . The strongest sex difference was observed for the mucosal high-risk HPV type 16 . Seroprevalence in younger women was 5 . 8-fold and in older women still 2 . 4-fold higher than in men of the same age . For two other mucosal high-risk types , seroprevalence was also slightly elevated in younger women ( HPV 18 , 2 . 1-fold and HPV 33 , 1 . 6-fold ) , however without statistical significance . Seroprevalence for cutaneous α , β and γ PV tended to be higher in younger women than in younger men ( median 1 . 3-fold ) , but HPV 2 was the only individual type for which this difference was statistically significant . In contrast , women >34 years showed a lower seroprevalence for these genera than men ( median 0 . 8-fold ) , with significant differences for the HPV types 75 , 9 , and 5 . Antibody reactivity to more than one HPV type was frequent ( Table 1 ) . While 24 . 5% of the sera ( 41 . 0% of the antibody positives ) were single positive , 11 . 5% reacted with two , 7 . 4% with three and 16 . 2% with more than three HPV types . Even seropositivities to more than 8 ( 6 . 1% ) and more than 16 ( 1 . 9% ) types were observed . Multiple seropositivity ( Figure 3 ) , here defined operationally as antibody positivity to at least half of the HPV types analysed within a species or genus , followed in general the patterns seen for type-specific seroprevalences ( Figure 2 ) . Multiple seroreactions were rare in children ( <15 years ) and were most prevalent in the second half of life ( >34 years ) . Mucosal and cutaneous α PV showed substantially less multiple seropositivity than β , γ , μ and ν types . Multiple seropositvity to μ and ν PV showed a steep increase already in young adults ( 15–24 years ) and thus was the earliest to occur in both genders . Multiple seropositivity to high-risk mucosal α PV in women ( but not in men ) first occurred and also peaked at 25 to 34 years . For cutaneous α PV , multiple seropositivity showed an isolated peak in the oldest age group in both genders . For β and γ PV , both genders showed two prevalence peaks , in women at around 35 and 60 years and in men some 5 to 10 years later .
Fundamental data on the humoral immune response to HPV infections is scarce . Most serological analyses performed in the past were case-control studies that mainly focussed on mucosal HR HPV types highly prevalent in cervical cancer and mostly on women at productive age . Little is known about the age distribution of HPV infections , especially in children , men and in old-aged individuals , and for many HPV types no serological data is available at all . To understand the natural history of HPV infections , investigations of a broad range of HPV types belonging to different species and genera are needed . This is the first large study to analyse simultaneously antibody reactions to 34 HPV types in a general population . The cross-sectional data presented here allow a detailed and comprehensive assessment of the seroprevalence in the adult German general population by HPV type ( and higher taxonomic order ) , sex and age . The data for the children originating from two hospitalized groups may be less representative for the German population . The conclusions of our study results might also be limited due to the use of unadjusted seroprevalence values . However , when age standardization was applied seroprevalence estimates changed only marginally . HPV of the same genus , with the exception of cutaneous α PV , showed similar antibody patterns and are therefore discussed as group . For comparisons with previously published prevalence data across different laboratories it is important to keep in mind that different assay formats and , probably even more important , different cut-off definitions may greatly influence absolute prevalence figures . Genus μ PV are associated with distinct common warts , HPV 1 with deep palmoplantar warts ( myrmecia ) and HPV 63 with cystic or punctate , mainly plantar warts [6] . Warts [51] , [52] and especially HPV 1 positive warts mostly occur at the age of 5 to 20 years [6] , [28] , [29] , [53]–[55] while patients with HPV 63-induced warts were 10 to 39 years old [54] , [56] . HPV 41 of genus ν originally isolated from a facial wart of a 15 years old girl was found mainly in plane hand warts but also in some skin carcinomas and actinic keratoses [1] , [3] , [11] , [12] . In our study , seroprevalence for HPV 1 ( 17 . 0% ) was the highest among all types analysed , antibodies to HPV 63 ( 10 . 6% ) were less prevalent . These findings are in agreement with a study reporting HPV 1 as the most prevalent type in warts from Germany ( 27 . 3% ) [57] and with other non-German studies reporting higher prevalences in warts for HPV 1 ( 44 . 1% and 31 . 5% ) than for HPV 63 ( 0 . 9% and 16 . 4% ) , respectively [54] , [58] . Serological studies using virus particles purified from warts as antigens also frequently found antibodies to HPV 1 among mostly adults in 10% [30] and 19 . 3% [20] of the control sera . However , HPV 1 antibody prevalences of 32% and higher in both patients with and without a history of foot warts have been reported [28]–[34] , [36] . The substantial seroprevalence found in all studies indicates a high frequency of infections with HPV 1 in the normal population . In addition , HPV 1-induced warts contain high viral loads [53] , [59] likely to result in more antigen presentation and thus in increased antibody production . In both sexes , antibodies against μ and ν PV were the first to be seen . Seroprevalence peaked after the age of 14 and declined only slightly thereafter . In line with this observation , Hamsikova et al . [37] and Pfister et al . [28] reported a seroprevalence peak for HPV 1 at 13–30 years and 11–20 years , respectively . Two other studies reported seroprevalence to peak in children younger than 15 years [29] , [35] . Assuming no newly acquired foot wart in adults and thus no new seroconversion , the data suggest a delayed but long lasting antibody induction detectable even decades after the initial infection . In our study , type-specific seroprevalences for HR mucosal HPV in adult women ( >14 years ) were highest for HPV 16 ( 10 . 9% ) followed by HPV 33 ( 4 . 7% ) , 18 ( 4 . 6% ) , 58 ( 4 . 1% ) , and 52 ( 0 . 7% ) . DNA prevalences for HR types in German women without cervical abnormalities were concordantly also highest for HPV 16 ( 14 . 6% ) followed by HPV 58 ( 2 . 7% ) , 52 ( 2 . 5% ) , 33 ( 2 . 2% ) , and 18 ( 1 . 7% ) [60] . Another study on type-specific HPV DNA prevalence in West German women also found HPV 16 ( 26 . 2% ) as the most frequent type , followed by HPV 31 ( 10 . 1% ) , 18 ( 5 . 3% ) , 58 ( 4 . 5% ) , 33 and 52 ( both <4% ) [61] . In adult men , we found the highest seroprevalence for HPV 58 ( 4 . 8% ) , inconsistent with DNA data identifying HPV 16 as the most prevalent HR type in men [62] . Antibodies to HR mucosal α PV were rare in children ( HPV 16 0 . 5% , other HR types 0 . 0–2 . 1% ) , which is consistent with seroprevalence rates for HPV 16 in children ranging from 1 . 5–7 . 6% reported by other studies [63] . In women , these antibodies increased after puberty ( reflecting the start of sexual activity ) and peaked at 25–34 years , whereas in men seroprevalence did not increase strongly until the second half of life . This antibody increase and peak in young women was reported by several other studies [64]–[71] and is in agreement with a DNA prevalence peak for mucosal ( predominantly HR ) HPV frequently found in women younger than 30 years . With increasing age , HPV DNA prevalence decreases [72]–[76] , however a second peak in women around 50 years [77] or 60 years and older [78]–[81] has been reported . Consistently with the latter studies , we found a second seroprevalence peak for HR HPV in women older than 45 years . HPV 16 serology in comparison to the other HR HPV was unique . Only for HPV 16 , seroprevalence was significantly higher in women than in men both among younger and older adults , while seroprevalence for the other mucosal HR types showed only a non-significant increase in women . Although studies on HPV seroprevalence in men are rare , the lower seroprevalence for mucosal HPV types 6 , 11 , and 16 assessed by virus-like particle ( VLP ) serology is well known [66] , [71] , [82]–[89] . Several biological and anatomical sex-dependent differences were discussed as possible explanations: HPV infections of the penis involve rather keratinized than mucosal epithelium which might be less susceptible for the virus , less productive , and less accessible for the immune system . Men might have rather transient infections with lower viral loads that induce a weaker antibody response than in women . Overall DNA prevalence of mucosal HPV types in cytologically normal cervical smears ranges from 1 . 4% to 44% [90] , [91] . In healthy men , overall HPV DNA prevalence values of 3 . 5% to 45% for mucosal types in exfoliated cells of the penis are in the same range [62] . In view of this similar prevalence in penile versus cervical samples , the assumption of lower susceptibility of men cannot be held up . It has been shown that persistent infection is associated with higher seroprevalence [92] , [93] . Thus , HPV 16 might not only be more prevalent in women compared with men but also more persistent , with higher viral DNA loads , and with a greater amount of intact virions . HPV 16 is the most prevalent HPV type in low-grade squamous intraepithelial lesions [94] , and these lesions are known to express L1 [95] . In addition , HPV 16 is possibly more immunogenic than other HR types [96] , [97] . The most heterogeneous antibody distribution with regard to both age and type was seen for cutaneous α PV , although they are closely sequence-related . However , due to the overall low seroprevalence , the power of this pattern analysis is low . While antibodies to HPV 3 were frequent in children and decreased with age , seroprevalences for HPV types 2 , 57 , 10 , and 77 were low in children and increased with age . These findings may suggest different natural histories of the individual wart-associated α PV types . HPV 3 and 10 are associated with plane , HPV 2 and 57 with common , and HPV 27 with intermediate warts [6] . HPV 77 has been found in tumours and warts of immunosuppressed but not in lesions of immunocompetent individuals [98] . HPV 2-associated warts have been most frequently found in the 20–40 years age group [53] , [54] , [99] , and in another study the prevalence of HPV 2/27/57-induced warts peaked at the age of 21–25 years [55] . For β and γ PV , age distribution patterns were very homogeneous . Seroprevalence for these types was low in children and increased with age . In middle-aged women , seroprevalence tended to be higher than in men of the same age . In older adults , this sex ratio shifted to a higher seroprevalence for men . We observed two seroprevalence peaks , both occurring slightly earlier in women than in men . At present , we can only speculate about potential reasons for the sex-associated serological differences for β and γ PV . In men , body hair is more abundant , the use of chemicals on the skin ( cosmetics ) is probably reduced , and for the population studied here sun exposure for large parts of the body during outdoor work might have been more frequent . Serological studies showed elevated prevalences of antibodies to β PV , mainly HPV 5 and 8 , in EV and in immunosuppressed patients , in patients with dermatological diseases like squamous cell carcinoma of the skin [20] , [23]–[25] , [38]–[41] or psoriasis [21] , [24] and in patients with second degree burns [20] . Feltkamp et al . found a statistically significant association of seropositivity with age and male sex for HPV 24 but not for HPV 5 , 8 , 15 , 20 and 38 [38] while in two other studies HPV 8 seropositivity was not correlated with age and/or sex [23] , [24] . DNA prevalence data indicate an ubiquitous distribution of these types in the population . Cutaneous HPV mainly of genus β are found in normal skin and plucked hairs of different body sites from healthy individuals , with up to 96% overall DNA prevalence in adults [10] , [100] , [101] . In samples from different skin regions of an individual , the same types are frequently found [100] , [102] . Type patterns in plucked eye brow hairs and on healthy skin of an individual frequently persist [101] , [103] . DNA from β and γ PV has been found in specimens taken with wet cotton-tipped swabs from the foreskin of infants within days after birth [104] thus demonstrating exposure . However , in the absence of additional data showing active virus infection in the infant's cells , it remains unclear whether these findings show infection to occur early in life . In adults , HPV DNA prevalence increases with age in both genders [100] , [105] . We observed increasing seroprevalence in females until around 40 and in males until around 50 years . This suggests that seroconversion for HPV of these two genera in contrast to μ and ν PV is extremely slow and occurs only years or even decades after the initial infection . Alternatively , this might indicate that despite early exposure active infection occurs much later . DNA of these types in immunocompetent individuals is present only in very low copy numbers [106] . A single contact with these types might therefore not result in antibody induction . However , accumulation of cutaneous HPV infections over lifetime due to an increased exposure via intense skin contacts could lead to higher overall viral loads and thus to an increasing seroprevalence with age . In addition , the weakening of the immune system in older age and/or perhaps increased sun-exposure might be responsible for the inability to control viral replication which consequently leads to higher viral loads and higher seroprevalences . For most of the analysed HPV types , seroprevalence peaked twice . A possible explanation for the second peak at ages beyond 55 years is a reactivation of latent infections perhaps by reduction of immune surveillance with increasing age followed by increasing viral load and antibody induction . For mucosal α PV in women , it might in addition be caused by changes in the genital epithelium associated with menopause . Alternatively , the seroprevalence in older age groups of this serum collection might be due to a cohort effect . Possibly varying behaviour in different birth cohorts might have influenced exposure , such as sexual behaviour for genital HPV . We speculate that also environmental factors , such as sun exposure or nutrition , may have influenced the extent of HPV replication and/or the functionality of the immune system . The sera of this study were collected in 1987 and 1988 . It remains to be examined whether this age-dependent distribution will also be present in sera collected 20 years later . We observed a substantial frequency of multiple seropositive reactions , e . g . 48% of HPV 5 positive sera reacted also with HPV 8 . Multiple infections with mucosal HPV [62] , [91] and even more often with cutaneous HPV both in immunosuppressed as well as immunocompetent individuals are common [100] , [107] . Multiple seropositivity may result from type-specific reactions to multiple infections and/or could be due to cross-reactive antibodies induced by an infection with only one or few HPV types . The antibody detection assay used here is not able to distinguish these two possibilities . To address this issue , we investigated i ) whether double seropositivity for a given pair of HPV types was observed more often than expected by chance and ii ) whether double seropositivity was correlated with sequence identity of the respective L1 proteins ( shown in supplementary material Figure S1 and Text S1 ) . If multiple seropositivity was due to cross-reactivity , it should be explainable by the degree of relatedness of HPV types . Our results showed that multiple seropositivity was not or only weakly correlated with L1 amino acid sequence relatedness , and thus we consider the measured antibodies to be mainly type-specific . However , cross-reactivity can only be appropriately investigated by means of absorption experiments or monospecific antisera which are currently not available for most HPV types . One limitation of this study is the use of a uniform , arbitrarily defined cut-off for seroprevalence calculations . For assays of antibodies to sexually transmitted genital HPV , cut-off definitions can be based on seroreactivities in groups of virgins [108] while for the cutaneous types representing the vast majority of HPV types analysed here the definition of a mostly uninfected and thus mostly seronegative group is not possible . In the absence of defined reference sera that could be used as international standards , any cut-off definition has an arbitrary component and seroprevalence values obtained by different laboratories in general should only be compared with caution . In our study all antigens were identically constructed L1 fusion proteins expressed in the same bacterial expression system . The full-length L1 fusion protein density on the beads for different HPV types was very similar , since MFI values obtained after staining of the carboxy ( C ) -terminal tag epitope with a monoclonal antibody varied less than two-fold . Thus , given the similar properties of the antigens the use of an uniform cut-off appears justified . To avoid false-positivity by low-level cross-reactivity and to increase type specificity of the seroprevalence values , a cut-off well above background levels was chosen . Thus , the cut-off is rather stringent and probably underestimates the true seroprevalence of cutaneous HPV infections . However , as shown in Figure 1 , the major conclusions are independent of the chosen cut-off . The age-dependent distribution of skin HPV antibodies as well as the unique pattern of HPV 16 antibodies in women are also present at very low seroreactivity levels ( MFI values well below the chosen cut-off ) . Direct comparison of GST-L1 fusion protein- and VLP-based ELISA with human sera has been performed for HPV 16 and 18 , and showed good correlation [48] , [50] . Of 46 monospecific monoclonal antibodies detecting conformational epitopes on VLP of 9 mucosal types of which 4 ( HPV 16 , 18 , 33 , and 52 ) are also used here all reacted with the GST-L1 fusion protein of the same type [50] demonstrating the ability of GST-L1 to bind type-specific antibodies . Based on these observations , it is reasonable to assume similar properties for the other 30 HPV types investigated here . We cannot entirely exclude cross-reactivity and thus unspecific antibody reactions . Like VLP , GST fusion proteins present conformational and neutralising epitopes but display a higher amount of linear/cross-reacting epitopes than VLP [50] . Thus , under the condition that only highly purified , intact VLP preparations are used as antigens , VLP-based assays may yield a higher ratio of specific to unspecific reactions than with GST-L1-based serology . In conclusion , the serological results presented here suggest different seroprevalence patterns of phylogenetically related HPV: Antibodies to cutaneous μ and ν PV appear late in childhood , those to mucosal high-risk HPV after puberty mainly in women , and seroprevalence for β and γ PV peaked late in life in both genders . The assays developed here allow further serological investigations to enlarge our still scarce knowledge on the natural history of especially cutaneous HPV infections . Interesting issues are e . g . effect of sun exposure on HPV prevalence , and the immune response in EV patients and patients with cutaneous warts in comparison to healthy people . In addition , seroepidemiological case-control studies might help to understand the potential role of cutaneous HPV in the development of non-melanoma skin cancer .
HPV L1 open reading frames were expressed via the pGEX4T3 vector ( GE Healthcare , München , Germany ) in E . coli Rosetta cells ( Merck , Darmstadt , Germany ) as double fusion proteins with amino ( N ) -terminal GST and a C-terminal peptide ( tag ) consisting of the last 11 amino acids from the large T antigen of simian virus 40 . The expression constructs for the L1 proteins of HPV types 16 and 18 have been described [48] . Constructs for full-length L1 of HPV types 1a , 2a , 3 , 4 , 5 , 8 , 9 , 10 , 15 , 17 , 20 , 23 , 24 , 33 , 36 , 38 , 41 , 48 , 49 , 50 , 52 , 57 , 58 , 60 , 63 , 65 , 75 , 76 , 77 , 92 , 93 , and 95 were generated in the same fashion . HPV genomes used as PCR templates , amplified regions , and cloning enzymes are listed in Table 2 . HPV sequences were verified by commercial sequence analyses and aligned by HUSAR ( Heidelberg Unix Sequence Analysis Resources , http://genome . dkfz-heidelberg . de/ ) . Sequences of 27 of the 34 L1 constructs were identical with the published HPV nucleotide sequence . Seven of the 11 sequence variations in L1 of HPV 5 , 8 , 10 , 16 , 18 , 41 , and 57 resulted in amino acid changes ( Table 2 ) . Eight of the mismatches were present already in the parental plasmids , indicating possibly mistakes in the published sequences . Of the three PCR-generated mismatches , only one lead to a threonine ( T ) to serine ( S ) amino acid change in HPV 10 L1 . This error was not corrected since T and S are functionally very similar amino acids . The sequence of the parental HPV 5 L1 ORF used here [109] , [110] was newly determined and submitted to GenBank ( AM922325 ) . In comparison to the most closely related published HPV 5 sequence ( HPV 5b [111] , D90252 ) , it showed 17 mismatches and a 27 nucleotide deletion ( 2 . 9% sequence variation ) and therefore probably represents a new subtype . Fusion protein expression was induced at room temperature by 0 . 25 mM isopropyl-β-D-thiogalactoside ( IPTG ) and six h after induction bacteria were harvested . The pellet from a 1 liter culture was resuspended in 10 mL of 40 mM Tris , 200 mM NaCl , 1 mM EDTA , pH 8 . 0 , 2 mM DTT containing complete protease inhibitor cocktail ( Roche , Mannheim , Germany ) , and lysed with a high pressure homogenizer ( Avestin , Ottawa , Canada ) . After incubation with 2 mM ATP and 5 mM MgCl2 for 1 h at room temperature , lysates were cleared from insoluble components ( e . g . cell membranes ) by centrifugation ( 14 , 000 rpm , 4°C , 30 min ) . For all HPV types , the proportion of insoluble fusion protein was low . Supernatants were stored with 50% glycerol at −20°C . Fusion protein expression was characterised by Coomassie-stained sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) as described [49] . Western blot analyses using GST and tag specific antibodies showed full-length fusion proteins of approximately 84 kDa and some additional minor bands of shorter fragments . N-terminal ( GST-reactive ) fragments were more prevalent than C-terminal ( tag-reactive ) fragments indicating incomplete translation rather than proteolysis as cause for the smaller fragments ( data not shown ) . Verification and concentration of full-length GST-L1-tag proteins in the lysates was determined by GST-capture ELISA [49] using a tag-specific antibody for fusion protein detection . For all constructs , glutathione-casein saturation with fusion protein was reached with 200 µg total lysate protein/mL or less . Half maximal binding ranged from 16 . 5 µg/mL ( HPV 3 L1 ) to 0 . 3 µg/mL ( HPV 48 L1 ) ( median 2 . 6 µg/mL ) indicating about 55-fold variation in GST-L1-tag concentrations in the lysates . Anonymous sera from 1797 individuals ( age 1–82 years , median 37 years; 758 males and 1039 females ) were analysed . These sera originated from three serum collections . The largest contribution is from the population-based German Nutrition Survey ( Nationale Verzehrstudie NVS , [112] , [113] ) collected in the German population aged 4–94 years between October 1985 and January 1989 ( n = 23 , 209 ) . The VERA study ( Verbundstudie Ernährungserhebung und Risikofaktorenanalytik ) is a random subsample of the NVS population aged 18 years and older collected in 1987 and 1988 ( n = 1988 ) [112] , [113] . We tested the available 1573 ( 79 . 1% ) VERA serum samples ( age 18–82 years , median 41 years; 651 males and 922 females ) . Sera from children ( below 19 years ) were obtained from two other collections: ( i ) 175 sera from children ( age 2–18 years , median 9 years; 71 males and 104 females ) treated in 2002 at the university hospital in Homburg ( Germany ) were randomly selected from a collection of stored diagnostic sera and ( ii ) 49 sera ( age 1–10 years , median 5 years; 36 boys and 13 girls ) collected for a previous HPV serology study [114] in 1991 and 1992 from children treated at the university hospital in Heidelberg ( Germany ) . The age structure of the tested VERA samples is indifferent from the age structure of the NVS population aged 18 years and older ( p = 0 . 9808 ) , and the age structure of our total study ( 1573 sera from VERA , 175 children sera from Homburg , and 49 children sera from Heidelberg , total n = 1797 ) is characteristic for the age structure of the total NVS population ( p = 0 . 9629 ) . This allows the conclusion that our study is likely to be representative of the German population . Sera were analysed simultaneously for antibodies to 34 HPV types by multiplex serology as described [46] , [47] . Briefly , GST-L1-tag fusion proteins from cleared lysates were affinity-purified in situ through binding to the glutathione casein-coated fluorescence-labelled polystyrene beads . Each fusion protein was bound to a spectrally distinct bead set . A total lysate protein concentration of 1 mg/mL was used for all lysates , an at least five-fold excess to saturate GST fusion protein binding on the beads . Monoclonal anti-tag antibody binding to the different antigen-loaded bead sets varied less than two-fold , indicating similar full-length L1 fusion protein density for the different HPV types . Fusion protein-loaded bead sets were mixed . Sera were pre-incubated at 1∶50 dilution in PBS containing 1 mg/mL casein , 2 mg/mL lysate from bacteria expressing GST-tag alone to block antibodies directed against residual bacterial proteins and GST-tag , 0 . 5% polyvinylalcohol ( PVA , Sigma-Aldrich ) , 0 . 8% polyvinylpyrrolidone ( PVP , Sigma-Aldrich ) and 2 . 5% Superchemiblock ( Millipore , Billerica , MA , USA ) to suppress unspecific binding of antibodies to the beads themselves [47] . Serum dilutions were incubated with the same volume of mixed bead sets , resulting in a final serum dilution of 1∶100 . Bound antibodies were detected with biotinylated goat-anti-human IgG ( H+L ) secondary antibody and streptavidin-R-phycoerythrin . A Luminex analyser ( xMAP , Luminex Corp . , Austin , Texas , USA ) was used to identify the internal colour of the individual beads and to quantify their reporter fluorescence ( expressed as median fluorescence intensity ( MFI ) of at least 100 beads per set per serum ) . A fusion protein consisting of GST and tag without intervening viral antigen served for background determination . The glutathione-casein coupled bead sets were loaded with their respective antigen in one batch . The efficiency of antigen loading on beads was quantified via the C-terminal tag as described [46] . Values ( mean MFI of 3 wells ) for the different antigens ranged from 4604 ( HPV 58 L1 ) to 9065 ( HPV 95 L1 ) with a median of 6180 MFI . Correct antigen loading was verified with 25 reference sera with known HPV antibody pattern . These sera originated from two earlier studies analysing the antibody reactivity to 16 [39] and 31 HPV types [45] by multiplex serology . All types except HPV 33 , 52 , 58 , and 60 were represented in the reference sera . Study sera were analysed once on three consecutive days . A quality control panel ( QC ) of 46 sera was included each day resulting in three QC data sets to determine inter-day variation . For inter-day variations of raw MFI values , Pearson correlation coefficients ( R2 ) for the individual antigens ranged from 0 . 761 to 0 . 986 ( median 0 . 963 ) for day 2 versus 1 and from 0 . 691 to 0 . 989 ( median 0 . 947 ) for day 3 versus 1 . The raw data of days 2 and 3 for each antigen were divided by the slopes of the regression lines of the QC data pairs of days 1 and 2 or days 1 and 3 , respectively , to correct for inter-day variation . The QC sera were also pooled and included as positive standard on each plate . Inter-plate variation coefficients ( CV ) calculated from these data for the various antigens ranged from 13 . 0% to 23 . 4% with a median of 16 . 6% . Auto-fluorescence of each bead set and background reactions resulting from binding of secondary reagents to the antigen-loaded beads were determined in one well per plate without human serum . After correction for inter-day variation , mean background values ( range 4 to 27 MFI , except for unusually high bead backgrounds of 150 and 350 MFI for HPV 4 L1 and HPV 33 L1 , respectively ) were subtracted from the raw MFI values and then antigen-specific reactivity was determined by subtraction of the MFI of GST-tag from the MFI of the specific antigen . Cut-off values to define seropositivity for all antigens were arbitrarily set to 200 MFI . Serological data were stratified by sex and age . Since sexual transmission is known to play a major role in mucosal HPV infection , the first age group encompassed children ( 14 years and younger ) , followed by age groups in 10 year intervals . The age distribution of our study and the German standard population as reported by [115] is similar ( p = 0 . 695 ) , and we present data stratified by age . Age standardization was applied but changed seroprevalence estimates only marginally . Therefore , unadjusted seroprevalences are reported throughout this manuscript . Statistical significance of differences in seroprevalence was assessed by two-sided Fisher's exact test . P-values below 0 . 05 were considered statistically significant . | Papillomaviruses ( PV ) are a large and highly diverse group of DNA viruses that infect cutaneous and mucosal epithelia of warm-blooded vertebrates . Of the more than 100 identified human PV ( HPV ) types , many cause benign lesions like warts and papillomas , and some also cervical , other anogenital , and oral cancers . For most HPV , transmission routes , pathogenesis , and time and duration of infection are only poorly understood . In the German general population , we investigated the prevalence of antibodies to the capsid proteins of 34 HPV types representative of all five PV genera ( alpha , beta , gamma , mu , and nu ) that contain HPV . We provide evidence for different age- and sex-dependent seroprevalence patterns of phylogenetically related HPV: antibodies to cutaneous mu and nu PV appear early in life , those to mucosal alpha PV after puberty , and those to beta and gamma skin PV accumulate in adulthood . | [
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| 2008 | Seroprevalence of 34 Human Papillomavirus Types in the German General Population |
It is generally assumed that most point mutations are fixed when damage containing template DNA undergoes replication , either right at the fork or behind the fork during gap filling . Here we provide genetic evidence for a pathway , dependent on Nucleotide Excision Repair , that induces mutations when processing closely spaced lesions . This pathway , referred to as Nucleotide Excision Repair-induced Mutagenesis ( NERiM ) , exhibits several characteristics distinct from mutations that occur within the course of replication: i ) following UV irradiation , NER-induced mutations are fixed much more rapidly ( t ½ ≈ 30 min ) than replication dependent mutations ( t ½ ≈ 80–100 min ) ii ) NERiM specifically requires DNA Pol IV in addition to Pol V iii ) NERiM exhibits a two-hit dose-response curve that suggests processing of closely spaced lesions . A mathematical model let us define the geometry ( infer the structure ) of the toxic intermediate as being formed when NER incises a lesion that resides in close proximity of another lesion in the complementary strand . This critical NER intermediate requires Pol IV / Pol II for repair , it is either lethal if left unrepaired or mutation-prone when repaired . Finally , NERiM is found to operate in stationary phase cells providing an intriguing possibility for ongoing evolution in the absence of replication .
Many endogenous and exogenous chemicals as well as radiations covalently damage DNA leading to major perturbations of DNA metabolism such as in replication and transcription . Cells have evolved sophisticated mechanisms to deal with DNA damage , collectively referred to as DNA Repair mechanisms . In principle , DNA Repair pathways refer to processes that effectively remove DNA lesions . Most repair pathways rely on the double-stranded nature of DNA that allows a damaged strand to be corrected by using the information present in the complementary strand . For instance Nucleotide Excision Repair ( NER ) uses dedicated enzymes that recognize damaged DNA , incise the damaged strand and remove a short oligonucleotide encompassing the damage . Regulation of the NER pathway requires high specificity in lesion recognition by means of molecular matchmaking and kinetic proofreading and proper coordination with the DNA Damage checkpoint response [1–4] . The resulting single-stranded DNA gap is accurately filled-in by a DNA polymerase and sealed by a ligase . This “cut and patch” repair mechanism has a huge effect on protecting cells against genotoxic compounds , indeed an NER-proficient strain is resistant to a UV dose that is almost 50-fold higher compared to an NER-deficient strain [5 , 6] . Nevertheless , a few lesions may escape repair and will be present at the time of DNA replication , thus perturbing replication fork progression . In that case , cells implement lesion tolerance pathways that are not genuine repair pathways since the lesion is bypassed without being removed [7] . A common lesion tolerance strategy , named Translesion Synthesis ( TLS ) , involves specialized DNA polymerases that are able to copy damaged templates . TLS occurs either directly at the fork or behind the fork during a gap-filling process [8] . Due to the low fidelity of the TLS polymerases , this process is intrinsically error-prone . In E . coli , Pol V , encoded by the umuDC locus , plays a prominent role in Translesion Synthesis ( TLS ) and mutagenesis [9 , 10] . Indeed , essentially all UV-induced mutagenesis is abolished when umuDC is inactivated . The primary role of Pol V in UV-induced mutagenesis is due to its unique property to mediate the specific insertion steps across the major UV lesions [11 , 12] . The early-time responses of E . coli cells to UV irradiation have been monitored by the kinetics of dNTP incorporation into newly synthesized DNA [13–16] . A common conclusion from these investigations is that , after irradiation of a wild type strain , following an initial abrupt drop , the rate of synthesis increases to progressively reach its normal speed over a period 40–50’ post irradiation . Compared to a wild type strain , inactivation of the TLS polymerases only modestly affects the recovery process [13 , 14] . This observation can be interpreted as follows: when the replication fork encounters a lesion the fork does not stop permanently . Indeed , when a DNA polymerase encounters a replication-blocking lesion in the leading strand , the replicative helicase continues unwinding the two strands but at a highly reduced rate due to its uncoupling from the DNA polymerase [17 , 18] before downstream re-priming eventually occurs [19] . Downstream re-priming events create gaps that are subsequently repaired most often by recombination with the sister chromatid or more rarely by TLS [20] . The observed reduction in the rate of DNA synthesis may thus reflect the reduced fork speed due to the uncoupling / re-priming process [5 , 6 , 21] rather than indicating a complete stop in replication fork progression as often suggested [7 , 22] . During the ≈ 50’ period , NER removes most of the lesion genome-wide to permit recovery of full synthesis rate [14–16 , 23] . Not surprisingly , in a uvrA strain the rate of synthesis post UV irradiation is strongly affected [14] . The notion that mutagenesis may be associated , at least in part , with NER has been suggested more than 40 years ago by Nishioka and Doudney who reported that UV mutagenesis coincides with the loss of photoreversibility of UV lesions within a 20 min post UV irradiation period in a wild-type but not in a uvrA strain [24 , 25] . These data were interpreted as evidence for a NER-dependent pathway that induces mutations in a wild-type strain at early time points following UV irradiation . Bridges extended this observation by showing that the NER-dependent mutation pathway was recA+ dependent [26] . Moreover , evidence for NER-dependent UV mutagenesis was described in vitro in E . coli crude extracts [27 , 28] . In NER-proficient S . cerevisiae , it was shown that mutations can occur prior to the first post-UV replication cycle [29] [30] while mutations are not fixed prior the first post-UV irradiation replication cycle in NER-deficient yeast cells , [31] . Studies on the repair of clustered lesions in double-stranded DNA have mostly been restricted to lesions induced by radiations such as Uracil residues , abasic sites or 8-oxo-G which are typical substrates for Base Excision Repair pathways ( for a review see [32] ) . The concept that closely spaced UV lesions are a potential source of mutations has been documented and discussed in bacteria [33 , 34] and in yeast where it was shown that mutations accumulate as a function of the square UV dose in wild-type cells but not in NER-deficient cells [30] . More recently , the occurrence of UV-induced mutations in non-proliferating yeast cells prior to replication was demonstrated [35] . While this class of mutations is characterized by the occurrence of the mutation in both DNA strands prior to replication in a NER-dependent way , the data did not allow distinguish between a linear or a quadratic dose-response curve . Despite all these data , the precise mechanistic and genetic links between NER and mutagenesis have largely remained unexplored . In the present paper , we show that in NER-proficient strains , a significant fraction of UV-induced mutagenesis occurs within the time frame of active NER , i . e . when replication is shutdown . Interestingly , these early time-point mutations disappear in NER-deficient strains . Thus , in proliferating cells , UV-induced mutations fall in two classes , mutations induced during replication ( RiM ) and NER-induced mutagenesis ( NERiM ) , respectively . We also show that mutations induced in a NER-dependent way accumulate with the square of the UV dose suggesting a “two-hit mechanism” . Normally , the NER machinery excises lesions as small damage-containing oligonucleotides and leaves a short gap that is filled-in; however , if following an initial NER incision event , another lesion resides in close proximity in the complementary strand , the second lesion may become exposed within the initial NER gap forming a so-called opposing lesion structure . The second lesion may be converted into a point mutation during filling-in of the NER associated gap . The mutagenic process associated with NER not only requires Pol V , but also DNA Pol IV and to some extent of Pol II . Additionally , we would like to stress that when two lesions are located in close proximity in the same strand , processing of one of them by NER may also trigger gap enlargement and subsequent requirement for processing by Pol IV/Pol II . For sake of simplicity , we will essentially discuss the situation were the closely spaced lesions are located in opposite strands . Our data are consistent with NERiM playing a significant role in UV mutagenesis in proliferating cells; indeed , even at relatively low levels of irradiation ( 40J/m2 ) , NERiM accounts for 1/3 of UV-induced mutagenesis . In addition we show that the role of NERiM becomes predominant in stationary phase bacteria suggesting an elegant way to generate genetic diversity in non-replicating microbes and perhaps in post-mitotic metazoan cells .
As discussed in the introduction , following UV irradiation , bacterial cells temporarily stop replication to allow removal of most lesions by NER before resuming replication [14–16 , 23] . We thought to exploit this feature as a way to differentiate mutations that are fixed during repair from those that arise during replication by analyzing the induction of mutations in various strains as a function of time after UV . We thus compared the kinetics of rifR mutation fixation in various strains following a single UV dose . The UV dose was chosen so as to affect survival in the various strains to a similar extent , namely ≈10% ( wild-type: 100 J/m2 , dinBpolB: 35 J/m2 , uvrA: 3 J/m2 ) . While rifR mutations accumulate rapidly in the wild-type strain ( t1/2 ≈ 30 min after irradiation ) , the kinetics of appearance of mutations in the uvrA and in the dinBpolB strains are very significantly delayed ( t1/2 ≈ 80–100 min after irradiation ) ( Fig 2A ) . Mutations fixed during replication appear with a delayed kinetics compared to NER-associated mutations . The plateau reached in the uvrA and dinBpolB strains corresponds to ≈ 25–30% and 15–20% of the level reached in the wild-type strain , respectively ( see also Fig 1E at 10% of survival ) . As indicated above , for a wild-type strain a dose of 100J/m2 corresponds to a similar level of survival ( ≈10% ) as a dose of 35J/m2 administered to a dinBpolB strain ( Fig 1C ) . According to our mathematical model ( see below ) , the level of RiM as deduced from the dinBpolB strain at 35J/m2 corresponds to only ≈15% of the total level of mutagenesis induced at 100J/m2 in the wild type strain . This is compatible with the observation that in the wild-type strain most of the induced mutagenesis occurs rapidly via NERiM and that only a small additional increase in mutagenesis appears at longer time points ( corresponding to ≈15% of RiM ) . To further establish this hypothesis we conducted UV-induced mutagenesis experiments in a thermo-sensitive dnaB helicase mutant . These experiments were conducted at a UV dose of 90 J/m2 corresponding to about 10% of survival ( Fig 2B ) . In a dnaBts strain , known to be a quick-stop replication mutant [45 , 46] , rifR mutants are induced at similar kinetics at both permissive ( 35°C ) and non-permissive ( 42°C ) temperature , implying that most mutations are fixed in a non-replicative manner ( Fig 2B ) . How may UV-induced mutagenesis be triggered during NER ? We envision two formal possibilities i ) the “faulty NER” model: at low frequency , the NER machinery makes a “mistake” and incises the non-damaged strand thus generating a lesion-containing gap or ii ) the “opposing lesion structure” model: a proper NER-mediated repair event occurs at a location where a second lesion resides in close proximity in the complementary strand . In both models , the gap resulting from NER incision contains a lesion that may lead to a mutation during subsequent gap filling . To distinguish between the two models , we analyzed the shape of the induced mutagenesis curve as a function of UV doses . In the wild-type strain , mutation frequency data exhibit a near-quadratic behavior with respect to UV dose ( Fig 3A and 3B ) while the relationship is linear in both dinBpolB ( Fig 3C ) and uvrA ( Fig 3D ) strains . The quadratic response in the wild-type strain suggests that NERiM results from the processing of opposing lesions ( see below ) . In contrast , the linear relationship of mutation frequency with dose in the dinBpolB and uvrA strains indicates a single hit model as expected when mutations are fixed during replication ( RiM ) . We propose the following working model for NERiM ( Fig 4A ) : normally , NER proceeds via excision of a short 12–13 nucleotide-long tract that contains a damaged nucleotide , followed by repair synthesis of the short gap by DNA Pol I . This process is error-free and leads to a spectacular increase in cell survival ( Fig 1C ) . At rare occasions a second lesion will be located in the opposite strand either within the initial incision gap or in close proximity , near the gap junctions . When the second lesion is located within the initial gap , a gap enlargement step may be necessary to allow a RecA filament to assemble downstream from the lesion in order to activate Pol V [47] . If the second lesion is located near the initial gap junction , it may become part of the gap following a gap enlargement step triggered by the distortion induced by the second lesion at the double-stranded / single-stranded junction via exonuclease ( or helicase ) processing . The resulting structure will be referred to as “opposing lesion structure” . Closely spaced photoproducts in complementary strands occur at high ( but physiologically relevant ) doses [48] . Published data suggest that opposing UV dimers are substrates for NER incision . Indeed , opposing cyclobutane dimers within the 5’-TTAA sequence are properly incised by UvrABC , in one or the other strand , without double strand break formation [49] . The frequency of opposing lesion structures increases with the square of the UV dose . The present model posits that repair of the opposing lesion structure requires both Pol V at the TLS step across the lesion and Pol IV / Pol II for yet unknown reasons ( see below the § about Damaged Primer Elongation ) ( Fig 4A ) . However , it does not exclude the potential participation of Pol III in the gap-filling reaction as previously suggested by Bridges and coworkers [50] . This model predicts similar UV-sensitivities for dinBpolB and umuDC strains , an expectation not met experimentally as a umuDC strain is found to be less UV-sensitive than a dinBpolB strain ( Fig 1C ) . The higher UV sensitivity of polBdinB compared to umuDC could be accounted for by the existence of NER-induced gaps that require processing by Pol IV/Pol II but not by Pol V . Such gaps may contain a specific UV lesion that can be bypassed by Pol IV/Pol II without need of Pol V ( as discussed above: the putative lesion at position 526 ) . Alternatively , when two lesions are in close proximity in the same strand , incision at one of them may generate a lesion-containing primer that requires Pol IV / Pol II for its extension ( see below ) but not necessarily Pol V as the gap may not contain a lesion . Such events account for greater UV sensitivity of dinBpolB compared to umuDC strains . The precise role of Pol IV ( and to a lesser extent of Pol II ) during the completion of the gap-filling reaction during NERiM remains a puzzle for which we offer some genetic and biochemical hints ( see below ) . To better define the requirement for Pol IV , we wondered whether its role in NERiM requires binding to the beta-clamp , the replication processivity factor [51] . For this purpose we used a mutant dinB gene carrying a five amino acid C-terminal deletion of the consensus beta-clamp binding motif [52] . Compared to a plasmid expressing dinB+ , a plasmid expressing the dinBΔ5 mutant only partially complemented a dinBpolB strain ( Fig 1A ) , suggesting that the interaction of Pol IV with the beta-clamp is important for its function in NERiM . Interestingly , Pol kappa , the eukaryotic ortholog of Pol IV was shown to be involved in NER [53] . The model proposed could explain the finding that mouse cells lacking Pol kappa are defective at excising lesions from DNA and filling NER gaps [54] . In order to estimate the mutation frequencies induced by UV lesions during NER ( NERiM ) or replication ( RiM ) as a function of UV dose ( D ) , we have generated the following mathematical model . RiM and NERiM will be calculated by multiplying the number of lesions subject to either replication or NER by the average rate of conversion of a lesion into a mutation in the rif assay . The following parameters have been used: We wondered whether the NER-induced mutation pathway described above and established within the frame of a chromosomal mutation assay ( rifR ) using UV lesions as the mutagen could be generalized to another mutagen and another assay . For this purpose we implemented a plasmid-borne reversion assay using as a mutagen oxidative lesions induced by the in vitro treatment of plasmid DNA with methylene blue plus visible light ( MB+light ) and its subsequent introduction in bacterial cells for mutagenesis monitoring . This mutation assay involves the reversion of a tetracycline sensitive to a tetracycline resistant allele carried by a pBR322-derived plasmid [58] . Plasmid DNA is randomly damaged in vitro with ( MB+ light ) and introduced into bacteria by transformation . The assay was shown to be highly specific for monitoring only true -2 frameshift revertants to tetracycline resistance [59] . This assay is highly sensitive as the level of induced mutagenesis is two to three orders of magnitude above the background level reached with untreated control plasmid [60] . Cells are plated on tetracycline and ampicillin plates to determine the TetS-> TetR mutation frequency . The ( MB+light ) treatment , that induces a variety of oxidative lesions , was previously shown to trigger -2 frameshifts in an SOS-dependent way [60]; although the chemical nature of the culprit lesion is unknown , it was shown not to be 8-oxo-dG [60] . Introduction into SOS-induced wild-type cells of plasmid DNA treated with ( MB+light ) robustly increases the -2 frameshift mutation frequency by two to three orders of magnitude above untreated control plasmid ( Fig 5A and 5B ) . The mutagenic response is reduced ≈5-fold when the ( MB+light ) treated plasmid is introduced into either uvrA , dinBpolB or uvrAdinBpolB strains ( Fig 5B ) . In a umuDC strain the induced-mutation frequency is fully abrogated [61] . These data , obtained within the frame of a plasmid-borne mutation assay , fully recapitulate the key genetic requirements of NERiM as established above with the chromosomal rifR assay . It thus further generalizes NERiM to different mutagenic treatments and to all types of point mutations , base substitutions and frameshift mutations . Moreover , the plasmid assay could be implemented in all strains at the same lesion density , thus validating the NERiM pathway established at doses leading to equal survival but with different lesion densities in the chromosomal context ( see further discussion in Conclusion § ) . Next , we wondered about the biological significance of NERiM , a mutation pathway that is coupled to NER instead of being linked to replication . A NER-dependent pathway appears to be advantageous to stationary phase cells in order to adapt to genotoxic conditions . To investigate directly this possibility we UV-irradiated stationary-phase bacteria contained in 1 to 3 day-old E . coli colonies ( doses 200–400 J/m2 ) and further incubated the colonies for periods ranging between 1 to 3 days . At the end of the incubation period , the level of rifR mutants was determined by suspending individual colonies in liquid medium and plating adequate dilutions on LB or LB + rifampicin containing plates . In wild-type bacteria , we observe a 30 to 50-fold increase in rifR mutagenesis in irradiated compared to non-irradiated control colonies . Strikingly , induced mutagenesis in these stationary phase E . coli cells was entirely dependent upon a functional excision repair system ( i . e . absence of induced mutagenesis in a uvrA strain ) suggesting that mutagenesis arose during NER ( Fig 6A ) . In order to determine which DNA polymerase was involved in the generation of these UV-induced mutations , we examined strains defective in each of the specialized DNA polymerases , Pol II , Pol IV and Pol V . In addition to the expected involvement of Pol V , UV-induced mutagenesis in stationary phase cells was totally dependent upon a functional Pol IV gene , while Pol II had essentially no effect . Thus the genetic control of stationary phase mutagenesis induced by UV irradiation is similar to NERiM except for the involvement of Pol II , an observation that remains unexplained . We also found that UV-induced mutagenesis in stationary phase cells is suppressed in a strain that cannot induce its SOS system ( lexA ( Ind ) - strain ) ( Fig 6B ) . It has also been reported that spontaneous mutagenesis in resting bacterial populations is controlled by the SOS-response in a cAMP-dependent way [62] . Using an F plasmid based reversion assay , it was shown that spontaneous -1 frameshift mutations in short runs depend upon Pol IV and recombination proteins in stationary phase cells [63] . Detection of lesions in stationary phase may be initiated during transcription by the Transcription Coupled Repair factor Mfd as suggested by a recent study on stationary phase mutagenesis in B . subtilis [64] . In B . subtilis it was also found that stationary-phase mutagenesis involves the yqjH gene that encodes a Pol IV homolog [65] . Our present working model for NER-induced mutations involving opposing lesions ( Fig 4A ) raises two major questions: how are the extended gaps formed and how are they subsequently filled-in ? We hypothesize the involvement of an exonuclease activity that will use as its substrate a normal NER gap formed at opposing lesion structures ( as defined above , Fig 4A ) . Similarly , in S . cerevisiae it was shown that the occasional processing of normal NER incision intermediates by Exo1 leads to extended gaps that are essential in checkpoint activation in non-dividing cells [66 , 67] . The rare occurrence of large NER gaps has previously been described in E . coli [68 , 69] . Indeed , large NER gaps ( ≈1500 bases ) were found at about 1% the frequency of the normal NER gaps for UV doses in the 60J/m2 range [68 , 69] . The enzymatic activities involved in gap enlargement in E . coli are unknown . E . coli possesses numerous exonucleases preventing us to unequivocally identify the responsible exonuclease ( s ) . Degradation by exonucleases of DNA templates containing chemically modified nucleotides has revealed that the degradation of lesion-containing templates is blocked at or near the lesion sites [70 , 71] . If this is the case , the subsequent gap-filling reaction requires a DNA polymerase able to extend a primer containing a lesion in the vicinity of its 3’-end ( Fig 4A ) . In order to support such a model , the ability of E . coli’s DNA polymerases to extend damaged primer extremities in vitro was analysed . Primers containing either a TT-CPD or a T ( 6–4 ) T photoproduct at their 3’-end ( S3 Fig ) were annealed to a single-stranded circular DNA substrate ( S4 Fig ) . These primer / templates were subsequently incubated in the presence of the β-clamp , the clamp loader , RecA and SSB proteins before addition of a given DNA polymerase [12] . For sake of comparison , an equivalent amount of DNA pol units of Pol I , II , III* , IV and V were added to these primer template substrates although this does not reflect the respective amounts of the different DNA polymerases in E . coli cells . Following a 30 minutes incubation period , replication products were analysed by PAGE . The data show that for the TT-CPD containing primer , Pol IV ( and Pol II ) show significantly greater extension capacities compared to Pol I and Pol V , while Pol III* is intermediate ( S4 Fig ) . With respect to the T ( 6–4 ) T bearing primer , Pol II exhibits the greatest extension potential ( S4 Fig ) . The lack of extension activity by Pol I is in good agreement with our in vivo data showing that NERiM is fully proficient in the polA1 strain ( Fig 1B ) . In good agreement with genetic data , Damaged Primer Extension of TT-CPD and T ( 6–4 ) T lesions by Pol IV and Pol II , rely on the presence of the β-clamp ( S5 Fig ) ( Fig 1A ) . These data support the idea that Pol IV / Pol II may be involved in NERiM by virtue of their capacity to extend primers that contain lesions at their 3’-extremity ( S6 Fig ) . | In this paper , we report the surprising finding that in addition to the well-known properties of Nucleotide Excision Repair ( NER ) in efficiently repairing a large number of DNA lesions , NER entails a mutagenic sub-pathway . Our data suggest that closely spaced lesions are processed by NER into a toxic DNA intermediate , i . e . a gap containing a lesion , that leads either to mutagenesis during its repair or to cell death in the absence of repair . The paper describes a new pathway for the generation of mutations in stationary phase bacteria or quiescent cells; it also provides an additional role for Pol IV , the most widely distributed specialized DNA polymerase in all forms of life . | [
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| 2017 | Processing closely spaced lesions during Nucleotide Excision Repair triggers mutagenesis in E. coli |
Cytosine methylation is one of the most important epigenetic marks that regulate the process of gene expression . Here , we have examined the effect of epigenetic DNA methylation on nucleosomal stability using molecular dynamics simulations and elastic deformation models . We found that methylation of CpG steps destabilizes nucleosomes , especially when these are placed in sites where the DNA minor groove faces the histone core . The larger stiffness of methylated CpG steps is a crucial factor behind the decrease in nucleosome stability . Methylation changes the positioning and phasing of the nucleosomal DNA , altering the accessibility of DNA to regulatory proteins , and accordingly gene functionality . Our theoretical calculations highlight a simple physical-based explanation on the foundations of epigenetic signaling .
In eukaryotic cells gene function is modulated by a myriad of epigenetic marks and interactions with signal molecules that control synergistically the production of RNA and proteins . Epigenetic marks are a set of heritable but reversible chemical changes of the DNA and histones that can trigger gene silencing and activation . One of the most important epigenetic marks is DNA cytosine methylation , which occurs in 60–90% of the CpG content in mammalian DNA . In fact , most ( CpG ) s , except those in regions with large tracts of CpG steps ( “CpG-islands” ) , are methylated [1] , [2] , and changes in the methylation pattern of DNA is a fingerprint of different pathologies , including cancer [3]–[10] . DNA methylation has a known role in gene expression regulation [11]–[13] , but despite extensive work [14]–[24] its mechanism of action is not well understood [25] , i . e . it is not clear whether and how the chemical properties of MeC impact gene expression regulation . A popular explanation suggests that it regulates the action of proteins containing methylated-DNA binding domains [7] . However , the prevalence of DNA methylation and the magnitude of the changes in the methylation pattern occurring under pathological conditions points towards a more general mechanism [26] , [27] . A promising hypothesis to rationalize the biological impact of cytosine methylation is that it affects the accessibility of the DNA within chromatin by modulating intrinsic nucleosome positioning . Although several works suggest that methylated DNA increases nucleosome rigidity [28]–[30] and that it is less prone to wrap around nucleosomes than normal DNA [30]–[32] , recent genome-scale studies suggest that nucleosome-bound sequences are slightly enriched in methylated cytosines ( MeC ) , which are placed in a subtle 10-base periodicity pattern [33] . It is thus unclear whether methylation intrinsically favours or disfavours nucleosome formation , whether it leads or not to changes in nucleosome positioning or phasing , and what is the preferential location ( if any ) of MeC . To shed light on these questions , we have performed a theoretical analysis of the impact of CpG methylation on the structure and stability of the nucleosome . We find that methylation of CpG steps decreases the stability of the nucleosome . Such effect increases with the number of MeCs , depends on the position of the MeC with respect to the histone core , and can be explained from variations in the mechanical properties of methylated versus un-methylated DNAs . Our results reveal that methylation is sufficient to induce changes in phasing and/or positioning of the DNA around the nucleosome , which in turn might modify the accessibility of DNA sequences to proteins controlling gene expression . Our study helps understand the important role of methylation in gene expression regulation .
Molecular dynamics simulations and free energy calculations of fully solvated and neutralized mono-nucleosomes were carried on the X-ray structure with PDB code 1KX5 [34] . To save computational cost we have removed the long histone tails protruding out from the core . We subjected the energy-minimized structure to 200 ns of MD simulation , and we used the last structure to introduce different number of CpG and methylated CpG steps in positions described in Table S1 in Text S1 . After energy minimization and initial thermalization , we performed MD for 100 ns for the selected single mutations and 200 ns for the multiple mutations ( see table 1 and the next section ) , gathering information concerning solvent interaction or solvent densities , energies of stacking and geometrical parameters . Differential binding free energies were computed using the thermodynamic integration method in its discrete formalism , exploiting a thermodynamic cycle sketched in figure 1B . In this method , the free energy between two states is computed by integration of the derivative of the energy of the system as function of the state parameter λ , known as coupling parameter [35] , which in our case describes either the methylated ( e . g . λ = 0 ) or the unmethylated state ( λ = 1 ) . For each window we collected 9 estimates for by using 9 blocks of 100 ps , which were then integrated through the entire mutation pathway to obtain mutation free energies ( with associated statistical errors ) . We measured the deformation energy for the methylated and un-methylated sequences using a mesoscopic energy model . This model describes the deformability along DNA helical parameters by an harmonic approximation , using the stiffness constants ( ki ) associated with the displacements with respect to the equilibrium values of the helical parameter [36] , [37] . The values for the parameters describing the equilibrium geometry and stiffness constants of naked DNA were derived from long atomistic MD simulations ( >200 ns , as found in the ABC consortium database [38] ) of a reduced number of short DNA duplexes in water . The parameters for methylated cytosine were extracted from Perez et al . [31] . Full details on all computational methods and on the analysis performed are provided as SI text .
We first studied the change in the stability of a nucleosome particle ( histone proteins and DNA ) induced by replacing cytosines with 5-methylcytosines in CpG steps located at representative positions along the DNA ( examples in Figure 1A , full list in Table S1 in Text S1 ) by means of the thermodynamic cycle shown in figure 1B . The starting conformations for our free energy calculations were obtained from a 200 ns molecular dynamics ( MD ) simulation of the nucleosome in physiological conditions , using as initial conformation the highest-resolution X-ray structure available of the nucleosome [39] . We produced 18 different mutated nucleosome models , where each mutation consisted on placing a single CpG step at different locations where either the minor , or the major grooves face the histones ( Figure 1A ) ; these two types of positions explore widely different geometrical placements for MeC in the nucleosome [40] . In addition , to study the effect of multiple methylations on nucleosomal stability we introduced several CpG steps simultaneously ( see SI and Table S1 in Text S1 ) . All the systems were extensively re-equilibrated prior to production runs . Nucleosomal and corresponding naked DNAs were used as starting points for TI calculations , where the reversible work associated with the methylation of the CpG step in nucleosomal and naked DNA was computed and processed to determine the change in nucleosome stability induced by cytosine methylation ( see Figure 1B and Suppl . Information for details on all calculations performed in Text S1 ) . MD/TI calculations yield a positive free energy variation in all cases , demonstrating that methylation of DNA decreases the stability of the nucleosome ( Figure 2 ) , in contradiction with recent genome-wide-association study ( GWAS ) [33] , but in agreement with many previous biophysical studies [25] , [30] , [32] , [41] , [42] . The disagreement with the GWAS conclusions could be attributed to an uncertainty of up to four base pairs in MNase-degradation nucleosome footprinting , which is close to half a DNA helical turn , and to the cell-to-cell variability of nucleosome positioning and methylations maps [43] . The MeC-mediated destabilization of nucleosome is cumulative for multiple methylations , and in some cases the expected destabilization is so large ( more than 20 kJ/mol ) that it could challenge the entire stability of the nucleosome . Our MD/TI simulations also show that the effect of methylation on nucleosome stability is phase/position-dependent ( Figure 2 ) . In general , major groove methylations ( i . e . those of CpG steps that face the histones through the major groove ) are much better tolerated than minor groove methylations ( i . e . those of CpG steps that face the histones through the minor groove ) . These results indicate that nucleosomes are more stable when the methyl groups in MeCpG steps are placed pointing towards the histones and not to the solvent . Analysis of the large amount of MD/TI data presented here ( Figure 2 ) shows that methylation is especially nucleosome-destabilizing at some specific positions , such as those located at ±26 base steps from the nucleosome dyad position ( mutations 10 and 16 ) , where the nucleosome-bound DNA is characterized by a kinked geometry and a value of the roll angle ( ∼−7 deg . Fig . S1A in Text S1 ) that is widely different to the equilibrium value of MeCpG steps ( ∼+14 deg . ) [31] , [44] . In comparison , methylation has a significantly lower stability cost when happening at major groove positions , such as −11 and 21 base pair from dyad ( mutations 9 and 12 ) , where the roll of the nucleosome bound conformation ( +10 deg . ) is more compatible with the equilibrium geometry of MeCpG steps . The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines , following the same position dependence as the single methylations . The multiple-methylation case reveals that each major groove methylation destabilizes the nucleosome by around 1 kJ/mol ( close to the average estimate of 2 kJ/mol obtained for from individual methylation studies ) , while each minor groove methylation destabilizes it by up to 5 kJ/mol ( average free energy as single mutation is around 6 kJ/mol ) . This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30] . The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp , which already contains multiple CpG steps in mixed orientations , and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation . The analysis of our trajectories reveals a larger root mean square deviation ( RMSD ) and fluctuation ( RMSF; see Figures S2–S3 in Text S1 ) for the methylated nucleosomes , but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation ( Fig . S1B , S1C , S4–S6 in Text S1 ) . The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2 , as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule ( Figure S5 in Text S1 ) . We find however , a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability ( see Figure S6 in Text S1 ) . This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome . To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation , we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36] , [37] , [44] . This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome ( full details in Suppl . Information Text S1 ) . As shown in Figure 2 , there is an evident correlation between the increase that methylation produces in the elastic deformation energy ( ΔΔE def . ) and the free energy variation ( ΔΔG bind . ) computed from MD/TI calculations . Clearly , methylation increases the stiffness of the CpG step [31] , raising the energy cost required to wrap DNA around the histone octamers . This extra energy cost will be smaller in regions of high positive roll ( naked DNA MeCpG steps have a higher roll than CpG steps [31] ) than in regions of high negative roll . Thus , simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove ( where positive roll is required ) that when it faces histones through the minor groove ( where negative roll is required ) . We have established that methylation affects the wrapping of DNA in nucleosomes , but how does this translate into chromatin structure ? As noted above , accumulation of minor groove methylations strongly destabilizes the nucleosome , and could trigger nucleosome unfolding , or notable changes in positioning or phasing of DNA around the histone core . While accumulation of methylations might be well tolerated if placed in favorable positions , accumulation in unfavorable positions would destabilize the nucleosome , which might trigger changes in chromatin structure . Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i ) the DNA could either detach from the histone core , leading to nucleosome eviction or nucleosome repositioning , or ii ) the DNA could rotate around the histone core , changing its phase to place MeCpG steps in favorable positions . Both effects are anticipated to alter DNA accessibility and impact gene expression regulation . The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects , but clear trends are visible in cases of multiple mutations occurring in unfavorable positions , where un-methylated and methylated DNA sequences are out of phase by around 28 degrees ( Figure S7 in Text S1 ) . Due to this repositioning , large or small , DNA could move and the nucleosome structure could assume a more compact and distorted conformation , as detected by Lee and Lee [29] , or a slightly open conformation as found in Jimenez-Useche et al . [30] . Using the harmonic deformation method , we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences . Consistently with our calculations , we used two extreme scenarios to prepare our DNA sequences ( see Fig . 3 ) : i ) all positions where the minor grooves contact the histone core are occupied by CpG steps , and ii ) all positions where the major grooves contact the histone core are occupied by CpG steps . We then computed the elastic energy required to wrap the DNA around the histone proteins in un-methylated and methylated states , and , as expected , observed that methylation disfavors DNA wrapping ( Figure 3A ) . We have rescaled the elastic energy differences with a factor of 0 . 23 to match the ΔΔG prediction in figure 2B . In agreement with the rest of our results , our analysis confirms that the effect of methylation is position-dependent . In fact , the overall difference between the two extreme methylation scenarios ( all-in-minor vs all-in-major ) is larger than 60 kJ/mol , the average difference being around 15 kJ/mol . We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position ( figure 3B ) . The large differences between the extreme cases can induce rotations of DNA around the histone core , shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove . It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences . Since there are roughly 1 . 5e88 possible 147 base pairs long sequence combinations ( i . e . , ( 4n+4 ( n/2 ) ) /2 , n = 147 ) , it is unfeasible to calculate all the possible sequence effects . However , using our elastic model we can provide a range of values based on a reasonably large number of samples . If we consider all possible nucleosomal sequences in the yeast genome ( around 12 Mbp ) , the energy difference between the best and the worst sequence that could form a nucleosome is 0 . 7 kj/mol per base ( a minimum of 1 kJ/mol and maximum of around 1 . 7 kJ/mol per base , the first best and the last worst sequences are displayed in Table S3 in Text S1 ) . We repeated the same calculation for one million random sequences and we obtained equivalent results . Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/mol , which translates into ∼0 . 5 kJ/mol per methyl group , 2 kJ/mol per base for the largest effects . Considering that not all nucleosome base pair steps are likely to be CpG steps , we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the sequence , and it appears that multiple minor groove methylations in a nucleosome are very likely to induce nucleosome repositioning . Changes in the phase of nucleosomal DNA could give rise to differences in gene activity , exemplified in figure 4 with two cases extracted from the yeast genome . We computed the relative probability to find a nucleosome centered in a given base pair using a Boltzmann-like probability distribution based on the differential elastic deformation energy . In the first example , figure 4A , both theory and experiment predict that the binding site of the transcription factor ABF1 ( green box ) is fully accessible . Upon CpG methylation , the predicted nucleosome probability curve changes ( red line ) and the histone core hides the ABF1 binding site . In figure 4B we show that methylation could induce a phase displacement that would change the accessibility of the recognition box of PHD1 . Full details on these calculations can be found in the SI material . Both cases represented in these figures illustrate the impact of methylation in modulating binding of regulatory proteins to DNA by a simple chemical mechanism that affects nucleosome positioning . In summary , the calculations reported here shed light on the physicochemical code behind epigenetic CpG methylation . State of the art calculations suggest that methylation disfavors nucleosome formation in a unique position-dependent manner , in agreement with recent experimental work [30] , and that methylation induces changes in nucleosome positioning and phasing , resulting in a different pattern of well-positioned nucleosomes . This can change the accessibility of DNA to effector proteins and can affect then gene regulation . The present results also suggest a novel role for methylated DNA binding proteins: to keep the MeC pointing towards the nucleosome exterior . Detachment of DNA binding proteins after methylation could lead to a spontaneous shift of the DNA's phase due to relaxation of the base steps towards more favorable positions . This relaxation modifies DNA accessibility and , accordingly , DNA read-out mechanisms . Overall our results support the existence of a basic physical code for the regulation of gene expression through chromatin organization . More complex mechanisms are probably built on top of it to define a fine control of the interplay between epigenetics , chromatin structure and gene regulation . | In Eukaryotic cells , control of the patterns of DNA cytosine methylation – a mechanism that acts on top of the genetic code – plays a key role in the regulation of gene expression . The large prevalence of DNA methylation in vivo , suggests a connection between the physical properties of methylated and un-methylated DNA with the control of gene expression . In this work we investigate the physical implications of DNA methylation in nucleosomal DNA , in particular its preferred location with respect to the nucleosome core-particle and the consequences of DNA methylation for the accessibility of the genetic material . We find that methylated DNA is less prone to form nucleosomes due to a reduced elasticity , especially when all methyl groups are pointing outwards from the nucleosome core , and that multiple methylation could give rise to changes in nucleosome positioning . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
]
| []
| 2013 | Understanding the Connection between Epigenetic DNA Methylation and Nucleosome Positioning from Computer Simulations |
Prevalence is a common epidemiological measure for assessing soil-transmitted helminth burden and forms the basis for much public-health decision-making . Standard diagnostic techniques are based on egg detection in stool samples through microscopy and these techniques are known to have poor sensitivity for individuals with low infection intensity , leading to poor sensitivity in low prevalence populations . PCR diagnostic techniques offer very high sensitivities even at low prevalence , but at a greater cost for each diagnostic test in terms of equipment needed and technician time and training . Pooling of samples can allow prevalence to be estimated while minimizing the number of tests performed . We develop a model of the relative cost of pooling to estimate prevalence , compared to the direct approach of testing all samples individually . Analysis shows how expected relative cost depends on both the underlying prevalence in the population and the size of the pools constructed . A critical prevalence level ( approx . 31% ) above which pooling is never cost effective , independent of pool size . When no prevalence information is available , there is no basis on which to choose between pooling and testing all samples individually . We recast our model of relative cost in a Bayesian framework in order to investigate how prior information about prevalence in a given population can be used to inform the decision to choose either pooling or full testing . Results suggest that if prevalence is below 10% , a relatively small exploratory prevalence survey ( 10–15 samples ) can be sufficient to give a high degree of certainty that pooling may be relatively cost effective .
Prevalence is a key epidemiological measure for assessing the burden of soil-transmitted helminths ( STH ) in human communities . The majority of data collected within national surveillance and treatment programs are in the form of prevalence , although infection intensity data based on egg counts from faecal samples can also be collected by using a variety of diagnostic techniques [1] . Treatment guidelines from the World Health Organisation ( WHO ) for control of STH species use prevalence data to categorise areas into high , medium and low risk and concomitantly define mass drug administration ( MDA ) treatment strategies accordingly [2] . In the context of public health decision making , intensity data are chiefly used to calculate the prevalence of individuals with low , medium and high intensity infections . Such an approach has been used as a proxy measure for STH infection-induced morbidity in populations [3] . In recent years , interest has started to turn from control of morbidity to the possibility of the elimination of STH infection through breaking the parasite’s transmission cycle in the human population [4 , 5] . Breaking transmission is achieved by driving infection prevalence through a critical threshold below which parasite reproduction cannot sustain transmission [6 , 7] . In this context , the ability to accurately measure a very low prevalence will become increasingly important as programmes target elimination . A variety of diagnostic methods are currently used in assessing prevalence in endemic areas , but most are based on microscopic examination of a standard quantity of stool and the subsequent calculation of the number of eggs it contains [8] . The most frequently used egg-counting method for STH diagnosis is the Kato-Katz ( KK ) technique [9] . Egg counting methods generally require minimal equipment , beyond a microscope , and testers can acquire the necessary skills within a week of training [10] . However , the sensitivity of these techniques is known to be poor under most conditions , although this is hard to quantify in the absence of a gold standard . This is largely a consequence of heterogeneity in the distribution of egg output in stool excreted by an infected individual with a given worm burden [11 , 12] . A survey of the literature suggests that the sensitivity of egg counting methods is highly inconsistent across studies . Sensitivity appears to vary according to parasite species , chosen diagnostic method , underlying prevalence/intensity of disease and even across different studies ( presumably indicating the importance of the degree of training of the operator/tester ) . Some patterns emerge however; the test-sensitivity to detect Ascaris lumbricoides eggs appears generally high across all background intensities ( sensitivity approx . 0 . 7 ) [8 , 13–15] while hookworm-larvae detection in stool is much less reliable , showing a tendency to drop from higher values at high prevalence ( sensitivity: 0 . 65 at 80% prevalence ) [15] to low values at very low prevalence ( sensitivity: 0 . 3 at 18% prevalence ) [14] . Molecular methods , such as Polymerase Chain Reaction ( PCR ) and quantitative PCR ( qPCR ) techniques , offer a significantly more sensitive diagnostic method by detecting STH DNA in the stool specimen , almost certainly from eggs , and amplifying it to ensure more reliable measures of concentration [14 , 16 , 17] . The initial quantity of DNA in the sample can be quantified indirectly by counting the number of cycles of amplification necessary to reach a predetermined detection threshold [16] . Sensitivities in the range of 98% are reported , depending on the efficiency of the protocol used for extraction of the DNA [14] . The main drawbacks for these techniques are the expense , the potential for contamination within the laboratory , the need for relatively advanced equipment with a reliable supply chain , and extensive training of laboratory staff . In most cases , field-collected samples need to be properly preserved and stored before they get transported to centralised facilities for subsequent processing and testing [18] . In this situation , pooling of stool samples represents an appealing approach whereby the time and cost ( translated to labour time needed to perform a number of DNA extractions and PCR runs ) can be significantly reduced while still maintaining the sensitivity of the diagnostic test performed and hence ascertaining the presence of STH species in all the samples collected . Pooling consists of combining equal portions from each sample that will make up the pool and combining them into one sample , usually with a homogenisation step to ensure sufficient mixing . The number of samples that make up a pool is a key variable of the protocol . Aggregating samples into a pool allows bulk properties of the constituent samples to be measured with a single test . There is a range of theoretical and experimental work on the estimation of faecal egg count by pooling , for STH infections in both animals and humans [19–21] . Comparisons of mean egg counts and egg count reduction rates from individual and pooled samples show reasonable levels of agreement , improving as pool sizes increase [22] . The most common approach is the use of pooling to detect presence of an infectious agent . Assuming sufficient sensitivity , a negative test for the pool implies all constituent samples are negative . The larger the pool size , the fewer tests that need to be made . The process of pooling naturally ‘averages’ the intensities of constituent samples and hence the sensitivity and specificity for detection at pool level may differ from that at the individual sample level . At low levels of infection , a pool consisting of a single positive sample will require greater sensitivity due to an effective dilution . The impact of pooling on the sensitivity of detection and egg count estimation has been investigated using the Kato-Katz diagnostic for STHs and schistosomiasis [23] . The results indicate that the detection sensitivity was considerably reduced by pooling , as expected , while estimated egg counts were generally increased . A similar study based on the POC-CCA diagnostic technique for schistosomiasis found much lower drops in sensitivity with pooling , perhaps reflecting the better inherent sensitivity of the method [24] . Other work has attempted to use detailed models of worm distribution and diagnostics [12] to address sensitivity and specificity for pooled samples for Schistosoma mansoni [25 , 26] . The relationship between the probability of individual samples being positive , and pools being positive , can be used to estimate prevalence from pooled results , but standard estimators can be strongly biased [27] . An extension of the pooling technique is to use it as a method to identify the test status of all samples . In this case , pools with a positive test result have their constituent samples individually tested to identify infected individuals [28] . In this paper , we examine using this approach to calculate the prevalence of STH infection in a population . We also assess the optimal design for a pooling scheme , in order to minimize the number of tests performed . The main aim of this study is to examine the circumstances under which pooling of samples from a survey to measure prevalence is potentially more cost-effective than testing all samples , where the cost of a survey design is a function of the number of tests or DNA extractions that are required . The question arises specifically in the context of the DeWorm3 project , a cluster randomized trial to test the feasibility of interrupting STH transmission using biannual MDA targeting all age groups funded by the Bill and Melinda Gates Foundation [4 , 29] . The project includes cross-sectional prevalence surveys conducted using qPCR , to be completed at baseline , mid-point and end-point of the trial . However , without prior information on the prevalence of infection within a cluster , it is impossible to say whether pooling will be more or less cost-effective than testing all samples individually , or indeed what sampling and pooling strategy might be most efficient . We construct probability distributions for the cost of testing and overall cost-effectiveness of pooling and use both to investigate the dependence of cost effectiveness on the underlying prevalence of STH infection in a given population and the number of samples aggregated into a pool . Note that the method is designed to be used for all stool sample diagnostics for faecal egg detection released by human helminths , not just STHs . We also examine the effect of uncertainty in the underlying prevalence on cost-effectiveness and look at how sparse prior information on prevalence can be used to make decisions on the sampling protocol to be employed .
Our method for using pooling to calculate prevalence is analogous to that outlined by Corstjens et al for identifying infected individuals through pooling [28] . Samples are grouped in pools of size n . Aliquots from each pool are then extracted and tested using qPCR . Assuming 100% sensitivity for the test , a negative result indicates that all constituent samples are also negative . For pools that test positive , all constituent samples are individually processed and tested to ascertain how many are positive . For a pool size of n and an underlying prevalence , π , the probability of a positive pool is p+=1− ( 1−π ) n ( 1 ) Assuming a total number of samples N , the number of positive pools , N+ , will have a positive binomial distribution N+∼Binom ( NT , p+ ) ( 2 ) where NT=N/n is the total number of pools . This expression assumes that the number of samples is exactly divisible by the pool size , which would be expected to be a feature of sampling design . The number of tests , T , required to calculate prevalence is also a random variable T=NT+nN+ ( 3 ) The cost of the testing strategy will be a function of the number of tests performed . Other factors that determine cost include equipment overheads , staff time and the details of the testing protocol as the number of tests needed increases [18] . While such detailed cost data are often not available , they can be acquired . We discuss this important aspect of cost-effectiveness calculation in detail in the Discussion section . Here , we consider the simplest possible case in which there is a fixed cost per test , independent of the number of tests being carried out . With this assumption , we also ignore the time and cost of preparing the pool . One of our key interests is whether a pooling strategy leads to a greater or lesser cost than simply testing all samples . As such , the relative cost , C , C=TN ( 4 ) is useful . If C > 1 , pooling is not cost-effective in comparison to testing all samples separately , while if C < 1 , it is more cost effective than the individual testing of all samples . The value of C will vary according to how the individual pools are constituted from the samples , so we present results in terms of its mean value and the shape of its distribution . We first derive some key properties of the mean relative cost C¯ and how it depends on pool size and underlying prevalence , which provides key insights into the conditions under which pooling can be advantageous . The mean relative cost is only a single statistic of relative cost . The criterion for deciding whether to proceed with a pooling approach will depend on the details of the relative cost distribution . A better basis for decision-making is the probability that relative cost is less than 1 , P ( C < 1 ) . A threshold , PT , can then be defined as the probability that pooling is more cost-effective than testing individual samples . If the condition P ( C < 1 ) ≥ 1−PT is met , then pooling will be chosen over testing all samples . Without some information on the prevalence of infection in the population , it is impossible to know whether pooling of any sort will be relatively cost-effective . Some information is required to make such a decision . Information on prevalence might come from limited exploratory testing of the population using standard egg-counting diagnostic methods such as Kato-Katz or molecular techniques . Additionally , there may be prior information from previous studies in the literature , data from national surveys or expert opinion based on local history or conditions . To combine these ideas , we create a Bayesian model combining prior information about prevalence and exploratory testing data from individual samples to generate the resulting probability distribution for relative cost ( See ( SI ) for derivation and analysis ) . We define the new relative cost random variable as CU to distinguish it from C , the relative cost when prevalence is known . Details of the derivation can be found in the SI , but the resultant expression for the probability of pooling cost effectiveness is given by P ( CU<1 ) =∑N+=0N+CP ( N+|d+ , dT , NT ) ( 5 ) Here , N+C= ( n−1 ) NT/n is the sample size , expressed in terms of pool test results . The conditional probability is P ( N+|d+ , dT , NT ) ∝∫π=01P ( N+|π , NT ) P ( d+|dT , π ) P ( π ) dπ ( 6 ) where the first probability under the integral is the binomial probability of N+ positive pools , the second is the probability of d+ positive exploratory tests out of dT trials with prevalence , π , and the last term , P ( π ) , represents other prior information about prevalence . The derivation of this relationship can be found in the SI . We use this model to examine the dependence of the probability of efficient pooling , and hence the decision to pool , on the amount and type of information available on the underlying prevalence of disease .
We first consider the case in which the underlying prevalence of infection , π , is known . The value of the relative cost , C , can vary in the range [1/n , 1+1/n] , where pooling is cost effective when C < 1 . The distribution of relative cost is examined further below and in the SI , but the dependence of relative cost on pool size and prevalence can be seen by considering the case in which the expected relative cost , C¯=1 . This acts as a discriminant , dividing cases in which the expected cost effectiveness is positive for pooling from those in which it is negative . In the SI , we calculate the prevalence as a function of pool size for unit relative cost as πc ( n ) =1−exp{−ln ( n ) n} ( 7 ) This discriminant characterises any combination of pool size and prevalence as , on average , cost effective or not ( Fig 1 ) . For a given pool size , prevalence below πc ( n ) are cost effective , on average . In the figure , pool size is an integer quantity and the continuous line of the discriminant in the figure is a series of points at whole number values . Critical prevalence has a maximum value πcmax=1−exp{ −1e }≃0 . 31 ( 8 ) Consequently , pooling is , on average , not cost effective for all prevalence greater than 31% , independent of the size of the pools . The maximum critical prevalence is achieved for n = e ≃ 2 . 72 . Hence the pool size which is cost effective for the largest range of prevalence is 3 , the nearest integer . As pool size increases , the critical prevalence for cost effectiveness falls . Within the cost-effective region , the optimally pool size ( on average ) for a given prevalence is shown by the dotted line in Fig 1A . Optimal pool size increases slowly with decreasing prevalence but doesn’t rise above 5 until prevalence is below 5% . Below this prevalence , however , optimal pool size grows rapidly with decreasing prevalence . Fig 1B shows how the mean relative cost effectiveness for optimum pool size varies with prevalence . As prevalence decreases below the value at which pooling becomes a viable option , the cost effectiveness gradually increases . For prevalence values around 2% , pooling on average requires only a fifth of the number of extractions and tests as would be required if testing samples individually . The effect of uncertainty in the underlying infection prevalence is analysed in detail in the SI . We present the main results in this section . The introduction of uncertainty in underlying prevalence has two effects on the probability distribution of relative cost . Firstly , uncertainty of the underlying prevalence increases the overall variance . The variance including uncertainty is ( See SI ) var ( CU ) =NT−1NTvar ( p+ ) +var ( C ) ( 9 ) where CU is the relative cost with uncertainty in the underlying prevalence . The variance is the sum of the variance associated with pool construction and that associated with the uncertainty in the underlying prevalence . For any reasonable number of pools employed , the first term is effectively var ( p+ ) . The mean cost effectiveness is also altered by the additional uncertainty in prevalence . The mean can be expressed as C¯U=1n+1−B ( d++1 , dT−π¯dT+n+1 ) B ( d++1 , dT−π¯dT+1 ) ( 10 ) where B ( … ) is the beta function , dT is the total number of exploratory tests and π¯=d+/dT is the expected underlying prevalence . In general , it is not clear whether C¯U is greater than or less than C¯; that is , whether the uncertainty in prevalence leads to a higher or lower relative cost . However , it is the case that for low prevalence situations where π¯<1/ ( n+1 ) , the cost is increased by uncertainty ( See SI ) . Fig 2 shows the effect of uncertainty in prevalence on relative cost . Fig 2A shows the effect on the critical prevalence/pool size relationship for dT = 10 , effectively correcting Fig 1A for the effects of uncertainty in underlying prevalence . Critical prevalence ( 100% ) is higher for larger pool sizes but lower for the most cost-effective lower pool sizes , but the effect is not large . The maximum prevalence for cost effective pooling is only marginally affected . The figure also compares contours for 80% and 50% relative cost between known and uncertain prevalence . Prevalence uncertainty increases the separation of the 100% and 80% cost contours , with 80% achievable only below a prevalence of 15% . A relative cost of 50% is not possible within the range of pool sizes examined . As shown in Fig 2B , the difference between the uncertain and known prevalence result drops as the sample size increases and the differences become negligible for sample sizes above 50 . Fig 3 shows the probability that pooling will be cost effective , given the number of exploratory tests ( on the left ) and the number of those that are positive ( along the bottom ) . The prevalence prior is set to be uninformative . The diagnostic test in the exploratory phase is assumed to have 100% sensitivity and specificity in this case and pool size is set at 5 . Taking the probability of cost effectiveness , PT = 80% as the pooling choice threshold , the green region of Fig 3 defines the exploratory results that would trigger the use of pooling for this size of pool . Five or more negative tests with no positives exceeds the threshold , as does 10 or more tests with one or fewer positives . Having reached the threshold for choosing pooling , it’s possible that further information can invalidate that choice . For example , 5 negative tests suggest pooling , but if the next test were positive , the support for pooling would drop to 60% , and as such below the chosen threshold . The band of yellow and orange result combinations that run diagonally across the figure correspond to the critical prevalence for pool size of 5 . Fig 2A shows this to be about 27% for 10 samples . The broken grey line tracks this prevalence across the table , approximately . Kato-Katz sensitivities for hookworm vary considerably ( 25–65% ) and appear to decline as prevalence reduces in a roughly linear fashion ( evidence from the literature on this is very variable and often hard to interpret ) . Our models of diagnostic sensitivity as a function of prevalence indicate that for hookworm , sensitivity is increasing approximately linearly with underlying prevalence . Fig 4 shows the support for pooling for hookworm with sensitivity varying from 30% at the lowest prevalences to 70% for 100% prevalence . This increases the number of samples needed to support a decision to pool , but does not change the basic structure i . e . support for pooling is only generated by finding very low numbers of positive samples .
Analysis of mean relative cost reveals some of the main qualitative and quantitative features of pooling as compared to complete processing of individual samples . The line defining neutral relative cost , C¯=1 , suggests that the maximum prevalence for which pooling could be advantageous is given by πcmax , which is approximately 31% . The closest integer pool size to this point of maximum effectiveness is at a pool size of three ( Fig 1A ) . The value of the maximum prevalence and the pool size at which it occurs do not vary significantly with uncertainty as to the underlying prevalence . Analysis shows that the most efficient pool size for prevalence values below πcmax remains below five down to approximately 5% prevalence . However , below 5% , there is a rapid increase in optimal pool size , with a 2% prevalence having an optimal pool size close to 10 . It should be noted that pool sizes of five are considerably smaller than those used purely for the detection of the presence of infectious agents , rather than assessing the prevalence of infection [19] . The relative cost for optimal pool size is shown in Fig 1B . The trend indicates that the relative cost is minimum for the lowest prevalence values . A prevalence rate below about 10% indicates that pooling is less than half the cost of testing all samples individually . The shape of the optimal pool size curve in Fig 1A suggests that , in the absence of accurate prevalence information , 5 is a good default pool size . However , in areas that have been subjected to multiple rounds of MDA , a larger pool size of 10 would lead to more cost effective pooling , assuming no loss of sensitivity . In particular , this would apply in the case of transmission assessment surveys ( TAS ) . Currently , there is no formally endorsed TAS for STHs , but they typically involve large sample sizes to compare measured prevalence to threshold values at the 1–2% level [30] . Key to the use of pooling in this context , however , would be assessing whether pooling sensitivity could still reliably be assumed to be 100% for pooled samples from lightly infected individuals . The issue of qPCR sensitivity to low egg counts and , by extension , sample dilution , is made complicated by the nature of the qPCR process . A positive result for a sample is the result of a detection threshold being reached within a certain number of amplification cycles ( the CT value ) . Detection sensitivity as a function of egg count or dilution therefore tends to have a binary character . If the threshold is attained within the maximum number of cycles used , the sensitivity tends to be close to 100% . When CT values get close to the maximum value , sensitivity drops rapidly to very low levels . Additionally , the detection threshold and the maximum number of cycles performed are calibrated with respect to the preparation protocol to be used for a batch of samples . As a result , point at which sensitivity falls away is very dependent on the particular experimental set up and hard to generalise . The characteristics of sensitivity as a function of dilution are the subject of current research . The introduction of uncertainty in the underlying prevalence both increases the variance of the relative cost and also biases its mean value . However , as shown in Fig 2 , even with quite poor information on prevalence , the bias in expected relative cost is only plus or minus a few percentage points . Relatively little information on prevalence is required to achieve good support for a pooling approach . If the exploratory data is assumed to have 100% sensitivity , as would be the case for qPCR , as few as 5 individual tests can be sufficient to reach 80% support for pooling if the underlying prevalence is very low ( Fig 3 ) . Support of 80% for individual sampling can be achieved with only a couple of data points , if the underlying prevalence is very high . There is a range of exploratory data results that give no clear indication for either strategy ( that is , support for pooling is between 20% and 80% ) . This occurs when exploratory results indicate prevalence is close to the critical value at which relative cost is 1 . The decision for or against pooling will be most difficult when the costs of the two alternatives are closely balanced . When exploratory data are collected through Kato-Katz diagnostics , the reduced sensitivity increases the amount of data necessary for a decision to be made ( Fig 4 ) . This situation is likely to be quite common , as most prevalence surveys are currently conducted using microscope-based techniques . However , in order to take advantage of this , the sensitivity of the Kato-Katz method would have to be well characterised . Since this feature of KK is so poorly documented and is context- and technician-dependent , it would be necessary to use the most pessimistic sensitivity figures , which would push up the amount of data required for a decision and biasing the decision process in the direction of caution and against pooling . However , the situation seems very different for the highly sensitive PCR diagnostic method; the method further benefits from inclusion of internal controls to ensure successful extraction of the DNA/target present and from the consistency of application with accepted ranges of the detectable value measured assuming a standardized level of technician competency . In this study , we have used a relative cost metric as the basis on which to discriminate between pooling and testing of all samples separately . Relative cost , as defined in the Methods section , has the advantage that it is simple to interpret and is a linear function of the number of tests performed . The cost metric comprises two parts; the function that converts the number of tests done into a cost ( the cost function ) and the way in which the cost of pooling and of testing all samples are compared . The cost of a sampling strategy is made up of the cost of consumables , such as reagents , primers and extraction kits , and the cost of staff time and equipment requirements . For consumables , there is the possibility of economies of scale through bulk buying . These would introduce non-linearities into the relationship between number of extractions and cost , which would apply to both strategies . However , the cost of consumables is likely to be swamped by the much larger costs of machinery purchase and staff salaries for highly-trained technicians . These costs are likely to scale linearly with numbers of tests , since they reflect the number of man-hours per test . Non-linear effects may arise if more equipment or technicians are required to manage the workload . Automated sample processing , extraction and PCR offers the opportunity to significantly reduce the cost of personnel at the expense of larger up-front equipment costs . The process described as testing in the main text comprises three stages . The most time-consuming stage , and hence the highest cost , is the extraction of DNA from samples , although the pooling process represents a small but not insignificant amount of work [23] . This requires bead-beating and centrifuging of samples [31] . The final PCR amplification of extracted DNA is much less labour intensive , but may be limited by the availability of PCR machines . For pooled samples , there is a preliminary stage of creating pools from individual samples [22] . Hence pooling strategies require some additional work , but it is small in comparison to extraction steps . Such assumptions do require validated approaches using the most efficient pooling approach . The small additional cost associated with pooling samples will reduce slightly the relative cost of pooling described in our analysis . Taking this into account , the neutral cost effectiveness curve of Fig 1A would drop down to slightly lower prevalence values . In order to assess the absolute cost of different sampling strategies , detailed cost information on the different stages of testing will be required . It is likely that this kind of cost information will be context-specific but it should be easy to collect . The relative cost metric used in the current work has the advantage that absolute costs cancel out , provided the cost functions are linear with the same cost per test . However , this may not be the best cost metric for decision making . It is possible that relative cost may be less important than the absolute difference in outlay between the two sampling approaches . In that case , the difference in cost may be a better metric . While this wouldn’t affect the expected cost-effectiveness as a function of prevalence and pool size as shown in Fig 1 , decisions based on exploratory data would now need to be based on the probability of achieving a given cost or saving with respect to testing all samples . This may result in different decisions being made than those suggested by analyses based on relative costs . The theory described in this paper can be easily adapted to address the use of different metrics . Two potential features of STH prevalence surveys not considered in this paper are the prevalence of the different species that make up the STH classification , and spatial or geographical heterogeneity in infection prevalence . In the case of multiple species , each species will require an independent PCR run for detection if using a singleplex or multi-parallel protocol . However , the pooling and DNA extraction stages of testing will act on all species present simultaneously and these are the costliest parts of the process . Hence the optimal protocol is probably to use pooling for all species of interest if it is indicated as optimal for any of them . Large-scale surveys are likely to comprise sub-regions across which prevalence will vary . Similarly , cluster-based trials will feature clusters containing a range of prevalence values in sub-populations ( or villages ) within the cluster . If a wide range of prevalence is indicated ( based on prior prevalence information ) , greater cost effectiveness may be achievable by assigning sub-regions or clusters to different sampling strategies . Whether this is justified will depend on what additional costs are incurred by having a dual sample strategy . However , these costs , if known , could be included in the decision-making analysis . The analysis presented here represents a basic framework for understanding the cost effectiveness of pooling strategies and a guide to decision-making about the best strategy to adopt in any given setting . Our findings provide a general picture of when to use pooling , how to judge pool sizes , and how prior information can be used to make better decisions . When considering pooling strategy and decisions in specific cases , details of pooling protocols , costs and decision-making priorities will need to be factored into the model and the appropriate calculations made as detailed in this paper . Acquiring detailed cost data is an important requirement in applying these methods . | Current diagnostic methods for assessing prevalence of soil-transmitted helminths ( STHs ) largely rely on microscopic visualization of helminth eggs , an inexpensive but insensitive method of detection . However , growing interest in going beyond control to break transmission of STH through mass drug administration will require highly sensitive assays to detect the low intensity infections that occur when prevalence is low within a population . Molecular tools , such as real-time PCR , offer the required sensitivity , but depend on well-equipped laboratories and adequately trained technicians . In addition , current assays are relatively expensive to perform at the scale required for surveys . Sample pooling is a technique that can be used to estimate prevalence from a set of samples , while potentially employing fewer tests for a given sample size , reducing cost . The decision in favour of or against pooling will determine how samples are collected , properly stored and analysed , and that needs to be established early in the study or program design process . Our work identifies the key determinants on which this decision should be made , what information is needed to make the choice and how the decision can be made . | [
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| 2019 | Calculating the prevalence of soil-transmitted helminth infection through pooling of stool samples: Choosing and optimizing the pooling strategy |
Tools for plague diagnosis and surveillance are not always available and affordable in most of the countries affected by the disease . Yersinia pestis isolation for confirmation is time-consuming and difficult to perform under field conditions . Serologic tests like ELISA require specific equipments not always available in developing countries . In addition to the existing rapid test for antigen detection , a rapid serodiagnostic assay may be useful for plague control . We developed two rapid immunochromatography-based tests for the detection of antibodies directed against F1 antigen of Y . pestis . The first test , SIgT , which detects total Ig ( IgT ) anti-F1 in several species ( S ) ( human and reservoirs ) , was developed in order to have for the field use an alternative method to ELISA . The performance of the SIgT test was evaluated with samples from humans and animals for which ELISA was used to determine the presumptive diagnosis of plague . SIgT test detected anti-F1 Ig antibodies in humans with a sensitivity of 84 . 6% ( 95% CI: 0 . 76–0 . 94 ) and a specificity of 98% ( 95% CI: 0 . 96–1 ) . In evaluation of samples from rodents and other small mammals , the SlgT test had a sensitivity of 87 . 8% ( 95% CI: 0 . 80–0 . 94 ) and a specificity of 90 . 3% ( 95% CI: 0 . 86–0 . 93 ) . Improved performance was obtained with samples from dogs , a sentinel animal , with a sensitivity of 93% ( 95% CI: 0 . 82–1 ) and a specificity of 98% ( 95% CI: 0 . 95–1 . 01 ) . The second test , HIgM , which detects human ( H ) IgM anti-F1 , was developed in order to have another method for plague diagnosis . Its sensitivity was 83% ( 95% CI: 0 . 75–0 . 90 ) and its specificity about 100% . The SIgT test is of importance for surveillance because it can detect Ig antibodies in a range of reservoir species . The HIgM test could facilitate the diagnosis of plague during outbreaks , particularly when only a single serum sample is available .
Plague , a bacterial infection caused by Yersinia pestis , is essentially a zoonosis of small mammals such as rodents . It is occasionally transmitted to man by the bite of an infective flea [1] . The bubonic form in humans can evolve rapidly to pneumonic form if not treated early . Plague is an acute , often fatal , and potentially epidemic disease . Accordingly , plague is classified as a class I notifiable disease , subject to International Health Regulations [2] . Plague remains a serious problem for international public health . Small outbreaks of plague continue to occur throughout the world , and at least 2000 cases of plague are reported annually [3] . The disease has displayed recrudescence and geographical extension in Madagascar [4] , [5] . It reappeared in Algeria in June 2003 after an absence of almost 60 years [6] , and decreased in Vietnam in 2003 [7] . No natural foci of plague have been described in Algeria [8] while in Vietnam plague persists in wild animal reservoirs and is subject to an intensified monitoring program . Bubonic plague is the major form of the disease encountered in the modern plague outbreaks . It affects mainly rural people in developing countries , whose level of education is very low . In Madagascar , plague foci matches with the poorest rural area with the most vulnerable population [9] , [10] . Re-emergence of plague is associated with low sanitary condition , waste , rats abundance and proximity to rodents [11] . In fact that plague currently affects poor and vulnerable people , development of simple and affordable test which may help and contribute to the control of the disease should be taken in consideration . The control of plague involves diagnosis and recognition of the disease [2] . For diagnosis , plague confirmation can be done by bacteriological culture ( isolation of Y . pestis strain ) , by rapid diagnostic test ( RDT ) for F1 antigen detection ( in endemic area without other confirmatory test ) or by serology ( four-fold rise in anti-F1 antibody titre in paired serum samples ) [12] . The isolation of Y . pestis from clinical sample ( pus of bubo , sputum ) requires a laboratory with at least level II biosafety put into place [13] . Moreover , bacteriology is time-consuming , expensive and sensitive to the presence of contaminants , to patient treatment and to delays in specimen transport . A RDT for the detection of F1 antigen , a capsular antigen of Y . pestis , was recently developed and could be used on bubo aspirate , sputum , sera , urine and organs removed post mortem [14] . This test may be useful for confirming clinical diagnosis and triggering alerts . It performed particularly well with bubo aspirate when bubonic plague was suspected . The development of this RDT constitutes a major advance in plague diagnosis , particularly in countries with poor medical infrastructures [15] . The serology for the detection of anti-F1 plague antibodies plays an important role in confirming plague diagnosis . Indeed , during the last outbreak of pneumonic plague in the Democratic Republic of Congo ( DRC ) in 2005 , plague diagnosis was confirmed by evidence of seroconversion based on the anti-F1 IgG titre in paired serum samples [16] . The ELISA method is essential for retrospective confirmation when the causal agent cannot be isolated and also when only serum is available . When neither the most appropriate specimen nor the pair of sera were available , the plague confirmation is “compromised” . A rapid test which can be performed with a unique serum collected during the early stage of the infection would be very helpful for the biological diagnosis . For the surveys of plague infection foci , the sero-epidemiological or sero-epizootic investigations of anti-F1 antibody prevalence in human or animal population could be achieved . The species involved in the plague cycle have been identified in Madagascar and Viet Nam , but this is not necessarily the case in other countries , such as Algeria . ELISA is an efficient tool for the serology but it is difficult to carry out in field either for diagnosis or for surveillance . It requires specific equipment , expensive consumables and specific antibodies to each species for the revelation of anti-F1 to be detected . The available ELISA for human and rodent IgG detection were respectively 91 . 4% and 100% sensitive and 98 . 5% and 100% specific [17] , [18] . A rapid test for anti-F1 IgG antibody detection with a “half-dipstick” format has been described for humans and animal reservoirs . Its sensitivity was 94 . 3% and its specificity 89 . 2% [19] . This format showed only a test line . A second line , absent from this test , and usually used for attesting the validity of the strip ( control line ) in immunocrhromatographic assays is of great importance in result interpretations . There is still a need to develop a simple , rapid and cost-effective test for the detection of plague-specific antibodies . First , a test which can replace the ELISA and which can be used in a large-scale in the field would be beneficial . Second , another test which can be used for biological diagnosis of plague would be very helpful . We aimed to develop two immunochromatography-based tests ( dipstick ) . The SIgT test is able to detect total ( T ) anti-F1 immunoglobulin ( Ig ) in different species ( S ) ( human and animal reservoirs ) . It was compared with ELISA . The SIgT test could have major applications in epidemiological investigations of plague and for surveillance . The HIgM test is able to detect human ( H ) anti-F1 IgM . Performance of the test with sera from individual with known status of plague was determined .
We developed two rapid tests based on a one-step , vertical-flow immunochromatography . The test consisted of a reaction pad , a conjugate pad and the absorbent pads . The reaction pad consists of a nitrocellulose membrane , with a pore size of 5 µm ( Whatman International , Chateau Giron - France ) . The BioDot machine ( BioDot , Irvine , California ) was used to spray a line of Protein A ( Pharmacia , Sweden ) at a concentration of 2 . 5 mg/ml to capture Ig for SIgT test or a line of anti-human IgM ( µ ) at a concentration of 2 mg/ml ( P . A . R . I . S , Compiègne – France ) to capture human IgM for HIgM test . Protein A possesses two distinct Ig-binding domains , one that binds the Fc region of IgG and the other that binds the Fab region of Ig [20] . A second line of monoclonal anti-F1 antibody ( MAbB18-1 ) was also sprayed on the upper part of the nitrocellulose . This Anti-F1 antibody reacts with the gold particles-F1 antigen conjugate and form a second reaction ( control line ) , indicating that the test has been correctly performed . The control line attests the validity of the test and should always appear . The conjugate pad consists of a polyester Accuflow P ( Whatman Schleicher & Schuell , England ) . The colloidal gold particles ( 40 nm diameter ) were conjugated to F1 antigen by the British Biocell International ( British Biocell International Cardiff , UK ) . The polyester was soaked in F1 conjugated at OD = 3 and was overnight lyophilised . The absorbent pads ( sample and wicking pads ) consist of cellulose filter paper ( Schleicher and Schuell , Ecquevelly - France ) . The sample pad was soaked in blocking buffer ( 0 . 1 M sodium borate supplemented with 1% Triton X-100 ) and overnight dried . The wicking pad was not processed prior to use . A membrane-based immunochromatographic card was prepared by fixing the different pads onto a plastic card with double-sided sticky tape ( Adhesives Research Ltd , Melville House United Kingdom ) : the reaction pad in the middle , the conjugate pad under the sample pad at the bottom and the wicking pad at the top of the card ( Figure 1 ) . The dipstick were trimmed to a width of 5 mm with the BioDot cutting machine and stored in a waterproof bag at 4°C . The dipstick test was performed on sera or seropads ( blood on blotting paper ) . Sera were previously diluted ten times in distilled water . For seropads , one pastille was soaked in 200 µl of distilled water and incubated for one hour at 37°C or at room temperature . This step allows the elution of the serum from the filter . The test was carried out in a plastic tube containing 150 µl of diluted sample . Diluted serum samples containing antibody absorbed from the bottom of the dipstick bound to the conjugated antigen , and the antigen-antibody binding formed complexes moved by capillary action into the nitrocellulose membrane . The complexes reacted with the immobilised protein A , generating a signal . Excess of the conjugate moved on the nitrocellulose and bound to the anti-F1 Mab , giving a second signal corresponding to the control line ( Figure 2 ) . Results were read after 15 minutes . Two pink lines appeared in positive sample and a single pink line in negative one . If the control line is absent , the test is invalid . The shelf life of the strip for long-term storage was assessed by testing serial dilution of the positive control sera with the dipsticks stored at 60°C twice per week for three weeks . Storage at 60°C for three weeks is equivalent of two years at room temperature [21] . The detection limit of the tests was obtained by testing in duplicate serial dilutions of the positive control . For SIgT test , it was expressed as the lowest concentration of MAb anti-F1 G6-18 giving positive result . For HIgM test , the detection limit was expressed as the highest titre of sera with positive result . The tests were evaluated on human and canine stored sera at Institut Pasteur de Madagascar and on small mammal samples freshly collected by the Pasteur Institutes of Algeria , Madagascar and Nha-Trang or from their frozen collection . The human samples tested were anonymous frozen sera from the collection of Institut Pasteur of Madagascar . Samples without turbidity , with sufficient quantity and with clearly identification number were selected and blind tests were performed . A total of 288 sera from the following groups were included in the study: For SIgT test , as the purpose of this test is to be an alternative method to the ELISA in the field , ELISA was used as reference methods . Both methods were performed with each sample . Human sera were tested by human IgG anti-F1 ELISA . The sensitivity of this ELISA was 91 . 4% , and its specificity was 98 . 5% [17] . Small mammals and dogs sera were tested according to a previously described ELISA [18] . For rodent sera , revelation used a goat anti-IgG ( H+L ) rat Horse Radish-Peroxidase H . R . P ( Sigma-Aldrich , USA ) and for insectivore and dog samples , a peroxydase conjugated protein A . Samples were considered positive when the absorbance was above the defined threshold . ELISA for rodent and insectivore was standardized by testing sera from experimentally infected animals and from animals caught in plague-free areas . For rodent ELISA , its' sensitivity and specificity were 100% [18] . For dogs , sera collected from animals in plague-endemic and plague-free areas were used to standardize the ELISA . The specificity of a positive ELISA-anti-F1 for this species was confirmed by F1 antigen-inhibition . Dog ELISA had a specificity of 97 . 3% . Sensitivity wasn't determined because dogs' positive controls were not available . For HIgM test , the evaluation was carried out with sera from individual of known plague status according to the biological diagnosis by bacteriology ( culture ) [13] . The purpose was to have another method for plague diagnosis when bubo aspiration or sputum for the culture or the pair of sera for the serology are not available . We compare the results of HIgM test with plague status . The data , such identification number of the sample and the results of the two compared tests , were entered in Microsoft Excel sheet and analysed by Pivot Table reports . Sensitivity ( Se ) and specificity ( Sp ) estimates are given as percentages with 95% confidence interval . The use of the human sera from the collection of Institut Pasteur de Madagascar , including the sera from plague patients , the sera from individual in plague–free area and the sera from patients affected by others diseases , was approved by the ethics committee of the Madagascar Ministry of Health .
We tested a total of 131 sera from human clinically suspected plague from which 65 were positive by ELISA . Compared to ELISA , the SIgT test gave 55/65 ( 84 . 6% ) positive results . Among ten SIgT-negative and ELISA-positive samples , six had a low IgG titre by ELISA and four were sera collected early . Among the 66 negative sera by ELISA , 63 were also negative with SIgT test and 3 were positive ( Figure 3 ) . Among the 71 negative controls tested; 70 were negative by SIgT test . The specificity of SIgT test was assessed by testing 86 samples from patients with other infectious diseases prevalent in Madagascar: 19 patients with schistosomiasis , 10 patients with cysticercosis , 14 patients with toxoplasmosis , 10 patients with hepatitis B , 10 patients with hepatitis C , 10 patients with streptococcosis and 13 patients with unknown diseases . All these sera were negative . The SIgT test performed on human samples and compared to ELISA had a sensitivity of 84 . 6% with 95% CI between 0 . 76 and 0 . 94 and a specificity of 98% with 95% CI between 0 . 96 and 1 . It had a positive predictive value ( PPV ) of 93 . 2% and a negative predictive value ( NPV ) of 95 . 6% with plague prevalence estimated to 0 . 6% under endemic situations in Madagascar highlands . SIgT test was evaluated with 352 sera from animal reservoirs of plague: 88 small mammals ( Rattus rattus , Rattus norvegicus ) from Madagascar , 134 from Viet Nam ( genera Rattus , Mus , Suncus ) and 130 from Algeria ( genera Lemniscomys , Psamommys , Atelerix ) . When compared to ELISA , SIgT test had a sensitivity of 87 . 8% with 95% CI between 0 . 80 and 0 . 94 and a specificity of 90 . 3% with 95% CI between 0 . 86 and 0 . 93 . It had a PPV of 70 . 6% and a NPV of 96 . 5% with rodent plague prevalence estimated to 10 . 5 in context of highland endemic foci . Dogs respond to Y . pestis infection by producing specific antibodies against the F1 antigen of Y . pestis . Sixty three samples from endemic ( 14 sera ) and from non-endemic ( 49 sera ) areas of Madagascar were tested . Compared to ELISA , SIgT test had a sensitivity of 93% with 95% CI between 0 . 82 and 1 . 0 and a specificity of 98% with 95% CI between 0 . 95 and 1 . It had a PPV of 92 . 8% and a NPV of 98% with a plague prevalence estimated to 23 . 8% under plague endemic area situation . We evaluated HIgM test with 23 acute-phase sera from bacteriologically-confirmed plague patient and 110 plague-negative ( 24 negative control from Madagascar and 86 with others infectious disease ) . All these sera were from the collection of Institut Pasteur de Madagascar . Results of HIgM tests are presented according to the patient status for plague ( Table 2 ) . HIgM test gave 19/23 positive results . Among the 4 discordant results ( HIgM test negative but plague status confirmed by bacteriology ) , 2 samples were collected within three days after the disease onset . HIgM test was found to have a sensitivity of 83% with 95% CI between 0 . 75 and 0 . 90 and a specificity of 100% . This test had a PPV of 100% and a NPV of 96 . 49% with plague prevalence estimated to 0 . 6% stated above . No cross-reactivity with HIgM test was detected within the 86 sera from patient infected with other diseases .
We developed and evaluated two tests for the qualitative detection of plague anti-F1 antibodies in sera: the SIgT test for total Ig anti-F1 antibodies during and after plague infection in humans , rodents and other animals and the HIgM test for anti-F1 IgM in humans . Using ELISA as reference method , SIgT test detected plague antibodies in human with a sensitivity of 84 . 6% and a specificity of 98% according to the reference test ELISA which sensitivity and specificity were respectively 91 . 4% and 98 . 5% [17] . By this comparison , 3 discordant results SIgT positive-ELISA negative were obtained with sera collected from bacteriologically-confirmed plague cases . Of the 10 samples SIgT test negative-ELISA positive , four sera were from bacteriologically-confirmed plague cases ( Figure 3 ) . These are SIgT test false negative . The limitation of this new test is about the low positive sample with ELISA that could be negative by SIgT test . Although less sensitive ( but specific ) , this new test would be useful in case of plague outbreak since it could give rapid information on the human plague situation in a studied area . It is also particularly interesting for retrospective investigation when only serum is available . In addition , since SIgT test detects plague-specific antibodies in many species of animal reservoirs , it is suitable for large scale serological survey of reservoirs in remote and impoverished areas endemic for plague . This could help to determine the risk of plague in a given zone , leading to a progress in disease prevention . SIgT test , used for canine sera proved sensitive and specific enough for this purpose , since it provided evidence of plague antibodies production in 93% of the samples collected from area of endemic plague , whereas over 98% of the samples from areas considered free of plague tested negative . Indeed , dogs are useful sentinels of plague prevalence , since animals living in or in adjacent to areas endemic for plague may be in contact with Y . pestis by infected flea bites or by consuming infected prey . They may develop high antibody titre without plague symptoms [22] . Moreover it is easier to manage dogs than small mammals' surveillance whose study is tedious ( number of samples to be collected and analysed ) . HIgM test was developed for the detection of anti-F1 IgM in humans to provide an alternative diagnostic method for plague , particularly when serum is the only clinical specimen available . HIgM test in plague confirmed cases gave a specificity of 100% and a sensitivity of 83% . This low sensitivity will generate some false negative results . However , of the 4 “false negative” samples; 2 were taken early ( within 2 days after onset of the disease ) before IgM was likely to be detectable in blood and 2 were collected 1 week after the onset of the disease . Owing to its high specificity , HIgM test could be used with significant advantage on serum samples collected during the acute phase as early as three days after onset of the disease . It could be performed with only a single serum sample while plague diagnosis by ELISA usually need a pair of sera ( early and late sera collected at 4–6 weeks interval ) [2] . Our aim was to develop some simple , rapid and affordable tools for a large scale use in plague monitoring ( seroepidemiological investigations ) and as an alternative test to ELISA . In the majority of endemic area , particularly in Madagascar , the poor sanitary and economic situation renders difficult the control and surveillance of plague . Bacteriology techniques including culture-isolation and mouse infection require appropriate laboratory . In developing countries , at the district level , simple tests like the dipstick assay can be implemented in the health centres . Most of the suspected cases of plague are detected in remote villages in rural area . As soon as transport of specimen from these places to a central laboratory is long and difficult , it is essential to possess an alternative tool for plague diagnosis and surveillance on site . A pilot assessment of the new tests by users at the periphery level could be considered to define the utility and performance of these tools in field conditions . In conclusion , the rapid serodiagnostic tests developed in this study are promising , not only for epidemiological studies , but also for the surveillance of reservoirs and active foci and for plague diagnosis . Application in case of bioterrorism attack can also be considered as Y . pestis is recognized as biological weapon [23] . | Plague is due to the bacterium Yersinia pestis . It is accidentally transmitted to humans by the bite of infected fleas . Currently , approximately 20 developing countries with very limited infrastructure are still affected . A plague case was defined according to clinical , epidemiological and biological features . Rapid diagnosis and surveillance of the disease are essential for its control . Indeed , the delay of treatment is often rapidly fatal for patients and outbreaks may occur . Bubo aspirate is the most appropriate specimen in case of bubonic plague , but its collection is not always feasible . The main current biological approaches for the diagnosis of human plague are F1 antigen detection , serology for antibody detection by ELISA and Y . pestis isolation . The biological diagnosis of plague remains a challenge because the clinical signs are not specific . In this study , we developed some simple , rapid and affordable tests able to detect specific plague antibodies . These tests can be used as alternative methods for plague diagnosis in the field and for plague surveillance . | [
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| 2009 | Development and Evaluation of Two Simple, Rapid Immunochromatographic Tests for the Detection of Yersinia pestis Antibodies in Humans and Reservoirs |
Although antibody responses to dengue virus ( DENV ) in naturally infected individuals have been extensively studied , the functionality of DENV specific memory T cell responses in relation to clinical disease severity is incompletely understood . Using ex vivo IFNγ ELISpot assays , and by determining cytokines produced in ELISpot supernatants , we investigated the functionality of DENV-specific memory T cell responses in a large cohort of individuals from Sri Lanka ( n=338 ) , who were naturally infected and were either hospitalized due to dengue or had mild or sub clinical dengue infection . We found that T cells of individuals with both past mild or sub clinical dengue infection and who were hospitalized produced multiple cytokines when stimulated with DENV-NS3 peptides . However , while DENV-NS3 specific T cells of those with mild/sub clinical dengue infection were more likely to produce only granzyme B ( p=0 . 02 ) , those who were hospitalized were more likely to produce both TNFα and IFNγ ( p=0 . 03 ) or TNFα alone . We have also investigated the usefulness of a novel T cell based assay , which can be used to determine the past infecting DENV serotype . 92 . 4% of DENV seropositive individuals responded to at least one DENV serotype of this assay and none of the seronegatives responded . Individuals who were seronegative , but had received the Japanese encephalitis vaccine too made no responses , suggesting that the peptides used in this assay did not cross react with the Japanese encephalitis virus . The types of cytokines produced by DENV-specific memory T cells appear to influence the outcome of clinical disease severity . The novel T cell based assay , is likely to be useful in determining the past infecting DENV serotype in immune-epidemiological studies and also in dengue vaccine trials .
Dengue viral infections are one of the most important emerging virus infections in the world [1] causing 390 million dengue infections annually , of which 96 million are clinically apparent [2][3] . 70% of infections occur in Asia and as a result of the high disease burden , dengue has been declared a priority infection by the WHO , UNICEF and World Bank [4] . Currently there are no effective antiviral drugs to treat acute infection , nor a licensed vaccine to prevent infection . The main hurdle in developing a safe and effective vaccine has been our poor understanding of the complex nature of the protective immune response in acute dengue infection and the presence of four dengue virus ( DENV ) serotypes that are highly homologous[5] . One of the main concerns , especially after the results of the recent phase IIb and a phase III dengue vaccine clinical trials has been the poor understanding of T cell correlates of protection [6–8] . Although DENV-specific , highly cross reactive T cells were believed to contribute to severe clinical disease [9–11] , [12] more recent data suggest that T cell responses are likely to be protective [13 , 14] . Extensive analysis of T cell responses from a large cohort of naturally infected individuals showed that DENV-specific T cell responses of higher magnitude , and which were multifunctional were directed towards HLA alleles associated with reduced disease susceptibility [13] . Polyfunctional T cell responses have shown to be protective in many virus infections [15–18] . However , in dengue infection , DENV-specific memory T cell responses are thought to produce highly pro-inflammatory cytokines and are also thought to be sub optimal in clearing the virus in secondary dengue infection[10 , 12 , 19] . Therefore , as the functionality of DENV-specific memory T cell responses can possibly influence the outcome of subsequent infection with DENV , we proceeded to investigate the functionality of DENV-specific T cell responses in a large cohort of naturally infected individuals who were either hospitalized due to dengue or who had mild/subclinical past infection . Although secondary dengue infections are known to associate with more severe disease [20 , 21] , the majority of both primary and secondary dengue infections lead to asymptomatic disease [21 , 22] . Therefore , in order to understand how previous dengue infections contribute to clinical disease severity , it would be crucial to determine the past infecting serotype and subsequent immune responses in naturally infected individuals who develop symptomatic and asymptomatic infection . Currently the most widely used method to determine past infecting serotype in DENV-seropositive individuals is the plaque reduction neutralization assay ( PRNT ) [23 , 24] . However , there have been many concerns regarding the variability of the PRNT results , especially in those who had been infected with multiple DENV-serotypes [25–28] . In addition , a recent study showed that individuals who demonstrated high neutralizing antibodies for a particular DENV serotype ( which suggested past infection with that serotype according to the PRNT ) later went on to develop DHF when infected with that particular serotype[28] . Collectively , these data suggests that the PRNT may not be the most suitable method for determining the past infecting serotype and other methods should be developed . In this study , we further investigated the use of a previously described panel of serotype specific peptides , from highly conserved regions of the DENV in determining the past infecting serotype [29] . We found that DENV-seronegative individuals did not generate any responses to these peptides , while 92 . 4% of DENV-seropositive individuals responded to peptides of at least one DENV serotype . In addition , during the study period , those who were hospitalized due to a primary dengue infection ( who were seronegative at the time of recruitment ) responded to peptides of only a single DENV-serotype . Therefore , as this novel T cell based assay appears to detect DENV-serotype specific responses , it has a potential to be used in dengue vaccine trials to determine the past infecting DENV-serotypes and thus pre-existing DENV immunity .
The study included 338 individuals attending the Family Practice Center , which is a primary health care facility of the University of Sri Jayewardenepura , Sri Lanka providing primary health care to over 2000 individuals living in the suburban areas of the Colombo district . Individuals registered at the Family Practice Center between the ages of 6 to 80 years were invited to participate in this study and were recruited following informed written consent . Ethical approval was granted by Ethical Review Committee of the University of Sri Jayawardanapura . Blood samples were obtained and an interviewer administered questionnaire was used to record demographic details . We had access to detailed medical records of all participants which included information regarding all hospital admissions , presence of co-morbid factors , drug history and vaccination records . None of the participants reported a past JEV infection . According to classification of WHO guidelines 2011[4] , of the 54/338 of those who were hospitalized due to dengue infection , 8 ( 14 . 8% ) were diagnosed with DF and 46 ( 85 . 2% ) had DHF . The Ministry of Health of Sri Lanka , has laid down criteria for admission of patients with suspected dengue infection to hospital and hospitals follow these criteria for admission . Therefore , any individual with a suspected dengue infection , who presents with any of the clinical features listed below , is admitted to hospital , following a clinical assessment carried out by the medical officers at the out-patient department . The criteria for admission are as follows: A person with fever and a platelet count <100 , 000/mm3 Presence of dengue warning signs such as abdominal pain or tenderness and persistent vomiting Patients with any clinical signs of plasma leakage such as the presence of pleural effusions , ascites or the presence of mucosal bleeding , lethargy and restlessness , liver enlargement >2 cm An increase in the haematocrit with concurrent rapid decrease in platelet count in a full blood count At the time of recruitment of the study participants , they were categorized as having hospitalized dengue based on the diagnosis card given by the hospitals . This diagnosis card carried information such as whether the patient has DHF or DF and the laboratory investigations while in hospital . As dengue antibody assays or dengue NS1 antigen assays are not routinely done in government hospitals , the dengue NS1 antigen test results or the dengue antibody test results were only available for some of these 54 patients . The other patients were diagnosed as having either DF or DHF based on clinical features . The individuals who had been hospitalized due to dengue were recruited at least 6 months after the episode of their hospitalization . The time of infection of those with mild/subclinical dengue infection was not known . A panel of 17 , 20mer peptides were synthesized in-house in an automated synthesizer using F-MOC chemistry . These peptides have been previously described [29] and were found to be serotype specific and originating from highly conserved regions of the four DENVs . There were four peptides specific to DENV-1 , five specific to DENV-2 , four specific for DENV-3 and four specific for DENV-4 . Of these 17 peptides , except for 2 peptides which induced both CD4+ and CD8+ T cell responses , all other peptides induced CD4+ T cell responses . Dengue NS3 peptides were again 20 mer peptides overlapping by 10 amino acids , which spanned the whole length of the DENV-3 NS3 protein . The synthetic NS3 20mer peptides were pooled together to represent the whole NS3 protein . The FEC control peptides that were used contain a panel of 23 , 8–11 amino acid CD8+ T cell epitopes of Epstein Barr virus ( EBV ) , Flu and CMV viruses and have been used as quality control in ELISpot assays [30] . Ex vivo Elispot assays were performed as previously described [31 , 32] in 242 individuals with mild/sub clinical dengue infection , 54 individuals with past hospitalized dengue and 42 seronegative volunteers ( a total of 338 individuals ) . Briefly ELISpot plates ( Millipore Corp . , Bedford , Massachusetts , USA ) were coated with anti-human IFNγ antibody ( Mabtech AB , Nacka , Sweden ) overnight . DENV-NS3 overlapping peptides and FEC peptides were added at a final concentration of 10 μM as previously described [12 , 33] . The live attenuated SA-14-14-2 JEV vaccine was used at a PFU concentration of ≥5 . 4 log PFU/ELISpot well . PHA was used as a positive control and an irrelevant peptide ( SARS 20 mer peptide ) was included as a negative control in addition to background control ( cells with media ) . The spots were enumerated using an automated ELISpot reader ( AID , Germany ) . Background ( cells with media ) was subtracted and data expressed as number of spot-forming units ( SFU ) per 106 PBMC . The DENV-3 NS3 peptides were tested in a single pool of peptides ( all the 20mer peptides were pooled together ) in duplicate . Mean of the response is indicated in the data points as SFC/million PBMC . All peptides that induced an IFN-γ response of more than mean ±3 standard deviations of the irrelevant peptide were considered positive . Cultured ELISpot assays were performed on 82 individuals with mild/sub clinical dengue infection , in 23 who were hospitalized due to dengue and 30 seronegative individuals as previously described [29 , 31 , 32] . All peptides that induced an IFN-γ response of more than mean±3 standard deviations of the irrelevant peptide were considered positive . All individuals were tested by an indirect dengue IgG capture ELISA ( Panbio ) for the qualitative detection of IgG antibodies to DENV . Pan Bio units were calculated according to the manufacturer’s instructions and accordingly , Pan Bio units of >11 were considered positive , 9–11 was considered equivocal and <9 was considered negative . JE Detect IgG ELISA ( Inbios ) was used for the detection of JEV specific-IgG antibodies . Calculation of the immune status ratio ( ISR ) was done according to the manufacturers’ instructions and accordingly an ISR of >5 was considered positive; an ISR of 2–5 equivocal and an ISR of <2 negative . Quantitative cytokine assays were done in duplicate on ex vivo ELISpot culture supernatants stimulated with DENV-NS3 , JEV , FEC and the wells that only contained media . Quantitative ELISA was done for granzyme B ( BioLegend , USA ) , TNFα ( BioLegend , USA ) and IL 2 ( Mabtech , Sweden ) according to manufactures instructions . Accordingly 2 . 4 pg/mL ≥ of granzyme B production , 3 . 5 pg/ml ≥ TNF-α production and 0 . 4 pg/mL ≥ of IL2 production was considered positive . PRISM version 6 was used in statistical analysis . As the data were not normally distributed , differences in means were compared using the Mann-Whitney U test ( two tailed ) . To compare means of three or more variables , Kruskal-Wallis test was used . Degree of associations between hospitalized dengue infection , mild/sub clinical dengue infection and type of cytokines produced by the PBMCs and response to serotype specific peptides of three or more DENVs or 4> DENVs was expressed as the odds ratio ( OR ) , which was obtained from standard contingency table analysis by Haldane’s modification of Woolf’s method . Chi Square tests or the Fisher’s exact test was used to determine the p value .
Although cross reactive T cells have been implicated in causing severe dengue [10 , 12 , 34] , more recent studies done in individuals naturally infected with the DENV suggest that DENV-NS3 specific T cell responses could be protective [13 , 14 , 35] . However , the frequency and functionality of memory T cell responses in these cohorts were not assessed based on past clinical disease severity . As DENV-NS3 has shown to be one of the most immunodominant DENV nonstructural proteins and since it was shown to induce predominantly CD8+ T cell responses , we proceeded to use one pool of DENV-NS3 overlapping peptides to determine the functionality of T cell responses [32 , 34] . Our initial aim was to determine if the frequency of IFNγ NS3 specific memory T cell responses were different in those with mild/sub clinical dengue infection when compared to those who were hospitalized due to dengue . We found that individuals who were hospitalized due to dengue did not have a significantly higher ( p = 1 . 0 ) frequency of NS3-specific IFNγ ELISpot responses when compared to those with mild/sub clinical dengue infection ( Fig 1A ) . Of the 54 individuals who were hospitalized 47 ( 87 . 0% ) had a positive IFNγ ELISpot response to the NS3 peptides , while 170 ( 70 . 2% ) of those with mild/sub clinical dengue infection were positive . Therefore , it appears that although the magnitude of IFNγ production by NS3 specific T cells were not different in those who were hospitalized and in those with mild/sub clinical dengue infection , DENV-NS3 specific T cells of those who were hospitalized were more likely to produce IFNγ ( p = 0 . 02 , odds ratio = 2 . 7 , 95% CI 1 . 2 to 6 . 3 ) . As production of a single cytokine does not indicate the functionality of DENV-NS3 specific T cells in those with varying severity of past infection , we next proceeded to investigate the relationship between functionality of DENV-NS3 specific memory T cell responses and past clinical disease severity . In order to characterize the functionality of DENV-NS3 specific memory T cells , we determined cytokine profiles of IFNγ , TNFα and granzyme B in individuals who were hospitalized and those with mild/sub clinical dengue infection . As determining the polyfunctionality of DENV-NS3 specific T cells at an individual cell level was beyond the scope of this study , we used ELISpot culture supernatants to determine cytokine production by antigen specific T cells . From our cohort , ELISpot culture supernatants of PBMCs stimulated with DENV-NS3 peptides of 70 individuals with mild/sub clinical dengue infection and 41 individuals who were hospitalized were further assessed for production of the above cytokines . However , DENV-NS3 specific T cells of only 7 ( 10% ) of those with past mild/sub clinical dengue infection and 7 ( 17 . 1% ) of those who were hospitalized , produced 3 cytokines ( IFNγ , TNFα and granzyme B , Table 1 ) . Although we also investigated IL-2 production by DENV-NS3 specific T cells in the ELISpot supernatants , there was negligible amounts of IL-2 production . DENV-NS3 specific memory T cell responses of those who were hospitalized were significantly more likely to produce IFN γ and TNF α in the absence of granzyme B when compared to those with past mild/sub clinical dengue infection ( p = 0 . 03 , odds ratio = 4 , 95% CI 1 . 1 to 14 . 3 ) . In addition , DENV-NS3 specific T cells of 12 . 9% of those with mild/sub clinical dengue infection produced granzyme B only , whereas granzyme B only producing DENV-NS3 specific T cells were not detected in any of those who were hospitalized due to dengue ( p = 0 . 02 , odds ratio = 0 . 08 , 95% CI 0 . 004 to 1 . 4 ) . Therefore , production of granzyme B alone by DENV-NS3 specific T cells appeared to be associated with the occurrence of mild/subclinical infection . The functionality of JEV specific memory T cell responses were also assessed in those with past hospitalized and mild/sub clinical dengue infection , as memory T cell responses to other closely related cross reactive flavi-viruses could influence the clinical disease outcome . Although , significant differences in the functionality of JEV-specific memory T cell responses were not observed in these two group of individuals , those with past mild/sub clinical dengue infection were more likely to produce granzyme B alone in response to JEV ( Table 1 ) . Following analysis of types of cytokine production from PBMCs when stimulated with DENV-NS3 , we proceeded to determine any differences in the quantity of cytokine production . There was no difference in quantity of granzyme B produced ( p = 0 . 5 ) in those who were hospitalized due to dengue ( mean 108 . 9; SD±196 . 5 pg/ml ) when compared to those who had past mild/subclinical dengue ( mean 146 . 3 , SD±335 . 6 pg/ml ) ( Fig 1B ) . Similarly , no difference was seen ( p = 0 . 1 ) in the quantity of TNFα production in those who were hospitalized due to dengue ( mean 36 . 9 , SD±149 . 7 pg/ml ) , when compared to those with past mild/subclinical dengue ( mean 31 . 2 , SD±135 . 7 pg/ml ( Fig 1C ) . Severe dengue infection such as DHF is known to associate with secondary dengue infections [20 , 36 , 37] possibly due to the presence of cross reactive DENV-specific antibody and T cell responses [10 , 38] . However , sub clinical dengue infection is also known to occur in those with secondary dengue infection [21] . Therefore , we next proceeded to determine if the number and the type of the past infecting DENV-serotype contributed to disease severity . We had previously described a novel T cell assay [29] , which uses several serotype specific peptides from highly conserved regions of the four DENVs . In this assay 4–5 peptides representing each DENV serotype are used to determine the serotype-specific T cell responses [29] . Therefore , as an initial step , we proceeded to determine the usefulness of these peptides in determining the past infecting serotype in a large cohort of individuals [29] . In order to further determine the usefulness of this assay we performed cultured ELISpot assays in 135 individuals in our study cohort . Of these 135 individuals , 30 of them were seronegative for the DENV , 23 individuals had been hospitalized due to dengue infection and 82 were seropositive but had sub clinical dengue infection . None of the DENV-seronegative individuals ( n = 30 ) responded to any of the serotype-specific peptides of the four DENV serotypes . Of these 30 seronegative individuals , 14/30 ( 46 . 7% ) , had received the JE vaccine . Despite been vaccinated with JE , they still not did respond to any of these DENV peptides , suggesting that these peptides are specific for dengue and do not cross react with JE specific T cells . 21/23 ( 91 . 3% ) individuals with past hospitalized dengue and 76/82 ( 92 . 7% ) of those with past mild/sub clinical dengue infection responded to at least one peptide of a DENV serotype . 37/105 ( 35 . 2% ) of seropositive individuals responded to peptides of two serotypes , 7/105 ( 6 . 67 ) responded to peptides of three DENV serotypes of , 2/105 ( ( 1 . 9% ) have responded to peptides of all DENV serotypes During the follow up a period of 15 months of the initial cohort of , 12/135 individuals in whom we had performed cultured ELISpot responses , developed a dengue infection requiring hospitalization . 7 of them were previously seronegative and 5 individuals were seropositive at the time of recruitment , but only responded to peptides of one serotype in this assay ( Table 2 ) . The decision to hospitalize these 12 patients was based on the criteria laid down by the Ministry of Health , Sri Lanka . In these 12 patients , dengue IgM and IgG was positive in all 5 patients with an acute secondary dengue infection and dengue-IgM was positive in all 7 who developed an acute primary dengue infection . The antibody tests carried out in hospital was reconfirmed by us , as we too bled the patients on day 21 following acute infection and performed dengue IgM and IgG antibodies . One of the previously seronegative individuals developed 2 episodes of hospitalized dengue infection . During the first dengue infection , only DENV-specific IgM was positive suggestive of a primary dengue infection . During the second episode of DHF , both DENV-specific IgM and IgG were positive , suggestive of a secondary dengue infection . Cultured ELISpot assays were carried out in 7 of the seronegative individuals and 5 of the previously seropositive individuals after the episode of DHF . 4/7 of the seronegative individuals responded to at least 1 peptide of the DENV-1 serotype , one responded to peptides of DENV-3 and one responded to peptides of DENV-4 . Of the 5 dengue seropositive individuals who developed DHF , all had previously responded to only one serotype . Following the episode of DHF , all 5 responded to an additional DENV serotype and again it was found that 4/5 of them responded to at least one peptide of DENV1 ( Table 2 ) . This suggests that 8/12 of these individuals were likely to have been infected with DENV1 serotype , which accounted for >90% of infections during the study period ( personal communication from Dr . Hasitha Tissera , Sri Lanka Epidemiology unit , Ministry of Health ) . The seronegative individual had two infections responded to two different serotypes ( DENV-3 and DENV-1 ) . In summary , using this T cell assay we found that none of the DENV seronegative individuals responded to these peptides and 97/105 ( 92 . 4% ) of those were seropositive responded . In addition , 6/7 seronegative individuals who later developed a primary dengue infection only responded to peptides of one DENV-serotype and five individuals who previously only responded to one serotype , responded to an additional serotype after developing DHF . The seronegative individual who developed two episodes of DHF responded to two different serotypes . Therefore , this novel T cell assay appeared to be a useful tool in determining the past infecting serotype . Since this novel T cell assay appeared to be a useful tool in determining the past infecting serotype , we used this assay to determine the past infecting serotypes in our DENV-seropositive cohort . As expected we found that the number of DENV serotypes that individuals responded to significantly increased with age ( p = 0 . 001 ) ( Fig 2A ) , suggesting that multiple infections are more frequent as individuals aged . In addition , as expected , individuals who were hospitalized due to dengue were more likely to be infected with multiple DENV-serotypes when compared to those who had a sub clinical dengue infection ( Fig 2B ) . For instance , 12 ( 57 . 1% ) of those who were hospitalized due to dengue responded to more than one DENV-serotype , whereas only 36 ( 47 . 3% ) of those who had a past mild/sub clinical dengue infection responded to more than one . In addition , those who were hospitalized due to dengue were significantly more likely to respond to peptides from three or more DENV serotypes ( p = 0 . 04 , odds ratio 4 . 44 , 95% CI 1 . 15 to 17 . 18 ) . We next proceeded to determine the frequency of DENV-NS3 specific T cell responses with infection with multiple DENV-serotypes . Although , infection with multiple DENV-serotypes increased with age , DENV-NS3 specific IFNγ responses did not increase significantly with age ( Spearmans r = 0 . 05 , p = 0 . 42 ) ( Fig 2C ) . There was no difference in the frequency of DENV-NS3 specific IFNγ ELISpot responses in those who were hospitalized due to dengue who were likely to have had a primary dengue infection ( responding to serotype specific peptides of only one DENV serotype ) when compared to those who had a secondary dengue infection ( those who responded to peptides of more than one serotype ) . No difference was again seen in individuals who had a past mild/subclinical infection which was probably a primary infection , when compared to those who had a secondary infection ( those who responded to peptides of more than one serotype ) . All four DENV-serotypes have known to cause severe clinical disease [39 , 40] . We investigated if the type of infecting DENV serotype determined the severity of dengue infection ( Fig 2D ) . Although those who were hospitalized due to dengue ( 47 . 6% ) were more likely to respond to peptides of DENV-1 ( suggestive of a past infection with DENV-1 ) , when compared to those with past mild/sub clinical dengue infection ( 39 . 5% ) , the frequency of infection with DENV-1 or any other DENV serotype was not significantly different between those who were hospitalized when compared to those who had a mild/sub clinical dengue infection ( p = 0 . 6 , Odds ratio = 1 . 4 ) .
Although DENV-specific T cells have been implicated in severe clinical disease [10 , 12 , 34 , 35] , more recent studies have shown that T cells are likely to have a protective role [13] . Extensive analysis of T cell responses by Weiskopf et al in individuals naturally infected with the DENV showed that the T cell responses of higher magnitude and which were multifunctional were directed towards HLA alleles associated with reduced disease susceptibility[13] . It was shown that DENV specific memory T cells of approximately 17% of naturally infected individuals produced 3 cytokines whereas approximately 50% were double positive . However , since the clinical disease severity in those who were naturally infected were not known in the study done by Weiskopf et al , in our study we aimed to determine the functionality of DENV-specific T cell responses in those with varying severity of past infection in a large cohort of naturally infected individuals . Similar to the observations made by Weiskopf et al , we too found that DENV-NS3 specific T cells of 10–17% of individuals produced 3 cytokines and 29–34% of individuals produced 2 cytokines . However , we found that while DENV-NS3 specific T cells of those with past mild/sub clinical dengue infection were more likely to produce only granzyme B , those who were hospitalized due to dengue were more likely to have DENV-NS3-specific T cells that produce TNFα and IFNγ or TNFα alone . In addition , we also found that although the magnitude and the frequency of DENV-NS3 specific T cell responses were not different in those with hospitalized and mild/sub clinical dengue infection , T cells of those with past mild/sub clinical dengue were more likely to produce IFNγ . Therefore , although multiple cytokine producing T cells were observed in both group of individuals , the type of cytokines produced appear to influence the outcome of clinical disease severity rather than the frequency of DENV-NS3 specific T cells . Virus specific CD8+ T cells have shown to consist of a heterogenous population and they are thought to adjust the type of cytokine/cytokines they produce depending on the challenge [41] . Therefore , granzyme B expressing T cells are more likely to be cytotoxic than T cells expressing either TNFα or IFNγ or are double positive for these cytokines [41 , 42] . Since loss of CD28 has shown to associate with acquisition of granzyme B and better cytotoxic potential [42] , it would be important to study DENV specific different effector memory and central memory T cell subsets in relation to disease severity and protection in individuals naturally infected with dengue . In this study we used DENV-NS3 peptides to determine DENV-NS3 specific T cell responses . Although NS3 is one of the most immunodominant proteins , it is not equally recognised by T cells of all naturally infected individuals [13 , 32 , 43] . Therefore , it would be important to investigate T cell responses to other non-structural proteins to get a more detailed overview of memory T cell responses in relation to past clinical disease severity . Since DENV-NS3 stimulates both CD4+ and CD8+ T cell responses , the cytokines produced by the T cells are likely to be from both subset of T cells . However , since CD8+ T cells are more likely to be cytotoxic and more likely to produce granzyme B than CD4+ T cells , it would be important to investigate the differences in both CD4+ and CD8+ memory T cell responses in patients with varying severity of past dengue infection . Currently the PRNT is used in many dengue vaccination trials and epidemiological studies to determine the past infecting serotype and protection against a particular serotype [8 , 24 , 44] . However , it was recently shown that that individuals who demonstrated high neutralizing antibodies for a particular DENV serotype suggestive of past infection with that serotype , later went on to develop DHF when infected with the same serotype [28] . Therefore , the presence of high titres of neutralizing antibodies against a particular DENV serotype as measured by the PRNT , does not appear to reliably indicate past infection with that serotype . Since it would be important to determine the past infecting serotype for determining correlates of protection and also in dengue vaccine trials , we proceeded to investigate the usefulness of a novel T cell based assay to determine the past infecting serotype . Using a panel of previously defined peptides for the four DENVs we have further investigated the usefulness of this novel T cell based assay in determining the past infecting DENV serotype [29] . 92 . 4% of DENV seropositive individuals responded to at least one DENV-serotype-specific peptide in this assay , whereas none of the DENV seronegative individuals responded ( N = 30 ) . During the study period , 7 previously seronegative individuals ( who had no responses to any of the peptides ) and 5 previously seropositive individuals who responded to only one serotype by this T cell assay developed DHF . Of the previously seronegative individuals , who did not respond to any of the peptides in this assay , 6/7 responded to peptides from only one DENV serotype following acute primary DHF . Of the 5 DENV seropositive individuals who responded to peptides of only one DENV serotype previously ( suggestive of a previous primary dengue infection ) , responded to only one more additional DENV serotype following an acute hospitalized dengue infection . Therefore , 11/12 individuals who developed DHF during the study period were found to respond to at least one peptide of another DENV serotype , for which they did not respond at the time of recruitment . Although documenting the infecting DENV-serotype during acute infection would have further strengthened these findings , we did not have the access to blood sample during the acute illness . Based on epidemiological data during this time period , DENV-1 was the predominant circulating serotype and was the cause of infection in over 90% of cases ( personal communication from Dr . Hasitha Tissera , Sri Lanka Epidemiology unit , Ministry of Health ) . Therefore , the fact that 8/12 individuals who developed DHF during the study period responded to DENV1 , is compatible with the epidemiological data of DENV-1 being the predominant circulating virus serotype , although we did not confirm the serotype during the acute illness due to the unavailability of the samples during the acute illness The PRNT which measures the neutralizing ability of antibodies is known to be influenced by the cross reactivity between different flavi-viruses [45 , 46] . DENV-specific T cells have also shown to highly cross react with other flaviviruses [11] . Flaviviruses such as Japanese encephalitis virus ( JEV ) co-circulate in the same geographical region [47] and many countries universally vaccinate all children with the JEV vaccine . Therefore , in assessing the burden of dengue and in determining the past infecting DENV serotype , it is crucial that such an assay does not cross react with other flavi-viruses . In this novel T cell assay , none of dengue seronegative individuals who had received the JEV vaccine responded to any of the DENV peptides . Therefore , this assay does not appear to pick up JE specific cross reactive T cell responses . This is a further added advantage over the PRNT , where low titres of neutralizing flavi-virus antibodies are detected in dengue seronegative individuals due to cross reactivity of the flavi-virus antibody responses[23 , 24 , 48] . Since this novel T cell based assay appeared to be a useful tool in determining the past infecting DENV serotype , we used this assay to determine the association of past infecting serotype and clinical disease severity . As expected , those with past hospitalized dengue were more likely to respond to more than one DENV serotype suggesting that they had hospitalized dengue due to a secondary dengue infection . However , 37 . 8% of those with past mild/sub clinical dengue infection responded to 2 serotypes and 5 . 4% responded to 3 serotypes suggesting that mild/sub clinical dengue infection is also common with both secondary and tertiary dengue infections . Interestingly , the number of past dengue infections did not appear to influence the frequency of DENV-NS3 specific IFNγ responses as the frequency and magnitude of responses were similar in individuals who responded to one , two or three DENV serotypes . In summary , the majority of individuals naturally infected with the DENV , had DENV-NS3 specific T cell responses , which produce multiple cytokines . However , DENV-NS3 specific T cells of those who had a past mild/sub clinical dengue infection were more likely to produce only granzyme B , whereas , T cells of those with past hospitalized dengue infection were more likely to be double positive for IFNγ and TNFα . In addition , we have also investigated the usefulness of a novel T cell based assay , which can be used to determine the past infecting DENV serotype . Since the peptides used in this assay do not appear to be cross reactive with JEV and since those who develop primary and secondary dengue appear to be making expected responses to these peptides , it is likely to be useful in determining correlates of protection in large epidemiogical studies and in dengue vaccine trials . | Although dengue viral infections cause severe clinical disease , the majority of individuals infected with the dengue virus ( DENV ) develop asymptomatic infection . The function of DENV specific memory T cells in relation to past clinical disease severity is incompletely understood . In this study , we sought to investigate the function of DENV specific memory T cell responses in a large cohort ( n = 338 ) of individuals who were naturally infected with the DENV but developed varying severity of clinical disease . We found that T cells of individuals who were hospitalized due to dengue and those with mild/sub clinical dengue infection produced multiple cytokines when stimulated with DENV-NS3 peptides . In addition , we have also validated a novel T cell based assay , which can be used to determine the past infecting DENV serotype . We found that 92 . 4% of DENV seropositive individuals responded to at least one DENV serotype of this assay and none of the seronegatives responded . Moreover , the peptides used in this assay did not cross react with Japanese encephalitis virus . Therefore , this assay is likely to be useful in determining the past infecting DENV serotype in immune-epidemiological studies and also in dengue vaccine trials . | [
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| 2015 | Functionality of Dengue Virus Specific Memory T Cell Responses in Individuals Who Were Hospitalized or Who Had Mild or Subclinical Dengue Infection |
Quantitatively understanding the robustness , adaptivity and efficiency of cell cycle dynamics under the influence of noise is a fundamental but difficult question to answer for most eukaryotic organisms . Using a simplified budding yeast cell cycle model perturbed by intrinsic noise , we systematically explore these issues from an energy landscape point of view by constructing an energy landscape for the considered system based on large deviation theory . Analysis shows that the cell cycle trajectory is sharply confined by the ambient energy barrier , and the landscape along this trajectory exhibits a generally flat shape . We explain the evolution of the system on this flat path by incorporating its non-gradient nature . Furthermore , we illustrate how this global landscape changes in response to external signals , observing a nice transformation of the landscapes as the excitable system approaches a limit cycle system when nutrients are sufficient , as well as the formation of additional energy wells when the DNA replication checkpoint is activated . By taking into account the finite volume effect , we find additional pits along the flat cycle path in the landscape associated with the checkpoint mechanism of the cell cycle . The difference between the landscapes induced by intrinsic and extrinsic noise is also discussed . In our opinion , this meticulous structure of the energy landscape for our simplified model is of general interest to other cell cycle dynamics , and the proposed methods can be applied to study similar biological systems .
Stochasticity is an inherent property of living cells [1–6] . However , it is still difficult to quantify the robustness and adaptivity of cellular networks , even for a small cellular network perturbed by intrinsic random fluctuations , due to the massive cross regulations and nonlinear nature of such biological systems . As the size of the network grows , determining how to characterize the global stochastic dynamics of the system becomes a tough problem . “Waddington’s epigenetic landscape , ” which utilizes potential energy to pictorially illustrate the dynamics and evolution of cellular networks , has been widely and repeatedly used for several decades [5 , 7] . Some beautiful efforts and frameworks aiming to quantify this landscape have been made [8–13] , but investigation into typical biological models still remains to be done . Furthermore , the energy landscape usually reshapes itself due to a variety of changes such as environmental signals [14] , cell-cell interactions [15] and the growth rate dependence of protein concentrations [16] . Determining how to explicitly quantify this transformation for specific systems is also a major task . The yeast cell cycle is an important biological process in which a cell reproduces itself through DNA replication and mitosis events , which are intimately related to the checkpoint mechanism [17 , 18] . Recent work has revealed the dynamic regulatory mechanisms of the cell cycle , and the cell cycle process is now considered a series of irreversible transitions from one state to another [19–21] . The cell-cycle regulatory network must also be robust and adaptive to external stresses and signal changes . To quantitatively characterize this robustness , and provide a global description of the cell cycle regulatory system , some fundamental questions must be studied . For example , how does the energy landscape reflect the robustness and successive phases of the cell cycle ? How does the landscape adaptively change in response to external signals ? Is there any information that the energy landscape cannot provide ? If so , does any other supplemental description exist ? Using a simplified budding yeast cell cycle model driven by intrinsic noise , we systematically explore the above issues from an energy landscape point of view by constructing a global quasi-potential energy landscape for the budding yeast cell cycle model . Our results demonstrate that the energy landscape of the cell cycle is globally attractive , and we show how the cell cycle regulatory network reduces fluctuations from its upstream process and enables long durations in the transition regime . We also describe how the landscape changes in response to external signals when nutrients become sufficient and the DNA replication checkpoint is activated . We also discuss the dynamic information provided by the non-gradient nature that the pure energy landscape cannot explain , and provide other approaches to take into account this non-gradient effect . In addition , we compare the difference between landscapes induced by intrinsic and extrinsic noise and discuss the finite volume effect . Overall , our energy landscape study shows that the budding yeast cell cycle is a robust , adaptive and multi-stage dynamical process .
We first assume that the DNA replication triggers the mitosis as a “domino” mechanism in the budding yeast cell cycle . That is , once the yeast cell passes the Start checkpoint , it will proceed through the whole cell cycle process spontaneously . Based on the key regulatory network [17] and our previous study on budding yeast [22] , the cell cycle regulatory network can be simplified and separated into G1/S , early M and late M modules , as shown in Fig . 1A . We ignore the G2 phase for simplification . Each module has a positive feedback , and different modules are connected with activation and repression interactions . The deterministic equations describing this three-module yeast cell cycle network are d x d t = x 2 j 1 2 + x 2 − k 1 x − x y + a 0 , ( 1a ) d y d t = y 2 j 2 2 + y 2 − k 2 y − y z + k a 1 x , ( 1b ) d z d t = k s z 2 j 3 2 + z 2 − k 3 z − k i z x + k a 2 y , ( 1c ) where x represents the concentrations of key regulators such as cyclins Cln1 , 2 , Clb5 , 6 and transcriptional factors SBF and MBF in the excited G1 and S phases; y represents the concentrations of key regulators such as cyclins Clb1 , 2 and transcriptional factor Mcm1/SFF in the early M phase; and z represents the concentrations of key inhibitors such as Cdh1 , Cdc20 and Sic1 in the late M/G1 phase . In this model , we assume simple forms to characterize the interactions . Thus , the first term in each equation , the second order Hill functions , represent the positive feedback in each module [23 , 24] . The second term represents the degradation rate of each regulator , while the third term represents the repression or inhibition interaction between different modules . The parameter a0 in Equation ( 1a ) characterizes the environmental nutrition condition [25 , 26] , and ka1 x and ka2 y are the trigger signals from x to y and y to z respectively . Starting from the excited G1 state , the system will finally evolve to a stable fixed point , the G1 state; if a0 is large enough , the system will enter the cell cycle process continually . With a proper parameter set , the model in Equation ( 1a ) ensures a successive event order from DNA replication in the S phase to mitosis in the M phase , as well as a long duration for both events in the cell cycle process . For this work , we will simply denote the set of equations in Equation ( 1a ) as dx/dt = b ( x ) , where x = ( x , y , z ) . The evolution trajectory in time and state space is shown in Fig . 1B and 1C , respectively . Here P1 ( x = ( 0 , 0 , zmax ) ) represents the G1 state—which is the globally stable state of our model where a0 = 0 . 001—and P2 is a saddle point used to represent the excited G1 state from which the yeast cell passes the Start checkpoint and enters the cell cycle process . The trajectory in Fig . 1C can be separated into three parts . The first part is from the excited G1 ( P2 ) to the S phase ( P3 ) , where x = ( xmax , 0 , 0 ) ; the second part is from P3 to the early M state before the metaphase/anaphase transition ( P4 ) , where x = ( 0 , ymax , 0 ) ; and the third part evolves from P4 to the stable G1 state ( P1 ) . Compared with the model used in [9] , our model does not rely on a quasi-steady-state assumption related to cell mass . More details about the network and model can be found in S1 Text and S1 Fig . Starting from the deterministic descriptions above , we now address the stochastic setup of the system . The noise can be classified into the intrinsic and extrinsic types [1] . Here we model the intrinsic noise through a Gillespie jump process [27] , in which the strength of the noise is determined by the reaction network structure . Denote the state of the system X = ( X , Y , Z ) where each component represents the number of molecules for the corresponding specie . We then translate each term in Equation ( 1a ) into a chemical reaction . Taking Equation ( 1a ) as an example , we have four associated reactions for the four terms . The state change vector ν for each reaction channel has the form ν1 = ν4 = [1 , 0 , 0] and ν2 = ν3 = [−1 , 0 , 0] , which corresponds to the plus or minus sign in the equation . Once a reaction fires , the state of the system X would be updated to X+ν . The reaction propensity function is determined by each term and the volume size ( or system size ) V , where ϵ ≡ V−1 characterizes the magnitude of intrinsic fluctuations [28] . In Equation ( 1a ) , the four propensity functions are a 1 ( X ) = V X 2 ( j 1 V ) 2 + X 2 , a 2 ( X ) = k 1 X , a 3 ( X ) = X Y V , a 4 ( X ) = a 0 V . We choose this form for the propensities because a ( X ) ∼ O ( V ) when X ∼ O ( V ) , and the stochastic process x ( t ) ≡ X ( t ) /V will tend to the deterministic process Equation ( 1a ) when the volume size V tends to infinity in this scaling . Equation ( 1b ) and ( 1c ) can be treated similarly . There are 12 reactions in total . The above setup is suitable for the intrinsic noise . For the extrinsic noise whose magnitude is independent of the considered system , we simply take the stochastic model as ẋ=b ( x ) +ϵẇ , where ẇ is the standard temporal Gaussian white noise . More details can be referred to S1 Text . To study the robustness and adaptivity of our cell cycle model , we construct the Waddington-type energy landscape based on the concept quasi-potential from large deviation theory [29] . For any stable steady state x0 of Equation ( 1a ) , the local quasi-potential S ( x; x0 ) with respect to x0 is defined as S ( x ; x 0 ) = inf T > 0 inf φ ( 0 ) = x 0 , φ ( T ) = x ∫ 0 T L ( φ , φ ˙ ) d t , ( 2 ) where inf is short for infimum , which means the least upper bound of a subset . φ is any connecting path and L is called the Lagrangian [29–31] . The concrete form of L is determined by the setup of the stochastic process defined through intrinsic or extrinsic fluctuations . In case that the driving noise is of white noise type , L can be obtained from path integral formulation [32] . S ( x; x0 ) tells us the difficulty of transition from state x0 to x under the noise perturbation . The local quasi-potentials starting from different stable steady states can be suitably integrated together to form a global quasi-potential S ( x ) , which is exactly our proposal to rationalize the Waddington landscape for any non-gradient system , i . e . the dynamic system whose driving force can not be simply represented by the gradient of a potential function [29 , 31] . To better understand the quasi-potential , let us consider a special case . We suppose the dynamics is simply a gradient system with a single-well potential driven by small noise , i . e . x ˙ = − ∇ U ( x ) + ϵ w ˙ , ( 3 ) where ϵ is a small parameter , and w ̇ is the standard temporal Gaussian white noise with 𝔼 w ̇ ( t ) = 0 and 𝔼 w ̇ ( s ) w ̇ ( t ) = δ ( s − t ) . It is obvious that U ( x ) is one correct choice for the Waddington potential . We have the Lagrangian L ( φ , φ˙ ) =|φ˙+∇U ( φ ) |2/2 and S ( x ) = 2U ( x ) in this case , and the corresponding minimizing path satisfies the steepest ascent dynamics φ˙=∇U ( φ ) with the boundary condition φ ( 0 ) = x0 , φ ( T ) = x , where x0 is the unique potential energy minimum ( see SI for details ) . This example shows that S ( x ) defined in Equation ( 2 ) gives the desired potential in the gradient case up to a multiplicative constant . It is also instructive to note that the quasi-potential S ( x ) = − lim ϵ → 0 ϵ ln P ( x ) , ( 4 ) where P ( x ) ∝ exp ( −2U ( x ) /ϵ ) is the stationary Gibbs distribution of Equation ( 3 ) . This result is also true for general dynamic systems [29] . For the Gillespie jump processes , there is no explicit form of S ( x ) . However , the invariant distribution P ( x ) satisfies the chemical master equation ∑ j a j x − ν j V P x − ν j V − a j ( x ) P ( x ) = 0 ( 5 ) and we can plug the WKB ansatz [33] P ( x ) ∝ exp ( −VS ( x ) ) into Equation ( 5 ) . Here the system size V plays the role of 1/ϵ , and this ansatz originates from Equation ( 4 ) essentially . The leading order term yields a Hamilton-Jacobi equation H ( x , ∇ S ( x ) ) = 0 , ( 6 ) where the Hamiltonian has the form H ( x , p ) = ∑ j a j ( x ) ( e p · ν j − 1 ) for the standard Gillespie jump process . From classical mechanics , the S ( x ) obtained from WKB ansatz is exactly the quasi-potential defined through Equation ( 2 ) . So even in the non-gradient case , we can still define S ( x ) as a generalization of the potential . That is why it is called quasi-potential . S ( x ) is also a Lyapunov function of the original deterministic system [34] Equation ( 1a ) . The quasi-potential inherits key properties as the real potential guarantees in a gradient system . Besides logarithmically equivalent to the invariant distribution S ( x ) ∼ −ϵ ln P ( x ) , the mean exit time τ that the system escapes from an attractive basin has the asymptotic form τ ∝ exp ( VΔS ) , where ΔS is the energy barrier height between the boundary of the basin and the stable state . The deeper the quasi-potential well is , the harder the system leaves a stable state . So the quasi-potential energy landscape describes the robustness of a system . This is similar for the extrinsic noise . For more properties about the quasi-potential , one may refer to [29–31] and S1 Text . In later text we will call S ( x ) the potential energy for simplicity . The computation of S ( x ) by solving the equation H ( x , ∇S ) = 0 is not straightforward although there are already powerful methods [35 , 36] . Since we are interested in both the energy landscape and the transition path , we choose to compute the energy landscape through the gMAM method [31 , 37] . The idea is to directly minimize the action functional Equation ( 2 ) through Maupertuis principle for the space of curves ( See SI for details ) . In our work , the constructed energy landscape S ( x ) is a function of three variables . For convenience of visualization and analysis , we plot S ( x ) in two dimensional planes with a certain dimension fixed . Therefore the global landscape is cut into different slices from various directions .
Using the described method , we constructed the energy landscape S ( x ) for a budding yeast cell cycle network . We will state our findings from the energy landscape along the evolving path , i . e . , from the excited G1 state ( P2 ) to the final steady G1 state ( P1 ) . We first focus on the section from P2 to P3 . Fig . 2A illustrates the slice of the energy landscape on the x-z plane where y = 0 . We can see that the G1 state is the global minimum on the energy landscape , and there exists an energy barrier between P1 and P2 to prevent small noise activation . Outside this potential well , the energy function S ( x ) along the first part of the trajectory ( from P2 to P3 ) is relatively flat , while the energy cost to deviate from the cycling path is high . Intuitively , we will call the cell cycle trajectory a “canal” to illustrate its flatness along the path . At the end of the first part of the cell cycle , the G1/S phase variable x gradually increases and represses z to zero . The S phase canal is quite narrow when it evolves near the vertex P3 . As the system evolves through P3 , x gradually triggers the activation of the early M phase variable y , at which point the activated y begins to repress x . This corresponds to the S/M transition of the yeast cell cycle and we denote it as the early M phase canal . The landscape of the S phase and S/M transition is illustrated in Fig . 2D where z = 0 . In the bottom right corner of Fig . 2D , the energy barrier between the canals of the S and early M phases greatly decreases the probability that the system passes the S/M transition without crossing P3 , hence ensuring the robustness of the S/M transition . More details about the formation of the early M canal and the S/M transition are shown in Fig . 2B ( the z = 0 . 3 plane ) and 2C ( the z = 0 . 05 plane ) . In Fig . 2B , the system is shown to be temporarily restricted to a small area on the x-y plane , isolated by barriers separating it from the G1 state and the early M canal . As z gradually decreases to 0 . 05 , Fig . 2C shows that this restricting area is still small but slowly shifts to a place with a larger x value . In addition , the early M canal looks more apparent . When z finally falls to zero ( Fig . 2D ) , the energy barrier between the S canal and the early M canal disappears . Now the cell can execute DNA replication events with a long duration across P3 , after which it successively evolves to the M phase . In the second part of the cell cycle , the system evolves through the early M flat canal to the vertex P4 over a sufficient duration for the mitosis event . When the system passes through P4 , y triggers the activation of the late M variable z , and the activated z begins to repress y . This is the transition from the early M phase to the late M phase , corresponding to the metaphase/anaphase transition in yeast cell cycle process . The landscape on the x = 0 plane looks very similar with the one on the z = 0 plane ( Fig . 2D ) and is shown in S6 Fig . Finally , in the third part of the cell cycle , the activated z represses y to zero and the system evolves to a G1 stable state ( P1 ) and waits for another cell cycle division signal . Since the main canal along the cell cycle trajectory in Equation ( 1a ) after P2 is flat , the above energy landscape itself cannot tell us the moving direction of the system on the canal , which impels us to investigate its non-gradient nature . From large deviation theory , we know that the most probable cycling path under Gillespie’s stochastic jump dynamics satisfies d x d t = ∇ p H ( x , ∇ S ) = F ( x ) , ( 7 ) where S ( x ) is the energy landscape under discussion , p ≡ ∇S , and H is the Hamiltonian of the considered system . In general , F ( x ) is not parallel to ∇S , and so we have additional non-gradient effects for the transition paths . This point is also emphasized in [8 , 9] . However , if the gradient force becomes zero , we have F ( x ) = b ( x ) , which exactly corresponds to the dynamical driving force in the deterministic Equation ( 1a ) ( See S1 Text for details ) . This is the case for the flat landscape along the canal in our results . In Fig . 3A , we illustrate the force strength of F ( x ) on the energy landscape in the z = 0 plane as an example . Along the cell cycle trajectory , we observe that the force strength F ( x ) is large in both S phase and early M phase canals , but extremely small near P3 and P4 . Furthermore , we find that this tendency basically corresponds to the restriction width of the canal in the transverse direction ( Fig . 3B ) . This means that , in the S phase and early M phase canals where the driving force is large , the cell cycle process will progress quite quickly , and hence does not require a strong restriction . As the process passes through P3 and P4 , however , the system evolves more slowly ( i . e . with more duration ) , and the driving force decreases and the canal width narrows in order to restrict fluctuations in the transverse direction . Thus , the dynamic properties around P3 work as an analogous DNA replication checkpoint . Similarly , the same characteristics around point P4 act as an analogous M phase checkpoint . Therefore , we suggest that this fast-slow dynamic and corresponding wide-narrow geometry of the landscape act as the dynamical mechanism keeping the cell cycle robust , precise and efficient . To further visualize the non-gradient force in the flat canal , we came up with an alternate way of constructing the energy landscape , which we will refer to as the local pseudo energy landscape . The main idea behind this approach is to temporarily remove the globally stable state P1 from our model and only focus on the downhill flat canal after the saddle point P2 . Thus the pseudo energy landscape is only a local landscape and no longer reflects the global stationary probability distribution ( See S1 Text for details ) . In Fig . 3C and 3D we illustrate the pseudo energy landscape constructed using this method . The result is a bit like combining the original landscape and the non-gradient effect together . From Fig . 3C , we can see that the effect of the force strength is replaced by the steepness of the canal in the tangential direction , while the landscape in the transverse direction remains the same . We emphasize that this point is essential to explain the directionality of the dynamic path along the flat canal , where we have observed a phenomenological fact that the gradient of the global potential S ( x ) becomes zero and the driving force b ( x ) gives the moving direction . In Fig . 3D we also show that the pseudo landscape on the z = 0 . 3 plane . Compared with Fig . 2B , we can see that the appearance of the S and early M canals is more apparent in the pseudo landscape . Furthermore , there exists a barrier between the S and early M canals ( in the bottom right corner of Fig . 3D ) , which further guarantees the complete disappearance of the specie z before the cell enters the M phase . In the previous results , we obtained and analyzed the energy landscape of the yeast cell cycle model in Equation ( 1a ) using a0 = 0 . 001 , ka1 = 0 . 001 , ka2 = 0 . 001 , and so on . However , when the external signal or stress changes , how does the energy landscape adaptively make such a transformation ? First , let us discuss the energy landscape’s response to DNA damage in the S phase of the yeast cell cycle . When there is DNA damage in the S phase , the DNA replication checkpoint is activated [38] . In the cell cycle process , the checkpoint mechanism ensures the completion of early events before the beginning of later events so as to maintain the progression order of the cell cycle [18] . We can decrease the parameter ka1 to 0 . 0001 to simulate this effect ( See S1 Text for more details ) , with the resulting new energy landscape on the z = 0 plane shown in Fig . 4A and 4B . The results show that the flat canal around P3 now turns into a small pit , which will keep the system in the P3 state until the DNA damage is repaired . Similarly , ka2 = 0 . 0001 can be used to simulate the M phase checkpoint . Secondly , the yeast cells will divide continuously when they are cultured in a rich medium [14 , 26] , and we can increase a0 to 0 . 01 to simulate this effect ( see [26] and S1 Text ) . The resulting energy landscape is shown in Fig . 4C , where the global three-dimension energy landscape is projected onto the y-z plane by choosing the potential minimum with respect to x for fixed y and z . The results demonstrate that the system shifts from an excitable system to a stable limit cycle system through bifurcation ( a0 = 0 . 0025 ) when nutrition is sufficient , and the stable G1 state disappears . As a comparison , we show a similar projected energy landscape with a0 = 0 . 001 in Fig . 4D , where the cell cycle process is an excitable system . The previous results are all based on the assumption that the system volume ( or system size ) , i . e . , the copy number of considered species , goes to infinity . In other words , we only studied the fluctuation effects for perturbations by noises that are small compared to the reaction rates b ( x ) in Equation ( 1a ) . This is a reasonable assumption for most biological systems . However , if the system nearly stagnates at some transition areas where its reaction rates are so small that they are of the same magnitude as the noise , the previous picture does not hold . In this case , some special phenomena will appear that do not fall into our traditional analysis . Here we use the classical Monte Carlo simulation method to study the finite volume effect . From Fig . 5 we can see that the landscape is generally unchanged except around two corners . The original flat landscape with long-lasting slow dynamical behavior near the checkpoints now turns into pits . In other words , these nearly degenerate points play the role of small potential wells along the canal . This is intuitively true because the stationary probability for each point on this unidirectional path is approximately determined by the speed with which the system passes across . Consequently the system transitions to having a multipeak probability distribution , where the additional peaks do not correspond to the stable points of ODEs in the usual case . These additional peaks are also found in the cell cycle process of mammalian cells [13] . This interesting finite volume effect is unique when our driving force strength is comparable to the noise strength in some places . The additional pits can be treated as a “fingerprint” for a multi-stage biological processes , in which the pits act as another kind of analogous checkpoint . We speculate that a similar phenomena also holds for the classical limit cycle dynamics . So far we have only discussed the intrinsic fluctuations determined by the reaction network itself , and ignored the extrinsic influence from environments in which the noise strength is independent of the network structure [39–41] . However , we also investigated the energy landscape of our cell cycle model perturbed by extrinsic noise using a similar approach , and the result is shown in S2 Fig . Compared with the landscape obtained in Fig . 2 and 3 , the general shape of the landscape is almost the same , but the width of the canal does not change as significantly as the one perturbed by intrinsic noise . This result coincides with the lower insensitive fluctuation strength in the extrinsic noise case ( S2 Fig . ) . This point , which indicates our cell cycle model is more tolerant with respect to intrinsic noise , can be used to help distinguish between intrinsic randomness and any environmental perturbations of the system , especially for a multi-stage biological process that periodically changes its reaction rates in time .
We performed a careful study of the budding yeast cell cycle process from an energy-landscape point of view . The energy landscape of the budding yeast cell cycle is mainly comprised of two parts on a global scale: a deep pit that holds a cell in its G1 state when the environment is not suitable for division , and one unidirectional flat canal that performs a robust and accurate cell cycle progress once the system is excited ( as summarized in Fig . 6A ) . When nutrients are sufficient , the original excitable system evolves into a stable limit cycle . Correspondingly , the deep pit lifts up , and the cell cycle can proceed efficiently without waiting in the G1 state ( Fig . 6B ) . Once a cell meets any accident during its division , the corresponding cell cycle checkpoint is activated . In this case , the flat canal is dug to form an additional pit , and the system is held there until the accident is resolved ( Fig . 6C ) . Furthermore , the super-slow dynamic stage on the canal corresponds to small pits if we take into account the finite volume effect . Those pits reduce the fluctuations from the cycle’s upstream processes and provides a longer stay duration when the system passes by ( Fig . 6D ) . Besides the global view , the energy landscape also contains massive details of the system . The unidirectional canal and the guardrail on both sides guarantee that each event occurs only once and in the right order . Although the global energy landscape defined through the stationary probability distribution does not contain the non-gradient effect , the local pseudo energy landscape we proposed clearly visualizes this unidirectionality brought by the non-gradient force . Now the strength of the non-gradient force along the canal is characterized by the steepness of the pseudo energy in the tangential direction . For a system perturbed by intrinsic noise , the fluctuation restriction ability in the transverse direction of the canal is highly related to the driving force strength at that point . However , for a system perturbed by extrinsic noise , this relationship is much weaker . In our simplified model describing the essential dynamics of the yeast cell cycle process , we assumed the “domino” mechanism of cell cycle regulation , which is different from the previous Tyson’s model [42] and its landscape [9] . In our model , only the G1 phase cyclins are controlled by the cell mass , and the mitosis event in the M phase is triggered by the completion of the DNA replication event . This “domino” mechanism is also found in the cell cycle regulation of higher eukaryotic organisms [43] . With our model and new methods , we clearly identified the DNA replication and M phase checkpoints in the constructed energy landscape . This landscape can reshape itself in response to environmental nutrients ( similar and consistent results in mammalian cell cycle [13] ) and checkpoint signals adaptively . Furthermore , we proposed the concept local pseudo energy landscape to characterize the irreversibility of the dynamic path along the flat canal in the landscape . These points , to the authors’ knowledge , have not been revealed in the previous studies . Due to the curse of dimensionality , we only performed our constructions for a three-node network model . When the problem is considered in higher dimensions , the computational cost increases exponentially . Even if this computational cost issue is resolved , however , determining how to save and exhibit such high-dimensional information remains a tough question . There is still a need to develop a systematic reduction method to analyze high dimensional problems . Even with such limitations , we believe our meticulous study of the energy landscape of the simplified budding yeast cell cycle model is of general interest to those studying other complicated cell cycle dynamics . Most of the insights we gained studying this simple model are independent of the number of dimensions and the specific formulation of the model , and therefore will be valuable to other systems and studies . | Quantitatively understanding the dynamic behavior of the yeast cell cycle process under noise perturbations is a fundamental problem in theoretical biology . By constructing a global energy landscape for a simplified yeast cell-cycle regulatory network , we provide a systematic study of this issue . Our results demonstrate that the cell cycle trajectory is sharply confined as a canal bounded by ambient energy barriers , with the landscape adaptively reshaping itself in response to external signals , such as the nutrients improving and the activation of DNA replication checkpoint in our work . After performing quantitative analysis based on the landscape , We found that along the cell cycle trajectory , the typical width of the canal narrows and broadens periodically . Interestingly , this is also basically in accordance with the force strength of the dynamics . Additionally , in places where the driving force strength is comparable to the noise level , some additional pits form that are associated with the checkpoint mechanisms . Overall , our energy landscape study shows that the yeast cell cycle is a robust , adaptive and multi-stage dynamical process . | [
"Abstract",
"Introduction",
"Methods",
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"Discussion"
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| []
| 2015 | Energy Landscape Reveals That the Budding Yeast Cell Cycle Is a Robust and Adaptive Multi-stage Process |
Computational protein design ( CPD ) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties . Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering . Most of Rosetta’s protocols optimize sequences based on a single conformation ( i . e . design state ) . However , challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states . This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols . Utilizing MSF , we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations . With this protocol , we designed de novo nine retro-aldolases on a conformational ensemble deduced from a ( βα ) 8-barrel protein . All variants displayed measurable catalytic activity , testifying to a high success rate for this concept of multi-state enzyme design .
Since the 1990s , computational protein design ( CPD ) has been a powerful tool of protein engineering . For example , CPD has been successfully utilized to increase thermostability of proteins [1–3] and to design new or altered binding specificities for metals [4] , DNA [5] or other ligands [6 , 7] . Additionally , CPD was applied to even more challenging tasks like the design of novel protein-protein interfaces [8 , 9] , de novo enzymes [10] or artificial folds not found in nature [11 , 12] . Classical CPD methods , referred to as single-state design ( SSD ) , optimize the amino acid sequence for the residue positions of a single backbone by means of an objective function [13] . A substantial contribution to the enormous success reached by SSD is due to refinements of the corresponding knowledge-based or statistical energy terms and the incorporation of backbone flexibility [14] . However , SSD is always a simplification because proteins populate conformational ensembles [15] . Moreover , certain design objectives such as negative design [16–18] , multi-specificity design [19] , the design of specific protein interfaces [20 , 21] or the mimicking of backbone flexibility [22] require the concurrent assessment of several conformational or chemical states . This is why multi-state design ( MSD ) methodology is an emerging field in CPD [23] that extends the application spectrum and promises high success rates . Even the design of stable proteins profits from using backbone ensembles [24] . Typically , the optimization strategy of MSD consists of an “outer routine” that suggests possible amino acids sequences and an “inner routine” that assesses the fitness of a given sequence by performing rotamer optimization on each of the considered states and combines the individual scores [25] . This combined score enables a sequence selection driven by the energetic contribution of multiple conformational and/or chemical states . For example , in order to increase specificity of protein-protein interactions , one can utilize negative design and penalize those sequences that favor undesired interactions [16] . One of the first applications of MSD was the design of topologically specific coiled-coil structures consisting of 11-fold amino acid repeats whose stability was assessed by using terms of a standard molecular-mechanics potential energy function [26] . Later on , the binding pocket of a ribose-binding protein was successfully redesigned by means of MSD based on a standard force-field [27] . Meanwhile , many of the common optimization algorithms used in SSD have been adapted for MSD , including Monte Carlo ( MC ) with simulated annealing [28] , genetic algorithms [29] , the FASTER approach [25] , dead-end-elimination [30] , and cluster expansion [31] . Rosetta [32] is currently the most flexible and most widely used CPD software suite and offers several multi-state applications; noteworthy are MPI_MSD [33] and RECON [34] . MPI_MSD provides a generic multi-state design implementation based on a genetic algorithm that optimizes a single sequence on multiple states given a fitness function . RECON starts by individually optimizing one sequence for each state; subsequently the computation of a consensus sequence is promoted by incrementally increasing convergence restraints . However , the current implementations of both methods are limited to certain design tasks and cannot make use of fine-tuned protocols like those required for enzyme design [35] or anchored design of protein-protein interfaces [36] . In order to overcome this limitation , we have developed MSF and our integration of this modular framework into Rosetta facilitates the transfer of already proven single-state protocols to an MSD environment . Here , by using MSF , we first corroborate the superiority of MSD for enzyme design based on two in silico benchmarks for ligand binding . Applying the same protocol , we then designed nine experimentally active retro-aldolases .
MSF is a programming framework that allows the user to develop and execute Rosetta protocols in an MSD environment . The modular software architecture of MSF significantly reduces the development efforts involved; see Fig 1 . MSF requires as input a set of states s1 , … , sn , e . g . structural conformations , and a population of sequences seq1 , … , seqm , which will be subsequently altered by the sequence optimizer . The evaluator determines n state-specific scores for each seqi according to the chosen Rosetta protocol . These n × m scores are the input of a user-defined fitness function , which combines the scores to determine the fitness of each sequence and communicates these values to the sequence optimizer . The task management is as follows for all protocols: one process controls the sequence optimizer and a user-defined number of evaluator-processes execute the protocol in parallel , which guarantees high scalability . Technical details and availability are described in S1 Text; MSF will be part of an upcoming weekly release of Rosetta . As has been shown , a genetic algorithm ( GA ) successfully samples sequence space in MSD calculations [16 , 27 , 33] . Therefore , we have implemented the sequence optimizer based on the well-proven GA of Rosetta . Briefly , a GA imitates the process of natural selection by maintaining a population of design sequences that are evolving for a number of generations , while the selection pressure of the fitness function eliminates less optimal solutions . The final output of MSF is a population of optimized sequences . By contrast , a standard SSD implementation that utilizes MC optimization generates one sequence . Both MSF and MPI_MSD rely on the Rosetta GA . However , MPI_MSD does not support the integration of existing SSD protocols such as enzyme design that requires the additional optimization of catalytic constraints . Thus , our aim was to offer a framework that minimizes the development effort of supplying SSD protocols with MSD capability . The architecture of MSF strictly separates the tasks of optimization and the application-specific assessment of states . The resulting modularity allows an informed Rosetta user to implement MSD for existing protocols in a straightforward manner . Most importantly , the functionality of the protocols is unchanged and all options remain available . In addition to protocol porting , the user has to set up an application-specific fitness function , which defines the design goal . If it is the goal to alter conformational , binding , or catalytic specificity , the fitness function often has to consider positive and negative design . For the assessment of one positive state s+ and one negative state s- , the following function has been proposed [25]: fitness+ , − ( seqi ) =Δscore+ ( seqi ) −wΔscore− ( seqi ) ( 1 ) Here , Δscorel ( seqi ) is the difference of scores calculated for seqi and seq0; seq0 is the optimal sequence determined in an SSD for the states sl ∈ {s+ , s−} and w is a weighting factor . Similar approaches , which were based on the computed transfer free energy from the target state to the ensemble of competing states [16] or on differences of Rosetta energies [33] guided the MSD of protein interfaces . Equally to MPI_MSD , our framework MSF supports the specification of a broad range of fitness functions . For the initial implementation of MSF , we have integrated enzdes and AnchoredDesign , two widely used Rosetta protocols . enzdes provides ligand binding and enzyme design functionality by repacking and redesigning residues around the binding/active site and by optimizing catalytic contacts . AnchoredDesign creates a protein-interface by transferring a key interaction identified in a natural binding partner of the target protein to a surface loop of the scaffold protein . Afterwards , the surface of the scaffold is redesigned with backbone flexibility to generate a novel binding partner of the target [36] . To validate AnchoredDesign in the MSF context , we redesigned the interface of the factor B serine protease domain from Homo sapiens ( PDB ID 1dle ) . For this single example , the MSD approach performed better that the corresponding SSD protocol; see S2 Text for details . In order to demonstrate the potential of MSF for a large number of cases , we focused on enzdes by performing in silico and in vitro experiments . For the in silico assessment , the fitness of the sequences was computed according to Eq 2 based on the Rosetta total score ( ts ) averaged over all states . In the following , we designate software protocols as program:protocol . For example , Rosetta:enzdes ( or for the sake of brevity enzdes ) and Rosetta:MSF:GA:enzdes ( MSF:GA:enzdes ) are the names of the SSD and MSD implementations of enzdes . The most obvious usage of MSD is its application to an ensemble representing the native conformations of a protein . In solution , a protein’s structure is dynamic and nuclear magnetic resonance ( NMR ) offers an experimentally determined estimation of protein dynamics . Interestingly , in previous analyses SSD protocols performed better on crystal structures than on NMR templates [22 , 37] . We speculated that this performance loss can be compensated , if MSD is applied to a whole ensemble and we decided to assess a ligand-binding design . Thus , for a first performance comparison of the SSD algorithm enzdes , and the MSD algorithm MSF:GA:enzdes , we chose an NMR ensemble of the human intestinal fatty acid binding protein ( hIFABP ) with bound ketorolac ( PDB ID 2mji ) . This ensemble consisting of ten conformations was prepared for ligand-binding design ( see Materials and Methods ) and the design shell contained 21 residue positions in the vicinity of the ligand . Our protocol allowed Rosetta to find a low energy sequence by arbitrarily choosing residues for these positions . For each of the individual conformations conf ( l ) , 1000 randomly seeded runsl ( i ) of enzdes ( SSD ) were started . Design quality was monitored by computing for each number of runs i the score tsSSDhIFABP ( i ) . This is the mean total score deduced from corresponding conformations ( Eq 6 ) given in Rosetta Energy Units ( REU ) . MSF:GA:enzdes ( MSD ) was applied to the full ensemble and the GA was started . Analogously to the SSD experiment , the mean total score tsMSDhIFABP ( j ) was computed for each generation j ( Eq 7 ) . As a second measure of design quality , we determined the native sequence similarity recovery ( nssr ) . Commonly , the performance of design algorithms is assessed by means of the native sequence recovery ( nsr ) [38–40] , which is the fraction of identical residues at corresponding positions of the native and the designed sequence . The concept of nsr is blind for a more specific comparison of residues beyond identity , which may impede a detailed assessment . In contrast , for the computation of nssr , all residue pairs reaching a BLOSUM62 score > 0 are considered similar and contribute to the nssr value ( Eqs 4 and 5 ) . The plots shown in Fig 2 indicate that the SSD and the MSD algorithm converged after 1000 runs or 800 generations , respectively , both with respect to sequence recovery and ts values of the chosen sequences . The mean nsr values of enzdes and of MSF:GA:enzdes were 20 . 00% and 27 . 14% , and the mean nssr values were 41 . 90% and 46 . 66% . Only two of the ten enzdes designs reached an nssr value ( 47 . 62% and 61 . 90% , respectively ) that was higher than the mean nssr of MSF:GA:enzdes . In summary , MSF:GA:enzdes performed better than enzdes suggesting the usage of MSD if sequences have to be designed for an ensemble . Altogether , the energies of models generated in SSD were on average 7 . 11 REU lower than those in MSD . However , a comparison of ts scores is no ideal means to compare SSD and MSD performance . In MSD , a sequence is a compromise that has to satisfy the constraints associated with all conformations in an acceptable manner . In contrast , SSD customizes a low energy sequence for each conformation . Thus , it is no surprise that the mean ts values of SSD sequences are superior to those of the MSD results . On the other hand , due to these specific adaptations based on single , less-native conformations , the SSD sequences are receding from the native ones , which are considered as close to optimal [41] . This undesired effect is less pronounced for MSD sequences computed on the whole native ensemble . We conclude that nsr and nssr are more suitable than ts values for a comparative benchmarking of SSD and MSD approaches . A standard dataset for the assessment of ligand-binding and enzyme design is the enzdes scientific sequence recovery benchmark . It consists of 51 representative proteins in which the ligand is bound with an affinity of 10 μM or lower [42] . During benchmarking , a given CPD algorithm redesigns residues of the design shell enclosing each ligand and the algorithm’s ability to recapitulate the native sequence ( nsr and nssr values ) is measured . However , for an assessment of de novo design algorithms , this approach may be misleading , because the required remodeling of a chosen protein is more demanding than the recapitulation of its native binding pocket . We created a more realistic benchmark that is devoid of a perfect backbone/rotamer preorganization and is more suitable for the assessment of de novo design algorithms . For feasibility reasons , we randomly selected 16 proteins prot ( k ) of the above 51 benchmark proteins . The corresponding ligands were removed and for each of the 16 apoproteins , an ensemble of 20 conformations was created using the Backrub server [43] , which generates near-native conformational ensembles [44 , 45] . Next , by superposition of each conformation with the corresponding crystal structure , the ligands were transferred to the binding pockets . Thus , the resulting dataset BR_EnzBench featured for each of the 16 prot ( k ) 20 backbone conformations that are near to native but lack the implicit pre-organization induced by a bound ligand in a crystal structure . We used BR_EnzBench to compare the performance of SSD and MSD for de novo ligand-binding design . All design shell residues were initially mutated to alanine and the conformations were energy-minimized to further increase the difficulty for CPD algorithms to recover the native sequence . To prevent a hydrophobic collapse of the alanine-only design shells , minimization was performed with backbone constraints . Thus , the CPD problem to be solved within the scope of this benchmark was to design a binding pocket by sequence optimization of the all-alanine design shells . For SSD with enzdes , all conformations of each protein were considered independently and for each conformation , 1000 randomly seeded designs were performed . Design quality was assessed by means of the three parameters nsr , nssr , and ts . The respective values were averaged for each of the 16 prot ( k ) ( Eqs 10 and 11 ) and are listed in Table 1 . Additionally , the convergence of the design process was followed by monitoring the mean performance for each number i of design runs ( Eqs 8 and 9 ) ; these values are plotted in Fig 3 . To conduct multi-state design by means of MSF:GA:enzdes , for each prot ( k ) , the 20 conformations were divided into four ensembles ensmk each containing five conformations . Note that the conformations that are combined in each of the ensembles ensmk are unrelated , due to the stochastic approach of the Backrub algorithm . The GA was started on a population consisting of 210 sequences and stopped after 600 generations , because convergence was reached . Analogously , nsr , nssr , and ts values ( Eqs 14 and 15 ) were determined for each MSD run and averaged for each of the 16 proteins . These results were added to Table 1 . As above , the convergence of the GA was followed be monitoring the mean performance for each generation j ( Eqs 12 and 13 ) ; these values are also plotted in Fig 3 . The protein-wise comparison ( Table 1 ) indicates that in 10 out of the 16 cases , the nsr and in 13 out of all 16 cases , the nssr values of MSF:GA:enzdes designs are superior to the corresponding values of enzdes designs . MSF:GA:enzdes recovers on average a higher percentage of native residues ( Δ nsr = 2 . 65% ) and a higher percentage of similar residues ( Δ nssr = 6 . 79% ) . Thus , with respect to the more adequate similarity measure nssr , MSD performs 15% better than SSD for this benchmark ( p = 0 . 004 , Wilcoxon signed rank test ) . In addition , multi-state designs have slightly better energies ( Δ ts = 2 . 51 REU ) , which is in contrast to the hIFABP results and is most likely due to the smaller ensemble size . Fig 3 reflects the differences in convergence speed of both algorithms and indicates that the better performance has its price: the MC optimization utilized by enzdes leads to acceptable design solutions even after a low number of runs . In contrast , the GA of MSF:GA:enzdes is slower and more than hundred generations are required to surpass the performance of the SSD algorithm . For this set of parameters , MSF:GA:enzdes required approximately five times the number of core hours needed by enzdes; further details of computational costs are given in S2 Text . The sequence recovery reached for the hIFABP ensemble and for BR_EnzBench strongly suggests that MSF:GA:enzdes is superior to enzdes in more complex design applications . However , it was unclear to us , whether the different concepts ( single-state versus multi-state ) or the different optimizers ( MC versus GA ) contributed most to performance . Choosing an MSD approach increases computational cost , which has to be substantiated by making plausible that the choice of the optimizer is less important . The performance of MSF:GA:enzdes on BR_EnzBench was assessed ensemble-wise by determining the values nssrMSD ( ensmk ) , which were averaged ( Eq 12 ) . As these ensembles contain not more than five unrelated conformations each , the nssrMSD ( ensmk ) values ( Eq 16 ) vary due to the small sample size and one can sort for each prot ( k ) the four ensmk on their nssrMSD ( ensmk ) value . The result is a ranking ensrank=uk ( 1 ≤ u ≤ 4 ) of the four ensembles and we created the set ES1 that contained the 16 ensembles ( one for each prot ( k ) ) with the lowest nssrMSD ( ensmk ) value . Analogously , we compiled the sets ES2—ES4; consequently , ES4 consisted of those 16 ensembles that had the highest nssrMSD ( ensmk ) value; for details see Materials and Methods . For these four sets ESi , we determined boxplots of the corresponding nssrSSD and nssrMSD values; see Fig 4 . The boxplots characterizing the SSD results are nearly identical; this finding indicates that the conformations allocated to the four sets ES1—ES4 give rise to a similar SSD performance . Moreover , the boxplots representing the nssrSSD ( ES1 ) and nssrMSD ( ES1 ) values are nearly identical ( median values 47 . 60% and 47 . 76% ) , which indicates that the optimizer GA is not generally superior to MC . Additionally the continuous increase observed for the nssrMSD ( ES1 ) - nssrMSD ( ES4 ) - but not for the nssrSSD ( ES1 ) – nssrSSD ( ES4 ) values - supports the notion that it is the combination of conformations that strongly affects MSD performance . We thus conclude that the MSD approach - and not the optimizer - contributes most to the performance of MSF:GA:enzdes . Because Rosetta has a certain bias in recapitulating native residues [46] , we assessed and compared the bias introduced by enzdes and MSF:GA:enzdes . For the assessment of the enzdes outcome , we selected the 13440 sequences representing the best designs on BR_EnzBench and determined nssrSSD ( aaj ) values . This distribution represents for all amino acids aaj the fraction of similar residues recovered at all design shell positions . Analogously , the distribution nssrMSD ( aaj ) was computed that indicates the fraction of similar residues recovered by MSF:GA:enzdes; for details see Materials and Methods . The two distributions , which are plotted in Fig 5 , indicate similar recovery rates that are below the optimal value of 100% for all residues . Generally , sequence recovery for large polar or charged residues ( D , E , H , K , N , R , S ) is low , which contributes to Rosetta’s weakness in accurately designing hydrogen bonds and electrostatics [47] . Interestingly , enzdes is slightly better in recovering polar and charged residues , whereas MSF:GA:enzdes clearly recovers a higher fraction of hydrophobic residues ( A , F , I , L , P , V , W , Y ) . This general trend is most evident in the two benchmark proteins with the most extreme differences in their individual nssrSSD and nssrMSD values: ARL3-GDP ( PDB ID 1fzq ) is a distinct GTP binding protein [48] from Mus musculus and both the ligand and the native binding pocket are considerably polar . Fig 6A shows that enzdes correctly recovers the residues interacting with the guanine group ( colored in teal ) of GDP , while MSF:GA:enzdes is less successful . On the other hand , in the glucose binding protein ( PDB ID 2b3b ) from Thermus thermophilus , four tryptophan residues provide tight binding to glucose by shape complementarity . Fig 6B shows that MSF:GA:enzdes recovers three critical tryptophan residues ( colored in teal ) in most designs , whereas enzdes prefers small polar residues that do not provide tight packing . We conclude that the representation of a protein by means of an ensemble improves hydrophobic packing but not the formation of polar interaction networks . Their design is considerably more difficult than hydrophobic packing due to the partially covalent nature of a hydrogen bond and the geometric requirements for orientations and distances [47 , 49] . Molecular dynamics ( MD ) simulation is a well-established and reliable method for modeling conformational changes linked to the function of proteins [50] . Thus , MD provides an alternative to the Backrub approach for the generation of ensembles to be utilized in MSD . We were interested in assessing the designability of conformations resulting from unconstrained MD simulations of length 10 ns . In analogy to BR_EnzBench , we compiled the dataset MD_EnzBench consisting of 1000 conformations generated for each of the 16 benchmark apoproteins by means of YASARA [51] . Again , all design shell residues were replaced with alanine prior to design; see Materials and Methods . To assess the structural variability of MD_EnzBench conformations , Cα-RMSD values of design shell residues were determined in a protein-specific all-against-all comparison and then averaged . Analogously , the structural variability of BR_EnzBench conformations was determined . Interestingly , the variety of the binding pockets generated by the MD simulations is much larger than that generated by Backrub: the mean RMSD of MD_EnzBench is 0 . 62 Å and that of BR_EnzBench is 0 . 17 Å , which indicates that a 10 ns MD simulation generates an ensemble with higher structural diversity than the Backrub server . As a control of design performance , the 16 × 20 nssrSSDBR_EB ( i=1 ) values of single enzdes designs generated for 20 protein-specific conformations from BR_EnzBench were summarized in a boxplot , which had a mean value of 43 . 88% . To assess the designability of the MD_EnzBench conformations , for each of the 1000 protein-specific conformations , one sequence was designed by means of enzdes and the resulting nssr values were averaged protein-wise . Fig 7 shows 100 boxplots each representing 16 × 10 nssr values resulting from ten conformations generated by the MD simulation in a 100 ps interval for each of the 16 prot ( k ) . The mean of these nssr values is 42 . 53% , which testifies to a satisfying design performance , given that only one sequence was designed for each MD conformation . Moreover , the boxplots indicate that performance did not decrease for conformations generated at later phases of the MD simulation: the median nssr , and the first and third quartile of the most left and the most right boxplots are 42 . 10% [35 . 40% , 45 . 89%] and 42 . 24% [34 . 78% , 50 . 00%] , respectively . In summary , these findings suggest that ensembles generated by MD feature higher conformational flexibility and appropriate de novo designability . The most convincing proof of concept for any CPD algorithm is the design of functionally active proteins . A non-natural reaction that is frequently chosen for enzyme design is the amine-catalyzed retro-aldole cleavage of 4-hydroxy-4- ( 6-methoxy-2-naphtyl ) -2-butanone ( methodol ) into 6-methoxy-2-naphthaldehyde and acetone [52] . This multi-state reaction comprises the attack of an active site lysine side chain on the carbonyl group of the substrate to form a carbinolamine intermediate that is subsequently dehydrated to a protonated Schiff base . The latter is then converted to the reaction products by acid/base chemistry [53 , 54] . The most active de novo retro-aldolase designs have been established on a jelly roll and several ( βα ) 8-barrel proteins [55–57] . For comparison purposes , we selected the indole-3-glycerolphosphate synthase from Sulfolobus solfataricus ( ssIGPS ) , a previously used thermostable ( βα ) 8-barrel scaffold . The native ligand was removed and the apoprotein was subjected to conformational sampling . Using the protocol validated with MD_EnzBench , three individual MD simulations were performed for 10 ns . A clustering of MD snapshots based on RMSD values helps to choose near-native conformations [58] . Thus , we used Durandal [59] to cluster the snapshots ( conformations ) generated with each MD run and picked four conformations from the largest cluster . These 3 × 4 conformations and the crystal structure of the apoprotein constituted the structural ensemble for the subsequent enzyme design . Enzyme design generally starts with the assembly of a theozyme , which is a model for the proposed active site that is based upon the geometric constraints dictated by the expected transition state ( s ) . To design retro-aldolase catalysis , we used a previously designed theozyme containing the carbinolamine reaction intermediate as transition state surrogate covalently bound to the catalytic lysine [56] . In addition , this theozyme contained an aspartate or a glutamate residue to function as general acid/base as well as a serine or a threonine residue to provide additional hydrogen-bonding interactions . Rosetta:match was applied to all conformations and created several thousand matched transition states ( mTS ) with catalytic triads Ki-[D , E]j-[S , T]k located at markedly different residue positions . A critical step of MSD is the compilation of the ensembles that are concurrently used as states . For enzyme design , ensembles ensmTS of mTS are needed and we compiled them the following way: first , mTS judged as binding the transition state only weakly were discarded . Second , mTS derived from different conformations were added to the same ensmTS , if identical catalytic triads were located at matching residue positions . Thus , each ensmTS contained a certain number of conformations accommodating the same catalytic triad . Third , the consistency of each ensmTS was assessed by superposing the transition states and by comparing the corresponding conformations . We chose 23 ensmTS consisting of 4 to 13 conformations ( states ) and their design and repack shells were defined by merging the output created by enzdes:autodetect for all conformations . MSF:GA:enzdes was executed with each ensemble until energetic convergence; see S3 Text for details of the protocol . In brief , to assess the designs we compared active-site geometry as well as total and interaction energies and the best 100 variants were subjected to MD simulations of 10 ns length . For each variant , we analyzed in detail catalytic site geometries of 100 snapshots ( see Materials and Methods ) and nine variants named RA_MSD1 to RA_MSD9 were chosen for biochemical characterization; see S3 Text . Because the catalytic efficiency and the conformational stability of initial designs are generally poor [60] , further optimization is commonly performed by using either Foldit or other software tools to revert unnecessary mutations back to the native sequence of the scaffold [56] , or by means of directed evolution [57] . However , we did not introduce subsequent stabilizing mutations into the sequences of RA_MSD1 to RA_MSD9 prior to a first experimental characterization . In doing so , we wanted to demonstrate the potential and also the limitations of multi-state designs . For a comparison of these novel designs with previous ones , we compiled a list of 42 retro-aldolases RA* from the literature ( see S3 Text ) that were also created in the ssIGPS scaffold by means of Rosetta . These RA* sequences differ on average at 15 positions from the native ssIGPS sequence; in contrast , our nine RA_MSD* sequences contain on average 21 amino acid substitutions . Moreover , RA* sequences deviate on average from RA_MSD* sequences at 24 positions , and 18 substitutions distinguish the most similar pairs of variants ( RA41 versus RA_MSD9 and RA90 versus RA_MSD8 ) . Even a previous ( RA114 ) and a new design ( RA_MSD1 ) , which share the same catalytic residues K210 and S110 , differ at 25 positions . Thus , although we utilized the same TS and the same scaffold that was used for the design of RA114—RA120 [56] , our MSD approach has generated a set of entirely novel catalytic sites located in the same shell as used for previous designs; see Fig 8 . The genes for RA_MSD1—RA_MSD9 were synthesized and expressed in Escherichia coli as fusion constructs with the gene for the maltose binding protein ( MBP ) . The fusion proteins were purified with metal chelate affinity chromatography via their N-terminal hexa-histidine tags , resulting in high yields ( 50–150 mg protein/l expression culture ) . RA_MSD5 could be produced in soluble form also without MBP , whereas the other designs precipitated in the absence of the solubility enhancer . All designs showed modest catalytic activity with low substrate affinity , leading to conversion rates in the presence of 500 μM S-methodol ranging from 3 × 10−7 to 1 . 7 × 10−5 s-1 ( Table 2 ) . For the best designs , namely RA_MSD5 and RA_MSD7 , the linear part of the substrate saturation curve was used to determine kcat/KM values of 3 . 47 × 10−2 and 1 . 41 × 10−2 M-1s-1 ( S1 Fig; Table 2 ) , which are similar to the values obtained for RA114 - RA120 [46] . Moreover , the RA_MSD5 designs with and without MBP displayed virtually the same kcat/KM values , excluding an influence of the solubility enhancer on activity . Due to the intentionally omitted step of secondary protein stabilization following the initial design process , eight of our nine designs were insoluble without MBP . We wanted to test whether protein stabilization would result in higher activity . Accordingly , we attempted to improve the stability of RA_MSD2 , which has the lowest activity of all designs ( Table 2 ) , by using the fully automated in silico method offered by the PROSS webserver [61] . The six conformations of RA_MSD2 were individually submitted to PROSS and the corresponding output sets that contained 6 to 21 stabilizing mutations were merged to five consensus sequences; see S3 Text , Table B1 . Variants RA_MSD2 . 4 and RA_MSD2 . 5 that contained the highest number of stabilizing mutations , could be produced in soluble form without MBP and were purified with high yield ( about 25 mg protein/l expression culture ) . Activity measurements showed , however , that the additional stabilizing exchanges did not drastically improve the conversion rate of RA_MSD2; see Table 2 . In summary , our results show that MSD ( based on a structural ensemble ) is comparably successful as SSD ( based on a single structure ) for establishing retro-aldolase activity on a thermostable ( βα ) 8-barrel scaffold , indicating that this particular reaction requires only a limited degree of conformational flexibility . However , catalysis is often linked to conformational transitions which can only be captured by MSD approaches . Moreover , in contrast to SSD , MSD offers a broader functionality and is for example also suited for more challenging tasks like negative design .
Two subsets of the scientific sequence recovery benchmark of Rosetta [42] were generated that contain 20 specifically prepared conformations of 16 proteins prot ( k ) with bound ligand . In order to exclude an erroneous conformational sampling , missing residues were reconstructed by using YASARA:loop_modeling [62] and the respective native sequences . Additionally , all ligands were removed prior to the conformational sampling of the resulting apoproteins . The dataset BR_EnzBench was created by using the BackrubEnsemble method of the Backrub server [43] to compute a conformational ensemble of 20 structures for each apoprotein . The second benchmark dataset MD_EnzBench was deduced from MD simulations of length 10 ns generated with YASARA ( version 14 . 7 . 17 ) and the YAMBER3 force field , which has been parameterized to produce crystal structure-like protein coordinates [51] . For each of the 16 apoproteins , 1000 conformations were sampled at an interval of 10 ps . After sampling , the native ligands were re-introduced in all conformations of both subsets by means of PyMOL:superpose [63] and the respective apoproteins . For the corresponding holoproteins of BR_EnzBench and MD_EnzBench , the same design and repack shells were utilized . These were determined protein-wise for each of the BR_EnzBench conformations by means of Rosetta:enzdes:autodetect and merged . In all conformations , design shell residues were replaced with alanine and prior to design , all conformations were energy-minimized by means of Rosetta:fastrelax with backbone constraints . Parameters of MD simulations , Rosetta:fastrelax , and the composition of design and repack shells are listed in S2 Text . The first generation of the 210 sequences consisted of the given seed sequence and 209 mutants each with a randomly introduced single point mutation . During each generation cycle , half of the population was replaced with sequences seqi generated by means of single point mutations and recombination . The replaced sequences were those with worst fitness values fitness ( seqi ) , which were computed for MSF:GA:enzdes according to: fitness ( seqi ) =1n∑l=1ntsl ( seqi ) ( 2 ) Here , n is the number of states ( e . g . conformations s1 , … , sn of a given prot ( k ) ) and tsl is the Rosetta total score for a sequence given a state l . In all equations , ts values are given in REU . For a given pair of residues aa1 , aa2 the nssr value was deduced from the scores of the BLOSUM62-matrix [64] as follows: nssr ( aa1 , aa2 ) ={1ifBLOSUM62 ( aa1 , aa2 ) >00else ( 3 ) For a given pair of sequences seq1 , seq2 of length n , the nssr value was determined as the mean value deduced for residue pairs seq1[i] , seq2[i]: nssr ( seq1 , seq2 ) =1n∑i=1nnssr ( seq1[i] , seq2[i] ) ( 4 ) For a given set of design solutions ds = {seq1 , … , seqm} and a native sequence seqnat , the value nssr ( ds , seqnat ) was computed according to: nssr ( ds , seqnat ) =1m∑i=1mnssr ( seqi , seqnat ) ( 5 ) The data set with PDB ID 2mji contains ten conformers of hIFABP and the bound ligand ketorolac; this ensemble has been deduced by means of solution NMR [65] . The set was downloaded from PDB and the ligand was parameterized using Rosetta:molfile-to-params [66] . Next , each of the ten conformations was energy-minimized via Rosetta:fastrelax with constraints . To obtain consistent design and repack shells , the shells determined by Rosetta:enzdes:autodetect for each conformation were merged . For SSD , enzdes was applied to each of the ten initial conformations conf ( l ) ( 1 ≤ l ≤ 10 ) . Using the default MC optimization and the parameter set ps_enzdes , sequences seql ( i ) were generated by means of 1000 randomly seeded runsl ( i ) ( 1 ≤ i ≤ 1000 ) . In order to control the convergence of the design process and for performance comparison , the seql* ( i ) with the best total score ( ts ) were chosen from seql ( 1 , … , i ) for each l and each i . Finally , the mean of the ten ts values was determined as a measure of design quality tsSSDhIFABP ( i ) reached in i SSD runs: tsSSDhIFABP ( i ) =110∑l=110ts ( seql* ( i ) ) ( 6 ) For MSD , all ten conformations conf ( l ) were considered as states and MSF:GA:enzdes was executed for 800 generations ( i . e . design cycles ) on a population consisting of 210 sequences with parameters ps_msf_enzdes . The initial population was seeded with the native sequence . The sequences representing a generation j were ranked with respect to ts values and the ten top scoring sequences seqlt ( j ) ( 1 ≤ t ≤ 10 ) were stored in order to allow for the subsequent performance comparison . Finally , the mean of the 10 × 10 ts values was determined as a measure of design quality tsMSDhIFABP ( j ) reached in j MSD generations: tsMSDhIFABP ( j ) =1100∑l=110∑t=110ts ( seqlt ( j ) ) ( 7 ) Further details of the analysis can be found in S2 Text; it lists parameters of Rosetta:fastrelax and the design protocol , and the composition of the design and repack shell . For SSD , enzdes was applied to each of the 20 initial conformations conf ( l ) ( 1 ≤ l ≤ 20 ) of each prot ( k ) ( 1 ≤ k ≤ 16 ) from BR_EnzBench . Using default MC optimization and the parameter set ps_enzdes ( see S2 Text ) , sequences seqk , l ( i ) were generated by means of 1000 randomly seeded runsk , l ( i ) ( 1 ≤ i ≤ 1000 ) . In order to control the convergence of the design process and for performance comparison , those seqk , l* ( i ) having the best ts value were chosen from seqk , l ( 1 , … , i ) for each k , l , and i . Finally , mean performance reached in i SSD runs was measured by means of the score ∈ {nsr , nssr} , where nsr is the native sequence recovery and nssr is the native sequence similarity recovery: scoreSSDBR_EB ( i ) =1320∑k=116∑l=120score ( seqk , l* ( i ) , seqnatk ) ( 8 ) tsSSDBR_EB ( i ) =1320∑k=116∑l=120ts ( seqk , l* ( i ) ) ( 9 ) Here , seqnatk is the native sequence of prot ( k ) , and ts is the total score . To score SSD performance reached for one prot ( k ) , the final score values were averaged over all conformations: scoreSSDBR_EB ( k ) =120∑l=120score ( seqk , l* ( 1000 ) , seqnatk ) ( 10 ) tsSSDBR_EB ( k ) =120∑l=120ts ( seqk , l* ( 1000 ) ) ( 11 ) To assess the performance of MSD , each of the 20 conformations of a prot ( k ) was assigned to an ensemble ensmk ( 1 ≤ m ≤ 4 ) consisting of five conformations each . These five conformations were considered as states and MSF:GA:enzdes was executed for 600 generations on a population consisting of 210 sequences with parameter set ps_msf_enzdes ( see S2 Text ) . The initial population was seeded with an all-alanine sequence . The sequences representing a generation j were ranked with respect to ts values and the five top scoring sequences seqk , mt ( j ) [1 ≤ t ≤ 5] were stored in order to allow for the subsequent performance comparison . Finally , mean performance values reached in j MSD generations were determined according to: scoreMSDBR_EB ( j ) =1320∑k=116∑m=14∑t=15score ( seqk , mt ( j ) , seqnatk ) ( 12 ) tsMSDBR_EB ( j ) =1320∑k=116∑m=14∑t=15fitness ( seqk , mt ( j ) ) ( 13 ) Here , seqnatk is the native sequence of prot ( k ) , score ∈ {nsr , nssr} is a sequence recovery , and fitness ( seqk , mt ( j ) ) is the mean ts score ( Eq 2 , n = 5 ) of a given sequence over the five conformations belonging to ensemble ensmk . To score MSD performance reached for one prot ( k ) after 600 generations , the final score values were averaged over all ensembles: scoreMSDBR_EB ( k ) =120∑m=14∑t=15score ( seqk , mt ( 600 ) ) ( 14 ) tsMSDBR_EB ( k ) =120∑m=14∑t=15fitness ( seqk , mt ( 600 ) ) ( 15 ) The 20 conformations of a given protein prot ( k ) from BR_EnzBench belong to one of four ensembles ens1k - ens4k . The performance values nssrMSD ( ensmk ) were determined for each prot ( k ) and each ensmk according to: nssrMSD ( ensmk ) =15∑t=15nssr ( seqk , mt ( 600 ) , seqnatk ) ( 16 ) Here , seqnatk is the native sequence of prot ( k ) . The values nssrMSD ( ensmk ) were used for a ranking ensrank=uk ( 1 ≤ u ≤ 4 ) of the four ensembles such that ensrank=1k is the one with the lowest nssrMSD ( ensmk ) value and ensrank=4k that with the largest one . Having ranked the ensembles of all prot ( k ) , sets of ensembles were created such that the set ES1=∪k=116ensrank=1k contained those ensembles that performed worst and ES4=∪k=116ensrank=4k those that performed best and the intermediates with rank = 2 and rank = 3 performed accordingly . For these four sets ESi , boxplots of the corresponding nssrSSD and nssrMSD values were determined . In order to assess the amino acid composition of the enzdes outcome , the 42 seqk , l ( 1 , … , 1000 ) with optimal ts values were identified for each of the 20 conformations l of all prot ( k ) ∈ BR_EnzBench . For these 16 × 840 sequences seqSSDk , the values nssr ( seqSSDk[i] , seqnatk[i] ) were determined ( Eq 3 ) by comparing design shell and native ( nat ) residues i . The distribution nssrSSD ( aaj ) represents for all amino acids aaj their recovered similarity at all design shell positions . To assess the amino acid composition for the MSF:GA:enzdes outcome , the 16 × 4 × 210 sequences seqMSDk of the final populations ( i . e . all seqk , m ( 600 ) ) generated for the four ensemble groups of each prot ( k ) ∈ BR_EnzBench were used to determine the values nssr ( seqMSDk[i] , seqnatk[i] ) . The distribution nssrMSD ( aaj ) represents for all amino acids aaj their recovered similarity at all design shell positions . The scaffold protein indole-3-glycerol phosphate synthase from S . solfataricus ( ssIGPS , PDB ID 1a53 ) , was downloaded from PDB and the ligand IGP was removed . To generate a structural ensemble , three MD simulations were performed with the apoprotein for 10 ns by means of YASARA and the YAMBER3 force field . Using Durandal:smart-mode:semi-auto[0 . 03 . 0 . 20] , the snapshots of each trajectory were clustered individually and four conformations were chosen from the largest cluster . These 12 conformations and the crystal structure of 1a53 were used for matching the transition state ( TS ) and grafting the theozyme of the retroaldol reaction [56] by means of Rosetta:match . Each of the resulting matched transition states ( mTS ) consisted of a catalytic triad Ki-[D , E]j-[S , T]k at three residue positions i , j , k that occured in one of the 13 conformations . Ensembles ensmTS of mTS used as input for MSF:GA:enzdes were generated as follows: first , mTS were discarded that were classified as weak TS binders or TS destabilizers . For example , matches with catalytic residues near the protein surface were eliminated . Second , mTS were grouped according to the composition and localization of the catalytic triad and those ensembles were selected that were compatible with most of the 13 conformations . Third , ensmTS were assessed with respect to the structural similarity of the superposed theozymes . In total , 23 ensembles ensmTS containing 4 up to 13 conformations were chosen . For each ensmTS , the design and repack shells were defined by merging the outcome of Rosetta:enzdes:autodetect for all corresponding conformations and MSF:GA:enzdes was executed on a population of 210 sequences that were seeded with the native sequence of ssIGPS . At convergence , the design process was stopped , which was the case after 97 to 710 generations . S3 Text lists more details of the design procedure like parameters of MD simulations and of Rosetta:match , and the specification of the TS . After MSD of retro-aldolases , the designs were filtered by ts values and active-site geometry . The best 100 designs were selected for 10 ns MD simulations in water and for one conformation of each design ensemble , 100 snapshots were generated . Two simulations were performed; the first one was based on the enzyme/TS complex . As a control , the second MD simulation was based on the enzyme/substrate complex and the substrate methodol was created by deleting the lysine-substrate bond of the TS . For each trajectory , catalytic distances , angles and torsion angles were plotted as boxplots and used to assess the designs; see S3 Text . Variant RA_MSD2 was chosen for solubilization experiments and all six conformations conf ( l ) of the corresponding ensemble were submitted to the PROSS server [61] , which was used with default settings allowing for mutations at all positions . For each input conf ( l ) , PROSS provided seven mutated sequences mut_seql ( i ) ( 1 ≤ i ≤ 7 ) containing an increasing number of putatively stabilizing mutations . For each i ( degree of stabilization ) , an MSA that contained all sequences mut_seql ( i ) computed for all conf ( l ) was generated and weblogo [67] was used to determine a sequence logo . Finally , consensus residues deduced from the sequence logos were accepted as mutations at sites that did not interfere with the catalytic center . All sequence logos are shown in S3 Text . The genes encoding the designed retro-aldolases were optimized for E . coli codon usage and ordered as synthetic gene strings from Life Technologies . Cloning was performed via BsaI restriction sites into pET28a ( Stratagene ) and pMalC5T ( New England Biolabs ) plasmids specifically modified for this method of cloning . Both vectors fuse an N-terminal his6-tag to the target proteins , pMalC5T additionally adds MBP . The cloning method is derived from golden gate cloning [68] . Details of plasmid construction and cloning procedure will be published elsewhere . E . coli BL21 Gold cells were transformed with the resulting plasmids . The cells were grown in Luria broth with 50 μg/ml kanamycin or 150 μg/ml ampicillin for pET28 constructs and pMAL constructs , respectively . At a cell density of OD600 = 0 . 5 protein production was induced by addition of 0 . 5 mM isopropyl-β-thiogalactopyranoside . After growth over night at 20°C the cells were harvested by centrifugation ( Avanti J-26 XP , JLA 8 . 1000 , 15 min , 4 , 000 rpm , 4°C ) . Cell pellets were resuspended in 50 mM Tris/HCl buffer ( pH 7 . 5 ) with 300 mM NaCl . Cells were lysed by sonication ( Branson Sonifier W-250D , amplitude 65% , 3 min , 2 s pulse/2 s pause ) . Cell debris was removed by centrifugation ( Avanti J-26 XP , JA 25 . 50 , 30 min , 14 , 000 rpm , 4°C ) and soluble proteins were purified by nickel chelate affinity chromatography ( GE Healthcare , HisTrap FF crude ) . The proteins were eluted with 50 mM Tris/HCl ( pH 7 . 5 ) containing 300 mM NaCl using a gradient of 10–500 mM imidazole . Fractions containing sufficiently pure protein were pooled and excess imidazole was removed by dialysis against 50 mM Tris/HCl ( pH 7 . 5 ) buffer containing 100 mM NaCl . Protein concentrations were determined by absorbance spectroscopy ( NanoDrop One , Thermo Fisher ) using extinction coefficients determined by the Expasy:ProtParam webtool . Retro-aldolase activity of the designs ( 30–50 μM ) was measured at 25°C in 50 mM Tris/HCl ( pH 7 . 5 ) , 100 mM NaCl and 5% ( v/v ) dimethyl sulfoxide ( for substrate solubility ) by following the formation of the fluorescent product 6-methoxy-2-naphthaldehyde from non-fluorescent S-methodol ( 70% ee ) . The substrate was synthesized as described in S3 Text . Fluorescence was measured in a Mithras LB 940 plate reader ( λex = 355 nm , λem = 460 nm ) using black 96 well micro plates . The concentrations of product were determined with the help of a calibration curve . For the determination of conversion rates , each measurement was repeated four times , for kcat/KM determinations all points were measured as triplicates . The wild-type scaffold protein ssIGPS and the solubility tag MBP served as negative controls and did not show any detectable activity . Further control measurements showed that conversion rates in the presence of 5% ( v/v ) dimethyl sulfoxide were identical to those in 3% acetonitrile , which has been used for the characterization of other retro-aldolase designs [46] . | Protein engineering , i . e . the targeted modification or design of proteins has tremendous potential for medical and industrial applications . One generally applicable strategy for protein engineering is rational protein design: based on detailed knowledge of structure and function , computer programs like Rosetta propose the sequence of a protein possessing the desired properties . So far , most computer protocols have used rigid structures for design , which is a simplification because a protein’s structure is more accurately specified by a conformational ensemble . We have now implemented a framework for computational protein design that allows certain design protocols of Rosetta to make use of multiple design states like structural ensembles . An in silico assessment simulating ligand-binding design showed that this new approach generates more reliably native-like sequences than a single-state approach . As a proof-of-concept , we introduced de novo retro-aldolase activity into a scaffold protein and characterized nine variants experimentally , all of which were catalytically active . | [
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| 2017 | Rosetta:MSF: a modular framework for multi-state computational protein design |
HIV infection induces phenotypic and functional changes to CD8+ T cells defined by the coordinated upregulation of a series of negative checkpoint receptors that eventually result in T cell exhaustion and failure to control viral replication . We report that effector CD8+ T cells during HIV infection in blood and SIV infection in lymphoid tissue exhibit higher levels of the negative checkpoint receptor TIGIT . Increased frequencies of TIGIT+ and TIGIT+ PD-1+ CD8+ T cells correlated with parameters of HIV and SIV disease progression . TIGIT remained elevated despite viral suppression in those with either pharmacological antiretroviral control or immunologically in elite controllers . HIV and SIV-specific CD8+ T cells were dysfunctional and expressed high levels of TIGIT and PD-1 . Ex-vivo single or combinational antibody blockade of TIGIT and/or PD-L1 restored viral-specific CD8+ T cell effector responses . The frequency of TIGIT+ CD4+ T cells correlated with the CD4+ T cell total HIV DNA . These findings identify TIGIT as a novel marker of dysfunctional HIV-specific T cells and suggest TIGIT along with other checkpoint receptors may be novel curative HIV targets to reverse T cell exhaustion .
During chronic viral infections , high antigenic loads continually stimulate T cells leading to progressive loss of function termed “T cell exhaustion” [1] . Throughout this period , T cells increase expression of several inhibitory immune receptors that raise the threshold for activation , resulting in suppressed immune responses . While Programmed Death Receptor-1 ( PD-1 ) was one of the earliest surface markers of immune exhaustion identified [2–7] , we have shown that the surface glycoprotein , T cell immunoglobulin- and mucin domain-containing molecule ( Tim ) -3 , defines a state of T cell exhaustion with diminished proliferative and cytokine capacities in chronic viral infection [8 , 9] . Thus , the upregulation of these and other negative checkpoints receptors may serve as potential targets for the reversal of T cell exhaustion . Indeed , blocking the interaction of T cell negative checkpoint receptor pathways using targeted reagents against PD-1/Programmed Death-Ligand 1 ( PD-L1 ) , Tim-3 , Lymphocyte-activation gene 3 ( Lag-3 ) and CD160 has shown promise in reversing CD8+ T cell exhaustion [7 , 8 , 10–12] . Reagents targeting many of these receptors are rapidly advancing in the clinic and are showing efficacy in the control of viral infectious disease [13] as well as anti-tumor immunity [14–19] . A single dose of an antibody against PD-1 led to sustained clearance of hepatitis C virus infection in a small subset of individuals [13] . Blockade of the PD-1/PD-L1 axis in vivo demonstrated efficacy in restoring simian immunodeficiency virus ( SIV ) -specific T cell and humoral immunity , and led to a reduction of SIV viremia and in immune activation . However , this did not completely control virus , suggesting that additional therapies are needed . Importantly , not all features of the exhausted T-cells are restored by interfering with single pathways [2–4 , 8 , 20] . Synergistic simultaneous dual blockade has yielded more promising responses suggesting these co-inhibitory molecules are non-redundant [10 , 19 , 21 , 22] . T cell immunoreceptor with immunoglobulin and ITIM domains ( TIGIT ) is a recently described immune checkpoint receptor that belongs to the CD28 family and contains an extracellular IgV domain , a transmembrane domain , and a cytoplasmic tail containing two-immunoreceptor tyrosine-based inhibitory motif ( ITIM ) [23] . It has been reported to be expressed on natural killer ( NK ) cells , CD8+ T cells and CD4+ T cell subsets [23] and is induced upon activation [23–27] . TIGIT competes with DNAM-1 , a co-stimulatory molecule , and TACTILE , a co-inhibitory molecule , for the poliovirus receptor ( PVR ) a member of the nectin family of adhesion molecules that is expressed on dendritic cells ( DCs ) [23 , 24 , 28] . Several murine and human studies strongly suggest that TIGIT is a negative modulator of T cell and NK cell function [25 , 29–31] . A number of plausible mechanisms exist by which TIGIT can mediate inhibition of T and NK cell activation . Signaling through the TIGIT/PVR pathway with the standard recruitment of phosphatases via the intracellular ITIM domain of TIGIT can curtail T cell and NK cell responses [26] . This interaction has been shown to induce tolerogenic DCs to release the immunosuppressive cytokine IL-10 [25 , 31] . Furthermore , disruption of DNAM-1 homodimerization by TIGIT can abrogate the positive co-stimulatory signals required for activation [18] . Recently , potent anti-viral and anti-tumor responses related to enhanced CD8+ T cell effector activity were generated following synergistic dual blockade of PD-L1 and TIGIT in the mouse model of chronic lymphocytic choriomeningitis virus ( LCMV ) infection [18] and ex-vivo in patients with advanced melanoma [19] . To date , these results have not been replicated in any human viral disease , but over-expression of both TIGIT and PD-1 on virally exhausted T cells suggests that this is a promising avenue of exploration as a viable strategy to increase control or eliminate viral infections through T cell modulation . Given the potential to restore anti-HIV-specific CD8+ T cell responses by synergistic modulation of negative checkpoint receptors , we investigated the expression and function of TIGIT in HIV disease pathogenesis , and in the SIV non-human primate model of HIV/AIDS .
We assessed the surface expression of TIGIT on T cells from peripheral blood mononuclear cells ( PBMCs ) from HIV-infected individuals that were either acutely infected ( AI ) , non-controllers ( NC ) , cART suppressed ( AS ) , or elite controllers ( EC ) , and compared these results to age-matched HIV-uninfected healthy donors ( HD ) ( Table 1; Figs 1A–1D , S1A and S1B ) . We observed a significant expansion in the frequency of TIGIT+ CD8+ T cells in HIV-infected participants ( AS; 44 . 95%; EC 56 . 7%; NC , 64 . 5% ) , even among those with viral suppression , relative to HD ( median: 28 . 05%; Fig 1C ) . We observed a non-significant trend in the expansion of TIGIT+ CD8+ T cells in AI ( 40 . 4% ) relative to HD ( Fig 1C ) . TIGIT+ CD4+ T cells were significantly elevated among NC ( 24 . 5% ) compared to HD ( 16 . 05% ) ( Fig 1D ) . Among the HIV-infected NC , TIGIT+ CD8+ T cells inversely correlated with CD4 cell counts , but not with CD8+ T cell activation or plasma viral load ( Figs 1E and S1E ) . TIGIT+ CD4+ T cells did not correlate with CD4 cell counts in NC ( Fig 1F ) . Among EC , TIGIT+ CD8+ T cells trended to correlate with CD8+ T cell activation , while frequencies of TIGIT+ CD4+ T cells correlated with CD4+ T cell activation ( Fig 1G and 1H ) . We did not observe any other significant correlations with TIGIT+ T cells ( S1C–S1F , S1G and S1I Fig ) . Given the high levels of TIGIT in the midst of viral suppression , we assessed the relationship between TIGIT and the cellular HIV content in highly purified CD4+ T cells among HIV-infected “cART initiators” who met strict selection criteria of well documented and long-term persistent viral suppression ( L-AS; Table 1 ) . We did not observe a correlation with the frequency of CD8+ T cell or CD4+ T cell TIGIT expression and HIV RNA from purified CD4+ T cells ( S1H and S1J Fig ) . However , the frequency of TIGIT+ CD4+ T cells positively correlated with purified CD4+ T cell HIV DNA content , but not with frequency of TIGIT+ CD8+ T cells ( Fig 1I and 1J ) . These data indicate that TIGIT expression on CD4+ T cells may be linked to chronic HIV disease pathogenesis , residual immune activation , and the cellular HIV DNA content among those with viral suppression . HIV infection leads to an expansion of intermediately differentiated memory CD8+ T cells that are not fully mature effectors [32–34] . We profiled the pattern of TIGIT expression in the heterogeneous CD8+ T cell subpopulations and found TIGIT was significantly expanded on the CD8+ T cell intermediate/transitional and effector subsets with the highest expression of TIGIT on the effector CD8+ T cell subset ( Figs 2A and S2A–S2E ) compared to AS . In the naïve population TIGIT expression was relatively stable with only a significant difference seen between HD and the non-controllers ( Fig 2A ) . We did observe a statistically significant difference in TIGIT expression between the HD and AI group in the memory CD8+ T cell population ( Fig 2A ) . Thus , TIGIT is expanded on the intermediate/transitional and effector CD8+ T cell subsets during chronic HIV infection , consistent with a role for TIGIT as potential regulator of intermediate/transitional and effector T cell responses . We next profiled the expression of TIGIT on viral specific CD8+ T cells from chronically HIV-infected participants using matched HLA-I restricted pentamers for various HIV and CMV peptide epitopes . TIGIT was expressed on over half of all CD8+ T cells for specific for HIV-1 Gag ( 55 . 3% ) , Polymerase ( 54 . 7% ) , Envelope ( 54 . 3% ) , Nef ( 52% ) , and also for CMV pp65 ( 57 . 8% ) ( Fig 2B and 2C ) . Comparable levels of TIGIT on HIV and CMV specific CD8+ T cells were observed on a per cell basis as measured by Geometric Mean Fluorescence Intensity ( GMFI ) ( Fig 2C ) . We next assessed the effector phenotype and functional properties of TIGIT expressing CD8+ T cells to determine whether they retain features of immune exhaustion . We found that most of the TIGIT expressing CD8+ T cells co-expressed PD-1 with the frequency of TIGIT+ PD-1+ CD8+ T cells significantly expanded in chronic HIV infection ( AS , 18 . 65%; EC , 20 . 85%; NC , 38 . 15% ) compared to HD ( 13 . 65% ) ( Fig 3A and 3B ) . The frequency of TIGIT+ PD-1+ CD8+ T cells inversely correlated with CD4 counts ( Fig 3C ) and positively correlated with plasma viral load levels ( Fig 3D ) among all chronically HIV-infected individuals . We observed significantly higher frequencies of TIGIT+PD-1+ co-expression on HIV-Gag-specific CD8+ T cells compared to non-HIV-Gag-specific CD8+ T cells derived from PBMCs ( Fig 3E–3G ) . Furthermore , the majority of the TIGIT+PD-1+ HIV-Gag-specific CD8+ T cells retained a transitional/intermediate memory ( CD45RA-CCR7-CD27+ ) phenotype ( Fig 3H–3J ) . These results suggest TIGIT may render a large fraction of viral specific CD8+ T cells vulnerable to negative regulation . Given the high expression of PD-1 among TIGIT+ CD8+ T cells , we evaluated the functional status of the TIGIT expressing cells . We stained T cells with the nuclear antigen Ki-67 , which is associated with cellular proliferation , and observed that TIGIT+ cells expressed significantly more Ki-67 than TIGIT- CD8+ T cells ( Fig 4A and 4B ) . However , in contrast , Ki-67 expression was equivalently distributed between PD-1+ and PD-1- CD8+ T cells ( Fig 4C and 4D ) . Using intracellular cytokine staining , in response to stimulation with an overlapping 15mer HIV-1 Gag peptide pool , we observed that TIGIT+ CD8+ T cells produced significantly less IFN-γ , TNF-α and IL-2 compared to TIGIT- CD8+ T cells ( Fig 4E and 4F ) . We observed phenotypically the majority of the HIV specific cytokine responsive CD8+ T cells lacked TIGIT and PD-1 dual expression and were minimally represented in the TIGIT+PD-1+ subset . However , single expressing cells retain some functional responses ( Fig 4G ) . To directly assess the functionality of the TIGIT+PD-1+ subset in HIV infected individuals , CD8+ T cells expressing TIGIT and/or PD-1 on their surface were sorted to high purities ( S3A and S3B Fig ) , stimulated with or without anti-CD3 + anti-CD28 Dynabeads , and assessed for changes in TIGIT and PD-1 expression and their capacity to secrete 13 different cytokines . We found CD8+ T cells lacking TIGIT ( TIGIT-PD-1- and TIGIT-PD-1+ ) robustly upregulated TIGIT upon stimulation ( S3B and S3C Fig ) . Irrespective of PD-1 expression , the TIGIT expressing ( TIGIT+PD-1- and TIGIT+PD-1+ ) cells only marginally increased TIGIT expression ( S3B and S3C Fig ) upon stimulation . We harvested the supernatants and observed that TIGIT+PD-1+ cells had the lowest secretion of all cytokines assessed in comparison to the other three subsets ( S3D Fig ) . TIGIT+PD-1- cells produced less cytokines than TIGIT-PD-1+ cells . These data are partially in alignment with results observed in Fig 4G . However , it was notable that IL-10 production was almost exclusively produced by the TIGIT-PD-1+ cell subset . These data suggest that TIGIT+ CD8+ T cells , particularly TIGIT+PD-1+ co-expressing CD8+ T cells exhibit distinguishing features of exhausted T cells . Next , we evaluated the intracellular granular content of TIGIT expressing cells . We observed that TIGIT expressing CD8+ T cells contained significantly more perforin and granzyme B compared to non-TIGIT expressing CD8+ T cells ( Fig 4H and 4I ) . We observed no difference in the ability of TIGIT+ CD8+ T cells to degranulate compared to TIGIT- CD8+ T cells when stimulated with HIV-1 Gag peptide pool ( Fig 4J and 4K ) . However , upon stimulation with anti-CD3 + anti-CD28 Dynabeads , TIGIT+ cells degranulated less than TIGIT- cells ( Fig 4J–4L ) . To explore the regulation of TIGIT expression we stimulated HIV-specific CD8+ T cells from chronically HIV-infected individuals with HIV-1 Gag peptides . HIV-1 Gag peptide stimulation did not significantly increase the expression of TIGIT on HIV-specific CD8+ T cells , although we did observe an upregulation of TIGIT in a subset of individuals ( Fig 5A–5C ) . Several common gamma-chain ( γ-chain ) cytokines have been shown to directly upregulate negative checkpoint receptors on CD8+ T cells during retroviral infections [35] . To further understand the mechanism driving TIGIT upregulation , we explored the capacity of γ-chain and non-γ-chain cytokines to regulate TIGIT expression ( Fig 5D–5F ) . We found that IL-2 and IL-15 prominently led to a significant increase in TIGIT expression on CD8+ T cells from HIV-infected individuals unlike non-γ-chain cytokines IL-12 and IL-18 ( Fig 5E ) . This effect was not evident on CD8+ T cells derived from HIV-uninfected participants ( Fig 5F ) . Correspondingly , TIGIT expression on CD4+ T cells was upregulated primarily by IL-2 and IL-15 in HIV-infected individuals ( S4A Fig ) . IL-21 stimulation increased TIGIT expression on CD8+ T cells , but not CD4+ T cells ( S4B Fig ) . These data suggest TIGIT expression may be regulated by a peripheral cytokine milieu dominated by γ-chain cytokines present during HIV infection . Since TIGIT and PD-1 are co-expressed , and dual blockade in the mouse model limits in vivo LCMV replication [18] and elicits anti-tumor CD8+ T cell responses [19] , we evaluated the effects of TIGIT and PD-L1 blockade on HIV-Gag-specific CD8+ T cells using cells from chronically HIV-infected individuals at various stages of infection ( Table 2 ) . To evaluate the ex vivo HIV-specific T cell cytokine restoration , we used a modified version of our previously published in vitro short-term primary recall blockade assay [8] . Incubation with either anti-TIGIT mAb alone or anti-PD-L1 mAb alone significantly increased IFN-γ production , however dual blockade of both TIGIT and PD-L1 did not enhance IFN-γ responses over anti-TIGIT or anti-PD-L1 alone ( Fig 6A and 6B ) . We also observed that only dual blockade of TIGIT and PD-L1 significantly increased IL-2 production by CD8+ T cells ( S5A and S5B Fig ) . Given that virus-specific IL-2 producing CD4+ T cells have been associated with disease control in HIV infection we assessed the effects of TIGIT blockade on CD4+ T cells [36 , 37] . Similarly , only dual blockade of TIGIT and PD-L1 significantly increased IL-2 production over the single blockades alone from CD4+ T cells ( S5C and S5D Fig ) . Single blockade of PD-L1 significantly enhanced HIV-specific CD8+ T cell proliferation while single blockade of TIGIT did not improve CD8+ T cell proliferation ( Fig 6C and 6D ) . When both anti-TIGIT and anti-PD-L1 were combined there was significant increased CD8+ T cell proliferation compared to PD-L1 blockade alone ( Fig 6C and 6D ) . Though donor OM115 had the highest baseline levels of TIGIT+ CD8+ T cells among the group , no significant association was seen between the magnitude of IFN-γ production and proliferation by TIGIT blockade and baseline TIGIT+ CD8+ T cell expression ( r = 0 . 24 , p = 0 . 257 ) . These data suggest that HIV-specific CD8+ T cell proliferation can be markedly improved with simultaneous combination blockade of TIGIT and PD-L1 . To explore the role of TIGIT in the rhesus macaque model of HIV/AIDS we cloned rhesus TIGIT ( rhTIGIT ) ( GenBank: KR534505 ) and observed that it shares 88 . 11% sequence homology with human TIGIT ( S6A Fig ) . We reasoned that rhTIGIT expression and function would mimic our human HIV studies and be replicable in the SIV-infected rhesus macaque model of HIV/AIDS . RhTIGIT expression was significantly increased on CD8+ T cells derived from the lymph node ( LN ) ( 38 . 6% ) and splenic ( 60 . 9% ) compartments when compared to SIV-uninfected macaques ( LN 10 . 82% and spleen 25 . 55% ) , but not in PBMCs ( Figs 7A and S6B ) . Similar to what we observed in HIV-infected participants , the frequency of rhTIGIT+ CD8+ T cells from PBMC did not correlate with plasma SIV viral load . However , we did observe a significant correlation with the frequencies of rhTIGIT+ CD8+ T cells in LN and viral load ( Fig 7B ) . As observed in human HIV infection , rhTIGIT expression was more prominently expressed in SIV infection on effector memory ( EM , CD28-CD95+ ) , and central memory ( CM , CD28+CD95+ ) CD8+ T cells when compared to naïve ( N , CD28+CD95- ) CD8+ T cells from PBMCs , LN and from the spleen ( S6C Fig ) . In the tissues , it was notable that TIGIT expression was highest on the central memory CD8+ T cells when compared to PBMCs ( S6C Fig ) . As in HIV infection , stimulation with γ-chain cytokines such as IL-2 and IL-15 upregulated rhTIGIT on CD8+ T cells from SIV-infected animals ( S6D Fig ) . rhTIGIT was also expressed on ~40% of SIV Gag or Tat tetramer specific CD8+ T cells derived from PBMCs or secondary lymphoid tissues , even in animals with full cART suppression of peripheral SIV viremia ( Fig 7C ) . This was more prominently found in the tissues of SIV-infected animals where higher frequency of SIV-specific CD8+ T cells co-expressed both rhTIGIT and rhesus macaque PD-1 ( rhPD-1 ) ( S6E–S6H Fig ) . While the levels of Ki-67 expression did not differ between rhTIGIT+ and rhTIGIT- CD8+ T cells from SIV-infected rhesus macaques ( S7A–S7D Fig ) , CD8+ T cells lacking rhTIGIT from PBMC produced significantly more IFN-γ compared to rhTIGIT+ CD8+ T cells when stimulated with either phorbol 12-mysistate 13-acetate ( PMA ) + ionomycin or SIV Gag181-189 CM9 peptide ( Fig 7D and 7E ) . Given the similarities of rhTIGIT and human TIGIT , we evaluated TIGIT and PD-L1 blockade on SIV peptide stimulated CD8+ T cell responses . We found that dual blockade of rhPD-L1 and rhTIGIT enhanced SIV-specific CD8+ T cell proliferation in PBMCs and spleen while single blockade of rhPD-L1 enhanced SIV-specific proliferation in the spleen ( Fig 7F and 7G ) . Taken together , rhTIGIT pathway is active in the rhesus macaque model of HIV/AIDS and partially mimics human TIGIT expression and function during HIV infection .
In this report we profiled TIGIT expression on T cells in HIV-infected participants with various degrees of viral control and in SIV-infected rhesus macaques . We ( 1 ) unveil a role for TIGIT+ CD8+ T cells in HIV disease progression and demonstrate its relation to T cell exhaustion , ( 2 ) observe that TIGIT appears to associate with the cellular viral reservoir in CD4+ T cells , ( 3 ) we found that co-blockade of TIGIT and PD-L1 lead to a greater restoration of T cell function compared with a single blockade , and ( 4 ) by successfully cloning rhTIGIT ( GenBank: KR534505 ) we reveal the similarities in expression and function of rhTIGIT on T cells in the non-human primate model of HIV/AIDS . Our findings reveal a novel inhibitory pathway involved in the suppression of T cell responses during chronic viral infection , the blockade of which may contribute to the reversal of T cell dysfunction in the control or elimination of HIV infection . While TIGIT levels on CD8+ T cells tracked disease progression ( depletion of CD4 T cells and T cell activation ) , this was not evident across the various HIV-infected groups . There was a significant difference in TIGIT+ memory CD8+ T cells in acute infection compared to the uninfected group , but not in the global CD8+ T cell population . This suggests there may be a gradient , with an increase in global TIGIT+ CD8+ T cells in acute infection that becomes greater over time , which is distributed among the differentiated CD8+ T cell populations and may differ when compared to other negative checkpoint receptors which are found elevated during the early stages of HIV infection [4 , 38 , 39] . TIGIT induction appears to be driven by polyclonal TCR stimulation and this is a common feature among immune checkpoint receptors [38 , 40] . We observed HIV-1-Gag-SL9-specific CD8+ T cells did not increase TIGIT expression after HIV-1 Gag peptide stimulation as a group , however a subset of individuals with moderate levels of TIGIT increased expression after stimulation , and individuals that expressed high levels of TIGIT retained expression after stimulation . TIGIT remained elevated despite antigen in cART-suppressed individuals; previous studies have also shown that common γ-chain cytokines maintain the ability to regulate immune checkpoint receptor expression in the absence of antigenic stimulation [35 , 41] . Our studies align with these previous observations and suggest that a cytokine milieu conducive for the maintenance of an exhausted T cell profile persists in HIV and SIV infection even during viral suppression . TIGIT expression was found to be associated with T cell activation principally among EC who represent a small population of HIV-infected individuals able to spontaneously suppress their viral load ( <50 copies/ml ) in the absence of cART for prolonged periods of time [42] . However , over time a subset of EC will lose virologic control and develop viremia and CD4+ T cell loss [43 , 44] . In addition , EC maintain elevated levels of T cell activation despite viral control [45 , 46] . High TIGIT expression may reflect ongoing immune activation in the EC population . The institution of cART in those EC has led to a reduction in immune activation [47 , 48] . Given our finding , in addition to cART , some EC may benefit from TIGIT blockade to alleviate the persistent T cell immune activation thereby reducing the risk of adverse non-AIDS events that have been documented to occur in this population , however such strategies need to be considered carefully given the risk of autoimmunity as described in anti-PD-1 clinical trials in the oncology field [49 , 50] . Viral clearance of the chronic strain of LCMV ( clone 13 ) in mice by combined blockade of TIGIT and PD-L1 provided the first evidence of the advantages of targeting these two pathways [18] . In addition , targeting TIGIT and PD-L1 on CD8+ tumor infiltrating lymphocytes in patients with advanced melanoma synergistically improves potent anti-tumor responses [19] . Here we extend these finding to human and simian retroviral infections . This was significant given the expansion of dual expressing TIGIT and PD-1 CD8+ T cells in HIV infection despite pharmacological or immunological viral suppression . Our data shows the presence of TIGIT and PD-1 dual expressing HIV and SIV-specific CD8+ T cells and co-blockade of TIGIT and PD-L1 better enhanced proliferation of HIV and SIV-specific CD8+ T cell responses compared to single blockade . Although we see a significant increase among all HIV-infected individuals , it was evident that subsets of weak-responders exist and appear heterogeneous irrespective of stage of infection , viral load levels or viral suppression . Indeed , combinational blockade of CTLA-4 and PD-1 revealed a subset of weak-responders to anti-tumor activity . Different or expanded combinations of immune checkpoint blockers with anti-TIGIT may need to be considered in the arsenal to improve anti-viral T cell immunity in all individuals . Persistence of the cellular latent HIV reservoir has been a major barrier to the eradication of HIV [51] . One proposed strategy is to ‘Shock’ the latently infected cells to flush out virus with latency reversal agents ( LRAs ) [52–54] . The development of the ‘Shock’ strategies is advancing at a rapid pace with in vivo studies yielding activity in reactivating of latent virus . However , the ‘Kill’ component is less well developed . Shan and colleagues demonstrate that after reactivation of latent virus from CD4+ T cells , CD8+ T cells’ activity had the capacity to kill latently infected CD4+ T cells if appropriate pre-stimulation of HIV peptide and IL-2 was provided an in vitro latency assay using Bcl-2 as a survival signal to prolong the longevity of the latent CD4+ T cells [55] . Furthermore , recent studies show that HIV-infected individuals on cART retain broad HIV-specific cytotoxic T-lymphocyte responses that are able to target the mutated latent virus [56] . Blocking immune checkpoint pathways such as TIGIT and PD-1/PD-L1 can be harnessed to boost HIV-specific CD8+ T cells responses given that these pathways persist in the setting of viral suppression . Furthermore , given our findings showing the relationship with TIGIT expression on CD4+ T cells and the total cell associated HIV DNA , it remains unclear what role TIGIT may play in the establishment of the reservoir in CD4+ T cells , however it is likely related to the capacity of TIGIT’s ability control T cell activation or proliferation . Our study provides a novel role for TIGIT during HIV disease pathogenesis and our demonstration of a role of rhTIGIT in the non-human primate model of HIV/AIDS provides a platform to investigate our understanding of the complex networks of co-inhibition that can be tailored to each individual or viral infection . Improving CD8+ T cell functions may further aid in the ‘Shock and Kill’ approaches being considered to eliminate latent virus and improve T cell mediated vaccine responses to prevent or limit infection [57] .
We recruited participants from the following cohorts: University of California , San Francisco ( UCSF ) SCOPE and OPTIONS cohorts [8] the Hawaii HIV-1 ( HHC ) cohort , the Toronto-based cohort CIRC ( Maple Leaf Clinic and St . Michael’s Hospital , Toronto , Canada ) [58] and the Duke Human Vaccine Institute ( DHVI ) tissue repository . SCOPE specimens ( n = 80 ) were selected from the following groups: untreated non-controllers ( n = 20 ) ( participants who had never been treated with antiretroviral agents or who had been off therapy for at least six months ) , treated virologic controllers ( participants who had an undetectable plasma HIV-1 RNA level for the previous six months while on cART ) ( n = 20 ) , spontaneous “elite” virologic controllers ( participants who are untreated and who have at least three documented plasma HIV-1 RNA levels <2 , 000 copies/ml over at least a 12-month period ) ( n = 20 ) , Some of these participants had persistent plasma HIV-1 RNA levels <75 copies/ml ) and HIV-infected “cART initiators” ( n = 20 ) who meet strict selection criteria and well documented persistent viral suppression for over 1 . 5 years . Participants with acute HIV infection ( n = 24 ) were obtained for the OPTIONS cohort of primary HIV infection [8] and age-matched HIV-uninfected ( n = 20 ) ( Table 1 ) and chronically infected virally suppressed leukapheresesed individuals were obtained from the HAHC cohort [59] . Additional participants with chronic infection at various stages of infection were obtained from participants with various levels of viral control from the Toronto-based cohort CIRC cohort , HHC , and DHVI . All persons gave written informed consent to participate in the study and approval for the study was obtained from the University of Hawaii Committee of Human Subjects . Samples were obtained from Indian rhesus macaques ( Macaca mulatta ) housed at the Oregon National Primate Research Center ( ONPRC ) , which were SIV infected for other ongoing , unrelated studies . Oregon Health and Science University ( OHSU ) Institutional Animal Care and Use Committee ( IACUC ) Protocol #: 0989 . The OHSU Institutional Animal Care and Use Committee reviewed and approved all study protocols . All macaques in this study were managed according to the ONPRC animal husbandry program , which aims at providing consistent and excellent care to nonhuman primates . This program is based on the laws , regulations , and guidelines set forth by the United States Department of Agriculture ( e . g . , the Animal Welfare Act and its regulations , and the Animal Care Policy Manual ) , Institute for Laboratory Animal Research ( e . g . , Guide for the Care and Use of Laboratory Animals , 8th edition ) , Public Health Service , National Research Council , Centers for Disease Control , and the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) International . The nutritional plan utilized by the ONPRC is based on National Research Council recommendations and supplemented with a variety of fruits , vegetables , and other edible objects as part of the environmental enrichment program established by the Behavioral Management Unit . Paired/grouped animals exhibiting incompatible behaviors were reported to the Behavioral Management staff and managed accordingly . All efforts were made to minimize suffering through the use of minimally invasive procedures , anesthetics , and analgesics when appropriate . Animals were painlessly euthanized with sodium pentobarbital and euthanasia was assured by exsanguination and bilateral pneumothorax , consistent with the recommendations of the American Veterinary Medical Guidelines on Euthanasia ( 2013 ) Cryopreserved PBMCs were rapidly thawed and enriched for CD4+ T cells to high purities with an EasySep Human CD4+ T cell enrichment kit ( Stemcell Technologies , Vancouver , British Columbia , Canada ) . Cellular RNA and DNA from PBMC T-cell subsets cells were purified using the AllPrep DNA/RNA kit ( Qiagen , Ventura CA ) as specified by the manufacturer , quantified using a Nanodrop ( ND-1000 ) spectrophotometer and normalized to cell equivalents by qPCR using human genomic TERT for DNA and GAPDH or RPLP0 expression for RNA ( Life Technologies , Grand Island NY ) . Total cellular HIV-1 DNA ( integrated and unintegrated ) and RNA ( unspliced and multiply spliced ) was quantified with a qPCR TaqMan assay using LTR-specific primers F522-43 ( 5’ GCC TCA ATA AAG CTT GCC TTG A 3’; HXB2 522–543 ) and R626-43 ( 5’ GGG CGC CAC TGC TAG AGA 3’; 626–643 ) coupled with a FAM-BQ probe ( 5’ CCA GAG TCA CAC AAC AGA CGG GCA CA 3 ) [60] on a StepOne Plus Real-time PCR System ( Applied Biosystems Inc , Foster City CA ) . Cell associated HIV-1 DNA copy number was determined using a reaction volume of 20 μl with 10 μl of 2x TaqMan Universal Master Mix II including UNG ( Life technologies ) , 4 pmol of each primer , 4 pmol of probe , and 5 μl of DNA . Cycling conditions were 50°C for 2 min , 95°C for 10 min , then 60 cycles of 95°C for 15s and 59°C for 1 min . Cell associated HIV-1 RNA copy number was determined in a reaction volume of 20 μl with 10 μl of 2x TaqMan RNA to Ct 1 Step kit ( Life Technologies ) , 4 pmol of each primer , 4pmol of probe , 0 . 5 μl reverse transcriptase , and 5μl of RNA under identical cycling conditions . For HIV-1 DNA measurements , external quantitation standards were prepared from pNL4-3 in a background of HIV-1 negative human cellular DNA , calibrated to the Virology Quality Assurance ( VQA , NIH Division of AIDS ) cellular DNA quantitation standards . For HIV RNA measurements , external quantitation standards were prepared from full length NL4-3 virion RNA followed by copy number determination using the Abbott RealTime assay ( Abbott Diagnostics , Des Plains Ill ) and calibrated to VQA HIV-1 RNA standards . Patient specimens were assayed with up to 800 ng total cellular RNA or DNA in replicate reaction wells and copy number determined by extrapolation against a 7-point standard curve ( 1–10 , 000 cps ) performed in triplicate . Cryopreserved PBMC were rapidly thawed in warm 10% cRPMI ( RPMI 1640 medium; ( Hyclone , Logan , Utah ) supplemented with 10% fetal bovine serum ( FBS ) ( Hyclone ) , 1% penicillin-streptomycin ( Hyclone ) , 10 mM HEPES ( Hyclone ) , 2 mM L-glutamine ( Hyclone ) , and 10 μg/ml DNase I ( Sigma-Aldrich , Dorset , United Kingdom ) , washed with PBS + 2% FBS ( Hyclone ) ( complete RPMI ) . Cells were stained for viability with an aqua amine reactive dye ( AARD; Invitrogen , Carlsbad , California ) , then incubated with panels of conjugated anti-human monoclonal antibodies ( mAbs ) The following directly conjugated mAbs used in this study were obtained from BD biosciences ( San Jose , California ) : PE-Cy5-conjugated anti-CD38 ( Clone: HIT2 ) , V450-conjugated anti-CD45RA ( HI100 ) , FITC-conjugated anti-CD45RA ( HI100 ) , PerCP-Cy5 . 5-conjugated anti-CD27 ( M-T271 ) , Alexa Flour 700-conjugated anti-CD4 ( RPA-T4 ) , FITC-conjugated anti-HLA-DR ( G46-6 ) , APCH-7-conjugated anti-CD8 ( SK1 ) , FITC-conjugated anti-CD57 ( NK-1 ) , APC-conjugated CD107α ( H4A3 ) . mAb obtained from Beckman Coulter ( Fullerton , California ) ECD-conjugated anti-CD3 ( UCHT1 ) . mAbs obtained from eBioscience ( San Diego , California ) PE-Cy7-conjugated anti-CD28 ( CD28 . 2 ) , PerCP-eFluor 710-conjugated anti-TIGIT ( MBSA43 ) , PE-conjugated anti-TIGIT ( MBSA43 ) , Mouse IgG1 Kappa isotype control PerCP-eFluor 710 ( P3 . 6 . 2 . 8 . 1 ) , mouse IgG1 K isotype control PE ( P3 . 6 . 2 . 8 . 1 ) . mAbs obtained from Biolegend ( San Diego , California ) Brilliant Violet 605-conjugated anti-CCR7 ( G043H7 ) , APC-conjugated anti-PD-1 ( EH12 . 2H7 ) , mouse IgG1 Kappa isotype control PE ( MOPC-21 ) . Qdot 605-conjugated anti-CD8 ( 3B5 ) was obtained from Invitrogen ( Carlsbad , California ) . In some experiments cells were fixed with 1X Lyse buffer ( BD Biosciences ) followed by 1X BD FACS Permeabilizing solution 2 ( BD Biosciences ) and stained with FITC-conjugated Ki-67 ( 35/Ki-67 ) , FITC-conjugated interferon gamma ( IFN-γ ) ( 25723 . 11 ) , Alexa 700-conjugated Granzyme B ( GB11 ) , PE-conjugated perforin ( B-D48 ) ( abcam , Cambridge , Massachusetts ) . All cells were washed with PBS + 2% FBS and then fixed in 1% paraformaldehyde ( PFA , Electron Microscopy Sciences , Hatfield , Pennsylvania ) before acquiring ( within 18 hours ) on a custom four laser LSRFortessa flow cytometer ( BD Biosciences ) . Between 100 , 000 to 500 , 000 lymphocyte events were collected for each sample . Isotype controls or fluorescence minus one ( FMO ) samples were prepared to facilitate gating . Anti-mouse or anti-rat IgG-coated beads ( BD Biosciences ) were individually stained with each fluorochrome-conjugated antibody and used for software-based compensation . Data were analyzed using Flowjo Software version 9 . 5 ( Treestar , Ashland , Oregon ) . Cryopreserved PBMCs were rapidly thawed and enriched for CD8+ T cells to high purities with an EasySep Human CD8+ T cell negative selection enrichment kit ( Stemcell ) . Cells were surface stained with the following combination of mAbs: BV711-conjugated anti-CD3 , Alexa 700-conjugated anti-CD4 ( BD Biosciences ) , PerCP-eFluor 710-conjugated anti-TIGIT ( eBioscience ) , Qdot605-conjugated anti-CD8 , APC-conjugated anti-PD-1 ( Invitrogen ) . Cells were sorted on a BD FACS ARIA and checked for purity . Gating was facilitated by isotype controls . The four-sorted populations ( TIGT+PD-1+ , TIGIT+PD-1- , TIGIT-PD-1+ , TIGIT-PD-1- ) were seeded at 100 , 000 cells per well in a 96 well culture plate with 200 μl of 10% cRPMI . Sorted cells were stimulated with anti-CD3 + anti-CD28 Dynabeads ( Life Technologies ) for 48 hours in an incubator at 37°C with 5% CO2 , supernatants were harvested from the cultures and processed according to recommended manufacture procedure with a Milliplex MAP Human High Sensitivity T cell Panel ( EMD Millipore , Billerica , Massachusetts ) for GM-CSF , TNF-α , IL-13 , IL-12 ( p70 ) , IL-10 , IL-8 , IL-7 , IL-6 , IL-5 , IL-4 , IL-2 , IL-1β , IFN-γ . Samples were acquired on a Luminex 200 ( EMD Milipore ) . Samples were run in duplicate . The intra-assay CV% for the conditions of each cytokines were <10% . We used the following Biotin labeled pentamers: A*02:01 SLYNTVATL HIV-1-Gag , A*02:01 ILKEPVHGV HIV-1-Pol , B*07:02 IPRRIRQGL HIV-1-Env , B*07:02 TPGPGVRYPL HIV-1-Nef , and A*02:01 NLVPMVATV CMV-pp65-NV9 . All pentamers were obtained from Proimmune Ltd , Oxford , UK . Using protocol outlined previously [8] and stained with antibodies against CD3 , CD8 , TIGIT , PD-1 , TIGIT isotype control or PD-1 isotype control and acquired on the flow cytometer as above . In some experiments PBMCs were stimulated with HIV-1 Gag Peptide pool and evaluated for pentamer phenotype The TIGIT antibody clones 11G11 and 23G8 were generated in HuMab mice [61 , 62] immunized with a TIGIT-Fc fusion protein and selected based on their high affinity for TIGIT and ability to block TIGIT/PVR interaction . Clone 11G11 is a fully human IgG1 antibody that was engineered to contain a well-characterized set of mutation in the Fc that eliminate FcR interaction [63] . Clone 23G8 is a fully human IgG2 antibody that cross-reacts with macaque TIGIT . The anti-human PD-L1 antibody , clone 12A4 , is a fully human IgG4 ( S228P ) that was generated in HuMab mice immunized with PD-L1-Fc . This antibody was selected based on its ability to block the binding of PD-L1 to both PD-1 and CD80 . 12A4 cross-reacts with macaque PD-L1 . 123 Overlapping ~15mer HIV-1 clade B gag peptides obtained from the National Institute of Health AIDS Reagent Program . Stimulations were performed with a final concentration of 10 μg/ml peptide . T cell activator ( anti-CD3 + anti-CD28 mAb Dynabeads ) ( Life Technologies ) followed recommended manufacture procedure . In the intracellular cytokine stimulation assay studies , thawed cryopreserved PBMCs were stimulated for 12 hours in an incubator at 37°C with 5% CO2 with 5 μg/ml brefeldin A and 5 μg/ml monensin ( Sigma-Aldrich ) culture media , DMSO alone , pooled HIV-1 Gag peptides , or anti-CD3/CD28 dynabeads ( Life Technologies ) in the presence or absence of purified isotype IgG control , anti-TIGIT and/or anti-PD-L1 mAbs . After stimulation , the cells were washed and stained for viability with AARD and cultured with surface phenotype panel against CD8 , TIGIT or an isotype control antibody , followed by intracellular staining of CD3 and IFN-γ and acquisition on the flow cytometer as above . For the proliferation assay , thawed PMBCs were washed two times and resuspended in PBS supplemented with 0 . 01% BSA at a concentration of one million cells per milliliter . Cells were labeled with 1mM Carboxyfluorescein succinimidyl ester ( CFSE ) violet trace ( Invitrogen ) using protocols previously described [8] . Cells were stimulated for seven days with DMSO alone , pooled HIV-1 gag peptides , or anti-CD3/CD28 Dynabeads in the presence or absence of purified isotype IgG control , anti-TIGIT or anti-PD-L1 at 37°C with 5% CO2 . At the end of the stimulation , cells were washed and stained for viability with AARD and cultured with surface phenotype panel against CD3 , CD8 , TIGIT or an isotype control antibody and acquired on the flow cytometer as above . PBMCs were thawed and one million cells were plated per stimulation condition . Stimulation conditions included media alone , 25 ng/ml IL-2 ( Roche ) , 50 IU/ml of IL-12 ( MBL international , Woburn , Massachusetts ) , 50 IU/ml of IL-18 ( MBL international ) and 25 ng/ml IL-15 ( R&D Systems , Minneapolis , Minnesota ) . Cells were stimulated for six days in an incubator at 37°C with 5% CO2 . After stimulation , the cells were washed and stained for viability with AARD cultured with surface phenotype panel against CD3 , CD4 , CD8 , TIGIT or isotype control antibody and acquired on the flow cytometer as above . HIV-infected cryopreserved PBMC from individuals identified in Table 2 were stimulated for 12 hours in an incubator at 37°C with 5% CO2 . Stimulation conditions contained culture media , DMSO alone or pooled HIV-1 gag peptides , in the presence of brefeldin A ( Sigma-Aldrich ) , monensin ( Sigma-Aldrich ) , anti-TIGIT mAb anti-PD-L1 mAb or mouse IgG1 isotype control . After stimulation , cells were washed and stained for cellular viability with AARD and conjugated antibodies against CD8 and CD4 , followed by intracellular staining of CD3 and IFN-γ and acquired on a flow cytometer as described above . The repeated-measures , one-way ANOVA followed by Tukey’s multiple comparison , Wilcoxon matched-pairs signed ranked test was used for paired tests and the Spearman’s rho test was used for correlation analyses . Measures of central tendency are expressed as medians and interquartile ranges ( IQRs; given in the form 25th percentile , 75th percentile ) . Statistical analyses were conducted using Prism Graphpad release 5 . 0d ( Graphpad Software , San Diego , California ) or SPSS 22 . 0 ( IBM , Armonk , New York ) and the statistical significance of the findings was set at a p-value of less than 0 . 05 . Indian rhesus macaque ( Macaca mulatta ) TIGIT ( rhTIGIT ) : GenBank KR534505 . | HIV-1 infection contributes substantially to global morbidity and mortality , with no immediate promise of an effective vaccine . One major obstacle to vaccine development and therapy is to understand why HIV-1 replication persists in a person despite the presence of viral specific immune responses . The emerging consensus has been that these immune cells are functionally ‘exhausted’ or anergic , and thus , although they can recognize HIV-1 specific target cells , they are unable to effectively keep up with rapid and dynamic viral replication in an individual . We have identified a novel combination pathway that can be targeted , TIGIT and PD-L1which may be responsible , at least in part , for making these immune cells dysfunctional and exhausted and thus unable to control the virus . We show that by blocking the TIGIT and PD-L1 pathway , we can reverse the defects of these viral specific immune cells . Our findings will give new directions to vaccines and therapies that will potentially reverse these dysfunctional cells and allow them to control HIV-1 replication , but also serve in “Shock and Kill” HIV curative strategies . | [
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| 2016 | TIGIT Marks Exhausted T Cells, Correlates with Disease Progression, and Serves as a Target for Immune Restoration in HIV and SIV Infection |
Borrelia burgdorferi , the Lyme disease spirochete , dramatically alters its transcriptome and proteome as it cycles between the arthropod vector and mammalian host . During this enzootic cycle , a novel regulatory network , the Rrp2-RpoN-RpoS pathway ( also known as the σ54–σS sigma factor cascade ) , plays a central role in modulating the differential expression of more than 10% of all B . burgdorferi genes , including the major virulence genes ospA and ospC . However , the mechanism ( s ) by which the upstream activator and response regulator Rrp2 is activated remains unclear . Here , we show that none of the histidine kinases present in the B . burgdorferi genome are required for the activation of Rrp2 . Instead , we present biochemical and genetic evidence that supports the hypothesis that activation of the Rrp2-RpoN-RpoS pathway occurs via the small , high-energy , phosphoryl-donor acetyl phosphate ( acetyl∼P ) , the intermediate of the Ack-Pta ( acetate kinase-phosphate acetyltransferase ) pathway that converts acetate to acetyl-CoA . Supplementation of the growth medium with acetate induced activation of the Rrp2-RpoN-RpoS pathway in a dose-dependent manner . Conversely , the overexpression of Pta virtually abolished acetate-induced activation of this pathway , suggesting that acetate works through acetyl∼P . Overexpression of Pta also greatly inhibited temperature and cell density-induced activation of RpoS and OspC , suggesting that these environmental cues affect the Rrp2-RpoN-RpoS pathway by influencing acetyl∼P . Finally , overexpression of Pta partially reduced infectivity of B . burgdorferi in mice . Taken together , these findings suggest that acetyl∼P is one of the key activating molecule for the activation of the Rrp2-RpoN-RpoS pathway and support the emerging concept that acetyl∼P can serve as a global signal in bacterial pathogenesis .
The enzootic life-cycle of Borrrelia burgdorferi is complex and typically involves transmission between an arthropod vector ( Ixodes ticks ) and a mammalian host ( e . g . , Peromyscus rodents ) [1] . Accumulated evidence have shown that the alternative sigma factor RpoS plays a central role in this complex natural cycle of B . burgdorferi [2]–[8] . RpoS functions as a global regulator and governs differential expression of more than 10% of all B . burgdorferi genes , including the two major virulence genes ospA and ospC [9]–[13] . One unique feature about rpoS of B . burgdorferi is that its expression is directly controlled by the alternative second sigma factor RpoN ( σ54 ) at a −24/−12 σ54-type promoter . Mutation within this promoter region or inactivation of rpoN that encodes the second alternative sigma factor RpoN ( σ54 ) abolishes expression of rpoS and RpoS-dependent genes such as ospC [6] , [8] , [14] . This RpoN-dependent transcriptional activation appears to play a major role in modulating RpoS level in B . burgdorferi [3] , [5]–[8] , [14] , [15] . In addition , a small RNA dsrA also has been shown to be involved in post-transcriptional regulation of RpoS [7] . RpoN ( σ54 ) -dependent activation of transcription requires a highly conserved transcriptional activator , the so-called enhancer-binding proteins ( EBPs ) [16] . B . burgdorferi has a single EBP , Rrp2 , a homolog of NtrC family [17] , [18] . Members of NtrC family contain three putative functional domains: an N-terminal response regulator receiver domain , a central RpoN-activation domain , and a C-terminal helix-turn-helix ( HTH ) DNA-binding domain [19] . The central domain becomes activated upon phosphorylation at a conserved aspartic acid residue ( corresponding to D52 in Rrp2 ) within the N-terminal receiver domain . The activated central domain then contacts the Eσ54-holoenzyme through DNA looping , hydrolyzes ATP , and promotes open promoter complex formation for transcriptional initiation . Although direct biochemical evidence remains lacking , genetic data indicates that Rrp2 is the activator for the σ54–σS cascade of B . burgdorferi . First , a single point mutation of glycine ( G ) residue 239 to cysteine ( C ) within one of the ATP-binding motifs in the central activation domain of Rrp2 abolishes expression of rpoS and RpoS-dependent genes [4] , [18] , [20] . Second , when a rpoS promoter-cat reporter and an inducible rrp2 gene were cloned into a surrogate E . coli system , the reporter was activated only upon induction of rrp2 [6] . Thus , Rrp2 , RpoN , and RpoS appear to constitute a Rrp2-RpoN-RpoS pathway . Consistent with this notion , recent microarray analyses reveal that genes influenced by Rrp2 , RpoN , or RpoS largely overlap [2]–[4] , [20] . Given the importance of the Rrp2-RpoN-RpoS pathway to the infectious cycle of B . burgdorferi [3]–[5] , [20] , it is striking how little we know about the upstream event ( s ) that lead to its activation . Since Rrp2 is the upstream activator for the pathway , an understanding of the activation of Rrp2 is key to understand the mechanism of activation of this pathway . It is postulated that activation of Rrp2 is through a phosphorylation event by a cognate histidine kinase [21]–[23] . Because of the co-localization of rrp2 and hk2 in the genome ( 15 ) and because of the ability of Hk2 to phosphorylate Rrp2 in vitro [6] , Hk2 is predicted to be the cognate histidine kinase for Rrp2 . A recent study by Burtnick et al . [6] , however , showed that an hk2 mutant remains capable of activating Rrp2 under in vitro cultivation conditions , indicating that the molecular mechanism activating the Rrp2-RpoN-RpoS pathway is more complex than previously envisioned . In addition , the contribution of Hk2 during the infectious cycle of B . burgdorferi remains unknown because the previous hk2 mutant lost an important endogenous plasmid ( lp36 ) for mammalian infection [6] . Response regulators can be activated by factors other than their cognate histidine kinases . The best studied mechanisms are phosphorylation by non-cognate histidine kinases ( a phenomenon called “cross-talk” ) [24]–[28] and phosphorylation by small molecular weight high-energy donors , such as acetyl phosphate ( acetyl∼P ) or carbamoyl phosphate ( carbamoyl∼P ) [29]–[31] . While cross-talk appears to be quite rare ( 48 ) , emerging evidence indicates that acetyl∼P can function in vivo as a global signal by donating its phosphoryl group to certain response regulators [32] , [33] . B . burgdorferi possesses four predicted histidine kinases ( Hk1 , Hk2 , CheA1 , and CheA2 ) [17] , [34] as well as pathways for the synthesis and degradation of both acetyl∼P and carbamoyl∼P [17] . Burtnick et al . [6] proposed that Hk2-independent activation of Rrp2 could be activated by receiving a phosphoryl group from a non-cognate histidine kinase or a small phosphorylated compound . However , this hypothesis has not been tested experimentally . In this study , we generated an hk2 mutant suitable for in vivo study and showed that Hk2 was not required for the activation of the Rrp2-RpoN-RpoS pathway under in vitro growth conditions or during murine infection . We further showed that cross-talk among two-component systems is not likely to account for Rrp2 activation . Rather , the results obtained support the hypothesis that acetyl∼P functions as an important phosphoryl donor for Rrp2 , making this small molecule a key modulator of the activation of the Rrp2-RpoN-RpoS pathway in B . burgdorferi .
To study the mechanism of activation of the Rrp2-RpoN-RpoS pathway , we focused on the upstream activator Rrp2 , a putative response regulator . Burtnick et al . [6] recently reported that inactivation of hk2 , which encodes the putative cognate histidine kinase for Rrp2 , did not affect activation of the Rrp2-RpoN-RpoS pathway when spirochetes were cultivated in vitro . However , this hk2 mutant was not phenotypically characterized in vivo [6] . Thus , we sought to generate an hk2 mutant suitable for in vivo study . A suicide vector harboring a disrupted hk2 region was transformed into the infectious B . burgdorferi strain B31-A3 ( Fig . 1A ) [35] . Disruption of hk2 in the transformants was confirmed by PCR ( Fig . 1B ) and the absence of Hk2 expression was verified by immunoblot analyses ( Fig . 1C ) . Of note , inactivation of hk2 by the KanR cassette did not substantially affect expression of the protein encoded by the downstream gene , rrp2 ( Fig . 1C ) . Three transformed clones were further subjected to plasmid profile analyses ( data not shown ) . Two clones had a plasmid profile identical to that of parental wild-type B31-A3; one of these was designated hk2 and chosen for further study ( Table 1 ) . Under in vitro growth conditions , a combination of elevated temperature and increased cell density activates the Rrp2-RpoN-RpoS pathway , leading to the production of RpoS and RpoS-controlled proteins such as OspC [2] , [5] , [6] , [8] , [18] , [36]–[39] . To determine if Hk2 affects temperature and cell density-dependent activation of the Rrp2-RpoN-RpoS pathway , wild-type B . burgdorferi and isogenic hk2 mutant spirochetes were cultivated at elevated temperature ( 35°C ) and harvested at the late-exponential stage of growth ( 5×107 spirochetes per ml ) , conditions under which the Rrp2-RpoN-RpoS pathway is known to be activated . The hk2 mutant and its parental strain expressed similar levels of RpoS and OspC ( Fig . 1C ) . Under “non-inducing” conditions ( i . e . , low cell density or lower culture temperature ) , neither the hk2 mutant nor the parent strain expressed OspC ( data not shown ) . Thus , consistent with studies by Burtnick et al . [6] , the Rrp2-RpoN-RpoS pathway can be activated in vitro in an Hk2-independent manner . In vitro growth conditions only partially mimic the B . burgdorferi gene expression patterns observed during tick feeding and mammalian infection . For example , spirochetes grown under elevated temperature and high cell density conditions upregulate ospC but do not downregulate ospA [2] , [40]–[42] . Therefore , we next examined the phenotype of the hk2 mutant grown in mammalian host-adapted conditions by cultivating spirochetes in dialysis membrane chambers ( DMCs ) implanted in the peritoneal cavities of rats [2] , [40]–[42] . As shown in Fig . 2 , wild-type spirochetes cultivated in DMCs produced large amounts of OspC and undetectable amounts of OspA . An rpoS mutant exhibited the opposite phenotype , as previously reported [41] . In contrast , the DMC-cultivated hk2 mutant behaved much like its wild-type parent , indicating that Hk2 was not required for Rrp2 activation within this mammalian host environment . To further determine whether Hk2 is required for murine infection , groups of C3H/HeN mice were inoculated intradermally with various doses of either wild-type B . burgdorferi B31-A3 or its isogenic hk2 mutant . As shown in Table 2 , the infectivity of the hk2 mutant was similar to that of the parental strain . This result suggests that unlike Rrp2 , RpoN and RpoS [3]–[5] , [20] , Hk2 was not required for infection of mice by B . burgdorferi . The results described above indicate that Rrp2 could be activated by an Hk2-independent mechanism . To test the possibility that cross-talk may contribute to Rrp2 activation , we assessed the involvement of the other three B . burgdorferi histidine kinases identified to date [17] . We first constructed an hk1 mutant ( hk1 ) in B . burgdorferi 297 using a strategy similar to that described for generating the hk2 mutant ( Fig . 3A ) . The resulting mutant was verified using RT-PCR to test for the absence of hk1 expression and the lack of polarity on the downstream gene rrp1 ( Fig . 3B ) . Spirochetes were cultivated at elevated temperature and harvested at the late-exponential stage of growth . Unlike the rrp2 ( G239C ) mutant , which failed to express OspC , the hk1 mutant produced levels of OspC that were comparable to those of its wild-type parent , indicating that Hk1 is dispensable for Rrp2 activation ( Fig . 3C ) . It remained possible that Hk1 and Hk2 are involved in Rrp2 activation but that they may compensate for each other in a single knockout mutant . To rule out this possibility , we generated an hk1 hk2 double mutant in B . burgdorferi 297 by transforming the hk1 mutant with the suicide vector used for generating the hk2 mutant . Immunoblot analysis of the double mutant confirmed the absence of Hk2 in the hk1 hk2 mutant , and , more importantly , demonstrated that temperature and cell density-induced expression of OspC was unaffected despite the loss of both histidine kinases ( Fig . 4A ) . These results indicate that during in vitro growth , Hk1 is not responsible for Rrp2 activation in the absence of Hk2 . In addition to Hk1 and Hk2 , B . burgdorferi expresses two other histidine kinases , CheA1 and CheA2 , both of which are involved in chemotaxis [43] , [44] . To determine whether CheA1 or CheA2 participate in Rrp2 activation , we examined the ability of cheA1 and cheA2 mutants to produce OspC . As shown in Fig . 4B , both cheA mutants expressed normal levels of OspC , indicating that neither CheA1 nor CheA2 is required for Rrp2 activation under in vitro growth conditions . As a putative two-component response regulator , it is predicted that Rrp2 becomes activated upon phosphorylation of a conserved aspartate residue ( D52 ) located within its N-terminal receiver domain [6] , [18] ( Fig . 5A ) . Since deletion of each histidine kinase gene exerted no effect on the activation of the Rrp2-RpoN-RpoS pathway , we asked whether Rrp2 activation actually requires phosphorylation . Repeated attempts to replace the wild-type rrp2 with a mutated allele containing a D52A mutation were unsuccessful . As an alternative strategy , we reasoned that , if phosphorylation is important for Rrp2 activation , overexpression of a wild-type N-terminal Rrp2 fragment ( Rrp2-N ) ( phosphorylatable but not active ) would interfere with phosphorylation of endogenous full-length Rrp2 and therefore affect activation of the Rrp2-RpoN-RpoS pathway . Conversely , overexpression of a non-phosphorylatable mutant version of the Rrp2 N-terminus should have no effect . Accordingly , we constructed a series of shuttle vectors that carried the wild-type allele rrp2-N or the mutant alleles rrp2-N ( D52A ) or rrp2-N ( D52E ) under control of the constitutive flaB promoter ( Fig . 5A ) . Each constructed vector then was transformed into a non-infectious but highly transformable strain , B31 13A . The resulting transformants were verified by immunoblot analysis showing that each produced native full-length Rrp2 and the overexpressed Rrp2-N fragment ( Fig . 5B ) . We then evaluated the ability of these transformants to express OspC . Overexpression of wild-type Rrp2-N almost completely abolished expression of ospC ( Fig . 5B and 5C ) . These results were consistent with the expectation that the Rrp2-N fragment can successfully compete with native full-length Rrp2 for phosphorylation and , thus , interfere with Rrp2 and RpoN ( σ54 ) -dependent transcription of rpoS [14] , [15] . In contrast , cells expressing non-phosphorylatable Rrp2-N ( D52A ) or Rrp2-N ( D52E ) behaved like the vector control ( Fig . 5B and 5C ) , as would be expected if Rrp2 activation requires phosphorylation of D52 . Given that the Rrp2-RpoN-RpoS pathway is essential for mammalian infection , we hypothesized that overexpression of Rrp2-N , but not Rrp2-N ( D52A ) would affect the spirochete's ability to infect mice . To test this hypothesis , we re-transformed the corresponding shuttle vectors into the infectious strain B31-A3 . Positive transformants that had endogenous plasmid profiles identical to that of B31-A3 were then needle-inoculated into groups of C3H/HeN mice . As shown in Table 3 , although the strain overexpressing wild-type Rrp2-N was capable of infecting mice with a high dose of inoculation ( 1×105 spirochetes per mouse ) , its infectivity was greatly reduced; only 1 out of 5 mice was infected at the dose of 1×103 spirochetes ( Table 3 ) . In contrast , overexpression of Rrp2-N ( D52A ) exerted no such effect . Thus , overexpression of Rrp2-N impaired the activation of the Rrp2-RpoN-RpoS pathway both in vitro and in vivo , further supporting the hypothesis that phosphorylation of Rrp2 is likely required for the activation of the Rrp2-RpoN-RpoS pathway . Since Rrp2 activation appears to require D52 , but not the B . burgdorferi histidine kinases , we reasoned that small metabolic intermediates ( e . g . , carbamoyl∼P or acetyl∼P ) might be responsible for phosphorylation of D52 . The B . burgdorferi genome is predicted to encode a single pathway that can produce carbamoyl-P , the so-called arginine fermentation or ArcA-ArcB pathway , in which the enzyme arginine deaminase ( ArcA ) converts arginine to citrulline , which is then converted to ornithine and carbamoyl∼P by the enzyme ornithine carbamoyltransferase ( ArcB ) ( Fig . 6A ) . To assess the ability of carbamoyl∼P to influence Rrp2 activation , we used transposon mutagenesis to construct an arcA ( bb0841 ) mutant ( see Materials and Methods ) . The arcA mutant had no growth defect in vitro ( data not shown ) and produced levels of OspC similar to those of the wild-type parent strain ( Fig . 6B ) . Moreover , wild-type spirochetes cultivated in growth medium supplemented with an excess of arginine or ornithine showed no change in OspC expression ( data not shown ) . Collectively , these results argue that carbamoyl∼P does not donate its phosphoryl group to activate Rrp2 , at least under in vitro cultivation conditions . Acetyl∼P is the intermediate in the acetate kinase ( Ack ) – phosphate acetyltransferase ( Pta ) pathway . B . burgdorferi possesses genes predicted to encode both Ack ( BB0622 ) and Pta ( BB0589 ) [17] ( Fig . 7A ) . However , the B . burgdorferi genome encodes neither an AMP-ACS pathway that converts acetate to acetyl-coA nor other known pathways that produce acetyl-CoA . It also lacks the TCA cycle which utilizes acetyl-CoA for energy production . The genome does have a mevalonate pathway ( BB0683-BB0688 ) that requires acetyl-CoA for cell wall synthesis . Therefore , the Ack-Pta pathway appears to be the sole pathway for biosynthesis of acetyl-CoA required for cell wall synthesis As a short-chain fatty acid , acetate can diffuse into cells under neutral or acidic conditions [32] . Then the enzyme Ack can convert acetate to acetyl∼P , which in turn is converted to acetyl-CoA by the enzyme Pta . Thus , increasing concentrations of exogenous acetate can elevate intracellular levels of acetyl∼P [32] . To assess whether acetyl∼P plays a role in Rrp2 activation , wild-type B . burgdorferi B31-A3 were cultivated in BSK-H medium supplemented with increasing concentrations of sodium acetate ( NaOAc ) with the final medium pH adjusted to 7 . 0 . In order to detect the effect of acetate , cells were harvested at low density ( 5×106 spirochetes/ml ) when activation of the Rrp2-RpoN-RpoS pathway ( monitored by RpoS and OspC expression ) is low [37] , [38] . As shown in Fig . 7B , supplementation of NaOAc to the growth media dramatically increased the expression of OspC and RpoS in a dose-dependent fashion . This increase was not due to an elevated salt concentration ( or to osmotic shock ) since supplementation of the medium with as much as 150 mM NaCl did not reproduce this effect ( data not shown ) . To determine whether acetate-induced RpoS and OspC expression occurs via the Ack-Pta pathway , we attempted to generate ack and pta mutants but were unsuccessful . We reasoned that the Ack-Pta pathway may be indispensable for borrelial growth ( see discussion ) . As an alternative approach , we overexpressed Pta in wild-type spirochetes . We reasoned that if acetate-induced Rrp2 activation results from accumulation of acetyl∼P , then overexpression of Pta would reduce the level of acetyl∼P and abolish the acetate effect . A shuttle vector carrying the pta gene under the control of the flaB promoter was introduced into strain B31 13A . The resulting transformants were cultivated in the presence of 15 mM NaOAc at pH 7 . 0 and harvested at low cell density ( 5×106 spirochetes/ml ) . As shown in Fig . 7C , overexpression of Pta dramatically reduced acetate-induced Rrp2 activation as assessed by expression of OspC . These results are consistent with the hypothesis that acetate activates Rrp2 via accumulation of acetyl∼P . A combination of elevated culture temperature and increased cell density or lowered pH ( pH 6 . 8–7 . 0 ) induces RpoS and OspC expression [5] , [37] , [38] , [45] , yet the underlying mechanism remains unclear . Since temperature , cell density , and pH are capable of influencing intracellular level of acetyl∼P in other organisms , such as E . coli [32] , we sought to determine if overexpression of Pta also affects temperature and cell density-induced Rrp2 activation . Thus , spirochetes were cultivated at 23 or 35°C in standard BSK-H and harvested during late exponential growth ( ∼5×107 spirochetes/ml ) . Consistent with previous observation , elevated temperature and cell density induced OspC expression in wild-type spirochetes ( Fig . 7D , the left panel ) . However , overexpression of Pta dramatically inhibited such effect ( Fig . 7D , the right panel ) . These results suggest that the effect of environmental cues such as temperature- and cell density on RpoS and OspC expression might be through the small molecule acetyl∼P . To determine whether overexpression of Pta would affect mammalian infection by B . burgdorferi , we re-constructed a Pta-overexpressing strain in the infectious strain B31-A3 . One of the transformed clones harboring flaBp-pta had an endogenous plasmid profile identical to that of B31-A3 , and was chosen for subsequent infection study . As shown in Table 3 , overexpression of Pta resulted in a moderate reduction of infectivity; half of the mice ( 4 out of 8 ) were infected at the dose of 1×103 spirochetes . This result suggests that the AckA-Pta pathway contributes to mammalian infection , likely by synthesizing acetyl∼P , which can donate its phorphoryl group to Rrp2 . To determine whether Rrp2 can be directly phosphorylated by acetyl∼P , we performed an in vitro phosphorylation assay . Different amounts of purified recombinant Rrp2 , Rrp2-N , Rrp2-N ( D52A ) , or Rrp2-N ( D52E ) were incubated with 32P-labeled acetyl∼P in the reaction buffer at 37°C for 15 or 30 min . As shown in Fig . 7E , phosphorylated Rrp2 was readily detected in a time- and dose-dependent manner . Furthermore , phosphorylation of Rrp2 requires D52 , since wild-type Rrp2-N , but not Rrp2-N ( D52A ) or Rrp2-N ( D52E ) could be phosphorylated by acetyl∼P ( Fig . 7E ) . These results indicate that acetyl∼P can directly donate its phosphoryl group to Rrp2 in a histidine kinase-independent manner .
The discovery of the central regulatory network , the Rrp2-RpoN-RpoS pathway , was a significant advance in B . burgdorferi gene regulation . However , the dearth of knowledge regarding the mechanism underlying the activation of this pathway has been a major gap in our understanding of Borrelia host adaptation . In this study , we showed that temperature- and cell density-induced Rrp2-RpoN-RpoS activation occurs via a histidine kinase-independent mechanism . We further provided evidence suggesting the hypothesis that the high-energy metabolic intermediate acetyl∼P plays a key role in Rrp2 phosphorylation and , consequently , the activation of the Rrp2-RpoN-RpoS pathway . In this study we first extended the recent finding by Burtnick et al . [6] that Hk2 was not essential for Rrp2 activation under in vitro cultivation conditions , by further showing that the hk2 mutant was capable of activating the Rrp2-RpoN-RpoS pathway in a mammalian host-adapted model and establishing infection in mice . The fact that the hk2 mutant remained capable of upregulation of OspC and downregulation of OspA in the DMC model ( Fig . 2 ) indicates that this sensor kinase and its PAS sensing domain does not play a major in sensing mammalian host-specific signals for RpoS activation . We next tested the hypothesis that Hk1 , the only other B . burgdorferi histidine kinase with no assigned function , could be responsible for activation of the Rrp2 pathway . We found that the hk1 and hk1 hk2 mutants exhibited normal levels of temperature-induced Rrp2-dependent OspC expression . We further found that spirochetes lacking other histidine kinases identified in the B . burgdorferi genome , the chemotaxis histidine kinases CheA1 or CheA2 , also exhibited normal OspC expression . One caveat is that we have not tested cheA1 hk2 and cheA2 hk2 double mutants and thus cannot formally rule out a possible compensatory effect between Hk2 and CheA1 or CheA2 . Several groups have reported the existence of atypical response regulators in other bacteria whose activities do not require phosphorylation of their receiver domains [46]–[48] . These atypical response regulators either do not possess the conserved aspartate residue shown to function as the phosphorylation site ( e . g . , HP1021 and HP1043 in Helicobacter pylori ) [46] , or lack conserved residues for Mg++ chelation , which is essential for phosphorylation ( e . g . , FrzS in Myxococcus or NblR in Synechococcus ) [47] , [48] . However , Rrp2 retains all the conserved residues for phosphorylation ( D52 ) , Mg++ binding ( D8 , D9 ) , and signal transduction ( T80 , F99 , K102 ) . Thus , it is unlikely that Rrp2 is an atypical response regulator . Indeed , in this study , we showed that Rrp2 can autophosphorylate using acetyl∼P as its sole phosphoryl donor . Furthermore , overexpression of the phosphorylatable receiver domain of Rrp2 ( Rrp2-N ) , but not variants of Rrp2-N that carry the D52A or D52E mutations , interfered with endogenous Rrp2 activity . This result is consistent with the assumption that Rrp2 activation requires phosphorylation of D52 . Another evidence supporting phosphorylation-dependent Rrp2 activation is our previous observation that the ATPase activity of Rrp2 , an activity that is essential for its transcriptional activation function , also is dependent on phosphorylation of Rrp2 [15] . Of note , overproduction of a protein from a strong constitutive promoter ( e . g . , flaB ) could have pleiotropic effects . An ideal approach to study the function of Rrp2 phosphorylation would be to replace the endogenous copy of rrp2 with the D52A mutant allele . Despite multiple efforts , however , we failed to generate the desired strain . This lack of success is consistent with previous reports that inactivation of rrp2 may be lethal [6] , [18] . We hypothesize that phosphorylated Rrp2 may be important for cell growth . Consistent with this hypothesis , overexpression of Rrp2 exhibited a moderate growth defect ( data not shown ) . The finding that activation of RpoS and OspC requires phosphorylation of Rrp2 but does not require any of the four histidine kinases led us to hypothesize that the phosphoryl donor might be a high-energy central metabolic intermediate [29] , [31] , [32] . Indeed , bioinformatic analysis of the B . burgdorferi genome revealed one pathway capable of producing carbamoyl-P ( ArcA-ArcB ) and one pathway that can synthesize acetyl∼P ( Ack-Pta ) . Loss of ArcA , which should result in the inability to synthesize carbamoyl-P , had no effect upon Rrp2-dependent expression , suggesting that carbamoyl-P does not serve as the phosphoryl donor to Rrp2 . Acetyl∼P is the intermediate of the Ack-Pta pathway . The Ack-Pta pathway functions in acetogenesis through the conversion of acetyl-CoA obtained from pyruvate into acetate; operation of this pathway in the opposite direction enables other bacteria to use acetate as a carbon source by activating acetate to acetyl-CoA , which subsequently enters the tricarboxylic acid ( TCA ) cycle . In some organisms , such as E . coli , the pathway is reversible and thus can function in both acetogenesis and acetate activation [32] . The relatively small genome of B . burgdorferi , an obligate parasite , does not encode any enzyme known to convert pyruvate to acetyl-CoA , nor does it encode the enzymes of the TCA cycle . Instead , B . burgdorferi performs lactogenesis , converting pyruvate to lactate [17] ( Xu H . and Yang , X . F . , unpublished result ) . As such , the main function of the Ack-Pta pathway of B . burgdorferi is likely not for converting acetyl-CoA to acetate , but for generating acetyl-CoA from acetate . This acetyl-CoA could then be used for cell wall synthesis ( via the mevalonate pathway [BB0683-BB0688] ) and possibly for other metabolic pathways ( Fig . 7A ) . Furthermore , B . burgdorferi seems to lack other acetyl-CoA synthetic pathways ( e . g . , the AMP-ACS pathway , β-oxidation of fatty acids , and several amino acid degradation pathways ) . Thus , the Ack-Pta pathway appears to be the sole pathway for biosynthesis of acetyl-CoA . If so , one would predict that the Ack-Pta pathway is essential for spirochetal growth . This notion is consistent with the fact that we failed to generate either an ack or a pta mutant by either targeted mutagenesis or random transposon mutagenesis ( data not shown ) . What's the source of acetate for B . burgdorferi ? Our measurement showed that acetate concentration in mouse blood and the midgut of fed ticks is ∼1 . 0 M and ∼1 . 8 mM , respectively ( Xu H . and Yang , XF , unpublished data ) . One of the ingredients of the BSK-H medium , CMRL , also contains 0 . 61 mM acetate ( other ingredients of this complex medium , such as rabbit serum , also may contribute to the overall levels of acetate ) . Through diffusion or an unknown transport system , B . burgdorferi may obtain sufficient acetate from these environments for acetyl-CoA production . Acetyl∼P has drawn attention as a global regulator of gene expression via its ability to donate its phosphoryl group to a subset of response regulators under certain environmental conditions [32] . In E . coli , the intracellular acetyl∼P concentration can reach levels sufficient to phosphorylate a subset of response regulators [49] and thus influence the biological processes controlled by those proteins [32] . Although we have not yet measured the intracellular acetyl∼P levels to determine if this is also the case in B . burgdorferi , we were able to provide three lines of evidence to support the conclusion that acetyl∼P plays an important role in Rrp2 activation: ( i ) the activation of the Rrp2-RpoN-RpoS pathway can be induced by increasing concentration of exogenous acetate ( Fig . 7B ) ; ( ii ) overexpression of Pta reduced acetate-induced activation of the Rrp2-RpoN-RpoS pathway ( Fig . 7C ) ; and ( iii ) acetyl∼P served as a phosphoryl donor to Rrp2 in vitro ( Fig . 7E ) . Note that overexpression of Pta did not completely abolish OspC production , suggesting that a low level of Rrp2 activation still occurs . This might be due to the presence of low levels of acetyl∼P , as overexpression of Pta does not abolish the production of acetyl∼P . Alternatively , Hk2 may contribute to Rrp2 activation . We are currently in the process of testing this possibility by overexpressing Pta in the hk2 mutant . Nevertheless , this partial inhibition of RpoS and OspC expression by overexpression of Pta is consistent with the in vivo phenotype that overexpression of Pta resulted in a moderate reduction of spirochetal infectivity in mice ( Table 3 ) . It is well established that the Rrp2 pathway can be activated by many environmental cues such as temperature , pH , cell density , oxygen , and CO2 levels [37]–[39] , [45] , [50] , [51] . However , the underlying mechanism for these phenomena has not been elucidated . In this regard , it is striking that virtually all the environmental cues that activate the Rrp2 pathway also have been shown to influence the acetyl∼P pool in E . coli [32] . This observation is consistent with our hypothesis that acetyl∼P serves as a signaling molecule that responds to environmental cues and in response activates the Rrp2 pathway . Indeed , we showed that overexpression of pta greatly inhibited both temperature- and cell density-induced activation of Rrp2 ( Fig . 7D ) , suggesting that elevated temperature and increased cell density activate the Rrp2-RpoN-RpoS pathway in an acetyl∼P-dependent manner . Elevated temperature may increase acetyl∼P levels by enhancing diffusion of acetate into the cells and/or from increased transport efficiency via an unidentified transport system for acetate . Elevated temperature also increases cell growth rates that likely lead to increased levels of acetyl∼P [32] , [52] . The effect of increased cell density on acetyl∼P levels , on the other hand , can result simply by a change in extracellular pH . As cell density increases , the culture pH diminishes from 7 . 5 to 7 . 0 or lower [38] , which favors the passive diffusion of acetate into the cells [32] . One caveat of this study is that we used expression of RpoS and OspC as the readout for Rrp2 phosphorylation . An ideal approach for such study would be directly to detect the phosphorylated form of Rrp2 . Unfortunately this approach is not technically feasible since most forms of the Asp-phosphorylation are unstable and there is no antibody available for detecting Asp-phosphorylation . Thus , a common approach for studying phosphorylation of response regulators is to monitor the output product as a result of phosphorylation of a response regulator . In the case of Rrp2 , the only direct target gene identified thus far is rpoS and therefore , expression of rpoS faithfully reflects the activation of Rrp2 modulated by phosphorylation . One concern for this approach is whether the effect on RpoS expression observed in this study is through another transcriptional activator , BB647 ( BosR ) . BB647 is a fur homologue and was recently shown that inactivation of this gene significantly reduced rpoS and ospC expression [53]–[56] . Although it remains unclear how BosR fits into the Rrp2-RpoN-RpoS pathway , we found that neither overexpression of Rrp2-N nor overexpression of Pta affected the level of BosR ( data not shown ) , suggesting that the effects of Rrp2-N or Pta overexpression on RpoS and OspC was not through BosR , rather through Rrp2 . In summary , we have shown that temperature- and cell density-induced the activation of the Rrp2-RpoN-RpoS pathway proceeds independently of histidine kinases and carbamoyl-P . In contrast , biochemical and genetic manipulation of the acetyl∼P-producing Ack-Pta pathway dramatically impacts activation of the Rrp2-RpoN-RpoS pathway , providing strong evidence that acetyl∼P plays an important role in Rrp2 activation under in vitro growth conditions . We also provide evidence showing that , during mammalian infection , the Rrp2-RpoN-RpoS pathway is also activated via an Hk2-independent mechanism and that acetyl∼P plays an important role in this process . Then , what is the function of Hk2 ? One possibility is that Hk2 may play a role in sensing host signals and activating Rrp2 during the process of tick feeding . In this regard , we have examined the phenotype of the hk2 mutant in ticks and found that the hk2 mutant indeed has reduced infectivity via the route of tick infestation . Unfortunately , we have not been able to construct an infectious complemented strain and , thus , have been unable to show restoration of this defect , which prevents us from drawing a definitive conclusion on Hk2 function in the enzootic cycle of B . burgdorferi . Nevertheless , this preliminary finding suggests that Hk2 may contribute to Rrp2 activation during the process of tick feeding . In addition , spirochetes likely have increased levels of intracellular acetyl∼P in feeding ticks , as they encounter increased temperature [39] , as well as a massive influx of nutrients that leads to a dramatic increase of growth rates during this process [57] , [58] . Thus , we postulate that while acetyl∼P plays an important in activating the Rrp2-RpoN-RpoS pathway during mammalian infection , both acetyl∼P and Hk2 are likely involved in integrating complex environmental and host signals to modulate the Rrp2-RpoN-RpoS pathway during the process of spirochetal transmission from ticks to mammals .
All animal experimentation was conducted following the NIH guidelines for housing and care of laboratory animals and performed in accordance with Indiana University Institutional regulation after review and approval by the institutional Animal Care and Use Committee at Indiana University . Low–passage , virulent B . burgdorferi strain B31-A3 was kindly provided by Dr . P . Rosa ( Rocky Mountain Laboratories , National Institute of Allergy and Infectious Diseases , National Institutes of Health ) [35] . Strain B31 13A that lacks lp25 was kindly provided by Dr . F . T . Liang ( Louisiana State University ) [59] . The rrp2 mutant was described previously [9] [20] . The cheA1 and cheA2 mutants were kindly provided by Dr . Li ( New York medical college , NY ) [44] . Borreliae were cultivated in Barbour-Stoenner-Kelly ( BSK-H ) medium ( Sigma , St . Louis , MO ) supplemented with 6% normal rabbit serum ( Pel Freez Biologicals , Rogers , AR ) at 35°C unless indicated otherwise . A shuttle vector pBSV2 ( a gift from Dr . P . Rosa ) was maintained in E . coli strain TOP10 . Relevant antibiotics were added to the cultures in the following final concentrations: 300 µg/ml for kanamycin and 50 ng/ml for erythromycin . To generate an hk2 mutant in strain B31-A3 , a 2 . 5 kb fragment containing hk2 and its surrounding region was amplified with primers hk2-delF and hk2-delR ( Supplemental Table S1 ) and cloned into the cloning vector pCR-XL-TOPO ( Invitrogen ) . The plasmid was digested with Hind III ( 19 bp downstream of the 5' end of hk2 ) and ClaI ( 637 bp upstream of the 3' end of hk2 ) , and a kanamycin-resistance cassette driven by the flaB promoter was then inserted into the disrupted hk2 gene ( Fig . 1A ) . The suicide vector was confirmed by sequencing , and the plasmid DNA was transformed into B . burgdorferi strain B31-A3 as previously described [9] , [60] . Whole cell lysates from positive clones were analyzed by PCR and Western immunoblot analysis using a monoclonal antibody against Hk2 to confirm marker insertion and inactivation of hk2 . The plasmid profiles of the hk2 mutant clones were determined by PCR analyses with twenty-one pairs of primers specific for each of the endogenous plasmids [61]–[63] . Two of the three randomly picked clones had plasmid profiles that were identical to the parental strain B31-A3 [35] , and one of these was chosen for further study . Dialysis membrane chambers ( DMCs ) containing 1×103 organisms diluted from a mid-logarithmic growth culture at 33°C in vitro , were implanted into the peritoneal cavities of female Sprague-Dawley rats as previously described [40] , [42] . The DMCs were explanted 192 h after implantation; the spirochetes then were harvested , washed with 1x PBS buffer , and then examined by SDS-PAGE and silver staining . To construct a suicide vector for inactivation of hk1 , regions of DNA corresponding to 1 . 3 kb upstream and 1 . 3 kb downstream of hk1 regions were PCR amplified from B31-A3 genomic DNA . The resulting DNA fragments were then cloned upstream and downstream of an erythromycin-resistant marker ( ermR ) within the pCR-XL-TOPO cloning vector , resulting in suicide vector pXY245 . The inserts of pXY245 were confirmed by sequencing . The plasmid DNA was transformed into B . burgdorferi 297 strain BbAH130 as previously described [9] , [60] , resulting in a mutant with 3 . 4 kb deletion within hk1 ( except the 460 bp to the 5' end and 385 bp to the 3' end of hk1 ) and an insertion of the ermR marker . Loss of hk1 expression was confirmed by RT-PCR analysis . To construct the hk1 hk2 double mutant , the suicide vector pHX-hk2-kan DNA was transformed into the hk1 mutant . Kanamycin and erythromycin-resistant clones were selected and the loss of hk2 was confirmed by Western immunoblot analysis using an anti-Hk2 monoclonal antibody . To constitutively express the wild-type Rrp2 N-terminal domain , the DNA fragment corresponding to the Rrp2-N terminal region was PCR-amplified from B . burgdorferi B31-A3 genomic DNA using primers rrp2-N-F and rrp2-N-R ( Supplemental Table S1 ) . Two restriction sites , NdeI and PstI , were incorporated into the designated primers and used for insertion of the digested PCR fragment into the pBSV2-derived shuttle vector pJD55 [4] harboring a flaB promoter . Thus , expression of Rrp2-N was placed under the control of the flaB promoter , flaBp-Rrp2-N . The resulting shuttle vector , pJD55/rrp2-N , was verified by sequencing and then transformed into B31 13A and B31-A3 . To introduce a single amino acid substitution ( D52A or D52E ) into the Rrp2-N terminal domain on pJD55/rrp2-N , site-directed mutagenesis was carried out by using the QuikChange II XL Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , CA ) with the mutagenic PAGE-purified primers D52A-F/D52A-R and D52E-F/D52E-R ( Supplemental Table S1 ) as described by the manufacturer . Briefly , PCR was carried out as follows: 95°C for 50 seconds , 60°C for 50 seconds , 68°C for 10 minutes and 18 cycles . The resulting shuttle vectors with point mutations in Rrp2-N were verified by sequencing and designated pJD55-Rrp2-N ( D52A ) and pJD55-Rrp2-N ( D52E ) , respectively . To overexpress Pta , the DNA fragment corresponding to pta ( bb0589 ) was PCR amplified from B . burgdorferi B31-A3 genomic DNA using primers Bb589F and Bb589R ( Supplemental Table S1 ) and then subsequently cloned into pJD55 , which places pta under the control of the flaB promoter . The resulting shuttle vector was verified by sequencing and then transformed into B31 13A and B31-A3 . The arcA mutant was generated by transposon-mediated mutagenesis as part of an on-going transposon signature tagged mutagenesis ( STM ) study . Briefly , twelve independent mutant libraries , each having a unique 7 bp sequence tag , were created using modified versions of the suicide plasmid pMarGentKan derived from pMarGent [64] ( kindly provided by Dr . P . E . Stewart , Rocky Mountain Laboratories , National Institutes of Health , Hamilton , MN ) . The resulting plasmids were transformed into B . burgdorferi B31 5A18; transformants were selected on solid BSK-II media containing 200 µg/ml of kanamycin and 40 µg/ml of gentamicin as described previously [65] . Transposon insertion sites were determined by restriction digestion of the Borrelia genomic DNA , plasmid rescue in E . coli , and sequencing , as described previously [1] . SDS-PAGE and immunoblot analyses were performed as previously described [66] . Monoclonal antibodies against OspC , RpoS , and FlaB were described previously [20] , [38] . Monoclonal antibodies against Rrp2 and HK2 were produced using a previously described method [66] . Rrp2-N fragments were detected using a previously reported polyclonal rat antiserum specific against full length Rrp2 [18] . Three or four week-old C3H/HeN mice ( Harlan , Indianapolis , IN ) were subcutaneously inoculated with spirochetes at a dose of 105 spirochetes per mouse . Ear punch biopsy and tissue samples ( skin , heart , spleen and joint ) were collected at the time points indicated for each experiment and cultured in BSK-H medium supplemented with 1× Borrelia antibiotic mixture ( Sigma , Saint Louis , MO ) . A single growth-positive culture was used as the criterion for infection of each mouse . All animal protocols were approved by the Institutional Animal Care and Use Committee at Indiana University . RNA samples were extracted from B . burgdorferi cultures using the RNeasy® mini kit ( Qiagen , Valencia , CA ) according to the manufacturer's protocols . Three independent culture samples were used for each strain . Digestion of contaminating genomic DNA in the RNA samples was performed using RNase-free DNase I ( Promega , Madison , WI ) , and removal of DNA was confirmed by PCR amplification using primers specific for the B . burgdorferi flaB gene [67] . The cDNA was synthesized using the SuperScript III reverse transcriptase with random primers ( Invitrogen , Carlsbad , CA ) . To quantify the transcript levels of ospC , an absolute quantitation method was used by creating a standard curve in qPCR assay by following the manufacture's protocol ( Strategene , La Jolla , CA ) . Briefly , a cloning vector containing the ospC gene serves as standard template . A series of ten-fold dilution ( 100 to 107 copies/µl ) of the standard template was prepared and qPCR was performed to generate a standard curve by plotting the initial template quantity against the Ct values for the standards . The quantity of the ospC and flaB in cDNA samples were calculated by comparing their Ct values of the Standard Curve plot . Both standards and samples were performed in triplicate on an ABI 7000 Sequence Detection System using GREEN PCR Master Mix ( ABI , Pleasanton , CA ) . Levels of ospC transcript were reported as per 1000 copies of flaB transcripts . Purification of recombinant Rrp2 protein was described previously [15] . The PCR fragments encoding Rrp2-N , Rrp2-N/D52A and Rrp2-N/D52E were cloned into the expression vector pGEX4t-2 with a glutathione S-transferase ( GST ) at the N-terminus . Fusion proteins GST-Rrp2 , GST/Rrp2-N , GST/Rrp2-N/D52A and GST/Rrp2-N/D52E were expressed in E . coli under inducible condition of 1 mM IPTG at 37°C for 6 hours . Proteins were purified from cell lysates using GST SpinTrap ( GE Healthcare , Piscataway , NJ ) according to the manufacturer's manual . [32P]acetyl phosphate was synthesized as described by Quon et al . [68] . Briefly , the reaction mixture includes 0 . 3 U E . coli acetate kinase ( Sigma ) , 10 µCi of [32P]ATP ( 6000 Ci/mmol , PerkinElmer ) in AKP buffer ( 25 mM Tris-HCl [pH 7 . 4] , 60 mM KOAc , 10 mM MgCl2; final pH 7 . 6 ) and was incubated at room temperature for 20 min . [32P]acetyl phosphate was used either without further treatment or with further purification by filtering through a 30 kDa cut-off membrane to remove acetate kinase ( Amicon ultra with 30 kDa cut-off , Millipore ) . [32P]acetyl phosphate was mixed with recombinant Rrp2 ( 2 . 5 µl , 0 . 7 or 1 . 4 µg ) , Rrp2-N ( 2 µg ) , Rrp2-N/D52A ( 2 µg ) , Rrp2-N/D52E ( 2 µg ) for 15 min or 30 min at 37°C . The reaction was terminated by addition of SDS-PAGE loading buffer and then loaded to 12% SDS-PAGE without boiling . The gel was then exposed to a Kodak X-ray film . | Borrelia burgdorferi , the causative agent of Lyme disease , is maintained in nature in a complex enzootic cycle involving Ixodes ticks and mammals . A novel regulatory network , the Rrp2-RpoN-RpoS pathway , which governs differential expression of numerous genes of B . burgdorferi , is essential for this complex life cycle . In this study , we provide evidence showing that the activation of the Rrp2-RpoN-RpoS pathway is modulated , not by the predicted histidine kinase for Rrp2 , but rather by acetyl phosphate ( acetyl∼P ) , the intermediate of the Ack-Pta ( acetate kinase-phosphate acetyltransferase ) metabolic pathway . Based on our findings , we propose that during the enzootic cycle of B . burgdorferi , changes in environmental cues and nutrient conditions lead to an increase in the intracellular acetyl∼P pool in B . burgdorferi , which in turn modulates the activation of the Rrp2-RpoN-RpoS pathway . | [
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| 2010 | Role of Acetyl-Phosphate in Activation of the Rrp2-RpoN-RpoS Pathway in Borrelia burgdorferi |
Genomes contain tandem repeats that are at risk of internal rearrangements and a threat to genome integrity . Here , we investigated the behavior of the human subtelomeric minisatellites HRAS1 , CEB1 , and CEB25 in Saccharomyces cerevisiae . In mitotically growing wild-type cells , these GC–rich tandem arrays stimulate the rate of gross chromosomal rearrangements ( GCR ) by 20 , 1 , 620 , and 276 , 000-fold , respectively . In the absence of the Pif1 helicase , known to inhibit GCR by telomere addition and to unwind G-quadruplexes , the GCR rate is further increased in the presence of CEB1 , by 385-fold compared to the pif1Δ control strain . The behavior of CEB1 is strongly dependent on its capacity to form G-quadruplexes , since the treatment of WT cells with the Phen-DC3 G-quadruplex ligand has a 52-fold stimulating effect while the mutation of the G-quadruplex-forming motif reduced the GCR rate 30-fold in WT and 100-fold in pif1Δ cells . The GCR events are telomere additions within CEB1 . Differently , the extreme stimulation of CEB25 GCR depends on its affinity for Cdc13 , which binds the TG-rich ssDNA telomere overhang . This property confers a biased orientation-dependent behavior to CEB25 , while CEB1 and HRAS1 increase GCR similarly in either orientation . Furthermore , we analyzed the minisatellites‚ distribution in the human genome and discuss their potential role to trigger subtelomeric rearrangements .
Some chromosomal regions are more prone to rearrangement than others and thus are the source of genetic diseases and cancer . Among “at risk” sequences , tandem repeats like microsatellites and minisatellites that differ by the length of their repeat unit ( 1–10 nt and 10–100 nt , respectively ) are prone to changes in repeat number ( expansion and contraction of the array ) [1] . Mechanistically , this instability can be explained by the propensity of the motifs to misalign during template-directed repair of endogenous lesions , occurring stochastically or promoted by the nucleotide sequence themselves , which , for example , can perturb replication . Consistently , their instability is exacerbated by defects of replication proteins ( like Rad27 or Polδ ) that ubiquitously affect genome integrity [2]–[7] . Intrinsic features of repeated sequences also play a role in the formation of rearrangements [1] . Microsatellite instability caused by hairpin formation during replication has been well documented [8] but less is known about minisatellite instability . Sequence composition and its ability to interact with endogenous factors and/or to adopt secondary structures can be invoked . Among these are G-quadruplexes . They are four-stranded structures that some G-rich nucleic acids form spontaneously in physiological salt and pH conditions in vitro [9] . A growing body of evidence implicates these structures in several biological processes , like directed genome rearrangements [10] , [11] , telomere capping [12] , [13] , and control of gene expression at the transcriptional and post-transcriptional levels [14] , [15] . Recently , we showed that the GC-rich human minisatellite CEB1 forms G-quadruplexes in vitro and demonstrated that Pif1 , a conserved 5′-3′ helicase , unwinds these G-quadruplexes [16] . In Saccharomyces cerevisiae , Pif1 prevents the formation of G-quadruplex-dependent CEB1 internal rearrangements during leading-strand replication and , consistently , the treatment of WT cells with the potent G-quadruplex binder Phen-DC3 mimicks the absence of Pif1 [16] , [17] , [18] . A different but perhaps related feature of the human GC-rich minisatellites with respect to genome stability is their clustering in the chromosomal subtelomeric regions [19] , [20] that are subjected to pathological terminal truncations [21]–[24] . The genomic factors involved in the highly dynamic behavior of terminal regions being poorly identified , here we examined the fragility of the subtelomeric human minisatellites HRAS1 [25] , CEB1 [26] and CEB25 [27] and the role of their specific sequence features in the induction of Gross Chromosomal Rearrangements ( GCR ) in S . cerevisiae . To this end , we employed the GCR assay developed by R . Kolodner and colleagues [28] that measures the rate of the yeast chromosome V terminal deletion . We showed that the three minisatellites and sequence variants stimulated the formation of GCR in WT cells to different extents depending on several factors: the number of motifs in the tandem array , the ability to form G-quadruplexes , the presence of Cdc13 binding sites , their orientation which yields different type of rearrangements , and/or the activity of Pif1 and of the homologous recombination pathway . Altogether , these results point to GC-rich minisatellites as major at-risk regions of the genome not only for changes in repeat number but also for their propensity to generate structural variants .
To study the behavior of human GC-rich minisatellites in the formation of GCR , we employed the genetic assay developed by Chen and Kolodner [28] . In this sensitive assay , the left arm of chromosome V was engineered to measure the rate of the simultaneous loss of the CAN1 and URA3 markers located in the terminal non-essential part of the chromosome V . Cells that undergo a GCR event that results in the simultaneous loss of URA3 and CAN1 are recovered on media containing canavanine and 5-fluoro-orotic acid ( 5-FOA ) . Fluctuation analysis of the number of growing colonies provide a very sensitive GCR assay ( see Materials and Methods ) , ranging over several order of magnitude since in WT cells , the GCR rate is approximately 10−10 events per generation [28] . We inserted the minisatellites centromere-proximal to CAN1 within the non-essential NPR2 locus , together with the Hygromycin resistance gene ( hphMX ) ( Figure 1A ) . Importantly , the HYGR cassette has a GC-content of 58% , does not share homology with the yeast genome , and is devoid of potential G-quadruplex-forming sequences or Cdc13 binding sites . Hereafter , to compare strains with similar size inserts , the hphMX construct constitutes our “no minisatellite” control strain . Altogether , we examined three subtelomeric GC-rich human minisatellites: CEB1 [26] , CEB25 [19] , and the minisatellite located in the promoter of the HRAS1 gene [25] . They are tandem arrays with motif lengths of 39 , 52 , and 28 nt , respectively . The sequence of the consensus motif and additional features of these minisatellites are indicated in Table 1 . Furthermore , it is known that the CEB1 and CEB25 , but not the HRAS1 motifs , can form stable G-quadruplex structures in vitro [16] , [27] . All three minisatellites were inserted in both chromosomal orientations at the same locus . In the orientation ‘“G” , the G-rich strands of CEB1 is on the same strand as the G-rich 3′ ssDNA overhang of the chromosome V left-arm telomere ( distance is approximately 45 Kb ) , while in the orientation “C” , the C-rich strand is on the same strand as the G-rich 3′ overhang ( Figure 1B ) . All the rates measured throughout this study are reported in Table S3 . Hereafter , unless otherwise stated , the inserts we refer to are in the “G” orientation . First , we examined GCR rates in the absence of minisatellite inserts . Previous studies provided an estimated rate of 3 . 5 . 10−10 events/generation for WT cells [28] . Consistently , the control strain ( npr2::hphMX ) exhibits the same GCR rate as the parental ( NPR2+ ) RDKY3615 haploid strain ( 4 . 3×10−10 vs . 4 . 2×10−10 events/generation , respectively ) . Thus , adding approximately 1 . 8 Kb of a non-repeated GC-rich DNA to the 13 kb region permissive for rearrangements ( located between CAN1 and the first centromere-proximal essential gen , PCM1 ) has no detectable effect . Then , we measured the consequence of the insertion of the CEB1-WT-1 . 7 allele containing 43 motifs [16] , CEB25-WT-0 . 7 ( 13 motifs ) and HRAS1-0 . 7 ( 26 motifs ) . Compared to the control strain ( hphMX ) , these minisatellites strongly but differentially increased the GCR rate in WT cells: 20-fold for HRAS1 ( 8 . 48×10−9 events/generation ) , 1 , 620-fold for CEB1 ( 6 . 97×10−7 events/generation ) and 276 , 000-fold for CEB25 ( 1 . 16×10−4 events/generation ) ( Figure 1C ) . Pif1 is a conserved 5′-3′ helicase that suppresses GCR events by telomere healing [29] , [30] through direct removal of the telomerase from DNA ends [31] . Pif1 is also involved in G-quadruplex unwinding [16] . We constructed pif1Δ cells carrying the minisatellites . Consistent with previous findings [29] , [32] , in the “no-insert” and in our control insert strain , the GCR rates are increased approximately 1500–2250-fold ( 6 . 63×10−7 and 1 . 01×10−6 events/generation , respectively ) in the pif1Δ strain compared to WT . The presence of the minisatellites had various quantitative effects . Compared to the control pif1Δ strains , HRAS1 , CEB1 and CEB25 stimulated the GCR rate 3 . 6-fold ( 3 . 68×10−6 events/generation ) , 385-fold ( 3 . 89×10−4 events/generation ) and 120-fold ( 1 . 21×10−4 events/generation ) , respectively ( Figure 1D ) . If we now compare the WT and the pif1Δ cells carrying the same minisatellite , the absence of Pif1 increases the GCR rate of HRAS1 and CEB1 approximately 500- and 558-fold , but has no effect on CEB25 . This insensitivity to Pif1 reflects the already high rate of GCR induced by CEB25 in WT cells . The heterogeneous behavior of this set of minisatellites suggests that specific sequence features modulate their propensity to trigger GCR , in both WT and pif1Δ cells . The CEB1 motif forms G-quadruplexes that are efficiently unwound by Pif1 in vitro [16] , [18] . To determine the role of the G-quadruplex forming sequences of CEB1 on GCR rate , we first examined the behavior of the CEB1-Gmut-1 . 7 array which does not form G-quadruplex ( Figure 2A ) [16] . In the WT strain background , the insertion of CEB1-Gmut-1 . 7 yields a GCR rate of 2 . 06×10−8 events/generation . This is 65-fold higher than in the control strain but 30-fold lower than in the CEB1-WT-1 . 7 cells carrying the same number of G quadruplex forming motifs ( Figure 2B ) . These results indicate that the effect of CEB1 on GCR rate is both G-quadruplex-independent and –dependent . Similarly , we examined the behavior of the CEB1-Gmut-1 . 7 allele in pif1Δ cells . The GCR rate was stimulated 6-fold ( 6 . 32×10−6 events/generation ) compared to the control pif1Δ strain , but was 62-fold lower than in the CEB1-WT-1 . 7 cells ( Figure 2C ) . This level is similar to the GCR rate induction observed with the HRAS1-0 . 7 minisatellite also devoid of G-quadruplex-forming sequence . We conclude that , in both WT and pif1Δ cells , the induction of GCR by CEB1 strongly depends on its potential to form G-quadruplexes . To confirm the stimulating role of G-quadruplex , we compared the rate of GCR in cells treated or not with the G-quadruplex-stabilizing ligand Phen-DC3 [33] . The treatment of WT cells bearing CEB1-WT-1 . 7 with 10 µM Phen-DC3 yielded a GCR rate of 3 . 65×10−5 events/generation , 52-fold higher than in the untreated cells ( Figure 2D ) . We verified that this induction was not due to a better growth rate of cells having performed a GCR in the presence of the ligand ( Figure S1 ) . In contrast , Phen-DC3 failed to increase the GCR rate in CEB1-Gmut-1 . 7 cells ( 3 . 56×10−8 events/generation ) ( Figure 2D ) . We also assayed concentration effects and treatment with Phen-DC6 , a compound related to Phen-DC3 [33] . Clearly , the extent of GCR rate induction in WT cells carrying the CEB1-WT-1 . 7 minisatellite was stimulated by both ligands and is dependent on their concentration ( Figure 2E ) . Finally , since our previous studies examined G-quadruplex-dependent expansion/contraction of CEB1 in different chromosomal locations [16]–[18] , we determined the frequencies of CEB1 expansion/contraction in this chromosome V location . As previously observed on Chr . III and VIII , the CEB1-WT-1 . 7 array was rather stable in WT cells ( 3/192 rearrangements ) and became frequently rearranged upon treatment with Phen-DC3 ( 39/192 , p-value vs . untreated = 8 . 8e−10 ) or PIF1 deletion ( 16/192 , p-value vs . WT = 3 . 55e−3 ) ( Table 2 ) . This depends on the presence of the G-quadruplex-forming sequences , since the CEB1-Gmut-1 . 7 allele remained stable in the above conditions ( Table 2 ) . We conclude that the impairment of the G-quadruplex unwinding capability of the cell , either by adding G-quadruplex-stabilizing ligands in WT cells or by deleting PIF1 , stimulates the propensity of the G-quadruplex-prone CEB1 minisatellite to undergo a high level of expansion/contraction and to a lesser extent GCRs . Next , we examined the relationship linking the total number of motifs in the CEB1 array and the GCR rate , both in WT and pif1Δ cells . We observed that the rate of GCR in WT cells was positively correlated to the number of repeats ( p-value = 2 . 8×10−3 , Spearman's correlation test ) ( Figure 2F ) , with rates ranging from 1 . 1×10−8 events/generation for the allele of 0 . 66 kb ( 17 motifs ) to 1 . 59×10−5 events/generation ( 37 , 000-fold higher ) for the longest allele of 2 . 7 kb ( ≈70 motifs ) . The straight slope in logarithmic scale suggests that the relationship linking the motif number and the GCR rate is roughly exponential . Similarly , CEB1-Gmut also induces the formation of GCR in a size-dependent manner ( p-value = 2 . 8×10−3 ) ( Figure 2F ) , but with a lower slope: an allele of 1 . 9 kb ( ≈49 motifs ) induced a GCR rate only 4-fold higher than a 0 . 9 kb allele ( 23 motifs ) ( 3 . 52×10−8 and 8 . 64×10−9 events/generation , respectively ) . In the absence of Pif1 , the GCR rates also increased exponentially with the number of CEB1-WT repeats ( p-value = 3 . 97×10−4 ) ( Figure 2G ) . Hence , we conclude that the number of repetition of the minisatellite motif is an aggravating factor of the fragility of these sequences , being steeper with the G-quadruplex-forming ones . To determine the nature of the GCR events induced by CEB1-WT-1 . 7 , we isolated a set of Can/5FOA-resistant colonies from independent cultures to avoid sibling events and analyzed their genomic DNA by Southern blot . The DNA was digested with a restriction enzyme cutting in the centromere proximal part of CEB1 and successively visualized with a CEB1 and a telomeric probe on the same blot . In the majority of colonies isolated in the WT strain background ( 29/31 , 94% ) it revealed a smeared CEB1 hybridizing band , which co-hybridized with the telomeric probe ( Figure 3A ) . Similar events and proportion were found for the WT strain treated with Phen-DC3 ( 18/18 ) , pif1Δ cells ( 18/19 ) ( Figure 4C ) , and WT cells carrying the CEB1-Gmut-1 . 7 array ( 8/10 events ) . Thus , these GCR are likely telomere addition ( telomere have variable length in the cell population ) associated with a variable number of residual CEB1 motifs . Analysis of the median length of the smeared band allowed us to roughly determining the number of remaining CEB1 motifs . In untreated WT cells , the events were evenly distributed along the 43 CEB1-WT motifs with the median telomere addition at the 25th motif ( Figure 3B ) . In contrast , upon treatment of WT cells with Phen-DC3 , or deletion of PIF1 , telomere addition sites shifted significantly toward small fragments , with a median of 11 ( p-value = 6×10−4 ) and 15 ( p-value = 9 . 1×10−3 ) motifs , respectively ( Figure 3B ) . These results indicate that ( i ) irrespectively of the nature of the CEB1 array , most GCR events are telomere addition within CEB1 , ( ii ) telomere addition can occur at numerous places within the CEB1 array thus leaving a variable number of CEB1 motifs , and ( iii ) impairing the ability of cells to unwind G-quadruplexes ( Phen-DC3 and pif1Δ ) is associated with an increased loss of CEB1 motifs . To gain higher resolution mapping of the telomere healing events within the CEB1 motifs , we sequenced a set of CEB1-telomere junctions using Ion Torrent Next-Generation Sequencing technology after purification of appropriate DNA bands on agarose gel ( see Materials and Methods ) . We identified the CEB1-Tel junctions from 15 untreated and 12 Phen-DC3-treated WT cells ( Figure 3C and Figure S2 , respectively ) . Telomere additions occur mainly at regions of the CEB1 motif that exhibit limited homology to the yeast telomeric sequence . Precisely , 10/27 CEB1-telomere junctions lie in the longest sequence of homology between CEB1 and the telomeric sequence ( GGGTGG ) and 24/27 junctions have at least two nucleotides in common between CEB1 and the telomeric sequence ( shown in blue in Figure 3C ) . This result is consistent with previous observations showing that for de novo telomere addition to occur , homology to telomeric sequence of 2-bp ( TG , GG , and GT dinucleotide ) is sufficient and that a longer homology facilitates telomere healing [30] , [34] , [35] . The fact that 62% of the telomere additions occur in , or at , the junction with the G-quadruplex-forming sequence of CEB1 ( red lines Figure 3C ) is consistent with the fact that 60% of the TG , GG , and GT dinucleotides overlap this sequence . The distribution of the telomere addition within the CEB1 motif is not significantly different in untreated and Phen-DC3-treated WT cells ( Figure S2 ) . Hence , although the Phen-DC3 treatment strongly increases the rate of GCR ( Figure 2D ) and affects the position of the telomere addition in the array ( Figure 3B ) , the position of the CEB1-telomere junction remains unaffected and mainly lies in the G-quadruplex-forming sequence ( Figure 3C and Figure S2 ) . Altogether , these results suggest that the G-quadruplexes present within the CEB1 array in conditions where the capacity of the cell to unwind G-quadruplexes is impaired ( upon Phen-DC3 treatment or PIF1 deletion ) stimulate the formation of GCR associated with a decreased number of CEB1 motifs remaining in the final repair product . Telomere healing may occur by de novo telomere addition to a 3′ ssDNA extremity , especially in the absence Pif1 [29] , [34]–[36] , leaving a specific pattern of telomeric sequences [34] . However , among the 27 junctions we sequenced , we do not notice any obvious addition of a particular pattern of telomeric sequence in CEB1 . On the other hand , telomere addition could occur by capture of endogenous telomeric sequences by break-induced replication ( BIR ) [28] , [37] , [38] . We examined the effect of the deletion of the RAD51 or RAD52 genes that are required for BIR [37] , [38] but not for direct telomere addition by telomerase . It causes a 2-fold decrease of the GCR formation in strains bearing CEB1-WT-1 . 7 , with rates of 2 . 92×10−7 and 3 . 54×10−7 events/generation , respectively ( Figure 3D ) . The extent of the decrease is similar ( 3- to 5-fold ) upon Phen-DC3 treatment , with GCR rates of 1 . 13×10−5 and 7 . 12×10−6 events/generation in the rad51Δ and rad52Δ mutants , respectively ( Figure 3D ) . Interestingly , the molecular analyses of the nature of the events provided additional information . We found that the drop of the GCR rate in the absence of Rad52 is associated with a specific decrease of GCRs by telomere addition within CEB1 ( Figure S3 ) while the analysis of the CEB1-telomere junction sequences recovered from untreated or Phen-DC3-treated WT cells revealed the presence of SNPs around the junction in 4/6 strains ( Figure 3E ) . These SNPs are found either in the telomeric sequence only , or both in the CEB1 and the telomeric sequence around the junction ( Figure 3E ) . These intriguing observations suggest that in WT cells roughly half of the telomere healing events in CEB1 occur by BIR on an ectopic telomere sharing a region of limited homology with the CEB1 motif [28] . SNPs found at the junction may result from the correction of the heteroduplex formed between CEB1 and the telomeric sequence , and/or by misincorporation of nucleotides in the early BIR steps [39] . CEB1 strands strongly differ with respect to their GC composition ( GC-bias = 76 . 6% ) and the density of TG/GG/GT dinucleotide ( bias is 87% ) that seeds GCR by telomere healing ( Table 1 ) . We examined the behavior of CEB1 placed in the opposite orientation ( orientation C ) relatively to the distal telomere ( Figure 1B ) . Strikingly , in WT cells , the GCR rates of CEB1-WT-1 . 7 are similar in either orientation ( 6 . 97 and 7 . 47×10−7 events/generation ) ( Figure 4A ) and alike the G-strand , the GCR rates increase according to the total size of the array ( Figure S4 ) . Similarly , although occurring at various absolute rates , there is no significant orientation-dependent difference in all the other strains and conditions that we assayed ( Figure 4A and 4B , Table S3 ) . Namely , in WT cells carrying the CEB1-WT-1 . 7 array treated with Phen-DC3 ( 3 . 65 and 1 . 66×10−5 events/generation ) , CEB1-WT-1 . 7 in pif1Δ cells ( 3 . 89 and 4 . 6×10−4 events/generation ) , CEB1-Gmut-1 . 7 in WT ( 2 . 77 and 2 . 07×10−8 events/generation ) and pif1Δ cells ( 6 . 32 and 3 . 05×10−6 events/generation ) nor in cells carrying HRAS1-0 . 7 in WT ( 8 . 48×10−9 and 1 . 1×10−8 events/generation , ) and pif1Δ cells ( 3 . 68 and 3 . 2×10−6 events/generation ) ( Figure 4A and 4B , Table S3 ) . Hence , both in the WT and pif1Δ cells , the GCR rates induced by CEB1-WT-1 . 7 , CEB1-Gmut 1–7 , and HRAS1-0 . 7 are not affected by the minisatellite orientation on the chromosome . However , the pattern of rearrangements in the G and C orientations is very different ( Figure 3A and Figure 4C ) . In WT cells bearing CEB1-WT-1 . 7 in the orientation C , only 2/22 rearrangements are smears indicative of telomere healing . The DNA of two other colonies migrates at the size expected for an unaltered Chr . V . By PCR analysis of CAN1 and URA3 , we observed that clone 12 ( Figure 4C ) retained both genes . Sequencing identifies a mis-sense mutation in URA3 ( G411A ) and a frameshift in CAN1 ( del595G ) . It might be a rare case of two independent mutagenic events but more likely a mutagenic fill-in synthesis by BIR [39] , occurring in this case on the sister chromatid to restore a full-length chromosome V . The other clone has lost CAN1 and URA3 . Thus , it is a structural variant like the majority of events ( 19/22 ) , which manifest themselves as discrete bands of various sizes . Among them , 15 hybridize with both the hphMX and the CEB1 probes ( Figure 4C ) . The variable hybridization intensity of the CEB1 signal indicates that the amount of remaining CEB1 sequence in the rearranged chromosomes is different from one strain to another ( for example , compare lanes 6 and 10 in Figure 4C ) . It is interesting to note that in some cases ( 4/18 ) , two or more bands hybridizing both the CEB1 and hphMX probes are visible ( clones 1–3 , and 7 ) . To gain more insights into the nature of these rearrangements , we analyzed clones 1–4 by pulse-field gel electrophoresis and Comparative Genomic Hybridization ( CGH ) ( Figure S5 ) . All exhibit an abnormal migration of Chr . V , while the rest of the karyotype appears normal ( Figure S5A ) . As expected , CGH analysis revealed that the distal part of Chr . V containing URA3 and CAN1 is lost ( Figure S5B ) . Furthermore , complex changes in copy number on other chromosomes are detected ( details are reported in Figure S5 ) . To be noticed , Ty1 elements are present in the vicinity of the breakpoints , suggesting that they are preferred sites for GCR [40] . Thus , contrary to the prominent telomere additions observed in the G orientation , GCR induced by CEB1 in the C orientation are diverse and complex , as observed among spontaneous GCR events [28] , [40] . The similar rate but different product structures in the G and C orientations can be explained if they result from a similar initiating event but difference in repair; In the G orientation , BIR starting within CEB1 on a telomere substrate will process in the chromosomal distal direction and immediately heal the initiating lesion . In the C orientation , BIR on a telomere substrate will process in the proximal direction to copy the entire chromosome , thus leading to the formation of a dicentric molecule prone to secondary complex rearrangement ( s ) before stabilization [41] . Furthermore , to address the genetic requirements of these GCR events , we examined the role of the non-homologous end joining ( NHEJ ) and homologous recombination ( HR ) pathways . The GCR rate remains unchanged in the dnl4Δ mutant ( Figure S6A ) while we observed a small but significant 4-fold decrease of the GCR rate in the rad51Δ and rad52Δ mutants ( Figure S6A ) . In the absence of Rad52 , the remaining events are telomere additions ( 8/9 events ) ( Figure S6B ) suggesting that the HR pathway plays a major role in the formation of the structural but not telomere addition events generated by CEB1 in the C orientation . We next asked what could be the molecular reasons for the high GCR rate induced by CEB25 in orientation G , and the inability of Pif1 to suppress GCR induced by this construct in WT cells ( Figure 1C and 1D ) . The GCR rate is not dependent on Rad52 ( 3 . 9×10−4 events/generation ) and all events in WT cells ( 11/11 ) are telomere additions within CEB25 ( Figure S7 ) . Interestingly , we found that contrary to CEB1 , the GCR rate induced by CEB25 strongly depends on its orientation: the inversion of CEB25 caused a 516-fold decrease of the GCR rate in WT cells ( 2 . 24×10−7 events/generation ) . In pif1Δ cells , the GCR rate of CEB25 in the orientation C was close to the “no insert” control strain ( 2 . 41×10−6 vs . 1 . 01×10−6 events/generation ) . This strong orientation-dependency prompted us to investigate the sequence composition of the CEB25 motif . CEB25 has a GC content of 58% and exhibits an absolute GC-bias and GT/GG/TG dinucleotide bias ( Table 1 ) . Interestingly , it contains several consensus-binding sites for the 3′ telomeric overhang binding protein Cdc13 ( GTGTGGGTGTG , in which the first 4 nucleotides are critical [42] , underlined in Figure 5A ) [43] , [44] . Cdc13 , together with Stn1 and Ten1 , is a part of the CST complex involved in telomere capping and mutagenic DSB repair by addition of telomeric repeats at a 3′ ssDNA end [32] , [45]–[47] . This unique feature , compared to CEB1 and HRAS1 , led us to suspect that the recruitment of Cdc13 on CEB25 could be responsible for its GCR effect . To test this hypothesis , we conducted both in vitro and in vivo experiments . In vitro , we determined the affinity of the purified Cdc13 for the CEB25 motif upon gel shift assay ( Figure 5A ) . Cdc13 binds with high affinity to the CEB25 motif ( CEB25-WT ) , with a Kd = 6 . 4×10−11±10−11 M . Mutations of the Cdc13 binding sites present in the CEB25 motif ( CEB25-Cdc13mut ) resulted in a 44-fold lower affinity for Cdc13 ( Kd = 2 . 8×10−9±3×10−10 M ) ( Figure 5A ) . Then , to address the possibility that the high affinity of Cdc13 for CEB25 is responsible for the high GCR rate induced by this minisatellite only when the G-rich strand is in the same molecule than the distal telomere ( and thus can be directly extended by telomerase ) , we constructed and introduced in yeast a 1 . 4 kb CEB25 allele mutated for its Cdc13-binding sites ( CEB25-Cdc13mut-1 . 4 , same motif as in Figure 5A ) that kept the same GC content and did not change the G-triplets potentially involved in the G-quadruplex formation ( see below ) . Remarkably , in the orientation G , this construct induced a GCR rate of 3 . 07×10−7 events/generation . This is 713-fold higher than in the “no minisatellite” control strain , and 377-fold lower than with CEB25-WT-0 . 7 in the same orientation ( Figure 5B ) . Contrary to CEB25-WT , the GCR rate was not affected by the inversion of CEB25-Cdc13mut-1 . 4 ( 2 . 95×10−7 events/generation ) , indicating that the strong orientation dependency observed with CEB25-WT relies on the presence of the Cdc13-binding sites ( Figure 5B ) . Additionally , in the absence of Pif1 , CEB25-Cdc13mut-1 . 4 also shows a decreased GCR rate compared to CEB25-WT-0 . 7 in the orientation G ( 60-fold ) ( Figure 5C ) . Again , the GCR rate induced by CEB25-Cdc13mut-1 . 4 was similar in both the orientations G and C ( 3 . 86 and 2 . 89×10−6 events/generation , respectively ) , and close to the control pif1Δ strain ( 1 . 01×10−6 events/generation ) ( Figure 5C ) . Hence , the orientation-dependent and Pif1-independent behavior of CEB25-WT is associated with the ability of its motifs to bind the accessory telomerase subunit Cdc13 with high affinity . CEB25 contains a consensus G-quadruplex-forming motif ( Table 1 ) that forms a monomorphic G-quadruplex whose structure has been recently solved by NMR [27] . To investigate the potential involvement of G-quadruplexes in the fragility of CEB25 , we first examined the GCR rate of CEB25-Cdc13mut-1 . 4 in the WT and pif1Δ strains ( mutations of the Cdc13 binding sites does not change the G-triplets involved in G-quadruplex formation ) . We found that GCR rates were ( i ) similar in these strains ( Figure 5C ) , ( ii ) occurred at a low level comparable to CEB1-Gmut-1 . 7 ( Figure 4B ) and HRAS1 ( Figure 1D ) and ( iii ) lower than for CEB1-WT-1 . 7 ( Figure 4B ) . To investigate the potential role of the CEB25 G-quadruplex forming sequences , we synthesized a CEB25 allele mutated for both the G-tracts and the Cdc13 binding sites ( CEB25-Cdc13mut-Gmut-1 . 4 ) . Clearly , the Phen-DC3 treatment of WT cells bearing CEB25-Cdc13mut-1 . 4 and CEB25-Cdc13mut-Gmut-1 . 4 alleles in both orientations yielded no increase of the GCR rates ( Figure 5D ) . This did not depend on the absence of intact Cdc13 binding sites since the CEB25-WT-0 . 7 allele in the orientation C also remained insensitive to Phen-DC3 ( Figure 5D ) . Rather , the G-quadruplex-forming and the G-mutated versions of CEB25-Cdc13mut exhibited exactly the same rates of GCR in WT cells . This absence of effect of Phen-DC3 contrasts with the 22- to 52-fold inductions observed with CEB1-WT upon WT cells treatment ( Figure 4A ) . We then combined the deletion of PIF1 to the Phen-DC3 treatment , conditions that yielded synergistic destabilization of CEB1 [18] . We observed a weak 5 . 5- , 2 . 3- and 4 . 6-fold induction of the GCR rates upon treatment of cells bearing CEB25-WT-0 . 7 in the orientation C , and CEB25-Cdc13mut-1 . 4 in the orientations G or C , respectively ( Figure 5E ) . No induction was seen upon treatment of cells bearing the G-mutated version of CEB25-Cdc13mut ( Figure 5E ) . These extreme conditions revealed a slight G-quadruplex-dependent GCR induction by CEB25 . Since the minisatellites studied here induced the formation of GCR , we wished to gain more insights into the GC-rich minisatellite representation and localization in the human genome . Using Tandem Repeat Finder [48] , we determined a list of 353 , 460 minisatellites ( Table S4 ) . They are not evenly distributed along chromosome arms ( Figure 6A ) [20] , being enriched in the 10 and 5% terminal arm regions ( Figure 6B ) . Interestingly , it seems to relate to their GC-content since the 85 , 222 minisatellites ( 24% ) that have a GC-content higher than 50% preferentially localize at the most terminal parts of the chromosome , whereas the other minisatellites with a lower GC-content are evenly distributed along the arms ( Figure 6A and 6B ) . A similar bias has been previously reported for chromosome 22 [19] . Then , we examined the minisatellites having potential G-quadruplex-forming sequences . Five percent ( 18 , 906 ) of the minisatellites bear at least one G-quadruplex-forming sequence ( see Materials and Methods ) , and 96% ( 18 , 191 ) of these G-quadruplex-forming minisatellites are GC-rich ( Table S5 ) . Among the 504 minisatellites that contain at least 30 G-quadruplex-forming sequences due to their tandem repeated structure , 60% ( 313/504 ) lie within the terminal 10% of chromosome arms , among which 80% ( 253/313 ) lie within the terminal 5% , while keeping a constant GC-content ( Figure S8 ) . Hence , GC-rich and G-quadruplex-forming minisatellites appear to preferentially cluster towards the chromosomal extremities ( Figure 6C ) . The mutagenic behavior of HRAS1 , CEB1 and CEB25 arrays in yeast described here raises the possibility that the human GC-rich minisatellites play a role in GCRs of the terminal part of human chromosomes .
Spontaneous GCR in WT cells occurs at a very low rate ( 10−10 ) . It yields a variety of rearrangements that delete the non-essential distal chromosomal region and rescue the chromosome by telomere addition at breaks that contain limited homology to telomere-like seed sequences as well as through more complex genome rearrangements [28] . Two factors may increase the rate of GCR: an increased number of initiating lesions or defects in the repair pathways [28] , [29] . Regarding the later possibility , as previously reported , we observed that Pif1 plays an important role in suppressing the formation of GCR by telomere healing [29] , [30] , [32] , [49] . In all but one of our minisatellite insertions , GCR rates were increased by several orders of magnitude upon PIF1 deletion . However , in sharp contrast , the extreme GCR rate stimulated by CEB25 in WT cells remained roughly the same in pif1Δ cells . This insensitivity to Pif1 depends on the orientation of CEB25 relative to the distal telomere ( G-strand in the same orientation as the single-stranded telomere G-overhang is the most active ) in agreement with the ability of the motif to bind the endogenous Cdc13 yeast protein with high affinity ( Figure 5A and 5B ) . Clearly , the mutation of the three Cdc13-binding sites yields a ≈380-fold reduction in GCR , consequently abolishing the CEB25 orientation-dependent behavior . The simplest interpretation of these results is that the recruitment of Cdc13 to CEB25 is sufficient to overcome the suppressive effect exerted by Pif1 to prevent the recruitment of the telomerase [46] . This is consistent with the Pif1-independent de novo telomere addition at a long internal telomeric tract ( TG ) 81 introduced near an unrepairable HO break [32] . In our assay , due to its motif sequence and its organization in tandem repeats , the human CEB25 minisatellite fortuitously resembles a pseudo-telomere . On the other hand , the HRAS1 , CEB1 , CEB1-Gmut , CEB25-Cdc13mut and CEB25-Cdc13mut-Gmut tandem arrays devoid of Cdc13 binding sites also induce GCR but at various rates and in an orientation-independent manner . Among the parameters potentially involved in the fragility of CEB1 , its ability to form G-quadruplexes appeared as an important destabilizing feature . Compared to the CEB1-Gmut-1 . 7 construct , the G-quadruplex-prone CEB1-WT-1 . 7 allele stimulates the GCR rate in WT cells 30-fold ( Figure 2B ) and accordingly the conditions that shift the equilibrium toward the folded G-quadruplex state increase the GCR rate : 52-fold upon treatment with the G-quadruplex stabilizing ligand Phen-DC3 and 558-fold in the absence of the G-quadruplex unwinding helicase Pif1 ( Figure 2B and 2C , Figure 3D and 3E ) . However , it should be emphasized that a predictive G-quadruplex-dependent phenotype cannot be safely ascertained from the presence of a consensus G-quadruplex motif in a given sequence , nor its ability to form stable G-quadruplexes in vitro . Indeed , contrary to CEB1 , the CEB25 array did not responded in vivo to the three conditions that affect G-quadruplex-dependent events ( G quadruplex motif mutation , treatment with PhenDC3 or Pif1 deletion ) except slightly , when combining the Phen-DC3 treatment to the PIF1 deletion ( Figure 5E ) . This synergistic combination previously observed for CEB1 [18] appears as an extreme hypersensitive condition that may lead to the rare accumulation of unprocessed CEB25 G-quadruplexes . The distinct behavior of CEB1 and CEB25 may rely on different conformations of their respective G-quadruplexes affecting their folding and/or their processing in vivo . Besides sequence affinity to Cdc13 and potential to form G-quadruplexes , a third aggravating factor stimulating the GCR rate is the total number of motifs . Thanks to the sensitivity of this GCR assay , we found that the GCR rate of CEB1 arrays increased exponentially with the number of motifs without an apparent threshold in both WT and pif1Δ cells ( Figure 2F and 2G ) . Interestingly , a similar exponential relationship between the number of motifs and the propensity of the triplex-forming ( GAA ) n repeats [50] to form GCRs [51] and expansions [52] has also been reported in yeast . It suggests the intriguing possibility that the capacity of tandem arrays to form secondary structures is a relevant feature . Along this line , we know that a tandem of two and three CEB25 motifs is able to form a pearl-necklace monomorphic G-quadruplexes structure [27] . If CEB1 is also able to form a pearl-necklace G-quadruplexes structure , the size-dependent exponential increase of the GCR rate may reflect the cooperative behavior between the CEB1 motifs to fold into G-quadruplexes . Mechanistically , we recently reported that the CEB1 G-quadruplex prone array perturbs replication and lead to expansion and contraction events [17] . As we proposed , the blockage of the DNA polymerase ( s ) at the first G-quadruplex may be sufficient to trigger the accumulation of ssDNA between the replication forks and the polymerase and thus enhance the formation of G-quadruplexes per cell and per molecule in a manner related to the total number of repeats . This situation may be similar to the Pol2 slowdown observed at single G-quadruplex-forming motifs under treatment of Pif1-deficient cells with the replication inhibitor hydroxyurea [53] . In addition to the effect of G-quadruplexes , other non B-DNA secondary structures can be the source of sequence fragility [8] . However , we found that the HRAS1-0 . 7 and CEB25-Cdc13mut-Gmut-1 . 4 minisatellites , devoid of potential G-quadruplex or other secondary structures , also stimulated the GCR rate by 20- and 700-fold in WT cells , respectively . In addition , once the G-quadruplex-forming capacity of CEB1 was removed by site-directed mutations , we noted that the CEB1-Gmut-1 . 7 construct was still able to stimulate GCRs at a substantial level ( ≈2×10−8 events/generation ) , approximately 60-fold higher than in the control WT strain . Similarly , the structure-free ( ATTCT ) n microsatellite has been reported recently to induce chromosomal fragility in WT yeast cells , which increase with the number of motifs [54] . However , the slope of this length-dependent effect could not be derived from these experiments since only two different allele sizes have been assessed [54] . The analysis of CEB1-Gmut allele of various lengths ( 23–70 motifs ) revealed a length-dependent fragility in WT cells in an almost linear manner ( multiplying the number of motifs by two increased the GCR rate by 4 ) , in sharp contrast with the exponential slope observed with CEB1-WT ( Figure 2F ) . This difference suggests that the G-quadruplex-independent fraction of the CEB1 fragility does not involve a cooperative behavior between the motifs . What remaining sequence properties could account for this structure-independent fragility ? The GC-richness per se can be invoked , since it has been shown to slowdown DNA polymerases in vitro [55] . In the case of our minisatellites , however , three reasons argue against its essential role . First , with similar size arrays , the GCR induction is not clearly correlated to the GC-richness: HRAS1 ( GC = 67% ) and CEB1-Gmut ( 72% ) both stimulated the GCR rate ≈20-fold compared to the no insert strain , but ≈35-fold less than CEB25-Cdc13mut ( GC = 56% ) . Second , the hphMX insert , whose size and GC content is similar to CEB25-Cdc13mut-1 . 4 ( ≈58% ) , did not stimulate GCR above the no-insert control strain . And third , the density of TG/GG/GT dinucleotides that can seed telomere addition is similar in the CEB25-Cdc13mut , HRAS and hphMX insertions ( Table 1 ) . These observations suggest that the GC-richness is not per se the determinant triggering GCR . The remaining shared feature of these sequences is their organization in tandem . By itself , it may perturb the normal progression of replication due to the high local concentration of homologous templates or create long range specific chromatin structures that might be processed at the expense of maintaining genome stability . In addition to inducing truncated arrays and motifs by GCR , CEB1 also varies in size by increasing or decreasing the total number of motifs via SDSA and/or template switch without involving the flanking regions [16]–[18] . These events are extremely frequent , being detected in 8 . 3 and 20 . 3% of the cells upon deletion of PIF1 or Phen-DC3 treatment , respectively ( Table 2 ) . This is 100–1000 fold higher than the GCR rates ( 3×10−4 and 3 . 6×10−5 events/generation , respectively ) of the same construct . Thus quantitatively , expansion/contraction is the major outcome of CEB1 instability with the advantage to avoid the formation of potentially detrimental structural rearrangements . This is in agreement with numerous reports that compared internal rearrangements and GCR induced by different microsatellites [2] , [56]–[58] . Mechanistically , since the presence of CEB1 perturbs replication [17] , GCR events might result from the rare situations in which the template directed intra-motif interactions failed , allowing break-induced replication on an ectopic telomere sequence [51] or the recruitment of the telomerase to act . Consistent with a role of the homologous recombination pathway , we observed that the deletion of the RAD51 or RAD52 genes yield a ≈4-fold decrease of the GCR rate ( Figure 3D and Figure S4A ) . This is true in both orientations although the nature of the GCR events is different . The insufficient absolute frequency of GCR events ( <10−4 ) prevented us to determine whether or not the variation of the GCR rates were compensated by an increase of the expansion/contraction events that can be detected by Southern blot analyses of individual or small pool of colonies . Chromosomal rearrangements are potentially detrimental for cell functions and are the source of genetic diseases and cancer . Remarkably , subtelomeric regions are highly dynamic in primate and altered in approximately a third of the human pathologies involving chromosomal rearrangements [21] , [23] , [24] , [59] , [60] . However , the factors involved in the high propensity of these regions to break and rearrange have not been identified . The intergenic CEB1 and HRAS1 , as well as the intronic CEB25 minisatellites assayed here are located 400 kb–1 . 4 Mb away from the telomeres ( Table 1 ) , representative of the enrichment for GC-rich and the G-quadruplex-forming minisatellites at chromosome terminal regions in the human genome ( Figure 6A and 6B ) . In yeast , the orientation does not affect the fragility per se but the nature of the GCR . Hence , given the high number of GC-rich minisatellites clustering at chromosome ends in the human genome irrespectively of their orientation , these sequences are likely implicated in the generation of the various subtelomeric rearrangements [61] , [62] . But why these harmful sequences are massively present in the human genome ? And what could be the reasons of their terminal clustering ? A positively selected function could be to signal defects in replicating G-quadruplex-forming sequences [17] , [53] . In this regard , the arrangement in tandem of G-quadruplex-forming motifs presents at least two advantages . First , they would act as severe “tandem of problems” for replication machinery as revealed by their exponential size-dependent fragility . Hence , cells with a decreased ability to remove G-quadruplexes will experience replication difficulties preferentially at these G-quadruplex-forming minisatellites rather than at unique sequences present throughout the genome and enriched in proto-oncogenes [17] , [53] , [63] . Second , owing to the higher local concentration of homologous template compare to unique sequences , they will preferentially undergo internal rearrangements rather than inducing structural variations . Thus , we envision that GC-rich and G-quadruplex-forming minisatellites help signaling deficient replication machineries , and their clustering at chromosome ends and repetitive nature overall limit the potential formation of detrimental structural rearrangements .
The genotypes of the Saccharomyces cerevisiae strains ( S288C background ) used in this study are reported in Table S1 . All strains have been derived from RDKY3615 ( WT strains ) [28] or RDKY4399 ( pif1Δ strains ) [29] by regular lithium-acetate transformation . Correct insertion of the hphMX cassette with or without minisatellite at NPR2 ( position 804 , BamHI site ) , as well as the minisatellite size , have been verified by Southern blot . The CEB1-WT-1 . 7 and CEB1-Gmut-1 . 7 minisatellites have been synthesized previously [16] . Contractions and expansions of these minisatellites have been generated during the insertion procedure at the NPR2 locus and are thus independent clones . The CEB25-WT-0 . 7 , CEB25-Cdc13mut-1 . 4 , and CEB25-Cdc13mut-Gmut-1 . 4 minisatellites have been synthesized in vitro using PCR-based method as previously described [16] . The HRAS1 minisatellite of 0 . 7 kb ( HRAS1-0 . 7 ) has been obtained from P37Y8 ( gift from D . Kirkpatrick ) [64] . The motifs of the minisatellites used in this study are presented in Table S2 . Deletion of RAD51 , RAD52 , and DNL4 has been performed by transformation of the corresponding KMX cassettes amplified from the EUROSCARF deletants collection [65] . Primer sequences are listed in Table S6 . Liquid synthetic complete ( SC ) and solid Yeast-Peptone-Dextrose ( YPD ) media have been prepared according to standard protocols [66] . Plates containing Canavanine ( Sigma-Aldrich ) and 5FOA ( Euromedex ) have been prepared according to standard protocols [67] with minor differences: because npr2Δ cells exhibit a decreased resistance to acidic pH ( <4 . 0 ) [68] we adjusted the pH to 4 . 5–4 . 8 ( instead of 2 . 8–3 . 0 ) and compensated the decreased penetration of 5FOA at this pH by using it at a slightly higher concentration ( ≈1 . 5X instead of 1X ) . SC liquid media containing Phen-DC3 ( 1 , 5 , or 10 µM ) and Phen-DC6 ( 1 or 5 µM ) have been prepared as previously described [18] . The GCR rate has been determined by fluctuation analysis of 5FOA and canavanine-resistant cells . A ura+ colony is used to inoculate at least 10 independent cultures at a concentration of ≈102–3 cells/mL in 2–50 mL of SC media and grown with shacking at 30°C . When they have reached saturation ( 2 days ) , cells are spread on 5FOA/canavanine-containing plates and on YPD plates . A maximum of 108 cells was spread on 85 mm plates , and 109 cells on 145 mm plates . The number of cells spread was adjusted in order not to exceed 100 colonies per plate . For G-quadruplex ligands-containing SC media , cells undergo an overnight preculture in SC prior to inoculation with the ligand , and are grown at 30°C up to saturation . For pif1Δ cells bearing CEB1-WT , which exhibit an inherently high level of CEB1 internal rearrangements , which can influence the GCR rate ( strains ORT6543-1 , ORT7153-9 , and ORT6592-22 ) , the size of the parental minisatellite is determined by Southern blot from individual colonies plated on YPD . The colonies bearing the parental size of CEB1-WT are directly spread on YPD and 5FOA/Can-containing plates without additional liquid culture . After 4 days at 30°C , the number of 5FOA/Can-resistant colonies ( r ) is counted , as well as the total number of viable cells spread ( Nt ) derived from the number of colonies formed on YPD . The GCR rate ( M ) as well as the upper and lower 95% confidence intervals ( 95% CI ) have been calculated from r and Nt with Falcor [69] using the Lea and Coulson method of the median . For each strain and condition , 10 to 45 independent cultures have been performed , in at least two independent experiments . The rates , 95% confidence intervals , and the number of independent cultures performed are listed in the Table S3 . Colonies grown on YPD plates after the 2 days culture in SC media are inoculated in 96-well megaplaque for 24–48 hours . Pools of 4–16 colonies were made right before DNA extraction . DNA was digested with XbaI/EcoNI ( leaving 414 bp of flanking sequence ) and migrated O/N in a 0 . 8% agarose-TBE 1X gel at 50 V . Digestion products were analyzed by Southern blot using a CEB1 radiolabeled probe . Blots were scanned using a Storm Phosphorimager ( Molecular Dynamics ) or a Typhoon Phosphorimager ( GE Healthcare ) , and quantified using ImageQuant 5 . 2 as described in [18] . In order to avoid sibling events , DNA of 5FOA/Canavanine-resistant colonies from separate cultures is extracted , digested using either SacI or XbaI , and migrated in a 0 . 8% agarose-TBE 1X gel overnight at 50 V . Digestion products were analyzed by Southern blot as described previously using a radio-labeled CEB1 , hphMX ( from pAG34 ) , or telomeric ( from pCT300 ) probe . The position of the telomere addition is estimated by measuring the size of the center of the smear , and subtracting both 50 bp of flanking sequence plus the mean telomere size ( 300 bp in WT cells and 400 bp in pif1Δ cells [30] ) . DNA of colonies bearing a CEB1-telomere smear identified by Southern blot was digested by XbaI and migrated in a 0 . 8% agarose-TBE 1X gel overnight at 50 V . After staining of the DNA with BET , the DNA fragments containing the CEB1-telomere junction were cut and extracted from the gel using the Nucleospin Extract II ( Macherey-Nagel ) kit . Fragments were quantified , pooled , and precipitated . Samples were prepared for Ion Torrent Personal Genome Machine ( PGM , Applied Biosystems ) . Sequencing has been performed according to manufacturer instructions on a 314R chip . Reads have been validated and aligned on the S288c genome ( R64-1-1 , 2011-02-03 ) and custom CEB1-telomere templates using the in-built Torrent Suite 1 . 5 . 1 . Reads matching both the CEB1 and the telomeric sequences have been isolated and analyzed manually using Tablet 1 . 11 . 11 . 01 [70] and Microsoft Excel 2007 . The list of minisatellites ( motif comprised between 10 and 100 bp ) and their associated characteristics has been obtained form the Tandem Repeat Database [71] ( list generated on the 2010-10-31 by the TRF algorithm [48] on the Homo Sapiens hg19 release ) . Overlapping duplicates of the same repeat due to uncertainties in the algorithm have been eliminated . The human minisatellites are listed in Table S4 . The number of non-overlapping G-quadruplex-forming sequences per minisatellite have been determined using R software . The custom algorithm searches for 4 runs of 3 Gs in a window of 30 nt , with a minimal loop size of 1 nt , and consequently a maximal loop size of 16 nt [72] . They are listed in the Table S5 . A full length version of CDC13 WT was cloned into a pYES2 vector and expressed as a fusion with a C-terminal tag consisting of a 8 glycine linker , 5 strepII-tags ( IBA , Germany ) and a HAT-tag ( Clontech ) . Cdc13 overexpression was induced in 2% galactose for 16 hours at 30°C according to the method described by P . M . Burgers [73] . Briefly , after grinding cell pellets in liquid nitrogen , the lysate was clarified from DNA by precipitation in 0 . 1% polyethyleneimine , and the proteins were precipitated with ammonium sulfate at 60% saturation . After resuspension in 50 mM Tris pH 8 . 0 , 300 mM NaCl , 10% glycerol , the soluble fraction was loaded successively on a streptactin column ( IBA , germany ) followed by a Talon column ( Clontech ) . Purified protein was dialysed against storage buffer 2X without glycerol , and concentrated and stored at −80°C in 1x storage buffer ( 25 mM tris-HCl pH 8 . 0 , 250 mM NaCl , 0 . 5 mM DTT , 50% Glycerol ) . This procedure yielded homogeneous CDC13 estimated more than 90% pure by coomassie blue staining after protein separation by SDS-PAGE . Gel shift was carried out by incubating 20 pM of the 52-mer CEB25 WT oligonucleotide or the 52_mer-Cdc13mut version , end-labeled at the 5′ end using γ-ATP and T4 polynucleotide kinase , with indicated amount of CDC13 , in the following buffer: 5 mM Tris pH 8 . 0 , 2 . 5 mM MgCl2 , 0 . 1 mM EDTA , 2 mM DTT , 0 . 1 µg/µl BSA ( NEB ) , 50 mM NaCl , 0 . 2 M LiCl . After incubation at room temperature for 30 minutes , binding reactions were supplemented with 3% Ficoll and run on a 6% native polyacrylamide gel ( 37 . 5∶1 acrylamide/polyacrylamide ratio ) , at 4°C and 8 V/cm . Gels were dried on DE81 paper and quantified using a Typhoon phosphorimager . Data were fitted to a one-site-specific binding equation ( Y = Bmax*X/ ( Kd+X ) ) using Prism software ( Graphpad ) , yielding R2 values for goodness of fit of 0 . 91 and 0 . 95 for CEB25-WT and CEB25-Cdc13mut , respectively . Statistical tests have been performed with R software 2 . 13 . 1 [74] or Graphpad Prism 5 . 0b . The α-cutoff for statistical significance was set to 0 . 05 . Rearrangement frequencies of CEB1 have been compared using a two-tailed Fisher's exact test . Correlation between the number of CEB1 motifs and the rate of GCR has been determined using the Spearman correlation test . GCR rates , as well as the distributions of the position of telomere addition in the CEB1 array have been compared using a non-parametric test ( Mann-Whitney-Wilcoxon , two-tailed ) . A one-tailed χ2 test has been used to determined the enrichment of minisatellites in the 10 and 5 terminal percent of chromosome arms . | All genomes contain particular DNA sequences that are prone to break and rearrange . They can be lost or rescued at the expense of sequence variations and complex rearrangements . Using a sensitive yeast model system , we examined the fragility of the HRAS1 , CEB1 , and CEB25 GC-rich human minisatellites ( tandem repetition of motifs from 10 to 100 bp long ) . We observed that they all stimulate Gross Chromosomal Rearrangements but to very different extents , both in wild type and in cells deficient for the Pif1 helicase . Several intrinsic sequence features can account for these differences: the total number of repeats , the ability to form G-quadruplex secondary structures , or the ability to bind with high affinity the telomerase cofactor Cdc13 . The orientation on the chromosome dictates the type of GCR ( telomere addition versus other structural rearrangements ) while not affecting the GCR rate in most cases . Being enriched in the subtelomeric regions of the human chromosomes , this class of GC–rich minisatellite has the potential to trigger a large variety of human genome rearrangements . | [
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| 2012 | Stimulation of Gross Chromosomal Rearrangements by the Human CEB1 and CEB25 Minisatellites in Saccharomyces cerevisiae Depends on G-Quadruplexes or Cdc13 |
The human protozoan parasites Leishmania are prototrophic for pyrimidines with the ability of both de novo biosynthesis and uptake of pyrimidines . Five independent L . infantum mutants were selected for resistance to the pyrimidine analogue 5-fluorouracil ( 5-FU ) in the hope to better understand the metabolism of pyrimidine in Leishmania . Analysis of the 5-FU mutants by comparative genomic hybridization and whole genome sequencing revealed in selected mutants the amplification of DHFR-TS and a deletion of part of chromosome 10 . Point mutations in uracil phosphorybosyl transferase ( UPRT ) , thymidine kinase ( TK ) and uridine phosphorylase ( UP ) were also observed in three individual resistant mutants . Transfection experiments confirmed that these point mutations were responsible for 5-FU resistance . Transport studies revealed that one resistant mutant was defective for uracil and 5-FU import . This study provided further insights in pyrimidine metabolism in Leishmania and confirmed that multiple mutations can co-exist and lead to resistance in Leishmania .
The protozoan parasites Leishmania are distributed worldwide and cause different symptoms including cutaneous , mucocutaneous or visceral leishmaniasis , the latter potentially fatal if left untreated [1] , [2] . Treatments include pentavalent antimonials , amphotericin B , paromomycin or miltefosine [3] , [4] but these drugs have severe shortcomings including toxicity , high cost , and resistance development that need to be addressed in the near future for a better control of this parasitic diseases [5] . With 350 million people at risk , the impact of leishmaniasis on global heath is non-negligible and the search for new drugs or new formulations along with the development of effective vaccines is urgent . Several lines of evidences have suggested that the pyrimidine pathway would represent a viable target for drug intervention in protozoan parasites , at least in the related parasite Trypanosoma brucei brucei [6] but also in Leishmania [7] although recent data would suggest that the pyrimidine pathway may not offer potential for therapeutic intervention in Leishmania [8] . Leishmania spp . are able to synthetize UMP [9] , although they seem to prefer to import pyrimidines from their environment [10] . While purine transporters have been well studied in Leishmania and trypanosomes ( reviewed in [11] ) , our knowledge of the pyrimidine import machinery is considerably less detailed in these parasites . High affinity transporters for uracil have been reported in both Leishmania and Trypanosoma b . brucei [6] , [7] , [12] , although the gene responsible for this transport activity has not been identified in Leishmania . Leishmania and Trypanosome parasites synthesize pyrimidine nucleotides via both de novo and salvage pathways so they don't need preformed pyrimidine for their growth [8] , [13] . One often used strategy to gain insight in a metabolic pathway in Leishmania is to study resistance mechanisms to an antimetabolite . For example , the folate/pterin metabolism and transport in Leishmania was largely derived from studies of mutants selected for resistance to the antifolate methotrexate ( MTX ) [14] , [15] . Indeed , Leishmania parasites are auxotroph for folates and need to import these essential molecules from their environment to meet their folate requirements [14] , [16] . Studies of MTX resistance allowed the characterization of plasma membrane transporters of the folate/biopterin transporter family ( FBT ) , a distant family within the major facilitator superfamily [17] , [18] , [19] , [20] . Similarly , studies of sinefugin resistant mutants have allowed the discovery of the AdoMetT1 transporter , a member of the FBT family transporting S-adenosylmethionine [21] . We thus selected Leishmania cells for resistance to the pyrimidine analogue 5-fluorouracil ( 5-FU ) , a antineoplastic compound displaying a strong antileishmanial activity [22] . After its entry in mammalian cells , 5FU is converted into 5-fluorodeoxyuridine 5′-monophosphate ( 5-FdUMP ) and 5-fluorouridine 5′-monophosphate ( 5-FUMP ) , both of which can be further phosphorylated and incorporated into DNA and RNA , respectively . Thus , the antiproliferative property of 5FU ultimately results in the inhibition of DNA replication and inhibition of the processing and maturation of rRNA , tRNAm snRNA and mRNA precursors , leading to cell death . The enzyme thymidylate synthase ( TS ) which catalyzes the methylation of deoxyuridine monophosphate ( dUMP ) to deoxythymidine monophsophate ( dTMP ) is thought to be the main target of 5-FdUMP ( for a review see [23] ) but other 5FU targets are being revealed including spliceosomal snRNAs [24] and the RNA exosome component hRrp6 [25] . In order to increase our understanding of pyrimidine metabolism in Leishmania , a genomic analysis of in vitro generated 5FU resistant mutants was performed .
The Leishmania infantum WT ( strain MHOM/MA/67/ITMAP-263 ) and 5-fluorouracil ( 5FU ) -resistant strains ( Lin5FU500 . 1 , Lin5FU500 . 2 , Lin5FU500 . 3 , Lin5FU500 . 4 and Lin5FU500 . 5 ) were cultured as promastigotes at 25°C in SDM-79 medium supplemented with 10% heat-inactivated fetal bovine serum and 5 µg/ml hemin . The five Lin5FU mutants were derived from the sensitive WT strain by passaging them in increasing drug concentrations at steps 50 , 100 , 200 , 400 and 500 µM 5-FU ( Sigma-Aldrich , St . Louis , MO , USA ) . Revertants were obtained by culturing the resistant cell lines in absence of 5-FU for 30 passages . Cell growth was monitored by measuring the absorbance of culture aliquots ( 200 µl ) at 600 nm in a multiwell scanning spectrophotometer ( Multiskan , Thermo Scientific , Waltham , MA , USA ) . EC50 values were determined by measuring the absorbance of culture aliquots ( 200 µl ) grown in the presence of various concentrations of drugs at 600 nm in a multiwell scanning spectrophotometer ( Multiskan , Thermo Scientific , Waltham , MA , USA ) . EC50 represents the concentration of drug that inhibits 50% of the growth . All restriction enzymes used in this study were acquired from New England Biolabs . Synthetic oligonucleotides for PCR and cloning experiments were purchased from Integrated DNA Technologies . Cyanine fluorescent labelled nucleotides required for microarray probes preparation were from GE Healthcare . Transport assays were performed with [3H]-labelled isotopes purchased from either PerkinElmer ( uracil ) or Moravek Biochemicals ( 5-fluorouracil ) . For Southern blot and PCR analyses , genomic DNAs from parasite cells were isolated using the DNAzol reagent ( Invitrogen , Carlsbad , CA , USA ) as recommended by the manufacturer . Southern blots , probe labeling , hybridization , and washing conditions were done following standard protocols [26] . For single nucleotide polymorphisms ( SNPs ) validation , the complete coding regions of genes LinJ . 10 . 1370 , LinJ . 10 . 1430 and LinJ . 10 . 1440 were PCR amplified ( see Table S1 in Supplementary Material , primers denoted “PCR amplification” ) using genomic DNAs derived from the WT 263 strain and each of the five 5-fluorouracil resistant mutants . Southern probes to assay gene deletion and/or amplification events in 5-FU resistant mutants were obtained by PCR , using genes LinJ . 10 . 1380 , LinJ . 10 . 1390 and LinJ . 10 . 1420 as targets on WT genomic DNA with the appropriate set of primers ( Table S1 , Supplementary Material , primers denoted “Southern” ) . DHFR-TS ( LinJ . 06 . 0890 ) containing amplicons were detected by Southern blot using a PCR amplified probe derived from gene LinJ . 06 . 0910 ( Table S1 ) . The genes LinJ . 06 . 0890 ( DHFR-TS ) , LinJ . 10 . 1380 , LinJ . 10 . 1390 , LinJ . 10 . 1400 , LinJ . 10 . 1410 , LinJ . 10 . 1420 , LinJ . 10 . 1430 , LinJ . 10 . 1090 , LinJ . 21 . 1450 , LinJ . 34 . 1110 and LinJ . 34 . 3040 were amplified from a WT 263 genomic DNA preparation ( see Table S1 , Supplementary Material , primers denoted “pSP72 αHYGα cloning” ) . The PCR fragments were first purified on columns ( Qiagen , Valencia , CA , USA ) according to the manufacturer's recommendations , digested with both XbaI and HindIII then cloned into the Leishmania expression vector pSP72αHYGα ( described in [27] ) digested with the same enzymes . To check the integrity of all cloned open reading frames , final expression constructs were sequenced before being used in transfection experiments . Transfection and maintenance ( hygromycin selection at 600 µg/ml ) of these constructs into Leishmania infantum 5-FU resistant promastigotes was performed as previously described [28] . Each transfectant parasite populations were then plated on agar containing drug ( hygromycin ) for clone isolation and individual clones were further assayed for drug sensitivity . The Leishmania DNA oligonucleotides full genome microarray design was described previously [29] as well as prehybridization , hybridization and washing conditions for CGH assays . Genomic DNAs from L . infantum WT ( strain MHOM/MA/67/ITMAP-263 ) and from the five 5-fluorouracil resistant mutants were used as template for probe labelling essentially as described [29] . Normalization and statistical analysis of microarray data were performed in R using the LIMMA 3 . 12 . 0 package [30] . Background correction was done using the Edwards method , within-array normalization used loess and inter-array normalization was performed using A quantiles . The entire data set has been deposited in GEO under the accession number series GSE45866 . Genomic DNAs were prepared from mid-log phase clonal cultures of L . infantum 263 WT and from the five 5-FU resistant mutants . Paired-ends sequencing libraries were prepared with the Nextera DNA sample prep kit ( each strain tagged with a different index ) and libraries were sequenced on an Illumina HiSeq1000 platform with short 101-nucleotide reads . An average genome coverage of over 50-fold was obtained for the five independent mutants as well as the WT strain . This strategy allowed us to identify point mutations when comparing with the reference genome sequence of L . infantum JPCM5 [31] . Sequence reads from each clone were aligned to the L . infantum JPCM5 reference sequence available at TriTrypDB ( version 4 . 0 ) [32] using the software bwa ( bwa aln , version 0 . 5 . 9 ) with default parameters [33] . The maximum number of mismatches was 4 , the seed length was 32 and 2 mismatches were allowed within the seed . The detection of single nucleotide polymorphisms ( SNPs ) was performed using samtools ( version 0 . 1 . 18 ) , bcftools ( distributed with samtools ) and vcfutils . pl ( distributed with samtools ) [34] , with a minimum of three reads to call a potential variation prior to further analysis . The quality assessment software samstat ( v1 . 08 ) was used to generate quality reports [35] . Several python ( version 2 . 4 . 3 ) and bash ( version 3 . 2 ) scripts were created to further analyze the data and for the detection of copy number variations ( CNVs ) . The sequence data for L . infantum 263 WT and the mutant Lin5FU500 . 1 up to Lin5FU500 . 5 are available at the EMBL European Nucleotide Archive ( http://www . ebi . ac . uk/ena ) ( study accession ERP001815 and sample accession ERS179382 corresponding to L . infantum 263 WT; and study accession ERP002415 , samples ERS226502 , ERS226503 , ERS226504 , ERS226505 and ERS226506 corresponding to the L . infantum 263 mutants Lin5FU500 . 1 to Lin5FU500 . 5 , respectively ) . All the putative point mutations detected by whole genome sequencing were verified by PCR amplification and conventional DNA sequencing using primers detailed in supplementary material ( Table S1 ) . Parasite cultures were harvested during their mid-log phase . 1×108 cells were washed and resuspended in transport assay buffer ( 33 mM HEPES , 98 mM NaCl , 4 . 6 mM KCl , 0 . 55 mM CaCl2 , 0 . 07 mM MgSO4 , 5 . 8 mM NaH2PO4 , 0 . 3 mM MgCl2 , 23 mM NaHCO3 and 14 mM glucose , pH 7 . 3 ) supplemented with 250 nM of [3H] uracil ( 40 . 3 Ci mmol−1 ) ( PerkinElmer , Waltham , MA , USA ) or [3H] 5-fluorouracil ( 16 . 4 Ci mmol−1 ) ( Moravek Biochemicals , Brea , CA , USA ) . Radioactivity accumulation was measured as previously described [36] . The uptake was normalized to cell numbers and the background transport level was removed by subtracting the accumulation values obtained on ice from each of the test readings .
Of the various pyrimidine analogs commercially available , the antimetabolite 5-fluorouracil ( 5-FU ) has strong antileishmanial effects on L . major promastigotes with EC50 values in the low µM [7] . The activity of this drug was tested here against L . infantum WT promastigotes ( strain MHOM/MA/67/ITMAP-263 ) with an in vitro EC50 of 72±0 . 8 µM to 5-FU ( Table 1 ) , being apparently less sensitive than the L . major promastigote strain . Five independent cultures of L . infantum WT parasites were selected by stepwise selection in liquid medium with increasing concentration of the drug up to 500 µM . Cultures were named Lin5FU500 . 1 up to Lin5FU500 . 5 . Each populations were shown to readily grow in the presence of over 2000 µM 5-FU ( Table 1 ) . To evaluate the stability of the resistance phenotype , resistant populations of parasites were sub-cultured for at least 30 passages in absence of 5-FU . Three out of the 5 resistant cultures conserved their high level of resistance to 5-FU ( Lin5FU500 . 3 , Lin5FU500 . 4 and Lin5FU500 . 5 ) but an intermediate level at 927±83 µM was observed in Lin5FU500 . 1 , still being 14-fold more resistant than the WT parental strain ( Table 1 ) whereas resistance reverted to WT levels in Lin5FU500 . 2 ( Table 1 ) . Since unstable resistance in Leishmania is often associated with gene amplification events [37] , [38] , DNA microarrays covering the whole set of genes encoded by the genome of Leishmania infantum were used to perform comparative genomic hybridization ( CGH ) . The genomic DNA from each 5-FU resistant clone was isolated , labelled with fluorescent dyes and co-hybridized with the WT labelled DNA in order to detect any change in gene copy numbers between the two cell types . The analysis of the CGH results revealed a unique region of about 30 kb on chromosome 6 that was amplified ( 20-fold compared to the WT level ) in Lin5FU500 . 2 ( Fig . 1A , locus indicated in black ) . This locus was encompassing 6 genes ( from LinJ . 06 . 0860 up to LinJ . 06 . 0910 ) including the gene encoding for the bifunctional enzyme dihydrofolate reductase-thymidylate synthase ( DHFR-TS , LinJ . 06 . 0890 ) . This amplicon was found to correspond to an extrachromosomal circle since we could isolate it by standard plasmid preparation ( data not shown ) . Southern blot analysis confirmed that DHFR-TS was amplified in mutant Lin5FU500 . 2 but also in mutant Lin5FU500 . 1 ( Fig . 1B , panel P0 ) , an amplification surprisingly not detected by CGH . No amplification of the DHFR-TS locus was found in the three other mutants ( Fig . 1B , panel P0 ) . A marked decrease in the copy number of the DHFR-TS containing amplicons was observed in both Lin5FU500 . 1 and Lin5FU500 . 2 revertant cells grown for 30 passages in absence of 5-FU ( Fig . 1B , panel P30 ) . The role of DHFR-TS in 5-FU resistance was tested by transfecting the Leishmania DHFR-TS gene cloned into an expression vector in WT parasites as well as in the revertant strain Lin5FU500 . 2rev . Transfection of the DHFR-TS construct conferred respectively a 6- and 9- fold increase in EC50 values in L . infantum and in Lin5FU500 . 2rev when compared to control transfectants ( Fig . 1C ) . Since DHFR-TS gene amplification can also lead to MTX resistance in Leishmania [39] , [40] , we further tested whether Lin5FU500 . 1 and Lin5FU500 . 2 were also cross-resistant to MTX . The Lin5FU500 . 1 and Lin5FU500 . 2 resistant parasites were indeed 2- and 3-fold cross-resistant to MTX respectively when compared to the WT strain ( Table 1 ) . Mutants Lin5FU500 . 4 and Lin5FU500 . 5 were not cross-resistant to MTX but intriguingly , Lin5FU500 . 3 was 20-fold hypersensitive to MTX ( Table 1 ) . For a more in depth genetic analysis of the 5-FU resistant mutants , we also used a whole genome sequencing ( WGS ) approach to try to identify and map mutations that could explain the resistance phenotype observed in our panel of 5-FU resistant mutants . This strategy has proven useful to study resistance in Leishmania [41] , [42] , [43] . Thus , a single clone derived from the WT L . infantum strain and from each of our five resistant populations was sent for sequencing on an Illumina HiSeq1000 system . A total number of 17 , 437 , 747 reads was obtained for the WT strain , whereas between 24 , 501 , 618 and 39 , 361 , 109 reads were obtained for the 5 mutants , leading to an average genome coverage of 50- to 90-fold depending on the strains . Reads depth coverage over the 36 chromosomes of Leishmania was used to predict copy number variations ( CNVs ) , thus revealing either amplifications or deletions at the genome scale . These comparative analyses confirmed the amplification of the DHFR-TS locus on chromosome 6 observed by CGH in the mutant Lin5FU500 . 2 , with an increase in the number of reads of ∼32-fold compared to the WT strain ( data not shown ) . Strangely and similarly to CGH , WGS did not detect any DHFR-TS amplification in the mutant Lin5FU500 . 1 , although southern blot analyses clearly demonstrated the amplification of this locus in this mutant ( Fig . 1B ) . In the mutant Lin5FU500 . 4 , sequence reads analysis revealed a deletion of 6 genes on chromosome 10 with a 64-fold reduction in the number of reads overlapping this locus compared to the WT ( data not shown ) . This sequencing result was in line with CGH analysis ( Fig . 2A ) where the locus deletion on chromosome 10 had apparently occurred between gene LinJ . 10 . 1370 and gene LinJ . 10 . 1440 in the Lin5FU500 . 4 mutant ( Fig . 2B ) . Southern blot analyses with specific probes derived from genes localized within this putatively deleted region ( LinJ . 10 . 1420 , LinJ . 10 . 1390 and LinJ . 10 . 1380 ) confirmed indeed that this region was deleted in Lin5FU500 . 4 ( Fig . 2C ) , a result also supported by PCR experiments targeting genes within or outside this locus ( Fig . 2D ) . The WT versions of the six genes part of the deleted locus ( LinJ . 10 . 1380 up to LinJ . 10 . 1430 , see Fig . 2B ) were individually cloned in the expression vector pSP72αHYGα and transfected back into the mutant Lin5FU500 . 4 to assess their role in 5-FU resistance . None of the transfectants however regained sensitivity to 5-FU ( data not shown ) . Since the deletion on chromosome 10 was close to one of the genes encoding an FBT member ( LinJ . 10 . 1450 ) , we decided to test whether this FBT gene would have been responsible for the resistance phenotype observed in the Lin5FU500 . 4 mutant . The expression of the FBT gene was unchanged in the mutant ( results not shown ) and transfection of the FBT gene did not change the 5-FU susceptibility in this mutant , however ( data not shown ) . Since the CNVs analyses ( and CGH ) did not revealed any other amplification or deletion events except the ones observed on chromosomes 6 and 10 in our 5-FU resistant mutants , we then analyzed single homozygous nucleotide polymorphisms ( SNPs ) that were detected by WGS in coding regions ( Table 2 , n = 29 ) . Seven of the SNPs corresponded to silent mutations and none of the 29 homozygous SNPs were shared between mutants . A selection of seven SNPs within genes encoding the most interesting candidate enzymes possibly involved in 5-FU resistance were PCR amplified from genomic DNAs derived from mutants and PCR products were subjected to direct resequencing for SNP validation . Three SNPs were found to be sequencing errors ( indicated as “E” for sequencing error in Table 2 ) but four were called as real mutations ( indicated as “M” for mutation in Table 2 ) . The four validated SNPs were detected in genes encoding respectively for the enzyme thymidine kinase ( TK , LinJ . 21 . 1450 ) in mutant Lin5FU500 . 3 , the uracil phosphoribosyl transferase ( UPRT , LinJ . 34 . 1110 ) as well as a hypothetical protein ( LinJ . 34 . 3040 ) in mutant Lin5FU500 . 4 , and the uridine phosphorylase ( UP , LinJ . 10 . 1090 ) in mutant Lin5FU500 . 5 ( Table 2 ) . With the exception of the hypothetical protein LinJ . 34 . 3040 , the other genes have been linked previously to pyrimidine metabolism in kinetoplastids [6] , [44] and were thus further investigated along with LinJ . 34 . 3040 . To prove the implication of these SNPs in resistance to 5-FU , the WT version of each mutated gene was transfected in the L . infantum WT strain as well as in the three mutants in which they were detected and the EC50 of each transfectant was determined in the presence of 5-FU ( Table 3 ) . Transfection of the TK ( LinJ . 21 . 1450 ) , the UPRT ( LinJ . 34 . 1110 ) , and the UP ( LinJ . 10 . 1090 ) genes reverted resistance in Lin5FU500 . 3 , Lin5FU500 . 4 and Lin5FU500 . 5 , respectively ( Table 3 ) . The phenotype was less strong with LinJ . 34 . 1110 in Lin5FU500 . 4 but in general each mutation was specific to one mutant . Surprisingly , however , transfection of the UP LinJ . 10 . 1090 gene resensitized both the mutants Lin5FU500 . 4 and Lin5FU500 . 5 , even though the LinJ . 10 . 1090 gene was only mutated in Lin5FU500 . 5 ( Table 3 ) . The expression of the WT version of gene LinJ . 34 . 3040 coding for a hypothetical protein did not affect the resistance profile to 5-FU in any of the three transfected mutants ( data not shown ) . The glutamine ( Q ) to proline ( P ) substitution in the TK version found in Lin5FU500 . 3 is within the active site of the protein ( Fig . S1 ) . The Lin5FU500 . 3 mutant is highly resistant to 5-FU but hypersensitive to MTX ( Table 1 ) . Transfection of a WT TK version in the Lin5FU500 . 3 mutant sensitized parasites to 5-FU ( Table 3 ) . We also investigated the MTX sensitivity profile in the Lin5FU500 . 3 overexpressing TK strain . Interestingly , the overexpression of the TK enzyme in the Lin5FU500 . 3 mutant abolished the hypersensitivity to MTX in this mutant , thus restoring MTX susceptibility to a level close to that of wild-type cells ( Fig . 3 ) . Finally we investigated the ability of L . infantum WT parasites and mutant cells to import uracil and its analogue 5-FU . Transport assays using [3H]-uracil or [3H]-5-FU in the WT strain clearly established that both substrates were transported by this species ( Fig . 4A ) and most likely by the same transporter . Indeed , the uptake of [3H]-uracil was equally competed with either cold uracil or cold 5-FU ( Fig . 4B ) . In the five mutants tested , we observed a 50–80% decrease in accumulation with the exception of mutant Lin5FU500 . 4 where no accumulation was observed ( Fig . 4A and 4C ) . This lack of accumulation in Lin5FU500 . 4 was stable since we could not observe accumulation in revertant parasites grown for 30 passages without drugs ( data not shown ) . Sequence analysis of the mutant did not reveal a candidate mutation that could have identified the uracil transporter . As an alternative for isolating the transporter , we carried out functional cloning where a cosmid bank derived from wild-type L . infantum was transfected in Lin5FU500 . 4 and spread on hygromycin ( the cosmid marker ) plates . Individual colonies ( n = 4000 ) were incubated in 96 well plates and screened for 5-FU sensitivity . This approach has been useful to isolate an aquaglyceroporin involved in antimonial transport [45] and for purine transporters in Leishmania [46] , [47] . We carried these experiments twice and both times we succeeded in isolating a cosmid rendering Lin5FU500 . 4 sensitive to 5-FU . Analysis of these transfectants however indicated no transport of uracil but rather each cosmid encoded the uridine phosphorylase LinJ . 10 . 1090 UP gene .
Studies of resistance mechanisms to the model drug methotrexate have contributed importantly to our understanding of folate and pterin metabolism and transport in Leishmania ( reviewed in [14] , [16] ) . Similarly , studies of resistance mechanisms to the AdoMet analogue sinefungin has led to the isolation of an AdoMet transporter and increased our understanding of one carbon metabolism in Leishmania [21] . One of the main metabolic roles of reduced folates is in the generation of dTMP through the activity of the bifunctional enzyme DHFR-TS . Indeed Leishmania DHFR-TS null mutants are thymidine auxotrophs [48] . In order to further gain insight and link folate and pyrimidine metabolisms , we selected Leishmania cells for resistance to a pyrimidine analogue , 5- fluorouracil ( 5-FU ) , as this drug was shown previously to have considerable activities against Leishmania [7] . We selected 5 independent L . infantum mutants highly resistant to 5-FU and analyzed these drug resistant mutants by a combination of comparative genomic hybridization and whole genome sequencing , two approaches that have been useful in studying drug resistance mechanisms in Leishmania [29] , [42] , [43] , [49] . Our analysis has pinpointed several mechanisms of resistance including gene amplification , point mutations in key nucleic acid metabolism enzymes as well as transport defects and is consistent with observations made in 5-FU resistant cancer cells [50] , [51] , [52] . Thymidylate synthase is the main target of 5-FU in all eukaryotic cells studied [53] including kinetoplastid parasites [6] . It was thus not surprising to observe an extrachromosomal circular amplification of the bifunctional gene DHFR-TS in the Lin5FU500 . 2 mutant both by CGH ( Fig . 1A ) and by analyzing sequence reads ( data not shown ) . Growing this mutant in absence of drug led to a marked decrease of the circular amplicon and reversion of the resistance phenotype ( Fig . 1B and 1C ) . Amplification of the DHFR-TS gene also explained the observed MTX cross-resistance in this mutant ( Table 1 ) as MTX targets the Leishmania DHFR enzyme [39] . Growth curves were carried out in the folate rich medium SDM-79 . Folate concentration modulates MTX cross-resistance and this may explain the low level of MTX cross-resistance despite a 20-fold amplification of DHFR-TS . Transfection of the DHFR-TS gene confirmed its role in 5-FU resistance ( Fig . 1C ) . Resistance levels reached are lower than the resistant mutants and this may be due to the level of expression of DHFR-TS in transfectants . Southern blot analysis indicated that DHFR-TS was not only amplified in Lin5FU500 . 2 but also in Lin5FU500 . 1 ( Fig . 1B ) . Surprisingly , this amplification in Lin5FU500 . 1 was missed by both CGH and sequencing . This is difficult to explain because we have shown both CGH and sequencing reads depth to be quantitative [43] , which is further confirmed by ongoing work with several unrelated resistant strains . The DNA used for Southern blots and sequencing were prepared at different times but usually amplicons are stable in the presence of drugs . Southern blots at varying passages confirmed the stable amplification of DHFR-TS ( data not shown ) . While DHFR-TS amplification seems the only resistance mechanism in Lin5FU500 . 2 , this does not seem to be the case in Lin5FU500 . 1 since growth in absence of drug led to a decrease in the copy number of the amplicon but only a partial reversion ( Table 1 , Fig . 1B ) . Five point mutations ( including one silent mutation ) are possible candidates for resistance ( Table 2 ) ( e . g . the kinesin K39 , LinJ . 14 . 1190; the proteophosphoglycan pgp3 , LinJ . 35 . 0500; and 3 hypothetical proteins , LinJ . 15 . 0490 , LinJ . 33 . 2730 and LinJ . 36 . 1020 ) and await further additional functional studies . Sequencing of the genome of the five resistant mutants has also led to the identification of several point mutations in 5-FU resistant parasites , three of which were shown to be involved in 5-FU resistance in three independent mutants . In Lin5FU500 . 3 , we observed a point mutation in the active site of a thymidine kinase ( TK , LinJ . 21 . 1450 ) ( Fig . S1 ) . Transfection of the WT copy of the gene in Lin5FU500 . 3 showed that this is a key mutation involved in 5-FU resistance ( Table 3 ) . A mutation in TK would reduce the formation of 5-FdUMP ( Fig . 5 ) . However a mutation in TK would reduce the conversion of thymidine into dTMP , hence rending the cell more dependent on the DHFR pathway ( Fig . 5 ) , thus making the cell more susceptible to the DHFR inhibitor MTX ( Table 1 , Fig . 3 ) . In mutant Lin5FU500 . 4 we observed a mutation in the uracil phosphoribosyl transferase ( UPRT , LinJ . 34 . 1110 ) . The mutation was located between the flexible loop and the phosphoribosyl-pyrophosphate ( PRPP ) binding domain in UPRT ( Fig . S1 ) . The mutation in UPRT contributes only slightly to 5-FU resistance as suggested by transfection of the wild-type gene in the mutant ( Table 3 ) . The main route in Leishmania for 5-FU to become 5-FUMP and being incorporated into RNA is through the UPRT pathway ( Fig . 5 ) and this reduced ability in the mutant may lead to some levels of resistance to 5-FU . However , mutant Lin5FU500 . 4 has also no measurable accumulation of 5-FU ( Fig . 4 ) and this defect must contribute to resistance . In mutant Lin5FU500 . 5 , the mutation in the uridine phosphorylase ( UP , LinJ . 10 . 1090 ) is key in conferring resistance since transfecting back a wild-type allele completely reverted resistance in Lin5FU500 . 5 . The UP is the main enzyme for activating 5-FU for eventual incorporation into DNA ( Fig . 5 ) and this can explain resistance . The lack of UP would however require an alternate pathway for the generation of dUMP ( Fig . 5 ) and several non-UP pathways have been described in the related parasite T . brucei to lead to the synthesis of dUMP [6] . Transfection of UP ( LinJ . 10 . 1090 ) also rendered Lin5FU500 . 4 cells more sensitive to 5-FU ( Table 3 ) despite that LinJ . 10 . 1090 is not mutated in Lin5FU500 . 4 . Since Lin5FU500 . 4 does not transport 5-FU or uracil ( Fig . 4 ) and two independent functional cloning experiments screening for regained sensitivity to 5-FU led to the isolation of cosmids encoding UP ( LinJ . 10 . 1090 ) rather than the uracil transporter , it would suggest that UP is rate limiting and that even in absence of measurable uptake over 10 minutes , slow diffusion of 5FU may be sufficient for UP-mediated increased toxicity . The loss of measurable accumulation of 5-FU in Lin5FU500 . 4 was not reverted when growing the mutant in absence of drugs ( data not shown ) . A defect in accumulation can be due to either a decreased uptake or increased efflux . The ABC transporter MDR2 ( ABCB2 ) was shown to be involved in 5-FU resistance in L . amazonensis , most likely by an active extrusion of 5-FU from the parasite cell [22] but in our five resistant mutants , we did not observe any mutation in the MDR2 gene ( LinJ . 26 . 2700 ) , nor difference in mRNA levels tested by real time qRT-PCR ( data not shown ) . We have carefully scrutinized the sequencing data of Lin5FU500 . 4 for either gene deletion or point mutations in proteins with putative transmembrane domains . CGH and WGS analyses detected an 18 kb chromosomal deletion in mutant Lin5FU500 . 4 on chromosome 10 ( Fig . 2 ) . The deleted locus included six genes , from LinJ . 10 . 1380 to LinJ . 10 . 1430 . None of the gene products were predicted to have transmembrane domains but all six hypothetical proteins contained a domain of unknown function ( DUF ) 1861 ( GeneDB ) . DUF1861 containing members are present in Achaea , bacteria and Eukaryota and are the most divergent family of the furanosidase superfamily [54] . Even if their role in 5-FU resistance was not obvious , transfection of the individual WT versions of these genes was nonetheless performed in the mutant Lin5FU500 . 4 but none did restore sensitivity to 5-FU ( data not shown ) . The mutant Lin5FU500 . 4 had also a PCR-validated mutated hypothetical gene ( LinJ . 34 . 3040 , Table 2 ) . This protein has no predicted TM domains but transfection of the WT version of LinJ . 34 . 3040 did not change the resistance profile to 5-FU ( data not shown ) . Members of the equilibrative nucleoside transporter ( ENT ) were shown to transport purine and pyrimidine [55] , [56] , [57] . Four members of the ENT family are annotated in the parasite genome ( NT1 in 4 copies , NT2 , NT3 and NT4 ) but sequencing and Southern blot analysis have revealed that these genes are neither mutated nor deleted in the mutants ( data not shown ) , supporting a conclusion that the leishmanial uracil transporter is not part of the ENT family [58] . The defect in transport in Lin5FU500 . 4 may be due to a mutation that we have missed during the analysis of the sequence reads . Additional sequencing and analysis may reveal the identity of this mutation . Alternative in the transport of uracil may depend on more than one gene product and would require either the co-transfection of several genes mutated in the mutant and similarly would complicate its isolation by functional cloning . In summary , multiple factors contribute to 5-FU resistance in Leishmania . Resistance to 5FU affects mainly the salvage pathway of the parasite , which is the main way to provide pyrimidines for kinetoplastids [59] , but gene amplification and transport defect were also associated with resistance . These studies have confirmed the value of studying drug resistance to increase our understanding of pyrimidine metabolism and its interesting connection with folate/antifolate metabolism ( DHFR-TS , TK ) should be helpful in eventually developing specific inhibitors against Leishmania . | The human protozoan parasites Leishmania present the ability of both de novo biosynthesis and uptake of pyrimidines . The pyrimidine pathway is not well understood in these parasites . In the hope to better understand the pyrimidine pathway in Leishmania , five independent L . infantum mutants were selected for resistance to the pyrimidine analogue 5-fluorouracil ( 5-FU ) . Analysis of the 5-FU mutants by comparative genomic hybridization and whole genome sequencing revealed the amplification of the main target enzyme DHFR-TS , and point mutations in three important metabolic enzymes . Transfection experiments confirmed that these point mutations were responsible for 5-FU resistance . Transport studies also revealed that one resistant mutant was defective for uracil and 5-FU import . Overall , this study provided further insights in pyrimidine metabolism in Leishmania and confirmed that multiple mutations can co-exist and lead to resistance in these protozoa . | [
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| 2013 | Gene Amplification and Point Mutations in Pyrimidine Metabolic Genes in 5-Fluorouracil Resistant Leishmania infantum |
DNA signatures are nucleotide sequences that can be used to detect the presence of an organism and to distinguish that organism from all other species . Here we describe Insignia , a new , comprehensive system for the rapid identification of signatures in the genomes of bacteria and viruses . With the availability of hundreds of complete bacterial and viral genome sequences , it is now possible to use computational methods to identify signature sequences in all of these species , and to use these signatures as the basis for diagnostic assays to detect and genotype microbes in both environmental and clinical samples . The success of such assays critically depends on the methods used to identify signatures that properly differentiate between the target genomes and the sample background . We have used Insignia to compute accurate signatures for most bacterial genomes and made them available through our Web site . A sample of these signatures has been successfully tested on a set of 46 Vibrio cholerae strains , and the results indicate that the signatures are highly sensitive for detection as well as specific for discrimination between these strains and their near relatives . Our approach , whereby the entire genomic complement of organisms are compared to identify probe targets , is a promising method for diagnostic assay development , and it provides assay designers with the flexibility to choose probes from the most relevant genes or genomic regions . The Insignia system is freely accessible via a Web interface and has been released as open source software at: http://insignia . cbcb . umd . edu .
Recent increases in the amount of available genomic sequence have made it possible to largely automate the design and screening of probes via computational search algorithms . Large-scale computational prediction of DNA signatures was first undertaken for the Biological Aerosol Sentry and Information System ( BASIS ) , deployed at the Salt Lake City Olympic Games in 2002 [9 , 10] . The related BioWatch project operates by collecting and analyzing airborne microbial samples for known pathogens , using PCR probe-based detection methods . Newer aerosol detection systems , such as the Autonomous Pathogen Detection System ( APDS ) [11] , automate the process , and can identify a known bioweapon in 0 . 5 to 1 . 5 hours [12] . Similar techniques are not limited to aerosols , and can be used in clinical or agricultural settings [13] . The success of these assays depends on both the available sequence databases and the computational methods used to identify signatures that differentiate the threat organisms from the background . Signature design for both BASIS and BioWatch was handled by Lawrence Livermore National Laboratories ( LLNL ) , and what began as a simple proof-of-concept BLAST search at LLNL evolved into the sophisticated KPATH signature pipeline [14] . KPATH identifies sequences shared by a collection of target genomes , yet unique with respect to all other microbial genomes , and is notable for its ability to handle such a large search space . Other methods for probe selection more rigorously address hybridization efficiency ( binding energy , self-hybridization , etc . ) , but do not scale well for large target and background sets [15–18] . Most notable are the approaches that promise the scalability of KPATH combined with the hybridization considerations of the other methods [19 , 20] . Because of its history of use in real-world diagnostic systems , a more detailed description of KPATH is warranted . It consists of four major components . First , a whole-genome multi-alignment is performed on a set of target genomes . This produces a “consensus gestalt , ” which represents the sequences that are conserved in all the target genomes . Next , this consensus is matched against a database of background sequences using Vmatch [21] . This step computes all exact matches between the target consensus and the background . Matching sequences are masked out to create a “uniqueness gestalt , ” which represents all sequences that are shared between target genomes and unique with respect to the background . Third , signature sequences are supplied to the Primer3 program [22] , which designs PCR assays based on those sequences . Primer3 produces a set of oligos suitable for testing by a TaqMan PCR assay: a forward primer , a reverse primer , and an intervening probe oligomer [23] . Finally , assay candidates are screened using BLAST [24] for near matches that might disrupt the hybridization process , and ranked according to their satisfaction of PCR experimental constraints . The result of this four-stage process is a set of ranked , prescreened assays , which are then subjected to rigorous laboratory validation . The transition to these computational methods from previously manual design methods has resulted in greatly increased design efficiency by limiting the number of assays that fail during laboratory validation . While highly innovative , the KPATH pipeline is not publicly available , and many of the sequences and signatures remain secret . In addition , KPATH requires significant computing resources ( hours of computing time on a 24-CPU server [14] ) , which are beyond the means of many investigators . In contrast , Insignia is a transparent , highly accessible signature pipeline , with the entire system being controlled by a Web interface and all supporting software released under an open source model . Additionally , Insignia dramatically accelerates the discovery process by precomputing exact sequence matches for all genomes and storing this information in a specialized data structure for rapid retrieval . Using the Insignia Web interface , users select a desired signature length and a set of target genomes . After query submission , the system analyzes the stored match information , and identifies signature candidates in less than one minute . Candidates may then be further screened using experimental constraints ( melting temperature , GC content , etc . ) , or using further computational criteria , such as the existence of near matches that may cause cross-hybridization . The integrated Gemina database ( http://gemina . tigr . org ) , which includes detailed annotation and supplementary epidemiological information for major pathogens , provides further support for signature selection . This rich metadata allows the formulation of complex queries such as “find signatures shared by all enteric Escherichia coli , ” and it allows the user to search for signatures in the context of the surrounding annotation . Insignia can compute signatures for any microbial genome in GenBank ( both draft and complete ) , and screens signatures against a comprehensive background including all bacterial , archaeal , and viral sequences , plus additional eukaryotic sequences from the National Center for Biotechnology Information ( NCBI ) RefSeq database [25] .
Insignia was used to develop assays for the identification of V . cholerae at the species level using a TaqMan Real-Time qPCR format . The initial version of Insignia queried a database that was populated with ~300 bacterial genomes , including one strain of V . cholerae ( O1 biovar El Tor strain N16961 ) , and four near neighbors in the family Vibrionaceae ( three Vibrio and one Photobacterium species ) . Thus the question for Insignia was: among all available DNA sequences , what sequences are unique to V . cholerae ? The Insignia Web interface was used to retrieve all 20-mers unique to V . cholerae , from which 50 TaqMan assays were designed . A similar query with the current version of Insignia takes 10 s and returns 34 , 122 signatures of varying lengths . To test whether the signature assays were broadly inclusive of V . cholerae strains , the 50 assays were tested against a panel of 46 strains of V . cholerae comprising a global distribution of both clinical and environmental strains from all major serotypes . To test whether they excluded non-cholera vibrios , the assays were additionally tested against a panel of 22 nearest-neighbor species in the family Vibrionaceae , along with one E . coli control . Figures 1 and 2 show example inclusive and exclusive qPCR results , respectively . Figure 3 summarizes the validation results for the 50 assays , covering 69 organisms , and totaling 3 , 450 experiments . Each square in Figure 3 represents one experiment , with color indicating the qPCR Ct value ( the number of PCR cycles before amplification is detected ) . Green and yellow squares indicate relatively rapid amplification while orange and red indicate delayed or failed amplification . ( For a grayscale version of Figure 3 , see Figure S1 . ) As Figure 3 makes clear , most assays detected all V . cholerae strains , with approximately half of the assays providing strong detection capability for every one of these diverse strains . The effectiveness of some assays deteriorated slightly for the non-O1/O139 serotypes , although they still provided positive results . This was to be expected , however , given that only a single V . cholerae strain ( of serotype O1 ) was available to Insignia . Additional genomic sequences from the other serotypes would have undoubtedly removed many of these less-efficient signatures from the Insignia output . Gardner et al . explore this phenomenon further in the context of viral signature development [26] . In addition to successful detection of a wide variety of V . cholerae strains , all but one of the tested assays ( 98% ) were able to successfully discriminate between V . cholerae and its near neighbors . Furthermore , 1 , 115 of the 1 , 150 exclusive tests ( 97% ) had Ct values >50 , indicating that all of the tested V . cholerae signatures are either absent or significantly divergent from the other members of Vibrionaceae . Assay signature sequences are provided in Table S1 , inclusive and exclusive strain information in Table S2 , and detailed qPCR results for all 3 , 450 validation experiments in Table S3 . This information is also available from the Insignia Web site .
Our validation results indicate that whole genome signature discovery , whereby the entire genomic complement of organisms are compared to identify probe targets , is a promising new tool for diagnostic assay development . This approach provides assay designers with the flexibility to choose probes of the proper length from the most relevant genes or genomic regions , while avoiding sequences known to contain no suitable signatures . Insignia also achieves unmatched scale by screening all microbial genomes in GenBank against a comprehensive background , while providing rapid access to DNA signatures through its Web interface . Insignia outputs signature candidates , rather than high confidence , laboratory-validated signatures . However , our results demonstrate that most of these candidates can work quite well as laboratory assays . Due to the limited availability of genomic sequence in public databases ( relative to the diversity of all organisms ) , and the possibility of near-match cross-hybridization , it is difficult to validate a genomic signature via purely computational methods . Instead , Insignia provides a computational screening regimen that eliminates many invalid signatures , so that laboratory validation may focus on the most likely candidates . Additional sequencing will help overcome the computational limitation , and future work on Insignia will be focused on screening signature candidates for near matches that may result in cross-hybridization . In addition to the computational restrictions , limitations of TaqMan PCR have been demonstrated for rapidly diverging target genomes , such as hepatitis and HIV viruses [26 , 27] . However , for typical bacterial targets , TaqMan assays remain one of the most rapid and sensitive methods for signature detection . In the case where TaqMan is inadequate , different detection technologies , such as chip-hybridization methods , could be used to remove the TaqMan requirement for three adjacent probes and to provide greater signature redundancy . Insignia would easily support the design of such assays . Viruses pose significant challenges for all detection methods because of their small genomes and high mutation rates . The Insignia database contains thousands of viral genomes; however , for large target sets there are often no conserved signatures . To address highly divergent targets , future Insignia versions may include the ability to identify signatures with degenerate bases , for cases where no exact signature is shared between them . An alternative is to compute the minimum signature set , where each signature might not identify every target , but the set contains at least one identifying signature for each target . This approach is particularly suited for chip assays where signatures can be multiplexed . A related approach selects combinations of non-unique probes , such that certain viral strains can be identified by their hybridization pattern [28] . Insignia support for specialized viral diagnostics is left for future work .
The function of the match pipeline is to identify exact matches between all pairs of target and background sequences in the database . The size of the Insignia sequence database is currently about 60 billion nucleotides , and even with the linear-time algorithms described below , this is too large to search in real time . Some computational effort is saved by limiting targets to microbial genomes only , but the process of matching all pairs of target and background genomes remains expensive . To complete the matching phase within a reasonable amount of time , all exact matches of 18 bp or longer are first identified using MUMmer [29–31] , a linear time and space suffix tree matching algorithm . To expedite the process , MUMmer searches are partitioned across a 192-node Linux cluster . Even with the use of an efficient search algorithm , however , the size of the database and the high repeat content of many genomes cause the size of the output—the number of matches between all pairs of genomes—to reach unmanageable levels ( e . g . , the number of matches can be quadratic with respect to the size of the genomes ) . To combat this problem , matches are converted to a minimalized “match cover” data structure , described next . This structure saves substantial space and later provides a convenient mechanism for computing signatures . The match cover , Mtb , of a target genome t , with respect to some background genome b , is simply the list of intervals on t that are covered by contiguous , exact matches to genome b . To eliminate redundancy , all intervals contained within larger intervals are removed , but overlapping intervals are not merged . This assures that every subinterval matches contiguously to some portion of the background sequence , and every maximal match to the background is contained by a single interval ( Figure 4 ) . After construction of the match cover , the intervals are sorted by their start position , and stored as a list of ( start , length ) pairs . Because this structure only stores the target “half” of the match data , space requirements are reduced by eliminating irrelevant background match coordinates . What remains is a minimal set of intervals on genome t that exactly match some part of genome b . In addition to storing only the target half of the matches , the match cover eliminates redundant information caused by repetitive sequences . Take for instance , two potential target genomes t and u . Because all target genomes are , by default , part of the background , two match covers will be created , Mtu and Mut . Now assume an identical repeat occurs x times in t , and y times in u . A list of exact matches ( start t , start u , and length ) would require 3xy integer values to represent the repeat , while the match covers would require only 2 ( x + y ) combined values . Therefore , even when storing both halves of a match set ( t → u and u → t ) , the match cover is more efficient in dealing with repeats . This behavior was empirically tested for an all-versus-all comparison of ~300 bacterial genomes , and the match cover reduced the match list from its original size of 78 GB to just 2 GB . This 39-fold space reduction demonstrates the prevalence of repetitive matches in real data and the utility of the match cover structure . Considering the match cover is simply a list of intervals , standard data compression could be applied to obtain further space savings . The match cover is not a lossless conversion , however , because it discards information about where a match occurred in the background . The information is nonetheless sufficient for signature computation , where it suffices to know which regions of a target are unique . Furthermore , by excluding irrelevant background match positions , large background databases can be accommodated without drastically increasing the match cover size , and draft quality genomic sequences can be incorporated without difficulty . As the next section will show , the match cover encapsulates all the necessary information for signature discovery and allows for the rapid construction of signatures for any set of target and background genomes in linear time . For perspective , it is worth mentioning that the match cover is an equivalent , interval representation of matching statistics [32 , 33] . Both formalizations represent the longest contiguous match beginning at any position of a sequence , but our interval representation is space-efficient and easier to interpret in the context of signature discovery . Rahmann also leverages the properties of matching statistics in describing a “jump list” for the discovery of DNA probes [20] , and it is interesting to note that although the match cover and jump list were arrived at independently , they are analogous given their shared utilization of matching statistics . The function of the signature pipeline is to generate valid signatures for any set of target and background genomes . Because there are thousands of possible targets and many more backgrounds , combinatorics rules out the pre-computation of all signatures; however , it is possible to generate signatures from the match information with minimal overhead . The pipeline for doing so is divided into two parallel stages , corresponding to the two primary criteria a valid signature must meet: 1 ) a signature must be shared by all genomes in the target set; and 2 ) a signature must not exist in any genome in the background set . The first stage computes a list of k-mers ( DNA sequences of length k ) shared by the set of target genomes . This could be determined by computing a whole-genome multi-alignment among the targets; however , multi-alignment algorithms are too slow for a real-time application ( e . g . , 30 min to align three E . coli strains [34] ) . Alternatively , shared k-mers could be identified by intersecting k-mer tables for each target genome , but these tables would have to be constructed on the fly for each k ( since k is specified by the user at run time ) , which would also be costly . Instead , Insignia utilizes the pre-computed match cover to quickly infer shared k-mers for any length k greater than the minimum match length ( currently 18 bp ) used to build the match covers . To determine which k-mers are shared between a set of target genomes , one target is chosen as the reference r , and all match covers , Mrt , are intersected for each t in the target set . This intersection yields all matches shared by the target genomes relative to the sequence of the reference genome . Given the resulting match cover intersection Ir for a collection of targets , a k-mer in r is shared by all other target genomes if , and only if , it is entirely contained within a single interval of Ir ( Figure 5 ) . A parallel stage of the signature pipeline computes a list of k-mers unique to a target genome with respect to some background . Once again , the match cover information is leveraged to efficiently identify these k-mers . Assuming the same target reference r , all match covers , Mrb , are merged for each b in the background set . This produces a consolidated set of matches to the reference from the background . Matches smaller than k , and matches entirely contained by another interval , are irrelevant and can be removed . Given the resulting match cover union Ur for a collection of backgrounds , a k-mer in r is unique with respect to the background if , and only if , it is not entirely contained within a single interval of Ur ( Figure 6 ) . It is sufficient to compute unique k-mers with respect to a single target , because a sequence will only be reported as a signature if it is also shared by all target genomes . Thus , any single target is guaranteed to contain all of the ensuing signature sequences . The interval set operations for signature detection are extremely efficient . For MRT-sorted reference-target match intervals and T target genomes , the time complexity for finding shared-mers is O ( MRT log T ) , with the log component incurred by a priority queue of overlapping interval end points . Given the bounded number of possible target genomes , this component can be treated as a constant and the complexity becomes linear . The time complexity for finding unique-mers is also linear: O ( MRB ) for MRB reference-background intervals . The results of these two operations are then intersected to identify sequence signatures , i . e . , k-mers that are both shared by the targets and unique with respect to the background . Because all three of these operations are linear with regard to the number of match intervals and there cannot be more than l intervals for a sequence of length l , the complexity of extracting signatures from a match cover database is linear with regard to the size of the search space O ( l ) . For a typical target and background set , this translates to about one minute of processing , given the current database size and computational processing speeds . The Insignia signature pipeline is accessible by a Web interface , hosted at the University of Maryland Center for Bioinformatics and Computational Biology ( http://insignia . cbcb . umd . edu ) . This interface affords signature queries for any set of target genomes in the database , and displays results in the context of genome annotations for enhanced understanding and analysis . In addition , Insignia is closely coupled with the Gemina database ( http://gemina . tigr . org ) , which provides sequence and annotation data for all bacterial , archaeal , and viral genomes available from GenBank , along with genotypic and epidemiological information for all NIAID category A , B , C pathogens . To perform a signature query , the user specifies a reference genome , a set of target genomes , and a desired signature length . All reported signatures will be perfectly conserved among all genomes in the target set and absent from all other genomes . The reference genome , which is by definition one of the targets , serves as the coordinate system on which all signatures and genes ( annotation ) are based . Selection of the target genomes is carried out either through a list-based , tree-based , or query-based interface . In the list version , users are presented with a full listing of all genomes in the database , while the tree view arranges genomes in a taxonomy tree . The query interface available at the Gemina Web site facilitates text-based , controlled vocabulary queries of pathogen , host , disease , symptom , anatomy , transmission method , and geographic location attributes . After computing all signatures for a given query , users may filter and display the results based on various experimental constraints . For instance , hybridization probes may require a certain GC content and melting temperature , so signatures falling below some user-specified thresholds can be screened out . Results may also be limited to specific genes , genes with specific functions , or intergenic sequence . After specifying the desired filters , signatures can be displayed and downloaded in tabular format or displayed in a genome browser , along with annotation data , to highlight each signature's position context . To further support assay design , Insignia provides users with the ability to screen signatures for near matches and design signature-based primers . To search quickly for near matches , Insignia screens signature candidates against the National Center for Biotechnology Information ( NCBI ) databases using BLAST . This process helps eliminate signatures with near matches to background sequences and matches to sequences not included in the Insignia database , such as ESTs or environmental sequences . Once a set of signatures has been decided upon , the integrated Primer3 [22] software can be used to choose suitable primers and hybridization probes from the signatures . The nucleotide sequences of the probes and primers for each TaqMan assay were selected from the signature set identified by Insignia for V . cholerae O1 biovar El Tor strain N16961 . The probes and primers were designed outside of Insignia using commercially available design software ( Allele ID , Premier Biosoft International , http://www . premierbiosoft . com ) . All assays were designed for PCR to run under the same conditions . The primers and probes were synthesized commercially ( Invitrogen , http://www . invitrogen . com , and Sigma-Genosys/Sigma-Aldrich , http://www . sigmaaldrich . com ) . The probes were synthesized with the FAM fluorescent reporter dye at the 5′ end and with TAMRA quencher dye at the 3′ end . Genomic DNA was extracted from each inclusive and exclusive validation strain ( DNeasy Blood and Tissue Kit , Qiagen , http://www . qiagen . com ) , and species identity was confirmed for each strain sample by partial 16S rDNA sequencing ( MicroSeq ID , http://www . appliedbiosystems . com ) . Real-time PCR was performed in a reaction mixture with a total volume of 25 μl containing 100 ng of genomic DNA , 500 nM of each primer , 250 nM of each fluorogenic probe , and TaqMan Universal Master Mix ( Applied Biosystems ) . The Master Mix contained AmpErase uracil-N-glycosylase ( UNG ) , deoxynucleoside triphosphate with dUTPs , ROX as an internal passive reference , and an optimized buffer component . Amplification and detection were carried out in an ABI 7500 Real-Time PCR System ( Applied Biosystems ) with an initial step of 50 °C for 2 min , 95 °C for 10 min , followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min . All PCR assays were conducted in duplicate and Ct values were used to evaluate the extent to which each assay was inclusive of V . cholerae strains and/or excluded near-neighbor strains . Ct values of <21 were considered strong positive , and Ct values between 21 and 50 were binned in increments of 4 ( i . e . , 21–24 , 25–28 , etc . ) to simplify analysis of the relative efficiency of PCR across all assays and strains . | Now that the genome sequences of hundreds of bacteria and viruses are known , we can design tests that will rapidly detect the presence of these species based solely on their DNA . Such tests have a wide range of applications , from diagnosing infections to detecting harmful microbes in a water supply . These tests can detect a pathogen in a complex mixture of organic material by recognizing short , distinguishing sequences—called DNA signatures—that occur in the pathogen and not in any other species . We present Insignia , a new computational system that identifies DNA signatures of any length in bacterial and viral genomes . Insignia uses highly efficient algorithms to compare sequenced bacterial and viral genomes against each other and to additional background genomes including plants , animals , and human . These comparisons are stored in a database and used to rapidly compute signatures for any particular target species . To maximize its utility for the community , we have made Insignia available as free , open-source software and as a Web application . We have also validated 50 Insignia-designed assays on a panel of 46 strains of Vibrio cholerae , and our results show that the signatures are both sensitive and specific . | [
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| 2007 | Comprehensive DNA Signature Discovery and Validation |
Leptospirosis , an emerging zoonotic disease with worldwide distribution , is caused by spirochetes belonging to the genus Leptospira . More than 500 , 000 cases of severe leptospirosis are reported annually , with >10% of these being fatal . Leptospires can survive for weeks in suitably moist conditions before encountering a new host . Reservoir hosts , typically rodents , exhibit little to no signs of disease but shed large numbers of organisms in their urine . Transmission occurs when mucosal surfaces or abraded skin come into contact with infected urine or urine-contaminated water or soil . In humans , leptospires can cause a variety of clinical manifestations , ranging from asymptomatic or mild fever to severe icteric ( Weil's ) disease and pulmonary haemorrhage . Currently , little is known about how Leptospira persist within a reservoir host . Prior in vitro studies have suggested that leptospires alter their transcriptomic and proteomic profiles in response to environmental signals encountered during mammalian infection . However , no study has examined gene expression by leptospires within a mammalian host-adapted state . To obtain a more faithful representation of how leptospires respond to host-derived signals , we used RNA-Seq to compare the transcriptome of L . interrogans cultivated within dialysis membrane chambers ( DMCs ) implanted into the peritoneal cavities of rats with that of organisms grown in vitro . In addition to determining the relative expression levels of “core” housekeeping genes under both growth conditions , we identified 166 genes that are differentially-expressed by L . interrogans in vivo . Our analyses highlight physiological aspects of host adaptation by leptospires relating to heme uptake and utilization . We also identified 11 novel non-coding transcripts that are candidate small regulatory RNAs . The DMC model provides a facile system for studying the transcriptional and antigenic changes associated with mammalian host-adaption , selection of targets for mutagenesis , and the identification of previously unrecognized virulence determinants .
Leptospirosis is a neglected disease of global significance [1] , [2] . Pathogenic leptospires , shed in animal urine or free-living within contaminated water , enter the host through small abrasions in the skin or contact with mucous membranes of the eyes , nose or throat . Organisms disseminate almost immediately following acquisition , travelling via the bloodstream to multiple tissues [3] . L . interrogans , an extracellular pathogen , is thought to penetrate host tissues by intercellular migration [4] . In immunocompetent hosts , the majority of leptospires are thought to be cleared by opsonophagocytosis following the appearance of specific antibodies [5] . However , organisms that reach the kidneys , an immunoprivileged site [1] , adhere to and colonize the proximal convoluted renal tubules , where they replicate exponentially . The majority of human disease is caused by Leptospira interrogans serovar ( sv . ) Copenhageni for which Rattus norvegicus serves as a reservoir host [3] , [6] , [7] . Experimentally-infected rats can excrete up to 107 leptospires/ml of urine for months without clinical signs of infection , thus exemplifying the unique biological equilibrium that can exist between pathogen and reservoir host [8] , [9] , [10] . The genome sequences of several pathogenic and saprophytic Leptospira spp . , including L . interrogans sv . Copenhageni , are now complete [6] , [11] , [12] , [13] , [14] , [15] , [16] . L . interrogans sv . Copenhageni Fiocruz L1-130 harbors 3728 protein-encoding genes [11] , [12] . By comparative genomics , Picardeau et al . [14] identified 1431 “pathogen-specific” genes that are present within either or both of the pathogenic species , L . interrogans and L . borgpetersenii , but are absent from the free-living saprophyte L . biflexa . Although the majority ( 62% ) of these pathogen-specific genes encode proteins of unknown function , it is possible that some are required by Leptospira to respond to unique environmental cues encountered within the mammalian host . Along these lines , the genome of L . interrogans contains >200 protein-coding sequences potentially involved in gene regulation , including gene products associated with two component signal transduction systems , alternate sigma factors , anti-sigma factors , and anti-sigma factor antagonists [11] , [12] . Not surprisingly , the pathogen-specific group also includes numerous gene products whose annotated functions or cellular location suggest a potential role in virulence-related processes such as adherence , digestion of host tissues and extracellular matrix , and evading the host's innate and adaptive immune responses [14] , [17] . To identify novel leptospiral virulence determinants , investigators have manipulated in vitro growth conditions to simulate those encountered within the mammalian host , including increased temperature and/or osmolarity , iron starvation , and the presence of serum [18] , [19] , [20] , [21] , [22] . However , the extent to which these in vitro conditions faithfully reproduce those encountered by Leptospira in vivo is unclear . In an effort to characterize leptospires in a truly mammalian host-adapted state , we cultivated virulent low-passage L . interrogans sv . Copenhageni within the peritoneal cavities of rats using a modification of our dialysis membrane chamber ( DMC ) model [23] , [24] . Given that rats are a natural reservoir host for this species of Leptospira [2] , [25] , [26] , we reasoned that this model would be ideal for this purpose . Originally developed to study host adaption by Lyme disease spirochetes ( Borrelia burgdorferi ) [23] , [24] , this technique , which uses dialysis membrane tubing with an 8000 Da molecular weight cut-off , provides bacteria with access to host nutrients while protecting them from the host's cellular immune response . The DMC model has been instrumental in studying the contribution of mammalian host-specific signals to differential gene expression in B . burgdorferi on a genome-wide scale as well as enabling us to characterize the transcriptional and physiological changes integral to the mammalian host-adaptation process [23] , [24] , [27] , [28] , [29] . In recent years , high-throughput RNA sequencing ( RNA-Seq ) has replaced microarrays as the method of choice for genome-wide transcriptional profiling in bacteria [30] , [31] . Unlike microarrays , RNA-Seq allows transcription to be understood at the single-nucleotide level . Here , we used an RNA-Seq approach to compare the transcriptome of virulent low passage Leptospira interrogans sv . Copenhageni cultivated within DMCs with that of leptospires grown under standard in vitro conditions ( 30°C in EMJH ) . Using this approach , we determined the relative expression levels of “core” housekeeping genes under both growth conditions , and , more importantly , we identified 166 genes that are differentially-expressed by leptospires within the mammalian host , the majority of which are pathogen-specific ( i . e . , not present within saprophytic Leptospira ) . Most notably , our analyses highlight novel physiological aspects of mammalian-host adaptation by leptospires with respect to heme uptake and utilization . Moreover , we identified 11 novel non-coding ( ncRNAs ) transcripts which represent candidate small regulatory RNAs . In addition to providing a facile system for studying the transcriptional and physiologic changes leptospires undergo during mammalian infection , our data provide a rational basis for selecting new targets for mutagenesis .
Our extensive experience with cultivation of Lyme disease spirochetes in DMCs implanted into rats [23] , [24] , [27] , [32] , a natural reservoir for L . interrogans , led us to ask whether the DMC model could be used to generate mammalian host-adapted Leptospira . In preliminary experiments , we determined that virulent low-passage L . interrogans sv . Copenhageni strain Fiocruz L1-130 , diluted to low density ( 1×104 leptospires/ml ) in EMJH medium , undergoes exponential replication within DMCs , reaching a maximal density of ∼7×107 leptospires/ml within 8 days post-implantation ( data not shown ) . Importantly , leptospires recovered from DMCs explanted daily between 8 and 12 days post-implantation were vigorously motile by dark-field microscopy . The polypeptide profiles of leptospires in DMCs explanted between 9 and 12 days were highly similar ( data not shown ) . On the basis of these studies , we chose 10 days as our standard period for intraperitoneal implantation . As shown in Figure 1A , under these conditions , we noted numerous polypeptides whose expression was either increased or decreased in response to mammalian host-derived signals compared to in vitro-grown bacteria . The polypeptide differences between in vitro- and DMC-cultivated organisms were even more apparent by two-dimensional SDS-PAGE ( Figure S1 ) . While a comprehensive quantitative analysis of these differentially-expressed polypeptides is necessary to identify the corresponding leptospiral proteins , these data support our contention that virulent leptospires substantially alter their proteome in response to mammalian host-specific signals . With B . burgdorferi , successful mammalian host-adaptation within DMCs is determined by the reciprocal expression of the outer surface lipoprotein ( Osp ) A and OspC lipoproteins , which are OFF and ON , respectively , within the mammal [23] . However , no expression profile associated with host-adapted L . interrogans has been reported and only a handful of leptospiral genes/proteins have been shown to be reproducibly upregulated during mammalian infection . Among these is Sph2 , one of four sphingomyelinase-like proteins encoded by L . interrogans sv . Copenhageni [12] . Although most strains of L . interrogans encodes at least 3 distinct sphingomyelinase-like proteins ( Sph1 , Sph2 and Sph3 ) , only Sph2 is thought to be a “true” ( i . e . , enzymatically active ) sphingomyelinase [33] . Expression of Sph2 is upregulated in vitro in response to serum [21] and/or increased osmolarity [34] and during mammalian infection [35] . On the other hand , SphH , a closely-related pore-forming protein without sphingomyelinase activity [33] , [36] , is expressed constitutively in vitro [34] , [37] and by leptospires colonizing the renal tubules of infected hamsters [37] . Consistent with these previous studies , the level of Sph2 was substantially higher in DMC-cultivated leptospires compared to in vitro-grown organisms , whereas SphH was expressed at similar levels under both conditions ( Figure 1B ) . Immunoblots using antisera against LipL32 and LipL41 , two leptospiral lipoproteins expressed constitutively in vitro and during mammalian infection [38] , [39] , [40] , were performed as loading controls ( Figure 1B ) . We considered these data as strong indication that DMC-cultivated leptospires are in a mammalian host-adapted state . Having established the feasibility of using DMCs to generate mammalian host-adapted L . interrogans , we compared the transcriptional profiles of DMC- and in vitro-cultivated leptospires by RNA-Seq . To ensure that our data would be robust and reproducible , we generated Illumina TruSeq libraries from three biologically-independent samples for each growth condition . The sequence statistics and numbers of mapped reads for each biological replicate are summarized in Table 1 and displayed graphically in Figure 2 . The total number of reads ranged from ∼8–14 million per library , of which 79–94% of reads mapped to the L . interrogans sv . Copenhageni Fiocruz L1-130 reference genome [11] , [12]; only those reads that mapped to a single location on either Chromosome 1 or 2 were used to assess gene expression . The majority ( 43–55% ) of unique sequence reads mapped to protein-coding mRNAs annotated on Chromosome 1 , while ∼3–5% mapped to predicted ORFs on Chromosome 2; this 12∶1 ratio is consistent with the relative coding capacities of the two chromosomes [11] , [12] . As discussed below , a considerable number of reads ( 13–20% ) in both chromosomes mapped to non-coding regions that represent candidate small regulatory RNAs ( sRNAs ) ( Table 1 ) . The genome of L . interrogans sv . Copenhageni Fiocruz L1-130 harbors 3728 protein-encoding genes [11] , [12] . The vast majority ( ∼94% ) of these ( 3489 and 3499 in DMC- and in vitro-cultivated leptospires , respectively ) , were represented in our RNA-Seq data by a mean expression value of ≥1 ( Table S2 ) . We observed average mean expression values of 67 . 2 and 60 . 5 per gene in DMC- and in vitro-cultivated organisms , respectively ( data not shown ) . By comparative genomics , Picardeau et al . [14] identified 2052 “core” protein-coding genes that are shared between pathogenic ( L . interrogans and L . borgpetersenii ) and saprophytic ( L . biflexa ) Leptospira species . Not surprisingly , many of these core gene products are associated with housekeeping functions , such as motility , energetics and intermediary metabolism , DNA and RNA metabolism , and cell division [14] . Analysis of the protein-coding sequences for the 100 most highly-expressed genes ( i . e . , Top 100 ) in DMC-cultivated leptospires revealed that 66 are conserved ( i . e . , ≥40% amino acid identity over ≥80% of the coding region ) between pathogenic and saprophytic Leptospira spp . and , therefore , part of the core group ( Table S3 ) ; of note , the percentage ( 66% ) of core genes within our Top100 is similar to the overall percentage ( 55% ) of core genes within the entire L . interrogans sv . Copenhageni genome [14] . Consistent with their proposed housekeeping functions , 62 ( 94% ) of the 66 core genes within the Top 100 were expressed at similar levels in vitro and within DMCs ( Table S3 ) . Thirty-four of the Top 100 genes are pathogen-specific ( i . e . , no orthologous gene identified in L . biflexa ) , two of which ( LIC10465/ligA and LIC12653 ) are found only in L . interrogans ( i . e . , absent in L . borgpetersensii , L . licerasiae and L . santarosai ) . Eight of the 34 pathogen-specific genes within the Top 100 were upregulated by L . interrogans sv . Copenhageni within DMCs ( see Table S3 and below ) . We also surveyed both DMC- and in vitro-derived datasets for genes associated with key metabolic pathways . One unusual metabolic feature of pathogenic leptospires , compared to other spirochetes , is that they are unable to utilize glucose despite encoding a seemingly complete glycolytic pathway , relying instead on β-oxidation of long-chain fatty acids as sources of both carbon and energy [11] , [41] . By RNA-Seq , we detected uniquely mapped reads for all of the genes thought to be involved in glucose uptake and utilization ( KEGG pathway lic00010 ) , each of which was expressed at similar levels in DMCs and in vitro ( Table S4 ) . However , two genes , LIC13358 and LIC20119 , both encoding putative phosphoglucomutases , and LIC12908 , encoding the only glucose transporter identified in L . interrogans [11] , [12] , [42] , were expressed at extremely low levels , both in DMCs and in vitro ( Table S2 ) . These data support the findings of Zhang et al . [42] , who proposed that the inability of pathogenic leptospires to utilize glucose stems from insufficient glucose uptake and/or catalysis rather than an incomplete glycolytic pathway . As one might predict , we detected significant numbers of sequence reads for genes involved in the uptake and β-oxidation of medium and long-chain fatty acids ( KEGG pathway lic00071 ) , the citric acid cycle ( KEGG lic00020 ) , generation of NAD/NADP ( KEGG lic00760 ) , and oxidative phosphorylation ( KEGG lic00190 ) . All of the individual genes involved in these energetic pathways were expressed at similar levels under both growth conditions ( Table S4 ) . Using DESeq [43] , we identified 166 genes whose expression was either positively- or negatively-regulated by ≥2-fold ( adjusted p-value≤0 . 05 ) within the mammal ( Tables 2 and 3 ) . Although some variance was observed between biological replicates ( Table S2 ) , a heat map representing the expression data for all 166 differentially-expressed genes confirmed that each biological replicate clustered with its respective sample source ( Figure S2 ) . Of the 110 genes upregulated by L . interrogans within DMCs , 106 are on Chromosome 1 while only 4 are on Chromosome 2 ( Table 2 ) . All but 3 of the upregulated genes appear to be pathogen-specific ( i . e . , a paralogous gene/protein could not be identified in L . biflexa; 54 of these are unique to L . interrogans and an additional 7 are unique to serovar Copenhageni ( Figure 3 ) . Almost half ( 49/110 ) of the genes upregulated in DMCs encode hypothetical proteins ( Figure 4 and Table 3 ) , which is consistent with the overall percentage ( 40% ) of hypothetical genes annotated within L . interrogans [6] , [11] . Based on searches performed using the Conserved Domain Database [44] , [45] , none of the hypothetical proteins encoded by these genes contained readily identifiable functional domains ( data not shown ) . However , one gene ( LIC12986 ) recently was shown to be required for leptospires to survive within hamsters and to colonize the renal tubules of mice [46] . Of the remaining upregulated genes , 16 encode putative lipoproteins of unknown function [47] ( Figure 4 and Table 3 ) . Surface-exposed spirochetal lipoproteins have been implicated in a wide range of pathogenesis-related functions , including adherence to extracellular matrix components and nutrient acquisition [48] . However , because the mechanism ( s ) responsible for sorting individual spirochetal lipoproteins remain poorly understood , it is not possible to predict based on amino acid sequence alone which , if any , might function at the pathogen-host interface . By RNA-Seq , we identified 56 genes ( 47 on Chromosome 1 and 9 on Chromosome 2 ) that were downregulated in DMCs ( Tables 3 and 4 ) . All of the downregulated genes are pathogen-specific ( i . e . , not found in L . biflexa ) ; almost half ( 26/56 ) are unique to L . interrogans ( i . e . , not in L . borgpetersenii , L . santarosai or L . licerasiae ) ( Figure 3 ) . As with the upregulated gene subset , more than half ( 35/56 ) of the DMC-downregulated genes encode hypothetical proteins ( Figure 4 and Table 3 ) ; of note , almost half ( 43% ) of these appear to be transcribed in two polycistronic operons ( LIC10173-10177 and LIC12604-12616 ) . Interestingly , all of the genes within these two putative operons are pathogen-specific . Only one lipoprotein ( LIC20153 ) was expressed at lower levels in DMCs ( compared to 16 upregulated ) . Five genes related to de novo heme biosynthesis ( LIC20008/hemA , LIC20009/hemCD , LIC20010/hemB , LIC20011/hemL and LIC20014/hemE ) [74] were DMC-downregulated these findings imply that leptospires can scavenge heme from the mammalian host . The heme biosynthetic operon also contains genes encoding a two component system ( TCS ) . Signal transduction by the orthologous TCS in L . biflexa is required for regulation of heme biosynthesis [75] . Although both the histidine kinase ( HK; LIC20012 ) and the response regulator ( RR; LIC20013 ) were downregulated ( 2 . 50- and 2 . 22-fold; respectively ) in DMCs , the fold-change for the RR was not significant ( p = 0 . 097 ) . Based on their tandem arrangement and similar expression profiles , these heme biosynthetic genes appear to be transcribed as a single operon . LIC20017/hemG and LIC20018/hemH , encoding enzymes responsible for the last two steps in heme biosynthesis , respectively , are located downstream of the larger biosynthetic operon; both of these genes appear to be transcribed as monocistronic messages at similar levels in vitro and in DMCs ( Table 4 and data not shown ) . One of the advantages of RNA-Seq is that it allows visualization of uniquely mapped reads within non-annotated regions of the genome . Using the IGB browser , we detected at least 11 regions that were transcriptionally-active but not protein coding; these non-coding RNA ( ncRNA ) transcripts are novel candidate small regulatory RNAs ( sRNAs ) within L . interrogans ( Table 5 and Figure S3 ) . Five of these are homologous to known sRNA families ( tmRNA , RNaseP , PyrR binding site and two cobalamin sRNAs ) ( http://rfam . sanger . ac . uk/ ) [76] , [77] , [78] , [79] . The expression of 8 of the 11 putative sRNAs was validated by reverse-transcriptase PCR in L interrogans sv . Copenhageni strain RJ16441 ( Table 5 ) and all predicted sRNAs were highly conserved in the closely-related virulent serovar type strain Lai [80] . One of the predicted sRNAs , LIC1nc80 ( Figure 6 ) , was significantly DMC-upregulated ( 4 . 39-fold ) compared to in vitro-cultivated leptospires ( Table S2 ) . Further characterization of these candidate sRNAs ( i . e . , by Northern blot ) is required to understand their function ( s ) and relationships to the surrounding genes ( i . e . , 5′ UTR verses bone fide sRNA ) . To validate our RNA-Seq data , we performed quantitative reverse transcription-PCR ( qRT-PCR ) on a panel of 14 genes that were , according to DESeq analysis , upregulated ( LIC12631/sph2 , LIC11888 and LIC11889/flaB ) , downregulated ( LIC10175 , LIC10179 and LIC12615 ) , or unchanged ( LIC10191/loa22 , LIC12966/lipL41 , LIC13166/ompL36 , LIC10787/flaA-2 , LIC10068 , LIC10421 , LIC12339 , and LIC20001 ) in DMCs compared to in vitro . While there is some debate regarding the most appropriate leptospiral gene to use for normalization [21] , [81] , we selected LIC11352/lipL32 based on studies demonstrating that its expression was relatively unchanged under a wide-range of growth conditions , including increased temperature , increased osmolarity , and/or exposure to serum [21] , [38] , . Representative results are shown in Figure 7A; data for the entire panel are presented in Figure S4 . Overall , we saw strong agreement between our RNA-Seq and qRT-PCR datasets; the correlation coefficient ( R2 ) between RNA-Seq and qRT-PCR data across the entire panel was 0 . 8881 ( Figure 7B ) . We also used qRT-PCR to confirm the relative expression for two ( LIC1nc60/RNase P and LIC2nc10/cobalamin ) of the putative sRNAs ( Figure S4 ) ; of these , only LIC1nc60/RNaseP was upregulated ( 2 . 65-fold; p = 0 . 0054 ) within DMCs .
The identification of genes/proteins that are differentially-expressed by microorganisms only during infection and/or within specific host niches often provides insight into the parasitic strategies of pathogens . During natural and experimental infection in rats , L . interrogans rapidly disseminate hematogenously to all tissues but are cleared by 7 days post-inoculation from all sites except the kidneys [7] , [82] . The ability of leptospires to colonize and persist within renal tubules almost certainly involves unique virulent determinants [1]; however , the paucilbacillary nature of leptospiral infection , even within this preferred niche , hinders our ability to perform global gene expression studies on L . interrogans within host tissues . Prior studies , including several using microarray-based approaches [18] , [19] , [21] , [22] , [83] , have manipulated in vitro growth conditions to simulate the environmental signals encountered by leptospires within the mammal . Based on extensive studies with B . burgdorferi , another pathogenic spirochete , we and others have demonstrated that bone fide mammalian host adaptation is a complex and dynamic process that cannot be fully reproduced ex vivo [23] , [27] , [32] . We therefore used a rat peritoneal dialysis membrane chamber ( DMC ) model to generate sufficient L . interrogans in a mammalian host-adapted state to perform global transcriptional studies . Cultivation of leptospires within DMCs , in conjunction with next generation sequencing , enabled us to define for the first time the transcriptome of L . interrogans within the mammalian host . In order to transition from a free living to infectious state , leptospires must adjust their metabolism to utilize nutrients available within the mammalian host . Quite surprisingly , we found that the majority of genes implicated in central and intermediary metabolism were expressed by leptospires at similar levels in DMCs and in vitro . We interpret these data to suggest that EMJH , the medium commonly used to cultivate pathogenic and saprophytic leptospires in vitro , reflects the overall composition of nutrients available within mammalian host fairly well . Nonetheless , leptospires cultivated within DMCs differentially-regulated a handful of genes whose products are involved in metabolic and biosynthetic pathways , most notably , heme uptake and utilization ( see below ) . Although increased temperature often is implicated as an important stimulus for host adaptation , we observed very little overlap ( <10% ) between the cohort of genes that were upregulated in DMCs and those previously identified as being temperature-regulated in vitro [18] , [19] , [21] , [68] . Thus , differential gene regulation by leptospires within DMCs appears to be driven primarily by non-thermal mammalian host-specific stimuli . The relatively small pore size of the dialysis tubing used to cultivate leptospires within rat peritoneal cavities would exclude macromolecules and most serum proteins but allow for efficient exchange of nutrients ( i . e . , glucose , ions , and free amino acids ) present within serum . These are the same types of small molecules that leptospires likely encounter within proximal convoluted tubules , where the composition of the glomerular ultrafiltrate most closely resembles that of interstitial fluid [84] . Further experimentation is required to assess how closely DMC-cultivated leptospires resemble their counterparts within host tissues during acute and/or chronic infection . The DMC model does have some limitations . For instance , virulence genes associated with pulmonary haemorrhage may be expressed only within the context of lung tissue . Because bacteria within DMCs are prevented from interacting with host immune cells and immunoglobulin [85] , this model does not enable us to identify genes that are differentially-regulated in response to specific pathogen-host interactions and/or immune evasion . Although increased temperature often is implicated as an important stimulus for host adaptation , we observed very little overlap ( <10% ) between the cohort of genes that were upregulated in DMCs and those previously identified as being temperature-regulated in vitro [18] , [19] , [21] , [68] . We observed a similarly limited overlap between our RNA-Seq data and genes found to be differentially regulated in vitro in response to exposure to serum [21] and low iron [22] . We observed a somewhat higher , but nonetheless small , degree of overlap ( 16% ) between our RNA-seq dataset and genes identified by Matsunaga et al . [20] as being upregulated by physiologic osmolarity ( EMJH supplemented with 120 mM NaCl ) ; included in this overlap are lipL53 ( LIC12099 ) , sph2 ( LIC12631 ) , a putative CoA-transferase ( LIC12322 ) , phoD ( LIC13397 ) and hol ( LIC20148; see below ) . Thus , differential gene regulation by leptospires within DMCs appears to be driven by mammalian host-specific stimuli that are not readily reproduced in vitro . The relatively small pore size of the dialysis tubing used to cultivate leptospires within rat peritoneal cavities would exclude macromolecules and most serum proteins but allows for efficient exchange of nutrients ( i . e . , glucose , ions , and free amino acids ) present within serum . These are the same types of small molecules that leptospires likely encounter within proximal convoluted tubules , where the composition of the glomerular ultrafiltrate most closely resembles that of interstitial fluid [84] . Further experimentation is required to assess how closely DMC-cultivated leptospires resemble their counterparts within host tissues during acute and/or chronic infection . The DMC model does have some limitations . For instance , virulence genes associated with pulmonary haemorrhage may be expressed only within the context of lung tissue . Because bacteria within DMCs are prevented from interacting with host immune cells and immunoglobulin [85] , this model does not enable us to identify genes that are differentially-regulated in response to specific pathogen-host interactions and/or immune evasion . To date , >20 named species of Leptospira have been identified based on molecular taxonomic analyses [86] . Leptospira spp . can be further divided into three major groups based on pathogenicity: pathogenic ( 9 species ) , intermediate virulence ( 5 species ) and free-living saprophytes ( 6 species ) . The vast majority ( 69% ) of genes upregulated by leptospires in response to mammalian host signals are found only in pathogenic and intermediate virulence species ( i . e . , absent in L . biflexa ) , suggesting that their gene products may help promote infection and/or colonization within mammal . However , more than half ( 64/110 ) of these upregulated genes encode either hypothetical proteins or lipoproteins of unknown function without any obvious conserved/functional domains . While their functions remain to be determined , our finding that these protein-coding genes are differentially-regulated in response to mammalian host-specific signals make them attractive candidates for further experimentation in animals model and , in particular , their potential use as part of a mono- or multi-valent protein-based vaccine . Thirty-five of the 56 genes downregulated in DMCs encode hypothetical proteins . Interestingly , all but 7 of these are unique to pathogenic and intermediate virulence species , raising the possibility that these genes products , while not required for survival within the host , facilitate the transition from a free-living to infective state . Heme is the major source of iron in L . interrogans and also serves as a cofactor for proteins essential for respiration ( i . e . , cytochromes ) , biosynthesis of vitamin B12 , and detoxification of reactive oxygen intermediates ( i . e . , catalase ) . Unlike B . burgdorferi [87] and T . pallidum [88] , L . interrogans possess a complete set of genes required for de novo heme biosynthesis as well as the uptake and utilization of exogenous heme [58] , [74] , [89] . By RNA-Seq , expression of 6 heme biosynthesis genes was significantly downregulated in DMCs compared to in vitro , while heme oxygenase ( LIC20148/hol ) and phuR , encoding a TonB-dependent heme receptor , were upregulated; these data support the notion that pathogenic leptospires preferentially use exogenously derived heme within the mammal . Of the four putative fur orthologs encodes by L . interrogans , only one ( LIC12034 ) was upregulated in DMCs . Recently , Marcsisin et al . [46] demonstrated that inactivation of this gene had no effect on virulence in a hamster acute infection , implying that this Fur paralog is not responsible for downregulation of the heme operon within DMCs . Alternatively , downregulation of heme biosynthesis is not a prerequisite for survival in vivo . Because heme is highly toxic [90] , there is relatively little , if any , free heme within plasma [91] . In the glomerulus , the molecular weight cut-off for ultrafiltration is ∼70 kDa [92] . Thus , while L . interrogans is able to use haemoglobin ( 64 kDa ) as a source of heme in vitro [60] , this micronutrient is likely present in only minute amounts within the proximal tubules . Smaller molecules ( ≤20 kDa ) , only the other hand , easily pass through the glomerulus into Bowmen's capsule; it is worth noting that this molecular weight cut-off is essentially equivalent to that of the dialysis tubing used for our DMCs ( 8 kDa MWCO ) . Instead , leptospires may be using myoglobin ( 16 . 7 kDa ) , which is present in human plasma at concentrations similar to that of haemoglobin [93] . Both hemoglobin and myoglobin , released by red blood cell turnover and muscle tissue damage , respectively , are filtered by the kidneys and would be available to leptospires within the renal tubules . Small non-coding RNAs ( sRNAs ) are increasingly recognized as essential post-transcriptional gene expression regulators that enable bacteria to adjust their physiology in response to environmental cues [94] . Bacterial sRNAs range from 50 to 500 nucleotides and frequently are located within intergenic regions [95] . By diverse mechanisms , including changes in RNA conformation , interactions with DNA , other RNAs and proteins , sRNAs can modulate transcription , translation , mRNA stability and DNA maintenance or silencing [96] , [97] . Five of the 11 candidate sRNAs identified as part of this study are conserved in bacteria and known to carry out specific housekeeping functions , including RNase P ( LIC1nc60 ) , responsible for processing of tRNAs and other RNAs , and tmRNA ( LIC1nc10 ) , which acts as both a transfer RNA ( tRNA ) and mRNA to tag incompletely-translated proteins for degradation and to release stalled proteins [76] , [77] . We also identified two cobalamin riboswitches ( LIC1nc55 and LIC2nc10 ) , which act as cis-regulatory elements in 5′ untranslated regions of vitamin B12-related genes; allosteric rearrangement of mRNA structure is mediated by ligand binding resulting in modulation of gene expression or translation of mRNA [78] . LIC1nc55 lies upstream of LIC121374/btuB , which encodes a constitutively-expressed TonB-dependent outer membrane cobalamin receptor protein [98] . We also identified a candidate sRNA ( LIC2nc10 ) upstream of LIC20135; although annotated as a ferredoxin , LIC20135 contains a domain conserved within sirohydrochlorinin cobalt chelatases , an important enzyme involved in biosynthesis of vitamin B12 . Finally , LIC1nc20 contains a conserved PyrR binding site; this RNA element is found upstream of genes involved in pyrimidine biosynthesis and transport in Bacillus subtilis [79] . In L . interrogans , this sRNA was found downstream of genes encoding hypothetical proteins . In addition to these known sRNAs , we identified six transcriptionally-active , non-coding regions that encode novel candidate regulatory sRNAs . LIC1nc30 , LIC1nc50 , LIC2nc30 and LIC2nc40 were all identified in the 5′ untranslated regions for LIC14007 , LIC10702 , LIC20192 and LIC20276 , respectively , all of which encode proteins of unknown function . The remaining two putative sRNAs ( LIC1nc80 and LIC2nc20 ) are located in the 3′ untranslated region of genes , which are known to be a repository of sRNAs in other bacterial species [99] . The L . interrogans genome encodes >200 proteins whose annotations suggest a role in transcriptional regulation ( i . e . , sigma factors , anti-sigma factors and trans-acting factors ) , two-component signal transduction and the synthesis/degradation of cyclic nucleotides [11] , [12] . By RNA-Seq , the vast majority of these putative regulatory proteins were expressed at similar levels in vitro and in DMCs; this finding is not unexpected given that these types of regulatory factors typically are activated at the protein level by endogenously- or exogenously-derived small molecules and environmental stimuli [100] , [101] , [102] . Recent advances in Leptospira molecular genetics , including the development of site-directed [103] and transposon-mediated [104] , [105] , [106] mutagenesis techniques , now make it possible to determine the contribution ( s ) of genes that are regulated within DMCs . We anticipate that this approach will identify proteins involved in environmental sensing , mammalian host adaptation and/or the expression of specific virulence determinants in vivo .
All animal experimentation was conducted following the Guide for the Care and Use of Laboratory Animals ( Eighth Edition ) and in accordance with protocol ( ACC# 100570-0116 ) reviewed and approved by the University of Connecticut Health Center Institutional Animal Care and Use Committee . The UCHC laboratory animal care program is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care . The USDA Site ID: Customer Number 44 , Certificate Number 16-R-0025 , PHS Assurance Number A3471-01 . Virulent low-passage Leptospira interrogans sv . Copenhageni strains Fiocruz L1-130 , kindly provided by Dr . David Haake ( UCLA ) , and RJ16441 were cultivated in vitro under standard conditions at 30°C in EMJH medium [107] supplemented with 1% rabbit serum ( Pel-Freez Biologicals , Rogers , AR ) with 100 µg/ml 5-fluorouracil . Cultures were passaged in vitro no more than 3 times before being used for experimentation . To obtain L . interrogans in a mammalian host-adapted state , organisms were cultivated in dialysis membrane chambers ( DMCs ) as previously described [23] , [24] . Briefly , DMCs were constructed using standard dialysis membrane tubing ( Spectra-Por; 8000 MWCO ) . Prior to use , 8-inch strips of dialysis tubing were tied off at one end and then sterilized by boiling for 20 min in sterile water containing 5 mM EDTA , followed by two successive boiling washes in water alone . Dialysis bags were cooled to room temperature and then filled with ∼8–9 mls of EMJH medium ( supplemented with 10% vaccine-grade bovine serum albumin to maintain osmotic pressure ) containing 104 organisms per ml . Once filled , the tubing was tied and excess membrane removed from both ends . For implantation , female Sprague-Dawley rats ( 150–174 g ) were anesthetized by intramuscular injection of a mixture of ketamine ( 50 mg/kg ) , xylazine ( 5 mg/kg ) , and acepromazine ( 1 mg/kg ) . Using strict aseptic technique , a DMC was implanted into the peritoneal cavity of each rat . Analgesia ( carprofen; 5 mg/kg ) was administered on the day of surgery and once the following day . At designated time points ( typically 9–10 days post-implantation ) , rats were euthanized by CO2 narcosis and DMCs recovered . The contents of each chamber were removed by gentle syringe aspiration with an 18G needle; the needle was removed prior to expelling the DMC dialysate into a sterile 15 ml conical bottom tube . Bacteria were enumerated by dark field microscopy immediately following explant using a Petroff-Hausser counting chamber ( Hausser Scientific Co . , Horsham , PA ) . In vitro-cultivated L . interrogans , harvested at late-log phase ( 5×108–1×109 per ml ) and leptospires explanted from DMCs were processed for one- and two-dimensional SDS-polyacrylamide gel electrophoresis ( 1D and 2D SDS-PAGE , respectively ) as previously described [8] . Protein concentrations were determined using the DC protein assay kit ( Bio-Rad ) . Total protein separated by 1D SDS-PAGE was detected by SYPRO Ruby protein gel stain ( Sigma-Aldrich Inc , Ireland ) as per manufacturer's instructions . Images were visualized with the BioSpectrum AC Imaging System ( Ultra-Violet Products Ltd , UK ) . For immunoblotting , proteins were transferred to nylon-supported nitrocellulose , incubated with rabbit polyclonal antiserum directed against Sph2 [34] , LipL32 [38] and LipL41 [39] followed by goat anti-rabbit secondary antibody ( Southern Biotechnology Associates , Birmingham , Ala . ) . Blots were developed using the SuperSignal West Pico chemiluminescence substrate according to the manufacturer's instructions ( Pierce , Rockford , Ill . ) . 2D gels were loaded with 500 µg total protein and stained with silver as previously described [8] . Total RNA was extracted using TRIzol reagent ( Invitrogen ) from three biologically-independent samples of ( i ) in vitro-cultivated leptospires or ( ii ) leptospires cultivated in DMCs ( 2 rats per sample ) for 10 days as described above . Purified RNA was treated with Turbo DNAfree ( Ambion , Inc . Austin , TX ) as previously described [108] to remove contaminating genomic DNA . The integrity of DNase-treated RNAs use for RNA-Seq were assessed using the Agilent Bioanalyzer RNA NanoChip ( Agilent Technologies , Wilmington , DE ) to ensure that each had an RNA integrity ( RIN ) value ≥8 . One-hundred ng of total RNA was used for library generation according to Illumina standard protocols ( TruSeq RNA Sample Preparation Guide , Low-Throughput Protocol , Part # 15008136 Rev . A ) . cDNAs were normalized using a duplex-specific nuclease ( DSN ) approach according to the DSN Normalization Sample Preparation Guide , Early Access Protocol , Part # 15014673 Rev . C , which decreases the prevalence of highly abundant transcripts , such as rRNAs . 76-bp paired-end sequencing was carried out by Sequensys ( Prognosys Biosciences , La Jolla , USA ) on an Illumina Genome Analyzer IIx according to the manufacturer's instructions . Mapping of sequenced reads to Chromosome 1 and 2 of the reference genome of Leptospira interrogans sv . Copenhageni strain Fiocuz L1-130 ( NCBI Reference Sequence: NC_005823 . 1 and NC_005824 . 1 respectively ) [11] was carried out using the software tool segemehl [109] with accuracy set to 100% . To increase coverage , mismatched nucleotides at the lower-quality 3′ end were removed from the reads and the mapping was repeated until a match was found or the read length decreased below 20 nucleotides ( see [110] ) . Reads that mapped to ( i ) ribosomal or transfer RNAs or ( ii ) more than one reference genome location ( e . g . , paralogous genes ) were discarded . Uniquely mapped reads ( i . e . , mapped to a single genomic location ) were selected for further analysis , such as data visualisation and determination of differential gene expression . Normalization , differentially-expressed genes , regulatory fold-changes and statistical significance were determined using DESeq [43] . Read coverage used for graphical display was normalized as follows to compensate for different library sizes: the number of reads covering each nucleotide position was divided by the total number of mapped reads in the library and then multiplied with the number of mapped reads from the smallest library . Mapped unique reads were visualised with the Integrated Genome Browser ( IGB , version 5 . 5 ) ( http://bioviz . org/igb/releases . html ) [111] . Putative orthologous relations between proteins in other Leptospira serovars and/or species were determined using BlastP alignment ( ≥40% amino acid identify over ≥80% of the length of the smallest protein ) as previously described [14] . Protein sequence similarity between differentially-expressed genes identified in L . interrogans sv . Copenhageni and other Leptospira spp . ( L . interrogans sv . Lai strain 56601 [80]; L . borgpetersenii sv . Hardjo strain L550 [13]; L . santarosai sv . Shermani strain LT821 [15]; L . licerasiae sv . Varillal strain VAR010 [16]; and L . biflexa sv . Patoc strain Patoc1 Ames [14] ) was determined using GLSEARCH ( version 35 . 04 ) from the FASTA package [112] . GLSEARCH identifies the optimal alignment across the entire genome of each strain , translated into all six reading frames , and calculates the percent identity across the whole length of the corresponding sequence . Conserved domain searches were performed on full length protein coding sequences using the NCBI Conserved Domain Database interface [44] , [45] . The presence of fur boxes was investigated using the predictive computational tool SLiMSearch [62] . SLiMSearch , which can be used to determine the occurrences of a predefined motif in DNA and protein sequences , makes use of disorder and conservation masking to reduce the number of false positives . The fur box consensus sequence ( [GC]AT[AT]AT[GC]AT[AT]AT[GC]AT[AT]AT[GC] ) used to search the genome of Leptospira interrogans sv . Copenhageni was based on that of [61] . Putative functions of candidate sRNAs were identified by BLAST using the Rfam database , Wellcome Trust Sanger Institute ( http://rfam . sanger . ac . uk/ ) . DNase-treated RNAs ( ∼1 µg per sample ) , isolated from leptospires grown to late-logarithmic phase at 30°C in vitro and within DMC , were prepared as described above and converted to cDNA using SuperScript III ( Invitrogen ) in the presence and absence of reverse transcriptase ( RT ) according to the manufacturer's instructions . cDNAs were assayed in quadruplicate using iQ Supermix ( Bio-Rad ) using the primer pairs described in Table S1 . For relative quantitation of transcript levels , amplicons corresponding to each gene of interest were cloned into the pCR2 . 1-TOPO cloning vector ( Invitrogen ) , then purified recombinant plasmid DNAs for each amplicon were diluted ( 107–102 copies/µl ) to generate a standard curve . Reaction conditions for each primer pair were optimized to ensure that each had an amplification efficiency of >90% . Transcript copy numbers for each gene of interest were calculated using the iCycler post-run analysis software based on internal standard curves then normalized against copies of lipL32 ( LIC11352 ) present in the same cDNA . Normalized copy number values were compared within Prism v5 . 00 ( GraphPad Software , San Diego , CA ) using an unpaired t-test with two-tailed p values and a 95% confidence interval . | Leptospirosis , a global disease caused by the unusual bacterium Leptospira , is transmitted from animals to humans . Pathogenic species of Leptospira are excreted in urine from infected animals and can continue to survive in suitable environments before coming into contact with a new reservoir or accidental host . Leptospires have an inherent ability to survive a wide range of conditions encountered in nature during transmission and within mammals . However , we know very little about the regulatory pathways and gene products that promote mammalian host adaptation and enable leptospires to establish infection . In this study , we used a novel system whereby leptospires are cultivated in dialysis membrane chambers implanted into the peritoneal cavities of rats to compare the gene expression profiles of mammalian host-adapted and in vitro-cultivated organisms . In addition to providing a facile system for studying the transcriptional and physiologic changes leptospires undergo during mammalian infection , our data provide a rational basis for selecting new targets for mutagenesis . | [
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| 2014 | A Model System for Studying the Transcriptomic and Physiological Changes Associated with Mammalian Host-Adaptation by Leptospira interrogans Serovar Copenhageni |
In previous studies , we suggested that Acanthamoeba is a new aero-allergen and that patients who showed positive results for the skin-prick test response to Acanthamoeba cross-reacted with several pollen allergens . Additionally , patients with common antibodies reacted to the 13–15 kDa Acanthamoeba unknown allergen . We examined whether profilin of Acanthamoeba is a human airway allergic agent because of its molecular weight . We expressed recombinant Ac-PF ( rAc-PF ) protein using an Escherichia coli expression system and evaluated whether Ac-PF is an airway allergic agent using an allergic airway inflammation animal model . Airway hyperresponsiveness was increased in rAc-PF-inoculated mice . The number of eosinophils and levels of Th2 cytokines , interleukin ( IL ) -4 , IL-5 , and IL-13 were increased in the bronchial alveolar lavage fluid of rAc-PF-treated mice . The lungs of the rAc-PF-treated mice group showed enhanced mucin production and metaplasia of lung epithelial cells and goblet cells . In this study , we demonstrated that rAc-PF may be an allergen in Acanthamoeba , but further studies needed to identify the mechanisms of allergenic reactions induced by Ac-PF .
Allergic airway inflammation is characterized by the production of allergen-specific immunoglobulin ( Ig ) E and Th2 cytokines including interleukin ( IL ) -4 , IL-5 , and IL-13 which lead to the immune cell recruitment and sensitization of effector cells such as eosinophils , basophils , and mast cells [1] . These allergic reactions are typically induced by allergens . Marsh et al . described highly purified and well-characterized allergens such as the pollen of grasses , weeds , and trees , as well as house dust mites , fungal spores , and dander from animals [2] . Acanthamoeba , pathogenic and opportunistic free-living amoebae [3] , is protozoa genus that can survive in various environments and is isolated from the soil , dust , air , water seawater , swimming pools , domestic tap water , and contact lenses and cases [4] . Additionally , excretory-secretory ( ES ) proteins from Acanthamoeba species contain strong proteases [5 , 6] . Our previous studies demonstrated that Acanthamoeba may be aero-allergens [7 , 8] . Six Acanthamoeba trophozoite intranasal ( I . N . ) treatments induced allergic airway inflammation in mice [7] . Moreover , patients showing positive results to the skin-prick test response to Acanthamoeba exhibited higher Acanthamoeba-specific IgE levels compared to other patients and healthy persons [8] . Interestingly , patients who showed a positive skin-prick test response to Acanthamoeba exhibited cross-reactivity with several pollen allergens , including willow tree , poplar , elm , oak , velvet grass , and cockroach . In western blot analysis , chronic cough patients IgE antibodies reacted with ~15-kDa components of Acanthamoeba [8] . We examined profilin from Acanthamoeba as a potential human airway allergic agent because of its molecular weight ( 13–14 kDa ) and cross-reactivity with several pollen allergens in the skin prick test showing positive results for Acanthamoeba in chronic cough patients [8] . In Acanthamoeba , two isoforms of profilin ( Ac-PF ) have been identified: profilin-I and profilin-II [9 , 10] . Profilin , which is found in all eukaryotic organisms in most cells , is an actin-binding protein that interferes with nucleation and restructuring of new filaments [11–13] . Recent studies showed that profilin functions as a pan-allergen recognized by IgE in approximately 20% of birch pollen and plant food allergic patients [14] . BetvI , which is one of the main causes of Type I allergic reactions , is an allergenic protein from the pollen of the white birch ( Betula verrucosa ) and related to IgE binding in more than 95% of birch pollen allergic patients [15 , 16] . Valenta et al . prepared cDNAs encoding IgE-binding birch pollen protein that differed from BetvI . This protein appeared to act as an allergen in individuals allergic to pollens of grasses and weeds . Furthermore , IgE antibodies from birch allergic individuals showed cross-reactivity with human profilin [17] . Another study confirmed that 23% of 30 celery allergic patients were sensitive to profilin [18 , 19] . In this study , we expressed recombinant Ac-PF ( rAc-PF ) protein using an Escherichia coli expression system and evaluated whether Ac-PF is an airway allergic agent using an asthma animal model .
Acanthamoeba lugdunensis KA/E2 strain , isolated from human cornea inflammation patient in Korea , it was maintained in PYG medium . The KA/E2 strain has the same molecular characteristics as the A . lugdunensis L3A strain ( ATCC 50240 ) [20] . To obtain total protein , live trophozoites were incubated in PYG medium for one week at 25°C . Following centrifugation at 12 , 000g for 30 min , the total protein was extracted from the pellet according to protocol of manufacture ( Cell lysis , ThermoFisher Scientific Co . Waltham , MA USA ) . After obtaining total proteins , the ToxinSensor Gel Clot Endotoxin Assay Kit ( Gen-Script , Piscataway , New Jersey , USA ) was used to eliminate endotoxins . To amplify full-length Ac-RF , we designed primers based on the A . castellanii profilin I gene ( GenBank No . XP_004351646 . 1 ) . The primer sequences were as follows: Forward; 5′-GGA ATT CCA TAT GTC CTG GCA GAC GTA CG-3′ Reverse; 5′-CCG CTC GAG AAA GCC CTG ACC GAT GA-3′ . Total RNA was extracted from Acanthamoeba sp . KA/E2 trophozoite using 1 mL of LPS Solution ( Biozol , Eching , Germany ) , and cDNA was synthesized using MMLV reverse transcriptase ( Promega , Madison , WI , USA ) according to the manufacturer’s protocols . After confirming the PCR product , the fragment was subcloned into the C-terminal His-tagged fusion protein vector pET-26b . The constructs were transformed into the expression host E . coli BL21 ( DE3 ) . Expression of rAc-PF was induced with 0 . 5 mM isopropyl-thio-β-D-galactopyranoside for 4 h . The E . coli cell pellets were resuspended in lysis buffer [50 mM Tris-HCl ( pH 7 . 5 ) , 200 mM NaCl , and 1 mM dithiothreitol] . After sonication cell suspensions on ice ( Branson Sonifier 450 , Branson Ultrasonics , Danbury , CT , USA ) , the resulting cell lysates were centrifuged at 10 , 000 ×g for 45 min to remove insoluble cellular debris . The soluble and insoluble portions were fractionated on 15% SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) gels and visualized by Coomassie blue staining . The supernatants were collected and used for protein purification . The His-tagged profilin fusion protein was applied to a Ni-NTA ( Amersham Pharmacia Biotech , Amersham , UK ) column for purification . To evaluate the protease activity of rAc-PF , zymogram analysis was performed according to a previous study [7] . Briefly , rAc-PF and KA/E2 crude extract samples were mixed with 5× zymogram loading buffer and loaded on gelatin-gel , and electrophoresis was performed at 125 V for 60 min at 4°C . After electrophoresis , the gelatin-gel was incubated 30 min in zymogram renaturation buffer . After development , the gel was stained with Coomassie Brilliant Blue R-250 overnight and then distained . Four-week-old female Wistar rat was purchased from Samtako Co . ( Gyeonggi-do , Korea ) . Rats were immunized subcutaneously with a combination of 500 μg ( in 0 . 5 mL PBS ) of rAc-PF and 0 . 5 mL Freund’s complete adjuvant ( Sigma-Aldrich , St . Louis , MO , USA ) . After 2 weeks , a 2nd subcutaneous injection was performed with the same dose of rAc-PF and 0 . 5 mL Freund’s incomplete adjuvant . Two weeks after the final booster , rats were sacrificed and the serum was obtained . The rAc-PF and KA/E2 total proteins were loaded each well of a 12% acrylamide SDS-PAGE gel . The loaded proteins were transferred onto nitrocellulose membranes ( Amersham Biosciences , Amersham , UK ) , and then blocked with 5% skim milk in TBS-T at 4°C overnight . After one day , the membrane was incubated with primary antibody in 5% skim milk in TBS-T at 4°C overnight . The secondary antibody , anti-rat IgG-horseradish peroxidase conjugate ( Sigma ) was reacted for 1 h at 24°C . Reactants were analyzed using ECL kit ( Amersham Biosciences ) using the LAS-3000 program . Six-week-old female C57BL/6 mice were purchased from Orient-bio Co . ( Gyeonggi-do , Korea ) . The mice were maintained in a specific pathogen-free facility at the Institute for Laboratory Animals of Pusan National University during the experimental period . The mice were divided into 4 groups . Mice in the positive control group were intra-nasally administered 10 μg of Aspergillus protease ( Sigma-Aldrich ) , a well-known substance that provoke allergic airway inflammation , 6 times at intervals of 2 days . Using the same protocol , mice in the other two groups were treated with each 10 and 50 μg of rAc-PF . On the day before sacrifice , airway hyperresponsiveness ( AHR ) was measured , and then the animals were sacrificed . At 24 h after the last challenge , airway responsiveness was evaluated by measuring the change in lung resistance in response to aerosolized methacholine ( Sigma-Aldrich ) according to Kang et al . [21] . Briefly , to measure bronchoconstriction , the enhanced pause ( PenH ) was measured at baseline ( PBS aerosol; control ) and after exposure to increasing doses of aerosolized methacholine ( 0–50 mg/mL ) using whole-body plethysmography ( Allmedicus , Korea ) . In the plethysmography procedure , the mice were acclimated for 3 min , exposed to nebulized saline for 10 min , and treated with increasing concentrations ( 0 , 12 . 5 , 25 , and 50 mg/mL ) of methacholine using an ultrasonic nebulizer ( Omron , Japan ) . After each nebulization , the PenH values measured every three minutes during the experimental period were averaged . Graphs were generated showing the PenH values in response to increasing methacholine concentrations for each dose-matched group of mice . To obtain the BALF , the tracheas of the anesthetized mice were exposed and cut just below the larynx . A flexible polyurethane tube ( outer diameter; 0 . 4 mm , length; 4 cm; BD Biosciences , San Jose , CA , USA ) attached to a blunt 24-gauge needle ( Boin Medical Co . , Seoul , Korea ) was placed inside the trachea . Next , the lungs were lavaged once with 800 μL of sterile cooling phosphate-buffered saline ( PBS ) . The BALF samples were centrifuged for 5 min at 1500 ×g 4°C . The supernatants were moved to new microcentrifuge tube and frozen at -70°C; the remaining cell pellets were resuspended in 100 μL of PBS . Total cells were counted using a hematocytometer . To observe the differential cell counts , the same number of BALF cells was centrifuged on the slides at 500 rpm for 5 min using a Cytospin apparatus ( Micro 12TM , Hanil Co . , Seoul , Korea ) . The slides were dried and stained with Diff-Quick solution ( Sysmex Co . , Kobe , Japan ) . At least 500 cells per slide were evaluated to analyze differential leukocyte counts . Following sacrifice , the blood serum of mice was obtained by cardiac puncture and lung-draining lymph nodes ( LLNs ) and spleen were collected . The LLN in normal mouse ( PBS treated mouse ) is too small to get enough number of lymphocytes for ELISA experiment , therefore we used only LLN of rAc-PF and Aspergillus protease treated group for ELISA experiment . Each LLN and spleen were ground with MONOJECT and treated with ammonium chloride potassium ( ACK ) hypotonic lysis solution ( Sigma-Aldrich ) for 1 min at room temperature for red blood cells lysis . After lysis , lymphocytes and splenocytes were filtered through 100-μm meshes ( Small Parts , Inc . , Miramar , FL , USA ) and then washed three times . Next , the cells were counted with a hemocytometer and plated in 48-well plate as 5 × 106 cells/mL in RPMI 1640 containing 10% fetal bovine serum and penicillin/streptomycin . For CD3 stimulation analysis , 0 . 5 μg/mL anti-CD3 antibody was added to the plated cells . The plate was incubated at 37°C with 5% CO2 . After 72 h incubation , the culture supernatants were collected and stored at –20°C for enzyme-linked immunosorbent assay ( ELISA ) [22] . ELISA was conducted to analyze the changes in IL-4 , IL-5 , and IL-13 levels in the BALF supernatant and stimulated culture supernatant of lymphocytes and splenocytes with an ELISA kit ( ebioscience , San Diego , CA , USA ) . according to the manufacturer’s protocols . A 96-well immunoplate was coated with capture antibody and incubated overnight at 4°C . Plates were blocked with 1% bovine serum albumin for 1 h at 37°C . Test samples and standards were added to the wells and incubated overnight at 4°C . The plates were washed with 200 μL of PBS containing 0 . 1% Tween-20 5 times . Next , 100 μL of biotin-conjugated anti-mouse detection antibodies were added to each well and incubated for 1 h at room-temperature . The plates were washed as described above and avidin-conjugated horseradish peroxidase was added for 30 min incubation at room temperature in the dark . Tetra-methyl-benzidine ( 100 μL ) was used to reveal the blue color and 100 μL of diluted H2SO4 was added as the stop solution ( Merck , Darmstadt , Germany ) . The solution in each well changed from blue to yellow . The absorbance of the final reactant was measured at 450 nm using an ELISA plate reader . Lung tissues were fixed in formalin solution for 24 h and embedded in paraffin wax . Thin sections of embedded lungs were stained with hematoxylin and eosin ( H&E ) to analyze inflammatory cell infiltration and periodic acid-Schiff ( PAS ) reagent was used to quantify goblet cells . The sections were visualized by microscopy . The prevalence and severity of peribronchial and perivascular inflammation were scored by as previously described [23 , 24] . A grade of 0 was assigned when no inflammation was detectable , while grade 4 indicated a high percentage of airways and blood vessels in the section cuffing by inflammatory cells ( 0 = normal tissue; 1 = <25%; 2 = 25–50%; 3 = 51–75%; 4 = >75% ) . Severity scoring was based on the thickness of the bronchi or vessels surrounded inflammatory cells ( 0 = no cells; 1 = 1–3; 2 = 4–6; 3 = 7–9 cells thickness; 4 = 10 or more cells thick ) . To quantify goblet cell metaplasia , the percentage of PAS-positive cells in hyperplasia areas was examined from 8 tissue sections per mouse . Mouse lung epithelial cells ( MLE12 ) were obtained from ATCC ( Manassas , VA , USA ) ; 3 × 105 MLE12 cells were plated in 24-well plates and incubated overnight at 37°C . The cells were stimulated with rAc-PF and Aspergillus proteins for 3 h . Next , MLE12 cells were collected with 1 mL of QIAzol ( Qiagen , Hilden , Germany ) , and RNA extraction was conducted in accordance with the manufacturer’s protocols for transcription of 2 μg of RNA . Real-time PCR was performed to determine the levels of macrophage-derived chemokine ( MDC; CCL22 ) , eotaxin ( CCL11 ) , thymic stromal lymphopoietin ( TSLP ) , and IL-25 RNA . GAPDH was used as an internal reference . The primers and PCR conditions used have been described previously [25 , 26] . All experiments were conducted three times for statistical analysis . The mean ± SD was calculated from data collected from different mice . Significant differences were determined using t-tests ( and nonparametric tests ) of variance . Statistical analysis was performed with GraphPad Prism 5 . 0 software ( GraphPad Software Inc . , La Jolla , CA , USA ) . The study was performed with approval from the Pusan National University Animal Care and Use Committee ( IACUC protocol approval; PNU-2016-1358 ) , in compliance with “The Act for the Care and Use of Laboratory Animals” of the Ministry of Food and Drug Safety , Korea . All animal procedures were conducted in a specific pathogen-free facility at the Institute for Laboratory Animals of Pusan National University .
After expression of rAc-PF , an approximately 15-kDa recombinant protein was detected by SDS-PAGE ( Fig 1A ) . To confirm the expression of Ac-PF in Acanthamoeba , a polyclonal anti-rAc-PF antibody was produced and reacted with Acanthamoeba total proteins . An approximately 13-kDa protein in the Acanthamoeba total protein reacted with the anti-rAc-PF antibody . Because rAc-PF has 6 histidine tags , the size was slightly larger than Ac-PF alone ( Fig 1B ) . To evaluate the protease activities of rAc-PF , we conducted zymogram analysis and compared the results with those of the total protein of Acanthamoeba KA/E2 . The KA/E2 total protein showed strong protease activity , while rAc-PF did not show this activity ( Fig 1C ) . To evaluate the effect of Ac-PF on allergic airway inflammation in mice , Aspergillus protease ( Sigma-Aldrich ) , which is well known as a strong allergen , and rAc-PF were treated intranasally 6 times in each mouse . Airway hyperresponsiveness to a 0–50 mg/mL dose of methacholine was increased in the rAc-PF inoculated mouse group ( Fig 2A ) . Additionally , the numbers of eosinophils , neutrophils , and lymphocytes were increased in the BALF of rAc-PF-treated mice ( Fig 2B ) . The levels of Th2 cytokines , IL-4 , IL-5 , and IL-13 , in the BALF and culture supernatants of the LLN and spleen were increased in the rAc-PF-treated groups compared to in the control group ( Fig 3 ) . To examine whether Ac-PF influences bronchial trees , we focused on histological changes in the lung tissue . Lungs from treated and non-treated mice were isolated and stained with H&E to analyze inflammatory cell infiltration and PAS to quantify goblet cells . The lungs of rAc-PF and Aspergillus protease-treated group mice showed dramatic immune cell infiltration surrounding the bronchial trees and vessels , enhanced mucin production , and metaplasia of lung epithelial cells and goblet cells ( Fig 4A ) . For histopathological analysis , the inflammation score was determined as the prevalence and severity of inflammation . Peri-bronchiolar and peri-vascular inflammation scores were significantly higher compared to in non-treated mice . Additionally , PAS-positive cells ( goblet cells ) were significantly increased compared to in the control group ( Fig 4B ) . To evaluate whether rAc-PF influences lung epithelial cells , Th2 chemokine gene expression levels were examined after treating MLE12 cells with rAc-PF . After 3hr later , the treatment significantly increased the levels of MDC , TSLP , eotaxin , and IL-25 gene expression ( Fig 5 ) . However , expression levels of those genes of Apergillus protease treated cell were not increased at that time point . These chemokines are known as essential for the initiation and expansion of the Th2 response in lung epithelial cells .
In this study , we investigated the ability of Ac-PF to provoke allergic airway inflammation in an animal model . Ac-PF-treated mice showed similar symptoms to asthma , including goblet cell and immune cell infiltration , increased mucin production , and hyperplasia of respiratory epithelial cells , which causes the airway tract to become narrow , leading to increased airway hyperresponsiveness in a methacholine dose-dependent manner . Acanthamoeba are free-living , amphizoic and opportunistic protozoa that are common in nature [27] . A previous study demonstrated that Acanthamoeba trophozoites induced allergic airway inflammation in mice by inducing a Th2 response [28] . These allergic airway inflammation effects were closely related to the protease activity of excretory-secretory proteins ( ESP ) of Acanthamoeba [7] . Several studies showed that protease activity was related to allergens and led to morphologic changes and cytokine production [29] . Kheradmand et al . found that inactivated protease allergen fragments showed no allergenic potency , demonstrating that active protease is essential for the allergen effects [30] . However , we found that protease activity was not the only essential factor in the allergic airway inflammation effect of Acanthamoeba . Although most effects were significantly reduced after blocking the protease activity of ESP , some allergic symptoms and effects were observed after treatment with boiled ESP [9] . We predicted that protease activity acts as one of the strongest allergic factors , and other allergens may be present in Acanthamoeba ESP or their extracts . rAc-PF may be one of the protease activity free allergens , as some agents did not exhibit protease activity , but still exhibited allergic inflammation ability ( Fig 1C ) . Profilin is intermediate or major allergen in pollen and foods [31 , 32] . Carlsson et al . first identified profilin as a profilamentous complex of actin-associated protein essential for the spatial regulation of actin microfilament growth . This is an essential process in cellular movement and cell shape changes [33–35] . It was also proposed that profilin elicits Type I allergic reactions because of the three-dimensional structures of some allergenic profilins [36 , 37] . Fedorov et al . reported the allergen cross-reactivity , crystal structure , and IgE-epitope mapping of birch pollen profilin . The epitopes reside in conserved sequences , thus providing an explanation for the cross-reactivity [38] . Park et al . detected IgE antibodies in patients with a positive skin-prick test to pollen who also reacted with Acanthamoeba ES proteins and showed positive results in patients positive to Acanthamoeba [8] . Although profilin is regarded as an allergen , its role in allergic symptoms is controversial . Many studies have described sensitization to profilin and cross-reactivity of IgE with this allergen [16 , 17 , 31 , 38–40] , but the sensitization and immunological cross-reactivity do not always lead to allergy . Allergic airway inflammation is induced by allergens through several mechanisms . For example , an allergenic substrate can activate dendritic cells and induce IL-25 , TSLP , and allergy-related chemokines [41] . In this study , we found that Th2 chemokine gene expression in mouse lung epithelial cells was significantly increased on 3hr after rAc-PF treatment ( Fig 5 ) , and Th2 cytokine expression levels in the BALF , MLN , and spleen were increased by repeated nasal treatment with rAc-PF in mice ( Fig 3 ) . In previous studies , Aspergillus can elicit Th2 chemokine genes in vivo experiments , we also found airway inflammation by Aspergillus treatment ( Figs 3 and 4 ) . Although the level of chemokine genes related with Th2 in Aspergillus proteinase treated mouse lung epithelial cell was not elevated on 3 hrs after treatment in this study , they could elevate the gene expression on more early time [25] . Previous studies revealed that Acanthamoeba ESP activated dendritic cells , increasing the differentiation of naïve CD4+ T cells into T helper type 2 ( Th2 ) cells [28] . In conclusion , rAc-PF increased the numbers of lymphocytes , eosinophils , and neutrophils in the BALF and levels of Th2 cytokines . Further studies are needed to determine the mechanisms used by Ac-PF to enhance allergenic reactions . Additionally , alterations in immunocytes in vitro and how Ac-PF influences activation of innate immunity should be examined , which may facilitate diagnosis and the development of new treatments for allergic diseases in the future . | Recently , the number of asthma patients have increased sharply . Among patients with asthma have a high serum IgE titer , but despite this , some of these patients do not react to known allergens in skin prick tests , that suggests the presence of unknown environmental allergens . The protozoa Acanthamoeba live in very diverse environment including water , soil , air and even human nasal cavities , throat , pharynx and lung . In previous study , Acanthamoeba could be a new aero-allergen . Patients who showed positive results for the skin-prick test response to Acanthamoeba , their serum could be cross-reacted with several pollen allergens as well as Acanthamoeba total proteins . Additionally , the patients have common antibodies reacted to the 13–15 kDa Acanthamoeba unknown allergen . Profilin , which is found in all eukaryotic organisms in most cells , is an actin-binding protein that interferes with nucleation and restructuring of new filaments . Recent studies showed that profilin functions as a pan-allergen recognized by IgE in approximately 20% of birch pollen and plant food allergic patients . In Acanthamoeba , two isoforms of profilin ( Ac-PF ) have been identified: profilin-I and profilin-II . We examined profilin from Acanthamoeba as a potential human airway allergic agent because of its molecular weight ( 13–14 kDa ) and cross-reactivity with several pollen allergens in the skin prick test showing positive results for Acanthamoeba in chronic cough patients . In this study , we expressed recombinant Ac-PF ( rAc-PF ) protein using an Escherichia coli expression system and evaluated whether Ac-PF is an airway allergic agent using an asthma animal model . Our study showed that rAc-PF may be an allergen in Acanthamoeba , but further studies needed to identify the mechanisms of allergenic reactions induced by Ac-PF . | [
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| 2018 | Acanthamoeba profilin elicits allergic airway inflammation in mice |
Elite controllers ( ECs ) are a rare subset of HIV-1 slow progressors characterized by prolonged viremia suppression . HLA alleles B27 and B57 promote the cytotoxic T lymphocyte ( CTL ) -mediated depletion of infected cells in ECs , leading to the emergence of escape mutations in the viral capsid ( CA ) . Whether those mutations modulate CA detection by innate sensors and effectors is poorly known . Here , we investigated the targeting of CA from B27/B57+ individuals by cytosolic antiviral factors Mx2 and TRIM5α . Toward that aim , we constructed chimeric HIV-1 vectors using CA isolated from B27/B57+ or control subjects . HIV-1 vectors containing B27/B57+-specific CA had increased sensitivity to TRIM5α but not to Mx2 . Following exposure to those vectors , cells showed increased resistance against both TRIM5α-sensitive and -insensitive HIV-1 strains . Induction of the antiviral state did not require productive infection by the TRIM5α-sensitive virus , as shown using chemically inactivated virions . Depletion experiments revealed that TAK1 and Ubc13 were essential to the TRIM5α-dependent antiviral state . Accordingly , induction of the antiviral state was accompanied by the activation of NF-κB and AP-1 in THP-1 cells . Secretion of IFN-I was involved in the antiviral state in THP-1 cells , as shown using a receptor blocking antibody . This work identifies innate activation pathways that are likely to play a role in the natural resistance to HIV-1 progression in ECs .
ECs are a rare ( <0 . 5% ) and heterogeneous subset of HIV-1-infected subjects grouped together because they maintain undetectable viremia ( <50 copies⁄ml ) and normal CD4+ T cell counts in the absence of antiretroviral therapy ( ART ) . Peripheral virus is usually not detectable by conventional PCR methods but low-level replication is ongoing [1] . While their viremia is controlled , these individuals present a persistent low-grade inflammation and , when compared to ART-treated individuals , they are at a higher risk of hospitalization due to chronic inflammation-related problems such as cardiovascular diseases [2 , 3] . Genetic studies have shown that HLA alleles such as B57 and B27 contribute to the success of the CD8+ cytotoxic T lymphocyte ( CTL ) -mediated depletion of infected cells [4–6] . The retroviral CA protein is one of the most successful targets of the CTL response [7 , 8] , and this immune pressure drives the emergence of escape mutations in CA . CA is also the target of innate immune restriction factors that act on the retroviral CA core following its release in the cytosol upon entry , including the interferon-stimulated genes ( ISGs ) Mx2/MxB ( Myxovirus-resistance protein 2 or B ) and TRIM5α ( Tripartite motif-containing protein 5 , isoform α ) [9–11] . The dynamin-like GTPase Mx2 was first identified in 2013 as a key inhibitor of HIV-1 replication following type I interferon ( IFN-I ) treatment [12–14] . Mx2 inhibits viral core disassembly , impedes with viral genome nuclear import and possibly with post-nuclear entry steps [13 , 15 , 16] . The anti-HIV-1 activity of rhesus TRIM5α was described almost a decade earlier [17] . TRIM5α is part of a large family of proteins containing a tripartite motif [18] . At its C-terminus is a variable B30 . 2/SPRY domain that determines the specificity of the restriction , i . e . which viruses are targeted by a particular TRIM5α ortholog [19–21] . Recognition of an incoming retrovirus through interactions between TRIM5α and its specific CA target impairs the progression of the infection by several mechanisms including the accelerated disassembly of the retroviral CA core , accompanied by a decrease in the amount of reverse transcription ( RT ) products [22–24] . As a consequence , core components such as viral RNA and integrase are solubilized or degraded [25] ( reviewed in [26] ) . Mx2 and TRIM5α both act in a cell type- , species- and viral strain-specific manner and the CA N-terminal domain is the main viral determinant of sensitivity to both restriction factors [15 , 24 , 27–31] . In addition to its effector functions , TRIM5α acts as a pattern recognition receptor ( PRR ) , i . e . an innate sensor of the retroviral CA [32–35] . The TRIM5α N-terminal RING domain recruits the E2-ubiquitin conjugating enzyme heterodimer Ubc13 ( Ube2N ) /Uev1a ( or Uev2 ) to generate lysine 63 ( K63 ) -linked polyubiquitin chains [34 , 35] that can be anchored onto TRIM5α through the action of another E2 enzyme , Ube2W [32 , 33] . K63-linked ubiquitin , in association with the TAK1 kinase complex , leads to the activation of both NF-κB and AP-1 pro-inflammatory pathways [33 , 34 , 36 , 37] . Early studies showed that in contrast to Mx2 , the human ortholog of TRIM5α ( huTRIM5α ) does not significantly restrict laboratory strains of HIV-1 [17 , 38] . However , a more recent study showed that CTL escape CA mutants found in EC subjects carrying the alleles HLA-B27 or B57 ( B27/B57+ ) had increased sensitivity to restriction by huTRIM5α [39 , 40] . This observation constituted the first evidence that huTRIM5α could target HIV-1 , at least in these ECs . Consistently , genetic studies have repeatedly isolated polymorphisms in TRIM5 modulating disease progression [9 , 41–43] . However , whether endogenous TRIM5α can act as a PRR for HIV-1 CA in B27/B57+ ECs was not known . In addition , whether the CA mutants found in these subjects are more or less sensitive to Mx2 was not known either . We demonstrate here that in addition to blocking the replication of HIV-1 strains isolated from B27/B57+ subjects , endogenous huTRIM5α contributes to the induction of an antiviral state involving pro-inflammatory pathways , thereby shielding the cells against subsequent infections .
To define the restrictive potential of endogenous human Mx2 and TRIM5α against HIV-1 in B27/B57+ individuals , we included all 9 B27/B57+ subjects from the Canadian Slow Progressors cohort ( S1 Fig ) , and we were able to amplify CA sequences from 7 of them ( EC1 , EC3 , EC5-9 ) . Of note , EC9 had reverted from the EC status at the time of sample collection ( S1C Fig ) . Two additional isolates from B27/57 individuals ( NRC2 , NRC10 ) previously shown to be huTRIM5α-sensitive [39] were also included ( S1 Table; S1 References ) . As a control group , sera were obtained from 10 non-B27/B57 normal progressors , and we were able to amplify CA sequences from 8 of them ( NP1-6 , NP8 , NP10 ) . The control virus NRC1 [39] and a laboratory strain ( NL4-3 ) were also included in the study . CA sequences were amplified following RNA extraction from donors’ plasma and inserted into pNL4-3GFP and pNL4-3DsRed to generate chimeric vectors that were then sequenced ( S2 Table; S1 References ) . Some plasma samples yielded two or more CA variants ( S2 Table ) . The CA sequence of pNL4-3 ( NY5 ) was used as reference . Polymorphisms in the IW9 , KF11 , TW10 and KK10 epitopes and the CypA-binding loop of the N-terminal region previously associated with immune pressure were tallied [39 , 44 , 45] ( S2 Table , Fig 1A ) . A mean of 7 . 2 mutations were found in viruses isolated from B27/B57+ subjects ( n = 9 ) compared to 4 . 4 in viruses derived from subjects having other alleles ( n = 9 ) ( p = 0 . 049 ) . To further analyze differences in the whole CA sequence between the two groups of subjects , for each subject we enumerated the mutations previously associated with: i ) decreased core stability , ii ) escape from Mx2 restriction , iii ) increased sensitivity to TRIM5α restriction , iv ) increased resistance to cyclophilin A inhibitors ( CypI ) , v ) CTL escape or compensation to CTL escape , and vi ) unknown function ( Fig 1B; S3 Table; S1 References ) . Mutations known to modulate core stability and CypI resistance were found at a similar frequency in the two groups . CTL escape/compensatory mutations were more frequently detected in viruses derived from B27/B57+ vs other subjects ( p = 0 . 0077 ) . Mutations induced by CTL pressure were often associated with several other phenotypes . For instance , G116A was described to both increase sensitivity to TRIM5α and decrease restriction by Mx2 [39 , 46] , while R132K was linked with decreased core stability , increased CypI resistance and increased sensitivity to TRIM5α [46] ( S3 Table ) . Globally , there was an increase in the Mx2 resistance G116A polymorphism in the B27/B57 group of subjects ( p = 0 . 0176; Fig 1B ) . Other polymorphisms at the G116 position ( i . e . 116R and 116Q ) were observed but no other mutation previously reported to confer resistance to Mx2 was found . Interestingly , we detected about twice as many polymorphisms potentially conferring sensitivity to huTRIM5α in B27/B57+ subjects , relative to subjects bearing other alleles ( p = 0 . 0335 ) . Overall , sequence analyses suggest that CTL escape mutations may affect sensitivity to CA-targeting restriction factors . Using the Chi-square and Fisher’s exact tests , we could confirm that aminoacid 116 was more frequently mutated in viruses from B27/B57 subjects , and that the latter were also more likely to have viruses with at least one mutation altering the sensitivity to TRIM5α ( S2A and S2B Fig ) . Pro-inflammatory and IFN-I signaling induce an antiviral state against HIV-1 in human cells [48 , 49] , but the possibility that TRIM5α-mediated restriction of HIV-1 contributes to inducing the antiviral state has never been explored . We set up an assay to quantitate this antiviral state by performing two infections with HIV-1 vectors , 48 h apart . We used vectors carrying two different fluorescent markers to analyze the cells’ permissiveness to each vector simultaneously by flow cytometry , as shown in Fig 3A . We observed in both THP-1 and Jurkat cells that pre-infection with a TRIM5α-sensitive HIV-1 vector resulted in a significant decrease in infectivity of the second virus ( i . e . an antiviral state ) , regardless of whether this second vector was TRIM5α-sensitive ( NRC10; p = 0 . 0002; n = 17 ) or -resistant ( NRC1; p<0 . 0001; n = 25 ) ( Fig 3A , 3B and 3D ) . In the representative FACS dot plots shown in Fig 3A , pre-infection with EC5-2DsRed or EC9-2DsRed resulted in a 5 . 1- and 4 . 4-fold decrease in infectivity for the second virus , respectively . In the TRIM5 knockout cells , the reduction in infectivity of the second virus was smaller ( 2 . 5-fold and 2 . 7-fold ) . In addition , the strength of the antiviral state was significantly linked to the intensity of inhibition of the first virus by TRIM5α as determined by linear regression ( p<0 . 0001 for NRC1 and NRC10 as second virus in THP-1 and Jurkat ) ( Fig 3C , n = 24; Fig 3E , n = 21 ) or by direct Spearman correlation ( p = 0 . 0003 , r = 0 . 6497 in Jurkat and p = 0 . 0054 , r = 0 . 5443 in THP-1 ) ( Fig 3C , Fig 3E ) . Altogether , these results strongly suggest that upon infection with a restriction-sensitive HIV-1 , huTRIM5α induces an antiviral state resulting in the inhibition of viruses regardless of their sensitivity to huTRIM5α . Next , we investigated whether the interaction between TRIM5α and the CA lattice was sufficient to trigger an antiviral state . For this , we inactivated HIV-1 vector particles using AT-2 ( Aldrithiol-2 ) , a compound that covalently modifies the nucleocapsid protein zinc fingers and therefore abrogates productive infection , while maintaining the conformational integrity of the viral envelope and capsid [50] . Thus , TRIM5α-CA interactions are expected to occur but the viral life cycle is stopped pre-completion of reverse transcription . We infected TRIM5 knockout and control Jurkat and THP-1 cells with TRIM5α-sensitive vectors ( NRC10 , EC8 , EC5-2 ) that were treated or not with AT-2 , and could not detect any productive infection upon inactivation of the vectors with AT-2 , as expected ( Fig 4A and 4B ) . 48 h later , cells were infected with the NRC1GFP ( TRIM5α-insensitive ) or NRC10GFP ( TRIM5α-sensitive ) vectors . In the control cells expressing TRIM5α , we consistently observed an antiviral state inhibiting the second virus by ~1 . 5- to 3-fold , whether NRC1GFP or NRC10GFP was used ( Fig 4A and 4B ) . In all cases , we observed no significant difference between the antiviral state induced by untreated and AT-2-treated vectors , indicating that induction of an antiviral state does not require completion of reverse transcription nor subsequent steps . We also constructed psPAX2-based “empty” viral ( EV ) chimeric particles bearing the CA from the TRIM5α-sensitive EC5-2 and NRC10 that do not contain viral RNA . The amounts of regular and EV vectors used were equalized by reverse transcriptase activity . THP-1 cells were infected with NRC10_EV or EC5-2_EV , and then infected with NRC1-GFP 48h later ( Fig 4B ) . We observed that the antiviral state ( i . e . the inhibition of NRC1-GFP ) was modest when NRC10_EV or EC5-2_EV was used as the first virus , similar to the TRIM5α-insensitive NL4-3-DsRed ( Fig 4B ) . This result suggests that efficient induction of the antiviral state requires the presence of a factor that is absent from the psPAX2-based EVs . TRIM5α can activate innate immune pathways through its E3 ubiquitin ligase activity that cooperates with the E2 ligase complex Ubc13/UEV2A to generate K63-linked ubiquitin chains , which in turn activate TAK1 , the kinase that phosphorylates the IKK complex as well as the IKK-related kinases TBK1 and IKKε [51 , 52] . IKK and related proteins phosphorylate the NF-κB inhibitor IκB , leading to NF-κB activation [53 , 54] . TAK1 also mediates the activation of AP-1 through a different mechanism [55] . Thus , TRIM5α stimulates pro-inflammatory pathways leading to the activation of NF-κB and AP-1 , which may result in IFN-I secretion [33 , 34 , 37 , 56] . We tested whether these downstream mediators of TRIM5α-dependent signaling had a role in the antiviral state . Depletion of TAK1 and Ubc13 ( S5G Fig ) resulted in an attenuated antiviral state both in Jurkat and THP-1 cells ( Fig 5A and 5B ) . In addition , the TBK1/IKKε signaling inhibitor BX795 , which targets TBK1 [57] , abrogated the TRIM5α-dependent induction of an antiviral state ( Fig 5C; see effect on virus 1 in S6A Fig ) . These results indicate that the antiviral state dependent on huTRIM5α is mediated by the signal transducers Ubc13 , TAK1 and TBK1 . In order to evaluate the importance of IFN-I signaling in the induction of the antiviral state , cells were treated with an antibody against IFNα/βR2 1 h prior to infection with the first virus ( NRC10DsRed ) ( Fig 5D ) . In Jurkat cells expressing TRIM5α , a strong antiviral state was induced following infection with a TRIM5α-sensitive virus ( NRC10 ) , which was slightly reduced but not abrogated upon neutralization of the type I IFN receptor . By contrast , treatment with the neutralizing antibody completely prevented the induction of a antiviral state in THP-1 cells ( Fig 5D ) . Consistently , there were significantly higher levels of IFN-β in the supernatants of THP-1 cells expressing TRIM5α and infected with a TRIM5α-sensitive virus than in non-infected cells or in cells that did not express TRIM5α ( p = 0 . 0270 , effect of TRIM5α by 2-way ANOVA; Fig 5E ) . Using the same ELISA test , we could not measure any detectable levels in Jurkat cells . These results highlight the role of type I IFN in the establishment of the antiviral state in THP-1 cells but not in Jurkat cells .
The viral CA core stands under severe conformational constraints to remain functional [59] and is subjected to immune pressures from several sources , as it is targeted by both innate and adaptive immunity , e . g . restriction factors and CTLs . Here , we characterized the roles of Mx2 and TRIM5α in the successful control of HIV-1 that takes place in B27/B57+ individuals . First , we searched for footprints of CTL pressure and of modulation of sensitivity to Mx2 and TRIM5α in the CA sequences . Previous reports uncovered numerous mutation sites associated with escape from Mx2 restriction in vitro [27 , 46 , 60] . In our isolates , only one previously described Mx2 escape polymorphism was detected: G116A , a CTL escape mutation in the TW10 epitope that was more frequently detected in B27/B57+ subjects . We detected two other variants at this site , 116R and 116Q in 3 B27/B57+ subjects and 1 control that probably conferred resistance to Mx2 restriction as well . An intriguing point arising from our results is the overall low levels of restriction by Mx2 for most isolates . Natural evolution towards Mx2 escape has been reported previously in clinical isolates and was associated with the polymorphism 116A in HIV-1 Subtype C of Chinese origin [46] . In our analysis , restriction of CA isolated from B27/B57+ individuals was mostly undetectable , suggesting that this restriction factor does not participate in the control of HIV-1 in ECs . We cannot exclude , however , that incomplete depletion of Mx2 may have resulted in underestimating its restriction potency in these knockdown experiments . In contrast to Mx2 , we observed an increase in huTRIM5α sensitivity for CA from B27/B57+ subjects . Interestingly , the strength of restriction by either TRIM5α or Mx2 doubled in absence of the other restriction factor . This suggests the existence of a negative competition effect between the two CA-binding factors whereby each disturbs the other one’s function . A TRIM5α-dependent antiviral state was induced following infection with restriction-sensitive HIV-1 capsids . This antiviral state was reversed by depletion of the pro-inflammatory mediators TAK1 and Ubc13 . In THP-1 cells , the TRIM5α-dependent antiviral state was associated with NF-κB and AP-1 activation , and inhibiting these transcription factors reduced the antiviral state . IFN-I receptor blockade prevented the antiviral state in THP-1 but less so in Jurkat cells , consistent with the absence of significant IFN-β production in Jurkat cells . Thus , the antiviral state is associated with activation of pro-inflammatory pathways that were previously shown to be triggered by TRIM5α in host-virus mismatch-species contexts [33 , 34] . It is unclear whether the observed signaling strictly stems from CA-TRIM5α interactions , or whether TRIM5α might upregulate pro-inflammatory signaling stemming from other sensing events . AT-2 and Raltegravir treatments showed that the establishment of an antiviral state is independent of viral life cycle steps starting with reverse transcription . However , “empty” HIV-1 vectors devoid of viral RNA were less competent for the induction of the antiviral state , suggesting a possible role for viral RNA in this process . Interestingly , similar “empty” vectors based on N-MLV did activate the transcription of innate immunity-specific genes , probably in a NF-κB- and AP-1-dependent fashion , in the Pertel et al study [33] . We attribute this discrepancy to the much higher levels of N-MLV restriction by huTRIM5α ( ~100-fold , typically ) . Finally , both TRIM5α-sensitive and -resistant viruses were sensitive to the TRIM5α-dependent antiviral state , implying that unidentified , interferon-inducible effectors are involved ( see theoretical model in S8 Fig ) . In conclusion , this study shows that CTL escape mutants circumvent the restriction mediated by Mx2 but become more sensitive to the restriction factor TRIM5α . In addition to restricting the replication of sensitive HIV-1 strains found in B27/57+ individuals , TRIM5α induces an antiviral state in which permissiveness to subsequent HIV-1 infections , including with TRIM5α-insensitive viruses , is decreased . Future experiments will need to characterize this antiviral pathway in primary cells and to identify the effectors of the antiviral state . Replication-incompetent virus-like particles able to be sensed by and activate endogenous human TRIM5α may constitute the basis for the development of novel approaches aimed at decreasing permissiveness toward HIV-1 .
This study ( SL-04-061 ) was approved by the Institutional Review Boards at all participating sites . All patients were enrolled in the study following written informed consent . The study involved no animals . The ethics certificates are as follows: Centre Hospitalier de l’Université de Sherbrooke , 10–015; McGill University Medical Center , GEN-05-13 , GEN-04-039; Centre Hospitalier de l’Université de Montréal , SLA04 , 061; Centre Hospitalier Universitaire de Québec , 2012–432 CH09-08-080; Clinique Quartier Latin and Clinique Médicale l’Actuel , 5005–10:37:1824-04-2017; Ottawa Hospital Research Institute , 2006502-01H; Sunnybrook Health Sciences Center , 237–2009; University of Toronto , 09-0538-BE; Maple Leaf Medical HIV Research Institute , 5005–10:221622-03-2016; Canadian Immunodeficiency Research Collaborative , 5005–14:02:0031-03-2017; CascAIDS Research Incorporated , 5005–10:58:3231-01-2017; Providence HealthCare Society , H09-01476; Interchange Medical Clinic , 5005–10:37:1824-04-2017 . All patients were enrolled in the study following written informed consent . All donors were adults . HLA genotyping was completed as previously described [61] . Absolute CD4+ and CD8+ T cell counts and HIV viral load were obtained at the time of blood collection [62] . Viruses were extracted from the blood of 19 donors ( including 9 B27/B57+ individuals and 10 non-B27/B57 non-treated viremic progressors ) from the Canadian Slow Progressors cohort ( S1 Table and S2 Fig ) [62] . We incorporated 4 additional viruses , including NRC2 and NRC10 that were isolated from B27/B57+ individuals , NRC1 isolated from a normal progressor and the laboratory-adapted strain NL4-3 [39 , 63] ( S1 Table ) . Jurkat , THP-1 and HEK293T ( 293T ) cells ( obtained from J . Luban , University of Massachusetts Medical School ) were maintained in RPMI 1640 medium ( HyClone , Thermo Scientific , USA ) . CRFK were maintained in DMEM medium ( HyClone , Thermo Scientific , USA ) . All culture media were supplemented with 8% fetal bovine serum ( FBS ) , penicillin-streptomycin ( HyClone ) and Plasmocin ( InvivoGen ) . The replication-incompetent pNL4-3GFPΔEnvΔNef ( thereafter called pNL4-3GFP ) has a deletion causing a frameshift in env and gfp in place of nef [64] . pNL4-3DsRed was constructed by replacing GFP with DsRed using NotI and XhoI in pNL4-3GFP . Patient blood was collected in EDTA-containing tube and HIV-1 RNA was extracted from plasma using the QIAamp Viral RNA mini kit ( Qiagen ) . CA was amplified by RT-PCR using the SuperScript III One-Step RT-PCR System ( Life Technologies ) and the following cycling settings: 30 min at 55°C; 2 min at 94°C; 40 cycles ( 15 sec at 94°C , 30 sec at 55°C , 3 min at 68°C ) with primers 5' NL4-3P24FOR and 3’ p24-1084-Rev ( S4 Table ) . Final primer concentration was 200 nM ( primer sequences are listed in S4 Table ) . Alternatively , whenever no CA signals could be obtained , whole gag sequences were amplified using primers 5’ GAGFOR692BSSHII and 3’ HIVGAG2827 REV ( S4 Table ) followed by nested PCR on CA using Velocity ( Bioline ) with the following cycling settings: 2 min at 98°C; 30 cycles ( 2 min at 98°C 2 min , 30 sec at 55°C , 3 min at 72°C ) . The 734 bp CA amplicons were then purified from agarose gels . Separately , two Gag segments upstream and downstream of CA were amplified from pNL4-3 using primer pairs 5’ GAGFOR692BSSHII and 3' NL4-3_GAGBEFOREP24 REV ( 487 bp upstream segment ) or 5’ p24downstreamFor and 5' NL4-3GAG_AFTERGAGAPAI_REV ( 196 bp downstream segment ) using the same cycling parameters and gel purified . Finally , the three PCR products were mixed and diluted , and a final PCR of 25 cycles was conducted using 5’ GAGFOR692BSSHII and 3’ GAGREV1959APAI . Following purification , the 1335 bp PCR products were digested with BssHII and ApaI and separated on agarose gel . Products were purified again and ligated into pNL4-3DsRed or pNL4-3GFP cut with ApaI and BssHII , using the T4 DNA ligase ( New England Biolabs ) for 10 min at room temperature followed by 16 h at 16°C . Following JM109 bacterial electroporation and culture , plasmid DNA was purified using EZ-10 Spin Column Plasmid DNA Miniprep Kit ( BioBasic ) and the Gag region was Sanger sequenced . Plasmid DNA was then prepared from the same bacterial clones using a Qiagen MidiPrep kit and co-transfected into 293T cells in 10 cm culture dishes at 90% confluence together with the VSV-G-expressing pMD2 . G using polyethylenimine ( PEI; polysciences , Niles , IL ) [65] . Medium was changed 6 h post-transfection . Virus-containing supernatants were harvested 24 and 48 h later , pooled , clarified by centrifuging 10 min at 3000 rpm , 0 . 45 μm-filtered and stored at -80°C . The multiplicity of infection ( MOI ) of the HIV-1 chimeric vector particles was assessed by titration in permissive cat CRFK cells . Capsid sequences were amplified from NL4-3 , NRC10 , EC5-2 , EC8-2 and EC9-2 using FOR_PsPAX2_ClaI and REV_PsPAX2_ECORV were ligated into psPAX2 ( Addgene #12260 ) cut with EcoRV and ClaI . “Empty” viral particles were constructed by co-transfection of 293T cells with psPAX2 and pMD2G using the same procedure as that described above for NL4-3-based vectors . Amounts of psPAX2-based and NL4-3 chimeric vectors were normalized by reverse transcriptase assay using the EnzChek kit ( Molecular Probes ) . The lentiviral expression vector plentiCRISPRv2 ( pLCv2 ) was a gift from Feng Zhang ( Addgene plasmid 52961 ) and was used to simultaneously express the gRNA , Cas9 nuclease , and PuroR in THP-1 and Jurkat cell lines by lentiviral transduction [47] . gRNAs targeting exon 1 ( S5 Table and S5A Fig ) were designed according to Zhang’s protocol and inserted into pLCv2 leading to pLCv2-T5gRNA1 and pLCv2-T5gRNA2 . Viral particles were produced by co-transfection of pLCv2 , pMD . G and pΔR8 . 9 in 293T cells [66] . T5gRNA2 was selected to knock out TRIM5 based on Surveyor assay results ( S5B Fig ) . A control gRNA targeting the CAG hybrid promoter [67] was used . TRIM5 alleles were amplified from cells transduced with pLCv2-CAG or pLCv2-T5gRNA2 treated for 10 days with puromycin ( 1μg/ml; Invivogen ) , sequenced and submitted to the in silico TIDE assay which quantitates percentages of indels by sequencing decomposition , in comparison with the unedited control ( S5C , S5D and S5E Fig ) [68] . TRIM5 knockout was also validated by assessing N-MLV restriction for TRIM5α ( S5F Fig ) Knockdowns were obtained using the pAPM-based miR30 shRNA system [33] . pAHM was generated by removal of PuroR gene from pAPM by digestion with XbaI and NotI and replacement by HygroR amplified from pMIH [69] using XbaI_PAHM FOR and NOTI_PAHM REV ( S5 Table ) . miR30-based shRNAs were designed using the publicly available Katahdin algorithm ( http://katahdin . cshl . org/siRNA/RNAi . cgi ? type=shRNA ) and their sequences are indicated in S5 Table . An irrelevant shRNA ( Luc ) was used as a control . The 97 bp miR30 sequences were synthesized by Genscript ( NJ , USA ) , amplified by PCR using the primer pairs indicated in S3 Table , digested with EcoRI and XhoI and inserted into pAHM cut with the same enzymes . shRNA sequences were verified by Sanger sequencing . pAHM was cotransfected with pΔR8 . 9 and pMD2 . G in 293T cells . Lentiviral particles were harvested from supernatants as described above . THP-1 and Jurkat cells were spinfected in presence of polybrene ( 8 μg/ml ) for 1 . 7 h at 1800 rpm . Cells were allowed to rest for 72 h and treated with 250 μg/ml hygromycin ( Sigma ) for 10 days . Knockdown efficiency was verified using mRNA transcript levels quantification by RT-qPCR . TRIzol ( Life Technologies ) and chloroform ( Sigma-Aldrich ) were used to extract total RNA from cultured cells . Glycogen ( Life Technologies ) was added during the extraction to enhance RNA yields and cDNA was synthesized using the SuperScript IV ( Life technologies ) according to the manufacturer’s protocol . Amplification was performed using 400 nM of forward and reverse primers ( S6 Table ) , and 5 μl template ( 150–500 ng ) in 10 μl final volume according to the SensiFast SYBR Lo-ROX kit protocol ( Bioline ) . After 3 min incubation at 95°C , 40 cycles of amplification were performed as follows: 5 sec at 95°C , 10 sec at 60°C , 15 sec at 72°C . Each PCR was performed in duplicate and the threshold cycle ( Ct ) was determined using the MxPro software ( Agilent ) . Relative expression was calculated using the ΔCt method with GAPDH for normalization ( 2– ( Ct ( target ) -Ct ( GAPDH ) ) ) . To measure chimeric HIV-1 vectors sensitivity to TRIM5α , TRIM5 knockout and control cells were seeded into 96-well plates at 4 × 104 cells/well and recombinant human IFN-β was added at a final concentration of 10 ng/ml ( PeproTech , Rocky Hill , NJ ) . Cells were infected the following day with serial 2-fold dilutions of DsRed- or GFP-expressing chimeric vectors at MOIs ranging from 0 . 1 to 2 . 5 . The percentage of DsRed- or GFP-positive cells was determined after 48–72 h of infection . For this , cells were fixed in 4% formaldehyde ( Fisher Scientific , MA , USA ) and 1×104 to 5×104 cells were analyzed on a FC500 MPL cytometer ( Beckman Coulter , Inc . , CA ) using the FCS express 6 software ( De novo software , CA ) . The -fold restriction was calculated as the mean ratio of viral titers between TRIM5 knockout and control cells ( titer calculations only took into account the vector amounts leading to percentages of infected cells between 0 . 5 and 10 ) . Infections were repeated multiple times and mean -fold restrictions were calculated . To quantify the antiviral state , TRIM5 KO and control ( CAG ) cells were seeded into 96-well plates at 4×104 cells/well and infected the next day with the DsRed-chimeric vectors ( virus 1 ) at a CRFK MOI of 0 . 25–0 . 5 in Jurkat and 1–2 . 5 in THP-1 cells . Where indicated , cells were pre-treated for 45–60 minutes prior to the first infection with inhibitors of NF-κB ( BAY11-7085; ENZO ) or cJun ( SP600125; ENZO ) , or TBK1/IKKε ( BX795 , Tocris ) , or with the blocking antibody anti-IFNα/βR2 20 μg/ml ( clone MMHAR-2; PBL Assay Science ) . 60-minutes pre-treatments were also done with Raltegravir ( 20 μM; Merck ) . Pre-treated cells were thoroughly washed prior to the first infection in order to remove the pharmacological inhibitors . Where specified , viruses were pre-treated with 300 μM Aldrithiol-2 ( prepared in MetOH; Sigma ) for 2 h at 4°C or with MetOH only , with shaking [50] . AT-2 and MetOH-treated virions were then diluted 10 times , ultracentrifuged for 90 min at 20 , 000 g and resuspended in PBS . The absence of infectivity following AT-2 treatment was verified by FACS . 48 h post-infection with virus 1 , virus 2 ( NRC1GFP or NRC10GFP ) was added to the cells at an MOI similar to virus 1 . Two days later , cells were fixed in 3% formaldehyde and analyzed by FACS . Infection experiments were performed in triplicates . TRIM5-KO and control THP-1 cells were treated with 100 nM of Phorbol 12-myristate 13-acetate ( PMA , Sigma-Aldrich ) for 24 h while seeded on glass coverslips , then washed and placed in normal medium . After 72 h , differentiated THP-1 were infected with NRC1GFP or NRC10GFP for 72 h . Cells were then fixed and permeabilized as published previously [65] , and incubated with anti-P-p65 or anti-P-cJun ( 1:150 in 10% bovine serum; Cell Signaling ) at RT for 4 h . Following 4 PBS washes , cells were stained with Alexa Fluor 594-conjugated goat anti-rabbit ( Molecular Probes , Eugene , OR ) diluted 1:100 in 10% bovine serum for 1 h at RT . Slides were mounted as previously described [65] and pictures were acquired on the Axio Observer microscope ( Carl Zeiss , Inc . , Toronto , ON , Canada ) . IFN-β was quantified in culture supernatants 72 h post-infection using the Verikine High Sensitivity Human IFN-β ELISA kit according to the manufacturer’s instructions ( PBL IFN Source ) . The GraphPad Prism software was used for statistical tests and for generating graphs . Non-parametric tests were used when data did not fit Gaussian distribution . | Some HIV-1-infected individuals show a natural capacity to control viral propagation . In individuals that have the HLA B27 or B57 allele , HIV-1 control is associated with mutations in viral proteins that arise as a result of immune pressure from cytotoxic T lymphocytes . HIV-1 capsid protein mutations found in these subjects render HIV-1 more sensitive to detection by TRIM5α , a cytoplasmic innate effector that targets retroviral capsids . We show here that HIV-1 bearing such mutations is restricted by TRIM5α but not by Mx2 , another capsid-targeting innate effector . As a result , cells have decreased permissiveness to subsequent HIV-1 infections , a phenomenon that could contribute to the inefficient disease progression observed in these individuals . This knowledge might find applications in the development of immune interventions to increase human cells resistance to HIV-1 . | [
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| 2018 | HIV-1 capsids from B27/B57+ elite controllers escape Mx2 but are targeted by TRIM5α, leading to the induction of an antiviral state |
The majority of invasive human fungal pathogens gain access to their human hosts via the inhalation of spores from the environment into the lung , but relatively little is known about this infectious process . Among human fungal pathogens the most frequent cause of inhaled fatal fungal disease is Cryptococcus , which can disseminate from the lungs to other tissues , including the brain , where it causes meningoencephalitis . To determine the mechanisms by which distinct infectious particles of Cryptococcus cause disseminated disease , we evaluated two developmental cell types ( spores and yeast ) in mouse models of infection . We discovered that while both yeast and spores from several strains cause fatal disease , there was a consistently higher fungal burden in the brains of spore-infected mice . To determine the basis for this difference , we compared the pathogenesis of avirulent yeast strains with their spore progeny derived from sexual crosses . Strikingly , we discovered that spores produced by avirulent yeast caused uniformly fatal disease in the murine inhalation model of infection . We determined that this difference in outcome is associated with the preferential dissemination of spores to the lymph system . Specifically , mice infected with spores harbored Cryptococcus in their lung draining lymph nodes as early as one day after infection , whereas mice infected with yeast did not . Furthermore , phagocyte depletion experiments revealed this dissemination to the lymph nodes to be dependent on CD11c+ phagocytes , indicating a critical role for host immune cells in preferential spore trafficking . Taken together , these data support a model in which spores capitalize on phagocytosis by immune cells to escape the lung and gain access to other tissues , such as the central nervous system , to cause fatal disease . These previously unrealized insights into early interactions between pathogenic fungal spores and lung phagocytes provide new opportunities for understanding cryptococcosis and other spore-mediated fungal diseases .
Through the act of breathing , the mammalian lung is regularly exposed to a wide variety of airborne particles , such as dust , air pollutants , and microbes . Both physical and immunological barriers have evolved to keep the lung clear of foreign agents and facilitate efficient respiration . Inert particles such as dust and pollen are cleared effectively , as are most microbes . However , many disease-causing organisms such as bacteria and fungi that gain entry via the lung have developed strategies for evading clearance , allowing them to colonize the lung and ultimately escape and disseminate to other tissues [1] . One particularly successful inhaled human pathogen is Cryptococcus . This environmental fungus is found in association with soil , tree bark , and bird droppings , and upon inhalation can escape the lung and cause fatal fungal meningoencephalitis [2] . Immunocompromised patients are most at risk of developing cryptococcosis , but disease among healthy individuals is on the rise worldwide [3] . Mortality rates range from 20–80% , depending on patient status and antifungal drug availability [3–5] . Hundreds of thousands of people a year develop cryptococcosis and die , due in large part to limited treatment options for invasive fungal diseases and challenges in treating brain infections [6] . Much attention has been paid to the propensity of this fungus to invade the central nervous system and the mechanisms by which it crosses the blood brain barrier during disseminated cryptococcosis; however , the mechanisms by which Cryptococcus escapes the lung to cause disseminated disease remain poorly understood [7 , 8] . Proposed infectious particles in human cryptococcal disease are yeast ( its vegetative growth form ) and spores ( the products of sexual development ) [9–11] . Because of the technical challenges associated with isolating large populations of pure spores , the vast majority of studies of Cryptococcus pathogenesis have been carried out with the more tractable yeast form . Yeast were used to develop the mouse models of cryptococcal infection and disease over the last fifty years , and yeast have been shown to harbor unusual virulence traits [12] . Surprisingly , yeast are not phagocytosed efficiently by phagocytes in the absence of opsonization , which appears to be largely due to the presence of a polysaccharide coat . This covering ( a . k . a . capsule ) on the cell surface effectively masks immunoreactive epitopes , preventing efficient phagocytosis by host immune cells [13] . Once phagocytosed , however , Cryptococcus yeast can reside and grow within acidified phagolysosomes , presumably avoiding additional immune surveillance [14] . Finally , yeast can escape phagocytes by causing phagocyte rupture or through non-lytic exit via exocytosis [15] . Each of these traits ( phagocytosis resistance , intracellular survival , and efficient escape ) has been associated with the ability of Cryptococcus to cause disease . More recently , spores were also shown to cause disease in mice in a mouse intranasal model of infection [9 , 10] . In contrast to yeast , however , spores are phagocytosed rapidly and efficiently in vitro in the absence of opsonization [10 , 16] . The spore surface harbors exposed beta-1 , 3-glucan , mannose , chitin , and other immunoreactive carbohydrates that presumably facilitate recognition and uptake by phagocytes [17] . Once inside phagocytes , spores can germinate into yeast and grow vegetatively . These spore-derived yeast replicate , reside inside phagolysosomes , and escape phagocytes in a manner indistinguishable from phagocytosed yeast [10] . Given the distinct interactions that spores and yeast display with immune phagocytes in vitro , we hypothesized that spores and yeast would interact with mammalian lung immune phagocytes differently in vivo , leading to differences in disease progression and/or outcome . Initial experiments did not appear to support this hypothesis because intranasal infections of mice with either spores or yeast from highly virulent strains of Cryptococcus resulted in nearly identical survival curves [10] . While these experiments showed definitively that spores can be disease-causing particles , they did not inform mechanisms of pathogenesis . To determine whether spores and yeast cause disease via similar or distinct mechanisms , we carried out a series of mouse infections with spores and yeast derived from several different backgrounds of Cryptococcus . We monitored fungal dissemination kinetics , tissue distributions and burdens , interactions with phagocytes , host cytokine production , and host mortality . We discovered that , in fact , disease progression and outcome are profoundly affected by the nature of the infectious particles ( spore vs . yeast ) , and the degree of this effect is more pronounced in strains with virulence profiles that resemble natural isolates . In all strains tested , intranasal infection with Cryptococcus spores led to higher fungal burdens in the brain ( but not other tissues ) at endpoint relative to yeast . In strains from backgrounds in which the yeast are avirulent in the intranasal model of infection , mice did not develop disease when infected with yeast ( as expected ) ; however , the spores produced by those avirulent yeast strains caused 100% fatal disease in mice . Furthermore , in all cases , spore-infected mice showed much earlier and higher rates of dissemination from the lung via the lung draining lymph node , and this dissemination was dependent on lung phagocytes . These data indicate that spore interactions with host phagocytes specifically facilitate spore escape from the lung and promote dissemination to other tissues .
While spores have long been presumed infectious particles of Cryptococcus , it was only relatively recently confirmed that spores are virulent in a mouse model of infection . Spores produced by the congenic Cryptococcus neoformans var . grubii ( a . k . a . C . neoformans , serotype A ) strains KN99a and KN99α , cause fatal disease with kinetics indistinguishable from those of their yeast parents [9 , 10] . To determine whether spores of other Cryptococcus strains were also capable of causing disease , we produced spores from crosses between the virulent type strain H99 ( α ) and the Botswanan clinical isolate BT63 ( a ) ( both serotype A ) [18] and tested both yeast and spores in mice . Mice were infected intranasally with 1x105 a yeast ( BT63 ) , 1x105 α yeast ( H99 ) , 1x105 total a + α yeast ( a 1:1 mixture of BT63 and H99 ) or 1x105 spores ( from a BT63 x H99 cross ) with the expectation that each haploid spore germinates into one haploid yeast [17] . Survival times and fungal burdens in the brain and lung at endpoint were assessed . As was seen with KN99a and KN99α previously ( 10 ) , the BT63 and H99 strains caused disease as both yeast and spores . We observed no statistical difference in the mean time to death for spore-infected ( 21 days ) or yeast-infected ( 20 days ) mice ( Fig 1A ) . Also consistent with our previous observations for KN99a and KN99α , the BT63 x H99 spores yielded higher brain burdens at end point than yeast . Spore-infected mice harbored ~18-fold higher CFUs in the brain than yeast-infected mice at 28 days post-infection ( 9 . 2x106 vs . 5 . 2x105 CFU/g , respectively , p = 0 . 011 ) . There were no significant differences in numbers of CFUs from any other tissues ( Fig 1B ) . Thus , these data demonstrate that infection with BT63 x H99 spores causes fatal disease with the same kinetics as infection with their parental yeast . These data also indicate that infection with spores is associated with higher fungal colonization of the brain at end point . Given that spores isolated from crosses between the congenic KN99a and KN99α strains and the non-congenic H99 and BT63 strains behaved similarly with respect to virulence potential and brain CFUs , we concluded that 1 ) these phenotypes were not specific to the KN99a/α background and are likely relevant across Cryptococcus strains , and 2 ) the differences in brain fungal burdens between spore- and yeast-infected mice may reflect differences in disease progression between the two infectious cell types . In our experiments with the KN99a/KN99α and BT63/H99 strain pairs , the primary sign of disease in both spore- and yeast-infected mice was respiratory distress , at which point they were euthanized . Because infections with spores yielded higher brain fungal burdens than yeast at the time of euthanasia , we considered that the early respiratory disease produced by H99-derived strains prevented observation of the full effects of spore-mediated disease in the brain . We hypothesized that strains that do not cause rapid and fatal pulmonary disease would extend the timeline of disease progression and more clearly reveal differences between spore- and yeast-mediated disease . To test this hypothesis , we carried out spore- and yeast-mediated infections with strains known to show limited pulmonary virulence . Mice were infected with spores or yeast of Cryptococcus neoformans var . neoformans ( a . k . a C . deneoformans ) strains B-3502 ( a ) and B-3501 ( α ) . Mice infected with 2 . 5x105 yeast ( a alone , α alone , or a + α ) showed no signs of disease at any point during the experiment and were euthanized after 100 days ( Fig 2A ) . Mice infected with 2 . 5x105 spores , however , began to show signs of CNS disease starting at day 52 , with all mice exhibiting evidence of CNS disease leading to euthanasia by day 75 ( Fig 2A ) . This difference in disease outcome between yeast and spores indicates that by using avirulent yeast strains with limited effects on the lung , we were able to discern the natural progression of spore-mediated disease . In this case spores caused fatal meningoencephalitis when their yeast parents could not ( p = 5 . 6x10-5 by a Log-Rank test for Kaplan-Meier survival analysis ) . To determine potential differences in dissemination during yeast- and spore-mediated infections , we evaluated fungal organ burdens over the course of disease . We determined that yeast-infected mice harbored no fungal burden in any tissues outside the initial site of infection in the lungs at any time point ( Fig 2B ) . While the lung burden did show an increase during the course of the experiment , by 100 days post-infection the number of fungi had dropped to levels similar to those observed very early after infection ( Fig 2B ) . Histological analysis revealed analogous results in the lungs , showing an increased fungal burden and inflammation through day 21 and lower levels of inflammation and decreased numbers of cryptococcal cells at day 100 . No abnormal pathology or fungal burden was observed in the brains of yeast-infected mice at any point ( S1A Fig ) . These data demonstrate that there was no detectable dissemination of yeast from the lung to any other tissue tested and that respiratory infection with yeast was contained in the lung . In contrast , mice infected with spores showed dissemination of Cryptococcus throughout all tissues evaluated ( Fig 2C ) . As early as 14 days post-infection , Cryptococcus was detected in the kidneys of all mice , and by 21 days post-infection there was a detectable fungal burden in the brain of one animal . When spore-infected mice became moribund ( between 50 and 75 days post-infection ) , the fungal burdens within the brain exceeded 107 CFU/g in all animals ( Fig 2C ) . It should be noted that spores are produced during sexual development in the environment ( and not in mammalian hosts ) and that spores are unable to replicate without first germinating into yeast ( which can then replicate via budding ) . Thus , the observed fungal burdens are derived solely from spores that were introduced intranasally , germinated in the host , and grew vegetatively as yeast . Histological cross-sections of the lungs of spore-infected mice were very similar to those of yeast-infected mice through day 21 . However , at endpoint ( when spore-infected mice became moribund and were sacrificed ) , we observed increased inflammation and visible fungal burdens in the lungs as well as high numbers of fungal cells in the brain ( S1B Fig ) . Taken together , these data indicate that infection with spores led to increased dissemination of Cryptococcus from the lungs to other tissues , relative to infection with yeast , leading to fatal meningoencephalitis . Because the spores for the spore vs . yeast infection experiments were derived from genetically distinct ( non-congenic ) yeast parents , we considered that even though the parental yeast were avirulent , it was formally possible that the difference in virulence between yeast and spores was a consequence of diverse genotypic combinations created during sexual development that could confer pathogenic abilities not harbored by either parent . To test the possibility that the spore population was more virulent than yeast due to the presence of virulent recombinant spore progeny in numbers sufficient to cause fatal disease , we germinated spores from B-3502 x B-3501 crosses into yeast and used those yeast to infect mice . Mice were infected with either 1x105 spores purified from a B-3502 x B-3501 cross or 1x105yeast derived by germinating spores from a B-3502 x B-3501 cross and monitored for signs of CNS disease . As was observed previously , spores from a B-3502 x B-3501 cross caused fatal disease in all mice . In contrast , yeast from germinated spores did not cause discernable disease in mice , supporting the hypothesis that the difference between spore- and yeast-mediated disease was dependent on the infectious cell type , rather than the presence of new virulent genotypes in the recombinant spore population ( Fig 2D ) . At the same time , we tested whether the difference between spore- and yeast-mediated disease was a consequence of more efficient spore entry into the brain via the nares by infecting mice using an intratracheal route of infection . As was seen with intranasal infections , mice infected with spores succumbed to fatal disease , whereas yeast-infected mice did not ( Fig 2D ) . These data rule out the possibility that spore-specific properties ( such as small size relative to yeast ) led to enhanced disease via direct access to the brain through the nares . To confirm that spores and yeast reach the lung equally well after intranasal infection , we also infected mice intranasally with spores or yeast and assessed fungal burdens in the lungs 30 minutes after infection and found no difference . This result indicates that yeast and spores were equally capable of gaining initial entry to the lung ( S2 Fig ) . To test whether the differences between spore- and yeast-mediated disease were dependent on a lung route of infection , we introduced the yeast or spores directly into the bloodstream , bypassing the lung . Mice were inoculated via the tail vein with 1x106 yeast or 1x106 spores and monitored for signs of CNS disease . In contrast to the intranasally- and intratracheally-infected mice , all of the tail vein-infected mice succumbed to cryptococcosis by day 70 post-infection , regardless of whether they were infected with spores or yeast ( Fig 2E ) . Fungal burden analysis revealed a high fungal burden in all tissues , including the brain , which did not differ significantly between yeast- or spore-infected mice . These data show that yeast and spores are equally capable of causing fatal disseminated disease when introduced directly into the bloodstream , indicating that spore- and yeast-mediated differences in disease are dependent on a lung route of infection . Overall , we conclude from these experiments that spores and yeast are equally capable of infecting the murine lung , neither cell type accesses the brain directly from the nares , and both are fully capable of causing fulminant disease when introduced directly into the bloodstream . Thus , the differences in disease outcome resulting in higher brain burdens in spore-mediated infections are the results of events that occur in the lung prior to dissemination to the bloodstream . Having established that the differences in disease between spores and yeast were dependent on both cell type and route of infection , we considered that the host response to spores could contribute to lung-dependent differences in disease . Previous studies have demonstrated that the Th1/Th2 bias of the pulmonary immune response is an important factor that determines cryptococcal disease progression and dissemination out of the lung [19] . To determine whether infection with spores or yeast drives altered polarization of the pulmonary immune response , cytokine production by T-cells and inflammatory infiltrate was evaluated following infection with spores or yeast in the murine lung . Mice were infected intranasally with yeast ( B-3502 + B-3501 ) or spores ( B-3502 x B-3501 ) . Mice were also infected with H99 yeast for comparison to a known , highly virulent strain . At 11 days post infection , lungs were processed and evaluated to determine the total number of cells , the number of immune cells in the population , and the levels of immune cytokines following T-cell stimulation . As anticipated , mice infected with H99 yeast showed high numbers of total host cells in the lung , consistent with increased inflammation ( S3 Fig ) . In contrast , B-3502 + B-3501 yeast showed relatively low numbers of host cells in the lung , indicating little inflammation , and this was also the case for mice infected with spores ( B-3502 x B-3501 ) ( S3 Fig ) . Staining of surface markers allowed us to identify various leukocytes ( CD45+ ) by flow cytometry , including monocytes ( SiglecFlow , Ly6Glow , CD11bhi , Ly6chi ) , neutrophils ( SiglecFlow , CD11bhi , Ly6Ghi ) , and eosinophils ( SSChi , SiglecFhi , MHCIIlow , CD11clow , CD11bhi ) . Again , as expected , mice infected with H99 yeast showed high numbers of all identified cell types , consistent with a robust immune response . In contrast , B-3502 + B-3501 yeast elicited much less immune cell recruitment . This was also the case for B-3502 x B-3501 spores , and the total numbers of monocytes , neutrophils , and eosinophils in spore- and yeast-infected mice were not different ( Fig 3A ) . To assess the balance of lung Th1/Th2 responses , we determined the cytokine response of CD44+ effector T-cells using ex vivo stimulation and flow cytometric analysis . Our analysis focused on identifying IFNgamma production as hallmark of Th-1 responses and improved outcomes in cryptococcal infection [20] , and IL-13 as a marker of a non-protective Th2 response [21] . Higher expression of IL-5 has been shown to lead to increased pulmonary eosinophilia and higher cryptococcal burdens in the lungs of mice [22] . Overall , there were no significant differences in the cytokine responses assessed among the three groups of mice ( Fig 3B ) . While mice infected with H99 yeast did show an apparent higher average number of CD44+IFNgamma+ cells , which are typically associated with a more protective TH1 response , we hypothesize that this is due to H99 yeast eliciting a higher immune response overall , relative to yeast and spores of B-3502 and B-3501 . From these data we conclude that spores and yeast ( of the B-3502 and B-3501 backgrounds ) do not initiate detectably different immune responses in the lung with both cell types eliciting similar immune cell recruitment and Th1/Th2 bias . While there could be more subtle differences in the immune responses to yeast- and spore-mediated infections that were not detected here , we conclude that the overall immune response mounted in the lungs is unlikely to be the deciding factor in the different disease outcomes observed between spore- and yeast-infected mice . Having observed that overall lung immune response signaling in spore- and yeast-infected mice was largely the same , we hypothesized that direct interactions between cryptococcal cells and host cells would lead to differing dissemination patterns of spores and yeast . Previous studies showed that spores are more readily phagocytosed than yeast by alveolar macrophages in vitro ( by ~10-fold ) [10 , 23 , 24] . We further determined that preferential phagocytosis of spores occurs with a number of diverse phagocytic cell types , including RAW 264 . 7 cells ( macrophages ) , JAWS II cells ( dendritic-like cells ) , and murine bone marrow-derived phagocytes ( S4 Fig ) . Based on these findings , we hypothesized that the difference in dissemination ( and ultimately disease outcome after 50 days post-infection ) , between yeast and spores is dependent on their interactions with host lung phagocytes ( that occur in the first day after infection ) . Respiratory immune cells are known to traffic to the lung draining lymph nodes within 24 hours following infection [25]; thus , we anticipated that the first point of dissemination of spores would be to the mediastinal lymph nodes . To test this hypothesis , we assessed dissemination of Cryptococcus in spore- and yeast-infected mice at early time points after infection . Mice were infected intranasally with spores or yeast , and fungal burdens in the lungs , lymph node , spleen , and blood were determined at 1 , 5 , and 7 days post infection . Mice infected with spores or yeast showed similar levels of fungal burden in the lungs through day 7 post-infection and almost no dissemination to the spleen or blood at this time point ( Fig 4A and S5 Fig ) . In contrast , however , there was a striking difference in dissemination between yeast and spores to the mediastinal lymph nodes . Mice that had been infected with yeast exhibited no fungal burden in their lymph nodes through day seven , whereas mice infected with spores showed an increasing burden in this tissue through the time course of the experiment ( Fig 4B ) . As early as one-day post-infection there was a small , but markedly higher fungal burden in spore-infected mice than yeast-infected mice that was observed repeatedly and consistently across multiple experiments with various strains ( S6 Fig ) . From these data , we conclude that 1 ) spores disseminate to the lung draining lymph node very early ( less than 24 hours ) post-infection before gaining access to any other tissues , and 2 ) spores disseminate to the lung draining lymph node much more readily than yeast . These findings establish a correlation between efficiency of phagocytosis in vitro and efficiency of dissemination to the lung draining lymph node , supporting the hypothesis that lung phagocytes engulf spores in vivo and traffic them out of the lung . Both spore- and yeast-mediated disease are caused by overwhelming fungal burdens composed of yeast , indicating that spores must germinate in vivo . In vitro , spore germination is a relatively rapid , synchronous process , occurring within 10–12 hours in the presence of rich media under laboratory conditions [26] . If germination occurs in a similar time frame in vivo , then one would predict that any differences in interactions of phagocytes with yeast and spores that influence dissemination would occur within the first day post-infection . To test this prediction and its effects on dissemination , we carried out experiments both in vitro and in vivo to determine the effects of germination on phagocytosis . To assess spore germination in vivo , cells were recovered via bronchoalveolar lavage 18 hours after infection with spores or yeast , and host cells were lysed . Germination state of the recovered cryptococcal cells was evaluated by measuring their aspect ratios ( ARs ) , where the AR of dormant , ovoid spores is approximately 0 . 6 , and the AR of round yeast is close to 1 . 0 [26] . We found that cryptococcal cells recovered from spore- or yeast-infected mice were morphologically indistinguishable from one another ( average AR = 0 . 94 and 0 . 96 , respectively ) and nearly identical to the in vitro yeast control ( average AR = 0 . 95 ) ( all p-values > 0 . 4 ) ( S7 Fig ) . Furthermore , the germination states of these three groups ( spore-infected , yeast-infected , and yeast control ) , were all significantly different morphologically than the in vitro ungerminated spore control ( all p-values < 10−10 ) ( S7 Fig ) . Thus , we conclude that the vast majority of spores introduced into the mouse lung germinate into yeast within 18 hours . To assess the kinetics of phagocytosis during germination , we carried out in vitro phagocytosis assays on populations of spores that were germinated for different lengths of time . Spores were incubated in rich growth medium for 0 to 16 hours , fixed with formaldehyde , stained with calcofluor white , and assessed in a fluorescence-based phagocytosis assay with RAW macrophages . We discovered that as germination progressed , the ability of macrophages to take up Cryptococcus decreased nearly linearly as a function of time ( Fig 5A ) . This observation was also confirmed by visually quantifying phagocytosis of both formaldehyde- and UV-fixed spores in a time course of germination ( S8 Fig ) . By 12 hours , spores had completed the morphological transition to yeast , and by 16 hours the population consisted of actively budding yeast . Cells from both 12 and 16 hours after the initiation of germination were phagocytosed at rates identical to live yeast controls , indicating that yeast resulting from germinated spores are indistinguishable from vegetatively growing yeast in terms of phagocytosis . These data also indicate that phagocytosis of spores diminishes over the course of germination , suggesting that the window for efficient phagocytosis of a germinating spore population is limited . To determine whether the frequency of phagocytosis was strictly a feature of individual spores transitioning into yeast or whether spores and yeast were influencing the ability of phagocytes to take up other nearby cryptococcal cells ( either spores or yeast ) , we carried out phagocytosis assays with mixed populations of spores and yeast . To track cryptococcal cells of each cell type separately , we generated spores and yeast expressing GFP or mCherry . RAW macrophages were then co-incubated with spores or yeast ( expressing either GFP or mCherry ) or 1:1 mixtures of both spores and yeast ( expressing GFP and mCherry ) . Flow cytometry analysis was used to determine cryptococcal cell ( GFP+ or mCherry+ ) association with live CD11b+ macrophages . As anticipated , we found that GFP-spores and mCherry-spores readily associated with macrophages . In contrast , macrophages incubated with GFP-yeast or mCherry-yeast showed no GFP+ or mCherry+ signals above background levels ( Fig 5B ) . Macrophages co-incubated with the mixtures of spores and yeast did not show significant differences in the percentages of mCherry+ and GFP+ events compared to each cell type alone , indicating that spores and yeast do not influence phagocytosis of one another ( Fig 5B ) . From these data we conclude that differences in spore and yeast phagocytosis result from intrinsic properties of the two cell types and are not caused by alterations ( by either cell type ) in the general phagocytosis competence of macrophages . Based on the observations that spores are preferentially phagocytosed in vitro and that spore and yeast phagocytosis occur independently of one another in a mixed population , we hypothesized that spores would preferentially disseminate to the lymph node in a mixed infection in vivo . To test this hypothesis , we carried out mixed infections of spores and yeast using the mCherry+ and GFP+ strains described above . We then assessed the population composition recovered from the lungs and lymph node early in infection . Mice were infected intranasally with 5x106 mCherry-spores , 5x106 GFP-yeast , 5x106 mCherry-yeast + 5x106 GFP-yeast , or 5x106 mCherry-spores + 5x106-GFP yeast . We deliberately infected mice with mCherry-yeast + GFP-yeast at twice the dose used for GFP-yeast alone to ensure that the total dose matched that of mCherry-spore + GFP-yeast infected mice . Thus , any differences in dissemination to the lymph node between yeast and spores reflects differences between the two cell types and not differences in dose . Three days after infection , the fungal burdens in the lungs and lymph nodes were quantified , and 50 colonies from each mouse were assessed for mCherry or GFP signals . We discovered that the average lung fungal burden in mCherry spore- and GFP yeast-infected mice was 2x105 and 2x106 CFUs , respectively ( Fig 5C ) . Mice infected with mCherry-yeast + GFP-yeast contained an average lung fungal burden of 6x106 CFUs , and mCherry-spore + GFP-yeast mice contained an average of 3x106 CFUs . The higher lung fungal burdens in the groups of yeast-infected mice relative to spore-infected mice is unsurprising given that yeast are free to replicate from the onset of infection , whereas spores must undergo germination ( an ~12 hour process ) before they can replicate , leading to lower total CFUs in spore-infected mice . The composition of the colonies from the lungs of all mice was as expected for the composition of the infectious inoculum . That is , mCherry spore-infected mice harbored 100% mCherry positive colonies , GFP yeast-infected mice harbored 100% GFP positive colonies , and mice infected with a 50:50 mixture of mCherry-yeast + GFP-yeast harbored an average of 47 . 6% mCherry positive colonies and 52 . 4% GFP positive colonies . Mice infected with a 50:50 mixture of mCherry-spores and GFP-yeast harbored an average of 14% mCherry positive colonies and 86% GFP positive colonies , consistent with the expected delay in replication of the products of mCherry-spores ( Fig 5C ) . Consistent with earlier dissemination results ( Fig 4 ) , spore-infected mice showed consistently higher fungal burdens in the lung-draining lymph nodes than yeast-infected mice . The lymph nodes of mCherry-spore-infected mice exhibited an average fungal burden of 75 CFUs , whereas all but one GFP-yeast-infected mouse contained no fungal cells in their lymph nodes ( Fig 5D ) . Mice infected with mCherry-yeast + GFP-yeast averaged a lymph node burden of 58 . 6 CFUs , and mCherry-spore + GFP-yeast mice averaged 98 . 5 CFUs . Given our prior findings ( Fig 4 ) , the high fungal burden in the lymph nodes of the mCherry-yeast- + GFP-yeast-infected mice was unexpected; however , this value is heavily influenced by one lymph node that contained 198 colonies and is a significant outlier ( as defined by a Grubbs’ outlier test , p < 0 . 05 ) , likely due to contamination during dissection . Removal of this outlier decreases the average lymph node fungal burden of the mCherry-yeast + GFP-yeast group to 26 , which is consistent with all other experiments . Fungal composition in the lymph nodes reflected the inocula as one would expect for three of the four test groups; colonies from mice infected with mCherry-spores were 100% mCherry+ , colonies from mice infected with GFP-yeast were 100% GFP+ , and colonies from mice infected with a 50:50 mixture of mCherry-yeast:GFP-yeast were 46 . 6% mCherry+ and 54 . 4% GFP+ . In stark contrast , lymph node colonies from mice infected with mCherry-spores and GFP-yeast were heavily skewed toward mCherry+ ( 82 . 5% ) and away from GFP+ ( 17 . 5% ) ( Fig 5D ) . The high proportion of spore-derived cryptococcal cells ( mCherry+ ) in the lymph nodes of these mice was particularly striking given the overrepresentation of yeast-derived cells present in their lungs ( 14% mCherry-spore-derived , 86% GFP-yeast-derived ) ( Fig 5C ) . The observed fungal composition ( mCherry+:GFP+ ratio ) between the lungs ( 14:86 ) and lymph nodes ( 82 . 5:17 . 5 ) was significantly different ( p = 1 . 73x10-8 ) , indicating a clear selection in vivo for spore-derived ( mCherry+ ) colonies in the lymph node . From these data we conclude that intrinsic properties of spores determine their specific ability to traffic to the lung draining lymph node . These properties are non-transferrable to yeast and do not create a lung environment generally permissive for the escape of fungal cells . Given that spores were phagocytosed efficiently in vitro and trafficked to the lymph node more readily than yeast , we hypothesized that spores preferentially associate with antigen-presenting phagocytes in the host lung . To test this hypothesis , we infected mice with spores or yeast and evaluated their relative association with dendritic cells ( DCs ) and alveolar macrophages ( AMs ) early in infection . Dendritic cells are canonical antigen presenting cells , and alveolar macrophages represent 90% of the sentinel immune phagocytes in a naïve , resting lung ( and also have the ability to migrate for antigen presentation ) [27] . A hallmark of both cell types is the presence of the surface integrin CD11c . Mice were infected intranasally with 5x106 mCherry-spores , 5x106 mCherry-yeast , or PBS as an uninfected control . Six hours after infections , whole lung homogenates were stained and analyzed by flow cytometry to quantify cryptococcal association with CD11c+ lung phagocytes ( Fig 6 and S9 Fig ) . We first evaluated the total number of CD11c+ ( live , CD45+CD11c+ ) cells in all groups ( uninfected , spore-infected , and yeast-infected ) . We found that yeast-infected mice appeared to harbor more CD11c+ cells than uninfected and spore-infected mice . When we parsed CD11c+ cells into AMs ( live , CD45+CD11c+SiglecF+ ) and DCs ( live , CD45+CD11c+SiglecF- ) , we observed that the increased numbers of CD11c+ cells in yeast-infected mice were DCs ( Fig 6A ) . This effect was consistent across independent experiments ( S9 Fig ) but given high variation in the number of DCs between animals , generated a significance value of p = 0 . 14 ( Fig 6A ) . We then determined the percentage of each cell type ( CD11c+ , AM , and DC ) that were mCherry+ , indicating the association of at least one mCherry+ spore or yeast . We found that more CD11c+ cells were mCherry+ in the presence of spores than yeast ( 11 . 22% and 4 . 34% respectively ) , and this difference was robust ( p = 0 . 002 ) and reproducible ( p < 0 . 01 for all replicates ) , indicating that spores more readily associated with CD11c+ cells than yeast ( Fig 6B and S9 Fig ) . When CD11c+ cells were further parsed in to AMs and DCs , we discovered that the increased spore association with CD11c+ cells was primarily with AMs . AMs were more likely to show spore association than yeast ( 18 . 0% and 13 . 3% respectively , p = 0 . 019 ) , and this result was reproducible ( p < 0 . 02 for all biological replicates ) ( Fig 6B and S9 Fig ) . In contrast , DCs consistently displayed an extremely low percent spore and yeast association overall ( 1 . 23% and 0 . 11% respectively ) with very low numbers of overall mCherry+ events . Differences between spore and yeast association with DCs across experiments were highly variable with average p-values of p > 0 . 35 across biological replicates , suggesting that spores do not preferentially associate with DCs relative to yeast ( Fig 6B and S9 Fig ) . These cell association assays simply identified the presence or absence of mCherry+ signal for each CD11c+ phagocyte , so we could not discern the number of spores or yeast associated with each mCherry+ CD11c+ cell . To determine the ratio of cryptococcal spores or yeast to alveolar macrophages , we used direct microscopic visualization to count the number of spores or yeast per AM . Mice were infected with 5x106 mCherry spores or yeast . Six hours post infection , alveolar macrophages were recovered by bronchoalveolar lavage , and the fungal loads of 100 mCherry+ phagocytes per mouse ( n = 3 ) were counted . We found that on average , phagocytes from spore-infected mice contained two-fold more cryptococcal cells than yeast-infected mice ( 5 . 3 and 2 . 7 cells/phagocyte respectively ) and that this difference was robust ( p = 0 . 0002 ) ( Fig 6C ) . These data indicate that the difference in association between spores and yeast with alveolar macrophages should be adjusted to account for differences in numbers of fungal cells per mCherry+ event , which effectively doubles the magnitude of the difference in favor of spore association . Overall , from these data we conclude that early in infection , antigen presenting CD11c+ cells in the lung are at least 25% more likely to be associated with spores than yeast , those associations are primarily with alveolar macrophages ( and not lung dendritic cells ) , and twice as many spores as yeast per phagocyte account for those associations . Increased CD11c+ phagocyte association and dissemination to the lymph node by spores provides strong support for a Trojan horse model of dissemination . Critical to this model is confirmation that dissemination is in fact CD11c+ phagocyte-dependent . We hypothesized that a reduction in the population of CD11c+ phagocytes would attenuate early dissemination to the lymph node by Cryptococcus spores . To test this hypothesis , we took advantage of transgenic CD11c-Diphtheria Toxin Receptor ( CD11c-DTR ) mice , which are modified to express the Diphtheria Toxin Receptor ( DTR ) under the CD11c promoter . This selective expression allows for transient depletion of CD11c+ cells ( including lung DCs and AMs ) upon diphtheria toxin ( DT ) administration ( Fig 7A ) [28] . Both WT and CD11c-DTR mice were treated with DT or PBS via intraperitoneal injection . After 36 hours , mice were infected intranasally with 1x107 spores , and forty-eight hours post-infection the fungal burdens in lungs and lymph nodes were determined . As expected , average total fungal burdens observed in the lungs of WT mice were very similar , whether or not they were treated with DT ( 2x105 and 1 . 6x105 CFUs , respectively ) . CD11c-DTR mice that were not treated with toxin also harbored lung burdens similar to WT mice ( 4 . 2x105 CFUs ) ( Fig 7B ) . In contrast , there was a significant increase in CFUs in CD11c-DTR mice treated with toxin ( 3 . 4x106 CFUs , p < 0 . 01 compared to WT ) ( Fig 7B ) , indicating that CD11c+ cells are likely important for stemming the proliferation of Cryptococcus and controlling early fungal growth following spore infections . Examining fungal burdens in mediastinal lymph nodes revealed that WT mice , WT mice treated with DT , and CD11c-DTR mice not treated with toxin showed no significant differences in fungal burden ( WT = 180 , WT + toxin = 151 , CD11c-DTR = 156 , all comparison p-values > 0 . 6 ) ( Fig 7C ) . In contrast , the CD11c-DTR mice treated with DT showed a complete loss of dissemination to the mediastinal lymph nodes ( average LN fungal burden = 2 . 75 , p ≤ 0 . 01 compared to all other groups ) , despite the increased fungal lung burden in these same mice ( Fig 7C ) . Thus , depletion of CD11c-expressing cells in mice resulted in the inability of Cryptococcus spores to reach the mediastinal lymph nodes . These data indicate that spores disseminate to the draining lymph node of the lung via a mechanism that is dependent on CD11c+ phagocytes . Based on these data , we propose model in which spores that infect the lung are phagocytosed by CD11c+ phagocytes ( likely alveolar macrophages ) , survive inside those phagocytes , and are trafficked to the draining lymph node of the lung . Once they reach the lymph node , spores have access to the bloodstream , at which point they can access other tissues , including the brain ( Fig 8 ) .
Spores and yeast of Cryptococcus are likely natural infectious particles in human cryptococcosis and both have been shown to cause disease in a mouse model of infection [9 , 10] . However , the mechanisms by which Cryptococcus and other inhaled fungal pathogens escape the lung are poorly understood . Here , we evaluated the behaviors of spores and yeast from diverse strain backgrounds in a murine intranasal infection model to determine mechanisms of dissemination and disease . By comparing yeast- and spore-mediated infections , we discovered that disease outcomes can differ tremendously between infectious particles of the same organism , even when derived from the same strains . Most strikingly , using Cryptococcus yeast strains that typically do not cause disease in mice , we showed that spores derived from crosses between those avirulent yeast strains are fully virulent in mice and cause 100% fatal disease with symptoms reflecting meningoencephalitis . Thus , the nature of the infectious particle ( yeast vs . spore ) establishes the nature of the disease and its outcome ( up to 75 days later ) . This finding is particularly intriguing because all visible spores in the mouse lung germinate into yeast within the first 18 hours after infection . As such , different relationships that spores and yeast establish with the host in the first day after infection confer differences in disease outcomes 2 . 5 months post-infection . These findings suggest that early interactions with the host innate immune response in the lung set the stage for the nature of disease . This idea is consistent with our findings that mice infected with spores show earlier and higher rates of dissemination to the lung draining lymph node than mice infected with yeast , and these differences are dependent on CD11c-expressing resident lung cells . These data shed new light on the complicated relationship between resident immune cells of the lung ( i . e . alveolar macrophages and dendritic cells ) and infectious particles from a fungal pathogen . The critical roles of circulating lung phagocytes for protection against fungal infection in the lung have been clearly illustrated . In particular , depletion of phagocytes in the lungs of mammals results in rapid deterioration and death following infection with Cryptococcus [29] . However , our data also strongly suggest that at some frequency these immune cells that are normally tasked with defending against microbes can protect and chauffeur Cryptococcus out of the lung , leading to fatal disseminated disease . This type of Trojan horse mechanism of egress from the lungs has been observed for several bacterial pathogens , including Bacillus anthracis ( via alveolar macrophages ) and Francisella tularensis ( via dendritic cells ) [30 , 31] . The trafficking of Cryptococcus across the blood brain barrier has also been proposed to occur via phagocytes [8 , 32–34] . Here we present the first direct evidence of an inhaled fungal pathogen using a Trojan horse mechanism to escape the lung and cause disseminated disease ( Fig 8 ) . Most significant to this mechanism are our findings that 1 ) the efficiency of lymph node colonization in spore-infected mice is consistently orders of magnitude higher than in yeast-infected mice , 2 ) spores more readily associate with host lung phagocytes than yeast , and 3 ) trafficking of spores out of the lung is dependent on CD11c+ phagocytes . One prediction of this model is that the depletion of CD11c+ cells at the time of spore infections would lead to less dissemination to the bloodstream and consequently less dissemination to the brain , resulting in mitigation of disease or slower and/or lower fungal burdens in the brains . We made several attempts to test this prediction using the CD11c-DTR mice but discovered that CD11c-DTR mice treated with diphtheria toxin cannot tolerate subsequent infection with Cryptococcus spores or yeast . CD11c-DTR mice treated with even a single dose of diphtheria toxin and then infected with Cryptococcus experience uniform morbidity and mortality within 4 days , preventing longer-timeline experiments . As such , future experiments to test the long-term consequences of CD11c+ phagocyte depletion on spore dissemination from the lung to other tissues will require the development of a different system that imparts fewer pleiotropic effects . Regardless of the events that occur after egress from the lung , our data strongly suggest a direct link between the frequency of spore phagocytosis by alveolar macrophages in the lung and the efficiency of dissemination in vivo . This finding is consistent with prior studies in which the relationship between phagocytosis efficiency and virulence was established with yeast . For example , one study showed that more virulent clinical ( and laboratory ) yeast strains are phagocytosed more efficiently in vitro than less virulent strains [35] . Similarly , it was observed that deletion of the gene encoding antiphagocytic protein 1 ( app1Δ ) in yeast increased phagocytosis by alveolar macrophages both in vitro and in vivo and increased disseminated disease in immunocompromised mice [36] . Assuming that phagocytosis efficiency and dissemination are linked directly in vivo , one possibility is that yeast employ the same mechanism to disseminate in the host as spores , but the mechanism for spores is significantly more efficient because of enhanced recognition and phagocytosis by immune cells . Another ( not mutually exclusive ) possibility for increased lymph node colonization by spores is that intrinsic properties of spores imbue them with the ability to survive the host environment better than yeast , leading to more efficient dissemination from the lung . Spores are known to be more resistant than yeast to various forms of stress , including heat stress and reactive oxygen species [17] , and this general resiliency of spores could extend to the harsh environment of a phagolysosome or similarly stressful intracellular environments . Better intracellular survival of spores ( coupled with increased phagocytosis ) may explain their higher efficiency of dissemination to the lung draining lymph node relative to yeast . Consequently , these differences in rates of survival and dissemination could ultimately lead to more cryptococcal cells reaching the blood brain barrier in spore-infected mice than yeast-infected mice , resulting in more opportunities for Cryptococcus to infect the brain and cause meningoencephalitis in spore-infected mice . This model has major implications for the progression of mammalian cryptococcosis . Previous studies showed that environmental isolates of Cryptococcus were much less likely to cause disease in a mouse model of infection relative to clinical isolates ( all strains were infected intranasally as yeast ) [37] . Epidemiological data support the hypothesis that cryptococcosis in humans occurs after inhalation of Cryptococcus from environmental sources . If the majority of yeast strains isolated from the environment cannot cause disease ( at least in mice ) , there is likely an alternative condition , particle , or selection that occurs in environmental sources that confers disease . Because Cryptococcus has a defined sexual cycle ( through both opposite- and same-sex development ) , and population genetics studies of environmental isolates are consistent with sexual development occurring in nature , it follows that spores are likely to be produced in the environment . Given that spores are more resistant to environmental stressors than yeast , we hypothesize that spores harbor an enhanced capacity to cause disease ( relative to their yeast parents ) . This was borne out by experiments in which spores derived from both highly virulent strains and avirulent strains showed advantages in virulence relative to yeast ( e . g . higher brain CFUs at endpoint , more severe CNS symptoms , etc . ) . In fact , avirulent yeast parents gave rise to spores that caused fulminant disease in mice , lending credence to the idea that avirulent isolates ( in the yeast form ) might not be avirulent in the spore form . Such a finding could address the apparent paradox of how avirulent yeast isolates from the environment could be causing disease in humans . Thus far , studies of the spores of environmental isolates have been hampered by the fact that most are sterile or produce inviable spores [38 , 39] . Attempts to identify compatible mating partners for opposite-sex mating or stimulate same-sex mating of environmental isolates to levels required for robust spore isolation have not yet been successful . However , as more is learned about the signals that control sexual development and ultimately drive spore production , these technical challenges can be addressed . Taken together , the data presented here support a specialized role for spores in causing mammalian disease that is dependent on association with host lung phagocytes . This relationship appears to occur with both yeast and spores , but spores can more effectively capitalize on their hosts to persist , escape the lung , and disseminate to other tissues to cause disease ( Fig 8 ) . Key in spore-mediated disease is the relationship between spore germination and survival in the host . If spores do not germinate , they do not cause disease , but remaining a spore long enough to capitalize on the lung innate immune response of the host appears to confer a significant advantage during infection . While the full relationship between the rate of spore germination and the host response to a transitioning particle remains to be determined , understanding the fundamental differences between spore- and yeast-mediated disease is a key part of elucidating the disease process . Finally , understanding the pathogenesis of spores of Cryptococcus promises to open new avenues into the development of novel therapeutics that could be effective in the prevention of fatal cryptococcosis and other diseases caused by the spores of invasive human fungal pathogens .
RAW 264 . 7 cells . RAW 264 . 7 are adherent macrophages originally propagated from an Ableson murine leukemia virus-induced tumor in a male BALB/c mouse . Cells were cultured in RPMI 1640 + 10% FBS at 37°C and 5% CO2 . Cells were harvested using a cell scraper and passaged every 2–3 days upon reaching 80–90% confluency with a 1:5–1:10 split ratio . JAWSII cells . JAWSII cells are a dendritic-like mix of adherent and suspended cells originally derived from the bone marrow of a C57Bl/6 mouse ( sex unknown ) . Cells were cultured at 37°C and 5% CO2 in alpha-MEM ( with L-glutamine ) + 1mM sodium pyruvate + 5 ng/mL GM-CSF + 20% FBS . Cells were subcultured every 1–2 weeks by removing attached cells with rinsing with PBS , and incubating with 0 . 25% Trypsin + EDTA for 5 minutes at 37°C and splitting at a 1:2 ratio . All Cryptococcus strains were handled using standard techniques and media as described previously [40] . C . neoformans var . grubii strain H99 ( serotype A , mating type α ) , C . neoformans var . grubii strain BT63 ( serotype D , mating type a ) , C . neoformans var . neoformans strain JEC20 ( serotype D , mating type a ) , C . neoformans var . neoformans strain JEC21 ( serotype D , mating type α ) , C . neoformans var . neoformans strain B-3502 ( serotype D , mating type a ) , and C . neoformans var . neoformans strain B-3501 ( serotype D , mating type α ) were grown on yeast extract peptone dextrose ( YPD ) agar plates at 30°C and stored at 4°C . All experiments conducted with C . neoformans yeast cells employed a 1:1 mixture of a:α mating type unless indicated otherwise . Cryptococcus spores were created and purified as described previously [17] . Briefly , yeast of both mating types ( BT63 ( a ) with H99 ( α ) , B-3502 ( a ) with B-3501 ( α ) , JEC20 ( a ) with JEC21 ( α ) ) were grown separately on YPD agar for 2 days and then mixed in equal parts in phosphate buffered saline ( PBS ) . This suspension was then spotted onto aged V8 agar ( pH 7 . 0 ) and grown for 4–5 days at room temperature to facilitate a x α sexual development and the production of spores . Resulting crosses were then scraped off the plate , resuspended in a Falcon tube containing 65% Percoll/1X PBS , and subjected to gradient centrifugation at 10°C for 20 minutes and 4000 RPM in a swinging bucket tabletop centrifuge . The resulting spores were collected from the bottom of the Falcon tube using a 20G needle . Purity and total number of spores were determined by counting directly on a hemocytometer and plating to YPD to confirm the concentration of viable spores . For experiments to track and discern spore and spore-derived cryptococcal cells from yeast and yeast-derived cryptococcal cells we created two mating strain pairs expressing GFP-NATR or mCherry-NEOR in the JEC20 and JEC21 background . In both cases expression was driven by the histone H3 promoter , and the fluorescent protein contained a nuclear localization signal ( NLS ) from Nhp6b02 ( CNE04220 ) . To ensure that the integration of the expression cassette was consistent between strains , and that it did not interfere with any genes , the expression cassette was integrated at the site homologous to the “safe-haven” site previously identified in H99 [41] . In JEC21 this intergenic site landed between the genes CNA07550 and CNA07560 , and this site was targeted via homologous recombination . The left flank and right flank for homologous recombination were obtained from genomic DNA isolated from JEC21 using PCR . The resulting left flank was 756 bp in length and was created using primers CHO5360 and CHO536 ( primers listed in S1 Table ) , the right flank was 775 bp in length and was created using primers CHO5362 and CHO5363 . Using Gibson cloning , the flanks were then added to the previously created plasmid pCH1227 ( digested with Apa1 and HindIII , and fragments purified following gel extraction ) ( thanks to JM Davis ) . The resulting plasmid ( pCH1351 ) was sequence-verified and encodes a histone H3-driven enhanced green fluorescent protein ( EGFP ) with an 80-amino acid NLS and nourseothricin resistance ( NATR ) cassette with flanks for homologous recombination into JEC21 . pCH1351 was digested with SalI and introduced into JEC21 using biolistic transformation to create strain CHY3952 . Integration of the GFP expression cassette at the intended safe-haven locus was confirmed using PCR with primers annealing to GFP and to regions upstream or downstream of the flanks used for homologous recombination ( primers CHO5382 and CHO5383 ) . CHY3952 was then crossed with JEC20 to generate a congenic GFP-NLS-expressing a strain ( CHY3955 ) . An identical strain pair expressing mCherry and G418R was created by replacing the GFP-NATR cassette with and mCherry-G418R cassette via homologous recombination in strain CHY3952 ( Primers listed in S2 Table ) and then backcrossing with JEC20 . From this backcross , strains CHY4028 ( α ) and CHY4031 ( a ) , were isolated for use as a mating pair . All GFP and mCherry strains were confirmed to harbor a single integration of the H3-NLS-fluor-marker cassettes at the desired locus . The virulence , dissemination , and histology of Cryptococcus were determined using age-matched male and/or female mice ( between 6 and 12 weeks ) in the C57Bl/6 genetic background ( from Jackson Laboratory ) . CD11c-DTR mice were bred in-house under specific pathogen-free ( SPF conditions ) . Mice were housed and handled under appropriate guidelines in accordance with our IACUC-approved protocol #A005118-R01 . Numbers of mice needed for robust statistical outcomes were determined using an a priori power analyses as appropriate . For intranasal instillation , the indicated numbers of spores or yeast in a volume of 50 μl PBS were applied to the nares of anesthetized mice as described previously [10] . Direct blood stream infections were carried out by administration of 1x106 yeast or spores in 250μl PBS via injection into the tail vein . Mice were monitored twice daily , and euthanized if they appeared moribund ( i . e . exhibited hunched behavior , ruffed fur , difficulty walking , and/or >20% weight loss ) . Moribund mice were euthanized via CO2 asphyxiation , according to AMVA guidelines . Experiments assessing survival times were terminated once all mice succumbed to cryptococcosis or survived 100 days post-infection . Replicate data: For H99/BT63 survival curves ( see Fig 1A ) , a second biological replicate was carried out with 4 mice per group , also generating no significant differences among groups . For H99/BT63 CFU analyses ( see Fig 1B ) , a second biological replicate with 3 mice per group was carried out , also generating significant differences between CFUs in the brain ( p = 0 . 018 ) but not the lungs ( p = 0 . 30 ) or other tissues , including spleen , liver , kidney , and blood ( all p-values ≥ 0 . 1 ) . In all pairwise comparisons , significance values were generated using a two-sample , two-tailed Student's t-test . For B-3501/B-3502 survival curves ( see Fig 2 ) in which yeast-infected mice did not show any symptoms , mean time to death was assigned at 100 days post-infection . A second biological replicate of the B-3501/B-3502 spore vs . yeast survival experiment also resulted in a significant difference in time to death between spore- and yeast- infected mice ( p = 2 . 7x10-3 by a Log-Rank test for Kaplan-Meier survival analysis ) [42] . Panels 2B and 2C are representative of two independent biological replicates of fungal loads in organs . Dissemination of fungal burden in spore and yeast-infected mice was assessed by CFU determination in mouse organs as described previously [10] . Briefly , mice were euthanized at indicated time points , and organs ( including lungs , kidney , brain and one of the mediastinal lymph nodes ) were surgically removed , weighed , and homogenized by bead beating in sterile PBS . Tissue homogenates were serially diluted and plated on YPD agar . Colony forming units per gram of tissue were then determined after 2–3 days of growth at 30°C . For experiments determining the mCherry-NEO and GFP-NAT composition of the fungal burden , up to 50 colonies per organ per mouse were randomly selected , patched to fresh YPD plates , replica-plated to YPD-NAT and YPD-NEO agar and assessed for growth . For histological analysis , C57Bl/6 mice were infected with 2 . 5x105 B-3501 x B-3502 spores or their parental yeast strains . At days 3 , 6 , 14 , 21 and endpoint ( morbidity for spore-infected or 100 days for yeast-infected ) , 3 spore and 3 yeast-infected mice were sacrificed , and their lungs and brains were fixed in formalin . Cross sections of each organ were prepared , mounted , and stained with Mucicarmine by the histology lab at the UW School of Veterinary Medicine . Slides were imaged at 100X magnification with a Zeiss Axioskop 2 Plus microscope . To analyze the phagocytosis of live Cryptococcus spores and yeast by bone marrow-derived macrophages , RAW264 . 7 macrophages , and JAWSII dendritic like-cells , a CFU fungal association assay was used as described previously [24] . Bone marrow-derived macrophages were obtained from the femurs of wild type C57BL/6 . Femurs were flushed with 5 mL cold PBS and passed through a 70 μm filter . Red blood cells ( RBCs ) were lysed using ACK lysing buffer , the remaining cells washed , plated with 2x106 per petri dish in DMEM + 10% FBS with 3 ng/mL recombinant GM-CSF . On day 3 , the medium was refreshed , and on day 7 the cells were harvested with trypsin for in vitro assays . Phagocytes were co-cultured with spores or yeast for 4 hours at an MOI of 10:1 . Cryptococcus that had not adhered or been phagocytosed was then removed by washing 3 times with PBS . Macrophages were lysed with 0 . 01% Triton X-100 ( a concentration known not to affect the viability of Cryptococcus ) to release intracellular Cryptococcus; the lysate was serially diluted and plated on YPD to determine the number of macrophage-associated Cryptococcus cells . After 3 days of growth at 30°C , colony forming units were counted , and the percentage of macrophage association was calculated as ( # CFUs from lysate ) / ( CFUs introduced ) . To determine the efficiency of spore germination in a mouse lung , three mice were infected intranasally with JEC20 x JEC21 spores ( 3 mice ) and as a control , one mouse was infected with JEC20 + JEC21 yeast . Eighteen hours post-infection , the mice were sacrificed and cells in the mouse lung were collected by bronchoalveolar lavage and fixed in 4% formaldehyde . Cells were then observed by microscopy on a Zeiss Axioskop 2 Plus microscope at 10000X magnification . Approximately 20 cryptococcal cells per mouse were randomly chosen , and the width and length of each was measured and used to calculate their aspect ratios ( width/length ) . Lower aspect ratios indicated earlier germination states , while aspect ratios near 1 indicated fully germinated , spherical yeast . Dormant spores in PBS alone and vegetatively growing yeast collected from YPD medium were used as controls . To assess differences in uptake of partially germinated Cryptococcus spores compared to growing yeast cells , an adapted fluorescence-based assay was used [49] . RAW 264 . 7 cells were seeded into 96 well plates with 3x104 macrophages per well in 100μL per well in RPMI + 10% FBS and allowed to adhere overnight . Cryptococcus spores were allowed to germinate for 0 , 0 . 5 , 1 , 2 , 4 , 8 , 12 and 16 hours in YPD shaking at 30°C alongside vegetatively growing yeast . Fungal cells were fixed overnight at 4°C in 4% formaldehyde , stained with 50μM calcofluor white for 5 minutes , washed three times with PBS and counted using a hemacytometer . Medium was removed from the macrophages , and for each condition fungal cells were introduced to macrophages at an MOI of 100:1 ( 3x106 fungal cells/well ) in RPMI + FBS . A serial dilution control curve ( 1:2 dilutions from 3x106 to 9 . 4x104 cells ) was created for each condition . After a phagocytosis period of four hours , fluorescence of each well was measured using a plate reader , providing a value corresponding to the number of fluorescently labeled Cryptococcus cells in each well . Then extracellular ( non-phagoctyosed ) cell fluorescence was quenched with the addition of 100μl trypan blue , and fluorescence was measured again ( corresponding to the number of phagocytosed cells ) . Wells containing macrophages fixed prior to phagocytosis were included as a background fluorescence/no phagocytosis control . The number of cells phagocytosed for each germinated population was determined by using the best-fit line equation for each of the standard curves generated . Percent phagocytosis was calculated as [100 x ( # of cells phagocytosed/number of cells introduced ) ] . RAW 264 . 7 cells were plated on glass slides in a 24 well plate with 3x105 cells per well . JEC20 x JEC21 spores were germinated for different lengths of time ( 0 , 0 . 5 , 2 , 4 , 8 , 12 , 24 hours ) in YPD shaking at 30°C , at which point they were washed three times with PBS and fixed ( either by UV irradiation or in 4% formaldehyde ) , stained with 50μM calcofluor white for 5 minutes , washed three times with PBS and counted using a hemacytometer . A live spore and yeast control were included . Medium was removed from RAW 264 . 7 cells and replaced with 1mL of medium containing stained fungal cells at an MOI of 1:1 . Phagocytosis was allowed to progress for 4 hours , wells were washed 3 times with PBS , and glass slides were then fixed and mounted . For each condition , 10 randomly selected fields were observed with epifluorescence on a Zeiss Axioskop 2 Plus microscope at 630X magnification . The number of fungal cells bound to and internalized by macrophages was counted to determine the total number of fungal cells associated at each condition . This association was normalized to the 0 hr time point . The average extent of germination of the cells introduced , bound , and internalized was calculated for each condition as described in the methods for germination assays . Independent and mixed phagocytosis assays of mCherry-NEO and GFP-NAT spores and yeast by RAW 264 . 7 cells were assessed by flow cytometry . RAW 264 . 7 cells were seeded in 96 well plates with 1x105 cells per well in RPMI + 10% FBS and allowed to adhere overnight . mCherry spores , GFP spores , mCherry yeast or GFP yeast were introduced to macrophages at an MOI of 5:1 . To assess how the cryptococcal cell types influenced phagocytosis of one another , spores and yeast with alternate fluors were added together and introduced to macrophages , resulting in a final MOI of 10:1 . After 4 hours , macrophages were washed 3X with PBS to remove any unassociated cryptococcal cells . Macrophages were lifted from the wells by 10 minutes of incubation with Versene at 37°C and vigorous pipetting . Cells were washed once with RPMI + 10% FBS and then stained with a 1:200 dilution of anti-CD11b conjugated to PE-Cy7 and 1:1000 fixable Live/Dead yellow . Association of mCherry+ and GFP+ fungal cells with live macrophages ( CD11b+L/D- ) was quantified using flow cytometry on a BD LSRII cytometer . Mice were infected intranasally with 5x106 spores or yeast expressing mCherry ( 5 mice each ) or a PBS control ( 2 mice ) . Six hours post-infection , mice were euthanized by asphyxiation with CO2 , and whole lungs were recovered . Lung leukocyte isolation and surface staining for analysis by flow cytometry was carried out as described above . Live leukocytes ( L/D- , CD45+ ) were identified and further gated to identify alveolar macrophages ( CD11c+ , SiglecF+ ) and lung dendritic cells ( CD11c+ , SiglecF- ) . Association of cryptococcal cells with each of these subsets was determined by positive mCherry signal . In a separate experiment , the number of cryptococcal cells per phagocyte was determined microscopically . Mice were infected intranasally with 5x106 spores or yeast expressing mCherry ( 3 mice per cell type ) , and lung cells were recovered by bronchoalveolar lavage at 6 hours post infection . Red blood cells were lysed , and the samples were fixed and examined using epifluorescence on a Zeiss Axioskop 2 plus microscope at 1000X magnification . The number of mCherry+ cryptococcal cells inside 100 fungus-containing phagocytes per mouse was counted . CD11c-Diphtheria Toxin Receptor ( CD11c DTR ) mice were purchased from the Jackson Laboratory and bred in-house . WT C57BL/6 mice or CD11c-DTR mice were administered 500μL of 20ng/mL diphtheria toxin by intraperitoneal injection ( alongside non-treated controls ) . Depletion was allowed to progress for 36 hours , at which time mice were infected intranasally with 1x107 JEC20 x 21 spores . Two days post infection , mice were sacrificed , and their lungs and lymph nodes were homogenized and plated onto YPD to assess fungal burden through counting CFUs as described above . All experiments were carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health under University of Wisconsin-Madison Institutional Animal Care and Use Committee-approved protocol number A005118 . UW-Madison is an AAALAC accredited institution . | Little is known about how inhaled spores from human fungal pathogens cause infections and spread to other parts of the body . The most frequent cause of inhaled fatal fungal disease is Cryptococcus , which causes meningitis . To understand how Cryptococcus causes disease , we evaluated pathogenesis of two types of cells ( spores and yeast ) in a mouse model of infection . We compared yeast strains that cannot cause disease to the spore offspring they produced during sexual reproduction . We discovered that parental yeast that are not virulent produced spores that were fully virulent and caused fatal meningitis . This difference was associated with movement of spores to the lymph system; mice infected with spores had Cryptococcus in their lung draining lymph nodes , but mice infected with yeast did not . Furthermore , when we infected mice that lacked immune cells in their lungs , no spores were found in their lymph nodes . This indicates that instead of protecting mice from the spore infection , the immune cells moved spores out of the lung to the lymph system where spores could then spread to the brain . These insights into interactions between pathogenic fungal spores and lung immune cells provide new opportunities for understanding cryptococcosis and other spore-mediated fungal diseases . | [
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| 2019 | Infectious particle identity determines dissemination and disease outcome for the inhaled human fungal pathogen Cryptococcus |
Infections with high-risk human papillomaviruses ( HR-HPV ) such as HPV16 and 31 can lead to ano-genital and oropharyngeal cancers and HPV types from the beta genus have been implicated in the development of non-melanoma skin cancer . HPV replicate as nuclear extrachromosomal plasmids at low copy numbers in undifferentiated cells . HPV16 and 31 mutants have indicated that these viruses express an E8^E2C protein which negatively regulates genome replication . E8^E2C shares the DNA-binding and dimerization domain ( E2C ) with the essential viral replication activator E2 and the E8 domain replaces the replication/transcription activation domain of E2 . The HR-HPV E8 domain is required for inhibiting viral transcription and the replication of the viral origin mediated by viral E1 and E2 proteins . We show now that E8^E2C also limits replication of HPV1 , a mu-PV and HPV8 , a beta-PV , in normal human keratinocytes . Proteomic analyses identified all NCoR/SMRT corepressor complex components ( HDAC3 , GPS2 , NCoR , SMRT , TBL1 and TBLR1 ) as co-precipitating host cell proteins for HPV16 and 31 E8^E2C proteins . Co-immunoprecipitation and co-localization experiments revealed that NCoR/SMRT components interact with HPV1 , 8 , 16 and 31 E8^E2C proteins in an E8-dependent manner . SiRNA knock-down experiments confirm that NCoR/SMRT components are critical for both the inhibition of transcription and HPV origin replication by E8^E2C proteins . Furthermore , a dominant-negative NCoR fragment activates transcription and replication only from HPV16 and 31 wt but not from mutant genomes encoding NCoR/SMRT-binding deficient E8^E2C proteins . In summary , our data suggest that the repressive function of E8^E2C is highly conserved among HPV and that it is mediated by an E8-dependent interaction with NCoR/SMRT complexes . Our data also indicate for the first time that NCoR/SMRT complexes not only are involved in inhibiting cellular and viral transcription but also in controlling the replication of HPV origins .
Human papillomaviruses ( HPV ) constitute one of the largest human pathogenic virus families known to date . HPV Infections can cause skin warts , ano-genital warts and papillomas derived from mucosal or cutaneous epithelium . Persistent infections with certain HPV types such as HPV16 , 18 , 31 and others are a necessary risk factor for the development of cervical , other ano-genital and oropharyngeal cancer [1] . In addition , infections with HPV types from the beta genus have been implicated in the development of squamous cell cancer in epidermodysplasia verruciformis patients and non-melanoma skin cancer ( Beta genus papillomaviruses and skin cancer . [2] . HPV are small , naked viruses with a double-stranded , circular DNA genome of approximately 8000 bp . After infection , HPV genomes replicate as nuclear plasmids with 10 to 100 copies per cell in undifferentiated basal-like keratinocytes [3] . Viral genome copy numbers are kept constant by a random choice copy number control mechanism that is not well understood [4] . Upon differentiation of the host cell , viral genomes are amplified to several thousands of copies and as a consequence infectious virus is produced [3] . Viral proteins derived from the E1 and E2 genes function as sequence-specific DNA binding proteins and are involved in the initiation of DNA replication , control of viral transcription and segregation of viral genomes [5–7] . The E1 protein represents the viral replication initiator protein and acts as a replicative hexameric helicase [5 , 7] . However , despite being able to interact with the viral origin in vitro , E1 alone is not able to initiate replication in vivo and requires in addition the viral E2 protein [5–7] . E2 is a dimeric , sequence-specific DNA binding protein that recruits the E1 protein to the viral replication origin by protein-protein and protein-DNA interactions [6] . In addition , E2 can act either as a transcriptional repressor or activator which is dependent on the location of E2 binding sites ( E2BS ) in E2-responsive promoters and thus may also control replication by modulating viral gene expression [6] . The amino terminal domain of E2 ( ~200 residues ) is required for the activation of replication , modulation of transcription and attachment of PV genomes to mitotic chromosomes [6] . These functions are mediated by interaction with viral E1 and cellular proteins such as Brd4 which contact E2 via highly conserved residues among PV [8–11] . In addition to E2 , several human ( HPV1 , 5 , 11 , 16 , 18 , 31 and 33 ) and animal papillomaviruses ( bovine papillomavirus 1 , cottontail rabbit papillomavirus ( CRPV ) ) express transcripts in which an alternative exon from the E1 gene is spliced to a 3´-exon of the E2 gene [12–21] . This gives rise to a fusion protein between the E8 gene ( E9 for CRPV ) which overlaps with the E1 gene and the E2C domain of E2 . The corresponding proteins have been named E2C ( HPV1 , 11 , 16 , 33 ) , E8/E2 ( BPV1 , HPV16 ) , E8^E2 ( HPV18 ) , E8^E2C ( HPV5 , 16 , 18 , 31 ) or E9^E2C ( CRPV ) [12 , 13 , 15–19 , 21–23] . Genetic analyses of HPV5 , 11 , 16 , 18 and 31 genomes have revealed that the loss of E8^E2C expression leads to an over replication of viral genomes in undifferentiated cells suggesting a conserved function for E8^E2C in controlling genome replication [16 , 19 , 21 , 22 , 24 , 25] . Transcript analyses have indicated that the E8^E2C encoding transcript is the most abundant transcript processed at the splice donor site at nt . 1301 in both undifferentiated and differentiated HPV16 wt-positive keratinocytes [20] . Studies with HPV16 have surprisingly revealed that E8- genomes not only over-replicate in undifferentiated cells but also display increased levels of viral genome copies , transcripts and late proteins in differentiated cells than the HPV16 wt [20] . This suggested that E8^E2C not only restricts viral replication and transcription in undifferentiated cells but also limits virus production . The E2C part shared by E8^E2C and E2 proteins is responsible for DNA-binding and homo- and heterodimerization among E8^E2C and E2 proteins and thus over replication was initially thought to be due to binding-site competition between E2 and E8^E2C and/or the formation of inactive E2/E8^E2C heterodimers . However , E8^E2C proteins from HPV16 , 18 and 31 act in the absence of E2 as potent transcriptional repressors from both promoter-proximal and -distal E2BS which suggested that E8^E2C proteins are active and not only competitive repressors [13 , 26] . Studies using HPV16 and 31 E8^E2C revealed that repression from promoter-distal E2BS is dependent upon a KWK motif in the E8 domain [20 , 25 , 26] . Interestingly , KWK or WK motifs are not only highly conserved among E8^E2C proteins of HPV11 , 16 , 18 , 31 and 33 , where E8^E2C transcripts have been identified , but can also be always found in the predicted E8 genes in a large number of HPV belonging to the alpha-papillomaviruses [13] . HPV16 or 31 genomes with mutated E8 KWK residues replicate at increased copy numbers similar to E8 knock-out genomes suggesting a critical role for the E8 domain in limiting HPV replication [24 , 25] . In addition to its role as a transcriptional repressor , HPV31 E8^E2C protein also inhibits the E1/E2-dependent replication of the viral origin which is also dependent upon the E8 KWK-residues and can be achieved without heterodimer formation with E2 proteins [25 , 27] . This strongly suggests that ( 1 ) the over replication phenotype of HPV E8^E2C mutant genomes is due to the direct interference with the E1/E2-mediated origin replication and to the repression of viral transcription and ( 2 ) that the conserved E8 domain is important for both inhibitory activities . E8-domain dependent transcriptional repression by HPV31 E8^E2C occurs independently from other viral gene products in all human cells tested so far implying the involvement of host cell proteins [13 , 21 , 26 , 27] . Recent studies demonstrated that HPV31 E8^E2C does not require any of the cellular proteins recruited by E2 to mediate transcriptional repression of the viral E6/E7 promoter [28 , 29] . This indicated that E8^E2C proteins interact with a different set of host cell proteins to inhibit viral transcription and replication . In line with this , histone deacetylase ( HDAC ) inhibitors partially relieved transcriptional repression mediated by the HPV31 E8 domain and GST-pulldown experiments showed that HPV31 E8^E2C interacts in an E8-domain specific manner with cellular corepressor proteins such as HDAC 1 , 2 and 3 as well as TRIM28 and SETDB1 [27] . However , a proteomic approach using HPV31 E8^E2C identified none of these proteins but rather a different set of interactors which included ARG1 , BLMH , CASP14 , NCoR , TBLR1 and TGM3 [28] . The functional validation demonstrated a partial release of repression by HPV31 E8^E2C by siRNAs against NCoR but not against other interactors [28] . In addition , a partial release of repression was observed with an RNAi against HDAC3 but not against other HDACs [28] . This implied that both NCoR and HDAC3 are involved in the transcriptional repression of the HPV E6/E7 promoter by HPV31 E8^E2C but that additional cellular proteins might also be involved . In line with this , HDAC inhibitors showed no effect on E8^E2C´s ability to inhibit the E1/E2-dependent replication supporting the idea of additional interaction partners [27] . NCoR and the closely related SMRT protein have been first identified as transcriptional corepressors for several unliganded and orphan nuclear receptors but have also been found to interact with other cellular transcription factors [30 , 31] . NCoR and SMRT proteins form salt-resistant core complexes with HDAC3 , TBL1 ( transducin-beta like 1 ) , TBLR1 ( TBL-related 1 ) and GPS2 ( G protein pathway suppressor 2 ) [31 , 32] . In addition , NCoR/SMRT core complexes appear to recruit additional proteins in a transcription factor- or context-dependent manner to modulate transcription [33] . Transcription repression by NCoR/SMRT complexes has been suggested to be mainly due to HDAC3 which deacetylates histones and non-histone substrates [31 , 33] . However , recent data challenge this idea and indicate that deacetylase-independent mechanisms might be more important than previously thought [32 , 34] . NCoR has been suggested to be also a part of corepressor complexes different from NCoR/SMRT core complexes [30] , leaving it open which NCoR complex is recruited by HPV E8^E2C proteins . Furthermore , the contribution of E8^E2C-interaction partners to the inhibition of the E1/E2 dependent-replication of the viral origin has not been evaluated . Since highly conserved residues of E2 mediate key interactions with viral and cellular proteins to modulate replication and transcription , it is feasible that the conserved KWK residues in the E8 domain also mediate a conserved interaction with cellular partners . In this study we show for the first time that E8^E2C proteins also restrict the replication of HPV1 ( mu-PV ) and HPV8 ( beta-PV ) in normal human keratinocytes . The E8^E2C proteins from HPV1 , 8 , 16 and 31 interact with the NCoR/SMRT corepressor core complex consisting of GPS2 , HDAC3 , NCoR , SMRT and TBl1 and TBLR1 proteins in an E8 domain dependent manner . This interaction mediates the transcriptional repression and inhibition of E1/E2-dependent replication by E8^E2C proteins . In summary , our data reveal a novel , highly conserved role for the cellular NCoR/SMRT-corepressor complex in the control of HPV replication .
Previous studies have shown that the E8^E2C protein of HPV31 functionally interacts with NCoR and HDAC3 to inhibit transcription [27 , 28] . To get more insight into conserved HPV16 and 31 E8^E2C interactors , we established stable 293T cell lines expressing HA-tagged versions of HPV16 and 31 E8^E2C . E8^E2C proteins were immunopurified and associated proteins were identified by mass spectrometry . For both E8^E2C proteins an overlapping set of six interactors ( GPS2 , HDAC3 , NCOR , SMRT , TBL1 , TBLR1 ) could be identified that were either completely absent or whose intensities were greatly decreased ( >100-fold ) in the empty vector controls ( Fig 1A ) . To verify these results and address if these interactors depend on a functional E8 domain , stable cell lines were established that express HA-tagged repression-deficient E8^E2C-KWK mutant ( mt ) proteins . Cell extracts from the empty vector control , HPV16 and 31 E8^E2C wt or HPV16 and HPV31 E8^E2C KWK mt proteins were immunoprecipitated with α-HA and subjected to immunoblot analysis . This confirmed that the wt E8^E2C proteins interact with HDAC3 , NCOR , SMRT , TBL1 and TBLR1 , whereas binding to the E8^E2C KWK mt proteins was not detected or greatly reduced ( Fig 1B ) . Despite robust signals in the mass spec analysis , we were unable to confirm binding of E8^E2C proteins to GPS2 by Co-IP/immunoblot . In summary this finding confirms and extends previous studies that HPV31 E8^E2C interacts with NCoR , TBLR1 and HDAC3 [27 , 28] . GPS2 , HDAC3 , NCOR , SMRT , TBL1 and TBLR1 are known to form the stable NCoR/SMRT co-repressor complex that is recruited by unliganded nuclear receptors such as thyroid hormone receptor and retinoic acid receptor to inhibit their target genes [31 , 32] . To extend our findings to E8^E2C proteins from non-alpha-PV , we first investigated the phenotype of HPV1 , a mu-PV , and HPV8 , a beta-PV , E8 knock-outs genomes in normal human keratinocytes ( NHK ) . The ATG of HPV1 E8 ( nt . 1200–1202 ) and HPV8 E8 ( nt . 1312–1314 ) were mutated to ACG which causes a silent mutation in the overlapping E1 replication gene . Wt and mt genomes were transfected into NHK cells and low-molecular weight DNA was prepared 7d p . t , and analyzed by Southern blot after DpnI digestion to remove non-replicated genomes ( Fig 2 ) . Whereas wt HPV1 and HPV 8 genomes barely replicated , E8- genomes displayed a robust replication signal ( Fig 2 ) . To test if HPV1 and 8 E8^E2C proteins display transcriptional repression activity in an E8-domain dependent manner , expression vectors for wt E8^E2C proteins and mutant proteins without an E8 domain where lysine-2 was mutated to alanine and residues 3–10 were deleted ( E8^E2C K2A d3-10 mt ) were generated . Transfection experiments indicated that HPV1 and 8 E8^E2C proteins strongly repress activity from the pC18-Sp1-luc reporter plasmid which comprises four multimerized E2BS and a minimal promoter in NHK ( 10- and 6 . 8-fold ) , RTS3b ( 3- and 5 . 6-fold ) and HeLa cells ( 9- and 26-fold ) ( Fig 3A and 3B ) . In contrast , repression activity of both HPV1 and 8 E8^E2C proteins was completely lost when the E8 domain was removed . Immunoblot analyses of HA-tagged E8^E2C versions revealed that the deletion of the E8 domain resulted in proteins with enhanced stability which is similar to HPV16 and 31 [24 , 26 , 28] and indicated that the loss of repression activity is not due to a decreased protein stability ( Fig 3A and 3B ) . These data are consistent with the observations for HPV16 and 31 E8^E2C proteins that transcriptional repression is E8 domain dependent and can be observed in both normal and immortalized cells . To compare the activities of the HPV1 , 8 , 16 and 31 repressor proteins , titration experiments were carried out . This revealed that all E8^E2C proteins were equally able to repress transcription from the pC18-Sp1-luc reporter in both HeLa and RTS3b cell lines ( Fig 3C ) . In summary , tthis strongly indicated that E8^E2C´s repressive function is conserved among HPV . To test whether the HPV1 and HPV8 E8^E2C proteins also interact with components of the NCoR/SMRT complex , HeLa ( HPV1 ) and 293T ( HPV8 ) cells were transfected with wt or mutant E8^E2C expression vectors and whole cell extracts were immunoprecipitated with α-HA . Immunoblotting revealed that wt E8^E2C proteins interact with HDAC3 , NCoR , SMRT and TBLR1 whereas the deletion mutants did not or in a greatly reduced manner ( Fig 4A ) . An alignment of mu- and beta-PV E8 sequences revealed a conserved KLK motif present at residues 2–4 that resembles the functionally important KWK motif in alpha-PV ( Fig 4B ) . To investigate this , an HPV8 E8^E2C KLK mt was generated and tested for the interaction with the NCoR/SMRT components in HeLa and RTS3b cells . The HPV8 E8^E2C KLK mt was stably expressed but failed to interact with TBLR1 and HDAC3 and only weakly inhibited transcription in HeLa and RTS3b cells ( Fig 4C and 4D ) . The data suggest that the KLK motif is comparable to the alpha-PV KWK motif and is important for the interaction with NCoR/SMRT components . In summary , the data strongly indicate that HPV E8^E2C repressor proteins from mu- and beta-HPV also interact in an E8-dependent manner with the NCoR/SMRT complex . To further validate the interaction between E8^E2C protein and NCoR/SMRT complexes in intact cells , an immunofluorescence-based assay was applied . It has been previously reported that the co-expression of HPV18 E1 and E2 proteins in HeLa cells leads to the formation of distinct foci at the origins of replication of the integrated HPV18 genomes [37] . HeLa cells were first transfected individually with epitope tagged versions of HPV31 E1 , E2 and E8^E2C alone or in combination . Immunofluorescence analysis revealed that E1 , E2 and E8^E2C when expressed alone have a diffuse nuclear distribution ( S1 Fig ) . In contrast , their co-expression leads to 3–4 distinct foci that can be stained for E1/E2 , E1/E8^E2C and E2/E8E2C ( S1 Fig ) . Similar staining patterns were obtained when HPV16 E1 , E2 and E8^E2C were co-expressed ( S1 Fig ) . These data indicate that the HPV16 and 31 E1 and E2 replication proteins form nuclear foci in HeLa cells that strongly resemble HPV18 E1/E2 replication foci [37] . Furthermore these data indicate that E8^E2C repressor proteins are present in these foci . To address if constituents of the NCoR/SMRT complex are present in such foci , HeLa cells were cotransfected with HPV31 3xflag-E1 , E2-myc and E8^E2C-HA or E8^E2C-KWK mt-HA or HPV16 3xflag-E1 , E2 and E8^E2C-HA or E8^E2C-KWK mt-HA . Cells were stained for E8^E2C and HDAC3 , NCoR , SMRT , TBL1 or TBLR1 ( Fig 5 and S2 Fig ) . This revealed that E8^E2C proteins localize to E1/E2-foci independent from the E8 domain . Quantification of the signals for E8^E2C and NCoR/SMRT components in replication foci revealed statistically significant colocalization of HDAC3 , NCoR , SMRT , TBL1 and TBLR1 only with HPV16 or 31 E8^E2C but not with the respective E8^E2C KWK mt ( Fig 5 ) . These data indicate that NCoR/SMRT components are only recruited into replication foci in the presence of the wt E8 domain . In addition , we tested if colocalisation occurs when an origin of replication was provided by cotransfecting HPV URR plasmids . Distinctive HPV 31 E1/E2 foci were observed whose numbers and size increased when an HPV URR plasmid was cotransfected ( S3 Fig ) . The additional co-transfection of E8^E2C wt or KWK mt expression plasmids revealed that both proteins are recruited to these foci , but TBLR1 only significantly colocalizes with wt E8^E2C ( Fig 6 ) . Staining of E8^E2C upon transfection of RTS3b cells with the HPV8 URR plasmid and expression vectors for E1 , E2 and E8^E2C or E8^E2C KLK mt revealed that both the HPV8 E8^E2C wt and the mt protein are present in dot-like nuclear structures ( Fig 7 ) , but recruitment of NCoR , HDAC3 , SMRT and TBLR1 into these structures could only be observed in the presence of the wt E8^E2C protein ( Fig 7 ) . These results confirm that the NCoR/SMRT complex is recruited in an E8-domain dependent manner by E8^E2C proteins in vivo . Furthermore , the presence of NCoR/SMRT components in HPV16 and 31 E1/E2/E8^E2C foci suggests that the NCoR/SMRT complex is important for the repression of viral replication mediated by E8^E2C proteins . To functionally evaluate the role of the NCoR/SMRT complex during replication of the HPV origin , we made use of a luciferase based test system to measure replication activity . As previously described , the co-transfection of E1 and E2 expression vectors with an HPV16 URR reporter plasmid , which harbors the viral origin of replication and luciferase activity is driven by the major early promoter , results in an increase in luciferase activity which is mainly caused by an increase in copy number of the reporter plasmid [20] . We first validated the effects of E8^E2C in this experimental system and cotransfected HeLa cells with the respective URR plasmids and expression vectors for E1 , E2 and E8^E2C or E8^E2C KWK mt from HPV16 or HPV31 and measured luciferase activity and newly replicated URR plasmids after DpnI-digestion by qPCR ( Fig 8A ) . This revealed that the combination of HPV16 or 31 E1 and E2 induced both an increase in firefly luciferase activity ( 4- and 8-fold , respectively ) and of newly replicated plasmids ( 8- and 17 . 9-fold , respectively ) . The addition of HPV16 or HPV31 E8^E2C reduced luciferase activity ( 13- and 20-fold , respectively ) whereas the corresponding E8^E2C KWK mt did not ( 1 . 5- and 2 . 2-fold , respectively ) . Wt HPV16 or HPV31 E8^E2C inhibited the levels of newly-replicated plasmids ( 2 . 4- and 2 . 5-fold , respectively ) whereas the E8^E2C KWK mt had no effect ( Fig 8A ) . We then analyzed the effects on replication using HPV1 and HPV8 constructs in RTS3b cells as the HPV1 URR did not replicate efficiently in HeLa cells ( Fig 8B ) . The co-transfection of HPV1 and HPV8 E1 and E2 expression constructs with the respective URR plasmids induced luciferase activity 7 . 1- and 85-fold and plasmid replication 3 . 2- and 21 . 8-fold , respectively ( Fig 8B ) . The addition of E8^E2C repressed luciferase activity 20- and 13 . 7-fold and plasmid replication 1 . 9- and 12 . 8-fold , respectively . In contrast , the co-transfection with the HPV1 or HPV8 E8^E2C K2A/d3-10 mt had only minor effects on transcription ( 1 . 1- and 2 . 3-fold , respectively ) and replication ( 1 . 3- and 2 . 3-fold , respectively ) ( Fig 8B ) . In summary , these data strongly support the idea that the highly conserved function of the E8 domain in HPV E8^E2C proteins is both the repression of transcription and plasmid replication . To test the influence of NCoR/SMRT components on viral transcription and replication directly , siRNA-mediated knockdown experiments were carried out . HeLa cells were transfected with siRNA and 24h later with a mix of HPV31 reporter and expression plasmids . An efficient knock-down could only be achieved for HDAC3 , NCoR , SMRT and TBLR1 but not for GPS2 or TBL1 ( S4 Fig ) . Interestingly , the knock-down of a single complex component had no major effect on the repression activity of HPV31 E8^E2C ( S4 Fig ) . We therefore evaluated combined knock-downs of HDAC3/TBLR1 or NCoR/SMRT in HeLa and RTS3b cells by immunoblotting which revealed that the HDAC3/TBLR1 combination also reduces the NCoR and SMRT protein levels in HeLa cells and only NCoR in RTS3b cells whereas the NCoR/SMRT combination also reduces HDAC3 amounts in both cell types ( S5A Fig ) . Both siRNA combinations reduced the ability of HPV16 and 31 E8^E2C to repress replication to similar extents ( Fig 9A ) . Upon knock-down of HDAC3/TBLR1 or NCoR/SMRT repression of replication was reduced to 2-fold as determined by luciferase activity and fully restored as determined by DpnI digest and qPCR ( Fig 9A ) . Immunoblots indicated that the knock-down of HDAC3/TBLR1 had no influence on the nuclear levels of E1 , E2 or E8^E2C proteins ( S5B Fig ) . Similar to HPV16 and 31 , the siRNA combinations HDAC3/TBLR1 or NCoR/SMRT reduced significantly the repression activities of HPV1 and 8 E8^E2C in RTS3b cells ( Fig 9B ) . These data confirm that the recruitment of NCoR/SMRT complexes by E8^E2C is important for the inhibition of viral replication . The activity of the pC18-Sp1-luc reporter plasmid which harbors multimerized E2BS and a minimal promoter is repressed by HPV1 , 8 , 16 and 31 E8^E2C in a completely E8-domain dependent manner ( [24 , 26]; Figs 3 and 4D ) . In contrast to the assays using replicating HPV reporter plasmids the effects of siNCoR/siSMRT were different from siHDAC3/siTBLR1 . Only siNCoR/siSMRT strongly diminished transcription repression by HPV1 , 8 , 16 and 31 E8^E2C , whereas siHDAC3/siTBLR1 had only a ~2-fold effect on HPV16 and 31 that was not statistically significant ( Fig 10 ) . These data indicate that NCoR and SMRT are also important for E8-dependent transcriptional repression of non-replicating plasmids . However , the differential requirements for HDAC3 and TBLR1 may indicate that these proteins are mainly required for the inhibition of replicating plasmids . To confirm our findings in the context of replicating viral genomes , we used SCC13 cells , which show high transfection efficiencies and an E8^E2C-dependent replication of HPV31 genomes [21] . Cells were first transfected with control siRNA , siHDAC3/siTBLR1 or siNCoR/siSMRT and the next day with ligated HPV31 wt or HPV31 E8 KWK mt genomes . Forty-eight h later RNA was harvested and the amounts of viral transcripts spliced at the major splice donor in E1 and the major splice acceptor in E4 ( labeled E1^E4 ) and of HDAC3 , NCoR , SMRT and TBLR1 transcripts were analyzed by qPCR ( Fig 11 ) . The average knock-down efficiencies were 69–86% NCoR/SMRT complex components ( Fig 11 ) . HPV31 E8 KWK mt genomes expressed 3-fold more E1^E4 transcripts than wt genomes in the presence of siCon . The knock-down of HDAC3/TBLR1 and NCoR/SMRT significantly induced E1^E4 transcripts 1 . 8-fold and 2-fold , respectively , from wt genomes whereas transcription from E8 KWK mt genomes was not significantly changed ( 2 . 9- and 3 . 5-fold vs . wt; Fig 11 ) . These data confirm that NCoR/SMRT complex components repress viral transcription only in the presence of wt E8^E2C . To further extend our observations , we made use of the fact that an N-terminal fragment of NCoR has been shown to act as a dominant-negative inhibitor of Xenopus NCoR/SMRT activity [38] . This region is highly conserved among Xenopus NCoR , human NCoR and human SMRT . We therefore generated an expression plasmid for residues 1–304 of human NCoR with an N-terminal Flag tag and a nuclear localization signal ( pSG DN-NCoR ) . We first verified nuclear expression of DN-NCoR by immunofluorescence analysis of transfected cells ( Fig 12A ) and then analyzed the effects of DN-NCoR on the transcriptional repression activity of HPV31 E8^E2C . The expression of DN-NCoR prevented repression of the pC18-Sp1-luc reporter by HPV31 E8^E2C in a concentration-dependent manner ( 5-fold increase at the highest concentration of DN-NCoR compared to the empty vector control ) but had only a minor impact on the E8^E2C KWK mt ( 1 . 7-fold ) ( Fig 12B ) . To validate this in the context of HPV16 and 31 genomes , HPV16 wt , HPV16 E8 KWK mt , HPV31 wt or HPV31 E8 KWK mt genomes were cotransfected with either the empty vector or 0 . 5μg of the DN-NCoR expression vector into SCC13 cells . RNA was isolated 48h and analyzed by qPCR for the expression of spliced E1^E4 transcripts as described above . As expected E8 KWK mt genomes displayed 1 . 7- ( HPV16 ) and 2 . 0-fold ( HPV31 ) elevated transcript levels compared to the wt genomes in the presence of the empty expression vector ( Fig 12C ) . Upon co-transfection of DN-NCoR , the transcription of wt genomes was significantly elevated 1 . 6- ( HPV16 ) and 1 . 8-fold ( HPV31 ) , whereas transcription from the HPV16 and 31 E8 KWK mt genomes were unchanged ( Fig 12C ) . These data strongly support the idea that NCoR/SMRT complexes are only required to inhibit viral gene expression in the presence of wt E8^E2C . We then investigated the effect of the expression of DN-NCoR on the replication of the HPV31 wt or HPV31 E8 KWK mt genomes at 72h post transfection . The cotransfection of DN-NCoR significantly increased replication of wt genomes 3 . 3-fold , whereas replication of the E8 KWK mt genome was unchanged ( Fig 13 ) . These data further confirm that recruitment of NCoR/SMRT complexes by E8^E2C limits HPV genome replication .
A small number of alpha-PV types , most notably HPV16 , can cause cervical cancer and other malignancies of the ano-genital tract in persistently infected individuals . In addition , HPV that belong to the genus beta have been implicated in the development of non-melanoma skin cancer [1 , 2] . The replication of all PV genomes tested so far requires the viral E1 and E2 proteins , which form a complex that recognizes with high affinity the viral origin of replication in the URR . Aside from the conserved interaction with E1 , E2 proteins display a highly conserved interaction with the cellular Brd4 protein [10 , 11 , 39 , 40] . This interaction is responsible for the ability of E2 proteins to activate transcription from synthetic promoters [39 , 41] . Nevertheless , the function of the E2-Brd4 interaction in the context of a viral infection is less clear and might involve the regulation of viral transcription , cellular transcription , viral genome replication and/or partitioning of viral genomes in a virus type specific manner [42] . In addition to E1 and E2 , several PV have been shown to express a spliced mRNA that generates a fusion protein between the E8 ORF and the hinge and DBD/dimerization domains of E2 resulting in an E8^E2C protein . HPV16 and 31 E8^E2C knock-out genomes amplify their genomes to higher levels in normal and immortalized human keratinocytes [16 , 21 , 24] , extending a concept first established for BPV1 , that PV encode alternative E2 proteins that act as negative regulators of genome replication in undifferentiated cells [12 , 43 , 44] . Since the truncated BPV1 E2C protein and E8^E2C proteins harbour the DBD/dimerization domain of E2 , their mechanism of action was thought to be binding site competition and formation of inactive heterodimers with E2 [45] . However , studies with HPV16 and 31 E8^E2C revealed an unexpected requirement for the E8 part of E8^E2C in limiting HPV genome replication [24 , 25] . Mutation of the central KWK motif , that is conserved among a large number of alpha-PV [13] , resulted in E8^E2C proteins that lose transcriptional repression activity but are present at much higher amounts than wt proteins [24–26] . E8^E2C KWK mt proteins can partially diminish E1/E2-dependent replication in transfection experiments consistent with these proteins acting as binding site competitors and/or heterodimerization partners of E2 [25] . Nevertheless , HPV16 and 31 E8 KWK mt genomes over replicate to extents that are similar to E8^E2C knock-outs [24 , 25] which strongly suggests that the E8 part is crucial to limit HPV genome replication under physiological conditions . Our data indicate now for the first time that E8^E2C expression is also responsible for limiting the replication of phylogenetically diverse HPV types such as HPV1 ( mu-PV ) and HPV8 ( beta-PV ) in normal human keratinocytes ( Fig 2 ) . This finding may now allow developing tissue culture replication models for beta-PV to investigate the viral life cycle and oncogenic activities of viral proteins . Interestingly , HPV1 and 8 E8^E2C proteins display properties very similar to the HPV16 and 31 proteins: they repress transcription and E1/E2-dependent replication in an E8-dependent manner ( Figs 3 , 4 , and 8–10 ) emphasizing the idea that E8^E2C function is highly conserved among HPV . Previous studies have indicated that the E8 part of HPV31 E8^E2C interacts with the host cell proteins HDAC3 , NCoR and TBLR1 [27 , 28] . However , of these only HDAC3 and NCoR could be functionally validated by siRNA experiments [28] . By analyzing interaction partners for HPV16 and 31 E8^E2C in an unbiased manner , we were able to confirm HDAC3 , NCoR and TBLR1 and found in addition GPS2 , SMRT and TBL1 as common interactors . Interestingly , these proteins together form stable complexes that are known as NCoR/SMRT corepressor complexes . Consistent with these proteins being responsible for the repressive activities of E8^E2C , interactions with NCoR/SMRT complex components were greatly reduced when repression-deficient E8^E2C KWK mt proteins were analyzed in Co-IP assays . Interestingly , also HPV1 and 8 E8^E2C proteins interacted in an E8 domain-dependent manner with HDAC3 , NCoR , SMRT , and TBLR1 in Co-IP experiments . The mutation of a conserved KLK motif in HPV8 E8^E2C resulted in loss of activity and of binding to HDAC3 and TBLR1 . The sequence arrangement of beta-PV and HPV1 E8 , which have a conserved KLK motif followed by a three residue hydrophobic stretch ( MKLKhhh ) , resembles the HR-HPV E8 , in which a stretch of three hydrophobic residues is followed by the KWK motif ( MhhhKWK ) ( Fig 4B ) . The reversed order of basic and hydrophobic residues between alpha- and beta/mu-PV E8 may , however , indicate that the interactions with NCoR/SMRT complexes are not identical . Colocalization studies confirmed that only E8^E2C wt proteins but not E8^E2C KWK or KLK mt proteins recruit HDAC3 , NCoR , SMRT and TBLR1 into distinct nuclear structures that , in the case of HPV16 and 31 , correspond to E1/E2-positive replication foci . The individual knock-down of HDAC3 , GPS2 , NCoR , SMRT or TBLR1 by siRNA showed no significant reduction of repression by HPV31 E8^E2C . In contrast , knock-down of both HDAC3 and TBLR1 or NCoR and SMRT attenuated repression of E1/E2-induced replication by all investigated E8^E2C proteins . The functional difference between individual and combinatorial knock-downs may be due to the fact that NCoR and SMRT are functional homologues and two molecules of NCoR and/or SMRT are present in corepressor complexes [32] . Therefore NCoR , NCoR/SMRT and SMRT complexes are most likely present in many cells types . Thus only the combination of siNCoR/siSMRT would target all three complexes . Furthermore , the combined knock-down of HDAC3 and TBLR1 not only reduced the targeted proteins but also decreased NCoR and SMRT levels ( S5 Fig ) . Both siRNA combinations also specifically increased transcription from HPV31 wt genomes to similar extents whereas no activation of E8 KWK mt genomes was observed ( Fig 11 ) . Repression of the transcription reporter by HPV31 E8^E2C could be attenuated by an NCoR fragment ( DN-NCoR ) , shown to be a dominant-negative inhibitor of NCoR/SMRT repression activity [38] . Consistent with this , co-expression of DN-NCoR activated transcription and replication from HPV16 and 31 wt genomes but not from E8 KWK mt genomes ( Figs 12 and 13 ) . In summary our data provide strong evidence that the repressive activities of HPV E8^E2C proteins are mainly mediated by the interaction with NCoR/SMRT complexes . While NCoR/SMRT complexes have been shown to be involved in transcriptional repression of cellular genes , this is the first evidence that they also can inhibit viral replication origins . When using a non-replicating , synthetic reporter in the absence of E1 and E2 the knock-down of NCoR/SMRT had far more pronounced effects than the HDAC3/TBLR1 knock-down on the repression by E8^E2C . As this was not the case with replicating plasmids or HPV genomes the physiological relevance of this observation remains unclear . HDAC3 is the only known enzyme within the NCoR/SMRT core complexes and it is believed that the deacetylation of histones plays a major role in transcriptional repression by NCoR/SMRT [31] . Histone acetylation is thought to create an open chromatin environment which enables transcription activator recruitment . Furthermore , deacetylation of histones has been proposed to play a positive feed-forward role for repression by NCoR/SMRT as SMRT , NCoR , TBL1 and TBLR1 have been shown to bind preferentially to hypoacetylated histone 4 [46 , 47] . However , HDAC3 inhibitors only had a weak effect on transcriptional repression and showed no effect on the inhibition of E1/E2-dependent replication by HPV31 E8^E2C [27] . Furthermore , the efficient knock-down of HDAC3 alone had no discernible effect on replication repression by E8^E2C ( S4 Fig ) . In summary , these data do not suggest that HDAC3 is critical for the repression activity of E8^E2C . Interestingly , a recent publication showed that deacetylase-dead but not NCoR/SMRT-binding deficient HDAC3 mutants could rescue hepatosteatosis and repress lipogenic genes expression in a HDAC3 knock-out mouse model [34] . These findings indicate that the deacetylase activity of HDAC3 may not always be required for repression by NCoR/SMRT complexes but rather suggest an important role for NCoR and SMRT aside from activating the deacetylase activity of HDAC3 . NCoR/SMRT not only bind directly to HDAC3 , GPS2 , TBL1 and TBLR1 but have also been reported to interact with a large number of additional cellular proteins in a possibly context-dependent manner that contribute to the repression of transcription by NCoR/SMRT [32 , 48 , 49] . However , our proteomic analysis has not identified additional interaction partners common to HPV16 and 31 E8^E2C that are known NCoR/SMRT interactors . Future studies are required to address these questions . Our data indicate that E8^E2C does not act by reducing the protein amounts of nuclear E1 or E2 ( S5 Fig ) . Colocalization studies of HPV16 and 31 replication proteins revealed that the E1 protein is recruited into nuclear foci in the presence of wt E8^E2C ( S1 Fig ) . This indicates that E8^E2C proteins also do not interfere with the localization of E1 proteins to replication foci . In summary , our data indicate a highly conserved interaction between E8^E2C proteins and NCoR/SMRT complexes that is required to limit HPV replication .
The luciferase reporter plasmids pC18-SP1-luc , pGL 16URR-luc and pGL 31URR-luc have been previously described [13 , 21 , 26] . To generate pGL 1URR-luc and pGL 8URR-luc , HPV1 nt . 6939–103 or HPV8 nt . 7332–195 , respectively , were amplified by PCR using the cloned HPV1 or HPV8 genomes as templates and specific primers adding XhoI ( HPV1 ) or MluI ( HPV8 ) restriction sites at the 5´-end and an NcoI site at the 3´-end . Amplicons were cloned into the respective restriction sites of pGL3-basic ( Promega ) . The expression plasmids for HPV1 E1 ( pSG 1 E1 ) , HPV1 E2 ( pSG 1 E2 , HPV 16 E1 ( pSG 16 E1 , HPV16 E2 ( pSG 16 E2 ) , HPV 16 E8^E2C ( pSG 16 E8^E2C ) , HPV 16 E8^E2C KWK mt ( pSG 16 E8^E2C KWK mt ) , HPV 31 E1 ( pSG 31 E1 and pCMV neo 3xFlag-31E1 ) , HPV 31 E2 ( pSX 31 E2 ) , HPV 31 E8^E2C ( pSG 31 E8^E2C and pSG 31 E8^E2C HA ) and HPV 31 E8^E2C KWK mt ( pSG 31 E8^E2C KWK mt and pSG 31 E8^E2C KWK mt HA ) have been previously described [13 , 24 , 26 , 50–54] . To generate pIRESpuro 31E8^E2C-HA , pIRESpuro 31E8^E2C KWK mt-HA , pIRESp 16E8^E2C-HA and pIRESpuro 16E8^E2C KWK mt-HA , the respective coding sequences were PCR-amplified to add NheI and BamHI restriction sites and then cloned into pIRESpuro3 ( Clontech ) . Plasmid pSG 3xflag-16 E1co was generated by replacing an ApaI/EcoRV fragment of pSG16 E1co [20] with an N-terminal 3xFlag epitope E1 fusion fragment ( Life Technologies ) . To generate the expression vectors for HPV1 E8^E2C ( pSG 1 E8^E2C ) and HPV8 E8^E2C ( pSG 8 E8^E2C ) HPV1 nt . 1199-1232/3200-3797 or HPV8 nt . 1312-1343/3303-4200 were amplified using cloned HPV1 or HPV8 genomes as templates and specific primers adding EcoRI and BglII restriction sites . The resulting amplicons were cloned into EcoRI/BglII-digested pSG5 ( Stratagene ) . Expression plasmids for E8^E2C deletion mutants ( pSG 1 E8^E2C K2A d3-10 and pSG 8 E8^E2C K2A d3-10 ) , that lack the sequence for amino acids 3 to 10 and change residue 2 from lysine to alanine , were constructed by PCR . The expression vector for the HPV8 E8^E2C KLK mt was generated by PCR using a primer that changes nucleotides 1315–1323 that code for the KLK motif to AAA . The expression plasmids for the HA-tagged HPV 1 E8^E2C proteins ( pSG 1 E8^E2C HA , pSG 1 E8^E2C K2A d3-10 mt HA ) were generated by overlap-extension PCR resulting in the introduction of the HA-tag between residues 30 and 31 . To generate the expression plasmids for the HA-tagged HPV 8 E8^E2C proteins ( pSG 8 E8^E2C HA , pSG 8 E8^E2C K2A d3-10 mt HA , PSG HPV8 E8^E2C KLK mt-HA ) an oligonucleotide coding for the HA-tag was introduced into the SmaI-site of HPV8 at nt . 3539–3544 . To generate the expression vectors for HPV 8 E1 ( pSG 8 E1 ) and HPV 8 E2 ( pSG 8 E2 ) E1 and E2 coding sequences were amplified by PCR using the cloned HPV8 genomes as template and primers adding EcoRI and BglII restriction sites . The resulting amplicons were cloned between the EcoRI/BglII sites of pSG5 . Plasmid pSG 3xflag 8 E1 was generated by replacing the EcoRI/SacII fragment of pSG 8 E1 with an N-terminal 3xFlag epitope fused to a codon-optimized HPV8 E1 fragment ( Life Technologies ) . The HPV1 wild type genome was cloned as a KpnI fragment into plasmid pBS+ . To generate the respective E8- genomes the E8 ATG ( HPV1 nt . 1200–1202; HPV8 nt . 1312–1314 ) was mutated to ACG by overlap extension PCR . This exchange is silent in the overlapping E1 gene . Plasmid pSG DN-NCoR was constructed by inserting a synthetic gene ( Life Technologies ) consisting of the Flag epitope , a nuclear localization signal and the coding sequence of human NCoR ( residues 1–304 ) into the pSG5 plasmid ( Stratagene ) . All plasmid constructs were confirmed by DNA sequencing . To generate stable 293T cell lines 3 x 105 293T cells were transfected with Fugene HD ( Roche ) in 60-mm dishes with 1 μg of the respective expression vectors . Cells were selected with 0 . 4 μg/ml puromycin for 10–12 days . Cell pools were used for subsequent experiments . HeLa cells were maintained in DMEM ( Life Technologies ) and 10% fetal bovine serum ( FBS ) . RTS3b cells were expanded in E-medium supplemented with 10% FBS [26] . Normal human keratinocytes were maintained in KSFM ( Life Technologies ) . SCC13 cells were maintained in E-medium and 5% FBS in the presence of mitomycinC-treated NIH 3T3 J2 cells as described [21] . Low molecular weight DNA from transfected NHK or SCC13 cells was isolated 7 or 3 d , respectively , post transfection . DNA was digested with DpnI and EcoRV ( HPV1 ) , BglII ( HPV8 ) or XbaI ( HPV31 ) , and separated in 0 . 8% agarose gels . Blotting and hybridization to 32P-labeled HPV1 , HPV8 or HPV31 probes were carried out as previously described [24] . After exposure of the membrane to phosphoimager screens , HPV genomes were visualized and quantitated using the AIDA software package ( Raytest ) . For RNAi experiments cells were transfected with control siRNA ( siAllstar , Qiagen ) or siRNAs against NCoR ( ON-TARGETplus SMARTpool , Dharmacon L-003518-00-0005 ) , SMRT/NCoR2 ( ON-TARGETplus SMARTpool , Dharmacon L-020145-01-0005 ) , HDAC3 ( ON-TARGETplus siRNA , Dharmacon J-003496-09-0010 ) , TBLR1 ( Hs_TBL1XR1_10 , Qiagen SI03025925 ) , TBL1 ( ON-TARGETplus SMARTpool , Dharmacon L-012152-00-0005 ) , GPS2 ( Silencer Select , Thermo Fisher Scientific # 4392420 ) using HiPerfect ( Qiagen ) for HeLa cells and Lipofectamine RNAiMAX ( Invitrogen ) for RTS3b and SCC13 cells according to the manufacturer’s instructions . For siRNA transfections followed by immunoblot analysis 3x105 cells were seeded in 60mm culture dishes and lysed 72h after transfection . For luciferase-based reporter and replication assays approximately 3x104 cells were seeded in 24well dishes the day before transfection . The cells were either transfected directly with DNA with amounts as indicated in the figure legends or with siRNA followed by DNA transfection 24h later . DNA transfections were carried out using Fugene HD ( Roche ) and OptiMEM ( Invitrogen ) . To analyze viral transcription , SCC13 cells were co-transfected with 1 . 3μg of religated HPV genomes and pSG5 or pSG DN-NCoR ( 0 . 5μg ) using Fugene HD or first with the respective siRNAs and the following day with religated HPV genomes . To analyze genome replication , 4μg religated genomes and 1 . 5μg pSG5 or pSG DN-NCoR were used . Luciferase-based reporter assays were carried out 48 h after DNA-transfection as previously described [26] . For reporter-based replication assays , cells were harvested 48h after DNA-transfection . The DNA was purified using a BioRobot EZ1 Workstation and the EZ1 DNA Tissue kit ( Qiagen ) . The amount of replicated DNA was determined after DpnI digestion by quantitative real-time PCR using the primers flanking DpnI-restriction sites in the luciferase gene as described [20] . RNA was isolated 48h post transfection of HPV genomes using the RNeasy minikit ( Qiagen ) . RNA ( 1μg ) was reverse transcribed using the QuantiTect reverse transcription kit ( Qiagen ) . Twenty-five ng of cDNA was analyzed in a LightCycler 480 using 0 . 3 μM gene-specific primers ( PGK1 [24] , HPV16 E1^E4 ( 16 E1E4 880/3358 F 5′-TGGCTGATCCTGCAGCAGC-3′; 16 E4 3440 R 5′-AGGCGACGGCTTTGGTATG-3′ ) or HPV31 E1^E4 ( 31 E1E4 804 F 5′-TGTTAATGGGCTCATTTGGAA-3′; 31 E4 3373 R 5′- GGTTTTGGAATTCGATGTGG-3′ ) and LightCycler 480 SYBR green I Master ( Roche Applied Science ) as previously described [24] . Nuclear extracts from transfected cells , were prepared as previously described [55] . Immunoblots were performed as previously described [13] . The following primary antibodies were used at the indicated dilution: CDC47 ( Thermo Fisher Scientific MS-862-P; 1:1500 ) , c-Myc ( Santa Cruz BT Sc-40 ( 9E11 ) ; 1:500 ) , DYKDDDDK-Tag ( Cell Signaling # 2368; 1:1000 ) , HA-probe ( Covance MMS-101P; 1:1000 ) , HA-Tag ( ( C29F4 ) , Cell Signaling # 3724; 1:1000 ) , HA-Tag ( ( 6E2 ) , Cell Signaling #2367; 1:1000 ) , HDAC3 ( Cell Signaling #2632; 1:500 ) , HSP90 ( Santa Cruz sc-69703; 1:1500 ) , myc-Tag ( ( 71D10 ) Cell Signaling # 2278; 1:1000 ) , NCoR ( Bethyl Laboratories A301-145A; 1:1000 ) , SMRT ( Bethyl Laboratories 301-147A; 1:1000 ) , TBL1 ( Santa Cruz BT SC-137006; 1:500 ) ; TBLR1 ( Santa Cruz BT SC-100908; 1:500 ) . Bound antibodies were detected with anti-rabbit ( polyclonal swine anti-rabbit Immunoglobulin-HRP; Dako , Hamburg , Germany , 1:3000; anti-rabbit IRDye 680RD , Li-Cor 926–68071 , 1:15000; anti-rabbit IRDye 800CW , Li-CoR 926–32211 , 1:15000; anti-rabbit Fluorescent TrueBlot IRDye 800 , Rockland antibodies & assays # 18-3216-32 , 1:10000 ) or anti-mouse antibodies ( polyclonal rabbit anti-mouse immunoglobulin-HRP; Dako , 1:3000; anti-mouse IRDye 680RD , Li-Cor 926–68070 , 1:15000; anti-mouse IRDye 800CW , Li-Cor 926–32210 , 1:15000; anti-mouse Fluorescent TrueBlot DyLight 800 , Rockland antibodies & assays # 18-4517-32 , 1:10000 ) conjugated to horseradish peroxidase or to a fluorescent dye . Super-Signal West Dura reagent ( Perbio Science ) was used as HRP-substrate and chemiluminescent signals were recorded with a FluorSMax Imaging system ( BioRad ) . Fluorescent signals were recorded with a Li-Cor Odyssey Fc ( Li-Cor ) . For immunofluorescence microscopy approximately 2x105 HeLa or RTS3b cells were seeded and transfected on cover slips in 6well culture dishes . The cells were fixed and permeabilized with methanol/acetone for 2 minutes and incubated with primary antibodies ( DYKDDDDK-Tag , Cell Signaling # 2368 , 1:800; DYKDDDDK Tag ( 9A3 ) , Cell Signaling # 8146 , 1:1000; c-myc ( 9E11 ) , Santa Cruz BT Sc-40 , 1:100; HA-probe , Covance MMS-101P , 1:100; HA-Tag ( C29F4 ) , Cell Signaling # 3724 , 1:1000; HA-tag ( 6E2 ) , Cell Signaling #2367 , 1:100; HDAC3 , Cell Signaling #2632 , 1:100; Myc-tag ( 71D10 ) , Cell Signaling # 2278 , 1:200; NCoR , Bethyl Laboratories A301-145A , 1:100; SMRT , Bethyl Laboratories 301-147A , 1:100; TBL1 , Santa Cruz BT SC-137006 , 1:50; TBLR1 , Santa Cruz BT SC-100908 , 1:50 ) diluted in PBS -3% BSA in a humidified chamber for 1h at room temperature . Bound antibodies were detected with anti-rabbit ( anti-rabbit Alexa Fluor 555 , life technologies A-21428 , 1:2000; anti-rabbit Alexa Fluor 488 , life technologies A-21206 , 1:2000 ) or anti-mouse antibodies ( anti-mouse Alexa Fluor 555 , life technologies A-31570 , 1:2000; anti-mouse Alexa Fluor 488 life , technologies A-11029 , 1:2000 ) conjugated to a fluorescent dye . DNA was stained with DAPI . The pictures were obtained with a Zeiss Axiovert M200 microscope and the appropriate filter sets in combination with a Zeiss ApoTome to obtain optical sections with the highest E8^E2C signal while removing the out-of-focus image information . E8^E2C wt or mt positive foci of 4–9 cells were individually marked as regions of interest and the threshold for both channels was determined by a statistical method described by Costes et al . [56] implemented in the Axiovision40 4 . 8 . 2 software module . Co-localization of E8^E2C with the components of the NCoR complex is shown as the Pearson’s correlation coefficient . Three x 108 cells ( NanoLC-MS/MS analysis ) or 1 . 5 x 107 cells ( immunoblot analysis ) were harvested and then lysed in IP-buffer ( 50mM HEPES pH7 . 5 , 150mM NaCl , 10% ( v/v ) glycerol , 5mM EDTA , 1mM DTT , protease and phosphatase inhibitors or 50mM HEPES pH7 . 9 , 150mM NaCl , 0 . 3% ( v/v ) Igepal 630 , 1mM DTT , protease and phosphatase inhibitors ) . Immunoprecipitation was carried out using magnetic anti-HA-beads ( Miltenyi Biotech ) . Beads were washed with IP-buffer using μMACS columns and μMACS Separator ( Miltenyi Biotech ) . Bound proteins were eluted in 4 x SDS gel loading buffer ( Carl Roth ) heated to 95°C and an aliquot analyzed by HA-immunoblotting to monitor the efficiency of the IP . Complete protein eluates of the pull-down experiments were submitted to a short run ( 1cm in length ) on 1D SDS PAGE ( NuPAGE 12% precast Bis/Tris gels , Invitrogen ) for purification . The proteins were visualized by staining using the Novex Colloidal Blue Staining Kit ( Invitrogen ) according to the manufacturer’s instructions and the corresponding gel sectors were excised and subjected to tryptic in-gel digestion as described previously [57] . The resulting peptide mixtures were desalted with C18 Stage Tips [58] before LC/MS measurement . All LC-MS analysis were performed on a nanoLC ( Easy-nLC , Thermo Fisher Scientific , formerly Proxeon Biosystems ) coupled to a LTQ-Orbitrap-XL ( Thermo Fisher Scientific ) . Chromatographic separation of the peptides was performed on a 15-cm fused silica emitter of 75-mm inner diameter ( New Objective ) , in-house packed with reversed-phase ReproSil-Pur C18-AQ 3-mm resin ( Dr . Maisch GmbH ) . The peptide mixtures were injected onto the column in HPLC solvent A ( 0 . 5% acetic acid ) at a flow rate of 500 nL/min and subsequently eluted with a 124-min segmented gradient of 5%–80% HPLC solvent B ( 80% ACN in 0 . 5% acetic acid ) at a flow rate of 200 nL/min . MS data acquisition was conducted in the positive ion mode . The mass spectrometer was operated in the data-dependent mode to automatically switch between MS and MS/MS acquisition . Survey full-scan MS spectra were acquired in the mass range of m/z 300–2000 in the orbitrap mass analyzer at a resolution of 60 , 000 . An accumulation target value of 106 charges was set and the lock mass option was used for internal calibration . The 10 most intense ions were sequentially isolated and fragmented in the linear ion trap using collision-induced dissociation ( CID ) at the ion accumulation target value of 5000 and default CID settings . The ions already selected for MS/MS were dynamically excluded for 90 sec . The resulting peptide fragment ions were recorded in the linear ion trap . MS data were processed using the MaxQuant software suite ( 1 . 0 . 14 . 3 ) [59] . The Mascot search engine v . 2 . 2 ( Matrix Science ) was utilized to search the generated peak lists against a target-decoy database [60] consisting of the IPI human database ( ipi . HUMAN . v3 . 73 ) containing 89652 protein entries plus the sequences of the viral proteins used in the experiment and 262 commonly observed contaminants . In the database search , carbamidomethylation ( Cys ) was set as fixed modification , whereas oxidation ( Met ) and acetylation ( protein N termini ) were set as variable modifications . The mass tolerances for precursor and fragment ions were set to 7 parts per million ( ppm ) and 0 . 5 Dalton , respectively . Identified MS/MS spectra were further processed by MaxQuant for statistical validation and quantitation of peptides , sites , and protein groups [61] . False discovery rates [60] were set to 1% at site , peptide , and protein group level . HPV1 E1 ( VE1_HPV1A; P03111 ) , HPV1 E2 ( VE2_HPV1A; P03118 ) , HPV8 E1 ( VE1_HPV08; P06420 ) , HPV8 E2 ( VE2_HPV08; P06422 ) , HPV16 E1 ( VE1_HPV16; P03114 ) , HPV16 E2 ( VE2_HPV16; P03120 ) , HPV31 E1 ( VE1_HPV31; P17382 ) , HPV31 E2 ( VE2_HPV31; P17383 ) , GPS2 ( GPS2_HUMAN; Q13227 ) , HDAC3 ( HDAC3_HUMAN; O15379 ) , NCoR ( NCOR1_HUMAN; O75376 ) , SMRT ( NCOR2_HUMAN; Q9Y618 ) , TBL1 ( TBL1X_HUMAN; O60907 ) , TBLR1 ( TBL1R_HUMAN; Q9BZK7 ) . | Human papillomaviruses ( HPV ) have been shown to cause ano-genital and oropharyngeal cancers and have been also implicated in non-melanoma skin cancer . HPV have a two-stage replication cycle: in undifferentiated keratinocytes only a low level of genome replication without virus production can be observed whereas in differentiated keratinocytes high-level genome replication and virus production takes place . Previous studies have suggested that some HPV encode an E8^E2C protein that limits genome replication in undifferentiated cells . We now demonstrate that E8^E2C proteins from phylogenetically diverse HPV types interact with NCoR/SMRT corepressor complexes to limit viral transcription and genome replication . While NCoR/SMRT complexes are known to mediate the transcription repression functions of a wide variety of host transcription factors , this is the first evidence that NCoR/SMRT proteins also are involved in the repression of the replication of viral origins . | [
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| 2016 | Interaction of NCOR/SMRT Repressor Complexes with Papillomavirus E8^E2C Proteins Inhibits Viral Replication |
Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems . However , intronic control over gene expression is governed by a multitude of complex , incompletely understood , regulatory mechanisms . Despite this lack of detailed mechanistic understanding , here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs . Using only natural Saccharomyces cerevisiae introns as regulators , we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range . These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished .
Advancements and innovations in synthetic and computational biology have revolutionized our ability to rationally engineer libraries of single synthetic genetic elements ( such as promoters or ribosome binding sites ) and have increased our capacity to finely tune the expression of genes according to specification . Additionally , the rational tailoring of synthetic gene networks is gradually enabling the engineering of more complex genetic behaviors and control over various features of gene expression by altering a cells genetic code [1]–[5] or its extracellular signal concentrations [6] . Nevertheless , establishing reliable rules for applying regulatory genetic elements in the engineering of synthetic gene expression systems is still a major challenge in synthetic biology . One obstacle to reaching this goal is a lack of well-characterized genetic parts that can be readily used to accurately and predictably control gene expression in synthetic genetic contexts [7]–[10] . Gene expression is affected by a myriad of trans acting factors as well as interdependent cis regulatory elements such as promoters , upstream and downstream untranslated regions ( UTR's ) and introns . Since splicing of introns must be performed before translation can begin , it is a key step in controlling gene expression . However , deciphering how splicing regulation is encoded within pre-mRNA transcripts has proven to be a major challenge [11] , [12] . As a result , introns have been largely absent as a genetic “part” that can be integrated into the design of synthetic cellular systems . In this study we broaden the repertoire of genetic elements for bio-engineering by showing how introns can be used to regulate gene expression in a synthetic gene . We have constructed a synthetic gene expression library that tests the effect of most of S . cerevisiae's native introns with a quantitative fluorescent output ( Figure 1A ) , enabling in vivo , dynamic monitoring of intron-mediated regulation of gene expression in a synthetic gene context . Surprisingly , despite the mechanistic complexity of intronic splicing and of the splicing code , analysis of expression data from this novel library shows that a simple statistical model that integrates the few major known regulatory determinants of intron splicing in and around introns ( such as RNA secondary structure , GC content and sequence motifs ) accounts for the vast majority of gene expression variability observed when integrating many different introns into a synthetic gene expression system . The predictability of intron's effects is a major advantage in utilizing such elements for engineering purposes .
To create a synthetic intron reporter library we transformed yeast with a library of DNA transformation cassettes each containing a different native yeast intron . The cassettes were assembled using the Y-operation [13] , [14] by which introns were embedded in a Yellow Fluorescent Protein ( YFP ) fragment and concatenated to a common selection marker in high throughput ( Figure 1A , Materials and methods , and figure S1 ) . In this manner 240 strains were created , termed YiFP strains , where the sole difference between all strains is the native S . cerevisiae intron intervening the YFP gene . Introns were positioned in the YFP so as to both eliminate false positive splicing signals ( Text S1 ) and to mimic the natural location of introns in their endogenous context , which in S . cerevisiae is biased towards the 5′ end of the coding sequence [5] . We then assessed the contribution of introns to the regulation of gene expression by dynamic measurements of YFP expression . The entire library was cultured in 384-well plates together with reference strains harboring an intron-less YFP ( YFP-wt ) , and strains that had no YFP altogether . We monitored culture growth ( O . D600 ) and YFP fluorescence of each strain for 24 hours using a micro-plate reader , in four independent replicates . Our analysis shows that while growth characteristics remained coinciding for almost all intron library strains and controls ( Figure 1B , Text S1 , table S2 and figure S9 and Table S8 for outliers ) , YFP expression spanned over two orders of magnitude ( Figure 1C , figures S2 & S3 and figure S10 and Table S9 for outliers ) . Strains that had a signal-to-noise ratio ( SNR ) below 5 were classified as un-spliced . Importantly , YFP fluorescence levels were validated to consistently reflect YFP mRNA levels using quantitative real time PCR ( qPCR ) ( figure S4; r = 0 . 99; p = 2 . 3e-04 ) . Following normalization , the expression level of each intron strain was compared to that of the intron-less YFP strain to give a measure of relative expression level , that we relate to as “splicing efficiency” ( Figure 1D and table S1 ) . Interestingly , YiFP strains expression data shows that introns almost exclusively reduce reporter gene expression compared to the intron-less YFP reference strain ( Figure 1D , Splicing efficiency <1 ) . This finding highlight the differences between yeast and mammalian cells in which introns boost gene expression [15] , [16] . In addition , we observed that simple intron features such as intron length could not account for the variability in gene expression recorded in our library ( table S6 ) . For example , S . cerevisiae ribosomal protein genes ( RPGs ) introns are substantially longer than introns of non-RPG's , with means of 400 and 100 base pairs ( bp ) respectively; however , RPGs introns were not clustered to higher or lower splicing efficiencies in our library ( Figure 1E ) . Conversely , intron features known to significantly affect intron function such as secondary structure and GC content at intron-exon junctions , as well as certain sequence motifs were found to be dominant intron features that dictate splicing efficiently in a completely synthetic system . To assess how much of the changes in splicing efficiency stem from a wide distribution of splicing capacity in the population vs how much stems from single cell behavior , we performed high-throughput single cell analysis for all 240 library strains using automated microscopy imaging . We explored whether splicing efficiency of single cells from intron strains correlate with our splicing efficiency index ( Materials and methods and figure S5 ) . Results show that average expression from single cells is highly correlated with our splicing efficiency index ( Figure 1F , inset; r = 0 . 94 ) and that noise in YFP expression , i . e . cell-to-cell variability , within strains is highly correlated with the expression levels of single cells from the same strain , as also observed for fluorescently tagged yeast proteins [17] . This characteristic may play a role in setting a lower bound to the degree one can reliably down-regulate gene expression with introns . Nevertheless , we did identify a few introns that confer a lower or higher noise level than expected ( Figure 1F , marked in blue and red , respectively ) . The splicing of specific subsets of pre-mRNAs is modulated in response to various environmental conditions [18] , [19] . Interestingly , our results show that introns embedded within a synthetic gene expression system and exposed to four different conditions known to elicit changes in splicing levels do not respond accordingly , despite the fact that the change in growth condition was indeed being registered by the cells [18] , [19] ( Figure 1G and tables S2 & S3 ) . The loss of condition-specific splicing in synthetic expression systems indicates that introns are not sufficient for encoding splicing specificity . Additionally , in contrast to classes of genetic elements ( such as promoters ) that contain variants that are environmentally responsive , it seems that the entire repertoire of S . cerevisiae introns is insulated from environmental changes and may be used as robust regulators in changing environments . In addition to the canonical splicing signals ( 5′ & 3′ splice sites ( SS ) and branch point ( BP ) ) , which participate in splicing chemistry , splicing regulatory elements ( SREs ) within exons and introns are key factors that determine splicing efficiency and expression levels in higher Eukaryotes [2]–[5] , [20] . Evidence for SRE function in S . cerevisiae has been gradually emerging in recent years [3] . We used our library expression data to identify SREs and found ISEs and ISS motifs ( Figure 2A ) . We analyzed the positional distribution of motifs along the 240 introns of the library and found that the motifs are highly enriched near both splice sites ( Figure 2B , figure S6 and Table S4 ) . In order to test whether indeed the motifs can be used as independent entities to regulate intron dynamics we performed directed mutagenesis to the enhancer motif TTTATGCT in three nucleotides , transforming it into the silencer motif TTTGTGTA in two independent introns in two YiFP strains . Transforming these enhancers to silencers resulted in a reduction of 22% and 13% in their expression levels compared to the enhancer containing introns ( Figure 2C ) . This proof of principle opens possibilities for large scale re-encoding of introns with sequence motifs , demonstrating the mobility and utility of splicing motifs that reside within introns for engineering gene expression in synthetic systems . The cross-talk between introns and their surrounding exonic sequences regulates splicing through the formation of RNA structures that they create . RNA secondary structure and GC content of transcripts have been previously implicated with splicing efficiency and exon/intron definition in several organisms including yeast [21]–[25] . However , it is unclear whether the regulatory function of intron-exon junction structure transfers to synthetic contexts as do other sequence motifs , or whether it is lost completely in synthetic contexts as does the ability of introns to splice according to changes in the environment . To verify this we performed a detailed analysis of the correlation between local pre-mRNA folding and GC content and expression levels in our synthetic library . Specifically , we computed the local pre-mRNA folding energy ( FE ) and GC content profiles of all introns along a sliding window and tested the correlation of these values at each window with the expression levels that we measured . Our analysis demonstrates that introns with unfolded intron-exon junctions tend to exhibit higher expression levels , while introns that induced stronger RNA secondary structures at the intron-exon junction exhibit lower expression levels ( Figure 3A ) . Therefore , we conclude that FE and GC content at intron-exon junctions are significant modulators of synthetic gene expression . In accordance with previous reports on the effect of RNA secondary structure and GC content on splicing in endogenous genes [21]–[25] our findings provide evidence of intron-exon junctions structure-based regulation in several synthetic contexts . This suggests that junction structure is a modular , transferable regulatory feature that may be useful in the design of synthetic genetic circuits . Moreover , our results suggest principles for an informed design of intron/exon junctions to accurately tune synthetic gene expression systems . We inserted introns into additional positions within our synthetic gene expression system , collectively creating a gradient of junction folding strengths . These locations along the gene were selected to create intron-exon junctions with either very strong ( 165 bp from YFP start ) , strong ( original library , 195 bp from start ) , intermediate ( 370 bp from start ) , or weak RNA folds ( 461 bp from start ) . Introns were selected to collectively span the expression range measured in the original reporter library and were inserted in each of the 4 positions ( Figure 3B ) . Gene expression measurements of all strains with introns positioned at the strongest fold were decreased compared to the expression of the same introns in the original position that had a weaker fold . Expression data from these 40 unique strains support the notion that strong artificial junction folding strengths negatively regulate gene expression ( Figure 3C; p = 8 . 3e-03 ) . We did not , however , observe increased splicing at junctions with folding energies even weaker than that of the weak fold ( position 461 ) ( figure S7 ) . Additionally , since the splice sites in the original location of the complete intron library had relatively strong FE and high GC content this can also explain why these intron reporters displayed lower expression levels compared to an intron-less control . Collectively , our results of varying Intron-exon junctions demonstrate the robustness and wide applicability of intron-exon junction secondary structure design as an efficient tool for splicing mediated control of gene expression in synthetic expression systems , since junction fold strengths both regulate splicing efficiency and are fully transferable between different exonic locations . Deciphering the splicing regulatory “code” [11] , [12] , [26] is a major ongoing challenge of modern genetics . Hence , from a bio-engineering perspective it would be important to create a set of simple reliable rules for using introns in synthetic systems with accurate , user specified outcomes on gene expression even before the splicing code is completely understood . For this , dictating features of intron splicing in synthetic contexts must be defined and accurate predictions of their effect must be available . To this end , we incorporated the major determinants of intron splicing in synthetic contexts into a model that lays the basic rules and generates accurate and reliable predictions for tuning synthetic gene expression using introns . We compiled a dataset of intronic features using three independent approaches: first we manually defined simple , intuitive features such as intron length and distances from the branch-point position to both splice sites ( See SI for a complete list ) . Second , we computed various features related to the GC content and local pre-mRNA folding along each intron and intron-exon junctions , as mentioned before ( Figure 3A ) . Finally , we scored each intron for the presence of a sequence motif ( table S5 ) . We tested the contribution of each feature in the dataset to gene expression and the top-scoring features validated that RNA structures at intron-exon junctions ( r = 0 . 44; p = 8 . 21e-06 , table S6 ) as well as several intronic sequence motifs ( table S6 ) were the primary determinants of intron-mediated tuning of synthetic gene expression in this synthetic context . To assess the combined contribution of the various intron features to gene expression levels , we constructed a linear regression function that optimizes combinations of features that accurately account for the empiric expression levels ( Materials and methods and table S7 ) . The regression function was built by iteratively adding single features that yield the highest correlation to expression , considering only features with significantly high correlations . We found that local pre-mRNA folding energy at two specific locations spanning the 5′ splice site ( +3 nt and −12 nt ) as well as several sequence motifs are the principal expression determining features ( Figure 4A ) . Our model yielded correlations of more than 0 . 7 with the expression measurements using a combination of 8 features , and more than 0 . 76 using 13 features ( Figure 4B & 4C; r = 0 . 766; p<2 . 22e-016; empirical p<5e-03; see also Materials and methods and table S7 ) . In contrast , any individual intron feature was only able to explain up to 25% of the observed variation . Despite the detachment of introns from their native context , multiple regulatory mechanisms are still in play “out of context” , emphasizing the significance of analyzing and quantifying multiple intronic features when designing the integration of introns into synthetic expression systems . Notably , our model exhibited similar results when modeling was done for the major subgroups of intron-containing genes ( RPGs and non-RPGs , table S7 ) . To estimate the lower bound of our model's predictive power and account for any potential over-fitting we built new regression functions ( including the re-building of the feature database ) using a training set composed of 80% of the introns and calculated the correlation between the models' prediction and expression measurements of the remaining 20% ( Figure 4D ) . Our results demonstrated our ability to predict and design the effect of introns on expression in a specific location along a synthetic gene . The bioengineering value of the rules we uncovered and the model we devised as both prediction and design tools for synthetic biology depend , to a large extent , on whether they “transfer” reliably to other exonic contexts . To answer this , we tested our model experimentally on 40 strains placed at four different locations throughout the YFP gene ( 10 introns at each location , as previously mentioned ) . We then calculated the correlation between the measured expression and the model predictions using the same set of features for each intron in each location . Surprisingly , despite completely altering the introns exonic context four times , a combination of the eight top intron features maintained 80% of our original model's predictive power ( Figure 4E ) . The ability to maintain predictive power in the face of variable exonic context of introns highlights its gene expression engineering potential , especially in light of the significant and seemingly unpredictable change in gene expression of identical introns in different exonic contexts ( Figures S7 & S8 ) .
Synthetic biology aims to create new , finely tuned gene expression systems . A growing repertoire of genetic elements is continuously facilitating the design and construction of more complex synthetic biological systems . In order to enable engineering-level precision in the synthetic control of genetic circuits we must be able to control gene expression at all its levels of regulation – from transcription through splicing and translation . Here we use a combined experimental and computational approach to uncover and formulate rules for using introns in synthetic expression systems . We show that introns can be used to finely control gene expression in a wide dynamic range of expression levels ( Figure 1D ) , and that this tuning can be predicted and designed using a model that integrates several major intronic regulatory determinants ( Figure 4B ) . Our model for assessing the effect of introns on synthetic gene expression based on transcript sequence and structure remained predictive across several exonic contexts ( Figure 4E ) , suggesting that the rules we uncovered reflect genuine rules for intron-mediated tuning of gene expression in synthetic gene expression systems . Our finding that introns lose their environmental responsiveness when placed “out of context” can be utilized in the design of genetic systems tailored to be robust to changes in environmental conditions , in contrast to other genetic elements controlling transcription and translation , which are highly responsive to environmental conditions . The inability to accurately predict the effect of creating new combinations of genetic elements hinders synthetic biology's ability to streamline the design of novel genetic systems . Our findings and model enables the reliable and robust integration of natural introns , fundamental regulators of gene expression , into synthetic gene expression systems and should be useful for the accurate design and fine tuning of synthetic gene expression systems in general . Finally , our ability to predict the effect of introns through identification of the functional regulatory elements they encode opens the possibility to design synthetic introns with tailored splicing functions in synthetic gene expression systems .
A master strain containing a promoter-less YFP coding sequence ( CDS ) as well as a Cherry fluorescent protein driven by an independent TEF2 promoter , both inserted at the his3Δ1 locus was used . The master strain was transformed with a library of cassettes , each containing a URA3 selection marker under its own promoter and the YFP splicing reporter with a unique intron . 240 YiFP strains were arrayed on SD-URA+NAT agar plates in 384 colony format using a robotic colony arrayer ( RoToR , Singer instruments ) along with 10 replicates each of various control wells ( Text S1 ) . The aforementioned colony arrayer was used to inoculate the library into SD-URA in 384 well microplates ( Greiner bio-one , 781162 ) . Following over-night incubation , strains were diluted and cultured in the desired media to a starting O . D600 of ∼0 . 1–0 . 2 using a robotic liquid handler ( Perkin Elmer ) . A microplate reader ( Tecan Infinite M200 monochromator ) was used to measure growth ( Absorbance at 600 nm ) , mCherry ( E . x . 570 E . m . 630 ) and YFP expression ( E . x . 500 E . m . 540 ) . Single cell fluorescence measurements were performed using an automated microscope system as described in Cohen and Schuldiner , Methods Mol . Biol . 781 , 127–59 ( 2011 ) . Briefly , strains were cultured over-night and diluted in the same manner as in the microplate reader measurements . Following an incubation of four hours in 30°c in a shaking incubator ( LiCONiC Instruments ) , cells were then transferred onto glass bottom 384-well microscope plates ( Matrical Bioscience ) coated with Concanavalin A ( Sigma-Aldrich ) . The microscope plates were conveyed to an automated inverted fluorescent microscopic ScanR system ( Olympus ) , equipped with a cooled CCD camera . Images were acquired using a 60× air lens using YFP ( E . x . 490/20 nm , E . m 535/50 nm ) , mCherry ( E . x . 572/35 nm , E . m 632/60 nm ) , and bright-field channels . After acquisition images were analyzed using the ScanR Analysis software ( Olympus ) , and single cells were recognized based on the mCherry channel . Measures of cell size , shape and fluorescent signals were extracted . The top and bottom scoring single cells in terms of cell size and shape within each strain were gated out of further analysis to ensure homogenous and correct cell recognition , yielding a mean of 435±164 cells analyzed per strain ( minimum of 69 cells ) . mRNA level measurements were performed using quantitative real-time PCR ( qPCR ) . Strains were grown to mid-log and RNA purification was performed using the MasterPure yeast RNA purification kit ( Epicentre ) . cDNA was generated using the SuperScript III First Strand Synthesis kit ( Invitrogen ) . qPCRs were performed in a StepOnePlus Real-Time PCR system ( Applied Biosystems ) using Fast SYBR Green Master Mix , with ACT1 gene as reference . Relative expression results ( RQ ) were calculated using the StepOne software ( figure S4 ) . Intron transformation cassettes were ligated into the pGEM-T Easy vector ( Promega ) . Mutated transformation cassette was transformed into the master strain as previously described and positive clones were verified by PCR and sequencing . YFP and O . D . information were filtered using Butterworth IIR Low Pass Filter ( LPF ) with normalized cutoff frequency of 0 . 15 . Medium ( O . D . ) and background ( no YFP ) noise were subtracted , and YiFP or O . D . values were ignored if close to zero or negative ( replaced with NaN ) . The normalized unbiased expression level was calculated using the following equation:where i is the strain number , t is the time , YFP ( i , t , Cherry ) is the closet strain on plate without YFP and OD ( Blank ) is the O . D . level of a control well with medium only . YFP-wt strains expression calculations were done in the same manner . The time Interval threshold was set to be 6 hours , after which an Intron cannot be considered as spliced . In addition , introns with more than half NaN values were considered to be Not Spliced . The rest of the introns were examined based on self-crossing Signal-to-Noise Ratio ( SNR ) according to the following equation:where YFP not-filtered ( I , t ) and YFP filtered ( I , t ) are raw and filtered YFP data respectively and std is a standard deviation . Introns were termed Spliced for SNR_ratio higher than 5 in the time interval of the first 6 hours . The experiments were done in duplicates . The expression levels of n repeats were incorporated in the following manner: The average expression level was calculated for each duplicate . The joint expression matrix was obtained according to the following equation:where k is the strain number and n = 4 is the number of duplicates . The maximal expression level merging was done in the same manner . Introns that were considered to be spliced in the majority of the duplicates ( 3 or more when n = 4 ) , were considered to be spliced in the incorporated database . Splicing efficiency and maximal splicing efficiency were calculated using the following equations respectively:where i is the strain number . RNA secondary structure and folding energy predictions were done using rnafold ( Vienna ) function [28] . 2D distance calculations were done using RNA secondary structure predictions and the Dijkstra minimum path algorithm [29] . De-Novo Motifs & enriched sequences were identified using the HOMER ( Hyper-geometric Optimization of Motif Enrichment ) tool [30] . Only significant motifs were later used as expression predictors . Motifs distribution analysis was performed by generating a set of random motifs using internal motif permutation tests that preserve original motif properties . The location and significance level of the random motifs were calculated ( Table S4 ) . Calculation of distance between motifs was done by comparing their probability matrices using the following formulation:where freq1 and freq2 are the matrices for motif1 and motif2 , respectively . Empirically significant motifs with similarity score higher than 0 . 6 were merged . Prediction features were put into a linear regressor to assemble an expression predictor and a feature assembly list was calculated . Accumulation of features was done using greedy algorithm . In each feature assembly iteration k , spearman correlation was calculated . The adjusted correlation , which considers the number of features , value was calculated according to the following formula:where n is the number of measurement features , and R is the Spearman correlation in the k-th iteration . The robustness of the predicator results was validated using several statistical methods including permutation tests and cross validation analysis . See Text S1 for additional methods information . | Synthetic biology is gradually expanding our capability to engineer biology through rational genetic engineering of synthetic gene expression systems . These developments are already paving the way for the accelerated study of biology and applying engineered biological systems to major environmental and health problems . However , our capacity to intelligently modify and control gene expression depends on our ability to apply a broad range of genetic regulators in the engineering process . Here we show that Introns , pivotal regulators of Eukaryotic gene expression , can be rationally engineered to control a synthetic gene expression system of a Eukaryote . We developed a unique reporter-based system to evaluate the effects of engineering splicing in synthetic biology and show that the entire intron repertoire of S . cerevisiae can be accurately used to rationally engineer gene expression . Our results provide both a proof-of-concept for the integration of splicing into synthetic biology designs and a model that can be used by the scientific community for integrating splicing into their own designs . Following the extensive use of transcriptional ( promoter ) and translational ( UTR ) elements in synthetic constructs , our results introduce a new major regulatory system , splicing , that can be used to rationally engineer genetic systems . | [
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| 2014 | Accurate, Model-Based Tuning of Synthetic Gene Expression Using Introns in S. cerevisiae |
The ecdysis behavioral sequence in insects is a classic fixed action pattern ( FAP ) initiated by hormonal signaling . Ecdysis triggering hormones ( ETHs ) release the FAP through direct actions on the CNS . Here we present evidence implicating two groups of central ETH receptor ( ETHR ) neurons in scheduling the first two steps of the FAP: kinin ( aka drosokinin , leucokinin ) neurons regulate pre-ecdysis behavior and CAMB neurons ( CCAP , AstCC , MIP , and Bursicon ) initiate the switch to ecdysis behavior . Ablation of kinin neurons or altering levels of ETH receptor ( ETHR ) expression in these neurons modifies timing and intensity of pre-ecdysis behavior . Cell ablation or ETHR knockdown in CAMB neurons delays the switch to ecdysis , whereas overexpression of ETHR or expression of pertussis toxin in these neurons accelerates timing of the switch . Calcium dynamics in kinin neurons are temporally aligned with pre-ecdysis behavior , whereas activity of CAMB neurons coincides with the switch from pre-ecdysis to ecdysis behavior . Activation of CCAP or CAMB neurons through temperature-sensitive TRPM8 gating is sufficient to trigger ecdysis behavior . Our findings demonstrate that kinin and CAMB neurons are direct targets of ETH and play critical roles in scheduling successive behavioral steps in the ecdysis FAP . Moreover , temporal organization of the FAP is likely a function of ETH receptor density in target neurons .
Innate behaviors are stereotypic patterns of movement inherited from birth that require no prior experience for proper execution . Among such behaviors are fixed action patterns that , once initiated , run to completion independent of sensory inputs . Examples include courtship rituals , aggression displays , and ecdysis [1] . Ecdysis represents a “chemically-coded” behavioral sequence triggered by peptidergic ecdysis triggering hormones ( ETH ) , which orchestrate a downstream peptidergic cascade leading to sequential activation of central pattern generators underlying patterned motor activity [2 , 3] . The term FAP has fallen into disuse , since innate behaviors generally exhibit considerable plasticity . However the invariant nature of the ecdysis behavioral sequence makes it a clear example a classic FAP . In depth analysis of ecdysis behavior may provide a more thorough understanding of how hormones assemble and regulate behavioral circuitry in the brain , in particular circuits that operate sequentially . ETHs are released by peripheral peptidergic Inka cells in response to declining levels of the steroid hormone 20-hydroxyecdysone . Presence of Inka cells in more than 40 species of arthropods , along with the sequence similarity of ETH peptides in diverse insect groups , suggests that ETH signaling is highly conserved in insects [4] . Identification of the Ecdysis Triggering Hormone receptor ( ETHR ) gene in Drosophila melanogaster enabled elucidation of a complex downstream signaling cascade triggered by ETH [5] . The ETHR gene encodes two functionally distinct subtypes of G protein coupled receptors ( ETHR-A and -B ) through alternative splicing . The presence of two ETH receptor subtypes has been observed in all insect species thus far examined [6] . The two receptor subtypes show differences in ligand sensitivity and specificity and are expressed in separate populations of central neurons , suggesting that they have distinctive roles in ETH signaling . A diversity of ETHR neurons in the moth Manduca sexta and fruitfly Drosophila melanogaster has been identified [2 , 3] . One of the most striking properties of ETHR-A neurons is that they are virtually all peptidergic and conserved across insect orders . Groups of ETHR-A “peptidergic ensembles” express a range of different neuropeptides , including kinins , diuretic hormone ( DH ) , eclosion hormone ( EH ) , FMRFamide , crustacean cardioactive peptide ( CCAP ) , myoinhibitory peptides ( MIPs ) , bursicon ( burs and pburs ) , neuropeptide F ( NPF ) , and short neuropeptide F ( sNPF ) . We hypothesize that released ETH acts directly on the CNS to activate these peptidergic ensembles for control of specific central pattern generator circuits that elicit stereotyped ecdysis behaviors . However evidence for direct actions of ETH on these target ensembles is yet to be reported . Likely functions of certain ETHR-A peptidergic ensembles in Manduca have been inferred from pharmacological manipulations [3] . For example , serially homologous L3/4 neurons of abdominal ganglia in Manduca express a cocktail of kinins and diuretic hormones; exposure of the isolated CNS to these peptides elicits a fictive pre-ecdysis I-like motor pattern . Similarly , the IN704 peptidergic ensemble that co-expresses CCAP and MIPs is implicated in initiation of ecdysis behavior , since co-application of these two peptides elicits fictive ecdysis behavior . Homologous peptidergic ensembles in Drosophila exhibit characteristic patterns and time courses of calcium mobilization indicative of electrical activity coincident with successive steps in the ecdysis FAP [2] . Of particular interest are observations that bursicon , a hormone co-expressed in a subset of CCAP neurons , is required for ecdysis behavior [7] . Here we test hypotheses that two central peptidergic ensembles—kinin neurons and a subset of CCAP neurons ( CAMB ) that co-express CCAP , Allatostatin CC , Myoinhibitory peptide , and Bursicon—are direct targets of ETH and schedule pre-ecdysis and ecdysis behavior components of the ecdysis FAP , respectively in the fruit fly Drosophila . We show further that manipulation of ETHR expression levels and signal transduction specifically in these ensembles influences scheduling of the FAP . Finally , we examine possible mechanisms underlying timing of the switch from pre-ecdysis to ecdysis behavior and propose a model to explain mechanistically how these behaviors are sequentially activated .
Kinin cell ablation produced significant changes in scheduling of pre-ecdysis behavior . Of the individuals tested , ~25% ( 8 of 31 ) skipped pre-ecdysis entirely and initiated ecdysis behavior ( Fig 1A ) . Remaining animals performed pre-ecdysis , but its duration was highly variable , ranging from 2 . 8 min to 21 . 9 min . In contrast , control flies showed consistent pre-ecdysis duration of close to 10 min ( 9 . 1 ± 0 . 9 min ) . Kinin neuron ablation also weakened pre-ecdysis behavior: contraction frequency was reduced by 78% ( ~ 0 . 8 ± 0 . 6 contractions/min ) compared to controls ( ~ 3 . 6 ± 0 . 2 contractions/min ) . Animals lacking kinin neurons also exhibited delays in timing of head eversion following the switch to ecdysis ( 3 . 2 ± 1 . 6 min for kinin neuron-ablated animals vs . 1 . 2 ± 0 . 3 for controls ) . We next examined a kinin receptor mutant fly line ( Lkrf02594 ) carrying a piggyBac insertion in exon 1 of the kinin receptor gene ( Fig 1A ) . In homozygous kinin receptor mutant flies , pre-ecdysis duration is highly variable ( SD: ± 4 . 8 min . ) compared to control flies ( kinin-Gal4/+ , SD: ± 0 . 9 min; Lkrf02594/+ , SD: ± 1 . 4 min ) and weaker ( 1 . 4 ± 0 . 5 contractions/min ) compared to controls ( 3 . 5 ± 0 . 2 contractions/min ) . Variability was increased further by crossing Lkrf02594 with the kinin receptor gene deficiency line Df ( 3L ) Exel6105 ( SD: ± 6 . 7 min . ) . The high variability phenotype was rescued following precise excision of the piggyBac insertion using piggyBac transposase ( Lkrf02594 Rescued ) . Compared to kinin neuron ablation phenotypes , Lkrf02594 phenotypes are qualitatively similar , but less robust . This is likely due to the fact that the Lkrf02594 mutant is a hypomorph . Quantitative PCR measurements of receptor transcript number showed a reduction of ~30% in expression of the receptor ( Fig 1B ) , which is sufficient to affect pre-ecdysis behavior significantly . To more clearly define the cellular basis of kinin signaling important in pre-ecdysis regulation , we examined consequences of kinin receptor silencing using Gal4 drivers directed to pan-neuronal expression ( Elav-Gal4 ) , specifically to peripheral neurons ( Peb-Gal4 ) , motoneurons ( D42-Gal4 ) , or muscle ( 24B-Gal4 ) . We found that pan-neuronal silencing of kininR led to increased variability of pre-ecdysis duration and reduction of contraction frequency to 2 . 8 ± 0 . 7 contractions/min ( S2A Fig ) . Silencing the receptor in peripheral neurons using the Peb-Gal4 driver [9 , 10] produced a marked increase in variability of pre-ecdysis duration ( SD: ± 10 . 7 min ) and a 54% decrease in contraction frequency to 1 . 7 ± 0 . 4 contractions/min , similar to observations described after kinin cell killing and Lkrf02594 ( S2A Fig ) . Labeling of cells by the Peb-Gal4 driver was confined to peripheral neurons in the pre-pupal stage ( S2B Fig ) . No significant phenotypes were observed after silencing kininR using motoneuron ( D42-Gal4; [11] ) or muscle drivers ( 24B-Gal4; [12] ) . Both kinin cell killing and kinin receptor hypomorph flies resulted in significant mortality during larval stages , likely due to tracheal airfilling defects to be described elsewhere . For our experiments on pre-ecdysis regulation , we chose animals that exhibited normal , airfilled trachea during the 3rd instar . Peb-Gal4>UAS-kininR-RNAi flies showed no larval mortality or evidence of defective tracheal airfilling . The CCAP ensemble is large and diverse , comprised of neuronal subsets distinguished by the distinctive cocktail of peptides they express . As reported previously [2] , major behavioral changes occur upon ablation of the entire CCAP ensemble: Pre-ecdysis behavior is prolonged and both ecdysis and postecdysis behaviors are abolished ( S1 Fig ) . We proceeded to examine the functional consequences of ablating progressively smaller subsets of the CCAP ensemble using Gal4 drivers for MIP , burs , and pburs . All manipulations resulted in the same behavioral outcome as that obtained by removal of the entire CCAP ensemble ( Fig 1 and S1 Fig ) . Therefore , the minimal circuit required for the switch to ecdysis is composed of CAMB neurons labeled by the pburs-Gal4 driver ( Fig 1C and 1D ) . These animals show normal pre-ecdysis contraction frequency during the first 3 min of behavior ( 3 . 4 ± 0 . 3/min ) , but the frequency subsequently decreases gradually thereafter to 1 . 0 ± 0 . 3/min , whereupon it dissipates into weak body contractions that are not recognizable as ecdysis or post-ecdysis behaviors ( Fig 1D ) . The CAMB ensemble consists of bilaterally paired neurons in each of the abdominal neuromeres 1–4 . These neurons express a cocktail of peptides , including CCAP , AstCC , MIP and Bursicon , a heterodimeric protein composed of burs ( burs-α ) and pburs ( burs-β ) subunits [13] . Additional experiments tested consequences of electrically silencing CCAP neurons through expression of the inward rectifier potassium channel ( Kir2 . 1 ) ( S3 Fig ) . When Gal4 drivers for CCAP , AstCC , MIP , Burs , or Pburs were used for Kir2 . 1 expression , the switch to ecdysis failed to occur . In all of these manipulations , silencing of the CAMB ensemble is a common feature . Selective inactivation of CCAP-Gal4 through expression of Gal80 in neurons expressing AstCC , MIP , Burs , or Pburs restores normal timing of the switch to ecdysis ( see S4 Fig for maps of the ETHR ensembles silenced as a function of Gal4/Gal80 expression ) . All of these manipulations spare Gal4 expression in CAMB neurons , thus confirming them as the minimal ensemble necessary for the behavioral switch from pre-ecdysis to ecdysis behavior . Of particular note was the result obtained by electrical silencing of inhibitory MIP neurons lying outside the CCAP ensemble , which significantly accelerates the switch to ecdysis ( MIP; CCAP80>Kir2 . 1; S3B Fig ) . However , we also observed that the MIP-Gal4 , CCAP-Gal80 drivers resulted in some EGFP expression in CAMB neurons , suggesting inclusion of CCAP-Gal80 does not suppress Gal4 activity in CAMB neurons completely ( see arrows in S4B Fig ) , whereas inclusion of Burs-Gal80 or Pburs-Gal80 does so ( arrows in S4C and S4D Fig ) . Nevertheless , acceleration of the switch to ecdysis using the MIP;CCAP80>Kir2 . 1 suggests that inhibitory input ( s ) to the CAMB ensemble provided by MIP neurons outside of the CCAP ensemble could account for at least part of the delay in the switch to ecdysis behavior . MIP neurons lying outside the CCAP ensemble include neurons descending from the brain . In summary , these data further implicate the CAMB ensemble located primarily in AN1-4 as the minimal peptidergic circuit responsible for the switch from pre-ecdysis to ecdysis behavior . We have implicated kinin neurons in regulation of pre-ecdysis behavior and CAMB neurons in the switch to ecdysis behavior . To determine whether they become active at times corresponding to these sequential behaviors , we recorded timing of calcium mobilization in flies that express the calcium reporter GCaMP-3 in both kinin and CAMB neurons . GCaMP expression was confirmed by immunohistochemical staining ( Fig 2A ) ; besides staining in cell bodies , axonal projections of kinin neurons into the terminal plexus ( TP[11] ) of abdominal neuromere 9 ( AN9 ) were observed ( Fig 2A , arrow ) . Since the peptides ETH1 and ETH2 are released from Inka cells under natural conditions prior to onset of these behaviors [2 , 14] , we used a cocktail of ETH1 and ETH2 ( each at 300 nM or 600 nM ) in all experiments on the isolated CNS . The CNS prepared from pharate pupal flies 3–5 hr prior to ecdysis onset showed low-to-moderate levels of GCaMP fluorescence in cell bodies and TP ( Fig 2B2a ) . Following exposure to ETH , cell bodies of kinin neurons and their TP projections showed robust oscillatory fluorescence activity patterns , indicating fluctuations in cytoplasmic [Ca2+]i levels ( Fig 2B1 and 2C and S1 Movie ) . Notably , kinin and CAMB neurons mobilized calcium in sequential , non-overlapping fashion . When exposed to a cocktail of ETH1 and ETH2 ( 300 nM each to make a total of 600 nM ) , kinin neurons mobilize calcium within an average of 8 . 4 ± 1 . 4 min following ETH exposure and remain active for 9 . 1 ± 3 . 6 ( n = 19 ) min . The duration of kinin neuron activity under these conditions corresponds well with that of pre-ecdysis behavior in vivo ( Fig 1 ) . In contrast , calcium mobilization in CAMB neurons was delayed , starting only after termination of kinin neuron activity . These activity patterns are consistent with activation of pre-ecdysis behavior by kinin neurons and ecdysis behavior by CAMB neurons . In all preparations examined , pairs of kinin neurons in each of the AN1-7 neuromeres responded to ETH with synchronous , spike-like calcium oscillations . Calcium dynamics in all kinin neurons appear to be synchronized , suggesting they may be gap junctionally coupled . While transient increases and decreases of calcium levels in cell bodies and TP were synchronous , intensities in different neurons varied . Of particular note is the consistent observation that transient increases in [Ca2+]i occur first in the TP several seconds before increases in cell bodies occurred , suggesting that action potential activity originates in nerve terminals of kinin neurons . Synchronized spike-like activities in kinin neurons terminated simultaneously ( Fig 2C ) . Activation of CAMB neurons followed cessation of kinin activity within ~3 minutes . Onset of calcium dynamics in CAMB neurons was generally synchronized ( 20 . 0 ± 2 . 2 min ) , but synchrony was not as strong as was observed in kinin neurons . ETH-induced calcium dynamics in CAMB neurons remained high for more than 1 hr in the isolated CNS preparation . In our experimental setup , ΔF/F values of kinin neurons reached up to 20 . 5 ± 7 . 3% and values of CAMB neurons up to 28 . 9 ± 8 . 2% in response to 300 nM ETH over a base line noise level of less than 3% . Following exposure to a higher concentration of the ETH1 and ETH2 cocktail ( 600 nM each ) , activation patterns of both kinin and CAMB ensembles were accelerated ( Fig 2B1 and 2C ) . Kinin neurons became active within 6 . 0 ± 1 . 4 min ( duration: 5 . 0 ± 2 . 4 min ) and CAMB neurons initiated activity within 12 . 0 ± 3 . 3 min . Increasing ETH concentration from 300 nM to 600 nM decreased the latency to calcium mobilization from 8 . 4 min to 6 . 0 min . Compared to kinin neurons , calcium dynamics in CAMB neurons showed a larger range of intensity . Even though higher ETH concentrations accelerated onset of activity in CAMB neurons , sequential activity in kinin and CAMB neurons was maintained . Exposure to 600 nM ETH elicited the same pattern of synchronized spike-like activity in kinin neurons as was observed at the lower ETH concentration ( 300 nM ) , but intervals between the peaks were reduced . Even though neurons in the brain and SOG also express kinin [13 , 14] , these neurons did not respond to ETH . CAMB neurons also showed a similar activity pattern and duration as that observed following exposure to 300 nM ETH . Although some CAMB neurons showed activity before the end of kinin activity ( n = 5 out of 48 ) , most became active only after termination of kinin neuron activity . In our experimental setup , ΔF/F values of kinin neurons reached up to 23 . 6 ± 7 . 3% and values of CAMB neurons up to 24 . 2 ± 7 . 5% in response to 600 nM ETH over a base line noise level of less than 3% . We have shown that kinin neurons are early responders to ETH and are necessary for normal pre-ecdysis behavior . We next tested hypotheses that: 1 ) they are direct targets of ETH and 2 ) sensitivity to ETH affects the timing of pre-ecdysis behavior . This was accomplished by modifying ETHR expression levels in vivo through RNAi knockdown or overexpression using the Gal4>UAS system . We reasoned that ETHR knockdown in kinin neurons would decrease receptor density , thus reducing sensitivity to rising ETH levels . If kinin neurons indeed are direct targets of ETH , this manipulation should cause a delay in initiation of the behavior and reduce its duration ( Fig 3A ) . We employed two independent RNAi constructs , one an inverted repeat ( UAS-ETHR-IR2 ) , the other a symmetric UAS flanking a part of the ETHR coding sequence ( UAS-ETHR-Sym ) . Both RNAi constructs decreased pre-ecdysis duration , indicating that kinin neurons are direct targets of ETH . We next conducted the converse experiment , increasing ETHR expression using a UAS-ETHR construct; this manipulation should make kinin neurons more sensitive to ETH and cause pre-ecdysis initiation to occur sooner . As expected , overexpression of ETHR in kinin neurons using two independent UAS-ETHR lines increases pre-ecdysis duration: kinin-Gal4 ( 3 ) crossed with either UAS-ETHR-A ( 1 ) or UAS-ETHR-A ( 3 ) led to pre-ecdysis durations of 12 . 4 ± 0 . 3 min . and 10 . 8 ± 0 . 3 min . , respectively ( Fig 3B ) . Pre-ecdysis duration increases further when the copy number of kinin-Gal4 is doubled by combining expression on chromosomes 2 and 3 [kinin ( 2 , 3 ) >ETHR-A ( 3 ) ] ( 13 . 1 ± 0 . 3 min S . E . M . ) . Since our data support a functional role for kinin signaling in pre-ecdysis scheduling , we investigated the possibility that activity in kinin neurons is sufficient to initiate pre-ecdysis behavior through heterologous expression of TRPM8 and TRPA1 channels . However in both instances , temperature-dependent activation of these channels in kinin neurons did not result in initiation of pre-ecdysis . Presence of ETHR in CCAP neurons suggests they are direct targets of ETH . We tested this by employing RNAi knockdown of ETHR in progressively smaller subsets of the CCAP ensemble . We predicted that ETHR knockdown should lower sensitivity of these neurons to ETH , delaying the switch to ecdysis . On the other hand , overexpression of ETHR should increase sensitivity to ETH and hasten the switch to ecdysis . For behavioral analysis , we recorded timing of the switch to ecdysis behavior relative to pre-ecdysis initiation . We tested the effect of ETHR silencing in CCAP neurons and subsets thereof with three independent RNAi constructs , including two inverted repeats ( UAS-ETHR-IR1 and -IR2 ) targeting different regions of ETHR and a symmetric UAS flanking a part of the ETHR CDS ( SymUAS-ETHR ) . All RNAi treatments caused significant delays in the switch to ecdysis when driven by Pburs-Gal4 ( Fig 3A ) , CCAP-Gal4 , MIP-Gal4 , or Burs-Gal4 ( S5A Fig ) . The UAS-ETHR-IR2 construct proved to be most effective , delaying the switch to ecdysis by greater than 60 min when driven by Pburs-Gal4 . The converse experiment involved overexpression of ETHR-A in the entire CCAP ensemble . The switch to ecdysis is accelerated in flies overexpressing ETHR in CAMB neurons ( 6 . 3 ± 0 . 3 min . and 6 . 7 ±1 . 9 min; Fig 3B ) compared to control flies ( 9 . 1 ± 0 . 9 min ) . Significant acceleration of the switch to ecdysis also is observed using specific drivers of ETHR expression using CCAP-Gal4 , MIP-Gal4 ( 6 . 8 ± 2 . 7 min ) , and Burs-Gal4 ( 5 . 5 ± 1 . 2 min ) drivers , all of which express in the CAMB ensemble ( S5B Fig ) . These findings allow us to conclude that all components of the CCAP ensemble , including CAMB neurons , are direct targets of ETH and that timing of the ecdysis switch is strongly influenced by altered levels of ETHR expression . We have shown that CAMB neurons are direct targets of ETH and are likely to be important in the switch from pre-ecdysis to ecdysis behavior . To further investigate the role ( s ) of CAMB neurons in ETH-induced cellular activity , we focused on how manipulations of ETHR expression levels in these neurons influence timing of calcium mobilization . Our previous work correlated ETH-induced calcium dynamics in central peptidergic ensembles in vitro with behavioral scheduling of the ecdysis FAP in vivo [2] . In this study , we used similar techniques to record ETH-induced calcium dynamics in CAMB neurons expressing different levels of ETHR . Mean latency to onset of calcium dynamics in CAMB neurons of control preparations treated with ETH ( 300 nM ) is 16 . 6 ± 10 . 3 min . In preparations from flies in which ETHR expression in CAMB neurons is reduced through expression of ETHR-RNAi , onset of calcium dynamics is eliminated ( Fig 4A ) . In contrast , onset of calcium dynamics in CAMB neurons overexpressing ETHR is greatly accelerated: responses were registered within 5 . 0 ± 3 . 6 min . Thus , timing of calcium mobilization in CAMB neurons exposed to ETH depends on the level of ETHR expression in register with behavioral outcomes described in the previous section . We observed a significant rate ( ~19% ) of spontaneous calcium mobilization in CAMB neurons upon isolation and placement of the CNS in the recording chamber , a rate that increased ( ~40% ) in flies overexpressing ETHR in CAMB neurons ( Fig 4B ) . Spontaneous calcium mobilization was entirely absent in ETHR knockdown flies . Preparations that exhibited spontaneous calcium dynamics prior to ETH exposure were discarded . Our previous studies in Manduca showed that a balance between excitation and inhibition in segmental ganglia is important in the delay of ecdysis behavior initiation following ETH release[15] . We have shown here that ETH-induced calcium dynamics in CCAP and CAMB ensembles and ecdysis behavior also show a characteristic delay . We tested the possibility that inhibitory Go signaling in these neurons contributes to this delay through expression of pertussus toxin ( PTX ) , a known inhibitor of Gαo[16 , 17] . Indeed , expression of PTX in the entire CCAP ensemble ( CCAP>PTX ) accelerates the switch to ecdysis; presumably , disinhibition through block of Go signaling shifts the balance in favor of excitation ( Fig 5A ) . The ecdysis switch is similarly accelerated when PTX expression is confined to CAMB neurons ( Pburs>PTX ) . Next , we asked whether overexpression of Gαo , delays the switch to ecdysis , likely by favoring inhibition over excitation in CAMB neurons . This in fact does occur ( Fig 5B ) . Significantly , manipulation of Gαs or Gαi signaling pathways has no effect on scheduling of the ecdysis switch ( S6 Fig ) . These findings provide solid evidence that Gαo signaling functions in determining timing of the ecdysis switch . Overexpression of wild type Gαq in CCAP neurons ( CCAP>Gαq ) greatly accelerates timing of the switch to ecdysis ( Fig 5C ) , supporting a role for Gαq-mediated excitatory drive most likely resulting from ETHR activation . Overexpression of Gαq in kinin neurons [Kinin ( 2 , 3 ) >Gαq] increased the time to ecdysis onset , suggesting that this manipulation caused pre-ecdysis initiation to occur earlier than in control animals , thereby extending its duration . These data provide compelling evidence that both excitatory ( Gαq ) and inhibitory ( Gαo ) inputs to CAMB neurons play functional roles in timing of the switch to ecdysis . CCAP neurons are implicated as regulators of ecdysis behavior in both moths and flies . We therefore tested whether they are sufficient for ecdysis initiation by activating them selectively using the temperature sensitive TRPM8 channel[18] . TRPM8 is a non-selective cation channel gated by a 6-degree temperature shift from 24°C to 18°C . Flies expressing TRPM8 in either the entire CCAP ensemble ( CCAP>TRPM8 ) or a subset of CCAP—CAMB neurons ( Pburs>TRPM8 ) —were collected at stage P4 ( i ) ( positively buoyant; ~6 . 5–7 hr after pupariation ) [19] and held for ~5 hr prior to removal of the puparium and placement of the pre-pupa in halocarbon oil for detailed “puparium free” behavioral observation[2] . Control flies show no differences in scheduling of the pupal ecdysis behavioral sequence when temperature is lowered from 24°C to 18°C . However flies expressing TRPM8 in either the entire CCAP ensemble ( CCAP>TRPM8 ) or in the CAMB neuron subset ( CAMB>TRPM8 ) initiated ecdysis behavior upon lowering ambient temperature to 18°C ( Fig 6B ) . No pre-ecdysis behavior was observed in either case . In CCAP>TRPM8 flies , we observed robust ecdysis swinging frequency ( 1 . 4 ± 0 . 1 swings/min ) , with head eversion occurring within ~6 min ( 5 . 8 ± 2 . 9 ) of behavior initiation , compared to corresponding events in control flies ( 1 . 4 ± 0 . 03 swings/min; head eversion at 2 . 8 ± 1 . 8 min ) at the same temperature ( 18°C; Fig 4B and 4C and S2 Movie ) . After a 10 min observation period at 18°C , the temperature was returned to 24°C . Ecdysis contractions continue for 1–3 min , whereupon the natural ecdysis FAP invariably ensues in its entirety , starting with pre-ecdysis contractions ( Fig 6B ) . Intermittent ecdysis contractions occur for several minutes following initiation of pre-ecdysis behavior . The second bout of ecdysis behavior was somewhat abbreviated ( 2 . 9 ± 1 . 6 min ) compared to controls ( 4 . 4 ± 0 . 6 min ) . Similarly , activation of CAMB neurons by cooling Pburs>TRPM8 flies from 24°C to 18°C resulted in appearance of ecdysis contractions within minutes ( Fig 6B and S3 Movie ) . While ecdysis swinging behavior observed at 18°C appears normal , contraction frequency ( 0 . 7 ± 0 . 1/swings/min ) is considerably lower than that of natural behavior ( 1 . 6 ± 0 . 1 swings/min ) observed in controls at the same temperature ( Fig 6C ) . Furthermore , activation of CAMB neurons alone leads to a significant delay in head eversion , which occurs an average of 14 min later ( 13 . 8 ± 8 . 4 min ) and variability in timing of this event is high compared to controls . As observed using the CCAP-Gal4 driver , Pburs>TRPM8-induced ecdysis behavior is followed by the natural ecdysis behavioral sequence . The combined datasets suggest that CCAP and CAMB ensembles are sufficient to elicit ecdysis swinging behavior and head eversion . While the CAMB ensemble appears to be the minimum circuit sufficient for eliciting ecdysis , inputs from additional CCAP neurons are necessary for robust expression of the behavior and proper scheduling of head eversion . We have shown that kinin neuron activity ceases prior to activation of CAMB neurons . Since activity in these two neuronal ensembles is strictly non-overlapping , we asked whether initiation of CAMB neuron activity exerts negative feedback on kinin neurons . Our strategy was to employ RNAi silencing directed toward receptors of 3 out of the 4 peptides released by CAMB neurons: CCAPR , MIPR ( also known as sex peptide receptor , or SPR-IR ) , and rickets , the receptor for bursicon . In all experiments , no changes were observed in timing of the switch to ecdysis ( S7 Fig ) .
Our findings demonstrate that the kinin peptide ensemble is necessary for proper scheduling of pre-ecdysis behavior , if not itself sufficient to elicit it . Kinin cell ablation abolishes pre-ecdysis behavior in a significant percentage ( 25% ) of animals . The remaining 75% of individuals showed highly variable pre-ecdysis duration , ranging from 3–22 minutes , whereas duration of this behavior in control animals is tightly regulated at 9 . 1 ± 0 . 9 min . Furthermore , a 30% reduction in kinin receptor expression , caused by a piggyBac-element insertion into the promoter region of the gene , also disrupts fidelity of pre-ecdysis regulation; this phenotype is rescued by precise excision of the piggyBac insertion . Finally , RNA silencing of the kinin receptor in peripheral neurons using the Peb-Gal4 driver leads to reduced intensity of the behavior and greatly increases variability of pre-ecdysis duration . This is the first report demonstrating that kinin signaling affects scheduling of the ecdysis behavioral sequence via actions on peripheral neurons . Pan-neuronal silencing of kinin receptors also disrupted pre-ecdysis scheduling , but to a lesser extent . Manipulation of ETHR expression levels in kinin neurons also alters scheduling of pre-ecdysis behavior significantly , confirming that these neurons are targeted directly by ETH and that they play an important role in pre-ecdysis regulation . We reasoned that knockdown of receptor expression in these neurons would lead to a lower density of ETHR in the plasma membrane , thereby reducing sensitivity to ETH and delaying onset of pre-ecdysis . Our experimental results demonstrated a reduction of pre-ecdysis duration . We attribute this reduction to a delay in pre-ecdysis onset , brought about by the need for higher ETH levels for neuronal activation . Since timing of the switch to ecdysis ( controlled by CAMB neurons; see below ) is unaffected , pre-ecdysis duration is shortened . On the other hand , overexpression of ETHR in kinin neurons led to prolongation of pre-ecdysis duration . Following the same reasoning , this would result from premature kinin neuron activation attributable to higher sensitivity to rising ETH levels and overall longer pre-ecdysis . Kinins were identified originally using bioassays for myotropic and diuretic functions and they have well-known actions on muscle and transport activity in epithelia [20 , 21] . More recent studies demonstrated diverse functional roles for kinin signaling , including feeding , olfaction , and locomotory behavior [22–25] . In previous works , we demonstrated ETHR-A expression in kinin neurons of both Drosophila and Manduca , implicating them as direct targets of ETH [2 , 3] . Imaging studies have shown that abdominal kinin neurons in fly larvae exhibit periodic calcium oscillations under normal conditions and are involved in turning behavior [23] . These kinin neurons project to a terminal plexus in close association to kinin receptors , suggesting it functions as a site of peptide release . Interestingly , this same ensemble of kinin neurons in the pre-pupal preparation used here showed no such periodic activity , but instead exhibited synchronized calcium oscillations activity following exposure to ETH . This difference could be unique to the pharate stage ( i . e . , preceding ecdysis ) animal , during which insects generally are unresponsive to external stimuli . In our imaging studies , we observed that ETH-induced calcium dynamics are initiated from the terminal plexus region and subsequently moving anteriorly to the cell bodies . These observations suggest that this plexus serves critical functions in both sending and receiving signals . Evidence presented here for regulation of pre-ecdysis behavior by kinin neurons demonstrates a new function for this peptide in Drosophila , which is reinforced by previous observations in Manduca that application of kinin causes a fictive pre-ecdysis motor pattern in the isolated CNS [3] . How do kinin neurons function in the promotion of pre-ecdysis behavior ? While manipulation of kinin signaling clearly affects behavioral intensity and duration , we were unsuccessful in initiating pre-ecdysis through temperature-dependent activation of kinin neurons expressing either TRPM8 and TRPA1 . We conclude that , while kinin functions as a modulatory influence necessary for proper scheduling of pre-ecdysis behavior , other as yet unidentified signals are necessary for behavioral initiation . We have demonstrated here that CAMB neurons are both necessary and sufficient for the switch from pre-ecdysis to ecdysis behavior . This conclusion rests on results from a combination of experiments . First , CAMB cell ablation abolishes the switch to ecdysis , suggesting these neurons are necessary for the switch . Failure of ecdysis initiation is attributable to bursicon deficiency , since a previous report showed clearly that expression of the bursicon gene is required for initiation of pupal ecdysis [7] . Calcium mobilization in CAMB neurons is delayed for ~10 min after onset of activity in kinin neurons , which fits well with the ~10 min delay before appearance of ecdysis behavior following onset of pre-ecdysis behavior observed in vivo . Altered levels of ETHR expression in CAMB neurons clearly affects timing of the switch to ecdysis behavior: receptor knockdown delays the switch , whereas overexpression accelerates it . In vitro experiments confirm that altered ETHR expression levels affect timing of calcium mobilization in CAMB neurons in register with changes in behavioral timing . Finally , we show that activation of CAMB neurons through temperature-sensitive TRPM8 expression initiates ecdysis behavior in vivo . Thus , CAMB neurons are both necessary and sufficient for the switch to ecdysis behavior . However , activity in CAMB neurons alone does not result in robust ecdysis behavior . Expression of ecdysis behavior with parameters corresponding to that observed in wild-type flies requires activation of the entire CCAP ensemble . It is interesting that , in all TRPM8 activation experiments , removal of the temperature stimulus led to re-capitulation of the entire ecdysis FAP . This might be explained by positive feedback influences on the Inka cell to release ETH , possibly via EH neurons . Alternatively , the ecdysis motor circuit may exert negative feedback on the pre-ecdysis circuit , which when removed , causes a post-inhibitory rebound leading to activation of the pre-ecdysis circuit and the entire FAP . Our attempts to demonstrate such negative feedback here were inconclusive . CAMB neurons express a combination of CCAP , Ast-CC , MIP , and bursicon . In Manduca , application of a CCAP/MIP cocktail is sufficient to elicit fictive ecdysis behavior . It would be parsimonious to extrapolate this result to Drosophila , since both of these peptides are found in CAMB neurons . Nevertheless , in Drosophila it is clear that bursicon is a key signaling molecule necessary for ecdysis initiation [7] . It remains to be demonstrated precisely how absence of the bursicon gene blocks the switch to ecdysis . It will be interesting to elucidate possible functional roles of co-expressed peptides in CAMB neurons CCAP , Ast-CC , MIP in activation of the motor circuitry encoding the ecdysis motor pattern . How is timing of the switch to ecdysis determined ? Since both kinin and CAMB ensembles express ETHR , one would expect ETH to activate both ensembles simultaneously . Several previous studies provide evidence for the role of descending inhibition from cephalic and thoracic ganglia in setting the delay in the switch to ecdysis behavior [15 , 26 , 27] . Here we show that expression of pertussis toxin in CAMB neurons accelerates the switch to ecdysis , consistent with disinhibition of Gαi/o input ( s ) . We hypothesize that a balance of excitatory and inhibitory inputs to the CAMB neurons contributes to the delay in their activity , excitatory input coming from ETH via Gαq signaling and Gαi/o from an as yet unidentified transmitter descending from cephalic and/or thoracic ganglia . Our finding that RNAi-knockdown of MIP neurons lying outside the CCAP ensemble accelerates the switch to ecdysis behavior suggests one such possible inhibitory input . It is possible , if not likely that ETH drives both inhibitory and excitatory inputs to CAMB neurons , with ETHR-B-expressing inhibitory inputs preceding excitatory input . Such a scenario follows from the fact that sensitivity of Drosophila ETHR-B to ETH was shown to be ~450-fold higher than that of ETHR-A [5] . Therefore , as ETH levels rise in the hemolymph , ETHR-B-expressing inhibitory neurons would be activated well before ETHR-A neurons . ETH would effectively inhibit CAMB neurons indirectly prior to direct excitation via ETHR-A activation . Such a scenario pre-supposes that the EC50 values governing activation of ETH receptors determined previously from heterologous expression in mammalian CHO cells [5] are valid in Drosophila neurons . Data presented here suggests this is so . The EC50 value for ETH1 against ETHR-A was found to be ~414 nM , while the EC50 for ETH2 was determined to be ~4 . 3 μM . We applied a combination of ETH1 and ETH2 , each at a concentration of 300 nM , to the isolated CNS and obtained a pattern of calcium dynamics in kinin neurons lasting for ~10 min , which matches the duration of pre-ecdysis behavior under natural conditions . Furthermore , the switch to ecdysis behavior occurs ~10 min after initiation of calcium mobilization in kinin neurons , which corresponds to timing of the switch to ecdysis behavior in vivo . Doubling concentrations of the ETH1/ETH2 cocktail reduced the duration of calcium dynamics in kinin neurons to 5 . 5 min and accelerated the switch to ecdysis behavior . These results make it likely that the relative sensitivities of ETHR-B and ETHR-A are as established in Park et al . [5] and consequently activity in ETHR-B neurons would precede that of ETHR-A neurons . We have shown that altered levels of ETHR expression have significant consequences for timing of pre-ecdysis duration and timing of the ecdysis switch . These findings raise the possibility that scheduling of sequential steps in the ecdysis FAP may be a consequence of different sensitivities to the peptide ligand . In other words , delay in the switch to ecdysis could result from a lower density of ETHR in CAMB neurons , making them less sensitive to ETH . Possible differential sensitivity to ETH could be tested in variety of way , including assessing timing of responses to the ligand by acutely dissociated neurons and/or single cell PCR . We propose a mechanistic model to explain neural mechanisms underlying the Drosophila pupal ecdysis FAP ( Fig 7 ) . Principle players in orchestration of pre-ecdysis and ecdysis behaviors are the kinin and CAMB ETHR ensembles , respectively . As ETH levels rise in the hemolymph , ETHR-B neurons are activated due to their high sensitivity ( EC50 ~ 1 nM ) . These neurons release inhibitory signals acting through Gαi/o to inhibit CAMB neurons . As ETH levels rise further , kinin neurons receive direct excitatory input from ETH signaling via ETHR-A and Gαq to mobilize calcium from intracellular stores , leading to electrical activity in these neurons . ETH acts simultaneously on CAMB neurons , but inhibition from ETHR-B neurons descending from anterior ganglia prevents them from becoming active . As inhibition wanes , CAMB neurons become active , initiating the switch to ecdysis behavior .
All flies were raised at 25°C on standard cornmeal-agar media under a 12 hr light/dark regimen . Unless otherwise stated , transgenic flies were generated by p-element transgenesis in a w1118 background . Burs-Gal4 ( 2 ) , kinin-Gal4 ( 2 , 3 ) and pburs-Gal4 ( 2 ) were prepared in a pPTGal4 ( + ) [28] vector and introduced into yw . Primers used to generate the peptide Gal4 are summarized in S1 Table . UAS-ETHR-A ( 1 ) and UAS-ETHR-A ( 3 ) were prepared by cloning the complete coding sequence ( CDS ) of ETHR-A ( Genbank accession number AY220741 ) into pUAST . UAS-ETHR-IR1 ( 2 ) was generated by inserting the inverted repeat construct ( transformant ID dna697 ) obtained from the Vienna Drosophila RNAi Center ( VDRC ) into w1118 . The UAS-ETHR-IR2 stock was obtained from the VDRC ( transformant ID 101996 ) . SymUAS-ETHR was prepared by cloning a part of the ETHR CDS ( +1 to +546 in AY220742 ) shared by both ETHR-A and ETHR-B into sympUAST-w [29] . Kinin receptor mutant flies ( PBac{WH}Lkrf02594 ) were obtained from Exelixis ( Harvard University , Boston , MA ) . FMFRa ( Tv ) -Gal4 [2] and UAS-rpr , hid flies were provided by Paul Taghert ( Washington University , St Louis , MI ) . UAS-TRPM8 flies were provided by Benjamin White ( NIH ) . Other fly stocks used were CCAP-Gal4 [8] , CCAP-Gal80[30] , UAS-TRPM8[18] , UAS-PTX ( 3 ) [31] , UAS<w[+]<GαoQ205L , and UAS<w[+]<PTX ( 2 ) [16] . UAS-GαoQ205L , and UAS-PTX ( 2 ) were produced by removing the <w[+]< cassette from the last two stocks . Stocks from the Bloomington stock center ( Indiana University , Bloomington , IN ) include UAS-mCD8-GFP ( stock number , 5137 ) , w1118 ( 5905 ) , piggyBac transposase ( 8285 ) , UAS-GCaMP3 ( 32235 ) , Df ( 3L ) Exel6105 ( 7584 ) . Burs-Gal4 , and pburs-Gal4 were provided by Jae-Hyun Park ( University of Tennessee ) . Ast-CC-Gal4 , Ast-CC-Gal80 , MIP-Gal80 , Burs-Gal80 and Pburs-Gal80 were generated by the Young-Joon Kim lab . The Pburs;kinin combination Gal4 line was generated by crossing pburs-Gal4 and kinin-Gal4 . The kinin receptor RNAi line was from the VDRC stock center . Pebbled ( peb ) -Gal4 flies were provided by Walton Jones ( KAIST , South Korea ) . Other flies were obtained from the Bloomington stock center ( Indiana University , Bloomington , IN ) , including EHups-Gal4 ( stock number , 6310 ) , UAS-mCD8-GFP ( stock number , 5137 ) , w1118 ( stock number , 5905 ) , piggyBac transposase ( stock number , 8285 ) UAS-GCaMP3 ( stock number , 32235 ) , Df ( 3L ) Exel6105 ( stock number , 7584 ) , UAS-Kir2 . 1 ( stock number , 6595 ) , 24B-Gal4 ( stock number 1767 ) , D42-Gal4 ( stock number 8816 ) . RNA was extracted from whole bodies of 10 prepupal flies . cDNA was synthesized using the SuperScript III First-Strand Synthesis System ( Invitrogen ) . Real Time PCR was performed using the Bio-Rad CFX96 Real Time PCR Detection System ( Bio-Rad , USA ) in the UCR Institute of Integrative Genome Biology . Rp49 and actin were used as standards . Primers ( 5’ to 3’ ) used were as follows: KininR-F: ACGCACAGGATTCAC GGGAC KininR-R: CAGCCAATCGCAGCAAAAC Rp49-F: CCAAGA TCGTGAAGAAGCGCACCAA Rp49-R: GTTGGGCTACAGATACTG TCCCTTG Actin-F: CATCCACGAGACCACCTACA Actin-R: TTGGAGATCCACATC TGCTG We crossed Gal4 or Gal4-Gal80 transgenic flies with UAS-mCD8-GFP flies to produce progeny expressing GFP in peptidergic neurons and used them for GFP immunohistochemical staining . CNS of prepupae were dissected in phosphate buffered saline ( PBS ) and fixed in 4% paraformaldehyde in PBS overnight in 4°C , washed with PBS containing 0 . 2% Triton X-100 ( PBST ) , and incubated in 5% normal goat serum in PBST for 30 minutes at room temp . They were incubated in PBST with primary mouse antibody to GFP ( Invitrogen ) for 2 days at 4°C . Tissues were then washed with PBST and incubated with Alexa Flour 488-labeled goat anti-mouse IgG ( Invitrogen ) . GFP expression was observed under a confocal microscope ( Leica model SP2 ) with FITC filter . In vitro calcium imaging experiments were performed on pharate pupae ~3–5 hr prior to pupal ecdysis according to the following staging protocol . Stage P3 prepupae [19] were selected and checked for buoyancy every hour . Newly buoyant prepupae were thereby judged to be ~5–6 hr prior to pupal ecdysis . The CNS was extirpated in fly saline under minimal light exposure , and placed in a 300 μl chamber containing fly saline . The test chamber consisted of a metal frame slide with 9 mm hole in the center , under which a glass cover slip was attached . The cover slip was discarded after each experiment . In a previous study [2] , low-melting agarose was used to immobilize the isolated CNS . In the present study , the CNS adhered well to the new cover glass installed prior to each experiment , obviating the use of agarose for immobilization . Flies of the following genotypes were used: pburs;kinin>GCaMP ( Fig 2 ) , pburs>GCaMP;ETHR-IR3 ( Fig 4 ) and pburs>GCaMP;ETHR-IR3 ( Fig 4 ) . Calcium imaging instrumentation consisted of a Polychrome IV monochromator and TILL Imago CCD camera ( TILL Photonics , Munich , Germany ) . The microscope ( Olympus BX51WI ) was equipped with a 40x water immersion NA 0 . 8 objective . Binning on the chip ( 8x8 ) was set to give a spatial sampling rate of 1 μm/pixel ( image size 172 μm x 130 μm ) . Images were acquired at a sampling rate of 1 Hz . The excitation wavelength was 488 nm and exposure time was 25 msec . Fluorescent light passing an excitation filter ( 370–510 nm ) was directed onto a 500 nm DCLP mirror followed by a 515 long pass emission filter for EGFP . Images were acquired continuously for 1 hr; ETH was applied into a bathing media ~5 min after imaging onset . The volume of applied ETH was 3 . 6 μl . We used a cocktail of ETH1 and ETH2 for all experiments . 300 nM ETH ( 300 nM ETH1 plus 300 nM ETH2 ) and 600 nM ETH ( 600 nM ETH1 plus 600 nM ETH2 ) was added to a stagnant bathing bath with a micropipette . For imaging of CAMB neurons with modified ETHR expression levels ( Fig 4 ) , an Examiner A1 upright Zeiss microscope equipped with a 40x water immersion objective ( NA 0 . 8 ) , 480 nm LED ( CoolLED ) light source , and a Luca CCD camera ( Andor ) were used . Fluorescence intensity was calculated as ΔF/F; mean fluorescence over the entire 100 frames was taken , for each pixel , as an estimate for F . For the behavioral analysis of each fly line , we collected late stage P4 ( i ) buoyant pharate pupae ( ~ 2 hr prior to pupal ecdysis ) [19] and placed 5~8 pharate pupae ventral-side up in a small recording chamber containing wet filter-paper strips . Ecdysis recordings were performed at normal speed under a dissection microscope ( Wild Heerbrugg ) using an ExwaveHad digital color video-camera ( Sony ) and HITACHI Kokusai electric color CCD camera attached to RD-XS34SU or RD-XS35SU video recorder ( Toshiba ) . Flies expressing the cold-sensitive TRPM8 ion channel ( CCAP-Gal4>UASTRPM8; pburs-Gal4>UAS-TRPM8 ) were collected at the P4 ( i ) buoyant pharate pupal stage . Following removal of the puparium , animals were placed in a chamber containing halocarbon oil mounted on a Peltier device ( Echotherm chilling/heating plate; Torrey Pines Scientific , Inc . , San Diego ) . Videos were recorded as described in the previous section . Change in LKR gene expression level of mutant was compared with control using Student’s t-test ( p < 0 . 01 ) . Changes in pre-ecdysis duration observed in Kir expressing flies were compared with those of control flies using Student’s t-test ( p < 0 . 0001 ) . Statistical analyses performed on ETHR over-expression and suppression data sets were assessed using the non-parametric Mann-Whitney test at * p < 0 . 01 , ** p < 0 . 001 , *** p < 0 . 0001 . | In Drosophila , the pupal ecdysis behavioral sequence is composed of three distinct steps: pre-ecdysis , ecdysis , and post-ecdysis . We hypothesize that release of ecdysis-triggering-hormone ( ETH ) from endocrine Inka cells drives these stereotypical behaviors through sequential activation of peptidergic ETH receptor ( ETHR ) neuron ensembles in the central nervous system ( CNS ) . There are many questions about how a single hormone orchestrates a stepwise behavioral sequence . Here we present evidence implicating two central ETHR ensembles—kinin and CAMB—causally in pre-ecdysis and ecdysis behaviors . Using calcium imaging , we show a sequential pattern of activity in kinin and CAMB neurons that is temporally correlated with pre-ecdysis and ecdysis behaviors , respectively . Genetic manipulation of kinin and CAMB neurons demonstrates that timing of the behaviors is a function of: 1 ) sensitivity to the hormone , and 2 ) upstream inhibitory inputs . These findings provide insights into the molecular bases of behavioral orchestration by central peptidergic ensembles . | [
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| 2015 | Rescheduling Behavioral Subunits of a Fixed Action Pattern by Genetic Manipulation of Peptidergic Signaling |
Many animals can choose between different developmental fates to maximize fitness . Despite the complexity of environmental cues and life history , different developmental fates are executed in a robust fashion . The nematode Caenorhabditis elegans serves as a powerful model to examine this phenomenon because it can adopt one of two developmental fates ( adulthood or diapause ) depending on environmental conditions . The steroid hormone dafachronic acid ( DA ) directs development to adulthood by regulating the transcriptional activity of the nuclear hormone receptor DAF-12 . The known role of DA suggests that it may be the molecular mediator of environmental condition effects on the developmental fate decision , although the mechanism is yet unknown . We used a combination of physiological and molecular biology techniques to demonstrate that commitment to reproductive adult development occurs when DA levels , produced in the neuroendocrine XXX cells , exceed a threshold . Furthermore , imaging and cell ablation experiments demonstrate that the XXX cells act as a source of DA , which , upon commitment to adult development , is amplified and propagated in the epidermis in a DAF-12 dependent manner . This positive feedback loop increases DA levels and drives adult programs in the gonad and epidermis , thus conferring the irreversibility of the decision . We show that the positive feedback loop canalizes development by ensuring that sufficient amounts of DA are dispersed throughout the body and serves as a robust fate-locking mechanism to enforce an organism-wide binary decision , despite noisy and complex environmental cues . These mechanisms are not only relevant to C . elegans but may be extended to other hormonal-based decision-making mechanisms in insects and mammals .
During development , organisms often face unpredictable and unfavorable environmental conditions that may decrease their fitness . In some cases , organisms of the same genotype develop into alternate phenotypes , each better adapted to a particular environment . Alternative phenotypes entail changes in metabolism , developmental programs , behavior , or morphology [1] . To predict the capacity of the environment to provide for reproductive development , animals integrate external environmental conditions , the internal state of nutrient supplies , and other variables [2] . In many cases , the integration culminates in a decision between two mutually exclusive alternative phenotypes . Therefore , robust developmental mechanisms have evolved to ensure that the animals coordinate development exclusively into only a unified phenotype , as uncoordinated development will be detrimental [3] . Organism-wide binary decisions are common throughout the animal kingdom and include examples such as sex determination , changes in coloration as a function of season , and caste differentiation in insects [3] . However , our knowledge of the mechanisms that regulate the decision between alternative phenotypes and coordinate the outcome across a multi-cellular organism is fragmentary , drawing on principles derived from studies of different model organisms [4] . Insects coordinate the fate of an alternative phenotype by altering hormone amounts above or below a threshold , during a hormone sensitive period , prior to metamorphosis [2] . A threshold distinguishes the two alternatives , yet it remains unclear how the thresholding mechanism is regulated and how the amounts of hormone are maintained throughout the body post-decision . Transcriptional amplification mechanisms such as positive feedback loops have been shown to lock in binary fate decisions in phage [5] , bacteria [6] , and yeast [7] . A hallmark of such positive feedback mechanisms is that signals from a noisy environment can be forced into a bi-stable response by a threshold . Signal levels above the threshold will be amplified and maintained at a high abundance , acting as a memory module for a decision , thus enforcing a cell-specific fate . We sought to understand if such principles can be extended to hormonal regulation in multi-cellular organisms , specifically as a means to threshold , coordinate , and maintain the alternative phenotype in the selective environment . To understand the interaction between genetic and hormonal regulatory mechanisms that integrate environmental conditions and coordinate a discrete developmental fate , we looked at the dauer decision in the free-living nematode C . elegans . During this decision , C . elegans integrates environmental conditions and chooses between the mutually exclusive fates , dauer or reproductive development . In favorable environments—plentiful food , moderate temperatures , and low population density—C . elegans develops rapidly through four larval stages ( L1–L4 ) separated by molts , into a sexually reproductive adult . In unfavorable environments—high population density ( indicated by high levels of a constitutively secreted dauer pheromone ) limiting food or high temperature—animals can decide to develop into an alternative third larval stage , the dauer diapause , a developmentally arrested , long-lived form geared towards survival [8] . Dauer larvae do not feed and can endure harsh conditions , including starvation , desiccation , heat , and oxidative stress [8] . Accordingly , dauer larvae have profound morphological changes including an assault-resistant cuticle , pharyngeal constriction , and sealing of body cavities . Whereas adult nematodes live for about 3 wk , dauer larvae can survive several months . When returned to favorable conditions , dauer larvae resume development , molting into L4 larvae and adults [9]–[11] . In either reproductive or dauer mode , it is essential that the execution of the decision will be robust and that no mosaic phenotypes arise , as this will compromise survival . Moreover , understanding the decision-making process of diapause entry in C . elegans can illuminate analogous processes in parasitic nematodes whose infective stages are like dauer larvae and are regulated by some of the same signaling pathways [12] . A deeper understanding of the decision to become an infective juvenile as well as exit from this stage will facilitate the design of therapeutics that inhibit parasitic infection . Although the major signaling pathways regulating dauer formation have been identified , the cellular and molecular basis of this binary decision is not clear . Environmental cues are detected by multiple sensory neurons that integrate inputs into hormonal outputs by unknown means [13]–[16] . Molecular analysis has revealed at least four signaling pathways . Components of neurosensory structure and guanylyl cyclase signaling are involved in sensing temperature , nutrients , and dauer pheromone [17] , which regulate secretion of insulin/insulin-like growth factor and TGFβ peptides . Insulin and TGFβ signaling converge on a steroid hormone pathway , which metabolizes dietary cholesterol into several bile acid-like steroids , called the dafachronic acids ( DAs ) [18]–[23] . DAs serve as hormonal ligands for the nuclear hormone receptor transcription factor DAF-12 , which regulates the life cycle fate decision . Liganded DAF-12 promotes reproductive development , whereas unliganded DAF-12 , together with the co-repressor DIN-1S , directs the dauer fate . Thus , DAF-12 serves as a DA-responsive switch that determines whether an animal will undergo reproductive or dauer development [20]–[27] . The regulation of DAs during development as a function of environmental conditions remains largely unknown . Here we identify the times of integration and commitment to this life cycle fate choice and show that environmental conditions affect the threshold at which discrete levels of DA bypass the dauer fate . Higher amounts of DA are necessary to implement and coordinate the reproductive decision throughout the whole animal . We show that a positive feedback loop , which amplifies the amounts of DA in the hypodermis ( hyp7; WBbt:0005734 ) , is required under some circumstances to produce the higher amounts of DA , while a negative feedback loop keeps hormones with normal bounds . Finally , we demonstrate that the amplification of DA in the hypodermis is responsible for the irreversibility of the decision and the proper execution of reproductive programs . We propose that hypodermal amplification of a hormonal signal acts as a commitment mechanism that enforces the binary decision .
The decision between dauer arrest and reproductive growth is made at two points during early larval development . During late L1 , worms develop into the L2 stage in favorable environments or into the dauer-capable pre-dauer ( L2d ) stage in unfavorable environments . During the mid-L2d stage , worms commit to either the dauer or resume reproductive development as L3 larvae ( Figure 1A ) [10] , [28] . Golden and Riddle ( 1984 ) [28] showed that worms must be exposed to pheromone before the L1 molt in order to develop into the L2d stage and commit to the dauer fate before the mid-L2d stage . We re-visited these experiments and modified them to liquid culture to increase scale , homogeneity , and throughput . We measured the frequency of dauer formation in response to dauer pheromone while grown in the presence of sufficient food for adult development . Mean frequencies of life stages in favorable and unfavorable growth conditions were tightly distributed and highly reproducible , indicating the homogeneity of the liquid culture conditions ( Figure S1A–B ) . To identify when worms commit to reproductive development as L2 larvae , we performed a “shift-to-unfavorable” experiment by adding a high concentration of pheromone to synchronously hatched worms at progressive times . Worms stopped responding to pheromone at 18–20 hours post-hatch ( hph; Figure 1B; 16 . 6%±21 . 4% dauer formation ) , which coincides with the beginning of the L2 stage . After this time , animals initiated reproductive development despite exposure to unfavorable conditions . To identify when worms commit to the dauer fate , we performed a “shift-to-favorable” experiment by growing synchronously hatched worms in unfavorable conditions ( high concentration of pheromone ) and washing away pheromone at progressive times . L2d worms committed to dauer during mid-L2d at 33 hph ( Figure 1C; 29 . 1%±25 . 1% dauer formation ) , 18 h after the L1/L2d molt . Shifting worms to favorable conditions after this time did not affect their propensity to become dauers . We next identified when L2d worms commit to L3 . We reasoned that L2d worms exposed to favorable conditions for longer times would have a higher propensity to develop into adults . The “shift-to-favorable” experiment was modified by growing worms under unfavorable conditions to obtain L2d animals , followed by a shift to favorable conditions at 24 hph . Worms were then returned to unfavorable conditions after varying amounts of time . We found that a 3 h pulse into favorable conditions was sufficient to commit L2d animals to reproductive development ( Figure 1D; 0 . 02% dauer formation ) . Pulses at different start times during L2d had similar responses ( Figures 1E , S1D ) . The L2d stage is thus divided into two periods: integration ( from the beginning to the middle of the L2d stage , 16–33 hph ) and commitment and implementation of the dauer program ( 33–48 hph ) . We sought to find cellular and molecular candidates that could account for the dauer and L3 commitments . The two species of the DAs , Δ4-DA and Δ7-DA , are good candidates because synthetic DAs can fully rescue the Daf-c phenotypes of the null allele daf-9 ( dh6 ) ( WBGene00000905 ) as well as daf-7/TGFβ ( WBGene00000903 ) and daf-2/InsR ( WBGene00000898 ) mutants [21] . Partial reduction of daf-9 function results in animals that bypass the dauer stage yet exhibit abnormal gonadal morphogenesis and migration ( Mig; WBPhenotype:0000594 ) and occasionally aberrant cuticle shedding ( Cut; WBPhenotype:0000077 ) defects ( Figure 2A ) [18] , [19] . Exogenous DA can also rescue these phenotypes [20] , [21] , [23] , [24] . We thus hypothesized that a low amount of DA is required to bypass dauer and commit to L3 , whereas a high amount is required for normal development . To understand the physiological response to DA dose , dauer-constitutive daf-9 loss-of-function mutants were treated with increasing amounts of Δ7-DA and measured for dauer and reproductive adult fates . Most daf-9 ( dh6 ) null animals developed into abnormal adults when supplemented with a minimum of 10 nM DA ( Figure 2B , 74%±42% non-dauers ) , suggesting that a threshold of DA has to be crossed before committing to adult fate ( dauer bypass DA threshold ) . Increasing the levels to 25 nM DA decreased the frequency of dauers to 99%±1% with about 66% of animals developing normally ( Figure 2B ) . Further increase of DA to 50 nM increased the frequency of normal adults ( Figure 2B ) . For a distribution of Mig and Cut phenotypes , see . Similar results were observed with animals homozygous for daf-9 ( e1406 ) or daf-9 ( m540 ) ( Figure S2; worms were not synchronously hatched ) , both of which are strong loss-of-function alleles . By contrast , the weak loss-of-function allele daf-9 ( rh50 ) does not result in Daf-c phenotypes , but in highly penetrant Mig defects ( 95%±3% ) [18] . In these animals , only 10 nM of DA was required to rescue over 90% of the Mig phenotypes ( Figure 2C ) , revealing a 5-fold decrease in the amount of exogenous DA required to promote normal development compared to the stronger daf-9 mutants ( dh6 , e1406 , and m540; Figure 2S ) . Thus , daf-9 ( rh50 ) animals produce sufficient amounts of DA to bypass dauer development but require additional DA to develop into normal adults , consistent with our finding that different levels of DA are required for the two processes . Many Daf-c mutants have a slower developmental rate , but the basis of this is not well understood ( A . A . , unpublished observations ) . To test the effects of DA on developmental rate , daf-9 ( dh6 ) worms were synchronously hatched in different concentrations of DA and scored for developmental stage at 48 hph ( the time at which wild-type worms grown in favorable conditions are young adults ( YAs ) and worms grown in unfavorable conditions are dauers; Figure 1A ) and for egg production the following day . At 25–50 nM DA , worms developed into L4 , whereas worms supplemented with 75–175 nM DA already developed into YAs . Worms that were in the L4 or YA stages at 48 hph were gravid the next day . Exogenous addition of DA had no effect on wild-type growth rates ( Figure 2D ) . These trends indicate that increases of DA levels can accelerate growth rate until it matches that of wild-type worms ( Figure 2D ) . DA and dauer pheromone have opposite effects on dauer formation , with DA preventing and dauer pheromone promoting the dauer stage . We investigated the dose-response relationship when administered together , with respect to bypass of the dauer diapause and normal reproductive development . We hypothesized that the same dose of DA would be required to overcome dauer induced by pheromone as that seen in daf-9 ( dh6 ) null mutants . Synchronized populations of daf-9 ( dh6 ) worms were supplemented with a combination of DA and pheromone at different concentrations and scored for dauer , abnormal , and normal adult development at 48 hph ( Figure 3A–C ) . Unexpectedly , addition of pheromone at 1% , 3% , or 6% ( which induce 47%±4% , 92%±2% , and 95%±2% dauer in wild-type worms , respectively; Figure S1D ) increased the concentration of DA necessary to exceed the dauer bypass DA threshold to 30 , 45 , and 58 nM , respectively ( Figure 3F ) . Moreover , 90% of the population developed into normal adults if worms were supplemented with 30 nM of DA more than the amount required to bypass the dauer bypass DA threshold ( Figure 3D–F ) , similar to the concentration of DA needed to bypass the dauer bypass DA threshold in daf-9 worms without pheromone . These experiments demonstrate that dauer pheromone increases the amount of DA required to bypass the dauer bypass DA threshold and normal reproductive development . To understand the time of action of DAs and their role in life cycle fate decisions , we sought to identify three key points in the response to DA: ( i ) the time at which daf-9 ( dh6 ) animals start responding to DA to bypass dauer , ( ii ) the end of response to DA for the dauer decision , and ( iii ) the requirements of exposure to DA for normal development to maturity . Synchronously hatched daf-9 ( dh6 ) worms were shifted from media containing DA dissolved in EtOH to media containing EtOH alone ( downshift ) or vice versa ( upshift ) . Analysis of downshift experiments revealed that worms started responding to DA after 15 hph , the same time that wild-type worms commit to L2 mediated reproductive development ( Figure 4A ) . When DA was washed away before 15 hph , worms developed into dauers , despite previous exposures of DA indicating that previous exposures to DA had no effect on commitment to reproductive development . Removal of DA at time points after 15 hph prevented dauer formation to increasing extents , which could be divided into two phases: a minimum of 3 h on 100 nM DA during the responsive period was sufficient to prevent 61 . 7%±19 . 6% of the population from becoming dauers , but these animals developed as abnormal adults ( Figure 4A , 15 to 18 hph ) , whereas an additional 12 h were necessary to drive 100% of the population to normal adult development ( Figure 4A , 18 to 30 hph ) . To determine when daf-9 ( dh6 ) worms became refractory to DA , upshift experiments were performed during the L2d stage . Worms responded to DA until 33 hph , precisely at the same time that wild-type worms became refractory to pheromone ( Figure 4B; correlation coefficient = 0 . 996 ) . Next , we asked whether the total time exposed to DA or the specific time ( stage ) of exposure to DA were regulating the fate decision and development of normal adults . Pulse experiments revealed that worms committed to bypass dauer when exposed to DA at 15 hph or 24 hph for as little as 3 h ( Figure 4C , D ) . When DA was supplemented for 3 h at 24 hph worms developed into normal adults ( Figure 4D ) . These data correspond with addition of DA at 15 hph for 12 h , indicating that development into normal adults is a function of stage ( 27 hph , mid-L3 ) and a persistent exposure to DA ( Figure 4D ) . Similar results were seen with the daf-9 ( e1406 ) allele ( Figure S4 ) . In sum , DA can affect the decision during a specific temporal window ( 15 to 33 hph ) during the L2d stage , the same time that wild-type L2d worms integrate pheromone . Worms become committed to bypass dauer with a minimal exposure of 3 h in DA , but additional persistent exposure to DA over 12 h is necessary for normal adult development . We wanted to understand how the spatiotemporal and tissue-specific regulation of daf-9 is related to hormonal activity and stage commitments . The two bilaterally symmetric XXX cells ( WBbt:0007855 ) express daf-9 throughout all stages , suggesting that they may produce steady levels of DA [18] , [19] . Hypodermal daf-9 expression is more complex: hypodermal daf-9 is weakly expressed in L3 larvae growing in favorable , low-stress conditions , strongly expressed in L3 larvae growing in mild stress conditions , and not expressed under high stress conditions that trigger dauer formation [20] , [23] . First , we investigated the expression of daf-9 mRNA during L2 and L2d in favorable and unfavorable conditions by whole animal qPCR in wild-type worms . Second , we examined the expression of DAF-9 protein levels and distribution with a translational DAF-9::GFP fusion by fluorescent microscopy ( strain AA277; lin-15 ( n765 ) , dhIs64[daf-9::GFP , lin-15 ( + ) ]; Gerisch et al . , 2001 [18]; strain AA277 grows slower than N2 and therefore commitment to dauer occurs at 36 hph; Figure S5 ) . We found that daf-9 is regulated differently in favorable and unfavorable environmental conditions . In favorable conditions that promote reproductive development , total daf-9 transcripts were upregulated 7±1 . 1-fold at 16 hph and peaked at 30 hph , with 10-fold upregulation ( Figure 5A ) . All observed daf-9 upregulation was due to the daf-9a isoform as we were unable to detect the daf-9b isoform ( see Materials and Methods ) . Eighteen percent of worms started expressing hypodermal DAF-9::GFP at 21 hph , mid-L2 stage , reaching a maximum of 75%±12% at 30 hph , mid L3 ( Figure 5B; p<0 . 0001 ) . Presumably the delay between daf-9 upregulation detected by qPCR to that observed by GFP is due to the translation of mRNA to protein and slower developmental rate of the AA277 strain . Previous genetic experiments demonstrate that hypodermal daf-9 expression is regulated by DAF-12 and hormone biosynthetic genes [20] , [23] . To monitor daf-9 transcriptional regulation in specific tissues as a function of DA , we performed experiments using a pdaf-9::gfp transcriptional promoter construct in the daf-9 ( dh6 ) background . This promoter construct largely recapitulates the behavior of the translational fusion , suggesting that the majority of regulation occurs at the level of transcription . At low DA concentration ( 0–0 . 5 nM ) , expression was seen only in the XXX cells , and all animals developed as dauers ( Figure 5C–D ) . As DA concentration was increased to 0 . 75–7 . 5 nM , expression in the hypodermis dramatically increased by mid-L2 , suggesting positive amplification ( Figure 5C–D ) . Notably , within the range of 1–5 nM DA , animals bypassed dauer but exhibited the abnormal development phenotypes ( Figure 5C , D ) . Hypodermal expression was decreased at 10 nM or shut off ( 50–100 nM ) , suggesting suppression of daf-9 expression . At these higher concentrations ( >10 nM ) all animals developed into normal adults . Exogenous DA and hypodermal daf-9 upregulation have an inverse relationship; intermediate and high levels of DA promote intermediate , and low levels of hypodermal daf-9 , which correspond to states of abnormal and normal development , respectively . In unfavorable conditions , total daf-9 transcripts were not significantly upregulated in L2d animals committed to dauer ( Figure 5E , J; p = 0 . 14 ) . All observed daf-9 upregulation was due to the daf-9a isoform as we were unable to detect the daf-9b isoform ( see Materials and Methods ) . Nearly all worms grown in unfavorable conditions failed to show hypodermal DAF-9::GFP expression during L2d or dauer ( Figure 5F; 92%–100% , p = 0 . 18 ) . Shift-to-favorable experiments revealed a 40-fold upregulation of daf-9 transcripts in wild-type worms committed to reproductive development ( Figure 5H ) . When L2d worms were pulsed into favorable conditions at 24 hph for a 6-h window , 76%±12% showed hypodermal DAF-9::GFP expression with onset as early as 27 hph ( Figure 5G ) . Hypodermal daf-9 expression was retained even when worms were shifted back to unfavorable conditions . Conversely , 93%–99% of worms shifted to favorable conditions for 1 h did not express hypodermal DAF-9 ( Figure 5I ) . These results correlate temporally with the minimum time that wild-type worms require a pulse in favorable conditions to bypass dauer and suggest that hypodermal daf-9 expression could be a cause or consequence of a decision to develop into L3 . Worms committed to reproductive development had transcriptional upregulation of daf-9 in the hypodermis , likely resulting in the production of the high levels of DA . We asked whether the XXX cells play a role in hypodermal daf-9 upregulation . Notably , we observed that after a shift from unfavorable to favorable conditions , hypodermal DAF-9::GFP expression was observed in a spatiotemporal manner along the anterior posterior axis ( Figure 6 ) , first and most strongly in the head region before expression spread to more posterior regions . This led us to hypothesize that under these conditions , XXX cells ( located at the anterior ) might act as a source of DA , releasing a small amount that is amplified and propagated in the hypodermis from anterior to posterior . To test this hypothesis , we removed the XXX cells with a laser microbeam . We ablated XXX cells in worms expressing a translational DAF-9::GFP fusion developing in high pheromone concentration at 24 hph ( mid L2d , pre-commitment ) , and worms were allowed to recover in favorable conditions . Nearly all ( 30/31 ) XXX-ablated worms lacked hypodermal DAF-9::GFP expression and developed as dauers , while 29/31 control mock-ablated L2d animals developed into adults ( Figure 6B; p<1×10−10 , Figure S6A ) . Therefore , intact XXX cells are necessary for L2d larvae to respond to favorable conditions , committing to reproductive development and initiating hypodermal daf-9 expression . We next tested whether DA could rescue the dauer arrest caused by ablation of XXX cells . Worms were grown in high pheromone concentration and XXX cells were ablated at 24 hph ( L2d before commitment ) , and shifted to growth in favorable conditions supplemented with 0 , 1 , 5 , or 10 nM DA . An increasing frequency of both hypodermal DAF-9::GFP and normal adult development was observed as higher concentrations of DA were supplemented . Rescue with 1 nM DA yielded 22% adults and 78% dauers ( N = 18 ) , rescue with 5 nM DA yielded 56% adults and 44% dauers ( N = 30 ) , and rescue with 10 nM DA yielded 92% adults and 8% dauers ( N = 39; Figures 6C and S6B ) . All XXX-ablated worms supplemented with exogenous DA developed either as normal adults or as dauers , with none of the Mig or Cut phenotypes seen in daf-9 ( dh6 ) worms at these concentrations of exogenous DA ( Figure 2B ) . These results suggest that in the absence of the XXX cells , hypodermal daf-9 upregulation can be induced with as little as 1 nM DA , resulting in normal adult development . By contrast , in the daf-9 null background hypodermal daf-9 amplification is not possible , leading to abnormal development at low DA levels . To test whether XXX cells act as a source of DA later in development , we ablated XXX cells after commitment to L3 . Ablation at this time had no effect and resulted in worms that expressed hypodermal DAF-9::GFP and matured to adulthood ( Figure 6D; p = 2×10−9 , Figure S6C ) . Therefore , XXX cells act as a source of DA during the dauer decision and become dispensable later .
We analyzed the commitment of developing C . elegans larvae to reproduction ( L3 larvae ) or delayed reproduction ( dauer larvae ) in liquid culture , enabling a large and highly synchronized brood , amenable to facile and reproducible changes in environmental conditions . We showed that larvae exposed to favorable conditions for the first 12–18 h no longer entered dauer arrest when subjected to later pheromone exposure ( Figure 7A ) . Conversely , if grown in unfavorable conditions between 12 and 18 hph , worms were induced to the pre-dauer stage , L2d . Worms integrate environmental conditions during the L2d stage and become irreversibly committed to the dauer fate by 33 hph . However , L2d worms can commit to the reproductive fate if pulsed with favorable conditions for 3 h before the 33 hph commitment point is crossed ( Figure 7A ) . Our identification of the times at which these life cycle fate decisions occur allowed us to couple changes in the environment to the known molecular and cellular components involved in this decision . Timing of the decision period is congruent with the requirement for the hormone DA and the expression of the DA biosynthetic gene daf-9 . In this view , favorable conditions equate with the presence of DA , while unfavorable conditions equate with its absence . Starting from 15 hph , pulses of DA 3 h or longer will bypass dauer with no memory to previous exposures to DA . Similarly , daf-9 mutants stop responding to DA at 33 hph , mid-L2d stage . Thus , these periods of DA sensitivity overlap with the response to changes of population density in the environment . Our studies suggest that two thresholds of DA must be crossed to ensure proper reproductive development: DA levels above the dauer DA bypass threshold will specify reproductive development , and DA levels below the normal adult threshold will specify dauers . If worms produce DA levels between the two thresholds , they will develop into abnormal adults . Importantly , the two thresholds only become apparent in daf-9 mutants , which uncouple the dauer bypass threshold from the normal adult threshold . Addition of DA to daf-9 mutants indicates that 10 nM DA is sufficient to overcome the dauer DA bypass threshold in liquid culture and 1–5 nM are sufficient on plates , therefore committing worms to L3 development . These animals require an additional 30 nM DA to promote normal gonadogenesis and cuticle formation , thus developing into normal adults . Higher levels of DA increase developmental rate . In addition , dauer pheromone can raise both DA thresholds , thus increasing the fraction of dauers in a population . Therefore , worms that produce DA levels above the dauer DA bypass threshold , or lower the threshold itself , will develop into adults . The difference of 30 nM between both thresholds is constant regardless of the amount of pheromone to which worms are exposed . These observations suggest that pheromone has additional targets downstream or parallel to DA production that antagonize reproductive development ( Figure 7B ) . What might be the molecular and cellular correlates of these two thresholds ? The cytochrome P450 DAF-9 is limiting for DA production . daf-9 is expressed in the XXX cells from hatch and throughout development , and in the hypodermis starting from mid-L2 until L4 . The timing requirements for DA described above suggest that the commitment to adult development through the L2 stage is made early in L2 between 15 and 18 hph , a time that precedes visible hypodermal daf-9 expression . At high population density , the XXX cell appears to be source of DA required for the dauer decision , and the hypodermis amplifies DA production leading to normal development . When worms are shifted from unfavorable to favorable conditions , the dauer bypass DA threshold is lowered for a sufficient amount of time and the XXX cells presumably make a sufficient amount of DA to pass that threshold . Once the XXX cells release a small amount of DA , the hypodermis amplifies this signal leading to normal adult development . This amplification is visible as anterior to posterior propagation of hypodermal daf-9 expression originating in proximity of the XXX cells . If the XXX cells are ablated , there is no source of DA to trigger hypodermal daf-9 transcription and animals develop as dauer larvae . Hypodermal daf-9 amplification is triggered if XXX-ablated animals are supplemented with as little as 1 nM DA . Lastly , the onset of hypodermal daf-9 upregulation renders worms insensitive to removal of XXX , thus conferring the irreversibility of the decision and committing worms to the reproductive fate ( Figure 7A ) . In favorable conditions , the XXX cells and the hypodermis may share responsibilities . Under these conditions daf-9 expression in the XXX cells appears steady and hypodermal expression low . The XXX cells are sufficient but not necessary for committing to reproductive fate: rescue of the daf-9 ( dh6 ) putative null by a XXX cell-specific DAF-9 construct leads to adult development . Ablation of the XXX cells during the L1 stage , in worms grown in favorable conditions , results in 30% of animals developing as dauers [29] . However , the hypodermis can overcome this deficiency of XXX signaling by daf-9 upregulation [18] , . Hypodermal expression of daf-9 works as a homeostatic regulator since low amounts of DA increase transcription of daf-9 in a daf-12-dependent manner , whereas sufficient production of DA by the XXX cells is not followed by hypodermal upregulation of daf-9 during the L2 and L3 stages . From the daf-9 expression pattern , we infer that in favorable conditions DA is released in low levels over a long period of time , whereas worms developing in unfavorable conditions to adulthood release a burst of DA over a short period of time . This also implies that worms have a mechanism of counting and integrating hormone levels to reach the threshold of the dauer decision ( Figure 7B ) . We speculate that this could be achieved by various levels of DA swapping DAF-12/DIN-1 or other co-repressor complexes for DAF-12/co-activator complexes , a known mechanism in nuclear receptor signal transduction [30] . Consistent with the importance of the XXX cells to the dauer decision , many components of the dauer regulatory pathways are expressed in these cells , including ncr-1 , the Niemann-Pick C1 homolog , hsd-1 encoding a 3β-hydroxysteroid dehydrogenase , and sdf-9 and eak-6 , which encode tyrosine phosphatases , eak-3 , eak-4 , and eak-7 , novel proteins localized to the plasma membrane [18] , [19] , [27] , [29] , [31] , [32] . These components as well as others could regulate enzymatic activities , availability , or hormone transport to and from the XXX . Additional activities in the dauer pathways could regulate the amount of DA produced in the XXX cells and the adult DA threshold , in endocrine or target tissues . The spatiotemporal and homeostatic regulation of daf-9 provides a molecular mechanism that can explain the phases in the decision between dauer and reproductive development: integration of environmental and internal information , signaling of the decision , and implementation of a coordinated and irreversible decision . We propose that integration occurs by a process of information reduction and that signaling and implementation occur by a process of amplification and propagation upon reception of a discrete signal ( Figure 7B ) . Complex information from the environment is measured by at least six neuron pairs ( ASI , ADF , ASG , ASJ , ASE , and ASK [13] , [15] , [16] ) and reduced in complexity by the Insulin/IGF , TGFβ [33] , guanylyl cyclase [17] , and steroid hormone pathways [18]–[21] , [23] , [24] . We propose that the XXX cells then integrate information from these signal transduction pathways and commit to reproductive development by releasing DA ( Figure 7B ) . The latter phases , signaling and implementation of the decision , involve amplification and distribution of the discrete signal via a positive feedback loop in the hypodermis , thus ensuring a coordinated response over the whole animal . Amplification of the signal leads to independence from the integration apparatus . Should environmental conditions change and XXX cells cease to release DA , the amplification and diffusion over the whole body guarantees that all tissues will receive the necessary amounts of DAs required for normal adult development , thereby preventing inappropriate development in any cell lineage . In the future , it will be important to dissect further components of these life cycle commitments as well as the upstream mechanisms that weigh the decision within the XXX cells . Although there are clear differences across taxa , hormonal regulation in C . elegans bears many similarities to insect and mammalian hormonal regulatory mechanisms . First , external cues such as nutrients and photoperiod as well as internal cues such as body size or organ development affect developmental progression . Second , the hormone sensitive period overlaps with the environmental sensitive period and acts as a cue integrator also observed in the insects lepidoptera , hymenoptera , and diptera [34] . Third , developmental progression and coordination of development are relayed via the insulin/IIS and TGFβ/activin signal transduction pathways . In particular , the insulin/IIS pathway positively regulates reproductive growth in C . elegans [35] , [36] , insects [37] , and mammals [38] , [39] , and may do so by converging on steroidogenic pathways [20] , [23] , [37] , [40] . Conversely , a reduction of Insulin/IGF signaling increases the propensity for diapause in nematodes [4] , and insects [37] , as well as torpor in mammals [41] , [42] . TGFβ/activin signaling also controls steroidogenic enzymes and influences mammalian reproductive development [43] . Fourth , insect TGFβ/activin signaling regulates metamorphosis by controlling expression of a subset of steroidogenic enzymes through Insulin/IGF signaling and the prothoracicotropic hormone PTTH . PTTH is an insect peptide hormone that regulates developmental timing and body size at metamorphosis [44] . Its release from prothoracicotropic neurons and binding to its cognate receptor Torso in the prothoracic gland results in stimulation of steroidogenic gene expression and the production of Ecdysone [45] . In mammals , pulses of gonadotropin-releasing hormone ( GnRH ) may work analogously to PTTH to signal commitment to reproduction [2] , [38] . Although C . elegans lacks PTTH or GnRH hormones , conceivably other neuropeptides could take on this role . Fifth , the discrete spatial regulation of primary and secondary sources of steroid metabolism resembles somewhat those in insects , in which the precursor Ecdysone is produced in the PG , but the final product 20-hydroxyecdysone is converted in the peripheral tissues including the epidermis , midgut copper cells , Malpighian tubes and the fat body [44] . Last , hymenopterans and coleopterans have been shown to regulate alternative phenotypes by modulating the hormonal threshold via secreted pheromones [2] . The ant Pheidole bicarinata regulates the threshold of Juvenile Hormone ( JH ) , causing the differentiation between worker and soldier ants . Worker ants are determined by a sub-threshold dose of JH , while soldier ants are determined by above threshold amounts of JH . Ants that have committed to the soldier caste will secrete a soldier inhibiting pheromone , which raises the JH threshold in pre-committed ants [46] . We have demonstrated that a simple network architecture of positive feedback can both lock in a fate decision and convey irreversibility of a decision . Because the hormonal regulatory mechanisms found in the worm are similar to insect and mammalian systems , the relative simplicity of the C . elegans may prove beneficial in elucidating the environmental , cellular , and molecular mechanisms of decision-making involved in reproductive commitments in multi-cellular organisms . It will be particularly interesting to determine if the commitment role of hormonal amplification and feedback plays an analogous role in other animals as observed in C . elegans .
All worms were handled using standard growth and cultivation techniques using the bacterial strains HB101 and OP50 as food sources [47] . Unless otherwise stated all liquid cultures were grown in glass flasks at ∼1 worm per µl at 20°C in S complete medium supplemented with 7 . 5 mg/ml HB101 as described in [47] in an Innova 4230 incubator at 180 RPM . The wild-type strain used was N2 ( Bristol ) . Worms were hatched synchronously essentially as described by [48]; changes are described in the SOM . Crude pheromone was prepared as described in [28] . Each pheromone extract was tested on N2 worms ( 1 worm per µl ) and diluted so that 3% ( v/v ) would yield 90%±2% dauer arrest in a culture supplemented with 7 . 5 mg/ml of HB101 . Synchronous broods were grown as described above to the L2d stage by supplementing media with 3% ( v/v ) pheromone , partitioned into multiple parallel cultures , and grown in glass tubes . Shift to favorable: at specified times , broods were washed 3 times in S basal to remove pheromone . Cultures were re-suspended in S complete medium containing HB101 and calibrated for density . Shift to unfavorable: broods were supplemented with 3% ( v/v ) pheromone and grown in glass tubes . At specified time points ( L2d ) , worms were partitioned into a control sample and experimental samples , which were washed 3 times with S basal . Worms were suspended in S complete medium and allowed to grow for specific time periods until 3% pheromone ( v/v ) was added . Liquid culture: Δ7-DA was solubilized in 100% EtOH to necessary concentrations . Liquid culture assays were performed by adding EtOH-solubilized Δ7-DA in S basal medium . NG agar plate assays were performed by resuspending EtOH-solubilized Δ7-DA in S basal with OP50 and spreading on plates . Worms were picked onto Petri plates not more than 1 d after Δ7-DA was added to those plates . For the pdaf-9::gfp experiment Δ7-DA was added on 3 cm NG agar plates , seeded with OP50 . daf-9 ( dh6 ) , daf-9 ( e1406 ) , and daf-9 ( m540 ) worms were grown in liquid culture with different concentrations of Δ7-DA as described above . Worms were washed once with S basal medium to remove HB101 and mixed with S basal medium containing 1 mM sodium azide ( to limit worm movement ) , spotted onto a 24-well plate . Worms were scored for gonad migration and cuticle shedding . Phenotype frequencies were calculated as the means of three biological replicates ± standard deviation . Plots of daf-9 ( dh6 ) supplemented with 0% , 1% , 3% , and 6% pheromone as a function of DA were fit to a sigmoidal curve of the form f ( x ) = 1/ ( 1+xn ) , where x is a log transformed concentration of DA , and n is the hill coefficient of the slope . Each pheromone concentration hill coefficient and EC50 were used to solve the EC90; the concentration of DA to bypass 90% dauer formation or 90% normal adult formation ( Figure 3F , yellow and blue curves ) according to the equation: EC90 = ( 90/ ( 100-90 ) ) 1/n * EC 50 . Frequencies were calculated within each biological replicate and means of frequencies ± standard deviation were calculated between biological replicates . We determined the point of commitment at the measurement times with highest standard deviation as it represents the tipping point of a transition between non-committed to committed worms . We calculated a q-statistic based on a Tukey type multiple comparison test for differences among variances ( Zar 2009 [49]; Table S1 ) . Stage distributions were compared between three biological replicates in favorable and unfavorable conditions . A Bartlett's test [49] was used to determine if variances were significantly different between all stages of development . Analysis was performed by a one-way ANOVA ( Figure 5B , F ) . Significance of hypodermal upregulation in favorable versus unfavorable conditions after different time windows in favorable conditions was analyzed using a two-tailed t test between worms scored 30 hph ( Figure 5G , I ) . Significance of transcriptional upregulation was analyzed by one-way ANOVA across all time points ( Figure 5A ) and paired t tests between L2d uncommitted to L2d committed to dauer and to L2d committed to L3 ( Figure 5H , J ) . AA277 worms ( lin-15 ( n765 ) , dhIs64[daf-9::GFP , lin-15 ( + ) ] ) were grown to L2d stage in pheromone as described above . We found it necessary to use fluorescently labeled XXX cells as they migrate from the nose tip to the posterior region of the anterior bulb [50] . Worms were placed on glass slides with 5% agarose and 1 mM sodium azide in S basal , and laser microbeam ablations of the XXX cells were performed as described [51] . Worms were allowed to recover for 2 h before re-mounting on slides and verifying successful ablation by determination that no fluorescence signal was seen from either XXX cells . Worms were then transferred to either NG agar plates or NG agar plates supplemented with 1 , 5 , or 10 nM Δ7-DA . All ablations were coupled with mock-ablation controls . Statistical significance of observed differences between ablations and controls was determined using Fisher's exact test [49] . Strain AA277 was grown in liquid culture as described above . At specific times , worms were washed once in S basal medium and plated on glass slides with 5% agarose and 1 mM sodium azide in S basal . Worms were scored for hypodermal DAF-9::GFP under 40× magnification using a Zeiss Axiovert 200 microscope with a 200W mercury bulb . Anterior posterior DAF-9::GFP expression: Each worm was imaged using both Nomarski and fluorescence using a CoolSnap HQ camera ( Photometrics , Tucson , Arizona , USA ) run through Metamorph software ( MDS Analytical Technologies , Toronto , Ontario , Canada ) . Four to six worms were imaged per time period at 5 ms per Nomarski image and 400 ms per fluorescence image . Worms were straightened computationally , normalized to length , and mean grey value was quantified using custom software written in Matlab ( see Text S1 for details ) . Different concentrations of DA were added to NG agar plates , seeded with OP50 . One day later , 10 reproductive daf-9 ( dh6 ) pdaf-9::gfp adults ( grown in the presence of 250 nM DA ) were placed on each plate for egg laying . F1 progeny were scored for hypodermal daf-9 expression levels and dauer , molting , and gonadal cell migration phenotypes . Experiments were performed at 20°C and repeated at least twice . The GFP fluorescence was imaged through a Zeiss Axio Imager Z1 and photographed with an AxioCam MRm camera . Pixel intensity over a fixed area was measured with AxioVision 4 . 7 software . Synchronous populations of worms were grown at 20°C either in favorable ( 2 worms per µl , 15 mg/ml HB101 ) or in unfavorable conditions ( 3% pheromone v/v , 2 worms per µl , 15 mg/ml HB101 ) . At each time point , 104 worms were washed 3 times in S basal medium without cholesterol ( pH = 6 ) to decrease bacterial load and to wash off excess pheromone . Samples were concentrated in 100 µl volume and suspended with 1 ml TRIzol reagent ( Invitrogen , USA ) and mixed with 0 . 6 µl/ml Linear Poly acrylamide , used as a carrier [52] , flash frozen using liquid nitrogen , and stored at −80°C until processed . RNA purification using TRIzol was adapted from the manufacturer's protocol and is described in the SOM . RNA was subjected to quality control by Nanodrop spectrophotometry ( A260/280 ratio ) and Agilent Bioanalyser ( S28 to S18 ratio ) . Samples were processed if the A260/280 ratio was above 1 . 9 and the S28 to S18 ratio was above 1 . 8 . RNA was digested with RNase-free DNase ( Ambion , Austin , Texas ) according to the manufacturer's instructions . Total RNA was made into cDNA by reverse transcription reaction using Superscript III ( Invitrogen , San Diego , California ) . mRNA was selected for reverse transcription by using oligo dT primers . Reactions containing no reverse transcriptase were carried out in parallel . cDNA was purified on silica columns ( Qiagen , Venlo , Netherlands ) and diluted to 16 ng/µl for subsequent qPCR analysis . daf-9 transcripts were analyzed with three pairs of primers spanning different exons according to the WS190 gene model ( http://ws190 . wormbase . org ) . Each of the three amplicons was between 115 and 183 bp in length and included sequence from two exons ( Table S2 ) . All qPCR reactions were prepared using Roche SYBR Green I Master ( Roche Diagnostics ) and carried out in a Roche Lightcycler LC480 . Data analysis was performed according to the ΔΔCt method [53] . Efficiency values of each primer set were empirically determined by performing a dilution series on pooled cDNA . Transcripts were analyzed if they crossed the Ct threshold before 34 cycles . Control genes were determined empirically by measuring gene expression that did not change significantly ( Pearson correlation >0 . 995 ) during larval development ( L1 through L4 ) and dauer fate . daf-9 relative abundance was determined as follows: for mRNA processed from worms grown in favorable conditions , daf-9 was normalized to the geometric mean of control genes pmp-3 and- Y45F10D . 4 [54] . mRNA processed from worms grown in unfavorable conditions was normalized to relative abundance levels of ver-2 , a gene expressed only in the ADL neurons [55] . daf-9 fold change was determined by normalizing all time points to relative abundance in the L1 stage . Error bars represent mean fold change ± standard deviation across two technical replicates originating from three biological replicates ( six data points ) . Accession numbers from http://www . wormbase . org: Genes: daf-2: WBGene00000898 , daf-7: WBGene00000903 , daf-9: WBGene00000905 , daf-12: WBGene00000908 , daf-16: WBGene00000912 , din-1: WBGene00008549 , ncr-1: WBGene00003561 , hsd-1: WBGene00012394 , sdf-9: WBGene00004748 , eak-3: WBGene000022356 , eak-4: WBGene00009955 , eak-6: WBGene00008663 , eak-7: WBGene00010671 . Phenotypes: Mig , WBPhenotype:0000594 , Cut , WBPhenotype:0000077 . Cells: XXX: WBbt:0007855 , hyp7: WBbt:0005734 . | During development , many animals choose between mutually exclusive fates , such as workers , soldiers , or queens in bees or ants . The choice between states is uniform throughout the animal since mixtures of these fates are not observed in the wild . The nematode Caenorhabditis elegans larvae integrate environmental conditions and have two choices: mature into reproductive adults or arrest development as dauer larvae—a latent form that can survive harsh conditions . The decision between both fates is governed by the hormone dafachronic acid ( DA ) , however its regulation during development in response to environmental conditions has been unclear . In this study we show how two mechanisms are responsible for the integration of environmental conditions and the coordination of the decision between many tissues . We first show that a threshold mechanism integrates population density with the internal amount of DA made in the head . A normal population density has a low threshold of DA needed for worms to become adults , whereas a high population density increases this threshold and leads worms to develop into dauer larvae . We then show that the low levels of DA released from the head are amplified in the hypodermis ( the main body syncytial epithelium ) via a positive feedback loop , coordinating the decision over the animal . Disruption of this positive feedback yields abnormal adults . We propose that the positive feedback serves as a fate-locking mechanism enforcing an organismal binary decision—either adult or dauer—despite noisy and uncertain environmental conditions . | [
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| 2012 | Hormonal Signal Amplification Mediates Environmental Conditions during Development and Controls an Irreversible Commitment to Adulthood |
Throughout evolution , one of the most ancient forms of aggression between cells or organisms has been the production of proteins or peptides affecting the permeability of the target cell membrane . This class of virulence factors includes the largest family of bacterial toxins , the pore-forming toxins ( PFTs ) . PFTs are bistable structures that can exist in a soluble and a transmembrane state . It is unclear what drives biosynthetic folding towards the soluble state , a requirement that is essential to protect the PFT-producing cell . Here we have investigated the folding of aerolysin , produced by the human pathogen Aeromonas hydrophila , and more specifically the role of the C-terminal propeptide ( CTP ) . By combining the predictive power of computational techniques with experimental validation using both structural and functional approaches , we show that the CTP prevents aggregation during biosynthetic folding . We identified specific residues that mediate binding of the CTP to the toxin . We show that the CTP is crucial for the control of the aerolysin activity , since it protects individual subunits from aggregation within the bacterium and later controls assembly of the quaternary pore-forming complex at the surface of the target host cell . The CTP is the first example of a C-terminal chain-linked chaperone with dual function .
Many organisms , as diverse as bacteria , parasites , sea anemones or plants , produce membrane damaging proteins to protect themselves or to modify the behavior of their host [1] . Amongst these pore-forming proteins ( PFPs ) , we find bacterial pore-forming toxins ( PFTs ) . These are produced as soluble proteins that diffuse and bind to target cells via specific receptors . Many subsequently assemble into ring-like structures [2] , undergoing a conformational change with consequent exposure of hydrophobic surfaces . This drives spontaneous membrane insertion , leading to the formation of water filled pores . This peculiarity of PFTs , and PFPs in general , raises two interesting questions . The first is: since PFPs can adopt two quite different conformations , how is the folding reaction during biogenesis directed towards obtaining the soluble fold ? The second question is: what mechanisms prevent pore-formation from occurring in the producing cell ? To address these related questions , we have chosen the PFT aerolysin ( for review see [2] ) . Aerolysin is produced by the human pathogen Aeromonas hydrophila as an inactive precursor called proaerolysin . Conversion of proaerolysin to aerolysin involves proteolytic cleavage of a flexible 43-residue loop near the C-terminus ( Figure 1A and Figure S1A ) . Maturation occurs after secretion from the bacterium and processing by gut enzymes or proteases present at the target cell surface [3] , [4] . Since cleavage is essential for pore formation , it has been proposed that the role of the 43-residue C-terminal peptide ( CTP ) is to prevent premature oligomerization by steric hindrance , particularly within the producing bacterium . Our aim was to address the precise role of the CTP by combining computational techniques , site-directed mutagenesis , structural analysis , and functional assays . Our study reveals that the CTP drives the protein into the soluble state during biogenesis , protecting proaerolysin from aggregation possibly by promoting folding , a quite unexpected observation considering the C-terminal location of the peptide . Interestingly , mutagenesis of specific residues in the CTP not only affected the efficiency of proaerolysin folding both in vitro and in vivo , but also reduced the capacity of the CTP to prevent premature assembly of the heptamer , highlighting the dual role of the CTP in 1 ) preventing aggregation of the newly synthesized protein possibly by assisting folding , and 2 ) controlling the quaternary assembly of the active complex .
The crystal structure of proaerolysin has been solved at 2 . 8 Å resolution [5] . The protein is L-shaped , composed of a continuous globular N-terminal domain ( Figure 1A ) , Domain 1 , and an elongated region consisting of three discontinuous structural domains ( Figure 1A and Figure S1A ) . Domains 1 and 2 are involved in binding to cell surface receptors [6] . Domain 3 is involved in oligomerization and contains a loop that traverses the membrane upon pore formation [7] . Domain 4 has no known function but contains the CTP , which is folded as 2 anti-parallel β-strands connected by a short α-helix ( Figure 1A ) . To characterize the molecular interactions between the CTP and Domain 4 , we performed classical molecular dynamics ( MD ) simulations . MD aims at quantitatively describing structural and thermodynamic properties of biomolecules within physiological-like conditions [8] . The existing potential energy models used in MD have been shown to provide accurate representation of atomistic interactions , and they have been used to investigate the biophysical properties of a broad variety of molecular systems , including the PFTs α-hemolysin from S . aureus [9] , [10] . To remain as close as possible to experimental/in vivo conditions , we performed in MD simulations at room temperature ( 27°C ) and atmospheric pressure ( 1 atm ) , and the proteins were solvated by water molecules at physiological salt concentration . MD simulations reported here were all based on the X-ray structures of wild type ( WT ) proaerolysin ( for details about the structures used , see Methods ) . The loop connecting the CTP to the rest of the molecule is however not visible in any reported crystal structure of proaerolysin , probably due to its flexibility . Thus proaerolysin was de facto modeled in a situation mimicking a cleaved proaerolysin state ( here after termed aerolysin-CTP ) . During the 200 ns of MD , the CTP remained firmly bound to the protein ( Figure 1B ) . Native hydrogen bonds and salt bridges were preserved along the entire trajectory , as were secondary structure elements , both in the CTP and in Domain 4 ( Figure 1B ) . The mean conservation of secondary structure in the system , i . e . the percentage of residues in a β-sheet conformation along the MD simulation with respect to the initial crystal structure , was 86±5% over the last 100 ns . Since the MD simulations were performed with the absence of a covalent bond between the CTP and Domain 4 , these observations pointed to a strong binding affinity of CTP for Domain 4 . By computing electrostatic , van der Waals and solvation contributions to the binding of CTP to Domain 4 , we estimated their binding energy to be 115 kcal/mol . Our MD-based observations thus suggest that the CTP remains bound to aerolysin upon proteolytic activation of the protoxin . This was confirmed using two independent experimental approaches . First , we determined the structure of the proteolytically processed form of an aerolysin mutant that is unable to form heptamers , namely H132N [11] . The crystal structure of the trypsin-processed H132N mutant was solved by molecular replacement to 2 . 3 Å resolution ( PDB entry 3G4O ) ( [12] , Protocol S1 ) . Not only was the observed structure very similar to that of wild-type ( WT ) proaerolysin ( root mean square deviation RMSD of 0 . 74 Å for subunits A and 0 . 92 Å for subunits B in the dimer ) , it also contained the CTP , in an essentially identical conformation ( Figure 1C ) . As a second approach to investigate whether the CTP remains bound to the mature toxin following proteolysis , we took advantage of our E . coli expressed WT proaerolysin , which harbors a six-histidine tag at the C-terminus , i . e . at the end of the CTP . When proaerolysin was incubated with Nickel beads , it remained attached to the beads as expected and could be eluted with imidazole but not with urea ( Figure 1DE ) . When trypsin-processed toxin was incubated with Nickel beads , it also remained bound to the beads showing that the CTP had not been released upon proteolysis . Consistently with the non-covalent interaction between the mature toxin and the CTP , aerolysin could be released from the beads with urea ( Figure 1D ) . We had previously reported that processing of proaerolysin with trypsin leads to the release of the CTP from aerolysin [13] . This conclusion was based on the observation that fluorescence energy transfer was lost between a fluorescent probe , IEADANS , attached to an engineered cysteine on the CTP at position 445 and Trp-203 in Domain 4 [13] . Our current findings suggest that the previously observed release of the CTP was artefactually induced by the mutation and/or labeling of the cysteine at position 445 . Indeed , in wild-type proaerolysin , Ile-445 on the CTP is buried within a hydrophobic pocket in Domain 4 and labeling of Cys-445 with the bulky and polar IAEDANS fluorophore ( mimicked in Figure S1B ) must have triggered a severe perturbation at the CTP-Domain 4 interface , leading to premature release of the CTP upon trypsin cleavage . Both X-ray structures of WT proaerolysin and H132N aerolysin-CTP show the presence of a similar complex network of interactions between the CTP and Domain 4 composed of H-bonds ( 10 in subunit A , and 16 in subunit B ) , salt bridges ( Asp-207 with Arg-442 and Lys-198 with Glu-451 ) , and hydrophobic interactions . To identify key residues responsible for binding of the CTP to Domain 4 , we performed in silico alanine scanning on most of the CTP . In silico mutation of a given CTP residue to alanine has the effect of removing most of the native non-bonded interactions ( i . e . , electrostatic and van der Waals contributions ) with the local environment . By comparing the binding free energy of the WT species and its alanine mutant , it is possible to estimate the individual contribution of a given CTP residues to the binding affinity with Domain 4 . The greater the variation , the more the residue has a relevant role in the steady binding of the CTP to Domain 4 . As expected , mutation of solvent exposed residues showed little variation in the binding free energy ( Figure 2A ) . A low but significant variation was observed for certain polar residues , such as Asn-458 , which forms a hydrogen bond with Asp-222 , Asp-448 , which forms a salt bridge with Lys-198 , and especially Arg-442 , which forms a salt bridge with Asp-207 . The most dramatic variations in the binding free energy ( ∼6 kcal/mol ) were observed for three hydrophobic residues: Leu-441 , Phe-457 and Leu-462 . All three residues point inside a hydrophobic pocket in Domain 4 underlying the CTP ( Figure 2B ) . More specifically , Leu-441 interacts with Val-285 , Ala-204 , Pro-283 on Domain 4 and Leu-443 on the CTP , Leu-462 interacts with Val-217 , Leu-219 , and Ile-296 on Domain 4 and Ile-414 and Leu-443 on the CTP , and finally Phe-457 points straight into Domain 4 , and is blocked by steric hindrance with Val-197 and Leu-297 on the Domain 4 and Ala-411 and Leu-452 on the CTP . Since Phe-457 on the CTP points straight into Domain 4 , we investigated the effect of mutating this residue to glycine in silico using an MD setup similar to the one adopted for the WT species ( Video S1 ) . The mean conservation of the secondary structure of the CTP drastically dropped from 76±12% for the wild type to 17±4% for the F457G mutant ( Figure 3B ) . The CTP structure remaining after the simulation was a portion of the α-helix ( Figure 3A ) , which we determined to be the most stable structural element in an MD simulation of just the CTP in water ( Figure S2BC ) . Interestingly , the F457G mutation also affected the underlying Domain 4 ( Figure 3 ) . Indeed , after 100 ns of MD , the mean conservation of the secondary structure of Domain 4 ( including CTP ) was 86±5% for the wild type and 67±5% for the F457G mutant . By computing electrostatic , van der Waals and solvation contributions to the binding of the mutated CTP ( F457G ) to domain 4 , we estimated their binding free energy to be 75 kcal/mol . This represents a significant reduction with respect to the 115 kcal/mol previously estimated from the aerolysin-CTP MD simulation . The effects produced by a mutated CTP on Domain 4 prompted us to compare the structural features of aerolysin with and without its CTP . In silico , we removed the CTP from the proaerolysin crystal structure , and 200 ns MD simulation was performed ( Video S2 ) . Simulations performed in the presence and absence of the CTP were subsequently compared . The structural flexibility of each residue was quantified by calculating the root mean square fluctuation ( RMSF ) of the residue along the MD trajectory . Removing the CTP had no significant effect on the structure of Domain 2 and 3 ( Figure 4A , Domain 1 was omitted from the simulation since it is known to act as an independent folding unit [14] ) . In contrast , removal of the CTP led to an average increase of 6 . 8±3 . 2 Å of the RMSF for a given residue in Domain 4 ( Figure 4A ) , suggesting that the CTP stabilizes the structure of Domain 4 . The CTP also had an influence on the secondary structure of Domain 4 . This was assessed by tracking the percentage of secondary structure conservation along the two simulations , i . e . the percentage of residues adopting a ß-sheet conformation in the absence of CTP as compared to crystal structure of proaerolysin . In the absence of the CTP , the secondary structure conservation of Domain 4 was 67±10% ( Figure 4B ) , compared to 86±5% in the presence of CTP , and the RMSD ( root mean square fluctuation deviation ) after 200 ns of MD was 8 . 8 Å , compared to 3 . 6 Å in the presence of CTP . In silico removal of the CTP led to the unfolding of the β-strand encompassing residues Ser-272 to Ser-280 in Domain 4 ( Figure 4C ) . Interestingly , a further sequence-based analysis using order prediction algorithms identified the 268-282 segment as the most disordered region of Domain 4 ( for algorithms used see Protocol S1 ) ( Figure S2A ) , raising the possibility that the ß-structure observed for this segment in the proaerolysin crystal structure is in fact imposed by the CTP . It is interesting to note that induced folding of intrinsically unstructured elements often involves hydrophobic , rather than polar , interactions [15] as observed here for the CTP-Domain 4 interface . The above MD analyses suggest that the structure of Domain 4 strongly depends on interactions with the CTP . This raised the interesting possibility that the CTP acts as a stabilizer or an intramolecular chaperone during biosynthetic folding of proaerolysin . A prediction from this hypothesis is that when synthesized and translocated across the inner E . coli membrane , aerolysin , i . e . lacking the CTP , should not be able to reach a soluble form in the bacterial periplasm . To test this , we generated constructs encoding only the N-terminal signal sequence and the mature protein without the CTP . Constructs were generated to express the WT version as well as H132N as to avoid potential oligomerization in the periplasm . As mentioned earlier , residue His-132 is required for heptamer formation [11] . When extracts from bacteria harboring WT or H132N aerolysin-ΔCTP expression constructs were analyzed by SDS-PAGE and Coomassie blue staining , a band migrating at the expected ≈50 kDa molecular weight was observed upon induction with IPTG , showing that bacteria were able to produce aerolysin-ΔCTP and that the protein was not degraded by periplasmic proteases ( Figure 5 AD ) . When bacteria were processed to generate periplasmic and spheroplast fractions , proaerolysin , which migrates at ≈52 kDa , was recovered in the periplasmic fraction . In contrast WT or H132N variants of aerolysin-ΔCTP were only recovered in the spheroplast fraction ( Figure 5 AD ) . Care was taken to induce toxin expression with a low IPTG concentration ( 0 . 25 mM ) at a bacterial density of OD600 nm = 0 . 6 for only 2 hrs at 18°C , to avoid jamming of the translocation machinery across the inner membrane and impair periplasmic folding . That periplasmic translocation of both proaerolysin ( Figure 5B upper panel ) and aerolysin-ΔCTP ( Figure 5B lower panel ) did occur under these conditions was confirmed by Western blot analysis . A weak band corresponding to pre-proaerolysin , i . e . protein for which signal sequence cleavage had not yet taken place , could be observed in the spheroplast fractions ( Figure 5B ) . The bulk of both proaerolysin and aerolysin-ΔCTP however underwent processing by signal peptidase confirming that both forms of the toxin were properly translocated across the inner membrane ( Figure 5B ) . Our in silico alanine scanning analysis ( Figure 2A ) predicted that mutation of Leu-441 , Phe-457 and Leu-462 , and to a lesser extent Arg-442 , to alanine should affect binding of the CTP to Domain 4 . To test these MD-based predictions , we generated constructs to express these mutants in the E . coli periplasm . We also sought a mutation that would affect the secondary structure of the CTP but not the binding . We chose to change Ser-453 to proline since this residue localizes to the middle of the α-helix of the CTP ( Figure 1A ) and does not make contacts with Domain 4 . In agreement , in silico mutation of Ser-453 to alanine did not lead to a significant variation in the binding free energy ( Figure 2A ) . Due to the folding of its side chain back onto the protein backbone , proline imposes severe constraints to the backbone geometry leading to helix breaking . All proaerolysin mutants were detected in bacterial extracts showing that they were synthesized and not degraded to any significant extent ( Figure 5B ) . Proaerolysins L441A , R442A and L462A were recovered in significant amounts in the periplasmic fraction ( Figure 5D ) . Proaerolysin S453P was barely detectable in the periplasmic fraction ( Figs . 5CD ) , but following purification low amounts of the protein could be obtained . Proaerolysins F457A/G were essentially undetectable in the periplasmic fraction ( Figs . 5CD ) and neither could be recovered following purification on Nickel columns . These observations show that mutating Ser-453 to proline or Phe-457 to glycine induced aggregation of proareolysin in the bacterial periplasm , either due to the exposure of a hydrophobic patch or improper folding of part of the protein . The small amounts of toxin that could be purified for all mutants was however properly folded as indicated by the WT-like hemolytic activity of the mutants following trypsin cleavage ( Figure 6AB ) . We next investigated the in vitro folding of the mutant proaerolysins . For this , proaerolysins , WT and mutants , were unfolded in urea . We have previously shown that proaerolysin unfolding in urea or GdnHCl occurs in two steps , corresponding to the unfolding of Domains 2 to 4 , followed by the unfolding of the highly stable Domain 1 [14] ( Figure S3AB ) . All four proaerolysin mutants showed very similar urea unfolding curves ( Figure S3C ) . Following unfolding in 4 M urea , refolding of proaerolysins was triggered by dilution in a urea-free buffer . The efficiency of folding was indirectly monitored by measuring the hemolytic activity of the refolded proaerolysins after proteolysis with trypsin ( trypsin was added 5 min after dilution in the urea free medium ) . Hemolysis was followed as a function of time . Under these conditions , refolded L441A and S453P systematically showed a delayed hemolytic activity ( Figure 6C ) . These experiments reveal that in vitro folding into a soluble state of the L441A and S453P proaerolysin mutants was impaired and the extent correlated with the ability of the mutants to fold into a soluble state in vivo ( Figure 5 ) . Altogether , these computational analyses and experimental observations indicate that the CTP is required for proper folding of aerolysin and that both the structure of the CTP and its binding affinity to Domain 4 are important for proaerolysin to reach a soluble active state . Since aerolysin without the CTP could not be purified from bacteria , we studied the folding of aerolysin by cleavage of proaerolysin with trypsin followed by unfolding in urea . Unfolding transitions occurred at similar concentrations of chaotropic agents whether proaerolysin was processed by trypsin or not , suggesting proaerolysin and aerolysin-CTP have similar stabilities . Refolding was initiated by dilution into a urea free buffer and the tryptophan emission spectrum was measured at different times for more than 24 hrs . The maximum emission wavelength of proaerolysin rapidly shifted from 344 nm , corresponding to the unfolded protein , to 336 nm , corresponding to that of native proaerolysin . Under refolding conditions , the maximum emission wavelength of aerolysin –which had presumably lost its CTP during unfolding– however failed to reach that of native aerolysin-CTP , indicating that refolding did not occur or was partial . This was confirmed by circular dichroïsm ( CD ) in the far UV , which allows monitoring of secondary structure . As described previously , proaerolysin and aerolysin-CTP have very similar far UV CD spectra ( Figure 7A ) [16] . Upon unfolding and refolding , the spectrum of proaerolysin was to a large extent recovered ( Figure 7A ) . In contrast , the spectrum of aerolysin under refolding conditions showed typical features of a random coil , with a strong negative ellipticity in the 200 nm spectral region possibly due to protein aggregation ( Figure 7A ) . Thus , aerolysin was unable to reach a soluble state in vitro confirming the in vivo experiments ( Figure 5 AD ) . Interestingly , under refolding conditions , aerolysin did not fold into a molten globule-like structure , which has native like secondary structure ( Figure 7A ) . That aerolysin failed to reach a native conformation in vitro was confirmed by the lack of hemolytic activity after refolding from >4 M urea , in contrast to proaerolysin which refolded properly even from >6 M urea ( Figure 7B ) . All of the above-described observations point towards a role of the CTP in promoting the folding of proaerolysin into a soluble protein during biosynthesis . We finally investigated whether the CTP could also act when added in trans during in vitro refolding of aerolysin . We found that addition of 5-fold molar excess of synthetic CTP led to a significant and reproducible recovery in hemolytic activity of aerolysin , whereas addition of an irrelevant peptide did not ( Figure 7C ) . The fact that rescue was only partial is not surprising since having a covalently bound CTP , as in proaerolysin , greatly increases the effective concentration of the peptide , a situation that cannot be mimicked by ectopic addition of excess CTP . It had previously been proposed for Clostridium α-toxin , a toxin with 27% sequence identity and 72% similarity to aerolysin , that the role of the CTP is to inhibit the oligomerization process [17] . We found that this role is also fulfilled by the aerolysin CTP . Proaerolysin was cleaved in vitro with trypsin and oligomerization was allowed to proceed in the absence or presence of a five-fold excess of either synthetic CTP or a control peptide . Heptamer formation was delayed by the presence of CTP ( Figure 8AB ) . The role of the CTP in oligomerization was confirmed by the following observation . When proaerolysin was processed by trypsin in a pH 8 buffer to avoid oligomerization [11] and subsequently dialyzed against a neutral pH buffer to allow oligomerization , heptamer formation was only observed with a dialysis cut off that allowed the passage of the CTP ( 14 kDa ) but not with a cut off that retained the CTP ( 3 . 5 kDa ) ( Figure 8C ) . Thus the binding of the CTP to Domain 4 inhibits oligomerization . A corollary of the observation that the CTP inhibits oligomerization is that the CTP must be displaced from the mature protein for the process to occur and thus that weaker CTP binding should promote oligomerization . We first tested whether the S453P CTP would be released more readily than the WT CTP . To address this issue , WT and S453P proaerolysins were bound to Nickel charged NTA Surface plasmon resonance ( SPR ) sensor chips –via the His-tag at the C-terminus of the CTP– and cleavage of the CTP was induced by trypsin addition . After trypsin addition , a strong loss of signal was observed ( Figure 9A ) , presumably corresponding to the release of the mature toxin from the chip-bound CTP . From these curves , we estimated an apparent Koff of 4 . 5 . 10−3±0 . 6 . 10−3 s−1 for WT and 2 . 6 . 10−2±0 . 4 . 10−2 s−1 for S453P , confirming that the off rate of the S453P CTP was about 10 times higher than that of the WT CTP . These Koff values should , however , only be considered in a comparative and not an absolute manner . The structure of aerolysin H132N and the experiments of binding WT aerolysin-CTP to Nickel beads ( Figure 1DE ) indeed show that the WT CTP does not come off over a period of several hours , which is inconsistent with a Koff in the order of 10−3 s−1 . Binding of the CTP to the SPR chip therefore appears to have induced an accelerated release . That the S453P CTP has a lower affinity for the mature toxin was confirmed by the observation that upon binding of S453P aerolysin-CTP to Nickel beads , about 40% of the total aerolysin was recovered in the unbound fraction ( Figure 9BC ) , i . e . it was released from the bead-bound CTP , whereas less than 10% of the aerolysin was released when performing a similar experiment with the WT toxin ( Figure 1DE ) . Importantly , the CTP-free aerolysin fraction recovered from the S453P-treated beads had the same hemolytic activity as WT proaerolysin treated with trypsin ( 9±1 wells lysed in 60 min for CTP-free aerolysin ( n = 3 ) and 8±0 . 5 for trypsin treated WT proaerolysin , see methods ) . Three important conclusions can be drawn from this observation: 1 ) the CTP is not required for pore formation , confirming our previous findings [13]; 2 ) CTP-free aerolysin does not unfold –but might change conformation– since it retains its full activity; 3 ) CTP-free aerolysin does not undergo unproductive aggregation , thus the role of the CTP is not merely to prevent aggregation of monomers . If CTP release is necessary for oligomerization , then oligomerization should be accelerated when CTP binding is weaker . This is indeed what we observed when comparing oligomerization of WT and S453P: upon trypsin cleavage of S453P proaerolysin , oligomerization occurred faster than for WT ( Figure 10AB ) . To our surprise , we found that S453P actually already showed some hemolytic activity even in the absence of trypsin cleavage ( Figure 10C ) , which is never observed for WT proaerolysin . This activity was some 15 fold lower than upon trypsin cleavage , yet significant and reproducibly detectable . Hemolytic activity in the proaerolysin form was also observed for the other CTP mutants L441A , R442A and L462A , but to a lesser extent ( Figure 10C ) . These observations show that cleavage of the loop linking the CTP to Domain 4 is not essential and that peptide displacement is sufficient .
Guided by a combination of molecular simulations and in silico mutagenesis analysis and using a combination of structural and functional assays on WT and mutant toxins , we show that the CTP is essential for the folding of aerolysin into a soluble toxin . Due to the fact that it promotes folding but is not part of the final active conformation of the protein , i . e . the transmembrane heptameric pore , the CTP qualifies as a chain-linked molecular chaperone [18] . Chaperones comprise both proteins that favor the folding reaction of substrate proteins and proteins that control the quaternary assembly of multisubunit complexes . These two distinct roles can also be found in chain-linked , or intramolecular , chaperones and have been termed type I ( folding ) and type II ( assembly ) intramolecular chaperones [18] . Chain-linked chaperones can be short peptides ( ≈40 residues ) or independent folding units . They are often found in secreted or transmembrane proteins , a situation that requires the protein to be translocated across the plasma membrane in prokaryotes ( as for proaerolysin ) or the ER membrane in eukaryotes . As discussed below , due to the directionality of membrane translocation coupled to protein synthesis , type I intramolecular chaperones are found at the N-terminus of proteins . However , exceptions , such as aerolysin , exist . Indeed , an N-terminal chaperone prevents misfolding a priori , while a C-terminal chaperone would act a posteriori . In contrast , most documented type II intramolecular chaperones are C-terminal . Irrespective of their localization , chain-linked chaperones should not be part of the final structure , which implies that they must be cleaved off at some point . One of the earliest and best-characterized examples of a protein with an N-terminal intramolecular chaperone is Bacillus subtilis subtilisin , in which the 77 first amino acids fold into a well defined domain promoting the folding of the next 275 residues , acting as a type I chaperone , and is subsequently cleaved off by autoproteolysis [19] . C-terminal intramolecular chaperones have also been described . They are , however , generally of the type II , playing a role in controlling the quaternary assembly of proteins such as tail spikes of bacteriophages or fiber forming collagen [18] . Examples of type I C-terminal chaperones are rare and evidence is circumstantial [20] , [21] , [22] , [23] . Aerolysin thus appears to be the first example of a protein bearing a C-terminal chain-linked chaperone promoting the formation of soluble monomeric subunits and controlling assembly of the active complex , i . e . both type I and type II . The present studies indeed show that the aerolysin CTP acts as a type II chaperone in controlling the onset of heptamerization , a role consistent with its C-terminal location . More unexpectedly , we found that the CTP drives formation of soluble proaerolysin . Mutations in the CTP that affects its structure ( S453P ) or its binding to Domain 4 ( L441A , F457G ) indeed lead to aggregation of proaerolysin both in vivo and in vitro . Moreover aerolysin , devoid of CTP , also aggregated . Importantly , addition in trans of a 5 fold molar excess of synthetic CTP allowed partial recovery of activity . Upon CTP release , the trigger for which remains to be established , aerolysin remains folded , possibly with a somewhat different conformation , as illustrated by the full hemolytic activity of CTP-free aerolysin obtained from the S453P mutant . The unaltered activity of CTP-free aerolysin also indicates that the CTP plays a role in the biogenesis of the toxin and does not prevent unproductive aggregation of protein once folded . Altogether , these observations thus classify the aerolysin CTP as a chain-linked intramolecular chaperone . Our observations clearly indicate that the CTP prevents aggregation of proaerolysin during biosynthetic folding . As mentioned above , and as supported by the ability of the CTP to promote recovery of hemolytic activity upon in vitro folding of aerolysin , the CTP appears to do more than merely preventing aggregation as also suggested by the molecular dynamics studies . Confirming that the CTP promotes the folding of aerolysin and how it does so will require further investigation . Since proaerolysin is translocated from N- to C-terminus when crossing the inner Aeromonas membrane , the CTP appears last . In particular , it appears some 250 residues later than some of the residues it interacts with . What is also puzzling is that the CTP is not an independent folding unit that could guide folding of the rest of the protein , as is the case for most type I intramolecular chaperones . Our MD simulations suggest that when released from the protein , as mimicked by the F457G mutation , or when free in solution , the CTP is largely unfolded ( Figure S2BC ) . Therefore the CTP might stabilize folding intermediates . It has been proposed that , as a protein follows its folding landscape , the chaperone domain binds to , stabilizes and increases the population of molecules with native conformations . Thus , as opposed to general chaperones , which are thought to lack any structural information about the protein they fold , dedicated chaperones and possibly the aerolysin CTP could promote folding via conformational selection [24] , [25] , [26] and thus convey steric information . This hypothesis is consistent with the observation that one segment of Domain 4 with which the CTP interacts in the final structure , residues 269–279 , is predicted to be unstructured . Even though largely unfolded , such segments are likely to fluctuate between multiple folded states during short times , one of which could be stabilized by the CTP . A prediction from the conformational selection model for CTP-mediated proaerolysin folding is that folding should be affected by mutations in the CTP . This is indeed what we observed for the mutants suggested by in silico alanine scanning mutagenesis and in particular for the S453P and F457A/G mutations . As mentioned above , the CTP appears to force segment 268–272 into a β-strand conformation . Importantly , this segment is directly followed by the loop in Domain 3 that is to form one of the amphipathic ß-hairpins of the heptameric transmembrane ß-barrel pore ( Figure S2A ) . The ability of the CTP to control the folding state of the underlying β-strands ( note that Domain 4 shares multiple β-strands with Domains 3 and 2 ) suggests that the peptide also acts as a switch to control the pore formation process . Our observations indeed show that CTP release promotes oligomerization and that the CTP is not part of the final pore . Future studies will address what triggers release of the CTP . Our preliminary observations indicate that specific detergents can displace the CTP , consistent with the importance of hydrophobic interactions in CTP binding and suggesting that the target cell membrane may play a role . Future studies will also address whether CTP release triggers partial unfolding of Domain 4 and whether these changes propagate to Domain 3 helping overcome the energy barrier that leads to formation of the heptamer , the most thermodynamically stable conformation [14] .
Proaerolysin WT and mutants were expressed using a pET22b vector ( Novagen ) , which allows periplasmic expression of the toxin with a His6 tag on the C-terminus , as described [7] . Mutagenesis was carried out using the Quick Change Kit ( Stratagene ) . Briefly , BL21 [DE3] pLysS E . coli harboring the WT or mutant aerolysin expression plasmid were grown at 37°C to an OD600 of 0 . 6 . IPTG ( 0 . 25 mM ) was added and cultures were shifted to 16°C for protein production . Cells were harvested after ≈2 hrs ( reaching an OD600 = 1 . 2 ) . Periplasmic fractions were isolated by resuspending cells in T Buffer ( 0 . 1 M Tris-HCl pH 8 . 0 , 18% sucrose ) containing 5 mM EDTA and 0 . 2 mg/mL lysozyme . After agitation for 30 min at 4°C , the periplasm and spheroplasts were separated by centrifugation . For purification , the supernatant was further ultracentrifuged ( 100 000 x g for 2 h at 4°C ) , filtered ( 0 . 45 µm ) and dialyzed against 20 mM sodium phosphate buffer pH 7 . 4 , 0 . 5 M NaCl and loaded on a 1 mL HiTrap chelating column ( Amersham Pharmacia Biotech ) running on an AKTATM prime FPLC workstation . The protein was eluted in a 20 mM sodium phosphate buffer pH 7 . 4 , 0 . 5 M NaCl buffer with a linear gradient of imidazole ( 0–0 . 5 M ) . Finally , fractions containing the protein were dialyzed against 20 mM MES buffer pH5 , 150 mM NaCl before snap freezing and storing at −80°C . Protein concentration was determined by O . D . 280 measurements using an estimated ε≈13 . 05·104 M−1·cm−1 . The S453P single point mutant was dialysed into 20 mM Tris 150 mM NaCl pH 8 following purification due to its tendancy to oligomerizes unprocessed when dialyzed into the MES pH 5 buffer . Unless specified , processing of proaerolysin was performed by addition of 1/100 ( weight/weight ) of soluble trypsin ( Sigma ) and incubation for 10 min at room temperature . Where specified , the proaerolysin containing solution of 20 mM MES buffer pH5 , 150 mM NaCl was adjusted to pH 8 by addition of 1 M Tris buffer pH 8 . 7 to avoid oligomerization [11] . Pre-washed trypsin immobilized on agarose beads ( Sigma ) was added to the proaerolysin sample at 4°C and incubation was allowed to proceed on a rotary shaker for 2 hrs . The trypsin agarose beads were removed by centrifugation at 7000 rpm in a tabletop Eppendorf centrifuge . The degree of activation was assessed by SDS-PAGE and Coomassie blue staining . Activity of aerolysin was defined by its ability to lyse red blood cells . Serial dilutions of aerolysin starting at 20 µg/ml were incubated with a 0 . 5% solution of red blood cells in a 96 well plate . Activity was either recorded as number of wells fully lysed in 60 min at room temperature [7] or as the transmitted lightof the erythrocyte suspension monitored at 600 nm as a function of time in a given well using an automated 96 well plate reader at 37°C . Proaerolysin at a concentration of 0 . 4 mg/mL was submitted to proteolysis with trypsin bound to agarose beads as described above . A 5 fold mol/mol excess of synthetic propeptide ( EzBiolabs ) , control peptide , or an equal volume of buffer , was added to the sample . To initiate the oligomerization process ( which requires a pH<8 and is promoted by low salt ) , the sample was dialyzed at 4°C against 10 mM Hepes buffer pH 7 , 10 mM NaCl for 2–4 hours . The dialysis molecular weight cut off was 3 . 5 kDa , unless specified otherwise . Aliquots were removed at different time points and subjected to SDS-PAGE . Tryptophan fluorescence was measured as described [14] using a SpectraMax M2e spectrofluorimeter . Circular dichroism ( CD ) measurements were performed at 20°C using a Jasco J815 spectrometer using quartz cells of 0 . 01 cm path length [13] . Spectra between 190 and 250 nm were recorded in 20 mM Hepes buffer pH 8 , 50 mM NaF at protein concentrations between 0 . 1–0 . 3 mg/mL . For unfolding , proaerolysin WT or mutant or aerolysin was incubated in 4 M urea ( see Protocol S1 ) . Refolding was triggered by 1:10 dilution into urea free buffer . The refolding reaction was assessed by circular dichroism or hemolytic activity . The buffer blank solution was obtained by dilution of the respective buffers . WT proaerolysin in its dimeric form has been crystallized with a resolution of 2 . 8 Å ( entry 1PRE in protein databank ) . In this crystal structure , two loops located on top of Domain 4 proved too flexible to be crystallized , namely residues 207 to 211 and 423 to 439 . A crystal structure of dimeric proaerolysin mutant Y221G has been obtained with a higher resolution of 2 . 2 Å ( entry 3C0N in protein databank ) . In this crystal , residues 207 to 211 could be mapped in the crystal structure but loop 423 to 439 is still missing . This loop connects the CTP to the rest of the protein , and contains the site where cleavage takes place during aerolysin activation ( 420–427 ) . A model of wild-type aerolysin with the propeptide bound ( labeled aerolysin-WT ) to use in molecular dynamics simulations was obtained by using 3C0N structure , and mutating residue 221 back to tyrosine using 1PRE as a structural template ( wild-type rebuilding did not caused any steric problem since 3C0N and 1PRE were virtually identical ) . We assumed that this model would mimic the aerolysin structure after cleavage , i . e . C-terminal propeptide no longer covalently connected to the protein , but still bound to it . In fact , this model is structurally equivalent to the cleaved aerolysin H132N X-ray structure showed in this work . We modeled active aerolysin ( labeled WT ) by removing the propeptide from the previous model . Mutation F457G has been performed by removing the Phe-457 side-chain . Aerolysin contains six histidines . Their protonation state at physiological pH has been defined by the presence of proton donors and acceptors in their neighborhood in the crystal structure . We concluded that in H107 , H121 , H132 , H186 and H332 Nε atom is protonated , whereas in H341 Nδ is protonated . These model systems were solvated in a rectangular box of pre-equilibrated TIP3P water molecules , and their total charge was neutralized by the addition of Na+ and Cl− counterions . Molecular dynamics has been performed for aerolysin with CTP ( labeled Aero-CTP ) , without CTP ( labeled Aero ) and mutation F457G using the Amber parm99sb force field [27] on NAMD molecular dynamics engine [28] , using the SHAKE algorithm on all the bonds , and Particle-mesh Ewald for treating the electrostatic interactions in periodic boundary conditions [29] . We used an integration time step of 2 fs . The systems were energy-minimized by means of 1000 conjugate gradient steps , and subsequently gradually heated from 0 to 300 K in 1 ns at 1 atm . Simulations were run in the NPT ensemble at 1 atm and 300 K . Temperature was controlled by means of Langevin forces , using a damping constant of 1 ps-1 . Preliminary results confirmed that Domain 1 is bulky and , being connected by a long random coil to the large lobe ( Figure 1A ) , extremely flexible with respect to the rest of the protein . Since this resulted in no influence on the structure of the other domains , we decided to remove it in order to reduce the system size and therefore speed up the remaining computation . All simulations were run for at least 200 ns . RMSD of MD simulations showed that every system equilibrated in around 10 ns . Alanine scanning was performed on the single 200 ns molecular dynamics trajectory of CTP-WT system . A subset of 200 decorrelated frames ( one every 10 ns ) was extracted . On this subset , we calculated binding free energies of Ala mutant species using the MM-PBSA method , as implemented in the AMBER molecular dynamics package ( 24 ) . The Poisson-Boltzmann method was used to compute the electrostatic contribution to the solvation free energy . Ionic strength molarity was set to 0 . 1 M , the protein dielectric constant to 1 , and the solvent to 80 . Every residue being part of the CTP , excluding glycines and prolines , was scanned . These residues play a major role in the determination of strand flexibility , thus the alanine scanning is known to perform poorly . The MM-PBSA method was also used to estimate the binding free energy of WT and F457G mutated CTP to domain 4 . For both these measures , 200 decorrelated frames extracted from aero-CTP and F547G MD simulations were used , respectively . Analysis of MD trajectories , as well as rendering of protein structures , has been performed using VMD [30] . | Many pathogenic bacteria produce proteins , called pore-forming toxins , designed to perforate the plasma membrane of target cells thus perturbing host cell integrity and functionality . It is , however , important that these toxins do not form pores in the producing bacterium . To prevent this , bacteria initially produce them in a soluble state . After being secreted by the bacterium , the toxin subsequently acquires – often through a multimerization step– the ability to insert into the membrane . Here we were interested in the mechanisms ensuring that the toxin initially folds into the soluble state . Using as an example aerolysin from the human pathogen Aeromonas hydrophila , we show that the bacterium produces the toxin with a C-terminal extension of about 45 amino acids that promotes the folding of the protein into the soluble state . We find that by mutating or removing this extension , the protein folds poorly or not at all . Addition of the peptide in trans however lead to partial recovery of activity suggesting that this extension promotes folding , and being intramolecular thus results in a very high effective concentration . In addition to this chaperone role for correctly folding the monomeric form of the toxin , the C-terminal peptide is also crucial for controlling the folding of the quaternary structure of the mature pore complex at the surface of the target host cell . | [
"Abstract",
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| [
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| 2011 | Dual Chaperone Role of the C-Terminal Propeptide in Folding and Oligomerization of the Pore-Forming Toxin Aerolysin |
Persistent infection with the hepatitis C virus ( HCV ) is a major risk factor for the development of liver cirrhosis and hepatocellular carcinoma . With an estimated about 3% of the world population infected with this virus , the lack of a prophylactic vaccine and a selective therapy , chronic hepatitis C currently is a main indication for liver transplantation . The establishment of cell-based replication and virus production systems has led to first insights into the functions of HCV proteins . However , the role of nonstructural protein 5A ( NS5A ) in the viral replication cycle is so far not known . NS5A is a membrane-associated RNA-binding protein assumed to be involved in HCV RNA replication . Its numerous interactions with the host cell suggest that NS5A is also an important determinant for pathogenesis and persistence . In this study we show that NS5A is a key factor for the assembly of infectious HCV particles . We specifically identify the C-terminal domain III as the primary determinant in NS5A for particle formation . We show that both core and NS5A colocalize on the surface of lipid droplets , a proposed site for HCV particle assembly . Deletions in domain III of NS5A disrupting this colocalization abrogate infectious particle formation and lead to an enhanced accumulation of core protein on the surface of lipid droplets . Finally , we show that mutations in NS5A causing an assembly defect can be rescued by trans-complementation . These data provide novel insights into the production of infectious HCV and identify NS5A as a major determinant for HCV assembly . Since domain III of NS5A is one of the most variable regions in the HCV genome , the results suggest that viral isolates may differ in their level of virion production and thus in their level of fitness and pathogenesis .
The hepatitis C virus ( HCV ) is a major causative agent of acute and chronic liver diseases worldwide [1] . A hallmark of HCV infection is the high persistence , which is unusual for a virus of this group and which has been explained by numerous active and passive immune evasion strategies [2] , [3] . Although primary infection is often asymptomatic or associated with mild and non-specific symptoms , persistently infected persons have a high risk to develop chronic liver diseases in the course of one or several decades , the most serious outcomes being liver cirrhosis and hepatocellular carcinoma . It is this property of HCV infection and its high prevalence that explain the high medical relevance of this pathogen . Moreover , therapeutic options are limited and there is no prophylactic vaccine in sight [4] , [5] . The genome of HCV is a single strand RNA of positive polarity [6] . This RNA has a length of about 9 , 600 nucleotides and a very simple organization with only one long open reading frame . It is flanked by non-translated regions at the 5′ and 3′ end of the genome that are required for RNA translation and replication . The open reading frame encodes for an about 3 , 000 amino acids long polyprotein that is cleaved co- and post-translationally by cellular and viral proteases into 10 different products . The structural proteins core , envelope protein 1 ( E1 ) and E2 that build up the virus particle reside in the N-terminal region of the polyprotein and they are processed by host cell signalases and by signal peptide peptidase . C-terminal of E2 is the p7 protein that is required for virus assembly , release [7] , [8] and for infectivity in vivo [9] . The remainder of the polyprotein is processed into the nonstructural proteins 2 ( NS2 ) , NS3 , NS4A , NS4B , NS5A and NS5B . NS2 is a cysteine protease that cleaves at the NS2-3 site and that in addition is required for virus production [7] . NS3 is a bifunctional molecule that carries a serine-type protease activity in the N-terminal domain and a NTPase/helicase activity in the remainder . NS4A is a co-factor of the NS3 protease whereas NS4B is required for the induction of membrane alterations that probably serve as the scaffold for the formation of the membrane-associated replication complex . NS5A is an RNA binding protein assumed to be involved in some step of viral RNA replication and NS5B is the RNA-dependent RNA polymerase . By using a fully permissive cell culture system that supports the production of infectious HCV particles , we and others have recently gained first insights into the mechanisms underlying HCV particle assembly [10]–[12] . A key player of this process is the core protein . It is composed of an RNA binding domain , a domain for lipid droplet ( LD ) targeting ( domain 2 ) and a very hydrophobic C-terminal domain ( domain 3 ) that serves as the signal sequence of the C-terminal E1 protein [13] , [14] . Building on earlier observations that core protein accumulates on the surface of LDs [15] , [16] it was proposed that LDs are involved in the formation of infectious particles . A model was put forward by which core protein recruits the HCV replication complex ( RC ) to the surface of LDs where virus particle formation may be triggered [10] . Accumulation of core protein on the surface of LDs requires proteolytic removal of domain 3 by signal peptide peptidase [17] . This domain usually anchors the core protein to the ER membrane bilayer and thus precludes mobilization onto the surface of LDs that are surrounded by a monolayer membrane . However , upon removal of domain 3 , core becomes mobile and this mobility critically determines efficient virus assembly [11] , [12] . Since the core protein accumulates on the surface of LDs whereas the viral RC probably resides on ER or ER-derived membranes , nucleocapsid formation requires the translocation of the viral genome from the RC to core proteins . Cell culture adaptive mutations that enhance virus titres by up to several orders of magnitude without affecting RNA replication have been mapped to the nonstructural proteins constituting the RC . This led to the suggestion that the RC or non-structural proteins may play a role in this putative transfer of the viral genome to the core [18]–[21] . Indeed , one hot-spot of adaptation was found within NS5A arguing that this nonstructural protein may be an element involved in HCV assembly [21] . NS5A is an RNA binding phospho protein composed of 3 domains that are separated by trypsin-sensitive low complexity sequences ( LCS I and LCS II ) and an N-terminal amphipathic alpha-helix that stably anchors the protein to intracellular membranes [22]–[24] . According to the X-ray crystal structure of domain I , it forms a dimer with a claw-like shape that can accommodate a single strand RNA molecule [25] . The role of domain II and domain III of NS5A in the HCV replication cycle is unknow . However replication enhancing mutations were mapped to a region spanning the C-terminal part of domain I and LCS I arguing that these sequences are important for efficient RNA replication [26] , [27] . In contrast , domain III can be deleted or replaced by green fluorescent protein with no dramatic effect on RNA replication [28] , [29] . Taking advantage of a highly efficient HCV particle production system , in this study we identified a novel function of NS5A and demonstrate its crucial role in infectious virion assembly . Our data specifically identify domain III in NS5A as a key element in particle formation and provide insight how mutations in this domain lead to accumulation of core protein on LDs and thus may contribute to pathogenesis .
To identify the role of domains II and III of NS5A in the viral replication cycle we utilized the chimeric Jc1 genome and inserted a series of in frame deletions into the NS5A coding region ( Figure 1 ) . Since NS5A is a component of the HCV replication machinery , we first assessed the impact of these mutations on RNA replication in the context of a well established luciferase reporter virus genome ( Jc1-Luc; Figure 1A ) . In vitro transcripts of these Jc1-Luc constructs were transfected into highly permissive Huh7-Lunet cells in parallel with the Jc1-Luc wild type genome and a deletion mutant that lacked part of the envelope glycoproteins ( Jc1-Luc/ΔE1E2 ) . RNA replication was monitored by determination of luciferase activity at 4 , 24 , 48 and 72 h post transfection . The 4 h value was used for normalization and allowed correction for different transfection efficiencies [27] . As shown in Figure 2A , a deletion spanning almost the complete domain II and retaining only the C-terminal 35 amino acid residues did not affect RNA replication ( mutant Δ2222-2280 ) . However , complete removal of domain II abrogated RNA replication ( not shown ) arguing that the extreme C-terminal sequence of domain II is essential for RNA replication . Complete or partial deletion of domain III of NS5A had no significant impact on RNA replication . Although mutants Δ2328-2435 , Δ2354-2435 and Δ2354-2404 showed a clearly reduced replication level at 24 h post transfection , this was overcome 48 h or at latest 72 h after transfection arguing that these deletions had a rather moderate effect on RNA replication ( Figure 2A ) . Analogous results were obtained by using reporter-free Jc1 constructs for transfection and RNA determination by Northern-blot ( not shown ) . Based on these results we concluded that most of domain II ( Δ2222-2280 ) and the complete domain III ( Δ2328-2435 ) are not essential for RNA replication even though some of the deletions in domain III clearly delayed RNA replication kinetics . In the light of these surprising results we argued that domain II and domain III may have some other function , most notably in virus assembly and release . Given the impaired capacity of the luciferase reporter genomes to support infectious virus production , in all subsequent experiments only reporter-free constructs were used ( Figure 1A ) . To determine the impact of the NS5A deletion mutants on virus production we quantified core protein amounts in cells transfected with the various mutants and in the corresponding culture supernatants at different time points after transfection by using a core-specific ELISA ( Figure 2B , left and right panel , respectively ) . To account for different efficiencies of core protein accumulation within cells and core protein release , we also determined the relative core release as the ratio of core amounts detected in the culture supernatant to total core protein amounts ( intra- and extracellular core ) ( Figure 2C ) . Cells transfected with Jc1 released about 50–60% of total core protein into the supernatant . In contrast , relative core protein amounts released from cells that had been transfected with domain II deletion mutant Δ2222-2280 were somewhat reduced and ranged between 40% of total core at early time points after transfection and only 20% 72 h post transfection ( Figure 2C ) . In case of the domain III deletion ( Δ2328-2435 ) extracellular core was almost undetectable arguing for an important role of this NS5A domain for virus release . To map the region within this domain required for infectious particle production three smaller deletions were generated . Retention of the N-terminal 26 amino acid residues of domain III did not restore core release ( Δ2354-2435 ) . Inclusion of the 77 N-terminal residues ( Δ2405-2435 ) restored core release to only about 0 . 5–1% of the wild type ( Figure 2C ) . However , when we retained the N-terminal 26 and the C-terminal 38 amino acid residues , core release comparable to wild type level was observed ( Δ2354-2404 ) . These results suggest that the C-terminal region residing between amino acid residues 2404 and 2435 of domain III are crucial for virus production . In order to study the impact of the deletions in domains II and III of NS5A on assembly and release of infectious HCV particles , infectivity titers in the culture supernatant at 24 , 48 and 72 h after transfection with the various mutants , the Jc1 wild type or the envelope glycoprotein deletion mutant ( Jc1/ΔE1E2 ) were determined by using TCID50 assays . As shown in Figure 2D , infectivity titers of the wild type were in the range of 106 TCID50/ml at early time points after transfection whereas no infectivity was detected at all time points in case of Jc1/ΔE1E2 . Infectivity titers obtained with the domain II deletion mutant that still supports core release ( Δ2222-2280 ) were clearly reduced 48 and 72 h p . t . as compared to wild type and this reduction correlated with the reduction of core release ( Figure 2C ) . In agreement with the strongest impairment of core release , almost no infectivity could be detected in supernatants of cells that had been transfected with the two domain III mutants carrying the largest deletions ( Δ2328-2435 and Δ2354-2435 ) . Deletion of amino acid residues 2354–2404 or 2405–2435 resulted in about 10-fold or 100-fold lower titers as compared to the Jc1 wild type , respectively ( Figure 2D ) . These results were fully confirmed when we analyzed the analogous luciferase reporter virus genomes ( Figure 2A ) by determining luciferase transduction efficiency upon infection of naive Huh7 . 5 cells ( not shown ) . During these analyses we noted that cells transfected with Jc1/ΔE1E2 , which lacks part of the envelope glycoprotein ectodomains , still released well detectable amounts of core protein , but no infectivity . We therefore assumed that this core release is either non-specific , for instance due to cytotoxicity , or that part of the core protein can be released independent from the envelope glycoproteins . To address these possibilities , we constructed a double mutant which contained both the deletion in the E1E2-coding region and the largest deletion in domain III of NS5A ( Jc1/ΔE1E2/Δ2328-2435 ) . Interestingly , cells transfected with this mutant released much lower amounts of core protein as compared to Jc1/ΔE1E2 ( Figure 2B , C ) , arguing that a fraction of core protein can be released independent from functional envelope glycoproteins , but still in an NS5A-dependent manner . The results described above clearly show that domain III plays a primary role in the production of infectious HCV particles , but do not address the question whether assembly or release of these particles is affected . In case of a block of particle release , we would expect an accumulation of cell-associated ( intracellular ) infectious particles and thus a change of the ratio of extra- over intracellular infectivity . To address this possibility intracellular infectivity titers were compared to the corresponding supernatants . In case of the Jc1 wild type , about 90% of total infectivity was released into the supernatant and only 10% was detectable in transfected cells ( Figure 3 ) . A similar ratio was found with the domain II deletion mutant ( Δ2222-2280 ) and the deletion mutation retaining the 26 N-terminal and 38 C-terminal amino acid residues of domain III ( Δ2354-2404 ) . In case of the two largest domain III deletion mutants ( Δ2328-2435 and Δ2354-2435 ) intra- and extracellular infectivity titers were reduced by up to 4 orders of magnitude arguing that primarily , if not exclusively , infectious particle assembly was affected . No significant change of extra- over intracellular infectivity was found with the smallest deletion mutant lacking 31 amino acid residues close to the C-terminus of domain III ( Δ2405-2435 ) , but titer reduction was much less pronounced as compared to the two largest deletion mutants . These findings were confirmed for cells and supernatants harvested 72 h post transfection ( data not shown ) . We therefore conclude that the deletion mutations residing in domain III affected assembly of infectious virus particles and had little or no effect on virus release . Having confirmed an important role of NS5A for virus assembly , we assumed that NS5A might regulate the formation of HCV particles in a phosphorylation dependent manner and that the deletions we had inserted impact the phosphorylation status of this protein . To test this hypothesis we analyzed the phosphorylation status of the different truncated NS5A proteins by one-dimensional SDS-PAGE after phosphatase treatment . Huh7-Lunet cells were transfected with the different HCV genomes and harvested 72 h after transfection for Western blot analysis ( Figure 4 ) . In case of Jc1 the two phosphorylated variants of NS5A , the p56 basal phosphorylated form and the p58 hyperphosphorylated form , were readily detectable . After dephosphorylation of the proteins with λ-phosphatase , the p58 variant disappeared and the p56 variant migrated slightly faster than the non-treated protein , showing that both forms were phosphatase sensitive . For all domain III mutants , a basally and a hyperphosphorylated variant of NS5A were detected . The only mutant that showed a clear reduction in the amount of hyperphosphorylated NS5A carried the deletion in domain II ( Δ2222-2280 ) . Since this deletion did not significantly affect RNA replication and particle production , our results suggest that either very low amounts of hyperphosphorylated NS5A ( not detectable in this assay ) are sufficient for RNA replication and particle assembly or that hyperphosphorylated NS5A is dispensable for these processes . We have recently shown that the core protein appears to recruit the HCV replicase and the RNA genome to LDs , which may be the site for the assembly of infectious HCV particles [10] . Since deletions in domain III very much impair particle assembly , we assumed that these mutations may affect either the localization of NS5A on LDs per se or the core-dependent recruitment of NS5A to LDs . To address these possibilities we first studied the subcellular localization of the HCV proteins in cells transfected with the NS5A mutants or the Jc1 wild type or the E1-E2 deletion mutant . Seventy two hours after transfection cells were fixed and stained for NS5A , core protein and LDs . In case of Jc1 wild type , core protein accumulated on LDs ( Figure 5 , panel II ) . In addition , low amounts of NS5A were also found on LDs and this NS5A species colocalized with the core protein ( panel IV and V , respectively ) . In agreement with our earlier report [11] , core accumulation on LDs was very much pronounced when assembly was blocked at a rather late stage , which we achieved here by partial deletion of the ectodomains of E1 and E2 ( Jc1/ΔE1E2 ) ( Figure 5 , panel XII ) . Concommitant with core protein accumulation , we also found an accumulation of NS5A on LDs and a perfect colocalization with the core protein ( panel XIV and XV , respectively ) . Interestingly , in case of the mutant with the strongest impact on infectious particle assembly ( Jc1/Δ2328-2435 ) , both NS5A and core protein still accumulated on the surface of LDs ( Figure 5 , panel VII , IX ) , but their colocalization on the same LDs was no longer detectable ( panel X ) . The same phenotype was found with the double deletion mutant Jc1/ΔE1E2+Δ2328-2435 ( Figure 5 , panel XVI to XX ) . A more quantitative analysis of core and NS5A colocalization on LDs fully confirmed this observation ( Figure 6A , upper panel ) . Including the other NS5A mutants in the analysis , a clear correlation between core – NS5A colocalization on LDs and the efficiency of core release was detected . Most notably those deletions in domain III of NS5A that severely reduced the assembly of infectious particles also reduced core – NS5A colocalization on LDs ( Figure 6A , upper panel and Figure 6B ) . Interestingly , when we determined the colocalization of all these NS5A proteins with NS4B , which is most likely an integral component of the HCV replication complex , none of the mutations affected the colocalization with NS4B ( Figure 6A , upper panel ) . This observation is consistent with the result that the NS5A mutants support HCV RNA replication , in most cases comparable to wild type levels . The data thus imply that NS5A may exist in two different complexes . One complex containing the core protein and presumably responsible for particle assembly , and one complex containing NS4B and probably representing the ( membranous ) replication complex . Alternatively , NS5A may exist as a component of the replicase complex only , but this complex is no longer recruited to the core protein on the surface of LDs in case of the mutant lacking domain III . Another interesting observation emerged when we quantified the core protein accumulating on the surface of LDs in cells transfected with the various HCV genomes . As summarized in the lower panel of Figure 6A , core protein accumulation was highest for those NS5A mutants that do not support production of infectious virus particles ( Δ2328-2435 and Δ2354-2435 ) . In contrast , overall only low amounts of core co-localized on the surface of LDs in case of the Jc1 wild type , even though in a few cells colocalization was extensive ( one example is shown in Figure 5 ) . Likewise , in cells transfected with the NS5A mutants Δ2222-2280 and Δ2354-2404 that both support efficient virus production ( see also Figure 2D ) , only about 20% of LDs colocalized with core . An intermediate phenotype was found in case of the Δ2405-2435 NS5A mutant that supported about 100-fold lower virus titers as compared to wild type . This result correlates with the model that core protein residing on the surface of LDs is involved in virion assembly and that inefficient assembly results in an accumulation of core on LDs . In agreement with that assumption , core protein accumulation on the surface of LDs was also observed with the envelope deletion mutant ( Jc1/ΔE1E2 ) . However , in this case we observed a colocalization of core and NS5A , unlike the assembly-incompetent NS5A mutants . Although the data described thus far suggested that NS5A mutants lacking domain III no longer colocalized with core on the surface of LDs , the level of resolution achieved with these assays did not allow us to draw a firm conclusion about the localization of NS5A and core protein relative to each other . We therefore analyzed cells transfected with the NS5A domain III deletion mutant Jc1/Δ2328-2435 in parallel to cells transfected with Jc1 wild type or the envelope deletion mutant ( Jc1/ΔE1E2 ) by using deconvolution of images generated by spinning disk confocal microscopy . As shown in Figure 7A , in Jc1 transfected cells the core protein localized directly to the surface of the droplets , often in association with NS5A ( structure 1 in Figure 7A , upper panel ) . In some other cases NS5A was directly associated with the surface of LDs whereas the core protein accumulated at a distinct spot on the droplet surface which may correspond to the loading site of core onto LDs [12] ( structure 2 in Figure 7A , upper panel ) . In case of the envelope deletion mutant , core and NS5A still colocalized ( arrows in Figure 7A ) and core staining was much more intense reflecting an accumulation of core protein on the surface of LDs due to an assembly defect . A comparably high core accumulation on LD surfaces was found with the NS5A domain III deletion mutant consistent with the assembly block ( Figure 7A , lower panel ) . Most importantly , however , core and NS5A staining no longer co-localized arguing that domain III in NS5A is required for colocalization with core . These results were fully confirmed by immuno electronmicroscopy ( Figure 7B and the quantification summarized in Table 1 ) . Core and NS5A colocalized on the surface of the same LD in cells transfected with the Jc1 wild type ( upper panel ) . In contrast , in case of the domain III deletion mutant , only very rarely core and NS5A were found on the surface of the same LD even though the antigen amounts were much higher as compared to the wild type ( Figure 7B , lower panel and Table 1 ) . However , core – NS5A colocalization was detected in case of the envelope gene deletion mutant . In summary these data clearly show that domain III of NS5A is required for colocalization with core on the surface of LDs . In the light of an earlier report [10] the results thus imply that domain III-dependent recruitment of NS5A ( or the viral replicase ) to core-containing LDs is required for HCV particle assembly . NS5A is a component of the viral replicase and therefore involved in RNA replication . Recently we showed that replication-incompetent NS5A mutants could be rescued by trans-complementation [30] . Having now found that NS5A is involved in HCV assembly , we were interested whether this NS5A function can also be restored by trans-complementation . To this end we established a trans-complementation assay based on the co-transfection of a Jc1 genome carrying the deletion in domain III of NS5A ( Δ2328-2435 ) with a subgenomic luciferase-helper RNA that lacks the region encoding core to the C-terminus of NS2 ( Figure 1A ) . Replication of this helper RNA was determined by luciferase assay ( Figure 8B ) . In addition , for control purposes we included cotransfections of Jc1 wild type with the helper RNA as well as cotransfections of either of the Jc1 constructs with a defective helper RNA that carried the large domain III deletion in NS5A ( Figure 8A ) . Depending on the particular combination of the constructs , different outcomes were possible . Upon transfection of cells with the wild type helper RNA and the Jc1 wild type genome , we would expect the generation of infectious Jc1 particles containing the full length wild type genome or , in case of trans-packaging , the subgenomic helper RNA . In fact , upon inoculation of naive Huh7 . 5 cells with supernatants from such transfected cells , high level luciferase expression was detected indicating the formation of virus-like particles which had encapsidated the subgenomic helper RNA as a result of trans-packaging ( Figure 8C ) . Moreover , inoculated cells expressed high amounts of wild type NS5A and core protein indicating efficient infection with Jc1 wild type particles ( Figure 8D ) . Upon cotransfection of cells with Jc1 wild type and the defective helper RNA ( sg/lucΔ2328-2435 ) , infectious virus-like particles were generated that also transduced the luciferase gene ( Figure 8C ) . Formation of these particles was most likely due to trans-complementation of the defective NS5A of the helper RNA by wild type NS5A expressed from Jc1 and subsequent trans-packaging of the helper RNA into virus-like particles . In addition , infectious Jc1 particles were produced as determined by high level core and NS5A expression in cells inoculated with supernatant of the cotransfected cells ( Figure 8D ) . The epitope recognized by the NS5A-specific antibody used for this immunofluorescence staining resides in domain III thus allowing selective detection of wild type NS5A protein but not the domain III deletion mutant . In case of cells cotransfected with the Jc1-NS5A mutant ( Jc1/Δ2328-2435 ) and the wild type helper RNA ( sg/lucwt ) efficient transduction of the luciferase gene was observed arguing for trans-packaging of the helper RNA by the structural proteins provided by the Jc1 mutant ( Figure 8C ) . In addition , core expression in infected cells was observed arguing for a rescue of the Jc1 mutant by trans-complementation that was mediated by the intact NS5A protein provided by the helper ( Figure 8D ) . This assumption is supported by the fact that most cells expressing well detectable amounts of core protein did not express wild type NS5A protein indicating that these cells indeed had been infected with the Jc1-NS5A mutant . In contrast , cells expressing wild type NS5A ( expressed from the transduced helper RNA ) did not express core protein supporting the notion that these cells had been infected with virus-like particles that had packaged the subgenomic helper RNA . Finally , in supernatants of cells cotransfected with the Jc1 mutant ( Jc1/Δ2328-2435 ) and the defective helper RNA ( sg/lucΔ2328-2435 ) no infectivity was detected , neither by luciferase assay ( Figure 8C ) nor by immunofluorescence ( Figure 8D ) and TCID50 assay ( not shown ) . This result supports the notion that the NS5A protein expressed from the helper RNA carrying the deletion in domain III indeed is unable to support virus assembly . To exclude the possibility of RNA recombination between the Jc1-NS5A mutant and the wild type helper RNA resulting in a wild type Jc1 genome , supernatants from primary infected cells were passaged ( Figure 8A; 2nd passage ) . Supernatants from such inoculated cells were then analyzed for infectivity by using luciferase assay ( not shown ) and TCID50 assay ( Figure 8E ) . However , in no case infectivity was detected excluding the possibility that a Jc1 wild type with or without a luciferase gene was generated by RNA recombination . In conclusion these results demonstrate that also alterations affecting the assembly/virus release function of NS5A can be restored by trans-complementation . The data also show that subgenomic HCV RNAs can be packaged efficiently into infectious HCV-like particles .
A hallmark of the HCV replication cycle is its extraordinary dependance on host cell lipids . Several groups have shown that viral RNA replication is tightly linked to lipid synthesis pathways and sensitive to pharmacological intervention with statins and certain fatty acids [31]–[33] . More recently it became apparent that HCV particle morphogenesis and egress also depend on host cell lipids [34] . Taking advantage of the newly established cell culture system to produce infectious virus particles , evidence was obtained that HCV assembly occurs in close association with LDs [10] , [12] . It was suggested that core protein accumulates on the surface of LDs and recruits the RC , and thus the viral RNA , in order to trigger particle formation [10] . However , the mechanisms of RC recruitment and initiation of assembly are not known . In this study , we show that NS5A , in addition to core , is a key element of particle formation . The primary determinant within NS5A is domain III . Interestingly , deletions affecting this domain do not abrogate LD association of NS5A suggesting that LD association is mediated by some other domain ( s ) within NS5A , the most likely being domain I , eventually in conjunction with the N-terminal amphipathic alpha-helix ( 10 ) . However , domain III deletions affect colocalization with core protein on the surface of the same LD . This result implies that different types of LDs are formed in case of the mutant: those that contain primarily core protein and those that contain almost exclusively NS5A ( Figure 7B ) . The underlying mechanism is not known but it is possible that core protein has a strong binding affinity to LDs and thus heavily occupies the surface of LDs . This would leave only limited access for NS5A to the surface of such a LD . In case of wild type NS5A facilitated by an ( direct or indirect ) interaction between NS5A and core , both proteins can accumulate on the surface of the same LD . In case of the domain III deletion mutant , the core – NS5A interaction would be disrupted and thus , this mutant NS5A protein would primarily accumulate on LDs not occupied by core protein . Further studies are necessary to address this possibility . The fact that deletions in domain III perturb colocalization with core on LDs and at the same time impair infectious virus production provides compelling evidence that both events are linked . The data support a model in which core recruits either NS5A or the NS5A-containing RC via direct interaction with domain III to the assembly site . In agreement with this assumption a direct core – NS5A interaction has been demonstrated in an overexpression system by using coprecipitation and colocalization studies [35] , [36] . We also attempted to demonstrate a direct core – NS5A interaction by using infected or transfected cells as well as over-expression systems . Neither by co-immunoprecipitation with or without cross-linking nor by using fluorescence resonance energy transfer assays we were able to demonstrate such an interaction . Differences in the experimental set up as well as differences in the used HCV isolates ( genotype 2a ( JFH-1 ) versus genotype 1a and 1b in the previous studies ) could account for this discrepancy . However , it is well possible that in a more authentic ( virus production ) system such a core – NS5A interaction is very unstable and transient . It is also conceivable that within an assembly competent replication system only a minor fraction of total NS5A and core protein is enganged in an interaction . Moreover , once RNA transfer from the RC ( or NS5A ) to core has been initiated , a self-assembly process driven by core – RNA interaction may take place that no longer depends on an interaction between core and NS5A . It is even possible that core and NS5A do not directly interact but that exposed RNA regions not complexed by NS5A are recognized by core protein residing in close proximity to NS5A and sufficient to trigger assembly . In this respect colocalization of core and NS5A on the surface of the same LD or in close proximity to each other may be sufficient to allow particle formation . Finally , NS5A – core interaction may be indirect and facilitated by host cell factors . Several NS5A binding proteins have been described such as VAP-A/B and VPS35 that are involved in intracellular trafficking or apolipoproteins involved in the formation of lipoproteins [35] , [37] . The contribution of these host cell factors for virion formation remains , however , to be determined . It is also unclear why HCV utilizes this unique pathway of virion formation and where exactly envelopement of the nucleocapsid occurs . In agreement with earlier observations [10] , we found that E2 also accumulated in close proximity of LDs ( N . A . , M . Z . T . S . and R . B . , unpublished ) . It is therefore plausible that LDs tightly surrounded by ER membranes are the sites where nucleocapsids form , which could then acquire their envelope via budding into the ER lumen at sites in close proximity of LDs . Further studies are required to address this important question . At least two important consequences arise from an assembly model that is based on a RC ( NS5A ) – core interaction . First , specificity of RNA encapsidation would primarily be brought about by protein – protein rather than protein – RNA interaction . While the genome of several viruses , such as e . g . the hepatitis B virus , contain a distinct RNA element that is recognized by a viral protein to mediate selective genome packaging [38] , such a packaging signal would not be required for HCV . Second , mutations in NS5A affecting RNA binding or core interaction would interfere with assembly and thus lead to an accumulation of core on the surface of LDs . It is interesting to note that NS5A , especially the so-called V3 region of domain III is amongst the most variable sequences across the different genotypes and subtypes [39] . Therefore , it is well possible that NS5A variants differ in their efficiency for infectious particle assembly . Inefficient assembly results in an accumulation of core protein on the surface of LDs [11] and intracellular accumulation of core can lead to perturbation of host cell lipid metabolism such as interference with microsomal triglyceride transfer protein activity and very-low density lipoprotein ( VLDL ) secretion [40] . Thus , both core and NS5A may contribute to pathogenesis , especially HCV-induced steatosis . In agreement with earlier studies , we found that deletions affecting domain III have no or minimal impact on RNA replication [29] , [41] . However , insertion of a heterologous sequence into the coding region of domain III impairs virus production [42] . In the light of the present data the most likely explanation for this phenotype is that the insertion interferes with the assembly function of domain III . To our great surprise we also found that a nearly complete deletion of domain II ( Δ2222-2280 ) has no impact on RNA replication and almost no effect on virion production arguing that domain II serves some other purpose not directly contributing to the replication cycle such as interaction with the host cell . This result is to some extent at variance to a recent report by Tellinghuisen and colleagues [41] showing that a 10 amino acid deletion in domain II ( deletion B ) overlapping the larger deletion that we describe here ( Δ2222-2280 ) blocks RNA replication . This discrepancy may be due to the different experimental systems used in their and our study [genotype 1b ( Con1 ) derived subgenomic replicons versus JFH-1 derived genomes , respectively] . Moreover , it is interesting to note that only for deletion B Tellinghuisen and colleagues could not identify a single amino acid responsible for the non-replicating phenotype arguing that the complete region rather than a specific amino acid is required for RNA replication . Since the overall amino acid sequence homology of NS5A between Con1 and JFH-1 is about 61% , but only about 50% in case of domain II , it is possible that in JFH-1 other regions of the NS5A sequence are required for efficient RNA replication . Nevertheless , when we deleted the complete domain II , replication of JFH-1 was also completely blocked ( not shown ) . This observation fits to the results by Tellinghuisen and colleagues , who showed that 10 amino acid deletions affecting the C-terminal half of domain II prevent RNA replication completely [41] . By using a cotransfection approach of a NS5A mutant genome with a subgenomic helper RNA we demonstrate that both trans-complementation and trans-packaging occur . Trans-complementation means that the mutant RNA genome that due to a deletion of domain III is not assembly competent can be rescued by providing NS5A ( expressed in the context of a replicase ) in trans . The result is a virus-like particle that contains the mutant genome and thus upon infection of naïve cells can not spread in culture due to the assembly defect . The molecular mechanisms underlying this trans-complementation are not known and therefore we can only speculate . In one possible scenario mixed replication complexes form composed of the genomic NS5A mutant and the subgenomic helper , allowing wild type NS5A to interact with mutant RNA and thus ‘tagging’ the mutant RNA genome for encapsidation . Whatever the underlying mechanism is , preliminary results suggest that NS5A expressed on its own does not support trans-complementation , but only when NS5A is expressed in the context of the replicase ( NS3 to NS5B; N . A . , S . K . and R . B . unpublished ) . Thus , the pre-conditions required to rescue assembly-defective mutants by trans-complementation with NS5A appear to be similar to those required to rescue NS5A mutants that are defective in RNA replication ( 30 ) . Apart from trans-complementation we also observed trans-packaging which refers to the encapsidation of the subgenomic helper RNA into virus-like particles . These particles are infectious but due to the subgenomic nature of the encapsidated RNA , no infectious virus progeny is generated and thus there is no spread in cell culture . Trans-packaging has also been described for numerous other viruses including alphaviruses as well as several members of the pesti- and flaviviruses [43] , [44] . In these cases trans-packaging has been achieved by several different strategies including packaging defective helper constructs or helper cell lines that stably express the structural genes . Moreover , trans-packaging of subgenomic RNAs has been observed in vivo and is refered to as defective-interfering particles which means the formation of virus-like particles that contain an RNA subgenome and that upon co-infection of a cell with a wild type genome interfere with wild type RNA replication [45] . A recent report suggests that such particles are also formed in HCV-infected patients [46] . In summary , we demonstrate that NS5A is a major determinant for infectious virus production and show that domain III is most critical for this step in the viral replication cycle . In the light of this observation and given the essential role of NS5A for RNA replication , this protein is a novel and promising target for antiviral therapy . Several inhibitors targeting the replication function of NS5A have been described and it will be interesting to determine whether they also impact virus particle production and thus follow a two-pronged mode-of-action .
Plasmids pFK-Jc1 and pFK-Luc-JFH1 have been described previously ( Wakita 2005; Pietschmann 2006 ) . PFK-Jc1/ΔE1E2 that carries a deletion of 350 codons in the E1-E2 coding region ( removing amino acids 218–567 of the J6 polyprotein ) and the NS5A deletions were generated by PCR-based mutagenesis . All PCR-amplified DNA fragments were analyzed by automated nucleotide sequencing using an ABI 310 sequencer ( Applied Biosystems ) . Big Dye version 1 . 1 ( Applied Biosystems ) was used for cycle sequencing according to the manufacturer's protocol . Detailed information about DNA cloning is available in Protocol S1 . Huh-7 cell clones Huh7-Lunet [47] and Huh7 . 5 [48] that both are highly permissive for HCV RNA replication were used for electroporation and infection assays , respectively . Luciferase reporter virus-associated infectivity was determined as described elsewhere [49] . Infectivity of HCV variants lacking a reporter gene was determined by using a limiting dilution assay on Huh-7 . 5 cells [50] with a few minor modifications . Infected cells were detected by using a JFH1 NS3-specific rabbit polyclonal antiserum as primary antibody ( dilution 1∶500 ) and a peroxidase-conjugated goat anti-rabbit polyclonal antibody ( Sigma ) as a secundary antibody ( dilution 1∶1 , 000 ) . The tissue culture 50% infectivity dose ( TCID50 ) was calculated as described recently [51] . Intracellular infectivity assays as determined with freeze – thaw lysates of transfected cells were performed according to a published protocol [52] . In brief , 48 h post-transfection Huh7-Lunet cells were extensively washed with PBS , scraped off the plate and centrifuged for 5 min at 700×g . Cell pellets were resuspended in 1 ml of DMEM containing 5% FCS and subjected to three cycles of freezing and thawing using liquid nitrogen and a thermo block set to 37°C . Samples were then centrifuged at 10 , 000×g for 10 min at 4°C to remove cell debris , and cell-associated infectivity was determined by TCID50 assay . Culture supernatants from transfected cells were treated in the same way and infectivity was determined in parallel . Transient HCV RNA replication assays were performed as described previously [53] . In brief , plasmid DNA was restricted with MluI and used for in vitro transcription . Ten µg of run-off transcripts were used for electroporation of 4×106 Huh7-Lunet cells that were resuspended in 20 ml culture medium ( 12 ml in case of freeze and thaw experiments ) . Two ml aliquots were seeded per well of a 6-well plate and replication was determined by measuring luciferase activity in case of genomes containing the luciferase reporter gene at 4 , 24 , 48 and 72 h post-electroporation . Since luciferase activity measurable 4 h post transfection is derived from transfected input RNA , these values were used to normalize for transfection efficiency . In case of authentic virus genomes replication was monitored 0 , 24 , 48 and 72 h post electroporation by Northern blot analysis as described in Protocol S1 . For trans-complementation assays , Huh7-Lunet cells were co-transfected with 5 µg of Jc1 genomes and 0 . 5 µg of helper RNAs containing a luciferase reporter gene ( corresponding to a 1∶0 . 1 molar ratio , respectively ) . Electroporated cells were seeded as described above and replication of the non-reporter genome was determined by Northern-blot analysis . Transient replication of helper RNAs was determined by luciferase assay 4 , 24 , 48 and 72 h after electroporation . Values obtained 4 h post electroporation were used to determine the transfection effciency . Supernatants were harvested 24 , 48 and 72 h after eletroporation and concentrated three times by using Amicon columns ( Millipore , Schwalbach ) according to the instructions of the manufacturer . Release of infectious particles from co-transfected cells was determined by TCID50 assay by using the concentrated culture supernatants . Replication of trans-packaged subgenomic helper RNA was determined by luciferase assay performed with lysates of Huh 7 . 5 cells that had been inoculated with the concentrated culture supernatants of co-transfected cells . Core protein amounts were determined by using the Trak-C Core ELISA ( Ortho Clinical Diagnostics ) as recently described [51] . Since core protein amounts measurable 4 h after transfection are derived from transfected input RNA , these values were used to normalize for transfection efficiency . Huh7-Lunet cells electroporated with Jc1 genomes were seeded into a 10 cm diameter culture dish . Seventy two hours post electroporation cells were washed two times with ice-cold phosphate-buffer saline ( PBS ) and harvested by scraping into 500 µl RIPA buffer ( 50 mM Tris-HCl [pH 7 . 4] , 1% Nonidet P-40 , 0 . 25% sodium desoxycholate , 150 mM NaCl , supplemented with protease inhibitors ( 1 mM PMSF; 0 . 001 U/ml aprotinin and 4 µg/ml leupeptin ) . The cell lysate was divided into two aliquots , mixed with 4 volumes of acidified acetone/methanol and incubated at −20°C over night . Proteins were pelleted by centrifugation at 15 , 000×g for 15 min , pellets were air dried and resuspended in 200 µl of lambda phosphatase buffer ( 50 mM Tris HCl [pH 7 . 5] , 100 mM NaCl , 0 . 1 mM EGTA , 2 mM DTT , 0 . 01% Brij35 ) supplemented with 2 mM MnCl2 and 0 . 4% NP-40 . One hundred µl of the sample was incubated with 4 , 000 units of lambda protein phosphatase ( Biolabs ) at room temperature ( RT ) for 45 min . Samples were then mixed with SDS-PAGE loading buffer [200 mM Tris-HCl [pH8 . 8] , 5 mM EDTA , 0 . 1% bromophenol blue ( w/v ) , 10% sucrose ( w/v ) , 3% SDS ( w/v ) and 2% beta-mercaptoethanol ( v/v ) ] , separated by electrophoresis into an 8% polyacrylamide gel and transferred to a PVDF membrane . NS5A was detected by immunobloting using a JFH-1 NS5A-specific rabbit polyclonal antiserum ( dilution 1∶1 , 000 ) and peroxidase-conjugated goat anti-rabbit polyclonal antibody ( dilution 1∶25 , 000 ) . Bound secondary antibody was detected by using the ECL Plus Western Blotting Detection system ( Amersham ) according to the instructions of the manufacturer . Transfected Huh7-Lunet cells were seeded into 24 well-plates containing glass coverslips . Seventy two hours after electroporation , cells were washed twice with PBS , fixed with 4% paraformaldehyde in 150 mM sodium cacodylate buffer [pH 7 . 5] for 15 min at RT and permeabalized with digitonine ( 50 µg/ml ) for 5 min at RT . Permeabilzed cells were washed twice with PBS and blocked with PBS containing 5% ( w/v ) bovine serum albumine ( Sigma ) for 30 min at RT . NS5A was detected by using a NS5A-specific monoclonal antibody ( Austral Biologicals , San Ramon , CA ) at a dilution of 1∶200; core and NS4B were detected with monospecific rabbit polyclonal antisera C-830 ( dilution 1∶200 ) or serum #86 ( dilution 1∶100 ) , respectively . After 1 h at RT , cells were washed three times with PBS and incubated with a 1∶1 , 000 dilution of Alexa 488 , 546 or 647-conjugated secondary antibody ( Invitrogen , Molecular Probes ) in PBS - 5% BSA for 1 h in the dark . LDs were stained with 20 µg/ml of BODIPY493/503 ( Invitrogen , Molecular Probes ) during secondary antibody incubation . Cells were washed once with PBS , incubated for 1 min with a 1∶5 , 000 diluted solution of 4′ , 6′-diamidino-2-phenylindole dihydrochloride ( DAPI ) -PBS , and immediately washed four times for 10 min with PBS . Cells were mounted on glass slides with Slow-Fade Gold Antifade Reagent ( Invitrogen , Molecular Probes ) . For double staining images were acquired on a Nikon C1Si spectral imaging confocal laser scanning system on a TE-2000 E equipped with 60× Objective ( NA 1 . 4 ) . For 3-D reconstruction of samples stained with 3 or 4 markers , cells were imaged on an Ultraview ERS spinning disk ( PerkinElmer Life Sciences ) on a Nikon TE2000-E inverted confocal microscope equipped with a Plan-Apochromat VC 100X lens ( NA 1 . 4 ) . Channels were recorded sequentially onto an EM-CCD camera by using an emission discrimination option in the following order: 647/700 , 568/610 , 488/510 , 405/440 ( emission/excitation ) . For deconvolution , optical slices were acquired at 0 . 15-µm Z spacing resulting in a stack of 30–40 optical slices per cell . Colocalization of fluorescence signals was evaluated quantitatively for Pearson's correlation coefficient ( Rr ) by using the plugin ‘Intensity Correlation Analysis’ of the ‘Image J’ software . For each sample 50 cells were analyzed . Core-LD colocalization was calculated by counting the total number of LDs in a cell and the number of those LDs with an overlapping core protein signal . The plugin ‘RG2B’ colocalization software of Image J was used to detect overlapping signals . Deconvolution of image z-stacks was performed based on a theoretical point spread function by using Huygens Essential software ( v . 3 . 0 , Scientific Volume Imaging BV ) . The 3D projections of deconvolved images were reconstructed with the help of the simulated fluorescence process volume rendering algorithm of the Huygens Essential software . In case of trans-complementation assays ( Figure 8D ) , infected Huh 7 . 5 cells were treated as described above , but NS5A was detected with monoclonal antibody 9E10 [50] and images were acquired with an inverted fluorescence microscope ( Leica , Germany ) . For immuno-EM Huh7 . 5 cells grown in 10 cm2 dishes were transfected with viral RNA as described above . Forty eight hours post transfection the cells were fixed in 4% paraformaldehyde , 0 . 1% glutaraldehyde , and 1% acrolein in PHEM buffer ( 240 mM Pipes , 100 mM Hepes , 8 mM MgCl2 , 40 mM EGTA; pH 6 . 9 ) . Cells were scraped off the dish , pelleted , and incubated in 2 . 3 M sucrose overnight at 4°C . Subsequently , cell pellets were mounted on silver pins , flash frozen and stored in liquid nitrogen . The specimens were sectioned with a Reichert Ultracut S ultramicrotome with a Reichert FCS cryo-attachment using a Diatome diamond knife ( Diatome , Biel , Switzerland ) . Double-labeling of thawed cryo-sections was performed as described [54] , [55] . Protein A-gold ( Utrecht University , Utrecht , Netherlands ) of different sizes was used to label different viral proteins . EM specimens were examined using a Zeiss EM10 transmission electron microscope . Quantification of the labeling for anti-core and anti-NS5A was done using three different labeling experiments considering two grids per experiments . The average labeling per lipid droplet was estimated by considering 20 profiles of lipid droplets per grids and by counting the labeling on the surrounding membrane of the LD only . Statistical analysis was done by t-test using the free p- value calculater for 158 degree of freedom ( http://www . graphpad . com/quickcalcs/Pvalue2 . cfm ) . | The hepatitis C virus ( HCV ) is a major cause of acute and chronic liver diseases worldwide . In spite of high medical need there is no selective antiviral therapy available and a prophylactic vaccine is not in sight . Their development requires cellular replication systems that have become available just recently . One of the most fascinating insights gained with these systems is the finding that infectious HCV particles assemble in close association with an intracellular lipid storage compartment termed lipid droplets . In this study we show that nonstructural protein 5A ( NS5A ) , a component of the viral RNA replication machinery is a key factor for the formation of infectious HCV particles . We identify a distinct domain in NS5A as the primary “assembly determinant” and show that NS5A and the core protein , which is a major constituent of the virus particle , accumulate on the surface of lipid droplets . Deletions in NS5A disrupting the colocalization of core and NS5A on lipid droplets abrogate infectious HCV production . These studies unravel a unique pathway of infectious virus formation and identify NS5A as a factor modulating HCV replication and assembly and thus viral fitness . | [
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| 2008 | Essential Role of Domain III of Nonstructural Protein 5A for Hepatitis C Virus Infectious Particle Assembly |
Follicular thyroid carcinoma ( FTC ) and benign follicular adenoma ( FA ) are indistinguishable by preoperative diagnosis due to their similar histological features . Here we report the first RNA sequencing study of these tumors , with data for 30 minimally invasive FTCs ( miFTCs ) and 25 FAs . We also compared 77 classical papillary thyroid carcinomas ( cPTCs ) and 48 follicular variant of PTCs ( FVPTCs ) to observe the differences in their molecular properties . Mutations in H/K/NRAS , DICER1 , EIF1AX , IDH1 , PTEN , SOS1 , and SPOP were identified in miFTC or FA . We identified a low frequency of fusion genes in miFTC ( only one , PAX8–PPARG ) , but a high frequency of that in PTC ( 17 . 60% ) . The frequencies of BRAFV600E and H/K/NRAS mutations were substantially different in miFTC and cPTC , and those of FVPTC were intermediate between miFTC and cPTC . Gene expression analysis demonstrated three molecular subtypes regardless of their histological features , including Non–BRAF–Non–RAS ( NBNR ) , as well as BRAF–like and RAS–like . The novel molecular subtype , NBNR , was associated with DICER1 , EIF1AX , IDH1 , PTEN , SOS1 , SPOP , and PAX8–PPARG . The transcriptome of miFTC or encapsulated FVPTC was indistinguishable from that of FA , providing a molecular explanation for the similarly indolent behavior of these tumors . We identified upregulation of genes that are related to mitochondrial biogenesis including ESRRA and PPARGC1A in oncocytic follicular thyroid neoplasm . Arm-level copy number variations were correlated to histological and molecular characteristics . These results expanded the current molecular understanding of thyroid cancer and may lead to new diagnostic and therapeutic approaches to the disease .
Most thyroid cancers are classified as either classical papillary thyroid carcinoma ( cPTC ) , follicular variant of PTC ( FVPTC ) , or follicular thyroid carcinoma ( FTC ) based on histological architecture [1] . However , the distinction between follicular-patterned thyroid tumors , such as FVPTC , FTC , and benign follicular adenoma ( FA ) , still remains as a challenging problem [2] . Moreover , FTC and FA are indistinguishable by preoperative diagnosis as in practice they are often jointly referred to as follicular thyroid neoplasm ( FTN ) [3] . FTC accounts for approximately 10% of all thyroid cancers [4] and is known to harbor H/K/NRAS mutations , which are one of the molecular markers used for diagnosis [5] . However , H/K/NRAS mutations are also found in FVPTC and FA [6 , 7] . Therefore , these mutations are not sufficient as predictors of pure follicular histology or malignant potential in thyroid cancer . The recent publication of The Cancer Genome Atlas ( TCGA ) studied molecular characteristics of PTC including the subtypes of classical type , tall cell variant , and follicular variant [8] . It was the first comprehensive pan-genomic study of thyroid cancer . They concluded that classification with two molecular subtypes , BRAFV600E–like and RAS–like , represents the underlying signaling and differentiation properties better than pathological classifications . However , the analysis of TCGA was confined to subtypes of PTC and molecular characterization of FTC has not been performed . In particular , the TCGA study demonstrated that the mitogen-activated protein kinase ( MAPK ) signaling pathway in PTC , as well as differentiation of thyroid cells , was differently regulated depending on molecular subtypes . There are some other reports about differential activation of the MAPK signaling pathway through several different genetic events such as RET/PTC fusions , BRAF , and H/K/NRAS point mutations [9 , 10] . The initiation of those genetic alterations likely depends on some triggering factor such as radiation or chemical elements [11–15] . However , the association between clinical risk factors and genetic alterations has not been fully understood yet . We have performed a comprehensive RNA sequencing ( RNA-seq ) analysis to reveal the molecular characteristics of thyroid cancer including minimally invasive FTC ( miFTC ) and FA , and investigated their association to clinical data . Since there is no preceding large-scale RNA-seq study on miFTC and FA , we expect that our result will facilitate the discovery of new diagnostic and therapeutic approaches to thyroid cancer .
The mutational landscape of 180 thyroid tumors including 25 FAs , 30 miFTCs , 48 FVPTCs , and 77 cPTCs is illustrated in Fig 1 . Mutations in well-known cancer driver genes ( BRAF and H/K/NRAS ) and fusion gene rearrangements were identified in 37 . 22% , 25 . 00% , and 12 . 78% of total tumors , respectively . The patterns of genetic alteration differed between PTC and FTN; most fusion genes were observed in PTC ( 17 . 60% and 1 . 82% in PTC and FTN , respectively; p = 0 . 002 ) , while most mutations except BRAFV600E and H/K/NRAS were found in FTN ( 32 . 73% and 0 . 80% in FTN and PTC , respectively; p < 0 . 0001 ) . BRAFV600E was only identified in PTC and its frequency differed between cPTC and FVPTC ( 71 . 43% and 25 . 00% , respectively; p < 0 . 0001 ) . Many H/K/NRAS mutations were identified in FVPTC , miFTC , and FA ( 47 . 92% , 50 . 00% , and 24 . 00% , respectively ) . Only 1 . 30% of cPTC harbored NRAS mutations . Four tumors ( 6 . 67% of miFTC and 8 . 00% of FA ) harbored somatic DICER1 mutations ( E1705Q , D1810H , E1813G , and E1813Q; S1A Fig ) . These mutations were mutually exclusive with BRAFV600E and H/K/NRAS mutations in FA as well as miFTC . The expression level of DICER1 was increased with these somatic mutations ( S1B Fig ) . Among these , two mutations were previously reported in TCGA study ( D1810H and E1813G in TCGA-EL-A3GO and TCGA-EL-A3D5 , respectively ) . They also tended to be mutually exclusive with BRAF and H/K/NRAS mutations ( S1C Fig ) . Several mutations in the Ribonuclease III domain of DICER1 were previously reported in PTC and other types of cancer [16–19] , but DICER1E1705Q mutation was first to be identified in thyroid tumor . We found three missense mutations in EIF1AX ( G9V , R13C , and R13L ) which was recently proposed as a driver gene in PTC ( S1C Fig ) [8] . These mutations were occurred more often in FA than in PTC ( 12 . 00% and 0 . 80% , respectively; p = 0 . 015 ) and they were mutually exclusive with BRAFV600E and H/K/NRAS mutations . In addition , there were IDH1R132C and two PTEN missense mutations ( V343E and V175A ) in miFTC . Also , one FA sample appeared to have a somatic focal deletion of PTEN based on its lacked expression in tumor . Furthermore , we suggest some novel driver candidates: SOS1N233Y , SPOPP94R , EZH1Q571R , EZH1Y642F , and STK11R86fs . SOS1N233Y was identified as a recurrent hotspot in several cancers including uterine endometrial carcinoma , lung adenocarcinoma , and cancer cell lines [20] . SPOPP94R was localized to the MATH domain and most somatic mutations in SPOP occurred in this domain [21 , 22] . EZH1 is a member of the Polycomb group protein complex which are important components for prevention of cancer stem cell development [23] . In the TCGA dataset , SPOPP94R and EZH1Y642F tended to be mutually exclusive with BRAF and H/K/NRAS mutations ( S1C Fig ) . Mutations in STK11 were also reported in other types of cancer including poorly differentiated and anaplastic thyroid carcinoma [24 , 25] . Those mutations , SPOPP94R , EZH1Q571R , DICER1E1813G , DICER1E1813Q , and EIF1AXR13C , were confirmed as somatic mutations by polymerase chain reaction ( PCR ) and Sanger sequencing in tumor and matched normal tissues ( S2A Fig ) . We described all predicted fusion genes , breakpoint regions , and expression levels in S1 Table . All fusion genes including novel candidates were mutually exclusive with other mutations . Previously reported fusion genes in thyroid cancer , ETV6–NTRK3 ( 4 . 80% in PTC ) , CCDC6–RET ( 2 . 40% in PTC ) , NCOA4–RET ( 0 . 80% in PTC ) , SQSTM1–NTRK1 ( 0 . 80% in PTC ) , STRN–ALK ( 0 . 80% in PTC ) , and PAX8–PPARG ( 0 . 80% and 1 . 82% in PTC and FTN , respectively ) , were also identified [8] . ALK , RET , and NTRK1 represented aberrant overexpression after fusion gene breakpoint ( S3A Fig ) . Moreover , ETV6–NTRK3 and STRN–ALK were validated by fluorescence in situ hybridization ( FISH ) and immunohistochemistry ( IHC ) staining , respectively ( S3B and S3C Fig ) . In case of BRAF , we identified two novel candidate fusion genes , PICALM–BRAF ( 0 . 80% in PTC ) and NFYA–BRAF ( 0 . 80% in PTC ) , in addition to formerly reported SND1–BRAF ( 0 . 80% in PTC ) [8] . PICALM–BRAF was validated by reverse transcriptase PCR ( RT-PCR ) and Sanger sequencing ( S2B Fig ) . Additionally , we suggest other fusion gene candidates such as EZR–ERBB4 , FGFR2–KIAA1598 , FGFR2–WARS , PAX8–GLIS3 , THADA–LOC100505678 , and RNF213–SLC26A11 . From the above fusion gene candidates , EZR–ERBB4 , FGFR2–KIAA1598 , and RNF213–SLC26A11 were identified in other types of cancer [26–28] . ERBB4 also had aberrant overexpression after fusion gene breakpoint ( S3A Fig ) . THADA rearrangement was previously reported in FA and PTC [8 , 29] . PAX8 and GLIS3 are both related to thyroid metabolism and function [8] . Each subject showed different combinations of clinical risk factors such as age , smoking , alcohol drinking , menopausal status , and the presence of lymphocytic thyroiditis ( LT ) . To investigate the association of these risk factors with genetic alterations , we categorized the patients into three groups: 1 ) small size mutation , 2 ) fusion gene , and 3 ) driver-unknown ( Table 1 ) . The average age of the fusion gene group ( 39 . 2 ± 13 . 1 ) was younger than driver-unknown ( 52 . 3 ± 14 . 9 ) and small size mutation ( 47 . 4 ± 12 . 1 ) groups ( p = 0 . 002 ) . Moreover , tumors with fusion gene were found more frequently in young adults ( 20 . 00% of subjects age < 45 yrs . and 6 . 26% of subjects age ≥ 45 yrs . ; p = 0 . 006 ) . The percentage of pre-menopausal women in the fusion gene group ( 75 . 00% ) was higher than driver-unknown ( 23 . 53% ) and small size mutation ( 55 . 00% ) groups ( p = 0 . 01 ) . Patients harboring fusion gene were less likely to smoke than others , but it was not statistically significant . Also , the percentage of patients who drink alcohol was not different among the groups . Patients harboring H/K/RAS mutations had a lower frequency of LT ( 11 . 11% ) , which was defined by histologic findings in normal thyroid parenchyma , than BRAFV600E ( 37 . 31%; p = 0 . 002 ) and fusion gene ( 47 . 83%; p = 0 . 0004 ) groups . The fusion gene group showed higher frequency of LT compared with driver-unknown ( 26 . 92% ) and other mutation ( 21 . 05% ) groups , although this was not statistically significant . The result of K-means clustering via principal component analysis ( PCA ) on all study subjects is shown in S4A Fig . Tumor and normal tissues were distinctively separated in the PC2 axis even though some of them were grouped together in one of the K-means cluster . This cluster was associated with LT which was observed in 28 . 89% of study subjects ( S4B Fig ) . Samples with BRAFV600E mutation and LT were also separated from samples with BRAFV600E mutation and without LT when the same analysis was conducted with only tumors ( S4C and S4D Fig ) . In case of TCGA dataset , we were not able to distinguish an LT derived cluster although 22 . 89% of specimens harbored LT ( S4E Fig ) . The inconsistent result between TCGA and the current study could be raised from different gene set usage for each analysis; we used the Ensembl gene set instead of the UCSC gene set which was used in TCGA study . Within the most variable 500 genes in the Ensembl gene set applied to PCA , 91 genes were associated with immunoglobulin and only four genes were overlapped with the UCSC gene set . In order to decrease the gene expression variation affected by LT and increase that derived from oncogenic signal transduction , we used genes covered by the UCSC gene set for molecular classification . With this approach , we obtained three molecular subtypes in relation to oncogenic signal transduction: BRAF–like , RAS–like , which were proposed by TCGA , and a third which we refer to as Non–BRAF–Non–RAS ( NBNR ) . The three molecular subtypes that we identified showed a clear separation of samples by driver genes ( Fig 2A ) . We could get exceedingly similar result when the same analysis was performed on TCGA dataset ( S4F Fig ) . As the effect of gene expression derived by BRAF and H/K/NRAS was overwhelming in PCA due to their huge sample size , this analysis was performed on a partial TCGA dataset . BRAF–like consisted of BRAFV600E and fusion genes ( PICALM–BRAF , NFYA–BRAF , SND1–BRAF , FGFR2–WARS , ETV6–NTRK3 , SQSTM1–NTRK1 , CCDC6–RET , NCOA4–RET , and RNF213–SLC26A11 ) . None of FTN was clustered into BRAF–like because of their skewed proportion of BRAFV600E and fusion genes . RAS–like consisted of H/K/NRAS and fusion genes ( STRN–ALK , EZR–ERRB4 , FGFR2–KIAA1598 , ETV6–NTRK3 , and CCDC6–RET ) . Lastly , NBNR was associated with DICER1 , EIF1AX , IDH1 , PTEN , PAX8–PPARG , and other driver gene candidates ( Fig 2B ) . The aggressive pathologic characteristics , lymph node metastasis ( LNM ) and extrathyroidal extension ( ETE ) were correlated with the 3 molecular subtypes ( Fig 2C ) ; higher frequency of LNM ( 37 . 04% ) or ETE ( 61 . 73% ) was found in the BRAF–like group , while less or no LNM or ETE was observed in the RAS–like group ( 15 . 09% of LNM , 11 . 32% of ETE ) or NBNR group ( 0 . 00% of LNM , 8 . 70% of ETE ) ( For both categories; p < 0 . 0001 ) . To measure differentiation of thyroid cells and activation of the MAPK signaling pathway in three molecular subtypes , we implemented two scoring methods that were introduced by TCGA study: thyroid differentiation score ( TDS ) and ERK score ( Fig 2C ) [8] . Most BRAF–like tumors had low TDS , while RAS–like and NBNR tumors had high TDS . There was a strong negative correlation between TDS and molecular subtype classification ( Pearson correlation coefficient = -0 . 66 ) . The low level of TDS was derived from decreased expression level of 16 thyroid metabolism and function genes [8] . Many of these 16 genes were downregulated in BRAF–like , while RAS–like and NBNR maintained stable gene expression levels . In BRAF–like , significantly downregulated genes were DIO1 , DIO2 , TPO , SLC26A4 , and SLC5A8 . DUOX1 and DUOX2 were increased in RAS–like . On the other hand , NBNR had no differentially regulated gene except ESRRA overexpressed tumors ( See “The characteristic gene expression of oncocytic FTN” section ) , which represented downregulation of several genes: DIO1 , FOXE1 , GLIS3 , PAX8 , and SLC5A5 ( Fig 2D ) . The involvement of constitutive activation of the MAPK signaling pathway in the pathogenesis of PTC is well established [9] . ERK score strongly represented activation level of MAPK signaling pathway and there was very strong positive correlation between ERK score and molecular subtype classification ( Pearson correlation coefficient = 0 . 80 ) . As discussed in TCGA study , ERK score was highly elevated in most BRAF–like , but not in RAS–like samples . Although RAS–like represented lower ERK score than BRAF–like , it had some activated genes in the MAPK signaling pathway . However , NBNR did not have activated genes as represented by the ERK score ( Fig 2E ) . The mutational profile of miFTC and FA were very similar to each other . Moreover , that of EFVPTC was also similar to FTNs , while that of infiltrative FVPTC was similar to cPTC ( Fig 1 ) . All these tumors are follicular-patterned , which are occasionally hard to distinguish from one another . To identify the transcriptional difference among these follicular-patterned thyroid tumors , we performed PCA and differentially expressed gene ( DEG ) analysis . In PCA performed on EFVPTC and infiltrative FVPTC , PC1 axis clearly divided tumors which were classified as BRAF–like and RAS–like/NBNR ( Fig 3A ) . EFVPTC was mainly associated with RAS–like/NBNR rather than infiltrative FVPTC ( p = 0 . 0004 ) . When we performed PCA on miFTC and FA which are hard to distinguish by pathological examination , we could not find any cluster nor PC axis that separates miFTC and FA . Several clusters and PC axes divided those tumors , but all groups consisted of miFTC and FA . ( Fig 3B ) . The lower right corner and the upper central group were associated with H/K/NRAS and other driver genes ( e . g . , DICER1 , EIF1AX , IDH1 , PTEN , and PAX8–PPARG ) , respectively . DEG analysis also confirmed that miFTC and FA did not have significant transcriptional difference . Moreover , the transcriptome of EFVPTC which shows indolent behavior was also indistinguishable from miFTC and FA ( Fig 3C ) . We identified the increased ESRRA expression level of tumors in the lower left corner group in PCA performed on miFTC and FA ( Fig 3B and 3D ) . Pathway enrichment analysis [30] on chemical and genetic perturbations database showed that DEGs of the aforementioned cluster harbored genes that were upregulated by ESRRA and were related to mitochondria ( S2 Table ) . Remarkably , most of those tumors were oncocytic FTN ( p < 0 . 0001 ) ; 83 . 33% of oncocytic FTN ( five out of six ) was clustered into ESRRA overexpression group ( Fig 3D ) . Oncocytic FTN is characterized by remarkable accumulation of mitochondria [31] . In those tumors , expression level of ESRRA showed very strong positive correlation with expression level of PPARGC1A ( Pearson correlation coefficient = 0 . 83 using FPKM ) . Both ESRRA and PPARGC1A are key regulators of mitochondrial biogenesis [32 , 33] . DEG analysis demonstrated that the majority of genes in citric acid cycle ( TCA cycle ) were dramatically upregulated in oncocytic FTN ( Fig 3D ) . All of the oncocytic FTNs were classified as NBNR ( Fig 2C ) . To investigate detailed gene expression signatures in the three molecular subtypes , we performed pathway enrichment analysis on DEGs of each molecular subtype using the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway database [34] . The top 20 most significantly enriched KEGG pathways of upregulated and downregulated genes of each molecular subtype are illustrated in S5 Fig and S6 Fig , respectively . In BRAF–like , pathways including cell adhesion molecules ( CAMs ) , the extracellular matrix ( ECM ) receptor interaction , and focal adhesion were remarkably upregulated . The involvement of these pathways in the carcinogenesis of thyroid nodules and cancer invasiveness had been reported previously [35–37] . Moreover , the risk assessments based on TNM Classification of Malignant Tumors ( TNM ) stage and American Thyroid Association ( ATA ) risk stratification supported that BRAF–like is more aggressive than other molecular subtypes ( p = 0 . 030 and p = 0 . 001 , respectively ) . The p53 and MAPK signaling pathways were upregulated in both BRAF–like and RAS–like but not in most of NBNR . Numerous metabolism and calcium signaling pathways were downregulated in BRAF–like , and these pathways were barely downregulated in other molecular subtypes . Novel molecular subtype NBNR is composed of diverse kinds of driver genes and they had different gene expression profiles depending on the types of mutated or overexpressed gene . As we mentioned earlier , upregulated genes of ESRRA overexpressed tumors were significantly enriched for pathways related to TCA cycle , oxidative phosphorylation ( OXPHOS ) , PPAR signaling , and several metabolisms . Moreover , tumors with PAX8–PPARG rearrangement also showed increased metabolism and PPAR signaling pathways . The Wnt and mTOR signaling pathways were enriched in DICER1 and EIF1AX mutated tumors , respectively . To identify arm-level copy number variations ( CNVs ) of thyroid tumors , we defined arm-level jointly regulated blocks ( JRBs ) which demonstrate colocalization of overexpressed and underexpressed chromosome arms . Our previous study demonstrated high correlation between JRB and CNV status in cancer genomes [38] . In this study , we modified our former method to define arm-level JRBs . We successfully predicted aberration of chromosome arms which represent arm-level amplification and deletion ( Fig 4A ) . We illustrated the CNV landscape of all thyroid tumors in Fig 4B . cPTC had the lowest percentage of arm-level CNV , while miFTC , EFVPTC , and infiltrative FVPTC had high percentage of that . FA showed a lower percentage of arm-level CNV than miFTC ( Fig 4C ) . It was reported that chromosome 12 is more frequently amplified in FA and indolent tumors than aggressive tumors [39] . We also identified amplification of chromosome 12 in FTN but not in PTCs ( p = 0 . 008 ) . The percentage of arm-level deletion was higher in RAS–like than BRAF–like and NBNR ( Fig 4D ) . Similar to previous reports [8 , 39] , deletion of chromosome 22q was the most frequently identified arm-level CNV in RAS–like ( Fig 4E; p < 0 . 0001 ) . DEG test on RAS–like tumors with or without chromosome 22q deletion confirmed the reliability of detecting CNV by our approach . The moderately downregulated genes ( -1 < Log2 ( fold change ) < 0 , q-value < 0 . 05 ) were enriched to chromosome 22q when positional gene set enrichment was performed ( Fig 4F and S3 Table ) . However , there was no difference in other clinicopathological features whether chromosome 22q deletion occurred in RAS–like or not; only multifocality was more significantly frequent ( S4 Table; p = 0 . 037 ) . In BRAF–like , amplification of chromosome 18p was more frequent than other molecular subtypes , but it was not statistically significant ( p = 0 . 051 ) . The ratio of LT was elevated when chromosome 18p was amplified ( S5 Table; p = 0 . 032 , ) .
Recently , the genomic landscape of PTC has been well investigated [8] . This study reduced the rate of unknown oncogenic drivers in subtypes of PTC from 25% to 3 . 5% through discoveries of somatic alterations including EIFIAX , PPM1D , CHEK2 , and diverse fusion genes . However , the transcriptional and mutational landscape of miFTC , which has a greater tendency for hematogenous spread to lung and bone , is yet to be widely explored . In the present study , we performed RNA-seq on miFTC and FA , in addition to cPTC and FVPTC . We identified driver genes in 72 . 73% , 89 . 58% , and 92 . 21% of FTN , FVPTC , and cPTC samples , respectively ( Fig 5A ) . The patterns of genetic alteration differed between histological subtypes . cPTC and miFTC showed considerably different patterns of genetic alteration to each other . However , FVPTC represented an intermediate mutational status between cPTC and miFTC; EFVPTC and infiltrative FVPTC were similar to miFTC and cPTC , respectively . Furthermore , miFTC and FVPTC have higher percentages of arm-level CNVs than cPTC ( Fig 4C ) . This is consistent with previous studies that described a higher fraction of somatic copy number alterations in FVPTC and FTN than cPTC [8 , 40] . Taken together , our result suggests that different genetic alterations could lead to different tumor histology . In addition , we found that FA has a lower percentage of arm-level CNVs than miFTC . This result supports the hypothesis that FA is a preneoplastic condition of miFTC despite the similar patterns of genetic alteration between them . It has been suggested that several clinical risk factors including smoking [41] , alcohol drinking [42] , LT [43 , 44] , menopausal status [45] , genetic predisposition [46] , and early exposure to radiation [11 , 13 , 14] affect the development of thyroid cancer . However , there are few studies considering genetic alterations and clinical risk factors at the same time [12] . Therefore , we analyzed the possible association between types of genetic alteration and clinical risk factors to investigate the etiology of thyroid cancer ( Table 1 ) . The recent reports from Chernobyl cohort well demonstrated a relationship between fusion gene and thyroid cancer . [47 , 48] . Although there was no history of radiation exposure in our subjects , the younger age of the fusion gene group than other groups may reflects the involvement of environmental or genetic factors to the development of chromosomal rearrangement inducing thyroid cancer . Hashimoto’s thyroiditis is the main etiology of LT which is related to inflammation and immune reactions observed in thyroid . In this study , an elevated tendency of LT in the fusion gene group was shown and negative association between LT and H/K/NRAS mutation was also identified . These results suggest an etiologic role of LT in thyroid cancer development . Based on our findings , we suggest that some risk factors influence the types of genetic alteration . We believe that further study would allow better understanding of thyroid cancer development . Based on transcriptional landscape , 180 tumors were classified as BRAF–like , RAS–like , and NBNR ( Fig 2A ) . Our result in PTCs has similar context to the TCGA study , which classified subtypes of PTC as BRAFV600E–like and RAS–like . It was reported that FVFTCs in TCGA , which are classified as RAS–like , were often misdiagnosed as FTC by pathologists [49] . Moreover , as we mentioned earlier , arm-level copy number alterations were frequently observed in FVPTC similarly to FTC as well as H/K/NRAS mutations [40] . The aforementioned issues raised a question regarding the distinction between FTC and FVPTC in the point of biological and clinical relevance . In our analysis , EFVPTC and infiltrative FVPTC showed different mutational and transcriptional characteristics to each other ( Fig 1 and 3A ) . EFVPTC , which was recently re-classified as “noninvasive follicular thyroid neoplasm with papillary-like nuclear features” according to its indolent features [50] had highly similar gene expression profiles to miFTC or FA , ( Fig 3C ) . This result emphasizes that re-classification of thyroid cancers based on their mutational and transcriptional characteristics may be beneficial for stratified medicine . One of the goals of this study was to discover molecular markers to distinguish miFTC and FA . Differential diagnosis for FTC and FA is important for decisions to undergo surgery in clinic , but it is almost impossible due to their highly similar cytological features at present [2] . Several researchers have suggested markers based on gene expression levels [51–53] , but they are not widely adopted . In our analysis , we could not find any significant transcriptional difference between miFTC and FA ( Fig 3B and 3C ) . These results again suggest that miFTC is indolent and it could be treated minimally . However , the transcriptional difference between widely invasive FTC ( wiFTC ) and miFTC/FA is yet to be investigated as there was no wiFTC in the current study . Most tumors harboring EIF1AX mutations and PAX8–PPARG rearrangement were classified as RAS–like in TCGA study . However , they were distinguished from RAS–like and were classified as NBNR according to current and TCGA datasets ( Fig 2A and S4F Fig ) . Traditionally , thyroid cancer is well known to be associated with activation of the MAPK signaling pathway [9 , 10] . Our results suggested that NBNR involves totally different mechanism and pathways ( Fig 2E ) . Furthermore , NBNR exhibited distinct gene expression profiles within the class ( S5 Fig ) . We believe that accumulating data would lead to more effective molecular classification and to discovery of therapeutic targets . In BRAF–like , higher activation of ECM receptor interaction , CAMs , p53 , and MAPK signaling pathways than other molecular subtypes was identified ( S5 Fig ) . Furthermore , low level of TDS and downregulation of several metabolism pathways supported poor clinical presentation in BRAF–like ( Fig 2C and S6 Fig ) . We could not establish the clinical impact of molecular subtypes on locoregional recurrence ( n = 1 ) , distant metastasis ( n = 4 ) , and cancer-specific mortality ( n = 0 ) due to the short median follow-up of 37 months ( 1–100 months ) and low percentage of advanced thyroid cancer . However , the other aggressive pathologic characteristics LNM and ETE were observed much more frequently in the BRAF–like group ( Fig 2C ) , demonstrating its association with clinical presentation or aggressiveness . Collectively , we propose a schematic model of thyroid cancer progression integrating clinical risk factors , mutational and transcriptional landscape , and clinical presentation ( Fig 5B ) . The underlying mechanism of mitochondria accumulation in oncocytic FTN has not been elucidated clearly . We deduced that oncocytic FTN had distinct transcriptome among thyroid tumors containing extremely upregulated mitochondria-related metabolic pathways ( Fig 3D and 3E , and S5 Fig ) . This feature was in agreement with a recent study on eosinophilic chromophobe renal cell carcinoma which is also characterized by densely packed mitochondria [54] . The stimulation of mitochondrial biogenesis and OXPHOS by ESRRA and PPARGC1A is well established [32 , 33] and upregulation of two genes supported mitochondria accumulation in oncocytic FTN . The stimulation of high expression level of ESRRA and PPARGC1A is not fully studied here . However , we believe that our findings could provide important clues to understand the role of mitochondrial biogenesis in oncocytoma . Recently , there was a study that suggested mechanism of oncocytic thyroid tumor development [55] . They demonstrated that many oncocytic thyroid tumors harbored copy number gained mitochondrial biogenesis genes including ESRRA . In summary , this study demonstrates the transcriptional and mutational landscape of miFTC and FA together with cPTC and FVPTC . We revealed that thyroid cancers developed by different types of genetic alteration could be classified as three molecular subtypes ( BRAF–like , RAS–like , and NBNR ) based on gene expression profiles . The three molecular subtypes showed difference in chromosomal aberration , cell proliferation , differentiation , intracellular signaling , and metabolism . We propose that reclassification of thyroid tumors , especially follicular-patterned ones , on the basis of molecular characteristics would provide novel diagnostic implications .
This study was approved by the institutional review board of Seoul National University Hospital , in accordance with the Declaration of Helsinki ( approved ID: H-1108-041-372 ) . Written informed consent was obtained from each subject . Specimens from 180 patients ( 49 men and 131 women; 47 ± 13 years of age ) whose fresh frozen thyroid tissue after thyroid surgery were collected from March 2007 to January 2014 . We could collect 180 tumor tissue samples ( 25 FAs , 30 FTCs , 48 FVPTCs , and 77 cPTCs ) and 81 paired-normal tissue samples that matched with their tumor tissues . The diagnosis of each sample was determined based on pathological findings from thyroid specimens obtained after thyroidectomy . The clinical information of study subjects is shown in S6 Table . There were no patients who were exposed radiation previously . Pathological slides were reviewed by a specialized pathologist . cPTC was defined if the tumor has well-formed papillae with fibrovascular cores and characteristic nuclear features of papillary carcinoma . FVPTC was defined as a PTC with predominantly a follicular growth pattern more than 50% , no well-formed papillae . There are two subtypes of FVPTC: infiltrative FVPTC and EFVPTC regarding the tumor border—infiltrative border or a pushing border with smooth outlines and a capsule , respectively . Capsular invasion was identified in only two cases in EFVPTC and there was not capsular invasion in the other FVPTCs . Therefore , we did not categorize encapsulated FVPTC into two subgroups regarding capsular invasion . miFTC was diagnosed if the tumor is encapsulated by capsular invasion and/or small-caliber sized angioinvasion . FA was diagnosed with no capsular invasion and angioinvasion [56] . Extraction of RNA from frozen tissues was performed using the QIAcube and RNeasy Mini Kit ( Qiagen , Hilden , Germany ) or the Easy Spin RNA extraction kit ( Intron , Daejeon , Korea ) when tissue volume was small but high product yield was needed . RNA was assessed for quality and concentration measurement using an RNA 6000 Nano LabChip on a 2100 Bioanalyzer ( Agilent Inc . , Palo Alto , CA ) . The sequencing libraries were sequenced on a HiSeq 2000 platform ( Illumina , San Diego , CA ) . The sequenced paired-end reads were aligned to GRCh37 . p13 human reference genome using STAR 2-pass method [57 , 58] and PCR duplicates were removed by Picard MarkDuplicate ( http://picard . sourceforge . net ) . Filtered reads were further processed for variant calling using best-practice of GATK ( https://software . broadinstitute . org/gatk/best-practices/ ) , which includes insertion/deletion ( indel ) realignment and base quality score recalibration [59] . S8 Table shows a summary of sequencing throughput and alignment yield in our study subjects . We called somatic single-nucleotide variants ( SNVs ) from 81 matched samples using MuTect [60] . For non-matched samples , we applied SNV calling using the single sample mode of MuTect and GATK’s HaplotypeCaller . Moreover , GATK’s HaplotypeCaller was also used for indel detection . All variants called in these manners were annotated with information from several databases using ANNOVAR [61] . Furthermore , we used GATK’s DepthOfCoverage for counting alternative allele of mutation hotspots in common oncogenes . To discover driver mutations in thyroid cancers , we applied additional filtration criteria to variant calls , as follows: 1 ) not or rarely shown in public databases of normal individuals , such as Exome Aggregation Consortium ( ExAC ) ( http://exac . broadinstitute . org/ ) , 1000 Genomes projects [62] and Exome Sequencing Project 6500 ( http://evs . gs . washington . edu/EVS/ ) ( MAF ≤ 0 . 0001 for ExAC and ≤ 0 . 01 for other databases ) ; 2 ) nonsilent SNVs ( nonsynonymous and splice-site ) and frameshift indels; 3 ) genes that were annotated in COSMIC70 or PTC dataset of TCGA project . Driver candidates in TCGA were examined using cBioPortal for Cancer Genomics [63] . To discover fusion genes in thyroid cancers , we used MOJO ( https://github . com/cband/MOJO ) with TCGA GAF 3 . 0 reference . For filtering false positive calls , we applied further filtration to predicted calls: 1 ) fusion genes only shown in tumor samples; 2 ) discordant read pairs between gene A and B ≥ 2; and 3 ) genomic distance between predicted coordinates ≥ 100 kb or two genes located on different chromosomes . To validate the mutation of novel driver candidates , gDNA was extracted using QIAamp DNA Kits and QIAamp DNA FFPE Tissue Kit ( Qiagen , Hilden , Germany ) from fresh-frozen tissues , or formalin-fixed , paraffin-embedded ( FFPE ) tissue specimens . DNA was quantified using a Nanodrop ND-1000 Spectrophotometer ( NanoDrop Technologies Inc . , Wilmington , USA ) and used as template for PCR amplification . Amplification primers were designed with Primer3 [64] . PCR was performed on GeneAmp PCR System 9700 ( Applied Biosystems; Life Technologies , Carlsbad , USA ) using Hotstar Taq polymerase ( Qiagen ) as follows; 15 minutes at 95°C for initial denaturation , then 40 cycles at 95°C for 30 seconds , 60°C for 30 seconds , and 72°C for 60 seconds , then 5 minutes at 72°C for final extension . 20 ng of gDNA was used to amplify . Amplified products were purified with DNA purification kit ( HiYield DNA fragment extraction kit , Real Genomics , New Taipei City , Taiwan ) , and then analyzed on an Applied Biosystems 3730XL DNA Sequencing Facility ( Applied Biosystems ) . PCR primer and DNA sequencing services were provided by Cosmo Genetech ( Cosmo Genetech , Seoul , Korea ) . The sequencing results were analyzed using ABI Sequencing analysis software 5 . 2 ( Applied Biosystems ) . The primer sequences used in this study provided in S8 Table . RT-PCR and Sanger sequencing were performed across the fusion break points to identify the exact fusion junction of PICALM–BRAF . The tissue blocks were cut into 4μm slides , and total RNA from FFPE samples was isolated using a tissue kit ( Maxwell 16 LEV RNA FFPE purification kit , Promega , Madison , USA ) and an automatic extractor ( Maxwell MDx 16 , Promega ) . RT-PCR was performed on GeneAmp 9700 using Hotstar Taq polymerase ( Qiagen ) as follows;15 minutes at 95°C for initial denaturation , then 45 cycles at 95°C for 30 seconds , 62°C for 30 seconds , and 72°C for 60 seconds , then 5 minutes at 72°C for final extension . 30 ng of cDNA synthesized from total RNA was used to analyze . All PCR products were sequenced on both strands using the same primers and BigDye Terminator v3 . 1 Cycle Sequencing kits and a 3730 DNA analyzer ( Applied Biosystems ) . The specific fusion primers for PICALM–BRAF provided in S8 Table . To evaluate ETV6 rearrangement , we performed FISH analysis on FFPE tumor tissues using the Vysis LSI ETV6 spectrum Orange/Green probe ( Abbot Molecular , Illinois , USA ) . These commercially-available probes are designed as a dual-color probe where the two regions across the break-point . For microscopic evaluation , at least 100 intact and nonoverlapping cell nuclei were scored for the presence of a split signal using a Zeiss Axio Imager with appropriate filters . Pictures were captured using a digital microscope camera ProgRes MF ( Jenoptik , Germany ) and analyzed with the Isis software ( MetaSystems , Germany ) . The signal pattern interpretation was as follows: interphase nucleus with two colocalized green/orange fusion signals identified normal chromosomes , while a separated orange and green signals and green/orange fusion signals indicated rearranged gene . The positive threshold was defined as more than 10% of signals split and/or isolated orange signal in 100 tumor cells . To verity STRN–ALK , IHC staining was performed on FFPE tissue sections that were 4 m thick using an automated immunostainer ( Leica Microsystems , Milton Keynes , UK ) . Briefly , the slides were heated for 20 min at 100°C in Epitope retrieval solution , pH 9 . 0 ( Leica Microsystems ) . The slides were then incubated with a monoclonal mouse anti-human ALK antibody ( Novocastra , Newcastle Upon Tyne , UK ) at a dilution of 1:25 . This antibody was raised against a C-terminal portion of the tyrosine kinase domain of ALK and was intended for the qualitative identification of ALK molecules in paraffin sections by light microscopy . Staining intensity was scored as 0 ( no staining ) , 1+ ( weak cytoplasmic staining without any background staining ) , and 2+ ( strong cytoplasmic staining ) . Tumors with 1+ or 2+ expression in more than 10% of the tumor cells were deemed positive for ALK protein expression . For ALK IHC-positive cases , we subsequently performed IHC using an antibody against phosphorylated ALK ( phosphor Y1507 , Abcam , Cambridge , MA , USA ) at a dilution of 1:100 . According to Ensembl gene set , we counted the number of reads aligned to each gene using HTSeq-count and normalized them via regularized log ( rlog ) transformation method of DESeq2 [65 , 66] . In this study , DEGs were determined by DESeq2 to have q-value < 0 . 05 , |Log2 ( fold change ) | ≥ 1 , and baseMean ≥ 100 . The calculated p-values were adjusted to q-values for multiple testing using the Benjamini–Hochberg correction . The normalized gene expression values were applied to PCA using the most variable 500 genes . For heatmap display , the centered rlog values were applied to the K-means clustering algorithm using cluster 3 . 0 [67] . To identify pathways that were significantly enriched in DEGs , we applied them to the Molecular Signatures Database 5 . 0 [30] . As described by TCGA study , we calculated TDS using 16 thyroid metabolism and function genes . The rlog values from DESeq2 were first median-centered across 180 tumor samples , and then average values across the 16 genes in each tumor were determined as TDS . To numerically represent activation level of MAPK signaling pathway , we implemented and modified ERK score calculation from TCGA . We applied identical method that was described in TDS calculation using 52 MAPK signaling pathway genes [68] . For JRB analysis , we selected genes in autosomes that have average FPKM ≥ 1 . 5 and were classified as protein-coding gene in Ensembl database . After that , we sorted genes by chromosomal coordinate and applied three normalization steps as follows: 1 ) Log ( FPKM ) of gene ( gene A ) in ith tumor sample was Z-score transformed: Zi , A=Log ( FPKM ) −μσ where μ and σ represent average and standard deviation of Log ( FPKM ) of 81 normal tissues . 2 ) Z-score of gene ( gene A ) in ith tumor sample was Z-score transformed: ZZ , i , A=Zi , A−μiσi where μi and σi represent average and standard deviation of Z-score of ith tumor sample . 3 ) median Z-score of each chromosome arm of ith tumor sample was median-centered by subtracting the median Z-score of all chromosome arms . After normalization steps , we defined arm with median-centered Z-score ≥ 0 . 5 and ≤ -0 . 5 as overexpressed and underexpressed JRB , respectively . All statistical analyses were performed using SPSS version 20 . 0 ( IBM Co , Armonk , NY , USA ) . Data are presented either as frequencies ( % ) or as mean ± standard deviation . Comparisons of categorical variables were performed using either the Pearson’s χ2 or Fisher’s exact test ( if the number was < 5 ) , and the independent t-test was used for continuous variables . Adjusted p-values for age and sex were obtained by the binomial or multinomial logistic regression analyses for categorical variables and by either the linear regression or analysis of covariance ( ANCOVA ) for continuous variables . A post-hoc Bonferroni test were used to determine which groups have statistically different proportion of clinical risk factors . Statistical significance was defined as two-sided p-values < 0 . 05 . We submitted all the sequenced paired-end reads to EBI European Nucleotide Archive database with accession number PRJEB11591 ( Direct access: http://www . ebi . ac . uk/ena/data/view/PRJEB11591 ) . | Recently , The Cancer Genome Atlas proposed an improved classification of the subtypes of papillary thyroid carcinoma ( PTC ) based on gene expression profiles , which better represents cell signaling and differentiation . However , a molecular characterization of follicular thyroid carcinoma ( FTC ) , which has a greater tendency for hematogenous spread to lung and bone is not yet fully elucidated . In this study , we describe the first RNA sequencing data of minimally invasive FTC ( miFTC ) and benign follicular adenoma ( FA ) , which cause diagnostic difficulties due to their similar histological features . Additionally , classical PTC and follicular variant of PTC ( FVPTC ) were sequenced to compare their transcriptional and mutational landscape . BRAF , H/K/NRAS , fusion genes , and copy number variations were associated with tumor histology . Based on gene expression profiles , thyroid tumors were classified as three molecular subtypes regardless of histological subtypes , BRAF–like , RAS–like , and Non–BRAF–Non–RAS . In particular , we found identical gene expression profiles between miFTC , FA , and encapsulated FVPTC . Oncocytic follicular thyroid tumors have gene expression signatures related to mitochondrial biogenesis including ESRRA and PPARGC1A . These results expanded the current molecular understanding of thyroid cancer to its follicular types . | [
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| 2016 | Comprehensive Analysis of the Transcriptional and Mutational Landscape of Follicular and Papillary Thyroid Cancers |
Currently , the predominant onchocerciasis control strategy in Africa is annual mass drug administration ( MDA ) with ivermectin . However , there is a consensus among the global health community , supported by mathematical modelling , that onchocerciasis in Africa will not be eliminated within proposed time frameworks in all endemic foci with only annual MDA , and novel and alternative strategies are urgently needed . Furthermore , use of MDA with ivermectin is already compromised in large areas of central Africa co-endemic with Loa loa , and there are areas where suboptimal or atypical responses to ivermectin have been documented . An onchocerciasis vaccine would be highly advantageous in these areas . We used a previously developed onchocerciasis transmission model ( EPIONCHO ) to investigate the impact of vaccination in areas where loiasis and onchocerciasis are co-endemic and ivermectin is contraindicated . We also explore the potential influence of a vaccination programme on infection resurgence in areas where local elimination has been successfully achieved . Based on the age range included in the Expanded Programme on Immunization ( EPI ) , the vaccine was assumed to target 1 to 5 year olds . Our modelling results indicate that the deployment of an onchocerciasis vaccine would have a beneficial impact in onchocerciasis–loiasis co-endemic areas , markedly reducing microfilarial load in the young ( under 20 yr ) age groups . An onchocerciasis prophylactic vaccine would reduce the onchocerciasis disease burden in populations where ivermectin cannot be administered safely . Moreover , a vaccine could substantially decrease the chance of re-emergence of Onchocerca volvulus infection in areas where it is deemed that MDA with ivermectin can be stopped . Therefore , a vaccine would protect the substantial investments made by present and past onchocerciasis control programmes , decreasing the chance of disease recrudescence and offering an important additional tool to mitigate the potentially devastating impact of emerging ivermectin resistance .
Currently , the predominant onchocerciasis control strategy in Africa is annual mass drug administration ( MDA ) with ivermectin , which Merck & Co . have committed to donate for as long as needed to eliminate onchocerciasis as a public health problem . Since 2010 there has been a dramatic shift in onchocerciasis control policy in Africa , with programmes changing their aim from elimination of the disease burden to elimination of the infection where feasible . The World Health Organization’s ( WHO ) Roadmap on Neglected Tropical Diseases [1]—endorsed by the London Declaration on NTDs ( LDNTD , 31 January 2012 ) [2]—set goals for elimination of Onchocerca volvulus infection in selected countries of Africa by 2020 . The African Programme for Onchocerciasis Control ( APOC ) has pledged elimination of onchocerciasis where possible by 2025 [3] , and the Bill and Melinda Gates Foundation foresees that global elimination will be reached by 2030 [4] . We have previously indicated , based on mathematical modelling of onchocerciasis transmission and control with EPIONCHO , that the feasibility of eliminating the infection depends primarily on baseline ( pre-control ) levels of endemicity , patterns of transmission , magnitude of residual transmission between inter-treatment periods , therapeutic coverage and importantly , compliance to treatment , precluding a one-size-fits-all approach to elimination [5 , 6 , 7 , 8] . There is a consensus among the global health community , substantiated by mathematically modelling , that onchocerciasis in Africa will not be eliminated in all endemic foci with annual ivermectin MDA alone [9 , 10 , 11] , and that novel supportive health intervention technologies , including a vaccine , and/or alternative treatment and control strategies are badly needed [2 , 12 , 13] . Mass distribution of ivermectin is already compromised in large areas of central Africa ( including the Congo basin ) [14] , where another filarial infection , loiasis or eye-worm , is co-endemic with human onchocerciasis and ivermectin cannot be used for the treatment of individuals with high Loa loa microfilaraemia ( microfilariae in blood ) because of the risk of developing severe and possibly fatal or irreversible adverse reactions [15 , 16] . Currently , it is recommended that in areas co-endemic for these two filarial infections , where L . loa microfilarial prevalence is above a threshold of 20% [15] , ivermectin should not be distributed [16] as there is an unacceptable risk of severe adverse events ( SAEs ) . It has been estimated that approximately 14 million people live in high-risk loiasis areas in central Africa and are potentially affected by this contraindication [14] . However , the true extent of the overlap between onchocerciasis and loiasis , as well as the levels of infection prevalence and intensity for both infections and of L . loa microfilarial load in co-infected individuals within such co-endemic areas need to be ascertained [17] . Stopping ivermectin treatment following local elimination of infection brings the inescapable risk of infection recrudescence seeded by migrating and infective blackflies and/or humans from areas with ongoing transmission . Modelling has shown that the time to reach elimination varies considerably with the intensity of transmission , taking longer in high endemicity areas compared to low endemicity areas [5 , 6 , 9] . Hence , it is likely that the highest endemicity areas with the most intense transmission will become sources of infection to an increasing number of infection-free communities as progress towards global elimination goals advances . In addition to the above considerations , suboptimal or atypical responses to ivermectin have been documented in some communities , particularly in Ghana where mass ivermectin distribution first started . These responses manifest as a faster than anticipated rate of microfilarial reappearance in the skin following treatment [18 , 19 , 20 , 21] . This has raised concerns that the parasite may be developing incipient resistance to the embryostatic effect of ivermectin [18 , 19 , 20 , 21] . If ivermectin resistance were to develop , it could eventually spread and the likelihood of onchocerciasis elimination by MDA with ivermectin as a stand-alone strategy would be jeopardised . The Onchocerciasis Vaccine for Africa ( TOVA ) initiative is a response to the demand for new intervention tools for onchocerciasis control and elimination [13 , 22 , 23] . TOVA builds upon over 30 years of research aimed at developing and testing an O . volvulus vaccine , a project initiated by the Edna McConnell Clark Foundation ( 1985–1999 ) [24 , 25] and subsequently supported by the European Union and the National Institutes for Health of the USA . TOVA has identified three prime vaccine candidates ( Ov-103 , Ov-RAL-2 , and Ov-CPI-2M ) based on proven efficacy in animal model systems [22 , 26 , 27 , 28 , 29 , 30] , aiming to take at least one of these experimental vaccines to phase II efficacy trials by 2020 [22] . Here , we extend a previously developed onchocerciasis dynamic transmission model to: ( a ) investigate the potential impact of vaccination in areas where ivermectin is contraindicated because of onchocerciasis–loiasis co-endemicity , and ( b ) explore its potential influence on infection resurgence in controlled areas .
The analysis was performed using our deterministic onchocerciasis transmission model ( EPIONCHO ) which describes the rate of change with respect to time and host age ( in both sexes ) of the mean number of fertile and non-fertile female adult worms per host , the mean number of microfilariae ( mf ) per milligram ( mg ) of skin , and the mean number of L3 larvae per simuliid fly ( Fig 1 ) . The model has been refined from the original framework developed by Basáñez and Boussinesq [31] , to include age and sex structure of the host population [32]; the population-level effects of a single [33 , 34] and multiple [8] treatments with ivermectin , and increased programmatic realism related to patterns of treatment coverage and systematic non-compliance ( whose effects can be explored separately ) [8] . The assumed human age- and sex-structure of the population reflects demographic characteristics in savannah areas of northern Cameroon [32 , 35 , 36] ( Fig 2 ) , where the prevailing O . volvulus–Simulium damnosum sensu lato combinations ( i . e . savannah parasites–S . damnosum sensu stricto /S . sirbanum ) are responsible for the most severe sequelae of onchocerciasis [37 , 38] . The model captures age- and sex-specific host exposure to biting blackfly vectors ( Fig 2A ) , calibrated to reproduce observed pre-control microfilarial load ( infection intensity ) age profiles ( Fig 2B ) in Cameroon [32] , epidemiological patterns which are also seen in forest areas of Cameroon [35] and elsewhere in foci under vector control in the Onchocerciasis Control Programme in West Africa ( OCP ) area [39] . We assumed a stationary age distribution and a stable ( closed ) population . The model can reflect pre-control infection levels in a range of hypo- , meso- , hyper- and highly hyperendemic onchocerciasis foci ( Table 1 ) by varying the annual biting rate ( ABR ) of the simuliid vectors ( the number of bites received per person per year ) . A more detailed explanation of the model is provided in S1 File ( Text A , Text B , Table A , Table B and Table C ) . Our extended version of EPIONCHO assumes that the vaccine exerts two effects ( Fig 1 ) , a prophylactic effect against incoming L3 ( infective ) stage-larvae and a therapeutic effect against mf ( the stage responsible for transmission to vectors and onchocercal pathology ) . These effects—which are represented phenomenologically rather than mechanistically—are assumed to manifest , respectively , as a proportional reduction in the probability that an incoming L3 larva develops into a reproductively functional adult worm ( prophylactic effect ) , and as a proportional reduction in the skin microfilarial load ( therapeutic effect ) ( S1 File , Text C ) . Based on animal model data [26 , 27 , 28] , we assumed an initial prophylactic efficacy of 50% , and an initial therapeutic efficacy of 90% . We also explored higher initial vaccine efficacies of , respectively , 70% and 95% in a sensitivity analysis . We assumed that these initial prophylactic and therapeutic effects wane at a rate of 0 . 05 per year such that their mean duration is 20 years ( = 1 / 0 . 05 ) . As part of our sensitivity analysis , we varied this rate of decay ( mean duration between 5 and 50 years ) , in accordance with the range considered by previous modelling of a hypothetical schistosomiasis vaccine [40] . We modelled a vaccination programme targeting one- to five-year olds in its first year , representing an initial ‘catch-up’ campaign , followed by continuous vaccination of one-year olds subsequently ( hence each child would receive a single vaccination , as they become 1 ) . This was based on the age range included in the Expanded Programme on Immunization ( EPI ) [41] . We also considered a less intensive alternative programme , omitting the initial ‘catch-up’ component , and involving the vaccination of five-year olds only . A compromised schedule such as this could be necessary given the high number of vaccinations that are given to the one-year-old cohort in developing countries [42] . Vaccination coverage was assumed to be 80% based on EPI data on the 4-year average coverage of measles vaccine in Cameroon between 2010 and 2013 [43] , and more incidentally , the level of coverage assumed by a previous modelling paper on the potential long-term impact of a hypothetical schistosomiasis vaccine [44] . We based our estimate on the EPI data from Cameroon because: ( a ) it is a country with a high prevalence of onchocerciasis–loiasis co-endemicity , and therefore a potential beneficiary of an onchocerciasis vaccine , and ( b ) the demographic structure of EPIONCHO is based on data from this country [32] . We also varied the assumed level of coverage as part of our sensitivity analysis , choosing a more modest 60% coverage to reflect , perhaps , a lower degree of public confidence in a new vaccine compared to more familiar and established vaccines . We used the model to investigate ( 1 ) the beneficial impact of vaccination in terms of reductions in onchocerciasis infection and transmission in O . volvulus–L . loa co-endemic areas where ivermectin is contraindicated , and ( 2 ) the long-term dynamics of vaccine-induced protection against O . volvulus infection and how this may reduce the chance of infection recrudescence following elimination ( and cessation of ivermectin MDA ) . We investigated these scenarios using three principal model outputs , all presented after 15 years of a hypothetical vaccination programme . These outputs are: ( a ) the mean microfilarial load in the human population as a whole and the age-stratified contribution to this mean; ( b ) the overall annual transmission potential ( ATP , the average number of L3 larvae potentially received per person per year ) , and the age- and sex-specific contributions to the ATP; ( c ) the age-specific protection afforded by the vaccine against new infections . The age-stratified contribution to mean microfilarial load was obtained by multiplying the age- and sex-specific microfilarial loads ( Fig 3B ) times the proportion of the population within each corresponding demographic stratum ( Fig 2A for age and Fig 2B for sex ) . The sum ( grand total ) of the age-stratified contribution yields the overall mean microfilarial load . The age- and sex-specific contribution to the ATP was calculated as the product of the following factors: i ) the age- and sex-specific microfilarial loads; ii ) the proportion of the population within each corresponding demographic stratum; iii ) the proportion of blackfly bites taken on each demographic stratum ( Fig 3A ) ; iv ) the annual biting rate ( ABR ) ; and v ) the constraining density-dependent processes ( parasite establishment and fly survival ) acting on the development , to L3 larvae , of ingested mf within the blackfly [32] . The sum ( grand total ) of the age-and sex-stratified contribution to ATP yields the overall annual transmission potential .
Our modelling results indicate that the deployment of an onchocerciasis vaccine would have a substantial beneficial impact in O . volvulus–L . loa co-endemic areas where it may not be possible to deliver ivermectin MDA , or the population does not take treatment for fear of SAEs . However , these benefits take a considerable time to accrue since vaccinated individuals ( one to five year olds initially and then only one year olds ) need to age through the population into the more heavily exposed population age groups ( Fig 3A ) . After 15 years of vaccination , the overall mean microfilarial load in the population is projected to decrease by 30% in highly hyper- and hyperendemic onchocerciasis foci and by 32% in mesoendemic foci ( Table 2 ) . Assuming a more modest 60% vaccination coverage ( as opposed to the default 80% ) , the corresponding reductions are 23% ( highly hyperendemic ) , 22% ( hyperendemic ) and 24% ( mesoendemic ) ( S1 File , Table D ) . When the initial one- to five-year old ‘catch-up’ campaign is omitted and the programme comprises the continuous vaccination of five-year olds only ( but see below for a discussion on caveats associated with this approach ) , the reductions in the highly hyper- , hyper- and mesoendemic foci , again after 15 years , are 24% , 24% and 26% respectively ( S1 File , Table E ) . Fig 4 illustrates the profile of the age-specific contribution to overall mean microfilarial load , accounting for both demography of the population ( Fig 2 ) and infection ( Fig 3B ) . Although the reduction in the overall mean microfilarial load is somewhat modest compared to what could be achieved if it were possible to deliver ivermectin MDA [5] , it is highly relevant that the most substantial reductions occur among younger members of the population . Previous studies have highlighted the crucial role played by exposure to heavy infection early in life on the risk of onchocerciasis-associated morbidity , blindness and excess mortality [36 , 45] , and that for a given microfilarial load the relative risk of mortality is much greater in the <20 yr age group [43] . Hence , our modelling results suggest that an onchocerciasis vaccine would contribute to reduce the burden of disease and mortality in these populations , with most benefit afforded to those aged less than 20 years . In future , it will be important to determine whether a vaccine eliciting these anticipated reductions in onchocerciasis-associated disease and mortality could be delivered in a cost-effective manner . Like any intervention , this will crucially depend on the balance between the fixed and variable costs combined with the scale of the intervention ( economies of scale ) [46 , 47] . Currently , it is difficult to ascribe plausible costs to an onchocerciasis vaccination programme given the early stage of the vaccine’s development , and that no comparable vaccines or vaccination programmes exist for any other human helminthiasis . Besides , if ivermectin treatment were to be implemented in areas of onchocerciasis–loiasis co-endemicity with high risk of SAEs ( those with a loiasis prevalence ≥ 20% ) , it would have to be on a test-and-treat basis in order to identify and exclude those with a high loiasis microfilaraemia and therefore most at risk , which would raise the costs over those of routine community-directed treatment with ivermectin ( CDTI ) . In addition , measures would have to be put in place to monitor any SAEs that might occur and provide adequate care , and this would also elevate the costs of programmes based on ivermectin . These considerations would have to be taken into account in any cost-effectiveness comparison . The ATP is projected to decrease by over 20% ( Table 2 ) , representing reductions in onchocerciasis transmission which would diminish the risk of O . volvulus–L . loa co-endemic areas acting as sources of infection to areas where treatment programmes are in the process of being scaled down or stopped . The reduction in the intensity of transmission ( ATP ) , of 20% , is less than the reduction in the intensity of infection ( microfilarial load ) , of 30% , because older women ( particularly those aged ≥60 years ) , albeit comprising a relatively small proportion of the total population ( Fig 2A ) , are both heavily exposed to biting blackflies ( Fig 3A ) and , at baseline , also heavily infected ( Fig 3B ) . Like the age-specific contributions to the microfilarial load ( Fig 4 ) , the corresponding contributions to the ATP depicted in Fig 5 are most reduced in younger age groups , making these age groups , despite being most numerous in the population , the lowest contributors to infection transmission . Reductions in microfilarial load and ATP are only marginally increased by assuming a greater initial vaccine efficacy; of 70% for prophylactic ( against L3 larvae ) efficacy , and of 95% for therapeutic ( against mf ) efficacy ( Table 2B ) . By contrast , reductions in the assumed rate of waning of these vaccine effects have a marked impact on model outcomes ( Fig 6 ) . Therefore , it will be more important to invest in a vaccine with a slow rate of decay , effecting a long duration of protection—most likely mediated by the natural boosting effect of repeated infection challenges—than in an initially highly efficacious vaccine whose protective effects decay faster . This conclusion is in agreement with other modelling studies on potential schistosomiasis vaccines [44] . Therefore , our modelling helps to inform the most desirable properties of an onchocerciasis vaccine as an integral part of developing its target product profile ( TPP ) . Overall , the magnitude of the reduction in ATP elicited by an onchocerciasis vaccination programme would be unlikely to interrupt transmission per se and ultimately eliminate O . volvulus without concomitant and complementary interventions that can be safely and effectively implemented in areas of onchocerciasis–loiasis co-endemicity . Thus , in such areas of co-endemicity , it is envisaged that an onchocerciasis vaccine would represent an additional and complementary intervention strategy to be used in conjunction with interventions such as vector control or test and treat strategies using anti-Wolbachia macrofilaricidal drugs such as doxycycline [48] , both of which are currently under consideration for recommendation as alternative treatment strategies ( ATSs ) by APOC .
Based on elimination successes in Mali and Senegal [52 , 53] , lessons learned when stopping onchocerciasis control in the OCP , and projections of the ONCHOSIM model [54] , APOC has proposed provisional operational thresholds for treatment interruption followed by surveillance ( pOTTIS ) . These comprise a microfilarial prevalence ( by skin snipping ) less than 5% in all surveyed villages and less than 1% in 90% of such villages , as well as less than 0 . 5 L3 larvae per 1 , 000 flies [55] . It is important to emphasize that these pOTTIS are not necessarily equivalent to transmission breakpoints , which represent a parasite density ( and corresponding prevalence ) below which the worm population would not be able to maintain itself due to the presence of Allee effects [11 , 56] . The magnitude of the transmission breakpoints is likely to be very locale-specific , depending on factors such as the parasite distribution and reproductive biology resulting from prolonged treatment , and the prevailing vector biting rates and competence for O . volvulus [57] . Although the pOTTIS have been validated in some foci—with low pre-control endemicity and highly seasonal transmission by savannah members of S . damnosum s . l . [52 , 53 , 58]—they will not necessarily hold in all epidemiological settings; particularly those with high pre-control endemicity , transmission rates and vector density . Furthermore , the current entomological threshold within these guidelines is measured per 1 , 000 flies and not per 1 , 000 parous flies ( those which have previously fed on blood , laid eggs and survived gonotrophic cycle ( s ) ) . Consequently , it does not account for any potential differences in parity and survival rates among vector species in different seasons , or for different vector mixes when more than one simuliid species contributes to transmission in the same locale [59] . This , together with the poor sensitivity of skin snipping when infection levels are low [60] , can lead to misleading conclusions regarding the level of ongoing transmission and potentially to treatment being stopped prematurely . An onchocerciasis vaccine would offer protection to populations after ivermectin distribution has ceased , and may reduce the chance of infection recrudescence in areas where treatment may have been stopped early . In addition , the use of an onchocerciasis vaccine would mitigate the consequences of a potential spread of ivermectin resistant O . volvulus [20 , 21] . It is also important to note that models such as EPIONCHO , ONCHOSIM , and others have been primarily calibrated with data collected in transmission areas of African savannah [32 , 57] , an exception being the SIMON model [61] , parameterised for a forest setting but not currently used for decision support in Africa . It is also important to note that models such as EPIONCHO , ONCHOSIM , and others have been primarily calibrated with data collected in transmission areas of African savannah [32 , 53] , an exception being the SIMON model [57] , parameterised for a forest setting but not currently used for decision support in Africa . Forest simuliid species do not exhibit the same degrees of density-dependent constraint on the fraction of incoming microfilariae that successfully establish in the thoracic muscles of the flies [57] , resulting in higher numbers of L3 larvae per forest fly [62] and corresponding transmission potentials [63] . This could mean that forest blackflies are more efficient transmitters of infection , although little is known about other density dependencies that might mitigate this effect , such as the degree of density-dependent excess mortality inflicted on infected blackflies . Parasitological data on infection intensity ( microfilarial loads ) combined with entomological data on annual biting rates collected from communities in forest settings could help to infer vector efficiency indirectly , yet such data are somewhat scarce . It remains an important research need to parameterise onchocerciasis models to reflect the epidemiology and transmission of forest onchocerciasis , as it is in these areas that onchocerciasis–loiasis co-endemicity represents a barrier to elimination [14] , and the use of an onchocerciasis vaccine would be highly desirable as one of a number of complementary interventions forming a multipronged strategy . Other modelling studies have been conducted to explore the epidemiological impact of helminth vaccines such as for human [40 , 44] and zoonotic [64] schistosomiasis , and hookworm infection [65] . In particular the latter also explored vaccination of older age groups ( school-age children ) . We refrained from doing this because it has been shown that helminth vaccines may not be efficacious in hosts who are already infected due to the immunomodulatory effects of helminth infection [66] . However , in areas of intense onchocerciasis transmission where ivermectin has not yet been deployed , the under 5-year olds may be patently infected and the 1-year olds pre-patently infected . These challenges will have to be taken into account when optimising the design of the vaccines and vaccination programmes . Since skin and blood samples are seldom taken from these age groups during onchocerciasis surveys , data to inform the ( immuno- ) epidemiology of the infection in young children are scarce ( but see [67 , 68] ) . The development of O . volvulus-specific biomarkers for detection of active infection is a pressing research need [69 , 70] . A potential caveat of the vaccination strategy discussed in this paper would be the possibility of SAEs was there cross-reactivity between O . volvulus and L . loa with respect to the therapeutic effect of the vaccine against microfilariae . However , the amino acid identity between the three candidate O . volvulus proteins and their counterparts in L . loa amount only at 52% for Ov-RAL-2 , 58% for Ov-CPI-2M and 71% for Ov-103 , and therefore it is unlikely that there would be substantial cross efficacy at immunologically-mediated killing of microfilariae . Notwithstanding this seemingly low risk , this issue has not yet been tested in animal models of loiasis , but experimental models are being developed [71] that would allow investigation of this question if a patent infection could be established . More recently , a newly developed co-infection model of O . ochengi and L . loa microfilariae in Mongolian jirds ( Meriones unguiculatus ) has been established at the University of Buea , Cameroon , by Dr . Fidelis Cho-Ngwa ( personal communication ) . This immunocompetent jird model was developed for the simultaneous testing of potential macrofilaricides on O . ochengi and L . loa microfilariae in the same animal . This counter screen is important in confirming that a drug , whilst killing adult worms in vitro or in vivo , will not kill L . loa microfilariae in a host with a fully intact immune system ( as occurs in co-infected humans ) . This model could be also used to investigate the question of immunological cross reactivity ( the similarity between O . volvulus and O . ochengi for all three proteins mentioned above is ≥99% ) , by immunizing with the recombinant antigens and then challenging with O . ochengi and L . loa microfilariae , following their mortality and any ensuing pathology . Developing quantitative tools that allow rigorous exploration of the considerations described above will be essential for assessing the true cost-effectiveness of onchocerciasis vaccination . In particular , this work highlights the importance of developing spatially-explicit transmission models with which to investigate and quantify the probability of infection being re-introduced in successfully controlled areas from others with ongoing transmission . The results of the analysis clearly show the importance of obtaining reliable estimates of the duration of vaccine protection , i . e . the reciprocal of the rate at which vaccine efficacy would decay . This property of the vaccine will be more important than initial vaccine efficacy in terms of the long-term impact of vaccination campaigns | Novel and alternative strategies are required to meet the demanding control and elimination ( of infection ) goals for human onchocerciasis ( river blindness ) in Africa . Due to the overlapping distribution of onchocerciasis and loiasis ( African eye worm ) in forested areas of central Africa , millions of people living in such areas are not well served by current interventions because they cannot safely receive the antiparasitic drug ivermectin that is distributed en masse to treat onchocerciasis elsewhere in Africa . The Onchocerciasis Vaccine for Africa—TOVA—Initiative has been established to develop and trial an onchocerciasis vaccine . We model the potential impact of a hypothetical childhood vaccination programme rolled out in areas where co-endemicity of onchocerciasis and African eye worm makes mass distribution of ivermectin difficult and potentially unsafe for treating , controlling and eliminating river blindness . We find that , 15 years into the programme , a vaccine would substantially reduce infection levels in children and young adults , protecting them from the morbidity and mortality associated with onchocerciasis . Most benefit would be reaped from a long-lived vaccine , even if only partially protective . We also discuss how a vaccine could substantially reduce the risk of re-emergence of onchocerciasis in areas freed from infection after years of successful intervention . | [
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| 2015 | Human Onchocerciasis: Modelling the Potential Long-term Consequences of a Vaccination Programme |
Most mosquito control efforts are primarily focused on reducing the adult population size mediated by reductions in the larval population , which should lower risk of disease transmission . Although the aim of larviciding is to reduce larval abundance and thus recruitment of adults , nonlethal effects on adults are possible , including transstadial effects on phenotypes of adults such as survival and pathogen infection and transmission . In addition , the mortality induced by control efforts may act in conjunction with other sources of mosquito mortality in nature . The consequences of these effects and interactions may alter the potential of the population to transmit pathogens . We tested experimentally the combined effects of a larvicide ( Bacillus thuringiensis ssp . israelensis , Bti ) and competition during the larval stages on subsequent Aedes aegypti ( Linnaeus ) traits , population performance , and susceptibility to dengue-1 virus infection . Ae . aegypti that survived exposure to Bti experienced accelerated development , were larger , and produced more eggs with increasing amounts of Bti , consistent with competitive release among surviving mosquitoes . Changing larval density had no significant interactive effect with Bti treatment on development and growth to adulthood . Larval density , but not Bti or treatment interaction , had a strong effect on survival of adult Ae . aegypti females . There were sharper declines in cumulative daily survival of adults from crowded than uncrowded larval conditions , suggesting that high competition conditions of larvae may be an impediment to transmission of dengue viruses . Rates of infection and dengue-1 virus disseminated infections were found to be 87±13% and 88±12% , respectively . There were no significant treatment effects on infection measurements . Our findings suggest that larvicide campaigns using Bti may reduce the number of emerged adults , but survivors will have a fitness advantage ( growth , development , enhanced production of eggs ) relative to conspecifics that are not under larvicide pressure . However , under most circumstances , these transstadial effects are unlikely to outweigh reductions in the adult population by Bti and altered risk of disease transmission .
Aedes aegypti ( Linnaeus ) is regarded as one of the most important vectors of arboviruses that affect human health , including yellow fever , chikungunya , and dengue . Protecting humans from the diseases transmitted by this mosquito has historically been achieved by controlling mosquito populations . In conjunction with the use of pesticides , development and testing of non-pesticide control strategies and products is ongoing to determine their utility for the control of mosquito vectors of disease ( e . g . , genetically modified organisms , [1]; Wolbachia symbionts , [2] ) . Control approaches to reduce populations of Ae . aegypti have focused on the use of larvicides ( e . g . , temephos , Bacillus thuringiensis spp . israelensis ( Bti ) , methoprene ) , space spraying ( aircraft and vehicle-mounted ULV sprayers and portable thermal foggers of insecticides such as malathion and pyrethroids ) , source reduction , biological control ( larvivorous fish and copepods ) , and education of the public [3 , 4] . Despite the heavy reliance on larval control , there is little understanding of how larval control interventions ( insecticide , biological ) act in conjunction with other sources of larval mortality in nature to influence population size of adults , characteristics of surviving adult mosquitoes and subsequent consequences for risk of disease transmission . Density-dependent processes which induce mortality of mosquito vectors during the immature stages also influence recruitment to the adult stage as well as growth and development [5] . Density-dependence attributable to larval competition has been demonstrated for mosquito vectors of pathogens ( e . g . Ae . aegypti , [6]; Culex quinquefasciatus Say , [7]; Anopheles gambiae Giles , [8] ) and extends to other mosquito species ( e . g . , [9] ) . Specifically , there are greater numbers of larvae that die when there are many larvae than when there are few conspecific larvae , attributable in most instances to competition for food resources . Additionally , density-dependent size at metamorphosis may be the basis for population regulation in some mosquito species ( Aedes sierrensis Ludlow ) [10] . Density-dependent competition may also have other consequences on risk of disease transmission , such as changes in characteristics of the surviving adult mosquitoes . In other words , density-dependence in the larval stages may have transstadial effects that are realized in the adult stage . Alterations in adult traits or population genetics associated with biting behavior , adult survival , and vector competence for pathogens among survivors after density-induced mortality events could influence vectorial capacity . For example , inter- and intraspecific competition among container mosquitoes alter adult survival [11–13] and vector competence for arboviruses [14–16] . It is often assumed that larval control practices which cause mortality among juvenile mosquitoes will act additively with other sources of mortality resulting in lower adult population size . Although this may be the case in most instances , alternative outcomes are possible [9 , 17] . For example , larval competition can be severe in container inhabiting mosquitoes , such that many individuals die in the larval stage . If competition is reduced ( a lower number of larvae in a container ) , more individuals may survive to adulthood [18] . Results observed in other systems have demonstrated that competitive stress may enhance [19 , 20] or diminish [21] the lethal effects of pesticides [19 , 20] , hypothesized to be caused in part through changes in the food web . So , sources of mortality may have additive or non-additive effects . Additive refers to the arithmetic sum of individual effects , whereas non-additive refers to departure from additivity . If multiple causes of mortality are additive , total mortality is the sum of mortality from each source . Non-additive mortality may be observed in compensatory mortality , where the total number surviving is not affected by multiple mortality causes , or in overcompensation , where multiple causes of mortality result in lower total mortality than if acting alone [22] . Therefore , control measures aimed at reducing the larval population can have unanticipated outcomes on the adult numbers as well as characteristics of the adult survivors . Assessing the ultimate consequences of changes in larval density to the final population size or potential for pathogen transmission requires knowledge of the individual and combined effects . Exposure to pesticides during the larval stages can alter immunity which may influence susceptibility to pathogens and infectivity [23–26] . Insecticides like Bti may influence mosquito life history traits and susceptibility to pathogens through changes in microbial communities and nutrient dynamics [27] and by changes in the energy budget and immune system . For example , exposure to an organophosphate during the larval stages altered the expression of immune-related genes in larvae and adult female Ae . aegypti [28] . Additionally , insecticide resistance is energetically costly , including resistance to Bti [29] , which may compromise immune responses ( e . g . production of detoxification enzymes that result in metabolic resistance [30 , 31] , and interfere with pathogen infection [32] . The effect of intraspecific competition among larvae and exposure to the organophosphate malathion on Ae . aegypti and Aedes albopictus ( Skuse ) survivorship to adulthood and life history traits among survivors have been examined using Sindbis virus as a model system [28 , 33] . For both species , competition and the presence of malathion reduced survival to adulthood . The pesticide reduced larval density , eliminating the negative effects of competition that otherwise resulted in lengthened development time and small adults . Thus , malathion treatment led to short development time and large adults in high competition . For Ae . aegypti , but not Ae . albopictus , high competition conditions and the presence of malathion led to an increase in the number of mosquitoes with disseminated Sindbis virus infections [33] . These results suggest that competition and pesticides may influence disease transmission directly by altering recruitment to the population of adults and indirectly by altering phenotypes of adults ( size , competence for arboviruses ) . Although these initial studies suggest an indirect role of malathion on mosquito competence using a model arbovirus system , there is a need to evaluate these effects for pathogens important to human health [34] . The current study investigates the relationship between density-dependence and Bti , one of the pesticides currently used to control dengue vectors [35 , 36] . This paper reports on laboratory investigations to test Bti and density-dependent effects on Ae . aegypti traits , population performance , and infection with dengue-1 virus . Our first goal was to determine whether Bti affects adult survival and reproduction . Secondly , we investigated whether Bti interacts with intraspecific competition to alter adult survival , propensity to blood feed and barriers to infection and dissemination for dengue-1 virus . We explore possible effects using multiple levels within each Bti and density treatment . The estimates obtained from these experiments will facilitate parametrization of models aimed at investigating the interactive effects of larval crowding and Bti on risk of dengue transmission .
Mosquitoes used were F3 progeny of larvae collected from Key West , Florida . We used Ae . aegypti from Key West as this population vectored dengue virus in 2009 and 2010 . Larvae were hatched from eggs submerged in containers with 1 . 0 L tap water and 0 . 15 g of larval food consisting of an equal mixture of brewer’s yeast and lactalbumin at 25±1°C and a 14:10 L:D photoregime . First instar larvae were rinsed free of larval food and added to experimental microcosms consisting of 2 . 5 L cylindrical plastic containers with lids , 2 . 0 L tap water and 0 . 2 g larval food . No additional food was added during the experiments . The experimental containers were maintained under the same temperature and photoregime used during larval hatching . Each experimental container was stocked with 300 newly hatched Ae . aegypti larvae . Bti was applied to each replicate container on the same day the larvae were added . The concentrations of Bti applied were 0 ( control ) , 0 . 0009 , 0 . 0025 , 0 . 007 , 0 . 0194 , 0 . 054 , 0 . 15 , and 0 . 25 parts per million ( ppm ) using our own dilutions from a commercially available formulation with a potency of 3000 International toxic units ( ITU ) per mg . The levels of Bti used approximate the LD50 ( LD50 for a similar Bti product , VectoBac WG: active ingredient: 3 , 000 Bti ITU per mg , 0 . 017–0 . 018 ppm for 3rd instar Ae . aegypti [37] , as well as concentrations below and above the LD50 . No mosquitoes survived to adulthood in concentrations of 0 . 0194 ppm or higher . We replicated treatments with exposure to Bti three times , whereas treatments not exposed to Bti ( 0 ppm , control ) were replicated four times . Experimental containers were maintained in a walk-in incubator . On a daily basis experimental containers were examined for pupae which were transferred to plastic vials plugged with cotton to capture emerged adults . The position of treatment containers within the incubator were changed daily . Newly eclosed adult Ae . aegypti were recorded by sex and date and then transferred to 16 ounce ( by volume ) cylindrical cages . Mosquitoes were provided with a 20% sucrose solution . Adult females aged 7–10 days were allowed to feed on defibrinated bovine blood using an artificial feeding system ( Hemotek , Discovery workshops , Accrington , UK ) . Fully engorged females were separated from unfed and partially fed mosquitoes and held individually in cages . Mosquitoes that took partial blood meals were used in calculation of development and survivorship to adulthood . However , these mosquitoes were not used in calculation of adult survival , fecundity , and size . Each cage contained a plastic cup ( 30ml volume ) filled with water and lined with seed germination paper as an oviposition substrate . Fully engorged females were maintained using the same environmental conditions and access to water and sucrose . Mosquitoes were monitored daily and the date of death was recorded by treatment and replicate . As an indicator of body size , the wings of mosquitoes were dissected and measured by length ( axillary incision to wing tip ) from a photo using a microscope and image analysis software ( Media Cybernetics , Maryland , USA ) . The number of eggs oviposited by individual females during the first gonotrophic cycle was counted . For each container replicate we used all the mosquitoes available to measure survivorship to adulthood , development time , size , number of eggs oviposited , and adult survival .
Mean survivorship to adulthood was significantly affected by the concentration of Bti ( F7 , 16 = 58 . 01 , p<0 . 0001 ) , with no survivors at concentrations ≥ 0 . 0194 . Increasing concentrations of Bti were associated with significantly lower survivorship to adulthood ( Fig 1a ) . Development time of females , from treatments with survivors , was significantly affected by the presence of Bti ( F3 , 7 = 8 . 48 , p = 0 . 009 ) . In the absence of Bti , development times were significantly longer than treatments with Bti , except at the lowest level of Bti . In the presence of Bti , there was a consistent decrease in development time of females with increases in the concentration of Bti ( Fig 1b ) . Development time of males were unaffected by the presence of Bti ( F3 , 8 = 1 . 20 , p = 0 . 36; Fig 1b ) . Regression analysis showed a significant positive relationship between Ae . aegypti wing length ( mm ) and number of eggs oviposited ( y = 60 . 737x-111 . 76 , r2 = 0 . 35 , n = 212 , p<0 . 0001; where y = number of eggs and x = wing length in mm ) . Significantly longer wing lengths and more eggs were oviposited by Ae . aegypti from the highest Bti treatment than all other treatments ( F3 , 7 = 9 . 11 , p = 0 . 008; Fig 1c ) . The relationship between wing length and number of eggs oviposited did not differ between Bti treatments ( test for equal slopes , F3 = 1 . 17 , p = 0 . 32 ) . Thus , the slopes were not different between Bti treatments . There was an approximate 46% increase in mean number of eggs oviposited by females exposed to the highest amount of Bti relative to the control . A caveat to this interpretation is that females were not dissected and examined for retained eggs , thus potentially underestimating fecundity . A total of 558 adult females were monitored daily to record date of death , which was used to establish treatment dependent survival distributions . Survival analyses showed no significant differences in adult survival among Bti treatments ( χ2 = 5 . 13 , df = 3 , p = 0 . 16; Fig 1d ) .
Our results demonstrate that the outcome of mortality and phenotypic traits induced by Bti control efforts would not be expected to change at different amounts of larval crowding of Ae . aegypti . Female mosquitoes that survived exposure to Bti experienced accelerated development , were larger , and produced more eggs during the first gonotrophic cycle after being exposed to the highest concentration of Bti . Superior reproductive potential among survivors may facilitate recovery of local populations of Ae . aegypti following larviciding [18 , 43] . Mosquito larvae in nature may be exposed to sublethal concentrations of larvicide attributable to recolonization of container habitats with sublethal amounts of larvicide or exposure to less than the “full” lethal dose at time of widespread application of larvicides ( low-volume area-wide strategy ) [44] . For example , changes in life history traits in our experiments occurred at high exposure to Bti that resulted in ~80% mortality ( inhibition of adult emergence ) which approximates observations of rates of mortality ( 87% ) of area-wide ground applications of Bti to control dengue vector Ae . albopictus in residential neighborhoods [44] . Larvicide resistance in many populations of Ae . aegypti is further evidence for the occurrence of exposure to sublethal concentrations of larvicides in nature [45–48] . Our observations suggest that Ae . aegypti larvae may respond to reductions in the number of conspecifics attributable to Bti killing a fraction of the larvae . It is possible that selective mortality due to Bti allowed for survival of some individuals with rapid growth and development ( e . g . , lethal concentration increases with larval development ) [49] . However , it seems more likely that the mechanism relates to changes in density and availability of nutrition ( microbial biomass ) because consumption by mosquito larvae reduces digestible microorganisms [50] . Although we did not directly measure microbial populations , reductions in the number of larvae with associated increases in per capita nutrient resources are consistent with observed changes in growth and development . Other studies have shown strong and complex interactive effects of Bti on microbial communities and nutrient dynamics in microcosms [27] . Our observations support previous studies showing similar effects of pesticides on growth and developmental responses in Ae . aegypti , including fungal larvicide Metarhizium anisopliae ( Metschn . ) Sorokīn [43] , neurotoxin spinosad [51] , organophosphate malathion [28 , 33 , 52 , 53] , and botanical insecticides [54] . Most mosquitoes are sexually dimorphic and protandrous , where the latter here refers to the eclosion of males before females into a seasonal breeding population . Sex-specific development and size may explain why the developmental response of males ( demonstrating canalization ) differed from females along a gradient of Bti . Males are often smaller than females and develop more quickly . Competitive release among surviving larvae accelerated metamorphosis among females ( 21% reduction in development time in the highest amount of Bti than controls ) , whereas the same benefit was negligible among males with inherently shorter developmental time . Sex-specific reaction norms ( development , size , survival , nutrient reserves ) have been observed among several container dwelling mosquito species exposed to predators [55 , 56] competitors [57 , 58] , and varying nutrition [59] during the immature stages . Mosquitoes experienced a 46% increase in the number of eggs produced at the highest amount of Bti than controls , which covaried with size . This means that mosquitoes exposed to sublethal amounts of Bti did not incur a physiological cost of reproduction . Alternatively , any physiological cost of reproduction was outweighed by enhanced size ( 13% increase ) following exposure to Bti , presumably associated with reductions in the number of larvae . A caveat to this interpretation is that we did not measure cumulative lifetime fecundity ( net reproductive rate ) and so we cannot assess whether there are costs of reproduction later in life ( e . g . , daily fecundity through lifespan ) [60] . However , substantially fewer individuals would survive the second gonotrophic cycle assuming a mean probability of daily survival of 0 . 6–0 . 89 [61–63] and approximate duration of the gonotrophic cycle of three days in nature [64] . Size of Ae . aegypti females in our study were within the range and approximated the mean size observed in the field [65–67] . Fecundity increases with size of adult Ae . aegypti females [68–70] as in most insects [71] . However , the current study is one of a few demonstrating an indirect role of a pesticide enhancing the number of eggs produced by mosquitoes ( e . g . , [51] ) . The presence of Bti did not alter survival of adults and so Bti appears to selectively influence traits of adults . Lifespan and fecundity determine fitness and so the lasting effects of sublethal exposure to Bti are likely to minimally influence ( perhaps increase ) net reproductive rate . The influence of Bti on mosquito life history traits acted independently of those effects attributable to larval crowding , suggesting that competitively stressed mosquitoes respond similarly to this pesticide as do mosquitoes from less stressful conditions . This suggests that the efficacy of Bti in nature should be robust to spatial and temporal heterogeneity in the number of larvae in containers . Responses in population performance to treatments were less distinct than those observed for life history traits . λ' is heavily influenced by changes in survivorship to adulthood . Despite using a 6-fold difference in the initial density of larvae , survivorship to adulthood contributed less than that of development and growth effects , which in part , explains the lack of many treatment differences in λ' . Curiously , two of the three treatments at the highest Bti concentration and a density of 250 initial larvae failed to produce adult females which largely contributed to observed treatment differences . Although it is unclear what accounts for the observed effect , non-linearities in the relationship between density and mosquito responses have been observed in other container-dwelling Aedes species [11 , 72] . The highest concentration of Bti significantly reduced λ' , but lower concentrations did not . This result suggests that larvicidal controls that achieve less than about 75% mortality may not be effective at achieving substantial declines in Ae . aegypti populations . This interpretation agrees in general with transmission models of dengue that have found that substantial levels of larval control such as source reduction would be needed to reduce Ae . aegypti pupae per person in an environment [73] . Lengthened development and reduced growth were largely responsible for the density effect , suggesting that larval crowding and nutrition were not limiting factors that substantially influenced survivorship to adulthood . We observed strong transstadial effects of density with steeper declines in survival of adult Ae . aegypti females from crowded larval conditions , perhaps attributable to changes in larval biosynthesis of nutritive reserves [69 , 74–76] or an additional stressor on the mosquito’s physiology [28 , 77 , 78] . Our observations support findings from laboratory studies that used manipulations of larval nutrition and crowding and measured survival of adult Ae . aegypti females [11 , 13 , 76] . Measurements of daily survival rates of Ae . aegypti in nature and the laboratory have used size as an indicator of relative nutrition and crowding experienced during the immature stages . [66] analyzed laboratory results in which adult survival of Ae . aegypti increases with size , but decreases at the largest sizes ( i . e . , non-linear relationship between size and longevity ) . Similarly , the relationship between adult parous rate , used to measure mosquito survival , and size among wild-caught Ae . sierrensis females was curvilinear [79] . Using the same approach , field studies in Thailand and Puerto Rico found no relationship between parity status and size of Ae . aegypti [67] . A mark-release-recapture field study in Brazil showed that laboratory reared large adult Ae . aegypti males , but not females , had a survival advantage relative to smaller individuals from a low food diet [62] . Field studies often assume that heterogeneity in size of mosquitoes is largely determined by nutrition or larval crowding , however other factors may be responsible as well ( environmental temperature , genetics ) . Collectively , these studies suggest that ecological conditions that larvae experience may have a strong transstadial effect on daily survival rates of adult Ae . aegypti females . In the current study , the heterogeneity in sizes of adult females was comparable to those observed in Ae . aegypti in the laboratory [80 , 81] , but narrower than the range in the field [66] , where differences in dengue virus infection were observed . The reason for lack of heterogeneity in dengue virus infection in the current study is unclear . However , infection and viral dissemination rates in Ae . aegypti females were much higher than those observed in other studies [15 , 80 , 81] . Thus , dose-dependent effects of virus exposure to Ae . aegypti females may obscure more subtle effects attributable to ecological conditions experienced by larvae . For example , our ability to detect a treatment-induced increase , but not reduction , in infection and viral dissemination is limited due to the relatively high rates observed . Also , survivorship to adulthood was higher in the current study ( 41–57% ) compared to other studies ( 31–36% ) [15] that have observed density-dependent heterogeneity in dengue virus infection , suggesting less competitive stress . Previous studies have demonstrated that intra and interspecific larval competition and availability of nutrients enhanced susceptibility to dengue virus infection [15 , 81 , 82] . However these effects appear to be stronger for Ae . albopictus than Ae . aegypti [15] . Similarly , larval competition enhanced infection and viral dissemination of Sindbis virus in Ae . albopictus , but not Ae . aegypti [14] , suggesting species-specific differences in barrier ( s ) to infection and tolerance for stress . Given the prominent role that larvicides serve in Ae . aegypti control programs , there is a compelling need to understand how larvicides act in concert with other sources of mortality in larval habitats in nature . Here we show that low concentrations of a common larvicide had direct lethal effects on Ae . aegypti immatures and indirect effects on select life history traits . We would expect that larvicide campaigns may reduce the number of emerged adults , but survivors will have a lifetime fitness advantage ( growth , development , number of eggs oviposited ) relative to conspecifics . However , we did not find any evidence to suggest that exposure to Bti changes survival of adult females or their rates of infection with dengue virus , both components of vectorial capacity . Under most circumstances , these transstadial effects are unlikely to outweigh reductions in the adult population by Bti and altered risk of disease transmission . | Effective control of mosquito-borne diseases like dengue fever has historically been achieved by controlling vector populations . The use of larvicides that kill the larval stages of the yellow fever mosquito is a critical component of effective control . However , larvicides may act together with other components of the environment like larval crowding and alter the biology of adult mosquitoes . The authors found that mosquitoes that survived exposure to the larvicide Bacillus thuringiensis spp . israelensis ( Bti ) experienced faster development , were bigger , and produced more offspring . Larval crowding , but not larvicide Bti , had a strong effect on survival of adult Aedes aegypti ( Linnaeus ) females . Adult mosquitoes from crowded larval conditions had reduced survival compared to individuals from uncrowded larval conditions . Exposure to the larvicide Bti or larval crowding did not alter the ability of the surviving adult mosquitoes to become infected with dengue virus . Our findings suggest that larvicide campaigns may reduce the number of emerged adults , but survivors will have a fitness advantage ( growth , development , enhanced production of offspring ) relative to mosquitoes not exposed to the larvicide . However , under most circumstances , these effects are unlikely to outweigh reductions in the adult population by larvicide Bti and altered risk of disease transmission . | [
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]
| 2016 | Transstadial Effects of Bti on Traits of Aedes aegypti and Infection with Dengue Virus |
As the Lyme disease bacterium Borrelia burgdorferi traverses its enzootic cycle , alternating between a tick vector and a vertebrate host , the spirochete must adapt and persist in the tick midgut under prolonged nutrient stress between blood meals . In this study , we examined the role of the stringent response in tick persistence and in regulation of gene expression during nutrient limitation . Nutritionally starving B . burgdorferi in vitro increased the levels of guanosine tetraphosphate ( ppGpp ) and guanosine pentaphosphate ( pppGpp ) , collectively referred to as ( p ) ppGpp , products of the bifunctional synthetase/hydrolase RelBbu ( RelA/SpoT homolog ) . Conversely , returning B . burgdorferi to a nutrient-rich medium decreased ( p ) ppGpp levels . B . burgdorferi survival in ticks between the larval and nymph blood meals , and during starvation in vitro , was dependent on RelBbu . Furthermore , normal morphological conversion from a flat-wave shape to a condensed round body ( RB ) form during starvation was dependent on RelBbu; relBbu mutants more frequently formed RBs , but their membranes were compromised . By differential RNA sequencing analyses , we found that RelBbu regulates an extensive transcriptome , both dependent and independent of nutrient stress . The RelBbu regulon includes the glp operon , which is important for glycerol utilization and persistence in the tick , virulence factors and the late phage operon of the 32-kb circular plasmid ( cp32 ) family . In summary , our data suggest that RelBbu globally modulates transcription in response to nutrient stress by increasing ( p ) ppGpp levels to facilitate B . burgdorferi persistence in the tick .
The Lyme disease spirochete Borrelia burgdorferi is maintained in an enzootic cycle involving ticks and vertebrates [1–3] . Since B . burgdorferi is not transovarially transmitted , the bacterium must be acquired by Ixodes larval ticks feeding on an infected mammal , the host reservoir . The larvae then molt into nymphs and the following year take another blood meal where spirochete transmission to naïve hosts may occur , completing the cycle . In order to navigate these transitions , B . burgdorferi must not only evade the host immune system , but also adapt to stressful environmental conditions in the arthropod by altering its gene expression [3 , 4] . A vital environmental factor in the tick midgut is available nutrients , including a carbon source , fatty acids , and nucleotides [5] . B . burgdorferi has limited biosynthetic capabilities and must scavenge nutrients from its environment [6 , 7] . As B . burgdorferi enters the larval midgut , along with the nutrient-rich blood meal , replication commences , dramatically increasing the number of spirochetes as the blood meal is consumed [8–11] . The midgut becomes depleted of nutrients within weeks [12] . B . burgdorferi may have to persist in this austere environment for months as the larvae molt into nymphs that will not feed until spring of the following year [12 , 13] . When the nymphs feed , the midgut milieu suddenly becomes rich in nutrients as the blood meal enters , triggering dormant B . burgdorferi to prepare to transmit to a new host [3 , 7 , 14 , 15] . Several B . burgdorferi gene products important for persistence in the tick have been identified [13 , 16] , including BptA , a lipoprotein [17]; Dps/NapA/BicA , a bacterioferritin homolog [18]; GlpD ( glycerol-3-phosphate dehydrogenase ) , an enzyme involved in glycerol metabolism [19]; and proteins involved in cyclic-dimeric-GMP ( c-di-GMP ) metabolism: Rrp1 , a response regulator and diguanylate cyclase [20 , 21] , Hk1 , its cognate histidine kinase [22] , PdeB , a phosphodiesterase [23] , and PlzA , a c-di-GMP-binding protein [24] . However , none of these tick persistence factors have been shown to be associated with the requisite adaptation to nutrient limitations . Bacteria adapt to nutritional limitations by adjusting their growth and modifying their physiology through the stringent response [25–29] . Global cell reprogramming induced during the stringent response is mediated by increases in the levels of two related nucleotide alarmones: guanosine pentaphosphate ( pppGpp ) and guanosine tetraphosphate ( ppGpp ) , collectively abbreviated ( p ) ppGpp . These alarmones either directly or indirectly modulate transcription ( rRNA , tRNA and stress regulons ) , translation , DNA replication , cell morphology , and numerous aspects of cellular physiology and metabolism [25 , 26 , 29–32] . In Escherichia coli and many other bacteria , ( p ) ppGpp levels are controlled by the enzymes RelA and SpoT , where RelA is a monofunctional synthetase and SpoT is a bifunctional synthetase/hydrolase . Some gram-positive bacteria contain both functional domains in a single enzyme termed Rel or RSH ( RelA/SpoT homolog ) [33] . Typically , RelA synthesizes ppGpp in response to amino acid starvation [34] , while SpoT activity favors accumulation of ( p ) ppGpp in response to limiting fatty acids ( FA ) [35] , phosphate [36] , carbon [37] , or iron [38] . Synthetase activity transfers a pyrophosphate ( PPi ) from ATP to either GDP to form ppGpp ( and AMP ) or to GTP to form pppGpp ( and AMP ) . SpoT and SpoT-like domains hydrolyze either pppGpp to GTP and PPi or ppGpp to GDP and PPi . Until recently , pppGpp and ppGpp have been considered essentially equivalent regarding the cellular response elicited; however , studies in E . coli have shown that subtleties of the stringent response depend not only on the overall alarmone concentration , but also the relative amounts of pppGpp and ppGpp [39–41] . The effects of ( p ) ppGpp on transcription are complex and global [42–44] . ( p ) ppGpp affects the activity of RNA polymerase ( RNAP ) both directly and indirectly through DksA ( DnaK suppressor ) [45–47]; these interactions can increase or decrease transcription depending on the specific sequence near the promoter . Typically genes involved in vegetative cell growth whose expression is mediated by σ70 ( RpoD ) are downregulated while those involved in the stress response and/or adaptations to nutrient limitations are upregulated . Indirectly , increasing ( p ) ppGpp levels affects sigma factor selectivity as more RNAP is released from some σ70-promoters allowing alternative sigma factors , such as σS ( RpoS ) , to bind RNAP , further shifting the program of gene expression [25 , 29 , 45] . In addition , pppGpp production consumes GTP and thereby decreases the cellular GTP concentration , which is significant enough in some bacteria , such as B . subtilis , to inhibit transcription initiation [48] . The influence of ( p ) ppGpp levels on growth and survival during nutrient stress is also intimately entwined with various aspects of virulence in numerous pathogens [49 , 50] . The alarmone transduces signals from environmental cues to indicate when conditions are favorable to replicate , transmit , or persist . For example , ( p ) ppGpp regulates expression of the alternative sigma factor FliA in Legionella pneumophila to control replication and transmission in host cells [51] , and modulates the activity of transcription factors HilA and SlyA in Salmonella enterica serovar Typhimurium to induce expression of Salmonella pathogenicity islands 1 and 2 [52 , 53] . Therefore , while ( p ) ppGpp induces general physiological and metabolic changes to adapt to nutrient stress , the alarmone also triggers intracellular processes specific for microbial virulence in response to different environments [25 , 28 , 29 , 49] . Adaptive morphological changes in response to environmental stresses are also regulated by ( p ) ppGpp in many bacteria [31] . Elevated ( p ) ppGpp levels induced by starvation correlated with Mycobacterium smegmatis converting from bacilli to coccoid forms [54] . Myxococcus xanthus requires ( p ) ppGpp in order to initiate the pathway leading to myxospore formation during nutrient-limiting conditions [55] . Abolishing ( p ) ppGpp production in Helicobacter pylori causes the premature formation of coccoid forms [56] . Notably , B . burgdorferi undergoes conversion to a condensed non-motile morphology termed a round body ( RB ) during starvation in vitro and , to a certain extent , in the midgut of the flat tick , although the role of ( p ) ppGpp in this process has not been previously evaluated [57–60] . The B . burgdorferi gene product BB0198 ( RelBbu ) contains domains homologous to RelA and SpoT; RelBbu has been shown to be responsible for ( p ) ppGpp production and relBbu can heterologously complement an E . coli relA/spoT double mutant [61–63] . The conditions that modulate ( p ) ppGpp levels and the role of this important intracellular messenger in adaptation of B . burgdorferi to nutrient stress remain scarcely studied . In this work , we examine the in vivo role of relBbu in the tick-mouse model of Lyme disease as well as the in vitro role in survival and regulation of global gene expression by comparative RNA sequencing ( RNA-seq ) .
To adapt to the stress of nutrient starvation , bacteria increase ( p ) ppGpp levels , which invokes substantial physiological changes to aid survival . We assayed if B . burgdorferi increases ( p ) ppGpp levels during starvation by shifting in vitro cultures from the normal growth medium , Barbour-Stoenner-Kelly II medium containing 6% rabbit serum ( BSK + RS ) , to a starvation medium ( RPMI containing no serum ) . This definition of starvation was used because shifting cells to RPMI removes many of the nutrients present in BSK and was previously used to mimic nutrient stress and starvation conditions in the tick [60 , 63 , 64] . The starvation medium notably lacks rabbit serum , as well as neopeptone , yeastolate , N-acetylglucosamine , and bovine serum albumin . B . burgdorferi strain B31-5A4 ( wild type ) was grown in BSK + RS and labeled with 32P-orthophosphate ( 20 μCi ml-1 ) . Cultures were starved in RPMI or starved and then recovered in BSK + RS , aliquots were collected at each time point , nucleotides were extracted and samples were resolved by thin layer chromatography ( TLC ) . Both pppGpp and ppGpp levels increased during starvation ( Fig 1A , lanes 1–3 ) , with a significant increase in ( p ) ppGpp observed after 6 h in RPMI compared to cells growing in BSK + RS ( Fig 1B , 1 and 3 ) . Concomitant with increased ( p ) ppGpp levels was a decrease in cellular GTP levels and an increase in pyrophosphate ( PPi ) levels ( Fig 1A , lanes 2 and 3 ) . Spots corresponding to GTP and PPi in the cell extracts were determined by running α-32P -GTP and 32PPi as standards ( S1 Fig ) . To determine if ( p ) ppGpp levels changed during B . burgdorferi recovery from starvation , we starved 32P-labeled cells for 6 h in RPMI before returning them to BSK + RS for 10 min ( Fig 1A , lane 4 ) or 2 h ( Fig 1A , lane 5 ) . ( p ) ppGpp levels decreased significantly in B . burgdorferi returned to nutrient-rich medium ( BSK + RS ) for 2 h ( Fig 1B , 5 ) compared to 6 h in RPMI ( Fig 1B , 3 ) . PPi levels also decreased and GTP levels increased during recovery from starvation ( Fig 1A , lanes 4 and 5 ) . Therefore , B . burgdorferi modulates ( p ) ppGpp levels in response to nutrient stress , although the specific extracellular signal ( s ) remain to be identified for this pathogen . Since RelBbu is predicted to be a bifunctional enzyme responsible for both ( p ) ppGpp synthesis and hydrolysis , the expected response of relBbu gene expression to environmental signals and growth conditions was not obvious . Temperature , increasing from 23°C to 35°C , and pH , lowering from 7 . 4 to 6 . 8 , are both signals that have been proposed to regulate gene expression during transmission of B . burgdorferi during tick feeding [65–67] . We assayed relBbu gene expression in response to these environmental conditions by qRT-PCR and found no significant difference in relBbu transcript levels , suggesting that temperature , pH and growth phase do not control expression of the relBbu gene in vitro ( Fig 2A ) . We next examined if nutrient levels affect the amount of relBbu transcript by starving B . burgdorferi in RPMI medium and comparing relBbu transcript levels to those in normal growth medium ( BSK + RS ) by qRT-PCR . relBbu transcript levels decreased about threefold when compared to flaB transcript levels , which actually increased slightly , within 30 min of starvation compared to either the original culture or cells collected by centrifugation and returned to BSK + RS ( Fig 2B ) . There was no significant difference in the observed threefold decrease in relBbu transcript levels in cells incubated in RPMI and those starved in RPMI lacking amino acids , glucose or phosphate , indicating that these three components are not in vitro environmental cues that regulate relBbu transcript levels ( Fig 2B ) . When B . burgdorferi was starved in RPMI for longer times ( 6 h ) , relBbu transcript levels remained depressed , but recovered to levels similar to those observed before starvation after returning cultures to complete medium ( BSK + RS ) ( Fig 2C ) . The reduction in relBbu transcript was unexpected since ( p ) ppGpp levels increased during starvation , but may reflect a strategy to decrease the potential to hydrolyze ( p ) ppGpp by decreasing the amount of bifunctional RelBbu . The relationship between relBbu transcript levels , RelBbu protein levels , and coordination of synthetase and hydrolase activity requires further investigation . To assay if the relBbu gene product was responsible for the increase in ( p ) ppGpp levels observed during starvation , we disrupted the relBbu gene with a streptomycin/spectinomycin resistance cassette [68] to generate a relBbu mutant strain ( Fig 3A ) . The relBbu mutant strain was complemented using two different strategies: the relBbu gene was either fused to the flac promoter [69] and inserted into the shuttle vector pBSV2 [70] to yield pBS-flacp-relBbu or cloned along with 365 upstream nucleotides , which contain the native promoter [61] , and inserted into pBSV2 to yield pBS-relBbu ( Fig 3A ) . The relBbu transcript was present in the wild-type and complemented strains , but absent in the mutant strain by RT-PCR ( Fig 3B ) . To examine if ( p ) ppGpp production was dependent on the relBbu gene product , wild-type , relBbu mutant and complemented ( relBbu- pBS-relBbu ) strains were labeled with 32P-orthophosphate , shifted to starvation medium for 6 h and nucleotides analyzed as described for Fig 1 . The wild-type and complemented strains produced pppGpp and ppGpp under starvation conditions while the relBbu mutant strain did not ( Fig 3C ) . To examine if B . burgdorferi survival during nutrient starvation is relBbu-dependent , we assayed cell viability in vitro [71 , 72] . Wild-type , relBbu mutant and complemented ( relBbu- pBS-flacp-relBbu ) strains were grown to late log phase in BSK + RS medium before shifting to RPMI medium for 0 , 24 or 72 h . At these times , cultures were incubated with propidium iodide ( PI ) , which stains cells with compromised membranes ( i . e . , dead cells ) . Live cultures were wet-mounted and both differential interference contrast ( DIC ) and fluorescence images were collected and overlaid . As a positive control to ensure that PI stained nonviable B . burgdorferi , cells were heat-killed by incubating at 94°C for 5 min before PI staining: we found that 99% of cells were stained with PI following heat treatment . The relBbu mutant strain did not survive as well as the wild-type and complemented strains when incubated in RPMI for 24 and 72 h , as seen by the increased number of PI-stained spirochetes ( blue ) ( Fig 4A ) . Notably , many relBbu mutant cells assumed a condensed spherical morphology , termed round bodies ( RB ) [57 , 58] , during starvation . RBs were more frequently seen in the relBbu mutant than in wild-type or complemented strains . Many of the RBs stained with PI ( Fig 4A , arrows ) , but others did not ( Fig 4A , arrowheads ) , suggesting that some RBs remained viable . By quantifying the number of PI-stained cells , we found that survival of the relBbu mutant was significantly decreased compared to wild-type and complemented strains throughout starvation ( Fig 4B ) . Similar results were obtained when viability was quantified by enumerating the colony forming units of strains plated in semi-solid BSK following a time course of starvation . Again , the survival of the relBbu mutant ( Fig 4C; hatched bars ) during starvation was compromised compared to the wild-type and complemented strains ( Fig 4C; black bars and gray bars , respectively ) . Therefore , relBbu has a crucial function for survival of B . burgdorferi under nutrient stress . To further investigate the role of relBbu in RB formation induced by starvation , we developed a method to simply and rapidly visualize live Borrelia cultures without using fixative ( such as acetone or paraformaldehyde ) , which can affect morphology , and without expressing fluorescent proteins . Incubating B . burgdorferi with wheat germ agglutinin attached to a fluorophore ( WGA-Alexa Fluor 594 ) rapidly labels essentially all of the bacteria ( S2 Fig ) by binding to sialic acid and N-acetylglucosamine residues on the surface , so they are readily visible by fluorescence microscopy . Wild-type , relBbu mutant and complemented strains were grown in BSK + RS or shifted to RPMI for 48 h and then stained with WGA-Alexa Fluor 594 . Cells were wet-mounted on slides and immediately imaged . While all three strains had the same flat-wave morphology characteristic of the spirochete when grown in BSK + RS ( Fig 5A–5C ) , the majority of relBbu mutant cells converted to the RB phenotype under starvation conditions ( Fig 5E ) compared to wild-type and complemented cells ( Fig 5D and 5F ) . Wild-type and complemented strains still formed RBs , but at a lower frequency compared to the relBbu mutant . To more closely examine the morphology of the relBbu mutant RBs , samples that were starved for 48 h were subjected to scanning electron microscopy . There appeared to be two types of RBs formed from the relBbu mutant strain: one in which the membrane appeared intact and smooth as the cylinder condensed and contracted into a ball ( Fig 5G and 5I ) , and another in which the membrane was disrupted and folded ( Fig 5H and 5J , arrows ) and showed membrane blebbing ( Fig 5H , arrowheads ) . The morphology of B . burgdorferi cells grown in vitro was also examined as cells transitioned to stationary phase . The relBbu mutant strain again condensed to form RBs more often than the wild-type and complemented strains when cells were grown in BSK + RS well into stationary phase ( ~3 x 108 cells ml-1 ) as visualized by fluorescence microscopy of WGA-Alexa Fluor 594-stained cells ( Fig 6 ) . These results are similar to those found during starvation of the relBbu mutant strain and suggest the stationary phase environment may induce a similar response in B . burgdorferi . Taken together , these data suggest that RelBbu controls the decision to undergo , and possibly the program of , RB formation . Enzymes that metabolize ( p ) ppGpp have been shown in other bacteria to regulate numerous virulence factors , some of which mediate host interactions including , but not limited to , immune evasion , motility , transmission , and replication [49] . To test if relBbu in B . burgdorferi is required for mammalian infection , mice were intradermally needle-inoculated with either 105 or 106 of wild-type , relBbu mutant or complemented cells . Tissues were collected three and five weeks post injection , cultured for B . burgdorferi and monitored by dark-field microscopy for the presence of spirochetes . The relBbu mutant strain was able to infect mice and disseminate to the ear , ankle joints and bladder ( Table 1 ) , indicating that relBbu is not required for mammalian infection in the murine model of Lyme disease . The natural route of mammalian infection is transmission by tick bite . To examine if RelBbu is required for tick transmission , naïve larvae were fed to repletion on mice infected with wild-type , relBbu mutant or complemented strains . After molting to nymphs , five ticks were placed on a mouse and allowed to feed to repletion , and mice were assessed for infection three and five weeks later as described above . Wild-type and complemented strains were transmitted from nymphs to all infested mice , while only 3 of 12 mice were infected by nymphs carrying the relBbu mutant strain ( three independent experiments with one out of four mice infected in each experiment; Table 1 ) . There are two explanations , which are not mutually exclusive: RelBbu plays a role in tick transmission , but is not absolutely required and/or transmission is compromised due to low levels of relBbu mutants in nymphs due to a persistence defect . Our in vitro data suggest that relBbu is important for survival during starvation . We hypothesized that relBbu is required for persistence in the tick vector between blood meals , where B . burgdorferi experiences nutrient stress [12 , 13] . To test this hypothesis , naïve Ixodes scapularis larvae were allowed to feed to repletion on mice infected by needle inoculation with wild-type , relBbu mutant or complemented strains as described above . B . burgdorferi persistence in the tick was assayed by immunofluorescence ( IF ) microscopy ( Fig 7A ) . At each stage ( fed larvae , flat nymphs and fed nymphs ) , six ticks were dissected on a slide , fixed and processed for IF microscopy using anti-B . burgdorferi antibodies followed by Alexa Fluor 488 secondary antibodies ( green ) ; tick cells were labeled with WGA-Alexa Fluor 594 ( red ) . Fed larvae acquired all strains to a similar degree ( Fig 7A , top row ) . However , the relBbu mutant , while still present in flat nymphs , did not persist after the nymphs fed on uninfected mice ( Fig 7A , middle column ) . To confirm these results by another method , the Borrelia load per tick was quantified at each stage by qPCR . Total DNA was isolated and qPCR was performed using primers/probe to the B . burgdorferi flaB gene . The number of spirochetes per tick in fed larvae and flat nymphs was not significantly different in wild type- ( black circles ) , relBbu mutant— ( white circles ) or relBbu-pBS-relBbu- ( gray circles ) infected ticks ( Fig 7B ) . Again , the relBbu mutant did not persist from flat to fed nymphs: there were significantly fewer spirochetes detected in nymphs infected with the relBbu mutant strain compared to nymphs infected with the wild-type strain . Persistence was restored in the complemented strain ( Fig 7B ) . To examine global transcriptional changes occurring during nutrient stress , the transcriptomes of wild-type B . burgdorferi grown to stationary phase , starved for 6 h , and recovered from starvation were compared by RNA-seq . Two independent experiments were performed , comparisons were combined , and both DEseq and EdgeR analyses were used to calculate the significance of differential gene expression ( see Materials and Methods ) . Only genes whose transcript levels were significantly changed ( P < 0 . 05 ) and varied by twofold or greater were considered to be affected by nutrient stress or dependent on RelBbu ( S1–S10 Tables ) . Furthermore , only sequences that mapped uniquely to the genome were included and the differential expression of each significantly regulated gene was manually inspected and pseudogenes removed from the lists . These analyses likely underestimate the number of affected genes , particularly of the cp32s , due to the extreme sequence similarity in some regions of the genome [6 , 73 , 74] . When wild-type cultures were starved ( 6 h in RPMI ) , only 16 genes were upregulated compared to cells in stationary phase , with the majority encoding cell envelope proteins and lipoproteins ( CE ) or encoding proteins , mostly hypothetical , of unknown function ( U ) ( Fig 8A , black bars ) . Notably , glpF ( bb0240 ) , encoding the glycerol uptake facilitator , and dbpB ( bba25 ) , encoding a decorin-binding protein , were both significantly upregulated ( S1 Table ) . Forty genes were downregulated during starvation of wild-type cells with the majority , again , encoding hypothetical proteins ( Fig 8A , gray bars and S2 Table ) . During recovery of the wild-type strain from starvation ( 6 h in RPMI medium followed by 2 h in BSK II + RS ) , more genes were upregulated ( 97 genes ) than were downregulated ( 47 genes ) ( Fig 8B; S3 and S4 Tables ) . The majority of upregulated and downregulated genes during recovery encoded proteins of unknown function . The other functional categories containing numerous upregulated genes were: cell division ( CD ) ; cell envelope and lipoproteins , including the antigenic variation expression locus vlsE ( bbf0041 ) ; and metabolism ( MT ) , including the genes pfs , metK and luxS from the bb0374-bb0377 operon [75] ( Fig 8B and S3 Table ) . luxS was also downregulated during starvation of wild type ( S2 Table ) , suggesting transcript levels of this gene respond positively and negatively to nutrient levels . csrA ( bb0184 ) , which encodes the carbon storage regulator , is induced under conditions mimicking mammalian infection [76] and was upregulated during recovery . Additionally , two genes encoding proteins in the master pathway regulating genes required for infectivity [4] , a sensory transduction histidine kinase ( hk2; bb0764 ) and the alternative sigma factor σ54 ( rpoN; bb0450 ) , were induced during recovery ( S3 Table ) . Interestingly , the expression of the key transcriptional regulator of this pathway , rpoS , was not upregulated more than twofold . During recovery of the wild-type strain from starvation , the majority of downregulated genes encoded products of unknown function or in the cell envelope and lipoproteins category ( Fig 8B , gray bars ) , including the outer membrane protein P66 ( p66; bb0603 ) , which binds β chain integrins and has porin activity [16] . Two genes from the glycerol metabolism ( glp ) operon were downregulated: glpF and glpK ( bb0241 ) , encoding glycerol kinase ( S4 Table ) . Thus , glpF was induced during starvation in wild-type cells and repressed during recovery from starvation , results consistent with a proposed role in the tick [19] . dps/napA/bicA ( bb0690 ) , another gene whose product is important for persistence in the tick [18] , was also repressed during recovery ( S4 Table ) . Therefore , in wild-type cells , some of the genes involved in infection tend to be upregulated during recovery from starvation while the genes that play a role for persistence in the tick tend to be repressed . To examine the role RelBbu and ( p ) ppGpp have in global gene regulation during nutrient stress , we compared the transcriptomes of wild-type and relBbu mutant strains by RNA-seq in stationary phase , during starvation , and in recovery from starvation from two independent experiments ( twofold cutoff; P < 0 . 05 ) as described above . RelBbu directly or indirectly at least doubled the transcript levels ( i . e . , higher expression in the wild-type transcriptome than in the relBbu mutant transcriptome ) of 160 genes at stationary phase , 182 genes during starvation , and 93 genes during recovery from starvation ( Fig 9D and S5–S7 Tables ) . About a third of the genes upregulated under each condition were unique to that condition . Thirty-eight genes were upregulated in all three conditions , suggesting that RelBbu is important for their expression independent of extracellular nutrients . Cells in stationary phase and under starvation conditions shared more upregulated genes than were shared between starvation and recovery or stationary phase and recovery ( Fig 9D ) . RelBbu-dependent changes in transcript levels measured by RNA-seq were validated by qRT-PCR under stationary phase , starvation and recovery conditions ( S3 Fig ) . RelBbu-mediated upregulation and repression were confirmed in the majority of genes and conditions , but qRT-PCR generally underestimated the differences between wild-type and relBbu mutant strains found by RNA-seq . To gain insight into the biological processes influenced by RelBbu and ( p ) ppGpp in B . burgdorferi , we plotted the number of upregulated genes by functional category and shared response to the three conditions . For example , the 44 cell envelope and lipoprotein genes RelBbu upregulated under starvation conditions are fairly evenly distributed as unique to starvation , shared between stationary and starvation , shared between starvation and recovery , and shared by all three conditions ( Fig 9B ) . The majority of RelBbu-upregulated genes in all three conditions encode products of unknown function or cell envelope and lipoprotein genes ( Fig 9A–9C ) . Closer examination of the RelBbu-dependent transcriptome reveals the biological processes controlled by RelBbu and the stringent response . Many of the upregulated cell envelope and lipoprotein genes encode products that are known to bind host extracellular matrix proteins , including decorin ( bba24 and bba25 ) , laminin ( bbq47 ) , fibronectin ( bbk32 , bbm27 , bbp27 , and bb0347 ) , and collagen ( bba33 ) , suggesting that RelBbu has a role in the interaction of B . burgdorferi with its host ( S5–S7 Tables ) . In addition , the vlsE gene is upregulated by RelBbu under all three conditions , indicating some regulation by RelBbu that is independent of nutrient levels . vlsE is the expression site of a recombination system used for antigenic variation of the surface lipoprotein VlsE that allows B . burgdorferi to evade the host immune system during infection [77 , 78] . dbpA and dbpB were upregulated by RelBbu during starvation ( S6 Table ) , but not during stationary phase ( S5 Table ) , raising the possibility that regulation of these genes responds more dramatically to ( p ) ppGpp than other genes upregulated by RelBbu . dbpBA transcript levels also remain elevated during the recovery phase ( S7 Table ) , implying an intricate and subtle relationship between RelBbu , ( p ) ppGpp , and gene expression that modulates host-pathogen interactions . Transcript levels of ospC ( bbb19 ) , encoding an outer membrane lipoprotein , were RelBbu-upregulated during starvation and recovery but not stationary phase . OspC is essential for mammalian infection and its transcription is regulated by a complex dual sigma factor cascade involving RpoN and RpoS [3 , 4 , 16] . While levels of rpoN were not increased by RelBbu , rpoS ( bb0771 ) levels were upregulated in stationary phase , but unchanged during starvation and recovery . Additionally , the gene encoding the DNA-binding protein BosR ( bb0647 ) , which is an important regulator of RpoS-mediated virulence gene expression [4 , 79–81] , was induced by RelBbu during stationary phase and starvation ( S5 and S6 Tables ) . Glycerol metabolism genes in the glp operon were also regulated by RelBbu . glpF was upregulated in stationary phase and during starvation , while glpK was upregulated during starvation . Glycerol and the products of the glp operon have been shown to function in B . burgdorferi growth in vitro and tick persistence [19 , 20 , 82] . Our data support these observations and suggest a mechanism linking changing nutritional cues , gene regulation and control of carbon utilization . A group of genes encoding an oligopeptide transporter system was also upregulated by RelBbu . B . burgdorferi lacks the ability to synthesize most amino acids and is thought to scavenge peptides from the environment to fulfill this need [6 , 7] . The genes encoding oligopeptide binding proteins were upregulated in stationary phase ( oppA1 and oppA2 ) , starvation ( oppA1 , oppA2 and oppA3 ) , and recovery ( oppA2 and oppA5 ) ( S5–S7 Tables ) . Our findings also agree with a previously reported role for RelBbu in regulation of these transport proteins [83] . These data , along with the results from Iyer et al . [84] that expression of oppA1 and oppA3 was higher in ticks compared to mice , support a role for RelBbu in the tick . In addition , oppA5 was shown by Iyer et al . to be expressed at higher levels in mice than ticks [84] , while we discovered that its expression was increased only during recovery from starvation ( S7 Table ) . Expression of the genes encoding other components of the oligopeptide transport system such as permeases ( oppB1/oppC1 and oppB2/oppC2 ) and ATP-binding proteins ( oppD and oppF ) were not RelBbu-dependent . More genes are downregulated ( higher expression in the relBbu mutant than in wild type ) than upregulated by RelBbu under all conditions examined ( Figs 9 and 10 ) . Using the same parameters for significance and a twofold cutoff , 184 genes were repressed by RelBbu in stationary phase , 196 genes during starvation , and 125 genes during recovery from starvation ( Fig 10 and S8–S10 Tables ) . A higher percentage of the RelBbu-downregulated genes were common to all conditions ( 19% ) than were common among RelBbu-upregulated genes ( 9% ) . In fact , all the bacteriophage ( BP ) , cell motility ( CM ) , protein degradation ( PD ) , DNA replication and repair ( RR ) , and transcription ( TR ) genes downregulated by RelBbu during recovery were common to all three conditions ( Fig 10C , gray bars ) . Similar to the RelBbu-upregulated genes , stationary phase and starvation shared the most similar set of downregulated genes ( 59 genes ) . After genes of unknown function , the categories with the most RelBbu-downregulated genes were metabolism and translation ( TL ) ( Fig 10A–10C ) . While many of the repressed genes unique to stationary phase ( Fig 10A , red bars ) and recovery ( Fig 10C , blue bars ) are of unknown function , the repressed genes unique to starvation are mainly divided among metabolic , replication and recombination , and transport proteins ( TP ) ( Fig 10B , light green bars ) . The majority of downregulated translation genes encode 50S and 30S ribosomal proteins , as well as translation initiation and elongation factors , whose regulation was shared between all three conditions ( Fig 10 and S8–S10 Tables ) . RelBbu also downregulated expression of the RNA polymerase subunits rpoB ( bb0389 ) , rpoC ( bb0388 ) and rpoD ( bb0712 ) in all conditions ( S8–S10 Tables ) . Consequently , perhaps not surprisingly , RelBbu represses expression of ribosomal subunits and RNA polymerase subunits to mediate cellular adaptation to nutrient stress . RelBbu exerted similar control of the genes encoding proteins involved in transcriptional regulation: all of these genes that are repressed during recovery are common to all conditions , and stationary phase and starvation share all ten repressed transcriptional regulator genes ( Fig 10A–10C , TR ) . Most of the genes encoding known proteases and peptidases were repressed by RelBbu ( Fig 10A–10C and S8–S10 Tables ) . All of the protease genes repressed during recovery are common to all three conditions , while stationary phase and starvation share all protease genes but one ( Fig 10A–10C , PD ) . These include the ATP-dependent proteases encoded by clpP1 ( bb0611 ) , clpX ( bb0612 ) , ftsH ( bb0789 ) , and both lon paralogs ( bb0613 and bb0253 ) , which are repressed by under all conditions ( S8–S10 Tables ) . Additionally , the ATP-dependent proteases encoded by htrA ( bb0104 ) and hlsV ( bb0296 ) are repressed in stationary phase and starvation . The role of most proteases remains unknown in B . burgdorferi , but some are likely involved in protein quality control . ftsH , which is repressed in each condition , encodes a protease that , in E . coli , regulates phage λ life cycle by degrading the cII protein [85 , 86] . The group of contiguous genes encoding all the enzymes for the mevalonate biosynthetic pathway was also repressed by RelBbu . This appears to be the only route for biosynthesis of isoprenoids in B . burgdorferi [6 , 87] . During starvation , hmgs , fni , hmgr , mvaD , pmk , and mvk ( bb0683-bb0688 ) were all repressed by RelBbu ( S9 Table ) , while bb0683-bb0687 are repressed in recovery ( S10 Table ) and only bb0685-bb0687 are repressed in stationary phase ( S8 Table ) . Previous studies have shown that external acetate levels influence the mevalonate pathway and that transcript levels of most of the genes in this pathway are lower in B . burgdorferi in ticks than in dialysis membrane chambers ( DMCs ) in mice [84 , 87] . These data together with our results support a role for RelBbu in the tick . Many more genes on the cp32s were RelBbu-repressed ( 47 in stationary , 40 in starvation and 23 in recovery ) than were RelBbu-induced ( 11 in stationary , 8 in starvation and 12 in recovery ) ( S5–S10 Tables ) . The majority of these genes encode hypothetical proteins of unknown function , but many are located on the putative late phage operons [88 , 89] . These data , along with the repression of ftsH , raise the possibility that RelBbu regulates the B . burgdorferi prophages , a relationship that has been reported for λ [90 , 91] .
We found that ( p ) ppGpp levels increased when B . burgdorferi were starved for nutrients ( shifting from BSK + RS to RPMI ) for 30 min and 6 h ( Fig 1 ) . Recovery from starvation returned ( p ) ppGpp levels to those measured in actively growing cells . The production of ( p ) ppGpp in response to nutrient stress was RelBbu-dependent . There have been conflicting reports regarding changes in ( p ) ppGpp levels in B . burgdorferi during nutrient limitation . Our data agree with the results of Concepcion et al . [63] , but not Bugrysheva et al . , who found that ( p ) ppGpp levels did not increase during starvation for serum , yeastolate or neopeptone [61] . This discrepancy is likely due to our study and Concepcion et al . [63] both starving cells in RPMI while the other report used BSK , which is based on the cell culture medium CMRL and contains bovine serum albumin ( BSA ) and rabbit serum . The presence of BSA and associated fatty acids , and other lipids , as well as other components of CMRL , may not induce the stringent response and ( p ) ppGpp production . The specific signals that induce RelBbu-mediated ( p ) ppGpp accumulation in B . burgdorferi have not been identified . In many other bacteria , limiting amino acids activates RelA to synthesize ( p ) ppGpp , while the lack of other nutrients such as carbon , fatty acids , iron , and phosphate activate SpoT-mediated ( p ) ppGpp synthesis over hydrolysis . In bifunctional enzymes , like RSH and Rel , the synthetic/hydrolytic activities on the N-terminal region are coordinated by conformational changes and regulatory domains , such as ACT and TGS ( threonyl tRNA synthetase , GTPase , SpoT/RelA ) , which are in the C-terminal region [25 , 29 , 92 , 93] . Starvation for FA is communicated via FA-bound acyl carrier protein to the TGS domain in SpoT proteins to favor ( p ) ppGpp synthesis [35] . Regulation of ppGpp levels by FA is an attractive hypothesis in B . burgdorferi since the spirochete lacks the ability for de novo FA synthesis [6 , 7] and RelBbu contains a predicted C-terminal TGS domain . However , the function of the TGS domain of RelBbu is not known , as this domain has so far only been implicated in sensing FA in SpoT proteins [94] . Additionally , the TGS domain mediates RelMtb oligomerization as well as association with ribosomes/tRNA/mRNA in Mycobacterium tuberculosis [95 , 96] . Other candidates that may regulate RelBbu activity include components present in BSK + RS , but not in RPMI , such as neopeptone , yeastolate , and possibly metals associated with serum , but a detailed description of the nutrients and domains targeted that control ( p ) ppGpp levels will require a molecular dissection of the RelBbu enzyme and component analysis of extracellular medium . Along with the increase in ( p ) ppGpp levels during starvation , we also observed an accumulation of PPi and , predictably , a decrease in GTP levels . GTP is consumed by RelBbu to synthesize pppGpp . Exactly how pyrophosphate levels increase is unclear , but one possibility is that the activity of the regulatory glycolytic enzyme pyrophosphate phosphofructokinase ( PPi-PFK; BB0020 ) is decreased and less PPi is used to form fructose 1 , 6-diphosphate . PPi-PFK activity is reversible [97] , unlike ATP-PFK , so B . burgdorferi could be converting fructose 1 , 6-diphosphate to PPi and fructose 6-phosphate , thus increasing PPi levels [98] . In fact , accumulation of fructose 6-phosphate , a substrate of PFK , is a key regulator of the stress response during nutrient starvation via the universal stress protein in E . coli [99] . However , expression of ppi-pfk ( bb0020 ) was not affected in the relBbu mutant , so any regulation by RelBbu would likely be through a post-transcriptional mechanism . RelBbu did repress expression of a second pfk gene , bb0727 ( S8–S10 Tables ) , although BB0727 lacks PPi-PFK activity and is thought to be an evolutionary link between PPi-PFK and ATP-PFK [100] . Illuminating the role of BB0727 and its potential effect on PPi levels in the spirochete will require further investigation . Since discovered almost two decades ago by Brorson and Brorson [57] , the round body , or condensed cyst form , of B . burgdorferi has been largely ignored until recently . Although the physiological role of B . burgdorferi RBs remains unknown , it appears to be a morphological adaptation to environmental stress , particularly nutrient starvation [58 , 59] . While RBs represent an unusual spirochete morphology , they are not simply an in vitro culture artifact as they have been identified in vivo in tick midguts , are viable , and rapidly convert back to the distinctive flat-wave morphology of B . burgdorferi [57 , 60] . Our findings suggest that ( p ) ppGpp may be an important intracellular signal for RB formation during nutrient stress: strains unable to produce ( p ) ppGpp ( relBbu mutant ) not only more frequently form RBs , but they also have disrupted membranes and are less viable ( Figs 4 and 5 ) . One advantage to forming RBs may be to decrease the spirochete’s surface area , thus better adapting B . burgdorferi to environmental oxidative and osmotic stresses , as well as possible evasion from the tick immune system . Remarkably , a spherical spirochete is not without precedent: Sphaerochaeta , a recently isolated free-living spirochete from freshwater sediment , has never been observed with a flat wave or helical morphology [101 , 102] . The molecular mechanisms controlling RB formation are unknown , but Dunham-Ems et al . showed that an rpoS null mutant formed RBs more frequently when starved for nutrients , but had no decrease in viability [60] . Our results that lack of RelBbu increased RB formation and decreased survival during starvation , independent of alterations in rpoS transcript levels , suggest that ( p ) ppGpp influences the transition to RBs slightly differently than the RpoS-mediated pathway . In addition , there may be a connection between coenzyme A metabolism and RB formation: a coA-disulfide reductase ( cdr , bb0728 ) mutant is more likely to form RBs than wild type during starvation [60] and we found that dephospho-CoA kinase ( coaE ) transcript levels decreased during starvation and increased during recovery in wild-type cells ( S2 and S3 Tables ) . Our data demonstrate that RelBbu , and presumably ( p ) ppGpp , are important for B . burgdorferi persistence in the tick vector , specifically between the fed larvae and fed nymph , and likely initiate a program to adapt to the nutrient-limited environment of the tick midgut . Previous studies have identified a number of other B . burgdorferi genes that differ in expression between in vitro conditions designed to mimic flat and fed ticks , and others important for in vivo tick persistence [3 , 13 , 84 , 103] . IF microscopy data suggest that relBbu mutant strains do not survive the molt , as fewer spirochetes were seen in the midguts of flat nymphs infected with the mutant compared to those infected with the wild type , while qPCR data point to compromised survival of relBbu mutants during the nymphal blood meal . One possible explanation for this discrepancy is that DNA from nonviable B . burgdorferi in flat nymphs is still detected . We hypothesize that 25% of mice can be infected by transmission from relBbu mutant-infected nymphs ( Table 1 ) due to decreased spirochete loads in the nymphs , but RelBbu may play a role in transmission and host infection . In fact , expression of genes associated with virulence in the host is upregulated by RelBbu , but we found no qualitative differences in mouse infectivity . RelBbu-dependent tick persistence is likely due , at least in part , to upregulation of the glp operon: glpF , glpK and bb0242 ( which encodes a hypothetical protein ) are RelBbu-upregulated during starvation ( S6 Table ) , and glpF and bb0242 are RelBbu-upregulated during stationary phase ( S5 Table ) . Furthermore , glpF is upregulated in wild-type cells during starvation compared to stationary phase ( S1 Table ) and downregulated , along with glpK , in recovery compared to starvation ( S4 Table ) . Previous studies have shown that glycerol and the glp operon , which mediates glycerol uptake and metabolism , are important for tick persistence [19 , 20] . This operon is induced by glycerol and temperature , and in both larvae and nymphs compared to mammalian adapted B . burgdorferi in DMCs [19 , 84 , 104] . Moreover , the glp operon is upregulated by the intracellular second messenger c-di-GMP [20 , 105] , which has been implicated in the virulence of many pathogens [106 , 107] as well as in the persistence of B . burgdorferi in the tick [20–23 , 108–110] . Microarray analysis has revealed that c-di-GMP is a global transcriptional regulator affecting many genes , including the RpoS regulon , through the c-di-GMP-binding protein PlzA [20 , 24 , 105 , 108 , 111] . Comparison of the ( p ) ppGpp and c-di-GMP regulons provides new insights into subtle changes of the transcriptional landscape . For example , ( p ) ppGpp and c-di-GMP both induce expression of bb0240-bb0242 of the glp operon ( S5 and S6 Tables ) , but ( p ) ppGpp represses glpD ( bb0243 ) during stationary phase and recovery ( S8 and S10 Tables; S4 Fig ) , while c-di-GMP upregulates this gene [20 , 105] . GlpD is predicted to convert glycerol-3-P to dihydroxyacetone-P , a reaction directing glycerol-3-P to glycolysis [6 , 7] . Therefore , c-di-GMP may favor glycerol utilization for glycolysis while ( p ) ppGpp-mediated repression of glpD may direct glycerol to a different fate , such as phospholipid and lipoprotein biosynthesis [6 , 7] . During preparation of this manuscript , Bugrysheva et al . published a description of the RelBbu transcriptome [83]; there are many differences between this study and ours , notably they used 1 ) a mutant that is not infectious in mice [62] , and is likely missing plasmid components of the genome , 2 ) an oligonucleotide microarray , and 3 ) growth conditions that do not alter ( p ) ppGpp levels [61] . However , they did also find that RelBbu upregulates expression of the glp genes [83] , although our data suggest that the glpD gene of this operon is differentially regulated as seen in a plot of the RNA-seq reads mapped to this region ( S4 Fig ) . Previous work has also suggested that glpD expression follows that of glpF and glpK [20 , 104 , 105 , 112] . Further investigation will be needed to resolve these discrepancies concerning glpD regulation . Since both the enzymes that synthesize ( p ) ppGpp and c-di-GMP , RelBbu and Rrp1 , respectively , are important for B . burgdorferi survival in the tick and induce the glp operon , their levels and downstream effects are likely coordinated [26] . c-di-GMP levels in B . burgdorferi are regulated by three enzymes [109]: the response regulator Rrp1 is a diguanylate cyclase [105 , 113] that combines two molecules of GTP to form c-di-GMP and two molecules of PPi , while two phosphodiesterases , PdeA [114] and PdeB [23] , hydrolyze c-di-GMP to yield two molecules of GMP . As previously discussed , RelBbu is responsible for both synthesis and hydrolysis of ( p ) ppGpp: synthesis transfers PPi from ATP to GDP or GTP to yield ppGpp and pppGpp , respectively , and AMP; hydrolysis produces GDP or GTP and PPi . While there is no known enzyme directly linking ( p ) ppGpp and c-di-GMP , production of both second messengers consumes GTP ( in the case of pppGpp ) while PPi is a product of c-di-GMP synthesis and ( p ) ppGpp hydrolysis; thus , the two pathways could influence each other by affecting the concentration of substrates or products . In fact , ( p ) ppGpp and c-di-GMP recently were reported to have overlapping functions in Mycobacterium smegmatis [115] . The two nucleotide messengers may also coordinate their effects by targeting expression of the transcription factor BosR . BosR was identified as the Borrelia oxidative stress regulator [79 , 116] and more recently as an important global transcriptional activator of virulence gene expression mediated by the dual sigma factor ( RpoN-RpoS ) regulatory pathway [80 , 81 , 117 , 118] . While previous microarray studies did not find bosR transcript significantly upregulated by Rrp1 [20 , 105] , more recent work reported that c-di-GMP upregulates bosR transcriptionally and post-transcriptionally via the c-di-GMP-binding protein PlzA [111 , 119] . We found that RelBbu ( ( p ) ppGpp ) upregulates bosR expression ( S5 and S6 Tables ) , suggesting that RelBbu may have a dominant effect over Rrp1 on bosR transcript levels , thus offering an explanation for the differential regulation observed in rrp1 and plzA mutants . The RelBbu-dependent increase in bosR during stationary phase , but not in starvation , coincided with increased rpoS expression ( S5 Table ) . This difference could be explained by the phosphorylation state of Rrp2 , a response regulator that is required for rpoS expression [120 , 121] . In a previous study , the expression of bosR was not identified as RelBbu-dependent [83] . The mechanism ( s ) of ( p ) ppGpp and c-di-GMP coordination remains mysterious , but it appears not to be directly transcriptional as rrp1 and relBbu mutants do not affect each other’s transcript levels ( S5–S10 Tables ) [20 , 105] . Further studies are needed to elucidate the network of interactions between these two intracellular messengers . RelBbu-mediated mechanisms both activate and repress the expression of numerous genes during nutrient stress , with the suites of genes targeted being more similar during stationary phase and starvation compared to recovery from starvation . Our finding that RelBbu mediates changes in gene expression in the absence of starvation ( low or no ( p ) ppGpp production; Fig 1 ) indicates that proper transcriptional regulation may have an absolute requirement for ( p ) ppGpp . The complete absence of ( p ) ppGpp may alter the balance of sigma factor use by RNAP , enhancing the sensitivity of some genes more than others to ( p ) ppGpp regulation . Alternatively , RelBbu-mediated transcriptional effects may be independent of ( p ) ppGpp and instead due to other as yet undefined functions of RelBbu . Determining the significance of decreased gene expression from the plethora of plasmids in the relBbu mutant compared to the wild type ( RelBbu-upregulated genes ) must be carefully considered as these replicons can be lost during in vitro cultivation and transformation [122 , 123] . If this occurred , then all the genes on a given plasmid would appear to be repressed in the relBbu mutant ( RelBbu-upregulated ) . This was not the case in our data as each plasmid , including all of the cp32s , contained genes that were upregulated , repressed and not significantly changed under at least one condition ( S5–S10 Tables ) . However , there remains the possibility that a small percentage of the cells in the population have lost a plasmid , thus slightly skewing the regulatory effect on the transcriptome . This concern is not relevant to genes on the chromosome , which cannot be lost in viable cells , or for genes whose expression is higher in the relBbu mutant than in the wild type ( RelBbu-repressed ) as the spirochete does not typically gain plasmids . A number of genes upregulated by RelBbu during stationary phase and/or starvation encode adhesins whose products bind to the extracellular matrix of the host , such as erpX [124] , revA [125] , bbk32 [126] , and bba33 [127] , and are important for host infection [128–130] . Two adhesin genes , encoding decorin-binding proteins A ( dbpA ) and B ( dbpB ) [131] , were induced by RelBbu during starvation , but not in stationary phase ( S5 and S6 Tables ) . Binding of DbpA and DbpB to decorin is important for infection and dissemination in the host [132–134] . A low-nutrient environment may seem at odds with host infection , but the extracellular space can be nutritionally inhospitable . For example , the articular cartilage of synovial joints is a smooth connective tissue containing decorin , and this extracellular space is not well vascularized and low in nutrients [135] . Therefore , as B . burgdorferi migrates to the synovial joint , the spirochete could encounter a nutrient-poor environment that signals RelBbu to increase ( p ) ppGpp levels leading to the expression of dbpBA and binding to decorin , facilitating immune evasion and/or adhesion . Therefore , ( p ) ppGpp may provide the transcriptional regulation that differentiates dbpBA expression from the expression of other genes , such as ospC , governed by the RpoN-RpoS pathway [64 , 112 , 136–138] . Evasion of the host immune system during B . burgdorferi infection is accomplished , at least in part , by antigenic variation of the surface lipoprotein VlsE [122] . The epitope diversity is generated when a portion of the vlsE gene ( the expression locus ) is replaced by a silent vls cassette via gene conversion [77 , 78 , 139] . The mechanism of vlsE induction during infection remains unknown , but our transcriptome analysis showed that RelBbu upregulates the expression locus vlsE ( bbf0041 ) under all conditions tested ( S5–S7 Tables ) . In addition , vlsE was significantly upregulated in wild-type cells recovering from starvation ( S3 Table ) suggesting nutrient availability may be a signal for expression . Other factors that induce vlsE expression in vitro include oxygen tension [140] , pH [141] , AI-2 [142] , and mammalian epithelial cells [143] . Unexpectedly , RelBbu repressed expression of the “silent” vls cassettes . This result was somewhat surprising considering that these genetic elements have been considered to be transcriptionally inert . While the observed increase in vls cassettes expression is significant , the level of expression in the relBbu mutant is modest compared to vlsE ( S5 Fig ) . RelBbu-mediated repression of the vls cassettes coupled with upregulation of the vlsE expression site may represent a mechanism to ensure that only the variable vls cassettes inserted into the expression locus are transcribed . Sequencing of the vlsE locus from the relBbu mutant grown in vitro showed no difference in antigenic switching compared to wild type ( 6/6 relBbu mutant clones had the same vlsE sequence as the wild-type parental strain ) . RelBbu-mediated repression of another intriguing gene , cgtA ( bb0781 ) , was seen during all three conditions ( Fig 10 and S8–S10 Tables ) . CgtA is a small GTPase of the Obg family , which , in Vibrio cholerae , influences many cellular functions including repression of the stringent response , possibly by its interaction with SpoT [144] . CgtA has been implicated in numerous cellular processes including sporulation , DNA repair , and ribosome assembly via interactions with the 50S ribosomal subunit [145] . We found that almost half of the genes for 50S ribosomal subunits were also repressed by RelBbu during starvation ( S9 Table ) , raising the possibility that repression of some 50S ribosomal genes and cgtA may be part of the mechanism to inhibit translation during the stringent response . Furthermore , if CgtA modulates the stringent response [146] by increasing ppGpp hydrolysis [144] , then RelBbu-mediated repression of cgtA could be a positive feedback during the stringent response: cgtA is repressed as ( p ) ppGpp levels increase and ( p ) ppGpp is less likely to be hydrolyzed , thus accelerating ( p ) ppGpp accumulation . The converse would occur during recovery from the stringent response as ( p ) ppGpp is hydrolyzed . While the cp32s of B . burgdorferi are extensively homologous , we were able to distinguish paralog-specific transcripts using RNA-seq ( see Materials and Methods ) . RelBbu repressed 110 cp32 genes ( S8–S10 Tables ) and induced 31 cp32 genes ( S5–S7 Tables ) under all conditions examined . The cp32s , or at least some members , are lysogenic prophages of the bacteriophage ϕBB-1 [89 , 147] that can transduce between strains [122 , 148] . Induction of ϕBB-1 upregulates 30 genes that constitute a late operon from bbl42 to bbl28 ( using the paralog designations from cp32-8 ) [88] . blyA , one of the induced paralogous genes located on the cp32s , as well as other plasmids , encodes a holin predicted to be required for phage release [149] . In E . coli , ( p ) ppGpp controls the λ lysis-lysogeny decision via transcriptional regulation [91]: moderate levels of ppGpp maintain lysogeny [150 , 151] . This is consistent with our observations that the relBbu mutation induces prophage gene expression in B . burgdorferi , possibly via the FtsH protease , which promotes λ lysis in E . coli [85 , 86] . We hypothesize that RelBbu regulates prophage development in B . burgdorferi , but further studies are required to probe how our observed changes in gene expression relate to lysis , lysogeny and transduction . We have found that RelBbu is necessary for B . burgdorferi persistence in the tick in the tick-murine model of Lyme disease . The RelBbu-produced ( p ) ppGpp is a global regulator of the genetic programs engaged during nutrient limitations in the tick that link morphology , metabolism and survival during this heretofore insufficiently studied phase of the enzootic cycle .
All experiments involving the use of animals were approved by the University of Montana Institutional Animal Care and Use Committee ( Animal Use Protocol # 041-11SSDBS ) and in full compliance with the Guide for the Care and Use of Laboratory Animals from the National Institutes of Health . Low-passage B . burgdorferi strain B31-5A4 [152] ( a gift from George Chaconas ) and all mutant strains were maintained in Barbour-Stoenner-Kelly II ( BSK ) liquid medium , pH 7 . 6 , containing 6% rabbit serum ( RS ) ( Pel-Freez Biologicals ) [153] without gelatin [154] . Cultures were inoculated at 1 × 105 and grown at 23°C until late log phase ( 5 to 9 × 107 cells ml-1 ) or inoculated at 1 × 103 or 1 × 104 cells ml-1 and grown at 35°C to late log or stationary phase ( 1 to 3 × 108 cells ml-1 ) in BSK ( pH 7 . 6 or 6 . 8 ) + RS . Cell density was determined using a Petroff-Hausser counting chamber [154] . B . burgdorferi strains were starved by centrifuging cultures at 9 , 000 × g for 5 min at room temperature ( RT ) . Pellets were resuspended in RPMI 1640 without L-glutamine ( Mediatech , Inc . ) and without serum for the times indicated at 35°C . To quantify the number of live B . burgdorferi cells from in vitro cultures , strains grown in BSK + RS to late log phase were divided into two separate cultures and one was starved in RPMI ( as described above ) and the other kept in BSK + RS . At the times indicated , equal volumes of each culture were plated in semi-solid BSK + RS and grown at 37°C in an incubator with 5% CO2 as previously described [154] . After two weeks , colonies were enumerated . Each value is the mean ± SEM from at least three independent experiments . In order to generate a relBbu- strain , a region of the chromosome upstream of the relBbu gene was amplified by PCR using KOD polymerase ( Novagen ) with the primers rsh U866F and rsh 142R+Aat+Age and a region downstream of the relBbu gene amplified using primers rsh 1939F+AatII and rsh D3102R+AgeI ( S11 Table ) . PCR products were cloned into pCR2 . 1-TOPO ( Invitrogen ) and verified by DNA sequencing at the University of Montana Murdock Sequencing Facility . The upstream and downstream pieces were digested with AatII and AgeI and ligated together leaving a synthetic AatII site . The streptomycin/spectinomycin resistant cassette with the flgB promoter from B . burgdorferi [68] and trpL terminator from Bacillus subtilis [155] ( flgBp-aadA-trpLt ) was then inserted into the AatII site . The resulting plasmid was linearized by digestion with AhdI and ethanol-precipitated . Competent B . burgdorferi strain B31-5A4 was electroporated with 10 μg of linearized DNA as previously described [154] and transformed cells dilution plated in liquid BSK + RS [156] containing 50 μg ml-1 streptomycin in 96-well plates . Positive colonies were confirmed to have the relBbu gene replaced by the aadA cassette by PCR analysis . Two different strains were constructed to complement the relBbu- strain . The relBbu ORF was PCR-amplified using the oligonucleotides rsh 1F+NdeI and rsh 2004R+AatII and cloned into pCR2 . 1-TOPO . The inducible flac promoter [69] ( containing synthetic 3′ NdeI and 5′ AatII sites ) was inserted upstream of the relBbu ORF . The flacp-relBbu construct was then inserted into the AatII site of pBSV2 [70] containing the lacI gene under control of the B . burgdorferi flgB promoter in the MCS , as previously described [157] , to produce the construct pBS-flacp-relBbu . In the second construct to complement the relBbu- strain , the relBbu ORF and 365 nucleotides upstream containing the native promoter [61] were PCR-amplified using primers rsh U365+AatII and rsh 2004+AatII , cloned into pCR2 . 1-TOPO , subcloned into the AatII site of pBSV2 to generate pBS-relBbu , and verified by DNA sequencing . 10 μg of either pBS-flacp-relBbu or pBS-relBbu was used to transform the competent relBbu- strain and transformants selected in 200 μg ml-1 kanamycin and 50 μg ml-1 streptomycin as described above . RNA was isolated and qRT-PCR performed as previously described [158] . Briefly , RNA was isolated from 50-ml cultures of B . burgdorferi grown at 23°C or 35°C under the conditions described for individual experiments using TRIzol ( Invitrogen ) . Contaminating DNA was removed by treating samples with Turbo DNase ( Ambion ) followed by phenol/chloroform extraction . To ensure no DNA remained , samples were checked by PCR using the primers flaB 423F and flaB 542R before synthesizing cDNA . The SuperScript III kit ( Invitrogen ) was used to convert 1 μg of RNA to cDNA according to the manufacturer’s instructions . Primers and FAM-TAMRA labeled probes were designed using Primer Express 3 . 0 ( Applied Biosystems ) or MacVector ( MacVector , Inc . ) . TaqMan qRT- PCR was performed in 96-well plates using TaqMan Universal PCR Master mix ( Applied Biosystems ) with an Applied Biosystems 7300 Real-time PCR thermo cycler and cycling conditions: 50°C 2 min; 95°C 10 min; 95°C 15 sec and 60°C 1 min for 40 cycles . Transcript amounts of relBbu and flaB were calculated using a standard curve generated using known amounts of the relBbu ORF or a portion of the flaB ORF ( nucleotides 278–551 of the ORF ) cloned into pCR2 . 1-TOPO , respectively . B . burgdorferi genomic DNA was used to generate standard curves for all other target sequences . Transcript copy number of the gene of interest in each sample ( in triplicate ) was determined using the internal standard curve and then normalized to the number of flaB copies . Each value is the mean ± SEM from at least three independent experiments . B . burgdorferi cultures were pelleted by centrifuging at 10 , 600 × g for 5 min at RT . Cells were washed with 1 . 0 ml Dulbecco’s phosphate buffered saline ( 138 mM NaCl , 2 . 7 mM KCl , 8 . 1 mM Na2HPO4 , and 1 . 5 mM KH2PO4; dPBS ) and centrifuged again at 10 , 600 × g for 5 min at RT . Cells were resuspended in 100 μl dPBS and 1 μl wheat germ agglutinin ( WGA ) -Alexa Fluor 594 ( 1 mg ml-1 in dPBS + 5 mM MgCl2 ) ( Molecular Probes ) was added and gently mixed . After incubation at 37°C for 5 min in the dark , cells were collected by centrifuging at 10 , 600 × g for 5 min at RT , resuspended in 10 μl dPBS and wet mounted on slides . Slides were examined using an Olympus BX51 fluorescence microscope with 100x/1 . 30 NA or 40x/0 . 75 NA objectives . Images were processed using ImageJ ( National Institutes of Health; http://rsbweb . nih . gov/ij/ ) and Pixelmator ( Pixelmator Team , Ltd ) . To determine which B . burgdorferi were dead following incubation in starvation media ( RPMI without serum ) , cultures were collected by centrifugation at 10 , 600 × g for 5 min at RT and the cell pellet resuspended in 10 μl 0 . 85% NaCl . Propidium iodide ( 15 μM ) was added to a final concentration of 1 . 5 μM and cells incubated for 15 min at RT in the dark . Samples were wet mounted on slides and examined by fluorescence microscopy as described above . The percentage of live cells under each condition for each strain was calculated as follows: 100 – ( ( number of PI stained cells/total number of cells ) x 100 ) . Each value is the mean ± SEM from at least three independent experiments . B . burgdorferi persistence in ticks was assayed by IF microscopy using anti-B . burgdorferi antibodies as previously described [159 , 160] . Six ticks were crushed on a single slide and midguts separated in 10 μl of dPBS with 5 mM MgCl2 using 27-guage needles on silane-coated slides ( LabScientific , Inc . ) . Midguts were air-dried for 30 min before being fixed in acetone for 10 min . Slides were washed 3 × 10 min in wash buffer ( dPBS + 5 mM MgCl2 + 1% goat serum ( Gibco , Life Technologies ) ) and then incubated with rabbit polyclonal anti-B . burgdorferi antibodies ( a gift from Tom Schwan ) at 1:200 dilution for 1 h . Slides were washed again as above and the primary antibodies detected using goat anti-rabbit Alexa Fluor 488 antibodies ( Molecular Probes ) at 1:500 dilution for 1 h . Slides were washed 2 × 10 min in wash buffer . Tick cells were stained by incubating slides for 5 min with WGA-Alexa Fluor 594 at 1:200 dilution in wash buffer . Slides were then washed a final time for 5 min . Coverslips were mounted on slides with ProLong Gold ( Molecular Probes ) and sealed with Permount ( Fisher Scientific ) and allowed to dry prior to examination by fluorescence microscopy as described above . Persistence of B . burgdorferi in ticks was quantified by isolating DNA from fed larvae ( one week post feeding; groups of 5 ) , flat nymphs and fed nymphs ( one week post feeding ) by grinding with a pestle in a 1 . 5-ml tube and using the DNeasy Blood/Tissue kit ( Qiagen ) [161] . TaqMan qPCR was done as described above using the primers and probe to the flaB gene listed in S11 Table . Values are expressed as the number of spirochetes/tick based on the genome equivalents where one copy of the flaB gene = one genome = one spirochete . B . burgdorferi cultures were collected by centrifugation at 10 , 000 × g for 10 min at 4°C , the cell pellet resuspended in fixative ( 20 mM sodium cacodylate , pH 6 with 2 . 5% v/v glutaraldehyde ) and cells were fixed overnight at 4°C . Cells were then centrifuged again , washed once in ddH2O and fixed in 2% osmium tetroxide for 2 h at 4°C . Cell pellets were washed twice in ddH2O , resuspended in ddH2O and the cells were loaded onto a 0 . 6 μm filter using a 1-ml syringe . Cells were gently dehydrated for 10 min each in a graded ethanol series; 35% , 50% , 70% , 90% , 95% , and twice in 100% EtOH using a 1-ml syringe . The filter was removed after the final 100% EtOH wash and placed in 100% hexamethyldisilazane for 30 min . The filter was air dried and placed on an adhesive carbon tab on a 13 mm aluminum stub . Filters with bacteria were coated with gold and palladium in a Pelco Model 3 sputter coater for 30 sec . After coating , samples were imaged in a Hitachi S-4700 Field Emission scanning electron microscope . To determine the murine infectivity of B . burgdorferi strains , C3H-HeJ female mice were intradermally needle-inoculated in the hind leg with either 1 x 105 or 1 × 106 cells of wild-type ( B31-5A4 ) , relBbu- or complemented strains [162] . Three weeks after inoculation , mouse ear tissue was collected and cultured in BSK + RS containing 50 μg ml-1 rifampicin , 20 μg ml-1 phosphomycin and 2 . 5 μg ml-1 amphotericin B . Cultures were screened for B . burgdorferi growth by dark-field microscopy . To examine strains for dissemination , mice were sacrificed five weeks post inoculation and ear , ankle and bladder tissues collected , cultured and screened for B . burgdorferi . To examine tick acquisition of B . burgdorferi strains , naïve Ixodes scapularis larvae ( National Tick Research and Education Resource , Oklahoma State University ) were allowed to feed to repletion on infected mice . Fed larvae were collected and kept in a bell jar containing a saturated solution of K2SO4 to generate a 98% humidified atmosphere . One week after collection , fed larvae were assayed for B . burgdorferi by IF microscopy and the number of bacterium per tick were quantified by qPCR as described above . Flat and fed nymphs ( one week post repletion ) were analyzed for B . burgdorferi by IF microscopy and qPCR to quantify persistence . To test strain transmission from ticks to mice , five flat , infected nymphs were placed on each naive C3H-HeJ female mouse ( at least three mice per B . burgdorferi strain ) and allowed to feed to repletion . Mice were screened for Borrelia infection by culturing tissues collected three and five weeks after infestation , as described above . B . burgdorferi was grown to ~5 x 106 cells ml-1 and 500 μl collected by centrifugation ( 8 , 600 × g for 5 min at RT ) , resuspended in the same volume of phosphate-free BSK + RS and grown for 12 h before addition of 20 μCi ml-1 32P orthophosphate ( PerkinElmer ) . Cells were labeled for 24 h and 500 μl culture samples were collected by centrifugation at 9 , 000 × g for 7 min at RT . Cell pellets were resuspended in BSK + RS or RPMI containing 20 μCi ml-1 32P orthophosphate and incubated at 35°C for the indicated times . Samples were collected by centrifugation at 12 , 000 × g at 4°C for 5 min . Cell pellets were rinsed with 50 μl cold dPBS . Cells were lysed and nucleotides extracted by addition of 30 μl of cold 6 . 5 M formic acid ( Fisher Scientific ) . Samples were incubated on ice for 10 min and stored at -80°C . Cell debris was pelleted by centrifugation ( 20 , 800 × g , 5 min at 4°C ) before separation by thin layer chromatography ( TLC ) . Polyethylenimine ( PEI ) cellulose TLC plates ( EMD ) [163] were pre-run in ddH2O to remove impurities and dried before 8 μl of each sample was spotted on the plate and allowed to dry . Samples were resolved in 1 . 5 M KH2PO4 , pH 3 . 4 . Plates were dried , covered with plastic and exposed to an intensifying screen for 48–72 h . Screens were analyzed using a Fujifilm FLA-3000G Phosphorimager . Three independent experiments were performed and pppGpp , ppGpp and GTP levels quantified by densitometry using ImageGauge . Values represent ( p ) ppGpp / ( p ) ppGpp + GTP and error bars are SEM [164] . Statistical significance ( P < 0 . 05 ) was determined by one-way ANOVA with a Tukey’s post-hoc test . Total RNA was isolated using a hot phenol protocol [165] . Total RNA was treated with DNase I ( Roche ) following the manufacturer′s protocol . RNA integrity was measured using the Agilent 2100 Bioanalyzer . RNA with an RNA Integrity Number ( RIN ) above 9 . 0 was used for cDNA library construction . Directional ( strand-specific ) RNA-seq cDNA libraries were constructed with a ligation-based protocol as previously described [166] except a different 5′ end RNA linker ( 5′-ACACUCUUUCCCUACACGACGCUCUUCCGAUCU-3′ ) and corresponding forward primer for PCR ( 5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′ ) were used . Total RNA was depleted of rRNA using the Ribo-Zero RNA removal kit for gram-negative bacteria ( Epicenter ) . 250 ng of RNA was fragmented using the RNA fragmentation reagents ( Ambion ) per the manufacturers protocol at 70°C for 5 min . RNA was treated sequentially with tobacco acid phosphatase ( Epicenter ) and calf intestinal phosphatase ( New England Biolabs ) to remove 5′-end phosphates . Finally , RNA was treated with polynucleotide kinase ( T4 PNK; New England Biolabs ) without ATP to remove 2′-3′ cyclic phosphates for 4 h at 37°C per the manufacturer’s protocol . A 3′-end adaptor , based on the Illumina multiplexing adapter sequence ( oligonucleotide sequences 2007–2014 Illumina , Inc . all rights reserved ) blocked at the 3′ end with an inverted dT , was phosphorylated at the 5’ end using T4 PNK ( New England Biolabs ) per the manufacturer’s protocol . The 3′ multiplex adapter was ligated to the 3′ ends of the RNA using T4 RNA ligase ( New England Biolabs ) at 20°C for 6 h following the manufacturer’s protocol . RNA was size-selected ( 75–300 nt ) and purified over a denaturing 8% polyacrylamide/8M Urea/TBE gel . The 5′ ends were phosphorylated with T4 PNK ( New England Biolabs ) following the manufacturer’s protocol . The Illumina 5′ end adapter was ligated to the 5′ ends using T4 RNA ligase ( New England Biolabs ) . The ligated RNAs were size selected ( 100–400 nt ) and gel purified as described above . The di-tagged RNA libraries were reverse-transcribed with SuperscriptII reverse transcriptase ( Invitrogen ) using random nonomers per the manufacturer’s protocol . cDNA libraries were prepared from wild-type , relBbu- and complemented strains at three different time points and from two biological replicates ( for a total of 3 x 3 x 2 = 18 samples ) and were sequenced on an Illumina HiSeq 2000 with single-end 50-base-pair reads at the Campus Science Support Facilities Next Generation Sequencing unit ( http://www . csf . ac . at/facilities/next-generation-sequencing/ ) . The reads were demultiplexed and adapters were clipped with cutadapt . After quality control , the reads ( between 31–51 Mio reads per sample ) were mapped to the B . burgdorferi B31 reference genome ( GenBank Ids: AE000783 , AE001583 , AE000793 , AE001582 , AE000785 , AE000794 , AE000786 , AE000784 , AE000789 , AE000788 , AE000787 , AE000790 , AE001584 , AE000791 , AE000792 , AE001575 , AE001576 , AE001577 , AE001578 , AE001579 , AE001580 , and AE001581 ) with NextGenMap 0 . 4 . 10 [167] using standard parameters and a minimum identity threshold of 90%; multireads ( reads with mapping equally well to more than one location on the genome ) were pruned . NextGenMap mapped between 69% and 86% of the reads with a mapping quality larger than 20 . This corresponded to 26–41 Mio reads per dataset or a theoretical genome-wide coverage of 855-1368X . FeatureCounts [168] was used to calculate read counts for all datasets . We considered only genes present in the Schutzer annotation set [169] , thus ignoring all reads that map to tRNAs ( between 91% and 95% of the mapped reads ) . The final average coverage per gene was between 74X and 233X for the different datasets ( S12 Table ) . From the read counts , we calculated differential expression between various conditions/time points using edgeR and DESeq and filtered the results by adjusted P-value ≤ 0 . 05 . P-values were adjusted using Benjamini and Hochberg’s algorithm to control the false discovery rate . We observed very little variance between our biological replicates which , in turn , resulted in small differences between conditions being assigned very low P-values ( i . e . , a large number of genes were called “significantly differentially expressed” by edgeR ) . For this reason , we further filtered the list of DE genes by log-fold change ( LFC ) and considered only genes with a difference in normalized read counts ( ABS ( LFC ) ≥ 1 , which corresponds to an estimated twofold expression increase or decrease ) . We also extracted strand-specific , normalized depth-of-coverage signals ( i . e . , the counts of reads overlapping a particular genomic position ) from the read alignments using CODOC [170] and converted them to the BigWig data format to enable manual inspection in a genome browser . | Borrelia burgdorferi , the spirochete responsible for causing Lyme disease , is maintained in nature via cycling between an Ixodes tick vector and a vertebrate host . The spirochete must adapt to and survive extreme nutrient deprivation , which may last months between blood meals , to persist in the midgut of the tick vector . How B . burgdorferi survives extended periods under such nutrient limitations has not been previously examined . In this study , we demonstrated that the stringent response , governed by RelBbu , which synthesizes and hydrolyzes the alarmones guanosine tetraphosphate and guanosine pentaphosphate ( collectively termed ( p ) ppGpp ) , is necessary for persistence in the tick . RelBbu was also required for survival during in vitro starvation and relBbu mutants more readily formed round bodies , a morphological change recently implicated in persistence in the tick . These adaptations to nutrient limitations appear to be mediated by global changes in gene expression modulated by RelBbu activity . Our results highlight an important role for RelBbu , and presumably ( p ) ppGpp , in vivo for persistence of a pathogen in its arthropod vector . | [
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| 2015 | The Borrelia burgdorferi RelA/SpoT Homolog and Stringent Response Regulate Survival in the Tick Vector and Global Gene Expression during Starvation |
Recently diverged species typically have incomplete reproductive barriers , allowing introgression of genetic material from one species into the genomic background of the other . The role of natural selection in preventing or promoting introgression remains contentious . Because of genomic co-adaptation , some chromosomal fragments are expected to be selected against in the new background and resist introgression . In contrast , natural selection should favor introgression for alleles at genes evolving under multi-allelic balancing selection , such as the MHC in vertebrates , disease resistance , or self-incompatibility genes in plants . Here , we test the prediction that negative , frequency-dependent selection on alleles at the multi-allelic gene controlling pistil self-incompatibility specificity in two closely related species , Arabidopsis halleri and A . lyrata , caused introgression at this locus at a higher rate than the genomic background . Polymorphism at this gene is largely shared , and we have identified 18 pairs of S-alleles that are only slightly divergent between the two species . For these pairs of S-alleles , divergence at four-fold degenerate sites ( K = 0 . 0193 ) is about four times lower than the genomic background ( K = 0 . 0743 ) . We demonstrate that this difference cannot be explained by differences in effective population size between the two types of loci . Rather , our data are most consistent with a five-fold increase of introgression rates for S-alleles as compared to the genomic background , making this study the first documented example of adaptive introgression facilitated by balancing selection . We suggest that this process plays an important role in the maintenance of high allelic diversity and divergence at the S-locus in flowering plant families . Because genes under balancing selection are expected to be among the last to stop introgressing , their comparison in closely related species provides a lower-bound estimate of the time since the species stopped forming fertile hybrids , thereby complementing the average portrait of divergence between species provided by genomic data .
The genomes of incipient species diverge at heterogeneous rates , and recently diverged model species are key systems to investigate the causes of this heterogeneity [1]–[3] . Hybridization followed by introgression between recently diverged plant and animal species with incomplete reproductive barriers is one of the main processes generating the genomic heterogeneity in species divergence [4] . Indeed , some regions appear to be crossing the species barriers more readily than the genomic background ( in Helianthus [5] , Anopheles [6] , Quercus [7] , Mytilus [8] , Mus [9] and Drosophila [10] ) . Although much of this heterogeneity may be accounted for by stochasticity of the genetic drift process , natural selection may also play an important role . In particular , because introgressive hybridization brings genetic material from one species into the co-adapted background of another species , some chromosomal fragments are expected to be selected against and resist introgression [11] . On the other hand , selection can also promote introgression when a transferred chromosome fragment is advantageous in the recipient species . In such a situation , introgression can potentially mediate the transfer of adaptations . Examples of adaptive introgression involving the transfer of transgenes conferring adaptations such as herbicide or insect resistance via hybridization with close relatives of crop species [12] have been documented , but other examples in natural populations are strikingly rare [13] . In the Louisiana Iris species complex for instance , detailed experimental studies provided support for the transfer of adaptations ( flood and shade tolerance ) between Iris fulva and I . hexagona [14] . In Helianthus , a recent experimental study reported that herbivore resistance traits have introgressed from Heliantus debilis to H . annuus , thereby increasing adaptation of their naturally occurring hybrid H . annuus taxanus [15] . All these documented examples are thus associated with strong directional selection for adaptive traits recently evolved in one of the species and then transmitted horizontally . Theory predicts that adaptive introgression should also be a general property of alleles at genes evolving under multi-allelic balancing selection , such as the vertebrate MHC system , plant disease resistance or self-incompatibility ( SI ) genes [16] . In these systems , rare alleles enjoy a strong selective advantage [17] . Assuming that a given allele is absent from one of two related species , introgression of this allele would then be as strongly favored as a new allele arising by mutation , unless this is impeded by linked genes that are not well adapted to the recipient species . Thus , in multi-allelic systems evolving under balancing selection , repeated exchanges of alleles promoted by adaptive introgression may be expected between closely related species , as long as fertile hybrids can be formed . Therefore , in the course of evolution of strong reproductive isolation between incipient species , such genomic regions should be among the last to stop introgressing . In this study , we test whether multi-allelic balancing selection mediates introgression between closely related species . We do this by contrasting divergence of a portion of the gene controlling self-incompatibility specificity ( SRK ) with the background level of genomic divergence in two closely related plant species . The study system consists of two closely related Arabidopsis species , A . lyrata and A . halleri , whose genomes diverged approximately 2 million years ago [18] . The two species have overlapping distributions in Northern Europe [19] and relatively recent introgression has been demonstrated for a small fraction of nuclear genes [20] . SI prevents self-fertilization and some matings among relatives through recognition and rejection of pollen expressing identical specificity . Molecular and genetic analyses of the self-incompatibility locus ( S-locus ) in A . lyrata and A . halleri identified many specificities , and the SRK sequences often form monophyletic pairs of high sequence similarity , each of which probably represent the same SI specificity in the two species derived from one specificity in their common ancestor . We refer to these pairs as trans-specifically shared pairs of S-alleles . We use divergence at fourfold degenerate sites between alleles within trans-specifically shared pairs to estimate the divergence corresponding to the time of the last introgression event for S-alleles between the two species , and we find that introgression has occurred at a higher rate or continued over more extended periods of time at the S-locus than at the rest of the nuclear genome .
Our species-wide survey of sequence diversity reveals that a large fraction of alleles at the pistil self-incompatibility specificity-determining gene SRK ( S-locus receptor kinase ) are trans-specifically shared between the two species ( Figure 1 ) . Overall , we find 30 sets of SRK sequences in A . halleri and 38 sets of SRK sequences in A . lyrata . As is typical for S-alleles [21] , the sequences fall into sets of nearly identical ones ( presumably representing the same specificity , [21]–[23] ) and ones with many differences from all other sequences ( presumably representing functionally distinct specificities ) , with the most similar pairs within A . halleri and A . lyrata showing 44 and 51 differences , respectively , over a total of about 570 nucleotides . We then compared nucleotide sequences between S-alleles from the two species and find that the mismatch distribution ( Figure 2 ) is clearly bimodal . Most comparisons are in line with intraspecific comparisons and range between 45 and 218 differences over a total of about 570 nucleotides ( see also Figure S5 ) , but the distribution shows a distinct set of 18 highly similar interspecific pairs of sequences ( indicated by brackets in Figure 1 ) with at most 12 nucleotide differences . The numbers of non-synonymous differences within the 18 highly similar pairs of S-alleles ranged from 0 to 9 over a total of 380 non-synonymous sites . These sequences are more similar than pairs of alleles known to have retained the same specificity when comparing the closely related Brassica oleracea and B . rapa [24]–[27] . Even if these sequences currently occur in two different ( but closely related ) species , we therefore hypothesize that these pairs have retained identical specificity . We refer to these 18 pairs of S-alleles as “trans-specifically shared” pairs of alleles and note that they represent 60% and 47% of S-alleles found to date in A . halleri and A . lyrata , respectively . Two of these pairs ( AlSRK37/AhSRK04 and AlSRK16/AhSRK10 ) were previously identified and shown additionally to be shared trans-specifically with A . thaliana [28] . Phylogenetic reconstructions show that both synonymous ( Figure S2 ) and non-synonymous ( Figure S3 ) differences are strikingly low within trans-specifically shared pairs and high among pairs . Note that , by definition , SRK alleles we consider as trans-specific pairs are determined based on those S allele pairs that have the fewest differences , so the procedure could potentially lead to ascertainment bias . Yet , close examination of the next best candidates ( AhSRK03/AlSRK28 , AhSRK28/AlSRK03 , AhSRK23/AlSRK06 and AhSRK20/AlSRK04 , Figure 1 ) suggests that none of these pairs is likely to represent pairs of trans-specifically shared alleles ( detailed arguments are presented in Text S1 ) . Within SRK , several hypervariable ( HV ) regions have been identified in the domain responsible for binding the pollen protein ( S-domain ) and shown to be targets of positive selection , suggesting they are involved in determination of specificity [29] , [30] . Accordingly , HV regions from different specificities within species typically show an excess of non-synonymous substitutions [29] , [31] , [32] . In sharp contrast , we find that as compared to synonymous differences , non-synonymous differences are relatively less frequent in HV regions than in non-HV regions ( on average 0 . 7 and 2 . 3 differences in HV and non-HV regions respectively for non-synonymous differences , versus 1 . 1 and 1 . 6 differences respectively for synonymous differences , Table 1 ) . This contrast is significant by Fisher's exact test of independence ( odds ratio = 2 . 5 , p = 0 . 029 ) , suggesting that sequence pairs that putatively encode the same specificity tend to have similar HV region sequences for non-synonymous sites , but might differ at synonymous sites in these regions , whereas other regions may differ at both types of sites . If introgression occurs , then divergence might also be affected by the dominance of the S-alleles . Indeed , complex patterns of dominance relationships generally occur among alleles in sporophytic SI systems [33] and Billiard et al . [34] reported asymmetric selective pressures for dominant and recessive S-alleles because rare dominant S-alleles will tend to express their specificity more often than rare recessive ones ( a process similar to “Haldane's sieve”-the bias against the establishment of recessive beneficial mutations [35] , [36] ) . Hypothesizing that the introgression rate thus differs between dominant and recessive S-alleles , we tested for an effect of dominance on divergence between the two species . The range of variation observed for nucleotide differences across pairs of trans-specifically shared S-alleles cannot be explained fully by the stochasticity of the substitution process ( Fisher's dispersion index = 2 . 03 , P = 0 . 0103 ) , but there was no obvious relationship between number of nucleotide differences and level of dominance of the S-alleles , as inferred from the phylogeny of alleles as suggested by [37] . Thus , we find no evidence that dominance affects S-allele divergence between the two species . To test whether balancing selection resulted in adaptive introgression of S-alleles between the two species , we compared levels of divergence at fourfold degenerate sites between trans-specifically shared S-alleles with that of the genomic background , estimated from twelve unlinked control genes and two S-gene family members . These two sets of control genes give similar mean values of divergence ( K4fold = 0 . 0743 and K4fold = 0 . 0904 , respectively , Table 2 ) , which are about four times higher than the average for trans-specifically shared pairs of S-alleles ( K4fold = 0 . 0193 , Table 1 ) . Because a large number of S-alleles are actively maintained within species by balancing selection , each S-allele has individually a small effective population size [21] . Thus , estimates of divergence for S-alleles and reference genes cannot be compared directly because of differences in effective population sizes ( Figure 3 ) . To take this into account , we used coalescent simulations to test whether our data are compatible with a null model of speciation ( the “isolation with migration” model of Nielsen and Wakeley , [38] ) that assumes identical introgression rate for S-alleles and the genomic background . Under this model , we first used previously published species-wide polymorphism data in A . halleri and A . lyrata from [20] , [39] , [40] to estimate rates of introgression , splitting time t as well as θA = 4NAμ , where NA is the effective population size in their common ancestor and μ the substitution rate . The maximum likelihood estimates for directional rates of introgression are mhal→lyr = 2 . 775×10−7 , mlyr→hal = 2 . 912×10−7 , and θA = 1 . 7975 ( Table 3 ) . The t estimate is 2 , 533 , 980 years [1 , 307 , 952–5 , 166 , 833] , which is entirely consistent with the previous 2 Myrs estimate by Koch & Matschinger [18] . All estimates converge satisfactorily based on 10 replicate runs with different random seeds . To single out the NA estimate , we then used A . thaliana as outgroup to obtain a substitution rate at fourfold degenerate sites of μ = 1 . 296×10−8 substitutions per nucleotide per year ( [9 . 218×10−9–1 . 781×10−8] as 95% credible interval ) . The resulting estimate for NA is 253 , 892 with [13 , 772–663 , 510] as 95% credible interval . Based on these parameters , we then simulated the evolution of two species exchanging migrants at the rate estimated above . The simulations were entirely consistent with the data for the genomic background ( K = 0 . 0678 [0 . 0423–0 . 0955] , Figure 4 ) . In sharp contrast , conservatively assuming a reduction of effective population size for S-alleles by a factor 50 ( as expected if 50 different S-alleles segregate in each species ) only led to a modest reduction in divergence ( K = 0 . 0465 , Figure 4 ) , whose 95% credible interval [0 . 0305–0 . 0640] did not comprise the observed value for K ( K4fold = 0 . 0193 ) . Hence , the data are not consistent with equal introgression for S-alleles and the genomic background . This result is robust to the conservative use of the lower boundary of the 95%CI for either NA or t . Increasing the rates of introgression for S-alleles led to a sharp reduction in divergence between A . halleri and A . lyrata . The simulations best fitted the data when the directional rates of introgression were empirically increased for S-alleles by a factor 5 , with divergence value closely approaching the observed data ( K = 0 . 0182 , Figure 4 ) . A simpler analysis also confirmed that average net interspecific divergence [41] for S-alleles was lower than that at the genomic background ( Text S1 ) . For three pairs of S-alleles ( AlSRK01/AhSRK01 , AlSRK34/AhSRK05 , AlSRK37/AhSRK04 ) we also surveyed intra-allelic variation in at least 10 copies from each species . We found very little diversity among allelic copies within each surveyed allele in each species ( average synonymous diversity = 0 . 0064 , data not shown ) in accordance with their low expected effective population sizes . We examined the sequences for shared polymorphisms , and found none in any of these S-allele pairs . This suggests old and infrequent , rather than recent , introgression events since the separation of A . lyrata and A . halleri . Moreover , the estimated divergence among pairs of S-alleles was more heterogeneous than expected based on the Poisson distribution , suggesting that the last introgression event occurred at different times for different alleles .
The possibility of introgression of S-alleles may have important consequences for the extent of allelic diversity maintained within self-incompatible species . If introgression occurs , hybridizing species effectively share a common pool of S-alleles . If hybridization is restricted , the two species together can maintain more S-alleles than each species individually [42] . Such a process could be especially important in the first stages of the split because reproductive barriers may then be more leaky , and also because allelic diversity at the S-locus within incipient species may be decreased if founding events were associated with speciation . This process could be responsible for maintaining many highly divergent allelic lineages at the S locus within plant families , where trans-generic sharing of allelic lineages seems to be the rule , and loss of ancestral allelic lineages through strong bottlenecks within particular genera the exception , as has been described in the Solanaceae [43] . It can therefore be misleading to use a species' extant number of lineages at a gene under balancing selection to estimate the minimum population size at speciation . For instance , using polymorphism data for MHC in humans , Takahata [44] predicted that the number of breeding individuals in the human lineage could not be as small as 50-100 at any time of its evolutionary history , assuming two extant ancestral allelic lineages at HLA-B . According to our hypothesis of adaptive introgression mediated by balancing selection , variation can be efficiently “rescued” , and stronger founder events at speciation would still be compatible with extant variation at HLA-B , if some interbreeding occurred with the chimpanzee lineage after the split . Although identifying the functional types of alleles may not be simple in that case ( and recombination may confine the effect of balancing selection to a small region around the selected sites themselves ) , a detailed analysis of MHC alleles in the great apes would be of great interest to survey whether adaptive introgression mediated by balancing selection has indeed occurred in primates . A recent study by Koch and Matschinger [18] reported that , whereas A . lyrata and A . halleri were well separated in phylogenetic trees based on the nuclear encoded ITS region , several cpDNA haplotypes are shared between both species [18] . This was interpreted as ancestral polymorphism segregating for the chloroplast but not the nucleus . However , this interpretation is at odds with the smaller effective population size expected for the chloroplast ( approximately 1/2 for hermaphroditic species , [45] ) and the consequent low expected variability . Indeed most studies in plants have found low sequence diversity for chloroplast genes , taking into account their low mutation rate [46] , and also stronger differentiation among populations for chloroplast than nuclear markers [47] . In line with our results from S-alleles , we suggest the alternative interpretation that introgression occurred more readily for the chloroplast than nuclear genes , as has been reported in several instances ( e . g . [48] , [49] ) . The haplotype network of chloroplast sequences reported by Koch and Matschinger [18] also showed greater sharing of more basal haplotypes , suggesting that chloroplast introgression has become less common in recent times . Our results also shed light on the evolution of self-incompatibility specificities . Indeed , our data strongly suggest that purifying selection prevents the substitution of non-synonymous differences within HV regions , supporting a role for these regions in determining specificity . More specifically , the strength of purifying selection seems higher on the HV regions than on the rest of the sequence , and this could be related to strong selection against mutations altering specificities . Mechanisms selecting against mutant S-alleles with altered pistil specificities have been discussed by Uyenoyama et al . [50] . Inter-species exchanges of S-alleles may , however , be important in the evolution of new specificities . Chookajorn et al . [51] suggested that new specificities could evolve if sufficient variation could be maintained within the pollen ( or pistil ) S gene for enough time to allow variants of the other gene to co-evolve with them . Due to the small effective population size of individual S-alleles , this hypothesis requires population structure with very limited migration [16] . Speciation with some introgression of S-alleles leads to precisely the strongly subdivided population needed for this mechanism to work . Under this hypothesis , two alleles could slowly evolve to different specificities in two isolated species and then add to the number of S-alleles in each species after reciprocal introgression . Data testing the specificities of sequence pairs in the two species that differ at few amino acids might help determine whether new specificities have indeed arisen in one species or the other since they split .
We surveyed sequence diversity at SRK in two species-wide samples in A . halleri and A . lyrata over a total of about 570 nucleotides from the 3′ end of the S-domain using the strategy detailed in [31] . We identified and sequenced five and eight new putative S-alleles in A . halleri and A . lyrata , respectively . Overall , we analyzed 30 SRK sequences in A . halleri and 38 sequences in A . lyrata . In each case , the nucleotide sequence was obtained as a consensus over three independently obtained sequence products . All identified sequences in A . halleri and A . lyrata were amino-acid translated and aligned by ClustalW in BioEdit 7 . 0 . 5 [52] and adjusted by eye . On the overall set of sequences at SRK , we used MEGA 4 [53] to reconstruct a phylogeny using the Neighbor-Joining method based on the total number of differences per site or on the number of either synonymous or non-synonymous differences . Within each pair of trans-specifically shared sequences at SRK , we estimated the number of synonymous nucleotide differences per synonymous site between the A . halleri and the A . lyrata copy using the method of [54] with MEGA 4 . A homogeneous substitution process across all pairs is expected to result in an accumulation of nucleotide differences according to the Poisson distribution . We used Fisher's dispersion index to test whether the distribution of nucleotide differences across trans-specifically shared sequence pairs could be explained by the stochasticity of the substitution process alone . We used Fisher's exact test of independence to test whether synonymous and non-synonymous differences hit HV regions equally frequently . Background genomic divergence was estimated by the species-wide average nucleotide divergence at fourfold degenerate sites ( K4fold ) between the two species for 12 reference genes that had been previously sequenced [20] , [39] , [40] and two genes that are members of the S-domain gene family ( Aly10 . 1 , Aly10 . 2 ) . To determine whether difference in effective population size and thus coalescence time between S-alleles and genomic background may suffice to explain the low divergence of S-alleles , we applied the isolation with migration model of Nielsen and Wakeley [38] to polymorphism at fourfold degenerate sites in both species for the eleven reference genes plus Aly9 ( 12 genes in total , see table 2 ) as implemented in the IM program [38] . We chose to focus on fourfold degenerate sites only because differences in substitution rates have been reported among codon positions [55] . The program DNAsp [56] was used to generate a datafile containing fourfold degenerate sites only . The procedure was run with 10 different random seeds to ensure proper convergence of the six free parameters , i . e . θA , θlyrata , θhalleri , t , mhal→lyr , mlyr→hal ( polymorphism θ = 4Nμ in the common ancestor of A . halleri and A . lyrata , polymorphism in A . lyrata , polymorphism in A . halleri , splitting time and the rate at which genes introgressed into A . lyrata from A . halleri and into A . halleri from A . lyrata as time moves forward , respectively ) . The HKY mutation model [57] was used . To single out the NA estimate , we estimated the average per fourfold degenerate site mutation rate ( μ ) as follows . We used A . thaliana as outgroup to estimate the average net nucleotide divergence at fourfold degenerate sites between A . thaliana and A . halleri and between A . thaliana and A . lyrata for each reference gene . Assuming that the lineages leading to A . thaliana and the common ancestor of A . lyrata and A . halleri separated 5 million years ago [58] , we obtained a mutation rate estimate per site per year for each reference gene . We computed an average mutation rate per site per year ( μ ) by taking the geometric mean over genes . A mutation rate per generation was computed assuming a mean generation time of two years . The maximum likelihood estimates were then used to simulate divergence between two species isolated since one million generations but still capable of introgression . Ten thousand replicates of pairs of genes with the same number of nucleotides as the real data were performed using SIMCOAL2 [59] . The genomic background divergence was first used to confirm that the simulations parameters were appropriate . We then determined whether the observed divergence for S-alleles was consistent with the overall genomic rate of introgression by simulating the evolution of S-alleles in this system assuming that 50 S-alleles segregate in the species , and thus that the effective population size of each allelic class is reduced by a factor 50 . To remain conservative in this analysis , S-alleles were simulated under the 2 . 5% low boundary of the 95% credible interval for NA obtained from IM . Using the maximum likelihood estimate for NA , we then aimed to determine the extent to which introgression is increased for S-alleles relative to the genomic background . We did so by gradually increasing mhal→lyr and mlyr→hal for S-alleles by a multiplicative factor from one to ten until the simulated data came close to the observed divergence . The sequences reported in this paper have been deposited in the GenBank database under accession numbers EU878008- EU878026 . | The role of natural selection in promoting or preventing genomic divergence between nascent species remains highly debated . As long as reproductive barriers remain incomplete , genetic material from one species is indeed exposed to natural selection into the genomic background of the other species . In some cases , genomic co-adaptations developing independently in each species are believed to select against such transfers . Yet , theory predicts that the transfer of some chromosomal fragments may be favored by natural selection . In particular , this should occur for alleles at genes evolving under a particular form of natural selection , i . e . , multi-allelic balancing selection . We test this prediction using two closely related Arabidopsis species , and find a four-fold lower divergence at alleles at the gene controlling pistil self-incompatibility specificity than at the genomic background . We conclude that alleles at this gene have been transferred more readily between the two species than the genomic background . We suggest that natural selection may efficiently allow the maintenance of high allelic diversity and divergence across many species at S-loci as well as at all other loci under multi-allelic balancing selection , such as the MHC in vertebrates or disease resistance genes in plants . | [
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| 2008 | Repeated Adaptive Introgression at a Gene under Multiallelic Balancing Selection |
Escherichia coli pol V ( UmuD′2C ) , the main translesion DNA polymerase , ensures continued nascent strand extension when the cellular replicase is blocked by unrepaired DNA lesions . Pol V is characterized by low sugar selectivity , which can be further reduced by a Y11A “steric-gate” substitution in UmuC that enables pol V to preferentially incorporate rNTPs over dNTPs in vitro . Despite efficient error-prone translesion synthesis catalyzed by UmuC_Y11A in vitro , strains expressing umuC_Y11A exhibit low UV mutability and UV resistance . Here , we show that these phenotypes result from the concomitant dual actions of Ribonuclease HII ( RNase HII ) initiating removal of rNMPs from the nascent DNA strand and nucleotide excision repair ( NER ) removing UV lesions from the parental strand . In the absence of either repair pathway , UV resistance and mutagenesis conferred by umuC_Y11A is significantly enhanced , suggesting that the combined actions of RNase HII and NER lead to double-strand breaks that result in reduced cell viability . We present evidence that the Y11A-specific UV phenotype is tempered by pol IV in vivo . At physiological ratios of the two polymerases , pol IV inhibits pol V–catalyzed translesion synthesis ( TLS ) past UV lesions and significantly reduces the number of Y11A-incorporated rNTPs by limiting the length of the pol V–dependent TLS tract generated during lesion bypass in vitro . In a recA730 lexA ( Def ) ΔumuDC ΔdinB strain , plasmid-encoded wild-type pol V promotes high levels of spontaneous mutagenesis . However , umuC_Y11A-dependent spontaneous mutagenesis is only ∼7% of that observed with wild-type pol V , but increases to ∼39% of wild-type levels in an isogenic ΔrnhB strain and ∼72% of wild-type levels in a ΔrnhA ΔrnhB double mutant . Our observations suggest that errant ribonucleotides incorporated by pol V can be tolerated in the E . coli genome , but at the cost of higher levels of cellular mutagenesis .
Translesion synthesis ( TLS ) allows living organisms to tolerate DNA damage to their genome . The vast majority of TLS in Escherichia coli is catalyzed by the LexA-regulated damage-inducible polymerases II , IV and V , which alone , or in various combinations , are recruited to the sites of DNA damage [1] . The B-family pol II which is encoded by the polB gene , is a rare case of a specialized TLS polymerase possessing 3′-5′ exonuclease activity [2] . As a result , pol II-dependent replication of both undamaged and damaged DNA is quite accurate with the exception of an N2-acetylaminofluorene adducts , where it promotes −2 frameshifts [3] . Y-family polymerases , pol IV , encoded by the dinB gene [4] , [5] , and pol V , the product of the umuC and umuD genes [6] , are devoid of exonucleolytic proofreading and are characterized by low-fidelity DNA synthesis on undamaged DNA [7] , [8] . Nevertheless , pol IV is remarkably accurate when replicating past certain DNA lesions , such as N2-dG adducts [9] . While pol II and pol IV each appear to facilitate TLS of a narrow range of damaged substrates , pol V is able to accommodate a diverse spectrum of DNA lesions in its active site and bears the greatest burden of TLS in E . coli [1] , [6] , [10] . Pol V-dependent TLS is highly error-prone causing the majority of cellular mutagenesis after DNA damage [6] , [11] . Pol V , a heterotrimeric UmuD′2C complex [12] , requiring the presence of a RecA nucleoprotein filament ( RecA* ) for optimal activity [13]–[17] , has intrinsically low base substitution fidelity [18] , [19] . We have recently discovered that this polymerase is also characterized by substantially reduced sugar selectivity [20] . When the canonical Watson-Crick base pairing is preserved , purified pol V accompanied by accessory proteins readily incorporates all ribonucleotides ( ribonucleoside monophosphates , rNMPs ) except uracil and catalyzes efficient and highly processive RNA synthesis in vitro in the presence of all four rNTPs . The ability of pol V to incorporate ribonucleotides is dramatically enhanced by a Y11A substitution at the conserved steric gate residue of UmuC , and greatly reduced by an F10L substitution [20] . In contrast , a Y11F substitution affects sugar selectivity minimally [20] . All three alleles also have different effects on base substitution fidelity and TLS activity of the mutant polymerases . Because the Y11F mutant readily accommodates G∶T mispairs in the active site , it induces higher levels of mutagenesis than wild-type pol V [20] , but the ability of the wild-type polymerase and Y11F mutant to replicate damaged DNA is similar . The F10L_UmuC variant is characterized by a significant increase in the accuracy of nucleotide incorporation and moderate decrease in TLS activity . Consistent with this observation , cells expressing the F10L mutant exhibit low levels of UV-induced mutagenesis [21] . In contrast , the in vivo phenotype of strains expressing pol V with the umuC_Y11A substitution contradicts its in vitro biochemical properties . UmuC_Y11A is highly inaccurate in vitro , yet exhibits low mutability in vivo [20] , [21] . Furthermore , despite the observation that the UmuC_Y11A variant catalyzes TLS past a T-T cyclobutane pyrimidine dimer ( CPD ) in vitro at least as efficiently as the wild-type enzyme , it confers minimal UV-resistance to a ΔumuDC strain [21] . To explain these phenotypes , we suggest that the dramatic increase in rNMP incorporation promoted by UmuC_Y11A leads to the induction of downstream pathways involving rNMP processing . Presumably the rNMP-targeted repair pathways would not only reduce umuC_Y11A-dependent spontaneous and UV-induced mutagenesis , but also interfere with completion of TLS resulting in the observed decrease in UV resistance . Recent studies have demonstrated that similar to pol V , various DNA polymerases are able to incorporate ribonucleotides into DNA although in most cases less efficiently ( reviewed in [22] ) . Even replicative polymerases with much more rigorous steric exclusion mechanisms insert rNMPs in much higher amounts than it was previously assumed [23] . In addition to misinsertion during replication or repair , stable incorporation of rNMPs in the DNA backbone could result from the incomplete removal of RNA primers used during maturation of lagging-strand Okazaki fragments . Due to the presence of a reactive 2′ hydroxyl on the ribose ring , rNMPs embedded in genomic DNA could sensitize the DNA strand to spontaneous and enzymatic hydrolytic cleavage . They can also cause distortion to the structure of the double helix that disrupts the ability of DNA-binding proteins to recognize DNA , thereby interfering with subsequent replication and transcription processes . Therefore , efficient repair of RNA/DNA mismatches is a critical process for a living cell , so as to ensure maintenance of genome integrity and , ultimately , its viability . As a result , cells have evolved various pathways for recognizing and removing aberrant rNTP incorporated into DNA strands [24]–[27] . The major enzymes initiating this pathway are ribonucleotide-specific endonucleases , Ribonucleases H ( RNases H ) , which are present in organisms across all domains and are classified as types 1 and 2 based on sequence conservation and substrate preference [28] . Ribonucleases of both types are structurally related and have a similar mechanism of hydrolysis . However , while RNase HI cleaves the RNA moiety in the RNA/DNA hybrids with more than four sequential rNTPs embedded in a dsDNA strand , RNase HII enzymes can hydrolyze all kinds of hybrids , but prefer those which have a single rNTP embedded in DNA , rather than RNA/DNA duplexes with a stretch of riboses [29] . Although it is well established that RNase HI and HII are important for the release of rNMPs from a DNA duplex , the precise pathway initiated by these enzymes remains elusive . Based on in vitro studies , a general model describing the sequence of events that leads to the replacement of the ribose with deoxyribose has been developed for eukaryotic system . According to this model , after the phosphodiester bond of the nucleotide 5′ to the RNA-DNA junction is nicked by RNase H , an enzyme with 5′ to 3′ exonuclease activity makes a single cut 3′ to the rNTP , thus releasing the monoribonucleotide . After dissociation of the cleaved RNA , DNA polymerase fills the resulting gap and DNA ligase seals the nick [30] , [31] . Previous studies suggest that the 3′ cut is made by FEN-1-like proteins [32] and that RNase HII nicking activity is promoted by binding to PCNA [33] . The importance of this pathway in repair of rNMPs incorporated by a DNA polymerase during replication has been emphasized by the observation that the lack of RNase HII in yeast strains expressing a mutant pol ε with relaxed sugar selectivity , leads to replicative stress and genome instability [34] . The main hallmark of this instability , deletion of 2–5 base pairs in short repetitive sequences , was also demonstrated in strains encoding wild-type pol ε and shown to require the endoribonuclease activity of Top1 , a topoisomerase that relaxes supercoils by reversibly nicking duplex DNA [27] . The 2′-3′-cyclic phosphates formed after Top1-catalyzed cleavage between ribo- and deoxynucleotides , prohibit religation resulting in the generation of stable ssDNA breaks at the sites of incorporated rNMPs . Removal of all rNMPs is not a standard function of Top1 , and it targets only some of the ribonucleotides in DNA/RNA hybrid when RNase H2 is defective [27] . Originally , it was hypothesized that the formation of deletions in cellular DNA in the absence of RNase HII occurs through a misalignment mechanism and involves mismatch repair ( MMR ) proteins , but it was subsequently shown to be independent of the status of the MMR machinery [35] . More recently it was revealed that the MMR system in both prokaryotes and eukaryotes competes with RNase H mechanisms to remove misincorporated ribonucleotides and restore DNA integrity when isolated rNMPs in chromosomal DNA also distort the Watson-Crick base-pairing [27] . The pathway of rNMP repair in prokaryotes is much less understood . Despite having multiple cellular functions , RNases HI and HII , encoded by the rnhA and rnhB genes respectively [29] , are not essential for viability of bacteria , since the double mutants are viable , albeit temperature sensitive [36] . With respect to removal of rNMPs embedded in the genomic DNA , this means that other mechanisms can substitute for RNase H , or perhaps that prokaryotes can better tolerate DNA/RNA hybrid structures . Taking advantage of the different capacities for ribonucleotide incorporation by pol V variants with substitutions at , or adjacent to the steric gate , we examined rNMP-processing pathways that cause phenotypic changes in strains expressing the pol V variants . We discovered that mutations in rnhB ( encoding RNase HII ) , NER genes ( uvrA and uvrC ) , and unexpectedly in dinB ( encoding pol IV ) , play pivotal roles in modulating the extent of umuC_Y11A-dependent UV survival and mutagenesis . In addition , we show that in recA730 lexA ( Def ) ΔdinB strains lacking rnhB , rnhA helps to limit the extent of umuC_Y11A-dependent spontaneous mutagenesis imposed on the undamaged E . coli chromosome .
We have previously shown that the UV-resistance of recA730 lexA ( Def ) ΔumuDC cells expressing plasmid encoded umuC_Y11A is similar to that promoted by the vector plasmid alone [21] . This phenotype cannot be simply attributed to an inability to traverse the major UV-induced lesion because the highly purified pol V-Y11A enzyme bypassed the CPD efficiently in vitro [21] . Since UmuC_Y11A is characterized by low sugar discrimination fidelity [20] , it seems plausible that the poor UV-survival of strains expressing the Y11A variant might be explained by large numbers of ribonucleotides incorporated during TLS that trigger repair pathways directed at rNMP removal . These pathways would excise pol V-dependent TLS tracts mimicking a pol V-deficient phenotype . To test this hypothesis , we measured the UV-sensitivity of strains expressing wild-type pol V and variants , but lacking an enzyme implicated in the repair of ribonucleotide-containing DNA . Based upon previous studies , the most logical choice was to assay UV-survival in strains lacking the RNase H proteins; RNase HI , the product of the rnhA gene [37] with the capacity to release RNA from RNA/DNA hybrids with multiple sequential rNMPs , and RNase HII encoded by the rnhB gene [38] , which differs from the RNase HI by recognizing and cleaving a single ribonucleotide embedded in a DNA duplex . We therefore compared cell survival promoted by wild-type and mutant pol V variants in isogenic recA730 lexA ( Def ) ΔumuDC strains with ΔrnhA or ΔrnhB alleles alone , or in combination , after exposure to UV-light in a semi-quantitative “spot” assay . The plasmid-encoded pol V variants provide an excellent internal control for any effect of the RNase H proteins on cell survival , since the F10L mutant is essentially unable to incorporate ribonucleotides and should not exhibit any difference in the rnh+/− strains , whereas Y11A efficiently incorporates ribonucleotides and any rNMP-mediated repair would be most evident by comparison of the rnh+/− strains expressing this variant . The first thing to note is that the ΔrnhA strain is more sensitive to UV-light than the isogenic rnh+ or ΔrnhB strains ( Figure S1 ) , In this strain background , chromosomal duplication is dysregulated and unlike normal genome duplication which is initiated at oriC , is likely to be initiated at D-loops formed at oriMs and R-loops formed at multiple oriKs [39] . Presumably the added load of DNA damage to cells with highly irregular modes of replication contributes to the observed increased UV-sensitivity . In all strains analyzed , wild-type pol V conferred considerable UV-resistance ( Figure S1 ) . As previously reported [21] , in a recA730 lexA ( Def ) ΔumuDC rnh+ background , umuC_Y11A confers minimal UV-resistance compared to either pGB2 vector , wild-type UmuC , Y11F , or F10L mutants ( Figure S1A ) . In the ΔrnhA background , UV-survival of the Y11A variant was comparable to umuC_F10L and to the vector containing strain ( Figure S1B ) . In contrast , while Y11A exhibited roughly the same overall UV-resistance as F10L in the ΔrnhB strain , it was considerably more UV-resistant than the vector containing strain ( Figure S1C ) We were concerned , however , that these phenotypes were much less pronounced than we had anticipated , especially given the properties of the Y11A mutant in vitro [20] , [21] . The strains employed here carry the lexA51 ( Def ) allele which has a frameshift mutation in the C-terminus of LexA [40] , that leads to constitutive expression of all LexA-regulated genes [41] , including all three TLS polymerases , pol II , pol IV and pol V . Under these conditions , pol IV is the most abundant DNA polymerase in E . coli with an intracellular concentration of roughly 2500 molecules per cell [42] . Even though pol IV has not previously been implicated in the TLS of UV-induced lesions , we considered the possibility that the highly abundant enzyme could nevertheless compete with pol V to limit its access to stalled replication forks . To test this hypothesis , we generated isogenic recA730 lexA ( Def ) ΔumuDC ΔdinB ΔrnhA , ΔrnhB or ΔrnhA-ΔrnhB mutant strains and re-analyzed the effects of defects in RNase H on UV-survival of cells expressing wild-type pol V and its variants ( Figure 1 ) . As observed previously ( Figure S1 ) , the ΔrnhA allele rendered the strains more UV-sensitive than the isogenic rnh+ or ΔrnhB strains ( Figure 1B and 1D , where cells were exposed to 20 J/m2 UV light compared to 40 J/m2 in Figure 1A and 1C ) . However , the ΔrnhA allele had no effect on the relative extent of UV-survival provided by the four pol V-expressing plasmids . In particular , the umuC_Y11A expressing plasmid conferred the least UV-resistance that was only marginally greater than the pGB2 vector containing strain . A very different phenotype was observed in the ΔrnhB strain , where umuC_Y11A-dependent UV-survival was greatly enhanced ( compare pGB2 and Y11A in Figure 1C ) . A similar enhancement of Y11A-dependent UV-survival was also observed in the more UV-sensitive ΔrnhA ΔrnhB double mutant strain ( Figure 1D ) . Overall , our data are consistent with the possibility that RNase H II activity actually promotes rNMP-dependent UV-induced cell killing . The fact that the increase in UV-resistance of ΔrnhB Y11A-expressing cells is much more dramatic in a ΔdinB background compared to the isogenic ΔrnhB dinB+ strain implies that pol IV interferes with pol-V-catalyzed replication during TLS of CPDs . Such inhibition is surprising , since the prevailing models for TLS suggest that the two polymerases may cooperate to ensure efficient TLS [1] . Furthermore , It has been proposed that the more processive and catalytically efficient pol IV replaces pol V at the replication fork in order to protect the primer terminus from proofreading by the exonuclease-proficient enzymes [43] . To test whether the inhibitory effect on pol V is pol IV-specific , we generated isogenic recA730 lexA ( Def ) ΔumuDC rnhB+/− strains lacking pol II and determined UV-survival of cells expressing wild-type pol V and its variants ( Figure S2 ) . Deletion of pol II had very little effect on UV-resistance of the plasmid expressing strains . In general , in the rnh+ strains the relative UV resistance of Y11A was comparable to that promoted by the vector , pGB2 , and less than that conferred by the F10L plasmid . There was a modest increase in UV-resistance in the ΔrnhB strains ( Figure S2 ) , but this was comparable in the ΔpolB and polB+ strains and certainly much less evident than observed with the ΔdinB/dinB+ ΔrnhB strains ( c . f . Figure S1C and Figure 1C ) . Overall , our findings argue against a possible competition between pol II and pol V during the TLS of UV-induced lesions . To characterize the effect of rNTP processing on UV-resistance and mutagenesis in strains expressing umuC_Y11A , we focused on the generally more UV-resistant recA730 lexA ( Def ) ΔumuDC ΔdinB rnhB+/− strains , rather than the more sensitive ΔrnhA derivatives , where the interpretation of any results might be complicated due to more complex phenotypes involving constitutive and induced stable DNA replication [39] . As shown in Figure 2A , the UV-survival curves of the ΔdinB rnhB+ cells either lacking pol V , or expressing UmuC_Y11A are superimposable . Consistent with the semi-quantitative survival assay , the ΔdinB strain expressing UmuC_Y11A tolerates UV-damage much better in the absence of a functional RNase HII at all UV doses . In contrast , the absence of RNase HII has no effect on UV-resistance of strains either lacking pol V ( pGB2 ) , or expressing wild-type pol V . Next , we compared the levels of UV-induced mutagenesis in the various strains by assaying reversion of the hisG4 ochre allele ( Figure 2B ) . While there was a slight reduction in the level of UV-mutagenesis promoted by wild-type pol V in the ΔrnhB strain compared to the rnh+ strain , we observed an ∼9-fold increase in umuC_Y11A-dependent UV-induced mutagenesis in the ΔrnhB strain compared to the rnhB+ strain ( Figure 2B ) . Our observations therefore indicate that RNase HII plays a major role in preventing Y11A-dependent ribonucleotide-driven mutagenesis in E . coli . The rnhB gene encoding RNase HII is located in a multi-gene operon and is immediately upstream of the dnaE gene encoding the catalytic α-subunit of pol III [44] . To eliminate the possibility that the observed phenotypes of the ΔrnhB allele on Y11A-dependent mutagenesis might be non-specific , due to effects on expression of dnaE , we determined the levels of the α-subunit in isogenic dinB+/− rnhB+/− strains ( Figure S3 ) . In both the dinB+/− cells , we observed an ∼25% reduction in the amount of α-subunit in the ΔrnhB strain compared to the rnhB+ strain . The reduced levels of the α-subunit do not , however , explain the Y11A-dependent increase in UV-resistance and UV-mutagenesis in the ΔrnhB strains , since if reduced levels of the α-subunit allows greater access of pol V to a primer terminus , then we would have also expected to observe a significant increase in wild-type pol V UV-mutagenesis , when in fact , we actually observed a small decrease ( Figure 2B ) Since pol IV appears to inhibit pol V-dependent TLS in vivo , we reconstituted TLS reactions in vitro using a circular vector with a unique T-T CPD [45] , and a radiolabeled primer located five nucleotides 3′ from the CPD . To ensure maximal catalytic activity of both polymerases , the reaction conditions were optimized by including β-sliding processivity clamp , γ clamp-loading complex , and single-stranded DNA binding protein ( SSB ) . The reactions also included a RecA nucleoprotein filament ( RecA* ) , which has no noticeable effect on pol IV , but is required for pol V TLS in vivo [13]–[15] and in vitro [16] , [17] , [46] . The role of RecA* in pol V-catalyzed TLS is to transfer a molecule of RecA and ATP from its 3′-proximal tip to convert a barely active pol V to an activated UmuD′2C-RecA-ATP complex , termed pol V Mut [17] , [46] . All reactions were carried out in a similar manner with some containing a single DNA polymerase , pol IV ( Figure 3 , lanes 2 and 7 ) , wild-type pol V ( lanes 3 and 8 ) , or Y11A_UmuC pol V ( lanes 5 and 10 ) , while in other cases , pol V variants and pol IV were added simultaneously ( lanes 4 , 6 , 9 , and 11 ) . DNA polymerases were used either at equi-molar concentrations ( lanes 4 and 6 ) , or with a 10-fold excess of pol IV over pol V ( lanes 9 and 11 ) . As expected , pol IV by itself was unable to bypass a CPD adduct [18] even when used at elevated levels ( Figure 3 , lanes 2 and 7 ) . The major reaction product observed was located immediately adjacent to the 3′ base of the CPD , with a very small band corresponding to nucleotide incorporation opposite the 3′T of CPD , as previously reported [18] . Although the overall primer extension efficiency of pol V was lower than that of pol IV ( total primer extension by pol V ranged between 15 and 20% , while pol IV , depending on the concentration used , extended 35 or 85% of primers ) , the ability of pol V to replicate past the lesion was substantially greater . For example , ∼70% of primers extended by pol V to the −1 position ( relative to the CPD ) were further extended past the CPD . In contrast , and independent of the polymerase concentration used , only 3% of the primers bypassed the CPD when they were extended by pol IV . TLS catalyzed by wild-type pol V and Y11A pol V was similarly efficient and processive , even though the distribution pattern of products differed ( Figure 3 , lanes 3 , 5 , 8 and 10 , see also [21] ) . When pol IV and pol V were used at ∼ equi-molar concentrations ( lanes 2–6 ) , the extent of lesion bypass catalyzed by wild-type pol V and pol V UmuC_Y11A was unaffected by the presence of pol IV since the amount of reaction products extended past the lesion remained the same . The apparent increase in the proportion of replication products that accumulated opposite the template A immediately 3′ to CPD ( in lanes 4 and 6 compared to lanes 3 and 5 ) , is compensated by the increased proportion of elongated primers suggesting that pol V was unable to replace pol IV at the lesion site . When pol IV was present at ∼10-fold excess , which is roughly equivalent to the in vivo cellular ratio when maximally expressed during SOS-induction [42] , [47] ( lanes 7–11 ) , significant inhibition of pol V-dependent TLS was observed ( compare lanes 9 and 11 with lanes 8 and 10 ) . In addition , the general distribution pattern of reaction products was similar to that observed in the reaction containing only pol IV ( compare lanes 9 and 11 to lane 7 , and the amount of primer elongated past the lesion expressed as a percent of primers elongated to the −1 position , was reduced to 3% , which is equivalent to the results observed in reactions in the presence of pol IV alone ) . The data suggests that under certain SOS-inducing conditions , pol IV may bind to the 3′-primer terminus of nascent DNA strand and thereby prevent access of pol V to the replicating fork thus serving as a cellular “competitive” inhibitor of pol V-dependent TLS at DNA lesions that pol IV itself is unable to bypass . Our previous studies [21] , and those described above ( Figure 1 and Figure 2 ) , indicate that in a strain actively repairing errantly-incorporated ribonucleotides , expression of umuC_Y11A confers minimal UV-resistance compared to wild-type pol V , or other pol V variants ( umuC_F10L or umuC_Y11F ) . This phenotype , can , in part , be explained by the fact that the abundant ribonucleotides target the pol V-generated TLS tract for repair . As shown above , this process is initiated by RNase HII , which nicks the DNA backbone immediately 5′ of the misincorporated ribonucleotide , but the ribonucleotide must subsequently be physically replaced using other repair enzymes/polymerases with a limited ability to traverse UV-induced DNA lesions . To identify proteins involved in ribonucleotide removal , we constructed a series of isogenic recA730 lexA ( Def ) ΔumuDC ΔdinB strains with individual deletions of various DNA repair genes ( unpublished data ) and determined whether or not such an inactivation would lead to an increase in umuC_Y11A-specific UV-resistance , in a similar manner to that observed with defects in rnhB ( Figure 1D and Figure 2 ) . The control , or pol V-encoding plasmids were introduced into these repair deficient strains , and their sensitivity to UV-light was assayed in the semi-quantitative UV survival assay . In most cases , the ability of the particular pol V plasmid to confer UV resistance was the same as shown in Figure 4A , i . e . , the relative UV-sensitivity of each plasmid-expressing strain remained the same . Wild-type pol V was similar to Y11F , and both were better than F10L at promoting UV-survival , while Y11A conferred minimal UV-resistance ( unpublished data ) . However , a markedly different pattern of survival emerged in strains defective for nucleotide excision repair ( NER ) , such as uvrA , which plays a critical role in damage recognition ( Figure 4B ) , or uvrC , an endonuclease which cleaves the phosphodiester bond 3′ of the lesion , ( Figure 4C ) . As expected because of their deficiency in NER , the ΔuvrA and ΔuvrC strains were much more sensitive to UV-light than the isogenic parental strains . As a consequence , while cells shown in Figure 4A were exposed to 40 J/m2 UV-light , the NER deficient cells shown in Figure 4B and 4C were only exposed to 1 J/m2 UV . However , the key observation is that in the NER-deficient strains , umuC_Y11A conferred considerable UV-resistance that was roughly similar to that observed for wild-type pol V and the umuC_Y11F variant ( Figure 4B and 4C ) . Thus , the status of the NER machinery plays a critical role in the survival of cells exposed to DNA damage whilst concomitantly incorporating high levels of ribonucleotides . In addition to facilitating TLS , when expressed in a recA730 lexA ( Def ) background , pol V promotes high levels of spontaneous mutagenesis [15] . This mutagenesis is not the result of TLS of “cryptic” DNA lesions , but rather the ability of pol V to compete with E . coli's other DNA polymerases and gain access to undamaged genomic DNA where its low-fidelity synthesis is manifested as mutagenic events on the E . coli chromosome [48] . We have shown that despite its low sugar and base-substitution fidelity in vitro , when expressed in a recA730 lexA ( Def ) ΔumuDC strain , umuC_Y11A promotes low levels of spontaneous mutagenesis [20] . One obvious explanation , based upon our observations above , is that mutagenesis is limited via the actions of rnhB . However , the strain used in the earlier study also expresses pol IV and while it is believed that pol V and pol IV work together to promote spontaneous mutagenesis [43] , we could not exclude the possibility that in a similar manner to its negative effect on the TLS of CPDs , pol IV might actually block access of the error-prone pol V_Y11A polymerase to undamaged chromosomal DNA . To test this hypothesis , we assayed spontaneous mutagenesis in isogenic recA730 lexA ( Def ) ΔumuDC ΔdinB rnh+/− strains ( Figure 5 ) . Expression of wild-type pol V in the rnhB+ cells resulted in a substantial increase in the number of spontaneously arising His+ revertants compared to the same strain lacking pol V . In the isogenic ΔrnhA strain , there was a considerable ( 3–5-fold ) increase in the number of revertants promoted by pol V and variants , presumably because the increased number of R-loops in the ΔrnhA strain [39] help to hyperactivate the RecA730 protein [49] for its role in pol V-dependent mutagenesis [17] . While there was a 3 . 7-fold increase in the absolute number of Y11A-dependent mutations in the ΔrnhA strain compared to the rnh+ strain , when expressed as a percentage of wild-type pol V-dependent mutagenesis , umuC_Y11A mutagenesis actually decreased from 7 to 5% of the wild-type levels ( Figure 5 ) . In contrast , in the isogenic ΔrnhB strain , the number of umuC_Y11A-dependent revertants increased approximately 4 . 6-fold compared to the rnhB+ strain and reached ∼40% of the level of mutagenesis observed with wild-type pol V ( Figure 5 ) . Our studies therefore show that RNase HII clearly participates in a repair pathway that reduces the accumulation of rNMPs , as well as incorrect dNMPs incorporated into undamaged and damaged DNA by UmuC_Y11A . Based upon its in vitro properties [21] , we expected pol V umuC_Y11A to be as mutagenic , if not more so , than the wild-type pol V , but even in the ΔrnhB strain , Y11A-dependent mutagenesis was less than half of that observed with wild-type pol V ( Figure 5 ) , suggesting that perhaps additional repair pathways act to reduce the mutagenic consequences of rNMPs incorporated by the highly error-prone umuC_Y11A . Indeed , in the isogenic ΔrnhA ΔrnhB strain umuC_Y11A spontaneous mutagenesis increased significantly to ∼72% of the level observed with wild-type pol V ( Figure 5 ) . Thus , although Rnase HI alone does not appear to participate in the removal of ribonucleotides incorporated by umuC_Y11A , in the absence of Rnase HII , where there is likely to be a significant accumulation of ribonucleotides into DNA , Rnase HI helps reduce the mutagenic burden of errant ribonucleotide incorporation into the E . coli genome .
In order to maintain its genomic integrity a cell must protect its DNA from constant assaults coming from different sources . Among these is the attempt to replace the sugar moiety of a nucleotide , which appears to be one of the most persistent potential sources of “damage” . Incorporation of rNMP into the DNA backbone most frequently occurs during DNA replication and repair due to mistakes made by DNA polymerases . Seemingly a harmless event , assuming that the base of the ribonucleotide being incorporated is a correct Watson-Crick pair , it nevertheless can threaten the cell's well-being because the presence of rNMP with a reactive 2′ hydroxyl on the ribose ring makes the DNA strand more susceptible to spontaneous or enzymatic cleavage . It can also lead to a B- to A-form conformational DNA transition and disrupt interactions of DNA-binding proteins thereby compromising various DNA processing pathways . E . coli pol V appears to be one of the least discriminate DNA polymerases . The sugar selectivity of pol V can be significantly improved by an F10L substitution in the catalytic subunit UmuC and vice versa , a Y11A substitution in UmuC significantly reduces the ability of pol V to select a nucleotide with the correct sugar [20] . To prevent the deleterious effects of ribonucleotides incorporated into DNA , E . coli is equipped with enzymes capable of hydrolyzing the phosphodiester bond between ribo- and deoxyribonucleotides , thereby triggering repair pathways leading to removal of rNMPs . In the present study , we show that ribonucleotide-specific endonuclease RNase HII plays an important role in the correction of mistakes made by error-prone pol V . The basic mechanism of ribonucleotide repair appears to be evolutionary conserved as Nick McElhinny et al . , recently reported that RNase H2-dependent repair is necessary for the prevention of replicative stress and genome instability in yeast strains expressing a pol ε variant with compromised sugar selectivity [23] . However , in contrast to other studies demonstrating that deletion of RNase H2 increases spontaneous mutagenesis in yeast strains with wild-type DNA polymerases [25] , [50] , [51] , no increase in spontaneous or UV-induced mutagenesis was observed upon in a ΔrnhB strain expressing wild-type pol V ( Figure 2B and Figure 5 ) . Nevertheless , these data do not imply that RNase HII is not important for correction of pol V-dependent mistakes , but rather suggest that sugar selectivity of the polymerase must be significantly reduced for endonuclease function to be readily detectable . Indeed , the lack of RNase HII caused a significant increase in mutagenesis in strains expressing UmuC_Y11A for which ribonucleotide processing is most important . Therefore , the pathway initiated by RNase HII not only leads to removal of nucleotides with an incorrect sugar , but also to the repair of base substitutions , explaining the low mutability of the rnhB+ strain expressing highly error-prone UmuC_Y11A . While we observed a minimal effect of ΔrnhA alone on the level of umuC_Y11A-dependent spontaneous mutability , there was a dramatic increase in spontaneous mutagenesis in combination with the ΔrnhB allele ( Figure 5 ) . Presumably this is due to the accumulation of ribonucleotides in the absence of Rnase HII , and the propensity of the Y11A variant to catalyze synthesis of polynucleotide chains containing multiple sequential rNMPs [20] . It should also be noted that the role of RNase HII-initiated repair in determining UV-sensitivity of Y11A-expressing cells was most pronounced in ΔdinB strains . We assume that in cells expressing dinB ( Pol IV ) , there is competition for a primer-terminus between pol IV and pol V that limits the extent of the pol V-dependent TLS tract , which in the case of UmuC_Y11A will concomitantly reduce the number of incorporated rNMPs into the genome , and the need for RNase HII-mediated repair ( Figure S1C ) . In a similar vein , since the number of rNMPs incorporated by the wild-type pol V , UmuC_Y11F , and especially UmuC_F10L pol V , is significantly lower than that of UmuC_Y11A , the effect of RNase HII and pol IV on UV-resistance is negligible in the strains expressing these polymerases ( Figure 1 ) . RNase HII-initiated rNMP repair , which at the same time leads to the correction of base substitutions , readily explains the low mutability of strains expressing a pol V variant with an impaired steric gate . In the current study , we found that the low UV-resistance , which is somewhat unexpected for cells equipped with an efficient TLS polymerase such as UmuC_Y11A [20] , is also connected to ribonucleotide incorporation and repair . For example , we show that the sensitivity to UV-light in cells expressing umuC_Y11A is reduced by RNase HII to the level detected in the strain completely lacking pol V . The observation that removal of rNMPs actually diminishes cells viability implies that unlike yeast , in which unrepaired rNMPs lead to genome instability , bacterial cells can tolerate the presence of ribonucleotides in genomic DNA quite well . This assumption is supported by the findings that all the strains defective in rNMPs repair have similar colony size and growth rates independent of the sugar discrimination properties of a pol V variant . However , even assuming that E . coli is able to tolerate the presence of ribonucleotides in its genome , it nevertheless seems counterintuitive that activation of a pathway directed at the removal of rNMPs would reduce cell viability after UV-treatment . In order to explain these observations , we propose the following model: simultaneous attempts of the NER machinery to repair UV-induced lesions on the parental template strand and of the RNase HII-initiated pathway to remove numerous rNMPs from the TLS-tract in the nascent strand , lead to the formation of multiple and persistent DNA double-strand breaks that cause cell death ( Figure 6 ) . Pulse Field Gel Electrophoresis ( PFGE ) and bacterial COMET assays are currently underway to test this hypothesis . When either of these repair pathways is inactivated , UmuC_Y11A confers significant UV resistance ( Figure 1D , Figure 2A , and Figure 4 ) presumably because of its ability to facilitate TLS and in spite of the fact that ribonucleotides are concomitantly incorporated into the E . coli genome . However , the increased survival comes at the steep cost of increased cellular mutagenesis ( Figure 2B , Figure 5 ) . In summary , E . coli utilizes a variety of mechanisms to minimize pol V-dependent ribonucleotide incorporation into its genome . The first line of defense is a competition between pol IV and pol V during TLS that limits the access of the errant pol V to a stalled primer terminus . In the absence of pol IV , pol V can incorporate ribonucleotides , but these are rapidly removed from the genome in an RNase HII-initiated repair pathway . Concurrent with rNTP removal , NER of the UV-lesion results in double-strand breaks leading to cell death . The Rnase HII-mediated repair pathway minimizes both UV-induced and spontaneous mutagenesis to the bacterial chromosome and in its absence , and in the presence of a mutant pol V with a propensity to incorporate polyribonucleotides ( UmuC_Y11A ) , RNase HI serves as a backup to RNase HII to limit the mutagenic consequences of excessive ribonucleotide accumulation into the E . coli genome .
Most of the E . coli K-12 strains used in this study are derivatives of RW584 ( full genotype: recA730 lexA51 ( Def ) ΔumuDC596::ermGT thr-1 araD139 Δ ( gpt-proA ) 62 lacY1 tsx-33 glnV44 galK2 hisG4 rpsL31 xyl-5 mtl-1 argE3 thi-1 sulA211 [52] . All derivatives were made by standard methods of P1 transduction using P1vir [53] ( Table 1 ) . The various alleles were selected by conferring resistance to spectinomycin ( 20 µg/ml ) , zeocin ( 25 µg/ml ) , chloramphenicol ( 20 µg/ml ) and kanamycin ( 50 µg/ml ) respectively and subsequently confirmed by PCR [54]–[56] . The low-copy-number plasmids used for expression of UmuC variants are derived from pGB2 [57] . Spectinomycin resistant plasmids pRW134 , pJM964 , pJM963 , and pJM952 encode E . coli UmuD′ along with wild-type UmuC or F10L , Y11A , and Y11F variants , respectively and the proteins are expressed from the native umu promoter [20] . Ampicillin resistant derivatives were generated by replacing the BspHI-BspHI vector fragment encoding resistance to spectinomycin with a BspHI-BspHI fragment from pET22b+ encoding resistance to ampicillin . Bacteria harboring plasmids were grown in LB media containing appropriate antibiotics ( 50 µg/ml spectinomycin , or 100 µg/ml ampicillin ) . Cells were grown overnight at 37°C in Luria–Bertani ( LB ) plus spectinomycin . The next morning , the cultures were sequentially diluted 10-fold in eppendorf tubes containing SM buffer [58] . 10 µl of each serial dilution was then spotted on the surface of a 12×8 cm rectangular LB agar plate ( Nunc , ThermoFisher ) . The plates were irradiated with UV light ( 254 nm ) and incubated overnight at 37°C . Images of the irradiated plates/cultures were captured with a FluorChem HD2 imaging system ( Alpha Innotec ) . Cells transformed with the vector plasmid , pGB2 , or one of the low-copy number plasmids expressing wild-type pol V or UmuC variants were grown overnight at 37°C in LB media plus spectinomycin . The next day , cultures were diluted 100-fold into 10 ml fresh media and grown at 37°C until they reach an OD600 of 0 . 1 ( roughly 3 hrs ) . Cells were centrifuged and resuspended in an equal volume of SM buffer [58] and transferred to a petri dish . Aliquots were removed and saved as the unirradiated control for the experiments . The culture was irradiated at a UV fluence of ∼2 J/m2 per second and aliquots removed at 20 J/m2 increments . At least three independent cultures were assayed for each strain and all experiments were performed under yellow light to avoid unwanted photoreactivation . For UV survival assays , appropriate serial dilutions ( based upon trial assays ) were plated on LB agar plates containing spectinomycin and incubated overnight at 37°C . The surviving fraction was determined by dividing the number of viable cells exposed to UV by the number of viable cells in the unirradiated culture . Error bars represent the standard error of the mean ( SEM ) . To determine the number of spontaneously arising histidine mutants on the plate , as well as UV-induced mutants , the unirradiated cell culture was seeded on the Davis and Mingioli minimal agar plates [59] plus glucose ( 0 . 4% wt/vol ) ; agar ( 1 . 0% wt/vol ) ; proline , threonine , valine , leucine , and isoleucine ( all at 100 µg/ml ) ; thiamine ( 0 . 25 µg/ml ) ; and either no histidine , or histidine ( 1 µg/ml ) . On the plates containing no histidine , only pre-existing His+ mutants grew to form colonies . However , on the plates containing 1 µg/ml histidine , 100–200 His− cells are able to grow on the limiting amount of histidine , so that a viable cell count can be obtained under the exact same conditions where His+ mutant arise . When ∼4×107 bacteria were seeded , they grew to form a lawn , concomitantly exhausting the low level of histidine . Spontaneously arising His+ mutants grew up through the lawn and were counted after 4 days incubation at 37°C . To determine the extent of UV-induced mutagenesis , cells that had been irradiated with 20 J/m2 UV were used for analysis . This UV dose was chosen since even the UV-sensitive strains exhibited minimal cell killing at this exposure . These conditions therefore provide a window to observe UV-induced mutagenesis without the complications associated with differential levels of cell killing in the various strains . The UV-induced mutation frequencies were calculated as previously described [60] . This equation not only takes into account the number of mutants spontaneously arising on the low histidine plates , but also any effect of reduced cell viability on the number of pre-existing His+ mutants in the culture . The data reported in Figure 4 represent the average number of His+ mutants from 3 separate experiments ( ± standard error of the mean [SEM] ) . Cells were grown overnight at 37°C in LB plus appropriate antibiotics . The next morning , cultures were diluted 1∶100 in fresh LB , plus antibiotics and grown with aeration at 37°C until they reached an OD600 of ∼0 . 5 . Cultures were harvested by centrifugation , resuspended in 1× SDS sample buffer ( 50 mM Tris-HCl [pH 6 . 8] , 10% glycerol , 2 . 3% sodium dodecyl sulfate [SDS] , 0 . 1% bromophenol blue , 10 mM dithiothreitol ) , and immediately frozen in dry ice . Cells were lysed by multiple freeze-thaw cycles and boiled for 5 mins at 95–100°C . Extracts were immediately applied to a 15% SDS-PAGE gel . After separation , proteins were transferred to an Immobilon-P membrane ( Millipore ) using standard Western blot protocols . The membrane was incubated overnight with a 1∶1000 dilution of mouse monoclonal antibodies raised against the α-subunit of pol III ( kindly provided by Charles McHenry , University of Colorado ) . The membrane was then incubated with secondary anti-mouse alkaline phosphatase conjugated antibodies and visualized using the CSPD-Western light assay ( Applied Biosystems ) . Pictures were captured on a FluorChem HD2 imaging system ( Alpha Innotec ) . Wild-type pol V , the UmuC_Y11A variant and pol IV , β-clamp and γ-complex were purified as previously described [46] . pSOcpd plasmid , containing a unique CPD adduct , was also constructed as previously described [45] . All oligonucleotides were synthesized by Lofstrand Laboratories ( Gaithersburg , MD ) and gel purified prior to use . 5′-32P labeled M13-TT ( 5′ – GAT-CGA-TGG-TAC-GGA-CG ) primer was annealed to pSOcpd ssDNA templates at a 1 . 5∶1 molar ratio by heating in an annealing buffer ( 50 mM Tris-HCl ( pH 8 ) , 5 mM MgCl2 , 50 µg/ml BSA , 1 . 42 mM 2-mercaptoethanol ) for 10 min at 100°C followed by slow cooling to room temperature . 4 mM RecA ( New England Biolabs , Ipswich , MA ) was incubated with 0 . 25 µM 48-mer single-stranded oligonucleotide in the presence of 1 mM adenosine 5′[γ-thio]triphosphate ( ATPγS , Biolog Life Science Institute , Bremen , Germany ) in the 1× reaction buffer [20 mM Tris-HCl pH 7 . 5 , 8 mM MgCl2 , 8 mM DTT , 80 µg/ml BSA , 4% glycerol] at 37°C for 5 min to form RecA nucleoprotein filament on ssDNA ( RecA* ) . Reaction mixture containing 1 mM ATP , 50 µM dNTPs , 2 nM DNA templates , 100 nM SSB ( Epicentre Biotechnologies , Madison WI ) , 50 nM β clamp , and 5 nM γ complex in the 1× reaction buffer was preincubated for 3 min at 37°C . Purified pol V variants ( 80 nM ) were first combined with RecA* ( 0 . 25 µM ) to form pol V Mut and then added to the reaction mixture . When indicated , Pol V was either substituted , or mixed with indicated amounts of purified pol IV . Reactions were incubated at 37°C for 20 mins and terminated by adding 10 ml of 2× loading buffer [97% formamide , 10 mM EDTA , 0 . 1% xylene cyanol , 0 . 1% bromophenol blue] . The products were heat-denatured and resolved by denaturing PAGE ( 8 M urea , 15% acrylamide ) , followed by visualization on a Fuji image analyzer FLA-5100 . | E . coli pol V , a complex formed by umuC and umuD gene products , is a “founding” member of the Y-family of DNA polymerases that have been identified in all domains of life . The primary cellular function of Y-family polymerases is the replication of damaged DNA . We discovered that pol V is characterized by unusually poor sugar selectivity and readily incorporates ribonucleotides into DNA . The extent of ribonucleotide incorporation can be modulated by substituting amino acids at , or adjacent to , the “steric gate” in the active site of the DNA polymerase . Principally , by taking a genetic approach , supported by in vitro biochemical data , we show that SOS mutations triggered by pol V–catalyzed errant ribonucleotide incorporation are kept in check by the action of nucleotide excision repair operating in conjunction with RNase HII and , unexpectedly , by another error-prone Y-family polymerase , pol IV . Our studies provide new insight into a growing field investigating the processing of ribonucleotides that are misincorporated by DNA polymerases and how these basic mechanisms contribute to cell survival and mutagenesis . | [
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| 2012 | Mechanisms Employed by Escherichia coli to Prevent Ribonucleotide Incorporation into Genomic DNA by Pol V |
Collectively classified as white-rot fungi , certain basidiomycetes efficiently degrade the major structural polymers of wood cell walls . A small subset of these Agaricomycetes , exemplified by Phlebiopsis gigantea , is capable of colonizing freshly exposed conifer sapwood despite its high content of extractives , which retards the establishment of other fungal species . The mechanism ( s ) by which P . gigantea tolerates and metabolizes resinous compounds have not been explored . Here , we report the annotated P . gigantea genome and compare profiles of its transcriptome and secretome when cultured on fresh-cut versus solvent-extracted loblolly pine wood . The P . gigantea genome contains a conventional repertoire of hydrolase genes involved in cellulose/hemicellulose degradation , whose patterns of expression were relatively unperturbed by the absence of extractives . The expression of genes typically ascribed to lignin degradation was also largely unaffected . In contrast , genes likely involved in the transformation and detoxification of wood extractives were highly induced in its presence . Their products included an ABC transporter , lipases , cytochrome P450s , glutathione S-transferase and aldehyde dehydrogenase . Other regulated genes of unknown function and several constitutively expressed genes are also likely involved in P . gigantea's extractives metabolism . These results contribute to our fundamental understanding of pioneer colonization of conifer wood and provide insight into the diverse chemistries employed by fungi in carbon cycling processes .
The most abundant source of terrestrial carbon is plant biomass , composed primarily of cellulose , hemicellulose , and lignin . Numerous microbes utilize cellulose and hemicellulose , but a much smaller group of filamentous fungi has the capacity to degrade lignin , the most recalcitrant component of plant cell walls . Uniquely , such ‘white-rot’ fungi efficiently depolymerize lignin to access cell wall carbohydrates for carbon and energy sources . As such , white-rot fungi play a key role in the carbon cycle . White-rot basidiomycetes may differ in their substrate preference and morphological patterns of decay ( for review see [1] , [2] ) . The majority of lignin-degrading fungi , including Phanerochaete chrysosporium and Ceriporiopsis subvermispora , are unable to colonize freshly cut wood unless inhibitory compounds ( extractives ) are removed or transformed [2]–[5] . A few basidiomycetes , including Phlebiopsis gigantea , are pioneer colonizers of softwood because they tolerate and utilize resinous extractives ( e . g . , resin acids , triglycerides , long chain fatty acids , see Figure 1 ) which cause pitch deposits in paper pulp manufacturing [6] . It is this unusual capability that also led to the development of P . gigantea as a biocontrol agent against subsequent colonization of cut stumps by the root rot pathogen Heterobasidium annosum sensu lato ( now considered several species ) [7] , [8] and of harvested wood by blue stain fungi [9] , [10] . It seems likely that when applied to freshly cut wood , P . gigantea is able to rapidly metabolize accessible extractives and hemicellulose . As the hyphae continue to invade tracheids and ray parenchyma cells , the more recalcitrant cell wall polymers ( cellulose , lignin; Figure 1 ) are eroded . Little is known of how some white-rot fungi degrade conifer extractives [11] , [12] or interact with other fungi such as H . annosum [13] . White-rot fungi degrade major cell wall polymers through concerted action of hydrolytic and oxidative enzymes ( reviewed in [14] , [15] ) . Cellulose is attacked by a combination of exo-cellobiohydrolases and endoglucanases assigned to glycoside hydrolase families GH5 , GH6 , GH7 and possibly GH9 , GH12 , GH44 and GH45 [16] , [17] . In addition to these hydrolases , recent evidence strongly supports the involvement of lytic polysaccharide monooxygenases ( LPMOs ) in cellulose degradation [18]–[20] . Lignin degradation is catalyzed by an array of oxidative enzymes , especially lignin peroxidase ( LiP ) , manganese peroxidase ( MnP ) and versatile peroxidase ( VP ) belonging to class II of the plant-fungal-prokaryotic peroxidase superfamily . Recent genome investigations reveal that all efficient lignin degraders possess some combination of these class II ligninolytic peroxidases [21] , [22] . In P . gigantea , four MnP sequences were previously identified [23] . In addition to peroxidases , laccases have been implicated in lignin degradation [24]–[26] . To date , multiple laccase isozymes and/or the corresponding genes have been characterized from most white-rot fungi except P . chrysosporium , an efficient lignocellulose degrader that lacks such enzymes [27]–[29] . The mechanism ( s ) by which laccases might degrade lignin remain unclear as the enzyme lacks sufficient oxidation potential to cleave non-phenolic linkages within the polymer . Interestingly , laccase activity has not been reported in P . gigantea . Additional ‘auxiliary activities’ [30] commonly ascribed to ligninolytic systems include extracellular enzymes capable of generating H2O2 . These enzymes may be physiologically coupled to peroxidases . Among them , aryl-alcohol oxidase ( AAO ) , methanol oxidase ( MOX ) , pyranose 2-oxidase ( P2O ) , and copper radical oxidases ( such as glyoxal oxidase , GLX ) have been extensively studied . With the exception of P2O [31] , none of these activities have been reported in P . gigantea cultures . In short , the repertoire of extracellular enzymes produced by P . gigantea is largely unknown , and its mechanism ( s ) for cell wall degradation remain unexplored . Beyond extracellular systems , the complete degradation of lignin requires many intracellular enzymes for the complete mineralization of monomers to CO2 and H2O . Examples of enzymes that have been characterized from P . chrysosporium include cytochromes P450 ( CYPs ) [32]–[34] , glutathione transferases [35] , and aryl alcohol dehydrogenase ( AAD ) [36] . The role of such enzymes in P . gigantea , if any , is unknown . Herein , we report analysis of the P . gigantea draft genome . Gene annotation , transcriptome analyses and secretome profiles identified numerous genes involved in lignocellulose degradation and in the metabolism of conifer extractives .
Following an assessment of wood decay properties ( Figure 2 ) , P . gigantea single basidiospore strain 5–6 was selected for sequencing using Illumina reads assembled with AllPathsLG . Genome size was estimated to be approximately 30 Mbp ( Text S1 ) , somewhat lower than closely related members of the ‘Phlebia clade’ [23] , [37] such as C . subvermispora ( 39 Mbp ) and P . chrysosporium ( 35 Mbp ) [22] , [27] . Aided by 17 , 915 mapped EST clusters , the JGI annotation pipeline predicted 11 , 891 genes . Proteins were assigned to 6412 , 5615 , 6932 and 2253 KOG categories , GO terms , pfam domains and EC numbers , respectively . Significant synteny with P . chrysosporium was observed ( Figure S1 ) . Detailed information on the assembly and annotations is available via the JGI portal MycoCosm [38] . Principal component analysis ( PCA ) , based on 73 and 12 families of carbohydrate active enzymes ( CAZys , [16] ) and auxiliary activities ( AAs ) , [30] ) , respectively , clustered P . gigantea with other efficient lignin degraders ( [39] , Figures 3A and S2 ) . Gene numbers were extracted from 21 fungal genomes and excluded genes encoding putative GMC oxidases such as methanol oxidase , alcohol oxidase and glucose oxidase ( Dataset S1 ) . Highest contribution of PC1 ( 50% of variance separating white-rot and brown-rot fungi ) and PC2 ( 13 . 0% of variance ) ) values were those genes associated with degradation of plant cell wall polysaccharides and lignin , respectively ( Text S1 ) . Hierarchical clustering analysis with this dataset also categorized P . gigantea into a clade of white-rot fungi that included the polypore P . chrysosporium . The precise number and distribution of P . gigantea genes likely involved in lignocellulose degradation were similar , but not identical , to other polypores such as P . chrysosporium and C . subvermispora ( Figure 4 ) . Like P . chrysosporium and Phanerochaete flavido-alba , P . gigantea had no laccase sensu stricto genes . Interestingly , while both P . gigantea and the white-rot Russulales H . annosum are adapted to colonization of conifers , significant numbers of laccase sensu stricto genes were only observed in H . annosum ( Figure 4 ) . This important conifer pathogen also lacked GLX , LiP and representatives of GH5 subfamiles 15 and 31 . With regard to hemicellulose degradation , the genomes of conifer-adapted P . gigantea and H . annosum revealed increased numbers of genes involved in pectin degradation such as GH28 polygalacturonase , CE8 pectin methylesterase and CE12 rhamnogalacturonan acetylesterase ( Figure 4 ) . The major hemicellulose of conifer is galactoglucomannan ( [40] , Figure 1 ) but , in the case of mannan degradation , no significant increase in genes encoding GH2 β-mannosidase , GH5_7 endo-mannanase and GH27 α-galactosidase was observed relative to other wood decay fungi ( Figure 4 ) . Similarly , no significant differences in the number of genes involved in arabinoglucuronoxylan hydrolysis were identified , except for two transcriptionally convergent GH11 genes present in P . gigantea ( Text S1 ) . Encoding putative endo-1 , 4-β-xylanases , wood decay fungi typically harbor one or no GH11 genes . Auricularia delicata is another exception with three of these endoxylanases . Also unusual among white-rot fungi , none of the P . gigantea protein models were assigned to GH95 ( Dataset S1 ) . This family includes 1 , 2-α-fucosidases that hydrolyze the α-Fuc-1 , 2-Gal linkages in plant xyloglucans . The P . gigantea genome includes representatives for all the peroxidase families reported in basidiomycetes , including LiP , MnP , heme-thiolate peroxidases , and dye-decolorizing type peroxidases ( DyP ) , with the only exception of VP ( Text S1; Figures S8–S13 ) . MnP gene expansion is similar to that found in the C . subvermispora and H . annosum genomes . Beyond class II peroxidases and multicopper oxidases ( MCOs ) , genes encoding auxiliary enzymes involved in ligninolysis were also found such as GMC oxidoreductases ( Figures S14–S19; Table S5 ) and copper radical oxidases ( CRO , Figure 4; Table S4 ) . Among the latter group , GLX is coupled to P . chrysosporium LiPs via extracellular H2O2 generation [41] . Consistent with this physiological connection , the P . gigantea genome features both GLX- and LiP-encoding genes . GMC genes encoding putative AAO , MOX and glucose oxidase ( GOX ) may also be involved in H2O2 production by oxidation of low molecular weight aliphatic and aromatic alcohols . The P2O gene ( protein model Phlgi1_130349 ) lies immediately adjacent to a putative pyranosone dehydratase ( Phlgi1_16096 ) gene . This arrangement is conserved in several wood decay fungi and , in addition to peroxide generation , suggests a route for conversion of glucose to the pyrone antibiotic , cortalcerone [42] , [43] . Genes encoding AAD , members of the zinc-type alcohol dehydrogenase superfamily [44] , are also abundant in P . gigantea . Relatively few genes were predicted to encode CYPs which are generally considered important in the intracellular metabolism of lignin derivatives and related aromatic compounds ( Figure S19; Dataset S2 ) . The repertoire of P . gigantea genes contrasts sharply with that of brown-rot polypores , such as Postia placenta [45] , which lack ligninolytic class II peroxidases , cellobiohydrolases ( GH6 , GH7 ) , and endoglucanases fused to cellulose binding modules [21] , [46] ( Figure 4 ) . Unlike P . gigantea and other white-rot fungi , brown-rot fungi often lack genes encoding cellobiose dehydrogenase ( CDH ) and have relatively few lytic polysaccharide monooxygenase genes ( LPMOs ) . Formerly classified as GH61 ‘hydrolases’ , the LPMOs are now known to be copper-dependent monooxygenases [18]–[20] capable of enhancing cellulose attack by CDH and cellobiohydrolase ( CBH ) [47] , [48] . With the exception of Gloeophyllum trabeum , genes encoding GH74 enzymes have not been found in brown-rot fungi . Two such xyloglucanase genes were identified in P . gigantea ( Text S1 ) . In contrast to analysis of genes involved in lignocellulose degradation ( Figure 3A ) , white-rot and brown-rot fungi were not clearly separated by principal component analysis of 14 enzymes involved in lipid metabolism ( Figures 3B and S3 ) . However , P . gigantea was grouped near B . adusta and P . carnosa . These associations seem in line with the preferential colonization of softwood substrates by P . carnosa [49] and with the efficient degradation of conifer extractives by B . adusta culture supernatants [50] . The highest contribution to PC1 ( 26 . 0% variance ) and PC2 ( 6 . 8% variance ) were aldehyde dehydrogenase and long chain fatty acid CoA ligase , respectively ( Figures 3A and S3 , Text S1 ) . Also potentially involved in intracellular lipid metabolism , CYP52 and CYP505 clans of cytochrome P450s are associated with degradation of fatty acids and alkanes . Relative to other white-rot fungi , P . gigantea had a slightly greater number of CYP52-encoding genes whereas CYP505 gene numbers were similar ( Figure 4; Dataset S1; Figures S31 , S32; Tables S13–S15 ) . P . gigantea also diverges from other Agaricomycetes with respect to genes encoding proteins that are more distantly connected to lignocellulose degradation , including hydrophobins ( Figures S33 and S34; Tables S17–S19 ) , transporters ( Table S20 ) and non laccase MCOs ( Figure S20 ) . Detailed analyses are provided for CAZys ( Tables S7–S10; Figures S22–S30; Dataset S1 ) , peroxidases ( Figures S8–S13 ) , auxiliary proteins , cytochrome P450s ( Figures S31–S32; Table S13–S15 ) , potential regulatory genes ( Figures S4–S7; Tables S3 , S11–S12 ) and genes involved in secondary metabolite synthesis ( Table S16 ) . Transcript levels were determined in cultures in which the sole carbon source was glucose ( Glc ) , freshly harvested loblolly pine wood ( Pinus taeda; LP ) extracted with acetone ( ELP ) , or freshly harvested but not extracted loblolly pine wood ( NELP ) ( Text S1 ) . GC-MS analysis [51] identified the major extract components as resin acids ( 46% ) , triglycerides ( 13% ) and fatty acids ( 11% ) ( Text S1; Figure S35; Table S21 ) . Excluding genes with relatively low transcript levels ( RPKM values <10 ) in LP-containing media , transcripts of 187 genes were increased>2-fold ( p<0 . 05 ) in NELP or ELP relative to Glc . Of those Glc-derived transcripts with RPKM values>10 , 146 genes had higher transcripts in Glc relative to NELP or ELP ( Figure 5; Dataset S2 ) . Mass spectrometry ( nanoLC-MS/MS ) identified extracellular peptides corresponding to a total of 319 gene products in NELP and ELP cultures ( Dataset S2 ) . Most proteins were observed in both NELP and ELP culture filtrates , which contained 294 and 268 proteins , respectively . Approximate protein abundance , expressed as the exponentially modified protein abundance index ( emPAI ) [52] , varied substantially within samples . As expected , gene products with predicted secretion signals and high transcript levels were often detected . Other detected proteins ( e . g . MOX model Phlgi1_120749; [53] ) may be loosely associated with cell walls and/or secreted via ‘non-classical’ mechanisms ( [54]; www . cbs . dtu . dk/services/SecretomeP ) . Still other peptides correspond to true intracellular proteins released by cell lysis , e . g . ribosomal proteins ( Dataset S2 ) . Glycoside hydrolase gene expression was heavily influenced by media composition with transcripts corresponding to 76 genes increasing>2-fold in NELP- or ELP-containing media relative to glucose medium ( Figure 6 ) . Some of these genes were highly expressed with RPKM values well over 100 . For example , transcript and peptide levels matching GH7 cellobiohydrolase ( CBH1; model Phlgi1_34136 ) were among the ten most highly expressed genes ( Table 1 ) . Indicative of a complete cellulolytic system , this CBH1 was accompanied by upregulated transcripts and extracellular proteins corresponding to another CBH1 ( Phlgi1_13298 ) , a GH6 family member CBH2 ( Phlgi1_17701 ) and GH5_5 β-1 , 4 endoglucanases ( EGs; Phlgi1_86144 , Phlgi1_84111 ) , all of which feature a family 1 carbohydrate binding module ( CBM1 ) . Also highly expressed were putative β-glucosidases ( Phlgi1_127564 , Phlgi1_18210 ) and a GH12 ( Phlgi1_34479 ) . Other glycoside hydrolases likely involved in degradation of cell wall hemicelluloses include GH5_7 endomannanases ( Phlgi1_97727 , Phlgi1_110296 ) , a GH74 xyloglucanase ( Phlgi1_98770 ) , a GH27 α-galactosidase ( Phlgi1_72848 ) and a GH10 endoxylanase ( Phlgi1_85016 ) . Expression of oxidative enzymes implicated in lignocellulose degradation was also influenced by growth on LP-media ( NELP or ELP ) relative to Glc-containing media . Transcripts corresponding to five LPMO-encoding genes showed significant regulation ( P<0 . 01 ) in LP-medium , and three LPMO proteins were detected ( Phlgi1_227588 , Phlgi1_227560 , Phlgi1_37310 ) . An AAD-like oxidoreductase ( Phlgi1_30343 ) , possibly involved in the transformation of lignin metabolites , was also upregulated . However , we did not observe high expression of class II peroxidases under the conditions tested ( Dataset S2 ) . On the other hand , a DyP ( Phlgi1_85295 ) was significantly upregulated in certain LP-containing media ( Table 1 ) . The importance of these peroxidases is further supported by the high protein levels of another DyP , Phlgi1_122124 . Specifically , the latter protein showed emPAI values>17 after 5 days growth on LP media and , relative to Glc medium , its transcript ratios were>5-fold higher ( p<0 . 04 ) ( Dataset S2 ) . High DyP gene expression has been observed in white-rot fungi Trametes versicolor and Dichomitus squalens [21] , but no genes for these proteins are present in P . chrysosporium and C . subvermispora ( Figure 4 ) . The P . gigantea DyP ( Phlgi1_122124 ) was also abundant in media containing microcrystalline cellulose ( Avicel ) as the sole carbon source ( Dataset S2 ) . To identify enzymes involved in tolerance to and/or degradation of extractives , comparisons were made of gene expression in ground loblolly pine wood that had been extensively extracted with acetone ( ELP ) versus non-extracted loblolly pine wood ( NELP ) ( Figure 7A ) . In general , this treatment had little impact on gene expression . For example , glycoside hydrolase transcript and protein patterns showed only minor differences ( Figure 8 ) . Nevertheless , transcripts corresponding to 22 genes showed significantly increased levels ( >4-fold; p<0 . 01 ) in NELP relative to ELP ( Figure 7B; Table 2 ) . Of particular interest were genes potentially involved in metabolism of resin acids ( e . g . CYPs; [55] ) , in altering the accessibility of cell wall components ( e . g . , an endoxylanase ) , and in regulating gene expression ( e . g . proteins containing putative Zn finger domains or HMG-Box transcription factors ) . Integration of transcript profiles of genes involved in intracellular lipid and oxalate metabolism , together with TCA and glyoxylate cycles , strongly supports a central role for β-oxidation in triglyceride and terpenoid transformation by P . gigantea ( Figure 9 ) . Relaxing the transcript fold-change threshold ( >2-fold; p<0 . 01 ) and focusing on mass spectrometry-identified proteins revealed 14 additional genes potentially involved in metabolism and/or tolerance to loblolly pine wood extractives ( Table 3 ) . Among these extract-induced genes , lipases Phlgi1_19028 and Phlgi1_36659 likely hydrolyze the significant levels of triglycerides . The substrate specificity of aldehyde dehydrogenases such as Phlgi1_115040 is difficult to assess based on sequence , although several have been implicated in the degradation of pine wood resins by bacteria [56] . Secretome patterns in media containing microcrystalline cellulose ( Avicel ) as sole carbon source generally supported the importance of the same proteins in the metabolism of pine wood extractives ( Table 3 , Dataset S2 ) . Specifically , lipases Phlgi1_19028 and Phlgi1_36659 and aldehyde dehydrogenase Phlg1_115040 were more abundant in loblolly pine wood and in Avicel media relative to the same media without extractives . The role of peroxiredoxin ( Phlgi1_95619 ) and glutathione S-transferase ( Phlgi1_113065 ) are less clear , but transformations involving H2O2 reduction and glutathione conjugation are possible . A single MCO ( Phlgi1_129839 ) and its corresponding transcripts , were observed to be upregulated in ELP relative to NELP . Although lacking the L2 signature common to laccases [57] , the MCO4 protein may have iron oxidase activity provided that an imperfectly aligned Glu residue serves in catalysis ( Text S1; Figures S20 and S21; Table S6 ) .
The distinctive repertoire and regulation of P . gigantea genes underlie a unique and efficient system for degrading all components of conifer sapwood . Transcriptome and proteome analyses demonstrate an active system of hydrolases and LPMOs involved in the complete deconstruction of cellulose and hemicellulose . The overall enzymatic strategy is therefore similar to most cellulolytic microbes , but unlike closely related brown-rot decay Agaricomycetes such as P . placenta . With regard to ligninolysis , key genes were identified including LiPs , MnPs , CROs and GMC oxidoreductases . As in P . chrysosporium , the presence of both LiP- and GLX-encoding genes is consistent with a close physiological connection involving peroxide generation [41] . We also annotated non-class II peroxidases HTPs and DyPs some of which have been implicated in metabolism of lignin derivatives [58] , [59] . The distribution and expression of DyP-encoding genes are notable; with no genes present in P . chrysosporium and C . subvermispora but several highly expressed genes in T . versicolor , D . squalens [21] and P . gigantea ( Table 2 ) . Physiological roles of DyP are likely diverse , but oxidation of lignin-related aromatic compounds has been demonstrated [59] . In addition to lignin , oxidative mechanisms likely play a central role in P . gigantea cellulose attack . Of 15 LPMO-encoding genes , transcripts of six genes were regulated ( >2-fold; p<0 . 01 ) and peptides corresponding to three were unambiguously identified in NELP- or ELP-containing media . Our inability to detect any LPMO proteins in Avicel media ( Dataset S2 ) suggests induction by substrates other than cellulose [60] . Beyond this , the CDH gene was highly expressed ( transcripts and protein ) in LP media . The observed coordinate expression of CDH and LPMO may reflect oxidative ‘boosting’ as recently demonstrated [19] , [20] , [47] , [61] . However , we did not detect elevated transcripts or peptides corresponding to the two P . gigantea aldose 1-epimerase genes even though these were previously observed in culture filtrates of various white-rot fungi [21] , [62] , including Bjerkandera adusta , Ganoderma sp , and Phlebia brevispora [17] . Thus , it seems unlikely that enzymatic conversion of oligosaccharides to their β-anomers is necessary for efficient CDH catalysis . Softwood hemicellulose composition typically includes 15-20% galactoglucomannan while hardwoods contain 15–30% glucuronoxylan [40] . Consistent with an adaption to conifer hemicellulose , GH5_7 β-mannanases were highly expressed in both NELP and ELP cultures , together with a GH27 α-galactosidase ( Table 1 ) . GH11 endoxylanase and CE carbohydrate esterase peptides were also detected in the pine wood-containing media , but not in Avicel cultures ( Dataset S2 ) . In aggregate , these results demonstrate P . gigantea adaptation to conifer hemicellulose degradation . P . gigantea's gene expression patterns reveal multiple strategies for overcoming the challenging composition of resinous sapwood . Tolerance to monoterpenes may be mediated in part by a putative ABC efflux transporter ( Phlbi1_130987 , Figure 9 ) . Of the 51 ABC proteins of P . gigantea , this protein is most closely related to the GcABC-G1 gene of the ascomycete Grosmannia clavigera , a pathogen of Pinus contorta [63] . The GcABC-G1 gene is upregulated in response to various terpenes and appears to be a key element against the host defenses . Consistent with a similar function , our analysis showed the P . gigantea homolog to be upregulated>4 . 9-fold ( p = 0 . 02 ) in NELP relative to ELP media ( Dataset S2 ) . Other transcripts accumulating in NELP-derived mycelia included three CYPs ( Table 2 ) potentially involved in the hydroxylation of diterpenoids and related resin acids [55] . Differential regulation also implicates glutathione S-transferase , aldehyde dehydrogenase and peroxiredoxin in the transformation and detoxification of extractives ( Table 2 ) . Three putative transcription regulators were similarly regulated ( Table 3 ) . Aldehyde dehydrogenase- and AAD-encoding genes , some of which are upregulated in P . gigantea LP cultures relative to Glc cultures ( Tables 1 ) , are induced by aromatic compounds in P . chrysosporium [64] , [65] . Predicted to degrade triglycerides , a total of nine lipase-encoding genes were identified in the P . gigantea genome and four of these were upregulated>2-fold ( p<0 . 01 ) in LP media compared to Glc medium ( Dataset S2 ) . Two lipases displayed similar patterns of transcript and protein upregulation on NELP relative to ELP ( Table 3 ) , and the pine wood extractive also induced accumulation of these lipases in Avicel media ( Table 3 ) . Further metabolism of triglycerides is uncertain , although a putative glycerol uptake facilitator ( Phlbi1_99331 ) was highly expressed ( RPKM value of 2532 ) and significantly ( p<0 . 02 ) upregulated ( 2 . 1-fold ) in NELP compared to ELP ( Dataset S2 ) . Fatty acids activation and β-oxidation can be inferred by the expression of fatty acid CoA ligase ( Phlgi1_107548 , Phlgi1_126556 , Phlgi1_89325 ) , β-ketothiolase ( Phlgi1_27649 , Phlgi1_130767 ) , and fatty acid desaturase ( Phlgi1_100083 , Phlgi1_115799 ) . Upregulation of a mitochondrial malate dehydrogenase ( Phlgi1_22176 , Table 3 ) , together with relatively high transcript levels of other TCA cycle components ( citrate synthases Phlgi1_126205 , Phlgi1_100215; 2-oxoglutarate dehydrogenase , Phlgi1_126652 ) may complete fatty acid oxidation . In this connection , we also observed high expression of isocitrate lyase ( Phlgi1_21482 , Phlgi1_93159 ) and malate synthase ( Phlgi1_27815 ) , which partially explain oxalate accumulation [66] and strongly support an active glyoxylate shunt [45] , [67] ( Figure 9 ) . Upregulation of glycoside hydrolases , transcription factors , cyclophilins , ATP synthase and ribonuclease may also reflect broad shifts in metabolism or reduced accessibility of the unextracted substrate ( Tables 2 and 3 ) . Beyond genetic regulation , certain constitutively expressed genes are also likely involved in the degradation of all plant cell wall components , including complex resins and triglycerides . For example MOX ( Phlgi1_120749 ) is among the most abundant transcripts in both NELP and ELP ( Dataset S2 ) , suggesting an important role in H2O2 production associated with lignin demethylation [53] . Extracellular peroxide generation is key to peroxidase activity , and MOX fulfills this role along with CRO , AAO , and P2O . Along these lines , we also observed high extracellular protein levels of DyP ( Phlgi1_122124 ) under all culture conditions . Most problematic , many P . gigantea genes and proteins exhibited little or no homology to NCBI NR or Swiss-Prot entries . Some of these ‘hypothetical’ or ‘uncharacterized’ proteins are undoubtedly important , particularly those that are highly expressed , regulated and/or secreted . For example , of 92 genes upregulated ( >2-fold; p<0 . 01 ) in NELP relative to ELP , 51 were designated as hypothetical ( Table 2; Dataset S2 ) . Three of these featured predicted secretion signals and peptides were detected in one case . In the absence of biochemical characterization and/or genetic evidence , assigning function to these genes represents a major undertaking . Nevertheless , high throughput transcript and secretome profiling substantially filtered the number of potential targets from a genome-wide estimate of 4744 ‘hypothetical’ genes to the more manageable numbers reported here . More broadly , the results advance understanding of the early and exclusive colonization of coniferous wood by P . gigantea and also provide a framework for developing effective wood protection strategies , improving biocontrol agents and identifying useful enzymes [6] , [9] , [10] .
Wood wafers ( 1 cm by 1 cm by 2 mm ) were cut from the sapwood of aspen ( Populus tremuloides ) , pine ( P . taeda ) and spruce ( Picea glauca ) and sterilized by autoclaving . Following inoculation by contact with mycelium growing on malt extract agar ( 15 g malt extract [Difco , Detroit , MI] and 15 g agar per liter of water ) in Petri dishes , colonized wafers were harvested 30 , 60 and 90 days . Noninoculated wood wafers placed on the same media in Petri dishes served as controls . Wafers were removed 30 , 60 or 90 days later , weighed and percent weight loss was determined . Additional wafers were removed at the same time period , immediately frozen to −20°C and prepared for scanning electron microscopy as previously described [68] . The genome was sequenced using Illumina and annotated using the JGI Annotation Pipeline [69] . Assembly and annotations are available from JGI portal MycoCosm [38] and deposited to DDBJ/EMBL/GenBank under accession AZAG00000000 . The version described in this paper is version AZAG01000000 . The completeness of the P . gigantea genome was assessed by finding 99 . 1% of CEGMA proteins conserved across sequenced genomes of eukaryotes [70] ( Text S1; Tables S1 , S2 ) . Mycelium was derived from triplicate cultures of 250 ml basal salts containing: i . 1 . 25 g freshly-harvested , ground ( 1mm mesh ) loblolly pine wood that had been ‘spiked’ with acetone and thoroughly dried ( NELP ) ; or ii . the same material following extended acetone extraction in a Soxhlet apparatus and drying ( ELP ) . The composition of the extract ( Text S1 ) was determined by GC-MS [51] . Duplicate cultures of basal salts medium with glucose as sole carbon source served as a reference . After 5 days incubation , total RNA was purified from frozen mycelium as described [22] , [71] . Multiplexed libraries were constructed and sequenced on an Illumina HiSeq2000 . DNAStar Inc ( Madison , WI ) modules SeqNGen and Qseq were used for mapping reads and statistical analysis . Transcriptome data was deposited to the NCBI Gene Expression Omnibus ( GEO ) database and assigned accession GSE53112 ( Reviewer access via http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=ilovmswixtajjez&acc=GSE53112 ) . Experimental details are provided in Text S1 and all transcriptome analyses are summarized in Dataset S2 . With minor modification , NanoLC-MS/MS analysis identified extracellular proteins in culture filtrates as described [22] , [72] . For each of the two woody substrates ( e . g NELP and ELP ) , cultures were harvested after 5 , 7 and 9 days . Mass spectrometric protein identifications were accepted if they could be established at greater than 95 . 0% probability within 0 . 9% False Discovery Rate and contained at least two identified peptides . Protein probabilities were assigned by the Protein Prophet algorithm [73] . To verify the effects of pine wood extractives in a well-defined substrate , media containing microcrystalline cellulose ( Avicel ) were also employed [22] , [45] , [74] . Filtrates from these cultures , with or without addition of loblolly pine wood acetone extract , were collected after 5 days and analyzed . Approximate protein abundance in each of the cultures was expressed as the number of unique peptide and the exponentially modified protein abundance index ( emPAI ) value [52] ( See Text S1 for detailed methods ) . | The wood decay fungus Phlebiopsis gigantea degrades all components of plant cell walls and is uniquely able to rapidly colonize freshly exposed conifer sapwood . However , mechanisms underlying its conversion of lignocellulose and resinous extractives have not been explored . We report here analyses of the genetic repertoire , transcriptome and secretome of P . gigantea . Numerous highly expressed hydrolases , together with lytic polysaccharide monooxygenases were implicated in P . gigantea's attack on cellulose , and an array of ligninolytic peroxidases and auxiliary enzymes were also identified . Comparisons of woody substrates with and without extractives revealed differentially expressed genes predicted to be involved in the transformation of resin . These expression patterns are likely key to the pioneer colonization of conifers by P . gigantea . | [
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| 2014 | Analysis of the Phlebiopsis gigantea Genome, Transcriptome and Secretome Provides Insight into Its Pioneer Colonization Strategies of Wood |
We present a computational method for the reaction-based de novo design of drug-like molecules . The software DOGS ( Design of Genuine Structures ) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds . The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features . We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability , based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles . This enables the software to suggest a synthesis route for each designed compound . Two prospective case studies are presented together with details on the algorithm and its implementation . De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software . The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes , and for generating bioactive molecules with drug-like properties .
De novo design aims at generating new chemical entities with drug-like properties and desired biological activities in a directed fashion [1] , [2] . This goal corresponds to the major task of the early drug discovery process and comprises a considerable fraction of the effort spent by pharmaceutical companies and academic groups in order to develop new treatments for diseases . De novo design is complementary to high-throughput screening in its approach to find innovative entry points for drug development [3] . Instead of searching for bioactive molecules in large collections of physically available screening compounds , de novo design ‘invents’ chemical structures from scratch by assembling molecular fragments . Computer-assisted approaches to de novo design automate this process by generating hypothetical candidate structures in silico . Although related areas of computer-aided drug development ( e . g . virtual screening , quantitative structure-activity relationship modeling ) have gained substantial attention in terms of publication numbers , de novo design has witnessed a constant evolution ever since the first computational methods have emerged in the late 1980s [2] . A number of reviews on this topic have been published recently , providing a comprehensive overview of the field [1]–[4] . Most of the approaches to de novo design attempt to mimic the work of a medicinal chemist: molecules are synthesized ( virtually assembled from fragments ) , tested for their biological activity ( computationally evaluated by a scoring function ) , and the insight gained serves as the basis for the next round of compound generation ( optimization ) . De novo design methods differ in the way they search for , assemble , and score the generated molecules . For example , scoring can either be performed by computing some similarity index of candidate compounds and known reference ligands ( ligand-based approach ) or based on the three-dimensional ( 3D ) structure of a ligand-binding cavity ( receptor-based approach ) . Irrespective of the particular technique used , automated de novo design has always been confronted with the issue of synthetic accessibility [1] , [5] . It may be argued that this is one of the main reasons why de novo design software has only rarely been subjected to practical evaluation [3] . An overview of successful de novo design studies is provided in a recent review article by Kutchukian and Shakhnovich [4] . Only a small fraction of all molecules amenable to virtual construction can in fact be synthesized in a reasonable time frame and with acceptable effort . De novo design programs tackle this issue by employing rules to guide the assembly process . Such rules attempt to reflect chemical knowledge and thereby avoid the formation of implausible or unstable structures . For example , some assembly approaches prevent connections between certain atom types , and finally the formation of unwanted substructures [6] , [7] . Other strategies employ chemistry-driven retrosynthetic rules capturing general principles of reaction classes [2] . A prominent example of this kind of rule set is the RECAP [8] ( retrosynthetic combinatorial analysis procedure ) , which is also used by some de novo design tools [9]–[12] . The software SYNOPSIS [13] follows a conceptually even more elaborate approach by connecting available molecular building blocks using a set of known chemical reactions . This enables the software to suggest reasonable synthesis pathways along with each final compound . Here , we present a new approach to computer-assisted de novo design of ligand candidate structures , and describe its implementation in the software tool DOGS ( Design Of Genuine Structures ) . DOGS represents a medicinal chemistry-inspired method for the de novo design of drug-like compounds , placing special emphasis on the synthesizability of the designed molecules . The software not only suggests new compounds , but also provides at least one motivated , hypothetical synthesis pathway per ligand candidate structure . The assembly process is based on available molecular building blocks and a set of established reaction principles . This strategy forces the program to follow construction pathways that represent direct blueprints of possible synthesis routes . The synthesis pathways generated and output by the software include vendor catalog identifiers of the building blocks and references to the underlying synthesis protocols . DOGS grows new molecules in a deterministic and stepwise process: in each step , complete enumeration of a subspace of all possible solutions is performed . Following a greedy strategy , top-scoring intermediate products are submitted to subsequent growing steps . The quality of designed ( intermediate ) products is assessed by a ligand-based scoring scheme . Similarity to a reference ligand is computed by a graph kernel method . Two different graph representations of molecules ( molecular graph and reduced graph ) have been implemented to allow for different levels of abstraction from the two-dimensional molecular structure . In a recently published work , we have successfully applied DOGS in a first prospective study to designing a selective inhibitor of human Polo-like kinase 1 ( Plk1 ) in its inactive ( DFG-out activation-loop ) conformation [14] . One of the compounds suggested by DOGS was selected for synthesis based on a series of post-design analyses and human inspection . Following the proposed synthesis route , the compound was accessible and found to have the desired biological effect and selectivity profile in vitro . The Plk1 study focused on the practical use case and only provides a brief description of the method . Here , we disclose the algorithmic details and give a full description of the implementation . We present a theoretical evaluation of the software with respect to general properties of designed compounds , and show its ability to suggest well-motivated bioisosteric replacements . We also present two new prospective case studies: Three compounds designed by DOGS ( two suggested as modulators of γ-secretase and one as an antagonist of human histamine H4 receptor ) were selected for chemical synthesis and subsequently tested for in vitro bioactivity . In all cases , the proposed synthesis plan was readily pursuable as suggested by the software .
The DOGS algorithm builds up new candidate structures by mimicking a multi-step synthesis pathway . This strategy is supposed to deliver a direct blueprint for the actual synthesis of proposed candidate structures . For this approach , established reaction protocols need to be formalized in order to make them processable by a computer . Reactions were encoded using the formal language Reaction-MQL [15] . The specification of a reaction as a Reaction-MQL expression consists of a reactant side on the left and a product side on the right . A reactant is specified only by the substructure that is directly involved or essential for the reaction ( reaction center ) in order to make the description applicable to a broad spectrum of reactants with variable substituent groups ( R-groups ) . The product is described by bond rearrangements caused by the reaction ( Figure 1 ) . All Reaction-MQL representations used in this work feature reactants with variable R-groups to keep them as generic as possible . Catalysts and invariant reactants are not denominated in the reaction expressions . DOGS implements 83 reactions ( termed coupling reactions in the following ) , 58 of which are unique and 25 represent either charge variations ( reactants ) or regioisomer variations ( products ) of one of the unique reactions . The complete list of reactions is provided in Table S1 in Text S1 , supplementary material . Out of the 58 unique reactions , 34 describe ring formations . All reactions require one or two reactants ( referred to as one- or two-component reactions , respectively ) and result in a single product ( A→B; A+B→C ) . In case a reaction generates regioisomers , it is split into two separate Reaction-MQL expressions , each describing one of the regioisomer products . A subset of the Sigma-Aldrich ( Sigma-Aldrich Co . , 3050 Spruce St , St . Louis , MO 63103 , USA ) catalog containing 56 , 878 chemical building blocks was downloaded from the ZINC database [16] , [17] . These compounds served as a basis for the extraction of the final set of building blocks by a three-step preparation protocol . In the first step , building blocks were standardized , and unsuitable entries were eliminated . For this purpose , a preprocessing routine was developed and implemented using the software MOE ( version 2009 . 10; Chemical Computing Group , Suite 910 , 1010 Sherbrooke Street West , Montreal , Quebec , Canada ) : In the second step , the filtered compound set was subjected to a collection of preprocessing reactions . A set of 15 functional group addition ( FGA ) and functional group interconversion ( FGI ) reactions was compiled from the literature and encoded as Reaction-MQL expressions ( for a complete list of preprocessing reactions see Table S2 in Text S1 , supplementary material ) . FGA and FGI reactions are supposed to introduce reactive functional groups to building blocks to make them applicable to coupling reactions during the virtual compound construction process . Each time a building block was converted by any of the 15 reactions its original version was kept , and the converted building block was added to the library . The third and final step of the preparation process comprises the annotation of reactive substructures ( i . e . which building block can act as a reactant for which reaction ) . In order to be annotated as a reactant for a reaction , a building block has to match one of the reactant's substructure definitions exactly once . Forbidding the same functional group to be present multiple times is supposed to avoid unwanted side products or the need for excessive use of protecting groups in the actual chemical synthesis . ( Please note that in the current version of the software no additional effort is made to estimate the reactivity of competing functional groups . ) After annotation , building blocks are stored in a MySQL ( Oracle Corporation , 500 Oracle Parkway , Redwood Shores , CA 94065 , USA ) database . The resulting building block library accessible to DOGS contains 25'144 entries . DOGS generates new molecules by iterative fragment assembly . The design cycle comprises the modification of a current intermediate product by applying one of the chemical reactions from the library , i . e . the extension of the intermediate product ( growing step ) . The product of a design cycle is an intermediate compound , which is modified in the subsequent iteration . A design cycle features two steps: The algorithm evaluates every building block processed by the dummy reaction steps according to the scoring function . Each of the n top-scoring building blocks is considered as a potential starting point for a distinct synthesis pathway . Parameter n is defined by the user and controls the number of compounds resulting from a design run . Once the design of a new compound based on a selected starting building block is initiated it will be continued until one of two stop criteria is fulfilled . The first stop criterion controls the molecular mass of the designed compounds . The reference compound's mass ( 100% ) defines a relative lower ( 70% ) and upper ( 130% ) bound . A constructed molecule has to exhibit a molecular mass lying within these boundaries to be accepted as a valid final product . During the design of a new molecule the algorithm continuously adds building blocks until the constructed intermediate product exceeds the lower mass boundary . Up to this step the extension of the intermediate product is accepted even if its score value decreases . Once the molecular mass of the intermediate product exceeds the lower mass boundary , the algorithm will only accept a subsequent extension step if it leads to an improved score . In case the addition of a building block leads to a lower score or causes the molecular mass to exceed the upper mass limit , the last reaction step is neglected and the previous intermediate product is added to the list of final products . The second stop criterion is supposed to truncate the number of synthesis steps to keep proposed synthesis pathways short . A pathway is interrupted regardless of any other condition when it exceeds a user-defined maximal number of synthesis steps ( set to a value of four steps in all runs presented in this study ) . In this case , the intermediate product formed by the last valid reaction step is added to the list of final products , and a new synthesis pathway is initiated based on another starting building block . Figure 3 presents the core of the DOGS compound design algorithm . DOGS tries to construct at least one compound starting from each of the n building blocks considered to be the most promising starting points . It is possible that an initiated synthesis path does not produce a final product . This happens when the growing intermediate product does not offer an attachment point to add another building block before it exceeds the minimal mass limit . In such a case , DOGS automatically skips this particular synthesis and increments n by 1 to guarantee that at least n final products are generated . Typically , a run will result in more than n final products because synthesis pathways can split if more than one top-scoring intermediate product is generated . In this case , multiple final products will be designed on the basis of a starting building block . All steps of the design algorithm are deterministic , i . e . two runs of DOGS with identical parameters will deliver identical results . The scoring function assesses the quality of a molecule with respect to the design objective . Products of each stage of a virtual synthesis pathway ( dummy products , intermediate products , final products ) are evaluated by the same scoring function . DOGS employs a two-dimensional ( 2D ) graph kernel method ( ISOAK [19] ) for scoring the designed molecules . The graph kernel was originally developed for similarity searching in virtual screening of compound databases , where it has been applied successfully [20] . ISOAK can be readily employed as a scoring function for ligand-based de novo design , where , like in virtual screening , similarity to a given reference ligand ( a known bioactive compound ) forms the key objective . Briefly , ISOAK computes similarity values for two molecules based on their 2D topological structures . Molecules are interpreted as graphs , where atoms are represented as vertices and covalent bonds as edges between vertices ( molecular graph ) . Hydrogen atoms are removed from the graph . Vertices are ‘colored’ by one of eight pharmacophoric feature types assigned to the corresponding atom ( A: hydrogen-bond acceptor , D: hydrogen-bond donor , E: hydrogen-bond donor & acceptor , P: positive charge , N: negative charge , R: aromatic , L: lipophilic , 0: no type; the list of atom type definitions can be found in Table S3 in Text S1 , supplementary material ) . A recursive definition of similarity between compared atoms ( “two atoms are similar if their neighbors are similar” ) is iteratively employed until the process converges . Parameter α controls the influence of the graph neighborhood , where higher values increase the impact of the neighborhood . Based on calculated atom-pair similarities , an optimal assignment of each atom of the smaller graph to one atom of the larger graph is computed . The assignment maximizes the sum of atom-pair similarities , which gives the overall similarity of the compared molecules . Similarity values are adjusted for compound size by scaling by the number of non-hydrogen atoms . In addition to the molecular graph described in the previous section , a reduced graph representation of molecules was implemented as an alternative description of molecules . Reduced graphs only represent the overall topological arrangement of structural features . The motivation to use them for de novo design was to encode molecules in a representation featuring a higher level of abstraction from the molecular composition and constitution . Similar to the FeatureTrees [21] approach , the reduced graph representation employed by DOGS reduces cyclic substructures as well as clusters of ‘lipophilic’ and ‘no type’ atoms to single vertices ( Figure 4A ) . In general , each ring that is part of the smallest set of smallest rings ( SSSR [22] ) is converted to one vertex . Exceptions of this rule are fused ring systems with atoms belonging to more than two rings of the SSSR . In this case , it is not possible to represent each ring as a single vertex and still obtain a simplified acyclic graph representation of the molecule . Such ‘amalgamated’ ring systems are reduced to a single vertex as a whole ( Figure 4B ) . In order to distinguish the reduced graph representation of two adjacent rings that are connected by a bond and two fused rings ( rings sharing atoms ) , the corresponding vertices of reduced graphs representing the rings are connected by an edge of order one ( ‘single bond’ ) in the former case and two ( ‘double bond’ ) in the latter ( Figure 4C ) . Vertices of reduced graphs are labeled with bit vectors that store information about the atoms they represent . These bit vectors consist of ten bits ( one for each of the eight atom types , and two additional bits standing for ‘ring’ and ‘amalgamated ring system’ , respectively ) . A vertex bit is set if the corresponding feature is present in the set of atoms the vertex encodes . Vertices not only store the bit vector but also the number of atoms they represent . Accordingly , a benzene substructure would be converted to a single vertex which is labeled by a bit vector with bits for ‘ring’ and ‘aromatic’ set to 1 , and stores an atom count of six . Pyridine would be encoded in the same way , except for the bit ‘hydrogen-bond acceptor’ being also set to 1 . Bit vectors ( bv ) and atom counts ( ac ) are used to compute the similarity of two vertices A and B of reduced molecular graphs . The similarity is computed by multiplying two terms ( Eq . 1 ) . ( 1 ) Term 1 ( sdFactor ) returns a value between 0 and 1 depending on the difference between the atom count values of compared vertices ( Eq . 2 ) , defined as ( 2 ) Term 2 ( Ti ) is the Tanimoto index for bit vector comparison ( Eq . 3 ) . ( 3 ) where c is the number of bits set to 1 in both vectors , a is the number of bits set to 1 in bvA and b is the number of bits set to 1 in bvB . Component sdFactor can be seen as a penalty function for atom count differences modulating the Tanimoto index . In case the atom count of compared vertices is equal ( e . g . two six-membered rings are compared ) , fvc reduces to the Tanimoto index . If the difference between the atom counts exceeds five , fvc will return 0 regardless of the calculated Ti for the bit vectors . All other components of ISOAK including the edge comparison are identical to the molecular graph comparison . ISOAK can only processes graphs with a maximum vertex connectivity of six , i . e . a vertex of a graph processed by ISOAK must not have more than six directly connected neighbors . While this will not happen in molecular graphs ( typically , no element that is present in drug-like molecules will form more than six covalent bonds ) , such cases can occur in reduced graphs . For example , 1H-phenalene ( Figure 4B ) is represented as a single vertex and offers up to nine positions for substitution . Molecules containing vertices with more than six neighbors in their reduced graph representation are excluded from subsequent steps and discarded . The molecular representation used in a design run is selected by the user , i . e . a DOGS run is either based on the molecular graph or reduced graph scoring scheme . The DOGS software was implemented in the programming language Java ( Oracle Corporation , 500 Oracle Parkway , Redwood Shores , CA 94065 , USA ) version 1 . 6 and uses the Chemistry Development Kit ( CDK , version 1 . 0 . 2 ) [23] , [24] .
Our initial theoretical analyses of the algorithm were based on de novo designed compounds originating from ten distinct DOGS runs . Five trypsin inhibitors served as reference ligands for these runs ( Figure 5 ) . For each reference , a design run based on the molecular graph representation ( α = 0 . 875 , default of ISOAK ) and a second run applying the reduced graph representation ( α = 0 . 4 , selected based on preliminary empiricism ) was performed . The number of start fragments was set to 200 . The ten runs resulted in a total of 1'767 unique compounds . DOGS was employed to propose candidate structures as new modulators of γ-secretase , an aspartic protease that cleaves the amyloid precursor protein ( APP ) and generates potentially toxic amyloid-β ( Aβ ) peptides [31] . Formation and accumulation of soluble Aβ oligomers in the brain is thought to be a primary pathological event in Alzheimer's disease [32] . γ-Secretase modulators shift the product ratio of APP processing from the highly amyloidogenic Aβ42 peptides towards shorter Aβ fragments with a lower propensity to aggregate like Aβ38 [31] , [33] . Four different reference ligands known to modulate γ-secretase were selected . For each reference compound , two DOGS runs ( molecular graph representation , α = 0 . 875; reduced graph representation , α = 0 . 4 ) were performed . The resulting eight compound lists were visually inspected , and two appealing ligand candidates 3 and 4 were selected for synthesis ( Figure 10 ) . Synthesis plans were readily traceable as suggested by the software . One-step reactions yielded the desired products in both cases . Hence , DOGS demonstrated its ability to come up with compounds considered as promising candidates by medicinal chemists and proved to be chemically accessible as suggested ( cf . Figure S3 , Figure S4 , Protocol S1 and Protocol S2 in Text S1 , supplementary material ) . Synthesized compounds were tested for their ability to modulate the γ-secretase product spectrum as previously described [34] . CHO cells with stable overexpression of human APP and presenilin-1 were treated with increasing concentrations of 3 and 4 . Subsequently , concentrations of secreted Aβ peptides were detected in cell supernatants by sandwich ELISA using C-terminus specific antibodies that distinguish between Aβ38 , Aβ40 , and Aβ42 peptide species [34] . ELISA results indicate inverse modulation of γ-secretase activity ( cf . Figure S5 in Text S1 , supplementary material ) . Compound 3 induced a dose-dependent increase in Aβ42 levels with a concomitant decrease in Aβ38 levels . Similar results were obtained for compound 4 . This pattern of inverse γ-secretase modulation has previously been observed , e . g . with derivatives of the non-steroidal anti-inflammatory drug indomethacin [35] . Although inverse γ-secretase modulation is not the effect intended for potential treatment of Alzheimer's disease , these results clearly show that DOGS is able to design compounds with pharmacological activity on the macromolecular target . Compounds 3 and 4 can serve as tool compounds and – more importantly – as starting points for an optimization of the pharmacological profile by structural modification . Histamine is a biogenic amine involved in a plethora of signaling pathways as a messenger . Four subtypes of histamine receptors ( hH1R – hH4R ) are known in human . All subtypes belong to class A ( rhodopsin-like ) of the G-protein coupled receptor ( GPCR ) superfamily [35] , [36] . Some antagonists of hH1R and hH2R are approved drugs for the treatment of allergic reactions and ulcer . Clinical trials of hH3R antagonists for the therapy of diseases of the central nervous system , such as epilepsy , schizophrenia and sleep/wake disorders are currently in progress [37] . We applied DOGS to provide ideas for new selective antagonists or inverse agonists of hH4R . For this purpose , two reference ligands ( an inverse agonist and an antagonist ) were employed ( Figure 11 ) . For each reference , the molecular graph representation ( α = 0 . 875 ) as well as the reduced graph representation ( α = 0 . 4 ) was applied , resulting in a total of four DOGS runs . Three prioritized designs 5–7 are presented in Figure 11 . N-methylpiperazine is present in both reference compounds . This moiety is often used as a basic head group in H4 receptor ligands [38] . The positive charge of basic amines is believed to form a key interaction to a negatively charged amino acid side-chain of the protein [39] . While in compound 5 the N-methylpiperazine moiety is preserved , it is replaced in 6 and 7 by isofunctional groups . Both represent aliphatic rings with basic nitrogen atoms , which provide a chance to undergo the charge-mediated interaction with the receptor . Localization of aromatic ring systems of the reference compounds is also approximately kept within the proposed structures . The attempt to follow the synthesis scheme proposed for compound 5 was not continued after facing solubility problems of the aminothiazole building block , which led to extremely poor yields of the intermediate product . Awkward behavior of reactant building blocks represents one potential problem of the transition from in silico to bench synthesis , illustrating the demand of this endeavor . Compound 7 was deemed to be of special interest , as it combines two structural elements that can be found in reported H4R ligands: an alkylic linker chain with an ether bridge and a central triazole ring ( Figure 12 ) . Notably , both structural elements are absent from the reference compound . The moderate affinity of the triazole-carrying ligand 8 ( Ki = 35 µM ) [40] may be caused by a missing hydrogen-bond acceptor in the central part , an interaction site that is believed to play a role in ligand binding to H4R [39] . The oxygen atom of the ether bridge present in designed compound 7 and H4R ligand 9 [41] is able to act as a hydrogen-bond acceptor . The ISOAK scoring function of DOGS assigns this oxygen to the carbonyl oxygen of the reference , which also represents a hydrogen-bond acceptor . In order to test for the hypothesis that a combination of the features – as found in compound 7 – might lead to hH4R affinity , compound 7 was selected for synthesis and testing . The synthetic procedure was realized exactly as suggested by the software ( cf . Figure S6 , Protocol S3 in Text S1 , supplementary material ) . Binding affinity of compound 7 was determined in a competitive binding assay by measuring displacement of radioactively labeled [3H]histamine bound to hH4R [42] . Membrane preparations of insect Sf9 cells expressing hH4R together with G-protein subunits Gαi2 and Gβ1γ2 were performed to yield the protein . A similar assay was used to measure the activity on hH3R ( reference ligand: [3H]Nα-methylhistamine ) [43] . Compound 7 exhibits only very weak affinity to hH4R . From three measurements , a mean Ki of 436±137 µM was determined . Comparable results were found for the activity of 7 on the hH3R receptor ( Ki = 466±209 µM , averaged over four distinct tests ) . A reason for the weak affinity of 7 might be a missing hydrogen-bond donor in the central part , which has been suggested to play a role in the interaction of some known H4 ligands with the receptor [39] , [44] . In fact , the nitrogen atom of the indole moiety of reference compound JNJ7777120 can act as a hydrogen-bond donor . Introduction of a hydrogen-bond donor to the central part and the exchange of the piperazine head group against N-methylpiperazine represent comparably small structural changes to compound 7 and might be considered as first steps to improve binding affinity . Additionally , compound 7 was tested against a panel of 30 other human GPCRs ( assays were performed by Cerep , Le bois l'Evêque , 86600 Celle l'Evescault , France; human GPCRs tested: A2A , A2B , A3 , α1A , α1B , α2C , β1 , β2 , CCK1 ( CCKA ) , D1 , D3 , D4 . 4 , H1 , H2 , M1 , M2 , M3 , M4 , M5 , NK1 , δ2 ( DOP ) , κ ( KOP ) , μ ( MOP ) , 5-HT1D , 5-HT2A , 5-HT2B , 5-HT2C , 5-HT4e , 5-HT6 , 5-HT7 ) . Notably , an agonistic effect on the κ opioid receptor ( 21% of the effect of the reference agonist U50488 , EC50 = 1 . 2 nM , n = 2 ) , and antagonistic effects on the δ2 opioid receptor ( 76% residual activity of the receptor in the presence of the reference agonist naltrindole ( IC50 = 0 . 37 nM , n = 2 ) and the 5-HT1D receptor ( 62% residual activity of the receptor in the presence of the reference agonist methiothepin , IC50 = 1 . 1 µM , n = 2 ) were observed . For other GPCRs in the panel only weak responses in the single digit or low double-digit percent range were found . These findings suggest that , while lacking high affinity and selectivity to the primary target hH4R , compound apparently 7 features a general pharmacophore motif of aminergic GPCRs ligands . Although the DOGS design approach is capable of suggesting compounds of practical relevance , a potential improvement to scoring would be to directly incorporate knowledge of a particular pharmacophore , i . e . the requirement for a particular spatial arrangement of potential interaction sites . This is only implicitly considered by the current scoring scheme , which can lead to high scores for designs exhibiting a spatial rearrangement of interaction sites . We therefore consider combining the design algorithm with scoring functions capable of taking 3D pharmacophore models into account in future versions of the software . In conclusion , we present a detailed description of a new method for automated de novo design . The software had already shown its potential to suggest selective and potent new compounds together with a pursuable route to synthesize them in a previous study [14] . Here , we provide in-depth insight into the algorithm and analyze it theoretically . In addition , two prospective case studies on automated design of bioactive compounds are presented . An important feature of the algorithm is its minimal demand for prior knowledge about the biological target . A single reference compound is sufficient to have the algorithm come up with suggestions for active compounds . This feature might be of special merit for drug discovery addressing structurally unexplored targets . However , despite these advances generating innovative and patentable molecules with biological activity from scratch remains a demanding goal . Current software solutions to this problem are far away from being ‘click-and-harvest’ applications guaranteed to produce readily exploitable results . De novo design relies on the thoughtful intervention and support of a human expert . Nevertheless , it can be a valuable source of inspiration and new ideas for medicinal chemistry . | The computer program DOGS aims at the automated generation of new bioactive compounds . Only a single known reference compound is required to have the computer come up with suggestions for potentially isofunctional molecules . A specific feature of the algorithm is its capability to propose a synthesis plan for each designed compound , based on a large set of readily available molecular building blocks and established reaction protocols . The de novo design software provides rapid access to tool compounds and starting points for the development of a lead candidate structure . The manuscript gives a detailed description of the algorithm . Theoretical analysis and prospective case studies demonstrate its ability to propose bioactive , plausible and chemically accessible compounds . | [
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| 2012 | DOGS: Reaction-Driven de novo Design of Bioactive Compounds |
The photoreceptors of the Drosophila compound eye are a classical model for studying cell fate specification . Photoreceptors ( PRs ) are organized in bundles of eight cells with two major types – inner PRs involved in color vision and outer PRs involved in motion detection . In wild type flies , most PRs express a single type of Rhodopsin ( Rh ) : inner PRs express either Rh3 , Rh4 , Rh5 or Rh6 and outer PRs express Rh1 . In outer PRs , the K50 homeodomain protein Dve is a key repressor that acts to ensure exclusive Rh expression . Loss of Dve results in de-repression of Rhodopsins in outer PRs , and leads to a wide distribution of expression levels . To quantify these effects , we introduce an automated image analysis method to measure Rhodopsin levels at the single cell level in 3D confocal stacks . Our sensitive methodology reveals cell-specific differences in Rhodopsin distributions among the outer PRs , observed over a developmental time course . We show that Rhodopsin distributions are consistent with a two-state model of gene expression , in which cells can be in either high or basal states of Rhodopsin production . Our model identifies a significant role of post-transcriptional regulation in establishing the two distinct states . The timescale for interconversion between basal and high states is shown to be on the order of days . Our results indicate that even in the absence of Dve , the Rhodopsin regulatory network can maintain highly stable states . We propose that the role of Dve in outer PRs is to buffer against rare fluctuations in this network .
The ability of Drosophila to perceive color and motion depends on the specific patterning of several Rhodopsin proteins throughout its retina [1]–[3] . The fly retina is a complex three-dimensional structure that consists of a lattice of approximately 800 simple eyes known as ommatidia [4] . As shown in Figure 1 , each ommatidium is a bundle of eight photoreceptor neurons ( PRs ) , with six motion detecting PRs ( R1–R6 ) on the perimeter ( “outer” PRs ) and two smaller , color detecting PRs ( R7 & R8 ) in the middle ( “inner” PRs ) [5]–[12] . Beginning at the third instar larva , photoreceptors arise following the passage of a morphogenetic furrow across the eye imaginal disc , a monolayer of epithelial cells . As the furrow passes , cells are recruited to ommatidia in a stereotyped manner wherein the R8 photoreceptor is recruited first and then followed by pairs of outer photoreceptors: R2 and R5 , R3 and R4 , then R1 and R6 . Finally , R7 joins the group of cells . During pupation , photoreceptors express specific Rhodopsins ( for details of the process , see [13] , [14] ) . A well-studied genetic network controls Rhodopsin protein expression in the eight PR cell types , and enforces a “one-neuron , one-receptor” rule across the majority of the retina , such that each PR expresses one and only one of five types of Rhodopsin proteins ( Rh1 , Rh3 , Rh4 , Rh5 , or Rh6 ) [15] . In outer PRs , each cell expresses Rh1 exclusively . There are two major types of ommatidia: in a random subset consisting of approximately 35% of ommatidia , inner PRs exhibit coupling such that when the R7 cell expresses Rh3 , the R8 cell expresses Rh5; in the other 65% of ommatidia , when R7 expresses Rh4 , R8 expresses Rh6 ( for exception , see [16] ) . Several regulators of Rhodopsin patterning have been discovered and their regulatory interactions are well-characterized [16]–[21] . The K50 homeodomain protein Defective proventriculus ( Dve ) was recently shown to enforce the “one-neuron , one-receptor” rule in the outer PRs and in the subset of Rh4-expressing R7 cells [22] . In outer PRs , Dve acts together with the activator Orthodenticle ( Otd ) in an incoherent feedforward loop motif to repress Rh3 , Rh5 , and Rh6 . In the inner PRs , a second coherent feedforward loop that includes the inner PR factor Spalt ( Sal ) , represses Dve thus allowing Rhodopsin expression . In dve mutants , Rh3 , Rh5 and Rh6 are de-repressed in outer PRs at levels that vary among cells . Importantly , at the time of Rh expression , Dve is expressed in outer PR cell types where it represses Rh3 , Rh5 and Rh6 ( Figure 1 ) . Dve's effect on Rhodopsin expression , however , is modulated by cell-type specific inputs onto the promoters of each rhodopsin gene ( Figure 1 and [22] ) . Most of these inputs have previously been shown to affect rhodopsin expression in inner photoreceptors only . However , Otd and Hazy/Pph13 ( a Q50 homeodomain protein ) are expressed in all PRs similarly to Dve [18] , [21] , [23] . Both Otd and Hazy/Pph13 have been shown to be necessary but not sufficient factors for expression of specific rhodopsins in vivo ( Figure 1 ) and sufficient to activate their expression in vitro [24] . Here , we quantitatively studied a molecular null mutation in dve . Since the variable nature of this phenotype requires a quantitative analysis ( Figure 2 ) , we developed image analysis algorithms to identify each ommatidium in the retina and discriminate individual PRs in 3D confocal stacks of retinae . We applied these methods to quantify cell-specific effects in dve mutants in thousands of cells by measuring relative protein levels for the three Rhodopsins in the eight different PR types over a time course of four weeks . We measured a wide cell-to-cell variability in Rhodopsin expression . Our ability to precisely quantify Rhodopsin levels enables detection of subtle differences among the outer PR cell types , which manifest in the de-repression of Rhodopsins . We use stochastic models to understand the underlying causes of the observed Rhodopsin distributions . This allows us to attribute differences among cells to the rates of molecular processes such as protein and mRNA synthesis and degradation rates . On the basis of our modeling , we propose a functional role for Dve in outer PRs as a buffer against rare fluctuations in the Rhodopsin regulatory network .
Thirty dve mutant retinae collected at four different developmental time points were analyzed by confocal microscopy and automated image analysis algorithms ( see Methods ) . We developed algorithms that automatically analyze three-dimensional confocal stacks of entire retinae and computationally extract ommatidia from the stacks ( Figure 3 ) . Within each ommatidium , the algorithms identified photoreceptor cells and assigned the correct photoreceptor type . For most retinae , our algorithms identified >500 ommatidia , and the number that were sufficiently well-resolved to allow automatic identification of individual photoreceptors was typically in the range 70–200 ommatidia per retina . We quantified Rhodopsin protein levels using a local relative intensity measure ( Il ) across an interval of z slices identified by the algorithm to maximize both the number of ommatidia with well-resolved PRs and the number of slices used for quantification ( Figure S1 and Text S1 ) . This intensity measure , Il , is calculated in each z slice such that each PR's intensity value ( Ipr ) is normalized by the local ommatidia background ( Iomma ) , and averaged over the z slices: Il = <Ipr/Iomma> . In order to quantitatively compare Rhodopsin protein levels across individual retinae , we overcame several technical challenges resulting from imaging within the complex retinal tissue ( see Methods and Figure 4 ) . Using Il to quantify protein levels , we demonstrated that Rhodopsin distributions are reproducible across replicates ( see Methods and Figure S2 ) . We applied our quantification approach using Il to measure cell-specific Rhodopsin expression in dve mutants across several time points . By comparing the measured distributions for different Rhodopsins in different PRs , we observed PR specific effects in de-repression . The distribution of Rhodopsin protein levels we measured should be informative of the processes of mRNA and protein production and degradation for different rhodopsin genes . Previous studies have derived the form of the equilibrium distributions for several different models [26]–[28] . In the simplest model , a constitutively active promoter produces transcripts that are translated into protein [27] . Assuming that protein lifetimes are significantly longer than mRNA lifetimes , the equilibrium distribution of protein levels is a gamma distribution , , with shape parameter α and scale parameter β . The two parameters are related to the rates of mRNA production ( ) and degradation ( ) , and of protein production ( ) and degradation ( ) as follows: , . We expect this model to be appropriate when transcriptional activity is uniform across cells . For example , since wild-type flies strongly repress Rh3 , Rh5 , and Rh6 in outer PRs , their very low expression levels in those cells should be well-modeled by a uniform basal transcription rate . To test this , we measured Rhodopsin distributions in wild-type flies . We found excellent fits to gamma distributions for all three Rhodopsins , shown in Figure 7 ( insets ) and Table 1 . The difference between Rh3 and Rh5 was pronounced in their values of α ( they had identical values of β ) , indicating differences in their basal promoter activities or protein turnover rates , rather than differences in translation or RNA stability . In contrast to these results from wild-type retinae , the distributions in dve retinae were not fit as well by this simple model ( see Figure S3 ) . While in some cases the fit was good at the tails of the distributions , the fit at the modes exhibited significant deviations . The simple model , therefore , predicts fewer cells expressing low levels of Rhodopsin than observed . Comparing dve and wild-type distributions in Figure 7 , appears that the mode corresponds to the subpopulation of cells that exhibit basal expression . These deviations from the simple model indicate that protein expression is strongly non-uniform across cells . One possibility is that Rhodopsin promoters can interconvert reversibly between on and off states , and mRNA is produced only when the promoter is on . In several different regimes of this promoter on/off interconversion model , protein levels are predicted to exhibit a gamma distribution . For example , if the gene's inactivation rate is much larger than both the activation rate and the mRNA degradation rate , then mRNA levels will have a gamma distribution; hence , if protein levels closely track mRNA , the gamma distribution will be observed [26] . Likewise , if the promoter state interconverts significantly faster than proteins are degraded , a gamma distribution is predicted [28] . Our data exhibits significant deviations from both gamma distributions ( Figure S3 ) as well as the general solution obtained in [28] ( data not shown ) . Our data are much more consistent with a two-state network model in which proteins can be produced at either a low , basal rate ( off state ) or a high rate ( on state ) , and interconversion between the two states is much slower than all other processes . We emphasize that these two states are not necessarily different states of the promoter alone – they may result from differences in other processes and thus correspond to the overall state of the network that regulates Rhodopsin production . Due to the slow interconversion between states , proteins levels in each state exhibit a gamma distribution; the overall distribution is a mixture of two gamma distributions , with parameter pon denoting the fraction of cells in the on state . Fits to the two-state model are shown in Figure 7 , where the blue line indicates the distribution for cells in the on state ( parameter values are given in Table 1 ) . While such mixture models can suffer from over-fitting , here we have avoided this problem by explicitly measuring the wild-type distributions , which correspond to the off state ( Figure 7 , inset ) . The wild-type off state is similar in its position and shape to the dve off state across time points , indicating that the fits are reliable . The model allows us to infer the fraction of cells in the on state at each time point ( Table 1 ) . For Rh5 , we see initially a very small fraction of cells are on ( pon = 0 . 02 ) ; over the course of 2 weeks , cells get induced until approximately 2/3 of cells are on , a value that is maintained through the 4 week time point . For Rh3 , pon remains at ∼0 . 3 until 4 weeks , when it decreases to 0 . 2 . For Rh6 , pon fluctuates in the range 0 . 2–0 . 3 over the time course . The difference between the on and off states of cells , and further implications of our modeling , are addressed in detail in Discussion . The existence of common regulatory mechanisms acting on different genes can often be inferred by measuring correlations of their expression levels within cells . Although Otd regulates three different rhodopsin genes , the presence of Dve in wild-type retinae maximally represses these genes in outer PRs , and correlations cannot be observed . In dve mutants , Rhodopsin expression is revealed in outer PRs . If Otd were acting alone , we would expect that fluctuations in Otd levels would lead to positive correlations between different pairs of Rhodopsins . However , the presence of other regulators in different outer PR cells could modulate the strength or even the sign of correlations . To test this , we plotted the distribution of relative levels for pairs of Rhodopsins that were co-stained , Rh3–Rh6 and Rh5–Rh6 ( Figure 8 ) . In the pooled data , across all replicates and photoreceptors , we found statistically significant positive correlations for Rh3–Rh6 at the last time point ( Spearman's ρ≈0 . 3 , p-value<10−4 ) and negative correlations for Rh5–Rh6 ( Spearman's ρ≈−0 . 35 , p-value<10−4 ) ( Figure S4 , upper panels ) . To verify these , we examined correlations within each replicate and within each photoreceptor ( Figure S4 , lower panels ) . For Rh3–Rh6 , although several replicates exhibit statistically significant correlations , there is no consistent pattern , although there is a strong tendency for positive correlations at the fourth week time point . Some positive correlations may be spurious , e . g . while the measure Il normalizes for variations in local mean intensity across the retina , higher-order intensity variations might account for weak positive correlation between Rh3–Rh6 . For Rh5–Rh6 , on the other hand , we find reproducible anti-correlation across all three replicates at the 4 week time point , which are consistent across PR types . One explanation for anti-correlation could be exclusion of different Rhodopsins due to limited space and dense packing within the rhabdomere . In that case , we would expect a cloud of points slightly elongated along a line of negative slope . Figure 8 is not consistent with this scenario . Instead , along the increasing Rh5 axis in the 4 week panel , we see a spread of points , with decreasing density in the Rh6 direction . Cells that highly express one Rhodopsin , tend to express the other type at a lower level , and there are few cells that co-express both Rhodopsins at high levels . We conclude the Rh5–Rh6 anti-correlation is the result of factors other than Otd acting in the outer photoreceptors ( see Discussion ) .
In this paper , we established the Drosophila compound eye as a quantitative system for studying cell-specific gene expression . We developed methods to image the complex three-dimensional tissue and automatically identify ommatidia and their constituent PRs . We quantified the cell-specific effects of removal of dve , a key transcriptional repressor that regulates Rhodopsin patterning . In wild type outer PRs , which exclusively express Rh1 , Dve represses the expression of Rh3 , Rh5 , and Rh6 . Our data shows that removal of Dve leads to a continuous and wide distribution of expression levels ( Figure 7 ) . This cell-to-cell variability exhibits PR-specific differences ( Figure 6 ) suggesting that each cell type may express different levels of Rhodopsin regulators ( Figure 1 ) . Importantly , we could only detect these differences by comparing distributions since mean Rhodopsin levels exhibit little change among outer PRs . The fact that Rh3 repression is greatest in R3 and R4 cells is consistent with the fact that the R3/R4 pair is known to undergo additional differentiation after PRs are recruited [29] . Thus , our analysis has revealed that cell fate differences in R3 and R4 yield distinct Rh3 expression in dve mutants . Furthermore , other co-recruited pairs ( R1/R6 and R2/R5 ) also appear to regulate Rh3 levels similarly , suggesting that each pair may have similar levels of Rh3 regulators . Surprisingly , however , such similarities between co-recruited pairs are not detected for Rh5 and Rh6 . This observation suggests that outer PRs exhibit differences in the amount and types of Rhodopsin regulators they contain . For example , the R3 cell exhibits a strong tendency to repress Rh5 , which is distinct from all other PRs ( Figure 6 ) . These differences in Rhodopsin regulation in outer PRs have not previously been shown . While some examples of differential gene expression among outer PR types are known [30]–[32] , further study of Rhodopsin regulation in outer PRs is needed to identify the molecular basis for the differences we observe . We showed that Rhodopsin distributions in outer PRs are consistent with a two-state model in which Rhodopsin production occurs at either high or basal rates , which we call respectively the on and off states ( Figure 7 ) . To understand the biological basis for these two states , it is instructive to compare the model parameters α and β between them , across time points , for each Rhodopsin ( Table 1 ) . In all cases , we find and . The most pronounced differences are between β values , which are an order of magnitude or more larger in the on state than the off state at most time points . Since is the ratio of protein translation to mRNA degradation rates , the model suggests that differences between the two states of cells in the dve mutant may stem from post-transcriptional regulation . The on state could be associated with stabilization of the transcript ( reduction of ) and/or increased translation ( increase of ) . Differences in Rhodopsin trafficking to the rhabdomere may comprise additional post-transcriptional differences between the states . Because our experiments measure protein level distributions , they do not provide information about the transcription rate independently of , i . e . only the ratio can be determined . Thus , transcriptional differences between on and off states cannot be inferred . However , under the reasonable assumption that , the observation that implies that Rhodopsin turnover rates ( discussed below ) increase significantly in the on state . Our model assumes that on ↔ off switches occur infrequently compared to the equilibration timescale of the gene expression and protein translation dynamics . This timescale was shown to be of order [28] , and can be estimated from measured values of Rhodopsin turnover rates . The best measurements are of Rh1 in the blowfly , Calliphora , a dipteran with the same compound eye organization and patterning of Drosophila . The half-life of Rh1 depends on exposure to light: photo-activated Rh1 has a half-life of 2 hours , while in the dark its half life is 5 days [33] . Subsequent studies suggest that half-lives are longer in Drosophila , e . g . photo-activated Rh1 half-life is ∼13 hours [25] . Our flies were raised in 12 h light-dark cycles , hence 1 day is an approximate upper bound for the equilibration timescale . The rate constant for on ↔ off switching is therefore predicted to be significantly slower than 1 event per day per cell , a result that is fully consistent with our observation that changes in pon occur over the timescale of weeks ( Table 1 ) . The timescale for on ↔ off switches , which is comparable to the organismal lifespan , is strikingly slow in view of other systems where stochastic activation occurs on the order of minutes [34] , [35] . To some extent , this difference could result from the fact that photoreceptors are post-mitotic cells with overall slower metabolic processes than the actively dividing cells used in previous studies . More important in our view , however , is the fact that the default state of rh3 , rh5 , and rh6 genes in outer PRs is off , and the strong Rhodopsin-specific activators are not expressed in these cells ( Figure 1 ) . Thus , the very slow timescale we infer for on ↔ off interconversion suggests that even in the absence of Dve , the Rhodopsin regulatory network in outer PRs can maintain two extremely stable states of Rhodopsin expression . We therefore propose that the functional role of Dve in these cells is to buffer against rare fluctuations in the Rhodopsin regulatory network . While the mechanism by which Dve buffers against fluctuations is not known , we provide a simple toy network model in which buffering operates directly through Dve's known behavior as transcriptional repressor of rhodopsin genes . The mathematical model presented in Text S1 is constructed by analogy with well-known bistable networks such as the lac operon [36] , [37] . The capacity of photoreceptors to produce large amounts of Rhodopsin protein requires up-regulation of the protein production machinery , which could be induced by Rhodopsin protein itself within a positive-feedback loop . In this case , once Rhodopsin levels increase beyond some threshold , protein production would kick into “high gear” , with concomitant increase in degradation pathways to allow for efficient turnover . Depending on the Rhodopsin mRNA level , the system can be either bistable or monostable , as we show in Text S1 . If Rhodopsin mRNA concentration is very low ( or very high ) , the system has a single stable fixed point , corresponding to low ( or high ) production . In an intermediate range of mRNA levels , the system exhibits bistability ( see Figure S5 ) . In the bistable regime , a cell that is in the low production state remains stably in that state . An increase of Rhodopsin levels by rare fluctuation is required to drive the system into the high production state . Once there , it remains stably in high gear . Within this network , the role of Dve is to buffer against fluctuations [38] by ensuring that the system remains in the monostable regime . Removal of Dve increases mRNA levels , moving the system into the bistable range . Thus , in this model Dve exhibits the quintessential hallmark of a buffer: it controls the stability of the system , not its state ( see Text S1 ) . Our toy network provides a plausible mechanism of buffering by Dve , but other scenarios are clearly possible , e . g . via additional regulatory interactions . The key point indicated by our results is that Rhodopsin production is not entirely determined by transcript levels . Removal of Dve renders the system poised for activation , making it susceptible to fluctuations . While such fluctuations can in principle occur without activators , it is known that the specific Rhodopsin activators Otd and Hazy/Pph13 are expressed in outer PRs [18] , [21] , [23] . The levels of these regulators and any others would thus be major determinants of the rate of fluctuations that drive the system from one stable state to the other . Using our approach , we measured correlations between levels of different Rhodopsin proteins . While we observed weak but significant positive and negative correlations between both Rh pairings ( Figure S4 ) , only the negative correlations of Rh5 and Rh6 at the 4 week time point were consistent across replicates and cell types . It is noteworthy that in wild-type flies in the inner R8 cell , Rh5 and Rh6 expression is strongly bimodal due to the presence of a double negative feedback loop between warts and melted [19] . Transcriptional reporters of warts [19] and melted ( D . Jukam , personal communication ) are expressed in low levels in subsets of outer PRs , suggesting that the major effectors of this negative feedback loop are present in outer PRs . The presence of these regulators could result in the anti-correlations of Rh5 and Rh6 revealed in dve mutants . Moreover , recent work has shown that in R8 photoreceptors , Rh6 acting through an uncharacterized pathway has the capacity to inhibit Rh5 expression [39] . Removal of Rh6 leads to progressive expression of Rh5 in R8 PRs , which becomes apparent only after 2 weeks . Our finding of Rh5–Rh6 anti-correlation in dve outer PRs , which develops only after 2 weeks , suggests that a similar Rh6-mediated repression could be active in the outer PRs and revealed in the dve mutant . Our results demonstrate the power of applying quantitative approaches to the study of systems-level problems in developmental biology . Our measurement of cell-specific Rhodopsin distributions enables detection of subtle differences among outer PR types , which were previously unknown . Our modeling of the distributions reveals that post-transcriptional processes play a major role in stochastic de-repression of Rhodopsins in outer PRs in the absence of Dve . More generally , we infer that the cellular state corresponding to basal Rhodopsin production is stably maintained by the Rhodopsin regulatory network even without Dve . On the basis of these findings , we conclude that Dve's role in outer PRs may be to act as a buffer against fluctuations in the genetic network that controls Rhodopsins .
All retinae were dissected from dve186 flies , a molecular null deficiency [22] . Flies were raised on standard corn meal-molasses-agar medium and grown at 25°C . Flies were staged and then dissected at 4 time points after eclosion: 0 weeks ( +/−1 day ) , 1 week ( +/−1 day ) , 2 weeks ( +/−1 days ) , 4 weeks ( +/−1 days ) . All retinae were stained with Alexa-488 conjugated phalloidin , which binds actin and is used to visualize the actin-dense rhabdomeres . Additionally , two antibodies are used to simultaneously visualize Rhodopsins: retinae were co-stained with one of two pairs , either mouse anti-Rh3 ( 1∶10 ) and rabbit anti-Rh6 ( 1∶2000 ) , or mouse anti-Rh5 ( 1∶200 ) and rabbit anti-Rh6 ( 1∶2000 ) . The fluorophores conjugated to secondary antibodies were Alexa 568 ( Rh3 ) , Alexa 568 ( Rh5 ) , and Alexa 633 ( Rh6 ) . Retinae were dissected and fixed for 15 minutes with 4% formaldehyde at 25°C . Retinae were rinsed twice , washed for at least 2 hours in PBX and then incubated overnight with the primary antibodies diluted in PBX . Retinae were then rinsed twice , washed in PBX for more than 4 hours and incubated overnight with secondary antibodies . Retinae were mounted in Prolong Gold following two additional rinses and a 2+ hour wash . All retinae were cured for at least 5 days but no more than 7 days . Prolong Gold exhibits the best refractive index matching between mounting media and objective oil . Images were acquired using confocal scanning laser microscopy ( Leica SP5 ) with a 40× oil immersion objective ( NA = 1 . 25 ) . Retinae were mounted on glass slides under a cover slip in Prolong Gold . Optical sections were collected every 250 nm , with 8-bit depth and a pixel size of 160 nm×160 nm . Each channel was scanned separately and images were line averaged . Retinae are approximately 100 microns deep with a maximal diameter of approximately 500 µm . Individual ommatidia radii are in the range 3–5 µm , and the distance between neighboring ommatidial centers is 12–15 µm . The centers of PRs within an ommatidium are approximately 2 µm apart , while the spacing between each PR is less than 250 nm , and thus just within the resolution limit . A single image stack typically consisted of ∼300 optical slices and saved as 3–4 GB of data ( Figure 2A ) . | Complex networks of genetic interactions govern the development of multicellular organisms . One of the best-characterized networks governs the development of the fruit-fly retina , a highly organized , three-dimensional organ composed of a hexagonal grid of eight types of photoreceptor neurons . Each photoreceptor responds to a particular wavelength of light depending on the Rhodopsin protein it expresses . We present novel computational methods to quantify cell-specific Rhodopsin levels from confocal microscopy images . We apply these methods to study the effect of the loss of a key repressor that ensures each photoreceptor expresses only one Rhodopsin . We show that this perturbation has cell-specific effects . Our measurement of the cell-type specific Rhodopsin distributions reveals differences between photoreceptor cells , which could not otherwise be detected . Using mathematical models of gene expression , we attribute this variability to stochastic events that activate Rhodopsin production . | [
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| 2012 | Stochastic De-repression of Rhodopsins in Single Photoreceptors of the Fly Retina |
Drug resistance of Salmonella enterica serovar Typhi ( Salmonella Typhi ) to first-line antibiotics is emerging in Central Africa . Although increased use of fluoroquinolones is associated with spread of resistance , Salmonella Typhi with decreased ciprofloxacin susceptibility ( DCS ) has rarely been reported in Central Africa . As part of a microbiological surveillance study in the Democratic Republic of the Congo ( DR Congo ) , Salmonella Typhi isolates from bloodstream infections were collected prospectively between 2007 and 2011 . The genetic relationship of the Salmonella Typhi isolates was assessed by pulsed-field gel electrophoresis ( PFGE ) . The antimicrobial resistance profile of the isolates was determined and mutations associated with DCS were studied . In total , 201 Salmonella Typhi isolates were collected . More than half of the Salmonella Typhi isolates originated from children and young adults aged 5–19 . Thirty different PFGE profiles were identified , with 72% of the isolates showing a single profile . Multidrug resistance , DCS and azithromycin resistance were 30 . 3% , 15 . 4% and 1 . 0% , respectively . DCS was associated with point mutations in the gyrA gene at codons 83 and 87 . Our study describes the first report of widespread multidrug resistance and DCS among Salmonella Typhi isolates from DR Congo . Our findings highlight the need for increased microbiological diagnosis and surveillance in DR Congo , being a prerequisite for rational use of antimicrobials and the development of standard treatment guidelines .
Typhoid fever is endemic in the Democratic Republic of the Congo ( DR Congo ) . Although antimicrobial resistance data are sparse for Central Africa , drug resistance to first-line antibiotics is clearly emerging [1] . Also in DR Congo , multidrug resistance ( MDR ) [defined as co-resistance to first-line antibiotics ampicillin , chloramphenicol and trimethoprim/sulphamethoxazole ( TMP-SMX ) ] in Salmonella enterica serotype Typhi ( Salmonella Typhi ) has been observed [2] , [3] . Facing widespread MDR , fluoroquinolones have become the drugs of choice for treating typhoid fever , but their increased use has been associated with a spread in low-level fluoroquinolone resistance - further referred to as decreased ciprofloxacin susceptibility ( DCS ) [4] . Salmonella Typhi with DCS is common in Asia and has been reported in East Africa and South Africa [5]–[7] , but apart from a single case reported from Cameroon [8] , and two cases in DR Congo , Salmonella Typhi with DCS has not yet been observed in the central African region [2] , [3] , [9] . The present study describes the antimicrobial resistance profile of a prospective collection of Salmonella Typhi isolates recovered as part of a microbiological surveillance study from blood cultures obtained from patients in DR Congo over the years 2007–2011 . Mutations associated with DCS were studied and the genetic relationship of the Salmonella Typhi isolates was assessed .
Ethical approval was granted by the Ethical Committee of the University of Antwerp , Belgium and from the Ministry of Health in DR Congo . The present study complies with the World Health Organization and international guidelines ( European Society of Clinical Microbiology and Infectious Diseases Study Group for Antimicrobial Resistance Surveillance and Clinical Laboratory Standards Institute ) on antibiotic surveillance for which no recommendation for an informed consent has been issued . The diagnostic procedure – blood cultures – is part of the standard diagnostic work-up of patients with a suspicion of bacteremia . Clinical information -as presented- and information about use of antibiotics was the standard information present on the laboratory request form . Data have been reviewed and analyzed anonymously . Between April 2007 and January 2011 , blood cultures were performed on 9 , 634 patients suspected of typhoid fever or other systemic infections . Patients were seen at health care facilities in seven out of 11 provinces in DR Congo: Kinshasa , Bas-Congo , Bandundu , Equateur , Kasai Occidental , Kasai Oriental and Oriental province ( Figure 1 ) . In Kinshasa , health care facilities involved in the detection and study of the epidemic increase of typhoid fever-associated peritonitis of 2004 were selected [3] . Health care facilities in other provinces were recruited based on the past or actual existence of microbiological laboratories , professional contacts and the accessibility to reliable shipment facilities . Criteria for blood culture sampling were clinical suspicion of bacteremia associated with a local ( pneumonia , urinary tract infection , meningitis or other ) or systemic ( typhoid fever , endocarditis ) infection diagnosed at consultation or admission . Typhoid fever was defined according to the case definitions of the Ministry of Health surveillance of communicable diseases [10] . At the start of the surveillance project , teams of clinicians and laboratory technicians were trained in indications and sampling of blood cultures . For children <14 years , 1–4 ml of blood was inoculated into a pediatric blood culture vial ( Bact/ALERT FP; bioMérieux; Marcy L'Etoile; France ) . For adults , 2×10 ml of blood was inoculated into aerobic blood culture vials ( Bact/ALERT FA; bioMérieux; Marcy L'Etoile; France ) . Age , gender , geographic origin , use of antibiotics prior to blood culture sampling and presumptive diagnosis ( focus of bacteremia including suspicion of typhoid fever ) were recorded . Inoculated vials were shipped to the Institut National de Recherche Biomédicale ( INRB ) in Kinshasa , incubated at 35°C and daily checked for growth by visual inspection of the chromatographic growth indicator . Grown cultures were Gram stained , subcultured and identified to the species level . Skin or environmental bacteria ( coagulase negative staphylococci , Corynebacterium spp . , Propionibacterium acnes and Bacillus spp . ) were categorized as contaminants , the other bacteria were considered as clinically significant organisms [11] . Suspected colonies of Salmonella were identified as Salmonella Typhi using standard biochemical methods ( characteristic aspect on Kligler Iron Agar ( acid from glucose , no gas , trace of H2S ) , negative tests for urease , oxidase , β-galactosidase and indole production tests , positive tests for lysine decarboxylase ) and the serotype of Salmonella Typhi ( O:9;H:d;Vi+ ) was confirmed with commercial antisera ( Remel , Lenexa , Kansas ) . Identity of Salmonella species isolates was confirmed using the Vitek II system ( Card GN21 341 , bioMérieux ) . At the National Reference Laboratory for Salmonella and Shigella ( Institute of Public Health , Brussels ) , the serotype of the Salmonella isolates was re-confirmed by slide agglutination with commercial monospecific antisera ( Sifin , Berlin , Germany ) , following the Kauffmann-White scheme [12] . For analysis in the present study , only the first isolate per patient was considered . Susceptibility tests for ampicillin , cefotaxime and TMP-SMX were performed using the Vitek II system ( Card AB AST-N156 , bioMérieux ) . For nalidixic acid , ciprofloxacin , chloramphenicol and azithromycin , minimal inhibitory concentration ( MIC ) values were determined using the E-test macromethod ( bioMérieux ) . Breakpoints for resistance were respectively ≥32 mg/l for ampicillin , ≥4 mg/l for cefotaxime , ≥32 mg/l for naladixic acid , ≥16 mg/l chloramphenicol ( considering intermediate susceptible isolates resistant ) and ≥4/76 mg/l for TMP-SMX using Clinical and Laboratory Standards Institute definitions [13] . DCS was defined according to European Committee on Antimicrobial Susceptibility testing ( EUCAST ) V 2 . 0 . guidelines , i . e . a MIC-value for ciprofloxacin >0 . 064 mg/l [14] . For azithromycin , EUCAST V . 2 . 0 . suggests MICs of >16 mg/l as defining resistance [14] . Multidrug resistance was defined as co-resistance to ampicillin , chloramphenicol and TMP-SMX . Screening for mutations causing DCS was performed by amplification and sequencing of the quinolone resistance-determining regions ( QRDRs ) of the gyrA , gyrB , and parC genes . The presence of the plasmid-mediated quinolone resistance qnr genes ( qnrA , qnrB , and qnrS ) was determined using PCR [15] . Pulsed-field gel electrophoresis ( PFGE ) was performed according to the PulseNet protocol for molecular subtyping of Salmonella [16] , using XbaI as restriction enzyme ( New England Biolabs , Leusden , The Netherlands ) . For cluster analysis , Bionumerics 5 . 1 was used ( Applied Maths NV , Sint-Martens-Latem , Belgium ) , with as comparison settings the Dice similarity coefficient and UPMGA dendrogram type ( optimization 0 . 50% , position tolerance 1 . 50% ) . PFGE profiles obtained were compared to PFGE of Salmonella Typhi profiles stored: In 1° the Institute of Public Health ( Brussels ) database , originating from Belgium ( n = 27 ) , Morocco ( n = 8 ) , Egypt ( n = 1 ) , Burkina Faso ( n = 1 ) , Niger ( n = 1 ) , Cambodia ( n = 20 ) , India ( n = 11 ) , Pakistan ( n = 7 ) , Bangladesh ( n = 3 ) , Indonesia ( n = 1 ) , Sri Lanka ( n = 1 ) , and Thailand ( n = 1 ) ; 2° the PFGE database of the Centre for Enteric Diseases ( CED ) of the National Institute for Communicable Diseases in South Africa , containing patterns for 730 Salmonella Typhi isolates . All data were entered in an Excel database ( Microsoft Corporation , Redmond , Washington , USA ) . Proportions were assessed for statistical significance using the Chi square test , considering p<0 . 05 as significant ( Stata 10 , StatCorp , Texas , USA ) .
From the 9634 blood cultures performed , 989 ( 10 . 3% ) clinically significant organisms were grown , including 201 isolates of Salmonella Typhi , representing respectively 2 . 1% of all blood cultures and 20 . 3% of all clinically relevant organisms . There were no annual or seasonal differences in isolation rates . The geographic site of cases , positive on blood cultures for Salmonella Typhi in DR Congo , is shown in figure 1 . Among the 201 Salmonella Typhi blood cultures isolates , 110 ( 54 . 7% ) were recovered from Bas-Congo and 67 ( 33 . 3% ) from Kinshasa . In Kinshasa , the isolation rate of Salmonella Typhi ( 67/5465 , 1 . 2% ) was lower compared to all other provinces in DR Congo ( 134/4064 , 3 . 4% , p<0 . 001 ) . The median age of patients infected with Salmonella Typhi was 15 years ( interquartile range 8–25 ) , but infection of young children was also common ( Table 1 ) . Over half of blood cultures were in children <10 years , yet 32 . 8% of the Salmonella Typhi isolates recovered were from children in this age group . The most affected age group were persons aged 10–19 years , in whom nearly 60% of the organisms isolated were Salmonella Typhi . In addition , this age group contributed to only approximately 10% of the blood culture samples , yet accounted for 30% of all Salmonella Typhi isolates recovered . In patients with clinically significant organisms , presumptive diagnosis of typhoid fever was made in 53 . 0% ( 524/989 ) of the cases . In the 201 patients from whom Salmonella Typhi was cultured , presumptive diagnosis of typhoid fever at the moment of sampling was made in 80 . 6% ( 162/201 ) of the cases . A total of 21 . 0% ( 34/162 ) of these patients suffered from abdominal distension and/or gastro-intestinal bleeding and were classified as complicated typhoid fever . Other presumptive diagnoses ( for several patients more than one presumptive diagnosis was mentioned ) included complicated urinary tract infection ( 14 . 4% ) , pneumonia ( 7 . 0% ) , meningitis ( 2 . 0% ) , malaria ( 5% ) and other non-specified causes of bacteremia ( 16 . 4% ) ; for three patients ( 1 . 5% ) , no data were available . Nearly half ( 93/201 , 46 . 3% ) of the patients had received antibiotics within 48 hours prior to sampling of blood cultures , mostly first-line antibiotics . Resistance against ampicillin , chloramphenicol or TMP-SMX were observed in 64 . 7% ( 130/201 ) , 41 . 3% ( 83/201 ) and 57 . 7% ( 116/201 ) of isolates , respectively . MDR and DCS were observed in 30 . 3% ( 61/201 ) and 15 . 4% ( 31/201 ) of isolates respectively; combined MDR and DCS occurred in 7 . 5% ( 15/201 ) of isolates . Isolates with DCS corresponded to nalidixic acid resistant isolates and vice versa . Only two ( 1 . 0% ) isolates had azithromycin MIC values exceeding 16 mg/l ( i . e . 24 mg/l ) ; both isolates were also resistant against ampicillin and TMP-SMX ( one also combined with DCS and nalidixic acid resistance ) . No cefotaxime resistance was observed . Fifty-one isolates ( 25 . 4% ) were fully susceptible to all three first-line antibiotics and 50 ( 24 . 9% ) were susceptible to all seven antibiotics tested . There was no apparent relationship between antimicrobial resistance and patient age , year of isolation , province of isolation or administration of antibiotics prior to isolation . All 31 isolates with DCS were analysed for mutations in the QRDRs of the gyrA , gyrB , and parC genes . No mutations were detected in gyrB or parC genes . No qnrA or qnrB genes were detected . For one isolate , the qnrS gene was detected . Apart from a mutation at codon 133 in the gyrA gene ( conferring a Glu to Gly change in the GyrA protein ) , which was also present in nalidixic acid susceptible strains , all 31 isolates had DCS-associated mutations in the gyrA gene , conferring the following amino acid mutations in the GyrA protein: ( i ) Ser83 changed into Phe or Tyr ( n = 22 ) , or ( ii ) Asp87 changed into Gly , Tyr or Asn ( n = 9 ) . Among 185 isolates tested for PFGE , 30 different profiles were observed ( Supplemental file ) . In 132/185 isolates ( 71 . 4% ) , an indistinguishable PFGE profile occurred . This profile was the main or single PFGE profile over time and geography , although its proportion was lower ( p = 0 . 02 ) in Kinshasa province ( 39/64 isolates , 60 . 9% ) compared to the other provinces in DR Congo ( 93/121 , 76 . 9% ) . In Kinshasa , the highest variation of profiles - in total 22 - was noted . Of the isolates showing the main/predominant profile , 31 . 1% ( 41/132 ) were MDR and 17 . 4% ( 23/132 ) were DCS , while 11 ( 8 . 3% ) showed a combined MDR and DCS . Comparison of the PFGE profiles of Salmonella Typhi from DR Congo with isolates from other geographic origins revealed one PFGE profile from a Salmonella Typhi isolate from DR Congo that was indistinguishable from a PFGE profile from an isolate recovered in Belgium , and 5 Congolese PFGE profiles that were indistinguishable from PFGE profiles of 9 Salmonella Typhi isolates recovered in South Africa .
This study was funded by Directorate General of Development Cooperation of the Belgian Government through Institutional Collaboration INRB-ITM ( Network Program on Laboratory Quality Management; Project 3 . 21 ) . The funders had no role in study design , data collection and analysis , decision to publish , or preparation of the manuscript . The authors declare that they have no conflicting interests in relation to this work . | Typhoid fever , caused by infection with Salmonella enterica serovar Typhi ( Salmonella Typhi ) , is an important health problem in sub-Saharan Africa . Multidrug resistance of Salmonella Typhi to the first line antibiotics is spreading and treatment of typhoid fever increasingly relies on fluoroquinolone antibiotics such as ciprofloxacin . Increased use of fluoroquinolones is however associated with spread of resistance as well . In sub-Saharan Africa , microbiological cultures to detect invasive bacterial diseases are frequently absent and the extent of the problem is poorly known . In the present study , 201 Salmonella Typhi isolates were collected between 2007 and 2011 in DR Congo , mainly from children and young adults . For the first time , widespread Salmonella Typhi multidrug resistance ( 30 . 3% ) and decreased ciprofloxacin susceptibility ( 15 . 4% ) is described in Central Africa . Decreased ciprofloxacin susceptibility was associated with point mutations in the quinolone resistance determining region of the gyrA gene . Resistance to azithromycin , an alternative for treatment of uncomplicated typhoid fever in the case of decreased ciprofloxacin susceptibility , was still rare ( 1 . 0% ) . Our findings demonstrate emergence of multidrug resistance and fluoroquinolone decreased susceptibility in DR Congo , and highlight the need for increased microbiological diagnosis and surveillance , being a prerequisite for rational use of antimicrobials and the development of standard treatment guidelines . | [
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| 2012 | Salmonella Typhi in the Democratic Republic of the Congo: Fluoroquinolone Decreased Susceptibility on the Rise |
The important human pathogen Pseudomonas aeruginosa has been linked to numerous biofilm-related chronic infections . Here , we demonstrate that biofilm formation following the transition to the surface attached lifestyle is regulated by three previously undescribed two-component systems: BfiSR ( PA4196-4197 ) harboring an RpoD-like domain , an OmpR-like BfmSR ( PA4101-4102 ) , and MifSR ( PA5511-5512 ) belonging to the family of NtrC-like transcriptional regulators . These two-component systems become sequentially phosphorylated during biofilm formation . Inactivation of bfiS , bfmR , and mifR arrested biofilm formation at the transition to the irreversible attachment , maturation-1 and -2 stages , respectively , as indicated by analyses of biofilm architecture , and protein and phosphoprotein patterns . Moreover , discontinuation of bfiS , bfmR , and mifR expression in established biofilms resulted in the collapse of biofilms to an earlier developmental stage , indicating a requirement for these regulatory systems for the development and maintenance of normal biofilm architecture . Interestingly , inactivation did not affect planktonic growth , motility , polysaccharide production , or initial attachment . Further , we demonstrate the interdependency of this two-component systems network with GacS ( PA0928 ) , which was found to play a dual role in biofilm formation . This work describes a novel signal transduction network regulating committed biofilm developmental steps following attachment , in which phosphorelays and two sigma factor-dependent response regulators appear to be key components of the regulatory machinery that coordinates gene expression during P . aeruginosa biofilm development in response to environmental cues .
Biofilms are composed of microorganisms attached to a solid surface and encased in a hydrated polymeric matrix of their own synthesis . Biofilms form when bacteria adhere to surfaces in moist environments . Biofilm-associated microorganisms have been shown to colonize a wide variety of medical devices and have been implicated in over 80% of chronic inflammatory and infectious diseases including chronic otitis media , native valve endocarditis , gastrointestinal ulcers , urinary tract and middle ear infections , and chronic lung infections in cystic fibrosis ( CF ) patients [1] , [2] . The human pathogen Pseudomonas aeruginosa is considered one of the primary causes of mortality in patients with CF , the most common life-threatening hereditary disease in Caucasians [3] , [4] . In addition , P . aeruginosa causes a variety of diseases in individuals predisposed to infections as the result of severe burns , wounds , urinary tract or corneal injury , or immunocompromised status [5]–[8] . Biofilm cells differ from their planktonic counterparts in the genes and proteins that they express , resulting in distinct phenotypes including altered resistance to antibiotics and the human immune system [2] , [9] , [10] . Thus , it is not surprising that biofilms are considered to be differentiated communities compared to their planktonic counterparts [9] , [11] . This is supported by the finding that various microorganisms , including P . aeruginosa have been shown to form biofilms in a stage-specific and coordinated manner . Biofilm formation is initiated with surface attachment by planktonic bacteria , followed by formation of clusters and microcolonies and subsequent development of differentiated structures in which individual bacteria as well as the entire community are surrounded by exopolysaccharides . The biofilm developmental cycle comes full circle when biofilms disperse [12] , [13] . This process has been shown to be governed by the activities of regulatory networks that coordinate the temporal expression of various motility , adhesion , and exopolysaccharide genes in response to inter- and intracellular signaling molecules and environmental cues . Vallet et al . [14] described a transcriptional regulator MvaT in P . aeruginosa that represses the expression of cup genes involved in the chaperone-usher fimbrial assembly pathway . MvaT deletion mutants exhibited enhanced attachment . In contrast , type IV pili and flagella deletion mutants exhibited reduced attachment indicating that attachment and biofilm formation are mediated by extracellular appendages [12] , [15]–[17] . Furthermore , the intracellular signaling molecule bis- ( 3′–5′ ) -cyclic diguanylic guanosine monophosphate ( cyclic-di-GMP ) , first described to control extracellular cellulose biosynthesis in Acetobacter xylinum [18] , [19] , has been demonstrated in several microorganisms to modulate biofilm formation via the production of exopolysaccharides or matrix components , control auto-aggregation of planktonic cells , and regulate swarming motility [20]–[32] . In P . aeruginosa , at least two pathways have been identified to modulate cyclic-di-GMP and thus , biofilm formation . These are the wsp chemosensory signal transduction pathway [25] and a genetic pathway composed of the phosphodiesterase BifA , the inner membrane-localized diguanylate cyclase SadC and the cytoplasmic protein SadB [20] , [21] , [33] . Both are involved in the reciprocal cyclic-di-GMP-dependent regulation of Pel and Psl exopolysaccharide production as P . aeruginosa transitions from a planktonic to a surface associated lifestyle . Both Pel and Psl exopolysaccharides contribute to the overall architecture of biofilms and are essential for surface interaction and biofilm initiation [34] , [35] . Expression of the pel and psl genes is coordinated by the global regulator RetS , a hybrid sensor kinase-response regulator protein , that plays a key role in the reciprocal regulation of virulence factors and biofilm formation required for acute versus chronic infection [36] . RetS belongs to the family of two-component regulatory systems ( TCS ) which translate external signals into adaptive responses by a variety of mechanisms , including control of gene expression and methylation of target proteins . RetS is postulated to act in concert with two other TCS sensor kinase-response regulator hybrids , GacS and LadS , to coordinate the expression of determinants involved in biofilm formation and the production of determinants required for cytotoxicity in P . aeruginosa via the small regulatory RNA rsmZ [36] , [37] . Inactivation of RetS results in reduced cytotoxicity but increased attachment and biofilm formation , while inactivation of both LadS and GacS results in increased virulence but decreased biofilm formation capacity [36] , [37] . This multi-component switch thus orchestrates the transition from the planktonic to the biofilm mode of growth by P . aeruginosa via phosphorylation events of the two-component regulatory system GacA/GacS [36]–[38] . Overall , the findings suggest that the transition to a surface associated lifestyle proceeds via several pathways , probably in response to environmental cues or signals present during attachment , and involves the coordinated transduction of phosphorylation events via two-component regulatory systems ( TCS ) . This raises the question of whether the transition to later stages of biofilm formation , which coincide with distinct phenotypes compared to planktonic and initial attached bacterial cells , also involves sensing of environmental signal ( s ) and requires the coordinated transduction of phosphorylation events ( phosphorelays ) . Here we demonstrate that P . aeruginosa exhibits distinct protein phosphorylation patterns at various stages of biofilm development . Furthermore , we report the identification of three novel two-component regulatory systems named BfiRS ( PA4196-4197 ) , BfmRS ( PA4101-4102 ) , and MifRS ( PA5511-5512 ) that coordinate phosphorylation events required for the progression of P . aeruginosa biofilm development in a stage-specific manner . These systems together form a coordinated signaling network that regulates three committed steps of the P . aeruginosa biofilm life cycle , in particular the transition to three later biofilm developmental stages following initial attachment , namely initiation of biofilm formation ( BfiRS ) , biofilm maturation ( BfmRS ) , and microcolony formation ( MifRS ) .
While phosphoproteomic analyses have become widespread in studies of regulation , signaling , development , the characterization of bacterial species and host responses during pathogenesis [40]–[49] , only a limited number of studies have demonstrated that bacterial phosphoproteomes are dynamic [44] , [46] , [48] . We therefore used a combination of 2D/PAGE and immunoblot analysis using commercially available anti-phospho Ser/Thr antibodies ( see Suppl . Fig . S1A-B for an example ) to probe for the presence of signal transduction events that occur over the course of biofilm formation . Immunoblots of whole cell extracts obtained from planktonic cells and biofilm cells representing five developmental stages ( reversibly and irreversibly attached cells , maturation-1 and -2 and dispersion stage; following 8 , 24 , 72 , 144 , and 216 hr of growth , respectively , see [12] , [50] for timing of biofilm stages ) were thus analyzed for the presence of planktonic- and biofilm-specific phosphorylation events . The planktonic mode of growth coincided with 24 phosphorylated proteins that were not phosphorylated following the transition of P . aeruginosa to surface-associated mode of growth ( Fig . 1A , stage-specific events ) . Additional stage-specific events were detected for biofilms differing in age . For instance , 8 hr and 24 hr old biofilms displayed 23 and 21 phosphorylation events , respectively , not detected at any other stage . Regardless of the biofilm developmental stage , 7 phosphorylation events were detected that were absent in planktonic cells ( Fig . 1A , biofilm-specific events ) . In both modes of growth , 26 proteins were constitutively phosphorylated . In addition to biofilm stage-specific phosphorylation of proteins , protein phosphorylation events were detectable at more than one biofilm growth stage indicating that the transition to surface-associated growth coincides with distinct protein phosphorylation and dephosphorylation events . As shown in Fig . 1A , these phosphorylation events are subcategorized as occurring during the reversible and irreversible attachment , biofilm formation and maturation stage depending on when and for how long protein phosphorylation was detected . For instance , four proteins were phosphorylated both in planktonic and reversible attached cells ( 8 hr biofilms ) but not at any other biofilm stage ( Fig . 1A , reversible attachment ) while 4 different proteins were phosphorylated only in planktonic cells and biofilm cells after 8 hr and 24 hr of growth under flowing conditions ( Fig . 1A , irreversible attachment ) . Furthermore , evidence of proteins being dephosphorylated over the course of biofilm formation was detected . Multiple proteins were found to be dephosphorylated at either a single or at multiple stages over the course of biofilm formation and maturation ( Fig . 1B ) . Moreover , the similarity of the biofilm phosphoproteome to the planktonic phosphorylation patterns decreased from 59% in 8-hr-old biofilms to 35% in 144-hr-old , mature biofilms . The reduced similarity in phosphorylation events between biofilms and planktonic cells was mainly due to biofilm specific phosphorylation events detected at one or more stages of development . Dispersion-stage biofilms ( 216-hr-old ) shared 43% similarity with the phosphorylation patterns of planktonically-grown P . aeruginosa cells ( not shown ) . The increase in similarity between the planktonic and the 216-hr-old biofilm phosphoproteomes is consistent with previous reports indicating that cells within dispersion-stage biofilms are returning to the planktonic mode of growth [12] , [50] . Protein phosphorylation in bacteria is not restricted to serine and threonine amino acid residues; however , the analysis of phosphorylation events by immunoblotting is limited to the availability of anti-phospho Ser/Thr ( and tyrosine ) antibodies . We therefore also purified phosphorylated proteins using metal oxide affinity chromatography ( MOAC , see Fig . S1 ) , a gel-independent approach allowing for the enrichment of phosphoproteins independent of the phosphorylation site with an up to 100% specificity [51] , [52] , followed by cleavable isotope coded affinity tag ( cICAT ) labeling and analysis by liquid chromatography tandem mass spectrometry ( LC-MS/MS ) . This quantitative mass spectrometric approach was used to analyze protein phosphorylation patterns of biofilm cells grown to the reversible , irreversible , maturation-1 and maturation-2 biofilm stages ( 8– , 24– , 7–2 , and 144-hr-old biofilms , respectively [12] ) in comparison to those of planktonic cells . Similarly to the results obtained via immunoblot analysis , the changes in phosphorylation events over the course of biofilm development detected using LC-MS-MS analysis appeared to be stage-specific ( two examples are shown in Suppl . Fig . S2 ) , with the similarity to the planktonic patterns decreasing from 72% in 8 hr biofilms to 38% in 144 hr biofilms ( Fig . 1C ) . The overall stage-specific ( de ) phosphorylation events as well as the differences in the phosphoproteome were similar to those detected by immunoblot analysis using anti-Ser/Thr antibodies . This is the first description of the dynamic changes of the phosphoproteome occurring during biofilm development . The combination of approaches used here has not been previously used to identify phosphorylated proteins in biofilms . The quantitative mass spectrometric approach by LC-MS/MS allowed for the simultaneous determination of peptide amino acid sequences by collision-induced dissociation ( CID ) in the MS/MS mode . Examples of two CID spectra are shown in Suppl . Fig . S2 . Proteins that were confirmed to be phosphorylated by immunoblot analysis were identified using a mass spectrometric approach as well . We thus identified 48 proteins that were differentially phosphorylated at one or more biofilm developmental stage including elongation factors , ribosomal proteins , several enzymes including reductases and GMP synthase , sigma factor RpoD ( Suppl . Table S1 ) and 11 regulatory proteins ( Table 1 ) . The majority of regulatory proteins found to be uniquely phosphorylated during planktonic growth were transcriptional regulators , while with the exception of PA2096 , all regulatory proteins found to be phosphorylated during surface attached growth were identified as belonging to two-component systems ( TCS ) ( Table 1 ) . Of those , the sensor/response regulator hybrid GacS and PA4197 ( BfiS ) were found to be phosphorylated as soon as 8 hr following attachment , and PA2096 and PA4101 ( BfmR ) following 24 hr of surface-associated growth ( Table 1 ) . Interestingly , PA4102 ( BfmS ) , the cognate sensor of PA4101 , was found to be phosphorylated following PA4101 phosphorylation after 72 hr of biofilm growth ( Table 1 ) . The reason for the difference in the timing of phosphorylation between sensor and response regulator is unclear . It is possible that this due to the different detection methods used . The probable TCS regulatory protein PA5511 ( MifR ) was phosphorylated following 72 hr of surface-associated growth . The stage-specific detection and phosphorylation of PA5511 as determined by LC-MS/MS analysis in conjunction with cICAT as well as the CID spectra of a tryptic peptide of PA5511 is shown in Suppl . Fig . S2 . Neither the cognate sensory protein PA5512 nor the response regulator PA4196 were identified in this study . This may be due to detection limitation ( low protein concentrations , poor protein solubility , poor ionization ) and/or limitation in the number of phosphorylated proteins identified ( see Suppl . Tables S1 and S2 for comparison ) . As PA4197 , PA4101 and GacS were all phosphorylated by 24 hr of biofilm growth , we asked whether the three proteins are modified simultaneously or in a sequential manner . We reasoned that if protein phosphorylation occurs in sequence , inactivation of one of the proteins would potentially prevent phosphorylation of the other proteins of the phosphorelay . We therefore analyzed GacS , PA4101 , and PA4197 mutant biofilm phosphorylation patterns for the presence/absence of these regulators . No evidence of phosphorylation of PA4197 , PA4101 , or GacS was detected in ΔPA4197 mutant biofilm phosphorylation patterns . However , phosphorylation of both GacS and PA4197 was detected in ΔPA4101 mutant biofilms , indicating that PA4101 phosphorylation may occur downstream of GacS and PA4197 . ΔgacS biofilm phosphorylation patterns showed an intermediate phosphorylation phenotype with PA4197 being phosphorylated but PA4101 phosphorylation lacking ( not shown ) . PA5511 was not detected in any of the mutant biofilms analyzed ( Suppl . Fig . S3 ) . The findings suggest that phosphorylation of regulatory proteins occurs in a sequential ( but probably indirect ) manner over the course of biofilm formation . To determine whether phosphorylation coincided with de novo gene expression or reflected biofilm-specific patterns of posttranslational modification , RT-PCR was used . PA4101 expression was detected to be biofilm-specific , while PA4197 and PA5511 were constitutively expressed regardless of the mode of growth ( Suppl . Fig . S4 ) . Similarly , retS and ladS were also constitutively expressed indicating that posttranslational modifications are essential for their activity . As differential and sequential phosphorylation of regulatory proteins was detected over the course of P . aeruginosa biofilm development , we asked whether inactivation of these regulatory proteins would alter or affect the stage-specific progression of biofilm formation . We therefore focused on biofilm-specific regulatory proteins . Since the proteins PA4101 , PA4197 , and PA5511 were found to be phosphorylated following 8 , 24 , and 72 hr of biofilm growth , respectively ( Table 1 ) , corresponding to three biofilm developmental stages [9] , [12] , mutants in these three genes were chosen and allowed to form biofilms for 144 hr in flow cells to test for biofilm formation defects . Under the conditions tested , wild type P . aeruginosa biofilms reached maturity following 144 hr of growth as characterized by biofilms being composed of large microcolonies exceeding 100 µm in diameter ( Fig . 2A ) . In contrast , PA4197 and PA4101 mutant biofilms lacked microcolonies after 144 hr of growth ( Fig . 2B ) and were only composed of a thin layer of cells at the substratum with an average height of 0 . 5 and 1 . 4 µm , respectively ( Table 2 ) . However , in contrast to PA4197 mutant biofilms , PA4101 mutant biofilms demonstrated the formation of some cellular aggregates which were less than 10 µm in height ( Fig . 2B ) . Furthermore , the mutant biofilms differed significantly from wild type biofilms with respect to biomass , surface coverage , and roughness coefficient . Complementation of both PA4101 and PA4197 mutants restored biofilm formation to wild type levels ( Fig . 2C , Table 2 ) . These results allowed us to firmly conclude that the mutant biofilm phenotypes are caused by a defect in the PA4197 and PA4101 ORF . Based on the role of PA4197 in the initiation of biofilm formation , we named the PA4197 ORF Biofilm initiation Sensor ( BfiS ) . BfiS is an unusual sensor that harbors a His kinase A domain typically found in two-component system ( TCS ) sensor proteins , a Histidine kinase-like ATPase domain involved in autophosphorylation but also in protein dephosphorylation events , and a PAS signal receiver domain [53] . The cognate response regulator BfiR ( PA4196 ) harbors a CheY-like signal receiver domain and a LuxR-like DNA binding domain , which is also present in the quorum-sensing regulatory proteins LasR , RhlR , and QscR and in response regulators with established roles in biofilm formation ( GacA , RocA1/SadA ) [53] . BfiR also harbors region 4 of Sigma-70 ( RpoD ) -like sigma factors , a domain involved in binding to −35 promoter elements [53] . Due to its role in biofilm maturation , we named the PA4101 ORF Biofilm maturation Regulator ( BfmR ) . The protein harbors an OmpR-like transcriptional regulator domain encompassing the common signal receiver and DNA-binding effector domains [53] . The cognate sensor BfmS ( PA4102 ) is unusual in that it lacks an autophosphorylation site typically found in sensor kinases [53] . As shown in Table 1 , the probable TCS regulatory protein PA5511 was phosphorylated following 72 hr of surface-associated growth . PA5511 mutant biofilms grown for 144 hr lacked clusters and microcolonies typically found in wild type biofilms following 72–144 hr of growth ( Fig . 2A–B ) . Complementation restored biofilm formation to wild type levels ( Fig . 2C , Table 2 ) . However , when placed in a PAO1 background ( PAO1/pJN5511 ) , overexpression of PA5511 resulted in biofilms composed of large microcolonies exceeding 250 µm in diameter ( compared to an average cluster diameter of 150 µm in P . aeruginosa PAO1 , Fig . 2A , D ) . Since cluster formation correlated with PA5511 expression levels , we named PA5511 Microcolony formation Regulator ( MifR ) . MifR harbors a CheY-like receiver and a sigma-54 interaction domain [53] . The protein is on average 30–50% identical to known P . aeruginosa NtrC-like enhancer binding proteins including PilR , FleQ , FleR , AlgB , CbrB , and NtrC [53] , [54] . The cognate sensor ( MifS , PA5512 ) is a typical sensor kinase harboring both a His kinase A and a His kinase-like ATPase domain [53] . Since individual carbon and nitrogen sources have been demonstrated to modulate P . aeruginosa in vitro biofilm development and architecture [16] , [55]–[58] , surface motility [59] and P . aeruginosa cell-cell signaling ( quorum sensing ) [60]–[63] , the biofilm architecture of all four mutant biofilms was tested using three different media including LB medium and two minimal media containing glutamate [17] or citrate [64] as sole carbon source . Under the conditions tested , the biofilm architecture of all three mutants was similar to the biofilm architecture shown in Fig . 2 independent of the media used . To determine whether the altered biofilm structure was due to arrested biofilm formation or attachment defects , we first determined whether the P . aeruginosa mutants are defective in attachment . Inactivation of BfiS , BfmR , and MifR ( PA4197 , PA4101 , PA5511 , respectively ) did not affect initial attachment to polystyrene compared to wild type biofilms as revealed by the crystal violet microtiter plate assay and confirmed by microscopy ( not shown ) . Furthermore , no difference in growth in broth or defect with respect to twitching , swimming , and swarming or Pel and Psl polysaccharide production was detected for any of the mutant strains ( not shown ) . In addition , no difference in transcript abundance , as determined by semi-quantitative RT-PCR , of genes involved in Pel and Psl polysaccharide biosynthesis compared to wild type was detected ( not shown ) . However , a ΔgacS mutant showed 10-fold reduced initial attachment compared to the wild type ( not shown ) , consistent with previous findings by Parkins and colleagues [65] . These findings implied that the novel regulatory proteins were involved in the regulation of biofilm formation at later stages following initial attachment . To determine the stage at which ΔbfiS and ΔbfmR mutant biofilms were arrested , the biofilm architecture of the mutant strains after 144 hr of growth was compared to the wild type P . aeruginosa biofilm architecture following 24 , 72 , and 144 hr of growth ( Table 2 ) . Based on the comparison of 5 biofilm variables , both mutant biofilms were more similar to 24-hr-old biofilms , with ΔbfmR forming more substantial biofilms than ΔbfiS or 24 hr wild type biofilms ( Table 2 ) . Arrest of biofilm formation at the 1-day time point correlated with the timing of BfiS and BfmR phosphorylation ( Tables 1–2 ) . Comparison of the ΔmifR biofilm architecture following 144 hr of growth to wild type biofilms indicated that ΔmifR biofilms were comparable to 72-hr-old biofilms . Since MifR was detected to be phosphorylated following 72 hr of biofilm growth ( Table 1 ) , our findings indicate that phosphorylation of MifR is essential for the progression of P . aeruginosa biofilms from the maturation-1 stage ( 72 hr ) to the maturation-2 stage ( 144 hr ) . To exclude the possibility that the ΔbfiS , ΔbfmR , and ΔmifR mutant biofilms may have disaggregated prematurely , the formation of mutant biofilms was monitored daily by confocal microscopy over a period of 144 hr . The ΔbfiS and ΔbfmR biofilms resembled wild type biofilms with respect to biomass and overall architecture at the 24 hr time point ( see Fig . 2A–B ) . However , while wild type biofilms continued to mature/develop upon prolonged incubation ( see Fig . 2A ) , no additional biomass accumulation or alteration in architecture was observed for ΔbfiS and ΔbfmR biofilms post 24 hr of growth . Furthermore , for ΔmifR biofilms , the progression of biofilm formation was indistinguishable from wild type P . aeruginosa biofilm formation for the first 72 hr of growth . However , continued incubation did not result in increased ΔmifR biofilm growth ( biomass , thickness ) or microcolony formation typically seen in wild type biofilms at the maturation-2 stage ( post 72 hr of growth , Table 2 , Fig . 2 ) . The findings clearly indicate that inactivation of these novel regulatory proteins did not cause biofilm disaggregation . Instead , our findings suggested that the mutant biofilms were incapable of progressing from the initial attachment stage to more mature biofilm stages . Since GacS was found to be phosphorylated in a BfiS-dependent manner following 8 hrs of growth , we asked whether a ΔgacS mutant forms biofilms similar in architecture to ΔbfiS biofilms . Inactivation of gacS resulted in the formation of biofilms following 144 hr of growth that were similar in appearance to 24-hr-old wild type biofilms . Closer inspection of biofilm formation by ΔgacS over a period of 144 hr , however , indicated that the biofilm architecture ( seen after 144 hr ) was due to accelerated biofilm growth followed by premature disaggregation of biofilms as compared to wild type biofilms . ΔgacS mutant biofilms were significantly thicker than wild type biofilms following 1 and 72 hr of growth under flowing conditions , forming microcolonies and clusters exceeding 150 µm in diameter ( Fig . 3 ) . At both time points , ΔgacS biofilms not only exceeded the average microcolony size typically seen for wild type biofilms of the same age , but also the biomass and thickness of wild type biofilms at more mature ages ( Fig . 3 , Table 2 ) . Continued growth for more than 72 hr , however , resulted in the disaggregation of ΔgacS mutant biofilms as indicated by the presence of large , detached clusters floating in the bulk liquid , and a significantly reduced attached biofilm biomass and biofilm thickness ( Fig . 3 , Table 2 ) . Thus , while growth of ΔgacS mutant biofilms following 24 hr post attachment was accelerated ( Fig . 3 ) , initial attachment was significantly reduced in this mutant ( not shown ) . These findings suggest that GacS may play a dual role in regulating biofilm formation , which in turn may be dependent on the phosphorylation status of GacS ( Table 1 ) . Based on qualitative and quantitative analyses , BfiS ( PA4197 ) and BfmR ( PA4101 ) mutant biofilm architecture appeared to be the result of arrested biofilm formation following initial attachment , while inactivation of MifR ( PA5511 ) coincided with biofilms impaired in microcolony formation at the maturation-1 stage . Since each of these biofilm developmental stages is characterized by a unique phosphorylation pattern ( Figs . 1 , 4 , Table 1 ) , we reasoned that if the mutant biofilms are indeed arrested in biofilm development , their phosphoproteomes will correspond to the stage at which they are arrested . We , therefore , analyzed the phosphorylation patterns of ΔbfiS , ΔbfmR , and ΔmifR biofilms grown for 144 hr in comparison to P . aeruginosa wild type biofilms grown for 8 , 24 , 72 , and 144 hr . The phosphoproteomes were analyzed using two approaches , ( i ) immunoblot analysis of whole biofilm cell extracts and ( ii ) LC-MS/MS analysis in conjunction with cICAT labeling following MOAC purification . The phosphoproteome of ΔbfiS biofilms as determined by LC-MS/MS was 74% similar ( 26% difference ) to planktonic cells while ΔbfmR biofilms shared 60% of all detected phosphorylation events with planktonic cells ( 40% difference ) . This is in contrast to the phosphoproteome of 144-hr-old P . aeruginosa wild type biofilms , which was 62–65% different from that of planktonic cells ( Fig . 4A ) . Furthermore , both mutant biofilms failed to exhibit phosphorylation events typically observed during normal biofilm development following 144 hr of growth ( see Fig . 1 , Suppl . Table S2 ) . For instance , ΔbfiS and ΔbfmR biofilms lacked all phosphorylated proteins typically found in mature , 144-hr-old biofilms . In addition , both mutant biofilms lacked evidence for MifR phosphorylation ( phosphorylated following 72 hr of wild type growth , Table 1 , Suppl . Fig . S3 ) . Instead , ΔbfiS biofilms exhibited stage-specific phosphorylation events typically detected in 8-hr- and 24-hr-old wild type biofilms: the Ser/Thr phosphoproteome contained 15 out of 23 phosphorylated proteins and 2 out of 21 phosphorylated proteins that are specific for 8-hr- and 24-hr-old wild type biofilms , respectively ( see Fig . 1 , Suppl . Table S2 ) . Similarly , the phosphorylation patterns of ΔbfmR biofilms indicated the presence of 24- and 72-hr stage-specific phosphorylated proteins ( not shown ) . The phosphorylation patterns of 144-hr-old ΔmifR biofilms were 62% different relative to planktonic cells , but only shared 58% similarity with mature , 144-hr-old wild type biofilms ( Fig . 4A ) . Furthermore , ΔmifR biofilms exhibited 8 out of 27 maturation-1 specific protein phosphorylation events , and only 16 out of 37 maturation-2 phosphorylation events ( Suppl . Table S2 , see Fig . 1 ) . We further reasoned that if the mutant biofilms are indeed arrested in biofilm development , their whole proteomes will also correspond to the stage at which they are arrested . We therefore compared the protein production patterns of 144-hr-old ΔbfiS , ΔbfmR , and ΔmifR biofilms to the 2D-patterns of P . aeruginosa wild type biofilms grown for 24 , 72 and 144 hr using 2D/PAGE , 2D ImageMaster Platinum software and heuristic clustering . As shown in Fig . 4B , cluster analysis based on protein similarity confirmed our previous findings obtained by microscopic and phosphoproteome analyses of mutant biofilms . ΔbfiS biofilms were more similar to 24-hr-old wild type biofilms than to wild type biofilms at more mature stages . The two protein patterns were more than 80% similar . In contrast , ΔbfmR biofilms were most similar to protein patterns obtained from 72-hr-old wild type biofilms ( 85% similarity ) , while those of ΔmifR biofilms were similar to both 72- and 144-hr-old biofilms sharing 76 and 82% similarity , respectively , to both protein patterns ( Fig . 4B ) . Based on analyses of biofilm architecture , as well as of protein production and phosphorylation patterns , our findings indicate that ΔbfiS biofilms are arrested in the transition from reversible to the irreversible attachment stage ( 8 hr to 24-hr-old biofilms , respectively ) . Inactivation of MifR appeared to result in the arrest of biofilm development in the transition between the maturation-1 and -2 stages ( 72 to 144 hr ) while ΔbfmR biofilms were arrested in the transition between irreversible attachment to maturation-1 stage . Our observations indicated that BfiS ( PA4197 ) , BfmR ( PA4101 ) and MifR ( PA5511 ) are essential in the stage-specific development of P . aeruginosa biofilm formation . To determine whether these regulatory proteins are only essential during biofilm formation or are also necessary for the maintenance of established biofilms , we asked whether inactivation of these regulatory proteins in mature biofilms would affect biofilm architecture . Complemented mutant strains , harboring the respective regulator genes under the control of the arabinose-inducible PBAD promoter , were allowed to grow for 144 hr in flow cells to maturity ( Fig . 2C , Fig . 5–0 hr ) in the presence of arabinose , after which time arabinose was removed from the growth medium to stop the transcription of the respective genes . The resulting biofilm architecture was viewed over a period of 144 hr post arabinose removal using confocal microscopy . P . aeruginosa wild type harboring an empty pJN105 vector was used as control . Loss of bfiS , bfmR , and mifR expression due to arabinose removal resulted in the collapse of the mutant biofilm architecture within three days . For ΔbfiS and ΔbfmR mutant biofilms , biofilm disaggregation was noticeable as early as 24 hr post arabinose removal ( not shown ) . The collapse was apparent by significant reduction ( P<0 . 05 ) of biofilm variables including biofilm biomass and thickness , which further decreased upon continued incubation ( Fig . 5 , Table 3 ) . Post 144 hr of arabinose removal , the biofilm architecture of the complemented mutants was similar to mutant biofilms lacking the respective regulatory gene ( Figs . 2 , 5 ) . In contrast , no reduction of the wild type biofilm architecture was observed ( Fig . 5 , Table 3 ) . These findings indicated that the three novel regulators are not only essential for the stage-specific progression of P . aeruginosa biofilms but also in the maintenance of the biofilm structure .
Evidence showing that biofilm development is a coordinated series of events coinciding with distinct phenotypes has led to the assumption that the formation of biofilms is a regulated progression [11] , [12] , [66] . However , biofilm development has been considered to be distinct from other developmental processes including the programmed differentiation seen in spore formation in Bacillus subtilis or fruiting body formation in Myxococcus xanthus [11] , mainly because no regulatory pathways have yet been identified that are responsible for regulating committed steps in the formation of biofilms with the exception of attachment . In this study we describe the identification and initial characterization of three novel two-component systems ( TCS ) essential in regulating three committed steps in biofilm development . Mutation in these regulatory pathways did not affect initial attachment , motility , or Pel and Psl polysaccharide production , but instead arrested biofilm development in the transition from reversible to irreversible attachment [8 hr to 24 hr , BfiRS ( PA4196-4197 ) ] , from initial attachment to the maturation-1 stage [ ( 24 hr to 72 hr , BfmRS ( PA4101-4102 ) ] , and following the maturation-1 stage [72 hr to 144 hr , MifRS ( PA5511-5512 ) ] ( Fig . 6 ) . To our knowledge , this is the first description of a regulatory program for stage-specific biofilm development . The stage-specific arrest in biofilm formation of the mutant strains coincided with the timing of phosphorylation of the respective regulatory or sensory proteins indicating that the phosphorylation status of the three novel two-component systems is essential for their function in regulating biofilm development by P . aeruginosa . Furthermore , the phosphorylation of these two-component systems occurred in sequence with BfiS being phosphorylated first , followed by GacS , and lastly , MifR ( Table 1 , Fig . 6 ) . The sequential phosphorylation of sensors/regulatory proteins is reminiscent of a regulatory cascade in which each phosphorylation event acts as a trigger for bacterial biofilm cells to transition to the next developmental stage ( Fig . 6 ) . Furthermore , the novel TCS systems described here appear to be linked via GacS to the multicomponent system RetS/LadS/GacAS/RsmA essential for regulating the switch between the planktonic and the sessile mode of growth . While it is not clear how the three two-component systems interact to form the observed sequential phosphorylation cascade , it is apparent from our observations that phosphorylation of each of the three novel TCS has to occur for P . aeruginosa biofilms to mature ( Fig . 2 ) . Possible scenarios for the sequential phosphorylation events to occur are by direct interaction or activation of a TCS system by one that is upstream in the cascade ( Fig . 6 ) , or indirectly . Since inactivation of each TCS system resulted in altered or arrested biofilms which failed to exhibit stage-specific protein production and phosphorylation events ( Figs . 1 , 4 , Suppl . Table S2 ) , it is likely that the mutant biofilms in turn do not produce the necessary signal ( s ) to activate or phosphorylate TCS system ( s ) that are further downstream . Thus , it is likely that inactivation of one TCS system ( in ) directly results in altered or arrested phosphorylation patterns and thus , lack of phosphorylation of downstream TCS systems ( as observed here ) . Independent of the mechanism , it is evident that inactivation not only disrupts the sequence of phosphorylation events but also leads to the collapse of mature biofilms to an earlier biofilm developmental stage at which the respective regulatory proteins play a role ( Fig . 5 , Table 3 ) . This is even more important as this biofilm collapse was observed under two different nutritional conditions , when grown on minimal medium using either glutamate or citrate as a sole carbon source ( see also Figs . 2 and 5 for comparison of LB and glutamate minimal medium ) . The finding suggests that while biofilm formation , architecture and cell-cell signaling is modulated by environmental and nutritional conditions resulting in biofilm development proceeding via distinctly different pathways [16] , [55]–[63] , it is possible that the novel regulatory proteins identified here play a role under more than one discrete culturing condition or pathway . The novelty of these TCS is further supported by the finding that a search for BfiS ( PA4197 ) and BfmR ( PA4101 ) homologues using BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) and BLINK ( precomputed BLAST , [53] ) , did not reveal any proteins that have been previously characterized in the literature . However , BfiS-like sensory proteins with identities ranging between 28–68% were detected in a variety of Gram-negative bacteria , in particular in α- , β- , and γ-proteobacteria ( Suppl . Table S3 ) . No homologues , however , were detected in λ-proteobacteria or E . coli , Klebsiella pneumoniae , and Enterobacter sp . Similarly , BfmR homologues were detected among proteobacteria including Yersinia sp . , Burkholderia sp . , Rhizobium sp . , Vibrio sp , Geobacter sp . , and M . xanthus with identities ranging between 50–92% ( Suppl . Table S3 ) . MifR homologues harboring a sigma-54 binding domain are present in both Gram-positive and Gram-negative bacteria including M . xanthus ( Suppl . Table S3 ) . The closest MifR homologue in M . xanthus was identified as the NtrC-like chemosensory regulator of development CrdA ( 48% identity ) . Inactivation of crdA has been shown to result in delayed M . xanthus multicellular development [67] . NtrC-like regulators belong to a family of transcriptional activators which control a variety of physiological processes in response to environmental signals [68] . This family of regulators control transcription from −12 , −24 promoters recognized by RNA polymerase that utilizes the alternative sigma 54 factor encoded by rpoN and its analogs . At least 8 NtrC-like transcriptional regulators are involved in coordinating M . xanthus fruiting body formation at distinct stages of the developmental process [69]–[71] . The preponderance of developmental promoters with sigma 54 hallmarks led to the suggestion that NtrC-like activators are key components of the transcriptional machinery that coordinates gene expression during M . xanthus development [72] . While fruiting body formation is governed by a cascade of RpoN-dependent transcription factors in starving cells , endospore formation in B . subtilis requires the consecutive activity of multiple sigma factors including Sigma E , F , G , and K . Their activity is regulated by posttranslational processes , either by cleaving the precursor molecules or by sequestration of sigma factors by “anti-sigma factor” proteins in response to intercellular cues , and compartmentalization [68] , [73] . Similarly , biofilm developmental processes appear to be controlled by sigma factors . Based on domain structure , two TCS regulatory proteins identified here regulate genes controlled by the sigma factors RpoD and RpoN [53] , [74] , [75] . BfiR harbors region 4 of Sigma-70 ( RpoD ) -like sigma factors , a domain involved in binding to −35 promoter elements . Activation of BfiR coincides with BfiS phosphorylation following 8 hours of surface attached growth and dephosphorylation of RpoD ( Table 1 ) . MifR harbors a sigma-54 binding ( RpoN ) binding domain and is dependent on the consecutive phosphorylation of BfiRS and BfmRS ( see Suppl . Fig . S3 ) . These results are consistent with the idea that biofilm development by P . aeruginosa is orchestrated by a regulatory cascade ( Fig . 6 ) that is analogous to other developmental systems including spore formation in B . subtilis or fruiting body formation in M . xanthus , requiring the consecutive action of at least two sigma factors and three two-component regulatory systems in response to environmental signals . In summary , we have evidence of three novel regulatory systems playing a role in the progression of P . aeruginosa biofilm development in a stage-specific manner . The only other regulatory system having been identified to play a role at later stages of biofilm formation , in particular the formation of large microcolonies and fluid-filled channels , is the three-component system SadARS ( RocS1RA1 ) , probably by controlling the expression of fimbrial cup genes [66] , [76] . In addition , coordinated transduction of phosphorylation events via two-component systems has also been shown to play a role in attachment . A multi-component switch composed of three unusual hybrid sensor kinases , RetS , LadS , and GacS , has recently been demonstrated to reciprocally orchestrate the transition from acute to chronic infection in P . aeruginosa , as well as to reciprocally regulate the transition between the planktonic and biofilm modes of growth by inversely coordinating repression of genes required for initial colonization , mainly genes responsible for exopolysaccharide components of the P . aeruginosa biofilm matrix [36] , [37] . While our study did not result in the identification of RetS or LadS , we identified GacS by two different approaches and confirmed GacS phosphorylation by immunoblot analysis ( Table 1 ) . GacS acts as a suppressor of RetS ( and vice versa ) with RetS regulating the suppressor activity of the membrane-bound sensor GacS by directly modulating its phosphorylation state [38] . The finding is consistent with our observation of GacS playing a dual role in biofilm formation , with phosphorylation acting as a switch in the function of GacS ( Fig . 3 , Table 2 ) : GacS participates in the planktonic/biofilm switch in its non-phosphorylated state , but limits/regulates the rate of biomass accumulation and biofilm development when phosphorylated . Since phosphorylation of GacS occurred following 8 hr of surface attached growth ( Table 1 ) and since RetS directly modulates the phosphorylation state of GacS [38] , the findings may suggest that RetS only remains functional for a period of 8 hours during initial attachment after which RetS is rendered non-functional . Here , GacS was found to be phosphorylated in a BfiS dependent manner . In turn , expression of the BfiS cognate response regulator , BfiR , was found to be RsmA dependent [77] ( see Fig . 6 ) . Taken together , our observations suggest a link between the multi-component switch RetS/LadS/GacAS/RsmA which reciprocally regulates virulence and the transition between the planktonic and the surface attached mode of growth and the previously undescribed signaling network which regulates developmental steps once P . aeruginosa has committed to the surface associated lifestyle ( Fig . 6 ) . Taken together , this work identifies a previously undescribed signal transduction network composed of BfiSR ( PA4196-4197 ) , BfmSR PA4101-4102 ) , and MifSR ( PA5511-5512 ) that sequentially regulates committed biofilm developmental steps following attachment by transcriptional and posttranscriptional mechanisms , which is linked via GacS and RsmA to the previously described multi-component switch RetS/LadS/GacAS/RsmA . Furthermore , the finding of sequential and essential regulatory steps in biofilm formation and the involvement of at least two sigma factors suggests that biofilm development is analogous to other programmed developmental processes . However , in contrast to known developmental processes , our findings suggest that both two-component regulatory systems and sigma factor dependent response regulators are key components of the transcriptional and regulatory machinery that coordinate gene expression during P . aeruginosa biofilm development .
All bacterial strains and plasmids used in this study are listed in Table 4 . The parental strain for all studies was P . aeruginosa PAO1 . All planktonic strains were grown in minimal medium containing glutamate as sole carbon source [17] at 22°C in shake flasks at 220 rpm . Biofilms were grown as described below at 22°C in minimal medium . In addition , biofilms were grown in VBMM medium containing citrate as sole carbon source [64] and 1/20 diluted Lennox Broth ( LB ) . Complementation experiments were carried out in minimal medium [17] with or without 0 . 1% arabinose . Antibiotics were used at the following concentrations: 50–75 µg/ml gentamicin ( Gm ) and 50 µg/ml tetracycline ( Tet ) for P . aeruginosa; 20 µg/ml Gm , 50 µg/ml ampicillin ( Ap ) , and 25 µg/ml kanamycin ( Km ) for E . coli . Biofilms were grown using a once-through continuous flow tube reactor system to obtain proteins and in flow cells to view the biofilm architecture as described previously [12] , [13] . Quantitative analysis of epifluorescence microscopic images obtained from flow cell-grown biofilms was performed with COMSTAT image analysis software [78] . Initial biofilm formation was measured by using the microtiter dish assay system , as previously described [16] . Preparation of crude protein extract and protein determination was carried out as previously described [50] . Phosphoproteins were enriched by metal oxide affinity chromatography ( MOAC ) essentially as described by Wolschin and colleagues [51] . MOAC has been demonstrated by Krüger et al . to result in up to 20-fold enrichment of phosphoproteins and to approach 100% specificity [52] . Briefly , 750 µg of cell extract were diluted with MOAC incubation buffer ( 30 mM MES , 0 . 2 M potassium glutamate , 0 . 2 M sodium aspartate , 0 . 25% Chaps , and 8 M urea ) to a final volume of 1 . 5 ml , and subsequently incubated for 30 min at 4°C in the presence of 80 mg of aluminum hydroxide . Unbound phosphoproteins were removed by washing the aluminum hydroxide slurry with incubation buffer . Then , phosphoproteins were eluted from the slurry using 100 mM potassium pyrophosphate and 8 M urea , desalted by methanol-chloroform precipitation , and subsequently vacuum-dried . The resulting phosphoproteins were then used for 2D/PAGE [12] , [17] or LC-MS-MS analysis in conjunction with cICAT labeling as described below . To probe for the presence of Ser/Thr-phosphorylated proteins , 2D-gels were blotted onto PVDF membranes ( Biorad ) , and probed using anti-Phospho- ( Ser/Thr ) Phe antibodies as previously described [13] . In addition , pull-down assays were used to enrich for Ser/Thr-phosphorylated proteins as previously described [13] using anti-Phospho- ( Ser/Thr ) Phe antibodies ( Cell Signaling Technologies , Danvers , MA ) . Both protein and phosphoproteins patterns were analyzed using the 2D ImageMaster software ( GE Healthcare , Piscataway , NJ ) . In addition , the heuristic clustering function provided by the 2D software was used for wild type and mutant biofilm protein patterns comparisons . The cICAT reagent kits were obtained from Applied Biosystems ( Framingham , MA ) and the cICAT sample preparation procedure was performed according to the manufacturer's protocols . Phosphoproteins isolated from planktonic PAO1 cells were used as a reference and were labeled with the cICAT light reagent , while all biofilm-derived proteins were labeled with the cICAT heavy reagent . The combined samples containing the light- and heavy-tagged proteins were purified by cationic exchange , subsequently subjected to avidin affinity chromatography , and the purified cICAT-tagged peptides subjected to partial tag cleavage . Peptide analysis was performed using a QStarXL mass spectrometer ( Applied Biosystems ) coupled to an Agilent LC system . A 5micron/300 Å Magic C18 AQ reversed-phase LC column ( Michrom BioResources , Inc . , Auburn , CA ) was utilized with a 220 minute gradient from 2–80% acetonitrile ( plus 0 . 1% formic acid , 0 . 01% trifluoracetic acid ) . Data dependant analysis was utilized to perform MS/MS on all ions above 500 m/z . Proteins obtained from 2D-gels were identified by MALDI-TOF mass spectrometry as previously described [12] , [13] and by LC-MS-MS . For the latter , tryptic digested proteins were first separated by reverse phase chromatography ( 2–70% acetonitrile plus 0 . 1% formic acid and 0 . 01% trifluoracetic acid , 90 min gradient ) and subsequently detected and fragmented using a QStarXL mass spectrometer ( Applied Biosystems ) . Determination of relative peptide abundances and protein identification were accomplished as previously described [12] , [13] and via analyses of TOF-MS and MS/MS data using Analyst QS 1 . 1 software with Bioanalyst , ProID , and ProICAT packages ( Applied Biosystems ) . Relative percent difference between two cICAT-analyzed samples was determined using the following formula: 100 U/ ( U+P ) , where U = total number of unique , unpaired peptide TOF-MS peaks detected ( i . e . : peptides present in only one of the two analyzed samples ) , and P = total number of cICAT peptide TOF-MS peak pairs detected ( i . e . : peptides present in both samples ) . Isogenic mutants were constructed by allelic replacement using sucrose-counter-selection as previously described [79] using the gene replacement vector pEX18Gm [80] . Complementation was accomplished by placing the respective genes under the control of an arabinose-inducible promoter in the pJN105 vector [81] . Primers used for strain construction are listed in Suppl . Table S4 . Swimming , swarming , and twitching motilities were assessed in tryptone or LB medium containing 0 . 3% , 0 . 5% , and 1 . 0% agar , respectively , as previously described [15] , [82] . Polysaccharide production was determined using the congo red ( CR ) binding assays as described [34] with the following modifications: Briefly , stationary phase cultures were adjusted to OD600 = 0 . 05 in LB containing 40 mg/L CR and incubated for 8 hr at 37°C with agitation after which time the cells were removed by centrifugation and the A490 of the supernatant was determined as a measurement of CR remaining in solution . RT-PCR was carried out to determine expression of genes encoding regulatory proteins , and proteins involved in Pel and Psl polysaccharide biosynthesis in planktonic and biofilm cells using 1 µg of total RNA [50] , [83] . PCR was carried out using primers listed in Suppl . Table S4 . mreB was used as control . Regulator homology searches and retrieval of regulator structure and conserved domain composition were accomplished using the National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov ) and the Pseudomonas Genome Database [53] . | Biofilms are complex communities of microorganisms encased in a matrix and attached to surfaces . It is well recognized that biofilm cells differ from their free swimming counterparts with respect to gene expression , protein production , and resistance to antibiotics and the human immune system . However , little is known about the underlying regulatory events that lead to the formation of biofilms , the primary cause of many chronic and persistent human infections . By mapping the phosphoproteome over the course of P . aeruginosa biofilm development , we identified three novel two-component regulatory systems that were required for the development and maturation of P . aeruginosa biofilms . Activation ( phosphorylation ) of these three regulatory systems occurred in a sequential manner and inactivation arrested biofilm formation at three distinct developmental stages . Discontinuation of bfiS , bfmR , or mifR expression after biofilms had already matured resulted in disaggregation/collapse of biofilms . Furthermore , this regulatory cascade appears to be linked via BfiS-dependent GacS-phosphorylation to the previously identified LadS/RetS/GacAS/RsmA network that reciprocally regulates virulence and surface attachment . Our data thus indicate the existence of a previously unidentified regulatory program of biofilm development once P . aeruginosa cells have committed to a surface associated lifestyle , and may provide new targets for controlling the programmed differentiation process of biofilm formation . | [
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| 2009 | A Novel Signaling Network Essential for Regulating Pseudomonas aeruginosa Biofilm Development |
O-linked glycosylation is an important post-translational modification of mucin-type protein , changes to which are important biomarkers of cancer . For this study of the enzymes of O-glycosylation , we developed a shorthand notation for representing GalNAc-linked oligosaccharides , a method for their graphical interpretation , and a pattern-matching algorithm that generates networks of enzyme-catalysed reactions . Software for generating glycans from the enzyme activities is presented , and is also available online . The degree distributions of the resulting enzyme-reaction networks were found to be Poisson in nature . Simple graph-theoretic measures were used to characterise the resulting reaction networks . From a study of in-silico single-enzyme knockouts of each of 25 enzymes known to be involved in mucin O-glycan biosynthesis , six of them , β-1 , 4-galactosyltransferase ( β4Gal-T4 ) , four glycosyltransferases and one sulfotransferase , play the dominant role in determining O-glycan heterogeneity . In the absence of β4Gal-T4 , all Lewis X , sialyl-Lewis X , Lewis Y and Sda/Cad glycoforms were eliminated , in contrast to knockouts of the N-acetylglucosaminyltransferases , which did not affect the relative abundances of O-glycans expressing these epitopes . A set of 244 experimentally determined mucin-type O-glycans obtained from the literature was used to validate the method , which was able to predict up to 98% of the most common structures obtained from human and engineered CHO cell glycoforms .
Glycosylation is a major post-translational modification of proteins , in which a carbohydrate moiety , called a glycan , is covalently attached to an amino acid of the polypeptide , to form a glycoprotein [1] . N-linked glycans are attached to an asparagine ( N ) residue appearing in the consensus sequence Asn-X-Ser/Thr , where X is not Pro , while O-linked glycans are attached to the hydroxyl group of a serine or threonine residue . A study of potential glycosylation sites indicated that three quarters of proteins may be glycosylated , with about 10% of these O-glycosylated [2] . Glycans are formed by the sequential addition of monosaccharides from nucleotide-sugar donors to the glycoprotein acceptor , a process that is catalysed by glycosyltransferase enzymes , which are located in the endoplasmic reticulum and Golgi apparatus . Mucins are a class of large glycoproteins that contain a large number of Ser/Thr in close proximity , which can be heavily O-glycosylated . The initial step of mucin-type glycosylation is the attachment of a GalNAc ( N-acetyl-d-galactosamine ) to an unoccupied Ser/Thr on the protein acceptor . Modification of mucin O-glycosylation is an important biomarker in cancer detection [3–8] . In the innate immune response , cell-cell recognition is dependent on the expression of a number of different carbohydrate epitopes on carrier proteins , which include both sulfated and non-sulfated versions of Lewis X ( Lex ) , Lewis A ( Lea ) , Lewis B ( Leb ) [9] and , more rarely , Lewis Y ( Ley ) antigens [10] . Of the several theoretical treatments of glycosylation which have now appeared , most have considered N-glycosylation rather than O-glycosylation [11] . The method of Kawano et al . [12] for predicting glycan structures from gene expression data was able to predict the appearance of a variety of glycosylated structures , including O-linked . The model by Gerken and co-workers focused on the initiation of O-glycosylation [13] . Liu et al . [14] described an object-oriented method of construction of networks of O-glycan biosynthesis that was used to predict levels of sialyl-Lewis X ( SLex ) , an important antigenic determinant , and more recently a computational approach based on MATLAB has been used to predict pathways of N- and O-linked glycosylation [15 , 16] . In the present work , we have taken an alternative , bottom-up , approach to modelling the de novo biosynthesis of mucin O-glycans . In order to facilitate computational analysis , we introduce a formal language ( see [17] ) for identifying individual glycan structures , a method for representing glycans graphically , based on these identifiers , and describe a method for generating networks of reactions based on the activities of enzymes involved in mucin protein O-glycosylation . A mathematical model of N-linked glycosylation has been developed , [18] whose structure identifiers are based on Linear Code; Spahn et al . have developed a Markov-chain model based on this system . [19] . As it seeks to uncover the nature of the reaction networks of O-glycosylation , this work both validates and extends the approach used by these earlier studies . With a rapidly increasing number of studies employing nuclease-based genome-editing technologies , such as zinc-finger nuclease ( ZFN ) [20] and CRISPR/Cas9 [21] , for biotechnological applications , it is important to consider the possible phenotypic effects that may result from knock-ins or knockouts of the glycosyltransferase genes , and the corresponding changes to the glycome . We anticipate that the methods we describe here will be of use in predicting such changes within the context of O-glycosylation networks .
We introduce a formal grammar [24] , Γ = ( ΣN , ΣT , P , S ) , where ΣN is a set of nonterminal symbols and ΣT is a set of terminal symbols . ΣN and ΣT are disjoint sets , meaning that they share no members in common . S defines a starting symbol and P is a set of production rules , each element of which maps a single non-terminal symbol to a string of one or more symbols drawn from ΣT∪ΣN , or to the null ( empty ) string , ϵ . Σ N = { Z , A , B , C , m , d , l } Σ T = { 2 , 3 , 4 , 6 , 8 , a , b , f , s , K , L , N , S , T , V , Y , [ , ] } P = Z → A T A → ϵ | B B V B → ϵ | [ C m l d ] C → ϵ | C m l d | C [ C m l d ] m → f | s | K | L | N | S | V | Y d → 2 | 3 | 4 | 6 | 8 l → ϵ | a | b S = Z The grammar generates a language L by the successive substitution of nonterminal symbols with the right-hand sides of production rules in P . The set ΣT ∪ ΣN is the alphabet of L , and strings of symbols generated by Γ are the words of the language . We define a structure identifier as a word of L that contains only symbols drawn from ΣT . The following sequence of strings serves as an example of a derivation within the grammar . For brevity , some steps are the result of several simultaneous applications of production rules . Z A T { Z → A T } B B VT { A → B B V } [ C m l d ][ C m l d ] VT { B → [ C m l d ] } [ C m l d ][ m l d m l d ] VT { C → C m l d , C → ϵ } [ m l d ][ m l d m l d ] VT { C → ϵ } [S l d ][ S l d L l d ] VT { m → S , m → L } [S6][ S 3 L 3 ] VT { d → 6 , d → 3 , l → ϵ } The final string in the list is a word in Γ denoting disialylated T antigen , commonly known as “diST” , a core-1 O-glycan . The linear string identifiers described in this work can be used to draw glycan structures in the manner of turtle graphics [26] . Reading the identifier from right to left , the drawing method acts according to the current symbol: if the symbol is an element of the set {f , K , L , N , S , V , Y , s} , it draws the symbol corresponding to the monosaccharide at the current drawing position; if the string character is a right bracket , ] , the current position and orientation information are pushed onto a stack , and are popped from the stack on meeting a left bracket . A two-pass approach is employed , with the bond framework being drawn on the first pass , and the sugar symbols drawn on the second . A suite of Perl scripts was written for the generation of structure identifiers by enzyme simulation , for parsing , and rendering as Scalable Vector Graphics ( SVG ) image files . A library of functions was written as a Perl module , which enabled ( i ) the translation of structure identifiers to and from the IUPAC condensed-form one-line notation; ( ii ) identification of common epitopes , such as Lex , based on regular-expression patterns; ( iii ) parsing of O-glycan strings by an LL ( 1 ) parser based on a simplified version of Γ; ( iv ) rendering of string identifiers as SVG , in either UOXF or CFG styles .
Not all of the structures encoded by the formal grammar are feasible , in that structures such as [S3][L3]VT are syntactically correct , but chemically impossible , since it describes a sialic acid ( S ) and galactose ( L ) both 3-linked to the same N-acetylgalactosamine ( V ) . In order to generate a set of biologically relevant O-glycans , therefore , a set of regular-expression based substitution rules was developed to mimic the actions of each of the enzymes shown in Table 2; throughout this work , numbers in bold face refer to the corresponding activities in this table . The rules were incorporated into a Perl script , which took a single O-glycan identifier as the initial substrate , and applied each of the substitutions in turn to output a set of products . The initial structure defaulted to the non-glycosylated site , ‘T’ , but any valid glycan structure could be supplied by the user as a starting point . The process was applied iteratively , such that each new product formed was presented as a substrate to every enzyme upon the next iteration . Where an enzyme rule could match at more than one position , as in the case of diantennary O-glycans , the identifier was split , using the current regular expression , and then each part substituted according to the same rule , before reassembling the parts , with the new string being added to the pool of possible products . Branching level and extension by poly-N-acetyllactosamine repeating units could be controlled by placing an optional limit on the total number of GlcNAc residues incorporated . Restrictions could be placed on individual enzyme activities by conditionals employing Boolean logic . The program could also be limited to use a subset of the enzymes . Simulations terminated after a prescribed number of iterations , or after any iteration in which no new products had been generated . The output of the program for three iterations of the method is shown in Fig 2 . A web-application front end to the enzyme simulator ( see Methods ) is available online at http://www . boxer . tcd . ie/glycologue . The enzymes of Table 2 can be divided into five main classes of activity: initiation ( 2 ) , core formation ( 5 , 6 , 8 , 9 ) , branching and extension ( 1 , 7 , 10 , 12 , 19 ) , sugar modification ( 20–22 ) and termination ( 3 , 4 , 11 , 13–18 , 23–25 ) . The terminal residue of an oligosaccharide is the monosaccharide appearing at its non-reducing end . In the current model , the two methods of termination were fucosylation or sialylation of a terminal galactose . Sulfation was the only type of non-glycosyltransferase modification that was considered . Oligosaccharide chains can be of type 1 ( ending in Galβ1-3GlcNAc- ) or type 2 ( ending in Galβ1-4GlcNAc- ) . The enzyme rules were reversed , so that a single monosaccharide was removed at each step of the simulation . Any O-glycan structure supplied as an initial substrate to the reversed enzyme simulator was considered to be predictable , or deducible , within the system if its final step was the removal of the terminal GalNAc from the protein by the enzyme ppGalNAc-T , according to VT -- ppGalNAc-Ts --> T . If the simulation ended with no new products formed , and without reaching the non-glycosylated site , the glycan was considered non-predictable within the system . The reaction data provided by the method described earlier , and depicted in Fig 2 , were used to generate network graphs in GraphViz ( www . graphviz . org ) , with O-glycan identifiers as nodes and with edges representing enzyme-catalysed reactions , colour-coded according to the monosaccharide being transferred . The enzyme simulator also allowed enzymes to be knocked out in silico , either individually or in groups , with each knockout resulting in a different reaction network . A web application , O-Glycologue ( see Methods ) was developed in order to view the structures obtained for a particular set of knockouts , and compare them with the structures obtained for the “wild-type” network , defined as the network obtained with all 25 of the enzymes active . The method is illustrated with an example taken from a study on salivary MUC7 glycans [45] , a triantennary core-2 structure with the structure identifier [S3L4[f3][s6]Y6][[S3L4[f3][s6]Y6][S3L4[f3][s6]Y3]L3]VT ( Fig 4A ) . The reversed reaction network is shown in Fig 4B , which successfully removed all monosaccharides in 17 iterations using the nine enzyme activities 1 , 2 , 5–7 , 11 , 16 , 19 and 20 . The network of reactions produced when the enzyme simulator was run in the forward direction with only these enzymes active is shown in Fig 4C . With all 25 enzyme activities enabled , 18 iterations of the method generated 13 , 127 , 561 unique O-glycans , in 34 , 215 , 049 reactions . All structure identifiers generated by the enzyme simulations were shown to be valid according to the parser . Different epitopes could be determined from the terminal sequences of the identifier string , and were counted as percentages of the total number of glycans formed: Lewis A ( [L3[f4]Y , 13 . 2% ) , Lewis X ( [L4[f3]Y , 25 . 0% ) , sialyl-Lewis A ( [S3L3[f4]Y , 4 . 2% ) , sialyl-Lewis X ( [S3L4[f3]Y , 8 . 4% ) , Lewis B ( [[f2]L3[f4]Y , 4 . 3% ) , Lewis Y ( [[f2]L4[f3]Y , 8 . 2% ) , H antigen ( [[f2]L3Y , 9 . 4% ) , A ( [V3[f2]L3[f4]Y , 1 . 9% ) , B ( [La3[f2]L , 17 . 5% ) , Sda/Cad ( [S3[Vb4]L , 12 . 7% ) and other ( 24 . 7% ) . Depending on the degree of branching , several different epitopes could appear together on the same O-glycan . Overall , 227 different pattern combinations of recognised epitopes could be distinguished , such as Lewis A with the H antigen . As a consequence of the method used to produce the network , in which the products at iteration n + 1 are dependent only upon those arising from iteration n , the growth function can be approximated by a discrete logistic map , ν ( n + 1 ) = bν ( n ) , b > 1 , with solution ν ( n ) = abn . Although the total population is therefore expected to grow exponentially , by setting a limit on the maximum number of GlcNAc residues incorporated in each O-glycan , it was possible to close the networks , so that eventually no further structures were added to the glycan pool ( Fig 5B ) . Under the assumption of irreversibility of each reaction , the network can be viewed as a rooted , directed acyclic graph G = ( V , E ) , where V and E are sets of nodes and edges , respectively , with each node representing a distinct O-glycan and edges representing enzyme-catalysed reactions in which O-glycans appear as substrates or products . The degree of a node is defined as the number of its immediate neighbours to which it is connected by an edge . For a directed graph , the number of incoming nodes is called the in-degree , and the number of outgoing nodes is defined as the out-degree . An important network property is the degree distribution , which is frequently expressed in terms of the probability , P ( k ) , that a randomly selected node will be of degree k . Many real networks possess the property of hierachical clustering of nodes [46] with a degree distribution that displays a power-law tail , P ( k ) ∼k − λ . In contrast , our reaction network displayed a Poisson-like distribution that is characteristic of random networks [47] . After 14 iterations , the average degree of the network , 〈k〉 , was calculated to be 4 . 36 , with the in-degree and out-degree averages each equal , at half of this value . A bilog plot of the degree-distribution of the network ( node degree frequency vs degree ) is non-linear , as shown in Fig 5C , indicating that the network is not self-similar [48] , or scale-invariant . That the degree distribution of a reaction network arising from a fully deterministic system has the characteristics of a random network may be a natural outcome of the method that was used to generate the glycan structure libraries . Since this method is essentially combinatoric , in that every possible acceptor-product is discovered from every substrate , we conjecture that its degree distribution can be described by a binomial function . Newman et al . [49] have shown that networks with a binomial degree distribution become Poisson when the number of nodes is large . Quantitative measures of the connectedness of the reaction network are provided by the α , β and γ indices [50] . The β index is the ratio of the number of edges , e , to the number of nodes , v: β = e v ( 1 ) The definitions of the non-planar versions of the α and γ indices , which allow for edges to cross at non-nodal positions in the plane , are α = ( e - v ) v ( v - 1 ) / 2 - ( v - 1 ) ( 2 ) and γ = 2 e v ( v - 1 ) . ( 3 ) The α index represents the number of cycles in a graph to the maximum number of possible cycles , and will take a value between 0 and 1 . The γ index is the ratio of the number of edges to the total number of edges in the fully connected network , v ( v − 1 ) . Local clustering coefficients were also computed , and averaged across the complete reaction network [51] . The clustering coefficient , Ci , provides a measure of the fractional degree to which nearest neighbours of node i are connected to each other . Let ki be the number of immediate neighbours of node i . Since there can be at most ki ( ki − 1 ) edges between ki nodes , for a directed graph , the value of Ci is defined as C i = E i k i ( k i - 1 ) ( 4 ) where Ei is the number of existing edges between the neighbours of node i . An average network clustering coefficient , 〈C〉 , was defined over the whole reaction network . The values of β and 〈C〉 , which were calculated at each iteration of the enzyme simulation , showed an increase overall , monotonically above the iteration 7 , while the non-planar γ index decayed uniformly from unity ( Fig 5D ) . The increase in β index approximated to linearity above iteration 8 . We simulated the effects of knocking out individual enzymes , observing the changes incurred in the topology of this reaction network . O-Glycan heterogeneity was most strongly influenced by the activities of Gcnt2 , C2/4Gn-T , β3Gn-T2/3/4/5/7 , β3Gn-T6 and β4Gal-T4 , as quantified by the changes in the indices in Fig 6A–6C . Changes to local clustering coefficients were also noticeable , although they were not as marked . In the absence of enzyme β3Gn-T2/3/4/5/7 ( 10 ) , the network closed after 20 iterations , and in the absence of β4Gal-T4 ( 1 ) , the network was closed after 14 iterations , since no further extension of antennae was possible in the absence of either of these activities . Changes to the α and γ indices were notable only for these two enzymes ( Fig 6B ) . Changes to the distributions of common epitopes are given in Table 3 . The occurrences of each epitope , expressed as a percentage of the total number of unique O-glycans , were obtained for 12-iteration networks with the enzyme knocked out as indicated , and from which the sulfotransferases ( 20–22 ) had been omitted . Excluded from the results are ppGalNAc-Ts and the knockouts of the sialyltransferases 17 and 18 , which showed no alteration from “wild type” ( wt ) . Since more than one epitope can be expressed on a single O-glycan , the numbers on each line in the table need not sum to 100 . The β4Gal-T4 knockout was found to eliminate all glycans expressing Lex , SLex , Ley and Sda antigens , indicating that it is an essential component of their biosynthesis; an increase in the percentage of O-glycans bearing the B antigen was also observed . The greatest decrease in the total number of glycans formed was observed with this knockout ( not shown ) . Single-enzyme knockouts of the N-acetylglucosaminyltransferases did not affect the distributions of these epitopes so markedly , as might be expected from their functions in core formation , elongation and branching , rather than termination . Knocking out the β-1 , 3-galactosyltransferase activity eliminated only O-glycans expressing the B antigen . The predictive power of the enzyme simulator was tested by comparing the in-silico generated O-glycans against fifteen published collections of such structures that had been identified experimentally: mucin O-glycans from human colon [52 , 53]; structures of MUC1 mucin glycoforms obtained from normal and cancerous breast epithelial cell lines [54]; poly-N-acetyllactosamine extended structures of leukosialin glycoprotein obtained from promyelocytic and myelogenous leukaemia cell lines [55]; leukosialin O-glycans expressed in T-lymphocytic leukemia [56] and erythroid , myeloid , and T-lymphoid cell lines [57]; O-glycans from salivary MUC7 , a major component of mucin glycoprotein 2 ( MG2 ) [45]; O-glycans of Tamm-Horsfall glycoprotein [58]; sulfated core-2 and core-4 oligosaccharides obtained from mucins associated with chronic bronchitis [59]; bovine serum fetuin , human serum IgA1 and secretory IgA , human neutrophil gelatinase B and glycophorin A O-glycans [60]; extended core-1 and core-2 O-glycans from Chinese hamster ovary ( CHO ) cells transfected with β3Gn-T3 [61]; MUC1 and MUC4 O-glycans from bovine and human milk [62] , normal human serum [63] and a human gastric adenocarcinoma cell line ( MKN45 ) [64]; mucin from normal descending colon [65]; recombinant mucins from engineered CHO cells [66] . In all , 244 unique O-glycan structures were collected from these studies and assigned structure identifiers . Multiple identifiers were assigned where a number of different configurations was possible . For example , the monosialylated forms of Galβ1-3 ( Galβ1-4GlcNAcβ1-6 ) GalNAc-R [64] were represented by the separate identifiers [L4Y6][S3L3]VT and [S3L4Y6][L3]VT . Each member of the set of experimentally determined O-glycans was supplied to the reversed enzyme simulator as the starting substrate , and tested for predictability within the system . Overall , 87% of the unique O-glycan structures were predicted by the method , which was able to reproduce any of the extended branched core 1–4 structures , with sialyl-Lewis X , Lewis Y , Lewis A or -B terminals and their 3′- and 6-sulfated variants . Table 4 lists the O-glycans determined experimentally that appeared in more than one of the studies , and thus independently verified , in descending order of frequency . Shown are the structure identifier , the supporting literature and a check next to those structures that were predicted in silico . Of the 45 oligosaccharides most commonly occurring , 44 were predicted by the model , giving a coverage of 98% .
From analysis of the grammar , and the results of the enzyme simulations , we predict that a highly heterogeneous population of mucin O-glycans is likely to result if even a limited subset of the enzyme activities of Table 2 is expressed . In-silico enzyme knockouts have identified β4Gal-T4 as a key regulator of the complexity of O-glycosylation networks , in keeping with our earlier observations on the influence of this enzyme on N-linked glycosylation in engineered Chinese hamster ovary cells [67] . The number of iterations was chosen according to the type of in-silico experiment: trends in the changes to the indices were discernable by iteration 15 , hence this value was chosen for the enzyme-knockout studies; 18 is the maximum number of iterations of the basic model that were possible within the available memory ( 32 GB ) , with all 25 enzymes active and no limitations placed on the number of GlcNAcs . Not all of the enzymes in the current model will be present in all species , or active at all times . The full network is therefore a chimeric construct , but one which could be tailored for specific cases as needed , by considering only the enzymes known to be expressed in a particular organism or tissue . The O-Glycologue web application , described in Methods , provides an easy way to experiment with the effects of knockouts or knock-ins of the enzymes of O-glycosylation . The transferase activities leading to cores 5 through 8 are as yet uncharacterized [1] , but could be added in future to account for such structures as are occasionally found in colonic tissues . The O-glycan structure [L4Y3L4[f3]Y6][L3]VT was also not predicted by the current model ( Table 4 ) . Although its appearance could be the result of a wider acceptor specificity of β3Gn-T2/3/4/5/7 ( 10 ) that would allow this enzyme to act according to the pattern *[Lb4[fa3]Y*T , it could also be the result of fucosylation of an inner GlcNAc by one of the several known α1 , 3-fucosyltransferase variants , such as FUT4 [68] . The pattern corresponding to the substrate acceptor in such a case would be *Lb4Y*T . An additional α1 , 3-fucosylation pattern that was evident from this data set is the sequence *L4[f3]Y6* , evident in ten of the non-predicted glycans from two studies [60 , 62] , and in the sole non-predicted structure of Table 4 . It is likely that a fucosyltransferase activity exists that is yet to be characterized , and which acts on type-2 chains with a preference for the 6-linked GlcNAc of core-2 or core-4 O-glycans . In the future , these reactions , as well as those of other fucosyltranserases that are distinguished by different substrate specificities , could be incorporated into the simulator either as additional rules or as refinements of the existing rule ( 11 ) . Some structures that were not predicted may also have been mischaracterised . For example , the non-predicted glycan structure described by Podolsky [52] , to which we assigned the identifier [S6][[S6L3Y6][S6L3Y3]L4Y3]VT , is in the same paper identified as a type-2 structure , which could be predicted . Our validation study therefore provides a lower bound on the number of structures that can be predicted . Certain poly-6-sialylated structures , including [S6][S6L3Y3[S6]L4Y3]VT , were not predicted . It is possible that a sialyltransferase activity exists in colon that recognises galactose at a distance from the non-reducing end of an oligosaccharide; for instance , an alternative reaction of ST6GlcNAc-I ( 18 ) might be CMP-S + *Y3Lb4Y*T = CMP + *Y3[Sa6]Lb4Y*T . Our analysis of the monosaccharide content of O-glycans extracted from the CFG database revealed that the frequency of occurrence of Neu5Ac was between two and three times the total of the remaining monosaccharides of lesser occurrence: Glc , GlcA , Kdn , and Neu5Gc . Of these , Neu5Gc , or N-glycolylneuraminic acid , is of particular interest because it is immunogenic in humans as a result of the silencing of CMP-N-acetylneuraminate monooxygenase ( EC 1 . 14 . 18 . 2 ) . This enzyme , which is active in other mammalian species , adds a single oxygen to CMP-N-acetylneuraminate to form CMP-N-glycolylneuraminate . Neu5Gc obtained in the diet can become incorporated into the cell surface glycome , especially that of cancerous tissue , making it a potential target for immunotherapy [69] . Sialic acids entering the cell via endocytic pathways become activated by the nuclear enzyme CMP-sialate synthase ( EC 2 . 7 . 7 . 43 , N-acylneuraminate cytidylyltransferase ) [70] . Together with the observation that CMP-Neu5Gc can readily substitute for the native donor in reactions catalysed by the sialyltransferases from other species [71] , a reasonable assumption is that Neu5Gc is incorporated into human glycoforms by this means . Thus , while Neu5Ac may be the dominant component of the sialylated epitopes expressed in O-linked and N-linked glycoproteins , a portion of such glycans generated by the enzyme simulator could be considered as terminating in Neu5Gc . If the sialyltransferase activities of Table 2 were allowed to act with CMP-Kdn as donor , an additional six structures from the validation study could be predicted by the model , increasing coverage of the data set to 89% . The notation we have described provides a succinct way to encode structural information for both graphical representation and modelling . Other linear string representations of carbohydrates exist , such as LINUCS [72] and Linear Code [31] , which are broader in scope than O-GalNAc glycosylation , and are supported by established glycoinformatic software tools , such as GlycoWorkbench [73] . An advantage of the modelling language described in this work is that it is able to encode the sialic acid Neu5Gc , which cannot be expressed in Linear Code . A more general , and widely supported carbohydrate encoding format is GlycoCT [32] . More recently , the Web3 Unique Representation of Carbohydrate Structures ( WURCS ) formalism was introduced with an even wider scope [74] . The GlycoForm web application , described in the methods , is able to output any O-glycan structure identifier as both IUPAC , Linear Code and GlycoCT condensed formats , making it interoperable with other software and databases . For the purposes of modelling and display , however , the advantages of the structure identifiers presented in this work are twofold; first , adherence to a strictly one-letter system for the monosaccharides reduces the memory requirements , which can be large when all enzymes of the model are allowed to act; second , the lexical analysis is simplified , since in the drawing algorithm each character can act as a single instruction . The method could be adapted to other systems , depending on the intended application . For instance , other enzyme activities could be included to account for branch termination by α-GlcNAc , as has been observed in porcine gastric mucins [10] , but not commonly on human glycoproteins [42] . The formal grammar could be modified to describe N-glycans , such as those expressed on immunoglobulins [75] , the hypermannosylated glycans produced by yeasts [76] , or glycans initiated through O-linked fucose [77] or mannose [78] . Additional reaction rules could be supplied , as needed , to support the enzyme activities of galactose 6-O-sulfotransferase and α-2 , 8-sialyltransferase . A limitation of the current implementation is that not all routes to a product may be included: for example , the simulated activity of Core-2 forming enzyme ( 5 ) does not recognise a 3-linked sialic acid on the lower arm of Core 1 . The alternative route to [Y6][S3L3]VT could be accommodated by including sialic acid as an option to the reaction pattern , similar to the case for reactions that allow sulfation of Gal or GlcNAc . Although we have restricted our subject to the enzymes of O-glycan biosynthesis , the actions of glycosidases , which are involved in O-glycan degradation , may have an important regulatory role . For example , it is known that α-l-fucosidase ( EC 3 . 2 . 1 . 51 ) is downregulated in certain types of colorectal cancer [79] , from which we infer that an increase in Lewis-type epitopes might be the result of both increased fucosyltransferase activity in Golgi and decreased fucosidase activity in either tissue or plasma . In the future , therefore , this model could be extended to include enzymes involved in the catabolism of O-linked glycoproteins . A quantitative analysis of O-linked glycosylation , incorporating the kinetic parameters of the enzymes involved , would be a natural extension , and development along these lines is proceeding . The web application , O-Glycologue , provides a convenient way to draw O-glycan structures from the identifiers used in this work , and to explore the wide variety of possible oligosaccharide structures formed by the activities of several known enzymes of O-glycosylation . While a MATLAB-based system for modelling N- and O-linked glycosylation has recently appeared [15] , the system described in this article requires neither installation by the user nor a commercial software license . To our knowledge , O-Glycologue is the first tool capable of testing the effects of knockouts of the enzymes of O-linked glycosylation on glycoform heterogeneity . As a knowledge-based system , it should be useful to glycobiologists interested in predicting the biosynthetic pathways forming particular O-glycans . Given that the glycoslation of mucins is known to change during cancer progression [7 , 69] , the software may be an aid to discovering the enzyme activities most responsible for the formation of particular cancer biomarkers . In conclusion , we have presented a method for encoding and displaying mucin-type O-glycans , and a method for generating reaction networks from enzymes known to act in O-glycosylation . The formal grammar and the enzyme reaction rules of Table 2 , together with an initial glycan identifier as an axiom , comprise the deductive apparatus of a formal system for the modelling and display of these O-glycans . Through an analysis of the reaction networks , we predict that β4Gal-T4 is a key regulator of mucin-type O-glycan heterogeneity , along with β3Gn-T2/3/4/5/7 , Gcnt2 , C1Gal-T , C2Gn-T and CHST4/6 . A comparison of the output of the model with experimentally derived glycans suggests the existence of several novel activities . This approach , which has been validated by structure predictions and the effects of enzyme removal , is intended to form a basis for future kinetic evaluations , and extensions to accommodate other types of glycan structure . | Our objective being to model the enzymes of mucin-type O-linked glycosylation , we first developed a model language to represent O-glycan structures succinctly in linear string form , to which a set of pattern-matching rules was then applied to simulate the activities of a set of 25 glycosyltransferase and sulfotransferase enzymes . The modelling language ( a formal language ) , together with the set of transformation rules representing the enzymes of the model . comprise the deductive apparatus of a formal system . The system , implemented in software , was able to predict a highly heterogeneous set of structures when all enzymes were allowed to act , including many clinically important epitopes such as sialyl-Lewis X . We studied the effects of single-enzyme knockouts on the properties of the resulting enzyme-catalysed reaction networks and determined the enzymes most likely to be responsible for heterogeneity . | [
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| 2016 | A Knowledge-Based System for Display and Prediction of O-Glycosylation Network Behaviour in Response to Enzyme Knockouts |
Feedback modules , which appear ubiquitously in biological regulations , are often subject to disturbances from the input , leading to fluctuations in the output . Thus , the question becomes how a feedback system can produce a faithful response with a noisy input . We employed multiple time scale analysis , Fluctuation Dissipation Theorem , linear stability , and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus , and we obtained a critical quantity in noise attenuation , termed as “signed activation time” . We then studied the signed activation time for a system of two positive feedback loops , a system of one positive feedback loop and one negative feedback loop , and six other existing biological models consisting of multiple components along with positive and negative feedback loops . An inverse relationship is found between the noise amplification rate and the signed activation time , defined as the difference between the deactivation and activation time scales of the noise-free system , normalized by the frequency of noises presented in the input . Thus , the combination of fast activation and slow deactivation provides the best noise attenuation , and it can be attained in a single positive feedback loop system . An additional positive feedback loop often leads to a marked decrease in activation time , decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation . On the other hand , a negative feedback loop may increase the activation and deactivation times . The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops . This principle may be applicable to other feedback systems .
It has been identified that feedback loops play important roles in a variety of biological processes , such as calcium signaling [1] , [2] , p53 regulation [3] , galactose regulation [4] , cell cycle [5]–[8] , and budding yeast polarization [9]–[13] . Although the detailed regulation of feedback loops may vary in different systems , the overall functions of feedback loop modules may be similar . For example , positive feedback loops are mainly used for promoting bi-stable switches and amplifying signals . One example is the cell cycle system [5]–[8] in which the mitotic regulator CDK1 activates Cdc25 , which in turn activates CDK1 , forming a positive feedback loop . Conversely , Wee1 and CDK1 inactivate each other , forming a double-negative feedback loop , equivalent to a positive feedback loop . The overall positive feedback regulation gives rise to a bi-stable switch that toggles between the inter-phase state and the mitotic-phase state . Another example is the system of yeast mating [9]–[15] , in which multi-stage positive feedback loops enable the localization of signaling molecules at the plasma membrane by amplifying signals to initiate cell polarization and mating . While most studies of feedback loops have been concerned with their roles in signal amplification , switch ( or switch-like ) responses [16]–[20] , and oscillations [21] ( See [22] , [23] for the latest review . ) , recently , another important aspect of feedback loops has drawn more and more attention: modulating ( accelerating or delaying ) timing of signal responses [22] , [24] , [25] . Intuitively , positive feedback could amplify signals inducing an expeditious activation , or delay an activation by setting a higher threshold such that the system is activated only when the response accumulates beyond that threshold [22] , [25] . Because characteristics of noises ( e . g . , the temporal frequency of a noise ) in a biological process are closely related to timing of a signaling system , feedbacks clearly play a critical role in noise attenuation [26]–[29] . Thus , one of the central questions on noise analysis is how the architecture of a feedback circuit affects its noise property . Some studies suggested that positive feedbacks tended to amplify noise and negative feedbacks typically attenuated noise [30]–[32]; on the other hand , some other studies demonstrated that the positive feedbacks could attenuate noises and there were no strong correlations between the sign of feedbacks ( negative or positive ) and the noise attenuation properties [28] , [33] . In their novel work [34] , Brandman et al . linked the effect of positive feedback loops on noise attenuation to the time scales of the feedback loops . They studied a canonical feedback module consisting of three components , i . e . , an output and two positive feedback loops , and . The output is turned on by the two positive feedback loops and , which are stimulated by an external ( or upstream ) stimulus and are also facilitated by ( Figure 1A ) . The output becomes active ( or stays inactive ) as the pulse stimulus is high ( or low ) . Through numerical simulations , Brandman et al . [34] showed that , if one of the positive feedback loops ( e . g . , loop ) was slow and the other one was fast ( termed as dual-time loops ) , the system could lead to distinct active output even in the presence of noise in the stimulus ( at the high state ) . Following this work , Zhang et al . [35] studied dual-time loops in producing a bi-stable response with a constant input ( unlike a pulse input in [34] ) . They concluded that dual-time loops were the most robust design among all combinations in producing bi-stable output for a slightly different system in which the stimulus could activate or without the participation of . Kim et al . [36] considered systems coupled with negative and positive feedback loops . By assuming all the positive feedback loops have the same time scale but different time delays , they obtained a system that was capable of performing fast activation , fast deactivation , and noise attenuation . What remains unclear are the sufficient and necessary conditions for a feedback system to achieve noise attenuation . Are two , or at least two , positive feedback loops ( as used in [34]–[36] ) required for controlling noise amplification in the input ? Is a fast loop necessary for a positive feedback loop system to achieve noise attenuation ? Are there any intrinsic quantities that connect the dynamic property of a system in absence of noises with the system's capability of noise suppression ? If such quantities exist , how do positive feedbacks or negative feedbacks affect them ? In this work , we find that the capability of noise suppression in a system strongly depends on a quantity that measures the difference between the deactivation and activation times relative to the input noise frequency . Specifically , this quantity , termed as the “signed activation time” , has an inverse relationship with the noise amplification rate , with larger signed activation time leading to better noise attenuation . In addition , the signed activation time , representing one of the essential temporal characteristics of the system in absence of noises , may be controlled by either negative or positive feedbacks . We explore the properties of the quantity through both analytic approach ( including linear stability analysis , multiple time scale analysis , and Fluctuation Dissipation Theorem ) and numerical simulations . We first consider the same modules as in [34] , and find that , for example , an additional positive feedback loop could drastically increase the signed activation time by speeding up the activation time while still keeping the deactivation time slow , as consistent with the previous observation [34] that dual-time-loop systems suppress noises better than single-loop systems . We next add a negative feedback loop to the positive-feedback-only system and show that a negative feedback loop usually slows down both activation and deactivation processes , leading to better or worse noise attenuation depending on which process ( between activation and deactivation ) is more significantly affected . Finally , we study the signed activation time and its relations to the noise amplification rate in different systems involving various feedbacks ( e . g . , positive , negative , and feedforward ) , including a yeast cell polarization model [14] , [37] , a polymyxin B resistance model in enteric bacteria [38] , and four connector-mediated models [39] . All simulations confirm that the capability of noise attenuation in those systems improves as the signed activation time increases .
A simple model with one positive feedback loop may have two components with one upstream stimulus ( inside the red dashed box in Figure 1A ) . In this system , the output is activated by , and is triggered by a stimulus and regulated by . The stimulus drives the output of the system with a high ( or low ) stimulus that corresponds to an active ( or inactive ) state of . Many biological circuits have positive feedback regulations of this nature [1] , [2] , [19] , [40] . For example , is a kinase to phosphorylate to , and once is activated , it catalyzes a conversion from an inactive form to an active form [5] . Neglecting the mechanistic details , while keeping the essential interactions , we model the dynamics of the above module by the following system of ordinary differential equations ( Text S1 ) : ( 1 ) where and represent normalized concentrations of and , respectively . The normalized stimulus , as a function of time , , usually varies ( continuously ) between two states , i . e . , an inactive state in which ( or the “off” state ) and an active state in which ( or the “on” state ) . The parameters , , , and are kinetic constants , and indicates the time scale for loop . Once the output of the system reaches the “on” state driven by the stimulus , how does system ( 1 ) respond to temporal noises in the input signal ? What are the strategies for effectively maintaining the system in the “on” state even with noises presented by the stimulus ? We find that the time scales , denoted by and ( Figure 2 ) , for the system to switch from the “on” state to the “off” state and from “off” to “on” respectively in the absence of noises in the signal , play a critical role . Specifically , when is significantly larger than the time scale of the noise , i . e . , , where is the frequency of the noise , the output of the system remains in the “on” state ( Figures 3A–3B ) . Intuitively , when the system in the stable “on” state receives a noisy signal with an instantaneous value possibly near , it needs time to react and detour to the “off” state . In the case of , before the system settles down to the “off” state , a noisy signal with an instantaneous value near shows up , forcing the system to synchronize with the new value of the input signal . If , the output recovers fast from the drift towards the inactive state , and is more likely to maintain around the “on” state . The above intuition suggests that the noise attenuation at the “on” state depends positively on and negatively on . Thus , the quantity , i . e . , the signed activation time , could be a good indicator of a system's ability of attenuating noise . To investigate how noise level in the solution depends on the signed activation time , we study the noise amplification rate , defined as the relative ratio of the coefficients of variation of the output ( ) and the noise ( ) [28]:First , we perform numerical simulations on system ( 1 ) ( Methods ) to study the relationship between and by varying the activation and deactivation time scales while fixing . This is achieved by changing the kinetic parameteres , and individually in the system , and is found decreasing in ( Figure 3C ) . Next , we hold constant , corresponding to no changes in all parameters , and vary the noise frequency . The trend of remains the same ( Figure 3D ) . We also consider the dependence of on and individually ( Figure S1 ) . In the single loop case , it turns out that is always decreasing in ( Figures S1A–S1C ) , but it might be increasing in ( Figure S1D ) . Similar results are also obtained for positive-positive-loop systems ( Figure S2 ) . Both suggest that neither deactivation nor activation alone can fully characterize the noise amplification rate , and the noise amplification rate is more likely determined by the difference between the deactivation and activation time scales . Next , we further explore this system through the following two analytical approaches . In the previous section , we have demonstrated that the noise amplification rate depends negatively on the signed activation time . Thus , if a system is persistent to noise at the “on” state , it should have a large signed activation time . In this section , by studying the dynamics of the noise-free system , we show that a small is necessary for a slow deactivation , but not sufficient . With a fixed small , larger or could lead to slower deactivation and faster activation . In many biological processes , such as cell cycle [5] , [6] , often two positive feedback loops and activate the output simultaneously ( Figure 1A ) . Similar to system ( 1 ) , the corresponding equations take the form: ( 13 ) Through direct numerical simulations , we find that the noise amplification rate decreases in the signed activation time ( Figures 4A–4B ) , following the same principle as in the single-positive-loop system ( 1 ) . The activation time scale decreases in and , while the deactivation time scale increases in and ( Figures 4C–4D , Table 1 ) . We also find that an additional feedback loop can lead to a faster activation ( red and black versus blue in Figures 4C–4D , bottom ) and a slower ( or similar ) deactivation ( red and black versus blue in Figures 4C–4D , top ) , compared to a single-positive-loop system , and a positive-positive-loop system can achieve similar activation and deactivation rates with larger ranges of kinetic parameters than a single-positive-loop system ( Table 2 ) . Consequently , noise attenuation can be better achieved in the positive-positive-loop system ( Figures 4E–4F ) . Below are details of the mathematical analysis for the roles of the additional positive feedback . In this section , we study how an additional negative feedback loop affects noise attenuation in a system . One of the simplest ways to introduce negative feedback to the single-positive-loop system ( 1 ) is to let deactivate ( Figure 1B ) [21] . In this case , the model becomes ( 20 ) Our analytical results show that the additional negative feedback loop leads to slower deactivation and slower ( or slightly faster ) activation compared to its single-positive-loop counterpart ( red and black versus blue in Figures 5C–5D ) . Moreover , the deactivation time scale increases in and , and the activation time scale decreases in and ( Figures 5C–5D , Table 1 ) , similar to the single-positive-loop ( Figures 3E–3F ) and positive-positive-loop systems ( Figures 4C–4D ) . Numerical simulations reinforce these findings and demonstrate that the noise amplification rate of negative-positive-loop systems decreases in the signed activation time , following the same principle as their single-positive-loop counterparts ( Figures 5A–5B ) . Below , we provide detailed analysis to show how the deactivation and activation time scales depend on various kinetic parameters , compared to the single-positive-loop case . In our analytical studies , we assume for simplicity . However , and are varied independently in numerical simulations . Unlike the simple models in the previous section , a yeast cell polarization signaling pathway model that we study next ( Figure 6A ) consists of more than three components and multiple feedback regulations [37] , [48] . Polarization in yeast cells ( a or cells ) is activated by pheromone gradients [48] . The pheromone ( L ) binds to the receptor ( R ) and becomes activated ( RL ) . The activated receptor facilitates the conversion of the heterotrimeric G-protein ( G ) into an activated -subunit ( G ) and a free G dimmer [49] . G is then deactivated to an inactive -subunit ( Gd ) , which in turn binds to G and forms the heterotrimeric G-protein . The free G recruits cytoplasmic Cdc24 to the membrane , forming the membrane-bounded Cdc24 ( Cdc24m ) , an activator of Cdc42 . Accumulation of the activated Cdc42 ( Cdc42a ) at the projection site is a key feature of polarization , and thus is regarded as the output of the proposed system . The activated Cdc42 participates in other polarization processes , forming positive or negative feedback loops . For example , the activated Cdc42 sequesters the scaffold protein Bem1 to the membrane , which then recruits Cdc24 to the membrane [50] . This forms a positive feedback loop . Other functions of Cdc42 include the activation of Cla4 ( Cla4a ) , an inhibitor of Cdc24 , resulting in a negative feedback loop [51] . Following the model proposed in [37] but ignoring the spatial effect , we have the following system of equations: ( 25 ) Here , denotes the concentration of the corresponding protein; [L] is the input signal , and [Cdc42a] is the output; the concentrations of G , Gd , the inactive form of Cdc42 , the cytoplasmic Cdc24 , and the cytoplasmic Bem1 are derived through conservation relations:Here , is the volume of the cell; is the surface area of the cell; , and are the total numbers of molecules per cell of the corresponding proteins . The two Hill functions and are defined asThese two functions represent two different ways of bringing Cdc24 to the membrane . One is by the free G ( function ) , and the other is through Bem1 . The Bem1 recruitment is known to be facilitated by G's binding to Ste20 [52] , and the influence from G is modeled by the function . Kinetic parameters take the same values as in [37] , and see also the caption of Figure 6 . Starting from zero Cdc42a , giving high ( [L]nM ) or low ( [L]nM ) constant inputs , the output reaches active and inactive states , respectively , which are clearly distinguished ( Figure 6B ) . Inputs with small amplitude ( Figure 6C ) can be detected by the system ( Figure 6D ) . On the other hand , the output is robust to noise when it is around the active state ( Figures 6E–6F ) . To study how the noise amplification rate depends on the relative time scales , we vary ten parameters systematically in their -fold ranges . All of them show the same decreasing trend of the noise amplification rate as a function of the signed activation time ( Figure 6G ) . This suggests that the negative relation between the noise amplification rate and the signed activation time , derived from the simple models , could also apply to models of complex interactions and combinations of positive and negative feedback loops . Such negative relationship may be a generic principle on noise suppression for input-output systems with feedback loops .
Our theoretical and numerical studies have demonstrated that it is not the sign of the feedback that determines the degree of noise attenuation . In searching for a general framework for a relation between feedback and noise attenuation , we have identified a critical quantity , termed as the “signed activation time” . Its relation with the system's ability of noise attenuation has been explored , and we have revealed that the noise amplification rate decreases in the signed activation time . These results are concluded through employing multiple time scale analysis , Fluctuation Dissipation Theorem , and linear stability analysis , combined with numerical simulations , in three feedback modules ( Figure 1 ) : single-positive-loop , positive-positive-loop , and positive-negative-loop systems . To test the generality of the conclusion , we have explored models ( Figure 1A ) with saturation effect , i . e . , modeling feedback loops by Hill functions ( Text S1 , Section 6 , Figures S5 , S6 ) , a yeast cell polarization model consisting of multiple intermediate components ( Figure 6 ) , a polymyxin B resistance model in enteric bacteria ( Figure 7 ) , and four connector mediated models ( Table 3 , Figure 8 ) . In all cases , the noise amplification rate has been confirmed to be a decreasing function in the signed activation time . To analyze the roles of multiple positive and negative feedback loops in our toy models , we have found that: 1 ) an additional positive feedback loop could drastically reduce the activation time scale , improving performance in noise attenuation; 2 ) the time scales in positive-positive-loop feedback systems are more robust to rate constant variations ( e . g . due to variability of organisms or variation of environments ) ; and 3 ) adding a negative feedback loop usually sustains both deactivation and activation processes , and thus its overall effect on the signed activation time could be either negative or positive . To obtain slow deactivation and fast activation , we have identified two key parameters , , the association constant of to , and , the association constant of to ( Figure 1A ) , that tightly control the deactivation and the activation time scales ( Tables 1 and 2 ) . Interestingly , under appropriate conditions , even the simplest single positive feedback loop system could display slow deactivation and fast activation , which were not observed in previous works [34]–[36] . The idea of connecting noise attenuation with the time scales of signal responses was mentioned in other works , for example , [53] , in which only the activation time scale was considered . However , we have shown that in our models neither the deactivation time scale nor activation time scale alone predict correctly the trend of the noise amplification rate ( comparing Figure 3C to Figures S1C–S1D , for example ) and the noise amplification rate is an interplay between the two time scales . Our proposed quantity , the signed activation time , provides a more consistent relation linking to the noise attenuation rate . Direct approaches for analyzing noise may be applied to feedback systems , such as the energy landscape method [4] , [35] , [54]–[56] and the methods used for noise attenuation or amplification in signaling cascades [28] , [57]–[60] and covalent modification cycles [53] . To characterize signaling time scales , we have studied the magnitude of eigenvalues and their corresponding eigenvectors of the Jacobian matrices at each distinct state of the signal . Questions concerning how the magnitude of signal output and signal duration depend on properties of pathway components ( e . g . , the effect of cascades ) were explored from a system control point of view in other works [61]–[63] . Our study features a novel approach using multiple time scale asymptotic expansion [41] . Different from the one-time-scale expansion , this approach provides an explicit relation between the solutions and the two separated time scales , suggesting that the single-positive-loop system can function as a low-pass filter and explaining why the relative size of noise time scale and a system's intrinsic time scales is important to noise attenuation . This approach may be applied to other biological systems with time scale separations . Our findings suggest that the negative relationship between the noise amplification rate and the signed activation time could be a general principle for many biological systems regardless of specific regulations or feedback loops . Notice that the deactivation and activation time scales are widely defined and could be measured without detailed knowledge of a system's internal structure . Thus , the underline system could be treated as a black box and its ability of noise attenuation could be estimated based on the signed activation time . In general , if a system prefers to better attenuate noise at the “on” state , the system should have a large signed activation time . We would like to point out that the studies done here mainly focus on time scale changes within a fixed system , although comparisons across different systems are likely to be consistent with our result ( e . g . the four connector-mediated models ) . However , we might not expect two drastically different systems with equal signed activation time to exhibit the same noise amplification rate , which is likely to depend on other factors in the system as well . We hope that the present work can shed some light on general principles of noise attenuation , in particular , their connections with timing of a system in the absence of noises .
All simulations are performed using Mathematica 6 . 0 . 0 . To compute the noise amplification rate , we useto approximate , whereWe useto approximate , whereThe noise is generated by dividing the time interval into sub-intervals of length , and on each sub-interval the signal takes a random number from a uniform distribution in . See Figure 3A for a typical noisy signal . See Text S1 . | Many biological systems use feedback loops to regulate dynamic interactions among different genes and proteins . Here , we ask how interlinked feedback loops control the timing of signal transductions and responses and , consequently , attenuate noise . Drawing on simple modeling along with both analytical insights and computational assessments , we have identified a key quantity , termed as the “signed activation time” , that dictates a system's ability of attenuating noise . This quantity combining the speed of deactivation and activation in signal responses , relative to the input noise frequency , is determined by the property of feedback systems when noises are absent . In general , such quantity could be measured experimentally through the output response time of a signaling system driven by pulse stimulus . This principle for noise attenuation in feedback loops may also be applicable to other biological systems involving more complex regulations . | [
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| 2010 | A Critical Quantity for Noise Attenuation in Feedback Systems |
The Wolbachia strategy aims to manipulate mosquito populations to make them incapable of transmitting dengue viruses between people . To test its efficacy , this strategy requires field trials . Public consultation and engagement are recognized as critical to the future success of these programs , but questions remain regarding how to proceed . This paper reports on a case study where social research was used to design a community engagement framework for a new dengue control method , at a potential release site in central Vietnam . The approach described here , draws on an anthropological methodology and uses both qualitative and quantitative methods to design an engagement framework tailored to the concerns , expectations , and socio-political setting of a potential trial release site for Wolbachia-infected Aedes aegypti mosquitoes . The process , research activities , key findings and how these were responded to are described . Safety of the method to humans and the environment was the most common and significant concern , followed by efficacy and impact on local lives . Residents expected to be fully informed and engaged about the science , the project , its safety , the release and who would be responsible should something go wrong . They desired a level of engagement that included regular updates and authorization from government and at least one member of every household at the release site . Results demonstrate that social research can provide important and reliable insights into public concerns and expectations at a potential release site , as well as guidance on how these might be addressed . Findings support the argument that using research to develop more targeted , engagement frameworks can lead to more sensitive , thorough , culturally comprehensible and therefore ethical consultation processes . This approach has now been used successfully to seek public input and eventually support for releases Wolbachia-infected mosquitoes , in two different international settings - Australia and Vietnam .
The Wolbachia strategy aims to ‘manipulate mosquito populations to make them incapable of transmitting dengue viruses between people’ ( www . eliminatedengue . com ) . Its potential emerged following the successful transference of the insect bacterium Wolbachia pipientis from the fruit fly Drosophila melanogaster into the Aedes aegypti mosquito [1] , [2] , [3] . Later studies showed that the bacterium spread effectively into wild populations , had a life-shortening effect on the mosquito , blocked the development of some dengue viruses and some strains had a life-shortening effect on the mosquito [4] , [5] . These properties would , in all likelihood , greatly reduce the mosquito's capacity to transmit the virus . To trial its effectiveness in real world conditions , required a series of field release through which Wolbachia-infected mosquitoes would be released into wild populations the aim being to replace these . The Wolbachia method is one of several strategies to emerge in recent year that use a range of new technologies to combat dengue fever . While some focus on genetic modification , others , like Wolbachia , use biological control [1] , [5] , [6] . However , these strategies are very different from their predecessors , notably source reduction and insecticide use , and are not without controversy . Moreover , many require open field releases to test their efficacy and potential uses . Significantly , these need to occur in the locations where dengue vectors are found , most commonly the homes , and places of work , education , worship and leisure of local residents at a release site . Most commentators recognize that the political and ethical complexities of community field trials are considerable and that public and government approval in conjunction with high quality science are of central importance . It is also widely acknowledged , that given the spread and increasing prevalence of dengue fever throughout the tropics , field trials will need to be undertaken in a variety of locales , regions and countries , both so called developed and developing . While public engagement is also recognized as critical to the use and future success of these strategies , many questions remain regarding how to proceed in ways that are ethical , and comprehensible to those being asked to trial these strategies in their homes and backyards . In 2008 an approach to engagement drawing on anthropological methodologies and insights was developed for the Wolbachia strategy . It was implemented in Cairns , Australia from 2008–2010 [6] and in January 2011 the first field release of Wolbachia-infected Ae . aegypti commenced . Drawing on anthropological methodologies and insights , this approach recognizes that different communities will have divergent expectations , knowledge , concerns , political structures and cultural sensibilities , that need to be understood and taken into account , if one is to engage sensitively , ethically and effectively [6] , [7] , [8] , [9] , [10] , [11] [12] . The most reliable way to do this , is to talk with residents at a potential release site about the new dengue control methods and ask what their concerns are , how they want to be engaged and what would constitute authorization [6] , [11] . From this research , an engagement framework is developed that is sensitive to local needs , expectations , knowledge and concerns . So , rather than simply adopting an engagement strategy that was developed elsewhere and implementing it in another setting , this approach uses social research to design an engagement framework and communication materials that are tailored specifically to potential release sites . In brief , it begins by undertaking systematic social research to: ( a ) document the socio-political context and identify the various publics and stakeholders at the potential release site , ( b ) determine how they want or expect to be engaged and the forms this should take , ( c ) explore what would constitute authorization , ( d ) identify any questions or concerns they might have about the Wolbachia strategy , ( e ) identify lay knowledge of the disease , its transmission , vectors , perceived risk , etc . and ( f ) develop responses to these . The results of this research are then used to design a community engagement framework tailored specifically to the sociopolitical setting , and the requirements and expectations of a given population [6] . This paper describes the use of this approach from June 2009 to September 2010 at the second potential Wolbachia release site - Tri Nguyen Island , in central Vietnam . It outlines the process , research activities , outcomes and key findings from the Vietnamese field site . It also highlights key public concerns and expectations about engagement and authorization and shows how these were used to develop a more targeted , culturally appropriate and comprehensible engagement framework and communication materials . Most significantly , the paper demonstrates the viability of this approach to community engagement for new dengue control strategies , in a ‘developing’ country context . It is hoped that by reporting on the methodology , process and results , that readers will be able to see the steps taken and assess the capacity of this approach to reflect and address local requirements and expectations , as well as its potential applicability to other programs .
Dengue fever has a long history in Vietnam and continues to represent a major public health problem [13] . Disease transmission occurs throughout the year in the south of the country but is limited to the warmer months in the northern and highland areas . Two vectors are active in disease transmission , the Ae . albopictus and Ae . aegypti mosquitoes [12] [14] , [15] . Historically , dengue control in Vietnam has focused on source reduction , container management , insecticides and community mobilization – the later relying on household visits by collaborators and the management of water storage containers [15] . Since 1989 , community-based biological control initiatives using Mesocyclops spp . to control mosquito breeding in household water containers have also been introduced [15] , [16] , [17] , [18] . These have also included successful community mobilization around the management of water storage containers and the presence of Mesocyclops spp . Tri Nguyen Island ( TNI ) or Hon Mieu ( ‘Island Shrine’ ) , as it was known historically , is located to the southeast of the city of Nha Trang ( NT ) in Khanh Hoa province , central Vietnam ( Figure 1 ) . It was selected as a potential release site for the Wolbachia strategy for a number of reasons . These include its physical isolation , its proximity to the Pasteur Institute in Nha Trang , famous for its work on infectious diseases , and residents' previous involvement in mosquito ecology and vector studies . Since the late 1940s and during the war with France , people from other provinces such as Quang Nam , Quang Ngai , Binh Dinh , Phu Yen moved to TNI . Today the island is stratified into 3 hamlets each with its own leader , which together represents one sector of the Vinh Nguyen ward of Nha Trang city , in Khanh Hoa province . In 2009 the population of TNI was 3253 residents , living in 710 households spread across three hamlets , each of which had its own political leaders [19] . The social research activities described here were undertaken over 16 months ( June 2009—September 2010 ) and included six weeklong fieldtrips to Tri Nguyen Island . Research activities centered on two key groups: a ) Residents of Tri Nguyen island and b ) health providers , government officials and scientists with responsibilities at the local , regional and national levels ( hereafter , Leaders ) . It is widely established that qualitative research methods are the most appropriate for assessing the views of a population , in part because of their emphasis on context and their documentation of knowledge and attitudes in a given geopolitical setting . In this study , key or recurring themes from the qualitative research were explored further using quantitative measures ( a household survey and anonymous questionnaire ) and results were triangulated ( compared , challenged or confirmed ) across different methods: interviews , observations , questionnaires and a series of community meetings and workshops - styled on a focus group . Importantly , the findings presented here should not be seen as isolated research activities , but as a body of interconnected data developed over time using iterative processes and then contextualized , triangulated and crosschecked . An overview of the research activities undertaken at each phase , the issues they explored , how participants were recruited and the outputs they produced , is provided in Table 1 . In the following section we describe the methods used at each step in the research process and how the key results were used to design an engagement framework and communication materials tailored to this potential release site . We do so on the assumption that successful engagement leading to a release using new dengue control methods is still somewhat rare and that it is the process as much as the results that will be of interest to others looking to engage communities around new disease control strategies . The first step in the process was to immerse the two social science staff in the science of dengue and the Wolbachia strategy and to identify any information about the history and demographics of the potential release site . This included an extensive literature review on dengue fever , bio-control , GM food and organisms in Vietnam and internationally , and the development of a database ( Table 1 ) . In June 2009 a PowerPoint presentation was developed ( Table 2 ) . It used the same slides and followed the same narrative structure as the presentation used at the Australian field site , to which Vietnam specific information was then added . Graphics with small amounts of text were used to communicate key messages around the following themes: increasing prevalence of dengue ( local , national , international ) ; disease transmission and vectors; current control measures in Vietnam; the Wolbachia strategy; the Australian pilot release; a potential release on TNI . A discussion was then facilitated to identify any questions or concerns and seek guidance on how to engage , whom to engage , what would constitute authorization ( Table 2 ) . In July 2009 this presentation was used at the first of three leaders workshops , with thirty national , provincial , district and commune leaders , scientists and local health providers in attendance . Participants were chosen purposefully , because of their roles as leaders or officials and formally invited to attend . They included Ministry of Health leaders and scientists , members of the Khanh Hoa People's Committee and Khanh Hoa Health Department , and community and union leaders from TNI and NT . Project scientists and social scientists from Vietnam and Australia were present at the workshop . At the first Leaders Workshop ( Hanoi , July 2009 ) project employees were introduced to participants , and presentations delivered on the impact of dengue fever in Vietnam , the science behind the Wolbachia method , the potential release strategy in Vietnam and progress at the Australian release site ( scientific and engagement ) . The presentation was approximately 20 minutes long , after which a discussion was facilitated while the second social scientist made observations on body language; interactions between participants and audio recorded the entire event - presentation and discussion . Participants were asked if they had any questions , thoughts or concerns and what their expectations around the strategy , engagement and authorization might be . Input was also sought to identify key stakeholders as well as feedback on the presentation and project communication materials . An anonymous questionnaire was distributed at the end of the leaders workshop . It asked participants to identify any concerns or questions , evaluate how acceptable the Wolbachia strategy was , how they wished to be engaged and what would constitute authorization . This questionnaire provided baseline data for evaluating responses to the Wolbachia strategy through time , and was an important mechanism for tracking responses to the project among the leaders group and later , local residents . This process was also used at the Australian field site [6] . In early September 2009 , a senior entomologist working for the Wolbachia project , who was well known to the local community , introduced project staff to Tri Nguyen ( TNI ) residents . Limited information on the history and demographics of TNI was publically available so a purposive sample of 10 in-depth interviews on the history , socio-political structure , social demographics and dengue history of TNI was undertaken with local residents and leaders . Purposive sampling involves the deliberate selection of individuals because of the crucial information they can provide – in this case local leaders with a detailed knowledge of the history and socio-political make up of the TNI community . These interviews , alongside informal discussions with local health and mosquito control staff and results from the Leaders workshop , were used to develop a detailed stakeholder contact list , which was added to over time . It categorized individuals and groups according to: level of influence ( local , national , international ) ; local expectations around engagement; marginality; and accessibility . This helped to determine who was engaged and when . In addition , results from the interviews were also used to improve the PowerPoint presentation and communication materials to be used at future community meetings and workshops . In the next stage of the process , the results from these interviews were used to develop a Household Survey that examined the following: political structure ( leaders , groups , organizations ) ; social demographics of TNI ( name , age , gender , occupation , education level , religion , family structure ) ; knowledge of dengue , its vectors , control methods and perceptions of risk; and local health issues of concern to residents . The survey provided a brief introduction to the Wolbachia strategy and sought to identify early responses and advice on engagement and authorization for a release . The survey was piloted with 10 residents , reviewed and later administered to 100 households randomly selected from a list of 710 provided by local authorities - approximately 14% of all households . The second Leaders Workshop was held in the mainland city of Nha Trang , and attended by 33 participants representing local ( TNI ) and district leaders , government representatives , scientists , local health providers and mosquito control staff . An update on the progress of the science , the Australian risk assessment and the release was provided and further advice sought on stakeholders , forms of engagement , authorization and the presentation and communication materials . As noted above , a discussion was facilitated and any questions or concerns were noted . The event was also audio-recorded for later transcription and analysis and the anonymous questionnaire distributed . During the next phase of the project , 46 community meetings , attended by 661 local residents , were held in TNI during four , one-week trips in January ( T1 ) , March ( T2 ) , May ( T3 ) and July ( T4 ) 2010 ( Table 1 ) . The aim of these meetings was to gauge the range of views on the Wolbachia strategy , the science , potential release , engagement and authorization using the same focus group style format as the Leadership Workshops . Discussion was facilitated around the following themes: questions raised , concerns , acceptability , how and whom to engage and authorization . The meeting was audio-recorded and the anonymous questionnaire distributed at the end ( Table 1 ) . During the second visit ( March 2010 ) local residents who had contracted dengue attended the meeting and spoke of their experiences during the presentation . In addition , new results from the independent Australian Risk Assessment and new experiments showing Wolbachia was not transmitted to predators who ingested the infected mosquitoes were added to the presentation . During the third ( May 2010 ) and fourth visit ( July 2010 ) , results of Vietnamese experiments indicating that ingesting infected mosquitoes did not affect or lead to transmission of Wolbachia among local predatory species was included . By this time we also had more information about government approval processes ( following the final Leaders Workshop ) and the likely time frame for this , so this to was incorporated into presentation . Other than these additions , the presentation was the same at each visit . For the community meetings on TNI , a small number of participants were approached directly and sampled purposefully ( i . e . health staff , hamlet and local union leaders and members ) based on the stakeholder list we had begun developing . However , the majority of participants were sourced through flyers , posters and announcements over the community loudspeaker prior to each visit . As such the sample was broadly representative , with participants self-selecting to be involved . We aimed to reach at least one person from every TNI household ( Table 1 ) . During the second ( March 2010 ) and third ( May 2010 ) visits , 20 in-depth interviews were also undertaken with residents from TNI and NT ( aged 18–60 years ) who could not attend the meetings . We approached marginalized or harder-to-reach groups identified during the Leaders Workshops and early interviews ( n = 10 ) with local leaders . This included fishermen who were often away from the island , women with domestic and employment duties and minority religious or ethnic groups who it was thought might otherwise not have been engaged . These interviews began with the PowerPoint presentation and explored the same issues as the workshops and meetings . They were audio recorded for transcription purposes . The third and final Leaders Group Meeting was held in Nha Trang and attended by 33 local , district and national leaders , local health and mosquito control staff and scientists . Presentations on the results of both the social and scientific research were provided , and further advice sought on regulatory pathways and approval processes in Vietnam . The anonymous questionnaire was also distributed . Two social scientists and at least one senior entomologist attended every meeting or workshop . Prior to any research or engagement , an extensive and detailed list of questions and answers posed by the public at the Australian field site , was made available to Vietnamese project staff . It was posted to the project's website in June 2009 ( see http://www . eliminatedengue . com/faqs for the current version ) and later , on the Vietnamese language version and developed into flyers provided to participants . As the research progressed , it was clear that this extensive list covered almost every question posed by participants in the Vietnam research . When new questions or issues did arise , they were answered , if possible . If it was not possible to answer a question , it was recorded so that a response could be sought from appropriate staff and later provided back to the person asking the question and the community . This practice helped to ensure that information across the field sites , project staff and research activities - meetings , workshops , interviews etc . - was accurate and consistent .
Results from the Household survey ( n = 100 ) indicated that residents were well versed on prevention activities and current control methods , i . e . covering water containers , insecticide use , bed nets etc . [20] . Although 65% of those surveyed correctly identified key domestic breeding sites , there was also a strong and recurring association between ‘dirty places’ , namely sewers , forested areas , and refuse and the mosquitoes thought to transmit dengue . Although 65% were able to identify the mosquito primarily responsible for dengue transmission in TNI , only 35% were able to explain the transmission cycle or describe symptoms – both of which were central to understanding the Wolbachia strategy ( for more details see Huong and McNaughton 2012 . The Household Survey ( n = 100 ) revealed that most residents ( 93% ) identified dengue fever as a dangerous disease within their community . The main reasons cited were that it can be fatal ( 83 . 9% ) and can spread very fast ( 40 . 9% ) . Residents looked first to local health workers ( 95% ) , followed by television ( 55% ) and local officials ( 41% ) as trusted sources of information on dengue and health . These and other results were used to develop a more targeted PowerPoint presentation on the Wolbachia strategy that focused on symptoms , the transmission cycle and the habitats of the vectors , three key gaps in local understandings . This presentation was used at 46 focus-group style meetings with 661 residents ( Table 1 ) . The most prominent and recurring issue for respondents across the residents' and leaders' meetings and interviews was the safety of the method for people , animals and the environment . Relatedly , participants wanted to know if it was safe to be ‘bitten’ by a Wolbachia-infected Ae . aegypti mosquito , if was safe to drink water with these mosquitoes , their larvae or pupae in it , and if this would lead to Wolbachia being transmitted into other organisms , especially people . For example , a member of the youth union asked “Is it a problem if we are bitten by Wolbachia-infected mosquitoes ? Can Wolbachia be transmitted into our body ? ” Some also expressed concerns that Wolbachia-infected mosquitoes might become susceptible to or able to transmit other diseases: “After releasing the Wolbachia-carrying mosquitoes , the dengue fever may be reduced , but how about other diseases; will it cause any other disease to come to our Island ? ” Responses to questions relating to the potential transmission of Wolbachia to humans , other organisms or the environment included but were not limited to the following: A discussion about the role of many project staff in blood feeding large numbers of these mosquitoes in the caged trials and laboratories ( including photos ) often ensued . Alongside safety , considerable discussion centered on why TNI had been chosen as a potential release site , if it would be the first to trial this strategy and who would be responsible if anything should go wrong . For example , “I heard many people who participated in your discussions ask each other why this method was not applied somewhere else but on Tri Nguyen Island . Is it safe if it is applied here ? ” ( Male , 25 years , member of the Youth union ) . Another resident expressed concerns about safety and responsibility as follows: For many participants , assurances were sought that Australia rather than Vietnam would be the first place to release these mosquitoes . In addition , residents wanted clear pathways of responsibility outlined so they knew whom to speak to should something go wrong . Several residents asked directly , “Which agency will be in responsibility in case the release strategy will cause additional impacts ? ” ( CM , T3 ) . Local government and health officials also wished to know who would be responsible in the event of any problems and sought greater clarity from each other and project staff and leaders , regarding their specific responsibilities during a pilot release . Clear lines of responsibility had been established and these were relayed to residents with responses like the following: Another common concern centered on the efficacy of the strategy , especially in the long term . One resident attending the group asked “…does it [Wolbachia] have any side effects after being introduced into mosquitoes ? It is a bacterium , so it must be harmful to some extent” . ( CM , T2 ) . Many participants were also concerned that the life shortening effect of Wolbachia would impact on the success of the strategy , “How can Wolbachia-infected mosquitoes help prevent the disease when they die early after being released ? ” ( CM , T1 ) . “I am concerned that it may be difficult for Wolbachia-infected mosquitoes to find another mosquito to copulate with , or that they may die before they can lay their eggs” ( CM , T2 ) . Many participants were interested in eliminating all mosquitoes or why current control methods were no longer as viable: “Why don't you try to kill all mosquitoes ? Why don't you spray chemicals to kill them all ? ” ( CM , T3 ) . The 2009 Household Survey ( n = 100 ) had indicated that while 86% found the Wolbachia strategy acceptable , the use of insecticides either inside ( 67% ) or outside ( 74% ) their homes was also viewed positively ( see Table 3 ) . Responses to questions relating to efficacy , focused in part on the role of the trials in determining the effectiveness of this strategy , and that results from the Australian releases would be reported back to the community during future engagement . They also included , but were not limited to , the following ( for more details http://www . eliminatedengue . com/faqs ) : The nature and scale of the pilot release were also prominent , recurring issues from the community meetings and interviews ( n = 20 ) . Respondents commonly sought a high level of detail regarding the release , its timing and scale . Questions focused on further details regarding how many mosquitoes would be released , if this would be in all or only some houses , and how long it would take for wild mosquitoes to be infected . There was a lot of discussion about what residents should do to assist the effectiveness of the strategy and what impact this might have on people's lives . For example: This question was answered as follows: During the Residents meetings ( n = 46 ) and interviews ( n = 20 ) assurances were often sought that the release would not negatively affect or inhibit local lives and livelihoods and that householders would be made aware of any activities they needed to undertake before or during a release . There was strong support for being advised and informed well in advance of a release “so that we are well prepared for it ? ” ( CM , T2 ) . In general , we responded to these questions as follows: The anonymous questionnaire , handed out at the end of each meeting included the question , “Do you have any concerns about the Wolbachia method ? ” which was used to track residents' perceptions of the project through time . As indicated in Figure 2 , the number of concerned participants declined significantly as the Residents' Meetings and interviews continued . During the final two visits to TNI in May and July 2010 , no participants objected to a release ( Figure 2 ) . Participants were asked at the Leaders workshops ( n = 3 ) , Residents' Meetings ( n = 46 ) and interviews ( n = 20 ) how they would like to be engaged about the Wolbachia strategy . There was a strong desire for public consultation across all groups , consistent support for in-community presentations and a strong preference for face-to-face interaction with the project team and senior health officials . There was much less support for the use of media , posters , brochures and leaflets . One of the most common requests related to the scale of the engagement . At the local level , participants consistently indicated that well before a release the project team should engage with every community member and provide ongoing information on the safety and benefits of the project well before a release . For example , “More people , all people should be invited . A small group of participants like this is not representative enough to make a decision . It is perfect if 100% of people agree” ( CM , T3 ) . Others suggested that , at minimum , one person from each household should be engaged . For example , “One person from every household should be invited . The main income earner in every household should be invited so that they can remember what they have heard and tell others . If you invite those who are too old , they may not have a good memory to tell others about what they have heard” ( CM , T3 ) . Participants were also asked what would be the best format to engage people on TNI about the strategy and in the lead up to a release if regulatory approval was given . There was an expectation of ongoing consultation about the strategy among residents , leaders and health staff , where updates on the science , safety , risk assessment , regulatory approval , pilot release strategy , results from the Australian release and a well-defined structure around roles and responsibilities would be provided . Some were also concerned that without this , people might forget what they had learned about the strategy and how to respond to a release . Community leaders and health professionals suggested that residents would come to them for information and guidance , especially if things did not go to plan . As such they sought to have clear pathways on any future roles and responsibilities they might have negotiated , outlined and communicated to residents well before a release . As well as calling for regular updates , participants consistently identified the importance of a large meeting attended by at least one representative from each household as well as local and provincial leaders – essentially a forum where people could raise their ideas , discuss benefits and concerns and make a collective decision ( Table 4 ) . There was also a strong preference for voting at such a forum , as one resident expressed it “Voting can be used . Those who agree will raise their hand . If the majority raises hands that means it is supported” ( CM , T1 ) ( Table 5 ) . As such a large public meeting held in the community or a vote was identified as a mechanism through which the project and the release would gain final and collective approval from the TNI community , alongside support of government officials ( regulators , Ministry of Health and scientists ) ( Table 5 ) . The anonymous questionnaire also asked whether Resident's would support a pilot release if ( a ) the Ministry of Health undertook a risk assessment and approval process , and ( b ) scientific data from the Australian release site proved to be positive . During the first phase of social research and engagement in January 2010 , 80 . 2% were in favor of the pilot release . By the final phase in July 2010 , this had risen to 99 . 4% ( Figure 3 ) . Of course , participants can and do change their minds and they could react differently when a release happens , and this is a limitation of this study . However , results from the Australian research did allow us to predict quite successfully how people would react and there was no last minute call to stop the release in Australia . Although a release has not yet occurred in Vietnam , the most recent engagement with TNI residents ( 2013 ) - where one person from every household was interviewed - 99% of householders were still in favor of the release , only a few months shy of its eventuality ( data not shown ) .
The approach described here produced a number of critical insights that helped determine the nature , scale , style and form of an engagement framework tailored specifically to the needs and wishes of officials and residents and the potential release site in Vietnam . It used systematic social research and consultation to ( a ) identify , inform and involve the public; ( b ) listen to their responses , questions and concerns; ( c ) examine the deeper cultural assumptions that underwrite these responses , including lay knowledge of dengue; ( d ) explore ways of responding to these issues i . e . scientifically , through education , the media , schools programs or new forms of participation; and ( e ) explore and enact suggestions regarding future engagement , participation , communication and authorization . Through this process we found that residents at the potential release site in Vietnam expected to be fully informed and fully engaged about the science , the project , its safety , risk assessments , the nature of the release and who would be responsible should something go wrong . Along with key health and government officials and representatives they provided advice on how best to engage their community and wanted the opportunity to meet with and ask questions of scientists involved in these programs and to have their concerns taken seriously and answered respectfully . This approach thus afforded the development of a more culturally appropriate and comprehensible engagement framework and communication materials that empowered those being asked to assess , critique and support a field trial or release . It has now been implemented at three socially and politically diverse and complex field sites ( seven in Australia , one in Vietnam ) in two countries , demonstrating its capacity to reflect local requirements and its potential for use in other programs and other regions . | In recent years , a number of new strategies using novel technologies for the control of dengue fever control have emerged . These strategies are notably different from their predecessors and not without controversy . Many also require open release field trials to test their efficacy . Public consultation and engagement are recognized as critical to the future success of these programs , but questions remain regarding how to proceed . In this paper we describe an approach to public engagement that uses social research to design an engagement framework and communication materials tailored to the concerns , expectations , and socio-political setting of potential trial release sites . This approach was developed and implemented in Australia ( 2008–2010 ) where the first publicly supported field trials occurred January 2011 . We report here on the implementation of this approach in Vietnam ( 2009–2010 ) where the second release will occur in 2014 . This paper describes the process , research activities , outcomes and key findings from the Vietnamese field site . It highlights key public concerns and expectations about engagement and authorization and shows how these were used to develop a more targeted , culturally appropriate and comprehensible engagement framework and communication materials . The paper demonstrates the viability of this approach to community engagement for new dengue control strategies , in a ‘developing’ country context . | [
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]
| 2014 | Designing a Community Engagement Framework for a New Dengue Control Method: A Case Study from Central Vietnam |
Plasmodium knowlesi , a malaria parasite originally thought to be restricted to macaques in Southeast Asia , has recently been recognized as a significant cause of human malaria . Unlike the benign and morphologically similar P . malariae , these parasites can lead to fatal infections . Malaria parasites , including P . knowlesi , have not yet been detected in macaques of the Kapit Division of Malaysian Borneo , where the majority of human knowlesi malaria cases have been reported . In order to extend our understanding of the epidemiology and evolutionary history of P . knowlesi , we examined 108 wild macaques for malaria parasites and sequenced the circumsporozoite protein ( csp ) gene and mitochondrial ( mt ) DNA of P . knowlesi isolates derived from macaques and humans . We detected five species of Plasmodium ( P . knowlesi , P . inui , P . cynomolgi , P . fieldi and P . coatneyi ) in the long-tailed and pig-tailed macaques , and an extremely high prevalence of P . inui and P . knowlesi . Macaques had a higher number of P . knowlesi genotypes per infection than humans , and some diverse alleles of the P . knowlesi csp gene and certain mtDNA haplotypes were shared between both hosts . Analyses of DNA sequence data indicate that there are no mtDNA lineages associated exclusively with either host . Furthermore , our analyses of the mtDNA data reveal that P . knowlesi is derived from an ancestral parasite population that existed prior to human settlement in Southeast Asia , and underwent significant population expansion approximately 30 , 000–40 , 000 years ago . Our results indicate that human infections with P . knowlesi are not newly emergent in Southeast Asia and that knowlesi malaria is primarily a zoonosis with wild macaques as the reservoir hosts . However , ongoing ecological changes resulting from deforestation , with an associated increase in the human population , could enable this pathogenic species of Plasmodium to switch to humans as the preferred host .
Until recently , it was believed that malaria in humans was caused by only four species of parasite ( Plasmodium falciparum , P . vivax , P . malariae and P . ovale ) . However , this perception changed when we discovered a large focus of human infections with P . knowlesi in the Kapit Division of Sarawak , Malaysian Borneo [1] . These infections had predominantly been mistakenly identified as P . malariae by microscopy , since both species have similar morphological characteristics [1] , [2] . With subsequent reports of human infections in other parts of Malaysia [3] , [4] , and in Thailand [5] , [6] , Myanmar [7] , Singapore [8] , [9] , the Philippines [10] , Vietnam [11] and Indonesia [12] , [13] , P . knowlesi is now recognized as the fifth species of Plasmodium responsible for human malaria . It causes a wide spectrum of disease and can lead to high parasite counts , severe complications and death [3] , [14] . In a recent study , we found that approximately 1 in 10 knowlesi malaria patients at Kapit Hospital developed potentially fatal complications , comparable to P . falciparum malaria , which is considered to be the most virulent type of malaria in humans [14] . P . knowlesi is primarily a simian malaria parasite and was first isolated from a long- tailed macaque ( Macaca fascicularis ) imported to India from Singapore in 1931 [15] . Subsequently , P . knowlesi has been detected in wild long-tailed macaques of Peninsular Malaysia [4] , [16] and the Philippines [17] , in pig-tailed macaques ( M . nemestrina ) of Peninsular Malaysia [16] and in banded leaf monkeys ( Presbytis melalophus ) in Peninsular Malaysia [16] . There has been no documented evidence of P . knowlesi or any other malaria parasites in monkeys in Malaysian Borneo , and although a monkey source for the hundreds of human P . knowlesi infections that have been described in the Kapit Division of Sarawak [1] , [3] , [14] appeared likely , it remained to be proven . Prior to our report in 2004 of the large focus of human infections in Sarawak , Malaysian Borneo , when we utilized molecular methods for characterisation and PCR assays for detection of P . knowlesi [1] , there had been only one confirmed case of a naturally-acquired P . knowlesi infection in a human [18] . That person got infected with P . knowlesi while spending a few weeks in the forest of Pahang , Peninsular Malaysia in 1965 . It is not known whether the large focus in Malaysian Borneo and subsequent recent reports of human knowlesi malaria in Southeast Asia represent a truly newly emergent malaria parasite in humans or whether human infections have been occurring for a relatively long period , but have gone undetected due to unavailability of molecular detection methods to distinguish between P . knowlesi and P . malariae . In order to identify the reservoir hosts and extend our understanding of the epidemiology and evolutionary history of P . knowlesi , we examined blood samples from wild macaques for malaria parasites , and analyzed the circumsporozoite protein ( csp ) gene and the mitochondrial ( mt ) genome of P . knowlesi isolates derived from humans and macaques .
Nested PCR examination of blood samples from 108 wild macaques ( 82 long-tailed , 26 pig-tailed ) , sampled from 17 different locations in the Kapit Division of Sarawak , showed that 101 ( 94% ) of the macaques were infected with malaria parasites . Long-tailed macaques had a higher prevalence of infection ( 98% ) than pig-tailed macaques ( 81% ) ( Fisher's Exact P = 0 . 009 ) ( Table 1 ) . By nested PCR assays , we detected 5 species of Plasmodium , with P . inui being the most common ( prevalence of 82% ) , followed by P . knowlesi ( 78% ) , P . coatneyi ( 66% ) , P . cynomolgi ( 56% ) , and P . fieldi ( 4% ) . Multiple species infections were very common , with 91 of the 108 ( 84% ) macaques being infected by two or more species of Plasmodium each . There was a higher prevalence of P . knowlesi among long-tailed macaques ( 87% ) than pig-tailed macaques ( 50% ) ( P = 0 . 006 ) . To compare the molecular identity of the parasites in macaques and humans , we first sequenced the P . knowlesi csp gene in blood samples from 31 patients admitted to Kapit Hospital and 16 wild macaques . Most macaques ( 10 of 16 ) , but only a minority of humans ( 3 of 31 ) contained 2 or more csp alleles ( Fig . 1A ) . Overall , we derived 48 csp allele sequences of P . knowlesi from the macaques and 34 from the human samples , with 61 different alleles observed in total . Three of these csp alleles were shared between human and macaques , three were shared by macaques , and the remaining alleles were detected in only macaques or humans ( Fig . S1 and Fig . 1B ) . We found that the central region of the P . knowlesi csp was composed of highly polymorphic repeat sequences ( Table S1 ) . Analysis of the aligned non-repeat regions of csp showed 19 polymorphic sites , of which 14 were shared polymorphisms in samples from both host populations ( Fig . S2 ) . The nucleotide diversity of csp was similar in both hosts ( π = 2 . 2×10−2 in humans and 2 . 4×10−2 in macaques ) , although the haplotype diversity was marginally higher in macaques ( H = 0 . 82 , SD = 0 . 03 ) than in humans ( H = 0 . 73 , SD = 0 . 06 ) . There was no clustering of csp allele sequence type associated with either host ( Fig . 1B ) . We also sequenced the ∼6-kilobase mtDNA genome of P . knowlesi parasites isolated from 25 malaria patients and 11 macaques . Each human sample had a single mtDNA haplotype , while all except one macaque sample contained multiple ( 2 to 6 ) haplotypes ( Fig . 2A ) . In total , we generated 54 complete mtDNA genome sequences , representing 37 different mtDNA haplotypes , with a higher number of haplotypes in the macaques ( 23 haplotypes from 11 samples ) than in the humans ( 17 haplotypes from 25 samples ) . Six of the haplotypes were found in more than 1 sample , and 3 of these were shared between the human and macaque hosts ( Fig . 2B ) . Forty-five single nucleotide polymorphisms ( SNPs ) and a 4-base insertion/deletion within the P . knowlesi mtDNA genome were identified ( Fig . S3 ) , and the level of nucleotide diversity ( π ) of mtDNA was estimated as 7 . 5±0 . 7×10−4 . The Bayesian coalescent approach [19] was used to estimate the time to the most recent common ancestor ( TMRCA ) for P . knowlesi . A nucleotide substitution rate for the mtDNA genome of 3 . 13×10−9 ( 95% HPD , 1 . 94–4 . 45×10−9 ) substitutions per site per year was estimated by comparing mtDNA sequences of P . knowlesi , P . fragile , P . cynomolgi , P . simiovale ( parasites of Asian macaques ) with P . gonderi ( a parasite of African mangabeys ) and Plasmodium sp . ( Mandrill ) , assuming parasite lineages separated when Asian Old World monkeys and African Old World monkeys diverged 10 million years ago ( MYA ) [20] . This derived rate yielded an estimate of 257 , 000 ( 95% HPD: 98 , 000–478 , 000 ) years before present as the TMRCA of P . knowlesi ( Fig . 3 ) . There was no evidence of recombination in the mtDNA of P . knowlesi ( Table S2 ) , and reconstruction of the haplotype genealogical network demonstrated that no distinct lineages of mtDNA were exclusively associated with either human or macaque hosts ( Fig . 2B ) . Our phylogeny-trait association tests based on association index ( AI ) [21] and parsimony score ( PS ) [22] of host-parasite phylogenetic substructure do not reject the null hypothesis of no association between parasite and host ( Table S3 ) . Hence , this further indicates the absence of a distinct lineage of P . knowlesi parasites associated with either human or macaque hosts . We observed an excess of unique mtDNA haplotypes , which appear at the edges of a star-like structure of the haplotype genealogical network ( Fig . 2B ) , and this is indicative of an evolutionarily recent population expansion of P . knowlesi . The signature of population expansion is also evident from the unimodal shape of the pairwise mismatch distribution ( Fig . 4A ) , and this is supported by a low Harpending's raggedness index ( r = 0 . 009 , P = 0 . 87 ) [23] . In addition we used the Tajima's D [24] , Fu and Li's D and F ( with out-group ) [25] and Fay and Wu's H [26] statistics to detect deviation from the model of neutral evolution considering that the deviation from neutrality could be due to demographic processes such as population expansion , population bottleneck or mutation rate heterogeneity [27] . We obtained significant negative values for all these statistics ( Tajima's D = −1 . 88 , P = 0 . 001; Fu and Li's D = −2 . 60 , P = 0 . 02; Fu and Li's F = −2 . 80 , P = 0 . 009; Fay and Wu's H = −10 . 33 , P = 0 . 044 ) , thereby providing further evidence for an expansion of the P . knowlesi parasite population . To further investigate the demographic history of P . knowlesi , we estimated the changes in effective population size of the parasite through time , using a coalescent approach called the Bayesian skyline plot [19] . The plot indicates that P . knowlesi underwent a rapid population growth between approximately 30 , 000 and 40 , 000 years before present ( Fig . 4B ) . In addition , we performed independent analyses for P . knowlesi mtDNA sequences derived from humans and macaques . Similar trends were reflected in the Bayesian skyline plots for each host ( Fig . S4 ) , showing that there are no differences between the demographic history of P . knowlesi for either host . We analysed mtDNA cytochrome b sequence data for long-tailed macaques in Southeast Asia using a similar approach , but did not find any evidence for changes in population size between 100 , 000 and 10 , 000 years before present ( Fig . S5 ) .
Our study shows that wild macaques in the Kapit Division of Sarawak , Malaysian Borneo are infected with the same 5 species of Plasmodium found in macaques of Peninsular Malaysia [16] , [28] , and that these macaques have a very high prevalence of P . knowlesi and P . inui . In previous studies , we found that P . knowlesi is the most common cause of hospital admission for malaria in the Kapit Division and there are approximately 90 knowlesi malaria admissions , predominantly adults , at Kapit Hospital per year [1] , [3] , [14] . The actual annual incidence of knowlesi malaria for the Kapit Division is probably higher , because not all persons with P . knowlesi infections may have sought treatment in hospital and there may be asymptomatic infections and misdiagnoses . Nevertheless , the restricted number of knowlesi malaria cases in the human population of 109 , 000 [14] , contrasts with the extremely high prevalence of P . knowlesi we detected in the wild macaques of the Kapit Division . These findings contrast with the absence of P . knowlesi infections in a survey of 99 long-tailed macaques in one region in Thailand [29] . In that study , the majority of macaques were trapped near a temple in a region where very few human knowlesi malaria cases have been reported [6] , and the absence of detectable P . knowlesi there could be due to the low abundance of mosquitoes of the Anopheles leucosphyrus group , which have been shown to be the most competent vectors of knowlesi malaria [28] . We previously identified one member of this group , Anopheles latens , as the vector for P . knowlesi in the Kapit Division [30] . This mosquito feeds outdoors after dusk and is attracted to humans and macaques at ground level , but prefers to feed on macaques at a higher elevation [31] . Our findings here , of the higher number of P . knowlesi csp alleles and mtDNA genome haplotypes detected per infection in macaques compared with humans , and the very high prevalence of P . knowlesi in macaques , suggest that presently there is a greater intensity of transmission of P . knowlesi by the vectors among wild macaques , than from macaques to humans . These results , including our observation that certain alleles of the P . knowlesi csp gene and mtDNA genome haplotypes are shared between macaque and human hosts , taken together with previous epidemiological [1] , [3] , [14] and entomological data [30] , [31] , strongly indicate that knowlesi malaria is a zoonosis in the Kapit Division and that wild macaques are the reservoir hosts . Our estimated TMRCA for P . knowlesi ( 98 , 000–478 , 000 years ago ) indicates that P . knowlesi is derived from an ancestral parasite population that predates human settlement in Southeast Asia [32] , [33] . Therefore macaques , which colonized Asia more than 5 million years ago [34] , were the most likely hosts during the initial emergence of P . knowlesi in this region . Our estimate also indicates that P . knowlesi is as old as , or older than the 2 most common human malaria parasites , P . falciparum and P . vivax , for which the TMRCA has been estimated to be 50 , 000–330 , 000 [35] , [36] years and 53 , 000–265 , 000 years [37] , [38] , respectively . Our analyses of the mtDNA data indicate that that P . knowlesi underwent a period of population expansion , estimated at 30 , 000–40 , 000 years ago , which coincides with a time when Borneo was part of mainland Southeast Asia [39] and the possibility of increased parasite admixture between macaque troops . This period is concordant with a time of exceptional human population growth in Southeast Asia , based on mtDNA sequence analysis [40] . We did not detect a similar population expansion of macaques , but this analysis was based on the cytochrome b gene alone . It would be preferable to analyze mtDNA sequences of macaques sampled in Borneo to determine whether they underwent a parallel historical population expansion . It is possible that the population expansion of P . knowlesi was not directly linked to expansion in any primate host , but was rather due to the expansion or adaptation of the mosquito vectors . In conclusion , our results indicate that P . knowlesi in Sarawak is zoonotic , with humans sharing parasites with the original and preferred hosts , the macaques , most likely since they first came into close contact in the forests of Southeast Asia . A multi-gene family ( KIR ) in P . knowlesi encodes proteins with sequence motifs mimicking host cell receptor CD99 in macaques [41] , and the observation that the KIR motifs are less perfectly matched to the human CD99 sequence also supports the hypothesis that the parasite is particularly adapted to macaque hosts . Humans acquire knowlesi malaria on occasions when they enter the habitats shared by macaques and mosquitoes of the Anopheles leucosphyrus group [4] , [16] , [28] , [30] , which are forest-dwelling mosquitoes that feed outdoors after dusk [28] , [31] . There is no evidence yet to suggest a host-switch by P . knowlesi , unlike other human malaria parasites such as P . vivax and P . falciparum that might have been part of ancient zoonoses [38] , [42] , but have since adapted to humans . However , it is possible that the current destruction of the natural forest ecosystem , with associated increase of the human population , may alter the parasite , macaque host and mosquito population dynamics and lead to an adaptive host-switch of P . knowlesi to humans .
Currently , Malaysia has no legislation governing the use of animals in research . Nevertheless , this study was carried out in strict accordance with the recommendations by the Sarawak Forestry Department for the capture , use and release of wild macaques . A veterinarian took blood samples from macaques following anesthesia by intramuscular injection of tiletamine and zolazepam . All efforts were made to minimize suffering by collecting blood from macaques at the trap sites and releasing the animals immediately after the blood samples had been obtained . The Sarawak Forestry Department approved the study protocol for capture , collection of blood samples and release of wild macaques ( Permits Numbers: NPW . 907 . 4 . 2-32 , NPW . 907 . 4 . 2-97 , NPW . 907 . 4 . 2-98 , 57/2006 and 70/2007 ) . A permit to access and collect macaque blood samples for the purpose of research was also obtained from the Sarawak Biodiversity Centre ( Permit Number: SBC-RP-0081-BS ) . Human blood samples were taken after written informed consent had been obtained from patients admitted to Kapit Hospital . This study was approved by the Medical Research and Ethics Committee of the Malaysian Ministry of Health ( Reference number: KKM/JEPP/02 Jld . 2 [133] ) , which operates in accordance to the International Conference of Harmonization Good Clinical Practice Guidelines . A total of 108 macaques were sampled from 2004 to 2008 . Ninety were from 5 major sites and the remainder from 12 different locations in the Kapit Division of Sarawak . All locations were within 2 km from longhouse communities where human knowlesi cases had previously been reported . After blood was obtained from anaesthetised animals , they were tagged with a microchip ( to prevent re-sampling ) and released . Human blood samples were obtained from 31 patients with knowlesi malaria admitted to Kapit Hospital between 2000 and 2006 . DNA was extracted from macaque and human blood samples as described previously [1] . DNA samples from macaques were examined using nested PCR assays with genus and species-specific primers based on the small subunit ribosomal RNA genes [1] . PCR primer sequences ( for P . knowlesi , P . coatneyi , P . cynomolgi , P . fieldi and P . inui ) and annealing temperatures are provided in Table S4 . We amplified and sequenced the complete P . knowlesi csp gene and performed sequence analysis as described previously [1] . At least two clones from each of the two PCR amplifications per sample were sequenced and for macaque samples , at times 5 PCR amplifications and cloning procedures were necessary before P . knowlesi sequences could be obtained . For amplification of the P . knowlesi mtDNA , two back-to-back primers were designed ( Pkmt-F1 , 5′- GGACTTCCTGACGTTTAATAACGAT-3′ and Pkmt-R1 , 5′-TGGACGTTGAATCCAATAGCGTA-3′ ) by using previously described mtDNA sequence of P . knowlesi [43] . PCR amplification was performed separately for each sample to prevent cross-contamination of DNA using the Elongase Amplification System ( Invitrogen ) . The PCR product for each isolate was gel purified , cloned into pCR-XL-TOPO vector ( Invitrogen ) and sequenced using BigDye Terminator Cycle Sequencing kit ( Applied Biosystems ) with 28 internal primers ( sequences in Table S5 ) that enabled sequencing of both DNA strands . The diversity of P . knowlesi mtDNA from 6 human samples was initially characterized . These were selected based on the criteria that each patient originated from a different geographical area of Kapit Division and patients had no records of recent travel history . Only females were chosen assuming that females travel less than the males . Sequencing data was obtained from at least 2 clones originating from separate PCR amplifications . Both DNA strands were sequenced from each clone and any nucleotide conflicts found were resolved following a third PCR amplification , cloning and sequencing . The remaining 19 human samples were randomly chosen from patients admitted between 2000 and 2006 . Following PCR amplification and cloning , these samples were haplotyped by sequencing single DNA strand of the mt genome and at least 2 plasmid clones were sequenced for each sample . Any single nucleotide polymorphisms ( SNPs ) or singleton polymorphisms detected were verified by sequencing the polymorphic regions in at least 2 plasmid clones originating from separate PCR amplifications , and both DNA strands were sequenced . The mt genome was selected to examine the evolutionary history of P . knowlesi , just as the mt genomes of P . vivax [38] and P . falciparum [35] were previously found suitable; it does not undergo recombination so intraspecific phylogenetic analysis can be performed and it shows no evidence of non-neutral polymorphism . DNA sequence data were aligned using the Lasergene package ( DNASTAR ) . Measures of genetic diversity were conducted using DnaSP v5 . 10 . 00 software [44] . A minimum spanning network connecting the mtDNA haplotypes of P . knowlesi based on statistical parsimony method was constructed using the TCS 1 . 21 software [45] . Host-parasite association was assessed based on the association index ( AI ) [21] and parsimony score ( PS ) statistics [22] , which account for phylogenetic uncertainty in analysis of phylogeny-trait correlations . The values of AI and PS statistics were calculated based on the posterior samples of trees produced by BEAST using the BaTS program [44][46] . The null distribution for each statistic was estimated with 1 , 000 replicates of state randomization . The demographic expansion of P . knowlesi was examined based on pairwise mismatch distribution using Arlequin v3 . 1 software [47] . Observed mismatch distribution was compared with that estimated under the sudden demographic expansion model using a generalized least-square approach [48] . The deviations from the population expansion model were tested using the Harpending's raggedness index [23] with a parametric bootstrap of 1000 replicates . Tajima's D [24] , Fu and Li's D [25] , Fu and Li's F [25] and Fay and Wu's H [26] statistics were performed using the software DnaSP v5 . 10 . 00 [44] . These statistics were calculated using the mitochondrial genome of P . coatneyi ( AB354575 ) as out-group . The evolutionary rate , time to the most recent common ancestor ( TMRCA ) and the past population dynamics of P . knowlesi were inferred using the Bayesian Markov Chain Monte Carlo ( MCMC ) method implemented in the BEAST package v1 . 5 . 4 [19] . The mean substitution rate of mtDNA and TMRCA of P . knowlesi were estimated based on a time-calibrated Bayesian phylogenetic analysis of non-human primate malarias ( P . gonderi , Plasmodium sp . ( Mandrill ) , P . simiovale , P . fragile , P . cynomolgi and P . knowlesi ) and human malarias ( P . falciparum , P . vivax , P . malariae and P . ovale ) ( Table S6 ) ( Figure 3 ) , assuming co-divergence of the parasites with their host lineages [38] , Asian Old World monkeys - African Old World monkeys at 10 MYA [20] . The accession numbers of sequences derived from GenBank database are as follows; P . falciparum ( M99416 ) , P . malariae ( AB354570 ) , P . vivax ( NC007243 ) , P . ovale ( AB354571 ) , P . gonderi ( AB434918 ) , Plasmodium sp . ( mandrill ) ( AY800112 ) , P . simiovale ( AB434920 ) , P . inui ( AB354572 ) , P . hylobati ( AB354573 ) , P . cynomolgi ( AB434919 ) , P . simium ( AY800110 ) , P . fragile ( AY722799 ) and P . coatneyi ( AB354575 ) . A General Time Reversible ( GTR ) substitution model with gamma distribution of rate variation among sites and a proportion of invariable sites as determined using Modeltest v3 . 7 [49] , an uncorrelated log-normal relaxed molecular clock model and a Bayesian skyline coalescent model ( 10 coalescent-interval groups ) were used for this analysis . One hundred million generations of the MCMC chains were run with sampling every 10 , 000 generations and the first 10 million generations were discarded as burn-in . The BEAST output was analyzed using the Tracer v1 . 5 program ( available at http://tree . bio . ed . ac . uk/software/tracer/ ) and uncertainty in parameter estimates was expressed as values of the 95% highest probability density ( HPD ) . The trees produced by BEAST were annotated using TreeAnnotator , and maximum clade credibility tree was visualized using the FigTree v1 . 3 . 1 program ( available at http://tree . bio . ed . ac . uk/software/figtree/ ) . Past population dynamics of P . knowlesi parasites in terms of the change in effective population size ( Ne ) through time were independently analyzed using the P . knowlesi mtDNA datasets for humans and macaque , and also by combining both human and macaque P . knowlesi mtDNA datasets . Using the estimated mean substitution rate and Bayesian skyline coalescent model , the MCMC chains were run for 100 million generations with sampling every 10 , 000 generations and the first 10 million generations were discarded as burn-in . The changes in effective population size ( Ne ) through time were also drawn for M . fascicularis and M . nemestrina based on the cytochrome b sequences obtained from GenBank ( Table S7 ) . A BEAST analysis to determine the mean rate substitution of the cytochrome b ( cytb ) gene of macaques was performed using cytb sequences of M . fascicularis , M . nemestrina and Papio anubis ( GenBank accession EU885461 ) , and assuming baboons and macaques diverged 6 . 6 MYA [50] . An estimated mean substitution rate of 4 . 56×10−8 substitutions per site per year was used to infer the Bayesian skyline plot for M . fascicularis and M . nemestrina . For each species , 100 millions generations were performed , with sampling every 10 , 000 generations and 10 percent of the sampling were discard as burn-in . For all analyses implemented in BEAST , at least 2 independent runs were performed and convergence of all parameters was determined based on Effective Sample Size ( ESS ) values of >200 . The sequences generated during this study have been deposited in GenBank: P . knowlesi mitochondrial genome sequence data under the accession numbers EU880446–EU880499 and P . knowlesi csp gene sequences under the accession numbers AY327558–AY327572 , DQ350272–DQ350306 , DQ641526–DQ641528 and GU002471–GU002533 . | We recently described the first focus of human infections with P . knowlesi , a malaria parasite of monkeys , and subsequently reported that these infections can be fatal . Whether mosquito transmission of infection depended on the monkey reservoir or was maintained by the human population was unknown . In the area of highest human infection incidence ( within the Kapit Division of Sarawak , Malaysian Borneo ) , we surveyed 108 wild monkeys and found most were infected with malaria parasites , including P . knowlesi . We observed that the number of P . knowlesi genotypes per infection was much higher in monkeys than humans , some genotypes were shared between the two hosts and no major types were associated exclusively with either host . Evolutionary analyses of sequence data indicate that P . knowlesi existed in monkeys prior to human settlement in Southeast Asia and underwent a recent population expansion . Thus , P . knowlesi is essentially zoonotic; humans being infected with these parasites from the original and reservoir monkey hosts probably since they first entered the forests of Southeast Asia . We consider that the current increase in the human population , coupled with ecological changes due to deforestation , could result in a switch to humans as the preferred host for this pathogenic Plasmodium species . | [
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| 2011 | Plasmodium knowlesi: Reservoir Hosts and Tracking the Emergence in Humans and Macaques |
Genotype I ( GI ) virus has replaced genotype III ( GIII ) virus as the dominant Japanese encephalitis virus ( JEV ) in the epidemic area of Asia . The mechanism underlying the genotype replacement remains unclear . Therefore , we focused our current study on investigating the roles of mosquito vector and amplifying host ( s ) in JEV genotype replacement by comparing the replication ability of GI and GIII viruses . GI and GIII viruses had similar infection rates and replicated to similar viral titers after blood meal feedings in Culex tritaeniorhynchus . However , GI virus yielded a higher viral titer in amplifying host-derived cells , especially at an elevated temperature , and produced an earlier and higher viremia in experimentally inoculated pigs , ducklings , and young chickens . Subsequently we identified the amplification advantage of viral genetic determinants from GI viruses by utilizing chimeric and recombinant JEVs ( rJEVs ) . Compared to the recombinant GIII virus ( rGIII virus ) , we observed that both the recombinant GI virus and the chimeric rJEVs encoding GI virus-derived NS1-3 genes supported higher replication ability in amplifying hosts . The replication advantage of the chimeric rJEVs was lost after introduction of a single substitution from a GIII viral mutation ( NS2B-L99V , NS3-S78A , or NS3-D177E ) . In addition , the gain-of-function assay further elucidated that rGIII virus encoding GI virus NS2B-V99L/NS3-A78S/E177E substitutions re-gained the enhanced replication ability . Thus , we conclude that the replication advantage of GI virus in pigs and poultry is the result of three critical NS2B/NS3 substitutions . This may lead to more efficient transmission of GI virus than GIII virus in the amplifying host-mosquito cycle .
Japanese encephalitis virus ( JEV ) , a mosquito-borne flavivirus , has a single-stranded , positive-sense RNA genome encoding three structural proteins ( capsid , precursor membrane protein , and envelope protein ) and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) [1] . JEV was first isolated in 1935 . Since then JEV has been classified into five genotypes ( GI-GV ) with variant geographic distribution in Asia and Australia . Traditionally , GIII virus is the most widely distributed and dominant JEV genotype in JEV endemic/epidemic regions [2] . The ecological factors of GIII endemic or epidemic areas are well characterized , and the virus is maintained primarily in the Culex tritaeniorhynchus-amplifying host ( pigs and avian species ) transmission cycle , and humans and horses are accidental , dead-end hosts [3–8] . An estimated 67 , 900 JE human cases occur annually with a 20–30% case-fatality rate , and 30–50% of surviving patients suffer neurological sequelae [9] . GI virus emerged in the 1990s and has gradually replaced GIII virus as the most frequently isolated JEV genotype from Culex tritaeniorhynchus , stillborn piglets , and JE patients in Japan , Korea , Vietnam , Thailand , Taiwan , China , and India [10–15] . We suggest that GI virus might compete with GIII virus in the same pig-mosquito cycle and exhibit a transmission advantage over areas previously dominated by GIII virus [11 , 16] . Emerging GI virus replicates more efficiently in Aedes albopictus mosquito-derived cells , birds , and ducklings than GIII virus [17–19]; however , it remains unknown if the replication efficiency of GI vs . GIII virus occurs in pigs and/or Culex tritaeniorhynchus mosquitoes , which play critical roles in local transmission of JEV . In addition , the experimental evidence associated with the difference in viral genomic factor ( s ) does not fully support the occurrence of GIII replacement during the past 20 years [11 , 18 , 20] . In this study , we investigated the in vitro and in vivo replication characteristics of emerging GI virus and previously dominate GIII virus in Culex tritaeniorhynchus mosquito vector and amplifying hosts ( pigs , young chickens , and ducklings ) . We did not include dead-end hosts in this study because of the remote possibility of the involvement of dead-end hosts in the transmission of JEV . More importantly , we constructed , generated and applied various genotypic chimeras of recombinant JEVs from infectious cDNA clones to determine the viral genetic determinants that enhanced and increased the replication efficiency of GI virus over GIII virus . These genetic determinants would be critically important for selection of viral hosts and genes to monitor GI virus activity and evolution in a natural transmission cycle .
To analyze the growth curve of GIII and GI JEVs in mosquito-derived cells , we infected Aedes albopictus-derived C6/36 cells and Culex tritaeniorhynchus-derived CTR209 cells with GIII viruses ( CH1392 and T1P1 strains ) and GI viruses ( YL2009-4 and TC2009-1 strains ) at 28°C ( Fig 1A and 1B ) . The growth curves and virus titers from GI and GIII-infected C6/36 cells were similar with no statistical significance between them during a 60-hour infection period ( Fig 1A ) . In CTR209 cells , the growth curves were statistically non-significant differences ( p>0 . 05 ) between genotypes; although , GI YL2009-4 virus was less efficient and generated an average of 1 . 61–2 . 21 log lower viral titer than GIII-CH1392 virus ( Fig 1B ) . In addition , comparable ratios of viral NS3 proteins to α-tubulin were detected in GIII and GI virus-infected C6/36 and CTR209 cells at 48 hours post-infection ( HPI ) ( S1A , S1B , S1D and S1E Fig ) . The newly evolved WNV genotype ( WN02 ) showed an ability to adapt to mosquitoes reared at a higher temperature [21] . Therefore , we further compared GIII to GI viruses in CTR209 cells at an elevated temperature . When the temperature was increased to 34°C , similar titers for both GIII and GI viruses were observed even though higher relative intensity of NS3 proteins from GI viruses was detected in CTR209 cells ( Fig 1C , S1C and S1F Fig ) . This suggested GIII and GI viruses might have different efficiency to generate infectious particle [22] or the stability difference of NS3 protein [23] in infected CTR209 cells . In addition , the viability of GIII and GI virus-infected CTR209 cells was non-significantly different at 28°C and 34°C ( S2 Fig ) . To analyze viral replication in mosquitoes in vivo , we fed female Culex tritaeniorhynchus mosquitoes with pig blood mixed with 8x106 focus forming units ( ffu ) of GIII or GI JEVs . We determined the infection rate by detection of viral titer in each mosquito 14 days post-infection ( DPI ) . GIII CH1392 virus and GI TC2009-1 virus had higher infectious rates ( 58 . 33% and 66 . 67% ) than GIII T1P1 virus and GI YL2009-4 virus ( 25 . 00% and 16 . 67% ) whereas no genotypic differences were observed in infectivity or in viral titer among positive mosquitoes ( Fig 2 ) . These in vitro and in vivo results suggest that the Culex tritaeniorhynchus mosquito vector may play a minor role in genotype replacement . To evaluate the role of amplifying hosts in genotype replacement , we compared the in vitro replicative ability of GIII CH1392 and T1P1 strains to GI YL2009-4 and TC2009-1 strains in pig- and poultry-derived cells ( PK-15 , DF-1 , CER , and DE cells ) and primate-derived control cells ( VERO cells ) at 37°C ( Fig 3A–3C , S3A and S3B Fig ) . A similar growth curve of GIII and GI viruses was observed in VERO cells ( Fig 3A ) ; in contrast , GI YL2009-4 and TC2009-1 viruses produced 1–2 -log higher viral titers than GIII CH1392 and T1P1 viruses in PK-15 cells at 24 and 36 HPI ( p< 0 . 05 ) ( Fig 3B ) . In poultry-derived cells ( DF-1 , CER , DE cells ) , two GI viruses and GIII CH1392 virus replicated to similar viral titers but their viral titers were higher than GIII T1P1 virus ( Fig 3C , S3A and S3B Fig ) . Natural amplifying hosts for JEV have a higher body temperature of 40–44°C in avian species and 38–40°C in pigs . In pigs , fever can be as high as 41°C following JEV infection [24] . Therefore , we inoculated JEVs into VERO , PK-15 , and poultry-derived cells ( DF-1 , CER , DE cells ) and analyzed viral replication at 41°C . Interestingly , as the temperature increased , the overall viral titers of GI viruses were significantly higher than GIII viruses in amplifying host-derived cells and at the single time point of 36 HPI in VERO cells ( p< 0 . 05 ) ( Fig 3D–3F , S3C and S3D Fig ) . An apparently higher relative intensity of NS3 proteins was also observed at 48 HPI in GI virus-infected PK-15 and DF-1 cells but not in VERO cells at 41°C ( S1G–S1L Fig ) . However , the ratio of NS3 to β-actin was inconsistent with extracellular infectious particles in the GIII virus-infected DF-1 cells . This suggested two GIII viruses might have different efficiency to generate infectious particle [22] or the stability difference of NS3 protein [23] in infected DF-1 cells . The higher thermal stability at elevated temperature for GI viruses could be a contributing factor related to enhancement of viral replication of GI viruses . To determine the influence of viral thermal stability , we incubated equal amounts of GIII and GI viruses at 37°C and 41°C , and examined residual viral titers at 3 , 6 , 9 , and 12 hours post-treatment ( HPT ) ( S4 Fig ) . The viral infectious rates of both genotypes dropped to less than 50% after 3-HPT at 37°C and even more at 41°C ( S4A and S4B Fig ) . There was no statistical difference between the half-life of infectivity of GIII and GI viruses at both 37°C and 41°C ( S4C Fig ) . Besides , there was no genotypic difference on cell viability ( VERO , PK-15 , and DF-1 ) after GIII and GI virus infection except lower viability of GI virus-infected PK-15 cells , producing higher viral tier , at 41°C at 60 HPI ( p< 0 . 05 ) ( S2 Fig ) . These results suggest that the replication advantage of GI virus over GIII virus occurred in an amplifying host but was independent of external viral thermal stability and virus-infected cell viability . Enhancement of GI virus replication ability in pig- and poultry-derived cells was further investigated in vivo . JEV–infected pigs exhibited either an asymptomatic manifestation or developed fever with sufficiently high viremia to ensure the transmission of virus by engorged mosquitoes [8] . We subcutaneously inoculated 105 ffu of GIII CH1392 virus , GI YL2009-4 virus , and or PBS into ten-week old , specific-pathogen free ( SPF ) pigs ( three SPF pigs per group ) , and then monitored daily body temperature and viremia . At 3 DPI , three GI virus-infected pigs developed fever averaging 40 . 6°C while GIII virus-infected and PBS-inoculated pigs maintained normal body temperature until euthanasia at 8 DPI ( Fig 4A ) . No detectable viremia was observed in the infected pigs at 1-DPI . A significantly higher viremia was detected the following day in GI YL2009-4 virus-infected pigs with an average viral titer of 104 . 6 ffu/ ml compared to GIII CH1392 virus-infected pigs with a viral titer of 102 . 7 ffu/ml ( p< 0 . 05 ) . However , viremia was undetectable at 4-DPI in all virus- and PBS-inoculated pigs ( Fig 4B ) . The higher viremia in GI virus-inoculated pigs was consistent with the higher RNAemia detected in the GI virus-infected pigs at 2 DPI ( p< 0 . 05 ) ( S5A Fig ) . Although only a limited number of SPF pigs were used in this study , we found that GI YL2009-4 virus infection induced a higher viremia and higher fever than GIII CH1392 virus at 3 DPI . Viremic migratory birds are suspected of spreading GI viruses between countries but the role of avian species in local transmission of GI virus has never been determined [14] . Experimentally JEV-infected young chickens and ducklings showed an age-dependent ability to induce sufficient viremia for mosquito infection [25] . To compare the replication ability of GIII to GI virus in domestic avian species , we subcutaneously inoculated 104 ffu of GIII CH1392 and T1P1 strains , GI YL2009-4 and TC2009-1 strains or PBS into one-day old chickens ( Fig 4C ) and two-day old ducklings ( Fig 4D ) . As early as 1 DPI , we found that 100% ( 8/8 ) and 75% ( 6/8 ) of GI virus-infected chickens and ducklings developed viremia , while 37 . 5% ( 3/8 ) and 12 . 5% ( 1/8 ) of GIII virus-infected chickens and ducklings , respectively , developed viremia ( Fig 4C and 4D ) . GIII and GI virus titers were highest at 2 DPI and subsequently dropped the following 2 days ( Fig 4C and 4D ) . These results showed that GI viruses replicated to a significantly ( p<0 . 05 ) higher viremia ( 0 . 60–1 . 73-log higher ) as well as earlier and longer lasting than GIII viruses in chickens and ducklings . The higher viremia was supported by the higher RNAemia detected in GI virus-infected poultry compared to GIII virus-infected poultry at 2 DPI ( p< 0 . 05 ) ( S5B and S5C Fig ) . However , no clinical signs were observed in the JEV-infected and PBS-inoculated poultry during the 6-day observation period . To investigate the viral genetic determinants for the enhancement of GI virus infectivity , we initially analyzed the amino acid variances between seventy-one GIII viruses and seventy-seven GI viruses . Nine and twenty-four GI virus-specific and highly consensus substitutions were identified in structural and non-structural proteins , respectively , and three were non-conservative , charge-altering substitutions ( Table 1 ) . These substitutions as well as the other forty-three variations were observed between GIII and GI viruses used in this study . We also identified the cyclization structural variants formed by GIII and GI viruses in the untranslated region ( UTR , S6 Fig ) , which might influence viral RNA synthesis [26] . To pin-point the target gene , we constructed five infectious clones of derived chimeric viruses ( pCMV GIII/GI UTR , pCMV GIII/GI C-E , pCMV GIII/GI NS1-5 , pCMV GIII/GI NS1-3 , pCMV GIII/GI NS4-5 ) and one GI virus infectious clone ( pCMV GI ) by introducing the variant region of GI virus into the GIII virus infectious clone ( pCMV GIII ) ( Fig 5A ) . These infectious cDNA clones were transfected into BHK-21 cells and subsequently detected by the expression of viral NS1 protein using an immunofluorescence assay ( Fig 5B ) . Recombinant viruses , recovered from the transfected cells , formed similar sized plaques in BHK-21 cells ( Fig 5B ) . Next we analyzed the replication ability of five chimeric recombinant viruses ( rGIII/GI UTR , rGIII/GI C-E , rGIII/GI NS1-5 , rGIII/GI NS1-3 , and rGIII/GI NS4-5 ) , recombinant GIII ( rGIII ) virus , and recombinant GI ( rGI ) virus in C6/36 cells at 28°C and in vertebrate cells ( VERO , PK-15 , and DF-1 ) at 41°C ( Fig 5C–5F ) . All recombinant viruses reached similar titers in the range of 107 . 09 to 107 . 62 ffu/ml in C6/36 cells at 48 HPI . However , rGI virus exhibited a robust replication and significantly higher titer than rGIII virus in VERO ( 100 . 6-fold difference at 24 HPI , p< 0 . 05 ) , PK-15 , ( 101 . 1 and 101 . 4-fold differences at 24 and 48 HPI , p< 0 . 05 ) and DF-1 cells ( 100 . 6-fold difference at 48 HPI , p< 0 . 05 ) ( Fig 5D–5F ) . Interestingly the rGIII/GI NS1-5 and rGIII/GI NS1-3 viruses replicated as efficiently as rGI virus , and produced a significantly higher titer than rGIII virus in VERO , PK-15 , and DF-1 cells ( p< 0 . 05 ) . In contrast , rGIII/GI UTR , rGIII/GI C-E , rGIII/GI NS4-5 , and rGIII viruses all maintained a low replication efficiency . These results demonstrated that the major genetic determinants for the enhancement of GI virus infectivity are located within NS1-3 proteins . Conversely , we investigated the influence of the replication ability of GIII virus- derived NS1-5 and NS1-3 proteins on the rGI backbone for amplification in host-derived cells . The chimeric clones pCMV GI/GIII NS1-5 and pCMV GI/GIII NS1-3 were constructed to produce rGI/GIII NS1-5 and rGI/GIII NS1-3 viruses as described above ( Fig 5A and 5B ) . The recombinant viruses yielded similar titers compared to rGIII virus but produced significantly lower titers than rGI virus in VERO , PK-15 , and DF-1 cells at 41°C ( p< 0 . 05 ) ( Fig 5D–5F ) . These results further support the conclusion that GI-derived NS1-3 genes made a major contribution to enhanced replication of GI virus in host-derived cells at elevated temperature . To verify the role of NS1-3 genes in vivo , we compared the replication ability of rGIII/GI NS1-3 virus to rGIII virus by subcutaneously inoculating 107 ffu and 104 ffu of the viruses into ten-week old SPF pigs and 1-day old chickens , respectively ( Fig 6 ) . The rGIII and rGIII/GI NS1-3 virus-infected pigs developed viremia with an average titer of 102 . 45 and 103 . 05 ffu/m at 2 DPI . The following day , rGIII/GI NS1-3 virus-infected pigs reached peak viremia but no viremia was detected in rGIII virus-inoculated pigs ( Fig 6A ) . This difference was also observed in the plasma viral RNA collected from the infected pigs at 2 DPI ( S7A Fig ) . These results highlighted the higher and extended viremia in rGIII/GI NS1-3 virus-infected pigs compared to rGIII virus-infected pigs . In addition , we also observed that rGIII/GI NS1-3 virus induced significantly higher viremia and RNAemia in chickens with 5-fold and 6-fold increases in viral titer and viral RNA compared to rGIII virus ( p< 0 . 05 ) at 60 HPI , respectively ( Fig 6B and S7B Fig ) . These results further suggest that the major viral determinants for the enhancement of GI virus infectivity in pigs and chickens were located on NS1-3 proteins . To verify the specific substitution ( s ) of GI NS1-3 proteins involved in the enhancement of GI replication , we conducted a loss-of-function experiment by introduction of a single GIII virus-specific and highly consensus substitution for NS1-3 proteins of the rGIII/GI NS1-3 chimeric virus instead of the rGI virus ( Fig 7A ) . The influence of these substitutions on the replication ability of rGIII/GI NS1-3 virus was evaluated in C6/36 cells at 28°C or in VERO , PK-15 , and DF-1 cells at 41°C . As expected , the replication advantage of the rGIII/GI NS1-3 virus was consistently observed in PK-15 and DF-1 cells but not in C6/36 and VERO cells as compared to the rGIII virus at 48 HPI ( Figs 5 and 7 ) . However , the enhanced replication of the rGIII/GI NS1-3 virus was significantly reduced after the introduction of three single-substitutions ( NS3-S78A , NS3-P105A , or NS3-D177E ) in PK-15 cells ( p< 0 . 05 ) and five single-substitutions ( NS2A-I6V , NS2A-T149S , NS2B-L99V , NS3-S78A , or NS3-D177E ) in DF-1 cells ( p<0 . 05 ) , respectively . The NS2A-R187K substitution had a minor effect on viral titer in both cell lines ( p> 0 . 05 ) ( Fig 7D and 7E ) . These results suggest that NS1 substitutions might not be critical factors for influencing the phenotype of the rGIII/GI NS1-3 virus . The loss-of-function experiments were also used to evaluate substitutions for the degree of influence on the in vivo replication ability of rGIII/GI NS1-3 virus in 1-day old chickens . The rGIII and parental rGIII/GI NS1-3 viruses were inoculated as infection controls ( Fig 7F and S8A Fig ) . As expected , rGIII/GI NS1-3 virus replicated to a significantly higher viremia than rGIII virus , and yielded 104 . 25 ffu/ml ( Fig 7F ) and 106 . 93 viral RNA copies/ml ( S8A Fig ) in the chickens at 60 HPI . In contrast , the recombinant viruses encoding a single NS2B-L99V , NS3-S78A , or NS3-D177E substitution induced a significantly lower viremia or RNAemia than the parental rGIII/GI NS1-3 virus ( p< 0 . 05 ) . The other recombinant viruses encoding a single NS2A-I6V , NS2A-T149S , NS2A-R187K or NS3-P105A substitution showed a viremia comparable to rGIII/GI NS1-3 virus in chickens . These results further supported a conclusion that the residues NS2B-99 , NS3-78 , and NS3-177 were involved in the replication enhancement of GI virus in vitro and in vivo . To investigate the inter-dependency among the substitutions , we introduced single or multiple GI virus NS2B-V99L , NS3-A78S , and NS3-E177D substitutions into rGIII viruses ( Fig 8A ) . Seven mutant rGIII viruses were generated and their infectivity evaluated using C6/36 cells at 28°C and VERO , PK-15 , and DF-1 cells at 41°C . rGIII , rGI , and rGIII/GI NS1-3 viruses were included as controls ( Fig 8B–8E ) . As expected , all recombinants yielded similar viral titers in C6/36 and VERO cells but rGIII virus exhibited a significantly lower titer than rGIII/GI NS1-3 viruses in PK-15 and DF-1 cells at 48 HPI . Mutant rGIII viruses encoding single NS2B-V99L , NS3-A78S , double NS2B-V99L-NS3-A78S , or triple substitutions yielded significantly higher viral titers ( 0 . 59 to 1 . 00-log increase in viral titer ) in both PK-15 and DF-1 cells as compared to rGIII virus ( p< 0 . 05 ) . The effect of substitutions on viral replication was further evaluated by inoculating mutant rGIII viruses into 1-day old chickens . rGIII/GI NS1-3 virus replicated to higher titer ( 1 . 02 log ) ( Fig 8F ) and produced higher levels of viral RNA ( 0 . 99 log ) ( S8B Fig ) than rGIII virus at 48 HPI . With the exception of the NS2B-V99L substitution , the remaining mutant rGIII viruses encoding single and multiple substitutions had 0 . 68 to 0 . 96-log higher viral titers and 0 . 46 to 1 . 01-log higher viral RNA production than rGIII virus in 1-day old chicken . However , we were unable to detect an apparent synergistic effect among three substitutions .
Emerging GI virus has gradually replaced GIII virus as the dominant JEV isolated from human cases , stillborn piglets , and Culex tritaeniorhynchus since the 1990s . The mechanism of genotype replacement remains unclear , especially the role of the genetic determinants affecting the local pig-Culex tritaeniorhynchus transmission cycle . In this study , we identified the contribution of NS2B/NS3 mutations correlated with enhanced replication of GI virus in amplifying hosts: domestic pigs as well as in day-old chickens and two-day old ducklings . The role of Culex tritaeniorhynchus mosquito might be less significant in the genotype replacement . There are two geographic variants of GI JEVs: viruses from GI-a clade are mainly distributed in tropical areas of Asia and Australia [13 , 16 , 27] and viruses of GI-b clade used in this study are widely distributed in southern and eastern Asia [16] . Previous studies have suggested that GI-a or GI-b viruses can compete with GIII for the same mosquito vector and amplifying hosts but are less likely to co-circulate in the same geographic locations [13 , 20 , 27 , 28] . Mosquito vectors play a critical role in the occurrence of newly emerging flavi- and alphaviruses [29–32] . The previous reports indicated that GI-b virus replicated to higher titer in Aedes albopictus mosquito-derived cells [18] but inconsistent results on the infectivity of GI-a , GI-b , and GIII viruses were observed in Culex quinquefasciatus [33 , 34] . Culex tritaeniorhynchus is the primary mosquito vector for GI and GIII viruses and account for 93 . 58% and 74 . 22% of mosquito-derived isolates , respectively [20] . We used in vitro Culex tritaeniorhynchus-derived cells as well as in vivo Culex tritaeniorhynchus mosquitoes to study the infectivity of GI-b and GIII viruses in the current study . Our study results indicated that both genotypes of JEV replicated to similar viral titers regardless of the assay system used ( Fig 2 ) and suggested that the primary vector , Culex tritaeniorhynchus mosquito , for GI-b and GIII viruses is less likely to play a significant role in the genotype replacement of GIII to GI-b . Migratory birds were suspected of spreading GI-b virus from southern to southeastern Asia and thus associated with JEV genotype replacement [19 , 35] . The ardeid birds ( herons and egrets ) and pigs play an important role in local transmission of JEV [8] . All host-derived GI viruses are isolated from pigs [20] . However , JEV can infect young domestic poultry ( chickens and ducklings ) which are suspected of being involved in local transmission of JEV due to viral titers being sufficiently higher than the minimum infectious dose for mosquito hosts [25] . Earlier or higher viremia in GI-b virus-infected birds [19] and ducklings [36] suggested that a domestic avian-mosquito cycle might enhance the transmission of GI viruses . GI-b virus induced higher viremia than GIII virus in pigs , young chickens and ducklings ( Fig 4 ) . However , previous studies showed similar replication ability of GI-b , JE-91 strain , isolated in 1990 and GIII virus in DF-1 cells and ducklings [18 , 33] . We speculate that this difference could be a result of the genetic variation of earlier GI-b isolates and 3 days older ducklings used in the previous studies [18 , 33] . The NS2B/NS3 protein sequences of GI and GIII viruses used in the previous studies were unavailable . Higher viremia induced by GI-b virus could enhance viral transmission by mosquitoes since the infection rate of JEV was dose-dependent in mosquitoes [37] . In addition , GI-b virus produced higher levels of viral RNA than GIII virus in pig tonsil and nasal mucosa explants , potentially enhancing oronasal transmission between pigs [38 , 39] . Collectively , our study suggests that the replacement of GIII virus with GI-b virus as the dominant and circulating virus in the host-mosquito cycle is the result of enhancement of transmission efficiency in amplifying hosts , including pigs and domestic avian species , not in mosquito vectors . Mutation in the NS1 protein of Zika virus and the NS3 protein of HCV has been shown to enhance viral fitness during flaviviral evolution [29 , 40] . Genomic sequencing and analysis has suggested that substitutions in E , NS4B , and NS5 proteins were involved in the evolutionary advantage of GI virus [18 , 20] . Experimental evidence provided in our study , however , suggested that the enhancement of GI-b virus infectivity in amplifying hosts was associated with NS2B/NS3 substitutions ( Figs 7 and 8 ) , especially the residues NS2B-99 and NS3-78 in the protease domain and NS3-177 in the loop connecting protease and helicase domains of NS2B/NS3 proteins ( Fig 9 ) . Other substitutions such as NS2B-D65E , NS2B-A105P , NS3-N182S , or NS1/NS2A only played a minor role in improving viral fitness . The GI virus NS2B-99 , NS3-78 , and NS3-177 residues were also observed in GII viruses or GV viruses ( S2 Table ) . This implied the substitutions associated with genotype replacement of GIII virus by GI virus and geographical distribution of the other genotypes may be different . The NS2B/NS3 proteins were involved in viral RNA replication , polypeptide processing , and infectious particle assembly through enzymatic-dependent or -independent processes [41 , 42] . Thus , the GI-b virus NS2B/NS3 substitutions may enhance viral replication at post viral entry , as supported by the observation that infectivity rates were similar between rGI and rGIII viruses in the infectious center assay ( S9 Fig ) . Moreover , flaviviral NS2B/NS3 proteins harbor multiple strategies to evade host innate immunity [42 , 43] . JEV NS2B/NS3 protease was able to cleave interferon stimulator [44] . This interferon antagonistic ability of JEV was critical for efficient replication and increased virulence in mice [45] . A novel interferon antagonist of NS1 protein has recently been identified in newly emerging Zika viruses associated with the current epidemics [46] . In contrast to PK-15 and DF-1 cells , the virus titer ( Figs 7 and 8 ) and focus size ( S10 Fig ) had no significant influence by NS2B/NS3 substitution in interferon-deficient VERO cells , suggested that the virus titers and focus size may associate with interferon antagonism or other host factors . Therefore , we hypothesize those two possible mechanisms of enhancement in viral post-entry and innate immunity antagonistic ability result in the replication advantage of GI-b virus in amplifying hosts . Flaviviruses adaptation to elevated temperatures have been shown to enhance fitness in avian species and mosquito vectors [21 , 48] . The enhancement of GI-b virus infectivity in amplifying hosts was observed at elevated temperatures ( Fig 3A–3F ) . A West Nile virus study found that thermostability of replication was associated with higher viremia in avian species [48] . The GI-b NS2B/NS3 substitutions might enable NS2B/NS3 protein complex or NS3/NS5 replicase complex to interact more effectively with heat shock proteins for proper folding and hence stability at elevated temperature [23 , 49] . Thus , it is possible that higher body temperature or development of fever in amplifying hosts could positively modulate interferon activity of vertebrate hosts against viral infection [50 , 51] . Therefore , the influence of temperature on enzymatic activity , heat shock proteins-interacting ability , and interferon antagonistic ability of GI-b and GIII virus NS2B/NS3 proteins should be investigated in future studies . The study of GI-a and GIII virus infectivity in Culex quinquefasciatus were inconsistent in previous studies [33 , 34] . The NS2B-99 , NS3-78 , and NS3-177 residues were conserved in GI-a and GI-b viruses but additional substitutions were identified on E-141 , NS2A-105 , and NS5-438 . Thus , the virological factors underlying the replacement of GIII virus by GI-a virus may require re-evaluation of viral replication ability in both Culex tritaeniorhynchus and amplifying hosts using dominant , circulating GI-a isolates in the future . There are two limitations of the current study: the limited number of SPF pigs used to reveal the replicative advantage of GI vs . GIII viruses and the lack of analytical data to determine the viral dissemination and transmission ability in Culex tritaeniorhynchus . However , even with these limitations we should not underestimate the disease burden of JEV caused by GI virus infection . We have no doubt that GI viruses are more efficiently transmitted in the amplifying host-mosquito cycle and have similar virulence compared to the GIII virus in human [52] . Thus , we suggest that it is important to continually monitor GI virus evolution and clarify the role of avian species in local transmission of GI virus .
Animal Use protocols were approved by the Institutional Animal Care and Use Committees ( IACUCs ) in National Chung Hsing University ( NCHU ) ( protocol number: 102–107 ) and National Pingtung University of Science and Technology ( NPUST ) ( protocol number: 104–013 ) . All experimental protocols followed the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health . A total of 196 1 day-old specific pathogen free ( SPF ) chickens ( Gallus gallus domesticus ) and 80 2 day-old minimum disease-free ducklings ( Cairina moschata ) were purchased from JD-SPF Biotech Co . , Ltd and Livestock Research Institute ( Council of Agriculture in Taiwan ) , respectively . These animals were kept in isolators at the avian holding facility in NCHU . Thirteen second-generation SPF pigs ( Lee-Sung Strain ) were purchased from the Agricultural Technology Research Institute in Taiwan , and housed in the negative air-pressure animal facility certified by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) in the Animal Disease Diagnostic Center of NPUST , Taiwan . Avian and pig euthanasia performed by the use of CO2 inhalation and electrical stunning were approved by IACUCs in NCHU and NPUST , respectively . C6/36 cells ( provided by Dr . Yi-Ling Lin from Academic Sinica , Taiwan ) derived from the midgut of Aedes albopictus and CTR209 cells ( provided by Dr . Kyoko Sawabe from National Institute of Infectious Disease , Japan ) derived from embryos of Culex tritaeniorhynchus [53] were grown in Roswell Park Memorial Institute ( RPMI ) 1640 medium ( Gibco ) supplemented with 5% fetal bovine serum ( FBS , Gibco ) and in VP12 medium [54] supplemented with 10% FBS , respectively . Porcine kidney cells ( PK-15 , provided by Dr . Chienjin Huang from NCHU , Taiwan ) , chicken embryo fibroblast cells ( DF-1 , provided by Dr . Shan-Chia Ou from NCHU , Taiwan ) , chicken embryo related cells ( CER ) and duck embryo cells ( DE and CER , provided by Dr . Poa-Chun Chang from NCHU , Taiwan ) , and monkey kidney cells ( VERO , provided by Dr . Gwong-Jen J . Chang from Centers for Disease Control and Prevention ( CDC ) , United States of America ) were all grown in Dulbecco’s Modified Eagle Medium ( DMEM , Gibco ) supplemented with 10% FBS except for VERO cells with 5% FBS . Baby hamster kidney cells ( BHK-21 , provided by Dr . Wei-June Chen from Chang Gung University ( CCU ) , Taiwan ) were cultured in Minimum Essential Medium ( MEM , Gibco ) supplemented with 10% FBS . Mosquito cells or mammalian cells were maintained in incubators supplied with 5% CO2 at 28°C or 37°C . GIII subcluster II JE CH1392 and T1P1 viruses ( provided by Dr . Wei-June Chen from CCU , Taiwan ) , and GI-b subcluster II JE YL2009-4 and subcluster I TC2009-1 viruses ( from an already-existing collection in our lab ) were used in this study . JEV strains CH1392 , YL2009-4 , and TC2009-1 were isolated from pools of field-captured Culex tritaeniorhynchus in 1990 and 2009 , and T1P1 was isolated from the pool of Armigeres subalbatus in 1997 [55 , 56] . All viral stocks were amplified in C6/36 cells and stored at -80°C until used . JEVs or rJEVs were inoculated onto C6/36 , CTR209 , PK-15 , DF-1 , CER , DE , and VERO cells at an MOI of 0 . 5 at 28°C for mosquito cells and at 37°C for mammalian cells . Infected cells were washed three times with 1X PBS after a one-hour incubation , and then subsequently incubated at 28°C or 34°C for mosquito cells and at 37°C or 41°C for mammalian cells . The supernatants from infected cells were collected and stored at −80°C until used . All experiments were conducted in triplicate , and the viral titers in the supernatants were determined by the micro-antigen focus assay . The micro-antigen focus assay was used to determine viral titers in the supernatants of infected cells , in the plasma recovered from infected animals , or in the supernatants of homogenized mosquitoes . Briefly , VERO cells were seeded in to 96-well plates at a cell count of 2 . 25×104 cells/100 μL/well and incubated at 37°C , 5% CO2 overnight to allow a cell monolayer to form . The serially diluted samples were added into wells for one hour at 37°C . Infected VERO cells were overlayed with 1% methylcellulose mixed with DMEM medium supplemented with 2% FBS , and incubated at 37°C for 32 to 36 hours . The methylcellulose overlays were discarded by washing with 1X PBS , and the infected VERO monolayers were fixed with 75% acetone in PBS for 20 minutes . The fixed cells were air-dried and stained with mouse anti-JEV polyclonal antibody ( provided by Dr . Gwong-Jen J . Chang from CDC , USA ) , followed by staining with horseradish peroxidase ( HRP ) -conjugated goat anti-mouse IgG antibody ( Jackson ImmunoResearch , West Grove , PA ) . The foci were developed after the addition of Vector-VIP ( Vector Laboratories , Burlingame , CA ) into each well . The viral titer was calculated by the average number of foci-forming unit ( ffu ) per ml or per mosquito . Laboratory-hatched female Culex tritaeniorhynchus mosquitoes were fed with 10% sucrose and maintained at 28°C . Mosquitoes were starved for 1 day prior to the blood-feeding experiment . The mosquitoes were fed per os with a JEV viremic blood meal , a mixture of pig blood cells and 8×106 ffu/ml of JEV according to the previous study [57] and the stock titer of JEVs used in this study . The virus-infected mosquitoes were maintained in the different cage at 28°C . Infected mosquitoes were collected from cages by aspiration and homogenized individually at 14 days post- infection ( DPI ) . Thirteen ten-week old , JEV-seronegative SPF pigs were used to determine the replication ability of field-isolated JEVs ( nine pigs ) and rJEV ( four pigs ) . SPF pigs were anesthetized with stresnil ( China Chemical and Pharmaceutical Co . , Ltd ) and subcutaneously inoculated with PBS or 105 ffu of GIII CH1392 virus , GI YL2009-4 virus , or 107 ffu of rJEVs . Experimental pigs were monitored , the daily body temperatures were recorded , and clinical signs of infection were noted . Pig plasmas were recovered at different days post infection . All pigs were euthanized with electrical stunning at 8 DPI . Eighty 1-day old chickens ( 16 per viral group ) and eighty 2-day old ducklings ( 16 per viral group ) were subcutaneously inoculated with one dose of PBS or 104 ffu of GIII CH1392 virus , GIII T1P1 virus , GI YL2009-4 virus , or GI TC2009-1 virus . The daily activity and clinical signs of infection for chickens and ducks were monitored after JEV infection . We collected blood from four infected chickens or ducklings 1 day prior to infection and at 1 , 2 , 4 , and 6 DPI . Plasma was mixed with anticoagulant at a final concentration of 0 . 33% sodium citrate ( Sigma-Aldrich ) in 0 . 85% sodium chloride ( Sigma-Aldrich ) , and centrifuged at 3 , 000 rpm for 15 minutes . The ten-fold diluted plasma was recovered from the supernatant and stored at −80°C until used . One hundred sixteen 1 day-old chickens ( 4 per group in Figs 6 and 7; 6 per group in Fig 8 ) were subcutaneously inoculated with PBS or 104 ffu of rJEVs . The plasma was recovered from infected chickens at 48 or 60 HPI as described above , and stored at −80°C until used . The infectious clone encoding the full genome of GIII JE RP9 virus was constructed using pBR322 plasmid and referred to as pCMV GIII ( kindly provided by Dr . Yi-Ling Lin from Academic Sinica , Taiwan ) in this study . JEV viral RNA was transcribed by CMV promoter and terminated by SV40 poly-A terminator . The precise JEV 3’ terminal sequence was generated by a ribozyme sequence of hepatitis delta virus ( HDVr ) incorporated right after the 3’UTR of JEV ( 5 ) . To construct GIII and GI JEV chimeric infectious clones , we replaced five genetic fragments or the complete viral genome of pCMV GIII with the corresponding genes of GI JE YL2009-4 virus by blunt-end ligation with T4 DNA ligase ( New England Biolabs ) to generate six recombinant viruses ( rGI , rGIII/GI UTR , rGIII/GI C-E , rGIII/GI NS1-5 , rGIII/GI NS1-3 and rGIII/GI NS4-5 ) . We replaced the pCMV GI infectious cDNA clone with the corresponding gene fragment of GIII virus using the protocol of Gibson assembly reaction ( New England Biolabs ) and generated two additional recombinant viruses , rGI/GIII NS1-5 and rGI/GIII NS1-3 ( Fig 5A ) . All fragments were amplified by PCR reactions ( KOD , Novagen ) . PCR templates and primers are listed in S1 Table . cDNA constructs , extracted from the transformed competent cells using Mini-prep kit ( Qiagen ) , were sequenced to authenticate the complete viral genome insert . The site-specific mutation was individually introduced into pCMV GIII/GI NS1-3 or pCMV GIII by site-directed mutagenesis using the following reaction mixes: 1 . 5 mM MgSO4 , 0 . 2 mM dNTPs , 0 . 4 mM mutagenesis primers ( S1 Table ) , 0 . 5U KOD Hot Start DNA polymerase ( KOD , Novagen ) , and the respective cDNA clones . Mutated clones were identified in cDNA constructs , extracted from the transformed competent cells using Mini-prep kit ( Qiagen ) , and sequenced to authenticate the complete viral genome insert . BHK-21 cells were seeded into 12-well plates and grown at 37°C overnight . The next day , the mixture of 1 μg of the infectious clone and Opti-MEM ( Life Technologies ) was added into a mixture of Lipofectamine 2000 ( Life Technologies ) and Opti-MEM , and then the final mixture was incubated at room temperature for 30 minutes . Next the mixture was added onto the 80% confluent cells for five hours at 37°C , and then replaced with the culture medium . After a 3- to 4-day incubation , the production of rJEVs was detected in the transfected cells by an immunofluorescence assay utilizing mouse anti-JEV NS1 antibody ( provided by Dr . Yi-Ling Lin from Academic Sinica , Taiwan ) . The recombinant viruses secreted from transfected cells were harvested and subsequently amplified in C6/36 cells . Virus plaques were identified by plaque assay . The viral RNA was extracted from virus-infected C6/36 cells with RNeasy mini kit ( Qiagen ) , and transcribed into cDNA with the JEV 3’UTR primer 5’-AGATCCTGTGTTCTTCCTCA-3’ using the Superscript III transcription reaction ( Thermo Fisher Scientific ) . The complete genome of recombinant virus was confirmed by sequencing the PCR products amplified from the viral cDNA template . The statistical analysis was performed by GraphPad Prism v5 . 01 . Student’s two-tailed t-test was used to compare two data groups . The multiple-group comparison was calculated by One-way ANOVA , and the post test analysis performed by using Turkey’s Multiple or Dunnett’s Multiple Comparison Test . P<0 . 05 , the significant difference in the two-group and multiple-group comparison . | Flaviviral vertebrate amplifying host ( s ) , invertebrate vector ( s ) , genetics , and environmental factors shape the viral geographical distribution and epidemic disease pattern . Newly emerging dengue virus genotypes , West Nile virus clades , or Zika virus strains exhibited an enhancement in mosquito vector competence . However , hosts and viral determinants responsible for the occurrence of JEV genotype replacement remains unclear . Here , we demonstrated that emerging GI viruses with enhanced transmission potential in amplifying hosts such as pigs and avian species was encoded by three critical GI-specific mutations in NS2B/NS3 proteins . This discovery provides insight into the viral genetic mechanism underlying the GI virus advantage and adaptation in the pig/avian species-mosquito cycle . Our results also emphasize the importance of monitoring viral evolution in amplifying vertebrate hosts to clarify the role of avian species in local transmission of GI virus in JE endemic and epidemic countries . | [
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| 2019 | NS2B/NS3 mutations enhance the infectivity of genotype I Japanese encephalitis virus in amplifying hosts |
Prior to the meiotic divisions , dynamic chromosome reorganizations including pairing , synapsis , and recombination of maternal and paternal chromosome pairs must occur in a highly regulated fashion during meiotic prophase . How chromosomes identify each other's homology and exclusively pair and synapse with their homologous partners , while rejecting illegitimate synapsis with non-homologous chromosomes , remains obscure . In addition , how the levels of recombination initiation and crossover formation are regulated so that sufficient , but not deleterious , levels of DNA breaks are made and processed into crossovers is not understood well . We show that in Caenorhabditis elegans , the highly conserved Serine/Threonine protein phosphatase PP4 homolog , PPH-4 . 1 , is required independently to carry out four separate functions involving meiotic chromosome dynamics: ( 1 ) synapsis-independent chromosome pairing , ( 2 ) restriction of synapsis to homologous chromosomes , ( 3 ) programmed DNA double-strand break initiation , and ( 4 ) crossover formation . Using quantitative imaging of mutant strains , including super-resolution ( 3D-SIM ) microscopy of chromosomes and the synaptonemal complex , we show that independently-arising defects in each of these processes in the absence of PPH-4 . 1 activity ultimately lead to meiotic nondisjunction and embryonic lethality . Interestingly , we find that defects in double-strand break initiation and crossover formation , but not pairing or synapsis , become even more severe in the germlines of older mutant animals , indicating an increased dependence on PPH-4 . 1 with increasing maternal age . Our results demonstrate that PPH-4 . 1 plays multiple , independent roles in meiotic prophase chromosome dynamics and maintaining meiotic competence in aging germlines . PP4's high degree of conservation suggests it may be a universal regulator of meiotic prophase chromosome dynamics .
For a single diploid genome to be partitioned into two haploid genomes in meiosis , chromosomes must undergo a sequence of strictly regulated dynamic events during meiotic prophase . Chromosomes must encounter , assess homology , and form close pairing interactions with their homologous partners , to the exclusion of all other chromosomes . This pairing must then be locked in through synapsis , or the assembly of the synaptonemal complex ( SC ) , which is an intricate protein polymer running the length of each chromosome . Programmed DNA double-strand breaks ( DSBs ) must also be made by the Spo11 endonuclease to initiate meiotic recombination [1] . A subset of DSBs are repaired as crossovers ( COs ) , exchanges of DNA continuity between maternally- and paternally-derived chromosomes . In most organisms COs are essential for creating links ( chiasmata ) between homologs that enable their correct segregation into daughter cells . Progression through the series of pairing , synapsis , DSB initiation , and CO formation must be temporally coordinated to coincide with developmental requirements for gamete formation . How chromosomes assess homology , and limit synapsis to homologous partners , is an outstanding mystery . In many organisms , homologous pairing relies on DNA recombination . However , varying levels of homologous alignment can be observed prior to DSB formation in several organisms [2] . In Caenorhabditis elegans and Drosophila melanogaster , homologous chromosomes pair and synapse in the complete absence of recombination [3] , [4] . Since SC proteins containing coiled-coil domains tend to self-assemble [5] , SC polymerization must be actively prevented between non-homologous chromosomes . Levels of DSB formation also must be tightly regulated , since creating either too many or too few DSBs is deleterious to the completion of meiosis . Protein regulation via phosphorylation and dephosphorylation is an essential part of transient responses to cellular events , such as the cell cycle checkpoint or DNA damage response [6] . The reversibility of phosphorylation makes many signaling pathways and feedback regulations possible in a timely manner . In C . elegans , kinases such as CHK-2 , PLK-1 and -2 , and ATM/ATR homologs have been shown to play essential meiotic roles . The inner nuclear envelope protein SUN-1 is phosphorylated in a CHK-2 and PLK-dependent manner when chromosomes begin pairing , and loses its phosphorylation in late pachytene [7] . Failure to finish meiotic tasks such as synapsis or recombination triggers an extension of SUN-1 phosphorylation [8] , prolongs the distinct leptotene , zygotene , and early pachytene stages of meiotic prophase [9]–[11] , and extends the time window during which DSBs can be made and processed correctly [12] , [13] . CHK-2 is predicted to be essential for phosphorylation of other substrates in addition to SUN-1 , whereas ATM/ATR kinases regulate many DNA damage repair components in meiosis to ensure correct recombination outcomes [14] , [15] . Although the importance of these kinases has been demonstrated , the functions of phosphatases which counterbalance these kinases during meiotic prophase have received comparatively little attention , and remain ill-understood . In C . elegans , RNAi depletion of the pph-4 . 1 gene ( encoding a homolog of the catalytic subunit of the PP4 holoenzyme ) has previously been shown to result in the appearance of more than the diploid number of 6 chromosome pairs in late meiotic prophase , indicating a failure to form chiasmata [16] . Since any errors in chromosome pairing , synapsis , or recombination could result in failure to create chiasmata , which processes PPH-4 . 1 directly regulates during meiotic prophase remains an open question . It has been shown that budding yeast PP4 controls the non-homologous clustering of centromeres in early meiotic prophase through dephosphorylation of Zip1 , an SC central element protein . Additionally , PP4 is independently required for complete SC formation in budding yeast . [17] . Nonhomologous centromere pairing is thought to improve segregation of nonexchange chromosomes by holding them together until anaphase I [18] , [19] . This non-homologous coupling of centromeres at the onset of meiosis has been observed in yeast and some plants [20] , [21] , but its absence from animal meiosis suggests that the meiotic functional repertoire of PP4 has yet to be elucidated . In this work , we have discovered that four essential steps in meiotic prophase require PPH-4 . 1 activity: ( 1 ) synapsis-independent chromosome pairing , ( 2 ) prevention of nonhomologous synapsis , ( 3 ) programmed DSB initiation , and ( 4 ) post-DSB CO formation . The combined failure of all these processes in cells lacking PPH-4 . 1 activity leads ultimately to significant numbers of chromosomes without chiasmata , chromosome nondisjunction , and embryonic lethality . In contrast to yeast PP4 mutants that are defective in SC assembly , we find that C . elegans pph-4 . 1 mutants have robust but premature SC assembly between nonhomologous chromosomes or on folded-over single chromosomes . We further demonstrate that DSB initiation and CO formation , but not chromosome pairing , increase their dependence on PPH-4 . 1 in an age-dependent manner , suggesting an increased requirement for PPH-4 . 1 to make sufficient numbers of DSBs and COs in older animals . Since PPH-4 . 1 in C . elegans is 92% identical at the amino acid level with human PP4C , it is likely that the roles we have discovered for PPH-4 . 1 have functionally conserved parallels in human meiosis .
We characterized the predicted null allele pph-4 . 1 ( tm1598 ) , which deletes the first three exons of the pph-4 . 1 coding sequence ( Figure 1A ) . No evidence of maternal protein carryover was detected in pph-4 . 1 homozygous adults ( Figure S1 ) . Examination of self-progeny of mutant hermaphrodites showed that tm1598 has low embryo viability ( 3% ) with a high incidence of males ( 23 . 8% ) , indicative of X chromosome nondisjunction , in the surviving progeny ( Table S1 ) . Cross-progeny of mutant hermaphrodites with wild-type males showed significantly higher embryonic viability ( 9 . 8% ) , indicating both spermatogenesis and oogenesis are affected in pph-4 . 1 hermaphrodites . To characterize meiotic defects , we dissected gonads going through oogenesis from pph-4 . 1 hermaphrodites and scored the number of DAPI-stained chromosomes ( DAPI bodies ) . In a wild-type hermaphrodite , the six pairs of C . elegans chromosomes give rise to six chiasmate bivalents at diakinesis ( late meiotic prophase ) , demonstrating the successful formation of crossovers between all six pairs of homologous chromosomes . On the other hand , the presence of 7 or more DAPI-staining bodies in diakinesis oocytes indicates the failure of one or more chromosome pairs to undergo crossover formation . Similarly to previously-shown RNAi depletion [16] , pph-4 . 1 ( tm1598 ) mutant homozygotes showed frequent univalent formation ( Figure 1B ) . Interestingly , the univalent phenotype of pph-4 . 1 mutants grew worse with age: in adult worms 72 hours after the L4 larval stage ( 72 h post-L4 ) , the distribution of univalents significantly shifts toward higher numbers , such that over 50% of diakinesis nuclei contained 12 DAPI bodies , indicating that none of the six chromosome pairs in those nuclei succeeded in chiasma formation . This rate of failure was seen in less than 25% of nuclei in 24 h post-L4 mutant worms . The average number of bivalents per pph-4 . 1 nucleus was 1 . 55 at 24 hours post-L4 , and 0 . 71 at 72 hours post-L4 , indicating that roughly half of the already-compromised meiotic competence is lost over 48 hours in pph-4 . 1 mutants . To determine whether PPH-4 . 1 phosphatase activity is specifically required for meiosis , we constructed two phosphatase-dead transgenes containing single amino acid substitutions ( D107A or R262L ) , analogous to known mutations in the active site of mammalian PP4C that lead to loss of catalytic activity [22] , [23] , and created transgenic C . elegans lines with each construct . Although both mutant proteins were expressed at levels similar to wild-type PPH-4 . 1 ( Figure S1A ) , both pph-4 . 1 ( D107A ) and pph-4 . 1 ( R262L ) mutant gonads showed defective bivalent formation , similar to the null tm1598 allele . As a control , we constructed a single-copy transgene pph-4 . 1 ( WT ) with wild-type pph-4 . 1 coding sequence , but otherwise identical in structure and MosSCI insertion site to the point mutants . pph-4 . 1 ( tm1598 ) mutant worms homozygous for pph-4 . 1 ( WT ) had 6 bivalents per nucleus , indicating a full rescue of the mutation ( Figure 1B ) . Since the inability of either mutant transgene to rescue the pph-4 . 1 phenotype can be solely attributed to the point mutations , we conclude that the phenotype of the pph-4 . 1 ( tm1598 ) allele is also specifically due to the loss of phosphatase activity . To further characterize the meiotic defects of the tm1598 allele , we next analyzed homologous chromosome pairing in young ( 24 h post-L4 ) and old ( 72 h post-L4 ) animals . We monitored pairing of one locus on the right arm of chromosome V with fluorescence in situ hybridization ( FISH ) probes that label the 5S rDNA locus . Pairing of the X chromosome was visualized by immunofluorescence against HIM-8 , which binds to the left end of the X chromosome at the cis-acting pairing center ( PC ) [11] . Taking advantage of the fact that the distal-to-proximal position of meiocytes in the C . elegans gonad mirrors the temporal progression of meiotic prophase , we scored pairing over time by measuring the fraction of nuclei containing paired versus unpaired signals within each of 5 equal-length zones of the distal gonad ( Figure 2A , B ) . In contrast to wild-type animals which displayed increasing pairing of chromosome V , eventually reaching 100% , pph-4 . 1 animals never achieved pairing levels higher than 30% ( Figure 2C ) . In marked contrast to chromosome V , the X chromosome PC in pph-4 . 1 animals was found to achieve pairing frequencies indistinguishable from wild-type , and with the same kinetics ( Figure 2C ) . We found similar discrepancies between X and autosome pairing behavior when examining the right end of the X with FISH , and chromosomes I and IV with immunostaining of ZIM-3 , a protein localizing to pairing center ends of chromosomes I and IV [24] ( Figure S2A , B ) . As expected from their higher pairing frequency , X chromosomes are significantly more likely to remain bivalent at diakinesis than chromosome V ( Figure S2C ) . No difference in pairing ability was observed between 24 h post-L4 and 72 h post-L4 for either the X chromosome or chromosome V in pph-4 . 1 animals . Therefore , the age-related loss of chiasma formation in pph-4 . 1 mutants is not attributable to decreased chromosome pairing . Since pph-4 . 1 mutants exhibited reduced autosomal pairing , we next decided to assess the nature and extent of SC formation , through immunostaining of SC proteins . The SC is a zipper-like structure consisting of two lateral elements that run the length of each chromosome , and a central element that bridges the 100 to 200 nm distance between the lateral elements [25] . Since this distance is smaller than the diffraction limit of visible light , we visualized SCs of pph-4 . 1 mutant animals with three-dimensional structured illumination microscopy ( 3D-SIM ) [26] , which permits the resolution of SC lateral elements as two separate strands with central elements located in the middle [27] . By visualizing the central element protein SYP-1 and the lateral element protein HTP-3 , we found that C . elegans pph-4 . 1 mutants nearly always formed complete SC , with SYP-1 localizing between parallel HTP-3 axes separated by approximately 150 nm ( Figure 3A ) . This observation supports the conclusion that SC structure in pph-4 . 1 mutants is canonical and pph-4 . 1 activity is not required to form the SC itself . This result contrasts with observations from budding yeast , in which SC formation itself depends on PP4 [17] . Since pph-4 . 1 mutants displayed normal SC structure but mispairing of allelic loci , we concluded that SC must be forming between non-homologous chromosomes . This led us to consider whether non-homologous synapsis could be responsible for autosomal mispairing in pph-4 . 1 mutants , by trapping chromosomes with the wrong partner . To assess this possibility , we examined autosomal pairing in worms lacking the SC central element protein SYP-2 . Meiotic prophase chromosomes in syp-2 mutant animals do not form SC , but engage in appreciable levels of synapsis-independent homologous pairing [10] . We observed levels of homologous pairing in syp-2 single mutants ( Figure 3B ) that were in agreement with previous studies . In contrast , syp-2; pph-4 . 1 double mutants displayed significantly lower pairing levels , indistinguishable from pph-4 . 1 single mutants . Since the defect in chromosome V pairing in syp-2; pph-4 . 1 mutants cannot be explained by promiscuous SC formation , we conclude that PPH-4 . 1 activity is required for the synapsis-independent pairing of autosomes . To quantitatively confirm the nature of the nonhomologous synapsis we inferred , we traced the three-dimensional paths of wild-type and pph-4 . 1 SCs in 3D-SIM images . Wild-type nuclei at late pachytene invariably showed full-length synapsis of all 6 chromosome pairs ( Figure 4A ) . In contrast , we observed a variety of synaptic aberrations in many pph-4 . 1 nuclei , including full-length synapsis of nonhomologous chromosomes , multivalent synapsis between three or more chromosomes and self-synapsis of unpaired chromosomes , which we infer to be foldback synapsis based on length ( Figure 4C , E ) . Manual tracing of pachytene chromosome complements from wild-type and pph-4 . 1 nuclei showed that 20 out of 20 wild-type nuclei had six fully-synapsed chromosomes , whereas 15 out of 20 pph-4 . 1 nuclei had synaptic aberrations detectable by 3D-SIM imaging of SYP-1 and HTP-3 staining ( Figure S3 ) . Staining of the ZIM-3 protein , which binds to the PCs of chromosomes I and IV , often revealed more than two synapsed foci in pph-4 . 1 , but not in wild-type nuclei ( Figure 4B , D ) , indicating full-length synapsis of distinct non-homologous chromosomes . In contrast to the autosomal PCs , the X chromosome PC was nearly always both paired and synapsed homologously in pph-4 . 1 mutants ( Movie S1 ) . Homologous synapsis of the X chromosome , but not the autosomes , is also a consequence of mutations in the axial element gene htp-1 or him-3 [28]–[30]; we therefore performed immunostaining to examine whether HTP-1/2 and HIM-3 proteins are normally localized to the SC in pph-4 . 1 mutants . We observed robust loading of HTP-1/2 and HIM-3 onto axes concomitant with HTP-3 in pph-4 . 1 mutants ( Figure S4 ) ; therefore , the nonhomologous synapsis phenotype cannot be explained by a failure of HTP-1/2 or HIM-3 to load onto chromosomes . The extent of nonhomologous pairing and synapsis we observed did not fully explain the high frequency of univalent chromosomes at diakinesis . Although the X chromosomes pair and synapse at nearly 100% frequency in pph-4 . 1 animals , they must nevertheless fail to form chiasmata in at least 25% and 50% of cases in young and old adults , respectively , based on our observed frequencies of nuclei containing 12 univalents . Since failure to form chiasmata despite successful pairing suggests problems with recombination , we next assessed recombination in wild-type and pph-4 . 1 mutant animals . First , we performed immunostaining against the strand-exchange protein RAD-51 in wild-type and pph-4 . 1 mutants , and quantified RAD-51 focus number per nucleus in each of seven equal-length zones of the distal gonad . RAD-51 foci became visible in wild-type gonads after the transition zone , and their number peaked in mid-pachytene with an average of around 5 foci per nucleus ( Figure 5A ) . Most C . elegans mutants with unpaired or incorrectly paired chromosomes accumulate RAD-51 numbers that exceed wild-type levels , due to the inability to repair recombination intermediates from a homologous chromosome template [10] , [31] , [32] . However , pph-4 . 1 gonads displayed greatly reduced RAD-51 focus numbers . We also observed reduced levels of the single-strand binding protein RPA-1 in the mutant compared to the control ( Figure S5 ) . These findings raised the possibility that RAD-51 and RPA-1 loading at DSB sites requires PPH-4 . 1 activity . To test this , we induced DSBs by exposing worms to 10Gy of γ-rays at 24 h post-L4 , and visualized early pachytene RAD-51 foci 2 hours later . We found that RAD-51 focus numbers had increased in irradiated mutant animals , to a level qualitatively comparable to irradiated wild-type animals ( Figure 5A ) , suggesting that pph-4 . 1 mutants have a reduced number of programmed DSBs , but are competent to load recombination proteins onto any breaks that exist . Next , we assessed whether lowered RAD-51 levels in pph-4 . 1 mutants could be explained by fast , premature repair . In meiotic prophase , programmed DSBs are preferentially repaired using the homologous chromosome as a template , rather than the sister chromatid [33] . However , when interhomolog bias is defective , DSBs can be rapidly repaired from the sister chromatid [29] . To test if this were the case in pph-4 . 1 mutants , we examined RAD-51 focus formation in the rad-54 mutant background , in which DSBs are created but cannot be repaired , leaving RAD-51 foci to persist [34] , [35] . In rad-54 mutants , the number of RAD-51 foci thus reflects the total number of DSBs created . In accordance with previous observations [36] , rad-54 single mutants displayed RAD-51 focus numbers much higher than wild-type ( Figure 5B , Figure S6 ) . In contrast , rad-54; pph-4 . 1 double mutants had significantly fewer RAD-51 foci compared to rad-54 single mutants , indicating the net number of DSB initiations is reduced in pph-4 . 1 mutants . These results lead us to conclude that loss of PPH-4 . 1 specifically compromises the ability to make wild-type levels of programmed DSBs . rad-54; pph-4 . 1 double mutants had significantly more RAD-51 foci than pph-4 . 1 single mutants , demonstrating that nuclei without PPH-4 . 1 are still capable of making appreciable numbers of DSBs . However , this residual DSB initiation activity in the absence of PPH-4 . 1 decreases with maternal age: in rad-54; pph-4 . 1 animals at 72 h post-L4 , the number of RAD-51 foci attains a level that is roughly half that seen at 24 h post-L4 in all zones after RAD-51 foci first form . Interestingly , the number of RAD-51 foci in rad-54 single mutants is also significantly lower at 72 h post-L4 , compared to 24 h post-L4 , in zones 4 , 5 , and 6 , suggesting that reduction of DSB initiation may be intrinsic to aging . We next inquired whether PPH-4 . 1 was required for recombination at steps subsequent to DSB formation . First , we assessed the number of presumptive CO sites in wild-type and pph-4 . 1 mutant animals by detection of COSA-1 ( Figure 6A ) , a protein shown to localize to sites designated for CO repair in C . elegans [37] . COs in many organisms are subjected to the phenomenon of interference , in which CO formation inhibits the formation of further COs nearby . In C . elegans , this interference operates over the length of entire chromosomes , limiting COs to one per chromosome pair [38] , [39] , resulting in 6 COSA-1 foci in wild-type meiotic pachytene nuclei [37] . We started to detect COSA-1:GFP foci in mid-pachytene and observed nearly 100% occurrence of 6 COSA-1 foci per nucleus in late pachytene , 1 per chromosome pair , in control animals . The number of COSA-1 foci in each late pachytene nucleus was 6 in both 24 h and 72 h post-L4 control animals . In contrast , in pph-4 . 1 mutants , we observed a significant reduction in COSA-1 foci , with a significant proportion of nuclei having no foci . Additionally , the number of COSA-1 foci in pph-4 . 1 underwent an even further decrease with advancing maternal age: in mutant animals at 72 h post-L4 , the distribution of focus numbers shifted significantly towards zero compared to 24 h post-L4 animals , suggesting the creation of fewer COs . These observations qualitatively agree with the increasing number of DAPI bodies observed in older animals . However , using COSA-1 focus numbers to predict the observed number of DAPI bodies from the same time points in Figure 1 reveals a positive offset ( Figure 6B ) : the number of COSA-1 foci exceeds the predicted number of chiasmata in both 24 h and 72 h post-L4 animals . This discrepancy can be accommodated by postulating probabilities less than 100% for COSA-1 foci to mature into a CO in pph-4 . 1 mutants; adjusting for lower probabilities gave predicted chiasma distributions that more closely match the observed DAPI body numbers . For 24 h post-L4 worms , a success rate of 85% led to an optimal match between DAPI body numbers and COSA-1 foci , while for 72 h post-L4 worms the optimally-matching rate was 39% . The decrease in the correlation between COSA-1 foci and chiasmata suggests that in the pph-4 . 1 mutant , advancing age leads to fewer COs in two ways: by reducing the initial number of COSA-1 foci , and also reducing the probability of a COSA-1 focus maturing into a chiasma . To examine further whether CO formation capacity requires PPH-4 . 1 as inferred from the COSA-1 data , we took advantage of the fact that the X chromosome is normally paired and synapsed in pph-4 . 1 mutants ( Movie S1 ) . If the dearth of chiasmata on the X chromosome were solely attributable to reduced DSB formation , then irradiation-induced DSBs ought to allow the X chromosomes to receive a chiasma in many cases , since chiasma failure caused by a lack of DSBs can be rescued by inducing artificial breaks with γ-rays [3] . Similar considerations for the autosomes , which attain low but non-negligible levels of homologous synapsis , suggested that increasing DSB number through irradiation should result in a measurable shift toward fewer univalent chromosomes ( and thus fewer observed DAPI bodies ) at diakinesis . Contrarily , if PPH-4 . 1 were required for carrying out post-DSB steps of CO formation at a wild-type level of competence , then creating new DSBs would not necessarily lead to a reduction in unpaired chromosomes . To test these possibilities , we exposed pph-4 . 1 animals at 20 h post-L4 to 10 Gy of γ-rays to induce DSBs , and counted DAPI bodies in diakinesis nuclei 18 hours later . We found no difference in the distribution of univalents between irradiated and non-irradiated pph-4 . 1 mutants ( Figure 6C ) . We confirmed the ability of the given dose of γ-rays to cause DSBs by irradiating spo-11 ( me44 ) animals in parallel , and observing a significant increase in bivalent numbers , compared to unirradiated controls ( Figure 6D ) . Since the artificial introduction of DSBs in the pph-4 . 1 mutant did not lead to a detectable decrease in univalent number , in spite of the abundance of homologously synapsed X chromosomes , we conclude that PPH-4 . 1 is required for wild-type levels of CO formation in addition to its roles in pairing , synapsis , and DSB initiation . Since a previous study showed that PP4 promotes crossover interference in budding yeast [17] , we decided to test whether the normal operation of interference was intact in pph-4 . 1 mutants . We irradiated worms 18 h post-L4 with 10 Gy of γ-rays , and examined COSA-1 foci 8 h post-irradiation . We found 1 out of 227 control nuclei , and 3 out of 189 pph-4 . 1 mutant nuclei , displaying two COSA-1 foci on a single HTP-3 stretch . Since this difference is not significant ( P = 0 . 3338 , Fisher's exact test ) , we conclude that the mechanism limiting COSA-1 foci to one per chromosome in C . elegans does not require PPH-4 . 1 for its function . Many meiotic mutations causing non-homologous synapsis result in a shorter region of the leptotene/zygotene transition zone marked by crescent-shaped nuclei with unresolvable chromosomes , as well as promiscuous loading of SC central elements [28] , [29] , [32] . In contrast , we observed that pph-4 . 1 animals at 24 h post-L4 had longer transition zone regions as scored by nuclear morphology , compared to the wild-type ( Figure 7 ) . However , transition zone lengths dramatically and unexpectedly decreased with age in pph-4 . 1 mutants . In 72 h post-L4 pph-4 . 1 mutants , seven out of eight gonads measured had very few leptotene/zygotene nuclei . In these gonads , nuclei progressed directly from a premeiotic appearance to an early pachytene appearance . This transition is accompanied by immediate loading of the central element of the SC ( Figure S7A ) after the mitotic zone , suggesting that as pph-4 . 1 mutants age , synapsis cannot be delayed in response to the lack of homologous pairing . At 48 h post-L4 , transition zone lengths in pph-4 . 1 animals were highly variable and overlapped both the 72 h and 24 h distributions , suggesting that loss of transition zone morphology occurs at around 48 h post-L4 in pph-4 . 1 mutants . The age-dependent loss of transition zone nuclei and earlier appearance of full-length synapsis in pph-4 . 1 mutants suggests that chromosomes have less time to actively search for partners , and are less able to delay synapsis in response to nonhomology , as they age . However , younger pph-4 . 1 mutant animals show no increase in autosomal pairing levels relative to older animals . Therefore we infer that young pph-4 . 1 mutants retain the ability to delay synapsis in the absence of homologous pairing , but this delay does not lead to higher pairing levels due to the absence of PPH-4 . 1 . The SUN-1 protein is normally phosphorylated during the transition zone and early pachytene , and meiotic errors are correlated with persistence of SUN-1 phosphorylation [8] . We therefore tested whether the phosphorylation state of SUN-1 in pph-4 . 1 mutants changed in correlation with the shortened transition zone in older germlines , using an antibody that specifically detects SUN-1 phosphorylated on Ser8 ( SUN-1:Ser8p ) ( Figure 7 ) . We measured the proportion of the gonad occupied by the SUN-1:Ser8p signal at 24 h , 48 h , and 72 h post-L4 as an indication of the persistence of SUN-1 phosphorylation . At all timepoints , we observed a significant increase in the proportion of the meiotic zone ( from TZ entry to cellularization ) containing cells positive for SUN-1:Ser8p in pph-4 . 1 mutants compared to wild-type ( Figure 7B ) . SUN-1:Ser8p is limited to nuclei with a transition zone or early pachytene appearance in all wild-type gonads , and in pph-4 . 1 mutants at 24 h post-L4 . However , at 72 h post-L4 , pph-4 . 1 mutant gonads also contain late pachytene nuclei with SUN-1:Ser8p staining ( Figure S7B ) . This observation explains the persistence of SUN-1:Ser8p-positive nuclei in aged pph-4 . 1 mutants with shorter transition zones and suggests that SUN-1 phosphorylation and nuclear morphology are uncoupled in older pph-4 . 1 mutants . In contrast to the extension of SUN-1:Ser8p , nuclei positive for SUN-1:Ser12p were dramatically reduced in 72 h post-L4 pph-4 . 1 gonads ( Figure S7A ) , indicating that SUN-1 phosphorylation at Ser8 and Ser12 are independently regulated in pph-4 . 1 animals . The persistence of SUN-1:Ser8p beyond its normal range suggests the possibility that PPH-4 . 1 is normally required for its dephosphorylation . Further work will be required to test for direct interactions between PPH-4 . 1 and SUN-1 . We noted a significant increase in the proportion of SUN-1:Ser8p in wild-type worms at 48 h and 72 h post-L4 compared to 24 h post-L4 . This observation suggests that aging presents intrinsic difficulties to meiosis , thus prolonging the time meiotic tasks take to complete . This age effect agrees with previous observations that show higher rates of apoptosis ( a sign of meiotic errors ) with increasing maternal age [40] . Taken together , these results imply a role for PPH-4 . 1 in maintaining correct meiotic progression with advancing maternal age .
This study has demonstrated multiple requirements for PPH-4 . 1 in essential aspects of meiotic prophase chromosome dynamics . In the absence of PPH-4 . 1 activity , autosomal pairing is reduced and promiscuous synapsis occurs between non-homologous chromosomes or within single chromosomes folded in half . Furthermore , DSB formation and crossover repair are not only defective without PPH-4 . 1 but deteriorate even further with advancing age . Our results explain the earlier observation of univalent chromosomes in a C . elegans PPH-4 . 1 knockdown [16] as the aggregate outcome of failures in all of these processes . The defect in autosomal pairing in the absence of PPH-4 . 1 has multiple possible causes . Mutations in plk-2 [41] , sun-1 [42] , hal-2 [43] , and the SC component htp-1 [29] have all been shown to compromise synapsis-independent pairing . Defective phosphoregulation of any of these proteins could cause defects in homologous pairing . Rad53 , the budding yeast homolog of CHK-2 , is dephosphorylated by PP4 to turn off the S phase checkpoint during the mitotic cell cycle [44] . It is possible that C . elegans CHK-2 or its substrates could have altered activity in pph-4 . 1 mutants , leading to defects in homologous pairing . Previous studies in budding yeast showed that two SC components , Hop1 and Zip1 , become hyperphosphorylated in the absence of PP4 [17] , [45] . Mammalian SC components HORMAD1 and HORMAD2 undergo developmentally-regulated phosphorylation [46] proposed to be part of a synapsis-monitoring system , as phosphorylated HORMAD1 is preferentially found on unsynapsed axes . Mutations in the C . elegans SC axial element proteins HIM-3 and HTP-1 have also been shown to cause nonhomologous synapsis of the autosomes [28]–[30] . While little functional information exists about SC phosphorylation , it is possible that dephosphorylation of SC components by PPH-4 . 1 plays a role in the restriction of SC assembly to homologous axes . The number of homologous recombination sites marked by RAD-51 foci drop precipitously in pph-4 . 1 and pph-4 . 1; rad-54 mutant animals , indicating that normal DSB initiation depends on PPH-4 . 1 . Interestingly , rad-54 single mutants also showed an age-related drop in RAD-51 foci in mid-meiotic prophase . Recent studies showed that mutations in rad-54 and other genes that cause a block in CO repair result in perdurance of the zone in which programmed DSBs are made [12] , [13] . This suggests that the number of DSBs we observe in rad-54 mutants do not reflect the wild-type level of DSBs , but rather an increased number due to a longer period of DSB initiation . We observed the same levels of RAD-51 foci up to zone 3 between young and old rad-54 animals , whereas older animals have fewer foci in zones 4 , 5 , and 6 . This suggests that the later prolongation of DSB formation , rather than the intrinsic mechanism of DSB initiation , specifically degrades with age . In contrast , the significantly lowered number of DSBs in pph-4 . 1; rad-54 double mutants compared to rad-54 single mutants demonstrates a strong dependence on PPH-4 . 1 activity for DSB initiation . COSA-1 foci , like RAD-51 foci , are less numerous in pph-4 . 1 worms compared to wild-type worms , and decrease further with maternal age . Our results strongly suggest that nonhomologous synapsis and reduced DSB formation contribute jointly and independently to the reduction of COSA-1 foci , with impaired DSB formation responsible for the age dependence . It is thought that each COSA-1 focus marks the site of a future chiasma in normal meiosis [37] . However , in pph-4 . 1 mutants , the observed number of chiasmata falls short of the number predicted by COSA-1 focus numbers , in an age-dependent manner . The simplest explanation is that in pph-4 . 1 mutants , COSA-1 foci do not always mature into chiasmata , with the chance of failure increasing over time . Additionally , inducing DSBs with γ-irradiation does not promote bivalent formation in excess of non-irradiated controls , despite the presence in pph-4 . 1 mutants of homologously synapsed X chromosomes and some autosomes . Taken together , these lines of evidence indicate that PPH-4 . 1 plays a role in CO formation in addition to its role in DSB initiation . Furthermore , budding yeast PP4 has been shown to promote single-end invasions [17] and DNA synthesis steps of DSB repair [47] . These functions of PP4 may be conserved during CO formation in meiosis . Loss of the C . elegans DSB-promoting factor DSB-2 [12] also produces defects in DSB and CO formation that worsen with age . DSB-2 contains several SQ motifs that are potentially substrates for the ATM/ATR DNA damage kinases . In budding yeast , Mec1 and Tel1 ( ATM/ATR ) phosphorylate Rec114 , which limits DSB formation by Spo11 [48] . It will be interesting to see whether PPH-4 . 1 is required to dephosphorylate DSB-2 or its homolog DSB-1 [13] to create normal levels of DSBs . It is possible that the age effects in dsb-2 mutants , as well as in the rad-54 single mutation shown by the current study , are due to an increased sensitivity to as-yet unknown factors that accumulate or diminish over time . The persistent phosphorylation of SUN-1 at Ser8 raised the possibility that SUN-1:Ser8p is a substrate of PPH-4 . 1 . SUN-1:Ser8p has been shown to be a part of the checkpoint coupling formation of CO intermediates with meiotic progression [12] . Phosphomimetic versions of SUN-1 have been shown to extend the transition zone length , similar to young pph-4 . 1 mutants [8] . However , sun-1 phosphomimetic mutants differ from pph-4 . 1 mutants in that they do not show prominent defects in pairing , synapsis , or RAD-51 focus levels . The multiple , distinct meiotic defects of pph-4 . 1 mutants indicate that SUN-1 is not likely to be the only substrate of PPH-4 . 1 . Our observation that SUN-1:Ser8p persists longer with increasing age in wild-type animals suggests an intrinsic age-related decrease of meiotic competence , which is normally accommodated through multiple checkpoint mechanisms but is unmasked in various mutant backgrounds including pph-4 . 1 . The age-dependent decrease we have shown in the probability of COSA-1 foci maturing into chiasmata is interesting in light of this possibility . Since our study demonstrates a situation in which chiasma formation fails at a relatively late stage , markers of presumptive CO sites such as MLH-1 foci may outnumber chiasmata in systems where the ability to cope with meiotic errors is compromised . Although this is not likely to be the case in normal human male or female meiosis [49] , [50] , our results suggest the usual 1∶1 correspondence between MLH1 or COSA-1 foci and chiasmata can break down in pathological situations . The several roles of pph-4 . 1 revealed in the current study are presumably attributable to hyperphosphorylation of one or more proteins required for proper meiotic prophase functions; current and future studies will identify these substrates of PPH-4 . 1 and illuminate how the balance of phosphorylation and dephosphorylation regulates the dynamic activities of chromosomes in meiosis .
C . elegans strains were grown with standard procedures [38] at 20°C . Wild-type worms were from the N2 Bristol strain . Mutations , transgenes and balancers used in this study are as follows: LGI: rad-54 ( ok617 ) ; LGII: meIs8 [Ppie-1::GFP::cosa-1 + unc-119 ( + ) ] , icmSi18[Ppph-4::pph-4 . 1 ( WT ) + unc-119 ( + ) ] , icmSi20[Ppph-4::pph-4 . 1 ( D107A ) + unc-119 ( + ) ] , icmSi22[Ppph-4::pph-4 . 1 ( R262L ) + unc-119 ( + ) ] , LGIII: pph-4 . 1 ( tm1598 ) , hT2[bli-4 ( e937 ) let- ? ( q782 ) qIs48]; LGIV: syp-2 ( ok307 ) , spo-11 ( me44 ) ; LGV: nT1[unc- ? ( n754 ) let- ? ( m435 ) ]; Unknown LG: opIs263[Prpa-1::rpa-1::YFP + unc-119 ( + ) ] . For mutant analyses , we used homozygous mutant progeny of heterozygous parents . For all cytological assays we stringently age-matched worms by picking young adult hermaphrodites to single plates and allowing them to lay eggs for 3 hours . F1 self-progeny from this 3-hour laying period were picked from these plates at the L4 larval stage , 51–54 h after the beginning of the egg-laying period and analyzed at 24 , 48 , or 72 hours after the L4 stage . To create the transgenic pph-4 . 1 constructs , we obtained synthesized DNA ( GenScript ) starting 311 bases upstream of the initiation ATG , and ending 591 bases downstream of the stop codon , with introns 4 and 5 removed . This fragment was cloned into plasmid pCFJ151 at the AflII restriction site . Single-site mutagenesis was performed by PfuUltra mutagenesis PCR ( Stratagene ) with primers containing 1- or 2-base mismatches in the relevant codons ( D107A: GAC→GCT; R262L: AGA→CTA ) for the creation of phosphatase-dead mutations . Single-copy insertions of transgenes into chromosome II was performed using strain EG6699 as described [51] . The following antibodies used in the present study have been described previously: HIM-8 [11] , HTP-3 [52] , SUN-1:Ser8p and SUN-1:Ser12p [7] , SYP-1 [53] , ZIM-2 and ZIM-3 [24] . The RAD-51 antibody was rabbit polyclonal from SDIX/Novus Biologicals , cat# 29480002 , lot# G3048-009A02 , used at 1∶1000 . The HIM-3 antibody , was rabbit polyclonal from SDIX/Novus , catalog # 53470002 , used at 1∶500 dilution . For all cytological preparations , we followed protocols described in [54] . Images were acquired using either a DeltaVision OMX v2 ( for 3D-SIM images ) or personalDV microscope ( Applied Precision/GE Healthcare ) , using 60x or 100x oil immersion objectives ( Olympus ) and immersion oil ( LaserLiquid , Cargille ) at a refractive index of 1 . 513 . For 3D-SIM , Z spacing was 0 . 125 µm; raw images were reconstructed using the softWoRx suite . For conventional widefield images , the Z spacing was 0 . 2 µm , and raw images were subjected to deconvolution . Image post-processing involved linear intensity scaling and maximum-intensity projection; in some color-blended maximum intensity projections ( Figure 4 ) the DAPI channel was locally underweighted to better visualize SC staining . Whole-gonad images are montages of sequentially-acquired panels , composited using GIMP with “lighten” mode , with individual linear scaling parameters applied to each panel . For DAPI body counting , completely resolvable contiguous DAPI-positive bodies were counted in three-dimensional stacks; with this criterion , chromosomes that happen to be touching can occasionally be counted as a single DAPI body . Homologous pairing quantitation was performed as described [11] . Statistical significance of pairing was assessed by 2-tailed t tests comparing like zones between conditions . Worms at were exposed to 10 Gy ( 1000 rad ) of γ-irradiation using a 137Cs source . For visualization of RAD-51 foci , worms were irradiated at 24 h post-L4 , and fixed and processed for RAD-51 immunofluorescence 2 h after irradiation . For DAPI body counting , worms were irradiated at 20 h post-L4 , fixed and DAPI-stained 18 h after irradiation , and imaged for scoring of DAPI bodies as above . For COSA-1 counting , worms were irradiated 18 h post-L4 , and fixed and stained 8 h post-irradiation . For the adjustment calculation , we inferred chiasmata numbers from DAPI body counts by subtracting the DAPI body count from 12 . To obtain the distribution of chiasmata that would have been expected in those same nuclei from our COSA-1 counts , given a 100% probability of COSA-1 foci becoming a CO , we normalized the numbers of nuclei scored for COSA-1 to the numbers of nuclei scored for DAPI bodies . COSA-1 counts of 7 or 8 ( found in 13 nuclei total out of 1360 ) were corrected to counts of 6 . We then calculated an adjusted distribution of expected chiasma numbers for probabilities less than 100% for COSA-1 foci to mature into chiasmata . For this analysis we assume each COSA-1 site in each nucleus has an equal and independent chance ( Psuccess ) of maturing into a chiasma . Given a starting number of nuclei N with k COSA-1 foci ( Nk ) , we calculate the adjusted number N'k for a Psuccess value of p from the sum of all nuclei Nm multiplied by the corresponding factors derived from the binomial distribution: Optimal Psuccess values for the 24 h and 72 h distributions were found by minimizing the sum of squared differences between the observed DAPI body counts and the predicted counts given the value of Psuccess . Adjusting for these values of Psuccess gave predicted chiasma distributions that more closely match the observed DAPI body numbers . | Meiosis creates gametes by distributing diploid genomes containing homologous chromosome pairs into daughter cells that receive only one of each chromosome . To segregate correctly at the first meiotic division , chromosomes must pair and synapse with their homologous partners , and undergo crossover recombination , which requires breaking and repairing the DNA strands of all chromosomes . How chromosomes recognize their partners , and how a cell controls the amount of DNA breakage and recombination that occurs , are open questions . In this study , we observed meiosis in the nematode Caenorhabditis elegans to examine the role of Protein Phosphatase 4 ( PP4 ) . We found that in the absence of PP4 , chromosomes often paired and synapsed with non-homologous chromosomes , or synapsed with themselves by folding in half . Additionally , without PP4 activity , the number of DNA breaks and of crossover recombination events were both independently reduced . The latter two defects became even worse with increasing age , indicating that older animals require PP4 to a greater extent . These findings shed light on how protein phosphorylation controls meiotic events , and demonstrate unanticipated , important roles for PP4 . | [
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| 2014 | Protein Phosphatase 4 Promotes Chromosome Pairing and Synapsis, and Contributes to Maintaining Crossover Competence with Increasing Age |
The seed maturation program only occurs during late embryogenesis , and repression of the program is pivotal for seedling development . However , the mechanism through which this repression is achieved in vegetative tissues is poorly understood . Here we report a microRNA ( miRNA ) –mediated repression mechanism operating in leaves . To understand the repression of the embryonic program in seedlings , we have conducted a genetic screen using a seed maturation gene reporter transgenic line in Arabidopsis ( Arabidopsis thaliana ) for the isolation of mutants that ectopically express seed maturation genes in leaves . One of the mutants identified from the screen is a weak allele of ARGONAUTE1 ( AGO1 ) that encodes an effector protein for small RNAs . We first show that it is the defect in the accumulation of miRNAs rather than other small RNAs that causes the ectopic seed gene expression in ago1 . We then demonstrate that overexpression of miR166 suppresses the derepression of the seed gene reporter in ago1 and that , conversely , the specific loss of miR166 causes ectopic expression of seed maturation genes . Further , we show that ectopic expression of miR166 targets , type III homeodomain-leucine zipper ( HD-ZIPIII ) genes PHABULOSA ( PHB ) and PHAVOLUTA ( PHV ) , is sufficient to activate seed maturation genes in vegetative tissues . Lastly , we show that PHB binds the promoter of LEAFY COTYLEDON2 ( LEC2 ) , which encodes a master regulator of seed maturation . Therefore , this study establishes a core module composed of a miRNA , its target genes ( PHB and PHV ) , and the direct target of PHB ( LEC2 ) as an underlying mechanism that keeps the seed maturation program off during vegetative development .
Seed maturation is a highly coordinated developmental phase in which storage reserves , including seed storage proteins ( SSPs ) , are synthesized and accumulated to high levels . The maturation genes need to be repressed , however , in order to allow seedling development to occur . Indeed , these genes are not expressed in vegetative organs of the plant [1] . Research in the past decade with the model plant Arabidopsis has led to the identification of repressors of seed maturation genes in vegetative organs ( reviewed in [2] ) , including chromatin-remodelling ATPases [3]–[5] , polycomb group ( PcG ) proteins [6]–[9] , histone deacetylases [10] , and DNA-binding transcription factors [11]–[13] . However , our understanding of the molecular mechanisms that repress the seed maturation program during vegetative development remains fragmented , and thus continued efforts are needed to identify additional factors involved and , more importantly , the molecular and functional links between the various components . In Arabidopsis , ABA-INSENSITIVE3 ( ABI3 ) , FUSCA3 ( FUS3 ) , LEC1 and LEC2 are master regulators of seed maturation [14]–[17] , and they regulate one another [18] , [19] . ABI3 , FUS3 and LEC2 are closely-related members of a plant-specific B3-domain transcription factor family . LEC1 encodes a novel homolog of the CCAAT-binding factor HAP3 subunit . Loss-of-function mutations in ABI3 , FUS3 , and LEC1 give rise to pleiotropic seed phenotypes including a strong reduction of SSPs . These regulatory genes are predominantly expressed in the seed . When misexpressed in vegetative tissues , they induce ectopic expression of the SSP genes and even the formation of somatic embryos [15] , [17] , [20]–[22] . It remains poorly understood , however , how the expression and activity of these master regulators are in turn regulated . Small RNAs of 20–30 nucleotides ( nt ) have emerged as key sequence-specific regulators of gene expression that influence almost all aspects of plant biology ( reviewed in [23]–[26] ) . There are two major types of small RNAs in plants , microRNA ( miRNA ) and small interfering RNA ( siRNA ) . Plant miRNAs are generated from longer precursors arising from defined genomic loci – the MIRNA genes . The biogenesis of miRNAs involves several evolutionarily conserved families of proteins , including DICER-LIKE ( DCL ) , ARGONAUTE ( AGO ) , HUA ENHANCER 1 ( HEN1 ) , and HASTY ( HST ) . Plant miRNAs regulate target mRNAs temporally and spatially through transcript cleavage and/or translational inhibition . Conserved miRNAs tend to target transcription factor genes that play crucial roles in almost all aspects of plant development . Plants are rich in endogenous siRNAs , which can be classified into several types , such as trans-acting siRNAs ( ta-siRNAs ) , natural cis-antisense transcripts-associated siRNAs , and heterochromatic siRNAs . Here , we show that mutations in AGO1 resulted in the ectopic expression of seed maturation genes in seedlings . Taking advantage of the weak ago1 allele identified in this work , we were able to identify the miRNA species ( miR166 ) responsible for the repression of seed genes . We demonstrated that targets of miR166 , the class III homeodomain leucine zipper ( HD-ZIPIII ) family of transcription factor genes , PHB and PHV , are positive regulators of seed genes . Further , we provided evidence to suggest that PHB acts directly at LEC2 . This work thus uncovered an important role of miR166 in the repression of seed genes during seedling development .
We have recently conducted a genetic screen in Arabidopsis to isolate mutants exhibiting ectopic expression of a soybean β-conglycinin gene promoter:GUS transgene ( βCG:GUS ) , which is normally expressed only in the seed [5] , [27] , [28] . Here , we describe the characterization of one of the mutants identified from the screen , initially named essp5 ( ectopic expression of seed storage proteins 5 ) . The essp5 mutant plants exhibited strong ectopic GUS activity in leaves , but not in other organs ( Figure 1A–1D ) . In addition , the mutant plants had pleiotropic developmental defects , such as late flowering , narrow and dark green leaves , shorter siliques and fewer seeds ( Figure 1B and Figure S1 ) . The mutation segregated as a recessive allele and was mapped to the AT1G48410 gene ( Figure S2 ) , which encodes AGO1 , the major effector protein that associates with small RNAs [29] . A single missense mutation in this gene was identified that would lead to the conversion of a leucine residue at position 740 to a phenylalanine . The leucine 740 is a highly conserved residue in the PIWI domains of AGOs from diverse species ( Figure 1E ) . A number of mutant ago1 alleles have been described previously and their pleiotropic morphological phenotypes have been documented [29]–[32] . The morphological phenotypes of essp5 resemble those documented for weak ago1 alleles . We obtained T-DNA insertion mutants of ago1 , including ago1-36 ( SALK_087076 ) , ago1-39 ( SALK_089073 ) , ago1-40 ( SALK_076199 ) , ago1-41 ( SALK_096625 ) , and ago1-42 ( SALK_116845 ) ( Figure 1F ) , crossed them with βCG:GUS and examined GUS expression in F2 progeny seedlings . The T-DNA insertion lines , regardless of their insertion sites , all displayed similar morphological phenotypes: long rod-shape cotyledons , delayed emergence of true leaves , and premature death with only a couple of small true leaves . As shown in Figure 1G–1J , ectopic GUS activity was clearly observed in several T-DNA alleles with insertion sites located throughout the gene . Furthermore , we performed an allelism test to provide additional evidence that essp5 is a weak ago1 allele . A weak ago1 allele ( ago1-25 ) that exhibits similar morphological phenotype [33] was crossed with essp5 and the F1 progeny were examined for GUS activity . As shown in Figure S3 , the F1 seedlings displayed ectopic GUS expression , indicating that the essp5 GUS phenotype cannot be complemented by a weak ago1 allele . Together , these observations suggest that essp5 is allelic to AGO1 and thus designated as ago1-100 . To find out whether the endogenous seed maturation genes are indeed ectopically expressed in ago1 mutant seedlings , we performed northern blot analysis to profile the expression of both the 2S and 12S storage protein genes . As shown in Figure 2A , the transcripts of the storage protein genes are highly accumulated in the two strong alleles , ago1-41 and ago1-42 , both with T-DNA insertion sites located in the 5′ end of the gene but barely detectable in ago1-100/essp5 and other weak alleles with insertion sites located in the middle and 3′ end of the AGO1 gene . We further examined the transcript levels of the four master regulators by quantitative real-time RT-PCR ( qRT-PCR ) . In line with the ectopic expression of the storage protein genes , all the master regulators are expressed to varying levels in the mutants , especially ago1-42 ( Figure 2B ) . In addition , we also profiled the temporal expression pattern of the maturation genes , using 2S2 as a marker . ago1-41 seedlings from 5 d to 19 d after germination were examined . As shown in Figure 2C , the 2S2 transcript peaked in abundance at around 13 d , but was clearly detected throughout the time course . These expression analyses clearly demonstrate the involvement of AGO1 in the repression of seed maturation genes . AGO1 associates with miRNAs and some endogenous siRNAs to mediate their activities [34] . AGO1 association also stabilizes the small RNAs such that ago1 mutants show a reduction in the steady-state levels of miRNAs and siRNAs [35]–[36] . To confirm that the ectopic expression of seed maturation genes in ago1 mutants is due to defects in small RNA biogenesis or activity , we examined seed gene expression in seedlings of loss-of-function alleles of genes commonly involved in small RNA biogenesis . For this purpose , we obtained mutant alleles of HEN1 , hen1-5 ( SALK_049197 ) and hen1-6 ( SALK_090960 ) , and of HST , hst-1 [37] , hst-15 ( SALK_079290 ) and hst-16 ( SALK_056352 ) . We introduced these mutations , individually , into the βCG:GUS background and examined GUS expression in the F2 generation . We were able to detect clear ectopic GUS activity in the hen1 backgrounds ( hen1-5; Figure 3A and 3B ) , but not in the hst alleles . We then generated double mutants between ago1 , hen1 , and hst . As shown in Figure 3C–3J , both ago1-41 hst-16 and hen1-5 hst-16 double mutants exhibited a high level of expression of the storage protein genes and the master regulators . The ago1-41 hen1-5 plants were very small , which precluded seed gene expression analysis . These results indicate synergistic genetic interactions among AGO1 , HEN1 , and HST in repressing seed genes during seedling development and , more importantly , the involvement of a small RNA pathway ( s ) in this repression process . Since AGO1 , HEN1 and HST are essential players in small RNA biogenesis and are involved in several small RNA pathways [38] , it was necessary to determine which pathway underlies the mutant phenotype . To this end , we took advantage of pathway-specific components to define the specific pathway responsible for the ago1 mutant phenotype . Specifically , RDR2 is an essential component of the heterochromatin pathway and RDR6 is required for the biogenesis of trans-acting siRNAs . We obtained and introduced the rdr2-1 ( SAIL_1277_H08 ) and rdr6-11 [39] mutations into the βCG:GUS background by genetic crosses and examined GUS expression in the F2 progeny . A large number of F2 seedlings were stained for GUS and no ectopic GUS activity was observed in either population . This genetic evidence suggests that it is unlikely that the trans-acting siRNA or the hc-siRNA pathway is involved in the repression of seed genes in seedlings . Since AGO1 , HEN1 , and HST all act in miRNA biogenesis , a miRNA ( s ) is thought to be a strong candidate for the repression of seed genes during vegetative development . To provide evidence that a miRNA pathway is indeed underlying the mutant phenotype , we examined the steady-state levels of a number of conserved miRNAs in ago1 and other mutant backgrounds . In ago1 mutants , it was documented previously that the accumulations of a number of conserved miRNAs decline markedly and their target gene transcripts are concomitantly elevated [35]–[36] . Here , we performed a miRNA northern blot analysis to examine and compare the accumulation of conserved miRNA species in ago1 , hen1 , hst , and the two double mutants , ago1 hst and hen1 hst . As shown in Figure 4 , we confirmed the published observation for ago1 in that all the miRNAs examined were clearly reduced . More importantly , we observed further reduced accumulation of most examined miRNAs in ago1 hst and hen1 hst double mutants compared to the single mutants ( Figure 4 ) . These findings are consistent with a genetic model for explaining the ectopic seed gene expression in ago1and other mutants: the steady-state level of a specific miRNA was reduced below a threshold to lead to the ectopic expression of its target gene , which encodes a positive regulator of seed maturation genes leading to the ectopic expression of seed genes in leaves . Post-germination repression of seed genes is critical in order for the seedling to develop normally . We thus reasoned that such a fundamental developmental program should be controlled by a conserved miRNA ( s ) . Therefore , to find out which miRNA was involved in conferring the essp5/ago1-100 GUS phenotype , we over-expressed each of the 15 conserved miRNA species , as listed in [23] and Table S1 , in the essp5/ago1-100 background and examined GUS expression of the resulting transgenic plants . The transgenic plants overexpressing different miRNAs displayed unique morphological phenotypes , which are consistent with previously published observations ( reviewed in [23] ) . Analysis of leaf GUS expression was conducted in the T2 generation . For each miRNA transgene , multiple independent transgenic lines were analyzed ( in most cases 10 lines ) ; and for each line , at least 20 T2 progeny homozygous for essp5 were stained for GUS activity . We only observed loss of leaf GUS activity in miR166 and miR156 overexpressing lines . In this study , we have focused on the characterization of miR166 . In total , we only obtained four miR166 transgenic lines , miR166ox-1-4 , of which two showed clear loss of leaf GUS activity ( miR166ox-1-2 ) while the other two ( miR166ox-3-4 ) did not show as obvious a change compared with essp5/ago1-100 seedlings ( Figure 5A–5D ) . The extremely low rate of positive transgenic plants for miR166 is likely due to the fact that some transgenic seedlings failed to develop the shoot apical meristem and could not survive in soil , as observed by others [40] . To confirm that the loss of leaf GUS activity in the transgenic lines was indeed due to the elevated accumulation of miR166 , a northern blot analysis was performed . As shown in Figure 5E , there were clearly higher levels of miR166 in lines miR166ox-1-2 than lines miR166ox-3-4 . In addition , we observed the formation of aberrant structures on leaves of miRNA166ox-1-2 ( Figure 5F ) . Similar aberrant structures were observed by Zhou et al in miRNA166 overexpressors [40] . These observations suggest that the reduction of miRNA166 and the concomitant accumulation of its target gene transcripts are likely the cause underlying the ectopic GUS phenotype of essp5/ago1-100 seedlings . To demonstrate that the specific loss of miR166 can cause the ectopic expression of seed maturation genes , we obtained the recently developed transgenic lines that exhibit a dramatic reduction in miR165/166 accumulation achieved by the expression of a short tandem target mimic ( STTM165/166 ) [41] . RNA blot analysis was performed to examine the expression of seed storage protein genes in these transgenic lines , using 2S2 as a probe . As shown in Figure 5G , the 2S2 gene is clearly expressed in the strongest line ( STTM165/166-48 ) , but not detectable in a weaker line ( STTM165/166-31 ) . This observation indicates that miR166 plays an important role in repressing seed genes in seedlings . It has been well established that the miR165/166 family miRNAs target the transcripts of the HD-ZIPIII genes , controlling their expression level and domain , to fulfill their roles in plant development including leaf polarity determination [42]–[45] . The HD-ZIPIII family consists of five transcription factors ( REV , PHB , PHV , AtHB8 , and AtHB15 ) , and they play both redundant and unique roles in diverse plant developmental processes [46] . In this context , it is worth noting that the transcript level of PHB was found to be decreased in miR166 overexpressors ( Figure S4 ) . To investigate whether the HD-ZIPIII proteins are responsible for conferring the ectopic GUS phenotype of essp5/ago1-100 , we introduced loss-of-function mutations in PHB and PHV genes into essp5/ago1-100 by genetic crosses and examined leaf GUS expression in F2 and F3 seedlings . A large number of F2/F3 seedlings were examined and no clear loss of leaf GUS activity was observed in phb essp5 or phv essp5 . We further introduced phb phv double mutations into the essp5/ago1-100 background , but still saw no detectable loss of leaf GUS activity . Obviously , the potential redundancy among the five HD-ZIPIII genes could be confounding the genetic analyses above . Next , taking advantage of the previously identified gain-of-function mutations in HD-ZIPIII family genes , we investigated whether these proteins are sufficient to cause the ectopic expression of seed genes . These gain-of-function alleles have mutations in the miR166 target regions to cause a mismatch between the miRNA and the target mRNA and thus render the transcripts resistant to miRNA-mediated degradation and consequently the ectopic accumulation of HD-ZIPIII transcripts . First , the gain-of-function mutations phb-1d [47] and phv-1d [44] were introduced into the βCG:GUS background by genetic crosses and ectopic GUS activity was examined in F2 seedlings ( Figure 6A–6I ) . Meanwhile , another gain-of-function phb allele driven by the CaMV 35S promoter , 35S:PHB G202G [43] , was also introduced into the βCG:GUS background by Agrobacterium-mediated transformation and GUS activity was examined for each independent T1 plant ( Figure 6A and 6J–6M ) . As shown in Figure 6A–6M , ectopic GUS activity was clearly observed for phb-1d , phv-1d , and 35S:PHB G202G . Further , we performed northern blot and qRT-PCR analyses to examine the ectopic expression of endogenous seed maturation genes in the gain-of-function mutant plants ( Figure 6N and 6O ) . Two representative maturation genes 2S2 and 2S3 were clearly expressed in the mutant seedlings ( Figure 6N ) . Similarly , the four master regulators were all elevated to varying levels ( Figure 6O ) . In addition , a gain-of-function REV mutant , rev-10d [42] , was also analyzed in the βCG:GUS background but no ectopic GUS activity was detected . In summary , our gain-of-function genetic evidence indicates that the HD-ZIPIII proteins PHB and PHV are each sufficient for ectopic expression of seed genes . HD-ZIP proteins are plant-specific transcription factors and named for the combination of homeodomain and leucine zipper domains at their N termini [48] . They bind a palindromic DNA sequence in vitro as dimers [49] . To determine whether PHB acts directly at maturation gene loci , we performed chromatin immunoprecipation ( ChIP ) experiments to examine PHB occupancy at the promoters of these genes . For the ChIP assays , we generated Arabidopsis plants transgenic for a YFP-tagged gain-of-function allele of PHB under the control of the PHB native promoter ( PHB:PHB G202G-YFP ) . Morphologically , the transgenic plants resemble the phb-1d mutant ( Figure 7A–7D ) . When the transgene was introduced into the βCG:GUS background , it resulted in ectopic GUS activity ( Figure 7E–7H ) . Expression of the master regulators of seed maturation in the transgenic seedlings was also examined by qRT-PCR . As shown in Figure 7I , these genes were ectopically expressed to similar levels compared to those of phb-1d , and the expression levels in homozygous seedlings were clearly higher than those in the hemizygous siblings . These observations demonstrate that the PHB:PHB G202G-YFP plants resemble phb-1d . In addition , we observed , at a low frequency , disorganized growth and/or formation of somatic embryo-like structures in the transgenic plants ( Figure 7J–7L ) . Some parts of these plants could be stained by the neutral lipid dye fat red ( Figure 7M–7O ) , indicating a high level accumulation of seed storage-specific triacylglycerols in these plants ( fat red/sudan red stains only seed storage-specific lipids ) . ChIP was performed with anti-GFP antibodies and an Arabidopsis line transgenic for GFP driven by the CaMV 35S promoter ( 35S:GFP ) was used as a negative control . The ChIP DNAs were analyzed by qPCR to examine the enrichment of promoter region genomic DNAs of the four master regulator genes . One region in the LEC2 promoter was highly enriched relative to the 35S:GFP and no antibody controls ( Figure 7P ) , but no enrichment was found for the promoter regions of other maturation genes examined ( Figure S5 ) . The enrichment level in homozygous plants was about double that in the hemizygous siblings , consistent with the ectopic expression level of seed genes in these plants ( Figure 7I ) . Interestingly , a partial palindromic sequence , aaAATCATTAC , was found in the vicinity of the enriched genomic region in the LEC2 promoter , but not at other maturation loci . This sequence is very similar to the HD-ZIPIII binding consensus sequence , GTAAT ( G/C ) ATTAC , derived from an in vitro binding site selection experiment [49] . These observations suggest that PHB , when ectopically expressed , binds to the LEC2 promoter and activates the expression of the gene . LEC2 can , in turn , activate a network of maturation-related genes including ABI3 , FUS3 , LEC1 , and the SSP genes ( Figure 8 ) .
In this work , we first identified a weak EMS ago1 allele , which exhibited ectopic expression of a GUS reporter driven by a seed gene promoter . Taking advantage of the weak ago1 allele and its GUS phenotype , we then performed a series of transgenic and genetic analyses to search for the molecular mechanisms underlying the mutant phenotype . We first demonstrated that miR166 reduction is a major cause of the mutant phenotype and further showed that the targets of miR166 , PHB and PHV , are sufficient for derepressing seed maturation genes in seedlings . Finally , our ChIP assay using a tagged PHB transgenic line suggests that PHB may act directly at the LEC2 promoter ( summarized in Figure 8 ) . However , in addition to LEC2 , PHB may also regulate other factors that in turn regulate seed maturation genes directly or indirectly . Future studies , such as ChIP-seq , are needed to address this issue . Therefore , this work has added miR165/166 to the documented repertoire of postgermination repressors of the embryonic program ( reviewed in [2] ) , and more importantly , established PHB , and possibly PHV , as direct positive regulators of the master regulator of seed maturation LEC2 . A major future challenge in the field is to find out the genetic and molecular relationships amongst the various players , including transcription factors , chromatin remodelers and modifiers , and the newly added miRNA , and build an integrated genetic network . Given the well-established expression patterns and roles of miR166 and its targets in leaf polarity determination ( reviewed in [50] , [51] ) , an obvious outstanding question is why the normal expression of the PHB and PHV genes in the adaxial domain of leaf primordia in wild type plants is not sufficient to cause the ectopic expression of seed maturation genes . miR165/166 is concentrated in the abaxial domain to restrict the expression of the HD-ZIPIII transcription factor genes to the adaxial domain in the lateral organs in Arabidopsis [42]–[44] and maize [45] . In phb and phv gain-of-function mutants , the expression of PHB and PHV is not restricted to the adaxial domain but extends into the entire primordium . We observed ectopic expression of seed maturation genes only in these gain-of-function mutants , indicating that the normal , adaxial expression of the HD-ZIP III genes is not sufficient to activate the seed maturation program . There could be at least two underlying reasons . First , the ectopic expression of the seed maturation genes in the phb and phv gain-of-function mutants only occurs in the abaxial domain . In this scenario , the lack of necessary co-factors or the presence of negative factors in the adaxial domain may prevent the HD-ZIPIII genes from activating the seed maturation genes . Alternatively , it might be a matter of thresholds – the adaxial domain normally does not have sufficient levels of HD-ZIPIII expression to trigger the seed maturation program , but when the miRNA is compromised , the expression level is high enough to trigger the program . Our preliminary observation is in support of the first scenario . GUS expression along the adaxial-abaxial axis in essp5/ago1-100 was examined and GUS activity was found only on the abaxial side ( Figure S6 ) . In addition , interestingly , GUS was also observed in both the upper and lower epidermal cells ( Figure S6 ) . The seed maturation program is a tightly regulated developmental process . Mechanisms are in place to not only ensure its repression during seedling development but also prevent its precocious induction during early embryogenesis [2] , [52] . The induction of seed maturation is also referred to as the morphogenesis-to-maturation phase transition of embryogenesis . While our studies have established miR165/166 and implicated miR156 as players in the repression of the seed maturation program in vegetative development , two recent studies have also revealed important roles of miRNAs in regulating the morphogenesis-to-maturation phase transition [53] , [54] . Of these , the work of Nodine and Bartel [53] demonstrated that miR156 and two of its target genes SPL10 and SPL11 play a major role in early embryo patterning and in preventing the precocious expression of maturation genes . An obvious question is whether miR165/166 also acts similarly in early embryogenesis to control the morphogenesis-to-maturation phase transition . Previous studies have shown that PHB and PHV promote embryonic development , and that the expression of these genes must be repressed by miR165/166 for embryonic development to proceed normally . For example , Grigg et al showed that serrate ( se ) mutants cause ectopic expression of PHB and PHV in the root pole of embryos , and that the embryonic lethal phenotype of se mutants can be rescued by loss-of-function mutations in PHB and PHV [55] . Smith and Long also showed that PHB and PHV promote shoot development during embryogenesis [56] . These studies focused on the roles of the miR165/166-PHB/PHV module in early embryo patterning . Our finding that this module plays an important role in repressing seed maturation genes during seedling development prompted us to test its role in the morphogenesis-to-maturation phase transition . We performed a ChIP analysis using a transgenic line expressing a tagged PHB driven by its endogenous promoter ( PHB:PHB-YFP ) . Preliminary data suggests that PHB acts directly at LEC2 during embryogenesis ( Figure S7 ) . Future investigations are needed to sort out the contributions of each miRNA to the repression of the seed maturation program during the pre- and post-maturation stages .
Seeds of mutants including the T-DNA insertion mutants were obtained from the ABRC , unless otherwise indicated . Seeds were stratified at 4°C for 3-d . Then the seeds were sowed on soil or on agar plates containing 4 . 3 g/L Murashige and Skoog nutrient mix ( Sigma-Aldrich ) , 1 . 5% sucrose , 0 . 5 g/L MES ( pH 5 . 7 ) , and 0 . 8% agar . Plants were grown under 16 h-light ( 22°C ) /8 h-dark ( 20°C ) cycles; except that the phb-1d/+ and phv-1d/+ mutants were grown at 17°C during reproductive development as described [47] . Homozygous T-DNA insertion mutants were identified by genotyping . The mutant essp5 was isolated from the same genetic screen as essp1 and essp3 [5] , [27] . For genetic mapping of the essp5 mutation , mutant plants were crossed with wild type plants of the Ler ecotype . A total of 644 homozygous essp5 mutants were collected from the F2 segregating population . Genomic DNA extracted from these seedlings was used for PCR-based mapping with simple sequence polymorphism markers , and the essp5 locus was mapped to a ∼127 kb genomic interval on BACs F11A17 , T1N15 and F9P7 on chromosome one ( 17 , 852–17 , 979 kb ) . Sequencing of the genomic region revealed a mutation in At1g48410 . The modified GUS staining solution ( 0 . 5 mg/mL 5-bromo-4-chloro-3-indolyl-glucuronide , 20% methanol , 0 . 01 M Tris-HCl , pH 7 . 0 ) was used [5] . Seedlings immersed in GUS staining solution were placed under vacuum for 15 min , and then incubated at 37°C overnight . The staining solution was removed and samples were cleared by sequential incubation in 75% and 95% ethanol . Fat red staining was performed by incubating samples in a saturated solution of Sudan red 7B ( Sigma ) in 70% ethanol for 1 h at room temperature . Samples were then rinsed with 70% ethanol [57] . Plants grown on MS media were used for gene expression analyses . RT-PCR and RNA blot analyses were preformed as described previously [5] . Probes for detecting transcripts of the CRA1 , CRB , and CRC genes were designed based on Pang et al [58] . Real-time PCR was conducted using the Bio-Rad CFX96 real-time PCR detection system and the SsoFast™ EvaGreen® Supermix kit ( Bio-Rad Laboratories , Inc . ) . Data from three biological replicates were analyzed by the software Bio-Rad CFX96 Managertm V1 . 6 . 541 . 1028 , using Actin8 as the internal reference . DNA oligonucleotides used as probes or in real-time PCR are listed in Table S1 . RNA isolation and hybridization for miRNA detection were performed as described [59] , [60] . 5′-end-labeled 32P antisense DNAs or an LNA oligonucleotide ( for miRNA166 ) were used to detect miRNAs from total RNAs ( 10 µg each sample ) . Oligonucleotide probes used are listed in Table S1 . Genome sequences surrounding the selected MIRNA genes ( listed in Table S1 ) were amplified by PCR from genomic DNA isolated from wild-type Arabidopsis ( Col ) . The amplified DNA was first cloned into the pDNR221 vector ( Invitrogen ) , confirmed by sequencing , and then recombined into the pEarlyGate100 Gateway-compatible destination vector [61] where the MIRNA genes are under the control of the CaMV 35S promoter . The constructs were introduced into essp5 homozygous or heterozygous plants ( essp5/+ ) . PCR primers used for amplifying the MIRNA genes are listed in Table S1 . Transgenic plants were selected on Basta , allowed to grow to maturity and seeds were collected , and GUS expression was analyzed in the next generation . For the construction of the PHB:PHB G202G-YFP transgene plasmid , the PHB promoter was PCR amplified from Arabidopsis ( Col-0 ) genomic DNA by Fusion DNA Polymerase ( NEB , M0530 ) using primers EcoRI-PHBpr and PHBpr-NcoI , and inserted into the pBluscript SK vector . The plasmid was then fully digested by SpeI and partially digested by EcoRI . The full-length promoter fragment was purified and ligated with the pEARLEYGATE 104 vector [61] to generate the plasmid pEG104-PHBpro . The PHB G202G coding sequence was amplified from cDNAs made from 35S:PHB G202G transgenic plants [43] with primers PHBf and PHBr , cloned into the pENTR-D-topo vector ( invitrogen ) , and subsequently cloned into the destination vector pEARLEYGATE104 by LR reaction . The generated plasmid pEG104-PHB G202G were digested by NcoI and SpeI , and the PHB G202G-YFP fragment was recovered and ligated with pEG104-PHBpro to obtain the pEG104-PHB:PHB G202G-YFP plasmid . The PHB:PHB-YFP transgene plasmid was constructed using a similar strategy . Primers are listed in Table S1 . Chromatin immunoprecipitation ( ChIP ) was carried out as described previously [62] . One gram of twenty-day-old Arabidopsis plants grown on MS agar was used for each ChIP . The sonicated chromatin was immunoprecipitated with 5 µL of anti-GFP antibody ( ab290 , Abcam ) . Quantitative ChIP PCR was performed with three technical replicates , and results were presented as percentage of input . ChIP experiments were performed at least two times . See Table S1 for primer sequences used for ChIP-PCR and construction of the PHB:PHB G202G-YFP transgene . | Seed development can be conceptually divided into two phases: namely the morphogenesis phase , in which cell division is active and all the major organs are formed , and the maturation phase , in which cells enlarge and storage reserves are synthesized and accumulated . Expression of the seed maturation program is tightly controlled such that it only occurs during the late phase of seed development . To uncover the molecular mechanisms underlying the repression of seed genes during vegetative development , we performed a reporter-assisted genetic screen , and one mutant identified is a weak allele of ARGONAUTE1 ( AGO1 ) that displays ectopic seed gene expression . We then performed a series of transgenic and genetic analyses to search for the molecular mechanisms underlying the mutant phenotype . We first demonstrate that the decrease in miR166 in ago1 is a major cause of the mutant phenotype . Further , we show that the targets of miR166 , type III HD-ZIP transcription factors PHB and PHV , are sufficient for derepressing seed maturation genes in seedlings , likely by binding directly to the promoter of a master regulator gene of maturation . Thus , this work establishes a miRNA–mediated pathway that represses the embryonic program and also establishes PHB/PHV as direct activators of the maturation program . | [
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| 2012 | MicroRNA–Mediated Repression of the Seed Maturation Program during Vegetative Development in Arabidopsis |
Nigeria carries the highest burden and diversity of neglected tropical diseases ( NTDs ) in sub-Saharan Africa and is preparing to scale up its efforts to control/eliminate these diseases . To achieve this it will require a range of internal technical support and expertise for mapping , monitoring and evaluating , operational research and documenting its success . In order to begin to evaluate this potential in Nigeria , this study collated and analysed information for lymphatic filariasis ( LF ) , onchocerciasis , schistosomiasis and soil-transmitted helminths ( STH ) , which are currently being targeted with preventive chemotherapy through mass drug administration ( MDA ) . Information from 299 scientific articles published on the selected NTDs in 179 journals between January 2008 and September 2013 was extracted and systematically compiled into a geo-referenced database for analysis and mapping . The highest number of articles was from the southern geo-political zones of the country . The majority of articles focused on one specific disease , and schistosomiasis and STH were found to have the highest and most wide ranging research output . The main type of study was parasitological , and the least was biotechnological . Nigerian authors were mostly affiliated with universities , and there was a wide range of international co-authors from Africa and other regions , especially the USA and UK . The majority of articles were published in journals with no known impact factor . The extensive database and series of maps on the research capacity within Nigeria produced in this study highlights the current potential that exists , and needs to be fully maximized for the control/elimination of NTDs in the country . This study provides an important model approach that can be applied to other low and middle income countries where NTDs are endemic , and NTD programmes require support from the expertise within their own country , as well as internationally , to help raise their profile and importance .
The impact of neglected tropical diseases ( NTDs ) on the health and economy of neglected communities is gaining increasing international attention with a call for global efforts to eliminate or eradicate 10 NTDs by 2020 [1][2] . NTDs are a group of infections that mainly affect people living in remote rural areas , urban slums or conflict zones [2] . In recent years , international funding for NTD control and elimination has increased through support by several donors including pharmaceutical companies , governments , UK Department for International Development ( DFID ) , the United States Agency for International Development ( USAID ) , Bill & Melinda Gates Foundation , World Bank , World Health Organization ( WHO ) , non-governmental development organizations ( NGDOs ) and other research agencies [1][3]–[5] . There is a need to build on this momentum but a key issue , which has been highlighted , including in the WHA Resolution , is the need for increased research efforts and for strengthening capacity in endemic countries for both research and implementation . However , financial support has not been evenly distributed with some countries overwhelmed with significant donor support , while other countries with an equally large burden of diseases have attracted little or no funding [6] . This increase in funding may be more appropriately and evenly channelled if potential synergies between different disease programmes were better identified . To date NTD programmes have not fully exploited the benefits of these synergies [7][8] , despite many of the diseases being co-endemic . The delivery systems could be shared thus making the interventions for NTDs more cost effective [7][9] . NTD programmes can be integrated into primary health care services and existing vaccination or micronutrient campaigns , or the school based distribution of anthelminthic drugs to achieve greater coverage and reduce operational costs [4] . The opportunities to maximise the benefits of one programme on another abounds . For example , the potential benefit of repeated doses of ivermectin for onchocerciasis on the prevalence of Wuchereria bancrofti ( LF ) [10][11] , and the combination of ivermectin and albendazole for LF on soil-transmitted helminths ( STH ) prevalence and scabies [1][12] have been demonstrated . In Nigeria , the opportunities and importance of scaling up interventions in a coordinated way across NTD programmes has recently been highlighted by Okorie and others [9] and in the five-year NTD Master Plan launched by the Federal Ministry of Health ( FMoH ) [13] . The NTD programmes are currently delivered through the State Ministries of Health , and the Local Government Authorities following the technical recommendations from the FMoH . The need for strong technical knowledge as well as managerial skills at the State and local level is essential in order to train and supervise the various cadres of workers , organize appropriate health education , and monitor and evaluate the outcomes . This can most effectively be achieved through strong partnership and collaborative links with the different sectors within the country , including academia , which may be able to provide a range of in-country technical support and expertise for mapping , monitoring and evaluating , operational research and documenting programmatic success . The availability of technical capacity , in terms of research scientists and institutions , in developing countries is a major challenge for the control of NTDs [1][14] . The need for developing and supporting this capacity within NTD endemic countries cannot be over emphasized as it would guarantee national ownership and help to make the programmes accountable and sustainable [2][5] . Although the FMoH in Nigeria have a lot of field capacity at state and local government level that could be utilized , additional support is required from the researchers within the country to execute its projects . In order to begin to evaluate this potential in Nigeria , and to develop a model approach for other countries , information from the published literature on the scientific research capacities for the select group of NTDs being targeted with preventive chemotherapy through mass drug administration ( MDA ) were collated and analysed [1] . This paper addresses the issue of country capacity for research as a resource to inform NTD programmes with reference to Nigeria , which carries the highest burden and diversity of NTDs in sub-Saharan Africa [15] . The NTDs most prevalent in Nigeria include lymphatic filariasis ( LF ) , onchocerciasis , schistosomiaisis , STH , trachoma , leprosy , Buruli ulcer and human African trypanosomiasis ( HAT ) [13] . This study however , focused on the diseases amenable to MDA including LF , onchocerciasis , schistosomiasis , STH as well as Loa loa filariasis . The latter is not officially classified as an NTD , but poses a major problem for LF and onchocerciasis programmes in co-endemic areas due to the risk of severe adverse events associated with the use of ivermectin [16] .
A systematic search for published articles on the selected NTDs was conducted using electronic sources including PubMed , BiomedExperts , Google Scholar , Google and African Journal Online ( AJOL ) . Search terms included Nigeria , in combination with each of the diseases , including alternative names of the disease , and the parasites; lymphatic filariasis , ( elephantiasis ) , Wuchereria bancrofti , onchocerciasis ( river blindness ) , Onchocerca volvulus , schistosomiasis , Loa loa ( loiasis ) and soil-transmitted helminths ( Ascariasis , Taeniasis , Trichuriasis and Hookworm ) . Additional references were identified within the collated articles , and then from the references within those articles . Websites of the various institutions in Nigeria ( when available ) were also searched for the publication list of scientists in related departments . All articles with Nigerian authors and those published between January 2008 and September 2013 were included in the study to provide information and perspective on the most recent research capacity and activity . This information on researchers who are currently active will be useful to many internal and international partners , donors , and scientists who wish to collaborate with NTD researchers in country . The following information was extracted from each article into a data base i ) article authors and title ii ) publication year iii ) disease focus and iv ) journal name . Additional information on the journal and author composition was entered into the database and included v ) journal website if available , vi ) journal impact factor based on the Journal Citation Reports ( JCR ) of 2013 , journal website or Researchgate website ( www . researchgate . net ) and vii ) number of Nigerian authors , viii ) number of international authors and ix ) the country of international co/authors . Based on the lead authors' institution , information on the location was also entered for mapping purposes and included x ) geopolitical zone xi ) state xii ) place ( town , city ) , and xiii ) geographical coordinates ( latitude , longitude ) of the location of the institution . To understand the range of studies being conducted across the country , the type of study of each article was broadly classified based on the material and methods described , and included the following; parasitological ( if parasitological techniques were used ) ; entomological ( if the study had a vector component ) ; social/anthropological ( if the study included the use of questionnaire , focus group discussion and/or observations ) ; clinical/physical examination ( if the participants of the study were subjected to clinical examination for the pathology of the disease ) ; hospital based ( if the study was conducted in a hospital ) ; biotechnological ( if molecular techniques were used ) and review ( if the disease or epidemiological aspects were reviewed ) . Multiple categories could be included in the database . Further , to understand the range of institutions involved in NTD research in Nigeria , the type of institution was broadly classified based on the lead author's affiliation , and included the following; University , Research Institute , College/Polytechnic ( including colleges of health and education , polytechnics , schools of technology ) , Diagnostic laboratory , Hospital , Ministry of Health , and Non-government/Development Organizations ( NGO/NGDOs ) . Data were entered into and examined using univariate and bivariate tabulations in Excel ( Microsoft office 2007 ) , and mapped using the geographical information systems software ArcGIS 10 ( ESRI , Redlands , CA ) . First , the number of articles were quantified by year and disease focus , and mapped by state and geo-political zone and to highlight areas where multiple diseases were being researched . Second , the type of study and institutional affiliation associated with each article was examined by disease focus , and then mapped to highlight the distribution of the different technical and academic capabilities and institutional foci available across the country . Third , the number of international co-authors , by disease focus and their country of origin were examined to identify international collaborative trends . Finally , the number of different journals and their impact factors were examined to determine if the disease focus and international collaboration had an influence on publication trends and research exposure .
A total of 299 published articles on LF , onchocerciasis , schistosomiasis , STH and L . loa between January 2008 and September 2013 were identified and collated into a database ( Table S1 ) . The number of articles published per year ranged from 35 to 58 , with an average of 53 articles per year ( Table 1 ) . Based on the lead author's institution location , all six zones of the country had published on the NTDs and there were articles from 31 States and the Federal Capital Territory ( Table S1 ) . No articles on these NTDs were published from Bayelsa , Bauchi , Gombe , Yobe and Jigawa States . The highest number of articles were from the southern geo-political zones in the South West ( n = 71 ) , South East ( n = 63 ) and South South ( n = 60 ) , and from states within these zones including Edo State ( n = 25 ) , Enugu State ( n = 22 ) and Ogun State ( n = 19 ) as shown in Figure 1 . The number of publications varied by disease , with 41 articles on LF ( 13 . 7% of the total ) , 58 articles on onchocerciasis ( 19 . 4% ) , 107 articles on schistosomiasis ( 35 . 1% ) , 81 articles on STH ( 27 . 1% ) , and 6 articles on L . loa ( 2 . 0% ) . Only a small number of articles ( n = 8; 2 . 7% ) published on more than one disease ( Table 1 ) . There was no observable trend in the number of articles per year for any disease , however , most articles on LF were published in 2011 ( n = 14 ) , on onchocerciasis in 2010 ( n = 14 ) , on schistosomiasis in 2012 ( n = 24 ) , on STH in 2010 and 2011 , on L . loa in 2008 ( n = 3 ) , and on multiple diseases between 2011 and 2013 ( n = 7 ) . The institutional location of where the disease specific research was carried out is shown in a series of maps in Figure 2a–e . Further overlapping maps were produced to highlight the locations and states where more than one NTD was being researched ( Figure 2f–h ) , despite only a small number of articles being published on more than one disease . Overall , the most common type of studies identified from the articles were parasitological ( n = 214 ) and/or social/anthropological-based ( n = 109 ) , and the least common were biotechnological ( n = 11 ) and reviews ( n = 12 ) . The type of studies carried out according to disease focus are shown in Table 2 , and highlight that parasitological studies were the most common type of study for all diseases except for L . loa and one study on multiple diseases . The distribution of the study types varied across the country , with parasitological and social/anthropological-based studies widely distributed across all zones and most of the 31 states ( Figure 3a ) . Entomological , clinical/physical examination and hospital based studies were carried out in all zones , however , the states varied; entomological ( 11 states ) , clinical/physical ( 15 states ) and hospital based ( 13 states ) . Reviews were carried out in seven states of the North Central zone and in all three southern zones ( South West , South East and South South ) , and biotechnological studies were only carried in two states of the South East ( Imo State ) and South West ( Lagos State ) zones ( Figure 3b , c ) . The articles published were from nine different types of institutions across the country ( Table S1 ) . Approximately ninety percent ( 89 . 6% ) of the institutions were public owned while most of the hospitals ( 88 . 9% ) were teaching hospitals . The most common type of institutional affiliation ( of lead author ) was the University ( n = 198 ) accounting for two thirds ( 66 . 2% ) of the total . Hospitals ( n = 36 ) and Research Institutes ( n = 20 ) represented 12% and 6 . 7% respectively , and authors from the Ministry of Health ( n = 2 ) and NGO/NGDOs ( n = 3 ) were found to lead least in research articles on these NTDs ( Table 3 ) . The trends were similar for the different diseases with the majority of authors being affiliated with a University however , there was some variation among the other institutional types , and notably , a higher proportion of STH articles were affiliated with hospitals ( 23 . 5% ) . The maps showing the distributions of the different institution types showed that the University affiliation was found in all zones and 29 of the states involved in NTD research ( Figure 4a ) . Research Institute affiliations were found in North Central , North West , South South and South West zones across five states ( Figure 4b ) , and hospital affiliations in the North Central and three southern zones ( South West , South East and South South ) , across 13 states ( Figure 4c ) . A total of 45 ( 15 . 1% ) of the 299 articles in the database were found to have international co-authors , with at least one Nigerian co-author included on the article ( Table S1 ) . In total , 192 international co-authors were tallied , with 29 international co-authors on LF articles ( 15 . 1% ) , 48 co-authors on onchocerciasis articles ( 25% ) , 85 co-authors on schistosomiasis articles ( 44 . 3% ) , 25 co-authors on STH articles ( 13% ) , none of L . loa and small number on articles published on multiple diseases . Overall , the international co-authors were from 22 countries from different regions of the world . The different countries were recorded 108 times on the 45 published articles ( accounting for multiple international co-authors from same country ) . The most frequently cited country was USA ( n = 29 ) followed by the UK ( n = 13 ) , Burkina Faso ( n = 10 ) ( reflecting a link with the African Programme for Onchocerciasis Control ( APOC ) ) , and Brasil ( n = 7 ) with fewer co-authors reported from countries within Africa including Ghana ( n = 6 ) , Cameroon ( n = 4 ) , South Africa ( n = 3 ) , Togo ( n = 2 ) , Senegal ( n = 2 ) , Uganda ( n = 2 ) , Zambia ( n = 2 ) , Tanzania ( n = 1 ) , and elsewhere in Europe and the Asia-Pacific regions ( Figure 5 ) . The 299 articles were found to be published in 176 different journals , with 14 journals ( 8% ) specifically with Nigerian research related titles . The Nigerian Journal of Parasitology had the highest number of publications ( n = 18 ) . The majority of articles ( n = 209 ) were published in journals with no known impact factor ( IF ) , while a total of 90 articles were in journals with IFs ranging from 0 . 25 to 5 . 85 . The overall IF average was 1 . 99 , and for LF articles was 2 . 43 , onchocerciasis 1 . 89 , schistosomiasis 2 . 16 , STH 1 . 58 and L . loa 0 . 81 . There were different trends between articles published by a combination of Nigerian-international co-authors and those by Nigerian authors alone , with a higher number of Nigerian-international co-authored articles published in journals with IFs over 2 , than Nigerian-only authored articles which were published in journals with a wider range of IFs ( Figure 6 ) .
This critical analysis of research capacity in Nigeria highlights that there is a wide range of skilled researchers and institutions working on NTDs across many regions of the country . The relatively high number of publications indicates that there is significant potential to expand on and utilize this academic and technical human resource to collect , collate and map more epidemiological data , which are currently lacking [13] . The focus on publications over the past five years highlights the scientists who are most active at a time when the NTD momentum could best harness this local research capacity . This resource could also be used to help address specific NTD programmatic issues , and to highlight the barriers and successes to elimination across all diseases . It could also be used to enhance collaboration between the various institutions , donors , partners and international stakeholders , which is critical for Nigeria at the present time , given the emphasis on the scale up of implementation activities and integration of programmes . There was a distinct difference between the number of publications by the geopolitical zones and states within them , with the highest research output coming from researchers in the southern zones . The classification or research output was based on location of researchers , hence , it may be possible that researchers may have carried out studies in other locations where they were not based . This north-south divide in NTD focus and capacity presented in this study , needs to be better understood to determine if it is related to the availability of resources , specific needs , opportunities , or even safety given the ongoing civil unrest in the northern zones [17] . Identifying these barriers and gaps is critical , and extending collaborative programmatic research activities to the north where there is significant co-endemicity of NTDs will be important , especially as it will likely be the region most difficult to eliminate them [13] . Addressing the elimination of NTDs in fragile conflict/post-conflict areas and countries is a significant challenge as they often have the highest disease burdens and least resources to cope [18]–[20] . The highest and most wide ranging research output was for schistosomiasis and STH . Notably however , most of the studies only focused on one disease , despite an extensive geographical and institutional overlap highlighted in our combined maps . A similar lack of integrated research was evident with LF and onchocerciasis , two diseases that often geographically overlap , have similar drug regimes and major programmatic barriers associated with the co-endemicity of L . loa . The lack of integrated disease research may be related to the fact that until recently , most NTD programmes and research groups had a more vertical disease-specific approach [21]–[24] . Now there is a significant national and international move to a horizontal or integrated programmatic approach with the development of NTD Master Plans , including mapping , implementing , and monitoring [25] . This programmatic paradigm shift will also affect future research , and has already started to be addressed in Nigeria [9] . For example , the FMoH recently launched a national guideline for the co-implementation of interventions to eliminate/control malaria and LF [26] , which are both transmitted by the same Anopheles vectors in Nigeria [27] . The main type of research published was parasitological based ( predominately microscopy ) , and those involving the use of questionnaire and focus group discussions . These types of studies are human resource intensive and do not require specialized or state-of the-art equipment , which may be the reason for their wide distribution . Researchers with these skills and experience could be an important human resource to the NTD Programmes , and help to collect and collate a range of epidemiological data across the country over time . Moreover the affiliated institutions could be used as a network of sites for training and/or conducting multi-centre and multi-disease studies [28][29] . In contrast , there were a limited number of molecular based studies in the country , which suggests there is a need to increase access to specialized high-tech equipment and develop molecular and other laboratory skills through training programmes . This is increasingly important as technologies advance and specialized laboratory personnel could play a major role in the elimination process [30][31] . Most articles were published by researchers affiliated with a University . In the Nigerian context , promotions for scientists and researchers , especially in the universities , are closely tied with publication output [32]–[35] . This in itself drives scientists to conduct and publish more research . This established academic infrastructure and human capacity , which is wide spread across the country , provides an opportunity for state level NTD programmes to link with local universities to conduct and publish operational research . This may increase the visibility of their work and help to inform other potential donors and international stakeholders of progress and challenges . It may also help to develop a collaborative network of programmatic-specific researchers within Nigeria , as well as externally between Nigerian and international scientists [6] . This study highlights a wide range of international collaborations and co-authors from Africa and other regions of the world , especially from the USA and UK . The impact of international research collaboration has not been extensively studied but could provide additional opportunities in terms of equipping laboratories , training scientists , providing mentorship , as well as helping to define an individual's career path , skills in grant writing , study design and publishing [2] . It was observed that articles with international co-authors , tended to be published in higher IF ranking journals . This trend has been reported elsewhere [36] , and may translate to more international exposure and a wider readership , than those articles published in Nigerian journals alone . The importance of continuing and increasing international collaborations , including those within Africa ( south south ) and exposure to the work/research cannot be emphasized enough at a time when the country is scaling up efforts to eliminate multiple NTDs [37] . Although , it is possible that some papers by Nigerian authors may have been missed because they were not in the databases searched , this extensive geo-referenced database and series of maps on the research capacity within Nigeria , highlights the current potential that exists and can be built on for the control/elimination of NTDs in the country . This locally available capacity can be harnessed and strengthened by further investments ( such as establishing up-to-date research facilities , conducting training activities , etc . ) from the funding agencies/donors involved in NTD research and implementation in Nigeria . This database will promote an evidence-based approach to capacity strengthening and create the basis from which to build collaborative links within and outside the country as well as creating linkages across disciplines . This is important as Nigeria has recently been considered a priority country for early assistance to address the large burden of NTDs , and emphasizes the importance of providing clear , sound , and timely advice on technical aspects of programmatic activities [38] . Most importantly , this database provides an important model that can be applied to other low and middle income countries where NTDs are endemic . This study focussed only on the lead author information and the authors acknowledge that the FMoH have a lot of field capacity at state and local government area level which could also be utilised . However , the immediate country needs are to improve the knowledge and capacity of the State Ministry of Health staff responsible for the implementation of NTD programmes and through them at the Local Government Area ( District ) level . This will ensure that programmes supported by the State , bilateral donors and NGDOs are effectively implemented whilst research capacity is deployed to support the parallel implementation research needs , monitoring and evaluation and surveillance . The study demonstrates what is necessary in other countries and the urgency in building and developing capacity as stressed in the WHA Assembly resolution 66 . 12 of 2013 [4] . This is becoming critical in other highly populous countries of Africa , who are falling behind the curve as far as the WHO Road Maps targets are concerned , specifically in terms of the number of drugs that are required to be delivered to address what has become known as the “implementation deficit” . | Nigeria carries the highest burden and diversity of neglected tropical diseases ( NTDs ) in sub-Saharan Africa and is preparing to increase the control and elimination of these diseases . The aim of this study was to provide information on the disease focus and type of studies carried out by scientists working on lymphatic filariasis ( LF ) , onchocerciasis , schistosomiasis , soil-transmitted helminths ( STH ) ) and Loa loa filariasis in Nigeria . Information on these diseases from all published literature on the scientific articles by Nigerian authors published between January 2008 and September 2013 was collated and mapped . The results show that many institutions are working on NTDs in Nigeria and their capacity could be readily enhanced with training and resources to boost their skills and to increase their range of technical activities and research visibility , which will also help to provide essential technical and laboratory support to the national NTD programmes . | [
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| 2014 | Neglected Tropical Diseases: A Systematic Evaluation of Research Capacity in Nigeria |
Schistosomiasis is the most important helminthic disease of humanity in terms of morbidity and mortality . Facile manipulation of schistosomes using lentiviruses would enable advances in functional genomics in these and related neglected tropical diseases pathogens including tapeworms , and including their non-dividing cells . Such approaches have hitherto been unavailable . Blood stream forms of the human blood fluke , Schistosoma mansoni , the causative agent of the hepatointestinal schistosomiasis , were infected with the human HIV-1 isolate NL4-3 pseudotyped with vesicular stomatitis virus glycoprotein . The appearance of strong stop and positive strand cDNAs indicated that virions fused to schistosome cells , the nucleocapsid internalized and the RNA genome reverse transcribed . Anchored PCR analysis , sequencing HIV-1-specific anchored Illumina libraries and Whole Genome Sequencing ( WGS ) of schistosomes confirmed chromosomal integration; >8 , 000 integrations were mapped , distributed throughout the eight pairs of chromosomes including the sex chromosomes . The rate of integrations in the genome exceeded five per 1 , 000 kb and HIV-1 integrated into protein-encoding loci and elsewhere with integration bias dissimilar to that of human T cells . We estimated ~ 2 , 100 integrations per schistosomulum based on WGS , i . e . about two or three events per cell , comparable to integration rates in human cells . Accomplishment in schistosomes of post-entry processes essential for HIV-1replication , including integrase-catalyzed integration , was remarkable given the phylogenetic distance between schistosomes and primates , the natural hosts of the genus Lentivirus . These enigmatic findings revealed that HIV-1 was active within cells of S . mansoni , and provided the first demonstration that HIV-1 can integrate into the genome of an invertebrate .
Schistosomiasis is considered the most important helminthic disease of humanity in terms of morbidity and mortality , and is one of the major neglected tropical diseases ( NTDs ) [1–4] . Whereas > 90% of cases occur in Africa , where the major burden of disease lies , a recent outbreak in Corsica confirmed its re-emergence in Europe [5] . To accelerate discovery of intervention targets for schistosomiasis and to provide exploitable insights into the parasite biology and pathogenesis , concerted efforts are in train to produce reference genome sequences of the human schistosomes and related helminths [6–11] . To capitalize on the investment in flatworm poly-omics to identify novel control strategies , high-throughput systems for comprehensive studies of gene function have now become essential . However , because parasitic flatworms at large are difficult to maintain in the laboratory due to complex developmental cycles , they remain recalcitrant to genetic/cellular manipulations , presenting a significant bottleneck for adapting state-of-the-art approaches to elucidate gene function [12] . Current large-scale approaches , mainly involving medium-throughput RNAi screening [13 , 14] , currently provide a veneer only of information on gene function since the knowledge of characteristics and regulation of specific gene expression remains limited . To profoundly probe function at scale , protocols for routine manipulation of the genome need to be established and optimized; genes need to be disrupted , transgenes inserted , and expressed in a sustainable , and even tunable , fashion . There has been progress in developing functional genomics for schistosomes and some other flatworms [12] . Vesicular stomatitis virus glycoprotein ( VSV-G ) -pseudotyped murine leukemia virus ( MLV ) was shown to transduce schistosomes , integrating the provirus into the chromosomes of Schistosoma mansoni [15 , 16] . ( Production of viruses with foreign viral envelope proteins is termed pseudotyping; pseudotyped is undertaken to increase host species and cell type tropism and/or enhance stability of the virions [17] . ) Germ-line transgenesis has been achieved by transducing S . mansoni eggs with VGV-G MLV , enabling the establishment of stable lines of transgenic parasites [16 , 18] . In addition , eggs also might be transducible by pseudotyped human immunodeficiency virus-1 ( HIV-1 ) [19] . Lentiviruses such as HIV-1 possess a desirable attribute for functional genomics , since these viruses can infect both dividing and terminally differentiated non-dividing cells; MLV can infect the former but not the latter [20 , 21] . However , critical details are missing on the capacity of lentiviruses to integrate into chromosomes of flatworms and transcribe transgenes . In particular , given evolutionary divergence of flatworms and humans , the natural host HIV-1 , it is necessary to characterize the preferred regions of integration before using lentiviral vectors for functional genomics . Here we report that infection of S . mansoni with pseudotyped HIV-1 lead to attachment of virions , reverse transcription of the RNA genome of HIV-1 , and integrase-catalyzed insertion of the provirus into the genome of the blood fluke , and characterize the sites of integration . HIV-1-based manipulation of these parasites should enable advances in functional genomics for schistosomes and related platyhelminth pathogens .
The successful attachment of VSV-G pseudotyped HIV-1 to the tegument of schistosomes was demonstrated using an antibody specific for VSV-G . Specific binding was observed to the surface of both schistosomula ( Fig 1A–1F ) and adult worms ( S1 and S2 Figs ) following exposure to the virions . An evident fluorescence signal emitted by Alexa Fluor 488-labeled anti-VSV-G antibody was detected and measured using spinning disk confocal microscopy ( Fig 1G and 1H ) . Moreover , the signal intensity observed mainly in the surface of the virion-transduced parasites significantly increased over three hours exposure ( S2 Fig ) . These results demonstrated time-dependent attachment of the virions to the schistosome tegument . In addition , the binding pattern seen on the tegument of both the schistosomules and adult worms revealed a focal rather than general binding to the surface of this developmental stage ( 1D , 1E , S1E and S1F Figs ) . Schistosomes not exposed to virions and incubated with VSV-G primary antibody and schistosomes exposed to virions and incubated with the secondary antibody only did not exhibit fluorescence , thereby indicating specific binding by both the primary and secondary antibodies . Although , autofluorescence was evident in schistosomules and adult worms ( Fig 1A and 1B and S1D Fig ) , that ‘fluorescence’ pattern was distinct and readily distinguished from the Alexa Fluor 488 . signal ( Fig 1H ) . Quantitative PCR ( qPCR ) of DNA extracted from schistosomula exposed to active or heat-inactivated virus was performed employing HIV-1 specific primers to estimate the copy number of HIV-1 cDNA molecules . Both early , strong-stop and late , positive-strand HIV- specific cDNAs were detected in parasites exposed to active HIV-1 , whereas few copies were detected in parasites exposed to heat-inactivated virions ( Fig 2A [early strong-stop; P ≤ 0 . 05 , Student’s t-test] and 2B [late , positive-strand; P ≤ 0 . 01] ) . These findings established that reverse transcription of the RNA genome of HIV-1 had proceeded in the cells of virion-exposed parasites . Thereafter , integration of HIV-1 cDNA into the schistosome genome was investigated . Integration of the provirus in human cells has been earlier assessed using a quantitative two-step Alu-based nested PCR [22] . We modified this approach to target multi-copy endogenous elements present in the schistosome genome; a method termed ‘quantitative Retrotransposon Anchored PCR’ ( qRAP ) [23] . Genomic DNA ( gDNA ) extracted from schistosomula was subjected to nested PCR employing a primer specific for the gag gene of HIV-1 in tandem with primers specific for several endogenous retrotransposons known from the genome of S . mansoni ( Fig 2C ) . The relative copy number of integrated HIV-1 as estimated by qRAP was significantly higher in schistosomes transduced with active virions compared to the negligible signals from parasites exposed to heat-inactivated virions , at both 24 and 48 hours after transduction ( Fig 2D and 2E ) . These findings indicated that HIV-1 cDNA reached the nuclei of schistosomes , and that the proviruses integrated into the genome of the parasite , at least in regions proximal to the endogenous retrotransposons employed as anchors for the qRAP [22] . Curiously , two inhibitors of reverse transcriptase , azidothymidine and nevirapine , each with a discrete mode of action , and an inhibitor of integrase , the diketo acid derivative 118-D-24 , failed to block these events , as determined by qRAP targeting integration events ( S3 Fig ) . In order to identify and map integrated HIV-1 proviruses within the reference genome of S . mansoni [6] , an Illumina sequencing-based approach that utilized PCR to enrich for the integration events was adapted from a procedure named TraDIS ( Transposon Directed Insertion-site Sequencing ) , which had been employed to characterize transposons in bacterial genomes [24] . The latter had been successfully adapted to localize integrations of VSVG-pseudotyped Murine Leukemia Retrovirus ( MLV ) in somatic and germ line-derived cells of schistosomes [16] . Illumina libraries were prepared from genomic DNA and then amplified to enrich for integration sites ( S1 and S2 Tables ) . High throughput sequencing of the TraDIS libraries constructed from both the 5- and 3’long terminal repeats ( LTRs ) of HIV-1 yielded >25 , 000 paired sequence reads with HIV-1 start sites from libraries constructed from both the 5’- and 3’-long terminal repeats ( LTRs ) of the lentivirus . About ~8 , 000 integrations were identified ( Table 1 ) , comprising 1 , 827 and 6 , 258 non-redundant events from the 5’- and 3’-LTR libraries , respectively . Of these sites , most were unique clusters where neighboring integrations were separated by > 250 bp . Mapping the integrated proviruses to the schistosome reference genome revealed a broad distribution of integrations throughout all eight chromosomes of the parasite , comprising chromosomes 1 to 7 and Z/W , the sex chromosomes . Notably , integrations into the mitochondrial genome were also mapped ( Table 2 ) . Similar findings were apparent from analysis of sequences mapped from either the 5’-LTR- or 3’-LTR-end libraries . It was evident that numerous integrations of HIV-1 provirus into the schistosome genome had been catalyzed by integrase , given the presence of the diagnostic dinucleotides CA at the 3’-LTR and TG at the 5’-LTR termini of HIV-1 at the integration junctions immediately flanking the schistosome genome ( Fig 3A and S1 Table ) ( S4 Fig ) . Fig 3B and 3C present representative integration boundaries ( vertical red bar ) between the 3’-LTR termini of the provirus and the schistosome genome within the chromosome 1 , or 5’-LTR-ends and chromosome Z/W , respectively . Sequences of a series of junctions of additional , representative integrations recovered using TraDIS from the 3’-LTR-end library are shown in S4 Fig . Proviruses of HIV-1 distributed throughout the eight pairs of chromosomes of S . mansoni . A frequency distribution of integration events along the entire ~65 Mb length of chromosome 1 ( Chr 1 ) illustrated the rate of integrations throughout the nuclear genome as recovered using TraDIS . Some regions represented integration hotspots with a rate exceeding five integrations of HIV-1 provirus per 100 kb of chromosome . HIV-1 integrase-mediated events are indicated with arrowheads above and below the windows; here the dinucleotides CA and TG were characterized in the sequenced analysis at the termini of the integrated 3’-LTR and 5’-LTR of the provirus , respectively ( Fig 4A ) , evidence of catalysis by HIV-1 integrase [25] . Examples of integration events within chromosomes 2 and 5 , as detected in the 5’-end LTR and 3’-end LTR libraries , are shown in Fig 4B and 4C . The event characterized in chromosome 2 lies within exon 10 of Smp_146570 , a histidyl-tRNA synthetase-related protein ( Fig 4B ) , and that in chromosome 5 lies in exon 6 of Smp_061540 , an amino acid transporter homologue ( Fig 4C ) . The genes were inferred by protein orthology and coordinates of the integration events are provided ( Fig 4 legend ) . Note also numerous other integration sites , the positions of which are indicated with the arrowheads; blue colored arrowheads indicate events detected in the 5’-LTR-end library and red-colored in the 3’-LTR-end library ( Fig 4B and 4C ) . Further analysis of the integration events revealed that exons contained 4% of the integrations , introns contained ~34 . 5% , whereas ~62% were detected within non-coding regions . By comparison , 4 , 39 and 57% of the S . mansoni genome is composed of exons , introns , and non-coding regions , respectively ( Fig 5A ) . Despite the apparent concordance of integration frequencies with genome composition of exons , introns and non-coding regions , statistical analysis revealed a significant tendency of proviral HIV-1 to integrate into non-coding regions ( binomial proportion one-tailed test , P ≤ 0 . 01 ) ; this was dissimilar to MLV , which did not show bias for any particular region [16] . Given the availability of a curated RNA-seq database from discrete developmental stages [6] , we compared expression levels of genes where HIV-1 integrations were identified to the transcriptomes at large of cercariae , schistosomula at three and 24 hours following mechanical transformation from cercariae , and adults . Bias was not evident for integration of HIV-1 into genes actively transcribed at these developmental stages ( Fig 5B ) . Given that both the qRAP and TraDIS approaches revealed that HIV-1 provirus had integrated widely into the schistosome genome , we undertook Whole Genome Sequencing ( WGS ) to precisely quantify integrated HIV-1 proviruses . Following WGS of genomic DNA from virion-exposed schistosomules to a depth of 48X coverage , Illumina reads were aligned to the genomes of both S . mansoni and HIV-1 . Alignments were curated to remove false positive integrations and reads entirely of schistosome origin . Reads were assigned to mapping categories according to their position in either genome: 1 ) sequence reads containing HIV-1 adjacent to schistosome sequence , i . e . integration site within the read; 2 ) independent , i . e . pairs of sequence reads with one read aligned to the schistosome genome and the other to HIV-1; and 3 ) read pairs that matched only HIV-1 , i . e . reads solely of lentivirus origin ( S5A Fig and S4 Table ) . The WGS data revealed 82 reads among the three categories , and 60 integrations , 35 and 25 events within categories 1 and 2 , respectively ( S5A Fig ) . S5B Fig , presents a representative alignment of reads mapped to the genomes of S . mansoni and HIV-1 , i . e . sequence reads containing partial HIV-1 sequence and partial schistosome sequence . This particular HIV-1 integration event , within the ZW chromosome , was one of 35 events in category 1 ( above ) ( S5 Fig ) . Subsequently , numbers of integrations per worm were estimated . The WGS library was constructed using 1 , 700 ng of genomic DNA from ~5 , 000 schistosomula exposed to virions . Given the diploid genome of S . mansoni has an estimated mass of 0 . 79 picogram [6] , the total number of nuclei in the WGS was ~2 . 2 x 106 . WGS was performed to a depth of sequence coverage of ~48 haploid genome-equivalents , resulting in 207 million paired ( and properly mapped ) reads among which 60 integrations were detected . Given that a diploid genome is equivalent to 7 . 29 million reads , the number of expected integrations per nucleus is 60 x 7 . 29/207 = 2 . 1 integrations ( 95% confidence intervals 1 . 6 , 2 . 7 ) , representing ~2 , 100 integrations per schistosomulum ( it has been estimated that the 48 hour-old schistosomulum comprises ~1 , 000 nuclei [26] ) .
This report characterizes the integration of HIV-1 provirus within the chromosomes of Schistosoma mansoni . It also provides , to our knowledge , the first demonstration of integration of HIV-1 into the genome of an invertebrate . Inoculation of cultures of schistosomes with HIV-1 led to attachment of virions to the surface of the blood fluke , reverse transcription of the RNA genome of HIV-1 , and integration of the provirus into the genome . Notwithstanding that transduction of these parasites was facilitated by pseudotyping virions with VSV-G , and that transduction proceeded in the absence of CD4 and other cellular receptors expressed on activated T and other receptive cells , biochemical processes that evolved for parasitism of primates [27] by lentiviruses proceeded within the cytoplasm and nuclei of schistosome cells , including reverse transcription of the RNA genome , assembly of a pre-integration complex , transit to the chromatin and integrase-catalyzed integration of the provirus into the chromosomes . Lentiviruses traverse the nuclear envelope , and consequently HIV-1 can productively transduce non-dividing target cells , such as macrophages . By contrast , gammaretroviruses such as MLV cannot transverse the nuclear envelope; rather , MLV accesses the chromosomes at mitosis following dissolution of the nuclear membrane [28 , 29] . HIV-1 possesses a conclusive advantage over MLV for functional genomics since it can infect both dividing ( as can MLV ) and terminally differentiated non-dividing cells ( which MLV cannot ) [20 , 21] . For example , HIV can transduce non-dividing human cells , including stem cells , prior to differentiation , and terminally differentiated cells , such as monocyte-derived macrophages , astrocytes and microglia [30 , 31] . The pre-integration events in the developmental cycle of HIV are essentially discrete from the events of the MLV life cycle: the pre-integration complex ( PIC ) of HIV mixes with chromatin after transit through the intact nuclear envelope whereas the MLV PIC mixes with chromatin during mitosis [32] . Whether the PIC of HIV can integrate in the chromosomes without passage through the nuclear envelope remains to be determined but this seems unlikely given the dissimilarity in structure of the PICs of HIV and MLV . It is feasible that HIV-1 entered the nuclei of S . mansoni cells during interphase , and integrated into non-dividing cells [33] . HIV-1 also may access the chromosomes at mitosis although evidence that this mode of replication leads to productive infection is not yet available . Nonetheless , one or both of these routes may have led to the widespread integration of HIV-1 in the schistosome genome . In view of earlier demonstrations of transduction of schistosomes by MLV [15 , 16] and the present findings with HIV-1 , it is clear now that pseudotyped retroviruses can accomplish chromosomal integration and vertical transmission in schistosomes . Moreover , infection by HIV-1 appears to be efficient: based on titers of virions to which these parasites were exposed and , assuming the presence of ~104 virion particles in one pg/ml of p24CA ( the major structural protein of the HIV-1 virion capsid; there are ~1 , 000 p24CA proteins in the mature virion ) [34] , each schistosomulum was exposed to ~0 . 5 x 106–1x106 virions . Since the WGS analysis detected ~2 , 100 integrations per schistosomulum , we estimated an integration efficiency of ~0 . 25–0 . 5% . Spinoculation , centrifugation of the worms in the presence of virions [developed for human T cells [22 , 35] , delivered >5-fold increase of viral entry and increased numbers of integrations ( S6 Fig ) . It should be noted that natural infection of schistosomes by HIV-1 in people co-infected with both pathogens is highly unlikely to occur given that schistosomes do not express HIV-1 receptors . In this study , the HIV-1 virions were pseudotyped with G protein of vesicular stomatitis virus ( VSV-G ) , which binds with a highly ubiquitous LDL family receptors ( LDLR ) and endows the pseudotyped virus with pantropism [36] . This process does not happen in natural conditions . This is supported by the lack of HIV-1 sequences in the curated genome of S . mansoni [6 , 8] , which represents an established laboratory strain of S . mansoni that is maintained in rodents [37] . Given the mechanism of productive infection by HIV-1 of CD4+ T lymphocytes , events in schistosomes would have taken place in concert with cellular factors . Some processes , including endogenous reverse transcription , can proceed in vitro outside host cells [38–40] , and perhaps proceeded here in the presence of a minimal number of human cellular factors incorporated into the virion during virion production . However , chromosomal integration and other processes require specific factors in human cells , suggesting that orthologues or even less conserved surrogates from schistosome were adequate . Homologues of factors that contribute to establishment of infection appear to be present in S . mansoni ( S5 Table ) , including a capsid-binding protein ( Smp_094810 ) , reverse transcription complexes-interacting protein ( Smp_100090 ) , importins involved in the nuclear translocation of the viral pre-integration complex e . g . Smp_051210 , Smp_142770 , and integrase-interacting protein ( Smp_125050 ) . However , HIV-1 exhibited divergent integration preference in schistosomes compared to T cells , a site preference influenced by lens-epithelium-derived growth factor , LEDGF/p75 . LEDGF/p75 stabilizes and tethers the intasome , a tetramer of integrase proteins bridging the termini of the provirus , to the nucleosome [41 , 42] . Absence of an orthologue of LEDGF/p75 may account for discrete integrations profiles between schistosome and human cells . Integrase-dependent integration of full-length proviruses took place; the intact termini of the LTRs provided cogent evidence of catalysis by integrase [43] . Recovery of twice as many events from the 3’-LTR library suggested that mutations of the provirus also occurred such that rearrangements , truncations or deletions of the HIV-1 proviral genome influenced the efficiency of the TraDIS targeting the 5’-LTR . These phenomena occur during HIV-1 infection [44–46] , but may be less surprising in the exotic setting of tissues of a schistosome . Truncated versions of MLV occur in schistosome chromosomes [15 , 16] . Variants of truncated HIV-1 genomes and integration junctions that lacked CA residues at the termini of the HIV-1 provirus suggested integrase-independent recombination of incomplete reverse transcripts . These integrations may have resulted from homologous recombination or from non-homologous end joining [47 , 48] . The entire lentiviral cDNA containing the central flap , a plus strand overlap in the 3’-terminus of pol , has an advantage over truncated cDNAs for importion into the nucleus [49 , 50] . Yet completion of reverse transcription did not appear to be essential for insertion of viral cDNA into the schistosome genome . Perhaps HIV-1 exploited an alternative nuclear import mechanism in schistosomes . Numerous integrations of 3’-end HIV-1 cDNAs , which may represent intermediate products of reverse transcription , supported this possibility . In like fashion , truncated forms of integrated HIV-1 provirus that appear to represent products of incomplete reverse transcription frequently occur during HIV-1 infection in humans [51] . Since spinoculation enhanced contact of virions with the surface of the schistosome , the virions may have transduced the tegument rather than deeper tissues . The tegument of blood stream forms of the schistosome is syncytial , with multiple nuclei that are not in cell cycle synchrony [52 , 53] . This arrangement may have facilitated contact of provirus with chromosomes , and recombination of incomplete reverse transcripts of HIV-1 into the genome . Integrated proviruses preferred less gene rich regions but nonetheless proviruses were distributed in both coding and non-coding regions in the genome of S . mansoni . Within the Retroviridae , species-specific preferences for sites of integration have evolved so that , for example , HIV-1 prefers actively transcribed genes in euchromatin whereas MLV exhibits bias for promoters of actively transcribed genes , enhancers and CpG islands [54] . Indeed , half the MLV integrations occur within < 2% of the genome of some human cell lines [55] . By contrast , the distribution of HIV-1 integrations in schistosomes was reminiscent of site selection in human chromosomes by prototype foamy virus , a spumavirus , and the Alphavirus avian sarcoma leukosis virus ( ASLV ) [56] . ASLV prefers open euchromatin but does not show bias for transcriptional elements [57] . Other differences were seen including the inactivity of inhibitors of reverse transcriptase and integrase in schistosomes and integration into the mitochondrial genome . The inactivity of integrase inhibitor 118-D-24 on HIV-1 integration seemed consistent with the finding that numerous integrations may have occurred independent of insertion by integrase . However , the same or higher efficiency of reverse transcription in the worms treated with azidothymidine and nevirapine , suggested distinctive mechanisms of elimination of all these compounds , the inhibitors of reverse transcription and integrase , from schistosome cells . This might have been accomplished by aquaporins and other transporters active in schistosomes [58 , 59] . Moreover , the inhibitors may not have entered schistosome cells that had been infected with HIV-1 . Integration into the mitochondrial genome was notable given that this phenomenon may not have been reported in HIV-1 infected human tissues , as indicated by absence of reports of this type of event within the Retrovirus Integration Database , a public database for retroviral insertion sites [60] . A compelling attribute of HIV-1 versus gammaretroviruses such as MLV is the ability of the former to efficiently infect both dividing and terminally differentiated cells . Here we tested the HIV-1 transduction in the schistosomula and adult worms where many cells are differentiated and do not proliferate . Since we sought to investigate reverse transcription and chromosomal integration , access to substantial quantities of RNA and DNA from the parasites facilitated these analyses . Therefore , it was less challenging to investigate these biochemical processes of HIV-1 in blood stage schistosomes , schistosomula and adults ( rather than eggs ) . In future studies we plan to investigate the feasibility of deriving transgenic lines of schistosomes by transducing eggs with VSVG-HIV-1 , given that eggs of schistosomes have been successfully transduced with pseudotyped MLV [61] . Eggs of S . mansoni have been exposed also to pseudotyped HIV-1 virions carrying transgenes encoding microRNA-adapted short hairpin RNAs targeting genes expressed in schistosome eggs . The HIV-exposed eggs were inoculated into the pulmonary circulation of mice , which led to phenotypic changes in the inflammatory response , presumably following gene knockdown [19] . Establishment of transgenic lines of schistosomes , derived from retrovirus- and lentivirus-transduced eggs , expressing transgenes including Cas9 nuclease , should now be achievable . This would enable generation of specific knock-out lines using CRISPR-Cas9 gene editing as recently demonstrated for a parasitic nematode [62] and induction of stable gene knock-down from expression of shRNAs from integrated transgenes delivered using lentiviral vectors [14 , 19 , 63] . The performance of cis-regulatory elements to drive the expression of transgenes , insulator elements to prevent chromatin silencing position effects , and the use of selection markers need to be further investigated . How the widespread integration of HIV-1 in the schistosome chromosomes influences gene expression awaits investigation , as does the impact of the integration bias . The high-throughput approaches employed here to estimate the number of integration events investigated genomic DNAs pooled from large numbers of schistosomules , and hence the relatively high number of integrations represents what took place within the population of pooled HIV-transduced parasites . Transducing schistosomes with high titers of HIV-1 may also facilitate insertional mutagenesis-based forward genetics . Manipulation by transgenesis , knockout and/or gene editing by CRISPR/Cas 9-related approaches [64 , 65] can be predicted to enhance understanding of these pathogens , their somatic stem cells [66] , reproduction , longevity in infected hosts [10 , 67] , and intervention targets . These approaches may also facilitate establishment of sex-biasing gene drives to block the spread of schistosomiasis [68] . To summarize and conclude , this report presents enigmatic findings that reveal that certain steps of HIV-1 replication are active within cells of the human blood fluke , Schistosoma mansoni , a parasitic flatworm responsible for the major neglected tropical disease ( NTD ) schistosomiasis . Facile manipulation of schistosomes using lentiviruses should enable advances in functional genomics in these and related NTD pathogens including tapeworms , in particular concerning their non-dividing cells . Such approaches have hitherto been unavailable , and the lack of these kinds of tools underpins why the NTDs are neglected: the helminth NTD pathogens have not been readily tractable to laboratory investigation . Unlike the retrovirus MLV , which we investigated previously [15 , 63] , HIV-1 integrates into non-dividing cells . This represents a distinct advantage in applications such as transient transformation of adult and schistosomula stages , and thus is a substantial advancement in the functional genomics of these important parasites . A corollary of the findings is that lentiviral pre-integration complexes exploit either evolutionary conserved mechanisms or that HIV-1 can employ diverse strategies of nuclear import and integration . Although HIV-1 has been considered as a specialist virus because it uses species-specific receptors for host cell entry , the new findings suggest , rather , that it is generalist in use of intracellular pathways at post-entry steps of infection .
Mice experimentally infected with S . mansoni , obtained from the Biomedical Research Institute , Rockville , MD were housed at the Animal Research Facility of the George Washington University Medical School , which is accredited by the American Association for Accreditation of Laboratory Animal Care ( AAALAC no . 000347 ) and has an Animal Welfare Assurance on file with the National Institutes of Health , Office of Laboratory Animal Welfare , OLAW assurance number A3205-01 . All procedures employed were consistent with the Guide for the Care and Use of Laboratory Animals . Maintenance of the mice and recovery of schistosomes were approved by the Institutional Animal Care and Use Committee of the George Washington University . To produce vesicular stomatitis virus glycoprotein–pseudotyped HIV-1 ( VSV-G-HIV-1 ) virions for transduction of schistosomes and analysis of reverse transcription , nuclear import and integration , HEK293T ( ATCC , Manassas , VA ) cells were co-transfected with HIV-1 proviral clone pNL4-3 ( T cell-tropic HIV-1 subtype B isolate ) [69] and the pcDNA-VSV-G plasmid at a 4:1 ratio using Metafectene as described [70] . The resulting viruses were harvested 48 h later , passed through a 0 . 45 μM diameter pore size membrane and then incubated in 10 mM MgCl2 and 50 U/ml of RNase-free DNase I ( Roche , Indianapolis , IN ) at 37°C for two hours . Thereafter , virus particles were concentrated by centrifugation ( Beckman SW-28 rotor , 100 , 000xg , 24 , 000 rpm ) for two hours at 4°C through a sucrose ( 30% in PBS ) cushion . Pellets of virions were re-suspended in DMEM after which the activity of reverse transcriptase ( RT ) [71] and concentration of HIV-1 p24 antigen ( as measured using the Alliance HIV-1 p24 Antigen ELISA kit , Perkin Elmer , Waltham , MA ) were determined in order to estimate the virion titer . Schistosomes were transduced with virions at titers ranging from 0 . 5 to 1 μg capsid p24CA per ml . Although the NL4-3 virus cannot replicate in schistosome cells because of its tropism for human T cells , nonetheless the studies undertaken here were performed under BSL2 containment . Schistosome-infected mice and infected Biomphalaria glabrata snails were provided by the NIAID Schistosomiasis Resource Center at the Biomedical Research Institute ( Rockville , MD ) through NIH-NIAID Contract HHSN272201000005I for distribution through BEI Resources . Schistosomula were obtained by mechanically transformation of cercariae released from infected B . glabrata snails as described [72] . Briefly , cercariae were concentrated by centrifugation ( 2 , 000 rpm/10 min ) and washed 3 times in schistosomula wash medium ( DMEM supplemented with 2% penicillin , streptomycin , fungizone , and 10 mM HEPES ) [72] . Cercarial tails sheared off by repeated passes through a 22G emulsifying needle were removed by Percoll gradient centrifugation , schistosomula were washed three times in schistosomula wash medium and cultured at 37°C under 5% CO2 in air in modified Basch’s medium [72] . Adult schistosomes were recovered from mice by portal perfusion , washed in 1x PBS supplemented with penicillin , streptomycin and fungizone , and cultured as described [72] . Schistosomula ( ~103–104 ) were cultured in 24-well tissue culture plates in one ml of modified Basch’s medium [72] for one day after transformation . Thereafter , the culture medium was replaced with 500 μl of intact or heat-inactivated ( 2 hours , 65°C ) VSV-G pseudotyped HIV-1 virions , 500 μl of schistosomula medium and 8 μg/ml of the cationic polymer polybrene [16] to a final volume of one ml . The plate was subjected to centrifugation with the virus ( 1 , 000 x g , 60 min , 23°C ) , i . e . spinoculation [22] , and 24 hours later the culture medium was replaced with fresh medium . Schistosomula were harvested at 24 and 48 hours after incubation with the VSV-G pseudotyped HIV-1 virions , washed 3 times with 1x PBS , snap froze in dry ice , and stored at -80°C . In some experiments the schistosomula were cultured in the presence of the virions without spinoculation . Spinoculation resulted on 5- to 6-fold increase on reverse-transcription and genome integration efficiency measured by qPCR and qRAP , respectively ( S6 Fig ) . Schistosomula and adult worms were exposed to VSV-G pseudotyped virions in the presence of polybrene . At intervals from 0 to three hours , the presence of VSV-G pseudotyped virions attached to parasites was investigated . In brief , at indicated time points the culture medium was removed , schistosomes were washed 3X with Tween-20 in PBS ( PBS-T ) to remove unattached virions , after which formaldehyde fixation was undertaken for one hour to cross-link virions bound to the parasite surface . Fixed schistosomes were permeabilized in 0 . 2% Triton X-100 in PBS for 15 min and washed with PBS-T for 5 min , 3 times . Non-specific epitopes were blocked by incubating schistosomes overnight in 5% normal horse serum in PBS and thereafter probed with primary antibody , rabbit anti-VSV-G antibody ( Sigma-Aldrich , St . Louis , MO ) diluted 1:500 in PBS for two hours at room temperature . The parasites were washed for 5 minutes 3 times with PBS-T , probed with secondary antibody , Alexa Fluor 488 chicken anti-rabbit antibody ( Life Technologies , Frederick , MD ) diluted 1:500 in PBS , washed with PBS-T and mounted on slides with Fluoromount-G ( Southern Biotech , AL ) . The schistosomes were examined using a Zeiss Axio Observer A . 1 inverted microscope fitted with an AxioCam ICc3 camera ( Zeiss ) and/or Zeiss 710 Cell Observer spinning disk confocal laser scanning microscope . Micrographs were captured with a 10X objective for adult schistosomes , 40X and 63X objectives for schistosomula . Manipulation of digital images was undertaken with the assistance of AxioVision 4 . 6 . 3 software ( Zeiss ) , with manipulations were limited to insertion of scale bars , adjustments of brightness and contrast , cropping and the like . Image enhancement algorithms were applied in linear fashion across the entire image and not to selected aspects . The intensity of the fluorescence from schistosomes exposed to HIV-1 virions was quantified using ImageJ , https://imagej . nih . gov/ij/ . In brief , 20 sections of the same area were selected at random for each micrograph , 10 sections of the background and 10 sections within the parasite , and the signal intensity for each obtained . The mean and standard deviation of signal intensities were determined for each panel , and the ratio of parasite signal intensity to the background signal calculated . Total genomic DNA was isolated from active- or heat-inactivated lentivirus-transduced schistosomula harvested 24 or 48 hours after transduction , using the AquaPure system ( Bio-Rad , Hercules , CA ) , and employed as template for qPCR targeting the negative-strand strong-stop and the positive-strand HIV-1 cDNA [73 , 74] . Briefly , sequences of the primers amplifying the strong-stop DNA were: forward primer M667 ( 5’-GGCTAACTAGGGAACCCACTG-3’ ) , reverse primer AA55 ( 5’-CTGCTAGAGATTTTCCACACTGAC-3’ ) , and Taqman probe Er-LTR ( 5’-FAM-GTCACACAACAGACGGGCACACACTA-TAMRA-3’ ) specific for the R-U5 region of the LTR of HIV-1 . The second set recognizes the positive-strand DNA ( late reverse transcription product ) and consisted of primers: FOR-LATE ( 5’-TGTGTGCCCGTCTGTTGTGT-3’ ) , REV-LATE ( 5’-GAGTCCTGCGTCGAGAGATC-3’ ) , and Taqman probe Lt-LTR-Prb ( 5’-FAM-CAGTGGCGCCCGAACAGGGA-TAMRA-3’ ) specific for the U5-Ψ LTR region . Quantitative PCRs were performed in triplicate , using 96-well plates ( Bio-Rad ) , with a denaturation step at 95°C , 3 min followed by 40 cycles of 30 sec at 95°C and 30 sec at 55°C , using a real-time thermal cycler ( iCycler , Bio-Rad ) fitted with the Bio-Rad iQ5 detector . Reactions were carried out in volumes of 20 μl with 0 . 3 μM primer-probe sets , Perfecta qPCR FastMix , UNG ( Quanta Bioscience , Gaithersburg , MD ) and 100 ng of total DNA isolated from active- or heat-inactivated HIV-1-transduced schistosomula as template . Ten-fold serial dilutions of DNA from 8E5 ( derivative of CEM ) cells , a human T lymphoblastoid line that contains a single copy of HIV-1 LAV provirus per cell , was used as the quantitative standard [75] . Findings are presented as copy numbers of negative-strand ( strong-stop ) or positive-strand HIV-1 cDNAs per ng of schistosome gDNA . Total genomic DNA from schistosomula exposed to active or heat-inactivated virions and harvested 24 or 48 hours after transduction was isolated as above . Based on the Alu-PCR approach to quantify the copy number of integrated HIV-1 provirus in human cells [22] , and on an endogenous retrotransposon-anchored PCR technique ( RAP ) we have previously employed to identify transposons and proviral transgenes integrated in the genome of S . mansoni [15 , 76] , we developed a quantitative anchored PCR-based approach ( qRAP ) , to identify and quantify retrovirus integrations into the schistosome genome [23] . In brief , qRAP includes two consecutive PCRs ( Fig 2C ) ; the first , retrotransposon anchored PCR ( RAP ) , is a multiplex PCR using a specific primer for the gag gene of HIV-1 in tandem with primers specific for endogenous retrotransposons present at high copy number and apparently interspersed throughout the genome of natural populations of S . mansoni [6 , 77] . Second , RAP products are used as template for quantitative PCRs , targeting the LTR sequence of HIV-1 . The RAP was performed using 100 ng template gDNA from populations of active- , heat-inactivated- lentivirus-transduced schistosomes or control untreated parasites , Platinum Taq DNA Polymerase High Fidelity ( Invitrogen ) and primers specific for the retrotransposons SR1 , SR2 , fugitive , Boudicca and SMα in combination with the gag-specific primer in a 50 μl reaction . Two primer mixes were used: mix 1: SR1F ( 200 nM ) , SR1R ( 200 nM ) SR2F ( 200 nM ) , SR2R ( 200 nM ) and gag ( 1 . 2 μM ) ; mix 2: fugitive F ( 200 nM ) , fugitive R ( 200 nM ) Boudicca F ( 200 nM ) , SMα ( 200 nM ) and gag ( 1 . 2 μM ) ( S3 Table ) . RAP cycling conditions were 94°C for 2 min followed by 20 cycles of 94°C for 30 s , 55°C for 30 s and 68°C for 10 min , with a final extension at 68°C for 10 min . RAP products were employed as template in a quantitative PCR targeting gag performed as described [23] . S3 Table provides the sequences of the primers and Taqman probe employed in the RAP and qPCR . Quantitative PCRs were performed in triplicate , using 96-well plates ( Bio-Rad ) , with a denaturation step at 95°C of 3 min followed by 40 cycles of 30 sec at 95°C and 30 sec at 55°C , using a real-time thermal cycler ( iCycler , Bio-Rad ) fitted with the Bio-Rad iQ5 detector . Reactions were carried out in 20 μl volumes with gag primer-probe sets , Perfecta qPCR FastMix , UNG ( Quanta Bioscience , Gaithersburg , MD ) , and as template , 5 μl of the RAP amplicons ( diluted 1 in 10 ) or matched dilutions of non-preamplified samples , i . e . , dilutions of gDNA that were not amplified by RAP . Quantification was undertaken using copy number standards , as above . LTR copy number was estimated by interpolation of the PCR signals from a standard curve [78] . LTR copy numbers from schistosomes exposed to virions are presented as fold-increase of RAP-preamplified copy number compared to the non-preamplified copy number or relative copy number of provirus , i . e . copy number of RAP-preamplified qPCR products divided by copy number of non-preamplified qPCR products [23] . The PCR efficiency for the primers/probe set was estimated to be 100 ± 5% by titration analysis [78] Two reverse transcriptase inhibitors , the nucleoside analogue azidothymidine ( AZT ) and the non-nucleoside analogue nevirapine ( NVP ) , and the integrase inhibitor 118-D-24 , C11H9N3O4 [79] were employed to pre-treated schistosomula 24 hours before exposed to pseudotyped HIV-1 virions . Schistosomula were exposed to 10 μM AZT , 10–50 μM NVP , 100 μM of 118-D-24 or corresponding vehicle controls , spinoculated in the presence of VSV-G pseudotyped-HIV-1 virions ( above ) , for 24 and/or 48 hours after which genomic DNA was isolated from the drug-exposed worms . Reduction in the integrated-provirus copy number was not detected using qRAP following exposure to inhibitors of reverse transcriptase or integrase compared to controls ( S3 Fig ) . These findings suggested that schistosomes , unlike mammalian cells , may not activate the drugs to toxic forms , and/or that schistosome cells pump out the drugs quickly [58] or indeed that the drugs did not enter the schistosomes , so that the HIV-1 enzymes were not inhibited . In overview , however , given both the similarities and dissimilarities between human cells and schistosomes in the ability to support reverse transcription and integration and the dissimilarities in the effects of these three inhibitors , there likely exist differences in the physiology of the schistosome versus human cells in regard to the HIV-1 developmental cycle . The amino acid sequences of human host cellular factors associated with HIV-1 reverse transcription and pre-integration complexes during the upstream events of the retrovirus life cycle were employed as queries in Blastp searches of the public databases http://blast . ncbi . nlm . nih . gov/Blast . cgi including the draft genome version 5 . 0 of S . mansoni , aiming to identify schistosome orthologues/ homologues that might be capable of interact with HIV-1 during the infection , reverse-transcription and provirus integration . Several tentative candidates , including cyclophilin A and importin-α3 , 4 involved in the HIV capsid binding and nuclear translocation of reverse transcription complexes , respectively , were identified with identity percentages ranging from 23% to 64% ( S5 Table ) . The presence of predicted schistosome homologues of human factors associated with HIV-1 reverse transcription and pre-integration complexes may explain why VSV-G-HIV-1 virions infect schistosome cells and complete the reverse-transcription and integration of the provirus in the genome of the transduced parasite . However , further studies , including the identification , cloning and functional characterization of these schistosome factors , are needed . Total genomic DNA ( 1 , 700 ng ) isolated from lentivirus-transfected schistosomula as described above , was used directly for preparation of amplification-free 200–400 bp paired end Illumina libraries using a protocol based on a previously described method [80] but using Agencourt AMPure XP beads for sample clean up and size selection . DNA was precipitated onto beads after each enzymatic stage with an equal volume of 20% polyethylene glycol 6000 and 2 . 5 M NaCl . Beads were not separated from the sample throughout the process until after the adapter ligation stage , after which new beads were used for size selection . This library was sequenced directly as a Whole Genome Sequencing Library ( WGS ) ( S1 Table ) without being subjected to TraDIS , as described below ( Illumina sequencing ) . Two hundred ng of genomic DNA prepared from schistosomula exposed to pseudotyped HIV-1-virions was used to prepare an Illumina library , as described above , but using double stranded Splinkerette V1 . 2 adapters formed by annealing the oligonucleotide ‘Splinkerette V1 . 2 top’ G*TTCCCATGGTACTACTCATATAATACGACTCACTATAGGTGACAGCGAGCGC*T ( the asterisk indicates phosphorothioate; phosphorothioate linkages resist nuclease degradation [81] ) and the oligonucleotide ‘Splinkerette V1 . 2 bottom’ , G*CGCTCGCTGTCACCTATAGTGAGTCGTATTATAATTTTTTTTTCAAAAAA*A . Adapter-ligated fragments , 423 ng , were employed for the amplification of the 3’- or 5’-termini of the sites of integration of HIV-1 into the schistosome genome . Nested oligos , detailed in S2 Table , were used to amplify the 3’- and 5’-ends of integrated HIV-1 proviruses using the Kapa Hifi Hotstart Ready mix . The thermal cycles of the first PCR , ‘PCR1’ , comprised denaturation at 95°C , followed by 18 cycles of 98°C , 20 sec , 58°C , 20 sec and 72°C , sec , and concluded with 72°C for 5 minutes . The second PCR , ‘PCR2’ , commenced using 24 μl of ‘PCR1’ ( after thermocycling ) , followed by the conditions as for ‘PCR1’ with 12 ( rather than 18 ) cycles of 98°C , 20 sec . S7 Fig presents a schematic of the construction plan for the Transposon Directed Insertion-site Sequencing ( TraDIS ) libraries from schistosomula exposed to pseudotyped HIV-1 virions . Libraries were denatured using 100 mM NaOH and diluted to 6 pM in a hybridization buffer to allow the template strands to hybridize to adapters immobilized on the surface of the flow cell . Cluster amplification was performed in an Illumina cBOT using the V3 cluster generation kit . Thereafter a SYBRGreen QC was performed to measure cluster density and determine whether to pass or fail the flow cell for sequencing , followed by linearization , blocking and hybridization of the R1 sequencing primer . The hybridized flow cells were loaded onto a HiSeq 2000 for 100 cycles of sequencing-by-synthesis using the V3 SBS sequencing kit . Subsequently , the linearization , blocking and hybridization step was repeated in situ to regenerate clusters , release the second strand for sequencing and to hybridize the R2 sequencing primer followed by another 100 cycles of sequencing to produce paired end reads . These steps were performed using proprietary reagents according to the manufacturer's recommendations , https://icom . illumina . com/ . The RTA1 . 8 analysis pipelines were employed to analyze data obtained from the Illumina HiSeq instrument ( S1 Table ) . Generating TraDIS libraries followed the above method , modified as follows . First , the reaction mix was spiked with 30% phiX to increase nucleotide diversity ( PhiX Control v3 , catalogue no . FC-110-3001 , Illumina , San Diego , CA ) . Second , the forward primer was specific to the construct ( S2 Table ) and the reverse primer was Spl_rev_seq ( S2 Table ) . Third , 150 bp paired end reads were produced on a MiSeq instrument . Illumina reads produced from the whole genome sequences were aligned to the reference genome of S . mansoni [6] and to plasmid pNL4-3 , which includes the entire sequence of HIV-1 ( GenBank AF324493 . 1 ) in parallel using SMALT , http://www . sanger . ac . uk/resources/software/smalt/ . Reads that aligned to both references were checked manually for false positives , and the integration positions investigated ( S4 Table ) . Representative false positive integrations , and events that lacked of strong evidence of integration are indicated in S4 Table , bottom table . For modified TraDIS sequence analysis , as the expected DNA fragment was around ~300 bp , FLASH [82] was first run to locate overlap between read pairs . Read pairs were merged into a single read for subsequent analysis if there was ≥10 bp overlap . After a round of quality control to remove PCR and splinkerette adaptors at sequence ends , four categories of reads remained for closer investigation: 5’-merged , 5’-paired reads , 3’-merged and 3’-paired reads . These reads were aligned to the reference genomes of S . mansoni and to HIV-1 , as above . Integrations were considered to be authentic if: i ) the Illumina sequence began with the lentivirus ( for 3’-library CTCAGACCCTTTTAGTCAGTGTGGAAAATCTCTAGCA corresponding the 3’- 37 bp of LTR; for 5’-library TTGTCTTTTTTGGGACCAAATTAGCCCTTCCA corresponding the 3’-32 bp of LTR ) ; ii ) the start of the sequence was not immediately followed by splinkerette adaptor sequences; iii ) the remainder of the sequence uniquely mapped to ≥ 30 bp the S . mansoni reference; and iv ) the mapping quality was Q30 , corresponding an alignment error rate of 0 . 1% . PCR duplicate mappings were deleted . Multiple matches within 250 bp of each other were classified a single , unique match in the genome assembly . We categorized the integration clusters as exon , intron , and intergenic regions ( Fig 5A ) based on annotation of the genome of S . mansoni [6] in GeneDB , http://www . genedb . org/Homepage/Smansoni . The confidence interval for the number of integrations in a cell was calculated for the binomial proportion of successful events p = 60 / 207 , 576 , 406 . Here , 207 , 576 , 406 is the number of properly placed paired reads from the sequencing run , which is equivalent to number of sequenced genomic 100-base segments from which an integration ( if present ) would be detectable . The frequency was scaled using the number of segments per diploid genome and , using the R library binom and the “exact” method; confidence intervals were calculated for a confidence limit of 0 . 95 . Sequence data generated here are available at the European Nucleotide Archive ( ENA ) accession number ERP002117 , http://www . ebi . ac . uk/ena/data/view/ERP002117 . | Schistosomiasis is a major neglected tropical disease ( NTD ) , which afflicts > 200 million people in developing countries . The genome sequence of the schistosome parasite has been decoded; it includes > 10 , 000 genes . New approaches to control this NTD are sought and genomic information may provide targets for new treatments . Methods to determine the role and importance of specific genes would facilitate these tasks . The retrovirus HIV-1 , the causative agent of HIV/AIDS , has been extensively studied and modified for use in biomedical research . Using a lab-modified form of HIV-1 , we manipulated the genome of Schistosoma mansoni , one of the major species of schistosomes . Lab-modified HIV-1 infected schistosomes and inserted in the chromosomes of the parasite . These chromosomal insertions were mapped using next generation sequencing and were distributed throughout the chromosomes including the sex chromosomes . The findings were notable since they revealed that HIV-1 was active within cells of S . mansoni , and they provide the first demonstration that HIV-1 can integrate into the genome of an invertebrate . They pave a route forward for investigating new therapies for schistosomiasis . | [
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| 2016 | HIV-1 Integrates Widely throughout the Genome of the Human Blood Fluke Schistosoma mansoni |
The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate . Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics . We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands , just as it has been observed for small-molecule-binding sites in the past . Using extensive computational docking experiments on a test set of 241 protein complexes , we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor . This observation appears to be robust to variations in docking programs , types of non-cognate protein probes , sizes of binding patches , relative sizes of binding patches and full-length proteins , and the exploration of obligate and non-obligate complexes . The accuracy of the docking scoring function appears to play a role in defining the correct site . The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods .
Specific protein-protein interactions are essential for maintaining a robust phenotype . A deeper understanding of these interactions would allow the identification of cognate ligands[1] and drivers of specificity , opening a pathway to manipulating the corresponding interaction interfaces in drug design applications[2] . While it has been estimated that a protein on average interacts with 3–10 other proteins[3] , the Protein Data Bank[4] ( PDB ) contains a disproportionally small fraction of known protein complexes . For most of the PDB entries neither the ligand protein nor the protein binding interfaces are known . In response to this important problem , a number of methods have been developed to predict protein binding interfaces using structural information , which may be available in the form of known experimental or computational three dimensional models[5] . The methods to predict protein interfaces can be grouped into two main approaches: ( 1 ) homology-based and ( 2 ) ab initio . Homology-based predictions of interfaces rely on the knowledge of known protein complexes to infer the likely binding sites in similar proteins . These methods can be very powerful[6 , 7] , but their applicability is limited by the amount of known interfaces . Within the category of “ab initio” protein interface predictions a number of studies have attempted to identify distinctive features of interfaces[8–14] often employing various machine learning approaches . These features include residue composition[15] , residue conservation[16–18] , hydrophobicity[19 , 20] , planarity[15] , predicted secondary structural features[14 , 21] , electrostatics[22] , accessible surface area , among others . Some studies found that different subtypes of protein interfaces ( e . g . transient interfaces , interfaces between homo- and heteromers , etc . ) have distinct sequence features , which can be exploited to predict some of the interface residues from sequence[14 , 23] . For example , these features suggest that interfaces for obligate complexes are somewhat more hydrophobic and larger than other interfaces[15 , 24] . Similarly , binding site hot-spots have been predicted using residue composition , conservation analysis , or other structural features such as desolvation effects[13] . However , a generic conclusion after many studies and using larger and more diverse test sets is that protein interfaces do not have a specific composition or other universal features they share[18 , 25 , 26] . This is arguably the expected conceptual conclusion as it is difficult to conceive a universal external evolutionary pressure that would unify interfaces[27] . Current success rates for protein binding interface predictions on a residue level are just barely statistically significant when compared to random predictions[28] . Relevant to the current study are the works that discuss the possible generality of binding site locations , both for small molecule and protein ligands . In the case of the former , it has been observed as early as in the 1980s that small organic molecules , both substrates and non-substrates tend to bind to similar , energetically favored “sticky” sites irrespective of their relevance to the target . These observations were made by experimental studies that soaked target proteins in organic solvents and examined the crystal[29] or NMR[30] structure for invariable small molecules sticking to energetically favorable sites . Computational methods such as the GRID[31] , or the Multicopy Simultaneous Search ( MCSS ) [32] approach , as well as some of the most competitive methods currently available[33] , are also based broadly on this observation . It was observed in the late 1990s that protein superfolds ( frequently occurring proteins that share their overall structural topology but have a range of distinct functions ) have “supersites” . In other words , despite substantial sequence divergence and the evolved distinct functions , the 10–15 superfolds that dominate about half of the structural fold population of the genomes[34] usually have very similar binding site locations[35] . This observation was subsequently revisited and expanded to remote homologs with insignificant sequence similarity to the cognate ligands for a range of different fold topologies[36 , 37] . Docking programs have been used successfully to predict partner-specific interface residues such as the Atomic Contact Frequency ( ACF ) [38] or the Residue Contact Frequency ( RCF ) method[39] and others[40] . These approaches require the prior knowledge of the cognate ligand from other , indirect sources , such as high throughput screening methods . In the current work , we explored the generality of the phenomenon of binding supersites . We report the surprising observation that protein-protein interaction sites serve as generic protein binding sites . Protein ligands , irrespective of their relevance to the receptor protein , tend to bind to the cognate protein interface . This behavior does not depend on the docking program used , the range and type of protein ligand probes employed , or more technical conditions such as the size of the binding sites considered . Based on this new observation we introduce a docking-based , ab initio method for binding site prediction that does not require prior knowledge of the cognate ligand . Binding interfaces are determined by the frequency of a receptor residue interacting with a range of unrelated protein ligands in extensive docking simulations . A conceptual insight brought to light by our work is that protein shapes evolved to allow a surprisingly small number of suitable surface patches for interactions that are apparently sampled by a wide range of possible ligands . Alternatively , it may be that a variety of unique residue patterns that evolved for recognizing a specific cognate protein ligand also present an energetically relatively favorable site for non-cognate proteins .
We explored the hypothesis of whether protein-protein interaction sites also serve as generic binding sites for a range of non-cognate ligands , and as such , behave similarly to protein-small-molecule-binding sites[30 , 32 , 41 , 42] . This would qualitatively generalize the observations made about supersites in superfolds[35] . We explored the preferred binding sites for a set of unrelated ligands on a large set of receptor proteins . Surprisingly , we found that unrelated ligands have a strong tendency to dock to the same general area of a receptor as its cognate ligand . We illustrate this in Fig 1 , where three , topologically different ligands ( all beta– 2jjs . C; mixed alpha and beta– 3h33 . A; and a small protein fold with few secondary structures– 2v86 . A ) , sharing no detectable structural or sequential similarity to the cognate ligand , all have a strong tendency to dock to the cognate protein binding site on the receptor protein ( 1cnz—3-Isopropylmalate dehydrogenase from Salmonella typhirium ) . We explored the overall phenomenon by docking 13 different ligand probes , six immunoglobulin folds and seven randomly picked small protein folds on a combined target dataset of 241[43 , 44] proteins with structurally defined protein binding sites . We ranked the residues in the receptor protein based on the RIF score ( see Methods ) . The statistical significance of the agreement of the top ranked residues and the cognate binding site was assessed by using hypergeometric distribution to model the probability of correctly selecting an interface residue by chance . Out of the 241 target proteins , in 157 ±2 cases ( or 65 . 2 ± 0 . 9% ) the binding site was docked by a variety of unrelated ligands in a statistically significant manner . We evaluated the performance by randomly selecting 2000 models from the total set of 26000 docked models ( 13 X 2000 per ligand probe ) and calculated the average performance and the standard deviation . We further broke down results by complex and database type . Performance on the Docking Benchmark[44] and NOX[43] databases were 70 . 3% ± 1 . 1 and 61 . 1 ± 1 . 6 , respectively . Furthermore , the NOX database contained a relatively well-balanced set of obligate ( 73 ) and non-obligate complexes ( 60 ) , and the results on these subsets were 68 . 7 ±1 . 9 and 51 . 8 ±2 . 2 , respectively . We also evaluated the results using sensitivity/specificity ROC curves ( Fig 2 . ) and obtained an Area Under the Curve value of AUC = 0 . 79 for the combined set , while 0 . 83 and 0 . 77 for the Docking Benchmark and NOX databases , respectively . All these suggest that the observation about protein binding supersites is a generic feature of proteins , with some fluctuation of specific success rates depending on the choice of test database . We also explored how well the cognate ligands bind to and define the annotated functional site of the receptor proteins in comparison to unrelated ligands . ( Fig 3 ) Interestingly , while the cognate ligands have a tendency to better recognize the interface , this tendency is statistically not significantly different from the results obtained for unrelated ligands . We further subdivided our results as a function of different ligand probes and ligand sizes , while also exploring two alternative docking programs , ZDOCK and GRAMM , to examine the role that variations in the scoring functions play in detecting supersites . We found little dependence on the type of probe used with either docking program ( Fig 3 ) . The differences in results obtained using individual probes are mostly statistically insignificant . The success rate for the NOX dataset depending on the ligand probes ranged between 54 . 1 to 65 . 4% with an average success rate of 60 . 1% ± 3 . 9% using ZDOCK , while the success rate ranged from 36 . 8% to 51 . 9% with an average of 44 . 7% ± 4 . 4% using GRAMM . ZDOCK appears to yield slightly better results with the immunoglobulin superfamily probes , while GRAMM works better with the non-immunoglobulin set of ligand probes . If we use a consensus prediction from all 13 ligand probes , the performances in the case of ZDOCK and GRAMM are 60 . 1% and 44 . 7% , respectively . The better performance of ZDOCK suggests that the energy function may play a role in defining the “stickiness” of protein binding supersites . ZDOCK[45] uses a statistical pair potential with a limited set of amino acid residue types , while the GRAMM[46] energy function is arguably more general using a step function that includes a classic repulsion term . We compared the actual interface residues predicted by the two docking programs , ZDOCK and GRAMM . Although the entire set of interface residues predicted by the two docking programs were not identical , for 40% and 79% of the 241 proteins in the data set , the two docking programs predicted more than 10 or more than 5 interface residues in common out of 15 , respectively . To put these numbers in a statistical context: the expected number of residues that are common out of 15 residues between any two random draws , —in protein sizes 100 , 150 , 200 , 250 and 300 are: 2 . 27 , 1 . 58 , 1 . 16 , 0 . 82 , and 0 . 81 residues , respectively . Consequently , the two programs have a strong tendency to locate binding sites similarly . The corresponding p-values of observed common residues between ZDOCK and GRAMM are all significant at any protein size . We explored an additional aspect of the potential impact of the employed energy function . ZDOCK ranks the generated docked poses by their energy score , so we explored if there is a difference in performance between the top-scoring and bottom-scoring docked poses . Indeed , this phenomenon can be observed once we plot the performance of the first and last 200 docked poses ( Fig 4 ) . There is a weak but persistent tendency that energetically higher ranked poses are more useful in identifying binding sites ( Fig 4 ) . These small differences disappear as the number of sampled conformations approach 200 and beyond . The differences between the accuracy of ZDOCK and GRAMM and between top-ranked and bottom-ranked docked poses of ZDOCK suggest that a more accurate energy function will identify binding sites more accurately because the relative affinity of non-cognate ligands will be better captured . When considering the possible reasons for the existence of protein binding supersites , besides the general energetic preferences of certain “sticky” areas of the protein , one could also consider receptor-shape-driven causes . For instance , one could speculate that in the case of small proteins it might be a geometrical artifact that only a confined area is suitable to accept interactions . However , the distribution of the size of receptors in the current work has a large range ( <100 residues to >700 residues ) for which the ability to detect supersites appears to be uniformly high ( Fig 5 ) . We further dissected the possible differences in performance between the two docking approaches . First , we compared the performance of these techniques using 2000 models generated by the methods , irrespective of the size of the identified binding interface , with the performance when using only a subset of the docked complexes that have the most common interface sizes; in the current work , formed by 9 residues ( Table 1 ) . Though the GRAMM docking method appears to sample a larger fraction of all the residues in the protein ( 85 . 9% vs 72 . 1% ) as well as the interface residues ( 99 . 6% vs 97 . 5% ) , ZDOCK identifies a larger number of true interface residues ranking in the top 15 positions ( 60 . 1% +/- 3 . 9 for ZDOCK vs . 44 . 7% +/- 4 . 4 for GRAMM ) . In case of considering 9–residue patches only , as expected , the total number of residues sampled ( 40 . 7% for ZDOCK and 54 . 6% for GRAMM ) as well as the interface residues sampled ( 39 . 6% for ZDOCK and 76 . 8% ) is smaller , which apparently has a strong influence on the method performance . In particular , the GRAMM docking method performs significantly worse when a subset of docked complexes , consisting only 9-residues is used in the analysis with a % significance of 21 . 6 ±3 . 4 compared to 48 . 4 ±4 . 7 using ZDOCK . We used 13 different ligand probes and by default 2000 docked conformations to locate the binding site of a receptor protein . This amounts to 13x2000 = 26 , 000 docked poses . We gradually reduced the number of docked poses and found that with 13 ligands as few as 200 docked conformations are sufficient to establish the same results as before , with 2000 poses ( Fig 6 ) . Another aspect of the binding site exploration is the number and variety of probes employed . Upon plotting the performance of all the 13 probes independently , it is clear that these perform in a relatively tight range and that the observed small differences most likely can be acknowledged to the particular set of test proteins used . As an empirical test , the accuracy using ZDOCK changes from 65 . 4 when averaged over a subset of 6 randomly selected different probes to 63 . 2 when averaged over all 13 different probes . We found that randomly selecting 2–3 probes already provides robustly the same performance results as running all 13 probes ( Fig 6 ) . It has been shown that docking based methods are less successful to predict the correct binding pose and binding site when targeting uncomplexed receptors , especially the ones that undergo substantial conformational change upon binding to their cognate ligand . In our case we do not restrict our analysis to the cognate ligand and to a few ( or one ) docked poses with the lowest energetics , as such an approach is likely to be insensitive to small conformational changes . Non-cognate ligands bind with much lower affinity , and we are capturing the relative preference of any ligand to dock to the cognate binding site . We manually identified 95 target proteins in our combined set for which we could locate a PDB structure in an uncomplexed form . The F-scores of "apo" and "holo" forms for this subset of 95 target proteins is include in the Supplementary Information ( S1 Fig ) . On this subset , the success rate of capturing binding sites has an average F-score of 0 . 27 and 0 . 26 for the complexed and uncomplexed targets , respectively , a statistically insignificant difference . An important aspect of this study is to explore if the observed phenomenon is a function of fold types , or something more general . The distribution of protein folds is very uneven[34] , with 12 superfolds populating about one third of the human genome . It has been discussed in the literature that these superfolds have a tendency to preserve their ancient/general binding interface despite their divergence into a range of distinct functions[35] . We analyzed our dataset to examine whether the well-performing interface detections using unrelated ligand probes work disproportionally well for these superfolds . Of the 241 protein chains , 91 belong to one of the top 12 CATH[47] superfamilies , roughly recapitulating the proportion of superfolds in biological systems . The success rates for the 91 superfamily and 131 non-superfamily classified cases are 71 . 0% and 62 . 2% using ZDOCK , respectively , and then 30 . 0 and 40 . 8 using GRAMM ( Table 2 ) . These small and non-systematic differences suggest that there is no preference for superfamily proteins , and that supersites are characteristic to all protein folds . Further breaking down of the results in a benchmark database dependent fashion shows that the general performance on the Docking Benchmark dataset is significantly better with ZDOCK than with the GRAMM docking approach , and for the NOX dataset these differences are substantially reduced ( Table 3 ) . However , no systematic preference emerged of supersites in superfolds , in fact , non-superfold subsets outperform in two out of four subsets ( ZDOCK with NOX database , and GRAMM with Docking Benchmark ) . In order to understand some of the differences in performances , we examined the specific superfamily classifications of the proteins represented in the two datasets ( Table 4 ) . In the Docking Benchmark , we found a highly skewed distribution of superfolds , where 66% of the superfamily classification is “immunoglobulin-like” while 14% are classified as the Rossman fold . Meanwhile , the NOX dataset is slightly better balanced , with the Rossman , TIM-barrel , and Immunoglobulin-like folds comprising 48 . 8% , 19 . 5% , and 17% of the dataset , respectively . It is possible that ZDOCK is better tuned to dock immunoglobulin like folds and their over-representation has shifted the results higher in the Docking Benchmark dataset . Slightly different interface definitions can drastically change the number of residues involved in the interface . A recent study suggests that even in the case of nearly identical definitions , the disagreement between different definitions can be substantial , suggesting that a ~0 . 8 F-score as a practical upper limit for prediction methods[48] . In addition , residues not involved in direct contact with a ligand can have a profound effect on binding , as illustrated by a number of studies[2] . Meanwhile , random predictions are distributed with a peak around 0 . 1 F-score[28] but many individual random predictions reach up as high as 0 . 2 F-score . Current protein interface prediction methods that provide results on a residue level and with an F-score accuracy , report statistically significant but generally speaking fairly low accuracies[28 , 49 , 50] . For instance , Table 3 in Taherzadeh et al . [49] published this year , reports seven methods , with F-score performances in the range of 0 . 18–0 . 31 . These methods typically use different benchmark datasets therefore a substantial part of the variation among the performance probably can be acknowledged to that fact . To put our results in this general context we converted our performance into F-score evaluation and obtained an average F-score of 0 . 35 using ZDOCK and 0 . 22 using GRAMM , which compares well with the recent values in the literature using other methods to identify protein-protein interfaces . The good performance is especially promising as our approach is based on the direct evaluation of a single feature while all other methods are using a combination of a number of features in machine learning setting . In this work , we have shown that protein binding supersites exist in proteins , i . e . the protein binding interface provides an energetically-preferred binding site for many alternative , non-cognate proteins as well . There were previous , anecdotal studies that noted that even non-cognate ligand have tendency to accumulate around the cognate site , as it was shown in case of chymotrypsin when docked with a non-native binder , lysosyme[40] . Other recent studies also pointed in the direction of our current observation[51 , 52] . Employing an energy landscape based analysis it was observed that binding sites can be identified without the prior knowledge of the cognate ligand . In that study , in a strict filtering protocol , the few lowest energy binders were identified for subsequent mapping of their preferred binding poses . Though this approach delivered an effective prediction method , it left open the following question—are these low energy binding poses related to the cognate binding partner , and thereby representing similar binding affinities , and likely , a similar binding interface ? Also , the observations were not generalized , the successful cases were not analyzed in terms of protein topology , to illustrate if the observations go beyond the original observations made about superfolds , where binding sites are preserved despite a long evolutionary history of sequence divergence . We observe that these sites can be effectively detected by employing an extensive docking sampling with a range of unrelated protein ligand probes . In another study the Hex docking approach was used in cross docking experiment and suggested the existence of “favored” sites[53] . The authors have noted a tendency of these sites to be closer to the center of mass of the protein and explored residue type preferences of binding patches . A wide variety of probes were used with different topologies but the phenomenon was not generalized in terms of distribution on folds , to see if these observations are generic over all fold types or work mostly for superfolds as it was established in 1998[54] . The accuracy of this approach to detect protein binding sites is comparable to other state-of-the-art techniques . However , it uses a mostly orthogonal input in comparison to many existing technologies , and as such , a practical outcome of this study is both a new , standalone binding site prediction algorithm and an opportunity to improve existing binding site predictions by incorporating this information with other existing techniques that use residue preferences , conservation , geometrical definitions , among others . On the conceptual level , our observations argue that possibly a combination of geometrical restraints ( shape of the local molecular surface ) and energetically preferred residue patterns are responsible for establishing these supersites . Given past experience and our current results , we believe that the number of combinations of how an energetically “sticky” patch can be established varies substantially . However , the fact that docking algorithms , which combine shape complementarity with a scoring function that assesses interactions , are able to capture many of these sites suggests a path forward in the characterization of protein interfaces . Docking methods were benchmarked in a number of studies that showed a lack of strong correlation between calculated and experimental binding affinities[55] . The current study implicitly confirms this observation when we show that the success of identifying binding interfaces does not depend in a statistically significant manner on whether one uses cognate or non-cognate ligands , albeit a small trend favoring cognate ligands can be detected . This suggests that more generic energetic features are captured .
Two different datasets were employed in this study . A set of 108 protein chains from the Docking Benchmark[44] and another set of 133 protein chains from the NOX database[43] , 73 and 60 of which are obligate and non-obligate complexes , respectively . The protein binding interfaces were identified from the three dimensional structure of the complexes using the CSU[56] program . A residue was considered to be at the interface if any of its atoms is within 3 . 5 Å of any atom of the interacting protein in the complex and establishes a legitimate contact type according to the CSU classification . In our approach we use a total of 13 ligand probes , none of which are known partners or share any detectable sequence similarity to known ligands for the query proteins in our data set . Six of these ligand probes were immunoglobulin folds ( PDB[57] codes: 1i85 . D , 2jjs . C , 2wbw . C , 1t0p . B , 2ptt . B , 3udw . C ) , as we assumed this fold evolved to be particularly suitable and generic to explore protein surfaces . Seven others were selected randomly . PDB entries were split into chains and clustered at 25% sequence identity level . All protein solved by NMR and not within the range of 70–250 residues were removed . From the remaining set we selected 7 proteins ( between 70–120 residues ) with different topologies compared to one another ( 1whz . A , 2eaq . A , 2v86 . A , 2w8x . A , 2y2y . A , 3h33 . A , 5cuk . A ) . Two different docking programs , ZDOCK[45] and GRAMM[58] were used to generate a maximum of 2000 docked complexes for each of the protein chains in our dataset with each of the 13 ligand probes . The 2000 complex structures for each receptor-ligand probe pair were analyzed using CSU to identify the residues at the interface , Rik , where i is the residue position number and k is the kth docked complex structure . If a residue is at the interface , then I ( Rik ) = 1; otherwise , I ( Rik ) = 0 . A Residue Interface Frequency ( RIF ) , Ni was determined for each residue at position i in the receptor protein by summing over all the 2000 docked structures . The residues were then ranked based on the Ni values , and the top 15 ranking residues were considered most likely to be at the interface . The residues were also ranked similarly by using a subset of the 2000 complex structures all of which contained exactly nine residues at the interface ( the most frequent interface patch size during the simulations ) . This subset generally consisted of between 150 and 300 complex structures . The actual number of interface residues varies with each receptor protein . We considered the number of true positive predictions of interface residues in the top 15 rankings assigned by our method . The performance of the current RIF method was evaluated using a statistical significance test by comparing it with a random prediction . The probability of randomly selecting x interface residues in the top K predicted residues ( K = 15 in our case ) for a query protein chain with N is the total number of residues sampled during the extensive docking simulation and M actual interface residues is given by the probability mass function of the hypergeometric distribution: P ( X=x ) = ( Mx ) ( N−MK−x ) / ( NK ) An interface prediction is considered significant if P ( X = x ) < 0 . 05 . The performance is expressed as %significance=NumberwithP ( X=x ) <0 . 05TotalnumberinthedatasetX100 Theoretically , the hypergeometric distribution can be exposed to some instability when very small numbers of discrete residues are assessed for significance; therefore , performance was also evaluated empirically , by randomly sampling 15 residues from the surface exposed residues sampled during the extensive docking simulation of the query protein 200 times and finding the average number of interface residues , μ , and the standard deviation , σ . A Z-score was then calculated , Z = ( N–μ ) / σ , where N is the actual number of interface residues in the top 15 using the RIF method . The prediction was considered significant if Z > 1 . 97 . The % significance evaluated using the hypergeometric distribution and the random sampling method yielded identical results . Receiver operating characteristic curves ROC were calculated by plotting the true positive rate ( sensitivity ) against the false positive rate ( 1- specificity ) . Corresponding Area Under the Curve values were obtained . Functional residues are a small fraction of the total residues , so true negatives far outnumber true positives . Therefore methods that heavily reward true negatives , such as the “specificity” and the “accuracy” , are less appropriate than ones that do not , such as the “F-Score”[59] and appropriately F-scores were used in a number of previous studies . Therefore success of a functional residue prediction was also assessed by the F-score , the harmonic mean of precision and recall ( 2*precision*recall / ( precision + recall ) ) , where precision is the ratio of true positives to the sum of true and false positives and recall is the ratio of true positives to the sum of true positives and false negatives . | Protein–protein interactions are key to understand the molecular level mechanisms of regulation in the cell . However , there is still a limited understanding of what distinguishes a protein-protein binding site from the rest of the surface . This lack of knowledge is manifested in the relatively low accuracy of computational methods that try to predict protein interfaces . In this work we report a new conceptual insight about protein interfaces . Our results suggest that protein interfaces serve as generic binding sites to any ligand . This also means that in the absence of the known binding partner it is still possible to define protein interfaces by extensive docking studies of randomly selected , unrelated ligands , as they have a strong tendency to bind to the cognate binding site . This insight was leveraged in a new binding interface prediction algorithm that alone outperforms state of the art approaches that often combine a variety of features . | [
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]
| 2019 | Protein—protein binding supersites |
Hepatitis C virus ( HCV ) is a positive-strand RNA virus that frequently causes persistent infections and is uniquely associated with the development of hepatocellular carcinoma . While the mechanism ( s ) by which the virus promotes cancer are poorly defined , previous studies indicate that the HCV RNA-dependent RNA polymerase , nonstructural protein 5B ( NS5B ) , forms a complex with the retinoblastoma tumor suppressor protein ( pRb ) , targeting it for degradation , activating E2F-responsive promoters , and stimulating cellular proliferation . Here , we describe the mechanism underlying pRb regulation by HCV and its relevance to HCV infection . We show that the abundance of pRb is strongly downregulated , and its normal nuclear localization altered to include a major cytoplasmic component , following infection of cultured hepatoma cells with either genotype 1a or 2a HCV . We further demonstrate that this is due to NS5B-dependent ubiquitination of pRb and its subsequent degradation via the proteasome . The NS5B-dependent ubiquitination of pRb requires the ubiquitin ligase activity of E6-associated protein ( E6AP ) , as pRb abundance was restored by siRNA knockdown of E6AP or overexpression of a dominant-negative E6AP mutant in cells containing HCV RNA replicons . E6AP also forms a complex with pRb in an NS5B-dependent manner . These findings suggest a novel mechanism for the regulation of pRb in which the HCV NS5B protein traps pRb in the cytoplasm , and subsequently recruits E6AP to this complex in a process that leads to the ubiquitination of pRb . The disruption of pRb/E2F regulatory pathways in cells infected with HCV is likely to promote hepatocellular proliferation and chromosomal instability , factors important for the development of liver cancer .
Among viruses that infect the human liver , hepatitis C virus ( HCV ) is a leading cause of morbidity and mortality worldwide [1] . Chronic infection with HCV is a major risk factor for the development of cirrhosis as well as hepatocellular carcinoma ( HCC ) [2 , 3] . The incidence of this cancer has increased dramatically in recent years in Japan and the United States , reflecting prior increases in the prevalence of HCV infection , and in Japan HCV has replaced hepatitis B virus as the leading infectious cause of liver cancer . The strong association between HCC and HCV infection is particularly notable in that HCV is a positive-strand RNA virus , classified within the genus Hepacivirus of the family Flaviviridae [4] . Its 9 . 6-kb genome replicates in association with membranes within the cytoplasm of infected cells , and encodes a single polyprotein that is processed by both cellular and viral proteases into ten individual structural and nonstructural viral proteins . Although inflammation associated with chronic hepatitis C is likely to contribute to the development of HCC , there is strong evidence that one or more of the proteins expressed by the virus contribute directly to carcinogenesis . The HCV core protein , a component of the putative viral nucleocapsid , has been shown to modulate the hepatocyte cell cycle [5 , 6] . Other studies suggest that expression of the nonstructural ( NS ) proteins , NS3 ( a serine proteinase/helicase ) , NS5A ( a replicase-associated phosphoprotein of uncertain function ) , or NS5B ( the viral RNA-dependent RNA polymerase ) may also affect control of cellular proliferation [7–10] . Moreover , transgenic mice expressing a high abundance of the core protein develop steatosis and HCC [11] . Liver cancer also developed in transgenic mice expressing a much lower abundance of the entire viral polyprotein , but not in a companion transgenic lineage expressing a higher abundance of the structural proteins ( core , E1 , E2 , and p7 ) only [12] . None of these transgenic mouse lineages had demonstrable hepatic inflammation in advance of the development of HCC . Together , these data suggest a direct role for both structural and nonstructural HCV proteins in oncogenesis . At least four different pathways that regulate either cell proliferation or cell death , the retinoblastoma ( pRb ) /E2F , p53 , transforming growth factor-β ( TGF-β ) , and β-catenin pathways , are commonly altered in HCCs [2] . Among them , pRb plays a major role in controlling the G1- to S-phase transition and mitotic checkpoints through a repressive effect on E2F transcription factors [13] . pRb functions as a tumor suppressor , and the gene which encodes it ( RB ) is frequently mutated in various types of tumors , including retinoblastomas , small-cell lung carcinomas , and osteosarcomas [14] . In previously published studies , we demonstrated that pRb protein abundance is negatively regulated in cells supporting the replication of subgenomic and genome-length HCV RNA replicons [8] . Similar to the DNA virus oncoproteins E1A of adenovirus and E7 of human papillomavirus ( HPV ) , we found that the RNA-dependent RNA polymerase of HCV , NS5B , forms a complex with pRb in these cells , targeting pRb for degradation and resulting in a reduction in its abundance . This leads to the activation of E2F-responsive promoters in cells containing HCV RNA replicons , and promotes progression of the cell cycle from G1- to S-phase in cells expressing NS5B [8] . While potentially important with respect to the development of HCC in people with chronic hepatitis C , the molecular mechanisms underlying these observations have not been characterized . Here , we show that pRb is downregulated not only in cells bearing HCV RNA replicons , but also in human hepatoma cells infected in vitro with different genotypes of HCV . We demonstrate that the downregulation of pRb occurs via the ubiquitin-proteasome system , and that it is dependent upon the activity of a known E3 ubiquitin ligase , E6-associated protein ( E6AP ) . E6AP forms a complex with NS5B and pRb , and pRb is ubiquitinated in an NS5B-dependent manner . Our findings reveal a novel mechanism that regulates pRb abundance in HCV-infected hepatocytes , and offer an enhanced understanding of the events leading to the development of HCC in chronically infected patients .
We demonstrated previously that the abundance of pRb is downregulated post-transcriptionally in cells supporting replication of both subgenomic and genome-length HCV RNA replicons derived from a genotype 1b strain of HCV , HCV-N [8] . However , these earlier studies did not determine whether pRb is also downregulated during the course of HCV infection , because genome-length HCV-N RNA is not capable of producing virus that is infectious in cultured cells [15] . To address this question , we used a genotype 2a virus strain , JFH1 , that is capable of undergoing the complete viral life cycle in Huh-7 cells in vitro [16–18] . pRb abundance was reduced significantly within 48–72 h of infection with JFH1 virus ( Figure 1A and 1B ) , with quantitative analysis of immunoblots indicating that the pRb abundance 120 h after infection was approximately 20%–30% that of uninfected cells ( Figures 1C and S1 ) . The activity of pRb is normally regulated through its phosphorylation by cyclin-dependent kinases , and we also observed an equivalent reduction in the abundance of phospho-pRb in JFH1-infected cells using an antibody specific for pRb phosphorylated at residues 807/811 ( Figure 1D and 1E ) . Coincident with the reduction in total pRb abundance , confocal microscopy demonstrated a striking cytoplasmic relocalization of pRb in JFH1-infected cells ( Figure 2A ) . Our earlier studies demonstrated that expression of the NS5B RNA polymerase is responsible for the downregulation of pRb in replicon-bearing cells , and that NS5B interacts directly with pRb through a Leu-X-Cys-X-Glu homology domain ( LH314–318 ) overlapping the polymerase active site [8] . Since we lack antibody capable of labeling the genotype 2a NS5B protein within infected cells , we labeled the viral replicase complex in these cells with a broadly reactive polyclonal antibody to NS5A . NS5A is known to colocalize with HCV RNA and other HCV nonstructural proteins within the cytoplasmic “membranous webs” that are thought to be sites of viral RNA synthesis [19] . These studies revealed numerous JFH1-infected cells with an abnormal cytoplasmic accumulation of pRb , and a clear colocalization with the HCV replicase complex as labeled with anti-NS5A ( Figure 2A , frame iv ) . Although the sensitivity of confocal microscopy for detection of pRb renders it difficult to deduce quantitative differences in nuclear pRb abundance in infected versus noninfected cells , we noted similar cytoplasmic relocalization of pRb in cells infected with a cell culture–infectious genotype 1a virus ( H77S ) developed recently in our laboratory [20] ( Figure 2B ) . Some H77S-infected cells demonstrated nearly complete relocalization of pRb to the cytoplasm ( Figure 2B , frame ii arrow ) , while in most infected cells , there was partial cytoplasmic relocalization ( Figure 2B , frame iv arrow ) . A quantitative analysis of multiple cells indicated that there was a significant increase in the proportion of total pRb present in the cytoplasm of infected versus noninfected cells ( Figure 2C; p < 0 . 001 ) . Confocal microscopy also confirmed a striking reduction in nuclear phospho-pRb in JFH1-infected cells ( Figure 2D; compare frames i and iii ) . Phospho-pRb was found to accumulate within the cytoplasm following treatment with epoxomicin ( Figure 2D , frame vi ) . These results are consistent with an interaction occurring between NS5B and pRb within the cytoplasm . While pRb is normally confined to the nucleus , it does undergo nuclear-cytoplasmic shuttling involving phosphorylation-dependent nuclear export mediated by exportin1 [21] . It is likely that NS5B interacts with and traps pRb in the cytoplasm prior to its initial transport to the nucleus , or perhaps after nuclear export . Although our previous studies demonstrated that NS5B downregulates the abundance of pRb post-transcriptionally [8] , the mechanism by which this occurs is not clear . We confirmed our earlier observations that the stability of pRb is reduced in cells supporting HCV RNA replication by carrying out additional pulse-chase labeling experiments in Huh-7 2–3 cells which contain a genome-length RNA replicon [22] . These results indicated a significantly shortened half-life for pRb in the replicon cells , compared with clonally related cells ( 2–3c cells ) from which the HCV RNA had been eliminated by prior interferon treatment ( Figure 3A ) . Since the abundance of pRb is regulated through proteasome-dependent pathways in the absence of HCV protein expression [23 , 24] , we considered it likely that HCV may also regulate pRb in a proteasome-dependent fashion . To test this hypothesis , we determined whether proteasome inhibitors [25 , 26] could restore the abundance of pRb in 2–3 replicon cells . As shown in Figure 3B , epoxomicin , a potent and selective synthetic inhibitor of multiple protease activities of the proteasome , caused a marked increase in pRb abundance , nearly to normal levels , in 2–3 cells ( Figure 3B , top panels ) . In contrast , epoxomicin treatment caused a slight increase in pRb abundance in the cured , HCV-negative 2–3c cell line ( Figure 3B , lower panels ) , consistent with the normal regulation of pRb abundance by proteasomal degradation [23 , 24] . We also observed similar , cell-type–specific restoration of pRb abundance in 2–3 cells after treatment with lactacystin , an irreversible inhibitor of the 20S proteasome , or MG115 , a reversible inhibitor of 20S and 26S proteasomes ( Figure S2 ) . Immunofluorescence analysis confirmed the rescue of pRb expression following MG115 treatment of 2–3 replicon cells , but revealed that pRb was localized primarily within the cytoplasm , and not the nucleus , in most 2–3 cells following treatment with the proteasome inhibitor ( Figure 3C , arrows ) . Importantly , pRb retained its normal nuclear localization in MG115-treated 2–3c cells that lack HCV protein expression ( Figure 3C , bottom panels ) . The retention of pRb in the cytoplasm of MG115-treated replicon cells ( Figure 3C ) is consistent with the interaction of NS5B discussed above . This interaction appears to trap the tumor suppressor protein within the cytoplasm and prevent its translocation to the nucleus in advance of its degradation by the proteasome . To determine whether inhibition of the proteasome would similarly rescue pRb expression in cells infected with HCV , we treated JFH1-infected cells with epoxomicin . As shown in Figure 3D , this resulted in a marked increase in the abundance of pRb ( compare lanes 1 versus 2 ) , as well as an increase in high-molecular-mass pRb-immunoreactive protein . While the identity of this high-mass pRb-immunoreactive protein is uncertain , it is likely to represent ubiquitinated pRb ( see below ) . The abundance of phospho-pRb was also increased following epoxomicin treatment of JFH1-infected cells , although there we observed no discernable high-molecular-mass phospho-pRb species ( Figure 3D , lanes 3 versus 4 ) . Considered collectively , these results indicate that HCV regulates the abundance of pRb by promoting its proteasome-dependent degradation . Although overexpression studies have suggested that the NS5B polymerase itself may be regulated by polyubiquitination and proteasome-mediated degradation [27] , epoxomicin treatment did not enhance , but rather reduced , the abundance of NS5B in both HCV-infected and replicon cells ( Figures 3B and 3D ) . Although pRb abundance is normally regulated through proteasome-dependent pathways , such regulation does not necessarily require the ubiquitination of pRb [23 , 24] . The increase we observed in the abundance of high-molecular-mass pRb-immunoreactive protein in lysates of JFH1-infected hepatoma cells prepared following treatment with epoxomicin ( Figure 3D , lane 2 ) suggests that HCV infection might promote the polyubiquitination of pRb . To assess this possibility , we immunoprecipitated pRb from lysates of Huh-7 . 5 cells that were infected with the JFH1 virus , then analyzed the precipitates in immunoblots using antibody to ubiquitin . We similarly studied infected cells that had been treated with epoxomicin for 20 h prior to lysis . These results revealed that HCV infection induces polyubiquitination of pRb ( Figure 4A , lane 1 ) . A significant abundance of polyubiquitinated pRb was not detected in lysates from mock-infected cells , even following treatment with epoxomicin ( Figure 4A , compare lanes 3 and 1 ) . We also demonstrated HCV-dependent polyubiquitination of pRb in the 2–3 replicon cells by transfecting the cells with a vector expressing Flag-tagged ubiquitin , followed by immunoprecipitation ( IP ) of lysates with anti-Flag antibody and immunoblotting with anti-pRb ( Figure 4B ) . While a small amount of polyubiquitinated pRb was detected in lysates of the cured 2–3c cells following treatment with MG115 ( Figure 4B , lane 6 ) , this was readily detected in lysates of untreated 2–3 replicon cells , and increased by MG115 treatment ( Figure 4B , lanes 7–8 ) . We obtained similar results in cells treated with lactacystin ( unpublished data ) . Importantly , the anti-Flag precipitates from the 2–3 cells also contained abundant NS5B , indicating that NS5B was associated with the ubiquitinated pRb in these cells ( Figure 4B , lanes 7–8 ) . We observed no high-molecular-mass forms of NS5B , suggesting that there is no appreciable ubiquitination of NS5B under these conditions . These results confirm that HCV infection and/or RNA replication induce polyubiquitination of pRb as a prelude to its degradation by the proteasome . Since our prior studies revealed that NS5B binds to and induces the degradation of pRb [8] , we asked whether ectopic expression of NS5B would also induce pRb ubiquitination . We transfected normal Huh-7 cells with vectors expressing Flag-tagged HCV nonstructural proteins ( NS3-4A , NS4B , NS5A , and NS5B ) , and immunoprecipitated cell extracts with anti-pRb , followed by immunoblotting with anti-ubiquitin antibodies . Overexpression of Flag-NS5B , but not other HCV nonstructural proteins , reproducibly resulted in polyubiquitination of pRb ( Figure 4C ) . In contrast , the ectopic expression of an NS5B mutant ( D318N/D319N ) containing substitutions within the LH314–318 domain that mediates the interaction of NS5B with pRb [8] resulted in only minimal ubiquitination of pRb ( Figure 4C; compare lanes 5 and 6 ) . These data indicate that the interaction of NS5B with pRb leads to targeted destruction of pRb via the ubiquitin-proteasome pathway , representing a striking parallel to the mechanism by which the HPV E6 protein mediates destruction of p53 [28] . A PROSITE ( http://ca . expasy . org/prosite/ ) search indicated that the NS5B protein does not contain a RING finger , or a HECT or ubiquitin interaction motif , making it unlikely that NS5B itself possesses ubiquitin ligase activity [29] . Thus , NS5B is more likely to induce the ubiquitination of pRb by mediating its interaction with a cellular ubiquitin ligase . In searching for this protein , we focused on two recognized cellular E3 ubiquitin ligases: the human homolog of the murine “double minute 2” protein ( MDM2 ) , which is involved in ubiquitination pathways that normally regulate the abundance of pRb and p53 [23 , 24 , 30] , and E6-associated protein ( E6AP ) which is recruited by the HPV E6 protein to mediate the ubiquitination of p53 [28] . Importantly , E6AP is also known to form a complex with ubiquilin-1 ( hPLIC-1 ) , a ubiquitin-like protein that has been shown to interact with NS5B [27 , 31] . Also , recent studies indicate that E6AP mediates the ubiquitination and degradation of the HCV core protein [32] . To assess the role of these two ubiquitin ligases in NS5B-mediated degradation of pRb , we used siRNA interference to examine the impact of MDM2 and E6AP knockdown on pRb abundance in the 2–3 replicon cells . Using pools consisting of four different specific siRNAs , we were able to achieve effective reductions of each targeted protein ( Figure 5A ) . MDM2 knockdown resulted in a modest but variable increase in pRb abundance ( Figure 5A; compare lane 8 with lanes 1 and 2 ) , while knockdown of E6AP reproducibly restored pRb abundance to a level approaching that of the control 2–3c cells ( Figure 5A; compare lane 9 with lanes 1 and 2 ) . Two different control siRNAs failed to increase pRb abundance in the replicon cells ( lanes 6 and 7 ) . This was also the case with an siRNA pool specific for an E3 ligase sharing the C-terminal HECT domain of E6AP , neural precursor cell–expressed developmentally downregulated protein 4 ( NEDD4 ) ( lane 8 ) [33] , indicating that the effect of E6AP knockdown was specific to that protein . None of the siRNAs tested significantly enhanced the abundance of pRb in the control , HCV-negative cells ( Figure 5A , lanes 1–5 ) . To further assess the specificity of the E6AP knockdown , we transfected 2–3 and 2–3c cells with each of the four individual siRNA molecules present in the E6AP pool tested in Figure 5A . These results demonstrated robust enhancement of pRb abundance in cells transfected with three of the E6AP-specific siRNAs ( E5 , E6 , and E7 ) , while none of the siRNAs influenced pRb abundance in the control 2–3c cells ( Figure 5B ) . Transfection of siRNA E8 reduced the abundance of E6AP , but had no apparent effect on pRb abundance ( Figure 5B , lane 10 ) . It is possible that this might reflect the involvement of an E6AP splicing variant , as the E5 , E6 , and E7 siRNAs all target exon 9 of the E6AP gene , while E8 targets exon 10 [34] . Alternatively , we observed kinetic differences in the rates of pRb restoration with these siRNAs , most likely reflecting different efficiencies of E6AP knockdown ( unpublished data ) . Transfection of siRNAs E5 and E6 resulted in increased pRb abundance within 48 h , while this was not observed with siRNA E7 until 96 h or more after transfection . As a final proof of specificity , we mutated one of the E6AP-specific siRNAs ( E5 ) , altering the base at two consecutive positions ( E5mut; Figure 5C , bottom ) . Compared with the wild-type E5 siRNA , transfection of E5mut resulted in neither knockdown of E6AP or restoration of pRb abundance ( Figure 5C; compare lanes 5 and 6 ) . Consistent with these results , overexpression of both E6AP and NS5B in Huh-7 cells enhanced the downregulation of pRb observed previously with ectopic expression of NS5B alone [8] ( unpublished data ) . A hallmark of E6AP and other HECT domain ligases is their ability to form a physical complex with the molecule undergoing ubiquitination . Since the results described above suggest that E6AP may play a role in the NS5B-induced ubiquitination of pRb , we sought evidence for an interaction between E6AP , pRb , and NS5B . We immunoprecipated pRb present in 2–3 cell extracts using antibody to pRb , and demonstrated that the precipitate contained detectable E6AP ( Figure 6A , lane 6 ) . In contrast , similarly prepared precipitates from the cured 2–3c cells did not contain appreciable amounts of E6AP ( Figure 6A , lane 5 ) , nor did precipitates generated from extracts of the 2–3 replicon cells using antibodies to MDM2 or Flag ( Figure 6A , lanes 4 and 8 ) . These results suggest that HCV induces the formation of a complex involving pRb and E6AP . As we had previously shown that NS5B interacts with pRb and targets it for degradation [8] , we considered it likely that NS5B was responsible for the E6AP–pRb complex observed in lysates of the replicon cells . We confirmed the interaction of NS5B with pRb by demonstrating the coimmunoprecipitation of NS5B with pRb in lysates of 2–3 cells ( Figure 6B , lane 6 ) . For reasons that remain unclear , the amount of NS5B present in the pRb precipitates was markedly reduced when the replicon cells were treated with epoxomicin prior to preparation of the extracts ( Figure 6B; compare lanes 5 and 6 ) . The overall abundance of NS5B was also reduced by epoxomicin treatment ( Figure 6B; compare lanes 1 versus 2 , and Figure 3A and 3B ) , possibly reflecting nonspecific cellular toxicity and a related reduction in viral RNA replication . To demonstrate that the formation of the pRb–E6AP complex was dependent upon NS5B and not other HCV proteins expressed by the replicon RNA , we transfected normal Huh-7 cells with vectors expressing various nonstructural HCV proteins , as shown in Figure 4C . Extracts prepared from these transfected cells were precipitated with antibody to pRb , then immunoblotted using antibody to E6AP . Only expression of wild-type NS5B resulted in the formation of a complex between pRb and E6AP ( Figure 6C , lane 5 ) . Importantly , this was not observed in cells expressing an NS5B mutant , D318N/D319N , that fails to bind pRb [8] ( Figure 6C; compare lanes 5 and 6 ) , or in cells ectopically expressing other nonstructural proteins of the virus: NS3-4A , NS4B , and NS5A . Taken together , the data shown in Figure 6 indicate that NS5B forms a complex with pRb and E6AP . Consistent with the results of the siRNA knockdown experiments described above , these data provide strong support for a role for E6AP in the NS5B-dependent degradation of pRb . To determine whether E6AP in fact functions as an E3 ligase for pRb , we assessed the ability of recombinant E6AP to direct the ubiquitination of pRb in a reconstituted in vitro ubiquitination reaction using recombinant E1 and E2 proteins . However , these experiments failed to demonstrate ubiquitination of pRb by E6AP , either in the presence or absence of NS5B ( Figure S3 ) . These results suggest that E6AP may not be responsible for the NS5B-dependent ubiquitination of pRb , or that this process requires another cellular or viral protein partner that was not included in the reconstituted in vitro ubiquitination reaction . To distinguish between these possibilities , we transfected the 2–3 replicon cells with vectors expressing either wild-type E6AP or a dominant-negative E6AP protein , E6AP-C840A , that contains a single amino acid substitution within the C-terminal HECT domain , ablating its E3 ligase activity [35] . Quantitation of immunoblots from three independent experiments demonstrated that the overexpression of E6AP-C840A resulted in a reproducible increase in the abundance of pRb in the 2–3 replicon cells ( Figure 7A [compare lanes 4 versus 6] and 7B ) . This increase in pRb abundance was clearly apparent in immunoblots of serial 2-fold dilutions of the cell lysates ( Figure 7C ) . In contrast , overexpression of wild-type E6AP caused either no change or a slight increase in pRb abundance in 2–3 cells , and did not appreciably alter pRb expression in the cured 2–3c cells ( Figure 7A ) . These results provide additional evidence that E6AP is required for NS5B-dependent ubiquitination of pRb , and are consistent with the effects of siRNA knockdown of E6AP ( Figure 5 ) and the presence of an NS5B–E6AP–pRb complex in lysates of the replicon cells ( Figure 6 ) . We conclude that E6AP is the E3 ligase responsible for NS5B-dependent ubiquitination of pRb in vivo .
We have shown here that pRb is ubiquitinated and degraded in a proteasome-dependent fashion in cultured human hepatoma cells infected with HCV . These observations enhance the biological relevance of prior studies showing that pRb is downregulated by the HCV polymerase protein , NS5B , expressed by autonomously replicating RNA replicons [8] . In showing that the cellular E6AP and viral NS5B proteins form a complex that regulates pRb abundance , we have also provided an enhanced mechanistic understanding of this process . E6AP was identified originally as a ubiquitin ligase that downregulates p53 in cells expressing the HPV E6 protein [28 , 36] . The E6–E6AP complex targets additional proteins for ubiquitination , including a set of PDZ domain proteins [37 , 38] and NFX1–91 , a repressor of the hTERT promoter [39] . Interestingly , E6AP has also been shown recently to ubiquitinate and regulate the stability of the HCV core protein [32] . The partial restoration of pRb abundance we observed in the 2–3 replicon cells following either siRNA knockdown of E6AP ( Figure 5 ) or overexpression of a dominant-negative E6AP mutant ( E6AP-C840A; Figure 7 ) provides strong evidence that the ubiquitin ligase activity of E6AP is required for HCV regulation of pRb . Importantly , neither E6AP knockdown nor E6AP-C840A expression altered the abundance of pRb in the clonally related 2–3c cells that had been cured of the HCV replicon by prior treatment with interferon . We also found that the ability of NS5B to form a complex with E6AP is dependent upon the NS5B LH314–318 domain [8] that mediates the interaction of NS5B with pRb ( Figure 6C; compare lanes 5 and 6 ) . These data thus lead us to propose a model in which NS5B interacts with hypophosphorylated pRb within the cytoplasm and recruits E6AP to the complex , thereby inducing the ubiquitination and subsequent degradation of pRb via the proteasome . Such a model is consistent with the cytoplasmic redistribution of pRb , as well as the colocalization of pRb and viral nonstructural proteins ( specifically NS5A ) that we observed by confocal microscopy in cells infected with HCV in vitro ( Figure 2A and 2B ) . Several possible explanations exist for the inability of E6AP to ubiquitinate pRb in an NS5B-dependent fashion in reconstituted in vitro reactions ( Figure S3 ) . First , it may be that additional cellular or viral proteins are required for ubiquitination . Strong evidence exists for functionally important interactions between the NS5B polymerase and several cellular proteins other than pRb , including vesicle-associated membrane-associated proteins A and B ( VAP-A and VAP-B ) , cyclophilin B ( CypB ) , and protein kinase C–related kinase 2 ( PRK2 ) , which putatively regulates NS5B by phosphorylation , as well as hPLIC-1 , mentioned above [27 , 40–43] . The deletion of a 21–amino acid C-terminal hydrophobic domain in the NS5B used in our assays ( which is required for its solubility ) [44 , 45] , could also have affected the in vitro assays , potentially compromising the ability of the polymerase to undergo the conformational changes required for efficient interaction of the LH314–318 domain with pRb [8] . This domain overlaps the Gly-Asp-Asp motif within the active site of the NS5B polymerase , and is sequestered within the interior of the protein in its fully folded form [46] . Prior studies indicate that pRb abundance is regulated by several different mechanisms , reflecting in turn its role as a master regulator of the cell cycle . The E3 ligase MDM2 plays a prominent role in its regulation in the absence of HCV infection [23 , 24] . While we observed a variable increase in pRb abundance following siRNA knockdown of MDM2 in the 2–3 replicon cells ( Figure 5A; compare lanes 6–7 and 10 ) , this effect was typically less than that observed with E6AP knockdown ( compare lanes 9 and 10 ) . Moreover , in contrast to E6AP , we were not able to demonstrate an interaction between NS5B and MDM2 ( unpublished data ) . pRb is also targeted for proteasome-mediated degradation by the E7 oncoprotein expressed by high-risk HPVs [47] . Although recognized for many years , the mechanism underlying its regulation by E7 has remained for the most part obscure . However , an active cullin 2 ubiquitin ligase complex has been reported recently to be associated with the HPV type 16 E7 protein , and has been implicated in this process [48] . The Epstein-Barr virus latent antigen 3C mediates degradation of pRb by recruiting the SCFSkp2 ubiquitin ligase [49] . Thus , the steady-state abundance of pRb appears to be regulated through the actions of three distinct E3 ligases: by MDM2 in normal cells , by SCFSkp2 in Epstein-Barr virus–infected B cells , and ( as our data suggest ) by E6AP in HCV-infected hepatocytes . The human cytomegalovirus pp71 protein also promotes the degradation of pRb and its associated family proteins , but does so in a proteasome-dependent , ubiquitin-independent manner [50] . In addition , gankyrin , an oncogenic ankyrin-repeat protein that is overexpressed in most HCCs , associates with pRb and reduces its stability [51] . While the abundance of pRb may be regulated through a diversity of mechanisms , its activity in controlling E2F transcription factors is largely regulated by phosphorylation [14] . The sequential phosphorylation of pRb plays a pivotal role in the G1/S-phase transition . In its hypophosphorylated state , pRb sequesters and represses the E2F family of transcription factors [13] . Mitogenic growth factors induce phosphorylation of pRb by activation of cyclin D–Cdk4/6 and cyclin E/Cdk2 complexes [52] . This results in release of E2F proteins from pRb , and promotes transcriptional activation of genes required for S-phase and DNA replication . It also appears to promote the nuclear export of pRb , at least in some cell types [21] . Conversely , growth-inhibitory signals reduce cyclin levels or induce Cdk inhibitors , resulting in decreased cyclin/Cdk activity , hypophosphorylation of pRb , and , subsequently , repression of E2F-target genes . Importantly , some ubiquitin ligases are known to catalyze ubiquitination in a phosphorylation-dependent manner . For example , ubiquitination of p27Kip1 is triggered by phosphorylation on Thr187 by Cdk2–cyclin E/A kinase complexes [53 , 54] . Phosphorylated p27Kip1 is then recognized by SCFSkp2 , which polyubiquitinates p27Kip1 , targeting it for degradation by the proteasome . At present , we have no direct evidence that phosphorylation influences the ubiquitination and degradation of pRb . However , the relatively low abundance of cytoplasmic phospho-pRb in epoxomicin-treated hepatoma cells infected with HCV ( Figure 2D , frame vi ) suggests that NS5B may interact preferentially with hypophosphorylated pRb . pRb is also subject to acetylation and sumoylation , both of which regulate pRb function within the cell cycle [55 , 56] , and could also influence how pRb is ubiquitinated . The effect of such modifications on the regulation of pRb by HCV will require further study . Since the ectopic expression of NS5B stimulates the activity of E2F-responsive promoters , S-phase entry , and cellular proliferation [8] , it seems likely that HCV regulation of pRb abundance may enhance the proliferative rate of infected hepatocytes . This virus–host interaction may have evolved because it favors viral RNA replication , which is known to be stimulated in proliferating cells in vitro [57 , 58] . A more important question , however , is whether the NS5B-mediated degradation of pRb could contribute to the development of HCC in patients with chronic HCV infection . While the manner in which the expression of E7 by high-risk papillomaviruses contributes to cervical cancer [59] may hold some analogies for HCV , it is likely that HCV regulation of pRb promotes the development of liver cancer in a more indirect fashion . In addition to its role in controlling the G1- to S-phase transition , pRb regulates mitotic checkpoints that are critical controls in prevention of cancer , as they allow cells to repair chromosomal damage before DNA replication or cell division . The risk of oxidative chromosomal DNA damage is likely to be enhanced due to inflammation within the liver during chronic hepatitis C , increasing the importance of these mitotic checkpoints . However , to be fully functional , these checkpoints require competent p53 and pRb pathways [60 , 61] . Expression of the E6 and E7 proteins from high-risk HPVs can override DNA damage-induced G1 arrest [62] , while E6 proteins from high-risk HPVs are also able to compromise the G2/M DNA damage checkpoint [63] . We have shown previously that NS5B-mediated degradation of pRb results in an upregulation of the activity of the E2F-responsive Mad2 promoter [8] . Importantly , deregulation of Mad2 , an essential component of the mitotic spindle checkpoint , leads to aneuploidy and an increased risk of tumors , including hepatomas , in mice [64 , 65] . Thus , while it remains to be proven that HCV infection disrupts mitotic checkpoints through downregulation of pRb , it is a reasonable hypothesis . By downregulating pRb abundance , HCV infection would both stimulate hepatocellular proliferation within a local environment rich in reactive oxygen species and also impair the ability of the cell to respond appropriately to DNA damage . The net result would be increased chromosomal instability . Such a hypothesis is consistent with the diversity of chromosomal abnormalities found in HCC , as well as the typically lengthy period spanning the onset of HCV infection to the development of HCC , and should provide a useful framework for future investigations .
Human hepatoma cells Huh-7 and Huh-7 . 5 [66] were grown in Dulbecco modified Eagle medium ( Cellgro , http://www . cellgro . com/ ) supplemented with 10% ( v/v ) heat-inactivated fetal bovine serum , 100 U/ml penicillin G , and 100 μg/ml streptomycin , at 37 °C in a humidified atmosphere with 5% ( v/v ) CO2 . The NNeo/C-5B 2–3 , Huh-7–derived cell line containing autonomously replicating , genome-length , dicistronic , selectable HCV RNAs derived with the genotype 1b HCV-N strain were cultured with 500 μg/ml G418 , as described previously ( Cellgro ) [22] . Its companion , interferon-cured progeny cell line 2–3c , was generated and maintained as described previously , and contains no HCV RNA [58] . Cell culture–infectious genotype 1a H77S and genotype 2 JFH1 viruses were harvested from the supernatant fluids of cultures of RNA transfected Huh-7 . 5 cells , and stored at −80 °C until use [16 , 20] . For experiments with JFH1 virus , cells were inoculated at a multiplicity of infection ( MOI ) of 1–2 , and virus was allowed to adsorb to cells for 6–12 h at 37 °C prior to replacement of media . pRb abundance and cellular localization was ascertained by immunoblotting and confocal microscopy 48–120 h after infection . H77S infections were carried out at an MOI of ∼0 . 01 due to the lower efficiency of virus production , and cells were examined by confocal microscopy only . pCMV6-hE6AP , containing full-length cDNA of human E6AP cloned into the mammalian expression vector pCMV6 , was purchased from OriGene . A dominant-negative mutant , E6AP C840A , was generated by PCR mutagenesis using pCMV6-hE6AP as a template with the primers 5′-GCC TTT AAT GTG CTT TTA CTT CCG G-3′ and 5′-AGT ATG AGA TGT AGG TAA CCT TTC-3′ . Human ubiquitin B precursor cDNA was purchased from OriGene ( http://www . origene . com/ ) , and cDNA representing the mature ubiquitin was subcloned into the pGEM-T Easy cloning vector ( Promega , http://www . promega . com/ ) after amplification by PCR using the primers 5′-CCG GAA TTC ATG CAG ATC TTC GTG AAA ACC CTT AC-3′ and 5′-GCT CTA GAT TAA CCA CCT CTC AGA CGC AGG ACC-3′ to generate pTM-047 . After confirming the sequence of both strands of the insert , pTM-047 was digested with EcoRI and XbaI , and the 0 . 25-kb fragment containing the ubiquitin open reading frame was subcloned into pcDNA3 . 1/Zeo/IRES-3xFLAG to generate a 3xFLAG-tagged ubiquitin expression vector . pEGFP-C1 , pORF9-hRB1 , pCMV-tag4-NS3/4A , pCMV-tag4-NS4B , pCMV-tag4-NS5A , pCMV-tag4-NS5B wt , and pCMV-tag4-NS5B D318N/D319N were constructed as described previously [8] . Lactacystin , MG115 , and epoxomicin ( all from Calbiochem , http://www . emdbiosciences . com/ ) were prepared as solutions in DMSO . For treatment of replicon cells , 2–3 replicon and cured 2–3c cells were seeded into 6-well plates and grown to 50% confluence . Inhibitors were added to the culture media at the indicated concentrations , and cells were incubated for 10–12 h ( lactacystin or MG115 ) or 20 h ( epoxomicin ) , followed by preparation of cell extracts for immunoblots . For epoxomicin treatment of JFH1 virus–infected cells , the inhibitor was added 20 h prior to lysis of cells and preparation of cell extracts . siRNA oligonucleotide SMARTpools , each containing four siRNA oligonucleotides specific for human MDM2 ( M-003279–02 ) , E6AP/UBE3A ( M-005137–00 ) , and NEDD4 ( M-007178–01 ) , and individual E6AP/UBE3A siRNAs , were purchased from Dharmacon ( http://www . dharmacon . com/ ) . Negative control siRNAs ( 4611 and 4613 ) were from Ambion ( http://www . ambion . com/ ) . For knockdown experiments , 2–3 replicon and cured 2–3c cells were grown to 30% confluence in 6-well plates , and transiently transfected with 80 nM of siRNAs using Lipofectamine 2000 ( Invitrogen , http://www . invitrogen . com/ ) according to the manufacturer's instructions . Protein extracts were prepared for further analysis 72–120 h after transfection . Cells were seeded into 6-well plates 24 h before transfection and grown to 50% confluence . Before transfection , the culture medium was replaced with fresh medium without antibiotics . For overexpression of E6AP or E6AP C840A , 2–3 and 2–3c cells were transiently transfected with 4 μg of pCMV6 ( empty vector ) , pCMV6-hE6AP , or pCMV6-hE6AP-C840A , along with 0 . 25 μg of pEGFP-C1 ( Promega ) , using FuGENE 6 reagents ( Roche Diagnostics , http://www . roche . com/ ) . Protein extracts were prepared for immunoblots at 48 h after transfection . Cells were washed three times with chilled PBS , and incubated in chilled lysis buffer ( 20 mM Tris-HCl [pH7 . 5] , 150 mM NaCl , 10 mM EDTA-2Na , 1% [v/v] Nonidet P-40 , 10% [v/v] glycerol , and 2 mM DTT ) supplemented with 1 mM PMSF and 2 μg/ml aprotinin , or complete protease inhibitor cocktail ( Roche ) , for 30 min at 4 °C . Cell debris was pelleted by centrifugation at 13 , 000g for 30 min at 4 °C , and supernatants were used as soluble fractions . Protein concentrations were determined by the modified Bradford assay with BSA as a standard ( Bio-Rad , http://www . bio-rad . com/ ) . SDS-PAGE and subsequent immunoblotting were done as described previously [8] , using mouse monoclonal antibodies against β-actin ( AC-15; Sigma , http://www . sigmaaldrich . com/ ) , GAPDH ( glyceraldehyde-3-phosphate dehydrogenase; Ambion ) , Flag tag ( M2; Sigma ) , MDM2 ( SMP14; Santa Cruz Biotechnology , http://www . scbt . com/ ) , pRb ( G3–245; BD Biosciences , http://www . bdbioscences . com/ ) , and ubiquitin ( P4D1; Santa Cruz Biotechnology ) , and rabbit polyclonal antibodies against phospho-pRb 807/811 ( Cell Signaling Technology , http://www . cellsignal . com/ ) , E6AP ( sc-25509; Santa Cruz Biotechnology ) , NEDD4 ( sc-25508; Santa Cruz Biotechnology ) , and NS5B ( A266–1; ViroGen , http://www . virogen . com/; or provided as a generous gift by Dr . Craig E . Cameron , Pennsylvania State University , State College , Pennsylvania , United States ) . Membranes were probed with appropriate secondary antibodies conjugated with horseradish peroxidase , visualized by ECL reagents ( Amersham Pharmacia Biosciences , http://www . amersham . com/ ) , and exposed to x-ray films . Huh-7 2–3 and 2–3c cells were seeded into 8-well Labtek chamber slides and grown until 50%–60% confluent , with or without the addition of 20 μM of MG115 . After washing twice with PBS , the cells were fixed in methanol–acetone ( 1:1 [vol/vol] ) for 10 min at −20 °C , air-dried for 60 min at room temperature , washed twice with PBS , and incubated with blocking buffer ( 1% BSA in PBS ) overnight at 4 °C . pRb was visualized by staining with mouse monoclonal antibody G3–245 . After washing three times with PBS , slides were further incubated with a goat anti-mouse Ig secondary antibody conjugated with FITC for 1 h at room temperature . Slides were then washed three times with PBS , counterstained with diamidino-2-phenylindole 2HCl ( DAPI ) , mounted in Vectashield mounting medium ( Vector Laboratories , http://www . vectorlabs . com/ ) , and examined with a Zeiss AxioPlan2 fluorescence microscope ( http://www . zeiss . com/ ) . Huh-7 2–3 and 2–3c cells or JFH1-infected Huh-7 . 5 cells were cultured in Labtek chamber slides ( http://www . labtek . net/ ) and fixed with 4% paraformaldehyde in PBS for 30 min . Cells were permeabilized with Triton X-100 ( 0 . 2% ) for 15 min and blocked with 10% normal goat serum at room temperature for 1 h . Cells were then incubated with the appropriate dilutions of primary antibodies for 1 h followed by secondary antibodies for 1 h at room temperature . HCV antigen was visualized with rabbit polyclonal antibody to NS5A ( a generous gift from Dr . Craig E . Cameron ) followed by Alexa 594 secondary antibody conjugate , or with human polyclonal antibody and an FITC-labeled anti-human Ig secondary antibody; pRb was visualized with visualized by staining with mouse monoclonal antibody G3–245 ( BD Biosciences ) , and phospho-pRb by rabbit polyclonal antibody against phospho-pRb 807/811 ( Cell Signaling Technology ) followed by secondary antibodies: goat anti-mouse Ig conjugated to FITC or goat anti-rabbit Ig conjugated to Alexa 594 . Slides were washed and counterstained with DAPI , and mounted in Vectashield mounting medium , then sealed and examined with a Zeiss LSM 510 laser scanning confocal microscope within the Infectious Disease and Toxicology Optical Imaging Core at the University of Texas Medical Branch . Pulse-chase labeling of endogenous pRb protein was done as described previously [8] . For analysis of the interaction between pRb and E6AP in normal Huh-7 cells , cells were grown to 50% confluence in 10-cm dishes and transfected with 5 μg of pCMV-tag4 , pCMV-tag4-NS3/4A , pCMV-tag4-NS4B , pCMV-tag4-NS5A , pCMV-tag4-NS5B wt , and pCMV-tag4-NS5B D318N/D319N . At 48 h after transfection , 20 μM of MG115 was added to the transfected cells for 10 h . Cells were lysed in 1 ml of IP lysis buffer ( 20 mM Tris-HCl [pH7 . 5] , 150 mM NaCl , 10 mM EDTA-2Na , 1% [v/v] NP-40 , 10% [v/v] glycerol , 1 mM PMSF , and 2 μg/ml aprotinin ) , and extracts were prepared as described above . IP was performed with 500 μg of extracts using anti-pRb monoclonal antibodies , as described previously , and immunoprecipitated proteins were analyzed by immunoblot [8] . For analysis of the interaction between pRb and E6AP in HCV replicon cells , 2–3 replicon and 2–3c cured cells were lysed in IP lysis buffer , and soluble extracts were prepared . IP was performed with 500 μg of extracts using 1 μg of anti-FLAG ( M2; Sigma ) , anti-pRb ( G3–245; BD Biosciences ) , or anti-MDM2 ( SMP14; Santa Cruz Biotechnology ) monoclonal antibody , and immunoprecipitated proteins were analyzed by immunoblot . For analysis of the interaction between NS5B and ubiquitinated pRb , NNeo/C-5B 2–3 and 2–3c cured cells were transfected with 3xFLAG-tagged ubiquitin expression vector , and treated with DMSO or 20 μM MG115 for the last 10 h . At 48 h after transfection , cells were lysed in IP lysis buffer , and soluble extracts were prepared . IP was carried out using 500 μg of extracts and anti-FLAG monoclonal antibody , and immunoprecipitated proteins were analyzed by immunoblot . For detection of ubiquitinated pRb , 2–3 replicon cells and 2–3c cured cells were cultured to 50% confluence in 10-cm dishes and transfected with 5 μg of 3xFLAG-tagged ubiquitin expression vector . At 48 h after transfection , cells were treated with DMSO , 10 μM of lactacystin , or 20 μM of MG115 for 10 h , and then lysed in IP lysis buffer supplemented with 1 mM NaF , 1 mM Na3VO4 , 4 mM N-ethylmaleimide , and 6 nM ubiquitin aldehyde ( ubiquitination buffer ) , and soluble extracts were prepared . A total of 500 μg of extracts were used for IP with anti-FLAG monoclonal antibodies , and immunoblots were carried out with anti-pRb or antiubiquitin monoclonal antibodies . For detection of NS5B-dependent ubiquitination of pRb , normal Huh-7 cells were cultured to 50% confluence in 6-well plates , and transfected with 2 μg pCMV-tag4 , pCMV-tag4-NS3/4A , pCMV-tag4-NS4B , pCMV-tag4-NS5A , pCMV-tag4-NS5B wt , and pCMV-tag4-NS5B D318N/D319N . At 48 h after transfection , 20 μM of MG115 was added to the transfected cells for 10 h . Cells were lysed in ubiquitination buffer , and soluble extracts were prepared . IP was carried out using anti-pRb monoclonal antibodies , followed by immunoblotting with monoclonal antiubiquitin antibody . Reconstituted , in vitro ubiquitination reactions were carried out using purified recombinant pRb ( Abcam , http://www . abcam . com/ ) , recombinant NS5B protein with a 21–amino acid C-terminal deletion ( Replizyme , http://www . replizyme . com/ ) , and recombinant E6AP produced in baculovirus , essentially as described [35] .
The Entrez Protein ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=Protein ) accession numbers for the proteins discussed in this study are as follows: pRb ( NP_000312 ) , E6AP ( NP_061828 ) , MDM2 ( NP_002383 ) , NEDD4 ( NP_006145 ) , ubiquitin ( NP_061828 ) , HCV-N full-length polyprotein ( AAD44719 ) , and JFH1 full-length polyprotein ( BAB32872 ) . | Persons infected with hepatitis C virus ( HCV ) are at increased risk for liver cancer . This is remarkable because HCV is an RNA virus with replication confined to the cytoplasm and no potential for integration of its genome into host cell DNA . While it is likely that chronic inflammation contributes to liver cancer , prior studies with HCV transgenic mice indicate that the viral proteins are intrinsically carcinogenic . In this study , we have examined the interaction of one of these , the RNA-dependent RNA polymerase nonstructural protein 5B , with an important cellular tumor suppressor protein , the retinoblastoma protein ( pRb ) . pRb is a master regulator of the cell cycle , and altered expression of some of the many genes it regulates may lead to cancer . We show that the abundance of pRb is strongly downregulated in cells infected with HCV , and that nonstructural protein 5B targets pRb for destruction via the cell's normal protein degradation machinery . The E6-associated protein appears to play a role in this process , which is interesting as it also mediates the degradation of another tumor suppressor , p53 , by papillomaviruses . The loss of pRb function in HCV-infected cells likely promotes hepatocellular proliferation as well chromosomal instability , factors important for the development of liver cancer . | [
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| 2007 | Hepatitis C Virus Induces E6AP-Dependent Degradation of the Retinoblastoma Protein |
Centromere behavior is specialized in meiosis I , so that sister chromatids of homologous chromosomes are pulled toward the same side of the spindle ( through kinetochore mono-orientation ) and chromosome number is reduced . Factors required for mono-orientation have been identified in yeast . However , comparatively little is known about how meiotic centromere behavior is specialized in animals and plants that typically have large tandem repeat centromeres . Kinetochores are nucleated by the centromere-specific histone CENH3 . Unlike conventional histone H3s , CENH3 is rapidly evolving , particularly in its N-terminal tail domain . Here we describe chimeric variants of CENH3 with alterations in the N-terminal tail that are specifically defective in meiosis . Arabidopsis thaliana cenh3 mutants expressing a GFP-tagged chimeric protein containing the H3 N-terminal tail and the CENH3 C-terminus ( termed GFP-tailswap ) are sterile because of random meiotic chromosome segregation . These defects result from the specific depletion of GFP-tailswap protein from meiotic kinetochores , which contrasts with its normal localization in mitotic cells . Loss of the GFP-tailswap CENH3 variant in meiosis affects recruitment of the essential kinetochore protein MIS12 . Our findings suggest that CENH3 loading dynamics might be regulated differently in mitosis and meiosis . As further support for our hypothesis , we show that GFP-tailswap protein is recruited back to centromeres in a subset of pollen grains in GFP-tailswap once they resume haploid mitosis . Meiotic recruitment of the GFP-tailswap CENH3 variant is not restored by removal of the meiosis-specific cohesin subunit REC8 . Our results reveal the existence of a specialized loading pathway for CENH3 during meiosis that is likely to involve the hypervariable N-terminal tail . Meiosis-specific CENH3 dynamics may play a role in modulating meiotic centromere behavior .
Centromeres are loci that direct faithful segregation of chromosomes during eukaryote cell division . They provide a platform for the assembly of kinetochores , structures that bind to spindle microtubules and coordinate chromosome movement . Centromere behavior must be regulated differently in mitosis and meiosis [1] . In mitosis , centromeres from sister chromatids face in opposite directions ( bi-orientation ) , allowing the spindle to pull the replicated sisters apart at anaphase . In meiosis I , sister centromeres face in the same direction ( mono-orientation ) . This allows sister chromatids to move to the same side of the spindle in anaphase I , when homologous chromosomes are segregated apart . Chromosome segregation errors in meiosis I are a primary cause of spontaneous abortion and birth defects , highlighting the importance of studying meiotic centromere behavior [1] . The mechanism of mono-orientation has been illuminated by yeast studies . In Schizosaccharomyces pombe , the meiosis-specific cohesin subunit Rec8 fuses sister kinetochores together in a geometry that favors attachment to microtubules from the same side of the spindle [2] . This appears to be a conserved mechanism , because rec8 mutants in the plant Arabidopsis thaliana also show bi-oriented sister kinetochores in meiosis I [3] . Furthermore , fused sister kinetochores have been observed in maize meiosis I [4] . Other proteins required for mono-orientation in yeast , such as S . pombe Moa1p or the monopolin complex of Saccharomyces cerevisiae , are not found in animals or in plants [5] , [6] . These proteins may evolve rapidly . However it is also possible that the mechanism of bi-orientation has different features between yeast kinetochores that are nucleated by small DNA sequences and animal and plant kinetochores that assemble on megabase-scale tandem repeat arrays [7] . Centromere function requires the centromere specific histone H3 variant ( CENH3 , called CENP-A in human ) , which replaces histone H3 in centromeric nucleosomes and recruits many essential kinetochore proteins [8] . Unlike conventional histones , which are extremely well conserved , CENH3s are fast evolving [9] . Genetic experiments in A . thaliana and in S . cerevisiae , as well as localization studies in Drosophila melanogaster have shown that evolutionarily divergent CENH3s cannot substitute for one another ( although gene silencing experiments in human cells suggest greater promiscuity ) [10]–[13] . The N-terminal tail domain of CENH3s is even more hypervariable than the C-terminal histone-fold domain , and shares almost no similarity between plant species such as A . thaliana and maize ( Zea mays ) , let alone between plants and other eukaryotes [9] , [13] . As the histone-fold domain of CENH3 is sufficient for kinetochore localization , the role of the tail domain is enigmatic . We have shown that a CENH3 protein lacking the tail is targeted to kinetochores , but fails to complement an A . thaliana cenh3 null mutant [13] . However , replacing the CENH3 tail with the tail of conventional H3 . 3 in a GFP-tagged protein gives rise to viable but sterile plants ( cenh3 plants complemented with this transgene are referred to as GFP-tailswap ) [14] . This unexpected result suggested that the CENH3 tail might have a specific function in meiosis , even though CENH3 is required for both mitotic and meiotic kinetochore functions . Most studies of CENH3 dynamics and function in eukaryotes with tandem repeat centromeres are limited to mitosis , since the knockout of CENH3 is zygotic lethal . The viability and subsequent sterility of cenh3 GFP-tailswap plants enabled us to investigate the meiosis specific role of CENH3 in A . thaliana . Here we present a detailed analysis of the meiotic phenotype of A . thaliana plants expressing GFP-tagged CENH3 variants with alterations in the CENH3 tail domain . Sterility in these plants was caused by random chromosome segregation , with the first defects appearing at the onset of metaphase I ( the stage at which centromere behavior is expected to differ between mitosis and meiosis ) . Chromosome segregation defects in meiosis were explained by severe depletion of the GFP-tailswap CENH3 variant at meiotic kinetochores ( the same protein is loaded normally in mitosis ) . Depletion of CENH3 at centromeres also compromised the recruitment of the essential kinetochore protein Mis12 . Our results thus reveal that centromeres have a meiosis-specific assembly mechanism which involves the CENH3 tail domain . This previously unsuspected pathway may play a role in modulating kinetochore dynamics to ensure differential centromere behaviour during meiosis .
We previously observed sterility in GFP-tailswap plants but not in plants expressing GFP-CENH3 , suggesting that the N-terminal tail of CENH3 might have a specific role in plant reproduction [14] . The sterile phenotype of GFP-tailswap could result from the absence of the CENH3 tail , or from the presence of the H3 . 3 tail . To differentiate between these possibilities , we created a chimera in which the A . thaliana CENH3 tail was replaced with an unrelated CENH3 tail domain from maize ( Zea mays ) , and transformed it into cenh3-1 heterozygotes ( Figure 1A ) . This GFP-maizetailswap protein was targeted to kinetochores and rescued the embryo-lethal phenotype of cenh3-1 . In contrast , full-length maize CENH3 protein was targeted to A . thaliana kinetochores but failed to rescue cenh3-1 embryo lethality [13] . Complemented GFP-maizetailswap plants showed the vegetative phenotype previously seen in GFP-tailswap plants but were even more sterile than GFP-tailswap plants ( although one partially fertile GFP-maizetailswap plant was recovered ) ( Figure 1A ) [13] . A GFP tag can have a deleterious effect on CENH3 function [15] . Self-pollinated GFP-CENH3 plants are phenotypically indistinguishable from wild type and fully fertile , indicating that the GFP tag does not interfere with meiosis . We constructed an untagged tailswap transgene to test the role of the CENH3 N-terminus in a protein lacking an N-terminal GFP ( Figure 1A ) . Interestingly , cenh3-1 plants expressing a tailswap transgene without GFP were viable and fertile , indicating that the meiosis-specific role of the CENH3 tail domain is evident only when protein function is compromised by the presence of an N-terminal GFP tag . Together , these results suggest that either the H3 . 3 tail or the maize CENH3 tail in the place of the native Arabidopsis CENH3 tail can severely compromise plant reproduction when combined with a GFP tag . This is especially interesting because the CENH3 tail is not required for centromere localization during mitosis , and is extremely fast-evolving [9] , [16] . Sterility in GFP-tailswap plants could be caused by meiotic defects ( during sporogenesis ) , or by later defects in post-meiotic cell divisions ( gametogenesis ) . To investigate the cause of sterility , we analyzed the course of male meiosis in chromosome spreads from anthers . The major early events of meiotic homolog pairing and recombination appeared normal in GFP-tailswap ( Figure S2 ) . In prophase I , progressive condensation of chromosomes , homolog pairing , chiasmata formation and subsequent desynapsis of homologs ( at diplotene ) were similar in GFP-tailswap ( n = 317 ) and wildtype meioses ( Figure S2 ) . The first defects in GFP-tailswap were seen in metaphase I ( Figure 1B ) . In most mutant cells ( 98/112 ) , bivalent chromosomes congressed normally to the spindle midzone ( Figure 1B , panel F ) . However , a few ( 14/112 ) showed alignment defects such as widely spaced metaphase plates , and unaligned bivalent chromosomes ( Figure 1B , panel K ) . Chromosomes can congress to the metaphase plate in the absence of a centromere [17] . Such movements may be driven by chromosome arm-associated kinesins that are independent of the presence of a functional kinetochore [18] . Plant genomes do not contain chromokinesins , the animal proteins that perform this role , but functional counterparts may exist . A striking defect was seen in the shape of chromosomes during the metaphase I to anaphase I transition . In wild-type meiosis , bivalent chromosomes at this stage assume a rhombus- or linear-shaped configuration caused by tension between spindle microtubules pulling on the kinetochore and chiasmata that hold homologous chromosomes together ( Figure 1B , panel A ) . In GFP-tailswap meiocytes , wild-type metaphase configurations were rarely observed . Instead , bivalents were oval and irregularly shaped , resembling prometaphase I stage meiocytes ( n = 79 ) ( Figure 1B , panels F and K ) . Prometaphase stage chromosomes are rarely seen in wildtype meiotic spreads ( in a total of 392 prophase stage meiocytes , only 4 of this type were observed ) because this stage has only a short duration before the onset of metaphase I . Our observations suggest that meiocytes in GFP-tailswap stall at the prometaphase stage and proceed directly to anaphase I without going through a typical metaphase-anaphase transition stage at which tension is exerted by the spindle . During anaphase I , GFP-tailswap bivalents frequently segregated both homologs to the same side of the spindle , in addition to the normal behavior of resolving homologs and segregating them to opposite poles ( Figure 1B , panels G and L ) . In wild type , pulling forces from the meiosis I spindle help to resolve homologs at anaphase . In GFP-tailswap , homolog separation was more asynchronous than in wild type , yielding cells that contained a mixture of bivalents and univalents ( Figure 1B , panels G , H , L and M ) . The bivalents then separated their homologs at a later stage of anaphase I ( Figure 1B , panel M ) . Some cells contained lagging chromosomes at the spindle midzone , supporting the idea that attachment to the meiotic spindle is impaired ( Figure 1B , panels J and O ) . In rare instances ( 4/103 ) , sister chromatids separated prematurely during meiosis I ( in contrast to normal separation at anaphase II ) ( Figure 1B , panel N ) . As a result of random chromosome segregation , meiosis I in GFP-tailswap yielded predominantly unbalanced dyads with 6-4 , 7-3 configurations e . t . c . instead of the 5-5 segregation that is universally seen in wild type ( reductional segregation to give 5-5 dyads was seen in 6% of mutant cells ) ( Figure 1B , panels J and O ) . At interkinesis , an intermediate stage between meiosis I and II , wild-type chromosomes decondense and recondense . In GFP-tailswap , decondensation and recondensation were delayed in some chromosomes ( especially those located at or near the midzone ) ( Figure 1B , panels J and O ) . Instead of regrouping and aligning on the metaphase plate during meiosis II , GFP-tailswap chromosomes remained scattered throughout the meiocyte in a manner similar to anaphase I chromosomes in the mutant ( Figure 1B , panel Z ) . Anaphase II in wild type A . thaliana separates sister chromatids to form four groups of 5 chromosomes , each of which contains one haploid genome . In GFP-tailswap , sister chromatids separated while chromosomes were scattered , showing that anaphase II begins without correct chromosome alignment on the metaphase plate ( Figure 1B , panels V and AA ) . Chromosome decondensation also occurred at dispersed locations throughout the cell ( Figure 1B , panels X and AC ) . Instead of the wild type tetrad containing four haploid nuclei , GFP-tailswap meiocytes after meiosis II are polyads containing many small nuclei ( in A . thaliana , cytokinesis occurs after the tetrad is formed ) ( Figure 1B , panels Y and AD ) . Analysis of male meiosis in GFP-maizetailswap plants revealed meiotic chromosome segregation defects similar to those in GFP-tailswap plants ( Figure S3 ) . Based on the chromosome segregation phenotypes described above , it is clear that sterility in GFP-tailswap and GFP-maizetailswap is caused by a severe meiosis-specific defect in centromere function . Analysis of microspores ( pollen grains ) from GFP-tailswap plants revealed that each contained 1–8 nuclei instead of the single nucleus that is always seen in wild type ( Figure 2A–2F ) . These micronuclei varied in size , suggesting that they contained different numbers of chromosomes . Fluorescence in situ hybridization ( FISH ) using a probe that recognizes the 180 bp centromere tandem repeats confirmed that each micronucleus contained from 1–4 chromosomes ( Figure 2J–2L ) . This observation suggests that randomly scattered chromosomes that lie in close proximity reassemble their own nuclear envelope at the end of telophase II , resulting in multiple micronuclei within each microspore . A similar defect has been reported in A . thaliana separase ( esp ) mutants defective in the enzyme that releases sister chromatid cohesion [19] . In mammalian somatic cells , micronuclei formation is triggered by the depletion of factors required for chromosome segregation [20]–[22] . Formation of micronuclei might be a general feature of perturbations that drastically affect chromosome movement in mitosis or meiosis . Viable male and female gametes in GFP-tailswap are expected to contain a single nucleus with a haploid genome of five chromosomes , because most ( 95% ) of the viable progeny from self fertilized GFP-tailswap are diploid [14] , [13] . This can be contrasted with several plant meiotic mutants which show random chromosome segregation during meiosis [23]–[25] . After meiosis II , these mutants often contain more than the normal four nuclei within a single meiocyte . However , the microspores resulting from such meiocytes usually contain a single nucleus , in contrast to the multinucleate microspores of separase mutants and GFP-tailswap . In A . thaliana mutants with general meiotic defects ( for example , ask1 and spo11 ) , a high fraction of viable gametes are aneuploid [24] , [25] . When such plants are self-fertilized , a high fraction of viable progeny are aneuploid . Formation of micronuclei in the microspore selects against otherwise viable aneuploid pollen in GFP-tailswap , so >90% of progeny obtained by self-fertilization are diploid , despite random chromosome segregation in meiosis . We conclude that functional centromeres are required to package segregating chromosomes into a single pollen nucleus , and are thus necessary for accurate transmission of a haploid genome after meiosis . A defect in kinetochore attachment to spindle microtubules in GFP-tailswap is suggested by the lack of apparent tension in metaphase I chromosomes and by random chromosome segregation ( in the absence of pairing or recombination defects ) . The distance between opposing kinetochores ( interkinetochore distance ) during metaphase is a more precise measure of tension generated by the spindle during mitosis . To investigate interkinetochore distance in GFP-tailswap meiosis I , we used FISH with a centromere tandem repeat probe ( Figure 3A ) . In meiotic chromosome spreads , the outer limit of centromere DNA staining indicates the likely position of the kinetochore . Wild type cells at the metaphase I to anaphase I transition showed centromere DNA foci whose outer edges were separated by a 405±68 nm distance ( n = 15 bivalent chromosomes ) . Centromere DNA was clearly stretched out on either side of the non-hybridizing DNA representing chromosome arms ( Figure 3A , panel D ) . In GFP-tailswap chromosomes at an equivalent stage , centromere DNA extremities were much closer to each other at 234±30 nm ( n = 15 bivalent chromosomes ) ( Figure 3A , panel H ) . Furthermore , the centromere DNA stretch characteristic of wild-type chromosomes under tension was not seen in the mutant . We conclude that GFP-tailswap kinetochores may not be efficiently pulled by spindle microtubules . FISH analysis of metaphase I meiocytes in GFP-tailswap also revealed abnormal alignment of centromeres with respect to the cell plate ( Figure 3B ) . In wild-type meiosis I , centromeres from homolog pairs align in a direction perpendicular to the future division plane . In GFP-tailswap they aligned in multiple directions , presaging the random chromosome segregation that occurs at anaphase I . This data further supports the hypothesis that spindle microtubules fail to pull on kinetochores in GFP-tailswap , leading to a lack of tension and incorrect chromosome orientation during meiosis I . As meiotic kinetochores appeared to be non-functional in GFP-tailswap plants , we investigated whether the GFP-tailswap variant of CENH3 was localized to meiotic kinetochores ( Figure 4 and Figure 5 ) . We have previously shown that GFP-tailswap protein is localized normally to mitotic kinetochores , and that mitosis is accurate in GFP-tailswap plants [13] . A . thaliana male meiocytes can be extruded as a cell conglomerate by gently squeezing anthers [26] . We imaged meiocytes from GFP-CENH3 plants , and found that the GFP-tagged CENH3 protein was visualized at kinetochores in all stages of meiosis , as well as in haploid pollen grains . However in GFP-tailswap meiocytes , the protein was only faintly visualized at kinetochores during premeiotic and early prophase I stages ( Figure 4 and Figure 5 ) and was not detected in later stages of meiosis I ( starting from pachytene ) and meiosis II . Depletion of GFP-tailswap from meiotic kinetochores contrasted with somatic cells from the same anther , which showed GFP fluorescence at mitotic kinetochores that appeared identical to wild-type ( Figure S4 ) . To further understand the dynamics of the GFP-tailswap protein during meiosis , we analyzed kinetochore GFP fluorescence at all meiotic stages from anther squashes ( identification of these stages is described in the Materials and Methods ) . In premeiotic interphase of GFP-CENH3 meiocytes , kinetochore GFP fluorescence was bright and uniform ( Figure 4 ) ( n = 93 , 4 plants ) . GFP-tailswap meiocytes never showed bright kinetochore GFP fluorescence ( Figure 4 ) ( n = 119 , 5 plants ) . Instead , we categorized them into three classes of reduced fluorescence , where GFP-CENH3 fluorescence is class I: 7% of meiocytes showed reduced kinetochore signal ( class II ) , 47% had barely detectable fluorescence ( class III ) , and the remaining 46% showed no GFP at kinetochores ( class IV ) . This observation suggests that the GFP-tailswap protein is not replenished during premeiotic interphase , and that GFP-tailswap protein inherited from the somatic precursor cell is gradually removed from the centromere . Depletion of GFP-tailswap protein from meiocytes continued in subsequent stages of meiosis I ( Figure 5 and Figure 6 ) . Kinetochore GFP signal gradually disappeared during leptotene and zygotene stages of early prophase I ( Figure 5 ) . From late pachytene stage onwards until the completion of meiosis we could not detect GFP signal in any meiocytes ( Figure 5 and Figure 6 ) . We also used anti-GFP antibodies to immunolocalize GFP-CENH3 and GFP-tailswap proteins during meiosis , and found similar results ( Figure S5 ) . Residual GFP-tailswap protein may remain at kinetochores at a level below the detection limit . To verify that the GFP-tailswap mRNA was correctly spliced during meiosis , we extracted meiocytes from GFP-CENH3 and GFP-tailswap anthers using a capillary-based method [26] . RT-PCR and subsequent sequencing of cDNA showed that the GFP-tailswap mRNA was identically spliced in somatic and meiotic cells ( Figure S6 ) . As CENH3 is essential , specific depletion of GFP-tailswap from meiotic kinetochores explains the chromosome missegregation that leads to sterility in GFP-tailswap plants . The GFP-maizetailswap protein was also absent from meiotic kinetochores but present normally at mitotic kinetochores . Plants that co-express either GFP-tailswap or GFP-maizetailswap along with a wild-type endogenous CENH3 gene were fully fertile . However , the GFP-tailswap and GFP-maize-tailswap proteins were poorly loaded onto meiotic kinetochores even in the presence of functional endogenous CENH3 ( importantly , GFP-CENH3 loads normally in the presence of wild type CENH3 ) ( Figure S7 ) . Thus , the CENH3 tail domain appears to be required specifically for recruitment of the protein to meiotic kinetochores ( when protein function is compromised by a GFP tag ) . Kinetochore proteins that act only during meiosis have been described [1] . To our knowledge , this is the first example of an alteration in CENH3 that causes a meiosis-specific defect but allows for accurate mitosis . In A . thaliana , CENH3 is recruited to mitotic kinetochores after S phase , to replenish kinetochore CENH3 levels that were diluted by DNA replication [16] . If the GFP-tailswap and GFP-maizetailswap proteins were simply unable to replenish kinetochores after pre-meiotic DNA replication , we would expect to see half the GFP signal found at mitotic kinetochores in meiosis I cells . The fact that these proteins are greatly reduced at almost all meiotic kinetochores suggests that CENH3 chromatin is actively disassembled during meiosis in mutant plants . We believe that the GFP-tailswap and GFP-maizetailswap mutants reveal a meiosis-specific kinetochore assembly pathway whose existence was previously unsuspected . CENH3 is required to recruit a large number of essential kinetochore proteins in other organisms . To further characterize the effects of the GFP-tailswap variant on kinetochore assembly , we performed immunostaining on GFP-tailswap and control GFP-CENH3 anther squashes with antibodies raised against the A . thaliana kinetochore proteins CENP-C and MIS12 [27] , [28] . CENP-C antibodies did not yield specific staining of kinetochores in meiocytes from either GFP-CENH3 or GFP-tailswap plants . However , MIS12 staining was observed at kinetochores in GFP-CENH3 meiocytes ( n = 44 ) , but not in GFP-tailswap meiocytes ( n = 33 ) ( Figure 7C and 7K ) . Somatic cells from both GFP-CENH3 and GFP-tailswap plants showed kinetochore localization of MIS12 ( Figure 7G and 7O ) . Although MIS12 may be recruited in a CENH3-independent way in human cells [20] , our results show that loss of A . thaliana CENH3 in meiosis also depletes MIS12 from the kinetochore . As MIS12 is a component of the KMN network that connects kinetochores to spindle microtubules , we predict that this will compromise kinetochore-microtubule attachment [29] . Furthermore , MIS12 is important for mono-orientation during meiosis in maize [4] . In summary , severe depletion of the GFP-tailswap protein during meiosis and downstream effects on kinetochore assembly can explain the chromosome segregation defects observed in the mutant . Depletion or removal of CENH3 or other essential kinetochore proteins from the centromere results in compromised kinetochore function , which destabilizes the formation of a normal spindle [30] , [31] . To gain insight into kinetochore-spindle microtubule interactions in GFP-tailswap , we visualized microtubules in meiocytes with anti-alpha-tubulin antibodies ( Figure 8 ) . A bipolar spindle is formed during metaphase I in mutants , but it was longer and more disorganized than the wild-type meiosis I spindle ( Figure 8 , panel E and I ) . Although we cannot conclude that kinetochores in GFP-tailswap are completely non-functional , our data is consistent with previous studies showing that kinetochores are not required to assemble a bipolar spindle in either mitosis or meiosis [32] , [33] . It is possible that interactions between spindle microtubules and chromosome arm-binding kinesins can organize a spindle in the absence of fully functional kinetochores . In general , longer spindles are correlated with smaller kinetochores , and with abnormal chromosome movement [20] . This provides further evidence that kinetochores are functionally compromised in GFP-tailswap meiocytes . Meiosis II spindles were even more disorganized in GFP-tailswap ( Figure 8 ) . Many meiocytes at this stage contained more than two spindles ( multipolar spindles ) . Spindles were frequently perpendicular to each other or generally lacking the neat parallel appearance of spindles in wild-type meiosis II ( Figure 8 , panels F , G , J and K ) . These phenotypes may explain the inability of chromosomes to align on the metaphase II plate . Some kinetochores in GFP-tailswap meiocytes appeared to lack nearby spindle microtubules ( Figure 8 , panels G and J ) . However , it is difficult to conclude from our data that kinetochores fail to bind stably to spindle microtubules in the mutant , because the detection limit of tubulin staining in meiocytes is unknown . Furthermore , we cannot easily distinguish kinetochore microtubules from interpolar microtubules . If GFP-tailswap has a specific defect in recruitment to meiotic centromeres , can we suppress this phenotype by imposing a mitosis-like behavior on kinetochores in meiosis I ? REC8 is a meiosis-specific cohesin subunit that functionally replaces its mitotic counterpart RAD21 . REC8 is required to hold sister kinetochores together and force them to orient towards the same side of the spindle [3] , [2] . In S . pombe rec8 mutants , sister kinetochores in meiosis I show bipolar attachment to the spindle and segregate apart from each other , much as they do in mitosis [34] . The role of REC8 in mono-orientation is conserved in plants , as shown by A . thaliana rec8 spo11 mutants ( the spo11 mutation is needed to prevent chromosome fragmentation , as rec8 mutants cannot repair meiotic double-stranded breaks ) [3] . To test whether CENH3 recruitment employs the mitotic loading pathway when REC8 is removed from meiotic kinetochores , we generated rec8 spo11-1 cenh3-1 triple mutant plants carrying the GFP-tailswap transgene . If removing REC8 in GFP-tailswap mutants fully converted kinetochores from meiotic to mitotic behavior , we expected to see two experimental readouts . First , GFP-tailswap would be recruited to meiotic kinetochores in a manner similar to GFP-CENH3 . Second , chromosomes in meiosis I would show a mitosis-like segregation pattern similar to the rec8 spo11-1 mutant , because functional centromeres would be restored . GFP-tailswap protein was not loaded onto meiotic kinetochores in rec8 spo11-1 cenh3-1 GFP-tailswap plants , showing that REC8 removal does not restore the mitotic CENH3 loading pathway during meiosis ( Figure S8 ) . In rec8 spo11-1 mutants , chromosomes remain as univalents during prophase I and separate their sister chromatids at anaphase I as they do in mitosis ( 10-10 segregation instead of 5-5 segregation ) ( Figure 9 and Figure S8 ) [3] . By contrast , meiosis I in rec8 spo11-1 cenh3-1 GFP-tailswap plants showed random segregation of the unpaired univalent chromosomes , confirming that kinetochores were still non-functional ( Figure 9 and Figure S8 ) . The inability of GFP-tailswap to load onto meiotic kinetochores suggests that the meiosis-specific CENH3 loading pathway is still functional even if we impose mitotic chromosome-like behaviour in meiotic cells by removing REC8 from centromeres . Unlike animal gametes , the haploid cells produced by plant meiosis undergo mitotic divisions to generate mature gametes . Male meiosis in GFP-tailswap produces a few viable pollen grains , presumably in those rare cases where a complete haploid genome is clustered into a single microspore nucleus . These haploid microspores ( and rarely , viable aneuploid microspores ) must undergo mitotic divisions , so we asked whether the GFP-tailswap protein was recruited to kinetochores after meiosis , when mitosis resumes . Although a large majority of GFP-tailswap microspores ( 407/504 or 81% , n = 6 plants ) are dead due to micronuclei formation and did not show GFP fluorescence , remaining microspores ( 97/504 or 19% ) in the mutant showed GFP fluorescence at kinetochores at a level equivalent to wildtype ( Figure 10A and 10B ) . Some of the microspores that showed kinetochore fluorescence contained fewer than five GFP foci , and are unlikely to contain a full haploid genome ( these microspores should lead to inviable pollen ) ( Figure 10C ) . Kinetochore localization of GFP-tailswap in haploid spores was brighter than that seen in early stages of meiosis ( class II and class III ) . Furthermore , we did not observe GFP-tailswap at kinetochores from pachytene until telophase II of meiosis . We conclude that GFP-tailswap is loaded afresh onto mitotic kinetochores after meiosis and is not simply carried over from those meiocytes that showed faint localization . This result confirms the existence of two distinct kinetochore assembly pathways , for mitosis and meiosis respectively . It also raises the question of how the GFP-tailswap variant of CENH3 recognizes mitotic kinetochores after meiosis . If GFP-tailswap is truly removed from kinetochores in meiosis , there must be a targeting mechanism that does not require the prior presence of CENH3 in centromeric nucleosomes .
Centromeres are differentially configured during mitotic and meiotic cell divisions , resulting in separation of either sister chromatids or homologous chromosomes during anaphase . Segregation of sister chromatids to the same spindle pole in meiosis I depends on meiosis-specific proteins such as monopolin components and Moa1p , but may also involve specialized functions of constitutive kinetochore proteins . The essential kinetochore protein CENP-C recruits Moa1p in S . pombe [35] . In maize , MIS12 has a role in fusing kinetochores , facilitating mono-orientation during meiosis I [4] . In addition , linker histone H1 variants have a meiosis-specific role in plants and are present only at meiotic centromeres in lily [36]–[39] . CENH3 is essential in both mitosis and meiosis , but our viable yet sterile A . thaliana mutants have uncovered a meiosis-specific loading pathway for CENH3 that most likely depends on its fast evolving N-terminal tail domain . Male and female meiosis are differentially affected in GFP-tailswap plants , although the basis for this is unclear [14] . Sex specific meiotic chromosome segregation defects have also been observed in tobacco plants defective for linker histone H1 variants [39] . During DNA replication , CENH3 nucleosomes are randomly partitioned between replicated sister chromatids and voids created in chromatin are filled by fresh loading of free CENH3 . The cell cycle loading time of CENH3 , and the chaperones that direct it to kinetochores , can vary between organisms [16] , [40] , [41] [42]–[45] . Our results may indicate that CENH3 has meiosis-specific chaperones that cannot recruit the GFP-tailswap and GFP-maizetailswap variants . In C . elegans , CENH3/HCP-3 is also differentially recruited during mitosis and meiosis [46] . Furthermore , C . elegans CENH3 has a different distribution from outer kinetochore proteins in meiosis , and is dispensable for meiotic chromosome segregation [46] , [47] . These properties are probably related to the holocentric nature of C . elegans chromosomes , whereas the meiosis-specific defect we have found confirms that CENH3 is essential for meiotic segregation of monocentric A . thaliana chromosomes . Severe depletion of GFP-tailswap at meiotic kinetochores suggests that endogenous CENH3 undergoes a removal and reloading process in meiosis . If this is the case , reloading must be closely coupled to removal , as we never observed meiotic cells from GFP-CENH3 plants that lacked kinetochore fluorescence . There are precedents for dynamic reorganization of CENH3 chromatin during particular developmental stages . In C . elegans meiosis , CENH3/HCP-3 is removed , and kinetochores are assembled de novo when mitosis resumes [46] . Parental CENH3 is also greatly depleted in the fertilized zygote of A . thaliana , and replaced by CENH3 synthesized from the zygotic genome [48] . In an alternative model to explain GFP-tailswap dysfunction , CENH3 at meiotic kinetochores might assume a different conformation that is destabilized by the combination of a bulky tag with the absence of the CENH3 N-terminal tail domain . Meiosis specific kinetochore architecture could involve an altered nucleosome structure ( such as a conversion between octameric and tetrameric forms ) , or a change in the arrangement of CENH3 nucleosomes [49]–[51] . CENH3 levels can be regulated by proteolysis [52] , and GFP-tailswap and GFP-maizetailswap proteins may be specifically degraded in meiotic cells ( possibly because they are not loaded into centromeric chromatin ) . The fact that A . thaliana has a centromere DNA structure similar to most animals and plants favors the hypothesis that meiosis-specific CENH3 assembly will be a conserved phenomenon . The involvement of the CENH3 N-terminal tail in meiosis-specific centromere assembly is intriguing because the tail is so fast-evolving , and because it is dispensable for accurate mitosis . This requirement is not absolute , as the H3 . 3 tail substitution in the absence of GFP is fertile – others have proposed that a GFP tag on CENH3 might disturb higher order chromatin structure [15] . The CENH3 tail ( 81 amino acids ) is longer than the H3 tail ( 43 amino acids ) , so GFP is closer to the histone-fold domain in the GFP-tailswap protein . However , we do not think this is the sole cause of sterility in GFP-tailswap , because GFP-maizetailswap plants are even more sterile , and the maize CENH3 tail ( 61 amino acids ) is longer than the H3 tail . The N-terminal tail of histone H3 can contain many functionally important post-translational modifications , and CENH3 can also be post-translationally modified [53] , [54] . Meiosis-specific modifications of CENH3 seem unlikely to be significant , because the amino acid sequence of the tail changes so rapidly , and important modified residues would be expected to be conserved . The N-terminal tail may interact with as yet unidentified meiosis-specific histone chaperones , with other proteins important for mono-orientation , or even with centromere DNA directly [55] . If the latter is the case , this binding must be especially critical during meiosis . The “centromere paradox” refers to the fact that centromere DNAs and CENH3 are remarkably fast evolving , despite their essential function [56] . It has been proposed that differences between the centromeres of two parents cause hybrid defects when they are crossed , leading to speciation [56] . Centromere differences could reduce the fitness of hybrid offspring by affecting either meiosis or mitosis . We previously reported that a cross between two phenotypically indistinguishable parents with CENH3 differences ( GFP-CENH3 and wild type ) caused mitotic chromosome segregation errors in the fertilized zygote [14] . Now we have found that meiosis ( particularly in the male ) can be specifically affected by changes in CENH3 , albeit in plants that contain only a single altered CENH3 protein . This result is analogous to the observation that a centromere DNA polymorphism in the monkeyflower Mimulus guttatus can cause male meiotic defects when it is homozygous [57] . We speculate that CENH3 interactions with centromere DNA may be altered by the M . guttatus centromere DNA polymorphism , and that A . thaliana GFP-tailswap protein may fail in meiosis because it cannot interact with centromere DNA appropriately . The existence of a meiosis-specific loading pathway for CENH3 further supports the concept that rapid evolution reduces hybrid fertility by weakening kinetochore function in meiosis .
Plants were grown under a 16 hr light/8 hr dark regime at 20°C . GFP tailswap plants have been previously described [14] , [13] . Plant transformations used the floral dip method . The rec8 ( Ler accession ) and spo11-1 ( Ws-0 accession ) mutants have been described [3] . rec8 spo11-1 cenh3-1 triple mutants expressing GFP-tailswap were generated by selfing REC8/rec8 SPO11-1/spo11-1 CENH3/cenh3-1 plants carrying the GFP tailswap transgene . Primer sequences for genotyping are listed in Table S1 . The GFP-maizetailswap transgene fuses the Zea mays CENH3 N-terminal tail ( amino acids 1–61 ) and the A . thaliana CENH3 histone fold domain ( amino acids 82–179 ) . GFP-maizetailswap was constructed by overlapping PCR and cloned as a SalI-XbaI fragment into the binary vector CP93 [13] . The tailswap transgene without an N-terminal GFP was constructed by overlapping PCR and cloned into CP93 from SalI to XbaI . Primer sequences for overlapping PCR are listed in Table S1 . Developmental analysis of unfertilized fixed ovules by differential interference contrast microscopy was performed as described [58] . Male meiotic spreads were prepared as described [59] except for a modification in the enzyme cocktail for tissue digestion , which contained 0 . 3% cellulose and 0 . 3% pectolyase in 10 mM citrate buffer ( pH 4 . 5 ) . FISH analysis on male meiotic chromosome spreads was performed as described [13] . The distance between centromeres at the metaphase I to anaphase I transition was measured using NIH Image J software . Immunolocalization of alpha-tubulin in meiocytes and microspores was carried out as described [60] . The primary antibody was a mouse monoclonal anti-alpha-tubulin ( Sigma T6199 ) . The secondary antibody was a goat anti-mouse IgG ( Sigma F0257 ) . Images were captured with a Deltavision deconvolution microscope . Immunolocalization of GFP and MIS12 was performed as described previously [28] . We used an anti-GFP antibody from Acris ( R1461P ) . To visualize GFP fluorescence in meiocytes , anthers were dissected from unfixed , fresh flower buds using insulin needles in a drop of staining solution ( 50% glycerol , 1% PBS and 1 µg/ml DAPI ) . After removing other floral tissues , anthers were fully submerged by fresh addition of 10–20 µl of staining solution onto the slide . A thin coverslip was placed on the slide and gently pressed with the plunger end of the insulin syringe until meiocytes were finely extruded out from the anther sacs . After sealing the coverslip with valap wax ( vaseline∶lanolin∶paraffin wax in a 1∶1∶1 ratio ) , slides were imaged using a Deltavision deconvolution microscope . Images were captured at 60× magnification with an exposure time of 0 . 5 seconds . Z-stacks with a step size of 0 . 2 µM were captured and further transformed into two-dimensional ( 2D ) flattened projections using SoftWoRx software ( Applied Precision ) . TIF files were edited using Adobe Photoshop and Illustrator . In live meiocytes stained with DAPI , it was difficult to accurately pinpoint the early stages of meiosis , especially the premeiotic and early prophase I stages . However , we could gauge the approximate stage by looking for certain landmark phenotypes . In the premeiotic stage , the chromosomes are highly decondensed and diffuse , and thus DAPI stains the whole nucleus . Therefore , premeiotic stage meiocytes were identified as being spherical or round in appearance . In premeiotic stage meiocytes , the G1 phase cells were identified as ones that showed round/spherical GFP fluorescence , whereas in S-G2 phase meiocytes , GFP signals are more elongated as a result of chromosome doubling . We never observed the clear separation of replicated sister kinetochores seen in mitotic G2 cells ( paired GFP foci ) . As meiosis proceeds , chromosomes start to condense . When the round appearance of chromosome mass became more irregular , they were identified as leptotene stage meiocytes . During zygotene , pairing of homologous partners starts and hence there were 5–10 GFP signals corresponding to the centromeres . During pachytene , pairing is complete and thus we detected 5 GFP signals corresponding to 5 fused kinetochores . In pachytene , chromosomes threads are much compact than early stages . From metaphase I onwards , meiotic stages were distinguished by their chromosome segregation behaviour using DAPI staining . Male meiocytes were extracted with a microcapillary-based method as described previously [26] . | There are two types of cell division in eukaryotes . Mitosis produces cells with identical copies of the genome , while meiosis produces gametes with half the number of chromosomes found in the parent cell . Faithful genome inheritance is controlled by centromeres , chromosomal structures that allow duplicated chromosomes to be pulled apart correctly during cell division . Centromeres are differentially configured during meiosis ( relative to mitosis ) so chromosome number can be reduced by half . Centromeres are built upon a specialized DNA packing protein , CENH3 . Here we describe altered forms of CENH3 that are loaded correctly during mitosis but are severely depleted from centromeres in meiotic cells . As CENH3 is essential for chromosome inheritance , plants expressing these versions of the protein are sterile because they produce very few viable gametes . Differential loading of CENH3 during meiosis may play a role in modulating chromosome inheritance to form haploid gametes . | [
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| 2011 | Meiosis-Specific Loading of the Centromere-Specific Histone CENH3 in Arabidopsis thaliana |
Insulin-like peptides ( ILPs ) play highly conserved roles in development and physiology . Most animal genomes encode multiple ILPs . Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes , including development , reproduction , longevity and several specific stress responses . Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy . Notably , we show that ILPs regulate each other transcriptionally , uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family . Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated . A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk , feedback and compensation . This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe . Our findings also provide insights into how large hormonal networks regulate diverse traits .
The organization and integration of multiple signals endow intercellular regulatory networks with information processing capabilities . For example , hormones modulate physiology and maintain homeostasis in variable environments [reviewed in 1] , and morphogens give rise to intricate patterns during development [reviewed in 2] . Nevertheless , how simple circuits are organized into complex networks that perform sophisticated functions is not fully understood . The ILPs are a superfamily of hormones that regulate many processes , including development , cell proliferation , energy metabolism , neuronal function , reproduction stress resistance , and longevity [3]–[15] . Canonical ILP signaling is mediated by a receptor tyrosine kinase pathway that culminates in the regulation of FOXO transcription factors and other regulatory molecules [16] . In Caenorhabditis elegans , this occurs via the DAF-2 ILP receptor tyrosine kinase , which signals through DAF-16 FOXO [6] , [17]–[19] . The importance of ILP signaling is underscored by the conservation of both the signal transduction pathway and the processes they regulate . Indeed , a C . elegans ILP ( INS-6 ) resembles human insulin structurally and can bind and activate the human insulin receptor [20] . Most animal genomes encode multiple ILPs: humans have 10 [21]; Drosophila melanogaster has 8 [22]–[24]; and C . elegans has 40 [25]–[27] . Small-scale studies have shown that certain ILPs can regulate other ILPs [4] , [24] , [28]–[31] , and that ILPs can act as either agonists or antagonists of their receptor to differentially affect multiple processes [5] , [24] , [27] . How do such simple interactions between these hormones generate complex functionality ? Here , we address this question by an integrated analysis of the C . elegans ILPs during larval development , stress resistance , reproduction and lifespan . We systematically tested the function of C . elegans ILPs in the control of diverse phenotypes . In contrast to the common notion of broad redundancy among ILPs [32] , we now provide evidence supporting a combinatorial code of action that maps the ILPs to multiple phenotypes . We also uncover the existence of a C . elegans ILP-to-ILP regulatory network that reveals the mechanisms through which multiple functionally diversified ILPs interact to regulate complex developmental and physiological traits . Thus , our analysis of the ILP-to-ILP network provides organizational principles for multiple-gene families and signaling networks .
As in many animals , the C . elegans daf-2 insulin/ILP receptor pathway affects multiple physiological processes , including development , aging , pathogen resistance , thermotolerance and reproduction [6] , [8] , [9] , [17] , [33]–[37] . The C . elegans ILP pathway also regulates entry into a specialized form of larval arrest known as dauer that forms preferentially under adverse conditions , such as high temperature , high population density , and low dietary sterols and food levels [reviewed in 6] . Under favorable conditions , animals exit the dauer stage to resume reproductive growth . Dauer exit is also regulated by the ILP pathway [34] , [38] , which suggests that ILPs function to regulate developmental plasticity in response to complex environmental cues [5] , [31] . Previous studies , which focused on a few ILPs , suggest that different phenotypes are modulated by distinct ILPs [4] , [5] , [11] , [26] , [27] . These ILPs can exhibit complex functional interactions in the regulation of certain phenotypes [4] , [5] . Together , these observations have raised the possible existence of an ILP combinatorial code in regulating physiology , in contrast to the prevailing notion of widespread redundancy as a feature of the ILPs and other gene families [32] . We tested this possibility by mapping the relationships between the 40 C . elegans ILPs , ins-1 to ins-39 and daf-28 [25]–[27] , and their developmental and physiological outputs . We systematically tested mutants in 35 ILPs for 8 distinct developmental and physiological phenotypes ( Figures 1 and S1 ) . Thirty-four of these mutations delete part or all of the coding sequence of an ILP and are predicted to be null mutations . One mutation , ins-10 ( tm3498 ) , contained a deletion in the genomic sequence and a duplication that overexpressed the intact coding sequence , which represents a gain-of-function allele ( Table S5 , Figure S2 and Materials and Methods ) . To minimize genetic background effects , all mutants were outcrossed 6 times to wild type . We used well-established procedures to score the ILP mutants and applied several statistical criteria to classify the phenotypes as high or low confidence based on statistical significance and reproducibility ( see Materials and Methods ) . We also confirmed the roles of many ILPs that showed new phenotypes or represented key conclusions in this study by rescuing their phenotypes with a transgene bearing the wild-type copy of the corresponding gene , as described in the following sections and in Table S3 . We included our previously published work in the analysis for comparison ( Figures 1 and S1 , Table S2 ) [4] , [5] . Importantly , we implicated distinct combinations of ILPs in every process tested and ascribed new functions to more than half of the C . elegans ILPs ( Figure 1 ) : 66% ( 23/35 ) of those tested showed at least one high-confidence phenotype , and 89% ( 31/35 ) showed high- or low-confidence phenotypes . We focused our analysis on the high-confidence hits . ILP-to-ILP signaling regulates several physiological processes [4] , [24] , [30] . To investigate its global nature , we used quantitative real-time PCR ( qPCR ) to identify changes in the mRNA levels of all 40 ILPs in each of 35 ILP mutants ( Figure 2A , S2 , and Table S5 ) . Surprisingly , we found that ILP-to-ILP signaling extends to many members of this family , demonstrating the presence of an ILP-to-ILP regulatory network ( Figure 2A ) . Out of a possible 1190 , we observed only 101 ILP interactions ( Figure 2A ) , which suggests that the inter-ILP regulation is sparse . These regulatory relationships also appear specific and diverse: each ILP is wired to a unique combination of regulators and targets , and regulation could be either negative ( 52% ) or positive ( 48% ) in a target-specific manner . These relationships showed an intermediate modularity of 0 . 49 , reflecting a mix of cross-regulation and compartmentalization in ILP gene expression . Thus , like the phenotypic screens , the qPCR data show that the diversification of C . elegans ILPs beyond functional redundancy also extends to their gene expression . For comparison , we also analyzed the changes in expression of all 40 ILPs in mutants that impair the ILP signaling pathway , using daf-2 ( e1368 ) ( a reduction-of-function allele ) , and daf-16 ( mu86 ) ( a null allele ) [18] , [34] . Many ILPs were up-regulated in the daf-2 ( e1368 ) background , suggesting compensation . Many of the ILPs that were regulated by other ILPs were also affected in the daf-2 ( e1368 ) and daf-16 ( mu86 ) backgrounds , suggesting that these changes were mediated through the canonical ILP signaling pathway . In general , daf-2 ( e1368 ) and daf-16 ( mu86 ) tend to cause larger effects on gene expression , suggesting that they might be closer to the upper limit of the gene expression changes , as might be expected if the central pathway for ILP signalling is disrupted . Some ILPs that were regulated by other ILPs were not affected by daf-2 ( e1368 ) or daf-16 ( mu86 ) ; this difference could be due to residual signaling activity retained in daf-2 ( e1368 ) [34] or the use of alternative pathways for inter-ILP regulation . To understand inter-ILP communication , we built a network based on these qPCR results for graph theory analysis , treating each ILP as a node and each regulatory interaction as an edge ( Figure 2B ) . In this network , the edges are directed ( reflecting the regulation of one ILP by another ) and signed ( indicating positive or negative regulation ) to represent the flow of information . We discovered three major properties of this network . First , the ILP network had “small world” properties defined by two key parameters: the characteristic path length that measures the average minimal number of edges between all possible pairs of ILPs , and the clustering coefficient that measures the density of local interconnections [50] , [51] . Compared with random networks with the same number of edges and nodes , the ILP network has a short path length , 3 . 17 , and a high clustering coefficient , 0 . 13 ( Figures S3A to S3C ) . Respectively , these properties might suggest that within these genetic circuits , signals can be communicated relatively efficiently from one ILP to another because they are separated by very few intervening ILPs , and that information is processed by local genetic circuits . These are consistent with the parallel processing we observed in the dauer entry sub-network , which is discussed below . Second , the ILP expression network displayed hierarchical regulation . Plotting the number of regulators ( in-degree ) versus the number of targets ( out-degree ) of each ILP ( Figure 2C ) reveals a regulatory hierarchy where several ILPs had an exceptionally high number of regulators or targets . This organizational feature suggests different functional attributes for the ILPs . ILPs with few inputs and many outputs are putative upstream regulators; ILPs with similar numbers of inputs and outputs likely act in relays or processing circuits; and ILPs with many inputs and few outputs could serve as downstream integrators or effectors . Third , important nodes for network communication tend to affect more processes . We calculated the betweenness centrality for each ILP , which measures its importance as a link between other ILP pairs in the network ( Figure 2D ) [52] . ILPs with higher betweenness centrality were more likely to be pleiotropic ( Figures 2D to 2E ) , similar to protein-interaction networks where proteins with high betweenness centrality tend to be essential [52] . Thus , ILPs with high betweenness centrality may act as bottlenecks during information flux in a wider range of processes . Our network analysis was robust to missing edges , such as those from subtle gene expression changes that did not rise to statistical significance . The top ranked ILPs for each network parameter were similar despite the addition or removal of 25% of random edges ( Figures S3H to S3K ) , indicating that we have sampled the network sufficiently . To relate ILP function to network organization , we mapped the high-confidence ILPs identified in each screen onto the network , which provided three global observations . First , the ILPs with phenotypes were spread over the network ( Figure 3A ) , suggesting that signaling across many parts of the network was important for its overall function . Second , the ILPs with more specific phenotypes from the non-sensitized screens were segregated into different locations ( Figures 3B to 3F and 3J ) , consistent with the observations that gene expression defects in these ILP mutants do not propagate over the entire network ( Figure 2A ) . The separation of critical nodes in the network could limit the number of physiological defects when one ILP is perturbed . Third , our sensitized screens for dauer entry revealed another functional level of non-critical ILPs distributed over much of the network ( Figures 3F to 3I ) . This suggests distributed processing , which could reduce the severity of a phenotype by providing alternate routes of communication . Together , these mechanisms contribute to functional specificity , which is an aspect of the ILP combinatorial code . To address how ILPs combinatorially regulate a specific process , we analyzed genetic interactions among deletion mutations of ILPs involved in dauer entry . We tested 56 double mutant combinations by selecting a diverse subset of 13 ILPs identified from each of the three dauer entry screens , encompassing ILPs showing high and low penetrance ( Figures 1B to 1D ) . To classify genetic interactions , we first determined how the fraction of dauer entry in the double mutant differed from the expected fraction in an additive model based on the single mutant phenotypes ( Materials and Methods ) . We then subdivided the interactions based on whether the corresponding single mutants had the same or opposite phenotypes ( Figure 4 , Table S6 ) . This analysis revealed a level of diversity in gene interactions not predicted by simple redundancy . Diverse genetic interactions ( defined in Figure 4 ) were observed in 47% ( 26/56 ) of the double mutants , of which 38% ( 10/26 ) were additive or synergistic . This result indicates that while the choice between dauer arrest and reproductive growth is binary , the likelihood of a given choice is specified by a graded combination of ILP activities . Strikingly , 9 of these 10 additive or synergistic interactions were seen in double mutants with null mutations in either ins-35 or daf-28 , suggesting that these ILPs are important genetic hubs in dauer entry , consistent with their strong dauer entry phenotypes . The remaining 53% ( 30/56 ) of the double mutants showed no effect or no interaction ( Figure 4 , Table S6 ) , indicating that ILPs are not promiscuous in their interactions during dauer entry , even with other ILPs involved in the same process . These results reveal how signals from pairs of ILPs are integrated to regulate dauer entry . Our findings also demonstrate functional differences among ILPs that regulate dauer entry , and indicate that the effect of an ILP depends on genetic background . Information processing is strongly influenced by the signaling motifs within the network and the overall network architecture [53] . While regulatory interactions serve as a roadmap for information flow among ILPs , genetic interactions between ILPs reflect how their activities are integrated to generate a physiological outcome . To assess information flow and processing , we combined regulatory and functional data for the ILPs whose genetic interactions were extensively defined for the dauer entry phenotype ( Figure 5 ) . The connectivity and synergistic or additive genetic interactions indicate parallel signaling in the dauer entry sub-network ( Figures 5A to 5B ) . The major signals that inhibit dauer entry come from three main branches ( daf-28 , ins-6/ins-33 and ins-6/ins-35 ) , because mutants in these branches have the strongest phenotypes ( Figure 1C ) . To generate graded probabilities of dauer entry , signals from these three branches are integrated in an additive or synergistic manner based on their genetic interactions ( Figures 4 and 5B ) . This network organization was supported by the phenotypes observed when we disrupted the daf-28 , ins-6/ins-33 and ins-6/ins-35 branches using combinations of null mutations . In the ins-33 and daf-28 double deletion mutant , we observed a strong synergistic response with a high proportion of dauers even at 25°C ( Figure 5D , Table S7 ) . Strikingly , in the ins-33; daf-28; ins-35 triple deletion mutant , up to 80% dauers were observed at 25°C ( Figure 5D , Table S7 ) , which is nearly comparable to daf-2 mutants . These results reinforce the idea that the daf-28 , ins-6/ins-33 and ins-6/ins-35 branches are major pathways for regulating dauer entry . The different connectivities within each branch of the ILP network suggest that they use different information processing strategies ( Figure 5 ) . In the daf-28 branch , daf-28 inhibits ins-26 , which likely serves as a compensatory regulation based on their synergistic interaction ( Figures 4 and 5B ) . The effect of this compensation is likely to be regulation of ins-5 as both daf-28 and ins-26 inhibit ins-5 . In contrast , the ins-6 branches have a bifurcated topology where ins-33 and ins-35 process inputs from ins-6 . A non-additive interaction was observed between ins-6 and ins-35 , as well as between ins-6 and ins-7 , which is downstream of ins-35 ( Figures 4 and 5C ) ; while an additive interaction between ins-6 and ins-33 indicates compensation ( Figures 4 and 5B; see below ) . At a downstream level , non-additive or non-synergistic interactions occur within the ins-33 or ins-35 branches , but not the daf-28 branch . Crosstalk occurs between the daf-28 and ins-6 branches ( Figure 5A ) , which may coordinate their signaling activities . ins-3 is likely to act as a negative modulator providing feedback to the dauer entry sub-network at multiple levels; such circuits are associated with noise reduction and homeostasis . Unlike most ILPs , the ins-3 mutation decreased dauer entry in several backgrounds ( Figures 1D and 4 ) . ins-3 expression was activated by ins-6; while ins-3 in turn inhibited ins-6 expression , as well as other ILPs in the daf-28 branch ( Figures 2A and 5A ) . While both ins-14 and ins-17 show high and low-confidence dauer entry phenotypes , respectively , they are likely to act separately as modulators in the main dauer entry sub-network ( Figure 5 ) for two reasons . First , they are not directly connected to the ins-6 and daf-28 branches of the expression network ( Figures 5A to 5C ) . Second , they have weaker interactions with the genes in the daf-28 and ins-6 branches ( Figure 4A ) . One exception is an additive interaction between ins-35 and ins-14 ( Figures 4 and 5B ) , which might represent cross-talk at the downstream level . ILPs could also exert either strong or weak effects ( Figure 1 ) . For example , although ins-6 and daf-28 both regulate dauer entry and exit , ins-6 null mutations had a stronger effect on dauer exit , whereas daf-28 null mutations had a stronger effect on dauer entry [5] . Our results reveal that this feature is common in the whole ILP system ( Figure 1 ) . These specificities are not due to some ILPs being generally strong signals , while others are generally weak , because the relative effects of the ILPs can be reversed depending on the phenotypes . Our integrated analysis provided a mechanistic explanation for the phenotypic specificity of daf-28 and ins-6 ( Figure 1C ) during dauer entry . Loss of daf-28 is compensated by ins-26 , because ins-26 was up-regulated in daf-28 mutants ( Figures 2A and 5A ) and because ins-26; daf-28 double mutants have a more severe phenotype than either single mutant ( Figures 4 and 5B ) . However , ins-26 is a weak compensator , as indicated by its weak phenotype ( Figure 1C ) . Additionally , daf-28 mutants up-regulate ins-5 ( Figures 2A and 5A ) , an ILP that can promote dauer entry ( Figures 1 and 4 ) . As opposed to compensation , increased ins-5 expression contributes to the mutant phenotype of daf-28 , because removing ins-5 suppressed the daf-28 mutation ( Figures 4 and 5C ) . These two targets of daf-28 therefore contribute to its strong dauer entry phenotype . In contrast , ins-6 is compensated by daf-28 and ins-33 , because both daf-28 and ins-33 were up-regulated in ins-6 mutants ( Figures 2A and 5A ) , and both ins-6; daf-28 and ins-33; ins-6 double mutants had a more severe phenotype than the respective single mutants ( Figures 4 and 5B ) . Both daf-28 and ins-33 were strong compensators , as indicated by their strong phenotypes ( Figure 1C ) . Thus , the weak ins-6 phenotype could be explained by compensation from two strong regulators . Together , these results show that connectivity within the ILP network serves as an important determinant of functional differences among ILPs .
Most animals , including humans , encode multiple ILPs in their genomes , which regulate multiple processes [4] , [5] , [14] , [15] , [23] , [24] , [26] , [27] , [54]–[58] . However , the biological function of large ILP ensembles remains an open question . Our systematic analysis of C . elegans ILPs revealed that they are organized into an ILP-to-ILP network that provides several regulatory mechanisms for graded signaling , functional diversity , robustness to gene perturbation and information flow . In turn , these functional properties of the ILP network generate aspects of a combinatorial code that links ILPs to developmental and physiological outputs . Thus , our findings challenge the notion that broad redundancy is the central feature of the C . elegans ILP family . Large gene families are often proposed to employ a combination of redundancy and diversity to regulate biological processes [59] . Here , we reveal the specific implementation of an ILP combinatorial code that coordinates aspects of development and physiology ( Figure 1A ) . Different ILPs generally affect different combinations of processes , which support the idea that redundancy is not evolutionarily stable unless the genes have additional functions [59] , [60] . The high-confidence phenotypes indicate that many single ILPs can significantly contribute to different phenotypic outputs . This combinatorial coding of phenotypes therefore argue against simple redundant mapping between ILPs and their outputs , but show that the complexity of these gene-phenotype relationships is generated at least in part by inter-ILP communication . The intermediate modularity of the ILP phenotypes raises the possibility that multiple ILP signaling centers exist in the animal , which could provide differential contributions to different processes . In addition to the regulatory connectivity that underlies phenotypic specificity , spatial specificity in ILP signaling could also be a complementary mechanism in achieving the specific patterns of ILP phenotypes . This model will need to be tested in the future by tissue- or cell-specific rescue of the ILPs , coupled with the elucidation of their downstream target tissues where the DAF-2 ILP receptor acts . Undirected networks have been recently used to group the C . elegans ILPs based on similarities in their expression patterns [31] . Here we show that the C . elegans ILPs are organized at the level of ILP-to-ILP regulation in a directed regulatory network , where signals in different branches are processed differently and modulated by cross-talk . This is exemplified in the different connectivities between the ins-6 and daf-28 branches of the dauer entry subnetwork , whose distinct signals are ultimately integrated to set the probability of dauer entry . This network organization thus contributes to the graded nature of the ILP combinatorial code . This property generates different probabilities of dauer entry that result in different fractions of developmentally arrested dauers versus reproductive adults within a population . Dauers can survive environmental insults that kill reproductive adults and can thus serve as a hedge at the cost of delayed reproduction . Therefore , the advantage of this graded response provided by the parallel circuit organization is the ability to optimize the trade-off between fast reproduction versus survival in response to variable environments . These findings further underscore how circuit organization in a network contributes to the phenotypic outputs of a multi-gene family . Compensation and distributed , parallel processing in the ILP network provide robustness against gene or network perturbation . Robustness in preventing dauer entry allows for rapid reproduction , ensuring that animals develop as dauers only in extreme conditions , such as when the environment impinges on more than one ILP . In addition , the connectivity of the ILP network show that specific compensatory circuits are organized to generate strong and weak regulators , an important component of the combinatorial code . Extensive genome-wide studies in yeast indicate that complete or partial functional redundancy can occur among duplicated gene pairs [61] , [62] where the loss of one gene can be compensated by responsive circuits that increase the expression of a second homologous gene [63] . Although compensatory circuits are often hypothesized as a feature of gene families that lead to redundancy , we show that its actual implementation can lead to more complex outcomes than previously proposed . Instead of global redundancy , the gradation provided by the ILP network is consistent with the idea that partial redundancy , as well as overlapping and distinct functions , could serve to encode diverse inputs [59] , [60] . ILP-to-ILP signaling in diverse animals uses similar signaling motifs , such as feedback , compensatory inhibition and feedforward circuitry [4] , [24] , [28]–[30] , [64] , [65] , which may provide similar biological functions despite component differences [53] . Our findings suggest how simple circuits can be organized to generate complex network functions; like signaling motifs , these principles may also apply to networks in general . Because our results indicate the importance of specificity versus redundancy in multi-gene families is a consequence of network organization , we propose that large-scale connectivity-based approaches have general utility in dissecting the regulatory mechanisms employed by different families of intercellular signals in different animals . In summary , we have delineated the C . elegans ILP-to-ILP regulatory network based on functional criteria , which provides a distinct approach to existing ILP networks based on expression similarities [31] . This ILP-to-ILP regulatory network , coupled with our systematic genetic analyses , serves as a mechanistic framework for understanding information processing by ILPs . Our findings suggest that the multiple ILPs provide the ability to organize circuits into a network with diverse points of regulation , which in turn produces an intricate combinatorial code to orchestrate development and physiology . Together , this represents a new avenue to understand how hormonal systems compute the development and physiology of the organism .
C . elegans were cultivated at 20°C under standard conditions except where otherwise stated . The strains used are listed in Table S1 . All ILP deletions were independently confirmed using PCR from genomic DNA with primers different from those used by the C . elegans Knockout Consortium to isolate the mutation . ins-10 ( tm3498 ) had increased expression of the coding region from our qPCR experiments ( below ) . PCR using genomic DNA from 6× outcrossed ins-10 ( tm3498 ) mutants with primers that annealed to the start and end of the ins-10 coding sequence amplified a genomic fragment that contained the full ins-10 coding sequence which was verified by sequencing ( data not shown ) . Because ins-10 ( tm3498 ) also contained a deletion in the endogenous ins-10 locus , which we verified independently from the C . elegans Knockout Consortium , these results indicate that ins-10 ( tm3498 ) involves at least a deletion and duplication of the ins-10 coding region that led to ins-10 overexpression . All mutant strains used in this study were obtained from the Knockout Consortium [66] . Double and triple mutants were generated by standard genetic methods . See Table S1 for strain list . Deletions were regularly verified using PCR . All the phenotypic assays were conducted on fresh NGM plates seeded with fresh OP50 unless specified otherwise , using animals that were well fed for at least 2 generations . The lifespan and dauer assays were replicated in different labs . The identity of each strain was blinded for most assays . We generated plasmids to rescue the phenotypes of the ILP mutants . These plasmids contain the entire coding region of the gene of interest and the 5′ and 3′ intergenic regions up to the next open reading frame . Genomic regions for ins-3 , ins-4 , ins-5 , ins-14 , ins-15 , ins-21 , ins-23 , ins-26 , and ins-27 were subcloned using a recombineering method [69] from the corresponding fosmids into the pQL60 , a vector derived from the original pPUB in which the unc-119 marker was removed . Genomic regions for ins-31 , ins-33 and ins-35 were amplified by PCR and subcloned into pCR-Blunt TOPO ( Invitrogen ) . The transgenic lines bearing extrachromosomal arrays were generated by microinjection of the rescue construct at different concentrations ( see Table S1 ) as well as ofm-1::gfp as a coinjection marker ( 25 ng/µl ) and pBluescript as a carrier DNA up to a final concentration of 100 ng/µl of DNA . For ins-12 , a mini-gene was synthesized , subcloned into the MosSCI plasmid pCFJ352 [70] with the corresponding the 5′ and 3′ intergenic regions up to the next open reading frame and integrated into the QL35 strain using MosSCI [71] . Modularity of the ILP-to-phenotype and the mRNA-to-ILP matrices were estimated by rearranging the rows and columns of the matrix to find highly interconnected groups and then assessing matrix-wide the ratio of the number of inside to outside group connections . We used the adaptive BRIM ( Bipartite Recursively Induced Modules ) algorithm [49] , [76] , which is a heuristic method , implemented in MATLAB [49] , [76] to maximize a bipartite modularity value Q . This Q value is dependent on modularity of the matrix; by definition , a perfectly modular matrix is comprised of clusters of completely isolated groups ( ) , and modularity declines as the number of cross-group connections increases ( ) . Because the modularity calculation is based on a stochastic algorithm that produced different matrix arrangement each time the algorithm is run , we performed the calculation 30 times and took the average of the modularity . The average modularity value of ILP-to-phenotype matrix is ( highly reproducible ) and that of mRNA-to-ILP is To evaluate the statistical significance of the modularity , we utilized two null models . The first model is a Bernoulli random null model in which the null matrix has the same total number of interactions as the original matrix , albeit randomly positioned . The second is a probabilistic degree null model in which each interaction in null model is assigned a probability . The ILP-to-phenotype and mRNA-to-ILP matrices are significantly different against the Bernoulli random null model ( p<0 . 001 in both cases ) ; however , when compared against the probabilistic degree null model , which is a stronger statistical test , the p-values of both matrices are greater than 0 . 05 . These results suggest that both matrices are weakly modular . | Insulin signaling is widely implicated in regulating diverse physiological processes ranging from metabolism to longevity across many animal species . Many animals have multiple insulin-like peptides that can regulate the activity of this signaling pathway . For example , while humans have ten , including the well-studied insulin hormone , the nematode Caenorhabditis elegans has forty such peptides . The similarity among these insulin-like peptides led to the predominant notion that widespread redundancy occurs among these peptides . Contrary to this notion , we find that the forty insulin-like peptides in the nematode C . elegans have specific and distinct effects on eight different physiological outputs that range from development , stress responses , lifespan and reproduction . Interestingly , we also find that these peptides regulate each other at the transcriptional level to form a signaling network . In addition , we observe that this network is organized into parallel circuits , whose activities are affected by compensation , feedback and crosstalk . Finally , the organization of the network helps to explain how different combinations of peptides generate specific outputs and captures the complexity of how these peptides orchestrate an animal's physiology through distinct peptide-to-peptide signaling circuits . | [
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| 2014 | An Insulin-to-Insulin Regulatory Network Orchestrates Phenotypic Specificity in Development and Physiology |
Mass drug administration ( MDA ) is part of the current trachoma control strategy , but it can be costly and results in many uninfected individuals receiving treatment . Here we explore whether alternative , targeted approaches are effective antibiotic-sparing strategies . We analysed data on the prevalence of ocular infection with Chlamydia trachomatis and of active trachoma disease among 4 , 436 individuals from two communities in The Gambia ( West Africa ) and two communities in Tanzania ( East Africa ) . An age- and household-structured mathematical model of transmission was fitted to these data using maximum likelihood . The presence of active inflammatory disease as a marker of infection in a household was , in general , significantly more sensitive ( between 79% [95%CI: 60%–92%] and 86% [71%–95%] across the four communities ) than as a marker of infection in an individual ( 24% [16%–33%]–66% [56%–76%] ) . Model simulations , under the best fit models for each community , showed that targeting treatment to households has the potential to be as effective as and significantly more cost-effective than mass treatment when antibiotics are not donated . The cost ( 2007US$ ) per incident infection averted ranged from 1 . 5 to 3 . 1 for MDA , from 1 . 0 to 1 . 7 for household-targeted treatment assuming equivalent coverage , and from 0 . 4 to 1 . 7 if household visits increased treatment coverage to 100% in selected households . Assuming antibiotics were donated , MDA was predicted to be more cost-effective unless opportunity costs incurred by individuals collecting antibiotics were included or household visits improved treatment uptake . Limiting MDA to children was not as effective in reducing infection as the other aforementioned distribution strategies . Our model suggests that targeting antibiotics to households with active trachoma has the potential to be a cost-effective trachoma control measure , but further work is required to assess if costs can be reduced and to what extent the approach can increase the treatment coverage of infected individuals compared to MDA in different settings .
Trachoma , a ‘Neglected Tropical Disease’ , is the leading infectious cause of blindness worldwide and there are currently an estimated 46 million people with the active stage of the disease [1] . The disease mostly affects impoverished populations where people cannot afford treatment and access to running water is scarce . The World Health Organization ( WHO ) advocates the ‘SAFE’ strategy ( Surgery for trichiasis , distribution of Antibiotics , Facial cleanliness and Environmental improvement ) to work towards the Global Elimination of Trachoma as a public health problem by 2020 . Annual mass drug administration ( MDA ) of antibiotics , to reduce the prevalence of the aetiological bacterium , Chlamydia trachomatis , is recommended for at least three years to members of communities in which the prevalence of Trachomatous Inflammation – Follicular ( TF ) in 1–9 year-olds is 10% or greater [2] . WHO recommends azithromycin as the first-line ( oral ) antibiotic for all , except infants under the age of 6 months who are given topical tetracycline [2] . MDA is advocated because screening individuals is not cost-effective and there is a poor correlation between active disease and infection of an individual [3] , [4] , [5] , [6] . Field-ready and cost-effective diagnostic tests for infection with C . trachomatis are currently unavailable . The ‘SAFE’ strategy has had success in reducing the prevalence of active trachoma in certain populations [7] , [8] , [9] . However , there are many costs associated with implementing MDA , particularly if the antibiotics are not donated , and many uninfected individuals receive treatment [10] , [11] , [12] . It has been estimated that there are fifty-seven countries endemic for trachoma [13] . Control programmes in eighteen of these countries currently receive azithromycin donated by the manufacturer , Pfizer , through the International Trachoma Initiative [14] . However a large disparity remains in certain countries between the number of individuals in the target population requiring treatment and the number of individuals receiving antibiotics [14] . If antibiotics can be successfully targeted to groups of infected individuals within a population rather than being administered to the whole population , the number of antibiotic doses required per population may be reduced . This saving of antibiotic resources could be utilised by other populations who require treatment to reduce trachoma . However the targeting method would only be justified if the method is as effective in reducing transmission as MDA and is also cost-effective . Households with active trachoma are potential targets for antibiotic distribution . Trachoma clusters by household [15] , [16] , [17] , [18] and we have previously shown that in most communities intra-household transmission is very efficient [19] . We found on average 71% of incident infections to be the result of household transmission ( with the remainder due to transmission between households ) . An alternative approach to targeting treatment would be to limit treatment to children because they are the principal reservoir and source of infection in most communities . Children in some communities have been shown to have a relatively high prevalence of active disease [20] , [21] , [22] and a high burden of infection [23] . Here we investigate whether targeting antibiotics to households that have at least one member with active disease or to children alone is effective in the prevention of ocular chlamydial infection by analysing data on the prevalence of C . trachomatis and active disease from four endemic populations in West and East Africa ( two in The Gambia , and two in Tanzania ) with different baseline trachoma prevalence . We calculate the cost-effectiveness of targeted household treatment compared with MDA on the basis of a mathematical model and previously published data on the costs of these interventions [10] , [11] .
Conjunctival swabs were collected from a total of 4 , 436 individuals living in four endemic populations , which had not received prior interventions for trachoma control , in West and East Africa ( Upper Saloum District and Jali village in The Gambia; Kahe Mpya sub-village and Maindi village in Tanzania ) and the presence of infection was assessed using Polymerase Chain Reaction ( PCR ) amplification of a target sequence in the common cryptic plasmid of the bacterium C . trachomatis . Standard procedures current at the time of these surveys were followed to prevent contamination , described in [23] and [24] . In Maindi village , quantitative PCR amplification of the omp1 gene was used to indicate presence of infection . In all four studies clinical observations were made by experienced trained observers using a ×2 . 5 binocular loupe and pen torch or direct sunlight . In The Gambia the more detailed clinical diagnosis “FPC” system [25] was used but subsequently converted to the simplified WHO grading system [26] for this analysis . In Tanzania the simplified grading system was used . Active disease was defined as the presence of TF and / or Trachomatous Inflammation – Intense ( TI ) . Detailed demographic information was collected including individual age , gender , and household membership . Full descriptions of the study populations and laboratory methods have been published elsewhere [17] , [24] , [27] , [28] and details on community structure are summarized in [19] . Pre-control prevalences of infection in these populations ( all ages ) were 7 . 2% , 22 . 1% , 9 . 5% and 36 . 0% respectively . The age distribution of the prevalence of infection in these four communities is given in Table S1 . The proportion of people present and consented to being screened for trachoma in the four data sets was 0 . 84 Upper Saloum district , 0 . 98 Kahe Mpya sub-village , 0 . 99 Jali village , and 0 . 86 for Maindi village . The work presented in this paper is based on further analyses of the data obtained in the original studies which had been granted ethical clearance [17] , [24] , [27] , [28] and did not involve collecting further information . For this reason additional ethical approval was not sought . The sensitivity and specificity of active disease ( TF and TI ) as a marker of infection were calculated among individuals in each community . We also calculated the sensitivity and specificity of active disease exhibited by at least one member of a household as a marker for infection of at least one household member ( which we refer to as the household sensitivity and specificity ) . Ocular chlamydial infection probably elicits only a limited protective immune response against re-infection and can be described by a simple Markov model where each individual may be either susceptible or infected . We have previously analyzed a susceptible→infected→susceptible ( SIS ) model where the population is structured into households [19] , [29] , [30] . Here we have extended this model to allow for different transmission parameters among ‘children’ ( those aged less than ten years ) and ‘adults’ ( those individuals aged 10 years and older ) ( Text S1 ‘Model of Ocular Chlamydia Transmission’ ) . We chose this classification of age because children under the age of ten are considered to be the principal reservoir of infection . Transmission parameters of each model for each dataset were estimated from the survey data using maximum likelihood , assuming endemic equilibrium . The most parsimonious yet adequate model for each dataset was selected using the Akaike Information Criterion ( AIC ) [31] . The transmission model was written in R ( version 2 . 7 . 2 ) . The rate of recovery from infection was taken as the reciprocal of the average duration of infection estimated from a Gambian cohort with frequent follow-up [4] ( 18 . 6 weeks for children , 7 . 1 weeks for adults and 17 . 2 weeks on average for the non-age-structured model ) . The effectiveness of different treatment strategies was assessed using the most parsimonious model identified for each of the communities ( Text S1 ‘Model Selection’ , Table S2 ) . With the exception of Upper Saloum district , the transmission models included a greater contribution of children to transmission than adults . Active disease at the household level was incorporated into the model at each round of treatment . At the time of treatment , each household was assigned a disease status by sampling from a Bernoulli distribution where the probability of a household having at least one individual with active disease was taken to be a function of the number of infected individuals within a household at the time of treatment . This probability function was calculated for each dataset on the basis of the observed distribution of infection and active disease in households of different sizes ( Figure S1 ) . The outcome of three annual rounds of azithromycin treatment was investigated in all four populations as this is the number of treatment rounds recommended by the WHO prior to re-assessment of the prevalence of active disease when the baseline prevalence of TF in children is greater than 10% . For a transmission model parameterised to Maindi village , annual rounds were predicted to result in infection returning to almost baseline level in all strategies within one year after a treatment round suggesting that the treatment rounds need to be more frequent for this higher transmission setting . Therefore the effect of six bi-annual rounds was investigated for this setting . Stochastic simulations of the model were used to examine four possible treatment scenarios: A single treatment with azithromycin was assumed to be 95% efficacious in clearing infection [32] . We did not explicitly model treatment of infants aged <6 months with topical tetracycline instead of oral antibiotics . We assumed treatment coverage to be 80% in a ) , b ) and d ) . One hundred simulations were run for each strategy to compare the effectiveness of each strategy . Further details of the stochastic model ( written in R ) are given in Text S1 ‘Stochastic Simulation Model’ . The cost-effectiveness of different antibiotic distribution strategies ( compared with the ‘doing nothing’ option ) from a government and societal perspective was assessed using previously published cost data from Mali and Nepal [10] , [11] ( summarised in Table S3 ) and the results from the stochastic simulations . The cost data were collected in 1998 and 2000 respectively for the studies in Mali and Nepal . Using the most recently available consumer price index for the two countries ( 2007 ) [33] , [34] , [35] , the costs were converted to the value of US$ in 2007 . Costs included the generic price of azithromycin per tablet , drug delivery costs per population size , and opportunity costs ( the amount of money not earned per recipient whilst they attend the treatment campaign ) . Delivery costs in the Mali study consisted of governmental ( salaries and vehicle investment ) and distribution ( dispatching , training of nurses and other health workers , per diems and fuel ) costs specific to each strategy . Delivery costs in the study from Nepal were composed of salary and transportation costs and not the training of health workers . The delivery costs were higher for household-targeted treatment as they accounted for the extra training and salaries of nurses to diagnose trachoma in Mali and the increase in transport costs in Nepal ( in this study two trips per community were assumed for this strategy: one for screening and one for treatment ) . We assumed that MDA was distributed via a central site . In agreement with the study in Mali , we assumed that opportunity costs equal to half a day or one hour's wages were incurred by individuals aged ≥10 years receiving treatment during MDA or HTT respectively . We assumed that individuals aged 10 years or older received an average of 3 . 43 azithromycin tablets and those under the age of 10 received an average of 1 . 02 tablets . ( Text S1 ‘Cost Effectiveness Analysis’ ) . One hundred stochastic simulations were performed for each strategy in each community and the costs were applied to the resulting simulations . The total cost of azithromycin was calculated by multiplying the number of individuals receiving treatment by the price per tablet and the mean number of tablets received in that age group . The delivery costs were scaled linearly to the size of the population in the four endemic areas under study and were assumed to occur at each round of treatment . A discount rate of 3% per year was applied to all costs . Two estimates of total drug costs , delivery costs and opportunity costs were obtained using the two different sets of cost data and the mean cost of the two was calculated . Cost-effectiveness was calculated on the basis of the median effectiveness observed in the simulations , with lower and upper bounds based on the inter-quartile range of the simulations and the upper and lower costs from the two cost studies .
In all four communities ( Upper Saloum district and Jali village in The Gambia , and Kahe Mpya sub-village and Maindi village in Tanzania ) the sensitivity of active disease as a marker of infection was higher and specificity lower at the household level compared with the individual level ( Table 1 ) . Limiting clinical diagnosis in a household to children under the age of 10 years resulted in a similar household sensitivity and specificity compared to undertaking clinical diagnosis in all age groups ( Table S4 ) . Targeting treatment to households , in which at least one resident has active disease , was predicted to result in post-treatment dynamics similar to MDA ( Figure 1 ) . The household-targeted approach had a slightly higher rate of return of infection and therefore the probability of eliminating infection five years after the last treatment round was predicted to be somewhat lower than the probability of eliminating infection after MDA ( absolute difference between the probabilities in each setting was −0 . 22 , −0 . 04 , −0 . 25 and −0 . 12 for Upper Saloum district , Jali village , Kahe Mpya sub-village and Maindi village respectively ) . However if all individuals in targeted households were treated , then the probability of eliminating infection was predicted to greatly increase in each setting , being greater than MDA ( absolute difference between the probability of eliminating infection after HTT with 100% coverage within the targeted households and the probability of eliminating infection after MDA was 0 . 26 , 0 . 69 , 0 . 07 and 0 . 44 respectively ) ( Figure 1 ) . Limiting MDA to children under the age of 10 years resulted in an initial decrease in the prevalence of infection in the untreated older population ( Figure S2 ) but the probability of eliminating infection in the whole community was greatly reduced compared to the other treatment scenarios investigated ( Figure 1 ) . There was a relatively smaller difference in effectiveness between the different treatment scenarios in the communities with relatively low baseline prevalence ( Upper Saloum district and Kahe-Mpya sub-village ) but HTT with 100% coverage , remained the most effective treatment scenario . Modifying the model to account for variation in the efficiency of transmission among households resulted in faster return of infection for all treatment strategies in the simulations and the probability of eliminating infection was lower five years after the last treatment round ( Figure S3 ) . However , the relative impact of the different strategies remained robust to this additional complexity . A household-targeted approach resulted in a similar number of infected individuals receiving treatment compared with MDA , but reduced the number of treatments given to uninfected individuals ( Figure 2A ) . Assuming 80% therapeutic coverage and that azithromycin was not donated , HTT was predicted to be more cost-effective than MDA in all four communities when including the cost of generic azithromycin ( Table 2 ) . Assuming azithromycin was donated , HTT was predicted to be more cost effective when opportunity costs for individuals collecting drugs in the MDA approach were included ( Table 2 ) . Otherwise , MDA was estimated to be more cost effective . We did not calculate the cost-effectiveness of targeting treatment to children because the model simulations showed it to be the least effective of the four treatment scenarios at controlling infection . If a visit to a household facilitates treatment of all members , then there was a large increase in the number of incident infections averted compared with either MDA or targeted approaches with 80% coverage in hyperendemic settings ( Figure 2B ) . As a result , the household targeting strategy in which all members of diseased households are treated was predicted to be significantly more cost-effective in the areas with high baseline prevalence , even when azithromycin was assumed to be donated ( Table 2 ) .
A targeted approach for distributing azithromycin would result in fewer antibiotic doses distributed per head of population than MDA , thus saving medication for use by other trachoma endemic populations in need of treatment to reach ‘the Global Elimination of Trachoma as a public health problem by the year 2020’ . However the approach would only be warranted if it is as effective in reducing ocular C . trachomatis prevalence in a population as MDA and as cost-effective . Our results have indicated that targeting antibiotics to households with at least one member with active disease has a similar effect to MDA in the reduction of infection . Active disease was found not to be 100% sensitive as a marker of infection at the household level and this explains the small differences observed between the two strategies . However , we have shown that HTT results in a large reduction in the number of uninfected individuals receiving antibiotics compared to MDA ( 26%–51% reduction ) . When antibiotics were assumed to be donated , opportunity costs incurred by individuals taking time to collect tablets from the MDA program resulted in HTT being more cost-effective . Although the large majority of trachoma control programmes currently operate using donated azithromycin , we also estimated the cost-effectiveness of HTT assuming antibiotics were purchased at the generic price , to give a monetary value to the amount of antibiotic used in each strategy for the donor's perspective of the strategy and because some small scale programmes operating at village levels do purchase the drug [36] , [37] . In this case the dominating cost was that of the antibiotics and so HTT was estimated to be more cost-effective . If all members of visited households were assumed to be treated as a result of the visit by the treatment team , a much higher chance of eliminating infection from the community in all settings compared with MDA was predicted . The success of this approach will depend on the extent of household transmission and the degree to which household visits can boost treatment coverage . For example , in a community such as Kahe Mpya where household transmission was estimated to be limited [19] , this approach can be hypothesised to be less effective . Baseline surveys of the prevalence of disease could be used as an indicator for the likely degree of household transmission , enabling the selection of communities that would benefit from a targeted approach . A large effort is typically required to achieve high coverage levels for MDA control programs [38] . In contrast , analogy can be drawn with other disease control programmes , such as vaccination for polio and measles , in which a house-to-house strategy of administering vaccination achieves much higher coverage than a fixed point campaign [39] , [40] . Whether all household members can be reached with a single household visit remains to be investigated and further work is required to address whether coverage of infected individuals can be improved with HTT at what additional costs . The cost per incident infection averted was greatly reduced when 100% of targeted-household members were assumed to be treated in areas with a relatively high prevalence of infection at baseline ( Jali and Maindi villages ) , both when assuming azithromycin was and was not donated . In low prevalence settings the additional benefits of treating all household members were less apparent in our simulations because we investigated the effect of only three annual rounds of treatment , which , in these settings , were sufficient for any treatment scenario to have a greater than 50% chance at eliminating infection . There are some caveats to our cost analysis: the cost data used in the study are a decade old and the linear scaling of delivery costs to the size of each community may not be appropriate for some costs ( for example the time taken to perform a round of HTT may depend not only on the size but also on the geography of the population ) . However , the two cost studies referred to were the only published cost data at the time of our study that included the full cost of HTT . We assumed that individuals aged ≥10yr received a mean of 3 . 4 tablets whilst those aged<years received 1 tablet . This is a simplification and does not include azithromycin suspension given to younger children and topical tetracycline given to infants under 6 months . However , this would increase the total cost of antibiotics further , making targeted treatment more cost-effective . We assumed that MDA occurred via a central site distribution . The WHO states that MDA can be carried out either via central site or by house to house distribution [2] . If we had assumed the latter for MDA there would have been a smaller difference in the distribution costs between MDA and HTT , ( the only difference would be the cost of screening for active disease ) and so HTT would have appeared more cost-effective in comparison to MDA . We took the assumption from the Mali cost study that MDA via a central site would result in adult antibiotic recipients having an opportunity cost of half a day's wages and HTT one hour's wages . However the WHO advises that MDA should be performed outside of the farming season [2] to try to minimise opportunity costs and improve the treatment coverage . In our analysis opportunity costs had a small impact on the cost-effective estimates but further studies could be performed to analyse what proportion of the recipient population's activities are interrupted by the different treatment campaigns . The costs involved in treatment scenarios are likely to vary from country to country and by size of the community treated . Our work has investigated HTT in populations of approximately 1 , 000 people . If such an approach were to be implemented on a district or even country-wide scale , economies of scale will have to be considered e . g . a large number of nurses ( or volunteers ) will have to be trained for screening and there may be societal costs incurred as such personnel may stop working on other health programmes . Further studies are required to investigate these differences . The delivery costs of targeting treatment to diseased households could be reduced in a number of ways which need to be researched further . We currently assume separate visits to households to assess disease and provide antibiotics . Assessment and treatment could be administered in a single visit , thereby reducing transport and salary costs . Furthermore , village volunteers could be trained to assess clinical disease to reduce the costs of ophthalmic nurses ( a scheme which has been trialled with success in Ghana [41] ) . We also assumed that all residents would be screened for active disease at each round of HTT . Firstly , this could be limited to children under the age of ten: we have shown here that this approach has the same sensitivity as screening all ages but the difference in cost between the two approaches remains to be ascertained . Secondly , in practice , as soon as one person in a household is found to have active trachoma , the remainder of the household would not need to be screened . Therefore the cost of HTT in this work may be an overestimate in the higher prevalence settings where it is likely that in some households not all residents would be required to be screened . Further data are required to elucidate how the cost of screening for identifying target households will vary for different levels of prevalence and household clustering , including settings where WHO currently recommends HTT ( active trachoma prevalence of 5%–9% in 1–9 year olds ) . Data on active trachoma , analysed in this study , were collected in a scientific setting by experienced observers . The accuracy of trachoma grading may be more variable in a programmatic setting . A consequence of this would be that that sensitivity and specificity of active disease as a marker of infection at the individual level could worsen . However , this may be less significant at the household level , where diagnosis of just a single case of active disease is sufficient for treatment of that household . Further field studies would help understand the implications of trachoma grading error on HTT . The original analyses of the cost data from Nepal and Mali differed from our work . The study in Nepal [11] , [42] compared MDA of children to HTT of all ages . The study found the two strategies not to be significantly different from one another in the reduction of active disease and the costs involved ( although this could be explained in part by the low power of the study ) . The original study in Mali [10] found HTT to be significantly less effective than MDA of the whole population with respect to the reduction of active disease prevalence one year after one round of treatment ( although the age-adjusted odds ratio for prevalence active disease after HTT in relation to MDA was 1 . 56 with 95% confidence intervals of 1 . 00–2 . 43 indicating the strategies could have had the same outcome ) . The study found HTT to be more cost-effective except in low transmission settings . A difference between our work and the previous cost analyses is that here the cost was calculated as a cost per incident infection averted over five years rather than a change in point prevalence between baseline and one time point in the previous cost analysis . Measuring the number of infections avoided is not feasible in the field but measuring the cost-effectiveness in this way from model simulations gives a better insight into the impact of each treatment scenario on cumulative exposure to infection and therefore the ocular disease process . Limiting treatment to children is another way to target treatment . Our models predicted that the prevalence in adults declines when children under the age of ten are treated , in agreement with House et al . [43] , but this strategy is not as effective as MDA or HTT because the probability of eliminating infection is reduced in all four communities . Women could be included along with children in the target group as explored in the study in Mali [10] . However we did not investigate this strategy because the number of transmission parameters to be estimated would have been too large for the size of our dataset and the prevalence of infection did not differ largely between males and females in the study communities ( excluding Maindi ) [23] . Besides , there is considerable risk that specifically excluding adult males from treatment schedules would jeopardise community support for drug distribution . Another method to target treatment would be to ‘graduate’ communities from MDA once the prevalence of ocular C . trachomatis infection is below a certain threshold , as suggested by Ray and colleagues [44] . Their study predicted graduating communities to be efficacious and drug-sparing ( assuming a diagnostic test for infection becomes available in a field-ready format ) , by fitting a stochastic model allowing for heterogeneous transmission between communities , to the Upper Saloum district and Kahe Mpya sub-village data and a group of communities in Ethiopia . Therefore two separate analyses of the Tanzanian and Gambian data sets have resulted in two different suggestions for targeting treatment . Here we fitted and simulated under a model of transmission which allows individuals to be infected by an infected member of their household or community at two different rates , specific to the setting . The Upper Saloum district contains 14 villages and this analysis grouped the villages together as one population . The Tanzanian sub-village contains balozis ( groups of roughly 10 households that form an administrative unit ) that we also grouped together . Additional analysis would be required to understand the relationship between within household transmission and heterogeneous community transmission where several of the communities constitute a larger population . This would then allow comparisons of the different targeting strategies to be made . A recent study in Ethiopia [45] found that communities which had received MDA with azithromycin was associated with an odds ratio of 0 . 51 ( 0 . 29–0 . 90 ) for childhood ( 1–9 years ) mortality one year after commencement of MDA compared to children in communities which did not receive the antibiotic . If this phenomenon extends to other settings then the impact of HTT with azithromycin on child mortality should be examined . Caveats to our model of transmission have been described previously [19] . Infection status of individuals was characterised through PCR of ocular swabs . Standard precautions at the time of data collection were performed to prevent contamination of infection data ( although the risk of contamination cannot fully be ruled out due to the absence of negative field controls ) . Sensitivity analysis of the assumption that each household is at equal risk of becoming infected found that increasing the level of heterogeneity in the household transmission parameters resulted in a faster rate of return of infection after treatment with a lower probability of eliminating infection for each treatment strategy . Further studies are needed to quantify differences in households' risk of becoming infected . Individuals were assumed not to move from one age group to the next but this is a reasonable simplification as the time spent in the lower age group ( ten years ) by each individual is far longer than the average duration of infection . We have assumed that the relationship between active disease and infection remains constant in a household after treatment . This requires further investigation but preliminary analyses of follow-up data from Upper Saloum District and Kahe Mpya sub-village indicates that households with at least one person with active disease at baseline can predict which households will contain individuals with ocular chlamydial infection at follow-up time points more accurately than households with active disease at follow-up . The model did not include interventions to improve facial cleanliness ( F ) or the environment ( E ) , the interventions advocated by WHO to accompany the distribution of antibiotics [2] . The exclusion of these interventions allowed the predicted effectiveness and cost-effectiveness of the different distribution strategies to be shown clearly . Inclusion of ‘F’ and ‘E’ would reduce the rate of return of infection and increase the probability of eliminating infection by an uncertain factor but is unlikely to alter the rank order of the impact of the different distribution strategies . If the cost of implementing ‘F’ and ‘E’ is independent of the antibiotic distribution strategy then the relative differences between the cost-effectiveness of implementing trachoma control for different antibiotic distribution strategies would remain unchanged . The exclusion of ‘F’ and ‘E’ from the model may explain why infection was observed to return relatively slowly in Maindi village following two rounds of treatment whereas our model predicts infection to rapidly return for an area with such a high baseline prevalence of infection . Changes in hygiene could have arisen in the village through residents receiving radio broadcasts by the National Trachoma Control Programme informing individuals to improve face washing and latrine usage [46] or alternatively , by simply the presence of the intervention itself , altering individuals' behaviour . Our model suggests that targeting treatment to households that have at least one resident with active trachoma is as effective as MDA in a diverse variety of settings and can be more effective if the strategy increases the coverage of infected individuals . We also show that HTT is drug-sparing and has the potential to be more cost-effective but to have a better understanding of this in settings for which azithromycin is donated , more studies are required to evaluate whether HTT can improve antibiotic coverage levels of infected individuals and whether the cost can be further reduced compared with costs recorded in the studies in Mali and Nepal . The results of these studies will provide a better understanding of efficient and effective antibiotic distribution approaches for trachoma control programmes in countries with limited resources . | Repeated ocular infection with the bacterium Chlamydia trachomatis leads to the development of trachoma , a major cause of infectious blindness worldwide . Mass distribution of antibiotics , a component of the current trachoma control strategy , has had success in reducing infection in some areas , but results in a large number of uninfected people receiving antibiotics . We have previously shown that transmission of the bacteria between people in the same household is very efficient . Here , we investigated the effectiveness and cost-effectiveness of targeting antibiotics to households with active trachoma ( inflammatory disease ) compared to mass distribution , using data from four trachoma-endemic populations and a mathematical model of transmission . We found a high correspondence between households with active trachoma and infected households . In all populations the household targeted approach was predicted to be as effective as mass distribution , but it reduced the number of uninfected individuals receiving antibiotics , making the targeted strategy more cost-effective when antibiotics are not donated . Assuming antibiotics are donated , we predicted the targeted strategy to be more cost effective if it increases the proportion of infected individuals receiving treatment . Further work to address the feasibility and the cost variability in implementing the targeted approach in different settings is now required . | [
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| 2010 | Targeting Antibiotics to Households for Trachoma Control |
Five independent groups have reported microarray studies that identify dozens of rhythmically expressed genes in the fruit fly Drosophila melanogaster . Limited overlap among the lists of discovered genes makes it difficult to determine which , if any , exhibit truly rhythmic patterns of expression . We reanalyzed data from all five reports and found two sources for the observed discrepancies , the use of different expression pattern detection algorithms and underlying variation among the datasets . To improve upon the methods originally employed , we developed a new analysis that involves compilation of all existing data , application of identical transformation and standardization procedures followed by ANOVA-based statistical prescreening , and three separate classes of post hoc analysis: cross-correlation to various cycling waveforms , autocorrelation , and a previously described fast Fourier transform–based technique [1–3] . Permutation-based statistical tests were used to derive significance measures for all post hoc tests . We find application of our method , most significantly the ANOVA prescreening procedure , significantly reduces the false discovery rate relative to that observed among the results of the original five reports while maintaining desirable statistical power . We identify a set of 81 cycling transcripts previously found in one or more of the original reports as well as a novel set of 133 transcripts not found in any of the original studies . We introduce a novel analysis method that compensates for variability observed among the original five Drosophila circadian array reports . Based on the statistical fidelity of our meta-analysis results , and the results of our initial validation experiments ( quantitative RT-PCR ) , we predict many of our newly found genes to be bona fide cyclers , and suggest that they may lead to new insights into the pathways through which clock mechanisms regulate behavioral rhythms .
Most organisms exhibit rhythms of behavior and physiology that occur with “circadian” or daily periods . Such rhythms are driven by endogenous biological clocks regulated via the rhythmic expression of a core set of pacemaker genes [4–7] . A salient feature of many circadian genes , conserved across a wide span of evolutionary divergence , is the cyclic expression of their mRNAs [5 , 6] . Several studies have exploited this characteristic to identify novel clock-related genes . Five such reports , based in the model organism Drosophila melanogaster ( the fruit fly ) , utilize microarray technology to discover clock-related genes that exhibit cyclic mRNA expression in fly heads [1 , 8–11] . These cumulatively identify hundreds of rhythmic transcripts; yet , there is a striking lack of overlap among the lists of identified genes . This raises doubts as to the fidelity of reported expression patterns [12–14] . Given the importance of rhythmic transcription to circadian clock function , we revisited these studies , attempting to identify the root causes of existing incongruities and to find transcripts that exhibit truly cyclic expression . Our analyses suggest that compilation and statistical prescreening of all available data lead to substantial reductions in the false discovery rate ( FDR ) . In addition , we find data quality and algorithm choice to play essential roles in determining the transcripts ultimately detected by any particular analysis . We introduce a novel procedure , as well as improvements to previously published techniques , that identify a core set of rhythmically expressed transcripts with high statistical fidelity . Intriguingly , our list includes 133 transcripts not found in the original reports .
Experimental approaches used to generate data were similar among the five fruit fly circadian microarray studies ( Table S1 ) . Wild-type Canton S ( CS ) , yellow white ( y w ) , or cinnabar brown ( cn bw ) strains ( the latter two are mutants marked with pigmentation phenotypes , but are generally considered to be wild-type with respect to circadian behavior ) were entrained in a 12-h light/12-h dark ( LD ) environment before release into constant darkness ( DD ) . Samples of flies were subsequently collected over one to several days at regular intervals . Each sample was used to generate probes for a single Affymetrix Drosophila 1 oligonucleotide array . Sample hybridization , expression detection , and low-level chip analyses were performed according to Affymetrix guidelines and with Affymetrix software ( MAS 4 or 5; McDonald et al . [8] being the single exception in which dCHIP software was used to produce average expression values ) . However , higher-level analyses , particularly the algorithms used to identify cycling expression profiles , differed greatly among the five reports . To assess the degree of overlap among the studies , we compared the reported lists of cycling transcripts between any two , then among any three , four , and finally all five studies . Overlap between any two lists peaked at 27 . 8% . For any combination of three , four , or all five studies , overlap plummeted to 17 . 4% , 10 . 4% , and 9 . 7% , respectively ( Table S2 ) . The seven genes found in common among all five reports include most , but not all , of the known cycling circadian genes ( period being the most obvious exception ) . Overlap values are substantially higher than those predicted by chance ( Table S2; italicized values ) , but are low enough to reveal a surprising degree of disparity . This suggests one , or both , of two possibilities: that the different cycling gene detection algorithms employed introduce bias , or that the five datasets are genuinely inconsistent with each other . The former possibility has been suggested in the reports themselves and in several review articles [1 , 8 , 9 , 12–14] . To our knowledge , two other studies have suggested , but none have directly examined , the latter [3 , 15] . To determine the contribution of algorithm dependent bias to differences in genes identified among the original reports , we analyzed each dataset with its native technique ( the algorithm published with it ) and the waveform correlation method described in McDonald et al . [8] ( see Methods ) . Comparison of the two lists of cycling transcripts generated from each dataset reveals significant disparity , with an average overlap of just 21 . 8 ± 6 . 8% ( mean ± standard error of the mean ) , a range of 12 . 5% to 41 . 7% ( Table S3 ) . These observations are consistent with the notion that algorithms introduce bias , recognizing different transcripts even when applied to the very same dataset . A second potential explanation for the low-level overlap is that the five original datasets are genuinely inconsistent . Such inconsistencies can be attributed to several possible factors . While similar in a broad sense , the five studies do differ in seemingly minor details ranging from selection and treatment of fly populations to mRNA isolation , cDNA probe generation , chip hybridization conditions , and chip lot; even the use of different Affymetrix chip scanners has been shown to produce significant bias in expression profiling [16] . Differences of this nature ( generally referred to as lab-dependent differences ) are extremely difficult to quantify; yet , their contribution to end-of-analysis inconsistencies can be profound , the greater and/or more numerous the difference ( s ) , the larger the inconsistencies . To determine if such differences could be observed among the five reports , we applied a single algorithm ( our reproduction of the McDonald algorithm ) to all five datasets and compared the lists of identified cycling genes ( Table S4 ) . This treatment removes algorithm-dependent bias , leaving lab-dependent differences as the only major source for inconsistencies in identified cycling transcripts . Surprisingly , we observed overlap among the identically derived lists to be even less than that observed among the originally published ones ( Tables S2 and S4 ) . The highest degree of identity observed between any two lists was 24 . 4% , with an average of just 11 . 4 ± 1 . 8% ( range , 4 . 4%–24 . 4% ) , opposed to an average of 22 . 9 ± 1 . 2% ( range , 16 . 5%–27 . 8% ) observed among our comparisons of the original reports . Only three transcripts were found in common among all five reports , opposed to the seven found among the originally reported cycling transcripts . These results strongly suggest that the primary data are inconsistent , presumably due to significant lab-dependent differences , yielding unique lists of genes even when data are identically processed . The successful identification of similar lists of genes appears to depend on consistency both with respect to raw data and the methods used to process it . Irregularities in either or both , as are clearly observed among the five extant studies , lead to significant disagreement among the lists of identified cycling genes . A potential explanation for the observed inconsistencies is the use of different fly strains [15] . At least three behaviorally wild-type strains were used among the five studies ( CS , y w , and cn bw ) . As we were primarily interested in identifying cycling transcripts that may play a role in pathways that mediate central clock output to animal behavior , we chose to examine array data only if it corresponded to strains that exhibit well-conserved behavioral rhythms . Comparing the behavioral profiles in LD and DD of the three experimental strains , we observed a relatively high degree of correlation between CS and y w strains ( r2 = 0 . 75 ) , but cn bw flies were less well-correlated with CS ( r2 = 0 . 30 ) and y w ( r2 = 0 . 46; Figure S1 ) . Based on these observations , our analysis included data from CS and y w , but not cn bw . Subsequently , our analysis consisted of five major steps: ( 1 ) ANOVA-based statistical prescreening for significant time-dependent changes in expression; ( 2 ) correlation analyses assessed via the Pearson correlation coefficient; and ( 3 ) a Fourier transform–based technique ( F24 ) followed by ( 4 ) permutation-derived p-value assessment of all analyses ( cross-correlation , auto-correlation , and F24 ( Figure 1 ) . ( 5 ) Steps 1–4 were performed on randomized datasets to approximate the FDR . Our analyses were also assessed via a simple power statistic . To limit the impact of lab-dependent differences , we compiled data from all of the reports , creating a single meta-dataset . We expected truly cycling patterns to reinforce each other , whereas those found to be cycling in individual reports ( most likely due to errant noisy signals ) would cancel and thereby suppress each other . This allowed us some measure of control over experimental noise without having to identify its precise causes . Data were compiled , sorted according to the LD or DD time of collection , and preprocessed with a log transformation [1–3] and subsequent standardization procedure [17] . The resulting dataset exhibited a normal distribution with an average expression of 0 and variance of 1 for each array ( see Methods; Figure S2 , LD data in part A , DD data in part B ) . These procedures removed several hundred transcripts; more than 12 , 000 were left to consider . Even with a relatively restrictive p-value of 0 . 05 , a dataset of this size would be expected to produce some 600 false positives ( the p-value times the total number probes to be tested ) [18] . Such a number of false positives ( it is interesting to note that fewer than 600 unique transcripts were identified as cycling among the five original reports ) could obscure results , making determination of truly cycling transcripts difficult via the reduction of statistical confidence [18] . Application of a priori filters is a practical solution to this problem . Based on sound assumptions , these can reduce the number of comparisons , while at the same time enriching the remaining population for the characteristic ( s ) of interest [19 , 20] . We reasoned that truly cycling genes would necessarily exhibit statistically significant changes in expression versus time . To identify transcripts that exhibited this trait , we applied an ANOVA filter after data were preprocessed and sorted ( Figure 1; Methods ) . Our ANOVA screen identified transcripts that exhibited statistically significant changes in expression , and removed those that did not from further consideration ( see Methods ) . This produced a set of 372 transcripts that exhibited significant time-dependent changes in expression in LD , and a set of 679 transcripts in DD ( Table S7 ) . All data were then processed via two separate pathways . In the first , expression values were grouped by timepoint and averaged together ( eight to 13 arrays per timepoint ) such that each probe set was represented by two six-point time courses , one derived from all available LD data , the other from DD data . We reasoned this procedure would suppress noise ( the distribution of noise would be random , averaging would tend to suppress it ) while concomitantly reinforcing true signals . However , this technique exhibited a significant shortcoming; it essentially ignored variation within each timepoint . To account for this limitation , we processed all data via a second route . After the ANOVA screening , data were appended such that two profiles , 8–13 d long , one derived from LD data , the other from DD data , were produced for each probe set . In this dataset , variance was not suppressed by averaging . As the majority of our post hoc tests ( autocorrelation being the only exception ) were insensitive to the order of the days of data in the appended profile—a 3 d–long profile would give the same result regardless of the ordering of the days for all cross-correlations and F24—we chose to use a single ordering of the days for all post hoc tests . Before our post hoc tests were applied to the appended datasets , the data for each day in the multiday profiles ( data for each single day came from a single report/lab ) underwent one additional standardization procedure such that the data for each day would exhibit a mean of 0 and variance of 1 . This was intended to minimize any artifactual expression level differences that may have been present in data from each report even after our initial transformation and standardization procedures . It also allowed for roughly equal weighting of each day in the expression profile; that is , expression values for each day were constrained such that their location and scale would be the same , while profile contours remained unchanged . To characterize the expression patterns of transcripts that passed the ANOVA screen , we performed a series of post hoc analyses: cross-correlation to four model waveforms , a sin wave , a previously reported characterization of per expression [21] , wild-type LD CS behavior , and wild-type DD CS behavior; we also employed an autocorrelation technique . Whereas the standard application of autocorrelation would have required two or more days of data [2] , we used a method that allowed autocorrelation to be applied to a single day of data . Autocorrelation requires two representations of the same profile . The first representation ( A ) is held constant as the second ( B ) is shifted out of register with it one timepoint at a time . With each shift , data points at the extremities are “lost” as they are moved beyond the point where they can be paired with a point from the other representation . To avoid this loss of data , we considered each representation circularly . The point lost from the end of B after a single shift would be moved to B's beginning such that all points would be compared to all points at each registration ( e . g . , six timepoints at five nonidentical registrations for the six-point-long averaged time courses ) . In addition , we considered the absolute , rather than raw , value of the Pearson correlation coefficient . This allowed us to detect 24-h rhythmicity with a single day of data—two 24-h sin waves exactly 12 h out of phase with each other will produce an absolute Pearson correlation coefficient of 1 . Two tests were used to approximate the statistical significance of our post hoc tests . First , to determine power ( essentially , a measure of how well each method detected known cycling genes ) , an empirical procedure was used . We tested each method's ability to detect six well-known cycling circadian genes ( Clk , cry , per , tim , vri , and Pdp1; see Methods ) . The power statistic was calculated as n/6 , n being the number of the known cycling genes detected . Next , to determine the FDR , we utilized a method similar to that employed by significance analysis of microarrays ( SAM [22] ) . After each post hoc analysis was applied to a particular dataset , the data were randomized ( within each transcript ) , and the post hoc test was repeated . Genes detected as cycling in the randomized dataset were scored as false positives . The FDR was calculated as the ratio of false positives to positives detected in the original ( nonrandomized ) dataset . Figure 1 presents a schematic diagram of our data organization and preprocessing steps ( Figure 1A ) as well as a flow diagram of all subsequent analysis procedures ( Figure 1B ) . To determine if our compiled dataset would yield results that were more statistically reliable than those derived from any single dataset , we applied our technique to the LD data from each report , provided reports contained replicate arrays for each timepoint ( a requirement for ANOVA prescreening ) , and compared results with those obtained from the meta-dataset . First , we examined how each dataset performed with respect to its FDR . We found the compiled dataset to exhibit an FDR lower than those generated from the original , individual datasets ( Table 1; list IDs refer to lists of overlapping genes—identified by Affymetrix probe set ID—presented in Dataset S1 , an addendum to Table 1 ) , suggesting that compilation leads to results in which false positives are suppressed . Intriguingly , we also discovered that ANOVA prescreening drastically reduced the FDR in the individual and compiled datasets . We also assessed datasets with respect to power . Power observed in the appended LD meta-dataset was as good or better than that observed in any individual set . Maximum power was observed under F24 analysis in the meta-dataset , but in only two of the three individually tested datasets . Other methods exhibited much lower power values . It is possible that differences in power between individual reports may be due to differences in data quality , but such determinations should be treated with skepticism as they rely on a very small sample size ( Table 1 ) . To determine if any one of the original datasets correlated better than the others when compared with the meta-dataset , we directly compared each originally published list with the list of transcripts we identified in our LD meta-dataset . With respect to number of cycling transcripts found in common , we observed each of the original reports to perform similarly; between 27 and 38 transcripts were found in common with the 177 identified in our analysis of the LD meta-dataset , but very few transcripts were found in common between two or more of the original reports and/or the meta-dataset . However , when we considered the number of genes found in common as a percentage of all those found in any one of the original reports , there appeared to be some differences among the five original reports: Claridge-Chang et al . [1] , 24%; MacDonald et al . [8] , 26%; Ceriani et al . [11] , 27%; Ueda et al . [10] , 30%; and Lin et al . [9] , 43% ( Table S6 ) . In our initial post hoc analyses of the DD data , FDRs were found to be much less favorable than those exhibited by the corresponding LD analyses . This is not surprising , as endogenous transcript cycling is known to be less robust and much less widespread than that driven and/or enhanced by the presence of strong entraining stimuli , particularly light cues [5] . Based on these observations , we decided to focus the majority of our efforts on the LD datasets . Under our initial analysis conditions , FDRs were higher than the generally desired value of 0 . 05 or less . Even our best performing analyses , sin-wave cross-correlation of averaged data and F24 of appended data , exhibited FDRs of 0 . 13 and 0 . 12 , respectively . To determine if adjustment of our selection parameters could improve the FDR , we performed a simple FDR optimization on the F24 procedure , chosen as it detected the largest number of genes with the most favorable FDR . We considered three groupings of the appended LD data: transcripts that passed the ANOVA screen ( ANOVA p ≤ 0 . 05 ) , transcripts that did not pass the ANOVA screen , and a third grouping that considered all transcripts . In each group , we varied the threshold of the F24 permutation–derived p-value and observed the impact on the FDR . In the ANOVA-screened group , an F24 permutation p-value of 0 . 02 or less produced FDR values of 0 . 05 or better with almost no impact on the number of transcripts detected ( 167 versus 177 ) , and no impact on power ( power = 1 ) . In the combined grouping , an FDR of 0 . 05 or better required an unreasonably stringent p-value of 0 . 0004 or less . The group that failed to pass the ANOVA screen also failed to produce an FDR ≤ 0 . 05 at our most stringent p-value , 0 . 0001 ( Figure S4 ) . Within our meta-dataset , ANOVA prescreening improved the FDR , and its application was necessary to achieve acceptable FDR values with a reasonable degree of stringency ( i . e . , p ≤ 0 . 02 ) . To determine the extent to which our ANOVA prescreening procedure improved the FDR , we performed the two most successful ( with respect to number of genes identified and the associated FDR ) analysis procedures ( sin-wave cross-correlation applied to the averaged LD dataset and F24 analysis applied to the appended LD dataset ) with and without ANOVA prescreening . ANOVA prescreening significantly improved the FDR with little or no reduction observed with respect to power . In both cases , removal of the ANOVA prescreening procedure increased the number of identified genes , but at significant detriment to statistical fidelity ( sin-wave cross-correlation to averaged data , 52–probe sets FDR = 0 . 13 with ANOVA , 602–probe sets FDR = 0 . 47 without; F24 to appended data , 177–probe sets FDR = 0 . 12 with ANOVA versus 1 , 688–probe sets FDR = 0 . 38 without; Table 1 ) . In an expanded analysis , we examined the effect of ANOVA prescreening on the LD and DD datasets . We found ANOVA to significantly improve the FDR except where the number of identified cycling transcripts was very small ( n ≤ 10 ) . In these cases , ANOVA seemed to have no discernable effect ( Table 2 ) . To determine if data compiling or ANOVA prescreening could be of benefit to other previously reported cycling-gene–detecting algorithms , we applied both procedures to the McDonald and F24 algorithms . We determined FDRs for both methods with and without application of the ANOVA screen . Under both analyses , the ANOVA prescreening procedure significantly reduced the FDR , from 0 . 57 to 0 . 14 for the McDonald algorithm and from 0 . 34 to 0 . 12 for F24 analysis ( Table 3 ) . The number of identified transcripts was reduced , but power remained unaffected , and the ANOVA screen passed all known cycling genes , suggesting the transcripts it rejected to be false positives . Our tests indicate that regardless of subsequent algorithmic steps , prescreening of the data has a drastic effect on the FDR , and could potentially be of benefit to other similar analyses . Compilation of data also leads to improvements in observed FDRs , but these appear to be less drastic than those introduced by the application of ANOVA prescreening ( Table 1 ) . The originally published reports utilized three distinct classes of cycling transcript detection algorithms: cross-correlation to sin-waves [10 , 11] , autocorrelation [1 , 9] , and Fourier transform analysis [1] . We performed all three classes of analyses on our averaged and appended meta-datasets with and without ANOVA prescreening , followed by rigorous statistical testing ( see Methods ) . In addition , we used cross-correlation to compare expression profiles with models derived from experimental observations ( per mRNA expression and LD and DD locomotor behavior; see Figure S3 ) . These novel expression models were constructed from a published period mRNA expression profile [21] and our own unpublished behavior data . Results strongly depended on the method and/or expression models employed . F24 analysis , when applied to the appended dataset , appeared to exhibit the greatest sensitivity , detecting a greater number of transcripts with a more favorable FDR than any other technique . However , some transcripts identified by cross-correlation to our experimentally derived expression models were not detected via F24 , suggesting that some cyclic expression patterns are poorly matched by sinusoidal waveforms ( Table 4; for a complete listing of all ANOVA and post hoc scores , see Dataset S2 , a supplemental addendum to Table 4 ) . Reduction in the stringency of our detection thresholds allowed F24 to detect some of these transcripts , but at significant detriment to the FDR ( unpublished data ) . Surprisingly , the results of our initial sin-wave cross-correlation poorly matched those of the corresponding F24 analysis . This was unexpected , as the two techniques are essentially equivalent [2] . The source of this discrepancy was a threshold cutoff applied to our sin-wave cross-correlation method ( transcripts had to exhibit an absolute Pearson correlation coefficient of 0 . 95 or better ) that had no equivalent step in F24 . When this step was removed from our analysis , agreement between the F24 and sin-wave cross-correlation results was found to be nearly 100% ( 174 of 177 possible matches; unpublished data ) . While the majority of transcripts were identified either by sin-wave cross-correlation or F24 analysis , a small group was not . A total of 21 genes were discovered by one or more of our novel waveform cross-correlation analyses: eight by the LD behavior waveform , 12 by the DD behavior waveform , and 17 via the per expression waveform ( Table 4 ) . All 21 of these genes were found among our list of novel genes; none were detected as cycling in the original five reports . These results suggest that use of a single cyclic expression detection technique is not sufficient to identify all cycling patterns . Even with our battery of techniques , we were unable to characterize nearly half ( 160 of 372 ) of the LD profiles that exhibited significant changes in expression with respect to time as assessed by the ANOVA screen ( Figure 2 ) . These may be characterized by the use of additional expression models and/or reduction in the stringency of our initial selection criteria . Under our initial analysis of the LD data , a total of 214 probe sets passed all selection criteria for one or more of our post hoc tests . Of this group , 81 were in common with one or more of the original five reports , including the known cycling circadian genes Clk , cry , per , tim , vri , and Pdp1 . Remarkably , 133 transcripts identified in our analysis were not found in the original reports , including 21 that no sin-wave–based method was able to detect . Nine members of this novel group were found in a similar meta-analysis conducted by Wijnen et al . [3] ( Table 4 , last column ) , but in none of the original five reports . To examine the LD data more closely , we prepared cluster dendrograms [23 , 24] of the transcripts that passed our ANOVA prescreening procedure ( see Methods; Figure 2 ) . Examination of the expression profiles reveals a wide assortment of patterns not detected by our analyses ( present in Figure 2B , absent in Figure 2A ) , further evidence that a limited number of correlation techniques and/or expression models likely fails to detect a large number of robustly cycling expression patterns . To examine all LD data considered for a randomly selected number of genes , we plotted the average standardized expression values for all arrays at each of the six timepoints for three separate classes of transcripts . Class I transcripts were those found in the majority ( three or more ) of the original reports that were also detected by our analysis ( Figure 3A ) . Class II consisted of genes identified as cycling in just one of the original reports that failed to be detected as cycling by our analysis ( Figure 3B ) . Class III included those transcripts that were identified as cycling by our technique , but did not appear as cycling in any of the original reports ( Figure 3C ) . Classes I and III appear to be robustly cycling , whereas class II exhibits no significant rhythmicity . This is consistent with our notion that transcripts identified in just one study are likely to be false positives , whereas those found in multiple reports or our compiled datasets are more likely to be bona fide cycling genes . Plots of the average standardized LD expression data for all genes ( listed by Affymetrix probe set ID ) can be produced from Dataset S3 , a supplemental addendum to Figure 3 . To determine if the expression profiles of our newly discovered genes were indeed cycling , quantitative RT-PCR was used . We randomly selected three genes , two from our initial analysis ( CG8008 and CG17100: CG8008 was found in none of the original reports , whereas CG17100 was found in Lin et al . [9] and Ueda et al . [10] , but in no other report we are aware of ) , and another detected under less stringent criteria ( kraken ) . While this manuscript was in preparation , three separate reports confirmed not only that CG17100 ( i . e . , clockwork orange [cwo] ) exhibits cyclic expression , but that it is a bona fide member of the circadian machinery [25–27] . A cursory examination revealed phase and amplitude values to be comparable between the compiled microarray and RT-PCR expression data for CG8008 and CG17100 , but not kraken . In addition , we performed RT-PCR on a group of four genes detected in our meta-analysis ( well-known cycling circadian genes tim , per , clk , and vri ) and one or more of the original reports ( Figure 4 ) . We assessed cycling of all RT-PCR–derived expression profiles with the same method applied to the microarray data ( Table 5 ) . For six of the seven genes tested , cycling in the RT-PCR data was confirmed under one or more of our analysis procedures . This included CG8008 and cwo . kraken failed to pass our analysis procedure , not surprisingly , as it was detected under less stringent conditions , suggesting it to be a likely false positive .
We introduce a novel analysis method that compensates for variability observed among the original five Drosophila circadian array reports . The principles of ANOVA-based prescreening and rigorous post hoc statistical testing we use are of benefit to our and two previously published techniques ( the MacDonald algorithm and F24 ) . We identify a set of cycling transcripts previously found in one or more of the original five reports as well as a novel set . Based on the statistical fidelity of our meta-analysis and the results of our pilot RT-PCR validation experiments ( our assessment of expression rhythmicity was found to be consistent between array and RT-PCR data ) , we predict many of our newly found genes to be bona fide cyclers ( e . g . , cwo ) , and suggest that they may lead to new insights into the regulatory pathways through which clock mechanisms regulate behavioral rhythms .
To produce the behavioral activity histograms ( Figure S1 ) , the activity of n ∼ 100 flies of the indicated genotype was recorded via the Trikinetics Drosophila Activity Monitoring system ( http://www . trikinetics . com ) . After 3–5 d of LD entrainment , data were recorded at 30-min bins for the last LD and first DD days . Data for every fly of each genotype were averaged together and presented as a histogram . Error bars indicate the standard error of the mean across all flies of the indicated genotype at the indicated time . An Excel-based macro ( Keegan et al . [26]; available from the Allada lab upon request ) was used to process behavioral data and produce the histogram figures . Data for a few of the reports were accessible through public Web sites . To obtain data and algorithms not available through publicly accessible Web sites , we contacted authors . All data used to produce this report are available upon request . Files that contain the individually formatted results from each of the original reports were too numerous and large to be included with this manuscript on the PLoS Computational Biology Web site . In each table where our characterized genes are presented ( Tables 4 , S6 , and S7 ) , each gene is referred to by its Affymetrix probe set ID and , depending on availability from the Affymetrix Drosophila 1 microarray annotation database , the gene name , symbol or CG number , CT number , GenBank accession number ( http://www . ncbi . nlm . nih . gov/genbank ) , and FlyBase accession number ( http://flybase . bio . indiana . edu ) . Other gene lists refer to transcripts by their Affymetrix Drosophila Genome 1 microarray probe set . Complete annotation for these can be obtained free of charge from the Affymetrix Web site: ( https://www . affymetrix . com/analysis/netaffx/index . affx ) . The original reports utilized a variety of methods to annotate transcripts , Affymetrix probe set numbers , CG number , CT number , gene ID , gene names and/or symbols , etc . We developed a Matlab-based code ( MathWorks , http://www . mathworks . com ) that could quickly provide the Affymetrix probe set number for any input found in the Affymetrix database ( gene name , CG number , CT number , gene ID , gene symbol , etc . ) such that each list could be represented with the same annotation . A second Matlab code was implemented to perform comparisons between any two lists of genes . It produces an output file that contains the list of genes found in common between any two input lists . Gene lists for this comparison were obtained directly from the primary literature [1 , 8–11] . All codes used to produce this report are available upon request . As described in the text , we only used data prepared from y w and CS flies . Arrays prepared from cn bw flies were excluded from our analyses . Several reports contain multiple days worth of data . As our goal was to create a single-day , six-point-long time course , we grouped data from timepoints and whole-day multiples of the same timepoint together: ZT0 and ZT24 were counted as ZT0; CT0 and CT24 were counted as CT0 . ZT , or zeitgeber ( “time-giver” ) , time refers to 24-h-long days in the presence of an entraining stimulus , which in the case of an LD regime the stimulus is light; ZT0 corresponds to the time when lights turn on , ZT12 when they turn off . Similarly , CT or circadian time refers to 24-h–long days under constant conditions , which is complete darkness in the case of a DD light regime; CT0 corresponds to the time when the stimulus ( light in this case ) would have been expected to turn on , and CT12 corresponds the time it would have been turned off if it were present . We performed this grouping on LD data and DD data such that we were able to produce a six-point LD time course and six-point DD time course , each with n ≈ 10 arrays per timepoint . Most datasets followed a ZT or CT 0 , 4 , 8 , 12 , 16 , 20 sampling regime . Two other sampling windows were used in the original reports , ZT/CT 1 , 5 , 9 , 13 , 17 , 21 and ZT/CT 2 , 6 , 10 , 14 , 18 , 22 . We included data sampled at the ZT/CT 1 , 5 , 9 , 13 , 17 , 21 , but excluded data sampled at ZT/CT 2 , 6 , 10 , 14 , 18 , 22 . This allowed us to include the greatest possible number of arrays while controlling for expression differences that could be attributed to sampling intervals differentially phased by more than 1 h . We applied the algorithm described in McDonald et al . [8] to the data from each of the five reports . As the McDonald algorithm was originally used on DD data , and our aim was to reproduce the original technique as closely as was possible , we applied it to DD data only; LD data were not considered in our McDonald algorithm analysis . First , data were grouped as indicated above ( Data grouping ) . Then , DD data from each of the reports were treated exactly as described in McDonald et al . [8] . Briefly , expression data for all replicates of a given timepoint were averaged together , and a standard error was calculated . This produced a single six-point DD expression time course for each transcript . The average expression across all six timepoints had to exceed a mild filter ( the averaged average-difference scores had to exceed a value of 20 ) . The expression profile of each transcript that passed this filter was normalized according to the method reported in McDonald et al . [8] Expression profiles then had to pass the following significance test: where peakavg expression and troughavg expression refer to the peak and trough expression values of a particular transcript expression profile , and SEpeak expression and SEtrough expression refer to the standard error of expression at the peak and trough points , respectively . A Matlab code was generated to automate this test . Profiles had to exhibit 1 . 5-fold differences between the peak and trough expression values . Last , a Perl script developed by Mike McDonald [8] was used to derive correlation scores between each transcript expression profile and several differentially phased sinusoidal waves with a period of 24 h . A correlation threshold score of 0 . 90 was used . We found reproduction of the McDonald algorithm to nearly , but not exactly reproduce the original results when it was applied to the McDonald dataset . Reproduction by McDonald produced a list identical to ours , slightly different than that published in the original report . ( Independent reproduction of the technique by two parties [Keegan and McDonald] identified 136 probe sets , these include 132 of the originally reported 135 probe sets , and four probe sets not found in the originally published analysis; personal correspondence with Mike McDonald . ) Comparison of lists of identified genes was performed using a Matlab code described above ( see the section Comparison of original gene lists ) . Data processed with the McDonald algorithm were not standardized . Matlab- and Excel-based codes are available upon request . Perl scripts are available through Mike McDonald and/or Michael Rosbash [8] . The McDonald algorithm was applied to each of the datasets ( to the raw , nonstandardized data ) as described above . For each dataset , the output from the McDonald algorithm–based analysis was compared with the originally published output . Thus , for each report , we had the lists of transcripts identified under two different algorithmic techniques , the McDonald algorithm and whatever technique was native to the report . The lists of identified genes were compared using the Matlab code described above ( see the section Comparison of original gene lists ) . Expression values were transformed as described in Claridge-Chang et al . [1]: for each timepoint , the expression level relative to the mean ( taken in log2 coordinates ) over that experiment was computed . This procedure was separately performed on the LD and DD data expression values from each report . This led to the subsequent exclusion of any negative expression values ( an artifact of MAS 4 and 5 expression condensation algorithms ) from further analysis . To achieve location and scale normalization among all arrays , we performed the following standardization procedure on each array after data were log transformed: where i = 1 , j = 14 , 010 ( the number of transcripts probed by each array ) . Xi is the expression value of the ith transcript , Xavg is the average expression of all expression measures Xi to Xj , and XSD is the standard deviation of expression for all expression measures Xi to Xj [17] . This normalization procedure sets the mean expression of each array to 0 , and the variance to 1 . Data from McDonald et al . [8] were standardized as described above , but as McDonald expression values represented an average calculated from three to five arrays , not the expression from individual arrays as was reported in each of the other studies , the standardized McDonald data were subsequently weighted such that each expression value was counted three times ( the McDonald et al . [8] text specifies three to five replicates per timepoint , but does not indicate the exact number of replicates used for each timepoint; we used the most conservative estimate , three replicates for each timepoint ) . The standardized data were grouped as indicated above ( see the section Data grouping ) and used in all analyses described below . After transformation and standardization , the remaining expression values were grouped according to LD or DD timepoint . When present , whole-day multiples of a single timepoint were grouped together ( i . e . , ZT4 and ZT28 were treated as ZT4 ) . All expression measures within a given LD or DD timepoint were averaged together . The standard error of the mean for each timepoint was also determined . After transformation and standardization , the remaining expression values were grouped according to report and then LD or DD timepoint . The data for each day in the multiday profiles ( data for each single day came from a single report/lab ) underwent one additional standardization procedures such that the data for each day would exhibit a mean of 0 and variance of 1 . This allowed for roughly equal weighting of each day in the expression profile; that is , expression values for each day were constrained such that their location and scale would be the same , but profile contours remained unchanged . In all correlation-based analyses , missing data ( due to incomplete time series ) and those data that failed to pass the log2 transformation were ignored . Under fast Fourier transform ( F24 ) analysis , missing data ( three timepoints in the LD dataset ) and those data that failed to pass the log2 transformation were replaced with a value of 0 . After grouping and standardization ( described above ) , we had two datasets , a six-point LD time course with nine to ten arrays per timepoint , and a six-point DD time course with ten to 13 arrays per timepoint . Data from the LD and DD sets were treated separately . For each of the 14 , 010 transcripts on the arrays , a single-factor ANOVA was performed across all six timepoints . Each timepoint was treated as a group , and arrays at each timepoint were treated as the individuals within that group . We applied a fairly liberal selection criterion; a nonadjusted ANOVA p-value of 0 . 05 or less was required for any particular transcript to pass the screen . Data were grouped and sorted in Excel , then exported via tab-delimited text files to a Matlab code created to automate ANOVA processing . Our code utilized the embedded Matlab ANOVA function , and is available upon request . Construction of expression models . As our primary interest was in identifying transcripts that exhibit cycling patterns , and not in finding the specific peak and trough expression timepoint ( s ) responsible for ANOVA results , we decided not to perform standard post hoc analyses . Instead , we used correlation techniques to identify transcripts that exhibited significant cycling . We developed three model waveforms . The first was a simple sinusoidal wave with a 24-h period . To account for the total range of possible phases , our model included 24 individual sin waves phased at hourly intervals to account for the total range of possible phases across a 24-h period . As our data took the form of six-point time courses , our models were also represented as six-point time courses . To accomplish this , we sampled each of the 24 sin waves at 4-h intervals , creating a single six-point representation for each of the 24 continuous sin waves . Thus , our sin model was composed of 24 differentially phased six-point sin wave profiles . Our second model was constructed from previously reported per expression data [21] . NIH image ( http://rsb . info . nih . gov/nih-image/ ) was used to derive approximate numerical values of per mRNA expression from Figure 2A of So et al . [21] , a Northern blot profile of per expression . We sampled this plot at 4-h intervals in 24 different phases to produce 24 differentially phased six-point per expression profiles . Our last model was similarly produced from behavior profiles of CS flies in LD or DD . The LD model was derived from the averaged LD behavior profile of n ≈ 100 flies ( wild-type CS ) . As with the two previous models , the initial waveform was sampled at 4-h intervals in 24 phases . The DD model was created by application of an identical technique to an averaged DD behavior profile of n ≈ 100 flies ( wild-type CS ) . The LD behavior-derived model was used to probe LD expression data , whereas the DD behavior-derived model was used to probe DD expression data . For correlation to appended data , the models described above were self-appended until the requisite profile length was achieved . Production of raw correlation scores and correlation p-values . We used a Matlab code to perform Pearson correlation analyses between each transcript expression profile and our expression models . LD expression data were correlated with the sin expression , per expression , and LD behavior-based models . DD expression data were correlated to the sin expression , per expression , and the DD behavior based models . The code performed correlation between each transcript's expression profile and the selected expression models . When processing our averaged datasets , profiles were considered only if they possessed expression values for each of the six timepoints . Similarly , when processing the appended datasets , we required that each profile possess at least six data points . For incomplete appended profiles ( those that had more than six but fewer than the total number of timepoints ) , missing data points were ignored . As each expression model contained 24 individual waveforms ( one corresponding to each of the 24 hour-phased representations of the model; see the section Construction of expression models above ) , 24 correlations were performed for each transcript to model comparison . The code found the model phase that exhibited the highest absolute correlation score ( accessed via the Pearson correlation coefficient ) of these 24 correlations , recording the score and the model phase . To assess the significance of each correlation , we employed a permutation technique . First , correlation analysis was performed between a transcript and a given expression model , as described above . Then , the original transcript expression profile was randomized , and the highest absolute correlation score was recomputed and stored . This process was performed until all possible permutations of the averaged expression profile ( six timepoints yield 720 possible arrangements of each average expression profile ) or 10 , 000 randomly generated permutations of the appended profiles had been tested . Our correlation p-value was calculated as the number of permutation correlations that exhibited a higher correlation score than the real ( nonpermuted ) expression profile divided by the total number of permutations tested ( 720 or 10 , 000 ) . This p-value can be interpreted as the probability that a correlation greater than that observed in the original ( nonpermuted ) profile could occur given any random arrangement of the expression values it contained . After the correlation score and correlation p-values were calculated by our Matlab code , we applied two filters to the output . ( 1 ) We required transcripts to exhibit a Pearson correlation score of at least 0 . 95 . ( 2 ) To be deemed significant , correlations had to exhibit a permutation-derived p-value of 0 . 05 or less . Permutation-derived FDR calculations . In addition to evaluating the significance of each individual correlation , we also wished to determine the FDR , the rate at which any positive result can be expected to be false , a measure of experiment-wide , as opposed to individual-test , significance [17] . We derived an FDR value for each of the expression models with a method similar to SAM [22] . First , to determine the number of positive results from the true data , we performed correlation analysis as indicated above , and determined the number of transcripts that passed all of our selection criteria . Next , to approximate the number of false positives , the expression profiles of all 14 , 010 transcripts was randomized . Correlation analysis was then performed on the randomized dataset . We scored transcripts that passed our selection criteria after randomization as false positives . Our FDR was then calculated using the following formula: ( number of false positives / number of original positives ) . Power approximation . To approximate the statistical power ( essentially , a measure of how well any method could detect known cycling genes ) , we used a simple empirical procedure . We tested each method's ability to detect six well-known circadian genes ( Clk , cry , per , tim , vri , and Pdp1 ) . Our power statistic was calculated as n/6 , where n was the number of the known cycling genes detected . Whereas the standard application of autocorrelation would have required two or more days of data ( a data profile at least two times the length of the period we were primarily interested in finding ) , we used a method that allowed for us to perform autocorrelation on a single day of data . Autocorrelation requires two representations of the same profile . The first representation ( A ) is held constant as the second ( B ) is shifted out of register with it one timepoint at a time; with each shift , data points at the extremities are “lost” as they are moved beyond the point where they can be paired with a point from the other representation . To avoid this loss of data , we considered each representation circularly . The point lost from the end of B after a single shift would be moved to B's beginning such that all points would be compared to all points at each registration ( six timepoints at five nonidentical registrations for the six-point–long averaged time courses ) . In addition , we considered the absolute , rather than raw , value of the Pearson correlation coefficient . This allowed us to detect 24-h rhythmicity with a single day of data—two 24-h sin waves exactly 12 h out of phase with each other will produce an absolute Pearson correlation coefficient of 1 . Autocorrelation was applied to averaged profiles only if they possessed expression values for each of the six timepoints . Autocorrelation was applied to appended profiles if they possessed six or more data points . The autocorrelation p-value was determined using the same procedure employed for determination of cross-correlation p-values . Our autocorrelation routine was implemented as a C++ script . Discrete Fourier transform ( DFT ) –based analysis , as nearly as we were able to reproduce the technique , was performed as described in the Wijnen studies [2 , 3] . Briefly , the F24 score represents the normalized spectral power determined for the 24-h period component in the expression profile . Under the Wijnen scaling of power , all values , regardless of expression profile length , will scale between 0 and 1 , with 1 representing a perfectly sinusoidal signal . Thus , the power from profiles of different lengths can be directly compared [2 , 3] ) . The example of Matlab code below was produced from our best understanding of the sample code provided in Wijnen et al . [2]: In the following example code , fast Fourier transform data are presented as a tab delimited text file imported as an array . Each row is the expression for a single transcript , and each column is the expression for a single timepoint . Prior to implementation of the code , the data for each expression profile were standardized by subtraction of the mean and division by the standard deviation . For i = 1:number_of_expression_profiles transcript_i = FFT_data ( i , : ) ; transcript_i _squared= transcript_i . /sqrt ( sum ( transcript_i . ˆ2 ) ) ; y=fft ( transcript_i _squared ) ; z=y . *conj ( y ) ; fft_power=z/length ( transcript_i _squared ) *2; end; where fft refers to the DFT; as stated in the Matlab help file for this function , fft ( X ) is the DFT of vector X . For length N input vector x , the DFT is a length N vector X , with elements: The portion of our code presented above simply performs the power calculations: our complete code produces a labeled output file with power and power p-values for all determinable periods . The complete code is available upon request to the authors . Clustering was performed as has previously been described [23] . We used freely available software to produce the cluster images [24] . Standardized expression values were averaged together for each timepoint to produce a single six-point LD time course for each gene . The standardized , averaged expression values for each transcript were then normalized such that all transcripts exhibited an expression range from 0 to 1 . The Pearson correlation coefficient was selected as our distance metric . We performed three separate experiments . Flies were entrained in an LD environment for 3–6 d and then collected at 4-h intervals through a single day . RNA was isolated from fly heads using the Invitrogen Trizol reagent [39] . DNAse I treatment of isolated nucleic acid was utilized to eliminate DNA contamination of samples . SYBR green reagent was used along with the following oligos to perform a real time PCR analysis of mRNA levels . Expression levels were quantified using an ABI 7900HT along with Sequence Detection System software ( Applied Biosystems , http://www . appliedbiosystems . com ) . Expression profiles were assessed using all four of our cross-correlation methods , autocorrelation , and F24 . Table S5 contains sequence data for all oligos used to perform our RT-PCRs . RT-PCR data for CG17100 ( cwo ) were adapted from Lim et al . [26] . | Circadian genes regulate many of life's most essential processes , from sleeping and eating to cellular metabolism , learning , and much more . Many of these genes exhibit cyclic transcript expression , a characteristic utilized by an ever-expanding corpus of microarray-based studies to discover additional circadian genes . While these attempts have identified hundreds of transcripts in a variety of organisms , they exhibit a striking lack of agreement , making it difficult to determine which , if any , are truly cycling . Here , we examine one group of these reports ( those performed on the fruit fly—Drosophila melanogaster ) to identify the sources of observed differences and present a means of analyzing the data that drastically reduces their impact . We demonstrate the fidelity of our method through its application to the original fruit fly microarray data , detecting more than 200 ( 133 novel ) transcripts with a level of statistical fidelity better than that found in any of the original reports . Initial validation experiments ( quantitative RT-PCR ) suggest these to be truly cycling genes , one of which is now known to be a bona fide circadian gene ( cwo ) . We report the discovery of 133 novel candidate circadian genes as well as the highly adaptable method used to identify them . | [
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| [
"biochemistry",
"mathematics",
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| 2007 | Meta-Analysis of Drosophila Circadian Microarray Studies
Identifies a Novel Set of Rhythmically Expressed Genes |
The development of classically activated monocytic cells ( M1 ) is a prerequisite for effective elimination of parasites , including African trypanosomes . However , persistent activation of M1 that produce pathogenic molecules such as TNF and NO contributes to the development of trypanosome infection-associated tissue injury including liver cell necrosis in experimental mouse models . Aiming to identify mechanisms involved in regulation of M1 activity , we have recently documented that during Trypanosoma brucei infection , CD11b+Ly6C+CD11c+ TNF and iNOS producing DCs ( Tip-DCs ) represent the major pathogenic M1 liver subpopulation . By using gene expression analyses , KO mice and cytokine neutralizing antibodies , we show here that the conversion of CD11b+Ly6C+ monocytic cells to pathogenic Tip-DCs in the liver of T . brucei infected mice consists of a three-step process including ( i ) a CCR2-dependent but CCR5- and Mif-independent step crucial for emigration of CD11b+Ly6C+ monocytic cells from the bone marrow but dispensable for their blood to liver migration; ( ii ) a differentiation step of liver CD11b+Ly6C+ monocytic cells to immature inflammatory DCs ( CD11c+ but CD80/CD86/MHC-IIlow ) which is IFN-γ and MyD88 signaling independent; and ( iii ) a maturation step of inflammatory DCs to functional ( CD80/CD86/MHC-IIhigh ) TNF and NO producing Tip-DCs which is IFN-γ and MyD88 signaling dependent . Moreover , IL-10 could limit CCR2-mediated egression of CD11b+Ly6C+ monocytic cells from the bone marrow by limiting Ccl2 expression by liver monocytic cells , as well as their differentiation and maturation to Tip-DCs in the liver , showing that IL-10 works at multiple levels to dampen Tip-DC mediated pathogenicity during T . brucei infection . A wide spectrum of liver diseases associates with alteration of monocyte recruitment , phenotype or function , which could be modulated by IL-10 . Therefore , investigating the contribution of recruited monocytes to African trypanosome induced liver injury could potentially identify new targets to treat hepatic inflammation in general , and during parasite infection in particular .
Inflammatory immune responses against invading pathogens require the recruitment of immune cells to the site of infection . However , the infiltration of infected tissue by activated immune cells may result in tissue injury justifying a profound understanding of the mechanisms underlying the recruitment and activation of inflammatory cells . Experimental infections with African trypanosomes , extracellular blood-borne parasites that cause sleeping sickness in humans and Nagana disease in cattle in sub-Saharan Africa [1] , [2] , are used as model systems to study infection-associated liver pathogenicity [3] . In murine models , the control of parasitemia is mostly mediated in the liver by IFN-γ- and MyD88-dependent generation of classically activated monocytic cells ( M1 ) that secrete trypanotoxic molecules TNF and NO and exert phagocytic activity [4] , [5] , [6] , [7] . Within M1 , CD11b+Ly6C+CD11c+ inflammatory DCs have been identified as the main population producing TNF and NO during Trypanosoma brucei infection [8] . These TNF and iNOS producing DCs ( Tip-DCs ) originated from bone marrow CD11b+Ly6C+ monocytic cells recruited to the liver , spleen and lymph nodes of infected mice . On the other hand , the production of TNF and NO contributes to infection-associated pathogenicity including liver cell apoptosis/necrosis , resulting in organ failure and thus negatively affecting survival of the T . brucei infected host [3] . In this respect , IL-10 was shown to limit liver inflammation/injury and prolong survival by dampening the IFN-γ producing activity of T cells [9] , [10] and by directly limiting the differentiation of CD11b+Ly6C+ monocytic cells to functional Tip-DCs [8] . Furthermore , IL-10 triggers the expression of genes associated with alternative activation of monocytic cells ( M2 ) that could contribute to anti-inflammatory and wound-healing processes during African trypanosome infection [10] , [11] . Since the recruitment of bone marrow derived CD11b+Ly6C+ monocytic cells to inflamed tissue and their subsequent differentiation in M1-type , TNF and NO producing CD11b+Ly6C+CD11c+ Tip-DCs may have a negative impact on the outcome of T . brucei infection , interfering with their recruitment to inflamed tissue could lead to new anti-disease treatments . Therefore we scrutinized the potential pathways governing the recruitment of inflammatory CD11b+Ly6C+ monocytic cells to the liver as well as their subsequent differentiation to functional Tip-DCs during T . brucei infection .
T . brucei infection in C57Bl/6 mice is characterized by a massive expansion of CD11b+Ly6C+ monocytic cells in the liver [8] . These cells are closely related to CD11b+Ly6C+CCR2+ inflammatory monocytes that are recruited in infected tissues during microbial infection [12] . To identify pathway ( s ) possibly involved in the recruitment of liver CD11b+Ly6C+ monocytic cells , a custom in-house developed mRNA array was used to screen for the expression of chemokine genes in total liver extracts at day 6 post infection , i . e . when the first most important peak of parasitemia and the highest expansion of CD11b+Ly6C+ monocytic cells in the liver ( reaching 27 . 8±2 . 5% within liver non-parenchymal cells in infected animals versus 2 . 1±0 . 3% in non-infected mice ) occur [8] . Seven chemokine genes were found to be significantly induced ( more than 2-fold as compared to gene expression in total liver extracts from non-infected mice ) and their expression in total liver extracts was subsequently confirmed by RT-PCR ( Table 1 ) . The identified genes included ( i ) T cell-attracting chemokine genes; Cxcl10 ( IP-10 ) and Cxcl9 ( Mig ) acting through the CXCR3 receptor and ( ii ) monocyte-attracting chemokine genes; Ccl3 ( MIP-1α ) , Ccl4 ( MIP-1β ) and Ccl5 ( RANTES ) , all ligands for the CCR5 chemokine receptor; Ccl2 ( MCP-1 ) , the preferential ligand for chemokine receptor CCR2; and Mif that interacts predominantly with the CD74 receptor but also potentially with CXCR2 and CXCR4 . These data raised the possibility that signaling through CCR5 or CCR2 chemokine receptor or through Mif may be involved in the recruitment of liver associated CD11b+Ly6C+ monocytic cells during T . brucei infection . This hypothesis was supported by the observation that liver CD11b+Ly6C+ monocytic cells expressed CCR2 , CCR5 and CD74 on their surface , although at different levels ( Fig . 1A ) . To evaluate whether CCR2 , CCR5 or Mif signaling plays a role in the recruitment of CD11b+Ly6C+ monocytic cells to the liver , CCR2 KO , CCR5 KO , Mif KO mice and their respective WT counterparts were infected with T . brucei . In CCR5 KO and Mif KO mice , no difference in the percentage of liver , blood or bone marrow CD11b+Ly6C+ monocytic cells was observed compared to WT mice on day 6 of T . brucei infection ( Figure 1B ) . In infected CCR2 KO mice , the percentage of CD11b+Ly6C+ monocytic cells was drastically reduced ( >75% ) as compared to infected WT mice in both the liver and the blood , and coincided with an increase of the percentage of CD11b+Ly6C+ monocytic cells in the bone marrow ( Figure 1B , 1C ) . Similar modulations were observed for absolute numbers of CD11b+Ly6C+ monocytic cells in the liver , blood and bone marrow of the respective mouse strains during infection ( Figure 1D ) . These data suggest that the recruitment of CD11b+Ly6C+ monocytic cells to the liver of T . brucei infected mice consists of a two-step process comprising a CCR2-dependent , yet CCR5- and Mif-independent egression step from bone marrow to blood , followed by an extravasation step from blood to liver . Since in CCR2 KO mice CD11b+Ly6C+ monocytic cells accumulated in the bone marrow , it was unclear whether CCR2 also played a role in blood to liver extravasation during infection . To address this question , CD11b+Ly6C+ monocytic cells were purified from the bone marrow of T . brucei infected WT and CCR2 KO mice , labeled ( using PKH26 and CellVue labeling kits respectively ) , and co-injected at a 1-1 ratio in infected WT recipient mice . Twenty four hours later , liver non-parenchymal cells from recipient mice were analyzed for the presence of labeled cells . As shown in figure 2 , the 1-1 ratio of transferred WT to CCR2 KO CD11b+Ly6C+ monocytic cells could be traced back in infected WT recipient mice , demonstrating that the absence of CCR2 signaling in monocytic cells had no effect on blood to liver migration of CD11b+Ly6C+ monocytic cells during T . brucei infection . To verify whether expression of CCR2 by cells other than CD11b+Ly6C+ monocytic cells affected the recruitment of the latter cells from the blood to the liver , CD11b+Ly6C+ monocytic cells isolated from the bone marrow of T . brucei infected WT mice and labeled with PKH26 were transferred into infected CCR2 KO or WT recipient mice . The percentage of transferred CD11b+Ly6C+ cells found back in the liver after 24 hours was not significantly different between CCR2 KO or WT recipients ( 0 . 4±0 . 2% or 0 . 3±0 . 1% , respectively ) . Thus , these data indicate that the CCR2 chemokine receptor signaling in CD11b+Ly6C+ monocytic cells is crucial for their bone marrow emigration but dispensable for their blood to liver extravasation during T . brucei infection . During T . brucei infection , up to 70% of the CD11b+Ly6C+ liver monocytic cells can co-express CD11c , classifying them as inflammatory CD11b+Ly6C+CD11c+ dendritic cells [8] . The percentage of CD11c+ cells within the CD11b+Ly6C+ monocytic cell population in the liver of T . brucei infected mice was not affected by the absence of CCR2 ( Figure 3 ) . Also , the percentage of TNF+ and iNOS+ cells within CD11b+Ly6C+ monocytic cells ( Figure 3 ) and their expression levels of co-stimulatory molecules CD80/CD86 and MHC class II ( not shown ) was similar in WT and CCR2 KO mice , indicating that CCR2 signaling is not involved in the differentiation of CD11b+Ly6C+ monocytic cells to functional Tip-DCs during T . brucei infection . However , in agreement with the reduced percentage of CD11b+Ly6C+ monocytic cells in the liver of CCR2 KO mice ( Figure 1 ) , we observed a significantly reduced percentage of TNF+ and iNOS+ Tip-DCs within the total liver non-parenchymal cell population of CCR2 KO mice compared to WT mice ( Figure 3 ) . The reduced percentage of Tip-DCs in the liver of infected CCR2 KO mice associated with ( i ) a significant reduction of TNF , NO and IFN-γ secretion in liver non-parenchymal cell cultures and ii ) a reduced percentage of liver IFN-γ+CD4+ and IFN-γ+CD8+ T cells compared to infected WT mice ( Figure 4 ) . Although TNF and IFN-γ are involved in the control of parasitemia during T . brucei infection [4] , [13] , parasitemia in WT and CCR2 KO mice was similar indicating that reduced production of TNF and IFN-γ in CCR2 KO mice was still sufficient for efficient parasite control ( not shown ) . On the other hand , the expansion of Tip-DCs and their concomitant production of TNF and NO may contribute to liver injury and reduced survival of T . brucei infected mice [8] . In this context , infected CCR2 KO mice exhibited lower levels of serum ALT than infected WT mice on day 28 post infection ( Figure 5A ) , i . e . when the lethal pathogenic features of the disease become apparent . Reduced liver injury in T . brucei infected CCR2 KO mice was further confirmed by histological analysis ( Figure 5B ) . Finally , decreased liver injury in the absence of CCR2 signaling correlated with a significantly increased survival time ( from 32±3 days in WT mice to 53±4 days in CCR2 KO mice ) ( Figure 5C ) . Thus , in the absence of CCR2 signaling , the reduction in the percentage of CD11b+Ly6C+ monocytic cells and concomitant reduction in Tip-DC percentage in T . brucei infected mice lowers the production of TNF and NO in the liver , reduces liver injury while preserving parasite clearing capacity and increases the survival time of the host . To further support a role for Tip-DCs in pathogenicity during T . brucei infection , we transferred their CD11b+Ly6C+ monocytic cell precursors purified from the bone marrow of WT infected mice into infected recipient CCR2 KO mice on day 6 post infection . Twenty four hours after transfer a significantly increased concentration of TNF and increased ALT activity in blood serum of recipient CCR2 KO mice was observed ( Figure 6 ) . In contrast , transfer of CD11b+Ly6C+ monocytic cell purified from TNF KO mice affected neither TNF nor ALT levels of recipient CCR2 KO mice ( Figure 6 ) . Although up to 52±4% of WT CD11b+Ly6C+ monocytic cells co-expressed CD11c and MHC-II after transfer , reflecting their differentiation and maturation towards Tip-DCs , TNF and ALT levels in recipient CCR2 KO mice did not reach levels achieved in infected WT mice at the same time point post infection ( Figure 6 ) . This may result from the low percentage of transferred cells recovered in the liver ( 0 . 9±0 . 1% of the non-parenchymal cell fraction corresponding to about 1 . 2±0 . 2×105 cells ) , which is likely due to the systemic nature of T . brucei infection [14] . These data strongly suggest that Tip-DCs contribute to pathogenicity during T . brucei infection . IL-10 has been shown to limit the generation of Tip-DCs from CD11b+Ly6C+ cells during T . brucei infection [8] . In agreement , treatment with neutralizing anti-IL-10R antibody on days 7 and 9 post infection , i . e . at the peak of IL-10 production [8] increased the differentiation of CD11b+Ly6C+ monocytic cells towards inflammatory DCs and their subsequent maturation to Tip-DCs in the liver ( Figure 3 ) . At this point , it was not established whether IL-10 also directly influenced the recruitment of liver associated CD11b+Ly6C+ monocytic cells . To consider this possibility , WT and CCR2 KO T . brucei infected mice were treated with neutralizing anti-IL-10R antibody . In WT but not in CCR2 KO mice this treatment mimicked the phenotype observed in IL-10 KO mice [8] , killing the mice at 11±1 days post infection with increased liver injury ( serum ALT levels: 810±68 versus 223±39 U/ml in anti-IL-10R and control antibody treated WT mice at day 10 post infection ) . In the liver as well as in the blood of infected anti-IL-10R antibody treated WT mice the percentage of CD11b+Ly6C+ monocytic cells within total liver non-parenchymal cells doubled compared to infected control antibody treated WT mice ( Figure 7 ) . In contrast , the percentage of liver and blood CD11b+Ly6C+ monocytic cells did not change in CCR2 KO mice upon anti-IL-10R antibody treatment , showing that IL-10 regulates peripheral CD11b+Ly6C+ monocyte percentages in a CCR2-dependent way . Of note , the percentage of bone marrow CD11b+Ly6C+ monocytic cells did not change in anti-IL-10R treated WT or CCR2 KO mice , suggesting that IL-10 signaling did not alter bone marrow monopoiesis ( Figure 7 ) . Interestingly , gene expression level of the main CCR2 ligand Ccl2 ( fold induction compared to non infected mice ) was further induced in total liver extracts upon anti-IL-10R antibody treatment in infected WT mice ( from 5 . 3±0 . 9 in control antibody treated mice to 16 . 4±1 . 4 in anti-IL-10R antibody treated mice , p<0 . 05 ) . In addition , blood serum levels of CCL2 increased upon anti-IL-10R treatment ( Figure 7 ) . These data indicate that IL-10 limits CD11b+Ly6C+ monocyte cell recruitment to the liver and the blood during T . brucei infection in a CCR2-dependent manner , likely by limiting the production of CCL2 . While the generation of Tip-DCs from liver associated CD11b+Ly6C+ monocytic cells in T . brucei infected mice was impaired by IL-10 , the mechanisms triggering this generation are undefined . In this regard , although IFN-γ and MyD88 signaling were documented to contribute to TNF and NO production in T . brucei infected mice [5] , [15] , it is unknown whether IFN-γ and/or MyD88 signaling are involved in Tip-DC differentiation during infection . As shown in Figure 3A , the percentage of CD11c+ inflammatory DCs within the liver CD11b+Ly6C+ population was not affected in the absence of IFN-γ or MyD88 signaling during T . brucei infection . However , infected IFN-γ KO and MyD88 KO mice had significantly lower percentages of TNF and iNOS producing cells within the CD11b+Ly6C+CD11c+ inflammatory DCs ( Figure 3A ) . Also , expression levels of co-stimulatory molecules CD80/CD86 and MHC class II were reduced in liver inflammatory DCs from IFN-γ or MyD88 KO compared to WT mice ( not shown ) . To further determine the role of MyD88 and IFN-γ signaling in the generation of Tip-DCs , we co-injected in a 1-1 ratio CD11b+Ly6C+ monocytes isolated from the bone marrow of T . brucei infected WT and MyD88 KO on the one hand , and from infected WT and IFN-γR KO on the other hand , in infected WT recipient mice . Twenty four hours later , the 1-1 ratio of WT versus KO transferred cells was maintained in the liver ( Figure 3B ) , suggesting that IFN-γR or MyD88 signaling was not involved in blood to liver extravasation . In addition , ( i ) similar percentages of transferred CD11b+Ly6C+ monocytic cells from WT , IFN-γR KO and MyD88 KO expressed CD11c ( 54±3% , 52±4% and 50±4% respectively ) and ( ii ) the expression of MHC-II ( Figure 3B ) and CD80/CD86 ( not shown ) molecules was significantly reduced on transferred IFN-γR KO and MyD88 KO CD11b+Ly6C+ monocytic cells compared to WT , confirming our findings in T . brucei infected IFN-γ KO and MyD88 KO mice that maturation of CD11c+CD11b+Ly6C+ inflammatory DCs towards a mature Tip-DC phenotype is dependent on both IFN-γ and MyD88 signaling . Together , these data indicate that IFN-γ and MyD88 signaling are not required for the differentiation of CD11b+Ly6C+ monocytic cells towards CD11b+Ly6C+CD11c+ inflammatory DCs , but play a role in their further maturation to functional Tip-DCs by inducing the expression of iNOS , TNF and costimulatory molecules .
Murine monocytic cells comprise of distinct functional subsets that can be distinguished based on their expression of the surface markers CD11b , Ly6C , CX3CR1 and CCR2 [16] . One subset consists of CD11b+CX3CR1lowCCR2highLy6Chigh “inflammatory monocytes” , referred to here as CD11b+Ly6C+ monocytic cells , which migrate from the bone marrow to inflamed organs following infection [12] . There , inflammatory or pathogen-derived molecules can induce activation of CD11b+Ly6C+ monocytic cells to CD11c , CD80/86 , MHC class II molecule expressing , TNF and iNOS-producing DCs ( Tip-DCs ) [17] . Tip-DC activity can be beneficial to the host by controlling growth of Listeria , Brucella , Leishmania or influenza virus pathogens [17] , [18] , [19] , [20] , however it may also be pro-pathogenic by contributing to tissue damage such as during infection with influenza virus or the African trypanosome Trypanosoma brucei [8] , [20] . Herein , we have attempted to unravel the pathways underlying the recruitment of CD11b+Ly6C+ monocytic cells to the liver of T . brucei infected mice and the factors regulating their differentiation to Tip-DCs allowing a better understanding of the mechanisms underlying African trypanosomiasis-associated pathogenicity . Screening of total liver extracts for genes with upregulated expression in T . brucei infection yielded chemokine genes previously implicated in monocyte trafficking: Ccl2 ( signaling through CCR2 ) , Ccl3-4-5 ( signaling through CCR5 ) and Mif ( signaling through CD74 , CXCR2 , CXCR4 ) [21] , [22] , [23] , [24] , [25] . FACS analyses revealed that liver CD11b+Ly6C+ monocytic cells from infected mice expressed high level of CCR2 , low level of CCR5 and CD74 , and marginal level of CXCR2 and CXCR4 ( not shown ) . No difference in the percentage of CD11b+Ly6C+ monocytic cells was observed in the bone marrow , blood and liver of infected CCR5 KO or Mif KO mice . In contrast , percentages of CD11b+Ly6C+ monocytic cells drastically dropped in liver and blood while increasing in bone marrow of infected CCR2 KO mice . Transfer experiments revealed that neither CCR2 , nor CCR5 or Mif ( not shown ) contributed to extravasation of bone marrow-derived CD11b+Ly6C+ monocytic cells from blood to liver of T . brucei infected mice . Together , these data show that CD11b+Ly6C+ monocyte recruitment to the liver of T . brucei parasite infected mice involved an egression step from the bone marrow that is CCR2-dependent , followed by a CCR2-independent extravasation step triggered by yet unidentified factors released by the inflamed tissue , as documented during bacterial infection [22] , [26] . CD11b+Ly6C+ monocytic cells entering the liver of T . brucei infected mice first differentiate to CD11b+Ly6C+CD11c+ inflammatory DCs and subsequently maturate to CD80/CD86 high , MHC-II high , TNF and NO secreting Tip-DCs . Both this differentiation and maturation step can be limited by IL-10 [8] . In addition , we show here that treatment of T . brucei infected mice with anti-IL-10R antibody strikingly increased the percentage of CD11b+Ly6C+ monocytic cells in blood and liver but not in the bone marrow , excluding a role for IL-10 on the differentiation of the inflammatory monocyte progenitor ( the so-called macrophage and DC precursor , MDP ) [27] , [28] . However , increased percentage of peripheral CD11b+Ly6C+ monocytic cells was not observed in CCR2 KO mice treated with anti-IL-10R antibody . In addition , CCL2 protein levels in the blood and gene expression of Ccl2 in total liver extracts of infected mice was increased by anti-IL-10R treatment during infection . Although many cell types can produce CCL2 , we found that Ccl2 gene expression was induced in liver CD11b+Ly6C+ monocytic cells during infection and further upregulated upon IL-10 neutralization , suggesting that IL-10 negatively regulates a CCL2 dependent positive feedback loop for liver CD11b+LyC6+ monocyte recruitment . CD11c− and CD11c+ CD11b+Ly6C+ monocytic cells contributed equally to the induced expression of Ccl2 ( not shown ) . Finally , the percentage of CD11b+Ly6C+ monocytic cells was found increased in liver and blood of IL-10 KO mice ( not shown ) , further supporting a role for IL-10 in regulating peripheral CD11b+Ly6C+ monocytic cell numbers . Taken together , these data suggest that besides impairing differentiation and maturation of CD11b+Ly6C+ monocytic cells to Tip-DCs , IL-10 can counteract the CCL2/CCR2-mediated recruitment of CD11b+Ly6C+ monocytic cells/Tip-DCs to liver and blood of T . brucei infected mice . MyD88 and IFN-γ signaling have been implicated in the generation of Tip-DCs in infection models including Leishmania , Brucella and Listeria [18] , [19] , [29] . Knowing that during T . brucei infection , the production of TNF and NO is impaired in MyD88 KO [5] and IFN-γ KO mice ( unpublished observation ) , we investigated whether MyD88 or IFN-γ signaling affected the differentiation/maturation of CD11b+Ly6C+ monocytic cells to Tip-DCs by using KO mice and co-transfer experiments . The percentage of liver associated CD11b+Ly6C+ monocytic cells , their extravasation and their differentiation to inflammatory DCs was unaffected by the absence of MyD88 or IFN-γ signaling . On the other hand , inflammatory DCs from infected MyD88 KO mice and IFN-γ KO mice expressed lower levels of CD80/86 , MHC class II molecules and produced less TNF and iNOS protein , indicating that MyD88 and IFN-γ signaling are involved in the maturation of inflammatory DCs to functional Tip-DCs . The currently identified T . brucei-derived MyD88 signaling triggers , the glycosylphosphatidylinositol-anchored VSG and DNA , inducing the production of TNF and NO , and endotoxin/LPS-like substances from the trypanosome could represent candidates contributing to Tip-DC maturation . While trypanosome DNA has been suggested to interact with TLR9 , TLRs that could interact with other trypanosome derived molecules have not been identified . Moreover , it cannot be excluded that increased LPS release into the blood circulation due to secondary bacterial infection and/or increased gut permeability contributes to TLR4-dependent Tip-DC maturation during trypanosome infection [5] , [30] , [31] , [32] . The function of DCs has been poorly examined in the context of African trypanosome infection with the exception of the work from Dagenais et al . showing that splenic conventional CD11chighCD8+ and CD11chighCD8− DCs during T . brucei ( rhodesiense ) infection contribute to activation of VSG-specific Th1 cell responses through the coordinated upregulation of costimulatory molecules , secretion of IL-12 , and presentation of VSG peptides to T cells [33] . Conventional DCs in contrast to Tip-DCs are believed not to derive from monocytes [34] . However , Tip-DCs can exhibit T-cell stimulatory capacity [19] , [20] . In agreement , in the liver of T . brucei infected CCR2 KO mice , reduced percentage of Tip-DCs associated with reduced percentage of IFN-γ producing T cells , referred to previously as “pathogenic” T cells during African trypanosome infection [35] . Our observation that IFN-γ signaling is necessary for the generation of mature functional Tip-DCs in infected mice supports a positive cross-regulation between T cells and Tip-DCs mediated by IFN-γ as was suggested during Leishmania infection [19] . In addition to their immunostimulatory function , Tip-DCs may contribute to parasite control during T . brucei infection by producing TNF that is essential for this process [4] . Accordingly , T . brucei infected MyD88 KO mice and IFN-γ KO mice exhibited reduced Tip-DC percentage and reduced production of TNF , correlating with an inability to efficiently control parasitemia [5] . However , although Tip-DCs and TNF levels were reduced in CCR2 KO mice , control of parasitemia was unaffected . In this respect , it cannot be omitted that the present work only focused on the role of liver associated CD11b+Ly6C+ monocytic cells while the role of resident liver monocytic cells , including CD11b−Ly6C+ monocytic cells and CD11b+ liver Kupffer cells , remains to be addressed . While absence of CCR2 could only affect recruited CD11b+Ly6C+ monocytic cells , absence of IFN-γ and MyD88 signaling can impair the activation of both recruited and resident liver monocytic cells during infection . Moreover , the relative contribution of recruited versus resident liver monocytic cells to parasite control and induction of pathogenicity may differ . Indeed , >75% of T . brucei are removed from circulation by resident liver Kupffer cells arguing for a major role of these cells in parasite control [36] . On the other hand , a more pro-pathogenic function for recruited CD11b+Ly6C+ monocytic cells/Tip-DCs is supported by the observation that in CCR2 KO mice ( this study ) or in T . brucei infected mice treated with IL-10 [8] , reduced percentage of recruited CD11b+Ly6C+ monocytic cells/Tip-DCs associated with reduced pathogenicity and increased survival without affecting parasitemia . In the same vein , drastic shortened survival time of T . brucei infected IL-10 KO mice due to excessive tissue injury coincided with increased levels of CD11b+Ly6C+ monocytic cells/Tip-DC in the liver but normal parasite clearance capacity [8] , [37] . Finally , our observation that the transfer of CD11b+Ly6C+ monocytic cells from infected WT mice that can maturate to Tip-DCs [8] , but not from TNF KO mice , increased TNF concentration and ALT activity in serum of infected CCR2 KO recipients supports a role for these cells in the induction of liver injury during T . brucei infection . In conclusion , we have unraveled the pathways involved in the recruitment of a major pathogenic Tip-DC population in the liver of T . brucei infected mice ( Figure 8 ) . The development of Tip-DCs is a multi-step process including ( i ) a CCR2-dependent egression of CD11b+Ly6C+ inflammatory monocytic cells from bone marrow , followed by ( ii ) a differentiation step to immature inflammatory DCs ( CD11c+ but CD80/CD86/MHC-IIlow ) which is IFN-γ and MyD88 signaling independent and ( iii ) a maturation step of inflammatory DCs to functional ( CD80/CD86/MHC-IIhigh ) TNF and NO producing Tip-DCs which is IFN-γ and MyD88 signaling dependent . IL-10 could inhibit the CCL2/CCR2-mediated egression of CD11b+Ly6C+ monocytic cells from the bone marrow as well as their differentiation and maturation to Tip-DCs in the liver . Liver injury in various etiologies can result from uncontrolled activation of monocyte-derived cells recruited to the liver via CCR2 signaling and can be modulated by IL-10 [38] , [39] , [40] , [41] , [42] . African trypanosome infections thus represent a useful model to unravel these activation mechanisms and hereby to identify new tools , including monocyte associated , IL-10 inducible genes we have previously identified [10] , [11] , to treat hepatic inflammation .
The experiments , maintenance and care of mice complied with the guidelines of the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes ( CETS n° 123 ) . The experiments for this study were approved by the Ethical Committee for Animal Experiments of the Vrije Universiteit Brussel , VUB , Brussel , Belgium . Trypanosoma brucei AnTat 1 . 1E parasites were a kind gift from N . Van Meirvenne ( Institute for Tropical Medicine , Belgium ) . Female mice kept under filter barrier were used at 8–12 weeks of age . Wild type C57BL/6 mice were from Harlan , The Netherlands . CCR2 KO , CCR5 KO and Mif KO C57BL/6 mice were kindly provided by Dr . D . Engel , University Clinic of Bonn , Germany , Dr . F . Tacke , RWTH-University Hospital Aachen , Germany and Prof . R . Bucala , Yale University School of Medicine , New Haven , USA ) . IFN-γ KO , TNF KO and MyD88 KO C57BL/6 mice were bred in our animal facility . Parasites stored at −80°C were used to infect cyclophosphamide treated F1 ( C57BL/6× BALB/c ) mice bred in-house by i . p . inoculation . At day 4 post infection , mice were bled , and parasites were purified by diethyl-aminoethyl-cellulose chromatography . C57BL/6 mice were then infected i . p . with 2000 purified parasites . Parasitemia was monitored by tail blood puncture . When required , mice were injected i . v . with 100 µl of a 1 mg/ml PBS solution of anti-IL-10R antibody ( 1B1 . 3a , kindly provided by Dr . M . Moser and C . Coquerelle , ULB , Gosselies , Belgium with the agreement of Schering-Plough Biopharma ) or control antibody ( rat IgG1 , BD Biosciences ) on days 7 and 9 of infection and sacrificed on day 10 . Liver non-parenchymal cells were isolated as follows . Animals were euthanized ( CO2 ) and livers were perfused through the portal vein with 10 ml of 100 U/ml collagenase type III ( Worthington Biochemical Corporation ) in HBSS . Then , the liver was minced and incubated in 10 ml of a 100 U/ml collagenase III solution in HBSS ( 20 min , 37°C ) . The resulting cell suspension was passed through a 70 µm nylon mesh filter and then washed by adding 30 ml of HBSS supplemented with 2 mM EDTA and 10% FCS followed by centrifugation ( 300 g , 10 min , 4°C ) . After erythrocyte lysis , pellet was resuspended in 10 ml of Lymphoprep ( Lucron Bioproducts ) and overlayed with 10 ml of HBSS supplemented with 2 mM EDTA and 10% FCS . After centrifugation ( 430 g , 30 min , 17°C ) , the layer of low-density cells at the interface containing non-parenchymal cells was harvested . CD11b+Ly6C+Ly6G+ liver granulocytes were not retained within the non-parenchymal fractions due to their high density characteristic . Bone marrow cells were isolated from hind leg bone of CO2 euthanized animals by perfusion with 20 ml of HBSS supplemented with 10% FCS ( HBSS/FCS ) . The resulting cell suspension was passed through a 100 µm nylon mesh filter , centrifuged ( 300 g , 10 min , 4°C ) and resuspended in HBSS/FCS . Blood was isolated on EDTA by heart puncture of euthanized mice , followed by thorough erythrocyte lysis , centrifugation ( 300 g , 10 min , 4°C ) and resuspension of blood immune cells in HBSS/FCS . Immune cells and used buffers were kept on ice during isolation protocols and subsequent analysis . CD11b+Ly6C+ monocytes were isolated from the bone marrow of infected WT , CCR2 KO , TNF KO , MyD88 KO or IFN-γR KO mice by MACS purification on day 6 post infection . CD11c+ and Ly6G+ cells were first depleted via negative MACS selection . CD11b+Ly6C+ monocytes were then isolated via positive CD11b selection on magnetic separation columns according to the manufacturer's protocol ( Miltenyi Biotec ) resulting in purity ranging from 90–95% . WT and CCR2 KO or MyD88 KO or IFN-γR KO bone marrow cells were then fluorescently labeled using respectively PKH26 ( Sigma-Aldrich ) and CellVue membrane labeling kits ( Polysciences ) according to the manufacturer's protocol . Labeled cells were adoptively transferred at a 1-1 ratio ( total of 4×106 cells/mouse ) through tail vein injection in infected recipient mice on day 6 post infection . Alternatively , CellVue labeled WT or TNF KO monocytes from infected mice were injected in CCR2 KO mice on day 6 post infection ( 8×106 cells/mouse ) . Twenty four hours after transfer the recipient mice were sacrificed and presence of labeled cells was analyzed in the liver non-parenchymal cell fraction . Liver cells were resuspended at 2×106/ml in RPMI 1640 ( Gibco ) supplemented with 10% FCS , 100 U/ml penicillin , 100 µg/ml streptomycin , 0 . 1 mM non-essential amino acids , 2 mM L-glutamine , and 5×10−5 M 2-mercaptoethanol ( all from Invitrogen Life Technologies ) and cultured in vitro ( 37°C ) . Cytokines were quantified in culture supernatants collected after 2 days with specific sandwich ELISAs for IFN-γ ( PharMingen ) or TNF ( R&D Systems ) , in accordance to the manufacturers' protocols . NO2 quantification was assayed by a standard Griess reaction as described [37] . For surface markers , cells were stained for 30 min at 4°C using conventional protocols . Cells were pre-incubated with anti-FcγR Ab ( clone 2 . 4G2 ) before adding ( 1 µg/106 cells ) : FITC-conjugated or APC-conjugated MHC-II ( clone M5/114 . 15 . 2 ) , PerCP-Cy5 . 5-conjugated or PE-Cy7 conjugated CD11b ( M1/70 ) , FITC-conjugated CD80 ( 16-10A1 ) , FITC-conjugated CD86 ( Gl-1 ) , FITC-conjugated Ly6C ( AL-21 ) , PE-conjugated or APC-conjugated CD11c ( HL3 ) , PE-conjugated CCR5 ( C34-3448 ) , unconjugated CCR2 ( MC-21 , a gift of Dr Matthias Mack , University of Regensburg , Regensburg , Germany ) , FITC-conjugated CD74 ( In-1 ) . Dead cells were excluded by 7-AAD staining . For intracellular TNF staining , cells were cultured 6 hours in the presence of brefeldin-A ( BD Bioscience ) . For intracellular IFN-γ staining , cells were cultured 2 hours with anti-CD3 before adding brefeldin-A ( BD Bioscience ) . Four hours later , cells were fixed , permeabilised ( Fix/Perm kit , eBioscience ) and analyzed . Antibodies used for intracellular staining were APC-conjugated TNF ( clone MP6-XT22 ) , unlabeled rabbit iNOS ( M19 ) and APC-conjugated anti-rabbit IgG . Cells were analyzed on a FACSCanto II and analysis was performed using FlowJo . Antibodies were purchased from BD Biosciences , eBioscience or R&D Systems . CD11b+ Ly6C+ cells were isolated from liver non-parenchymal cells by negative selection for CD4 , CD8 and CD19 followed by positive Ly6C selection , using a two-step labeling with Ly6C-PE ( BD Bioscience ) and anti-PE beads ( Miltenyi Biotec ) on magnetic separation columns according to the manufacturer's protocol ( Miltenyi Biotec ) with a purity ranging from 90–95% . Three ×106 purified CD11b+Ly6C+ liver cells were put in Trizol ( Invitrogen ) and stored at −80°C . For total liver extracts , pieces of liver ( 0 . 5×0 . 5 cm ) were minced , washed with HBSS and after centrifugation put in Trizol . Gene expression in liver extracts or isolated populations was analyzed by quantitative real time PCR using the conditions described [43] . Results of the PCR analyses were normalized against the house-keeping gene S12 . Primers used were: Cxcl-9 ( Forward: TCCTTTTGGGCATCATCTTC , Reverse: TTCCCCCTCTTTTGCTTTTT ) , Cxcl-10 ( Forward: GGATGGCTGTCCTAGCTCTG , Reverse: ATAACCCCTTGGGAAGATGG ) , Ccl3 ( Forward: CGGAAGATTCCACGCCAATTC , Reverse: GGTGAGGAACGTGTCCTGAAG ) , Ccl4 ( Forward: GCCCTCTCTCTCCTCTTGCT , Reverse: GTCTGCCTCTTTTGGTCAGG ) , Ccl5 ( Forward: ACAGGTCAAACTACAACTCCA , Reverse: TCAGCTCTTAGCAGACATTGG ) , Ccl2 ( Forward: CACTCACCTGCTGCTACTCATTCAC , Reverse: GGATTCACAGAGAGGGAAAAATGG ) , Mif ( Forward: CTTTTAGCGGCACGAACGAT , Reverse: AAGAACAGCGGTGCAGGTAA ) . Liver were fixed in Bouin solution ( Sigma ) . Histological sections embedded in paraffin were stained with Hematoxylin-eosin-saffron for microscopic evaluations . Liver alanine aminotransferase ( ALT ) was measured in serum samples , using commercially available kits ( Boehringer Mannheim , Mannheim , Germany ) . All comparisons were tested for statistical significance using the unpaired t test with Welch's correction from GraphPad Prism 4 . 0 software . | Most infections are associated with host inflammatory responses that can result in multiple organ failure and death . It is therefore essential to understand the mechanisms balancing host immune response and tissue damage . Mouse models of African trypanosome infection represent valuable tools to study the mechanisms contributing to the inflammatory ( pathogenic ) or anti-inflammatory ( anti-pathogenic ) immune response . We recently identified TNF and NO producing DCs ( Tip-DCs ) as major contributors to liver pathogenicity in Trypanosoma brucei infected mice . Herein , the role of different chemokine and cytokines in the generation of Tip-DCs was investigated . Tip-DCs originated from bone marrow derived monocytes that egressed to the blood in a CCR2 chemokine receptor dependent manner . Then , monocytes extravasated to inflamed liver where IFN-γ and MyD88 signaling promoted their maturation to Tip-DCs . Both the egression of monocytes from bone marrow and their IFN-γ/MyD88 dependent maturation to Tip-DCs was counteracted by IL-10 , hereby reducing liver pathogenicity . Liver injury , affecting millions of persons worldwide with often lethal consequences , frequently results from uncontrolled activation of recruited monocyte-derived cells that can be modulated by IL-10 . Thus , the mechanisms regulating liver immunopathogenicity during parasitic infection identified herein could lead to new therapeutic policies in the field of hepatic inflammation . | [
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| 2010 | Tip-DC Development during Parasitic Infection Is Regulated by IL-10 and Requires CCL2/CCR2, IFN-γ and MyD88 Signaling |
The human body is a complex organism , the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system . The nature of musculoskeletal interconnection facilitates stability , voluntary movement , and robustness to injury . However , a fundamental understanding of this network and its control by neural systems has remained elusive . Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure , predicted function , and neural control of the musculoskeletal system . We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points . We demonstrated that , using this simplified model , a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury . Finally , we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex . This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury , inspiring future advances in clinical treatments .
The interconnected nature of the human body has long been the subject of both scientific inquiry and superstitious beliefs . From the ancient humors linking heart , liver , spleen , and brain with courage , calm , and hope [1] to the modern appreciation of the gut–brain connection [2] , humans tend to search for interconnections between disparate parts of the body to explain complex phenomena . Yet , a tension remains between this basic conceptualization of the human body and the reductionism implicit in modern science [3] . An understanding of the entire system is often relegated to a futuristic world , while individual experiments fine-tune our understanding of minute component parts . The human musculoskeletal system is no exception to this dichotomy . While medical practice focuses in hand , foot , or ankle , clinicians know that injuries to a single part of the musculoskeletal system necessarily impinge on the workings of other ( even remotely distant ) parts [4] . An injury to an ankle can alter gait patterns , leading to chronic back pain; an injury to a shoulder can alter posture , causing radiating neck discomfort . Understanding the fundamental relationships between focal structure and potential distant interactions requires a holistic approach . Here , we detail such an approach . Our conceptual framework is motivated by recent theoretical advances in network science [5] , which is an emerging discipline built from an ordered amalgamation of mathematics ( specifically , graph theory [6] ) and physics ( specifically , statistical mechanics [7] ) , computer science , statistics [8] , and systems engineering . The approach simplifies complex systems by delineating their components and mapping the pattern of interactions between those components [9] . This representation appears particularly appropriate for the study of the human musculoskeletal system , which is composed of bones and the muscles that link them . In this study , we used this approach to assess the structure , function , and control of the musculoskeletal system . The use of network science to understand the musculoskeletal system has increased in recent years [10] . However , the framework has largely been employed to investigate the properties of local muscle or bone networks . For example , the local structure of the skull has been examined to investigate how bones can be categorized [11] . Additionally , studies of the topology of the musculoskeletal spine network have been conducted to evaluate stresses and strains across bones [12] . A few studies do exist that address the entire musculoskeletal system , although they do not use the mathematical tools that we employed here [13 , 14] . The current study differs from previous work in its assessment of the entire musculoskeletal system combined with the mathematical tools of network science . Within this broader context , we focused on the challenge of rehabilitation following injury to either skeletal muscle or cerebral cortex . Direct injury to a muscle or associated tendon or ligament affects other muscles via compensatory mechanisms of the body [15] . Similarly , loss of use of a particular muscle or muscle group from direct cortical insult can result in compensatory use of alternate muscles [16 , 17] . How the interconnections of the musculoskeletal system are structured and how they function directly constrains how injury to a certain muscle will affect the musculoskeletal system as a whole . Understanding these interconnections could provide much needed insight into which muscles are most at risk for secondary injury due to compensatory changes resulting from focal injury , thereby informing more comprehensive approaches to rehabilitation . Additionally , an understanding of how the cortex maps onto not only single muscles but also groups of topologically close muscles could inform future empirical studies of the relationships between focal injuries ( including stroke ) to motor cortex and risk for secondary injury .
Using the Hosford Muscle tables [18] , we constructed a musculoskeletal hypergraph by representing 173 bones ( several of these are actually ligaments and tendons ) as nodes and 270 muscles as hyperedges linking those nodes ( muscle origin and insertion points are listed in S9 Table ) . This hypergraph can also be interpreted as a bipartite network , with muscles as one group and bones as the second group ( Fig 1a ) . The 173 × 270 incidence matrix C of the musculoskeletal network is thus defined as Cij = 1 if vi ∈ ej and 0 otherwise , where V = {v1 , · · · , v173} is the set of nodes ( bones ) and E = {e1 , · · · , e270} is the set of hyperedges ( muscles ) . This hypergraph representation of the body eliminates much of the complexity from the musculoskeletal system , encoding only which muscles attach to which bones . All analysis was applied to only one half ( left or right ) of the body , because each cerebral hemisphere controls only the contralateral side of the body . Therefore , we further simplified our model by assuming left–right symmetry; in any figures in which both halves of the body are shown , the second half is present purely for visual intuition . The bone-centric graph A and muscle-centric graph B ( Fig 1b ) are simply the one-mode projections of C . The projection onto bones is A = CTC , and the projection onto muscles is B = CCT . Then , the diagonal elements were set equal to zero , leaving us with a weighted adjacency matrix [5] . We obtained estimated anatomical locations for the center of mass of each muscle ( and bone ) by examining anatomy texts [19] and estimating x- , y- , and z-coordinates for mapping to a graphical representation of a human body ( Fig 1c ) . To measure the potential functional role of each muscle in the network , we used a classical perturbative approach . To maximize simplicity and the potential for fundamental intuitions , we modeled the musculoskeletal system as a system of point masses ( bones ) and springs ( muscles ) . We stretched a muscle-spring and observed the impact of this perturbation on the locations of all other muscles . Physically , to perturb a muscle , we displaced all bones connected to that muscle by the same amount and in the same direction , stretching the muscle , and we held these bones fixed at their new location . This process is also mathematically equivalent to simply altering the spring constant attributed to the particular muscle-spring . The system was then allowed to reach equilibrium . We fixed bones at the midline and around the periphery in space to prevent the system from drifting . To quantify the impact of perturbing this single muscle-spring , we defined the movement of the ith node as follows: mid2r→idt2=∑j≠i∈V[SijAijl→ij ( xij-‖l→ij‖ ) ]-βdr→idt , where lij is the displacement between nodes i and j , xij is the unperturbed distance between nodes i and j , m is the mass of the node ( which we have set equal to unity for all nodes in the network ) , β = 1 is a damping coefficient , ri is the position of the ith node , A is the weighted adjacency matrix of the bone-centric graph , and Sij represents the sum of all spring strengths of the muscles that nodes i and j are both connected to . To normalize muscles’ restoring force on nodes , we let the spring strength of a muscle q ∝ 1/ ( k − 1 ) . Here , we have set all bones to have equal weight and all muscles to have equal spring constant , which is a simplification of the actual physical anatomy . For a discussion of how to account for additional physical properties , such as bone weight and muscle strength , and supplementary results using these properties , see S5 Text . Moreover , sample trajectories that provide an intuition for the dynamics of our model have been included in the Supporting information ( S8 Fig ) . To measure the potential functional role of each muscle in the network , we stretched a muscle hyperedge and measured the impact of the perturbation on the rest of the network . Rather than perturbing the network in some arbitrary three-dimensional direction , we extended the scope of our simulation into a fourth dimension . When perturbing a muscle , we displaced all of the nodes ( bones ) contained in that muscle hyperedge by a constant vector in the fourth dimension and held them with this displacement ( Fig 1d ) . The perturbation then rippled through the network of springs in response . We sequentially stretched each muscle hyperedge and defined the impact score of this perturbation to be the total distance moved by all nodes in the musculoskeletal network from their original positions . The displacement value is the summed displacement over all time points , from perturbation onset to an appropriate cutoff for equilibration time . Here , we solved for the equilibrium of the system by allowing dynamics to equalize over a sufficient period of time . Note that the equilibrium can also be solved for using a steady-state , nondynamic approach; we chose to use dynamics in this instance to more broadly support future applications . For each muscle , we calculated an index that quantifies how much the impact score of that muscle deviates from expected , given its hyperedge degree; we call this index “impact deviation” . We begin by constructing a null model that dictates the expected impact under a set of statistical assumptions . In the current study , we used several different null models with differing sets of assumptions , which we detail in later sections . Impact deviation was computed as follows: we calculated the mean , standard deviation , and 95% confidence intervals ( CIs ) for each of the null hypergraph degree categories from an ensemble of 100 null hypergraphs . The distance from a given muscle to the mean ± 95% CI ( whichever is closest ) was calculated and divided by the standard deviation of that null hypergraph degree distribution . In this way , we calculated deviation from the expected value , in standard deviations ( similar to a z-score ) . Table 1 contains the muscles that lie outside the 95% CI of deviation ratios , relative to their hyperedge degree . Muscles can be naturally grouped according to the homunculus , a coarse one-dimensional representation of how the control areas of muscles group onto the motor cortex . For a given homunculus group , we calculated the deviation ratio as the number of muscles with positive deviation divided by the total number of muscles in the group ( Table 2 ) . To understand both the function and control of the musculoskeletal system , we were interested in defining groups of densely interconnected muscles using a data-driven approach . We performed a type of community detection by maximizing a modularity quality function introduced by Newman [20]: Q=∑ijBij−γPijδ ( gi , gj ) , where Pij is the expected weight of an edge in the Newman-Girvan null model , node i is assigned to community gi , node j is assigned to community gj , and δ is the Kronecker delta function . By maximizing Q , we obtained a partition of nodes ( muscles ) into communities such that nodes within the same community were more densely interconnected than expected in a network null model ( Fig 1b , right ) . Here , we also used a resolution parameter to tune the size and number of communities detected such that the number of communities detected matched the number of groups within the homunculus , for straightforward comparison . Specifically , we used a resolution parameter of γ = 4 . 3 to divide the muscle-centric matrix into 22 communities ( see S8 Table ) . We began by redefining the original muscle-centric matrix B following Jutla et al . [21]; we let k = Σi Bi , j , and then we applied a locally greedy , Louvain-like modularity maximization algorithm to the adjusted matrix B′=B-γkTk∑jkj [22] . The above method of community detection is nondeterministic [23] . That is , the same solution will not be reached on each individual run of the algorithm . Therefore , one must ensure that the community assignments used are a good representation of the network and not just a local maximum of the landscape . We therefore maximized the modularity quality function 100 times , obtaining 100 different community assignments . From this set of solutions , we identified a robust representative consensus community structure [24] . S1 Fig illustrates how the detected communities change as a function of the resolution parameter for the muscle-centric network . We use rewired graphs as a null model against which to compare the empirical data . Specifically , we constructed a null hypergraph by rewiring muscles that are assigned the same category ( Table 3 , defined below ) uniformly at random . In this way , muscles of the little finger will only be rewired within the little finger , and similarly for muscles in other categories . Importantly , this method also preserves the degree of each muscle as well as the degree distribution of the entire hypergraph . Categories were assigned to muscles such that the overall topology of the musculoskeletal system was grossly preserved , and changes were spatially localized . Specifically , we partitioned the muscles into communities of roughly size 3 , such that each muscle was grouped with the two muscles that are most topologically related . We then permuted only within these small groups . This is a data-driven way of altering connections only within very small groups of related muscles . To partition muscles into communities , we took a greedy approach to modularity maximization , similar to prior work [25] . Specifically , we maximized the modularity of the system , such that the change in modularity to move node n from community c′ to community c is given by dQnc=Hnc-Hnc′+B′nn-Vcc′ Here , H is the node-to-module degree matrix , B′ is the adjusted muscle-centric matrix , and V is a penalty term to ensure communities will be small and of roughly equal size . Specifically , Hnc=∑i=1NB′nmδcjm , where N is the total number of nodes in the system , cj is an indicator variable encoding the community assignment of node j , and δ is the Kronecker delta function . Furthermore , Vcc′= ( ∑j=1Nδcjc′-NK ) 2 , where K indicates the total number of communities . This term penalizes determining a set of communities that are highly unequal in size . To conduct multidimensional scaling ( MDS ) on the muscle-centric network , the weighted muscle-centric adjacency matrix was simplified to a binary matrix ( all nonzero elements set equal to 1 ) . From this data , a distance matrix D was constructed , the elements Dij of which are equal to the length of the shortest path between muscles i and j , or are equal to 0 if no path exists . MDS is then applied to this distance matrix to yield its first principal component using the MATLAB function , cmdscale . m . To construct the binary matrix , a threshold of 0 was set , and all values above that threshold were converted to 1 . However , to make analysis robust to this choice , we explored a range of threshold values to verify that results are invariant with respect to threshold . The upper bound of the threshold range was established by determining the maximal value that would maintain a fully connected matrix; otherwise , the distance matrix D would have entries of infinite weight . In our case , this value was 0 . 0556 × max ( B′ ) . Within this range of thresholds ( i . e . , for all thresholds resulting in fully connected matrices ) , results were qualitatively consistent . As a supplementary analysis , we also employed a method of constructing a distance matrix from a weighted adjacency matrix in order to preclude thresholding ( S5 Fig ) , and we again observed qualitatively consistent results . We calculated the correlation between impact score and muscle injury recovery times . Injury recovery times were collected from the sports medicine literature and included injury to the triceps brachii and shoulder muscles [26]; thumb muscles [27]; latissimus dorsi and teres major [28]; biceps brachii [29]; ankle muscles [30]; neck muscles [31]; jaw muscles [32]; hip muscles [33]; eye/eyelid muscles [34]; and muscles of the knee [35] , elbow [36] , and wrist/hand [37] . The recovery times and associated citations , listed in Table 4 , are average recovery times gathered from population studies . If the literature reported a range of different severity levels and associated recovery times for a particular injury , the least severe level was selected . If the injury was reported for a group of muscles rather than a single muscle , the impact score deviation for that group was averaged together . Data points for muscle groups were weighted according to the number of muscles in that group for the purpose of the linear fit . The fit was produced using the MATLAB function , fitlm . m , with option “Robust” set to “on . ” Robust regression is a method of regression designed to be less sensitive to outliers within the data , in which outliers are down-weighted in the regression model . We calculated the correlation between impact score deviation and the area of somatotopic representation devoted to a particular muscle group . The areas of representation were collected from two separate sources [38 , 39] . The volumes and associated citations are listed in Table 5 . In both studies , subjects were asked to articulate a joint repetitively , and the volumes of the areas of primary motor cortex that underwent the greatest change in BOLD signal were recorded . We then calculated the correlation coefficient between cortical volumes and the mean impact of all muscles associated with that joint , as determined by the Hosford Muscle tables . We found a significant linear correlation between the two measures by using the MATLAB function , fitlm . m , with option “Robust” set to “on . ”
To examine the structural interconnections of the human musculoskeletal system , we used a hypergraph approach . Drawing from recent advances in network science [5] , we examined the musculoskeletal system as a network in which bones ( network nodes ) are connected to one another by muscles ( network hyperedges ) . A hyperedge is an object that connects multiple nodes; muscles link multiple bones via origin and insertion points . The degree , k , of a hyperedge is equal to the number of nodes it connects; thus , the degree of a muscle is the number of bones it contacts . For instance , the trapezius is a high-degree hyperedge that links 25 bones throughout the shoulder blade and spine; conversely , the adductor pollicis is a low-degree hyperedge that links 7 bones in the hand ( Fig 2a and 2b ) . A collection of hyperedges ( muscles ) that share nodes ( bones ) is referred to as a hypergraph: a graph H = ( V , E ) with N nodes and M hyperedges , where V = {v1 , ··· , vN} is the set of nodes and E = {e1 , ··· , eM} is the set of hyperedges . The representation of the human musculoskeletal system as a hypergraph facilitates a quantitative assessment of its structure ( Fig 2c ) . We observed that the distribution of hyperedge degree is heavy-tailed: most muscles link 2 bones , and a few muscles link many bones ( Fig 2d and 2e ) . The skew of the degree distribution differs significantly from that of random networks ( two-sample Kolmogorov-Smirnov test , KS = 0 . 37 , p < 0 . 0001 , see Materials and methods ) [5] , indicating the presence of muscles of unexpectedly low and high degree ( Fig 2e ) . To probe the functional role of muscles within the musculoskeletal network , we employed a simplified model of the musculoskeletal system and probed whether the model could generate useful clinical correlates . We implemented a physical model in which bones form the core scaffolding of the body , while muscles fasten this structure together . Each node ( bone ) is represented as a mass , whose spatial location and movement are physically constrained by the hyperedges ( muscles ) to which it is connected . Specifically , bones are points located at their center of mass , derived from anatomy texts [19] , and muscles are springs ( damped harmonic oscillators ) connecting these points [40 , 41]; for a hyperedge of degree k , we created k ( k − 1 ) /2 springs linking the k nodes . That is , for a muscle connecting k bones , we placed springs such that each of the k muscles had a direct spring connection to each of the other k − 1 bones . Next , we perturbed each of 270 muscles in the body and calculated their impact score on the network ( see Materials and methods and Fig 1c and 1d ) . As a muscle is physically displaced , it causes a rippling displacement of other muscles throughout the network . The impact score of a muscle is the mean displacement of all bones ( and indirectly , muscles ) resulting from its initial displacement . We observed a significant positive correlation between muscle degree and impact score ( F ( 1 , 268 ) = 23 . 3 , R2 = 0 . 45 , p < 0 . 00001; Fig 3a ) , suggesting that hyperedge structure dictates the functional role of muscles in the musculoskeletal network . Muscles with a larger number of insertion and origin points have a greater impact on the musculoskeletal system when perturbed than muscles with few insertion and origin points [42] . We can gain further insights into the results of these analyses by explicitly studying the relation between impact score and statistical measures of the network’s topology . In S11 Fig , we show that the network function as measured by the impact score was significantly correlated with the average shortest path length . While the network statistics are static in nature , their functional interpretation is provided by the perturbative simulations of system dynamics . To guide interpretation , it is critical to note that the impact score , while significantly correlated with muscle degree , is not perfectly predicted by it ( Fig 3a ) . Instead , the local network structure surrounding a muscle also plays an important role in its functional impact and ability to recover . To better quantify the effect of this local network structure , we asked whether muscles existed that had significantly higher or significantly lower impact scores than expected in a null network . We defined a positive ( negative ) impact score deviation that measures the degree to which muscles are more ( less ) impactful than expected in a network null model ( see Materials and methods ) . This calculation resulted in a metric that expresses the impact of a particular muscle , relative to muscles of identical hyperedge degree in the null model . In other words , this metric accounts for the complexity of a particular muscle ( Table 1 ) . Is this mathematical model clinically relevant ? Does the body respond differently to injuries to muscles with higher impact score than to muscles with lower impact score ? To answer this question , we assessed the potential relationship between muscle impact and recovery time following injury . Specifically , we gathered data on athletic sports injuries and the time between the initial injury and return to sport . Critically , we observed that recovery times were strongly correlated with impact score deviations of the individual muscle or muscle group injured ( F ( 1 , 12 ) = 37 . 3 , R2 = 0 . 757 , p < 0 . 0001; Fig 3b ) , suggesting that our mathematical model offers a useful clinical biomarker for the network’s response to damage . We note that it is important to consider the fact that recovery might be slower in a person who is requiring maximal effort in a performance sport , compared to an individual who is seeking only to function in day-to-day life . In order to generalize our findings to the entire population , we therefore also examined recovery time data collected from nonathletes , and we present these complementary results in the Supporting information ( S6 Text ) . Finally , to provide intuition regarding how focal injury can produce distant effects potentially slowing recovery , we calculated the impact of the ankle muscles and determined which other muscles were most impacted . That is , for each individual ankle muscle , we calculated the impact on each of the remaining 264 non-ankle muscles and then averaged this over all ankle muscles . Out of the 264 non-ankle muscles , the single muscle that is most impacted by the perturbation of ankle muscles is the biceps femoris of the hip , and the second most impacted is the vastus lateralis of the knee . Additionally , the muscle most impacted by perturbation to hip muscles is the soleus . What is the relationship between the functional impact of a muscle on the body and the neural architecture that affects control ? Here , we interrogate the relationship between the musculoskeletal system and the primary motor cortex . We examined the cerebral cortical representation map area devoted to muscles with low versus high impact by drawing on the anatomy of the motor strip represented in the motor homunculus [43] ( Fig 4a ) , a coarse one-dimensional representation of the body in the brain [44] . We observed that homunculus areas differentially control muscles with positive versus negative impact deviation scores ( Table 2 ) . Moreover , we found that homunculus areas controlling only positively ( negatively ) deviating muscles tend to be located medially ( laterally ) on the motor strip , suggesting the presence of a topological organization of a muscle’s expected impact in neural tissue . To probe this pattern more deeply , for each homunculus area , we calculated a deviation ratio as the percent of muscles that positively deviated from the expected impact score ( i . e . , a value of 1 for brow , eye , face and a value of 0 for knee , hip , shoulder; see Table 2 ) . We found that the deviation ratio was significantly correlated with the topological location on the motor strip ( F ( 1 , 19 ) = 21 . 3 , R2 = 0 . 52 , p < 0 . 001; Fig 4b ) . As a stricter test of this relationship between a muscle’s impact on the network and neural architecture , we collated data for the physical volumes of functional MRI-based activation on the motor strip that are devoted to individual movements ( e . g . , finger flexion or eye blinks ) . Activation volumes are defined as voxels that become activated ( defined by blood-oxygen-level-dependent signal ) during movement [38 , 39] . Critically , we found that the functional activation volume independently predicts the impact score deviation of muscles ( Fig 4c , F ( 1 , 5 ) = 14 . 4 , p = 0 . 012 , R2 = 0 . 743 ) , consistent with the intuition that the brain would devote more real estate in gray matter to the control of muscles that are more impactful than expected in a null model . Again , impact deviation is a metric that accounts for the hyperedge degree of a particular muscle and is relative to the impact of muscles with identical hyperedge degree in the null model . Thus , the impact deviation measures the local network topology beyond simply the immediate connections of the muscle in question . As a final test of this relationship , we asked whether the neural control strategy embodied by the motor strip is optimally mapped to muscle groups . We constructed a muscle-centric graph by connecting two muscles if they touch on the same bone ( Fig 1c , left ) . We observed the presence of groups of muscles that were densely interconnected with one another , sharing common bones . We extracted these groups using a clustering technique designed for networks [45 , 46] , which provides a data-driven partition of muscles into communities ( Fig 1b , right ) . To compare the community structure present in the muscle network to the architecture of the neural control system , we considered each of the 22 categories in the motor homunculus [18] as a distinct neural community and compared these brain-based community assignments with the community assignments obtained from a data-driven partition of the muscle network . Using the Rand coefficient [47] , we found that the community assignments from both homunculus and muscle network were statistically similar ( zRand > 10 ) , indicating a correspondence between the modular organization of the musculoskeletal system and the structure of the homunculus . For example , the triceps brachii and the biceps brachii belong to the same homuncular category , and we found that they also belong to the same topological muscle network community . Next , because the homunculus has a linear topological organization , we asked whether the order of communities within the homunculus ( Table 3 ) was similar to a data-driven ordering of the muscle groups in the body , as determined by MDS [48] . From the muscle-centric network ( Fig 1b ) , we derived a distance matrix that encodes the smallest number of bones that must be traversed to travel from one muscle to another . An MDS of this distance matrix revealed a one-dimensional linear coordinate for each muscle , such that topologically close muscles were close together and topologically distant muscles were far apart . We observed that each muscle’s linear coordinate is significantly correlated with its homunculus category ( Fig 4d , F ( 1 , 268 ) = 316 , p < 0 . 0001 , R2 = 0 . 54 ) , indicating an efficient mapping between the neural representation of the muscle system and the network topology of the muscle system in the body . Our results from Fig 4d demonstrate a correspondence between the topology of the homunculus and a data-driven ordering of muscles obtained by considering the topological distances between them . This result could be interpreted in one of two ways: one reasonable hypothesis is that because most connections in the musculoskeletal network are short range , the finding is primarily driven by short-range connections . A second reasonable hypothesis is that while short-range connections are the most prevalent , long-range connections form important intramodular links that help determine the organization of the network . To arbitrate between these two hypotheses , we considered two variations of our MDS experiment: one including only connections shorter than the mean connection length and the other including only connections longer than the mean connection length . We found that the data-driven ordering derived from only short and only long connections both led to significant correlations with the homuncular topology ( F ( 1 , 268 ) = 24 . 9 , R2 = 0 . 085 , p < 0 . 0001 and F ( 1 , 268 ) = 5 , R2 = 0 . 018 , p = 0 . 026 , respectively ) . Notably , including both long and short connections leads to a stronger correlation with homuncular topology than considering either independently , suggesting a dependence on connections of all lengths . It would be interesting in the future to test the degree to which this network-to-network map is altered in individuals with motor deficits or changes following stroke .
By representing the complex interconnectivity of the musculoskeletal system as a network of bones ( represented by nodes ) and muscles ( represented by hyperedges ) , we gained valuable insight into the organization of the human body . The study of anatomical networks using similar methods is becoming more common in the fields of evolutionary and developmental biology [10] . However , the approach has generally been applied only to individual parts of the body—including the arm [49] , the head [11] , and the spine [12]—thereby offering insights into how that part of the organism evolved [50 , 51] . Moreover , even when full body musculature [13] and the neuromusculoskeletal [14] system more generally have been modeled , some quantitative claims can remain elusive , in large part due to the lack of a mathematical language in which to discuss the complexity of the interconnection patterns . In this study , we offer an explicit and parsimonious representation of the complete musculoskeletal system as a graph of nodes and edges , and this representation allowed us to precisely characterize the network in its entirety . When modeling a system as a network , it is important to begin the ensuing investigation by characterizing a few key architectural properties . One particularly fundamental measure of a network’s structure is its degree distribution [52] , which describes the heterogeneity of a node’s connectivity to its neighbors in a manner that can provide insight into how the system formed [7] . We observed that the degree distribution of the musculoskeletal system is significantly different from that expected in a null graph ( Fig 2e ) , displaying fewer high-degree nodes and an overabundance of low-degree nodes . The discrepancy between real and null model graphs is consistent with the fact that the human musculoskeletal system develops in the context of physical and functional constraints that together drive its decidedly nonrandom architecture [53] . The degree distribution of this network displays a peak at approximately degree two , that is then followed by a relatively heavy tail of high-degree nodes . The latter feature is commonly observed in many types of real-world networks [54] , whose hubs may be costly to develop , maintain , and use [55 , 56] but play critical roles in system robustness , enabling swift responses [55] , buffering environmental variation [57] , and facilitating survival and reproduction [58] . The former feature—the distribution’s peak—is consistent with the intuition that most muscles within the musculoskeletal system connect with only two bones , primarily for the function of simple flexion or extension at a joint . By contrast , there are only a few muscles that require a high degree to support highly complex movements , such as maintaining the alignment and angle of the spinal column by managing the movement of many bones simultaneously . These expected findings provide important validation of the model as well as offer a useful visualization of the musculoskeletal system . The musculoskeletal network is characterized by a particularly interesting property that distinguishes it from several other real-world networks: the fact that it is embedded into three-dimensional space [59] . This property is not observed in semantic networks [60] or the World Wide Web [61] , which encode relationships between words , concepts , or documents in some abstract ( and very likely non-euclidean ) geometry . In contrast , the musculoskeletal system composes a volume , with nodes having specific coordinates and edges representing physically extended tissues . To better understand the physical nature of the musculoskeletal network , we examined the anatomical locations of muscles with varying degrees ( Fig 2c ) . We observed that muscle hubs occur predominantly in the torso , providing dense structural interconnectivity that can stabilize the body’s core and prevent injury [62] . Specifically , high-degree muscles cluster about the body’s midline , close to the spine , and around the pelvic and shoulder girdle , consistent with the notion that both agility and stability of these areas requires an ensemble of muscles with differing geometries and tissue properties [63] . Indeed , muscles at these locations must support not only flexion and extension but also abduction , adduction , and both internal and external rotation . It is important to note that significant variation exists within the musculoskeletal system across individuals , and not all anatomical atlases agree on the most representative set of insertion and origin points . The results presented here reflect how the musculoskeletal system was presented in the text from which it was constructed [19] and therefore provide only one possible network representation of the musculoskeletal system . To assess the reliability of our results across reasonable variation of the musculoskeletal configuration , we created a second musculoskeletal network from an alternate atlas [64] . Using this second atlas , we observed consistent results , and we report these complementary analyses in S3 Text . It is also important to note that we mapped the first atlas [19] into a musculoskeletal graph composed of both bony and non-bony nodes . This choice equates the structural roles of bones and certain tendons and ligaments , which is admittedly a simplification of the biology . One justification for this simplification is that non-bony structures frequently serve as critical attachment points of muscles ( i . e . , the plantar fascia of the foot ) . Thus , it is reasonable to separate the musculoskeletal network into the two categories of muscles and structures that serve as muscular attachment points , as we did here . Nonetheless , this second category is quite heterogeneous in composition , and in future work , one could also consider constructing a multilayer graph , with a separate layer accounting for each type of muscular attachment structure . To confirm that our findings and interpretations are not significantly altered by the presence of non-bony muscular attachment points , we removed such points in an alternative atlas and observe that our main findings still hold ( see S3 Text ) . To better understand the functional role of a single muscle within the interconnected musculoskeletal system , we implemented a physics-based model of the network’s impulse response properties by encoding the bones as point masses and the muscles as springs [65] . Significantly , this highly simplified model of the musculoskeletal system is able to identify important functional features . While muscles of high degree also tended to have a large impact on the network’s response ( Fig 3a ) , there were several notable deviations from this trend ( Table 1 ) . The muscle noted to have the least impact relative to that expected is the orbicularis oculi , the muscle used for controlling movement of the eyelid . This muscle is small and relatively isolated in the body , originating and inserting on bones of the skull . The face muscles in general form a tight and isolated community , with few connections reaching outside that community . These factors likely contribute to the low impact of this muscle , and an analogous argument could be made for the remaining two muscles with less impact than expected , which are also muscles of the face . The muscles with more impact than expected are more numerous but almost entirely located in the upper limb or upper limb girdle . The extensor carpi radialis longus , anconeus , brachioradialis , and brachialis muscles are all intrinsic arm muscles , the latter three acting at the elbow . All of these muscles may have higher impact than expected in a null model because they can either directly or indirectly affect the movement of the many bones of the wrist and hand . The observed high impact of these muscles could be a result of the fact that they control the movement of a limb , and at the end of the limb are many bones whose movement depends directly on these muscles . The remainder of the high-impact muscles , with the exception of the piriformis , all attach the upper limb to the axial skeleton . These muscles are the coracobrachialis , infraspinatus , supraspinatus , subscapularis , teres minor , teres major , and pectoralis major muscles . These muscles , like the previous four , have the property that they control the movement of an entire limb , which likely contributes to their impact . Unlike the previous group , these muscles also connect to the axial skeleton , which may also add to their impact . Many of these muscles originate on bones of the shoulder girdle and have the potential to affect all other shoulder girdle muscles , and potentially all bones connected to those muscles . This same dynamic likely exists in the lower limb , which is reflected by the presence of the piriformis muscle of the pelvic girdle . An in-depth discussion of how local network structure and muscle configuration may interact with impact deviation is presented in S7 Text . In addition to our work presented in the Supporting information , further insight may be gained into the properties of these outliers by performing experiments to closely examine the bones that are impacted most by each of these muscles . While the network representation of a system can provide basic physical intuitions due to its parsimony and simplicity , it also remains agnostic to many details of the system’s architecture and function . It is a perennial question whether these first-principles models of complex systems can provide accurate predictions of real-world outcomes . We addressed this question by studying the relationship between the impact score of a muscle and the amount of time it takes for a person to recover from an injury . We quantified time of recovery by summing ( i ) the time to recover from the primary disability of the initial muscle injury and ( ii ) the time to recover from any secondary disabilities resulting from altered usage of other muscles in the network , due to the initial muscle injury [66] . We found that the deviation from the expected impact score in a null network correlated significantly with time of recovery ( Fig 3b ) , supporting the notion that focal injury can have extended impacts on the body due to the inherently interconnected nature of the musculoskeletal system . Indeed , muscular changes in one part of the body are known to affect other muscle groups . For example , strengthening hip muscles can lead to improved knee function following knee replacement [67] . Alteration of muscular function in the ankle following sprains can cause altered hip muscle function [68 , 69] , a result replicated by our model ( which found the biceps femoris and vastus lateralis were most impacted by ankle injury ) , and injury to limb muscles can lead to secondary injury of the diaphragm [70] . Our model offers a mathematically principled way in which to predict which muscles are more likely to have such a secondary impact on the larger musculoskeletal system and which muscles are at risk for secondary injury , given primary injury at a specific muscle site . It would be interesting in the future to test whether these predictions could inform beneficial adjustments to clinical interventions by explicitly taking the risk of secondary injury to particular muscles into account . Previously , prevention of secondary muscle injury has been largely relegated to cryotherapy [71 , 72] and has yet to be motivated by such a mechanistic model . Finally , an important question to ask is how this musculoskeletal configuration is evolutionarily advantageous and how evolutionary pressures may have optimized muscle impacts . Intuitively , one might expect that evolutionary pressures drive muscle impact down , perhaps by increasing muscular redundancy . A thorough investigation of the evolutionary advantages of the musculoskeletal network topology would be an interesting topic for future work . Given the complexity of the musculoskeletal network and its critical role in human survival , it is natural to ask questions about how that network is controlled by the human brain . Indeed , the study of motor control has a long and illustrious history [73] , which has provided important insights into how the brain is able to successfully and precisely make voluntary movements despite challenges such as redundancies , noise [74] , delays in sensory feedback [75] , environmental uncertainty [76] , neuromuscular nonlinearity [77] , and nonstationarity [78] . Here , we took a distinct yet complementary approach and asked how the topology of the musculoskeletal network may be mapped onto the topology of the motor strip within the cortex . We began by noting that the impact deviation of a muscle is positively correlated with the size of the cortical volume devoted to its control ( Fig 4c ) . One interpretation of this relationship is that those muscles with greater impact than expected in a null model by their immediate connections tend to control more complex movements and therefore necessitate a larger number of neurons to manage those movements [79] . A second interpretation builds on an evolutionary argument that muscles with more impact need a greater redundancy in their control systems [80] , and this redundancy takes the form of a greater cortical area . Local cortical volumes aside [81] , one might also wish to understand to what degree the larger-scale organization of the musculoskeletal network reflects the organization of the motor strip that controls it . Building on the recent application of community detection techniques to the study of skull anatomy [11 , 82 , 83] , we reported the modular organization of the muscle network: groups of muscles in which the muscles in one group are more likely to connect to one another than to muscles in other groups . More intriguingly , we observed that muscle communities closely mimic the known muscle grouping of the motor strip ( Fig 1b , right ) : muscles that tend to connect to the same bones as each other also tend to be controlled by the same portion of the motor strip . Furthermore , a natural linear ordering of muscle communities—such that communities are placed close to one another on a line if they share network connections—mimics the order of control in the motor strip ( Fig 4d ) . These results extend important prior work suggesting that the one-dimensional organization of the motor strip is related to both the structural and functional organization of the musculoskeletal network [84 , 85] . In fact , the results more specifically offer a network-level definition for optimal network control: the consistency of the linear map from musculoskeletal communities to motor strip communities . Finally , we interrogated the physical locations of the cortical control of impactful muscles . We observed that muscles with more impact than expected given a null graph tend to be controlled by medial points on the motor strip , while muscles with less impact than expected tend to be controlled by lateral points on the motor strip ( Fig 4b ) . This spatial specificity indicates that the organization of the motor strip is constrained by the physical layout of the body as well as aspects of how muscles function . Previous studies have examined a general temporal correspondence between cortical activity and muscle activity during movement [86] , but little is known about topological correspondence . The construction of a hypergraph from the human musculoskeletal system requires assumptions and simplifications that impact the flexibility of the current model . Most prominent is the reduction of the system into two categories: muscles and bones . These categories hold no additional information and therefore do not account for features of a muscle’s or bone’s internal architecture . This simplification introduces several limitations to the perturbative model , including the capability of modeling the functional architecture of complex muscles , or those with the ability to independently contract a subset of fibers . For example , the two-headed biceps brachii has an origin both on the scapula and supraglenoid tubercle , and it is possible to contract the fibers of one head separately from the fibers of the other head . Future work could extend our modeling framework to represent this complex functional architecture . Furthermore , nonmuscular soft tissue structures essential to the musculoskeletal system cannot be explicitly accounted for . These structures , including tendons and ligaments , can either be ( 1 ) encoded as bones , as in the main text network , or ( 2 ) excluded from the network , as in the supplement; neither option is completely anatomically accurate . In the case of bones , the model is unable to account for bone–bone interactions ( joints ) . The majority of muscles act at joints , and the exclusion of joints obfuscates the specific function of muscles . That is , the model accounts for the fact that muscles move bones but not how they move or in what direction . In the perturbative simulation , the lack of joint constraints allows bones to be placed at unnatural angles relative to adjacent bones . In addition , bones are modeled as point masses , which in the perturbative simulation may allow bones to undergo trajectories involving the passage through space that , in reality , is occupied by another bone . Future work could extend our modeling framework to account for these additional biophysical constraints . Insights generated by this model are a result of the input data . As individual variation exists within the musculoskeletal system , it similarly exists in muscle impacts . We have made an effort to use two input datasets to justify our main findings , but these findings may not be generalizable to all healthy musculoskeletal configurations . Specifically , the degree of a muscle , subject to individual variation , is likely to affect the impact of that muscle . Exactly how normative individual variation in muscle degree is related to variation in predicted muscle impact is an important question that , nevertheless , is outside of the scope of the current study . Lastly , the human musculoskeletal system is a complex and densely interconnected network . Neither muscles nor bones function as independent entities . As such , it is difficult to parse the function of a single muscle from effects due to surrounding muscles . Nonindependence of muscles can be partially eliminated by appropriate null model selection , and our results hold under a variety of choices . Nonetheless , the notion that muscles—and impact factors—are not truly independent should be considered when interpreting these results .
In summary , here we developed a novel network-based representation of the musculoskeletal system , constructed a mathematical modeling framework to predict recovery , and validated that prediction with data acquired from athletic injuries . Moreover , we directly linked the network structure of the musculoskeletal system to the organization of cortical architecture , suggesting an evolutionary pressure for optimal network control of the body . We compared the structure , function , and control of the human musculoskeletal system to a null system in which small groups of closely related muscles are rewired with each other . Our results suggest that the structure , function , and control of the musculoskeletal system are emergent from the highly detailed , small-scale organization , and when this small-scale organization is destroyed , so are the emergent features . Our work directly motivates future studies to test whether faster recovery may be attained by not only focusing rehabilitation on the primary muscle injured but also directing efforts towards muscles that the primary muscle impacts . Furthermore , our work supports the development of a predictive framework to determine the extent of musculoskeletal repercussions from insults to the primary motor cortex . An important step in the network science of clinical medicine [87] , our results inform the attenuation of secondary injury and the hastening of recovery . | While network science is frequently used to characterize networks from genomics , proteomics , and connectomics , its utility in understanding biomechanics , orthopedics , and physical therapy has remained largely unexplored . Indeed , current clinical practice and knowledge regarding the musculoskeletal system largely focuses on single areas of the body , single muscles , or single injuries and therefore remains agnostic to mesoscale or global features of the body’s architecture that may have critical implications for injury and recovery . We addressed this gap by representing the musculoskeletal system as a graph or network , in which we considered bones and the muscular connections between them . By modeling muscles as springs and bones as point masses , we developed a perturbative approach to interrogate the function of this network . Employing this model , we calculated the network level effects of perturbing individual muscles . Using this formalism , we are able to draw new parallels between this system and the primary motor cortex that controls it , and illustrate clinical connections between network structure and muscular injury . | [
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| 2018 | Structure, function, and control of the human musculoskeletal network |
The N-linked glycosylation motif at amino acid position 154–156 of the envelope ( E ) protein of West Nile virus ( WNV ) is linked to enhanced murine neuroinvasiveness , avian pathogenicity and vector competence . Naturally occurring isolates with altered E protein glycosylation patterns have been observed in WNV isolates; however , the specific effects of these polymorphisms on avian host pathogenesis and vector competence have not been investigated before . In the present study , amino acid polymorphisms , NYT , NYP , NYF , SYP , SYS , KYS and deletion ( A’DEL ) , were reverse engineered into a parental WNV ( NYS ) cDNA infectious clone to generate WNV glycosylation mutant viruses . These WNV glycosylation mutant viruses were characterized for in vitro growth , pH-sensitivity , temperature-sensitivity and host competence in American crows ( AMCR ) , house sparrows ( HOSP ) and Culex quinquefasciatus . The NYS and NYT glycosylated viruses showed higher viral replication , and lower pH and temperature sensitivity than NYP , NYF , SYP , SYS , KYS and A’DEL viruses in vitro . Interestingly , in vivo results demonstrated asymmetric effects in avian and mosquito competence that were independent of the E-protein glycosylation status . In AMCRs and HOSPs , all viruses showed comparable viremias with the exception of NYP and KYS viruses that showed attenuated phenotypes . Only NYP showed reduced vector competence in both Cx . quinquefasciatus and Cx . tarsalis . Glycosylated NYT exhibited similar avian virulence properties as NYS , but resulted in higher mosquito oral infectivity than glycosylated NYS and nonglycosylated , NYP , NYF , SYP and KYS mutants . These data demonstrated that amino acid polymorphisms at E154/156 dictate differential avian host and vector competence phenotypes independent of E-protein glycosylation status .
Since its identification in North America in 1999 , West Nile virus ( WNV ) has caused large human encephalitis epidemics in the United States resulting in 48 , 183 reported human cases of neuroinvasive and non-neuroinvasive disease and 2 , 114 deaths to date ( http://www . cdc . gov/westnile ) . First isolated in 1937 from a febrile patient in the West Nile district of northern Uganda[1] , the virus is now known to circulate in Africa , Asia , Europe , North America , South America and Australia[2–4] . WNV is a member of the Flaviviridae family and is closely related to other human pathogens such as Japanese encephalitis ( JEV ) , Saint Louis encephalitis and Murray Valley encephalitis viruses within the Japanese encephalitis virus ( JEV ) serocomplex[5 , 6] . The natural reservoir/amplification hosts of WNV are avian species with Culex spp . mosquitoes serving as the primary vectors for enzootic transmission[4] . Humans and most mammals are incidental hosts in the natural transmission cycle as they do not develop sufficiently high viremias to be infectious for mosquitoes . WNV has a single-stranded positive-sense RNA genome that is transcribed and translated in one open reading frame[5] . There are three structural proteins , capsid ( C ) , pre-membrane ( prM ) , envelope ( E ) , and seven nonstructural ( NS ) proteins NS1-NS2A-NS2B-NS3-NS4A-NS4B-NS5 that are translated and released after host and viral protease cleavage in the cytoplasm[5] . The prM-E proteins influence efficiency of virus infection , viral particle maturation and virus release[7] . The prM is cleaved from the E protein during virus replication and assembly , allowing the E proteins to dimerize and create a lattice-like structure around mature WNV virus particles during viral maturation and release[8] . The E protein facilitates cell attachment , allowing virus entry into the cell via the endosomal pathway[5] . Both the prM and E proteins contain N-linked glycosylation sites; however , only the prM N-linked glycosylation site is highly conserved among WNV isolates[7] . The WNV E protein N-linked glycosylation motif ( asparagine-tyrosine-serine/threonine; N-Y-S/T ) lies between amino acid ( aa ) positions 154–156 and exists in forms predicted to be glycosylated and not glycosylated with the site completely absent in some strains due to a four amino acid deletion across this motif[9–11] . The N-linked glycosylation motif is present in most WNV strains that have been isolated during significant outbreaks of human disease both in North America and globally ( Table 1 ) [7 , 12–14] . Glycosylation of the WNV E protein has been previously implicated as an important viral genetic element for flavivirus virulence and pathogenesis and has been associated with enhanced growth in mammalian , avian and mosquito cells[7] , and a murine neuroinvasive phenotype[15–18] with higher viremia and increased avian pathogenicity[19–21] . WNV virus-like particles ( VLPs ) with mutations that prevent glycosylation at the envelope motif , in contrast , have been associated with reduced particle production in mosquito , mammalian and avian cells however , VLPs lacking envelope glycosylation exhibited higher infectivity rates in mosquito cells compared to VLPs with glycosylated E proteins[7] . In other studies , a non-glycosylated NYI mutant virus demonstrated reduced transmissibility following oral exposure of Culex pipiens and reversion at the glycosylation motif was commonly identified in saliva samples of the orally exposed Cx . pipiens . Similarly , oral infectivity of the parental glycosylated WNV was found to infect Cx . quinquefasciatus more efficiently than the NYI mutant [22 , 23] . Other flaviviruses such as Zika virus ( ZIKV ) also exhibit lower oral infectivity and vector competence in Aedes aegypti when the N-linked glycosylation was ablated from the envelope protein via mutagenesis[24 , 25] . Many of the newly emergent and more virulent WNV strains , including the North American isolates , possess the E protein N-linked glycosylation motif ( NYS ) [16 , 17 , 19] . Previous reports have focused on preclusion of E protein glycosylation by mutagenizing NYS to IYS and AYS both of which have not been associated with any WNV isolate[22 , 23] . However , the effect of naturally occurring WNV amino acid polymorphisms on maintenance of transmission cycles has not been investigated . Four glycosylation motif sequences found in naturally occurring isolates from North America , Australia , Europe and Africa ( NYP , NYF , SYS , KYS , A’Del ) [9 , 26 , 27] were reverse engineered and infectious clone-derived viruses generated . An additional WNV glycosylation mutant virus ( NYT ) was generated that was designed to alter the amino acid at E-156 while maintaining a glycosylation competent motif . An SYP mutant was generated as a double mutant in order to serve as a control , where both the first ( E-154 ) and third amino acid ( E-156 ) residues were mutagenized . Avian host , vector competence and viral growth capacity of these WNV infectious clone-derived glycosylation mutant viruses were assessed in vitro and in vivo in American crows ( AMCRs ) and house sparrows ( HOSPs ) and in Cx . quinquefasciatus and Culex tarsalis , respectively .
C6/36 ( Aedes albopictus ) and Vero cells were maintained in Dulbecco’s Modified Eagle’s Medium ( Gibco , Invitrogen , Carlsbad , CA ) and duck embryonic fibroblast cells ( DEF ) were maintained in Eagle’s Minimum Essential media ( EMEM , ATCC ) with 10% heat-inactivated fetal bovine serum and 100U/mL and 100ug/mL of Penicillin/Streptomycin , respectively , at 28°C ( mosquito cells ) and 37°C for vertebrate cells . HD11 chicken monocytes were kindly provided by Dr . Kirk Klasing ( UC Davis ) and were maintained in RPMI 1640 media at 37°C . Vero cells were used for all plaque assays in this study . Assembly and rescue of the WNV infectious clone derived virus ( WNV . IC ) has been described previously[28] . Site-directed mutagenesis primers ( Table 2 ) were designed based on the glycosylation motif of the different WNV isolates ( Table 1 ) . Glycosylation mutant WNV plasmids were generated using a QuikChange site-directed mutagenesis kit ( Invitrogen , Carlsbad , CA ) . To assemble the infectious clones , the viral genomic cDNA was digested out from a bipartite-plasmid system , ligated , in vitro transcribed and viral genomic RNA transfected into BHK cells as described previously[28] . The SYP and A’DEL ( this mutant virus lacks the E αA’ helix ( A’DEL; ΔE157-E160 ) ) viruses were generated subsequently to assess the effect of mutating both the first and third amino acids in the glycosylation motif and to reduce the potential for genetic reversion at the glycosylation motif for in vivo studies and to assess the infectivity of a virus lacking the E154-156 motif , respectively . All viruses were harvested at 3 days post-transfection after observation of cytopathic effect ( CPE ) . Full-length sequencing of the rescued viruses was performed to ensure the introduction of appropriate mutations and that extraneous mutations were not introduced . All viruses were titrated on Vero cells and plaque diameters measured ( mm ) from five representative plaques for comparison . In order to assess the stability of the E protein N-linked glycosylation motif in cell culture , serial passaging was performed in C6/36 cells . All viruses were passaged five times in C6/36 cells . Cells were initially inoculated in triplicate at an MOI of 0 . 01 . Media was removed , virus was added and allowed to adsorb for one hour . After the absorption period , cells were washed twice with DPBS , after which fresh media was added . Cells were allowed to incubate for 7 dpi . , after which supernatant was harvested , diluted 1:10 and added to a new flask of cells . This process was repeated for five passages . All viruses were harvested at 7 days post-infection ( dpi ) at which point RNA was extracted using a Qiagen Viral RNA extraction kit ( Qiagen , Valencia , CA ) . Reverse transcription was performed using primer WNV4129R ( 5’ TTGAGGCTAGAGCCAAGCATAGCAG 3’ ) and Superscript II ( Invitrogen , CA ) . Generated cDNA was diluted 100-fold and then used in PCR reactions with Pfu Turbo polymerase ( Invitrogen , Carlsbad , CA ) and primers WN128F ( 5’GCCGGGCTGTCAATATGCTAAAAC 3’ ) and WN2506R ( 5’ GCTCTTGCCGCTGATGTCTATG 3’ ) . Amplicons were sequenced using primer WN1797R ( 5’ ATGACCCGACGTCAACTTGACAGTG 3’ ) . In order to confirm the glycosylation status of the WNV mutants , Vero cells were inoculated with the parental and mutant viruses at an MOI of 1 . At 50 hours post-inoculation ( hpi ) , supernatant was removed and cells were lysed . Cell lysates were treated with 500 units of Endoglycosidase F enzyme ( New England Biolabs ) , an amidase that cleaves the N-linked glycosylation moiety from the glycoprotein . Briefly , 10 μg of cell lysate was denatured with 1X Glycoprotein Denaturing Buffer ( 0 . 5% SDS , 40mM DTT ) at 100°C for 10 minutes . Cell lysate was then treated with glycoBuffer 2 , NP-40 and Endoglycosidase F ( + ) after which the mix was incubated for 1 hour at 37°C . The samples ( 1 μg ) were separated electrophoretically on a 4–20% reducing SDS gel . Separated proteins were electroblotted onto nitrocellulose membranes and immunostained with anti-WNV E mAb 3 . 67 . Vero , HD11 , DEF and C6/36 cells were inoculated in triplicate with each virus at an MOI of 0 . 1 . Viral titers were determined via a standard plaque assay on Vero cells and these titers were used to calculate MOIs for all in vitro growth kinetics experiments . Media was removed from the cells and 200μL of diluted virus was added to each well after which the cells were allowed to incubate with the virus at 28°C ( C6/36 cells ) or 37°C ( all other cell lines ) for one hour . After the incubation , cells were washed twice with DPBS and fresh media added to each well . The plates were incubated at the designated temperature ( including up to 44°C for high temperature DEF cell assessments ) through 4 dpi . 50μL of supernatant aliquots were harvested every 24 hours from each well and added to 450μL of DMEM supplemented with 20% heat-inactivated FBS . Samples were stored at -80°C until processed . Growth kinetics for each virus was determined by standard plaque assay on Vero cells . Viral titers were averaged , reported in log-based plaque-forming units/ mL and plotted by hpi . The parental strain and WNV glycosylation mutants were diluted to 105 PFU/well and exposed to varying pH ranges ( 5 . 8–7 . 0 ) at 0 . 2 pH unit increments in triplicate for 10 minutes at which point the pH was brought to a neutral level ( pH~7 . 4 ) by the addition of sterile PBS . Viral supernatants in the pH-neutralized buffer were 10-fold serial-titrated by plaque assay . Wild AMCRs were captured by cannon nets in Bellvue , Colorado ( U . S . Fish and Wildlife Scientific Collecting Permit number MB-032526 ) . House sparrows were trapped by Japanese mist nets in Bakersfield , California . In order to confirm that AMCRs and HOSPs had not previously been exposed to WNV or another endemic flavivirus , St . Louis encephalitis virus ( SLEV ) , birds were bled prior to inoculation and serum tested by plaque reduction neutralization assays ( PRNTs ) against these two viruses as previously described[29] . Detection of specific neutralizing antibodies within the sera of any AMCR or HOSP to either SLEV or WNV excluded the bird for use in vivo experiments . All AMCRs/HOSPs were held for at least two weeks prior to viral inoculation for cage adaptation and quarantine . 6–8 AMCRs were subcutaneously inoculated with 1 , 500 PFU of each virus ( NYS , NYT , NYP NYF , SYS and KYS ) . Six HOSPs were similarly inoculated with 1 , 500 PFU of each WNV glycosylation mutant , including a mutant lacking the E αA’ helix ( A’DEL; ΔE157-E160 ) . All AMCRs and HOSPs were examined for signs of disease twice daily for 14 days following inoculation and bled once daily from 1 to 7 dpi to assess viremias . Viral RNA was extracted from serum samples at peak viremias and from brain tissue of AMCRs that had succumbed to infection and sequenced over the N-linked glycosylation motif to identify potential compensatory mutations in the E154-156 or surrounding regions using the method mentioned previously . Four to seven-day-old Culex quinquefasciatus ( Sebring strain ) and Culex tarsalis ( BFS ) , reared at 28°C , 16:8 ( L: D ) photo cycle with 5% sucrose solution , were used for vector competence assessments . The Sebring strain was originally collected in 1988 from Sebring County , Florida[30] . The Cx . tarsalis Bakersfield Field Station ( BFS ) strain was established from Bakersfield , Kern County , California and had been in colonization since 1952[31] . Seven groups of 100 female Cx . quinquefasciatus were sugar-starved for 24 hours prior to the feed . Viruses ( NYS , NYT , NYP , NYF , SYS , SYP , KYS ) were diluted to 7 log10 PFU/mL and mixed with defibrinated chicken blood ( Colorado Serum Company , Denver , CO ) at a 1:1 ratio . Each group of mosquitoes was exposed to 2 mL of the virus: blood mix using a Hemotek feeding unit ( Discovery Workshops , Accrington , UK , ) . After one hour of feeding , fully engorged females were held at 28°C with 5% sucrose under a 16:8 ( L:D ) photoperiod through 14 days post-exposure ( dpe ) . The A’DEL virus did not produce viral titers higher than 6 . 3 log10 PFU/mL at the time of this study . Therefore , a separate experiment was performed where the NYS virus was also diluted to 6 . 7 log10 PFU/mL for comparison and artificial oral infections with A’DEL and NYS were performed as described above . At 14 dpe , 25 mosquitoes were removed from each virus group , anesthetized by exposure to triethylamine , legs removed and saliva collected by capillary tube as previously described[32] . The hind leg was removed from each mosquito and was placed in 0 . 5mL of diluent along with a BB ( Crossman Corporation , NY ) . Mosquito bodies were collected after salivation was completed and stored in 0 . 5mL of diluent with a BB . Mosquito bodies , legs and saliva were stored at -80°C until titrated by plaque assay . Only 0 . 2mL of each sample was 10- fold serially titrated and viral titers were calculated as log10 PFU/mL . A direct 2 . 5-fold conversion can be applied to identify titers from whole bodies , legs and expectorants , respectively . Bodies were homogenized using a mixer mill at 24 cycles/sec for 2 minutes . Homogenates were clarified via centrifugation for 5 min at 5 , 000 x g and assayed for the presence of virus by plaque titration on Vero cells . Infection rates were calculated as the number of virus-positive bodies as a percentage of the total number of mosquitoes assayed at 14 dpe . Dissemination rates were calculated as the number of virus-positive legs as a percentage of virus-positive bodies . Saliva samples from mosquitoes that exhibited virus positive legs were centrifuged at 5 , 000 x g for 5 min and titrated on Vero cells as described above . Transmission rates were calculated as the percentage virus-positive saliva as a percentage of virus-positive legs . Culex tarsalis were also exposed to artificial infectious blood meals and were processed in the same manner as the Cx . quinquefasciatus . One hundred female Cx . quinquefasciatus were individually intrathoracically inoculated with 0 . 14μL of 6 log10 PFU/mL ( ~140 PFU ) of the parental NYS virus and each of the mutant viruses ( NYT , NYP , NYF , SYS , SYP and KYS ) . Following inoculation , mosquitoes were held at 28°C with 5% sucrose through 7 dpe under a photoperiod of 16:8 ( L: D ) . At 7 dpe , 50 mosquitoes exposed to each virus were anesthetized as described above . Saliva and bodies were collected , stored and titrated as described previously . Infection rates were calculated as described above . The number of virus-positive saliva samples were expressed as a percentage of virus-positive bodies to calculate transmission rates For all negative samples , the LOD value was used for all statistical evaluations . One-way ANOVA tests were used to assess differences in mean peak viremia in AMCRs and HOSPs as well as between titers at various time points between mutant viruses in Vero , DEF , C6/36 and HD11 cells and plaque sizes . Tukey’s HSD adjustment for multiple comparisons were utilized for assessing mean differences . Pair-wise Fisher’s exact tests were used to analyze differences in infection , dissemination and transmission rates of each mutant virus compared to the parental glycosylated NYS WNV in mosquitoes . California and Colorado birds were collected , housed , transported and inoculated under the following approved permits and protocols: i ) University of California , Davis , Institutional Animal Care and Use Committee ( IACUC ) protocols 12876 and 12880 ii ) Colorado State University , Institutional Animal Care and Use Committee protocol 10-2078A iii ) USGS Master Station Banding Permit 22763 iv ) State of California Scientific Collecting Permits v ) Federal Permit MB082812 vi ) BUA 0554 by the University of California , Davis , Environmental Health and Safety Committee , and USDA Permit 47901 vii ) IACUC 10-2078A in the Animal Disease Lab at Colorado State University .
WNV mutants ( Table 2 ) were harvested from transfected BHK-21 supernatants after observation of cytopathic effect at day three post-transfection . Rescued mutants were titrated and full-length Sanger sequencing performed to confirm the incorporation of the correct mutations and that spurious mutations were not inadvertently introduced ( Fig 1A ) . A Western blot was performed to assess the glycosylation status of E viral proteins purified from Vero cells infected with the parental and mutant viruses . The E proteins of endoglycosidase untreated control NYS [NYS EndoF ( - ) ] and NYT [NYT EndoF ( - ) ] viruses ran at >50 kDa . ( Fig 1B ) . After treatment with endoglycosidase F , E proteins of both NYS and NYT viruses increased in mobility and migrated at <49 kDa ( Fig 1B ) , indicating de-glycosylation of NYS and NYT via endoglycosidase treatment . Mobility of NYS and NYT endoglycosidase-treated E proteins was indistinguishable from the untreated NYF , NYP , KYS and SYS E proteins , indicating that the nucleotide substitutions to create NYF , NYP , KYS and SYS successfully ablated glycosylation of the E protein[7 , 10 , 22 , 27 , 33] . Plaques from NYS , NYT , KYS were significantly larger than ( p<0 . 05 ) NYP , NYF and SYS plaques ( Fig 1C ) . Growth profiles of glycosylation competent viruses ( NYS , NYT ) were indistinguishable in Vero cells ( Fig 1D ) , with both NYS ( 9 . 3 log10 PFU/mL ) and NYT ( 9 . 3 log10 PFU/mL ) viruses reaching peak viral titers at 2 dpi ( p>0 . 1 ) . The nonglycosylated ( SYS , NYP and KYS ) mutants exhibited mean viral titers that were at least 5-fold lower than the glycosylated parental virus at 2 dpi ( p<0 . 005 ) and reached indistinguishable mean peak viral titers compared to the glycosylated NYS virus by 3 dpi ( p>0 . 5 ) . The NYF mutant showed a more significant growth restriction in Vero cells , with an ~50-fold reduction in titer compared to the NYS virus in Vero cells at 2 dpi ( p<0 . 0005 ) . Furthermore , the mean peak titer at 3 dpi was also at least 5-fold lower than either the NYS or NYT viruses ( p<0 . 009 ) . Since glycosylation of the E154-156 motif has been associated with differential protein stability that could be critical under low pH conditions in which the E protein is exposed within the endocytic vesicle , the assessment of infectivity was performed under an acidic pH range[34] ( Fig 1E ) . Both glycosylated variants ( NYS/NYT ) maintained infectious mean viral titers that were within 3-fold of the pretreatment mean viral titer at pH ranges of 6 . 6–7 . 0 . The non-glycosylated NYF mutant similarly exhibited a 3-fold drop in mean viral titer within this same pH range . In contrast , the NYP , KYS and SYS mutants all showed approximately 10-fold reductions in infectious titers at pH 6 . 6 versus 7 . 0 . Similar to results in Vero cells , growth of the glycosylated viruses , NYS and NYT , in C6/36 cells was statistically indistinguishable , reaching daily mean titer of 5 . 5 ± 0 . 1 log10 PFU/mL by 4 dpi ( Fig 2A ) . Both NYS/NYT WNVs produced significantly higher titers ( p<0 . 05 ) than NYP , NYF , SYS , SYP and KYS , between 1–4 dpi . All nonglycosylated mutants exhibited a delay in the detection of virus until 2 dpi . From 2–4 dpi , NYP , NYF , SYS , SYP and KYS titers were significantly lower ( p<0 . 05 ) than NYS and NYT by 100-1000-fold . Titers for KYS were initially detected at 3 dpi and exhibited the most significantly retarded ( p<0 . 05 ) growth in C6/36 cells in comparison with the other WNV mutants . Serial passaging was performed to assess genetic stability of the E protein glycosylation motif . After five serial passages in C6/36 cells , the NYS , NYT , NYP , NYF , SYP and KYS viruses were found to have retained the introduced mutations . One replicate of the third passage of SYS showed a mixed population phenotype at both positions E-154 and E-156 . The E-154 codon showed nucleotide changes from AGC ( Ser ) to AAC/T ( Asn ) /A ( Lys ) and codon E-156 showed changes from TCC ( Ser ) to T/CCC ( Ser/Pro ) . Since monocyte/macrophage cell populations have been implicated in the amplification and dissemination of WNV in avian infections[35 , 36] , mutants were compared in cultured avian monocytes ( HD11 cells ) ( Fig 2B ) . The NYS/NYT viruses demonstrated statistically indistinguishable growth while the NYF mutant exhibited a 7-fold lower viral titer ( p<0 . 05 ) at the earliest time point ( 0 . 5 dpi ) and the mean peak titer was 3-fold lower than that of NYS . The KYS mutant demonstrated the most restricted phenotype with a 16-fold lower mean peak viral titer compared to NYS virus ( p<0 . 05 ) . In contrast , the non-glycosylated NYP/SYS mutants generated higher mean viral titers 6 . 3 and 6 . 8 log10 PFU/mL , respectively compared to 5 . 6 log10 PFU/mL at 1 dpi; however , the mean peak titers at 3 dpi were indistinguishable to that of NYS . Temperature sensitivity in avian cells has previously been associated with differential avian host competence[37] . As such , DEF cells growth phenotypes of the WNV mutants were assessed at 37°C/44°C ( Fig 2C and 2D ) . All WNVs grew to titers >8 log10 PFU/mL at 37°C; however , differences in growth were apparent at 44°C . Glycosylated viruses manifested mean titers of >7 log10 PFU/mL supernatant at the elevated temperature; however , NYP , NYF , SYS , SYP and A’DEL mutants all grew to mean peak viral titers of only approximately 5 . 5 log10 PFU/mL supernatant at 44°C , demonstrating a >50-fold higher temperature sensitivity than NYS/NYT . In contrast , KYS failed to grow above 4 log10 PFU/mL supernatant at the elevated temperature , demonstrating >30 , 000-fold lower titer at 44°C than at 37°C compared to approximately a 100-fold and 1000-fold temperature sensitivity phenotype of NYS/NYT and alternative E-154-56 mutants , respectively . Since AMCRs are highly susceptible to WNV[29 , 38] , the impact of the various WNV mutants on avian survivorship was assessed . All crows inoculated with NYS , NYT , NYF and SYS mutants succumbed to infection at indistinguishable rates over a 6–7 dpi time period ( Fig 3A ) . Crows inoculated with NYS , NYT , NYF and SYS viruses all developed acute viremias >8 . 5 log10 PFU/mL sera ( Fig 3B ) . In contrast , NYP and KYS mutants both resulted in only 60% mortality rates ( Fig 3A ) and exhibited attenuated viremia production ( <7 . 5 log10 PFU/mL sera ) ( Fig 3B ) . No differences in peak viremia or viremia on any dpi were observed between the glycosylated NYS/NYT ( Fig 3B ) . The non-glycosylated NYF/SYS mutants demonstrated no significant differences in the magnitude ( p>0 . 9 ) or duration ( p>0 . 09 ) of peripheral viremias compared to the NYS/NYT . TheNYP/KYS mutants , although demonstrating indistinguishable viremia duration compared to NYS/NYT ( p>0 . 7 ) ( Fig 3B ) , exhibited attenuated viremia production with mean peak viral titers 1 , 200-fold ( p<0 . 008 ) and 68-fold ( p<0 . 1 ) lower , respectively . Neither reversions nor compensatory mutations were identified in the E154-156 motifs observed from serum viral RNA sequenced from each bird at the time point with the highest viremia . Viral sequence from brain tissue of AMCRs that succumbed to infection also failed to demonstrate any genetic changes over the 500-nucleotide amplicon surrounding the E N-linked glycosylation motif when compared to the inoculum consensus sequences . The viremia response to the different mutants was evaluated in an alternative avian species , HOSPs ( Fig 4A and 4B ) . In concordance with the AMCR viremia data , the NYF non-glycosylated mutant developed statistically indistinguishable mean peak viral loads compared to the glycosylated ( NYS/NYT ) viruses ( p>0 . 1 ) . In contrast , despite developing high viremias in AMCRs , the SYS mutant was debilitated in the HOSPs , producing a ~500-fold lower mean peak viral titer when compared to the NYS virus ( p<0 . 05 ) . The NYP mutant developed a mean peak viral load that was 91-fold lower but was not statistically significantly different from the NYS virus ( p = 0 . 571 ) . The KYS mutant mean peak viremia was 30 , 000-fold lower ( p = 0 . 044 ) than NYS . The NYT mutant elicited a significantly higher initial viremia than the NYS virus at dpi 1 ( p<0 . 01 ) ; however , no significant differences were observed from dpi 2–7 . In a separate study , the viremia potential of the A’DEL mutant was assessed compared to the NYS parental virus . Two of the six HOSPs inoculated with the A’DEL mutant failed to generate detectable viremias during the course of the 1–7 dpi serum sampling ( Fig 4B ) . A two-day delay in viremia onset in the four HOSPs that became viremic was observed with the NYS titers significantly higher than the A’DEL titers at dpi 1–3; however , the A’DEL birds subsequently developed significant viremias from 3–7 dpi and peak titers for individual birds showed no difference compared to NYS inoculated HOSPs ( p = 0 . 252 ) ( Fig 4B ) . Mortality rates were low in all groups and not significantly different between any virus infection group and thus have not been shown . Sequencing of HOSP serum samples was also not performed due to the aforementioned reason . Culex quinquefasciatus were orally exposed to WNV parental ( NYS ) and mutant viruses , NYT , NYP , NYF , SYS , SYP and KYS , in order to examine the effect of the variable glycosylation motifs on mosquito infectivity and subsequent transmissibility . Oral infectivity for the parental NYS virus ( 36% ) was significantly lower ( p<0 . 005 ) than that of the NYT mutant ( 68% ) and significantly higher ( p<0 . 005 ) than the NYP infection rate of 6% ( Fig 5A ) . The NYT infection rate was significantly higher ( p<0 . 005 ) than rates for NYP , NYF ( 48% ) , SYP ( 40% ) and KYS ( 24% ) mutant viruses . The infection rate for NYP was significantly lower ( p<0 . 005 ) than all the other viruses . Among the remainder of the nonglycosylated viruses , the SYS ( 56% ) infection rate was significantly higher than NYF ( p<0 . 05 ) and KYS ( p<0 . 005 ) . Oral infectivity between A’DEL* ( 10% ) and NYS* ( 20% ) , performed at a lower oral input titer due to the restricted growth of the deletion mutant ( A'DEL ) in cell culture , were not significantly different . No significant differences in the mean viral titers of positive mosquito bodies were observed for Cx . quinquefasciatus orally exposed to NYS/NYT viruses; however , mean body titers of both NYT and NYS exposed Cx . quinquefasciatus were significantly higher ( p<0 . 05 ) than NYP ( Fig 5B ) . Mean NYT viral titers were also significantly higher ( p<0 . 05 ) than NYP , NYF , SYS , SYP and KYS ( Fig 5B ) . Among the nonglycosylated viruses , NYP exposed mosquitoes exhibited a significantly lower ( p<0 . 05 ) mean viral load than SYS and SYP exposed mosquitoes ( Fig 5B ) . Mean viral loads of A’DEL* were significantly lower ( p<0 . 05 ) than NYS* ( Fig 5B ) . No disseminated infections ( <LOD ) in the three bodies positive for NYP were observed . There were no significant differences ( p>0 . 05 ) in dissemination rates between any of the viruses for which dissemination was observed ( Fig 5C ) . Disseminated infections were not observed for both A’DEL* and NYS* ( Fig 5C ) . Mean viral titers in Cx . quinquefasciatus legs were not significantly different ( p>0 . 05 ) between NYS/NYT ( Fig 5D ) . No NYP leg titers were observed above the limit of detection . Using the LOD for NYP dissemination resulted in a significantly lower ( p<0 . 05 ) titer compared to NYS , NYT , NYF , SYS and SYP leg titers . Transmission rates of NYT disseminated mosquitoes were significantly higher than those of NYP and NYF and transmission rate of NYS was significantly higher than NYP ( Fig 5E ) There were no other significant differences ( p>0 . 05 ) in transmission rates among NYS , NYT , SYS , SYP and KYS ( Fig 5E ) . There were no significant differences ( p>0 . 05 ) in mean viral titers in the saliva ( Fig 5F ) . When transmission rates were compared as a function of the total exposed mosquitoes , NYT exhibited a 36% transmission rate with a 20% transmission rate for NYS; however , this difference was not significant ( p = 0 . 3451 ) . Culex quinquefasciatus were intrathoracically ( IT ) inoculated with NYS or mutant viruses , NYT , NYP , NYF , SYS , SYP , KYS and A’DEL to assess viral growth and transmission potential independent of the midgut infection barrier . No significant ( p>0 . 05 ) differences were observed in infection rates ( Fig 6A ) or mean body titers ( 100% for all viruses; Fig 6B ) for any WNV in IT-inoculated mosquitoes . No significant differences ( p>0 . 05 ) were observed in transmission rates among the viruses ( Fig 6C ) . Mean viral saliva titers for NYS inoculated mosquitoes were not significantly ( p>0 . 05 ) different from NYT inoculated mosquitoes ( Fig 6D ) ; however , both NYS and NYT viruses had significantly higher mean saliva titers ( p<0 . 05 ) than NYP . Mean saliva NYP viral titers were also significantly lower ( p<0 . 05 ) than those of NYF , SYP and KYS infected mosquitoes ( Fig 6D ) . The in vitro passaging data revealed the stability of the introduced mutations at E154-156 after five passages in C6/36 cells , therefore viral sequencing was not performed on any mosquito samples . The NYP virus demonstrated the most attenuated infection , dissemination and transmission rates in Cx . quinquefasciatus . As such , the NYP mutant was assessed in an alternative mosquito species , Cx . tarsalis , in order to determine its relative effect in another important North American WNV enzootic vector species ( Fig 7 ) . Similarly , the infection ( Fig 7A ) , dissemination ( Fig 7C ) and transmission rates ( Fig 7E ) for NYS were significantly higher ( p<0 . 005 , 0 . 001 , 0 . 001 , respectively ) than those observed for NYP . Mean viral titers in NYS-positive mosquito bodies ( Fig 7B ) , legs ( Fig 7D ) and saliva ( Fig 7F ) were significantly higher ( p<0 . 005 ) than the mean viral titers for NYP-positive mosquito bodies , legs and saliva .
The N-linked glycosylation motif within the WNV E protein at position 154–156 has been implicated previously as an important molecular determinant associated with enhanced murine neuroinvasiveness , avian pathogenicity[15 , 18–21] and infectivity and transmissibility in Culex mosquitoes[22 , 23] . In this study , we investigated WNV viral replication , WNV-vector interactions , avian virulence and vector competence of variable amino acid polymorphisms at the E-154 or E-156 loci that resulted in predicted glycosylated ( NYS , NYT ) and non-glycosylated E proteins ( NYP , NYF , SYS , SYP , KYS and A’DEL ) . We demonstrate the novel finding that the amino acid identity at either E-154 or E-156 modulated WNV viral growth , avian virulence and vector competence independent of the predicted glycosylation of WNV-E protein . In previous reports , WNV mutants with abolished E-protein glycosylation such as NYF[27] , QYS[7] and AYS[39] exhibited attenuated in vitro viral growth profiles in comparison to NYS whereas mutants representing NYS to IYS[22] , NYE or NYP[19] , were reported to have a minimal impact on viral growth in C6/36 cells . Other groups have reported that mutating NYS to QYS resulted in strikingly higher levels of infectivity in mosquito cells but lower virus production when compared with NYS[7] . These previous reports focused on ablation of glycosylation and did not specifically assess the effect of the actual amino acid identity at E-154/ E-156 on phenotype . The results presented herein have demonstrated that introduction of amino acid polymorphisms at either E-154 or E-156 differentially modulated WNV growth in C6/36 cells . Although many of the nonglycosylated WNVs showed an increased sensitivity to low pH , they all grew quite well in Vero , HD11 and DEF cells at 37°C . Interestingly , although temperature sensitivity at 44°C has been associated with lessened avian virulence phenotypes of other WNV variants/mutants[28 , 37] , many of the nonglycosylated WNV mutants ( NYF and SYS ) that were highly sensitive for growth at elevated avian temperatures in vitro demonstrated high viremia levels in AMCRs ( NYF and SYS ) or HOSPs ( NYF ) . These data indicate that amino acid polymorphisms in this region of the E protein , although impacting temperature sensitivity do not necessarily alter avian host competence phenotypes . Previous work has demonstrated nonstructural genetic determinants to be positively correlated with both temperature sensitivity and avian host competence phenotypes[36 , 40] , indicating that structural determinants of temperature sensitivity observed herein could be dictated by factors unrelated to avian competence . Unlike previous studies in which the growth of WNV mutants in AMCR monocytes predicted viremia potential and virulence[35] , all viruses grew relatively well in chicken ( HD11 ) monocytes herein , indicating fundamental differences in the relative utility for avian cells from alternative avian sources with lessened susceptibility ( chickens ) to WNV to model avian virulence phenotypes in highly susceptible birds ( AMCRs ) . In previous reports , HD11 cells had demonstrated restricted growth for a Mexican isolate of WNV lacking a glycosylation motif ( NYP ) with limited avian virulence potential in AMCRs[41] . The attenuated phenotype was subsequently attributed to both the NYP mutation in addition to a prM mutation and full attenuation effects were observed in the presence of both mutations in AMCRs , HOSPs and House finches[41] . Significantly lower viral loads in young chicks inoculated with a non-glycosylated ( NYP ) WNV plaque variant when compared to glycosylated ( NYS ) WNV have also been reported[19] leading the investigators to ascribe the phenotypic differences to the loss of glycosylation; however , other sequence alterations in the plaque variants and the specific effect of the amino acid polymorphism , independent of glycosylation , could mediate these phenotypes . Herein , the most significantly debilitated phenotypes in AMCRs were observed in birds inoculated with the KYS and NYP mutants that both lacked glycosylation signals but were imparted by different mutations within the glycosylation signal sequence . Alternative mutations that also ablated glycosylation by different amino acid substitutions at these same loci , SYS/NYF , had minor effects on avian viremia potential in AMCRs . Similar observations were made with HOSPs with the exception of the SYS mutant which showed an attenuated viremia much like KYS and NYP . As potential further support of the role of the amino acid identity at these positions for dictating phenotypes independent of glycosylation status , it is intriguing that both mutants that showed the most significant mosquito and avian competence differences KYS and NYP also encoded the most non-conservative amino acid substitutions . These consisted of a polar , uncharged asparagine substituted with a positively charged lysine ( E-N154K ) or a polar uncharged serine substituted with an aromatic ringed , hydrophobic proline ( E-S156P ) for KYS and NYP mutants , respectively . The specific amino acid identity at the E-156 locus influenced mosquito oral infectivity independent of glycosylation status as the NYT virus had a higher infection rate than NYS . The NYP mutant exhibited a severely debilitated infection and dissemination phenotype while the NYF did not significantly affect these phenotypes compared to the NYS virus . The E-154 locus showed similar variable effects on vector competence with restricted infectivity of the KYS mutant observed when compared to the SYS mutant . With the exception of the NYP mutant virus , absence of E protein glycosylation did not significantly attenuate viral growth , infection and/or dissemination rates of NYF , SYS , SYP and KYS viruses when compared with NYS parental WNV in Cx . quinquefasciatus . Even after bypassing the midgut barrier of infection by intrathoracic inoculation , NYP produced significantly lower viral titers in the saliva . The attenuated profile of NYP virus in Culex spp . in the present study could explain the low frequency of isolation of WNV-NYP in nature[19 , 21] . While NYP avian serum titers could presumably overcome WNV oral infection thresholds[42] in highly competent avian hosts[21] , AMCRs elicited lower serum viremias with this virus . Furthermore , the restricted dissemination and transmission in alternative mosquito vectors such as Cx . tarsalis could preclude subsequent transmission . In contrast to our study , Murata et al reported the isolation and characterization of a small plaque variant with the NYP motif where no oral infectivity differences were observed with Culex pipiens pallens and disseminated viral titers were indistinguishable between the NYS and NYP viruses[19] . Alternative mosquito species used and viral genetic differences between the NYP viruses used in the two studies could explain the difference in results . Of note , the SYP virus that was generated as a double mutant to prevent reversion to glycosylated motif as well as to assess the duplicative effect of altering more than one amino acid within the motif did not exhibit an attenuated mosquito infection phenotype as observed for NYP . The additional E-154 mutation could have served to compensate for negative fitness effects of the E-S156P substitution possibly through structural modulation of the envelope surface projections . The lack of significant differences between NYS and NYF , SYS , SYP and KYS viruses suggests that WNV N-linked glycosylation is not a critical determinant of vector competence . The WN02 genotype that displaced the introduced WNV-NY99 genotype , is characterized principally by the incorporation of a nonsynonymous valine to alanine substitution at position E-159[11 , 43] . This new genotype has been reported to exhibit a shorter extrinsic incubation period in certain Culex spp . and more efficient transmission rates at warmer temperatures[44] . This E-159 locus , similar to the E-154-156 motif , is present on the surface of the mature envelope protein and could indicate that numerous amino acid variants in this surface exposed envelope domain could significantly modulate vector competence phenotypes . Taken together , the data indicated that specific amino acid identities at E154 or E-156 and potential other sites on the WNV E protein could be critical for modulation of mosquito oral infectivity and vector competence exclusive of the glycosylation status of the E protein . Interestingly , the midgut infection rate for NYT ( 70% ) was more than twice that of the NYS virus ( 34% ) , indicating that a single amino acid polymorphism at E-156 that maintained glycosylation status of the E protein could result in a significant effect on vector competence . Nevertheless , the role of glycosylation for modulation of this phenotype between these viruses cannot be discounted as in vitro studies with recombinant rabies virus glycoproteins have shown that differential glycosylation efficiencies of the N-linked moieties were dependent upon the hydroxyl amino acid in the glycosylation motif[45] . That study also showed that NYT motifs were preferentially and more efficiently glycosylated than the NYS motif[45 , 46] . Thus it is possible that higher glycosylation efficiency of NYT versus NYS potentially enhanced virus entry , release and/or subsequent infection rates in mosquitoes in the present study[7] . That being said , while the infection rate for NYT was significantly higher than NYS , NYT viral titers , dissemination and transmission rates remained similar to NYS . While no naturally occurring NYT WNV variants have been described , other flaviviruses such as Zika ( NDT ) , dengue ( NET ) , Wesselbron ( NHT ) , Sepik ( NHT ) , Rocio ( NYT ) , Spondweni ( NDT ) and Ilheus ( NYT ) viruses have been shown to encode a threonine at the hydroxyl amino acid of the N-linked glycosylation motif[24] . Other flaviviruses such as Yellow fever virus and St . Louis encephalitis viruses do not encode a glycosylated E protein[10 , 47 , 48] . Furthermore , non-glycosylated SLEV maintains a lower oral infection threshold , i . e . has been shown to be more infectious , in Culex mosquitoes when compared with WNV[42] , again demonstrating that the N-linked glycosylation is not a requisite for flavivirus infection of and transmission by mosquitoes . Reports by others have also demonstrated variable vector competence rates with non-glycosylated WNV isolates[49 , 50] and in this study , with the exception of NYP and A’DEL all other non-glycosylated viruses were transmitted by Cx quinquefasciatus . The effect of the different E154-156 mutations on avian viremia potential was variable and species-specific , indicating that different mutations could be competent for avian viremia generation in certain avian species . Despite having delayed viremia onset , even the A’DEL mutant was capable of eliciting avian viremia conducive for mosquito infection , although this mutant failed to demonstrate transmissibility in Cx quinquefasciatus . Data presented herein indicates that WNV E154-156 motif amino identity , rather than specific N-linked glycosylation , dictates acute viral titers in birds and vector competence in mosquitoes and the effect of a particular E154-156 moiety expressed on WNV avian and vector competence was species-dependent . The isolation of many of these E-154-156 mutants in the field is intriguing and coupled with the findings here indicates the strong possibility that non-glycosylated WNV variants could circulate at relatively high frequency . The frequency of WNV genotypes to express a particular E-glycosylation motif ( E154-E156 ) is likely due to selection pressures on viral replicative fitness by the dominant amplification host ( s ) and vectors utilized during local transmission events . Future studies should be designed to assess the potential fitness advantages for these variants in specific enzootic vectors and/or hosts . | West Nile virus ( WNV ) has been responsible for the largest human encephalitis epidemics in the continental United States . Avians and Culex mosquitoes are the primary hosts for WNV natural transmission cycles . The envelope ( E ) protein for WNV contains a variable N-linked glycosylation motif which influences avian replication , mosquito infectivity and vector competence . WNV isolates with variable E protein glycosylation motifs that have been historically associated with human cases of disease , were selected to generate WNV glycosylation mutant viruses via reverse genetics . Replication capacity and host competence of WNV glycosylation mutant viruses were compared in vitro and in vivo in American crows , house sparrows and Culex mosquitoes . The data demonstrated that N-linked glycosylation was not as crucial for WNV transmission and host competence as previously reported . Rather the amino acid identities of the glycosylation motif were more important in dictating WNV virulence phenotypes in both avian and mosquito host . | [
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| 2019 | N-linked glycosylation of the West Nile virus envelope protein is not a requisite for avian virulence or vector competence |
Craniofacial abnormalities , including facial skeletal defects , comprise approximately one-third of all birth defects in humans . Since most bones in the face derive from cranial neural crest cells ( CNCCs ) , which are multipotent stem cells , craniofacial bone disorders are largely attributed to defects in CNCCs . However , it remains unclear how the niche of CNCCs is coordinated by multiple gene regulatory networks essential for craniofacial bone development . Here we report that tumor suppressors breast cancer 1 ( BRCA1 ) and breast cancer 2 ( BRCA2 ) are required for craniofacial bone development in mice . Disruption of Brca1 in CNCC-derived mesenchymal cells , but not in epithelial-derived cells , resulted in craniofacial skeletal defects . Whereas osteogenic differentiation was normal , both osteogenic proliferation and survival were severely attenuated in Brca1 mutants . Brca1-deficient craniofacial skeletogenic precursors displayed increased DNA damage and enhanced cell apoptosis . Importantly , the craniofacial skeletal defects were sufficiently rescued by superimposing p53 null alleles in a neural crest-specific manner in vivo , indicating that BRCA1 deficiency induced DNA damage , cell apoptosis , and that the pathogenesis of craniofacial bone defects can be compensated by inactivation of p53 . Mice lacking Brca2 in CNCCs , but not in epithelial-derived cells , also displayed abnormalities resembling the craniofacial skeletal malformations observed in Brca1 mutants . Our data shed light on the importance of BRCA1/BRCA2 function in CNCCs during craniofacial skeletal formation .
Abnormal growth of the facial bones causes congenital craniofacial malformations [1] . Because most bones in the face derive from multipotent stem cells designated cranial neural crest cells ( CNCCs ) [2 , 3] , craniofacial bone disorders are largely attributed to defects in CNCCs [4 , 5] . Importantly , several bone-related diseases affect craniofacial bones , but not other bones , despite the fact that the genes involved in these diseases are expressed throughout the body [6] . This suggests that CNCCs have distinct mechanisms to regulate craniofacial bone development . Therefore , clarifying the role of CNCCs in craniofacial bone development is essential for understanding the etiology of facial bone anomalies . Prior studies have shown that mutations in several DNA damage response proteins lead to defects in craniofacial skeletal development [7 , 8] . For example , patients with Nijmegen breakage syndrome , which is caused by a mutation in NBS1 , a molecule critical for the initial processing of DNA strand breaks , display small lower jaws [9] . Also , Fanconi anemia , a disease frequently associated with congenital craniofacial bone anomalies , results from a genetic defect in a cluster of genes responsible for DNA repair [10 , 11] . However , it remains elusive whether DNA damage repair mechanisms in CNCCs contribute to normal craniofacial bone formation . The tumor suppressor genes breast cancer 1 ( BRCA1 ) and breast cancer 2 ( BRCA2 ) are key players in DNA damage response and homologous recombination ( HR ) , which are critical for the repair of DNA double-strand breaks to maintain the fidelity of the genome . Mutations in BRCA1/BRCA2 lead to genetic instability and frequently cause familial breast and ovarian cancers [12] . While little is known about how these tumor suppressor genes affect craniofacial development , a recent study has shown that non-syndromic cleft lip and palate , one of the most common human craniofacial deformities , are associated with dysregulation of the gene regulatory network via BRCA1 [13] . Since germline mutations in DNA repair genes have been linked to congenital defects [8] , it is tempting to hypothesize that the BRCA1/BRCA2-dependent DNA damage and repair machinery is critical for the prevention of craniofacial abnormalities . Therefore , it is important to understand how BRCA1/BRCA2 function in CNCCs during craniofacial development . The purpose of this study is to investigate the role of BRCA1 and BRCA2 in the multipotent cell population of CNCCs . We found that BRCA1 and BRCA2 are required for skeletogenic cell proliferation and survival in CNCC-derived bones , highlighting the essentiality of the BRCA1 and BRCA2 for craniofacial skeletal development .
To characterize the function of BRCA1 in craniofacial development , we disrupted Brca1 in a neural crest-specific manner using a wingless-related MMTV integration site1 ( Wnt1 ) -Cre driver line [14 , 15] . Mice lacking Brca1 in neural crests showed craniofacial abnormalities ( Brca1:Wnt1-Cre hereafter ) ( Fig 1A ) . While Brca1:Wnt1-Cre mice were born at Mendelian ratios ( S1 Table ) , they could not survive more than twenty-four hours , most likely due to the cleft palate phenotype ( data to be published elsewhere ) . Brca1:Wnt1-Cre mice displayed small heads with 100% phenotypic penetrance ( Fig 1A ) . Skeletal staining revealed that the CNCC-derived facial bones were hypoplastic in Brca1:Wnt1-Cre embryos ( Fig 1B ) . The size of the maxillary and mandibular bones was small , and the nasal-frontal bones and membranous bones on the skull base were defective in Brca1:Wnt1-Cre mice . These results thus suggest that BRCA1 is responsible for orchestrating the formation of CNCC-derived facial bones . Because epithelial-mesenchymal interactions are also critical for craniofacial morphogenesis [1] , we disrupted Brca1 in an epithelial cell-specific manner using a Keratin14 ( K14 ) -Cre driver line [16] . The epithelial cell-specific deletion of Brca1 did not result in any overt craniofacial abnormality ( S1 Fig ) . Together , these results indicate that BRCA1 is indispensable for facial development in ectomesenchymal cells , but not in epithelial cells . In this study , we further examined the function of BRCA1 in craniofacial bones , especially focusing on CNCC-derived skull bones . CNCC-derived osteogenic precursors migrate from the dorsal neural tube to the supraorbital ridge , and ectomesenchymal cells start to condense at embryonic day ( E ) 12 . 5 [17 , 18] . Therefore , we asked whether the skull malformations in Brca1:Wnt1-Cre mice can be attributed to the lack of early osteogenic condensation at E12 . 5 . To investigate the pathogenesis of skull defects in Brca1:Wnt1-Cre mice , we examined the expression of RUNX2 , an early marker of osteogenic precursors [19 , 20] . While osteogenic precursors were well induced in both control and Brca1:Wnt1-Cre mutants , osteogenic proliferation was reduced in Brca1:Wnt1-Cre skulls at E12 . 5 and E13 . 5 ( Fig 2A ) . Additionally , a large amount of apoptotic cells was observed in Brca1:Wnt1-Cre skulls ( Fig 2B , arrows ) . At late-gestation at E16 . 5 , however , CNCC-derived osteoblasts in Brca1:Wnt1-Cre embryos showed normal cell proliferation and survival at levels comparable to the controls ( S2 Fig ) . These results indicate that the defects seen in the frontal bones in Brca1:Wnt1-Cre embryos are due to the decreased osteogenic cell population in the frontal bone primordium . Because cell death in premigratory CNCCs frequently leads to craniofacial bone abnormalities [21] , we tested whether there is increased cell death in premigratory CNCCs in Brca1:Wnt1-Cre mutants . We performed TUNEL assay in control and Brca1:Wnt1-Cre mutant embryos at E8 . 5 . As a result , no excess of apoptosis occurred in Brca1 mutant embryos ( S3A Fig ) , suggesting that Brca1 does not play a major role in the survival of premigratory CNCCs . In addition , CNCC migration also appeared to occur normally in Brca1:Wnt1-Cre mutant embryos indicated by normal Sox10 expression pattern at E9 . 5 ( S3B Fig ) . Thus , BRCA1 is less likely to play a critical role in premigratory CNCCs . Interestingly , previous studies showed that Brca1 is highly expressed only from mid- to late- gestations in the facial tissues [22 , 23] , suggesting that BRCA1 may become critical after post-migration of CNCCs for the onset of craniofacial bone development . We examined whether BRCA1 is produced in CNCC-derived skeletogenic precursors at E12 . 5 , the stage when CNCC-derived osteogenic precursors start to form [17 , 18] . Our results showed that BRCA1 was abundantly produced in craniofacial skeletogenic precursors in control embryos , while its production was completely abolished in Brca1 mutants ( S4 Fig ) . Together , these results demonstrate that BRCA1 is critical for the early onset of craniofacial skeletogenesis , but not for premigratory CNCCs . It has been reported that DNA damage in neuroepithelial cells can induce cell death , leading to craniofacial bone deformities [24] . Because BRCA1 functions in DNA damage repair [12] , one might predict that the underlying etiology of skull abnormalities is DNA damage-induced apoptosis in Brca1:Wnt1-Cre embryos . To address this hypothesis , we examined whether there is increased DNA damage in craniofacial skeletogenic precursors in Brca1:Wnt1-Cre embryos . We carried out immunofluorescence using antibodies against γ-H2AX , a marker of unrepaired DNA damage [25] , in skulls from the embryos . Compared to the control , skulls from Brca1:Wnt1-Cre embryos showed increased numbers of γ-H2AX-stained skeletogenic precursor cells that are co-labeled with Cleaved Caspase-3 ( Fig 2C , arrows ) . The portion of nuclei of skeletogenic precursor cells stained positive for phosphorylated-Chk2 , a checkpoint kinase that is phosphorylated upon DNA damage and activation of central DNA damage kinase ATM , also increased ( Fig 2C and 2D ) , indicating increased DNA damage-induced check point response . These data indicate that BRCA1 plays an important role in osteogenic proliferation and survival by suppressing DNA damage at the early stages of osteogenesis . We also investigated whether BRCA1 plays a role in osteogenic differentiation . CNCC-derived skull tissues were harvested , and the expression levels of osteogenic genes were examined . The majority of the osteogenic genes tested , including Runx2 , Sp7 and Ibsp , were expressed normally in control and Brca1:Wnt1-Cre embryos ( S5A Fig ) . Furthermore , Brca1-disrupted preosteoblasts from CNCC-derived skulls were capable to differentiate and mineralize in vitro ( S5B Fig ) . Thus , Brca1 is dispensable for osteogenic differentiation . It has been reported that DNA damage triggers p53 stabilization , and if the damage is extensive or cannot be repaired , it induces apoptosis [26] . Therefore , we hypothesized that p53-mediated apoptosis may cause the craniofacial bone defects seen in Brca1:Wnt1-Cre embryos . Supporting this hypothesis , the protein levels of p53 were much higher in Brca1:Wnt1-Cre mutants than in controls ( Fig 3A ) , suggesting that DNA damage-induced p53 stabilization may be responsible for the pathogenesis of Brca1:Wnt1-Cre mice . To test this possibility in vivo , we superimposed null alleles of p53 into Brca1:Wnt1-Cre mutants in a neural crest-specific manner [27] . Consistent with our prediction , the p53 deletion partially rescued the craniofacial bone defects in Brca1:Wnt1-Cre mutants ( Fig 3B ) . Importantly , two copies of the p53 deletion alleles were able to rescue the skull defects more efficiently than one copy in Brca1:Wnt1-Cre embryos ( Fig 3B and 3C ) . While there was no significant difference in head width in all genotypes , sagittal skull length was also rescued along with the recovery of maxilla and mandibular bones ( Fig 3D–3F ) . We found that the p53 deletion did not have an effect on the dysregulation of cell proliferation ( Fig 4A ) , but sufficiently suppressed the enhanced cell death in Brca1:Wnt1-Cre embryos ( Fig 4B ) . The suppression of cell death in rescued mice was linked to reduced number of γ-H2AX-stained cells co-labeled with either Cleaved Caspase-3 or phosphorylated-Chk2 ( Fig 4C ) . Thus , reduction of p53 in Brca1:Wnt1-Cre predominantly rescues the phenotype through decreasing DNA damage-induced cell death , but not through increasing osteogenic cell proliferation . We also examined whether p53 cell cycle regulation is involved in rescuing the defects of Brca1:Wnt1-Cre embryos . Since p53 regulates cell cycle through regulation of p21 [28] , we analyzed p21 expression in Brca1 mutants and rescued embryos . The expression levels of p21 were comparable among controls , Brca1 mutants and p53-/- superimposed Brca1 mutants ( S6 Fig ) . This indicates that p21 regulation was not affected and that the cell cycle checkpoint regulation through the p53-p21 axis did not play a major role in its function in Brca1 mutants . Taken together , these data demonstrate that BRCA1 deficiency-induced pathogenesis of craniofacial bone defects can be partially rescued by inactivation of p53 through reducing DNA damage-induced cell death . BRCA2 , the second major hereditary breast cancer susceptibility factor , is also critical for maintaining genome integrity [8] . While BRCA1 has multiple functions in the repair process of DNA damage , BRCA2 appears to be mainly involved in HR [29] . To examine the role of BRCA2 in CNCCs , we disrupted Brca2 in an epithelial- and/or ectomesenchymal-cell specific manner in mice [30] . While the epithelial-specific deletion of Brca2 did not induce any overt craniofacial malformation ( S7 Fig ) , mice lacking Brca2 in CNCCs ( hereafter Brca2:Wnt1-Cre ) exhibited craniofacial bone defects resembling those of Brca1:Wnt1-Cre mice ( Fig 5A ) . CNCC-derived craniofacial bones , including the frontal-nasal and maxilla-mandibular bones , were severely compromised ( Fig 5A ) . The ratio of defective frontal bone area and sagittal length in the skull was measured ( Fig 5B and 5C ) . Similar to Brca1:Wnt1-Cre mice , CNCC-derived osteoblasts from Brca2:Wnt1-Cre embryos differentiated normally ( S8 Fig ) . On the other hand , both osteogenic proliferation and cell survival were severely attenuated in Brca2:Wnt1-Cre ( Fig 5D and 5E ) . With the strong phenotypic similarities in both Brca1:Wnt1-Cre and Brca2:Wnt1-Cre mutants , we conclude that BRCA1 and BRCA2 are critical for craniofacial skeletal development .
BRCA1 has a central role in DNA damage response and tumor suppression [31] , but its function in craniofacial bone development is not known . Conventional disruption of Brca1 in mice results in embryonic lethality due to increased cell death and/or restricted proliferation in neuroepithelial cells [32–34] , and mice lacking Brca1 in neural stem cells display severe brain defects [35 , 36] . These data indicate that BRCA1 may have a specific role in neuroepithelial cell proliferation and differentiation . Our findings support this notion that BRCA1 is critical for the neuroepithelial lineage cells , i . e . CNCC-derivatives . Our data showed that Brca1 disruption in CNCCs causes decreased proliferation and increased cell death at mid gestation ( Fig 2 ) , but not at the maturation stages of craniofacial osteogenesis ( S2 Fig ) . Thus , BRCA1 is likely required for early onset of craniofacial skeletogenesis . Similar to our Brca1:Wnt1-Cre mice , treacle ribosome biogenesis factor 1 ( Tcof1 ) haploinsufficiency leads to augmentation of p53 production , and genetic suppression of p53 in Tcof1 mutant mice rescues the craniofacial bone abnormalities [37] . Of note , Tcof1 haploinsufficiency results in neuroepithelial cell death , and loss of Tcof1 diminishes the accumulation of BRCA1 at DNA damage sites , providing a potential link between TCOF1 and BRCA1 in the DNA damage response [24] . It remains to be determined whether TCOF1 and BRCA1 genetically interact in craniofacial bone formation and whether TCOF1 is involved in the regulation of BRCA1 . Deficiencies in the DNA response and repair mechanism often lead to genome instability associated with cancer predisposition . Whereas the importance of BRCA1/BRCA2-dependent DNA damage response and repair in tumorigenesis is well known , it is unclear how the BRCA1/BRCA2-dependent pathway functions in multiple developmental aspects , especially during craniofacial formation . The main function of BRCA2 is to load Rad51 recombinase to single-stranded DNA to facilitate HR , while BRCA1 has been implicated in multiple aspects of cellular response in addition to its role in regulating HR . We found that Brca2:Wnt1-Cre mutants have craniofacial bone defects almost identical to those observed in Brca1:Wnt1-Cre mutants ( Fig 1 , Fig 2 and Fig 5 ) , suggesting that BRCA1- and BRCA2-dependent HR function is likely to be involved in regulating craniofacial bone development . It is possible that CNCCs encounter internal or external genetic insults that can result in DNA damage in early embryonic development during craniofacial bone formation . Indeed , it has been shown that oxidative stress increases during this time of the development process , which may lead to increased DNA damage [24] . Therefore , BRCA1/BRCA2-dependent HR is likely to be essential to ensure proper CNCC proliferation . Recently , it also has been shown that BRCA1/BRCA2 play an important role in protecting stalled replication fork stability in order to maintain genome integrity [12] . It is also possible that rapid CNCC proliferation during craniofacial bone formation may generate replication stress; BRCA1/BRCA2 are required to protect the replication fork's integrity , and their deficiency leads to increased DNA damage . Given the essential role of BRCA1 and BRCA2 in embryonic development , the Wnt1-Cre deletion of Brca1 or Brca2 resulted in relatively mild craniofacial phenotypes . It remains to be seen whether BRCA1 and BRCA2 function redundantly during neural crest cell development and their differentiation into skeletogenic derivatives . Generating a Brca1- and Brca2-double deficiency in CNCCs may facilitate the understanding of the role of BRCA1 and BRCA2 in craniofacial bone development . Nevertheless , although the importance of BRCA1/BRCA2-dependent function in tumorigenesis is widely recognized , our data show , for the first time , the robust requirement of BRCA1 and BRCA2 in craniofacial bone development . In summary , we demonstrate the function of BRCA1 and BRCA2 in CNCCs and highlight the importance of BRCA1/BRCA2-dependent function in DNA damage response and repair for proper craniofacial bone development .
The guidelines for this study were issued by the Center for Laboratory Animal Medicine and Care ( CLAMC ) . The experimental protocol was reviewed and approved by the Animal Welfare Committee and the Institutional Animal Care and Use Committee of The University of Texas Medical School at Houston under approval number AWC-15-0152 . Brca1-floxed mice [14] , Brca2-floxed mice [30] , p53-floxed mice [27] , Wnt1-Cre mice [15] , and K14-Cre mice [16] were obtained from NCI/NIH and The Jackson Laboratory . All mice were maintained in the animal facility of The University of Texas Medical School at Houston . The experimental protocol was reviewed and approved by the Animal Welfare Committee and the Institutional Animal Care and Use Committee of The University of Texas Medical School at Houston . The DNA from mouse embryos was analyzed by PCR . The sequences of each primers were listed ( S2 Table ) . The conditions for genotyping PCR were 94°C for 20 sec , 65°C for 20 sec , 72°C for 20 sec , repeated 40 cycles . Genotyping for Brca1 , PCR reaction yielded 450 bp for wild-type or 500 bp for Brca1 floxed DNA fragment . Genotyping for Brca2 , PCR reaction yielded 298 bp for wild-type or 376 bp for Brca2 floxed DNA fragment . Genotyping for p53 , PCR reaction yielded 270 bp for wild-type or 390 bp for p53 floxed DNA fragment . For genotyping of Cre , PCR reaction yielded 169 bp for Cre gene . Staining of bone and cartilage of embryos with Alizarin red/Alcian blue was carried out as described previously [38] . An Olympus SZX16 microscope equipped with a DP71 digital camera was used for capturing images . The length and area ratio of the frontal foramina were measured by ImageJ . Immunofluorescent staining , and TUNEL assays of paraffin sections were performed as previously described [38] . Pregnant females were injected intraperitoneally with BrdU ( Invitrogen , BrdU labeling reagent; 1 mL/100 g body weight ) and sacrificed 2 hours later . Primary antibodies used in immunofluorescence staining were as follows: RUNX2 ( 1:400 , Cell Signaling; no . 12556 ) , Ki-67 ( 1:100 , BD Biosciences; 550609 ) , Cleaved Caspase-3 ( 1:400 , Cell Signaling; 9664 ) , γ-H2AX ( 1:200 , Millipore; 05–636 ) . Slides were viewed with an Olympus FluoView FV1000 laser scanning confocal microscope using the software FV10-ASW Viewer ( version 3 . 1 ) . Cranial preosteoblasts were established from E18 . 5 embryos as described previously [38] . The frontal bones were subjected to five sequential digestions with an enzyme mixture containing 1 mg/mL collagenase type I ( Sigma-Aldrich; C0130 ) and 1 mg/mL collagenase type II ( Sigma-Aldrich; C6885 ) . Cell fractions ( from two to five of the sequential digestions ) were collected . Preosteoblasts were grown in α-MEM medium ( Sigma-Aldrich; M8042 ) supplemented with 10% ( vol/vol ) FBS ( Sigma-Aldrich; F4135 ) , 100 U/mL penicillin-streptomycin ( Sigma-Aldrich; P4333 ) , and 4 mM glutamine ( Sigma-Aldrich; G7513 ) . Osteogenic differentiation was induced using culture medium supplemented with 50 μg/mL ascorbic acid ( Sigma-Aldrich; A-4403 ) and 2 mM β-glycerophosphate ( Sigma-Aldrich; G-9891 ) . Facial tissues were homogenized using protein lysis buffers . After centrifugation , the supernatants were separated by SDS/PAGE , blotted onto a PVDF membrane , and analyzed with specific antibodies . The antibodies used were as follows: GAPDH ( 1:2000 , Cell Signaling; 2118 ) , p53 ( 1:2000 , Santa Cruz; sc-6243 ) . The Clarity Max ECL Substrate ( Bio-rad ) was used for chemiluminescent detection , and signals were quantified with Image Lab Version 5 . 0 ( Bio-Rad ) . Using TRIzol Reagent ( Thermo Fisher Scientific ) , total RNA was extracted from the frontal bones of control , Brca1:Wnt1-Cre and Brca2:Wnt1-Cre embryos at E17 . 5 . The total RNA was treated with DNase I ( Roche ) before cDNA synthesis . Total RNA from the cultured osteoblasts was purified using the RNeasy Plus Mini Kit ( Qiagen ) . cDNA was synthesized using iScript Reverse Transcription Supermix for RT-qPCR ( BioRad ) . Quantitative RT-PCR was carried out using SsoAdvanced Universal SYBR Green Supermix ( Bio-Rad ) using CFX Connect System ( Bio-Rad ) . The conditions for qRT-PCR were 95°C for 2 min , 95°C for 5 sec , 60°C for 30 sec , repeated 40 cycles . The sequences of each primers were listed below ( Table 1 ) . Data were normalized to GAPDH and quantified by 2−∆∆CT method . The Student’s t test was used for statistical analysis . A P value of less than 0 . 05 was considered statistically significant . | Craniofacial abnormalities , including facial skeletal defects , comprise approximately one-third of all birth defects in humans . Since most bones in the face derive from neural crest cells , which are multipotent stem cells , craniofacial bone disorders are largely attributed to defects in neural crest cells . However , it remains unclear how the niche of neural crest cells is coordinated by multiple gene regulatory networks essential for craniofacial bone development . Here , we show that tumor suppressor breast cancer 1 ( BRCA1 ) and breast cancer 2 ( BRCA2 ) are required for craniofacial bone development in mice . Our data shed light on the importance of the DNA damage response/repair machinery in neural crest cells via BRCA1/BRCA2 , providing novel insights into the mechanisms of craniofacial bone development . | [
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| 2018 | BRCA1 and BRCA2 tumor suppressors in neural crest cells are essential for craniofacial bone development |
The environmental pathogen , Mycobacterium ulcerans ( MU ) can infect both humans and animals and cause Buruli ulcer ( BU ) disease . However , its mode ( s ) of transmission from the colonized environment to human/animal hosts remain unclear . In Australia , MU can infect both wildlife and domestic mammals . Till date , BU-like lesions have only been reported in wildlife in Africa . This warrants a thorough assessment of possible MU in domestic animals in Africa . Here , we screened roaming domesticated animals that share the human microhabitat in two different BU endemic sites , Sedje-Denou in Benin and Akonolinga in Cameroon , for MU lesions . We screened roaming mammals and birds across 3 endemic villages of Sedje-Denou in Southern Benin and 6 endemic villages of Akonolinga in Cameroon . After approval from relevant authorities , specimens ( wound swabs and tissue fragments ) were collected from animals with open or active lesion and systematically screened to detect the presence of MU though the diagnostic DNA targets IS2404 , IS2606 and KR-B . Out of 397 animals surveyed in Akonolinga , 44 ( 11 . 08% ) carried skin lesions and all were negative for MU DNA . For Sedje-Denou , only 25 ( 6 . 93% ) out of 361 animals surveyed carried external skin lesions of which 2 ( 8% ) were positive for MU DNA targets . These MU infected lesions were found in two different villages on a goat ( abdominal part ) and on a dog ( nape area of the neck ) . Source-tracking of MU isolates within infected animal lesions was performed using VNTR genotyping and further confirmed with sequencing . One MU VNTR genotype ( Z ) was successfully typed from the goat lesion . The evolutionary history inferred from sequenced data revealed a clustering of animal MU isolates within isolates from human lesions . This study describes the first report of two MU infected lesions in domestic animals in Africa . Their DNA sequence analyses show close relationship to isolates from human cases . It suggests that MU infection should be suspected in domestic hosts and these could play a role in transmission . The findings further support the hypothesis that MU is a ubiquitous environmental pathogen found in endemic areas , and probably involved in a multiple transmission pathway .
Buruli ulcer ( BU ) , the necrotizing skin disease caused by M . ulcerans ( MU ) remains a major Public Health problem in more than 33 countries worldwide . Although the distribution of BU is global , its burden is highest in remote communities of West and Central African regions including Benin , Ghana , Ivory Coast , Democratic Republic of Congo and Cameroon; as well as some coastal localities of Australia . These African endemic areas recently reported the highest incidence of the disease with 89% of new cases reported in 2014 [1 , 2] . They also share the most pathogenic MU isolates which are characterized by the production of the polyketide-derived macrolide toxin mycolactone A/B , the main virulence factor for MU [3] . Mycobacteria are the etiology of important diseases in humans and a wide range of animals including cattle , sheep , goats , deer , possums , badger , elephants , dogs , cats , birds , amphibians , and fishes . [4] . Mycobacterium sp . such as M . bovis are pathogens that have become important agents at the interface of humans , domestic livestock and wildlife [5] . Animals such as rodents ( rats , mice and shrews ) , monkeys , guinea pigs , rabbits and armadillos are sensitive to experimental infection with MU and constitute good models for the study of BU disease [6 , 7] . However , humans were considered the only mammalian host of the disease . Animal cases of BU were reported in Australia in both wildlife ( possums , alpacas and koalas ) and domestic mammals ( cats , dogs and horses ) [8–12] . The possible role of animals in BU transmission has also been suggested by the positive correlation observed between the prevalence of MU DNA in possum feces and BU endemicity in Australia [9] . Thus , domestic animals could play a significant role as amplifiers of MU pathogen , influencing the transmission cycle of human infectious disease [10 , 13] . Hence , potential colonization of the domestic microenvironment by MU emphasizes the risks associated with the close contact between humans and peridomestic animals . A tentative mode of transmission involving both humans and animals has been postulated [10] . This model suggests that domestic animals can be infected when feeding in the infected environment , or , by simple contact with the soils or feces of wild animals . Humans can later be infected through direct contact with animal excreta , animal bites or through ectoparasites [10 , 14 , 15] . In some instances , BU infected patients with poor hygiene conditions may play an active role in the dissemination and distribution of the Mycobacterium in the environment [16] , and indirectly favor the focal distribution of the disease in new hosts such as animal hosts . On the African continent on the other hand , there are relatively few reports infection in animals . MU DNA has been detected only in small mammals such as mice ( Mastomys sp . ) with ulcerative lesions [17 , 18] . A study carried out in Benin by Durnez et al . [14] revealed a wide distribution of Mycobacterium sp . in terrestrial small animals ( mice , shrews and rats ) and insectivores , but not of MU . Although MU was detected in the feces of a peridomestic small mammal , Thrynomys swinderianus ( agouti ) in Ivory Coast [19] , it was completely absent in a wide range of fecal materials from domestic animals and humans in endemic villages of Ghana [20 , 21] . More significantly , domestic animals have not been investigated for BU infection . Thus , it is not yet known whether in Africa domestic animals play a role in the environmental cycle of MU as suggested for wildlife and small mammals in Australia . [17 , 18] . Humans and mammals ( as well as amphibians—turtles ) not only carry the bacteria but also develop active disease . Open sores and ulcers could represent a potential source of contamination and transmission to other hosts [8 , 9 , 11 , 12 , 16–18 , 22] . However , it is still unclear whether BU infected animals can develop the spectrum of human disease . Thus , we hypothesize in this studies that the overlapping ecology of human and animal habitats could favor the transmission of MU . We employed molecular tools to screen BU like-lesions in roaming domestic animals ( carnivorous , cattle and domesticated birds ) sharing the same habitat with humans in endemic localities of Sedje-Denou in Benin ( West Africa ) and Akonolinga in Cameroon ( Central Africa ) . In these regions , domestic livestock constitutes one of the main resources of the communities , which mainly survive by subsistence agriculture , fishing , breeding , hunting and informal commerce .
Approval for animal trapping and samples collection were obtained from the International Institute of Tropical Agriculture ( IITA-Benin ) and the Cameroon National Ethics Review Committee ( N°946/CE/CNERSH/SP ) . Additional permits were also sought from the local leaders of Sedje-Denou and Akonolinga districts in Benin and Cameroon respectively . No invasive procedure was performed on animals , and all animals carrying active external lesions were systematically treated free of charge by a veterinarian physician . Community consent was also obtained prior to animal’s surveys and owners helped to trap animals in each investigated community . All activities on animals were conducted in compliance with the national animal protection law ( MEPN-Benin and MINFOF-Cameroon ) in addition to other international guidelines . This study was conducted in two BU endemic areas in West and Central Africa ( Fig 1 ) . The endemic area of Sedje-Denou ( 6° 32’ N and 2° 13’ E ) is located in the Southern part of Benin in West Africa . Sedje-Denou is a locality under the Commune of Ze , which is the second most BU endemic locality in Benin with a prevalence of 0 . 45% [23] . The presence of rivers and wetlands make the locality a typical environment for MU . The region is bordered to the East by the Oueme River which has several tributaries across the villages with the creation of meadows and wet march lands . Risk factors for BU in the region include non-protection from swamps , BCG-vaccinated patients ( >5 years old ) , living or participating in agricultural activities near the river and improper care for wounds [24 , 25] . The predominant occupation of the inhabitants is farming and fishing . Three endemic villages namely Agbahounsou , Agodenou and Agongbo with a history of high burden of disease were investigated in this region . The specific study sites were chosen because of endemicity of BU and ecological risk factors , proximity to the Oueme River ( main BU risk factor ) and the meadow and march land which constitute water supply sources for roaming domestic animals from the villages [26] . The BU endemic area of Akonolinga ( 3° 46’ N and 12° 15’ E ) is in the Central region of Cameroon in Central Africa . Akonolinga health district is at approximately 70 km East to the West , and a 100 km North to South , with a surface of approximately 4500 km2 [27] . Subsistent farming , fishing and hunting are the main occupation of inhabitants of Akononlinga . Akonolinga is the most endemic area for BU in Cameroon . The disease prevalence in the region is high and ranges between 0 . 41–0 . 73% ( average 105 cases ) per year [27] . The district located in the Nyong valley is crossed by the Nyong River which has several tributaries throughout the villages . Risk factors for BU in the region include wading in swamps , living near cocoa plantations or wood , having activities in the Nyong basin , and improper care for wounds [28 , 29] . Six high endemic villages , Akonolinga urban , Edjom , Ekoudou , Nyeck , Nkolessong , and Yeme-Yeme were selected as study sites based on published epidemiological data and ecological characteristics favoring possible MU distribution , and proximity with the Nyong River , which constitutes the main water supply source for domestic animals from these communities . Field activities were stratified into two steps: a 2 days sensitization activity in the communities during which a community aid was trained in each village; and a survey step which lasted for about 2 to 3 days in each village . Prior to the community surveys roaming animals living in close habitat with humans were kept in a paddock . Animals were kept enclosed till the arrival of the research team in the houses where they were systematically screened for lesions . The trapped animals were identified as cattle ( goat , sheep and cow ) , carnivores ( dog and cat ) , omnivores ( pig ) and domesticated birds ( chicken and duck ) . Identified animals were further examined physically by palpation for the presence of any skin lesion or nodule . Sex and age were recorded , and the animals were marked to avoid recaptures . Wound swab and tissue fragments were systematically collected in duplicate from animals carrying skin/soft tissue lesion or open sore as previously described [18 , 19] . Characteristic BU lesions as well as those not likely to be BU lesions were investigated . After sample collection the lesions were cleaned , disinfected and treated by a trained veterinarian doctor . After clinical examination , a differential diagnosis was done by the veterinarian and antibiotic spray ( VETOSPRAY Lot H-011 , France ) and dermal antiseptic ( Betadine Lot 31650 , MEDIA Manufacturing , Paris , France ) were applied accordingly to stop the propagation of colonized microbial pathogens . For lesions suspicious of BU , the veterinarian applied a combination of rifampicin 10 mg/kg and streptomycin 15 mg/kg . In addition , animals were systematically treated with 10 mg/ml ivermectin injection 1% ( Lot 1308211–01 , Alfamec , WOERDEN-Holland ) to protect against any parasitic infection . Overall , 250 μl of each homogenate ( swabs and tissue fragments ) was used for DNA extraction . Total genomic DNA ( gDNA ) was extracted using the Qiagen DNasy Blood and Tissue Kit according to the manufacturer`s instructions ( Qiagen ) . Negative controls ( nuclease-free water , Sigma-Aldrich ) were added at a frequency of 10% ( 1 control per batch of 10 extractions ) to monitor potential cross-contaminations . TaqMan real time quantitative PCR analysis was performed on DNA extracts to screen for the presence of MU DNA according to Fyfe et al . [30] . Although no internal positive control ( IPC ) was included in the analyses , IS2404 negative samples were diluted 1/10 and retested for the detection of PCR inhibitors . All samples positive for the IS2606 and KR-B targets were tested in duplicate for data validation . Two positive controls ( MU Agy99 DNA ) as well as two no-template negative controls ( nuclease-free water , Sigma-Aldrich ) were used to guide the experiments against false positive and negative results . Variable Number Tandem Repeat ( VNTR ) typing of four VNTR loci ( MIRU1 , Locus 6 , VNTR19 and ST1 ) was performed as a confirmatory assay for all MU positive samples to decipher the MU strains and differentiate them from other mycolactone producing mycobacteria ( MPMs ) . VNTR genotyping was performed according to Williamson et al . [31] . Briefly , 5 μl of gDNA extract were amplified in 45μl PCR mixture consisting of 1X Flexi Taq-Polymerase buffer ( Promega , Germany ) , 0 . 4μM of each specific primer , 0 . 2 mM dNTP-mix , 1 . 5 mM MgCl2 , 1U Go-Flexi Taq-polymerase ( Promega , Germany ) , and sterile DNase-free water ( Sigma-Aldrich ) . The amplification process was performed in GenAmp PCR System 9700 ( Applied Biosystems ) . After an initial denaturation at 95°C for 2 min; MIRU1 , locus 6 and VNTR 19 loci were amplified by 40 cycles of 1 min steps respectively at 94°C , 58°C , and 72°C; and one last elongation step of 10 min at 72°C . The ST1 locus was amplified using the same conditions with 30 s denaturation at 94°C and 30 s primer-hybridization at 65°C as described by Hilty et al . [32] . The amplicons were separated on 1 . 5% agarose gel stained with Midorin Green Advance DNA Stain ( Nippon Genetics , Europ GmbH ) , and visualized under a UV light source ( UVP , Benchtop Variable Trans-illuminator , Cambridge , UK ) . Amplicons band sizes were analyzed by comparing to a 100bp DNA molecular ladder ( Promega , Germany ) and the VNTR repeat/copy numbers were estimated as previously described [32–34] . VNTR allelic profiles were defined according to the copy number of each amplified locus and were in the order of MIRU1 , Locus 6 , ST1 and VNTR 19 . BU patient specimens ( MU positive swabs ) from Benin and Cameroon were also genotyped to source-tack MU strains from the environment to humans . Genomic DNA of MU Agy99 was subsequently tested in duplicates as positive control . All PCR runs also included negative controls ( sterile DNase-free water , Sigma-Aldrich ) for quality control and for detection of potential contaminations . Negative samples were treated as above . Representative amplicons of amplified VNTR loci were confirmed with sequencing using the forward and reverse primers at the Molecular Biology Platform of Pasteur Institute ( Ivory Coast ) . Products of the expected size were purified using the QIAquick Gel Extraction Kit according to the manufacturer`s instructions ( Qiagen ) . Purified PCR products were subjected to BigDye Terminator v3 . 1 Cycle Sequencing Kit ( for Sanger sequencing ) in GenAmp PCR System 9700 ( Applied Biosystems ) according to the manufacturer’s instructions ( Applied Biosystems ) . Sequence products were thereafter subjected to purification using the Dye Terminator Removal Kit according to the manufacturer`s instructions ( Thermo Fisher Scientific ) . This second purification step was designed to remove unbound fluorescence labeled dideoxyribonucleotides ( ddNTPs ) and excess salt from sequence reactions prior to sequence analysis . Cleaned sequences were analyzed in ABI 3500xL Genetic Analyzer using the ABI9500 sequencing program ( Applied Biosystems ) . Sequence data were edited in MEGA 6 . 0 software and consensus sequences generated in FASTA format [35] . Multiple sequence alignment ( MSA ) of consensus data was performed in NCBI-BLAST and the evolutionary history ( phylogenetic analysis ) of MU isolates was inferred using the UPGMA method within MEGA 6 . 0 [36] . Reference sequences of MIRU1 orthologs were retrieved from GenBank .
A total of 361 domestic animals roaming around the human habitats were systematically investigated in the 3 endemic villages in Benin , with 161 ( 44 . 6% ) in Agodenou , 109 ( 31 . 19% ) in Agongbo and 91 ( 25 . 21% ) in Agbahounsou . Animal diversity includes carnivore species , bovidae species and bird’s species . Most animals investigated were among the Bovidae ( cattle ) family including sheep ( Ovis aries , 29 . 09% ) , goat ( Capra aegagrus hircus , 18 . 56% ) and cow ( Bos Taurus , 0 . 55% ) . Different species of carnivores were identified including dog ( Canis lupus familiaris , 4 . 43% ) and cat ( Felis catus , 3 . 05% ) . Pig ( Sus scrofa domesticus , 6 . 93% ) constituted the only omnivore identified . Two bird species were identified in the localities namely chicken ( Gallus gallus domesticus , 29 . 36% ) and duck ( Anas platyrhynchos sp . , 8 . 03% ) . Skin lesions ( open sores ) were observed in 25 ( 6 . 93% ) roaming domestic animals in the 3 endemic villages in Benin . Identified lesions were on the head , ear , abdomen , foot ( tight ) , tail , and the nape area of the neck; and 9 ( 36% ) of these were BU-like ( S1 Fig ) . There was no specificity on lesions distribution according to animal and animal body parts . However , most of the lesions ( 98 . 8% ) were observed on the head , abdomen and the foot of animals . Dogs ( 4/16 , 25% ) , goats ( 10/67 , 14 . 93% ) , ducks ( 2/29 , 6 . 9% ) , sheep ( 7/105 , 6 . 67% ) , pigs ( 1/25 , 4% ) and chickens ( 1/106 , 0 . 94% ) carried wound in the areas studied in Benin . Overall , 397 roaming animals were systematically investigated with 118 ( 29 . 72% ) in Edjom , 75 ( 18 . 89% ) in Nyeck , 66 ( 16 . 63% ) in Yeme-Yeme , 55 ( 13 . 85% ) in Ekoudou , 50 ( 12 . 59% ) in Akonolinga urban center , and 33 ( 8 . 31% ) in Nkolessong . Distribution of animals in the two study sites were similar . Forty-four ( 11 . 08% ) roaming domestic animals investigated in the 6 endemic villages in Cameroon carried at least one active external lesion . Identified lesions here were on the head , ear , tail , abdomen , leg , nape area of the neck , lateral side , back , and the sex of the animal; and 7 ( 15 . 91% ) of these were BU-like ( S1 Fig ) . As observed in Benin , up to 96% of the lesions appeared on the head , abdomen and the foot . Lesions distribution varied among animals and the wounded animals include dogs ( 30/54 , 55 . 56% ) , goats ( 8/73 , 10 . 96% ) , ducks ( 2/40 , 5% ) , sheep ( 2/47 , 4 . 26% ) and pigs ( 2/66 , 3 . 03% ) . Quantitative PCR ( qPCR ) analysis of IS2404 molecular markers performed on animal specimens detected bacteria DNA in the lesions of 9 ( 36% ) animals out of 25 found in endemic villages in Benin . Five ( 55 . 56% ) of the IS2404 positive specimens also tested positive for IS2606 . whereas only 2 ( 22 . 22% ) of the IS2404-positive lesions were positive to the ketoreductase B domain of MU plasmid pMUM001 . Overall , 2 ( 8% ) external lesions tested positive for all three targets , IS2404 , IS2606 and KR-B suggesting the presence of MU . MU infected lesions were therefore identified in two animals including one goat and one dog in Benin . Details on the distribution of MU targets according to animal species and BU locality in Benin are shown in Table 1 . ΔCts ( IS2606-IS2404 ) values of 2 . 62 and 2 . 83 cycles were obtained from the dog and goat lesion respectively , confirming the presence of MU in these lesions as previously defined by Fyfe et al . , 2007 [30] . In Cameroon , none of the 44 external lesion specimens subjected to qPCR analysis revealed the presence of MU . Eleven ( 25% ) lesions tested positive for IS2404 , whereas the IS2606 marker was detected in only 3 ( 27 . 27% ) IS2404-positive lesions . None of these specimens was positive to the KR-B domain in the 6 endemic villages investigated in Akonolinga . Details on the distribution of MU targets according to animal species and BU locality in Cameroon are showed in Table 2 . MIRU-VNTR analysis was performed for MU-positive and MU-negative animal specimens to confirm the BU diagnosis and infer the evolution history between isolates . Allelic profiles/genotypes were written in the sequential order of ( MIRU1 , Locus 6 , ST1 , and VNTR 19 ) according to the copy numbers of each amplified locus . Overall , genotypes for MU-negative specimens were undetermined because of the heterogeneity in loci distribution and the non-amplification of some VNTR loci ( S1 Table ) . The heterogeneity among VNTR loci further confirms the absence of MU in these negative samples . Concerning the genotyping process of positive lesions , the percentage distribution of VNTR loci were as MIRU1 ( 3/3 , 100% ) , Locus 6 ( 3/3 , 100% ) , ST1 ( 3/3 , 100% ) and VNTR 19 ( 2/3 , 66 . 67% ) . Only VNTR 19 was not amplified from one MU-positive specimen ( tissue fragment ) collected from the dog lesion . Agarose gel pictures showing each amplified loci are given as supportive information ( S2 Fig ) . Although MIRU1 gave two repeats ( 4 copies at 539 bp or 3 copies at 486 bp ) , only one repeat of Locus 6 ( 1 copy at 500 bp ) , VNTR 19 ( 2 copies at 340 bp ) and ST1 ( 2 copies at 423 bp ) was detected from the amplification of MU-positive specimens . Allelic diversity was found in MIRU1 and Locus19 resulting in the naming of two genotypes designated Z ( 4 , 1 , 2 , 2 ) and C- ( 3 , 1 , 2 , 0 ) from the goat and dog lesions , respectively ( Table 3 ) . The C- genotype , which is close to the human C genotype is characterized by the complete deletion of the VNTR 19 locus . Additional bands were observed from the amplification of VNTR loci of animal specimens , probably characterizing the presence of other MPMs in these samples ( S1 Table ) . We overlapped both animal and human VNTR genotypes within the study communities to observe MU isolates distribution . Animal lesions showed different MU genotypes to those circulating in Benin , Cameroon and Ghana . The MU Agy99 human isolate used in this study as positive control confirmed the known VNTR profile C ( 3 , 1 , 2 , 2 ) . This commonly distributed African MU C genotype was also found in lesions collected from patients based in BU localities of the Nyong valley in Cameroon ( Central Africa ) and the Oueme valley in Benin ( West Africa ) ( Table 3 ) . MU isolates of animal lesions have therefore undergone genetic variations of the MIRU1 and VNTR 19 loci in the BU infected localities of Agongbo and Agbahounsou in Benin . Discrimination of VNTR loci between animal and human lesions suggests heterogeneity of MU isolates . Repeats of amplified loci in MU-positive lesions from animals and humans were further confirmed with Sanger sequencing . Multiple Sequences Alignment ( MSA ) of sequenced data within NCBI-BLAST confirmed the presence of DNA compatible with MU animal specimens , sharing high similarity with previous MU isolates . MSA also showed significant sequences homology among Locus 6 , ST1 and VNTR 19 loci from animal and human lesions as observed with VNTR gel-based analysis . In contrast , MIRU1 locus with sequence deposited in GenBank constituted the main discriminating and polymorphic marker between animal and human lesions from this study and published data . The MIRU1 ortholog from animal MU-positive lesion showed 99% identity to MU Agy99 complete genome , 99% to MU subsp . shinshuense DNA nearly complete genome , 99% to MU subsp . shinshuense DNA complete genome , 98% to M . liflandii 128FXT complete genome , 98% to M . marinum M complete genome , 96% to M . marinum E11 main chromosome genome and 88% to M . intracellular MOTT-64 compete genome ( S3 Fig ) . The evolutionary history of MU isolate found in animal lesions was inferred by comparison of animal and human MU isolates ( data from this study ) with human MU isolates from other endemic regions ( Cameroon , Ivory Coast and Ghana ) and published data in GenBank . The phylogenetic tree ( Fig 3 ) revealed 2 clusters of MU isolates: one cluster of animal and human MU isolates from Africa ( Cameroon , Benin , Ghana and Ivory Coast ) , and one cluster of MU isolates from Asia ( Japan ) and other Mycolatone Producing Mycobacteria ( MPMs ) . It can be observed from this figure that human and animal MU isolates are clustered at the top of the phylogenetic tree in contrast to M . marinum , the common ancestor to mycobacterial species . Genetic comparison revealed that MU isolates from animals are less diverse and are more closely related to the strains circulating in humans in Cameroon , Benin and Ghana ( Agy99 ) . This animal strain is in contrast less closely related to the strains circulating in Ivory Coast and some parts of Ghana ( ScoA and ScoB strains ) ( Fig 3 ) .
This study describes the first report of MU colonized lesions in domestic animals in Africa . Two animal lesions from one goat and one dog were found to be infected with MU in the villages of Agbahounsou and Agongbo which are major endemic foci for human BU in Southern Benin . VNTR typing of infected lesions revealed two different genotypes , a Z genotype from the goat lesion and a C- genotype from the dog lesion . We overlapped both animal and human VNTR genotypes within the study communities to observe MU isolates distribution . The data revealed a divergence between the animal genotypes and the human C genotype circulating in the investigated areas . Source-tracking of MU sequences between animals and humans revealed close homology among loci orthologs from this study and previous studies . In addition , phylogenetic analysis clustered animal and human samples . This might suggest a common source of contamination which could be followed by a strain rearrangement to adapt into the new environment . Domestic hosts which are in close contact with populations could play a role in the transmission cycle of MU as previously reported with small mammals . For control measures , MU infection should be suspected in domestic hosts living near humans in BU endemic areas and presenting with ulcerative skin disease . | Buruli ulcer ( BU ) remains a major Public Health problem in rural communities in sub-Saharan Africa . There are several reports of the occurrence of BU in Wildlife as well as domestic animals in Australia leading to the suggestion that animals may play a role in the transmission of MU to humans . Report of BU in animals is however scanty in Africa and no significant link has been made between BU in humans and animals . BU-like lesions were investigated in 397 and 361 roaming domestic animals respectively from Sedje-Denou and Akonolinga . Wound swabs , and tissue fragments were collected from animals with active lesions . Overall , 2 ( 8% ) type I ( <5 cm ) animal lesions ( localized on the abdominal part of a goat and the nape area of a dog ) were colonized by MU in Benin . MU VNTR genotypes Z ( 4 , 1 , 2 , 2 ) and C- ( 3 , 1 , 2 , 0 ) were identified in the lesions of the goat and dog respectively . Significant homology was found between orthologous sequences of MU strains infecting animals and humans . The evolutionary history inferred from sequenced data revealed a clustering of animal MU isolates within isolates from human lesions . New reservoirs of MU were found through this study and allowed to a new interpretation of the life cycle of this mycobacterium from the risk environment to humans in Africa . | [
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| 2018 | Domestic animals infected with Mycobacterium ulcerans—Implications for transmission to humans |
Influenza viruses cause seasonal flu each year and pandemics or epidemic sporadically , posing a major threat to public health . Recently , a new influenza D virus ( IDV ) was isolated from pigs and cattle . Here , we reveal that the IDV utilizes 9-O-acetylated sialic acids as its receptor for virus entry . Then , we determined the crystal structures of hemagglutinin-esterase-fusion glycoprotein ( HEF ) of IDV both in its free form and in complex with the receptor and enzymatic substrate analogs . The IDV HEF shows an extremely similar structural fold as the human-infecting influenza C virus ( ICV ) HEF . However , IDV HEF has an open receptor-binding cavity to accommodate diverse extended glycan moieties . This structural difference provides an explanation for the phenomenon that the IDV has a broad cell tropism . As IDV HEF is structurally and functionally similar to ICV HEF , our findings highlight the potential threat of the virus to public health .
Influenza viruses are enveloped , segmented , single-stranded , negative-sense RNA viruses and belong to the family Orthomyxoviridae [1] . The genomes of influenza A virus ( IAV ) and influenza B virus ( IBV ) consist of eight RNA segments , whereas influenza C viruses ( ICV ) only have seven segments . Both IAV and IBV contain two major surface glycoproteins: the hemagglutinin ( HA ) , which binds to sialylated host cell receptors and mediates membrane fusion; and the neuraminidase ( NA ) , which destroys the receptor by cleaving sialic acid from host cell membranes , thereby releasing newly assembled virus particles [1] , and likely assisting initial invasion by destroying sialylated mucin decoys [2] . ICV , however , has only one major surface glycoprotein , the hemagglutinin-esterase-fusion ( HEF ) protein , which possesses all-in-one of receptor binding , receptor destroying and membrane fusion activities [3 , 4] . While IAV infects avian , human , swine , and many other mammalian species including dogs , horses , tigers and seals , IBV and ICV are found principally in humans and rarely infect other species [3] . ICV usually causes mild upper respiratory tract infections in children with cough , rhinitis and rhinorrhea as clinical symptoms [5 , 6] . The virus only occasionally spreads to the lower respiratory tract and causes bronchitis , bronchiectasie and broncho-pneumonia [7] . Encephalopathy has also been occasionally reported [8] . Seroepidemiological studies have revealed that ICV is widely distributed globally and that the majority of humans acquire antibodies against the virus early in life [9 , 10] . Aside from humans , there is evidence that ICV possesses the ability to infect animals [3] . Serological studies showed that antibodies against ICV are present in pigs [11–13] . In 1981 , fifteen strains of ICV were isolated from domestic pigs in China [14] , which showed characters highly related to viruses isolated from humans in Japan [15 , 16] . Furthermore , pigs have been shown to be susceptible to experimental infection with both pig and human ICVs , and the virus is able to be transmitted from the infected to uninfected contact pigs [14] , suggesting that interspecies transmission of ICV between humans and pigs might occur in nature . Dogs may also serve as a natural reservoir for human ICV due to the presence of viral replication and clinical symptoms in experimental infections and the prevalence of antibody to ICV among dogs [12 , 17–19] . In 2011 , an influenza C-like virus was isolated from swine in Oklahoma ( D/swine/Oklahoma/1334/ 2011 [D/OK] ) exhibiting influenza-like symptoms [20] . The genome of this virus also contains seven segments , and sequence analysis showed approximately 50% overall amino acid homology to either human or previous swine ICVs . D/OK did not cross-react with antibodies against human ICVs and , importantly , was unable to reassort with human ICVs or generate viable progeny [20–22] . However , the low seroprevalence rate observed in both swine and humans to D/OK ( 9 . 5% and 1 . 3% , respectively ) suggested that swine and humans are not likely to be a major reservoir of this novel virus [20] . Subsequent serological studies have showed that antibodies against D/OK are almost ubiquitously present in cattle , and several novel D/OK-like virus strains have been isolated from cattle with respiratory disease which could be divided into two distinct lineages represented by D/OK and D/bovine/Oklahoma/660/2013 ( D/660 ) [21 , 23] . These two genetic and antigenic distinct clades have been shown to reassort with each other [23] . In addition , D/OK has a broader cell tropism than human ICV and is capable of infecting ferrets , pigs and guinea pigs and transmit to naive animals by direct contact [20 , 24] . Based on these differences to ICV it was suggested that this virus warrants classification as a new genus of influenza virus , named influenza D virus ( IDV ) with cattle as the potential reservoir [21] . Subsequently , more IDVs or viral genomic segments were identified from China and France in cattle , suggesting the wide geographic distribution of IDV [25 , 26] . Interestingly , IDV is common in clinical samples of bovine respiratory disease complex ( BRDC ) , which is the leading cause of morbidity and mortality in feedlot cattle [23 , 27] . BRDC is a challenging multi-factorial disease caused by viral , bacterial pathogens and environmental factors , leading to severe clinical signs and deaths [28] . IDV was detected in clinical BRDC samples , co-infected with bovine coronavirus ( BCV ) , bovine viral diarrhea virus ( BVDV ) , bovine respiratory syncytial virus ( BRSV ) , bovine herpesvirus 1 ( BHV-1 ) , and Pasteurella multocida , Mannheimia haemolytica , Histophilus somni et al , suggesting IDV has the pathogenic potential in BRDC [23] . In addition , a latest serological study showed that antibodies to IDV were present in sheep and goats in United States , suggesting that small ruminants are also susceptible to IDV infection [29] . To further evaluate the infectivity and transmissibility of IDV , we expressed and purified the ectodomain of D/OK HEF , and determined that it also uses 9-O-Acetyl-Sia as its receptor by glycan microarray . We also solved the crystal structure of D/OK HEF , both in its native state ( resolution of 2 . 4 Å ) and in complex with its receptor analogue ( resolution of 3 . 1 Å ) , and the structure of the enzymatically inactive HEF ( resolution of 2 . 4 Å ) alone and in complex with two receptor analogs respectively ( both resolutions of 2 . 2 Å ) . Indeed , our results show that IDV HEF is functionally and structurally similar to ICV HEF , but with some distinct characteristics .
The ectodomain of HEF from D/OK strain was cloned and expressed using a baculovirus expression system based on a previously reported method [30–33] with slight modifications . To avoid the enzymatic cleavage of the receptor substrates , we generated the catalytic mutant for the binding experiments and for the structures of the relevant complexes . Previous studies have demonstrated that the residues S57 , D356 and H359 create a catalytic triad in ICV HEF esterase , which has been proved by site-directed mutagenesis and structural analysis [34 , 35] , and the sequence alignment between the ICV and IDV HEF proteins reveals that a highly conserved catalytic triad is also observed in IDV HEF protein ( S2 Fig ) . Thus , we designed the enzymatically inactive HEF protein ( HEF-mut ) containing S57A , D356A and H359A substitutions , and expressed it using the same method as wild type protein . Soluble protein was purified by metal affinity chromatography followed by ion-exchange and gel filtration chromatography . The proteins were tested in a series of assays to determine their biological functions . Large-scale glycan microarray analysis with 610 different glycans was used to investigate the receptor binding properties of the HEF-mut protein . The result revealed that the HEF-mut protein only binds robustly to 9-O-Ac-Sia glycan derivatives , with different relative fluorescence units ( RFU ) ( Fig 1A , S1 Table ) . The structural formulas of the top four binding glycans are shown in Fig 1A . In order to further characterize the binding properties of IDV HEF , we chose to use a more extended array with broader and paired ( Neu5Ac- or Neu5Gc-based , and α2–3 or α2-6-linked ) 9-O-Ac and non-O-Ac-sialoglycans [36] . The result , summarized in Fig 1B and S2 Table , indicate that IDV HEF-mut bind both α2–3 and α2-6-linked 9-O-Ac-Sias . Another interesting finding was the IDV HEF-mut can tolerate differentially modifications at C5 , not only bind to 5-N-Ac-Sias , but also 5-N-Gc-Sias ( Fig 1B ) . We also tried to perform the glycan array analysis of ICV HEF-mut protein , and to our surprise , the ICV HEF-mut protein did not bind to the glycan array with synthetic short glycans at all , which might be due to the lower binding affinity , and/or relative instability of the mutated protein . In addition , both the IDV and ICV HEF-mut proteins , but not HEF , display the hemagglutination activities ( Fig 2 ) and specifically binding activities to the Madin-Darby canine kidney ( MDCK ) cells and bovine submaxillary mucins ( BSM ) which are enriched in 9-Ac Sias ( Fig 3A and 3B ) . More importantly , the IDV HEF-mut displays much stronger binding capacity than the ICV HEF-mut , which is compatible with our glycan array analysis . HEF protein was further tested for its enzymatic activity using two substrates , BSM and p-nitrophenyl acetate ( pNPA ) . Firstly , we found that the receptors in BSM could be destroyed by treatment with HEF protein ( Fig 4A ) . Secondly , we performed an enzyme kinetics assay using pNPA as a substrate at three different temperatures ( 37°C , 25°C and 4°C ) . Both IDV and ICV HEF displayed obvious esterase activities while HEF-mut proteins exhibited no enzymatic activities ( Fig 4B–4D ) . With the decrease of temperature , the esterase activities of both IDV and ICV HEF decreased as well . However , both IDV and ICV HEF still retain noticeable esterase activities even at 4°C ( Fig 4D ) . This may explain why wild type HEF proteins do not show either hemagglutination activity or receptor binding activities clearly ( Figs 2 and 3 ) , as the receptors are being destroyed by their esterase activities , even at 4°C . To examine the host tissue tropisms of IDV and ICV , we used soluble recombinant HEF-mut proteins of these two viruses to stain paraffinized human , swine or bovine trachea sections . Interestingly , the apical surfaces of the human , swine and bovine trachea showed positive staining with ICV HEF-mut and IDV HEF-mut ( Fig 5 ) , indicating the apical surfaces of the trachea of these three species all exhibit the receptor 9-O-Ac-Sia . Moreover , the tracheas of swine and bovine display brighter staining than that of the human trachea ( Fig 5 ) . In order to study the molecular basis of IDV HEF and its receptor / substrate binding mode we solved the native X-ray crystallography structures of wild type and mutant HEFs both at a resolution of 2 . 4 Å ( Table 1 ) . A Dali search within the Protein Data Bank ( PDB ) revealed that the IDV HEF structure most resembles the ICV HEF structure which has two subunits HEF1 and HEF2 ( PDB code , 1FLC; Z score , 53 . 5 for HEF1 and 9 . 0 for HEF2 ) [35] . Following the initial domain nomenclature used in the description of the structure of ICV HEF , we divided the IDV HEF structure into three domains: receptor binding domain ( R ) , esterase domain ( consisting of E1 , E' and E2 subdomains ) and fusion domain ( consisting of F1 , F2 and F3 subdomains ) . Both the overall structure ( Fig 6A ) and the structural folds of individual subdomains ( Fig 6B ) of IDV HEF and ICV HEF are remarkably similar . The E domains are the most conserved regions and display root mean square differences ( RMSDs ) on main chain C atoms at 0 . 396 Å , 0 . 390 Å and 0 . 435 Å for E1 , E' and E2 , respectively , with corresponding sequence identities of 66 . 7% , 68 . 8% and 56 . 6% , respectively . A comparison of R domains reveals a RMSD of 0 . 642 Å and a sequence identity of 46 . 3% . The F1 and F2 subdomains share 42 . 1% and 41 . 2% sequence identities and display RMSDs of 0 . 567 Å and 0 . 817 Å , respectively . Notably , the F3 subdomain shares 56 . 8% sequence identity but the RMSD reaches 1 . 445 Å . The F3 subdomain contains the fusion peptide , which is important for the viral membrane fusion . To understand the molecular basis of IDV HEF receptor binding , the HEF-mut crystals] were soaked with glycans 9-O-Ac-3'SleC ( Tr323 , Neu5 , 9Ac2α2-3Galβ1-3GlcNAcβ-Sp0 ) and 9-O-Ac-3'SLN ( Tr322 , Neu5 , 9Ac2 α2-3Gal β1-4GlcNAcβ-Sp0 ) [ ( kindly provided by the Consortium for Functional Glycomics ( CFG , Scripps Research Institutes , Department of Molecular Biology , La Jolla , CA ) to determine the structures of the respective receptor complexes , which were resolved at a resolution of 2 . 2 Å ( Table 1 ) . Similar to ICV HEF , the receptor-binding site of IDV HEF is located near the top of the HEF1 globular head in a shallow cavity , surrounded by residues from four secondary structure elements: the 170-loop , 190-loop , 230-helix and 270-loop . Five base residues F127 ( C HEF: Y127 ) , W185 ( C HEF: L184 ) , Y231 ( C HEF: Y227 ) , F229 ( C HEF: F225 ) and F297 ( C HEF: F293 ) form the bottom of the cavity ( Figs 7 and 8 ) . However , one unique feature in the IDV HEF receptor-binding site observed is an open channel between the 230-helix and 270-loop . In ICV HEF , the positively-charged residue K235 of 230-helix and the negatively-charged residue D269 of 270-loop form a salt bridge interaction , pulling the 270-loop up to connect with 230-helix and close the channel observed in IDV HEF . Whereas for IDV HEF , at equivalent positions , T239 and A273 cannot form salt bridge interaction . For the IDV HEF-mut/receptor complex structures , interpretable electron density is observed for all the three glycan rings of 9-O-Ac-3'SleC , including Neu5 , 9Ac2 ( 9-O-Ac-Sia-1 ) , galactose-2 ( Gal-2 ) , and N-acetylglucosamine-3 ( GlcNAc-3 ) ; while for 9-O-Ac-3'SLN , electron density for the first two sugars is well defined ( Figs 7 and S1 ) . The glycan rings of 9-O-Ac-3'SleC go through the open channel between the 230-helix and 270-loop ( Fig 7F ) . The open channel in the receptor-binding cavity of IDV could provide a structural basis for accommodating different receptors with diverse glycan rings in different cell types , which should be tested in the future . Compared with the ICV HEF receptor complex , the 9-O-Ac-Sia-1 of the receptor bound to IDV HEF displays a very similar orientation ( Figs 7 and 8 ) [35] . The 9-O-acetyl group of the receptor docks into a nonpolar , hydrophobic pocket , formed by V275 , F229 , F240 and F297 ( Fig 8 ) . The acetyl carbonyl oxygen forms a hydrogen bond with the hydroxyl group of Y231 ( ICV HEF: Y227 ) . Y231 is highly conserved both in ICV and IDV HEF ( See alignment in S2 Fig ) . Importantly , the conserved amino acid Y127 ( Y98 in IAV HA , H3 numbering ) both in ICV HEF and IAV HA has been changed to F127 in IDV HEF , which is the same as IBV HA [37] . The absence of two hydrogen-bonding interactions between hydroxyl group of Y127 and the 8-hydroxyl and 9-amide of the ligand , pushes the 9-O-acetyl moiety of 9-O-Ac-sia-1 to the other side by ~1 . 1 Å . In exchange , two more hydrogen-bonding interactions between the carbonyl group of C4 and T171 mediated by water stabilize the sugar ring conformation and prevent its excessive shift and rotation . The 5-N-acetyl group of the ligand fits into a hydrophobic pocket formed mainly by W185 , and forms a hydrogen-bonding interaction with A172 . In addition , the carboxyl group of C1 formed two hydrogen-bonding interactions with S173 . Both 9-O-Ac-3'SleC and 9-O-Ac-3'SLN bind in a cis conformation ( Figs 7 and 8 ) , similar to the IAV avian SH-H7N9 HA binding with avian-like ( α2–3 ) receptor structures [38] . But the majority of the interactions between ligands and IDV HEF are made by the 9-O-Ac-Sia-1 moiety , whereas the glycan portion of the ligands do not make significant contacts with IDV HEF , except for a hydrogen bond between the Gal-2 of 9-O-Ac-3'SLN and S173 . The E domain of IDV HEF , harboring the receptor-destroying enzyme ( RDE ) activity , has a hydrolase fold that is highly similar to that of ICV HEF . The active-site architecture of the HEF sialate-9-O-acetylesterase is fully conserved in ICV and IDV HEF with S57 , D356 and H359 creating a catalytic triad and with the side chain of N117 and the NH groups of S57 and G85 forming an oxyanion hole ( Fig 9A ) [35] . The pocket is extremely conserved not only in IDV HEF and ICV HEF , but also in some nidovirus hemagglutinin-esterase ( HE ) proteins such as bovine coronavirus ( BCoV ) HE , porcine torovirus ( PToV ) HE and bovine torovirus ( BToV ) HE [39] . Phylogenetic analysis also shows the relationship between HEF and HE to be much closer than the relationship between HEF and HA or hemagglutinin-neuraminidase ( HN ) proteins ( Fig 10 ) , implying common ancestral origins . Due to the presence of cacodylate buffer in the crystallization conditions , S57 in our IDV HEF structure is covalently modified by the addition of a dimethylarsenic group . The covalent modification of active site serine by arsenic was first reported by Ian Wilson’s group in 2003 [41] , when they solved the structure of an acetyl esterase , HerE , and its complex with the inhibitor dimethylarsinic acid , and illustrated the mechanism of the broad scope inhibition of serine hydrolases by As ( V ) -containing organic compounds [41] . The electron density of the covalent modification of S57 is shown in Fig 9B . This modification is only observed in the wild type HEF structure , while in HEF-mut structure , there is no such electron density due to a S57A mutation . Another serine esterase inhibitor diisopropyl fluorophosphate ( DFP ) is known to bind covalently to the serine in the active site of serine esterases . DFP-treated ICV bound specifically and irreversibly to cells expressing 9-O-Ac-Sias . This provided a probe for detecting 9-O-Ac-Sias [4] and S57 was demonstrated as the active-site serine in the acetylesterase of ICV [42] . DFP has also been used to determine the active site of enzymes by solving the complex crystal structure [43 , 44] . In our IDV HEF complex structure with its receptor analogue 9-N-Ac-Sia , we found that the 9-N-acetyl group of the substrate was tilted up against the oxyanion hole as the covalent modification of S57 by arsenic blocked the insertion of the 9-N-acetyl group inserting into enzymatic site ( Fig 9D ) . In our IDV HEF structure , IDV HEF is proteolytically cleaved into the subunits HEF1 and HEF2 . For ICV , the HEF protein contain a monobasic cleavage site located in the stem region of the trimeric spike and can be cleaved by protease hydrolyses between R432 and I433 in to HEF1 and HEF2 [35 , 45] . This monobasic cleavage site was very conserved both in ICV HEF ( R432 ) and IDV HEF ( R439 ) , and the first eight residues of fusion loop were conserved between ICV and IDV HEF sequences too ( S2 Fig ) . To confirm the proteolytic processing , we isolated IDV HEF crystals and checked for HEF2 band both by SDS-PAGE and western blot with an antibody recognizing the hexa-His tag engineered at the C terminus ( S3A Fig ) . Furthermore , N-terminal amino acid sequencing of the HEF2 band revealed that the first five amino acids of HEF2 were IFGID ( S3B–S3F Fig ) , the same with ICV HEF . In all solved cleaved influenza A and B HA structures , the N-terminal HA2 fusion peptide inserts into an electronegative cavity composed of different HA protomers , except in the bat-derived H17 and H18 , which display an exposed fusion peptide [46 , 47] . The exposed fusion peptide has also been observed previously in the HEF protein of ICV [35] ( Fig 11A ) and in the cleavage site of IDV HEF structure ( Fig 11B ) . Unambiguous electron density was seen from the ninth residue ( F9 ) of the fusion peptide ( S4 Fig ) . Although we cannot observe the first eight residues in the structure , different orientations of F9 in the HEF structure and other known cleaved HA structures helped to confirm that the fusion peptide does not insert into the cavity .
Since the first ICV strain ( C/Taylor/1233/47 ) was isolated in 1947 during an epidemic of respiratory illness , hundreds of viruses have been isolated in clinical specimens [48–50] , due to its inconspicuous or mild symptoms and lack of suitable cell lines for virus isolation [51] . Recently , a dozen of novel IDV strains have been isolated from both pigs and cattle , strains that are distantly related to human ICV [20 , 21 , 23 , 25 , 26 , 52] . Ferrets , guinea pigs and small ruminants ( sheep and goats ) have also been found to be susceptible to the virus [20 , 24 , 29] . Clearly , the viral traits necessary for host switching are different between ICV and IDV . Here , we solved the structure of IDV HEF alone and in complex with its receptor analogs . We found that the overall structure of IDV is extremely similar to that of ICV , despite sharing a relatively low sequence identity of only 53% [20] . This finding is similar to the structural similarity observed between HAs of IAV and IBV [37] . The IDV HEF structure was modeled by Modeller 9 . 10 using the ICV HEF as template in a previous study [20] , and they had an overall similar structure with ICV HEF . However , the details of interactions between the ligands and receptor-binding site and substrate binding site were not predicted precisely . Notably , we found that the receptor-binding site of IDV HEF occurs in an open channel between 230-helix and 270-loop . By contrast , in the receptor-binding site of ICV HEF the K235 of 230-helix and D269 of 270-loop form a salt bridge interaction , pulling the 270-loop up to connect with 230-helix and close the channel . The open channel in the receptor-binding site of IDV HEF could provide the space to accommodate an array of glycan linkages found in diverse host receptors which might explain why IDV has a broad cell tropism . Although the receptor specificity and adaptability of viral surface protein is one crucial determinant for host jump , other viral proteins may also be restrictive barriers to viral host range . PA , PB1 and PB2 , which comprise the RNA-dependent RNA polymerase complex have long been implicated in playing a crucial role in determining host tropism [53 , 54] . Furthermore , innate immune responses , intracellular factors and cross-species contacts may also affect host adaptation [55] . Therefore , a better understanding of the ecological , evolutionary and molecular mechanisms of IDV is essential in order to explain the broader host and cellular range of IDV and accurately assess the risk of transmission to other host species . We determined that IDV HEF uses the glycan derivatives , 9-O-Ac-Sia as its receptor . We show that both ICV and IDV HEF proteins can bind to the trachea of human , swine and bovine . Considering the ability of IDV to transmit in ferrets and guinea pigs , and its pathogenicity in pigs and cattle , its public health threat for transmission to human must be monitored . A number of studies have examined occupational risk factors for zoonotic influenza virus infections , including open bird market workers , swine workers , meat processing workers , veterinarians and poultry workers , concluding that these populations are indeed at greater risk of infection with zoonotic IAV [56 , 57] . Therefore , the surveillance of IDV infection in animal farm workers with influenza-like illness must be performed . Moreover , given the major economic importance of cattle and swine , further research into the pathobiology of IDV in these hosts , especially putative role in BRDC , needs to be conducted . Another important phenomenon observed earlier is that IDV is unable to reassort with the ICV [20 , 21] . As the only glycoprotein on the virus surface , it was suggested that the HEF protein may affect virus reassortment by incompatible protein functions in ICV and IDV [20 , 21] . However , herein we have described the structure of IDV HEF and found it to be highly structurally and functionally similar to ICV HEF . Previous work has shown that there exist two discrepancies at the extremely conserved non-coding regions of seven RNA segments ( the first twelve nucleotides at each 3' end as well as the last eleven nucleotides at each 5' end ) between ICV and IDV genomes [20] . In addition , the non-conserved non-coding regions adjacent to each coding region are significantly variable [20] . Therefore , we propose that the packaging signal compatibility may be the important factor for the heterotypic incompatibilities between ICV and IDV . The esterase pocket of IDV HEF is highly conserved among ICV and nidovirus HE proteins and could represent a potential drug target for developing broad-spectrum inhibitors . Therefore , rational modification of the substrate analog based on our structures may provide a potential route for the development of novel therapeutics against both orthomyxovirus HEFs and nidovirus HEs . In conclusion , our functional and structural approach for the IDV surface protein HEF clearly reveals the virus’ similarities and distinctions in comparison to the ICV and that the virus could be a potential concern for public health as the viral HEF can bind human trachea epithelia .
Paraffin-embedded normal human tracheal tissue sections were purchased from Auragene Bioscience ( China ) . Formalin-fixed normal swine ( 5 months old domestic pig ) and bovine ( 8 months old cattle ) tracheal tissues were obtained from Zhongmu institutes of China animal husbandry industry with approval . Madin-Darby canine kidney ( MDCK ) cells ( NBL2 , obtained from cell resource center of Shanghai Institutes for Biological Sciences , Chinese Academy of Sciences ) were cultured in Dulbecco's modified Eagle's medium ( DMEM , Gibco ) supplemented with 10% fetal bovine serum in a humidified chamber containing 5% CO2 at 37°C . The gene of the HEF from D/OK strain ( NCBI accession no . JQ922308 ) encoding the ectodomains ( amino acid residues 3–605 after deletion of the signal peptide ) was cloned into the baculovirus transfer vector pFastBac1 ( Invitrogen ) in-frame with an N-terminal gp67 signal peptide for secretion and a His6-tag at the C terminus for purification [31 , 32] . To allow expression of an enzymatically inactive HEF protein ( HEF-mut ) , the codons for the esterase catalytic residues S57 , D356 and H359 were all substituted by Ala by site directed mutagenesis using the overlap extension PCR method and inserted to pFastBac1 in the same way . Recombinant pFastBac1 plasmid was used to transform DH10Bac Escherichia coli ( Invitrogen ) . Transfection and virus amplification were performed according to the Bac-to-Bac baculovirus expression system manual ( Invitrogen ) [30 , 33 , 58] . HEF proteins were produced by infecting suspension cultures of Hi5 cells ( Invitrogen ) for 2 days . Soluble HEFs were recovered from cell supernatants by metal affinity chromatography using a HisTrap HP 5 ml column ( GE Healthcare ) , then purified by ion-exchange chromatography using a RESOURCE Q 6 ml column ( GE Healthcare ) . For crystallization , the proteins were further purified by gel filtration chromatography using a Superdex 200 10/300 GL column ( GE Healthcare ) with a running buffer of 20 mM Tris–HCl and 150 mM NaCl ( pH 8 . 0 ) , and the collected protein fractions were concentrated to 10 mg/mL using a membrane concentrator with a molecular weight cutoff of 10 kDa ( Millipore ) . Both the wild type and enzymatically inactive ( with S57A , D356A and H359A mutations ) ICV HEF ( C/Johannesburg/1/66 ) protein ( NCBI accession no . AM410041 , amino acid residues 1–597 after deletion of the signal peptide ) were expressed and purified in the same as that of HEF-D/OK . HA of A/Anhui/1/2005 ( H5N1 ) was prepared as described in our previous report [38] . The initial screening trials were set up with commercial crystallization kits ( Molecular Dimensions ) using the sitting drop vapor diffusion method . Normally , 1 μL protein was mixed with 1 μL reservoir solution . The resultant drop was then sealed , equilibrating against 100 μL reservoir solution at 4 or 18°C . After optimization and seeding , diffractable crystals were obtained in a reservoir solution of 0 . 1 M PCTP ( Propionic acid , Cacodylate , Bis-tris propane system ) buffer pH 8 . 5 , 22 . 5% w/v PEG 1500 for both HEF and HEF-mut protein at 4°C . For receptor complexes , HEF-mut crystals were soaked in a reservoir solution containing 10 mM 9-O-Ac-3'SleC or 9-O-Ac-3'SLN at 4°C for 5 hr . For receptor analog complexes , HEF crystals were soaked in a reservoir solution containing 8 mM N -Acetyl-9- ( acetylamino ) -9-deoxyneuraminic Acid ( or 9-N-Ac-Sia , TRC , Canada ) at 4°C for 5 hr . All crystals were flash-cooled in liquid nitrogen after a brief soaking in reservoir solution with the addition of 17% w/v PEG 1500 . The X-ray diffraction data were collected at Shanghai Synchrotron Radiation Facility ( SSRF ) beamline 17U , with a wavelength of 1 . 000 angstrom , at a temperature of 100K . All data were processed with HKL2000 software [59] . The HEF structures were solved by the molecular replacement ( MR ) method using Phaser [60] from the CCP4 program suite [61] , with the structure of human ICV HEF ( PDB: 1FLC ) as the search model . Model building and refinement were performed using the COOT [62] and REFMAC5 [63] programs , respectively . The HEF receptor analog complexes were subsequently determined using the refined HEF structure as the input model . The receptor analogs were manually built using COOT based on the simulated anealing omit Fo-Fc maps and were further refined by PHENIX [64] . The stereochemical quality of the final models was assessed with the program PROCHECK [65] . Final statistics for data collection and structure refinement is represented in Table 1 . The microarray analysis was first performed by applying the IDV HEF-mut protein to the array at 200 μg/mL and detecting with a His antibody labeled with Alexa488 . The experiments were performed in replicates of six at CFG using a version 5 . 1 CFG array consisting of 610 glycans . The highest and lowest points from each set of six replicates were removed , so the average is of four values rather than six . This eliminates some of the false hits that contain a single very high or low point . Then , we chose to use a more specific array with broader and paired ( Neu5Ac- or Neu5Gc-based , and α2–3 or α2-6-linked ) 9-O-Ac and non-O-Ac-sialoglycans to further characterize the binding properties of IDV and ICV HEF [36] . Glycan microarrays were fabricated using epoxide-derivatized slides as previously described [36] . Printed glycan microarray slides were blocked by ethanolamine , washed and dried . Slides were then fitted in a multi-well microarray hybridization cassette ( AHC4X8S , Arrayit , Sunnyvale , CA , USA ) to divide into 8 subarrays . The subarrays were blocked with Ovalbumin ( 1% w/v ) in PBS ( pH 7 . 4 ) for 1 hr at room temperature ( RT ) , with gentle shaking . Subsequently , the blocking solution was removed and diluted IDV-HEF-mut and ICV-HEF-mut protein samples with 160 μg/mL were added to each subarray . After incubating the samples for 2 hr at RT with gentle shaking , the slides were washed . Diluted anti-His-HiLyte Flour 555 antibody ( LifeSpan BioSciences ) in PBS was added to the subarrays , incubated for 1 h at RT , washed and dried . The microarray slides were scanned by Genepix 4000B microarray scanner ( Molecular Devices Corp . , Union City , CA , USA ) . Data analysis was performed using Genepix Pro 7 . 0 analysis software ( Molecular Devices Corp . , Union City , CA ) . Heat map was generated according to the method previously described [36] . Ranked binding of IDV-HEF-mut and ICV-HEF-mut on the array . Binding was ranked as ( glycan RFU/ maximum glycan RFU ) *100 . Blue and white represent the maximum and minimum , respectively . The cell binding assays were performed in 96-well plates as previously described [46] . When the density of the MDCK cells in the wells reached 90% coverage , the plate was washed with PBS twice and fixed with ice-cold 100% methanol for 20 min . After PBST buffer ( PBS with 0 . 05% Tween-20 ) washing for three times , the wells were blocked with blocking buffer [PBS , 0 . 05% Tween-20 , 4% bovine serum albumin ( BSA ) ] . His-tagged HEF or H5 HA protein ( 10 μg/mL , 20 μg/mL , 40 μg/mL , 60 μg/mL , 80 μg/mL , 100 μg/mL ) was then added to each well and each concentration was performed in triplicates wells . After incubation at 37°C for 1 hr , the plate was washed three times with PBST buffer . Mouse anti-His antibody ( MBL , Japan ) was added to each well at a 1:1000 dilution and the plate was incubated for 1 hr . Then , the plate was washed and incubated at 37°C for 45 min with HRP-conjugated goat anti-mouse antibody ( Santa Cruz , USA ) at a dilution of 1:2000 . Peroxidase activity was detected using TMB and the reaction was stopped by adding 2M H2SO4 . Absorbance was measured at an optical density of 450 nm . Hemagglutination assay was performed in U-bottom 96-well microtest plates ( Becton Dickinson , USA ) according to the method previously described [66–68] . Briefly , two-fold serial dilutions in 25 μl PBS of purified HEF or HEF-mut protein ( 100 μg/mL to 0 . 1 μg/mL per well ) mixed with 25 μl of a 0 . 5% chicken erythrocytes suspension and incubated for 1hr at 25°C or 2 hr at 4°C . Then the hemagglutination effects were observed and the plates were screened by CTL-ImmunoSpot S5 Versa Analyzer ( Cellular Technology , USA ) . SLBA was performed as previously described [69] . Briefly , Corning 96 well EIA/RIA plates were coated for 16 hr at 4°C with BSM ( 60 μg/mL in PBS; Abnova ) at 100 μL per well . The wells were washed with PBST and treated with blocking buffer for 1 hr at RT . Twofold serial dilutions of proteins containing a C-terminal His6 tag were prepared in blocking buffer ( starting concentration 100 μg/mL ) and 100 μL samples of these dilutions were added to the glycoconjugate-coated wells . Incubation was continued for 1 hr after which unbound protein was removed by washing five times . Then the wells were incubated with mouse anti-His antibody ( 1:1000 ) , washed five times with washing buffer , incubated with HRP-conjugated goat anti-mouse IgG antibody ( 1:2000 ) , and washed five times . Finally , the bound proteins were detected using TMB , and the reaction was stopped with 2M H2SO4 . The absorbance of the resulting yellow color was read at 450 nm . To assess the enzymatic activities of IDV HEF protein towards 9-O-Ac-Sias , BSM coated plates were treated with samples from two-fold serial dilutions of IDV HEF protein ( starting at 1 μg/mL in PBS , 100 μL/well ) for 1 hr at 37°C . The destruction of 9-O-Ac-Sia receptor determinants was determined by SLBA with IDV HEF-mut protein ( 50 μg/mL in blocking buffer ) as described above . The activities of purified HEF and HEF-mut were tested using p-nitrophenyl acetate ( pNPA , Sigma–Aldrich ) as a substrate [70] . Proteins ( 50 μL ) were diluted to 12 . 5 ng/mL using PBS buffer in each well of a 96-well plate , after which the plate was incubated at different temperatures ( 37°C , 25°C and 4°C ) . Twofold serial dilutions ( 0–8 mM ) of preheated pNPA ( 50 μL ) were then added at corresponding temperatures . The absorbance at 405 nm was measured immediately in a spectrophotometer every 30 seconds for 1 hr at corresponding temperatures on a microplate reader ( SpectraMax M5; Molecular Devices ) . All assays were performed in triplicate , and the Km and Vm value for HEF were calculated using GraphPad Prism . Immunofluorescence assays were performed as described previously with slight modifications [71 , 72] . Briefly , paraffinized human , swine or bovine trachea tissue sections were deparaffinized , rehydrated and incubated with 2% BSA in PBS for 30 min at RT to prevent nonspecific binding . Purified HEF protein was precomplexed with primary antibody ( mouse anti-His-tag , MBL ) and secondary antibody ( Alexa Fluor 488 goat anti-mouse IgG , Invitrogen ) in a molar ratio of 4:2:1 , respectively , for 20 min on ice . The tissue binding was performed using precomplexed stock HEF ( 50 μg/ mL ) in 1% BSA–PBS . Tissue sections were then incubated with the HEF–antibody complexes for 3 hr at RT . Sections were counterstained with 4' , 6-diamidino-2-phenylindole ( DAPI ) ( Beyotime; 1:2 , 000 in PBS ) for nuclei for 20 min at RT . After thorough washing , the tissue sections were mounted and then examined by using Leica TCS SP8 laser scanning confocal microscopy . The IDV HEF crystal samples were applied to SDS-PAGE and subsequently transferred to polyvinylidene fluoride ( PVDF ) membranes at 50 V for 1 hr . For western blot , the proteins were identified with a mouse monoclonal antibody of Anti-His-tag-HRP-DirecT ( MBL , Japan ) and a Super Signal West Pico Chemiluminescent Substrate ( Thermo , USA ) . For N-terminal sequencing , the PVDF blot membrane was stained for 30s-50s in coomassie brilliant blue ( CBB ) R250 staining solution ( 0 . 1% CBB R250 , 1% acetic acid , 40% methanol in Milli-Q water ) and destained with destaining solution ( 50% methanol in Milli-Q water ) under visual control until protein bands were well visible . The PVDF membrane was dried and bands of interest were cut for the N-terminal sequencing with the Edman degradation method using PROCISE491 ( America Applied Biosystems ) . Atomic coordinates and structure factors have been deposited in the Protein Data Bank under accession codes 5E64 for IDV HEF in native state and 5E66 in complex with 9-N-Ac-Sia , and 5E5W , 5E65 , 5E62 for IDV HEF-mut and complexes with 9-O-Ac-3'SLN and 9-O-Ac-3'SleC , respectively . | Of the Orthomyxoviridae family of viruses , influenza A , B and C viruses all can cause disease in humans . Recently , a novel influenza D virus ( IDV ) with approximately 50% amino acid identity to human influenza C virus ( ICV ) is found in pigs and cattle . This novel virus can establish infection in other mammals including ferrets and guinea pigs . However , the cellular receptor for viral entry and the molecular mechanism for its broad host range is unclear . We performed combined structural and functional studies on the viral surface protein , hemagglutinin-esterase-fusion ( HEF ) , and demonstrated that IDV ( like ICV ) uses 9-O-acetylated sialic acid as its receptor , but the IDV HEF has an open receptor-binding cavity to accommodate diverse extended glycan moieties . Our findings reveal in exquisite detail how the receptors or substrates bind to the receptor-binding site or esterase active site , providing a clue for the development of novel therapeutics against the conserved esterase pocket . Furthermore , the IDV HEF can bind human trachea epithelia , indicating that the IDV virus may become a potential threat to public health . | [
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| 2016 | An Open Receptor-Binding Cavity of Hemagglutinin-Esterase-Fusion Glycoprotein from Newly-Identified Influenza D Virus: Basis for Its Broad Cell Tropism |
We describe the rudolph mouse , a mutant with striking defects in both central nervous system and skeletal development . Rudolph is an allele of the cholesterol biosynthetic enzyme , hydroxysteroid ( 17-beta ) dehydrogenase 7 , which is an intriguing finding given the recent implication of oxysterols in mediating intracellular Hedgehog ( Hh ) signaling . We see an abnormal sterol profile and decreased Hh target gene induction in the rudolph mutant , both in vivo and in vitro . Reduced Hh signaling has been proposed to contribute to the phenotypes of congenital diseases of cholesterol metabolism . Recent in vitro and pharmacological data also indicate a requirement for intracellular cholesterol synthesis for proper regulation of Hh activity via Smoothened . The data presented here are the first in vivo genetic evidence supporting both of these hypotheses , revealing a role for embryonic cholesterol metabolism in both CNS development and normal Hh signaling .
Hedgehog ( Hh ) ligands have numerous and fundamental roles in both embryonic development [1] , [2] and tumor biology [3] . Mammalian hedgehog proteins bind to the transmembrane receptor Patched ( Ptc ) and thereby relieve its repression of Smoothened ( Smo ) . Intracellular transduction of Smo activity requires processing of GLI proteins . The primary cilium has shown to be an essential structural component for proper Hh signaling in mammals with dynamic localization of Ptc in response to binding of Sonic hedgehog ( SHH; [4] , [5] , [6] , [7] ) . Functional SHH signaling requires removal of PTC from the cilium , translocation of SMO to the cilium , and activation of SMO by an as yet unknown mechanism [5] , [6] , [8] , [9] . It has been well established that cholesterol is an essential component of Hh signal transduction . Processing of the Hh ligand in the producing cell includes the covalent modification of cholesterol to the carboxyl end of the immature protein . Although cholesterol in Hh proteins is thought to facilitate the proper dispersal of SHH through the target field , it is not necessary for signal transduction [7] . More recently , however , metabolites of cholesterol and cholesterol biosynthetic pathway intermediates have been shown to have an intracellular role in SHH signal transduction . For example , pharmacological inhibition of cholesterol biosynthesis leads to defective responses to SHH ligand , independent of SHH processing , both in vitro and in vivo , with the in vivo effect mimicking Shh loss of function phenotypes [10] , [11] , [12] . Moreover , not only can cholesterol-derived oxysterols activate the Hh pathway in vitro [13] , but treatment of cells with oxysterols has been shown to cause translocation of PTC and SMO to the cilium in the absence of SHH ligand [8] . Mutations in enzymes required for cholesterol biosynthesis are associated with a number of human diseases [14] . The best known is Smith-Lemli-Opitz Syndrome , in which patients have central nervous system ( CNS ) malformations , including holoprosencephaly and microcephaly , and skeletal defects ( most often postaxial polydactyly ) caused by mutations in 7-dehydrocholesterol reductase ( DHCR7 ) [15] . Other disorders of cholesterol biosynthesis include desmosterolosis , lathosterolosis , X-linked dominant chondrodysplasia punctata ( CDPX2 ) , CHILD syndrome ( congenital hemidysplasia with icthyosiform erythroderma or nevus and limb defects ) and Greenberg skeletal dysplasia . Overlapping features of these disorders include abnormalities in the CNS , facial dysmorphisms , and skeletal defects , often including polydactyly or other digit patterning defects . Mouse models also exist for many of these disorders and have similar defects . The frequent occurrence in these human syndromes and mouse mutants of defects in neurodevelopment , craniofacial morphogenesis and skeletal growth and patterning has led to the proposal that abnormal Hh signaling may be the root cause of these embryological defects . This speculation is based principally on genetic experiments that have shown a role for Hh signaling in the development of all of these tissues , and the known role of sterol metabolism in Hh signal transduction . Despite this , there is relatively little direct evidence from these mouse models for a defect in Hh signaling . Here we describe the phenotype of the rudolph mouse mutant , an ethyl-nitrosourea ( ENU ) -induced mutation in hydroxysteroid ( 17-beta ) dehydrogenase 7 ( Hsd17b7 ) , which was the last enzyme of the cholesterol biosynthetic pathway to be identified and one of four proteins of the sterol-4-demethylase complex [16] . Rudolph mutants have severe developmental abnormalities in several tissues including the brain and appendicular skeleton . We find that tissues from rudolph mutants have an abnormal sterol profile consistent with impaired activity of the sterol-4-demethylase complex . We further demonstrate that the rudolph mutant has deficient responses to Hh signaling , both in vivo and in vitro . These results support the recently proposed model that functional intracellular sterol metabolism is required for proper cilia-mediated activation of the Hh signaling pathway .
We recently recovered the rudolph mutation via an ENU mutagenesis screen designed to identify recessive mutations affecting development of the mammalian forebrain . Rudolph mutants were first ascertained by a blood spot on the end of the nose and their abnormally curved forelimbs ( Figure 1B ) . The precursor to this nasal phenotype was sometimes evident at earlier stages as a blebbing of the craniofacial epithelium ( Figure S1A ) . Examination of the embryonic skeleton revealed that all long bones of the appendicular skeleton were significantly shorter than those of wild-type littermates while the axial skeleton and ribs appeared normal . ( Figure 1D , Figure S2 , Table S1 ) . Further analysis of the embryos revealed severe defects in CNS development . The telencephalic tissue was markedly reduced in size and highly disorganized in mutants at embryonic day ( E ) 16 . 5 ( Figure 1F , 1H ) . Mutants had a smaller neurogenic ventricular zone and clumps of cells in the developing cortical plate . Similar defects were seen in the E16 . 5 retina and spinal cord ( Figure 1J , Figure S3B ) . Initial cortical morphogenesis appeared largely normal ( Figure S3D , S3F ) . To further characterize the rudolph phenotype , we performed a molecular analysis of the cortical phenotype . We assessed cell proliferation at E14 . 5 by BrdU treatment of pregnant dams or immunostaining of embryos with Ki67 and found a marked decrease in proliferation in mutants ( Figure 2B ) . Some of the mitoses we detected were seen as foci of BrdU-positive cells ( inset in Figure 2B ) . We thus interpreted the clusters of cells seen histologically in the rud cortex to be neurogenic foci . To determine the cause of the reduced neuronal tissue , we assayed apoptosis at E14 . 5 using the TUNEL reaction and found increased levels of cell death in the mutant tissue , distributed throughout the cortex and enriched along the ventricular surface ( Figure 2D ) . An increased level of cell death was not seen in non-neural tissue ( data not shown ) . Decreased , disorganized neuronal proliferation and increased cell death were also evident at E12 . 5 ( data not shown ) . Immunohistochemistry for TuJI to identify differentiated neurons at E14 . 5 showed a marked decrease in differentiation in mutants compared to wild-type ( Figure 2F ) . In addition , foci of TuJI immunoreactivity appeared in regions of no differentiation , consistent with the disorganization of the cortex seen histologically . Disorganized proliferation was also evident in the developing rudolph retina , where we observed similarly abnormal neuronal differentiation , decreased cell proliferation and increased apoptosis , but at different stages of development . Whereas at E12 . 5 we saw no significant decrease in proliferation or apoptosis between wild-type and mutant ( Figure 2H; data not shown ) , at E14 . 5 we found a decrease in BrdU incorporation in the mutant retina ( Figure 2J ) and an increase in apoptosis ( Figure 2L ) . Furthermore , the pattern of neuronal disorganization we saw in the rud cortex was similarly evident in the rud retina at E14 . 5 ( Figure 2N ) . The phenotypes of the rudolph mutants exhibit a variability in severity that appears to be dependent on genetic background . For example , we noted increased blebbing on the developing head and limbs in embryos that came from a mixed background ( Figure S1B–S1D ) . Embryos from our ENU screen have a mixed genetic background with contributions coming from both A/J ( the mutagenized strain ) and FVB mice ( introduced as part of the outcross for mapping purposes ) . In this A/J; FVB background , embryos were recovered in approximately Mendelian ratios from E10 . 5–E18 . 5 ( Table S2 ) . No mutants have been recovered after birth , suggesting they are among the stillborn fetuses . Upon introducing the B6 background , the number of mutant embryos did not decrease significantly , but we then began to see a number of dead embryos from E11 . 5 and older ( 7 . 8% ) , an increase in the severity of the nasal blebbing ( Figure S1A–S1C: 14 . 8% ) and limb patterning defects ( 10 . 4% ) . Further introgression into the B6 background resulted in no significant decrease in recovery of mutant embryos but a large increase in the incidence of more severe blebbing ( 48 . 5% in pooled N1 , N2 , and N3B6 mice ) . Backcrossing to FVB rescued this defect , and blebbing is essentially absent in N3 FVB mice . The reduced fecundity of 129 mice limited our analysis of this genetic background , preventing any definitive comments on modifiers on the 129 background ( Table S2 ) . We initially mapped the rudolph mutation to Chromosome 1 using a whole-genome 768-marker single nucleotide polymorphism ( SNP ) panel [17] . Examination of the 255 predicted and known genes in the region suggested Hsd17b7 as a candidate for further analysis because of its known function in cholesterol metabolism and its reported expression pattern in limb buds and the developing nervous system [16] . Sequencing of Hsd17b7 revealed a point mutation in the sixth intron , 27 base pairs upstream of the intron-exon boundary ( Figure 3A ) . We analyzed transcripts by RT-PCR with primers spanning exon 7 and found the predominant PCR product in mutant tissue to be smaller than that of wild type ( Figure 3B ) . Sequencing of this product revealed a precise excision of the seventh exon in the smaller PCR species . The loss of exon 7 was confirmed with primers in the sixth and seventh exons ( Figure 3C ) . Interestingly , a small amount of this truncated transcript was present in wild-type cDNA , and , conversely , mutant tissues retained a very small fraction of the wild-type transcript ( Figure 3B , 3C ) . We hypothesize that these RT-PCR products represent two naturally occurring forms of the Hsd17b7 transcript and that the rudolph ENU mutation affects the ratio of their abundances . The phenotypes we observe in the rudolph mutants appear to be somewhat tissue specific and have differing expressivity in different strains . However , the variation in the cDNA splicing pattern does not differ among different tissues examined ( heart/lung , limbs , brain , or whole embryo ) or depend on varying genetic backgrounds , suggesting that tissue specific transcription and variation in genetic background account for the variability in phenotype ( Figure S1E ) . Loss of the seventh exon of HSD17B7 is predicted to encode a protein with an in-frame deletion of 19 amino acids . To assess the effect of this splicing mutation , we tested HSD17B7 expression by Western immunoblot analysis and found only trace levels of protein in mutant tissue lysates ( Figure 3D ) . Although the deleted seventh exon is part of a putative endoplasmic reticulum anchoring sequence , in vitro expression of a rud-GFP fusion protein results in deficient protein rather than mislocalization , suggesting that the rudolph deletion generates an unstable protein product ( Figure S4 ) . Embryos homozygous for a null allele of Hsd17b7 generated from a 129 genetic background do not survive past E10 . 5 , suggesting the rudolph allele is likely a hypomorph [18] , [19] . We analyzed the sterols present in liver and brain tissue from wild-type , heterozygous , and rudolph embryos at E12 . 5 by gas chromatography-mass spectrophotometry ( Figure 4 ) and found marked differences between wild-type and mutant tissues . The identified abnormalities in methylsterol abundances are consistent with reduced function of the Hsd1b7 enzyme in rudolph brain tissues ( Table 1 ) . Sterol species upstream of Hsd17b7 activity were present in increased amounts , the most prominent of which were the HSD17B7 substrates zymosterone and 4methyl-zymosterone , and a third ketosterol tentatively identified as methylcholest-7-en-3-one . Various mono- and dimethylsterols that do not normally accumulate in wild-type tissues were also present in increased amounts , including 4α-methyl-5 α-cholest-8-en-3β-ol , 4 α-methyl-5 α-cholest-7-en-3 β-ol , 4 α-methyl-cholesta-8 ( 9 ) , 24-dien-3 β-ol , and 4 , 4-dimethyl-5 α-cholest-8 ( 9 ) , 24-dien-3 β-ol . Desmosterol , a compound downstream of Hsd17b7 , was reduced . The predominance of zymosterone ( 5 α-cholesta-8 , 24-dien-3-one ) and 4 α-methylzymosterone in the brain compared to liver is in keeping with the known decreased activity of desmosterol reductase in the brain , especially fetal brain , and the retention of the 24-unsturated bond in certain sterol species in the normal brain . In wild-type brain , desmosterol ( 5 α-cholesta-5 , 24-dien-3β-ol ) is the most abundant 24-unsaturated sterol . In view of the important role of Hh signaling in patterning of the limbs , face , and brain , and the genetic and cellular evidence for a role of cholesterol metabolism in this pathway , we hypothesized that Hh signaling is perturbed in the rudolph mutant . Furthermore , recent evidence specifically implicates intracellular sterols in the regulation of the subcellular localization of the Hh signaling components , Patched and Smoothened , supporting the possibility that an abnormal sterol profile in rudolph mutants could disrupt Hh signaling [5] , [6] , [8] . To assess this , we generated mice homozygous for the rudolph mutation that carried the Patched-lacZ gene , a transcriptional target of Gli2 and thus a reporter of Shh signaling activity . In these embryos , we found reduced levels of Ptc-lacZ in the developing brain at both E11 . 5 and E14 . 5 ( Figure 5B , 5D; Figure S5; data not shown ) . We also noted decreased expression of another Shh target gene , Gli1 , in the retina and brain of rudolph mutants at E14 . 5 ( Figure 5F , Figure S5 ) . Furthermore , analysis using quantitative RT-PCR demonstrated reduced Ptc mRNA in rudolph brain tissue compared to wild-type ( 63% of wild-type , p = 0 . 053 , data not shown ) . All tissues with reduced SHH target gene expression have severe morphological abnormalities in the rudolph mutant and are known sites of Hh signaling . Because dorsal-ventral patterning of the neural tube also requires SHH signaling , we examined the dorsal-ventral character of the rudolph neural tube and found that , at both E10 . 5 ( Figure S6A–S6N ) and E12 . 5 ( Figure S6O–S6BB ) , all immunohistochemical markers for cell fate we tested showed normal patterns of expression along the dorsal-ventral axis of the rudolph neural tube . We also observed limb patterning defects in mice from a mixed A/J , FVB , B6 ( Figure 6A , 6B , Table S2 ) . As Hh signaling is important for proper patterning , we examined Hh signaling in the developing limb bud . Normal patterning of the limb results in elevated Shh signaling in the posterior portion of the developing limb bud as compared to the anterior . We observed Ptc expression in rud mutants ( n = 3 ) from a mixed background using both Ptc-lacZ expression and whole mount in situ hybridization and found one embryo with reduced Ptc expression in the posterior limb bud ( Figure 6D ) as compared to littermate control ( Figure 6C ) . The variable penetrance of the limb patterning phenotype is consistent with an incompletely penetrant reduction in Shh activity in the developing limb bud . Hh signaling is also involved in long bone growth ( where the relevant ligand is Indian hedgehog ) . We also see reduced expression of Ptc in the developing limb of rud mutants using either the Ptc-lacZ allele ( Figure 6F ) or an in situ riboprobe for Ptc ( Figure 6H ) . To further determine if Hsd17b7 expression affects SHH signaling , we generated primary mouse embryonic fibroblasts ( MEFs ) from wild-type and rudolph embryos and assessed their response to added SHH protein . In wild-type MEFs , treatment with SHH protein resulted in increased cell proliferation and increased Ptc and Gli mRNA levels , which is consistent with the known role of SHH as a mitogen in several systems and with Ptc and Gli being direct targets of SHH signaling . In contrast , the effects of SHH treatment were blunted in mutant cells ( Figure 7A–7C ) . We also generated MEFs from wild-type;Ptc-lacZ and rudolph;Ptc-lacZ embryos to measure SHH transcriptional activity via the accumulation of β-galactosidase . In this assay , wild-type;Ptc-lacZ MEFs responded to SHH treatment with increased β-galactosidase production , whereas rudolph;Ptc-lacZ MEFs did not ( Figure 7D ) . Together these data suggest that rudolph mutant mice have reduced intracellular signal transduction distal to the binding of SHH ligand in the SHH signaling cascade . In a parallel approach , we used Pzp53MED cells [20] , which are SHH-responsive cells carrying the Ptc-lacZ allele , to assess the role of Hsd17b7 in SHH signal transduction . We used lentiviral infection followed by clonal selection with plasmids for RNAi knockdown of Hsd17b7 to create cell lines with reduced levels of Hsd17b7 ( approx . 80% reduced , data not shown ) and then treated the lines with recombinant SHH , as done with the MEFs . Two independent RNAi clones did not induce significant β-galactosidase production upon SHH treatment while the control cell line responded robustly ( Figure 7E ) . The decreased response to SHH protein in vitro shows that the signaling defect is downstream of ligand binding to cell surface receptors . Recent data have shown that treatment with oxysterols can cause a change in the subcellular localization of PTC and SMO protein [8] , which is necessary but not sufficient for activation of the SMO protein [6] , [21] . The Hsd17b7 phenotype may be caused by dysregulated sterol biosynthesis affecting the localization and/or active state of the Smo receptor . We therefore examined the localization of a SMO-GFP fusion protein in wild-type and rudolph MEFs upon treatment with SHH , but found no decreased mobility of the SMO-GFP to the cilia in the mutant MEFs ( Figure 8 ) . Wild-type MEFs without SHH treatment had SMO-GFP throughout the primary cilia in only a subset of cells examined ( 43 . 3%: n = 29/67 ciliated , transfected cells in two independent experiments ) . Upon addition of SHH , SMO-GFP was found throughout the cilium in the majority of cells examined ( 89 . 6%; 65/73 cells ) . Mutant MEFs behaved similarly , with untreated cells having 38 . 7% SMO-GFP positive cilia ( 24/62 ) and SHH treatment leading to 86 . 7% ( 72/83 ) of cells with SMO-GFP throughout the cilium . In addition to the increase in cells with SMO-GFP throughout the cilium upon SHH treatment , we also note that untreated cells often had SMO-GFP largely at the base of cilium . SHH treatment resulted in very few cells showing SMO-GFP localization to the base of the cilium , but rather throughout the length of the cilium . Taken together , these data suggest that the rudolph mutation does not affect the localization of SMO within the primary cilium in response to SHH treatment .
Our study of the sterols present in brain tissues from the mutant mice is consistent with reduced Hsd17b7 enzymatic function , as we observe an increase in compounds of the cholesterol biosynthetic pathway upstream of the Hsd17b7 enzyme . While no patient has yet been identified with a defect in Hsd17b7 , increased levels of mono and dimethyl sterols have been reported in plasma and/or skin of patients with mutations in two other genes of the sterol-4-demethylase complex , SC4MOL ( sterol C4-methyloxidase like ) and NSDHL ( NAD ( P ) H steroid dehydrogenase-like protein ) [22] , [23] . To explain the phenotype of the rudolph mouse we considered several possible metabolic effects , including 1 ) a decrease in the cellular level of cholesterol , 2 ) a decreased level of another product of Hsd17b7 enzymatic function , and 3 ) teratogenic effects of high levels of the cholesterol precursors detected in our study . Several lines of evidence suggest the last to be the major cause of the phenotype we observe . If simply a deficiency of the end-product , cholesterol , caused the rudolph phenotype , one might expect mice carrying mutations in the cholesterol biosynthetic pathway to resemble each other . This is not the case , since , despite some phenotypic overlap in adjacent disorders in the pathway , overall there are significant phenotypic differences across the spectrum of mouse models of human cholesterol biosynthetic disorders [24] . The best-characterized disorder of cholesterol biosynthesis is Smith-Lemli-Opitz syndrome [25] , [26] , caused by mutations in DHCR7 , which encodes the 7-dehydrocholesterol reductase that converts 7-dehydrocholesterol to cholesterol [15] , [27] , [28] . Two mouse models with null alleles of Dhcr7 have abnormal phenotypes including cleft palate , but lack the striking brain phenotypes we found in the rudolph mutant [29] , [30] . The Dhcr7 mutant mice have decreased cholesterol and increased 7-dehydrocholesterol levels in serum and tissues [29] . The enzyme immediately preceding DHCR7 is SC5DL ( sterol C5-desaturase-like ) which is deficient in human patients with lathosterolosis [31] . A null Scd5 allele in the mouse is a neonatal lethal with craniofacial and limb defects and decreased cholesterol similar to those of the Dhcr7-deficient mouse , but with increased levels of lathosterol ( the substrate of Sc5d ) in all tissues [32] . Given that the Dhcr7 and Scd5 mouse models have a significant decrease in cholesterol levels , which is not apparent in the rudolph mutant , and that their phenotypes do not resemble the rudolph phenotype , we conclude that cholesterol deficiency does not cause the distinctive embryological abnormalities of the rudolph mouse . Nsdhl , another element of the sterol-4-demethylase complex , is the enzyme immediately preceding Hsd17b7 in the canonical cholesterol biosynthetic pathway [33] . NSDHL mutations in humans cause CHILD syndrome ( congenital hemidysplasia with ichthyosiform erythroderma and limb defects ) , a rare X-linked dominant disorder with presumed lethality for CHILD causing alleles [34] and , with hypomorphic NSDHL mutations , CK syndrome , a form of X-linked mental retardation [22] . Mutations in Nsdhl are found in the Bare patches ( Bpa ) and Striated ( Str ) mice [35] . Analysis of tissue samples from Bpa/Str females showed an accumulation of 4-methyl and 4 , 4′-dimethyl sterol intermediates . Human mutations in EBP cause X-linked dominant chondrodysplasia punctata ( CDPX2 , Conradi-Hunermann syndrome [36] ) . Patients with CDPX2 usually have normal plasma total cholesterol levels but increased levels of other sterols , including 8-dehydrocholesterol and cholesta-8 ( 9 ) -en-3β-ol [36] , [37] , [38] , [39] . Tattered ( Td ) carries a missense mutation in the Ebp gene and phenotypically resembles the rudolph mutation most among all the known cholesterol biosynthetis mouse mutants . Male Td embryos die between E12 . 5 and birth and have defects in the skeleton and brain similar to those we describe here [40] . The sterol profile of heterozygous female mice includes elevated 8-dehydrocholestrol and choles-8 ( 9 ) -en-3β-ol . All of these findings combined with our data suggest a model in which the accumulation of specific sterols leads to the defects we observe . One of the effects these inhibitory sterols may be to dampen the intracellular response to Shh signaling . Although rudolph mutants lack some of the classic features of the Shh null mice , such as holoprosencephaly , other more specific ablations of SHH signaling have some features resembling the rudolph phenotype . In particular , the skeletal defects we observe in the rudolph mutant are similar to those described in the Indian hedgehog ( Ihh ) and dispatched-1 ( Disp1 ) loss of function mice and the conditional ablation of Smo from the developing skeleton [41] , [42] , [43] . As Ihh is the most active Hh ligand in development of long bones , we suggest that the similarities between rud and the HH-signaling mutants reflect the conservation of intracellular signaling transduction mechanisms between the different Hh ligands , and that these defects are due to an insufficient response to secreted IHH in the cartilage . In addition , the disorganized retina in rud mutants also resembles that seen in embryos with an ablation of Shh using a Thy1-Cre [44] . The role of Shh and Ihh as mitogens in retinal neuroblast proliferation has been established [45] , and Shh , Ihh and Gli1 are expressed in retina at E12 and E13 [44] , [46] . The difference in timing between the cortical and retinal defects ( rudolph retinal molecular defects are seen at E14 . 5 , but not E12 . 5 , Figure S4 ) is consistent with the later expression pattern of Shh signaling components in the retina as compared to forebrain tissue . The developing rudolph forebrain phenotype has both similarities to and differences from the known effects of Shh loss of function . The decreased cell proliferation and increased apoptosis we find are completely consistent with a role for Shh as a mitogen for the developing neural tissue and with the observations that blocking SHH function can lead to cell death [47] , and that conditional ablation of Smo throughout the cortex by E9 using the Foxg1-Cre leads to increased cell death [48] . Emx1-Cre ablations of Shh and Smo in the dorsal telencephalon by E10 cause a smaller telencephalon featuring reduced proliferation and neuronal differentiation with increased cell death [49] . However , the striking disorganization of the cortex in rudolph mutants resembles more a loss of polarity phenotype , such as the cortical ablation of numb and numb-like [50] . Because Shh loss of function has not been demonstrated to directly affect polarity , we suggest that abnormalities of cortical signaling mechanisms other than Shh must be disrupted in the rudolph cortex to explain some of the developmental abnormalities of the CNS we observe . Because cortical dysplasia is not characteristic of any of the known human disorders of cholesterol biosynthesis , the extremely high CNS level of zymosterone , normally only a trace sterol in the brain , suggests that zymosterone or other 3-ketosterols in the rud brain could have a direct toxic effect or could otherwise impair neuronal differentiation . Two reports have recently described the phenotypes of Hsd17b7 null allele mice [18] , [19] . These embryos have major morphological abnormalities by E10 . 5 , precluding direct comparison to the phenotypes studied here . The forebrain did appear smaller in the Hsd17b7 homozygous null embryos , and development does not proceed past E9 . 5 . A sterol analysis performed in these mutants demonstrated increased Hsd17b7 enzyme substrates and unchanged cholesterol levels , similar to the results we report [19] . Maternal supply of cholesterol was also suggested to account for the normal embryonic cholesterol levels . Expression of Shh and Ptc was examined in the Hsd17b7 homozygous null embryos at E8 . 5 , but the domain and levels of expression did not differ from wild-type [19] . The phenotypic differences between the rudolph and Shh mutants may be due to the hypomorphic nature of the rudolph allele . However , we also note that the rudolph phenotype in the CNS begins to emerge around E12 . 5 , which is the time when the blood-brain barrier forms and synthesis of cholesterol within the CNS becomes separated from non-neural cholesterol synthesis [51] , [52] , [53] , [54] . The developmental consequences of reduced sterol concentrations in the early embryo could be mitigated by the maternal circulation in view of evidence that , in rodents , substantial amounts of maternal cholesterol can be transported to the fetus through the placental-fetal interface [55] , thus possibly compensating for a lack of early Hsd17b7 function in the initial patterning stages of embryonic development . We therefore propose that formation of the blood-brain barrier creates a neurodevelopmental compartment absolutely requiring endogenous Hsd17b7 function , which , when absent , results in the severe phenotypes we describe here . Perhaps the most intriguing aspect of this study is that it is the first in vivo validation of several recent studies suggesting an intracellular role for sterols in SHH signaling [8] , [11] , [12] . These cholesterol intermediates have been demonstrated to have a role in the transduction of HH ligand signaling as well as the subcellular localization of HH signaling components . The fact that the mutant MEFs demonstrate normal Smoothened localization to the cilium , but a compromised response to SHH ligand , suggests that normal sterol concentrations are required for proper activation of Smoothened [6] . Alternatively , an inhibitory sterol may be present at increased levels , preventing the activation of the pathway . Treatment with statins is a well-accepted method for lowering cholesterol levels in human patients by inhibiting HMG-CoA reductase ( Hmgcr ) , the rate-limiting step in cholesterol biosynthesis . Statin treatment could also have significant effects on local concentrations of oxysterols generated from intermediates in the cholesterol biosynthetic pathway further downstream . Mouse Hmgcr mutants , which should genetically mimic a total block in the pathway at the site of action of statins , are not viable past implantation [56] and are therefore not informative in this regard . It will be important to study further the role of cholesterol intermediates and metabolites in various physiological settings and signaling paradigms . In doing so , we may find that statin treatments may be having unintended consequences in human health in sites of adult Hh activity , including adult neurogenesis .
Rudolph mice were originally generated by ENU mutagenesis of A/J mice and then outcrossed to FVB/J mice ( both obtained from Jackson Labs , Bar Harbor , ME ) . Initial mapping was done with a whole genome SNP panel similar to one we previously described [17] , and the mutation mapped to a 19 . 6 Mb interval on chromosome 1 . Exon directed sequencing ( including some flanking intronic sequence to identify mutations potentially affecting splicing ) identified the rudolph mutation . Genotyping is done with either D1Mit454 or D1Mit524 microsatellite markers depending on strains involved . We maintained the colony with a combination of intercrossing and outcrossing to FVB . The C57BL/6J Ptc1-lacZ mouse was obtained from the Jackson laboratory and intercrossed with rud heterozygous mice;Ptc1-lacZ genotyping was done with standard lacZ primers . We have also performed backcrosses of the rud allele to mice on C57BL/6J and 129X1/SvJ backgrounds . All animals were housed in accordance with the Harvard Medical School ARCM regulations . Timed matings were checked for signs of copulation in the morning; vaginal plugs were noted and noon of that day was established as embryonic day ( E ) 0 . 5 . Embryos used for histological analysis were fixed with Bouin's fixative for at least forty-eight hours and processed for paraffin embedding using a Leica TP1020 automated tissue processor . Sections were cut at a thickness of 14 µm and stained with hematoxylin and eosin using standard techniques . Microscopy was done with a Leica DC500 or Zeiss AxioImager with ApoTome . TUNEL assay was performed with the In Situ Cell Detection Kit , TMR Red , following the manufacturer's instructions ( Roche ) . BrdU labeling was done with a BrdU Labeling and Injection Kit ( Roche ) . The TuJI antibody ( SIGMA ) was used at 1∶500 for 2 hours at room temperature on paraffin sections with citrate buffer antigen retrieval . Neural tube immunohistochemistry was performed using standard methods with antibodies from the Developmental Studies Hybridoma Bank . To measure the size of the skeletal elements , embryos were stained for cartilage and bone using standard methods [57] and photographed . The length of each element was calculated using NIH Image J software , and units were converted to mm using standards . Mouse embryonic fibroblasts were generated using standard methods and plated at a density of 20 , 000 cells/cm2 in the presence or absence of 200 ng/mL SHH amino terminal peptide ( R&D Systems ) . Cell number was determined with the CyQuant Cell Proliferation Assay ( Invitrogen ) , and β-galactosidase production was measured with the Galacto-Light Plus System ( Applied Biosystems ) . Assays were performed 48 hours after plating . Cell growth experiments were done with an initial culture of 6 , 000 cells in a 96-well plate . Lentiviral particles were made via transfection of 293T cells with plasmids including a plKO . 1 control and a validated RNAi construct against mouse Hsd17b7 ( Open Biosystems , Huntsville , AL; clone TRCN0000041646 ) . PzP53Med cells [20] were infected with 293T supernatant containing lentivirus . After puromycin selection , resistant cells were plated at clonal density and individual clones were isolated , maintained and analyzed with qRT-PCR for Hsd17b7 levels . Control and knock-down clones were treated with SHH protein as described above . Total RNA from either brains or MEF cultures was prepared with TRIZOL ( Invitrogen ) and cDNA was made with qScript cDNA synthesis kit ( Quanta ) or the SuperScript RTIII system ( Invitrogen ) . Hsd17b7 transcripts were analyzed with both random hexamer primed cDNA and gene specific primed cDNA synthesis ( primer: TTTTGGTACCTCAGCTCGGGTGATCCGATTTCTG ) . Hsd17b7 transcripts were analyzed with primers amplifying exons 6–8 ( F: TCTGTATTCCAGTGTGATGTGC; R: CTTTTGGCCCGTGACGTAAT; 259 bp ) or exons 6–7 ( F: TCTGTATTCCAGTGTGATGTGC; R: CCACATTATGGGTAGGAGCAA ; 100 bp ) . Quantitative RT-PCR was done on a BioRad iCycler using either total RNA with Taqman probes ( Applied BioSystems ) or cDNA with Perfecta SYBR Green SuperMix ( Quanta ) . SYBR-GREEN probes used were: Ptc-F ( CCTGCAAACCATGTTCCAGTT ) , Ptc-R ( TCGTAGCCCCTGAAGTGTTCA ) Gli1-F ( CCAAGCCAACTTTATGTCAGGG ) , Gli1-R ( AGCCCGCTTCTTTGTTAATTTGA ) , Gapdh-F ( ACTCCACTCACGGCAAATTC ) , and Gapdh-R ( TCTCCATGGTGGTGAAGACA ) . Section mount in situ hybridization was done as previously described [58] with hybridization at 60 degrees and with BM Purple ( Roche ) for visualization of riboprobes . Probes used are published: Gli1 [59] and Ptc [60] . Embryos were stained with lacZ using standard protocols [57] and then processed for paraffin histology as described above . Older embryos were fixed in 4% paraformaldehyde , cryoembedded in OCT , sectioned at 20 µm and stained on slides . Embryos were homogenized in 1% SDS Lysis Buffer and protein extracts were run on a 10% polyacrylamide gel . A rabbit polyclonal antibody was used for Hsd17b7 ( 1∶1000 , overnight at 4 degrees C ) , and a mouse monoclonal anti-actin antibody ( 1∶5000 , SIGMA , 60 minutes at room temperature ) was used as a loading control . Full-length mouse Hsd17b7 from wild-type and mutant tissue was initially cloned into a pENTR/D/TOPO vector ( Invitrogen ) and then into the pcDNA-DEST47 vector ( Invitrogen ) . DNA for either Hsd17b7-GFP or rud-GFP was co-transfected with Sec61β-mCherry ( gift of T . Kirchhausen ) into NIH3T3 cells using Fugene ( Roche ) following manufacturer's instructions . 48 hours after transfection , cells were fixed with 4% paraformaldehyde and counter-stained with DAPI . SMO localization within ciliated MEFs was observed by transfection of 20–30% confluent MEFs plated on 0 . 4% gelatin or poly-lysine coated coverslips in 24-well plates with the pBabePuro-A1∶Smo∶GFP plasmid ( [9] , gift of A . McMahon ) . Confluent cells were treated overnight with SHH peptide 48 hours after transfection . Immunocytochemistry was done by fixing with 4% paraformaldehyde/0 . 2 TritonX-100 for 20 minutes and blocking with 1% BSA for 60 minutes . Antibodies used were acetylated α-tubulin ( SIGMA ) at 1∶2000 for 60 minutes at room temperature and Goat anti-mouse Alexa Flour 594 ( Invitrogen ) at 1∶500 for 30 minutes at room temperature . Cells were mounted with VectaShield ( Vector Laboratories ) and imaged on a Zeiss AxioImager . Sterols were extracted from brain tissue as previously described with the addition of sterol-specific ions for the compounds of interest to this study [61] . Sterol levels are reported as a fraction of total sterols . | The molecules and signaling pathways that regulate growth and patterning of the developing embryo are still being elucidated , and one valuable experimental approach is the use of animal models , such as the mouse . We have identified a recessive mutation in the mouse , rudolph , that causes abnormal forebrain development and have determined that the mutated gene encodes hydroxysteroid ( 17-beta ) dehydrogenase 7 gene , an enzyme necessary for cholesterol biosynthesis . Cholesterol is essential for proper signal transduction of the hedgehog family of proteins , key regulators of both developmental biology and tumor progression . We show that hedgehog signaling is diminished in our rudolph mutant . Our conclusions from studying this mouse mutant support two recent hypotheses in developmental biology . First , several human malformation syndromes are known to be caused by defects in cholesterol metabolism , but support linking the malformation to abnormal hedgehog signaling has not definitively been made . Second , while in vitro studies have shown that proper levels of metabolic by-products of cholesterol are necessary for proper hedgehog signaling , our studies offer the strongest genetic animal model evidence to support this idea . | [
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| 2011 | Cholesterol Metabolism Is Required for Intracellular Hedgehog Signal Transduction In Vivo |
Gonad differentiation is a crucial step conditioning the future fertility of individuals and most of the master genes involved in this process have been investigated in detail . However , transcriptomic analyses of developing gonads from different animal models have revealed that hundreds of genes present sexually dimorphic expression patterns . DMXL2 was one of these genes and its function in mammalian gonads was unknown . We therefore investigated the phenotypes of total and gonad-specific Dmxl2 knockout mouse lines . The total loss-of-function of Dmxl2 was lethal in neonates , with death occurring within 12 hours of birth . Dmxl2-knockout neonates were weak and did not feed . They also presented defects of olfactory information transmission and severe hypoglycemia , suggesting that their premature death might be due to global neuronal and/or metabolic deficiencies . Dmxl2 expression in the gonads increased after birth , during follicle formation in females and spermatogenesis in males . DMXL2 was detected in both the supporting and germinal cells of both sexes . As Dmxl2 loss-of-function was lethal , only limited investigations of the gonads of Dmxl2 KO pups were possible . They revealed no major defects at birth . The gonadal function of Dmxl2 was then assessed by conditional deletions of the gene in gonadal supporting cells , germinal cells , or both . Conditional Dmxl2 ablation in the gonads did not impair fertility in males or females . By contrast , male mice with Dmxl2 deletions , either throughout the testes or exclusively in germ cells , presented a subtle testicular phenotype during the first wave of spermatogenesis that was clearly detectable at puberty . Indeed , Dmxl2 loss-of-function throughout the testes or in germ cells only , led to sperm counts more than 60% lower than normal and defective seminiferous tubule architecture . Transcriptomic and immunohistochemichal analyses on these abnormal testes revealed a deregulation of Sertoli cell phagocytic activity related to germ cell apoptosis augmentation . In conclusion , we show that Dmxl2 exerts its principal function in the testes at the onset of puberty , although its absence does not compromise male fertility in mice .
The DMX-Like 2 ( DMXL2 ) gene encodes a protein with multiple WD40 domains , which mediate protein-protein and protein-DNA interactions [1] , [2] , [3] . These WD40 domains correspond to a series of 44- to 60-amino acid sequence repeats with a characteristic sequence terminating in a tryptophan–aspartic acid ( WD ) dipeptide [4] . WD40 proteins function as platforms , recruiting multiple partners for a wide range of cellular functions , such as signal transduction , vesicular trafficking , cell-cycle control , chromatin dynamics and the regulation of transcription [2] , [3] . Little is known about DMXL2 and its various functions in different species . This protein was first described as rabconnectin-3α ( or Rbcn-3α ) in the rat brain , where it forms the stoichiometric rabconnectin-3 complex with WDR7 ( rabconnectin-3β or Rbcn-3β ) [5] . In the brain , DMXL2 has been implicated in the regulation of neurotransmitter exocytosis via its interactions with the small G-proteins Rab3-GEP and Rab3-GAP [1] , [6] . The second main function attributed to DMXL2 is modulating vacuolar-ATPase proton pump ( V-ATPase ) assembly and activity , through interactions with several of its subunits [7] , [8] . This function is highly conserved and has been described in numerous species , including yeast [9] , [10] , Drosophila [7] , zebrafish [11] , mice [12] and humans [13] , [14] . V-ATPases acidify intracellular compartments ( e . g . endosomes , lysosomes , and secretory vesicles ) and they play a well-established role in protein sorting , trafficking and turnover in a wide range of signaling pathways , including the Notch pathway [15] , [16] . DMXL2 has been shown to be involved in the Notch signaling pathway [7] , [13] , [14] , [17] during diverse morphogenetic processes , such as the formation of ovarian follicles in the female gonads in Drosophila [7] . A role for this protein in reproductive function has also been suggested in humans , in which DMXL2 has been implicated in a complex syndrome of congenital hypogonadotropic hypogonadism ( CHH ) associated with polyneuropathy and glucose metabolism disorders [18] . Three brothers in the family concerned were affected and presented incomplete puberty with a low testicular volume . Their testosterone and gonadotropin ( LH , FSH ) levels were low , due to a dysfunction of gonadotropin-releasing hormone ( GnRH ) neurons . In mice , decreasing the level of Dmxl2 expression in neurons ( Dmxl2wt/loxP ; nes-Cre ) resulted in 30% fewer GnRH neurons [18] and an impairment of their activation and maturation [19] . The fertility of these mice also seemed to be impaired , consistent with a hypothalamic dysfunction . Several clues to the functions of DMXL2 have emerged from studies of these models , but Dmxl2 knockout studies have never been reported . Furthermore , DMXL2 has never been directly implicated in the functions of reproductive organs in mammals . We show here that a complete loss-of-function of Dmxl2 results in neonatal lethality within a few hours of birth . Dmxl2-knockout pups present defects of olfactory information transmission associated with an absence of feeding . A glucose metabolism disorder was also detected , with male Dmxl2 KO pups displaying severe hypoglycemia . A description of the expression profile of Dmxl2/DMXL2 revealed a possible role in ovary and testis function . We therefore studied the specific effects of Dmxl2 knockout in the germ cells and supporting cells of the gonads of both sexes . Long-term fertility appeared to be unaffected in both sexes , but young male knockout mice produced less sperm than wild-type controls at the beginning of their reproductive life .
Heterozygous Dmxl2tm1a ( EUCOMM ) Wtsi mice were purchased from the IMPC . These mice carry a recombinant allele ( tm1a ) containing an IRES:LacZ trapping cassette and a promoter-driven neo cassette in intron 6 flanked by two FRT sites . Two loxP sites are also present , one at either end of exon 7 of Dmxl2 . Transcription of the tm1a allele generates a truncated/abnormal Dmxl2 mRNA and leads to LacZ gene expression ( Fig 1A ) [20] , [21] , [22] . Crosses of Dmxl2tm1a ( EUCOMM ) Wtsi mice generated Dmxl2wt/wt ( wild-type , WT ) , Dmxl2wt/tm1a ( heterozygous , HTZ ) and Dmxl2tm1a/tm1a ( knockout , KO ) offspring in Mendelian proportions . The pups were genotyped with a combination of primers provided by the IMPC ( Fig 1B and S1 Table ) . Neither a functional Dmxl2 transcript ( Fig 1C ) , nor DMXL2 protein ( Fig 1D ) was produced in the organs of newborn KO mice such as the brain , demonstrating the efficiency of the knockout . KO newborns died within 12 hours of birth . KO pups were of normal size and skin color at birth ( Fig 1E ) , but they looked weaker than control littermates , with poor motility , and their stomachs contained no milk , suggesting that they did not feed . We investigated the tissue expression profile of Dmxl2 , by assessing LacZ reporter gene expression in mutants ( whole-mount X-gal staining at E14 . 5 and P0 ) and by western blotting ( at P0 and in adults ) . At E14 . 5 , specific X-gal staining was restricted to the olfactory mucosa ( Fig 2A ) . At birth ( P0 ) , prominent staining persisted in the olfactory mucosa ( arrowhead ) ( Fig 2A ) but specific X-gal staining was observed in other tissues ( Fig 2B ) . Indeed , detailed dissection and fixation of the brain revealed faint , diffuse and scattered staining of both hemispheres of the cerebral cortex , confirming the expression of Dmxl2 in this organ [18] . The heart and kidneys were also strongly stained , and the gonads of both sexes displayed punctate staining , with enrichment in the cortical region of the ovaries , and in the seminiferous cords of the testes ( Fig 2B ) . We also tested various organs , including the liver , pancreas , digestive tract , adrenal glands , and skeletal muscles . These tissues displayed no staining or only nonspecific staining , like the WT control tissues . Western blots comparing WT and KO organs confirmed that DMXL2 was expressed specifically in the brain , heart , kidney and gonads of both sexes at P0 , whereas no expression was observed in the liver and pancreas ( Fig 3A ) . In addition , DMXL2 expression was detected specifically in the adrenal glands at P0 , but these glands displayed no X-gal staining . In adults , the DMXL2 protein was detected in large amounts in the brain , as previously reported in rats [1] , but also in the pancreas and adrenal glands ( Fig 3B ) . In addition to the larger described isoforms that predominate ( 341 kD and/or 338 kD ) , two or three smaller isoforms ( 180 to 250 kD ) were detected , depending on the tissue ( and the level of DMXL2 expression in the organ concerned ) . Weaker , but clearly detectable expression was also observed in the testes , epididymides , seminal vesicles , ovaries , uterus horns and mammary glands ( Fig 3C ) . Intriguingly , X-gal staining and western blotting demonstrated the presence of DMXL2 in the heart and kidneys at P0 ( Figs 2C and 3A ) , but this protein was no longer detectable in these organs in adult animals ( Fig 3B ) . We performed morphometric analyses of E18 . 5 Dmxl2 KO embryos to investigate possible developmental defects of the heart and kidneys that might explain neonatal death . However , these studies revealed no cardiac or renal malformations or any other organ defects capable of accounting for the premature death of these mice ( observations made by Prof . Manuel Mark , IGBMC , Illkirch ) . Dmxl2/DMXL2 has been implicated in glucose metabolism [18] . We therefore assessed the blood glucose and plasma insulin concentrations of newborn pups of the different genotypes ( S1 Fig ) . These concentrations were normal for female KO pups , but male KO pups were hypoglycemic ( pValue ≤ 0 . 001 ) relative to their WT and HTZ littermates , despite normal insulinemia . As hypoglycemia affected the male pups only and neonatal lethality displayed no sex bias , it appears unlikely that this feature is responsible for the lethality of Dmxl2 knockout . Strong Dmxl2 expression was detected in the olfactory mucosa as early as E14 . 5 , and the newborn KO pups did not feed . We therefore investigated the possible effects of the loss of function of this gene on the olfactory system . The olfactory system is known to regulate feeding behavior [23] . We therefore performed electro-olfactography ( EOG ) to investigate the functionality of the olfactory mucosa in KO pups . EOG signals result from the activation of the olfactory transduction cascade in a population of neurons located close to the recording electrode . This transduction cascade occurs in the neuronal cilia in contact with their environment . We stimulated the olfactory mucosa with various odorants , at several concentrations ( Fig 4A ) . The maximum amplitude of the response to odorants was measured and did not differ significantly between KO and HTZ ( control ) pups ( Fig 4A and 4B ) . The olfactory mucosa was , therefore , functional , and the peripheral olfactory sensory neuron cilia of KO pups were as capable of odorant detection as those of control newborn mice . DMXL2 is associated with synaptic vesicles in rat brain , potentially regulating their exocytosis and signal transmission [1] , [11] . We therefore investigated whether the olfactory information generated in the olfactory mucosa was efficiently transmitted to the neurons of the olfactory bulb . Neuronal activation in the olfactory bulb after odorant stimulation was assessed by c-Fos immunodetection in HTZ and KO pups ( Fig 4C and 4D ) . We observed significantly fewer c-Fos-positive neurons in the glomerular and external plexiform layers of KO olfactory bulbs than in those of HTZ bulbs , whereas neuron density was similar ( Fig 4D ) . The synaptic transmission of the olfactory signal from the olfactory mucosa to the olfactory bulb was , therefore , significantly altered in the absence of Dmxl2 . LacZ staining and western-blotting experiments showed that Dmxl2/DMXL2 was expressed in the gonads of both sexes ( Figs 2 and 3 ) . Studies of its transcription during gonad differentiation revealed a dynamic profile , with increases at sex-specific stages ( Fig 5A ) . Male and female gonads displayed similar levels of Dmxl2 transcripts at early stages of differentiation ( E12 . 5 ) , but Dmxl2 levels increased in the ovary a couple of days before birth ( between E16 . 5 and E18 . 5: pValue = 0 . 019 ) , reaching maximum values on P0 , before decreasing slightly . Dmxl2 transcript levels remained low in the adult ovary . The first few days after birth correspond to the breakdown of germ cell nests and the formation of the first follicles in mice [24] . In the testes , Dmxl2 transcript levels remained constant during fetal and early postnatal development , but increased markedly with the initiation of spermatogenesis after P5 ( Fig 5A ) . Indeed , Dmxl2 transcript levels had already increased by P15 , when early-stage pachytene spermatocytes are observed [25] , and they peaked at P28 , when the first elongating spermatids are detected [25] . Dmxl2 transcript levels remained constant thereafter in the adult testes . Having demonstrated the specificity of the DMXL2 antibody ( S2 Fig ) , we used immunodetection methods to detect the DMXL2 protein at various stages of gonadal differentiation ( P5 and P28 ) . A strong signal was detected in the germ cell cytoplasm in both male and female gonads ( Fig 5B ) . A faint signal was also observed in the supporting cells of both sexes ( i . e . granulosa and Sertoli cells ) . As KO pups died shortly after birth , gonad phenotype could be analyzed only on P0 . The morphological appearance of the gonads of female and male KO mice was assessed by classical histology methods ( S3 Fig ) and by the use of several markers of germ cells and supporting cells . Gonad size , organization and general appearance were similar in KO and control gonads . In particular , KO ovaries had numerous germ-cell nests at the cortex and a few primordial follicles were starting to form , as in P0 control ovary ( S4 Fig ) . Transcriptomic analyses were performed at P0 . Microarray analyses comparing KO and WT ovaries and testes highlighted only a few genes differentially expressed between KO and WT gonads: 51 genes for KO ovaries ( S2 Table ) , and 12 for KO testes ( S3 Table ) ( adjusted pValue <0 . 1 ) . Four of these genes were differentially expressed in the KO gonads of both sexes , as confirmed by RT-qPCR analyses for Aph1b and Fez1 ( S5 Fig ) . Despite the small number of differentially expressed genes in KO ovaries , two gene clusters with significant enrichment scores were identified ( DAVID analysis tool; enrichment score ≥1 . 3 ) [26] . One of these gene clusters related to stress responses ( enrichment score = 1 . 42 ) , whereas the other concerned WD40 proteins ( enrichment score = 1 . 68 ) . Indeed , in Dmxl2 KO ovaries , transcript levels for three other WD40 protein-encoding genes were affected according to the microarray data , which were confirmed by RT-qPCR for Coro2b , and Fbxw8 ( S5 Fig ) . In addition , Coro2b transcript levels were found to be upregulated in KO testes . This upregulation was not detected in global analyses . In conclusion , Dmxl2 loss-of-function at P0 induced the dysregulation of a larger number of genes in female than in male gonads . Nevertheless , the morphology of KO ovaries was unaffected , with primordial follicle formation occurring as in the control . We evaluated the effect of Dmxl2 loss-of-function at later stages , including spermatogenesis in the male gonad in particular , by generating mice with conditional knockouts of Dmxl2 in germ cells and/or in supporting cells of both sexes . We obtained conditional knockouts of Dmxl2 by first generating Dmxl2loxP/loxP mice ( exon 7 floxed ) by crossing Dmxl2wt/tm1a mice with FlpO ( FLP ) recombinase-expressing mice ( Rosa26-FlpO ) ( Fig 1A ) [27] . We then used several lines of Cre-expressing mice: a Vasa-Cre line , to generate a conditional Dmxl2 KO in germ cells ( Dmxl2loxP/-; Vasa-Cre: germ cell cKO ) [28] , Amh-Cre [29] , to produce a conditional Dmxl2 KO in Sertoli cells ( Dmxl2loxP/loxP; Amh-Cre: Sertoli cell cKO ) and Amhr2-Cre [30] for conditional Dmxl2 KO in granulosa cells ( Dmxl2loxP/loxP; Amhr2wt/Cre:: granulosa cell cKO ) . Double conditional knockouts ( dcKO ) were also generated , resulting in Sertoli and germ cell-specific Dmxl2 dcKO for males ( Dmxl2loxP/-; Vasa-Cre; Amh-Cre ) or granulosa and germ cell-specific Dmxl2 dcKO for females ( Dmxl2loxP/-; Vasa-Cre; Amhr2wt/Cre ) . These single and double conditional knockouts were studied at various postnatal stages . Females of the different genotypes were fertile . The histological features of the ovary were also similar between females of the different genotypes ( S6 Fig ) . In males , fertility tests performed until the age of six months showed no significant differences between Sertoli cell cKO ( 9 . 1 ± 2 . 6 pups per litter ) , germ cell cKO ( 9 . 4 ± 3 . 4 pups per litter ) , Sertoli and germ cell dcKO ( 9 . 6 ± 2 . 9 pups per litter ) and control Dmxl2loxP/loxP ( 9 . 2 ±3 . 4 pups per litter ) mice . In addition , histological analyses of testis sections from six-month-old mice of the various genotypes revealed no specific phenotype ( S7A Fig ) , and sperm parameters were similar in Sertoli and germ cell dcKO and Dmxl2loxP/loxP control mice ( S7B Fig ) . Dmxl2 expression increases greatly with the onset of spermatogenesis at puberty ( Fig 5A ) . We therefore studied sperm parameters and testis differentiation at the end of the first wave of spermatogenesis . We first assessed Dmxl2/DMXL2 transcript and protein levels in the testes of mice of the different genotypes . We found that the germ cells were the major site of Dmxl2/DMXL2 expression in adults ( Fig 6A and 6B ) . Nevertheless , Dmxl2 transcript detection was completely abolished only in Dmxl2 dcKO testes ( Fig 6C ) , which were therefore considered to display a testis-specific Dmxl2 KO . The sperm parameters of control ( Dmxl2loxP/loxP ) ( n = 12 ) , Sertoli cell cKO ( Dmxl2loxP/loxP; Amh-Cre ) ( n = 11 ) , germ cell cKO ( Dmxl2loxP/-; Vasa-Cre ) ( n = 4 ) and Sertoli and germ cell dcKO ( Dmxl2loxP/-; Vasa-Cre; Amh-Cre ) ( n = 4 ) mice were analyzed seven weeks after birth ( Fig 7A ) . Sperm concentration was more than 60% lower in mice with no Dmxl2 expression anywhere in the testes ( dcKO ) , and in mice lacking Dmxl2 only in the germ line ( germ cell cKO ) , despite a normal testis/body weight ratio ( S4 Table ) . The percentage of motile sperm was similar in the four mouse lines , demonstrating that only the total number of spermatozoa was affected . Stereological analyses of germ cell cKO and dcKO testis sections revealed a significantly larger fraction occupied by the Sertoli cell cytoplasm in the center of the seminiferous tubules than in WT sections , whereas the lumen area was significantly smaller ( Fig 7B and 7C; S8 Fig ) . In addition , the seminiferous epithelium occupied a smaller area in dcKO testes than in control ( pValue = 0 . 001 ) or germ cell cKO testes ( pValue = 0 . 05 ) , suggesting that the number of germ cells was smaller . We then used an RNA sequencing approach to characterize the molecular consequences of the absence of DMXL2 expression in testes from seven-week-old animals , comparing dcKO ( Dmxl2loxP/-; Vasa-Cre; Amh-Cre ) and Dmxl2loxP/loxP control testes . RNA-sequencing identified 363 genes as differentially expressed in dcKO testes relative to control gonads: 161 genes were upregulated and 202 were downregulated ( S1 File ) . We identified the cell types affected by Dmxl2 loss–of-function in the testes , by assessing the cellular expression profiles of the 363 differentially expressed genes based on RNA-sequencing data obtained from the Gene Expression Omnibus http://www . ncbi . nlm . nih . gov/geo/; accession number GSE43717; [31] ) ( S1 File ) . According to these data , Dmxl2 is weakly expressed in Sertoli cells and much more strongly expressed in the germ line , mostly in spermatogonia and spermatocytes , consistent with our findings ( S1 File ) . The heat maps of the 161 upregulated genes ( S9A Fig ) and of the 202 downregulated genes ( S9B Fig ) were mirror images . Indeed , the genes upregulated in dcKO mice were mostly genes expressed by Sertoli cells and spermatogonia , whereas those downregulated were mostly genes expressed by spermatocytes and spermatids . Accordingly , the leading functional annotation for downregulated genes was “spermatogenesis” ( Benjamini-Hochberg adjusted pValue = 5 x 10−4 , DAVID6 . 8; S2 File ) . These observations , together with our previous stereological data , suggest a defect affecting the first wave of spermatogenesis , with smaller numbers of spermatocytes and spermatids produced in dcKO gonads . As the number of Sertoli cells conditions the number of spermatogenic cells , we determined the numbers of SOX9-positive cells ( i . e . Sertoli cells ) in dcKO and control testes ( S10 Fig ) . No difference was found between the two genotypes . Additional in silico analyses on upregulated genes highlighted apoptosis ( apoptosis signaling , pValue = 0 . 0035 ) and endocytosis ( phagosome maturation , pValue = 0 . 0036; clathrin-mediated endocytosis signaling , pValue = 0 . 0123; macropinocytosis signaling , pValue = 0 . 0186 ) processes as canonical pathways significantly upregulated in dcKO gonads , which could be related to the decrease in the number of spermatogenic cells at this stage ( Ingenuity Pathway Analysis software , Qiagen; S3 File ) . Furthermore , the ERK1/2 pathway , which has been implicated in apoptosis and phagocytosis [32] , was the leading functional network detected , encompassing 30 of the 161 upregulated genes ( almost 20% of the upregulated genes ) . Three of the five members of the TAM ( Tyro3 Axl Merkt ) regulation pathway were highlighted in this network [33]: one receptor Tyro3 and two ligands: Gas6 ( growth-arrest-specific 6 ) and Pros1 ( Protein S ) ( Fig 8A ) . This pathway has been reported to be involved in the phagocytic activity of macrophages , but also in that of Sertoli cells , in which it plays a crucial role in ensuring fertility [34] , [35] . Transcript levels for the five members of the TAM family ( the three receptors , Tyro3 , Mertk , Axl , and their two ligands , Gas6 , Pros1 ) were analyzed in the testes of seven-week-old mice of the four genotypes ( control , Sertoli cell cKO , germ cell cKO and dcKO ) ( Fig 8B ) . Interestingly , the transcript levels of all these genes were significantly higher in dcKO than in control testes ( pValue <0 . 05 ) , highlighting a general enhancement of the TAM regulatory pathway and , potentially , of Sertoli cell phagocytosis in the absence of DMXL2 expression in the testes . This increase in phagocytic activity may reflect higher levels of germ cell apoptosis in dcKO testes . Cleaved caspase-3 ( cCasp-3 ) expression was detected by immunohistochemistry and cCasp-3-positive cells were counted in dcKO and control testes ( Fig 9A and 9B respectively ) . The proportion of apoptotic cells ( cCasp-3-postive cells/mm2 ) was significantly higher in dcKO testes ( pValue = 0 . 02 ) , despite variations within animals . In conclusion , in the absence of Dmxl2 expression in the testes , spermatogenesis appears to be less efficient , with higher levels of germ-cell apoptosis , whereas the phagocytic activity of Sertoli cells seems to be enhanced during the first wave of spermatogenesis .
We describe here , for the first time , the neonatal lethality of Dmxl2 KO ( Dmxl2tm1a/tm1a ) in mice , with a maximum survival of 10 to 12 hours after birth . This observation contrasts with that of a previous study in which Dmxl2tm1a/tm1a was reported to be lethal during fetal development [18] . We therefore introduced the Dmxl2 tm1a mutation into two different genetic backgrounds , the C57Bl/6N and FVB/NRj strains , in which neonatal lethality kinetics were similar . Our first observations of Dmxl2 KO pups revealed abnormal feeding behavior , with an absence of milk in the stomach . A complete inability to feed inevitably leaves to neonatal death , due to the absence of nourishment , and because the liquid derived from milk is essential for homeostatic processes in newborn [36] . Nevertheless , death linked to milk deprivation occurs within 12 to 24 h and therefore seems unlikely to be the sole cause of premature death in Dmxl2 KO mice . Morphometric studies revealed no major defect in organogenesis , but signs of neurological and metabolic problems were observed . The tissue-specific expression of Dmxl2 has been analyzed during fetal and postnatal development . Dmxl2 expression is strongly detected in the olfactory mucosa of the fetus , an area closely associated with migrating GnRH neurons [37] . DMXL2 has been reported to be crucial for the number , activation and maturation of GnRH neurons [18] , [19] . It therefore seems likely that the GnRH neuron defects observed in adult nes-Cre; Dmxl2 wt/loxP mice result from early fetal dysfunction . The transmission of olfactory information was impaired in Dmxl2 KO neonates , which displayed very poor olfactory bulb neuron activation . As olfaction is required for suckling behavior in rodents [23] , this neurological transmission defect in KO pups may contribute to the absence of feeding . However , this observation is insufficient to account for the premature death of these pups , and this specific example of neurological failure probably reflects more severe impairments , as observed in human patients , in whom DMXL2 haploinsufficiency leads to polyneuropathy [18] . During gestation , fetal homeostasis is essentially managed by the placenta , whereas , after birth , the neonate must rapidly adapt to a stressful situation with new metabolic needs . A failure to establish energy homeostasis rapidly leads to the death of the neonate , over a period of time similar to that observed for Dmxl2 KO mice . Indeed , death may occur within eight hours of birth in some cases of glucose homeostasis problems [38] , or between 10 and 14 hours after birth if autophagy mechanisms are disrupted [39] , [40] . Interestingly , neonatal Dmxl2 KO pups present signs of metabolic/homeostasis problems , such as hypoglycemia in particular . During the first few hours after birth , the pup experiences a period of starvation during which gluconeogenesis is not fully active [41] . Glycogen is stored in the liver during fetal development , to prevent hypoglycemia in the neonate . Glycogen is an important source of glucose , which is released via glycogenolysis [42] . This adaptive mechanism is managed by a hormonal network , in which insulin levels decrease and the secretion of glucagon and glucocorticoids increases . DMXL2 was not detected in the pancreas or liver at birth , but was found in the adrenal glands , focusing attention on glucocorticoids and the possible effect of DMXL2 on their secretion . Intriguingly , hypoglycemia was observed only in male KO pups , highlighting the sex-specific nature of DMXL2 function in glucose homeostasis and , possibly , in adrenal sex-specific functions . However , as the timing of death was similar for both male and female KO pups , hypoglycemia cannot be the main event causing premature death . Autophagy is the first source of energy after birth , before efficient glycogenolysis is established . Autophagy levels are low during embryogenesis , but this process is upregulated in various tissues at birth ( including the heart in particular ) and is maintained at high levels from 3 to 12 hours after birth [39] . Autophagy eliminates aberrant or obsolete cellular structures/organelles . It is the primary means of degrading cytoplasmic constituents within lysosomes . Autophagy is also important for the cellular response to starvation , as the amino acids it generates can be used directly as a source of energy , or converted into glucose by the liver . Mice with deficiencies of ATG5 or ATG7 ( autophagy-related proteins 5 and 7 , respectively ) , which are involved in autophagosome formation , die within the first 12 hours of birth [39] , [40] , a timing very similar to that observed for Dmxl2 KO pups . Many WD40 proteins have been implicated in autophagy: Atg18 ( autophagy-related protein 18 ) [43] , EPG-6 ( ectopic PGL granules 6 ) [44] , AMBRA1 ( autophagy/beclin-1 regulator 1 ) [45] , ALFY ( autophagy‐linked FYVE protein ) [46] and WDR47 ( WD40-repeat 47 ) [47] . A possible new function of the DMXL2 protein in autophagy should , therefore , be investigated , as it might explain the premature death of Dmxl2 knockout mice . In addition to its role in neurological or homeostatic processes , DMXL2 was also thought to be associated with reproductive functions . Indeed , Dmxl2 is expressed in the germ cells and supporting cells of the gonads of both sexes , with a timing during development suggesting involvement in the major events of ovary and testis differentiation . In the ovary , Dmxl2 expression increases after the onset of meiosis ( E14 . 5 ) to reach a peak at birth . This period corresponds to germ-cell nest formation , breakdown and primordial follicle formation . However , the histological features of the ovaries were identical in Dmxl2 KO mice and controls at birth , as some primordial follicles were already visible in the gonads of both genotypes . Furthermore , cell-specific Dmxl2 knockout did not result in any fertility problems or ovarian abnormalities in adult females . Analyses of gene expression in Dmxl2 KO gonads at P0 revealed molecular disorders in the ovaries , which seemed to be under stress and trying to adapt to the loss-of-function of Dmxl2 , possibly by increasing the expression of other WD40 protein-encoding genes ( i . e . Coro2b and Fbxw8 ) . More interestingly , the Aph1b gene was found to be upregulated in the gonads of Dmxl2 KO mice of both sexes . Aph1b encodes one of the four subunits of the γ-secretase complex , which plays a key role in the Notch signaling pathway , and this subunit is involved in the stability of the complex [48] , [49] , [50] . DMXL2 has been implicated in Notch signaling , in which it controls the V-ATPase pumps responsible for regulating the pH of the endocytic vesicles in which γ-secretase acts [51] , [52] , [13] , [14] . In Drosophila ovaries , DMXL2/Rbcn-3α has even been shown to be involved in follicle formation , through its control of the Notch pathway [7] . The Notch pathway plays an important role in folliculogenesis that has been conserved during evolution , from flies to mammals [7] , [53] , [54] . Nevertheless , we show here that DMXL2 is not crucial for Notch signaling in mouse ovaries . The potential decrease in γ-secretase activity in ovaries lacking Dmxl2 is probably counterbalanced by an increase in the stability of the complex ( via Aph1b upregulation ) . The important role of DMXL2/Rbcn-3α in folliculogenesis and female fertility is , therefore , not conserved in mice . In testes , only a few other genes in addition to Aph1b were deregulated at P0 in Dmxl2 KO gonads . Nevertheless , Dmxl2 expression in the testes began to increase between P5 and P15 , coinciding with spermatogenesis , suggesting a role in postnatal gametogenesis rather than early testis differentiation . Consistent with this hypothesis , histological/stereological observations and analyses of sperm parameters in the gonads of mice with a testis-specific Dmxl2 KO ( Sertoli and germ cell dcKO ) revealed a disruption of the first spermatogenic wave , resulting in a sperm concentration 60% lower than that in the controls . The seminiferous tubules presented an expended Sertoli cell cytoplasm , with a shorter lumen , suggesting higher levels of phagocytosis by the supporting cells . Transcriptomic and immunohistochemical analyses confirmed these observations , highlighting a decrease in the spermatocyte/spermatid fraction and an increase in apoptosis , accompanied by an increase in the levels of phagocytosis regulators . In particular , the TAM pathway was found to be upregulated in the absence of Dmxl2 expression . Three of the TAM proteins belong to the receptor protein tyrosine kinase ( RPTK ) : TYRO 3 , AXL and MERTK . Two related proteins , GAS 6 and Protein S ( Pros1 ) , act as their ligands . These five TAM proteins are expressed by Sertoli cells in the testes [34] . Males lacking the three TAM receptors ( TAM-/- ) are sterile due to an impairment of the phagocytic function of Sertoli cells , which is essential for the elimination of apoptotic germ cells [34] , [35] , [55] . TAM receptor dimers bind their two ligands , which in turn bind to the phosphatidylserine exposed at the surface of apoptotic cells [56] . In this study , germ cell apoptosis rates were significantly higher in the absence of DMXL2 ( dcKO testes ) , suggesting that the TAM regulation ( and thus , probably , the phagocytic activity of Sertoli cells ) is enhanced in mutants due to germ cell dysfunction . Mice without DMXL2 expression in the germ line had a phenotype similar to that of dcKO mutants , with low sperm concentrations at puberty ( Fig 7A ) . Together , these results suggest that DMXL2 exerts its principal function in germ cells , during the meiotic process occurring at the onset of spermatogenesis . Changes in its expression may affect germ cell differentiation , with higher rates of apoptosis and phagocytosis by Sertoli cells clearing abnormal spermatocytes/spermatids from the seminiferous tubules and resulting in a lower sperm concentration . Nevertheless , sperm production normalized at later stages of testis development , indicating that the functions of DMXL2 are essentially limited to the first wave of spermatogenesis or that compensatory processes occur after puberty . As suggested by Busada et al . for Rhox13 [57] , Dmxl2 expression in spermatogenic cells may be advantageous in mice , supporting early fertility by providing additional germ cells at the start of the animal’s reproductive life .
Animals were handled in accordance with the guidelines on the Care and Use of Agricultural Animals in Agricultural Research and Teaching ( Authorization no . 91–649 for the Principal Investigator , and national authorizations for all investigators ) . The protocol was approved by the Ethics Committee for Animal Experiments of the Jouy-en-Josas Institute and AgroParisTech ( Permit Number: 12/184 ) . Dmxl2tm1a ( EUCOMM ) Wtsi ( Dmxl2wt/tm1a ) mice with a C57Bl/6N genetic background were provided by the Wellcome Trust Sanger Institute ( International Mouse Phenotype Consortium ( IMPC ) : https://www . mousephenotype . org/data/genes/MGI:2444630 ) . The mutation corresponded to a knock-in of the targeting vector between Dmxl2 exons 6 and 10 ( see Fig 1 ) [20] , [21] , [22] . We backcrossed Dmxl2 mutant mice onto the FVB/NRj strain ( JANVIER Laboratories ) for 10 generations . Dmxl2wt/tm1a mice with this genetic background were crossed to generate Dmxl2wt/wt ( wild-type , WT ) , Dmxl2wt/tm1a ( heterozygous , HTZ ) and Dmxl2tm1a/tm1a ( knocked-out , KO ) mice . Conditional knockouts of Dmxl2 were obtained by crossing Dmxl2wt/tm1a mice with FlpO ( FLP ) recombinase-expressing mice ( Rosa26-FlpO ) [27] to remove the β-galactosidase cassette and the neomycin resistance gene and to create Dmxl2loxP/loxP mice ( in which the Dmxl2 exon 7 is floxed , see Fig 1A ) . Dmxl2 conditional knockout in germ cells was achieved by crossing Dmxl2loxP/loxP mice with Vasa-Cre mice ( FVB-Tg ( Ddx4-cre ) 1Dcas/J ) [28] to obtain Dmxl2wt/- ; Vasa-Cre mice in the F1 generation . Due to the mode of Vasa-Cre transmission , only F1 males ( Dmxl2wt/- ; Vasa-Cre ) of less than eight weeks of age were then used to generate Dmxl2loxP/- ; Vasa-Cre F2 mice ( germ cell cKO ) . The conditional knockout of Dmxl2 in granulosa cells was achieved by crossing Dmxl2loxP/loxP mice with Amhr2-Cre mice ( Amhr2wt/Cre ) [30] to obtain Dmxl2wt/loxP; Amhr2wt/Cre F1 mice . We then crossed Dmxl2loxP/loxP mice with Dmxl2wt/loxP ; Amhr2wt/Cre mice to obtain Dmxl2loxP/loxP ; Amhr2wt/Cre F2 mice ( granulosa cell cKO ) . Dmxl2 was knocked out specifically in Sertoli cells by crossing Dmxl2loxP/loxP mice with Amh-Cre mice [29] . The Dmxl2wt/loxP; Amh-Cre F1 mice were then crossed with each other to generate Dmxl2loxP/loxP ; Amh-Cre F2 mice ( Sertoli cell cKO ) . We also generated double conditional mutant mice ( dcKO ) by crossing young Dmxl2loxP/-; Vasa-Cre males ( six to eight weeks of age ) with either Dmxl2loxP/loxP ; Amh-Cre or Dmxl2loxP/loxP; Amhr2wt/Cre females to produce Dmxl2loxP/-; Vasa-Cre; Amh-Cre males ( Sertoli and germ cell Dmxl2 dcKO ) or Dmxl2loxP/-; Vasa-Cre; Amhr2wt/Cre females ( granulosa and germ cell Dmxl2 dcKO ) , respectively . Mice were housed under a 12 h light/12 h dark cycle at the UE0907 ( INRA , Jouy-en-Josas , France ) , with ad libitum access to food . Genomic DNA was obtained from tail biopsy specimens with the Kapa Express Extract kit ( Kapa Biosystems ) , according to the manufacturer’s instructions . Total-knockout animals were genotyped by PCR amplification of the Dmxl2 wild-type and tm1a LacZ alleles . Conditional-knockout animals were genotyped by the PCR amplification of Dmxl2 exon 7 , and the Vasa-Cre , Amhr2-Cre and Amh-Cre alleles ( see S1 Table for primer sequences and Fig 1 for the location of Dmxl2 primers ) . PCR was performed with the KAPA2G Fast Genotyping Mix , according to the manufacturer’s instructions ( Kapa Biosystems ) . For E14 . 5 embryos , maternal uterine horns were dissected out and transferred to cold 1 X PBS ( Eurobio ) for storage . The embryonic sacs were removed and used for genotyping . Embryos were rapidly rinsed in PBS , and fixed by incubation for 2 . 5 hours in a fixative solution containing 2% formaldehyde and 0 . 2% glutaraldehyde in PBS . The embryos were washed twice , for 30 minutes each , in PBS , and stained by overnight incubation at room temperature , in the dark , in 5 mM ferrocyanide , 5 mM ferricyanide , 20 mM MgCl2 , 1 mg/ml X-gal ( GX12836 , Genaxis ) , 0 . 02% NP-40 , 0 . 01% sodium deoxycholate , 20 mM Tris HCl pH 7 . 4 . The following day , they were briefly rinsed and incubated in PBS for 30 minutes , before final fixation by incubation overnight at 4°C in 4% formaldehyde . The fixed embryos were rinsed in PBS and cleared as described by Schatz and coworkers [58] . For whole-mount staining , freshly dissected tissues ( skinned heads , heart and lungs , digestive system ( from the stomach to the large intestine ) , urogenital tracts , and skeletal muscle ) from P0 animals were washed for 10 min in PBS supplemented with 0 . 01% Tween-20 ( PBS-T ) , fixed by incubation for 10 min in 4% paraformaldehyde ( PFA ) , and then subjected to two more washes in PBS-T , for 10 minutes each . For the brain and kidneys , pups were perfused with 4% PFA for 10 minutes and washed by incubation in PBS overnight at 4°C . All tissues were then stained by overnight incubation in 5 mM ferrocyanide , 5 mM ferricyanide , 4 mM MgCl2 , 0 . 1% Triton X-100 , 1 mg/ml X-gal , at 32°C , in a water bath . Tissues were washed for 10 min in PBS-T and then fixed again by incubation with 4% PFA overnight at 4°C . They were stored in 100% ethanol until imaging with a Leica M80 dissecting microscope fitted with a Leica DFC420 digital camera . Tissues from newborn WT or Dmxl2tm1a/tm1a ( Dmxl2 KO ) mice ( brain , heart , liver , pancreas , kidneys , adrenal glands , testes and ovaries ) , or from adult WT mice ( brain , heart , pancreas , kidneys , adrenal glands , lungs , liver , spleen , skeletal muscle , testes , epididymides , seminal vesicles , ovaries , uterus and mammary glands ) were collected and snap-frozen in liquid nitrogen . For protein extraction , tissues were ground on dry ice , and transferred to a Dounce homogenizer , in which they were lysed in radioimmunoprecipitation assay ( RIPA ) buffer supplemented with protease inhibitors ( Roche ) . Lysates were centrifuged for 20 min at 4°C and 16 , 000xg , supernatants were collected and the amount of protein present was determined by the Bradford method . For each tissue , we subjected 25 μg of protein diluted in Laemmli buffer to electrophoresis in 4–15% Mini-PROTEAN TGX gels ( Cat . 456–108310 , Bio-Rad ) . Stained proteins of known molecular weight ( range: 31–460 kD , Cat . LC5699 , Invitrogen ) were run simultaneously as standards . The bands obtained on electrophoresis were transferred onto a polyvinylidene difluoride membrane ( Hybond-P PVDF; Amersham ) . The membrane was blocked by incubation in phosphate-buffered saline containing 1/1000 Tween-20 ( PBS-T; Prolabo , France ) supplemented with 4% ( w/v ) nonfat dried milk , and was incubated overnight at 4°C with primary antibody ( anti-DMXL2 or anti-GAPDH; refer to S5 Table for a list of the antibodies used and the conditions in which they were used ) diluted in PBS-T supplemented with 4% ( w/v ) nonfat dried milk . The blot was then washed three times with PBS-T , incubated for 45 min in PBS-T supplemented with 4% ( w/v ) nonfat dried milk plus the peroxidase-conjugated secondary antibodies , and washed thoroughly in PBS-T . Peroxidase activity was detected with the ECL-Plus detection system for western blots , according to the manufacturer’s instructions ( Amersham ) . Immunoreaction signals were analyzed with an image analysis system ( Advanced Image Data Analyzer software , LAS 1000 camera , Fujifilm ) . Electro-olfactogram ( EOG ) recordings were made on the olfactory mucosa in an opened nasal cavity configuration , on hemi-heads of newborn HTZ and KO mice , as previously described [59] . The hemi-head was kept under a constant flow of humidified filtered air ( 1000 ml/min ) delivered close to the septum through a 9 mm glass tube . This tube was positioned 2 cm from the epithelial surface . The olfactory system was stimulated by blowing air puffs ( 200 ms , 200 ml/min ) through an exchangeable Pasteur pipette containing a filter paper impregnated with 20 μl of the odorant , enclosed in the glass tube . The odorants used were diluted in mineral oil ( hexanal from 1:10000 to 1:10; limonene from 1:1000 to 1:10 and carvone at 1:100 ) . EOGs were recorded at two separate centrally located positions on turbinates IIb and IIa . EOG signals were analyzed and peak amplitudes were measured with a Clampfit 9 . 2 ( Molecular Devices ) . Values were averaged for each set of conditions . Means ± SEM were plotted with GraphPad , and statistical analyses were performed with a Fisher-Pitman two-sample exact permutation test ( R software using the Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) ) to compare the response between HTZ and KO animals for a given concentration of odorant . Newborn pups were isolated from their dams and placed in a new cage for 30 min in a quiet room . They were then exposed , for 10 minutes , to odorants in a tea ball containing filter paper impregnated with 20 μl of a mixture of 12 odorants ( equimolar mixture of anisole , citral , heptanal , isoamyl acetate , lyral , lilial , octanol , 1-4-cineol , isomenthone , limonene , carvone , and pyridine diluted to a final concentration of 10-3M ) . Pups were killed 60 min after the end of the exposure period . Heads were skinned , and prepared as described in the “Immunohistochemistry” paragraph for c-Fos immunodetection . For all coronal olfactory bulb sections , we took four dorsal and ventral images . Images were acquired blind to treatment , at a magnification of x100 . They were analyzed with ImageJ ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://imagej . nih . gov/ij/ ) for the thresholding of specific c-Fos staining , as previously described [60] . The area of the olfactory bulb was measured after DAPI staining , for quantification of the proportion of the plexiform extern and the glomerular layer area displaying c-Fos staining . The same threshold was applied to all images from the same experiment on the same litter . DAPI staining in each area was also quantified , for the evaluation of neuron density . Median values were plotted with GraphPad , and statistical analyses were performed with Fisher-Pitman two-sample exact permutation tests ( R software , using the Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) ) to compare HTZ and KO animals for c-fos activation and neuron density . Glucose concentrations were determined in blood samples from newborn pups on P0 , with FreeStyle Optium Xceed Blood Glucose meters ( Abbott ) . Glucose concentrations were determined after three hours of starvation ( separation of the pup from its dam ) . Insulin plasma concentrations were determined for each pup , with the Mouse Ultrasensitive Insulin ELISA kit , according to the manufacturer’s instructions ( Alpco ) . Median values were plotted with GraphPad , and statistical analyses were performed with Fisher-Pitman two-sample exact permutation tests ( R software , using the Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) ) to compare male and female pups of each genotype . We studied Dmxl2 expression during gonad development , by extracting total RNA from pools of ovaries or testes at different developmental stages , with the RNeasy Mini or Micro kit ( Qiagen ) , depending on the amount of tissue . Three biological replicates were prepared for each stage and sex . The Maxima First-Strand cDNA Synthesis Kit ( Thermo Scientific ) was used to synthesize cDNA for RT-qPCR from 200 ng of RNA . RT-qPCR was performed in triplicate for all genes with the Absolute Blue SYBR Green ROX mix ( Thermo Scientific ) , in the StepOnePlus Real-Time PCR System ( Applied Biosystems ) . Based on the output of the GeNorm program , we used ActB , and Ywhaz as the reference genes for this study ( S1 Table ) . The results were analyzed with qBase Software [61] . Dissected tissues were fixed in by incubation in 4% PFA in PBS at 4°C for 2 hours ( P0 and P5 gonads ) , overnight ( P28 gonads ) or for 24 hours ( skinned heads of neonates ) . They were then cryoprotected with various concentrations of sucrose in PBS ( 0 , 12% , 15% , and 18% for gonads , or 0 , and 30% for heads ) . Tissues were finally embedded in Tissue-Tek O . C . T . Compound ( Sakura Finetek Japan ) and frozen at -80°C . Cryosections ( 7 μm for gonads , 20 μm for coronal head specimens ) were cut and stored at -80°C . Sections were air-dried , rehydrated in PBS and permeabilized by incubation with 0 . 5% Triton , 1% BSA in PBS for 30 minutes . The tissue sections were then incubated with the primary antibodies ( listed in S5 Table ) overnight at 4°C ( 2 days for c-Fos ) . Sections were washed several times in PBS and then incubated with secondary antibodies for 45 min at room temperature ( overnight for c-Fos ) . Finally , slides were rinsed in PBS , mounted in Vectashield mounting medium with DAPI ( Vector ) and images were acquired with a DP50 CCD camera ( Olympus ) . Freshly dissected P0 ovaries and testes were snap-frozen in liquid nitrogen . Three independent total RNA extractions were performed on pools of WT and KO ovaries and testes , with the RNeasy Mini ( for testes ) or Micro kit ( for ovaries ) ( Qiagen ) . RNA quality was checked with an Agilent Bioanalyzer and 200 ng of total RNA for each set of conditions was hybridized with a Mouse WG-6 v2 . 0 Expression BeadChip ( Illumina ) ( Pitié-Salpêtrière Postgenomics Platform–P3S , http://www . p3s . chups . jussieu . fr , Paris , France ) . Raw data were corrected for background by the “normexp” method , and quantile-normalized with the Limma package , through Bioconductor in the R statistical environment ( version 2 . 15 . 0 ) . Raw pValues were adjusted by the Benjamini-Hochberg method ( false discovery rate ) [62] . The quality of the expression data was checked by generating boxplots for raw expression data , density plots for normalized data , and by producing scatter plots and calculating Pearson’s coefficient for the correlation between arrays , with the Ringo package . The microarray data were assigned Gene Expression Omnibus number GSE115194 and are publicly available ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE115194 ) . RT-qPCR validations were performed as previously described , on three independent pools of gonads per genotype ( XX and XY , WT versus KO ) and the results were normalized against two housekeeping genes ( ActB and Ywhaz , according to GeNorm analyses ) with qBase Software . Means ± SD were plotted with Excel , and statistical analyses were performed by ANOVA followed by Fisher’s LSD test in InVivoStat software [63] . Six-week-old male and female conditional mutant mice were paired with wild-type FVBN mice for a period of six months . Breeding cages were monitored daily and gestations , birth dates and litter sizes were recorded . At the end of the breeding trial , the gonads and epididymides were harvested and either snap frozen for molecular analysis or fixed for histological analysis . The evaluation of male fertility was completed by the use of the IVOS I CASA system ( Computer Assisted Sperm Analysis , at Hamilton Thorne Inc . , Beverly , MA , USA ) to assess semen motility at the ages of seven weeks and six months . The cauda epididymis was plunged into 100 μl of TCF buffer ( Tris , citrate and fructose buffer ) and swimming spermatozoa were collected after incubation for 30 minutes at 37°C . A 4 μl aliquot was placed in a standardized four-chamber Leja counting slide ( Leja Products B . V . , Nieuw-Vennep , the Netherlands ) . Ten microscope fields were analyzed on an automated stage , using the predetermined starting position within each chamber . Statistical analyses were performed with the mean values for these ten fields , for at least 500 cells . Each sample was analyzed twice ( two different Leja wells ) . In total , 30 frames were captured at 60 frames/s , with software settings as follows: cell detection with a minimum contrast of 50 , a minimum cell size of 4 pixels , and a cell intensity of 80; the cutoff value for progressive cells was 50 μm/s for VAP and 80 . 0% for STR . Slow cells were considered to be static and had a VAP cutoff of 7 . 4 μm/s and a VSL cutoff of 6 . 6 μm/s . Median values were plotted with GraphPad , and statistical analyses were performed with Fisher-Pitman two-sample exact permutation tests ( R software , using the Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) ) to compare the sperm parameters of Dmxl2loxP/loxP males and the various conditional mutants . The dissected gonads were fixed in Bouin’s solution for 2 hours at room temperature for P0 gonads and overnight at 4°C for adult gonads . They were washed several times in 70% ethanol and then dehydrated in a series of solutions of increasing concentrations of ethanol ( 90% , 100% ) and butanol ( 50% , 100% ) . The tissues were then embedded in paraffin and 5 μm-thick sections were cut . Hematoxylin and eosin staining ( HE staining ) was performed by standard protocols for studies of tissue morphology . Images were captured with a Pannoramic Scan II ( 3DHISTECH ) digital slide scanner . For males , adult testis sections from control ( n = 6 ) , Sertoli cell cKO ( n = 6 ) , germ cell cKO ( n = 6 ) and double conditional KO animals ( n = 4 ) were analyzed in more detail . The volume fractions of the lumen , the residual Sertoli cell cytoplasm and the seminiferous epithelium were estimated on 200 seminiferous tubules per sample with the P2 grid of Appendix B of the chapter 4 of “Unbiased Stereology” [64 , 65] . For each experiment , medians values were plotted with GraphPad , and statistical analyses were performed with Fisher-Pitman two-sample exact permutation tests in R software , with the Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) . For immunohistochemical analysis , Bouin’s solution-fixed testis sections ( 5 μm ) from seven-week-old mice ( control and dmxl2 dcKO ) were deparaffinized and subjected to antigen retrieval by heating in 0 . 01 M citrate buffer , pH 6 . 0 in a pressure cooker for 5 minutes . The sections were then incubated for 10 min in H2O2 ( 0 . 3% ) and then for 30 min in a blocking and permeabilization buffer ( PBS/1% BSA/0 . 5% Triton ) . The sections were incubated overnight at 4°C with primary antibodies ( anti-cCasp3 and anti-SOX9 antibodies [66]; see S5 Table for the list of antibodies and dilutions used ) . The slides were then washed in PBS and incubated with a biotinylated anti-rabbit IgG for 45 minutes at room temperature . The primary antibody was omitted as a negative control . Antibody binding was detected with a Vectastain ABC kit ( Vector Laboratories , PK-6100 ) , and sections were counterstained with hematoxylin . Images were captured with a Pannoramic Scan II ( 3DHISTECH ) digital slide scanner . Cleaved caspase-3-positive cells were manually counted on virtual slides obtained with the Pannoramic viewer ( 3DHISTECH software ) . Seminiferous tubules were outlined manually and their surface area was obtained by the Pannoramic viewer . The total number of cCasp-3-positive or SOX9-positive cells was divided by total seminiferous tubule surface area ( μm2 ) and multiplied by 1 , 000 , 000 to obtain a number of positive cells/mm2 . Cell counting was performed on 10 ( for SOX9 ) or 30 ( for cCasp-3 ) round seminiferous tubules ( transverse sections ) on testes from 4 different animals of the control and Dmxl2 dcKO genotypes . Median values were plotted with GraphPad , and statistical analyses were performed with Fisher-Pitman two-sample exact permutation tests ( R software , using the Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) ) . Total RNA was extracted from the testes of seven-week-old Dmxl2loxP/loxP ( WT mice ) ( n = 3 ) and Dmxl2loxP/-; Amh-Cre; Vasa-Cre ( n = 3 ) mice with the RNeasy Mini kit ( Qiagen ) . RNA quality was checked with an Agilent Bioanalyzer and 1 μg of total RNA from each sample was sent to the High-throughput Sequencing Platform of I2BC ( Gif-sur-Yvette , Université Paris-Saclay , France ) for oriented library preparation and sequencing . At least , 50 million 75 nt reads were generated per sample ( SRA accession: SRP149657 ) . Sequence libraries were aligned with the Ensembl 89 genome , with STAR [67] , and gene table counts were obtained by applying RSEM to these alignments [68] . Statistical analyses of differential transcript accumulation were performed with R version 3 . 0 . 0 ( R Development Core Team , 2013 ) with the Bioconductor package DESeq2 version 1 . 0 . 19 [69] . Fold-changes in expression were estimated by an empirical Bayes shrinkage procedure , which attenuated the broad spread of fold-change values for genes with low counts with negligible effects on genes with high counts [69] . The pValues were adjusted for multiple testing by the Benjamini and Hochberg method [62] , and those with an adjusted pValue ≤0 . 05 were considered to be significant ( S1 File ) . RNA-sequencing data providing information about the gene expression profiles of different testis cell types were obtained from the Gene Expression Omnibus ( accession number GSE43717; [31] ) . FPKM files containing normalized RNA‐Seq data for purified Sertoli cells ( GSM1069639 ) , spermatogonia ( GSM1069640 ) , spermatocytes ( GSM1069641 ) , spermatids ( GSM1069642 ) and spermatozoa ( GSM1069643 ) were compiled and data concerning the genes differentially expressed in tDmx2 KO testes were extracted ( S1 File ) . FPKM values were log2-transformed to produce heat maps ( pheatmap: Pretty Heatmaps . R package version 1 . 0 . 8; Raivo Kolde ( 2015 ) ; https://CRAN . R-project . org/package=pheatmap ) . RT-qPCR validations were performed as previously described , with total testis RNA extracted from five animals per genotype ( seven weeks of age ) , and results were normalized against three housekeeping genes ( ActB , Ywhaz and H2afz ( S1 Table ) , selected on the basis of GeNorm analyses ) with qBase Software . For each experiment , median values were plotted with GraphPad , and statistical analyses were performed with Fisher-Pitman two-sample exact permutation tests in R software ( Rcmdr . Plugin . Coin package ( pValue<0 . 05 ) ) . | DMXL2 gene dysfunction underlies various human diseases , including breast cancer , non-syndromic hearing loss , and polyendocrine-polyneuropathy syndrome , demonstrating the large range of potential actions of DMXL2 . We show here that Dmxl2 expression is crucial for survival in mice , as neonates die within hours of birth when this gene is inactivated . The transmission of olfactory information is affected , leading to an absence of suckling and impaired feeding . Severe hypoglycemia is also observed in male neonates . We observed Dmxl2 expression in several organs , including the brain , heart and adrenal glands , potentially corresponding to some of the phenotypes observed in Dmxl2-deficient pups . We also described Dmxl2 expression in the reproductive tracts and gonads and showed that Dmxl2 inactivation specifically in the testes has a significant effect on the initial waves of spermatogenesis , resulting in very low levels of sperm production at puberty . Our results suggest that DMXL2 deficits should be considered in men with impaired fertility , as pathogenic variants of this gene may be associated with male infertility in humans . | [
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| 2019 | Dual role of DMXL2 in olfactory information transmission and the first wave of spermatogenesis |
Several studies have suggested investigation of health beliefs in children to be an important pre-condition for primary prevention of disease . However , little effort has been made to understand these in the context of podoconiosis . This study therefore aimed to explore the health beliefs of school-age rural children in podoconiosis-affected families . A cross sectional qualitative study was conducted in March 2016 in Wolaita Zone , Southern Ethiopia . Data were collected through in-depth individual interviews ( IDIs ) and focus group discussions ( FGDs ) , with a total of one hundred seventeen 9 to15-year-old children recruited from podoconiosis affected families . The study revealed various misconceptions regarding risk factors for podoconiosis . Most children believed barefoot exposure to dew , worms , snake bite , frog urine , other forms of poison , and contact with affected people to be major causes of the disease . Their knowledge about the role of heredity and that of long term barefoot exposure to irritant mineral particles was also weak . Though most participants correctly appraised their susceptibility to podoconiosis in relation to regular use of footwear and foot hygiene , others based their risk perceptions on factors they think beyond their control . They described several barriers to preventive behaviour , including uncomfortable footwear , shortage and poor adaptability of footwear for farm activities and sports , and shortage of soap for washing . Children also perceived low self-efficacy to practice preventive behaviour in spite of the barriers . Health education interventions may enhance school-age children’s health literacy and be translated to preventive action . Overcoming practical challenges such as shortage of footwear and other hygiene facilities requires other forms of interventions such as livelihood strengthening activities . Linking podoconiosis-affected families with local governmental or non-governmental organizations providing socio-economic support for households may assist school-age children in those families to sustainably engage in preventive behaviours .
Podoconiosis is an example of a lifestyle-related disease that develops later in life and affects millions of people with little experience of preventive behaviour . It is non-infectious ( and thus also termed ‘non-filarial elephantiasis’ ) and is characterized by bilateral swelling of the lower legs , commonly affecting people in the economically productive age groups [1 , 2] . In Ethiopia , over 1 . 5 million people are believed to live with podoconiosis [3] . Evidence to date indicates that the combination of inherited genetic susceptibility and barefoot exposure to soil rich in irritant mineral particles contributes to the cause of podoconiosis [2 , 4] . An estimate of heritability of podoconiosis is 63% while the risk ratio of siblings in affected families is 5 times higher than their counterparts in the general population [2] . Luckily , genetically susceptible individuals can entirely prevent the disease if they consistently protect their feet from exposure to irritant particles by wearing shoes starting at young age [5] . However , few children in podoconiosis-affected families engage in preventive behaviours such as regular use of footwear and foot hygiene in spite of their higher susceptibility to the disease . In the most recent study in an endemic setting in Ethiopia , the proportion of preschool children reported to have “all day , every day” use of footwear was only 31% [6] . Another study also reported poor hygiene among children [7] . Previous studies among adults in communities endemic for podoconiosis have reported higher level of misconceptions regarding the cause and prevention of podoconiosis [6–11] , and discussed the implications of the misconceptions to disease prevention behaviour and interpersonal interactions [6] . The beliefs that podoconiosis is contagious , caused by worms in the soil , indiscriminately inherited among relatives , caused by evil eye , curse , witch , or cold weather [8 , 9] were found to have negative consequences on preventive behavioural choices and interpersonal interactions [10] . The perceptions of adults regarding their own and children’s susceptibility to the disease were also reported to be inaccurate [6 , 11] . The perceptions that footwear does not permit farm activities and other duties , is uncomfortable for walking in the mud , smells bad in the hot season , wears out too quickly , softens the feet , and should be preserved for special events have all been identified as factors discouraging optimum use of footwear among people at high risk for the disease [11 , 12] . However , most of these studies focused only on adults . The studies that have investigated preventive behaviour among children [6 , 7 , 13] have explained it based on the parents’ health beliefs . Children are perceived as “active , purposeful beings who make sense of their world and contribute substantially to their own development” [14] , and whose cognitive developments occur intensively within the age of 7–15 years [15–17] . Researchers have acknowledged increasing levels of social autonomy of school-age children as they spend more time away from home with less parental supervision . This gives them the chance to develop independent beliefs about health [18] . Several studies have underscored the importance of investigating the dimensions of health beliefs in school-age children , particularly for control and prevention of diseases that arise from behaviour and habits established in childhood and continue to adult life [18 , 19–23] . The formation of values and behaviour in early childhood necessitates understanding of health beliefs of children [19] . This is supported by other studies which argue for the establishment of accurate beliefs about health in early childhood as habits in childhood are predictive of habits in adulthood [20–22] . Investigating health beliefs in children is also thought to enable better understanding of the impact of health education on the modification of health beliefs and encouragement of preventive behaviour [18 , 19] . Knowledge of the health beliefs of school-age children can be used to engage them as health messengers to their families and peers . A growing body of thought supports the belief that school-age children are not just passive recipients of health information . Rather , they can act as change agents who positively influence the behaviour of others in their communities through communicating health messages [24 , 25] . Investigating the health beliefs of school-age children may not only help promotion of footwear use for preventing podoconiosis , but also prevention of other neglected tropical diseases contracted through the feet . To our knowledge , no studies have actively involved children of this age group in the study of their health beliefs in the context of podoconiosis . The main purpose of the present study is therefore to explore various forms of health beliefs in school-age children .
Ethical approval was obtained from the ethics committee of the Armauer Hansen Research Institute ( AHRI ) ( Project reg . No . P035/15 ) and College of Health Sciences , Addis Ababa University ( Protocol number 047/15/Ext ) . The Wolaita Zone Administrative Bureau gave written permission to work in the community . The Mossy Foot International also allowed their outreach clinic site staff to help in the identification of study participants . Individual participation of children and group interviews was guided by the international guidelines for involving children in research [26–28] . A developmental psychologist helped during the recruitment and interviews to make sure that the content and format of questions was appropriate to the age and cognitive level of the child . Informed consent was obtained from caregivers and children also gave their assent to participate in the study . Taking into account likely difficulties understanding written consent forms due to low literacy [29] , a conversational style oral presentation of consent information was made in the local language to caregivers and children . Caregivers confirmed their permission for a child to participate in the study by signing or thumb-printing on the consent forms . Children expressed their assent verbally in the presence of their caregivers as a witness , to ensure the assent process is without any coercion . The use of verbal assent to children was approved by the ethics committee . The study was conducted in Wolaita Zone , one of the thirteen zones in Southern Nation Nationalities and Peoples Regional State ( SNNPRS ) . Wolaita Zone is located in the south-west of Ethiopia roughly between 6 . 30–7 . 10 N and 37 . 10–38 . 10 E , latitude and longitude respectively ( Fig 1 ) . According to the 2007 census report , the total population of the area was around 1 . 7 million; of whom 83 . 2% resided in rural areas . The dominant means of living is subsistence agriculture [30] . In this Zone , the point prevalence of podoconiosis has been calculated at 5 . 46% [31] . Since 1998 , Mossy Foot International ( MFI ) ( formerly , the Mossy Foot Treatment and Prevention Association ) , an international non-governmental organization , has been offering community-based prevention and control activities against podoconiosis in 15 outreach sites located at 15 to 65 km from the head office in Wolaita Sodo . Clinics in all outreach sites are run by community podoconiosis agents ( CPAs ) who are themselves patients , and social workers recruited from the local community . The MFI reaches the wider community through a group of network members who provide voluntary services of awareness and demand creation in collaboration with site workers . The organization has been serving over 30 , 000 registered patients for over a decade [32] . A cross sectional qualitative study was conducted in March , 2016 using in-depth individual interviews ( IDIs ) and focus group discussion ( FGDs ) methods . A purposive sampling technique was used to select three study sites with large numbers of registered patients and a relatively long history of establishment . The selected sites were Damot Pulasa Woreda ( district ) , Boloso Sore Woreda and Ofa Woreda . Study site staff members helped identify affected families and children eligible for interviews in those families . A theoretical sampling technique was used to determine the number of participants in the study , i . e . the process of sampling that continues until theoretical saturation is reached [33] . The major inclusion criteria for children were being part of a podoconiosis-affected family and age 9–15 years . Podoconiosis-affected families having at least one child between the age of 9–15 years were identified through the MFI site workers . One child per household was selected either for IDI or for FGD . None of the selected child participated in both activities . Children affected by podoconiosis or other forms of physical impairment were excluded from the study . Focus group discussions were disaggregated by gender: three with boys and three with girls . Twelve children participated in each FGD , giving a total of 72 participants . A total of 45 IDIs were held with children: 15 in each of the three sites . Together , 117 children participated in individual and group interviews . Semi-structured questions were developed in English and translated into the local language , Wolaitatto ( AT is the native speaker ) . Children were interviewed individually during home visits while FGDs were held in a place proximate for children coming from surrounding villages . All interviews were digitally recorded . Before assessing the beliefs of children about podoconiosis in both IDIs and FGDs , the mental image of children regarding their recognition of podoconiosis as a disease and its manifestations was assessed through drawings and their verbal description of the pictures they drew . Drawing exercises have been suggested as a tool to understand children’s imagery of disease and to assess their knowledge and conceptions [34 , 35] . To prompt recalling , the interviewer used the local term “na’u gediya kitisiya hargiya” , literally “disease that causes bilateral swelling of feet” . This term was used instead of common local terms such as “Kita” , “Inchricha” [8] , which were reported as derogatory and entailing demeaning connotation against affected persons [29] . Participant children were asked about their thoughts regarding the cause of podoconiosis , their perceptions of severity of disease , their appraisal of susceptibility to the disease , the advantages and disadvantages of various types of footwear , their perceptions of barriers to regular use of footwear and their self-efficacy beliefs to use footwear regularly in spite of these barriers . In the middle or at the end of FGDs , role plays were performed by children , which boosted their confidence to talk openly what goes on within the family and in the community . Whenever children found a given question difficult to understand , guidance and repeated clarifications were used to facilitate responses . While individual interviews lasted a maximum of 30 minutes , it took 1 hour to complete each FGD . At the end of the interviews , participants were provided brief information about the causes of podoconiosis and the role of consistent use of footwear in preventing the disease . After every interview , children received a piece of soap , a pen and a note book as compensation for their time , and this was suggested in a previous study as ethically appropriate if made in consultation with community members in the study setting [29] . Data were transcribed and translated into English and imported to NVivo software version 11 for analysis . Both deductive and inductive approaches were used to analyse the data . Deductive coding of themes in the data was based on Health Belief model constructs such as knowledge , perceived severity , perceived susceptibility , perceived benefits and barriers , and perceived self-efficacy . The Health Belief Model ( HBM ) provides a useful framework to understand and explain health beliefs in association with disease preventive behaviour [36–40] . A number of studies have used the health belief model to explain health beliefs of young and adolescent children [18 , 19 , 41 , 42] . A grounded theory approach was used to inductively identify newly emergent themes and subthemes as coding process proceeded . Grounded theory refers to a set of integrated and inductively generated concepts , categories , and themes that are formulated into a logical , systematic and explanatory scheme [33] .
Children were asked if they thought they might or might not be at risk . Some provided logical reasons for considering themselves at risk considering exposure to risk factors in the environment and lack of preventive actions taken to reduce their exposure to these risk factors . They believed that they would be at risk if they went barefoot and were aware that their exposure could be reversed through preventive actions . If I walk barefoot and simply step on harmful things , I think my feet may also swell like that of my father . As a result , whatever they complain about poverty , I urge them to buy shoes for me . ( FGD participant , boy , 13 years , grade 4 ) On the other hand , misperceptions around personal susceptibility to podoconiosis were also common . Some children thought that they were at risk based on exposure to factors that are not recognized to cause podoconiosis such as exposure to snake bite and sharing of contaminated water . Some children never thought about the risk of getting podoconiosis . This is mainly because they related occurrence of disease to ‘God’s plan’ . Some children thought that they were not at risk and not worried of getting the disease because their feet were healthy at the time of interview . When a boy was asked whether he had ever thought about getting podoconiosis , he replied “no , I have never thought about getting foot swelling as my feet are healthy now . ( IDI participant , boy , 12 years , grade 2 ) Participants in all individual and group interviews were aware of the negative consequences of the disease on affected individuals and their families . They noted that podoconiosis “causes illness and results in unexpected medical expenses” , “limits capacity to work” , “embarrassing” , “exposes family of affected person to starvation” , “causes illness and makes them bedridden due to swelling in groins” , “limits their ability to walk or run fast” , and “dissociates them from other people because of the bad smell and flies collected on wounds of their feet” . More importantly , they also revealed the impact of their parents’ condition on their own life , citing stress and starvation because of their parents’ illness . The participants held a positive outlook towards using footwear . They stated that using footwear protects the feet from dust , snakes , poison , dew , chilly weather , injuries by sharp things . Children also clearly stated the importance of foot hygiene in preventing podoconiosis . As a FGD participant states , “if people wash feet regularly , before and after wearing shoes , they cannot get the disease” ( FGD participant , boy , 12 years , grade 4 ) . The perceived discomfort of footwear , particularly in hot weather , was commonly thought to be a barrier to regular use . Children associated closed rather than open shoes with discomfort in hot weather . They repeatedly stated that using closed footwear in hot weather caused nail dystrophy and ‘mich’ . On the other hand , girls tended to perceive the disadvantages of both open and closed footwear . They thought that the regular use of sandals caused heel fissures while the regular use of closed plastic boots damaged the toenails . They recommended changing between different types of footwear for better foot health . Most children also said that they felt uncomfortable using any type of footwear when they engaged in farming activities . Children also said it was difficult to do sport wearing shoes . While some were worried about shortening of the shoes’ life , others were more concerned about injuring themselves . Another problem was the limited number of pairs of footwear they owned . Those who had a single pair of shoes kept them for special occasions such as school or church . Some children were concerned about the economic situation of their parents and refrained from wearing shoes regularly so as to preserve the ones they had . The poor quality of shoes purchased for children was also reported to negatively influence the motivation of children to use them regularly . Regarding foot washing , most children indicated that they washed their feet before putting on shoes , before going to bed in the evening and after performing certain activities barefoot . Almost all of the children reported that they washed their feet at least once a day . They did not perceive barriers to washing their feet regularly , except discipline or consistency . The availability of water nearby and the encouragement of parents were reported to be enabling factors . However , when observed , the participant children’s feet were not as clean as expected since they rarely used soap . As discussed earlier , because of the discomfort of wearing closed shoes for farm activities and hot weather , children tended to instead wear either open ( less protective ) types of footwear or to walk barefoot . They were asked if they thought they could use closed footwear for all conditions regardless of the perceived challenges . Most said they could not wear closed shoes while farming because they were too heavy and cause bad smell . On the other hand , for some participants , self-efficacy depended on the type of closed footwear . For instance , most children thought that they could use canvas or ‘sieve’ plastic boots ( with ventilation holes ) in all seasons or times of day . They thought that these boots let air circulate around the feet in hot weather and are comfortable for cold weather since they cover the whole foot . Canvas shoes were also thought to be good under all conditions .
To our knowledge , this study is the first to address health beliefs of school-age rural children in podoconiosis-affected families . We have explored dimensions of health beliefs that are important to public health intervention in relation to this condition and other Neglected Tropical Diseases . However , the findings of this study may not be generalizable to other settings as the participants belong to a homogenous cultural background . In addition , the present study emphasized exploration of the school-age children’s health beliefs . Further study is required to examine the interplay between school-age children’s health beliefs and the socioeconomic circumstances of their families . In conclusion , children held misconceptions regarding behavioral , environmental and genetic risk factors . They also perceived obstacles that threaten their ability to engage in preventive behaviors . Health education interventions may enhance school-age children’s health literacy and be translated into preventive action . Overcoming practical challenges such as shortage of footwear and other hygiene facilities requires other forms of interventions such as livelihood-strengthening activities . Linking podoconiosis-affected families with local governmental or non-governmental organizations providing socio-economic support for households may assist school-age children in those families to sustainably engage in preventive behaviors . | Podoconiosis is an example of a lifestyle-related disease that develops after childhood and affects millions of people with little experience of preventive behaviour . It is caused by prolonged barefoot exposure to irritant mineral particles , accompanied by inherited susceptibility . Children with a family history of podoconiosis are highly vulnerable to the disease . Several studies have underscored the importance of studying health beliefs in children for the early control of diseases that arise from risky behaviour and habits established in childhood , which continue into adulthood . This study attempted to explore the health beliefs of school-age children in podoconiosis affected families . The forms of health beliefs addressed in the present study were knowledge of podoconiosis risk factors and perceptions of severity of and susceptibility to the disease , benefits of and barriers to engaging in preventive actions such as footwear use and feet hygiene , and self-efficacy to perform preventive actions regardless of perceived barriers . Various forms of misconceptions and inaccurate risk perceptions were recorded . Participant children also reported several barriers that limited their engagement in preventive actions and low confidence to overcome them . These findings imply that school-age children at high risk of podoconiosis might benefit from an intervention that improves their knowledge about podoconiosis and enhances their self-efficacy to sustainably perform preventive behaviours . | [
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| 2017 | Health beliefs of school-age rural children in podoconiosis-affected families: A qualitative study in Southern Ethiopia |
Group A streptococcus ( GAS ) causes a wide variety of human diseases , and at the same time , GAS can also circulate without producing symptoms , similar to its close commensal relative , group G streptococcus ( GGS ) . We previously identified , by transposon-tagged mutagenesis , the streptococcal invasion locus ( sil ) . sil is a quorum-sensing regulated locus which is activated by the autoinducer peptide SilCR through the two-component system SilA-SilB . Here we characterize the DNA promoter region necessary for SilA-mediated activation . This site is composed of two direct repeats of 10 bp , separated by a spacer of 11 bp . Fusion of this site to gfp allowed us to systematically introduce single-base substitutions in the repeats region and to assess the relative contribution of various positions to promoter strength . We then developed an algorithm giving different weights to these positions , and performed a chromosome-wide bioinformatics search which was validated by transcriptome analysis . We identified 13 genes , mostly bacteriocin related , that are directly under the control of SilA . Having developed the ability to quantify SilCR signaling via GFP accumulation prompted us to search for GAS and GGS strains that sense and produce SilCR . While the majority of GAS strains lost sil , all GGS strains examined still possess the locus and ∼63% are able to respond to exogenously added SilCR . By triggering the autoinduction circle using a minute concentration of synthetic SilCR , we identified GAS and GGS strains that are capable of sensing and naturally producing SilCR , and showed that SilCR can be sensed across these streptococci species . These findings suggest that sil may be involved in colonization and establishment of commensal host-bacterial relationships .
Group A streptococcus ( GAS ) is a common human pathogen that has major healthcare and economic impacts . It causes a variety of human diseases ranging from superficial skin and throat infections to severe life-threatening diseases such as necrotizing fasciitis ( NF ) . There are approximately 10 , 000 cases of NF per year in the USA alone , estimated to cause 2400 deaths annually [1] . GAS is an exclusive human pathogen and many individuals carry GAS asymptomatically in their upper respiratory tract and other anatomic sites [2] . Little is known about what controls the conversion from a carrier to a pathogenic state in GAS infections . In fact , the distinction between commensals and pathogenic bacteria in general is blurred . This is mainly because both categories of bacteria use common modes of interaction with their hosts [3] . Both types of bacteria generally operate as communities and not as solitary microorganisms , hence , bacterial communication systems are key elements in host-bacterial interactions [4] . Bacteria communicate by secreting and subsequently sensing signal molecules [4] , [5] . These molecules are termed autoinducers because when their concentration exceeds a given threshold , their own expression , as well as that of other genes , is abruptly activated [4] , [5] . This activation usually occurs at high bacterial cell densities , which ensures that the increased gene expression only takes place in the presence of a sufficient number ( a quorum ) of cells , giving this mechanism the name quorum-sensing ( QS . ) In Gram-positive bacteria autoinducers are synthesized as a pro-peptide that is processed and secreted by ATP-binding cassette ( ABC ) transporters . The mature peptide is then sensed by two-component signal transduction systems ( TCSs ) . This triggers an up-regulated expression of the peptide itself ( autoinduction ) and often of the TCS and the ABC transporters , thus creating an ultrasensitive regulatory switch that subsequently leads to a change in the expression of an array of genes . These types of systems have been found to regulate various processes in streptococci including virulence , genetic competence , bacteriocin production , acid tolerance and biofilm formation [6] , [7] , [8] , [9] , [10] . By applying transposon-tagged mutagenesis ( STM ) on a strain isolated from a NF patient ( M14 type strain JS95 ) and using a murine model of human soft-tissue infections , we identified the streptococcal invasion locus ( sil ) [11] . Subsequent analysis showed that sil is composed of nine genes organized in four transcriptional units ( Figure 1 ) . A unit of a polycistronic mRNA , initiated from the P1 promoter , encodes a TCS SilA-SilB composed of a response regulator and a histidine kinase . An additional unit of polycistronic mRNA , initiated from the P3 promoter , which is transcribed in the opposite direction , encodes an ATP-binding cassette ( ABC ) transporter system SilD-SilE and the autoinducer peptide SilCR . However , in the NF strain JS95 , SilCR is not formed due to a start codon mutation ( ATG to ATA ) [11] , [12] . The third polycistronic mRNA unit , initiated from the P4 promoter , encodes bacteriocin-like peptides . Finally , the forth monocystronic mRNA initiated from the P2 promoter contains silC that highly overlaps silCR and is linked to the ability of GAS to spread in soft-tissues [11] , [13] . The basal level of transcription from the P3 and P4 promoters is extremely low but abruptly increases when a mature synthetic SilCR peptide is added to the culture medium of JS95 . The increased transcription of silE/D/CR is dependent on the dose of the added SilCR peptide and absolutely requires the TCS SilA-SilB [12] . We previously hypothesized that loss of SilCR and its regulatory potential might play a role in the highly invasive phenotype of JS95 and in the virulence of other GAS strains containing the sil locus [11] , [14] . In line with this notion , all the M14-type isolates identified in a survey of invasive GAS infections in Israel [15] , have a point mutation in the start codon of SilCR ( unpublished data ) , and the M4 and M18-type GAS strains have a truncated silD [16] . Streptococcus dysgalactiae subsp . equisimilis contains several taxonomic units including group G streptococci ( GGS ) [17] . GGS are close genetic relatives of GAS , and are usually regarded as commensals [18] , but recently were shown also to cause human invasive infections [19] , [20] . GGS and GAS occupy similar niches in the host and can exchange genetic material [21] , [22] , [23] , [24] , which in streptococci usually requires sensing and communication across species [25] . Since sil has been suspected to be acquired by horizontal gene transfer [11] , we also examined sil presence in GGS . Here we show that while sil is present only in a small fraction of GAS strains , its prevalence in GGS is much higher . We discovered that GAS and GGS strains naturally produce SilCR and demonstrate , for the first time , that the peptide can be sensed across these streptococci species .
We have previously shown that upon addition of synthetic SilCR peptide the P3 and the P4 , promoters of sil ( Figure 1 ) are stimulated in the GAS strain JS95 ( Table S1 ) , and this upregulation absolutely requires the TCS SilA-SilB [12] . SilA belongs to the AlgR/AgrA/LytR family of transcription regulators that bind DNA via a LytTR-type domain [26] . To activate promoters , these regulators form dimers and bind to two direct repeats [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] . Indeed , upstream of the P3 and P4 promoters we identified two sets of direct imperfect repeats , which we termed DR1 and DR2 , respectively ( Figure 2A ) . To examine whether these repeats are involved in the activation of the P3 and P4 promoters , we fused the DNA segments depicted in Figure 2A to a promoterless gfp reporter and both elements were subsequently cloned into an E . coli-Gram-positive shuttle vector , yielding the plasmids pP3-gfp and pP4-gfp ( Figure 2A ) . When transformed into the JS95 strain , both pP3-gfp and pP4-gfp produced the green fluorescent protein ( GFP ) only in the presence of synthetic SilCR ( Figure 2B ) . The production of GFP by both promoters was absolutely dependent on intact SilA-SilB , since no GFP accumulation was observed in a JS95 mutant-deficient of the TCS ( Figure 2B ) . These results support the notion that binding of SilCR to the histidine kinase sensor , SilB , leads to activation of the transcription regulator SilA , which in turn stimulates the P3 and P4 promoters . A dose-response relationship between the rate of GFP accumulation and SilCR concentration in GAS JS95 harboring pP4-gfp demonstrated that a maximal rate was reached at a concentration of approximately 5 ng ml−1 of SilCR ( Figure S1A ) . To determine whether the SilA-SilB TCS is not only necessary but may be sufficient for the activation of the P4 promoter , we cloned the genes silA and silB together with their native promoter into the pP4-gfp plasmid , yielding the plasmid pP4-gfp silAB ( Figure 2A and Table S2 ) . When the plasmid was transformed into the JS95 mutant lacking SilA-SilB it complemented the SilCR-mediated GFP accumulation , demonstrating that SilA-SilB are expressed from the plasmid and are necessary for promoter activation ( Figure 2C ) . JRS4 is an M6-type GAS strain which does not possess sil . Due to lack of SilA-SilB , transformation of JRS4 with pP4-gfp was insufficient for the production of GFP either in the presence or the absence of SilCR ( Figure 2C ) . By contrast , JRS4 transformed with pP4-gfp silAB produced at least 3-fold more GFP in the presence of SilCR than in its absence , demonstrating that SilA-SilB can be functional also in a heterologous GAS background , and thus is also sufficient for P4 activation . It is apparent that a relatively high level of GFP was produced by the strain transformed with pP4-gfp silAB even in the absence of SilCR ( Figure 2C ) . This probably results from an excess of SilA that is expressed from the plasmid . At such high concentrations , even an unphosphorylated response regulator might be able to bind to the DR2 site and partially activate the P4 promoter . To define more accurately the DNA sequence necessary for activation of the P3 promoter by SilA , we deleted unidirectionally and progressively the 286 bp intergenic DNA segment located between blpM and silE from the direction of blpM ( P4 ) towards silE ( P3 ) ( Figures 2D and S2 ) . A deletion of the first 186 bp that included P4 and the DR2 site did not affect P3 activity , demonstrating that this site does not play a role in the stimulation of the P3 promoter ( Figure 2D ) . By contrast , the DR1 site was absolutely necessary for P3 stimulation . Deletion of part of the distal repeat ( 205 out of the 286 nucleotides ) , completely abolished P3 activity , indicating that both repeats of DR1 are necessary to stimulate the transcription from the P3 promoter ( Figure 2D ) . The consensus binding sequence of the LytTR domain is considered to consist of 9-bp repeats separated by 12 bp [26] . Since , in our case , the guanine nucleotide at position 10 is invariant in both DR1 and DR2 sites ( Figure 3 inset ) , we decided to include it in the consensus sequence and evaluate the relative contribution of the various bases in the first repeat of DR2 to the P4 activity . This was achieved by systematically introducing single-base substitutions in 9 of the 10 nucleotides composing the first repeat of DR2 in pP4-gfp , forming a collection of plasmids harboring the desired mutations . Each plasmid was transformed into JS95 and the rates of GFP accumulation in the corresponding transformants were determined in comparison to that of the original pP4-gfp plasmid harboring the WT DNA segment . Substitution of the nucleotides at positions 2 , 6 and 10 of DR2 had a marked detrimental effect on P4 activity , whereas the effect of substitutions at positions 1 , 4 , 5 , 7 , 8 and 9 was varied and less pronounced ( Figure 3 ) . To assess the importance of the spacing between the repeats of DR2 to P4 strength , we determined the effect of a nucleotide insertion ( cytosine at position 16 ) or a nucleotide deletion ( one of 6 adenines stretching between positions 14–19 ) as detailed above . Both modifications completely eliminated P4 activity ( Figure 3 ) , indicating that an exact spacing of the repeats is crucial for the SilA-dependent stimulation of the P4 promoter . To identify SilA-regulated promoters we performed a bioinformatics search using a position specific probability matrix based on the sequences of DR1 and DR2 ( Figure 3 ) . Since the genome of the M14-type strain JS95 has not been sequenced yet , the search was performed on the genome of the M4-type strain MGAS10750 ( GenBank accession no NC_008024 ) , containing sil ( Figure 4 ) . First we used a probability matrix that assumed independence between the positions . However , since only two binding sites have been identified ( DR1 and DR2 ) , the probability matrix was overfitted . To overcome this drawback , we used the data obtained from the experiments that yielded numerical values for the contributions of the various positions in DR2 to promoter strength ( Figure 3 ) , and performed the computation analysis according to the algorithm detailed in “Materials and Methods” . The search identified two new putative SilA-binding sites located upstream to promoters P5 and P6 in a neighboring location to sil ( Figure 4 ) . Like P3 and P4 promoters , P5 and P6 initiate divergent transcription of an ABC transporter and bacteriocin–like peptides , respectively ( Figure 4 ) . The mRNA initiated from the P6 promoter includes 6 ORFs; of which the first 4 were shown by us to be situated on the same transcript ( Figure S3A ) . As expected , their transcription was strongly stimulated by the presence of SilCR and was dependent on the TCS SilA-SilB ( Figure S3B ) . The first ORF displays homology [∼60% amino acid ( aa ) identity] with the bacteriocin BlpU of S . pneumoniae [28] . Since , orthologuous genes are present in Streptococcaceae [30] , [35] , [36] , we also designated this gene as blpU . ORF2 is an 80 aa-long protein that in its N terminal portion displays homology with a superfamily of membrane-bound CAAX metalloproteases [37] . This family of proteins has been implicated in protection against or maturation of bacteriocins [29] . The first 30 aa of ORF3 contain a signal leader peptide common to almost all Streptococcal bacteriocins . Although ORF4 contains a double-glycine motif required for processing of bacteriocins , it does not display homology to bacteriocin-related genes . The next two ORFs encode the insertion sequences IS1562 and IS904a , but they are truncated ( Figure 4 ) . The divergent P5 promoter , containing a putative SilA-binding site , initiates the transcription of a bacteriocin transporter homologous to BlpA of S . pneumoniae [28] . This gene appears to be truncated and its transcription was moderately stimulated by SilCR , as described below ( Table S5 ) . When sil was discovered , we found that it contains a lower GC content than the 38 . 5% average of the GAS chromosome [38] . Therefore , we suggested that sil might be a part of a genomic island [11] . To identify potential boundaries of the entire sil locus , in view of the adjacent positions of P5 and P6 , we now calculated the percentage of G+C using the EMBOSS program freak [39] across sil in the M4 strain MGAS10750 ( GenBank accession no NC_008024 ) . We identified a sharp decline in the GC percentage around spy_0482 ( Figure 4 ) . The region of low GC content stretches over 15 . 5 kbp and GC sharply increases to 38 . 5% around spy_0488 ( Figure 4 ) . The good concurrence between the transcription studies and the GC content changes made us redefine sil as the entire 15 . 5 kbp region . Finally , sequencing of the region upstream to SilA in the M14 strain JS95 verified that the entire sil locus of JS95 ( GenBank accession no GQ184568 ) is highly homologous to that of M4-type strain MGAS10750 ( GenBank accession no NC_008024 ) , as shown in Figure 4 . The prevalence of sil among GAS strains ranges between 12 to 18% [12] , [16] , [40] . Only two out of the 13 sequenced genomes , belonging to M4 and M18-types , contain sil . Nonetheless , all the other genomes possess blpM , spy_0484 and spy_0486 , which constitute an integral part of sil ( Figure 4 ) . Moreover , in all sequenced genomes , these genes are located in the same genomic location as in the M4-type sil-possessing strain , as shown for the sil-deficient M1-type SF370 strain ( accession no NC_002737 ) in Figure 4 . Intriguingly , GAS strains , which do not possess sil , still retain the bacteriocin-like genes blpM and spy_0484 , considered to be a part of sil . Furthermore , they also have genes encoding bacteriocin-like peptides BlpI and BlpJ [28] ( flanked by a part of the IS905 transposon element ) in the same location where sil would have been in these strains ( Figure 4 ) . This implies that due to their extensive sequence similarity , bacteriocins might serve as hotspots for genetic rearrangements . To identify all the genes directly and indirectly regulated by SilCR , we performed gene expression analyses using a microarray . As mentioned above , the genome of JS95 has not been sequenced yet , therefore we constructed , together with NimbleGen , a “universal” S . pyogenes array . This microarray covers all ORF's from 13 sequenced S . pyogenes strains as well as a number of ORF's from strain JS95 . A detailed description of the design of this array is found in “Materials and Methods” . Since sil transcription regulation was previously studied under conditions in which JS95 was grown to an early log in the absence and presence of 10 µg ml−1 of SilCR [12] , we first performed the microarray analysis under these conditions . It was found that in the presence of SilCR the transcription of 46 genes was altered by at least 2-fold; 4 of which were down-regulated [Figure 5 , Table S5 , and NCBI Gene Expression Omnibus ( GEO ) ( accession no GSE16961 ) ] . As expected from a previous study [12] and the data shown above , the transcription of genes initiated from the P3 , P4 and P6 promoters was strongly up-regulated by SilCR ( Figure 5 and Table S5 ) . As mentioned above , the transcription of the truncated blpA which is initiated from the P5 promoter was moderately up-regulated ( Figure 4 and Table S5 ) . Surprisingly , the transcription of silB ( promoter P1 , Figures 1 and 4 ) was slightly up-regulated , an effect previously undetected in real-time RT-PCR studies [12] . There was also an increase in the transcription of 37 genes that do not belong to sil ( Table S5 ) . To test the immediate response to SilCR , we performed the expression analysis using RNA samples that were prepared 10 min after SilCR addition . This period of time is sufficient for maximum transcription from the P3 and P4 promoters [12] . We found that under these conditions only genes having predicted SilA-binding sites were up-regulated ( Figure 5 and Table S5 ) . The spy_0416 gene located downstream of blpM ( nomenclature of M1 GAS SF370 strain accession no NC_002737 ) was also up-regulated but this is probably due to generation of a long read-through transcript from the P4 promoter . To ascertain that the transcription under these conditions is solely regulated via the SilA-SilB TCS , we performed transcriptome analysis using the JS95ΔsilAB mutant , and found that there were no significant changes in gene transcription in the presence and absence of SilCR ( Figure 5 and Table S5 ) . We also performed a genome-wide expression analysis at 50 ng ml−1 of SilCR , a concentration that is similar to that actually produced by GAS and GGS strains possessing a functional sil ( see below ) . We found that besides spy_0416 and spy_0150 ( nomenclature of M1 GAS SF370 strain accession no NC_002737 ) only sil genes were affected ( Figure 5 and Table S5 ) . These results taken together show that exposure of a sil-proficient GAS strain for a short time period to SilCR concentrations ranging from 50 ng ml−1 to 10 µg ml−1 , mainly affects sil genes . However , prolonged incubation alters also the transcription of genes that do not possess the proposed SilA-binding sites ( Figure 5 , Table S5 ) . To determine whether or not the change in these genes is SilA-mediated or is independent of SilA , requires additional analyses . Nonetheless , the fact that SilCR alters gene expression in a sil-deficient strain was reported by others [41] . In agreement with this observation , we found that sagA transcription was increased 4-fold when a ΔsilAB mutant was grown to an early log phase ( and not for 10 min as shown in Figure 5 ) in the presence of 10 µg ml−1 of SilCR ( not shown ) . We previously reported , using a dot blot assay based on anti-SilCR antibody , that GAS is able to produce SilCR [12] . Yet , a more careful examination showed that this assay is prone to generating false positive results . To overcome this drawback , we used the ability of JS95 transformed with pP4-gfp to detect SilCR in the nanomolar range ( Figure S1A ) , and searched for natively produced SilCR in the growth medium of different GAS strains . However , our attempts to trigger SilCR production by applying the stress conditions that induced competence in S . pneumoniae [42] , [43] , were unsuccessful . We therefore decided to switch on the autoinduction circle by providing sil-possessing strains with minute concentrations of SilCR , according to the strategy that is illustrated in Figure 6 , and designated here as a “Jump start” experiment . To do so , tested strains were exposed to 5 ng ml−1 of synthetic SilCR . After 3 h of incubation , the bacteria were removed by centrifugation and the supernatant was collected and diluted 10-fold into the culture medium of JS95 transformed with pP4-gfp , which served as the reporter strain ( Figure 6 ) . If a tested strain is able to produce its own SilCR , the latter will accumulate in the culture medium and then induce GFP production in the reporter strain ( Figure 6 right panel ) . If , however , the tested strain does not produce SilCR , the dilution of the supernatant medium prior to its addition to the reporter strain reduces the synthetic SilCR to a concentration that barely triggers GFP production ( Figure 6 , left panel , Figure S1A ) . By applying this strategy , we found that GAS strain IB7 naturally produces SilCR . A 10-fold dilution of WT IB7 supernatant triggered GFP production in the JS95pP4-gfp strain to a relative fluorescence ( RF ) value that was comparable to that obtained by 5 ng ml−1 of synthetic SilCR ( Figure 7A , white and grey bars ) . Mutants of IB7 , deficient of either silCR or silAB , did not induce GFP production above the RF value produced by 0 . 5 ng ml−1 of synthetic SilCR . Moreover , when anti-SilCR antibody was added to the 10-fold diluted supernatant of IB7 WT strain , it completely blocked GFP production ( Figure 7A , white and grey bars ) . This antibody captures the SilCR peptide and neutralizes GFP-production in a dose-dependent manner ( Figure S4 ) . Since GAS and GGS are close relatives and were shown to exchange DNA elements [22] , we screened several GGS isolates for sil . In contrast to ∼20% prevalence in GAS [12] , [16] , all the 12 GGS isolates tested possessed the locus as determined by PCR analysis ( Table S6 ) . In addition , 5 out of 8 ( 63% ) GGS isolates tested responded to exogenously added SilCR when transformed with pP4-gfp ( Table S6 ) , while only 4 out of 14 sil-containing GAS strains [JS95 , IB7 , T13 and T14 ( Table S1 ) ] were positive in this assay . All examined GGS isolates contained ATG and not ATA as a start codon of SilCR but only one out of the 5 examined had an un-truncated silD ( Table S6 ) . Finally , we checked the ability of GGS isolates to produce SilCR using the “Jump start” assay described above . Among all isolates tested , only N3 ( possessing an intact silD ) was able to produce SilCR ( Figure 7A , black bars ) . This ability was abolished when silE was interrupted by insertion-inactivation or when anti-SilCR antibodies were added to the reporter strain together with the supernatant of N3 WT ( Figure 7A , black bars ) . These data do not only suggest that GGS is capable of producing mature SilCR , but also that this peptide can be sensed by GAS . To demonstrate that the SilCR-mediated interspecies communication works in both directions , we performed a reciprocal experiment using GGS N9 harboring pP4-gfp as a reporter strain . Due to reduced ( 5 to 6-fold ) sensitivity of this reporter strain compared to that of JS95 transformed with pP4-gfp ( Figure S1A , B ) , we diluted the supernatant of the tested strain only 3-fold . A diluted supernatant of WT IB7 triggered GFP production in the N9 reporter strain to a RF value that was comparable to that produced by 10 ng ml−1 of synthetic SilCR ( Figure 7B ) . In contrast , supernatants of ΔsilCR or ΔsilAB mutants of IB7 , resulted in RF values equivalent to that produced by 1 . 7 ng ml−1 of synthetic SilCR , demonstrating that the mutants lost their ability to produce native SilCR . As shown above , anti-SilCR antibody reduced the GFP production to the background level . Taken together these results clearly show a bi-directional communication between GAS and GGS strains harboring functional sil . Since IB7 and N3 were positive in “Jump start” experiments , we sequenced the core region of sil stretching from silA to blpM in both strains ( GeneBank accession no , GQ184566 and GQ184567 , respectively ) and compared them to JS95 sil ( accession no , GQ184568 ) . As expected , silCR of IB7 and N3 contains ATG and not ATA as a start codon . Furthermore , these strains have an un-truncated silD . Since other GGS strains that could not produce SilCR possess ATG as the silCR start codon but have a truncated silD ( Table S6 ) , this strongly suggests that the ABC transporter system SilD-SilE is responsible for SilCR processing and secretion .
In this study we characterized in details the streptococcal invasion locus sil in GAS and GGS and showed , for the first time , that SilCR can be sensed across these streptococci species . sil resembles the two separate regulatory networks that use different but paralogous TCSs to trigger competence ( Com ) and bacteriocin production ( Blp ) in the mitis group , ( S . pneumoniae , S . gordonii , and S . sanguis ) [44] . Although the organization of the sil locus is more similar to that of the Blp system , and SilA-SilB is more closely related to BlpR-BlpH , SilCR belongs to the family of competence-stimulating peptides [11] . Like many response regulators linked to autoinducing peptides , SilA belongs to the LytTR family [26] . Response regulators containing LytTR are known to dimerize and bind to 9-bp repeats , which are separated by a 12 bp spacer [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] . We , however , found that the DNA element responsible for SilA-dependent activation of transcription is composed of two 10 bp repeats separated by a spacer of 11 AT rich bp . Recently a crystal structure of the LytTR domain of S . aureus AgrA bound to a 15 bp double stranded DNA was solved at a 1 . 6 Å resolution . It was shown that a side chain atom of LytTR forms contact with a nucleotide located at position 10 of the repeat [45] , thus this finding supports our notion that position 10 in the DNA repeat plays an important role in SilA-mediated activation of the P4 sil promoter . The bioinformatics and genome-wide transcription analyses performed here indicate that sil acts as an autonomous unit and contains almost all the genetic information necessary for its regulation by a QS mechanism . It is not known what controls the P1 promoter initiating the transcription of the silA/B TCS , however recent work shows that it is upregulated within the host [13] . The finding that sil is an autonomous unit supports the hypothesis that sil might represent an ancient mobile island that may have been acquired before GAS speciation . In GAS but not in GGS , subsequently a DNA segment stretching from the 5′ end of sil up to silE was lost from most strains . The presence of two truncated transposases ( IS1562 and IS904a ) within sil , a truncated silB gene located at the 5′ border of sil , and the fact that the truncated blpA gene is highly homologous to silE ( 84% identical amino acids ) may indicate that this location underwent an extensive genomic rearrangement . Furthermore , all GAS genomes that lost sil contain the same DNA segment , encoding the two bacteriocins , BlpI and BlpJ [28] , flanked by a part of the IS905 transposon , which is located at the same genomic position ( Figure 4 ) . This suggests that a second recombination event may have been responsible for sil excision , although other scenarios can not currently be ruled out . Since most GAS isolates do not possess functional sil during invasive and rapidly disseminating infections , it is possible that sil might play a role during colonization or at the early steps of disease progress . Having developed the ability to quantify SilCR signaling via GFP accumulation , we show here that SilCR can be produced by some GAS and GGS strains and sensed across these species . This could have an important impact for establishing colonization . Our finding that 2 out of 4 transcriptional units that are upregulated by SilCR encode bacteriocins supports this notion even more , because these peptides control bacterial growth [46] and could be instrumental in cases of competition in that stage . A conclusion that only a minor portion of GAS and GGS strains possess a functional sil may be premature because , the current as well as most previous studies , were performed on GAS and GGS strains isolated from severe diseases [12] , [15] , [16] , [40] , [47] . Thus , it will be intriguing to explore the prevalence and the role of sil among GAS and GGS strains from carriers in which a commensal host-bacterial relationship is established .
All GAS and GGS strains used in the study are described in Table S1 . For molecular cloning we used Escherichia coli strain JM109 , which was cultured in Laria-Bertani broth , Lennox ( Becton , Dickinson , and Sparks , MD , USA ) . GAS and GGS were cultured in Todd-Hewitt medium ( Becton , Dickinson ) supplemented with 0 . 2% yeast extract ( Becton , Dickinson ) ( THY media ) at 37°C in sealed tubes without agitation . To produce solid media , Bacto™ Agar ( Becton , Dickinson ) was added to a final concentration of 1 . 4% . When necessary , antibiotics were added at the following concentrations: for GAS and GGS: 250 µg ml−1 kanamycin ( Km ) , 50 µg ml−1 spectinomycin ( Spec ) and 1 µg ml−1 erythromycin ( Erm ) ; for E . coli: 100 µg ml−1 ampicillin ( Amp ) , 50 µg ml−1 Spec , 750 µg ml−1 Erm and 50 µg ml−1 Km . All the antibiotics were purchased from Sigma-Aldrich ( St Louis , MO , USA ) . Plasmids and primers used in this study are listed in Table S2 and Table S3 , respectively . The primers were synthesized by Syntezza Bioscience ( Jerusalem , Israel ) . Plasmid DNA was isolated by commercial kits ( Wizard®Plus Minipreps , Promega , Madison WI , USA ) , according to the manufacturer's instructions and used to transform E . coli , GAS and GGS strains by electroporation as described previously [48] . Restriction endonucleases , ligases and polymerases were used according to the recommendations of the manufacturers . Chromosomal DNA was purified from GAS as described previously [48] or by using the Wizard® Genomic DNA Purification Kit ( Promega , Madison WI , USA ) . Linear DNA fragments were purified using Certified™ low-melt agarose ( Bio-Rad , Hercules , CA , USA ) . PCR products were purified using a commercial kit ( QIAquick PCR Purification Kit , ( Qiagen , Hilden , Germany ) . DNA sequencing was performed using the Excel II cycle sequencing kit ( Department of Genome Technologies , Givat Ram , The Hebrew University , Jerusalem , Israel ) . All other procedures were conducted according to standard protocols [49] . RNA was purified by applying one of two procedures . JS95 or its derivative mutants were grown in THY to OD600 = 0 . 2 , the cultures were divided among several tubes and the desired amount of synthetic 96% pure SilCR ( BioSight , Israel ) was added to each tube , which was then incubated for 10 min at 37°C . Alternatively , bacteria were grown to the desired growth phase with or without 10 µg ml−1 of SilCR that was added at the beginning of culture growth . Total RNA from bacteria was isolated by hot acidic phenol extraction as previously described [53] . For microarray analysis the RNA concentration and integrity was verified by capillary gel electrophoresis in the Agilent 2100 Bioanalyzer . Only samples with an RNA integrity number equal or higher than 8 were used . RNA ( 4 µg ) was treated with RQ1 DNase ( Promega , Madison WI , USA ) and subjected to cDNA synthesis using MMLV reverse transcriptase ( Promega , Madison WI , USA ) , according to the manufacturer's protocols and as described previously [12] . Standard real time RT-PCR reactions were conducted using . SYBR-green mix ( Absolute SYBR GREEN ROX MIX , ABgene ) and fluorescence detection was performed using Rotor-Gene 3000 A ( Corbett life Science , Qiagen Germany GmbH ) according to manufacturer's instructions . RT-PCR primers ( Table S3 ) were designed using Primer Express™ software v2 . 0 ( Applied Biosystems ) . The cDNA amount of gyrase subunit A ( gyrA ) was used to normalize expression data for each target gene . Each assay was performed in duplicates with at least two RNA templates prepared from independent bacterial cultures grown on different days . The data were analyzed according to the standard curve method ( Rotor-gene analysis software 6 . 0 ) and are presented as abundance of transcript amount relative to that of gyrA . Bacteria harboring plasmids containing gfp ( Table S2 ) were grown and treated as described in the text , washed twice with phosphate-buffered saline ( PBS ) , concentrated 10-fold by resuspension in PBS at 1∶10 of the original volume and transferred in duplicates of 0 . 3 ml into 96-well flat bottom transparent plates ( FluoroNunc™ ) . The relative fluorescence data were measured in a fluorescence/absorbance reader Infinite F200 ( Tecan , Austria GmbH ) using the filter set 485/20 nm for excitation , 535/25 nm for emission . The fluorescence data were normalized according to the density of the cultures , which was determined by measuring OD at 595 nm . For simplicity the relative fluorescence values were divided by 100 for presentation . To measure promoter strength , the initial rates of GFP accumulation were calculated . For this purpose , cultures were grown to an OD600 = 0 . 2 and then various concentrations of synthetic SilCR were added . Fluorescence intensity was determined for time 0 and then for 4 additional time points , using samples withdrawn every 15 min . Promoter activity was calculated from slopes of fluorescence intensity as a function of time by performing least square analyses which yielded coefficients of determination greater than 0 . 95 ( R2>0 . 95 ) . For the “Jump start” experiments strains were grown in THY supplemented with 10% ( V/V ) of fetal bovine serum ( Biological Industries , Beit Haemek , Israel ) in the presence of 5 ng ml−1 of SilCR until late log phase ( OD600 = 1 ) . Then , the cultures were centrifuged for 10 min at 14 , 000 g and the supernatants were diluted 10 or 3-fold into the culture media of JS95pP4-gfp or N9pP4-gfp ( respectively ) which served as “reporter strains” . Relative fluorescence of the “reporter strain” was determined after an additional incubation of 2 h at 37°C , as described above . For the bioinformatics search for SilA-binding sites we used a probability matrix ( M ) . This matrix assumes independence between the different positions of the binding site , and accordingly the probability is where L is the length of the binding site and is the probability to find a specific nucleotide in position i . The probability matrix that takes into consideration the relative weights of the positions in the binding site motif is , where represents the weight assigned to each position of the motif; is as explained-above and is the frequency of the nucleotide in the genome . For the search over the genome of M4-type strain MGAS10750 containing sil ( GenBank accession no NC_008024 ) q ( cytosine ) and q ( guanine ) were designated as 0 . 192 and q ( adenine ) and q ( thymine ) were designated as 0 . 308 . The relative weights of the positions in the binding site motif were based on the results of the point mutations in DR2 , shown in Figure 3 . For each position , was determined as 1 minus the ratio of P4 activity for the specific mutant to that of the WT promoter . Although the mutation experiments were performed only on the first repeat , the weights were assumed to be suitable also for the second one . Mutating position 3 of the binding site was not successful , therefore was assumed to be equal to . The parameters of the two motifs are given in the Table 1 . Assigning small values to wi can result in a deprecated algorithm , therefore we also verified that the most probable binding sites found are not coincidental by computing a p value based on a comparison to random sequences ( see Table S4 ) . Using this model , the genome was screened for putative SilA binding sites by computing the probability of finding the two direct repeat motifs with a spacer of 11 nucleotides , starting at each position of the genome . The probabilities of finding each of the two repeats were multiplied and their natural log was computed and used as a score for comparing the different binding sites found . The statistical significance of each score was obtained by its comparison to scores of random sequences . We extracted a million random subsequences of 31 nucleotides ( the size of the entire binding site ) from the query , shuffled each of them and computed the score of each shuffled sequence . For each candidate binding site in the original genome sequence , the p-value of its score was obtained by the fraction of random 31-mers that exceeded this score . The candidate sites with a p-values lower than 10−5 were considered for further analysis and can be found in Table S4 . Searches for homologuos genes and proteins were performed using BLASTN and BLASTP [54] 2 . 2 . 20 programs against non-redundant databases with default parameters . Genomic regions alignments were performed using Integrated Microbial Genomes ( IMG ) program version 2 . 8 [55] . The GC plot was produced by freak software ( EMBOSS [39] ) using a stepping value of 100 and an averaging window of 500 . 12 GeneBank available GAS genomes: NC_002737 , NC_008022 , NC_006086 , NC_008024 , NC_008023 , NC_004070 , NC_007297 , NC_007296 , NC_003485 , NC_008021 , NC_004606 , NC_009332 . GeneBank accession no of IB7 sil locus - GQ184566 . GeneBank accession no of N3 sil locus - GQ184567 . GeneBank accession no of JS95 sil locus - GQ184568 . Microarray data - NCBI Gene Expression Omnibus ( GEO ) accession no GSE1696 . | Cell-to-cell communication in bacteria is termed quorum-sensing ( QS ) , which is triggered by signaling molecules called autoinducers . In streptococci , autoinducers are synthesized as immature peptides that are processed , secreted , and then sensed by two-component systems ( TCSs ) . As a result , the autoinducer's own expression is upregulated ( autoinduction ) , subsequently creating an ultrasensitive switch that turns on more genes . Group A streptococcus ( GAS ) is a human pathogen that causes many infections , including necrotizing fasciitis ( NF ) . Previously , we identified in a NF GAS strain a QS locus termed streptococcal invasion locus ( sil ) . Due to a mutation in the autoinducer peptide-SilCR , it is not produced by this strain . Here we sought to better explore sil and to examine if SilCR can be produced by other GAS strains , or strains of its close relative group G streptococcus ( GGS ) . To this end , we characterized the DNA promoter region responsible for the TCS-mediated activation upon sensing of SilCR , and based on bioinformatics and transcriptome analyses we identified genes that are directly affected by the autoinducer peptide . By converting SilCR response to fluorescence production and turning on the autoinduction circle with minute concentrations of synthetic SilCR , we discovered naturally SilCR-producing GAS and GGS strains , and showed that SilCR can be sensed across these species . Our study describes a novel way of cell-to-cell communications among streptococci . | [
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| 2009 | Functional Analysis of the Quorum-Sensing Streptococcal Invasion Locus (sil) |
Alpha-synuclein ( aSyn ) is the main component of proteinaceous inclusions known as Lewy bodies ( LBs ) , the typical pathological hallmark of Parkinson's disease ( PD ) and other synucleinopathies . Although aSyn is phosphorylated at low levels under physiological conditions , it is estimated that ∼90% of aSyn in LBs is phosphorylated at S129 ( pS129 ) . Nevertheless , the significance of pS129 in the biology of aSyn and in PD pathogenesis is still controversial . Here , we harnessed the power of budding yeast in order to assess the implications of phosphorylation on aSyn cytotoxicity , aggregation and sub-cellular distribution . We found that aSyn is phosphorylated on S129 by endogenous kinases . Interestingly , phosphorylation reduced aSyn toxicity and the percentage of cells with cytosolic inclusions , in comparison to cells expressing mutant forms of aSyn ( S129A or S129G ) that mimic the unphosphorylated form of aSyn . Using high-resolution 4D imaging and fluorescence recovery after photobleaching ( FRAP ) in live cells , we compared the dynamics of WT and S129A mutant aSyn . While WT aSyn inclusions were very homogeneous , inclusions formed by S129A aSyn were larger and showed FRAP heterogeneity . Upon blockade of aSyn expression , cells were able to clear the inclusions formed by WT aSyn . However , this process was much slower for the inclusions formed by S129A aSyn . Interestingly , whereas the accumulation of WT aSyn led to a marked induction of autophagy , cells expressing the S129A mutant failed to activate this protein quality control pathway . The finding that the phosphorylation state of aSyn on S129 can alter the ability of cells to clear aSyn inclusions provides important insight into the role that this posttranslational modification may have in the pathogenesis of PD and other synucleinopathies , opening novel avenues for investigating the molecular basis of these disorders and for the development of therapeutic strategies .
Protein misfolding and aggregation is an unavoidable and widespread problem in biology . Cells evolved a series of quality control mechanisms to ensure overall proteostasis and , in some cases , to exploit the plasticity of diverse conformational states , including those concealed in protein aggregates , as in the case of certain types of prions . In other instances , protein aggregates can be detrimental [1] , [2] . Protein inclusions made of alpha-Synuclein ( aSyn ) , known as Lewy bodies ( LBs ) are the pathological hallmark of Parkinson's disease ( PD ) and other disorders known as synucleinopathies [3] , [4] . The normal function of aSyn is still unclear , but it is thought to be involved in the regulation of dopamine neurotransmission , vesicular trafficking and in synaptic function and plasticity [5] . Although aSyn is phosphorylated at low levels under physiological conditions , a striking 90% of aSyn is phosphorylated at S129 ( pS129 ) in LBs [6] . However , the significance of pS129 in the pathogenesis of synucleinopathies is unresolved . While studies in Drosophila melanogaster [7] and transgenic mouse models of PD [8] showed that pS129 aSyn was pathogenic , studies in rats and in Caenorhabditis elegans failed to associate toxicity with phosphorylation and suggested a role of pS129 in the attenuation of aSyn induced neuronal dysfunction [9] , [10] . However , no differences in toxicity or aggregate formation were seen in a rat model [11] . Whether pS129 promotes or prevents aggregation remains largely controversial [12]–[14] . The yeast Saccharomyces cerevisiae is a powerful model for the study of protein misfolding due to the high conservation of the quality control systems with all other eukaryotes , including humans [15] . Although S . cerevisiae lacks an aSyn ortholog , heterologous expression of the protein induces toxicity in a concentration dependent manner and is associated with the formation of cytoplasmic protein inclusions [16] . Moreover , a network of highly conserved aSyn interactors was identified , suggesting the protein can be studied using simple models such as yeast , worms , or flies , in addition to mammalian models [17] , [18] . Several pathways involved in aSyn-associated toxicity in yeast are conserved in other eukaryotic models of PD . This is the case of apoptosis [16] , lipid droplet accumulation [16] , mitochondrial dysfunction [19] , [20] , proteasome impairment [16] , [21] , [22] , oxidative stress [21] , [23] , autophagy and mitophagy dysfunction [24] , [25] , vesicle trafficking defects [16] , [26] , and ER-to-Golgi trafficking impairment [20] , [27] , [28] . PD pathogenesis is thought to be exacerbated from inefficient protein clearance as consequence of dysfunction in protein degradation [29] , [30] . Clearance of aSyn can occur through direct proteolysis [31] , the ubiquitin-proteasome system ( UPS ) [32] and/or chaperone-mediated autophagy ( CMA ) [33] . However , under pathological conditions , aSyn inhibits the proteasome [22] , [34] and impairs chaperone-mediated autophagy , leading to the upregulation of macroautophagy ( hereafter referred as autophagy ) [35] , [36] . Autophagy dysfunction plays a central role in PD [24] , [35] , [37] and was shown to be required for aSyn degradation under pathological conditions [38] . Accordingly , increasing evidence suggests the existence of a complex cross-talk between different forms of autophagy and also between autophagy and the proteasomal degradation pathway , processes known to play distinct roles in the clearance of specific species of aggregated aSyn [22] , [34] , [35] , [39] , [40] . In yeast , the clearance of aSyn inclusions has been associated with both UPS and autophagocytic degradation pathways [41] . However , recent observations suggest the UPS may be less relevant in mediating aSyn clearance [25] . Here , we explored the power of yeast genetics in order to gain mechanistic insights into the role of S129 phosphorylation on aSyn biology . Using mutants of aSyn that attempt to mimic either phosphorylated ( S129E ) or unphosphorylated ( S129A or S129G ) states aSyn , we found that blocking phosphorylation increases aSyn toxicity and promotes the formation of cytosolic inclusions . Our data are consistent with the involvement of phosphorylation in the clearance of aSyn via autophagy . Altogether , our study provides insight into the role of S129 aSyn phosphorylation and opens novel avenues for additional studies in higher model systems .
To investigate the effect of aSyn phosphorylation in yeast , we used strains carrying two copies of human SNCA cDNA integrated in the genome , encoding either wild-type ( WT ) , S129A , S129G , or S129E mutant aSyn fused to GFP ( aSyn-GFP ) , in order to block ( S129A or S129G ) or mimic ( S129E ) phosphorylation , under the regulation of a galactose inducible promoter ( GAL1 ) ( Table 1 ) . These strains were previously described and characterized [42] and display a moderate level of aSyn toxicity compared to strains described in other studies [16] , [27] , [28] . Growth and viability ( colony-forming units , CFUs ) of the different strains were assessed upon induction of aSyn expression ( Figure 1A and S1A ) . We observed an initial lag phase of ∼2 hours in cells expressing either WT or S129E aSyn-GFP , compatible with the carbon source switch ( Figure S1A ) . Subsequently , these strains resumed growth and behaved similarly , growing slightly slower that the control cells ( Figure 1A ) . In contrast , cells expressing the S129A mutant exhibited a longer lag phase of ∼6 hours ( Figure S1A ) . During this period , cells expressing S129A aSyn-GFP lost viability , as indicated by propidium iodide ( PI ) staining of cells where membrane integrity was compromised ( Figure 1B ) . This was confirmed by spotting assays ( Figure S1B ) . In fact , 6 hours after induction of aSyn expression , 13 . 6±0 . 2% of the yeast cells expressing S129A aSyn-GFP were PI positive , compared to only 2 . 1±0 . 2% or 2 . 6±0 . 5% of cells expressing WT or S129E aSyn-GFP , respectively ( Figure 1B ) . Nonetheless , cells expressing S129A aSyn-GFP were able to adapt and to recover growth ∼7 . 5 hours after expression induction , with a similar growth rate to those expressing WT or S129E aSyn-GFP ( Figure 1A and S1A ) . These results suggest that yeast cells have mechanisms that allow them to cope with and to recover from toxicity induced by S129A aSyn . Still , cells expressing the mutant S129A aSyn-GFP failed to reach a final OD equivalent to that observed for cells expressing WT or S129E aSyn-GFP ( Figure 1A ) . Altogether , these results demonstrate that expression of S129A aSyn is more toxic for yeast cells than expression of WT or S129E aSyn . We then assessed the correlation between cytotoxicity and the subcellular distribution of aSyn-GFP . It was previously described that , initially , aSyn associates with the plasma membrane [16] . Upon increased accumulation of the protein , fluorescent foci appear adjacent to the plasma membrane and , finally , become cytoplasmic inclusions [16] , [27] , [41] . We imaged cells expressing the WT , S129A or S129E forms of aSyn-GFP by fluorescence microscopy and counted the percentage of cells displaying inclusions at different times after aSyn expression induction ( 1 . 5 , 3 and 6 hours ) ( Figure 2A ) . Interestingly , in addition to being the most toxic , the S129A mutant accelerated inclusion formation . At 3 hours post induction , the percentage of cells displaying inclusions was 3-fold higher for cells expressing the S129A mutant when compared to cells expressing WT protein ( 63 . 5±1 . 8% comparing to 23 . 7±5 . 8% ) ( Figure 2A ) . After 6 hours of expression induction almost all cells expressing S129A displayed inclusions ( 98±0 . 8% ) . In turn , the S129E mutation did not significantly affect the formation of aSyn-GFP inclusions ( Figure 2A ) . To confirm the effects observed with the S129A mutant were due to the inability of the protein for being phosphorylated , and not due to structural differences induced by the alanine residue , we also tested an aSyn mutant carrying an S129G substitution . Importantly , the results were identical to those observed with S129A aSyn-GFP ( Figure S2 ) . Thus , for subsequent experiments , we continued using the S129A aSyn-GFP phospho-resistant mutant . We next assessed whether the differences in toxicity and inclusion formation were due to different expression levels of the different variants of aSyn-GFP tested . Using western blot analyses , we found all variants were expressed at similar levels after 6 hours of expression induction ( Figure 2B ) . By using an antibody that specifically recognizes pS129-aSyn , we observed that human aSyn was phosphorylated on S129 residue by endogenous kinases ( Figure 2B ) . Thus , together with the fact that expression of both S129E and WT aSyn-GFP resulted in similar yeast growth phenotypes and percentage of cells with aSyn-GFP inclusions , we concluded that , in our yeast model , the S129E mutation mimics the phosphorylated state of aSyn at S129 ( Figure 1 and 2A ) . For this reason , we continued our study using just WT and S129A aSyn-GFP . In order to determine the biochemical state of WT and S129A aSyn-GFP we performed centrifugation in sucrose gradients , 6 hours after aSyn expression induction . We found that S129A aSyn-GFP is enriched in fractions corresponding to higher molecular weight species ( >200 kDa ) ( Figure 2C , left panel ) , and these differences were not due to differences in total protein levels ( Figure 2C , right panel ) . To further confirm this , we performed size exclusion chromatography ( SEC ) , another widely established method for the biochemical characterization of protein species . For WT aSyn-GFP , we detected species in fractions corresponding to molecular weights of 440 kDa ( Figure 2D and S3 ) . Interestingly , for S129A aSyn-GFP , we detected species in fractions corresponding to even higher molecular weights , since these eluted in the void volume of the column ( Figure 2D and S3 ) . The distribution of pS129 aSyn-GFP was also analyzed and showed a similar distribution to that of WT aSyn-GFP ( Figure 2D ) . This result convincingly demonstrates , for the first time , that in the yeast model considerable large oligomeric species of aSyn-GFP are formed , an issue that had not been previously resolved in the field . Several studies demonstrated that aSyn may disrupt multiple intracellular trafficking pathways in yeast [16] , [17] , [20] , [26]-[28] , [42] , [43] . However , the endoplasmic reticulum ( ER ) -to-Golgi vesicular trafficking impairment is one of the first defects following induction of aSyn expression [28] . This defect can be rescued by genes promoting ER-to-Golgi trafficking . In addition , genes negatively regulating ER-to-Golgi trafficking enhance aSyn toxicity [28] . To determine if the increased toxicity observed by the blockade of aSyn phosphorylation was also associated with ER-to-Golgi trafficking defects , we tested the effects of previously described suppressors ( Ypt1 , Ykt6 , Ubp3 , and Bre5 ) and enhancers ( Gyp8 and Pmr1 ) of aSyn toxicity in yeast . These modifiers of aSyn toxicity were co-expressed with either WT or S129A aSyn-GFP and the effect on toxicity and inclusion formation was evaluated 6 hours after induction ( Figures 3 and 4 ) . As in our yeast model WT aSyn-GFP is moderately toxic , the suppression of toxicity was not remarkable in the spotting assay but became evident when cells were assessed for PI-staining , a more refined method to determine cell viability ( Figure 3 ) . Our findings were consistent with what was previously described [28] . Among the suppressors tested , only Bre5 was not able to rescue S129A aSyn-GFP toxicity in both assays ( Figure 3 ) . Expression of Ypt1 , Ykt6 and Ubp3 significantly reduced the formation of both WT and S129A aSyn-GFP inclusions ( Figure 4A ) while the overexpression of Bre5 only decreased the formation of WT aSyn-GFP inclusions , and had no effect on S129A inclusion formation ( Figure 4A ) . Regarding the two enhancers of aSyn toxicity , Gyp8 and Pmr1 , expression of an extra copy of these genes exacerbated WT aSyn toxicity as expected [28] , and had a similar effect on S129A aSyn ( Figure 3 ) . Gyp8 and Pmr1 also increased the formation of aSyn inclusions in WT aSyn-GFP expressing cells without affecting the percentage of cells with S129A aSyn-GFP inclusions , which is already high and therefore precludes the ability to detect an additional increase ( Figure 4A ) . To determine if the phenotypes observed resulted from an effect of the modifiers on the levels of WT and S129A aSyn-GFP , we preformed western blot analyses . We found that total aSyn and pS129 levels were not affected by the co-expression of the modifiers ( Figure 4B ) . Altogether , these results indicate that ER-to-Golgi vesicle trafficking defects associated with S129A aSyn toxicity can also be rescued by suppressors of WT aSyn toxicity . We postulated that the different biochemical properties might arise from differences in protein clearance of the S129A aSyn-GFP . To test this , aSyn expression was stopped after 6 hours by replacing galactose by glucose ( to repress the GAL1 promoter ) and aSyn-GFP clearance was followed for 6 hours ( Figure 5A ) . We found 49±0 . 4% of cells expressing S129A aSyn-GFP contained inclusions , while only 8±1 . 2% of the ones expressing the WT form presented aSyn-GFP inclusions , after the clearance period ( Figure 5B and C ) . This corresponds to a reduction of about 50% and 70% when compared to the initial amount of cells with inclusions , respectively ( Figure 5C ) . At 0 hours of clearance ( 6 hours after aSyn expression induction ) , S129A expressing cells presented fewer inclusions per cell ( Figure 5D ) . These inclusions were larger and more heterogeneous in size than those formed by WT aSyn-GFP ( Figure 5E ) . After 6 hours of clearance , no significant differences in the size of the inclusions formed by WT or S129A aSyn-GFP were detected ( Figure 5E ) , due to an increase in size of the inclusions formed by WT aSyn-GFP compared to zero of clearance ( Figure 5E ) . To further investigate how phosphorylation affects aSyn inclusion formation and characteristics , cells were analysed by flow cytometry ( Figure 5F ) . At 0 hours of clearance , both strains presented a homogeneous distribution of the GFP fluorescence showing a single population of cells ( Figure 5F ) . However , after 6 hours of clearance , two populations were visible for both WT and S129A aSyn-GFP , one with weaker ( sub-population a ) and one with stronger GFP ( sub-population b ) signal ( Figure 5F ) . We found that the strain expressing S129A aSyn-GFP accumulated more cells in the population displaying stronger GFP signal ( 11±3 . 5% versus 1 . 4±1 . 0% for WT aSyn-GFP ) . Together with the results from the fluorescent microscopy , these findings are consistent with cells accumulating inclusions of different sizes , with the larger inclusions displaying stronger GFP signal ( Figure 5F ) . Next , we evaluated the protein levels of S129A and WT aSyn-GFP at 0 and 6 hours of clearance by western blot analyses . As expected from the results in Figure 2C , no differences were observed at 0 hours of clearance ( Figure 6A ) . In addition , at 0 hours of clearance , both WT and S129A aSyn-expressing cells presented similar levels of Triton-X-Insoluble protein ( TI ) , 55 . 37±2 . 23% and 50 . 69±9 . 47% , respectively ( Figure 6B ) . However , 6 hours after clearance , the levels of WT aSyn-GFP ( 41 . 93±6 . 04% ) were around 32% below those of S129A aSyn-GFP ( 73 . 01±19 . 45% ) ( Figure 6B ) , and this was accompanied by a significant increase of the S129A aSyn-GFP in the TI fraction ( 69 . 98±6 . 11% ) ( Figure 6B ) . Importantly , during the clearance period , the levels of pS129 aSyn did not change significantly for the WT aSyn-expressing strain ( Figure S4 ) . Based on the morphological and biochemical differences observed for inclusions formed by WT or S129A aSyn-GFP , we hypothesized that the dynamics of aSyn in the inclusions might differ . To test this , we first used high-resolution 4D imaging in WT and S129A aSyn-expressing cells . Images were acquired every 10 min for 18 hours after induction of aSyn expression ( Figure 7 and Supplementary Movie S1 and S2 ) . Initially , WT aSyn-GFP distributed preferentially along the plasma membrane . In contrast , S129A aSyn-GFP formed inclusions sooner than the WT aSyn-GFP ( Figure 7 ) . The percentage of cells with inclusions in the population of cells expressing WT or S129A aSyn-GFP was determined and the results confirm that S129A mutation in aSyn promotes inclusions formation in yeast ( Figure 7B ) . To further compare the protein dynamics in the inclusions formed by WT or S129A aSyn-GFP , we next used Fluorescence Recovery After Photobleaching ( FRAP ) and calculated kinetic parameters based on exponential fitting to the FRAP plots ( Figure 8 ) . Under the assumption that aSyn-GFP diffusion is very fast compared both to binding and to the timescale of the FRAP experiment , i . e . that binding dominates and diffusion is not detected in the FRAP recovery , an exponential fit to each FRAP recovery curve enables the determination of the aSyn-GFP immobile fraction ( IF ) and mean residence time ( T ) ( Figure S5A ) . After 6 hours of aSyn expression induction , the inclusions formed by the WT aSyn-GFP were homogeneous with respect to the relative fluorescence recovery profile ( Figure 8A ) . The residence time for these inclusions was 39 . 3±0 . 7 seconds and the immobile fraction , which corresponds to static or long-term bound aSyn-GFP , was 19 . 3±7 . 9% ( Figure S5A ) . In contrast , the inclusions formed in cells expressing S129A aSyn-GFP were heterogeneous , and could be distinguished in three main groups , based on their FRAP recovery profiles . In group I the immobile fraction was 55 . 7±7 . 5% and the mean residence time was 17 . 2±0 . 8 seconds . In group II , the inclusions behaved as the ones formed in cells expressing WT aSyn-GFP , with a mean residence time of 35 . 2±11 . 9 seconds and an immobile fraction of 24 . 1±12 . 4% . Finally , in group III , inclusions presented a lower mean residence time of 14 . 9±3 . 1 seconds and an immobile fraction of 12 . 7±7 . 0% . We next determined the mean area and fluorescence intensity of the inclusions analyzed in the FRAP experiments before photobleaching . Inclusions formed by S129A aSyn-GFP were in general larger and presented stronger fluorescence than those formed by WT aSyn-GFP , suggesting the existence of heterogeneity in the inclusions ( Figure 8B and C ) . To test whether inclusion heterogeneity was associated with reduced viability of cells expressing non-phosphorylatable S129A aSyn , we performed FRAP only in PI positive cells containing inclusions . In those cells , the initial fluorescence of the inclusions was not recovered at all and the fluorescence signal was unstable ( Figure S6 ) , in contrast to the patterns observed for the WT and S129A inclusions presented in Figure 8 . Next , we compared protein dynamics of S129A aSyn-GFP inclusions 6 hours after blocking aSyn expression with that of inclusions at 0 hours of clearance . Again , we distinguished three types of inclusions based on the protein dynamics profiles ( Figure 8A ) . However , after 6 hours of clearance , the immobile fraction as well as the mean residence time increased in the S129A aSyn-GFP inclusions when compared to 0 hours ( Figure S5B ) . The presence of inclusions with different aSyn-GFP dynamics in the cell led us to hypothesize that the protein was partitioning between distinct subcellular protein quality control compartments . In particular , we hypothesized these compartments might be either the “juxtanuclear quality control” compartment ( JUNQ ) , that is in close proximity to the nucleus and colocalizes with the proteasome , the “insoluble protein deposit” ( IPOD ) , that colocalizes with the autophagic marker Atg8 [44] , or P-bodies , cytoplasmic RNA-protein ( RNP ) granules that contain non-translating mRNAs as a cellular response to stress [45] . To verify these hypotheses , we performed fluorescence microscopy using established sub-cellular markers , namely Atg8 ( IPOD ) , Pup1 ( the beta 2 subunit of the 20S proteasome , JUNQ ) and Dcp1 ( P-Bodies ) . However , we observed no colocalization between WT or S129A aSyn-GFP with any of these markers ( Figure S7 ) . aSyn inclusions in yeast cells colocalize with diverse trafficking markers including Ypt1 ( ER-to-Golgi ) , Ypt31 ( late Golgi ) , Sec4 ( secretory vesicles-to-PM ) , Ypt6 ( endosome-to-Golgi ) , Vps21 and Ypt52 ( early-to-late endosome ) and Ypt7 ( LE-to-vacuole ) [27] . Considering that we observed that S129A aSyn also relates to defects in ER-to-Golgi trafficking , we asked whether blocking pS129 could alter the normal distribution of aSyn inclusions . Thus , we compared the colocalization of WT and S129A aSyn-GFP inclusions using the trafficking markers indicated above . We found that blocking pS129 did not significantly alter the localization of aSyn inclusions ( Figure S8 ) . aSyn has previously been shown to interact with membranes and , at low levels of WT aSyn expression , it localizes mostly to the plasma membrane [16] . Since aSyn also begins to aggregate at the membrane in small vesicles , we sought to investigate its association with the endocytic machinery . In order to visualize plasma membrane to vacuole endocytic trafficking , we used the monocarboxylate-proton symporter Jen1 , which undergoes internalization through the endocytic pathway with subsequent vacuolar degradation [46] , [47] . At the initial stages of aSyn inclusion formation we observed almost exclusive colocalization of WT and S129A aSyn-GFP inclusions with Jen1 , from the plasma membrane , in early endocytic vesicles , to the vacuole ( Figure 9 and Supplementary Movie S3 and S4 ) . As aSyn inclusion formation becomes more severe , the vesicles no longer reach the vacuole and accumulate in larger Jen1-positive inclusions , which are likely to be late endosomes ( Figure 9 ) . WT aSyn inclusions are present either as single vesicle or smaller clusters , while S129A aSyn inclusions at late stages of aggregation represent large clusters of vesicles ( Figure 9 ) . Next , we evaluated whether phosphorylation altered the clearance of aSyn in the cell . First , we tested protein degradation by the ubiquitin-proteasome system ( UPS ) . For this , we deleted the PDR5 gene in the strains expressing aSyn-GFP to ensure chemical inhibition of the proteasome by MG132 [48] . The expression levels of aSyn-GFP in the Δpdr5 mutants and in the original strains were similar ( Figure S9A ) . Western blot analysis revealed a marked increase in the levels of ubiquitinated proteins in MG132 treated cells , confirming pharmacological proteasome inhibition was achieved ( Figure S9B ) . However , we found that proteasome inhibition did not alter the levels of either WT or S129A aSyn-GFP , nor the percentage of cells displaying aSyn inclusions ( Figure S9C ) . We observed a striking increment in ubiquitinated proteins relative to 3 hours of aSyn clearance ( Figure S9B ) , suggesting the aSyn-induced proteasome inhibition was at least partially reversible . Afterwards , we analyzed the contribution of autophagy to the clearance of WT and S129A aSyn-GFP by comparing ATG8 induction and autophagic flux . We used the mCherry-Atg8 processing assay [49] , [50] , and inserted the reporter under the control of the endogenous ATG8 promoter in the genome of the strains expressing WT or S129A aSyn-GFP ( Table 1 ) . ATG8 induction was measured by quantifying the increase of total mCherry signal ( mCherry-Atg8 and free mCherry signal ) normalized to the loading control ( GAPDH ) ( Figure 10A ) , reflecting autophagy induction [49] , [50] . On the other hand , autophagic flux was quantified by measuring the vacuolar degradation of the Atg8 domain of the reporter ( ratio of free mCherry to total mCherry signal ) by western blot analysis [49] , [50] , reflecting the vacuolar transfer and degradation of autophagosomes ( Figure 10A ) . Interestingly , WT aSyn-GFP induced a 2-fold increase in the levels of Atg8 which decreased gradually during the clearance period ( Figure 10A ) . In contrast , the levels of Atg8 remained unaltered throughout induction and clearance in cells expressing S129A aSyn-GFP ( Figure 10A ) . No alterations in autophagic flux were observed with either WT or S129A aSyn-GFP . Using flow cytometry , we confirmed these results and established a correlation between autophagy induction ( measured by mCherry fluorescence intensity ) and aSyn-GFP signal ( Figure 10B ) . At 0 hours of clearance the mCherry-Atg8 median fluorescence intensity ( MFI ) in cells expressing S129A aSyn-GFP were considerable higher than those in cells expressing WT aSyn , and decreased gradually during the clearance period to near basal levels ( Figure 10B ) . These results are consistent with the western blot analysis described above ( Figure 10A ) . As expected , it was visible that , during clearance , the fluorescence of either WT or S129A aSyn-GFP decreases ( Figure 10B ) . However , in both cases a sub-population of cells with larger and brighter inclusions maintained stronger GFP fluorescence after 6 hours of clearance . In this sub-population , higher levels of mCherry-Atg8 were observed both in the cells expressing WT or S129A aSyn-GFP , indicating that autophagy induction is more pronounced in cells with bigger and brighter aSyn inclusions ( Figure 10B ) . However , in this sub-population , the cells expressing S129A aSyn-GFP displayed lower levels of mCherry-Atg8 than cells expressing WT aSyn-GFP both at 0 and 6 hours of clearance ( Figure 10B ) . A second line of evidence for the effect of pS129 on aSyn clearance by autophagy was obtained by genetically modulating this pathway . For these studies , we deleted ATG1 and ATG7 genes in the WT and S129A aSyn-expressing strains . Atg1 is a kinase playing an important role in autophagy initiation [51] and its mutant is defective in autophagy [52] while Atg7 is an activator of Atg8 and is required for the formation of autophagic bodies [53] . Deletion of ATG1 and ATG7 did not significantly affect WT or S129A aSyn-GFP expression levels after 6 hours of induction ( Figure 11A ) . However , when aSyn expression was turned off and clearance was followed during 18 hours , Δatg7 resulted in higher levels of WT and S129A aSyn-GFP ( Figure 11A ) . Deletion of ATG1 and ATG7 significantly increased WT aSyn pS129 levels after 0 hours of clearance ( 6 hours after aSyn expression induction ) , an effect that was not observed after 6 hours of clearance ( Figure S10 ) . Deletion of ATG1 and ATG7 also significantly increased the percentage of cells displaying aSyn inclusions after 6 hours of clearance ( Figure 11B ) . Flow cytometry experiments confirmed the fluorescence microscopy results , showing a significant increase in the population of cells displaying stronger GFP signal when autophagy was impaired due to ATG1 or ATG7 deletion ( Figure 11C ) . Moreover , Δatg1 and Δatg7 S129A aSyn-GFP expressing cells also exhibited a higher percentage of PI positive cells , indicating that impairment of autophagy increased S129A aSyn-GFP toxicity , an effect that was not observed for WT aSyn-GFP expressing cells ( Figure 11D ) . Altogether these results indicate that blocking aSyn phosphorylation impacts on autophagy induction , suggesting cells process phosphorylated aSyn in a distinct way .
Here , we found that WT aSyn-GFP is strongly phosphorylated at S129 by endogenous yeast kinases and S129E mutant aSyn-GFP mimics the behavior of WT protein , in particular with respect to cytotoxicity and inclusion formation . This contrasts with other reports that showed the S129E aSyn-GFP mutant fails to mimic the effect of aSyn S129 phosphorylation [54] , [55] . It is possible that the discrepancy is due to differences in the systems used but , importantly , it suggests S129E aSyn mutant might constitute a valid approach for the assessment of the cellular responses involved in the accumulation of phosphorylated aSyn . The S129A mutation was found to promote aSyn fibrillization [10] , [56] . However , this was not consensual in all cell and animal models where S129A was expressed . In SH-SY5Y cells , a neuroblastoma cell line with dopaminergic characteristics , S129A aSyn expression reduces inclusion formation [12] , while in a Drosophila model , S129A aSyn expression results in the accumulation of increased levels of aSyn oligomers , but not of mature fibrils [7] . In our yeast model , expression of the phosphorylation-deficient S129A aSyn-GFP resulted in an exacerbation of aSyn toxicity concomitantly with a reduction in cellular viability and an increase in aSyn inclusion formation . This phenotype does not appear to be related to specific structural consequences of this mutation on aSyn , but rather to the blockade of S129 phosphorylation , as similar results were obtained when S129G aSyn-GFP , a different aSyn phospho-resistant mutant , was expressed . Despite the accumulating evidence favoring the hypothesis that soluble aSyn oligomers , rather than insoluble protein aggregates , are the cytotoxic species in PD , the question is still unresolved [57] , [58] . In yeast , aSyn inclusions were described as clusters of vesicles [26] , [27] , [42] , raising questions about whether aSyn accumulations actually displayed biochemical properties compatible with the formation of protein aggregates . However , at least some of these accumulations are indeed amyloid-like and β-sheeted aggregates , as they react with thioflavin S [41] or thioflavin T [59] . Here , we biochemically characterized the aSyn-GFP species that are formed in yeast cells using two complementary approaches ( sucrose gradients and size exclusion chromatography ) and clearly demonstrate the formation of large oligomeric species . Moreover , we established a correlation between increased inclusions formation and exacerbation of cytotoxicity and the formation of oligomeric species with higher molecular weight for the S129A aSyn-GFP mutant . Interestingly , we did not observe differences in the TX-insoluble fractions of cells expressing wither WT or S129A mutant aSyn . These results are consistent with other reports where large aSyn soluble oligomers are also considered to constitute the toxic species [60] . Among the various cellular defects that have been implicated in the etiology of synucleinopathies , vesicular trafficking impairment has emerged as a major component of aSyn-dependent toxicity in yeast and in other model organisms [16] , [17] , [20] , [26]–[28] , [42] , [43] . Both genetic and chemical modulation of vesicular trafficking modulate aSyn toxicity [17] , [20] , [28] . In this study we show that pS129 blockade exacerbates vesicular trafficking defects that can be relieved by overexpression of YPT1 , YKT6 and UBP3 , genes that increase forward transport between ER and Golgi . Ypt1 , the Rab guanosine triphosphatase whose mammalian ortholog Rab1 is able to prevent dopaminergic neuron loss [28] , plays an essential role in the tethering and docking of the transport vesicle with the Golgi [61] . Ykt6 , the soluble NSF ( N-ethylmaleimide–sensitive factor ) attachment protein receptor protein ( SNARE ) , increases forward transport by increasing the likelihood of membrane vesicles from the ER tethering to Golgi target membranes [62] . In turn , the ubiquitin protease Ubp3 , together with its cofactor Bre5 , function to deubiquitinate the COPII coat protein Sec23p , and likewise promote vesicle exit from the ER [63] . Interestingly , the Bre5 cofactor , which also suppresses aSyn toxicity [28] , was not able to restore S129A aSyn-GFP induced trafficking defect . This suggests that aSyn phosphorylation may modulate the way the protein interacts with components of the trafficking pathway , as one might expect . Moreover , GYP8 and PMR1 whose overproduction negatively regulates ER-Golgi trafficking , exacerbate S129A aSyn-GFP toxicity . Gyp8 , is a negative regulator of Ypt1 , that therefore inhibits ER-to-Golgi trafficking [64] , while Pmr1 is the major Golgi membrane P-type ATPase ion pump responsible for transporting Ca2+ and Mn2+ ions into the Golgi apparatus , both of which are important for proper processing and trafficking of proteins through the secretory pathway [65] . Proteostasis is a central concept in the context of several disorders [66] . An imbalance between the rates of protein synthesis , clearance , and aggregation , caused by proteostasis dysfunction , could favor accumulation and/or formation of protein oligomers and inclusions that contribute to cytotoxicity [67] . We found that blocking aSyn phosphorylation impaired the turnover of aSyn . During clearance of S129A aSyn , we observed a significant increase of the TX-insoluble fraction , concomitantly with the attenuation aSyn-induced cytotoxicity . Our observations are consistent with those in a study in Drosophila where reduced aSyn toxicity was correlated with an increase in detergent-insoluble aSyn [68] and suggests this may constitute a defense mechanism . Using high-resolution 4D imaging we found that WT aSyn has a lag phase during which it preferentially associates with the plasma membrane , whereas the S129A forms inclusions almost immediately . Furthermore , blocking aSyn phosphorylation alters the dynamics of aSyn in inclusions suggesting there are distinct populations of aSyn accumulating with different kinetics in inclusions . Based on this , we defined three groups of inclusions based on the recovery after photobleaching . The presence of an immobile fraction of aSyn is common to all FRAP recovery curves , albeit at different proportions for each type of inclusions . However , blocking aSyn phosphorylation enables the protein to establish transient interactions with inclusions , which occur significantly faster than with WT aSyn . When the synthesis of aggregation-prone proteins surpasses the degradation capacity of the cell , different quality-control mechanisms that are conserved from yeast to mammalian cells actively sequester aggregated proteins as a protective cellular response [58] . Misfolded aggregated proteins partition between two cellular compartments: the JUNQ and the IPOD compartment , which may serve a protective function and facilitate aggregate clearance . We postulated that the differences in sizes , number and dynamics of the inclusion formed by WT and S129A aSyn might reflect the distribution of aSyn between these compartments where misfolded proteins display distinct relative exchange rates with the soluble cytosolic pool [44] , [69] , [70] . Non-phosphorylated S129A aSyn accumulating in a less mobile fraction could indicate the protein was localizing to the IPOD , the preferred destination of protein aggregates . Likewise , the S129A aSyn in group III indicated the exchange of soluble aSyn with the cytosolic pool , as described for the JUNQ compartment . Unexpectedly , we found no colocalization of WT or S129A aSyn with neither IPOD nor JUNQ , suggesting that aSyn is not actively sorted to these cellular compartments in yeast . Moreover , we also did not observe colocalization of WT or S129A aSyn-GFP with P-bodies . Alternatively , we did detect colocalization of S129A aSyn-GFP with several vesicular markers including Ypt31 ( late Golgi ) , Sec4 ( secretory vesicles-to-PM ) , Ypt6 ( endosome-to-Golgi ) , Vps21 and Ypt52 [EE-to-late endosome ( LE ) ] and Ypt7 ( LE-to-vacuole ) . We observed that initial stages of aSyn inclusion formation occur in conjunction with receptor-mediated endocytosis of the plasma membrane symporter Jen1 . The alteration in the course of endocytosis caused by the presence of aSyn in endosomal compartments affects trafficking , causes accumulation of vesicles and eventually leads to cell death . The deficiency in the delivery of late endosomes carrying aSyn to the vacuole is more pronounced in the strain expressing S129A aSyn . This prompted us to evaluate the effect of this mutant on endocytosis . The clustering of the endosomal compartment caused by the presence of aSyn on the membrane , slows down endocytic trafficking and eventually blocks internalization of vesicles . While small inclusions of WT and S129A aSyn are cleared , large clusters of S129A aSyn accumulate proximally to the vacuole and , with new cycles of endocytosis , the size of the clusters becomes unbearable for the cell . Given that early endosome functions are essential for autophagy and for endocytosis-UPS , deregulation of endocytosis by the presence of aSyn in vesicles disrupts not only vesicular trafficking but also major degradation pathways . Vesicles and membranous structures were also observed to accumulate at the periphery of LBs in PD brains [26] , reinforcing the relevance of this mechanism in the context of aSyn pathobiology . Posttranslation modifications modulate the degradation of aggregate-prone proteins by the UPS and/or autophagy . Ubiquitination generally determines whether a protein is degraded via the UPS or autophagy [71] . Interestingly , phosphorylation of mutant huntingtin ( Htt ) , the protein associated with Huntington's disease , was found to precede and regulate additional posttranslational modifications , including ubiquitination , SUMOylation , and acetylation , enhancing its normal clearance by the proteasome and lysosome [72] . In particular , acetylation of mutant huntingtin promotes its targeting to autophagosomes , facilitating its specific degradation by the autophagy/lysosomal pathway [73] . The impact of aSyn S129 phosphorylation on its clearance only started to be investigated recently . In neuronal cell lines , it was observed that proteasome inhibition results in increased levels of pS129 aSyn as an outcome of either increased activity of the kinase ( s ) involved or decreased phosphatase activity , together with decreased degradation of pS129 aSyn by the proteasome in an ubiquitin-independent manner [74] , [75] . Recently , overexpression of GRK6 , one of the kinases capable of phosphorylating aSyn at S129 [76] , was found to moderately increase aSyn toxicity in a rat model of familial PD [77] . In contrast , another recent study showed that the overexpression of another kinase , the Polo-like kinase 2 ( PLK2 ) [78] , is protective by mediating selective autophagy clearance of pS129 aSyn [79] . This apparent discrepancy could be the reflex of the different efficiencies of these kinases to phosphorylate aSyn at S129 . In our yeast model , we completely abolished aSyn phosphorylation by replacing S129 with neutral and phosphorylation-resistant amino acids ( alanine or glycine ) . In fact , our results are consistent with those recently reported [79] , since we observed that blocking aSyn phosphorylation compromises its degradation . The clearance of the inclusions formed by S129A aSyn was slower than that of inclusions formed by WT aSyn . Moreover , our findings suggest that autophagy is the main mechanism involved in aSyn clearance in our yeast PD model . Whereas the accumulation of WT aSyn led to a marked induction of autophagy , cells expressing the S129A mutant failed to activate this pathway . Thus , we postulate that S129 phosphorylation might constitute a switch to sense and induce the autophagocytic pathway , and that blocking phosphorylation impairs autophagic induction , albeit without altering autophagic flux . Genetic impairment of yeast autophagy by deletion of ATG1 and ATG7 did not significantly affect the levels of WT or S129A aSyn after 6 hours of induction suggesting that , at this time point , clearance by autophagy is not superimposed to protein synthesis or that cells could compensate autophagy impairment through other clearance pathways , as suggested by studies performed in other cellular models [38] , [39] , [80] . However , ATG7 deletion increases the defect on the clearance of S129A aSyn-GFP but it also has a significant effect on WT aSyn-GFP clearance . This effect could be due to the accumulation of different aSyn species formed by S129A or WT aSyn , as we clearly demonstrated in this study . While soluble and smaller oligomeric species of aSyn could be more easily cleared by the proteasome and CMA , as reported in other models [32] , [33] , [81] , [82] , the larger oligomeric species formed by S129A aSyn-GFP could specifically require autophagy function for clearance . Our observation that aSyn accumulation leads to impairment of proteasome function , as previously observed [83] , is also consistent with this hypothesis , and this impairment is at least partially reverted when aSyn expression is blocked . Pharmacological inhibition of the proteasome did not alter the clearance of inclusions in our yeast model , in agreement with previous studies [25] . However , it is now evident that autophagy and proteasome function are deeply interconnected and that inhibition of either one of these pathways results in the compensatory upregulation of the other [38] , [80] . Thus , we postulate that upon aSyn-mediated proteasome impairment , autophagy is upregulated as a compensatory mechanism to deal with the excess of aSyn . In this study , we provide evidence supporting a novel link between aSyn phosphorylation , aggregation and cellular toxicity using a simple but powerful model organism . The finding that the phosphorylation state of aSyn on S129 can have an impact in the ability for cells to clear aSyn inclusions opens novel avenues for intervention in synucleinopathies through the modulation of aSyn phosphorylation .
The yeast strains used in this work are described in Table 1 . VSY71 to VSY74 contain double genome insertions of GAL1pr-SNCA ( WT , S129A or S129E ) -GFP or of the empty vectors and were previously described [42] . Plasmids p304 GAL1pr-SNCA ( S129G ) -GFP and p306 GAL1pr-SNCA ( S129G ) -GFP were generated by site directed mutagenesis of the corresponding plasmids carrying the WT SNCA gene . These plasmids were linearized with EcoRV for integration into W303-1A and W303-1B strains , respectively . The correct insertion of the integrative vectors was verified by PCR and the haploid strains were used to generate a diploid strain by mating . Diploids were selected in minimal medium by URA and TRP prototrophy . Haploid strains carrying the double insertion of SNCA S129G were obtained by sporulation and tetrad dissection followed by analysis of auxotrophy and mating type verification . The phenotypic characterization was performed in several haploid strains obtained from dissected spores from independent tetrads and independent mating . NLS-TagRFP657 was cloned into pESC ( GAL1pr , LEU2; Stratagene ) [84] and used in fluorescence microscopy to visualize cells nuclei . VSY71 to VSY73 strains with the deleted PDR5 , ATG1 or ATG7 genes , were constructed by gene replacement with a cassette generated by PCR comprising the kanamycin gene ( KanMX4 ) resistance gene flanked by 250 bp gene specific homologous 5′- and 3′- targeting regions . PDR5 , ATG1 or ATG7 deletion was confirmed by PCR ( Table 1 ) . VSY71 to VSY73 2xmCherry-ATG8 strains contain single genome insertion of the pRS305 2xmCherry-ATG8 plasmid and were constructed by integration of the EcoRV-linearized pRS305-derived plasmid ( Table 1 ) . The 2xmCherry-ATG8 fragment was excised from the pRS316 2xmCherry-ATG8 plasmid [85] ( a kind gift from Dr . Kuninori Suzuki , Tokyo Institute of Technology ) , by SacI/KpnI digestion and inserted in the empty pRS305 plasmid , to generate the pRS305 2xmCherry-ATG8 plasmid . VSY71 to VSY73 Pup1p-RFP strains contain an integrated RFP ( tdimer2;12 ) at the 3′ end of the Pup1 endogenous loci and were constructed by integration of the EcoNI-linearized pRS305-derived plasmid . The Pup1p-RFP tag was excised from the original plasmid [86] ( kindly provided by Dr . Isabelle Sagot , Institut de Biochimie et Génétique Cellulaires ) , by SacI/HindIII digestion and inserted in the empty pRS305 plasmid , to generate the pRS305 Pup1p-RFP plasmid . The genes encoding modifiers of aSyn toxicity YPT1 , YKT6 , BRE5 , UBP3 , GYP8 , and PMR1 were kindly provided by Dr . Aaron Gitler , Stanford University , cloned in Gateway entry clones [28] and used to generate expression clones on the pRS based gateway vectors pAG305GAL ( YPT1 , YKT6 , UBP3 , GYP8 , and PMR1 ) or pAG303GAL ( BRE5 ) [87] . These plasmids were integrated in the VSY71 , VSY72 and VSY73 genome to generate new strains ( Table 1 ) . The vesicles markers Ypt1 , Ypt31 , Sec4 , Ypt6 , Vps21 , Ypt52 and Ypt7 used on fluorescence microscopy experiments were constructed by Gitler and co-workers [27] and were obtained from Addgene ( pAG416GPD-Cerulean-YPT1 , 18848; pAG416GPD-Cerulean-YPT31 , 18849; pAG416GPD-Cerulean-SEC4 , 18844; pAG416GPD-Cerulean-YPT6 , 18845; pAG416GPD-Cerulean-VPS21 , 18842; pAG416GPD-Cerulean-YPT52 , 18843; pAG416GPD-Cerulean-YPT7 , 18847 ) . These clones were used to generate entry clones by recombination cloning into a Gateway pDONR221 vector ( Invitrogen ) . These clones were then used to generate new integrative vectors in the pAG305 GPD-Cerulean-ccdB vector which were verified by DNA sequencing . These plasmids were integrated in the VSY71 , VSY72 and VSY73 genome to generate new strains ( Table 1 ) . The P-bodies marker encoding the gene DCP1 cloned in pBG1805-DCP1 was obtained from the Open Biosystems Yeast ORF Collection and used to generate an entry clone into Gateway pDONR221 vector ( Invitrogen ) . This clone was then used to generate a new integrative vector in the pAG305GPD-ccdB-DsRed vector which was verified by DNA sequencing . This plasmid was integrated in the VSY71 , VSY72 and VSY73 genome to generate new strains ( Table 1 ) . Construction of pESC JEN1-mCherry plasmid was performed by ligating BamHI-JEN1-XhoI fragment generated by PCR amplification of chromosome DNA with oligonucleotides containing flanking corresponding restriction enzyme sites into BamHI-pESCmCherry-XhoI plasmid . mCherry was subcloned into pESC ( LEU ) plasmid following XhoI/NdeI restriction sites . Yeast transformations were carried out using a standard lithium acetate procedure and all the genome insertions were confirmed by two independent PCRs following standard procedures [88] . For aSyn expression induction experiments , yeast cells were pre-grown in YEP-Raffinose ( peptone 2% , yeast extract 1% , raffinose 1% ) liquid media at 30°C , with orbital agitation ( 200 rpm ) for 24 hours ( doubling time: ∼3 hours ) . After 24 hours , optical density at 600 nm ( OD600 nm ) was measured and yeast cells were diluted to a standardized OD600 nm = 3×10−3 ( ∼2 . 5×105 cells/mL ) in YEP-Raffinose liquid media and grown at 30°C , with orbital agitation ( 200 rpm ) . After 24 hours , OD600 nm was measured . The volume of yeast culture needed to inoculate a new culture with an initial standardized OD600 nm = 0 . 2 ( ∼7×106 cells/mL ) was centrifuged ( 3000 rpm , at 30°C for 4 min ) . Cells were then resuspended in YEP-Galactose ( peptone 2% , yeast extract 1% , galactose 1% ) liquid media and incubated at 30°C , with orbital agitation ( 200 rpm ) , for 6 hours . The cell viability was assessed by counting CFUs after incubation of culture aliquots for 2 days at 30°C on YEP-glucose agar plates . For spot assays cell suspensions were adjusted to OD600nm = 0 . 05±0 . 005 and used to prepare 1/3 serial dilutions that were applied as spots ( 4 µl ) onto the surface of the YPD rich medium used as control or YEP-Galactose medium and incubated at 30°C for 2–3 days . For aSyn clearance experiments , OD600 nm of the 6 hours induced cultures was measured and the volume of yeast culture needed to inoculate a new culture with an initial standardized OD600 nm = 0 . 2 ( ∼7×106 cells/ml ) was centrifuged ( 3000 rpm , at 30°C for 4 min ) . Cells were washed in PBS and resuspended in YEP-Glucose ( peptone 2% , yeast extract 1% , glucose 2% ) liquid media and incubated at 30°C , with orbital agitation ( 200 rpm ) , for 6 hours . For fluorescence microscopy or flow cytometry analysis , adenine was added to the growth media at a final concentration of 0 . 16 mg/mL to avoid background interactions by the red pigment production due to ade2 auxotrophic marker of the used yeast strain . Adenine supplementation did not alter growth phenotypes of the tested yeast strains . Yeast cell membrane integrity was evaluated with PI staining using a BD LSR Fortessa . Yeast cells were incubated with PI 5 µg/mL for 15 min . As a positive control , cells boiled for 10 min were used ( data not shown ) . Autophagy induction was determined measuring fluorescence intensity of mCherry-Atg8 under the regulation of the natural promoter [85] in cells co-expressing WT or S129A aSyn-GFP , using a BD FACSAria III equipped with a 561 nm laser for excitation and a 600 LP mirror in conjunction with a 610/20 BP filter for detection ( BD Biosciences , San Jose , CA ) . Fluorescence intensity of WT or S129A aSyn-GFP was measured in simultaneous using a 488 nm laser for excitation and a 502 LP mirror in conjunction with a 530/30 BP filter for detection ( BD Biosciences , San Jose , CA ) . A minimum of 10 . 000 events were collected for each experiment . Data analysis was performed using FlowJo software ( Tree Star Inc . , Ahsland , OR , USA ) . Results were expressed as median fluorescence intensity ( MFI ) of a molecule . For total protein extraction , yeast cells were lysed in Tris-HCl buffer pH 7 . 6 supplemented with protease and phosphatases inhibitor cocktail ( Roche , Mannheim , Germany ) , with glass beads ( 3 cycles of 30 seconds in the beadbeater and 1 min on ice ) . Cell debris was removed with a smooth centrifugation ( 700 g , 3 min , 4°C ) and the supernatant was collected . The supernatant was sonicated ( 10 seconds at 10 mA , Soniprep 150 from Sanyo ) . Protein concentration was determined using the BCA protein assay kit ( Thermo Fisher Scientific Inc , Illinois , USA ) . The same amount of total protein was loaded in the SDS-PAGE for the detection of mCherry-Atg8 levels . As WT and mutant aSyn expression cells exhibit slightly distinct growth rates , equal volumes of total protein , corresponding to the same number of cells ( normalized based on OD600 nm ) were applied to the SDS-PAGE for aSyn quantification in induction , clearance and proteasome pharmacological inhibition experiments , in order to avoid bias in the protein measurement levels due to a cell dilution effect . Protein sample buffer ( 200 mM Tris-HCl pH 6 . 8 , 6% 2-mercaptoethanol , 8% SDS , 40% glycerol , 0 . 4% bromophenol blue ) was added to each protein sample and heated for 10 min at 100°C before acrylamide gel loading . Protein samples were run in SDS-PAGE and were transferred to a nitrocellulose membrane using a Trans-Blot Turbo transfer system ( Bio-Rad ) , as specified by the manufacturer . Immunoblotting was performed following standard procedures with the listed antibodies: aSyn ( BD Transduction Laboratories , San Jose , CA , USA ) , pS129-aSyn ( Wako Chemicals USA , Inc . , Richmond VA , USA ) and DsRed ( Clontech Laboratories , Inc . USA ) . GAPDH ( Ambion , Cambridgeshire , UK ) or PGK ( Life Technologies , Grand Island , NY , USA ) were used as loading control . The band intensity of the different immunoblots signals was estimated using ImageJ software ( NIH , Bethesda , MD ) and normalized against the corresponding GAPDH or PGK signal . In particular , aSyn levels were determined by calculating the ratio between aSyn/GAPDH and normalized to the control ( mean ± SD ) ; pS129-aSyn levels were determined by doing the ratio between both values: ( pS129/GAPDH ) / ( aSyn/GAPDH ) and normalized to the control ( mean ± SD ) . Atg8 induction was quantified by the determination of the fold increase of total mCherry signal ( mCherry-Atg8 and free mCherry signal , detected with anti-DsRed ) normalized to GAPDH [89]; autophagic flux was quantified by measuring the vacuolar degradation of the Atg8 domain reporter ( ratio of free mCherry to total mCherry signal ) [50] . Total protein was extracted and quantified with the BCA protein assay kit ( Thermo Fisher Scientific Inc , Illinois , USA ) . 200 µg of total protein was incubated with 1% Triton X-100 on ice , for 30 min . Protein fractions were separated by centrifugation at 15000 g for 60 min at 4°C . The top soluble protein fraction ( Triton-soluble , TS ) was collected and the insoluble protein fraction ( Triton-insoluble , TI ) pellet was resuspended in 40 µl of 2% SDS Tris-HCl buffer pH 7 . 4 by pipetting and subsequent sonication ( 10 seconds ) . Equal volumes of TS and TI were loaded and resolved by SDS-PAGE . Total protein from cells expressing WT or S129A aSyn was obtained and applied on a 5 to 30% sucrose gradient as described before [90] , [91] . Fractions were collected , precipitated for 4 hours at 4°C in trichloroacetic acid , washed in acetone three times and suspended in protein sample buffer ( 0 . 5 M Tris-HCl , pH 6 . 8 , Glycerol , 10% SDS , 0 . 1% Bromophenol Blue ) . Proteins were resolved by SDS-PAGE and estimation of the molecular sizes for each fraction was previously described [92] . Size exclusion-fast protein liquid chromatography ( SEC-FPLC ) was performed with total protein lysates from cells expressing WT or S129A aSyn-GFP extracted as described for sucrose gradient [90] , [91] , centrifuged at 16000 g for 4 min and filter with 0 . 45 µM PVDF ( Whatman ) to remove any insoluble particles . Samples ( 3 mg of protein in final volume of 500 µL ) were analyzed on a Superose 6 10/300 GL column ( GE Healthcare , Uppsala , Sweden ) using a FPLC system with UV-M II detector ( GE Healthcare , Uppsala , Sweden ) . The samples were eluted with 50 mM ammonium acetate , pH 7 . 4 at a flow rate of 500 µL/min and the UV absorbance was monitored at 280 nm . To estimate the molecular weight of the protein samples , High Molecular Weight and Low Molecular Weight gel filtration calibration kits were used ( GE Healthcare , Uppsala , Sweden ) . Fractions of 500 µL were collected , precipitated overnight at 4°C in trichloroacetic acid , washed in acetone three times and resuspended in protein sample buffer ( 0 . 5 M Tris-HCl , pH 6 . 8 , Glycerol , 10% SDS , 0 . 1% Bromophenol Blue ) , and were resolved by SDS-PAGE . For time-lapse imaging , VSY yeast cells transformed with pESC-Leu GAL1pr NLS-TagRFP657 were pre-grown overnight in Synthetic complete ( SC ) medium without leucine ( SC-Leu ) raffinose liquid media at 30°C , with orbital agitation ( 200 rpm ) . OD600 nm was measured and yeast cells were diluted to a standardized OD600 nm = 0 . 8 ( ∼2 . 4×107 cells/ml ) in SC-Leu raffinose liquid media and grown at 30°C , with orbital agitation ( 200 rpm ) . After 6 hours , cells were seeded on concanavalin A-coated 4-well microscope plates ( Greiner Bio-One GmbH ) for about 10 min . Then all media was replaced by SC-Leu Galactose for aSyn and NLS-TagRFP657 expression induction . Confocal 3D movies were acquired using a dual point-scanning Nikon A1R-si microscope equipped with a PInano Piezo stage ( MCL ) , using a 60x PlanApo IR water objective NA 1 . 27 and PlanApo VC oil objective 960x ) NA 1 . 40 , 0 . 35 micron slices , and 0 . 5% laser power ( from 5 mW 488 laser and 40 mW 561 laser ) . Prior to imaging the point-spread function was visualized with 100 nm fluorescence beads in order to adjust the correction ring of the objective to the coverslip thickness . Movies were acquired in resonant-scanning mode . Z-stacks were acquired every 10 min for 18 hours with some exceptions . Each z-series was acquired with 0 . 35 micron step size and 30 total steps , images - in galvano scanning mode ( 0 . 4 micron slices ) . Image processing was performed using NIS-Elements software . The percentage of cells with aSyn inclusions , number of aSyn inclusions per cell and size of aSyn inclusions were determined by fluorescence microscopy using a Zeiss Axiovert 200 M ( Carl Zeiss ) widefield fluorescence microscope equipped with a cooled CCD camera ( Roper Scientific Coolsnap HQ ) to acquire images containing at least 700 cells per strain , which were then manually counted using ImageJ . Colocalization studies were performed with a Zeiss Axiovert 200 M ( Carl Zeiss ) widefield fluorescence microscope equipped with a cooled CCD camera ( Roper Scientific Coolsnap HQ ) or a Zeiss LSM 710 inverted laser scanning confocal microscope ( Carl Zeiss ) using a Plan-Apochromat 63x/1 . 4 oil immersion objective . EGFP fluorescence was detected using the 488 nm laser line of an Ar laser ( 25 mW nominal output ) and a custom wavelength detection window set to 493–556 nm . mCherry and RFP fluorescence were detected using a 561 nm DPSS laser ( 15 mW ) and a custom detection window set to 569–797 nm . Yeast cells were grown as described above . At the indicated time points cells were collected by centrifugation and resuspended in PBS and 0 . 5% low melting agarose on a microscope slide . FRAP experiments were performed using a Zeiss LSM 710 inverted laser scanning confocal microscope equipped with a large incubator ( Pecon , Erbach , Germany ) maintained at 30°C . Images were acquired using a PlanApochromat 63x/1 . 4 objective . A series of 80 z-stacks consisting of 5 different focal planes spaced 0 . 7 µm apart ( frame size 512×512 , pixel width 91 nm and pixel time 4 . 44 µs ) were acquired at intervals of 2 seconds with pinhole set to 1 Airy unit . In each FRAP experiment a single inclusion , focused at the central focal plane of the z-stack , was bleached using the 488 nm laser line at 100% laser transmission on a circular region of interest ( ROI ) with a diameter of 8 pixels ( 0 . 35 µm radius ) for 32 ms . For imaging , the transmission of the 488 nm laser was set to 0 . 3% of the bleach intensity . Image processing and fluorescence intensity measurements were performed in ImageJ using an in-house developed macro to extract the average fluorescence in the bleached area I ( t ) at the central plane of the z-stack at each time point t and the average cellular fluorescence intensity T ( t ) which was used to normalize FRAP recovery curves as described previously [93] . where T0 is the fluorescence in the total cell area before bleaching and I0 is the fluorescence in the bleached region before bleaching . Post-bleach values were additionally set to zero to facilitate comparison of the curves . FRAP recovery curves were fit to a single-exponential curve assuming a binding-dominant ( diffusion-uncoupled ) regime where protein diffusion occurs much faster than binding kinetics [94] . where IF is the immobile fraction and koff is the off rate of binding which can be used to determine the mean residence time T = 1/koff . Curve fitting was performed on OriginPro 8 ( OriginLab Corporation ) . Statistical analysis were performed using unpaired tow-tailed t-test; one way ANOVA with Bonferroni's or Tuckey multiple comparison test; two-tailed unpaired t-test with Welch's correction; two-tailed Mann-Whitney test , where appropriate . P-value ≤0 . 05 was considered statistically significant . Statistics were performed using Prism 5 and SigmaStat ( GraphPad Software Inc . ) . | Protein aggregation is a common hallmark in neurodegenerative disorders , but is also associated with phenotypic plasticity in a variety of organisms , including yeasts . Alpha-synuclein ( aSyn ) forms aggregates that are typical of synucleinopathies , and is phosphorylated at S129 , but the significance of phosphorylation in the biology and pathophysiology of the protein is still controversial . Exploring the power of budding yeast , we found phosphorylation reduced aSyn toxicity and inclusion formation . While inclusions formed by WT aSyn were homogeneous , those formed by S129A aSyn were larger and heterogeneous . Interestingly , clearance of aSyn inclusions was reduced in cells expressing S129A aSyn , correlating with deficient autophagy activation . The finding that phosphorylation alters the ability of cells to clear aSyn inclusions provides novel insight into the role phosphorylation may have in synucleinopathies , and suggests posttranslational modifications might constitute switches cells use to control the aggregation and clearance of key proteins , opening novel avenues for the development of therapeutic strategies for these devastating disorders . | [
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| 2014 | Phosphorylation Modulates Clearance of Alpha-Synuclein Inclusions in a Yeast Model of Parkinson's Disease |
Living in a social environment requires the ability to respond to specific social stimuli and to incorporate information obtained from prior interactions into future ones . One of the mechanisms that facilitates social interaction is pheromone-based communication . In Drosophila melanogaster , the male-specific pheromone cis-vaccenyl acetate ( cVA ) elicits different responses in male and female flies , and functions to modulate behavior in a context and experience-dependent manner . Although it is the most studied pheromone in flies , the mechanisms that determine the complexity of the response , its intensity and final output with respect to social context , sex and prior interaction , are still not well understood . Here we explored the functional link between social interaction and pheromone-based communication and discovered an odorant binding protein that links social interaction to sex specific changes in cVA related responses . Odorant binding protein 69a ( Obp69a ) is expressed in auxiliary cells and secreted into the olfactory sensilla . Its expression is inversely regulated in male and female flies by social interactions: cVA exposure reduces its levels in male flies and increases its levels in female flies . Increasing or decreasing Obp69a levels by genetic means establishes a functional link between Obp69a levels and the extent of male aggression and female receptivity . We show that activation of cVA-sensing neurons is sufficeint to regulate Obp69a levels in the absence of cVA , and requires active neurotransmission between the sensory neuron to the second order olfactory neuron . The cross-talk between sensory neurons and non-neuronal auxiliary cells at the olfactory sensilla , represents an additional component in the machinery that promotes behavioral plasticity to the same sensory stimuli in male and female flies .
A fundamental question in neuroscience is how do animals integrate sensory information , together with context , internal state and prior social interaction , into an appropriate behavioral response [1] . The manner by which prior social interaction affects behavioral responses is a well-described phenomenon associated with changes in gene expression [2 , 3] . Causal links between past interactions , regulation of specific genes , and modulation of behavior , can be functionally dissected in model organisms that allow for genetic manipulation of genes and neuronal function , such as Drosophila melanogaster . We and others have previously demonstrated mechanisms in fruit flies by which social interaction shapes the expression of certain genes , leading in turn to long-lasting changes in behavior and physiology [4–6] . Social interaction is mediated by different mechanisms , one of which is pheromone communication , involving chemical cues that are emitted by one individual and perceived by another individual , predominantly of the same species ( for review see [7] ) . In flies , sensory perception of pheromonal cues is mediated by olfactory and gustatory sensory neurons found within hair-like structures called sensilla . Odorant molecules are dissolved in the aqueous environment of the sensilla , where they bind to receptors located on dendrites of sensory neurons . This stimulates the neuron , which delivers sensory signal to the central nervous system ( for review [7–13] ) . Cis-vaccenyl acetate ( cVA ) is a male-specific Drosophila pheromone that was originally identified as an aggregation pheromone [14] . cVA elicits dimorphic responses in male and female flies , inducing aggression in the former and promoting sexual receptivity in the latter [15–20] . Currently , different innate responses to cVA exhibited by male and female flies are best explained by a wiring difference in the brain , whereby the third order sensory neurons project to distinct target neurons within the lateral horn [21] . In addition to innate responses , there are several examples where exposure to cVA induces different behavioral responses that depend on the context in which it is presented , and prior social encounters with other flies [22 , 23] . For example , long-term exposure to cVA when male flies interact in a group reduces cVA-dependent individual aggression [24 , 25] , while exposure to cVA that is present on mated females , plays a role in memory formation for unsuccessful courtship in males , resulting in courtship suppression in future encounters [26–28] . This exemplifies the contextual component of the response to a single stimulus , the mechanisms by which it is achieved are still largely unknown . cVA is sensed by Or67d and Or65a receptors in sensory neurons [15 , 16 , 18 , 29] . cVA sensing also requires a soluble protein in the olfactory sensillar lymph , the odorant binding protein LUSH , which facilitates its movement through the lymph and its binding to Or67d receptors [30 , 31] . Lush belongs to a family of 52 fly odorant binding proteins ( Obps ) , the function of which is poorly understood [32 , 33] . Insect Obps are globular proteins secreted from auxiliary cells that are located adjacent to olfactory and gustatory sensory neurons , and are believed to participate in facilitating the transport of hydrophobic odorants within the soluble environment of the sensillar lymph , or in their degradation [32–36] . Two independent studies identified transcriptional regulation of Obps in response to social stimuli [6 , 37] , the functional implication of which is not known . In this study , we identified an Obp family member , Odorant binding protein 69a ( Obp69a ) as a new player in the machinery that modulates behavioral responses to cVA . We demonstrate that Obp69a exhibits sexually dimorphic expression in fruit flies and is regulated inversely in male and female flies in response to similar social cues via the activation of cVA sensing neurons . Downregulating and upregulating Obp69a levels modulate cVA related behavioral responses oppositely in male and female flies , suggesting a link between prior social interaction , Obp69a levels and modulation of social responsiveness in future interactions .
To further explore the previously identified connection between social conditions and odorant binding proteins in Drosophila [6 , 37] , we compared the expression levels of candidate genes between male and female flies , and in response to simple social conditions . Our analysis focused on genes with known functions within pheromone sensing sensilla ( trichoid sensilla ) such as Lush [30] , cyp6a20 [24] and est-6 [38] , and additional under-studied odorant binding proteins such as Obp28a , which was previously shown to be sensitive to courtship song [37] , and Obp69a , which is known to be expressed in trichoid sensilla [32 , 39] and is sensitive to environmental stimuli [40] . Pair-wise comparisons of candidate genes in total RNA extracted from heads of groups of five adult virgin male and five adult virgin female flies revealed an overall trend of higher expression levels in male flies ( Fig 1A ) , however only Obp69a exhibited a significant difference when corrected for multiple comparison ( Fig 1A P<0 . 01 ) . We next tested whether expression levels of the different candidate genes are sensitive to basic social conditions . Male and female flies were subjected to three simple social conditions over the course of three days post-eclosion . One cohort of flies ( single ) was subjected to social isolation from eclosion , a second cohort ( grouped ) was subjected to group housing in groups of five flies of the same sex , and a third cohort ( grouped with females/males ) was subjected to mixed-sex housing , i . e . five males and five females ( see illustration in Fig 1B ) . Relative mRNA levels of each of the candidate genes was analyzed by RT-qPCR using total RNA extracted from intact heads ( males , Fig 1C–1G and females , Fig 1H–1L ) . In male flies , a significant increase was documented in the relative levels of Obp69a in single-housed flies compared to grouped and grouped with females ( Fig 1D P<0 . 01 ) . No significant difference in relative Obp69a expression levels was observed between virgin male flies that were housed in groups and male flies that interacted and mated with female flies ( P>0 . 05 Fig 1D ) . In agreement with Wang , et al . who described regulation of cyp6a20 in response to social isolation [24] , we detected a significant reduction in cyp6a20 transcript levels in single housed male flies ( Fig 1E P<0 . 01 ) . In female flies , we observed a five-fold increase in Obp69a transcript levels in females that were housed with male flies , compared to single or grouped housed females ( Fig 1I P<0 . 01 ) . No significant difference in relative Obp69a levels was observed between the other two female cohorts ( Fig 1I P>0 . 05 ) . Lush , Cyp6a20 , est-6 and obp28a showed no significant expression difference across all conditions ( Fig 1C , 1F–1H , 1J–1L P>0 . 05 ) . This set of experiments suggests that in both male and female flies , exposure to male flies affects Obp69a expression , but in an opposite manner . Obp69a is expressed in trichoid sensilla of the third antennal segment , the major olfactory sensory organ of the fly [32] . Therefore , we hypothesized that olfactory sensory signals , presumably pheromonal cues , might underlie the observed changes in Obp69a transcript levels . To test this , we asked whether exposure to male scents can induce Obp69a transcriptional change . Single housed male flies were exposed to other males through a mesh , restricting physical interaction but allowing odor , and possibly visual and auditory cues to pass , for the duration of three days . Obp69a expression levels were then measured and compared to single and grouped housed males ( Fig 2A ) . Exposure to male scents was sufficient in reducing Obp69a expression levels , mimicking the effect of group housing ( Fig 2A P<0 . 001 ) . In female flies , analogous experiments revealed that Obp69a expression is also sensitive to male scents . Exposure to male cues via a mesh was sufficient in increasing relative Obp69a expression levels to similar levels as females that were grouped with males , suggesting the effects observed in females are not caused by mating , but rather by exposure to male signals that can pass through a mesh barrier , most probably olfactory cues ( Fig 2B P<0 . 001 ) . Control experiments were done to measure the levels of lush and est-6 under the same experimental set up , revealing no significant regulation ( S1A–S1D Fig , P>0 . 05 ) . These results suggest that Obp69a transcription is regulated oppositely in male and female flies in response to a male signal , most likely a pheromone cue present in male scents . To identify the component in male scents that induces changes in Obp69a transcript levels , we took a candidate-based approach and tested whether exposure to cVA , a male specific pheromone , is sufficient in mimicking the transcriptional regulation of Obp69a in male and female flies following exposure to male scents . Single male flies were exposed to 10μg cVA over the course of three days , after which their relative Obp69a transcript levels were compared to those from single male flies that were exposed to the solvent , as a negative control , or to males housed in groups of five , as a positive control . A five-fold reduction in Obp69a expression levels was detected in single male flies exposed to cVA , compared to negative controls , similarly to that observed in the positive control ( Fig 2C P<0 . 001 ) . Exposing virgin females to 10μg cVA over the course of three days increased Obp69a transcript levels compared to females that were exposed to the solvent alone , mimicked the effect of exposure to male scents and housing with male flies ( Fig 2D P<0 . 001 ) . lush and est-6 expression levels were also measured in response to cVA exposure , revealing no significant regulation in both cases ( S1A–S1D Fig ) . So far , this data demonstrates that exposure to cVA is sufficient in affecting transcription of Obp69a oppositely in male and female flies , decreasing its expression in male flies and increasing its levels in female flies . The observed sexual dimorphism in Obp69a gene regulation following exposure to cVA prompted us to ask whether Obp69a participates in the responses of male and female flies to social interactions that involve cVA sensing . To explore this direction , we first characterized Obp69a spatial expression pattern and the appropriate genetic tools for manipulating its levels . We compared Obp69a transcript levels that were isolated from heads and bodies and discovered that while it is expressed in both tissues , it is highly enriched in heads ( Fig 3A P<0 . 001 and ModeEncode ) . Further dissections revealed that it is expressed in antenna , as removal of the antenna diminished its relative expression in male and female heads ( compare whole heads to heads lacking antenna Fig 3B and 3C and also see in situ hybridization by Pikielny et al [39] ) . To explore this further , we used two GAL4 driver lines; a Minos transposable element inserted within the coding region of Obp69a ( Mi{ET1}Obp69a GAL4 ) , and a newly created GAL4 line in which the coding sequence of Obp69a was swapped with GAL4 by homologous recombination . In line with previous work by Larter , et al . , [32] membrane-bound GFP showed equivalent expression in the third antennal segment using both Obp69aMi-GAL4 and Obp69a-GAL4 drivers ( Fig 3E and 3F ) . Expressing a newly created GFP-fused version of Obp69a resulted in fluorescent signal within cells and the sensory sensilla ( Fig 3G and 3H ) , suggesting that Obp69a is produced in auxiliary cells and secreted to the sensillar lymph . Expression of Obp69a-GFP was also validated using Western-Blot analysis ( Fig 3I ) . To test our ability to modulate Obp69a expression in the relevant anatomical context , we expressed Obp69a-RNAi in different cell types , and assessed Obp69a transcript levels . Driving Obp69a-RNAi using Mi{ET1}Obp69a GAL4 ( the Minos insertion does not impair Obp69a expression ) resulted in more than five-fold reduction in Obp69a levels ( Fig 3J P<0 . 05 ) . A significant reduction of Obp69a expression was also observed in LUSH positive cells and in Obp28a positive cells ( Using Lush GAL4 and Obp28a GAL4 , respectively , Fig 3J P<0 . 05 ) , but not when using Or67d GAL4 or nompA GAL4 ( expressed in sensory neurons and techogen type of auxiliary cells , respectively ) ( Fig 3J P>0 . 05 ) . These results reinforce previous findings [32] showing that Obp69a is expressed in non-neuronal cells of the thormogen subtype in the antennae , and suggest that Obp69a is mutually expressed with LUSH in cVA sensing sensilla ( which harbors Or67d receptor ) . Having the tools to manipulate the expression of Obp69a specifically in Obp69a producing cells , we proceeded to test whether it plays a role in cVA related social interactions . cVA sensing is necessary for adequate sexual receptivity in female flies , and promotes aggressive interaction in male flies [29] . In addition , prolonged exposure to cVA , a normal outcome of male group housing , reduces aggressive behavior , while social isolation induces it [25] . To determine whether the correlation between Obp69a transcript levels and the behaviors that are associated with the different social conditions reflects a cause-and-effect relationship , we used Obp69a KD or over-expression to reproduce the expression levels observed in group housing and social isolation , respectively . If Obp69a participates in modulating aggression along the social isolation-group housing axis , decreasing its levels in single housed male flies should reduce the extent of aggressive displays . Hence , we measured males’ aggression under naturalistic conditions that rely on the presence of endogenous cVA on rival male flies [16 , 25] . Single male flies in which the levels of Obp69a were down-regulated by RNAi exhibited dramatic reduction in aggressive behavior , as measured by the number of lunges in 30 min , compared to genetic controls ( Fig 4A P<0 . 001 ) . Since grouped male flies rarely exhibit aggression , we chose to test whether over expressing Obp69a can enhance aggression in single male flies . Increasing Obp69a levels via expression of Obp69a-GFP significantly induced aggressive displays , as reflected by increased number of lunges ( Fig 4B P<0 . 001 ) and shortened the latency to first aggressive display ( Fig 4C P<0 . 01 ) . Thus , increasing the levels of Obp69a can enhance aggressive behavior . This implies that changes in Obp69a levels , most likely in the antenna , can regulate the rate of aggressive displays , in which down-regulation decreases , and up-regulation increases aggressive behavior in single male flies . To test the behavioral relevance of Obp69a transcriptional regulation in female flies , we first validated that exposure to male scents without mating promotes sexual receptivity . Virgin Wild-Type ( WT ) female flies were exposed to male scents over the course of three days via mesh , and their sexual receptivity was subsequently assessed in a courtship assay by measuring the time until copulation from the moment the male partner exhibited the first courtship display . A 50% increase in receptivity was observed in meshed females compared to controls ( Fig 4D P<0 . 01 ) , indicating that exposure to male scents , which upregulates Obp69a transcription in females , facilitates receptivity in female flies . To determine whether the correlation between Obp69a transcript levels and cVA induced receptivity is causally linked , we reduced or increased Obp69a expression in female flies and measured receptivity towards mature male flies . Down-regulation of Obp69a by expressing Obp69a-RNAi resulted in no significant changes in sexual receptivity , compared to genetic controls ( S2A Fig , P>0 . 05 ) . However , when female flies were exposed to male scents shortly before introducing a male partner , a significant reduction in sexual receptivity was observed in the experimental group ( Fig 4E P<0 . 05 ) , suggesting that Obp69a is necessary for promoting receptivity in response to cVA exposure . Increasing Obp69a levels by means of expressing Obp69a-GFP in the absence of previous exposure to cVA did not affect female receptivity ( S2B Fig , P>0 . 05 ) . However , short exposure to a sub-optimal concentration of cVA ( 1μg ) , significantly shortened the time required to reach successful copulation compared to genetic controls ( Fig 4F P<0 . 01 ) . Our results in female flies imply that Obp69a can alter the magnitude of the stimulating effect of cVA exposure on sexual receptivity . cVA is sensed by two types of olfactory receptor neurons ( ORNs ) : Or67d neurons and Or65a neurons . The former mediates acute responses to cVA , and the latter mediates chronic responses [15 , 18 , 25 , 29 , 41] . To identify which of the two neurons is relevant for cVA dependent regulation of Obp69a , we used the potassium-rectifying channel Kir2 . 1 to inhibit the activity of cVA sensing neurons in single and grouped housed male flies . Inhibition of the relevant sensory neuron is expected to diminish the difference in Obp69a levels between the two conditions . The regulation of Obp69a levels by single and grouped housing was maintained in male flies in which Or67d neurons were inhibited ( Fig 5A ) . In contrast , inhibiting Or65a neurons diminished the difference in Obp69a levels between single and grouped flies ( compare Or65a GAL4/+; UAS Kir2 . 1/+ to the corresponding genetic controls , Fig 5A P<0 . 01 ) . This result suggests that the activity of Or65a in male flies is necessary to reduce Obp69a levels in response to the presence of other males . Performing similar experiments in female flies , analyzing the fold-difference between virgin female flies and female flies that were housed with male flies , revealed that inhibition of Or65a does not block the induction in Obp69a levels in response to interaction with male flies ( Fig 5B ) . Surprisingly , inhibition of Or67d neurons in female flies did not only prevent the increase in Obp69a in female flies that interacted with male flies , but also led to a significant reduction in Obp69a levels ( Fig 5B P<0 . 01 ) . This could be explained by opposing effects of Or65a and Or67d neurons , whereby Or67d neurons are necessary for Obp69a induction in response to cVA , and Or65a neurons reduce its expression , an effect only revealed when Or67d neurons are inhibited . To test whether activating cVA sensing neurons is sufficient to elicit changes in Obp69a transcript levels , we expressed the red-shifted channel rhodopsin CsChrimson [42] in Or67d or Or65a expressing neurons , and subjected flies to three 15 min long optogenetic activation sessions , after which relative Obp69a expression levels were analyzed . Notably , a 2-fold decrease in relative Obp69a mRNA levels was observed in naïve male flies following artificial activation of either Or67d or Or65a neurons , when compared to similar flies that were kept in the dark ( no activations ) ( Fig 5C and 5D , P<0 . 05 ) . Obp69a expression levels did not change in genetic controls subjected to similar conditions ( Fig 5C and 5D , P>0 . 05 ) . The changes in Obp69a expression in response to activation of Or67d or Or65a neurons was only evident in single male flies , as the activation protocol conducted on grouped housed male flies did not affect Obp69a transcript levels ( S3A and S3B Fig , P>0 . 05 ) , possibly as a result of chronic endogenous activation of these neurons during group housing [25] . Activating Or67d neurons in virgin female flies led to a significant increase in Obp69a expression levels compared to controls ( Fig 5E P<0 . 05 ) , while activation of Or65a neurons resulted in a three-fold decrease in Obp69a expression levels ( Fig 5F P<0 . 01 ) . The opposing effect of Or65a and Or67d neuronal activation on Obp69a transcription in female flies , together with the results from the inhibition experiments , suggests that in female flies , exposure to cVA and activation of Or67d neurons increases Obp69a levels , while activation of Or65a neurons decreases its levels . Altogether , the effects of neuronal activation on Obp69a levels imply a causal link between cVA exposure and regulation of Obp69a via the activity of cVA sensing neurons . The regulation of Obp69a by cVA exposure and optogenetic activation , together with the fact that Obp69a is expressed in non-neuronal auxiliary cells prompted us to further explore possible mechanisms by which neuronal activation regulates Obp69a expression in auxiliary cells . There are two possible models that can account for the interplay between neuronal activation and transcriptional regulation in auxiliary cells: ( a ) a local direct interaction between the sensory neuron and auxiliary cells that converts neuronal activation to transcriptional changes within nearby auxiliary cells . ( b ) a non-direct mechanism in which sensory information is relayed to downstream neurons and eventually reaches auxiliary cells , perhaps via an afferent mechanism . To discriminate between the two models , we induced depolarization of cVA sensing neurons via optogenetics , and at the same time blocked synaptic vesicle release to downstream neurons using shibirets . If the interplay between sensory neuron and auxiliary cell is based on an indirect afferent mechanism , depolarizing the sensory neurons while inhibiting synaptic vesicle release is expected to block information flow to auxiliary cells and produce no transcriptional change . Conversely , if the mechanism relies on direct interaction between the two , blocking synaptic vesicle release is not expected to suppress the depolarization effects , and thus Obp69a levels are expected to change . To this end we expressed UAS-csChrimson and UAS-Shibirets in Or65a neurons and compared Obp69a levels in male flies that were subjected to three conditions: optogenetic activation ( positive control ) , inhibition of synaptic vesicle release in the absence of activation ( negative control ) , and combined activation and inhibition of synaptic vesicle release . While activation of Or65a neurons decreased Obp69a relative levels , the other two conditions resulted in no change in Obp69a levels ( Fig 6A P<0 . 01 ) . This result suggests that information transfer from Or65a neurons to the second order olfactory neurons is necessary for regulating Obp69a levels , supporting the indirect model . However , this does not preclude the existence of a local connection that depends on exocytosis from the neuron to the auxiliary cell , which would also be blocked by Shibirets . Similar activation and inhibition of synaptic vesicle release experiments performed on Or67d neurons in female flies indicated that the induction of Obp69a transcription in response to optogenetic activation is blocked by inhibition of synaptic vesicle release as well ( Fig 6B P<0 . 05 ) . Altogether , this set of experiments suggests that the dimorphic regulation of Obp69a in auxiliary cells requires active neurotransmission from the sensory neuron to the second order olfactory neuron , somehow conveying the information back to Obp69a producing cells .
The ability to incorporate experience obtained from prior interaction into a behavioral response is critical for survival and reproductive success . Here we used Drosophila to investigate mechanisms that link prior social interaction to modulation of sex specific pheromone communication and discovered Obp69a as novel player in the machinery that connects social interaction to modulation of sex-specific behaviors . Obp69a exhibits higher expression levels in male flies compared to females , a difference that can be explained by the slightly larger number of pheromone-sensing trichoid sensilla in male flies [43] . Regardless of this , Obp69a transcription is inversely regulated in males and females in response to cVA or to artificial activation of cVA sensing neurons in the absence of cVA , suggesting a causal link between cVA perception and Obp69a transcriptional regulation . In male flies , the activity of Or65a neurons is necessary to reduce Obp69a in response to cVA exposure . In female flies , Or67d and Or65a neurons have an opposite regulatory effect on Obp69a expression . This may be related to the different roles of cVA as a pheromonal cue under different mating states; serving as an attractant for virgin females but losing its attractive value after mating [41] . Until now , the dimorphic behavioral responses to cVA were thought to depend mostly on dimorphic wiring of the third order sensory neurons to distinct target neurons in male and female brains [21] . Our findings suggest an additional layer to this equation , showing that the soluble environment of the olfactory sensilla is different between male and female flies . The mechanism that converts activation of cVA sensing neurons into regulation of Obp69a within the auxiliary cells is not known . Nonetheless , our data imply that it depends on active neurotransmission from the sensory neurons to the second order olfactory neurons in the brain , and eventually back to Obp69a producing cells ( see model in Fig 6C ) . Still , it is not clear whether the opposite regulation of Obp69a in female and male flies results from wiring differences that relay the information to auxiliary cells , or from inherent dimorphic transcriptional programs within Obp69a producing cells . We used genetic manipulations of Obp69a to mimic the effects of social conditons on its expression levels and to explore the behavioral consequences of its modulation . Manipulating Obp69a expression to generate low levels , as in grouped housed flies , decreased aggressive displays in single housed male flies , while conversely , high levels of Obp69a facilitated aggression . The connection between Obp69a levels and aggressive behavior , together with its co-expression with LUSH , suggests that Obp69a plays a role in the machinery that generates aggressive behavior in the presence of cVA . This is consistent with previous work by Billeter , et al . , which proposed the existence of a LUSH-independent cVA detection system [23] . These results propose that long exposure to cVA during group interaction which reduces Obp69a levels along with other physiological changes such as Cyp6a20 up-regulation , may participate in reducing aggression , to promote aggregation and to allow mating . In female flies , down-regulation of Obp69a reduced receptivity , while over-expression boosted receptivity upon short exposure to suboptimal levels of cVA . This suggests , that regulating Obp69a levels can fine-tune the responsiveness of virgin female flies to the presence of male flies , promoting their receptivity . The genetic approach used in this study , is limited in that it does not prove a direct causal link between the effect of social conditions on Obp69a levels , and subsequent modulation of behavioral response in future interactions . In other words , it is possible that the cVA dependent changes in Obp69a levels and the modulation of behavior in response to decreasing or increasing Obp69a levels , represent two independent processes . Nonetheless , considering the causal link between cVA sensing and Obp69a regulation , the causal relationships between Obp69a levels and the extent of male aggression and female receptivity in response to cVA , and the fact that these changes correspond to changes in behavior that happen naturally following exposure to cVA , we propose a model to combine the two parts: exposure to cVA during social interactions regulates Obp69a , which in turn participates in modulating cVA-dependent behavioral responses in future interactions , suggesting the existence of a feedback loop linking cVA and Obp69a . This may serve to integrate prior interactions in the form of cVA concentration , and presumably time of exposure , into sensitivity to the same pheromone on future encounters ( see model in Fig 6C ) . The biochemical mechanism that shapes these responses still needs to be resolved , including whether Obp69a binds cVA directly , and whether it interacts with other players within the sensilla such as the receptor , LUSH and odorant degrading machinery . The proposed modulatory function of Obp69a is not the first example in which Obps modulate behavioral response to a certain stiumilus . Previously , it was shown that Obp56h can modulate mating behavior [44] . Another odorant binding protein in Drosophila , Obp49a , was shown to act in sugar sensing sensilla to inhibit responses to sugar in the presence of bitter compounds [45] . This , along with the fact that the principles by which olfactory information is processed within the nervous system is conserved from fruit flies to mammals , suggests that the functional role of Obps may also be conserved . In vertebrates , the nasal mucus consists of abundant levels of odorant binding proteins , the function of which is still poorly understood [46–49] . However , there is evidence to suggest that verterbrate Obps can also function to modulate sensory preception [49] . There are several well-characterized examples in the animal kingdom of how the same sensory stimulus induces dimorphic innate behavioral responses in males and females . In most cases , this strongly depends on the existence of dimorphic neurons and wiring schemes . For instance , male and female mice respond differently to young pups: female mice exhibit maternal behavior towards pups , while male mice show aggressive/infanticidal reactions . Recently , Scott , et al . demonstrated that these dimorphic responses rely on a set of sexually dimorphic dopaminergic neurons within the anteroventral periventricular nucleus ( AVPV ) , the activation of which induces maternal care in female mice , and aggression in male mice [50] . Another study documented differences in sensory processing of pheromone stimuli only in neurons of the medial amygdala , but not in olfactory bulb neurons , suggesting that the dimorphic responses in mice are not encoded at the level of the first and second sensory neurons [51] . A different study demonstrated that eliminating pheromone sensing in adult female mice via surgical removal of the VNO or deletion of the gene TRPC2 produced male-like behavioral responses in females [52] . These findings resemble studies in Drosophila in which female flies expressing the male specific FruM protein display courtship rituals towards other female flies , presumably via developmental feminization of their nervous system [53] . Male flies lacking FruM do not display courtship towards virgin female flies , but can acquire the potential to court when grouped with other flies [54] . Mice that undergo parasitic infection by Toxoplasma gondii present another intriguing example for encoding the valence of the same stimuli differently in males and females . The infection abolishes the innate aversion of female mice to bobcat urine , but does not affect male response to the same stimuli . These behavioral differences are correlated with a sex-specific changes in gene expression in the frontal cortex of male and female mice , including differential expression of olfaction related genes , suggesting that the parasite affects the processing of olfactory information [55] . The above mentioned studies exemplify the central role of dimorphic neuronal circuits in determining sex-specific behavioral responses , and raise the question of how the brain integrates past interaction into the modulation of these behavioral responses . Our findings suggest that such integration occurs not only within the brain , but also in the olfactory sensilla , most likely via an indirect interaction between neurons and auxiliary cells , the result of an intricate interplay by the activity of different types of sensory neurons in male and female flies .
Flies were raised at 25°C in a 12-h light/12-h dark cycle in 60% relative humidity and maintained on cornmeal , yeast , molasses , and agar medium . Canton S flies were used as the wild-type strain . All transgenic fly lines were backcrossed at least 5 generations into a white Canton S background . A UAS-Obp69aRNAi line was obtained from the Vienna RNAi collection , Obp69a-Minos-GAL4 and Obp28a-GAL4 flies were obtained from Bloomington , Lush-GAL4 was a gift from Richard Benton , Nomp-A GAL4 line was a gift from Yun Doo Chung , and UAS UNC84-GFP , UAS mCD8-GFP , Or67d-GAL4 and Or65a-GAL4 flies were obtained from HHMI Janelia Research Campus . Obp69a GAL4 was generated by inserting a GAL4 coding sequence into the Obp69a Locus using homologous recombination . Obp69a 5’ and 3’ homology regions of 3Kb were amplified by High Fidelity PCR Kit ( Hy Labs ) from wild type Canton-S genomic DNA using the following primers: 5' TGTACTTAGGAAAATGGA 3' , 5' TTTTGCTTCTCCCCAAAAATTGCTA 3' for the 5’HA arm and 5' CGCTAACCAACCTAAATA 3' , 5' AATTTGCTCAAGTTCCCCA 3' for the 3’HA arm . The amplified fragments were cloned into pC31B-JMKS4 . 2 GAL4-KanR donor vector . This vector contains tdTomato marker under the GMR promoter for visualizing positive donor integration into the MiMIC insertion site of Bloomington stock #35109 . Integrant lines were isolated to serve as donors of Obp69a GAL4 DNA substrate for homologous recombination [56] using Bloomington mobilization stock #6934 containing heat-shock-inducible FLP recombinase and I-SceI endonuclease . Transgenic GFP-eyed flies were individually balanced to establish stable lines . The UAS Obp69a-GFP fused transgenic line was generated accordingly; Obp69a coding region was amplified by High Fidelity PCR Kit ( Hy Labs ) from wild type Canton-S c-DNA library using the following primers: 5’ gctAGATCTatggttgcaaggcatttta 3’ and 5’ attCTCGAGcccaagtagcactattatc 3’ ( uppercase letters represent Bglll and XhoI restriction enzyme sites , respectively ) . The amplified fragment was cloned in frame up-stream to the E-GFP sequence in the pJFRC81-10XUAS-IVS-Syn21-GFP-p10 vector ( addgene , UAS ) and sent for injection into y1 w67 c23; P{CaryP{attP2 and y1 M{vas-int . Dm}ZH-2A w*;P{CaryP}attP40 sites ( BestGene Inc . , USA ) . All transformants were picked from individually injected flies . All behavioral observations were performed at 25°C , 65% relative humidity and at the same time of day ( 1h after Lights ON ) with 3–4 day old flies unless indicated otherwise . Total RNA was extracted from frozen intact fly heads using TRIZOL reagent . Each sample consisting of 15 frozen heads unless otherwise stated . cDNA was synthesized from total RNA extracts using BIORAD cDNA synthesis kit . cDNA samples were used as templates in a RT-qPCR machine ( BIORAD CFX96 ) using primers for Obp69a , lush , cyp6a20 , est-6 , obp28a . relative expression was quantified by ΔΔCT method using rpl32 as a loading control . Each sample was run in triplicates . Each experiment was repeated at least three time using independent sets of genetic crosses . Obp69a Primers: F–CCTACGATCATAAAGCAGGTGAGA R–TCACCGACTTGTCAATCACATCT . Lush primers: F–CGCAGGATCTTATGTGCTACAC R–CATTTCCGGGGGAACCAGAT Est-6 primers: F—AGCACGCAGGAGTCATTGGA R—CGTCACCGTCTACAGTTCCAAAA Cyp6a20 primers: F—TACTGGAAGCGCCGGGGCATTC R—CCTCATGGTCTCATCAATGACC Obp28a primers: F—ATGCCTATCTGCAGGAAATG R—GCGTCCAGAATTCCGATGTT RPL32 primers: F—ATCGATATGCTAAGCTGTCGCA R—GGCATCAGATACTGTCCCTTGAAG Fluorescent images were captured using Leica SP8 confocal microscope . | In this work , we used Drosophila melanogaster as a model organism to explore a basic question in neuroscience: why do different individuals experience the same sensory stimuli , such as smell differently , and moreover , why does one individual experience identical stimuli differently on different occasions ? Focusing on sex specific behaviors in fruit flies , we identified odorant binding protein 69a ( Obp69a ) as a new player in the machinery that promotes behavioral plasticity to the same sensory stimuli in male and female flies . | [
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| 2018 | Odorant binding protein 69a connects social interaction to modulation of social responsiveness in Drosophila |
We experimentally and numerically investigate the evolutionary dynamics of four competing strains of E . coli with differing expansion velocities in radially expanding colonies . We compare experimental measurements of the average fraction , correlation functions between strains , and the relative rates of genetic domain wall annihilations and coalescences to simulations modeling the population as a one-dimensional ring of annihilating and coalescing random walkers with deterministic biases due to selection . The simulations reveal that the evolutionary dynamics can be collapsed onto master curves governed by three essential parameters: ( 1 ) an expansion length beyond which selection dominates over genetic drift; ( 2 ) a characteristic angular correlation describing the size of genetic domains; and ( 3 ) a dimensionless constant quantifying the interplay between a colony’s curvature at the frontier and its selection length scale . We measure these parameters with a new technique that precisely measures small selective differences between spatially competing strains and show that our simulations accurately predict the dynamics without additional fitting . Our results suggest that the random walk model can act as a useful predictive tool for describing the evolutionary dynamics of range expansions composed of an arbitrary number of genotypes with different fitnesses .
A competition between stochastic and deterministic effects underlies evolution . In a well-mixed system such as a shaken culture of the yeast microorganism Saccharomyces cerevisiae , stochastic competition between individuals , mutations , and selection dictate the dynamics of the population [1] . In spatially structured environments , active or passive dispersal of individuals also plays an important role . The local “well-mixed” dynamics must be coupled to the motion of individuals , leading to strikingly different evolutionary dynamics , even in the absence of selection [2–7] . A model laboratory system that can be used to explore the coupling between local “well-mixed” effects and spatial deterministic and stochastic dynamics is a microbial range expansion [8] , in which a population expands into an unoccupied region of a hard agar Petri dish . Non-motile microbes expand outwards from their initial position due to a combination of growth coupled with random pushing by neighboring cells and leave behind a record of their genetic competition as they cannot move and cease reproducing once the population becomes too dense [8] . A frozen genetic pattern of four competing strains of E . coli marked by different fluorescent colors can be seen in Fig 1 . Spatial structure is present in the frozen genetic patterns because the microbes at the expanding frontier produce daughter cells of the same color that migrate only a small fraction of the front circumference within a generation . Hallatschek et al . [8] identified the key role of genetic drift in producing these sectored patterns; the small population size at the front of an expanding population [9 , 10] enhances number fluctuations ( i . e . genetic drift ) , eventually leading to the local fixation of one strain past a critical expansion radius R0 . The decrease in genetic diversity as the small number of individuals at the frontier expands is referred to as the “Founder effect” [11] . Outside of the laboratory , range expansions occur naturally during the spread of invasive species such as the bank vole in Ireland [12] or the cane toad in Australia [13] , and played a role in the evolutionary history of humans when migrating out of Africa [14] . In these natural expansions , populations may have many competing genotypes , or alleles , each instilling a different fitness . Even if a population is originally clonal , mutations may create new alleles that compete with one another to proliferate , a phenomenon known as clonal interference [15] . An allele’s fitness is often determined by its corresponding expansion velocity . Faster expanding individuals will colonize more territory and will block slower strains from expanding , resulting in the increased abundance of ‘faster’ alleles at the frontier [13 , 16 , 17] . If the curvature of a microbial colony can be neglected and its front is sufficiently smooth , it has been shown both theoretically and experimentally that the domain wall of a faster expanding strain will displace a slower expanding strain at a constant rate per length expanded after an initial transient , resulting in a characteristic triangular shape [17] as shown on the right side of Fig 1 . If the curvature of the expansion is not negligible , the sector boundaries will trace logarithmic spirals [17] . Even in the most simple scenario when de-novo mutations and mutualistic or antagonistic interactions are ignored , the dynamics of many competing alleles with varying fitnesses at the front of a range expansion have neither been quantified theoretically nor explored in laboratory experiments . Prior laboratory experiments focused on the dynamics of a single sector of a more fit strain ( representing a competing alelle ) of yeast sweeping through a less fit strain [17] in regimes where stochastic wandering of genetic boundaries was not expected to be important . Recent experimental work studied how fast a single more fit strain swept through a less fit strain in a range expansion and compared the dynamics to the same strains in a well mixed test tube [9] . In this paper , we experimentally and numerically investigate the dynamics of four competing strains ( alleles ) of E . coli with varying selective advantages initially distributed randomly at the front of a radial range expansion . The eCFP ( blue ) and eYFP-labeled ( yellow ) strains expanded the fastest , followed by the non-fluorescent ( black ) strain , and finally the mCherry-labeled ( red ) strain . The differences in expansion speeds are reflected in Fig 1 as follows: the yellow/blue bulges at the front of the expansion are larger than the black bulges which are larger than the red bulges . The significant random undulations at the frontier , however , significantly mask the selection-induced bulges . As is evident from Fig 1 , the size and location of a monoclonal sector can be described by the locations of its boundaries . When two boundaries collide , they either annihilate if the neighbors to the left and right of the collision are the same or coalesce if the neighbors are different , as illustrated by the A and C respectively on the left side of Fig 1 . We therefore describe our expansions as a one-dimensional line of annihilating and coalescing random walkers , a description that has been used extensively in previous work ( see Ref . [2] for a review ) . To account for the radial geometry of our colonies , we allow the frontier to inflate , corresponding to the increasing perimeter of the colony as its radius increases . Past the radius R0 where genetic domains originally form , we describe the random motion of genetic domains by a diffusion constant per length expanded Dw ( see Fig 1 ) [18] . If dx characterizes the displacement of a domain wall perpendicular to the expansion direction and dL is the distance the colony has expanded ( the radius that the colony has grown ) as illustrated on the right side of Fig 1 , where we neglect the circumferential curvature in this small region , we define the diffusion constant per length expanded as 2Dw = dVar ( x ) /dL where Var ( x ) ≡ 〈x2〉 − 〈x〉2 is the variance and the brackets indicate an average over many domain walls . Note that Dw has dimensions of length . Similarly , differences in expansion velocities between neighboring strains will lead to the deterministic displacement of domain walls per length expanded as the faster expanding strain will reach the contested point on the front before a slower growing strain as mentioned above [17]; we characterize this deterministic motion by a dimensionless “wall velocity , ” [18] v w i j = d 〈 x 〉 / d L , where i is the strain to the left of the domain wall and j is the strain to the right . Note that v w i j = − v w j i . The dynamics of an arbitrary number of neutral competing strains in an expansion ( i . e . v w i j = 0 for all domain walls ) is well understood as the dynamics can be described as a one-dimensional system of annihilating and coalescing random walkers [19–21] which is equivalent to a one-dimensional q-state Potts model [22 , 23] governed by zero-temperature Glauber dynamics [24] or a q-opinion Voter model [25 , 26] . Many theoretical predictions and analyses of this system exist; of particular relevance to this paper are the relative annihilation and coalescence rates per collision as q is varied [27–29] and the calculation of spatial correlation functions [28] . To map standard linear results onto an inflating ring ( i . e . including R0 in the models ) , one can use a conformal time transformation [30–32] . Fewer results are available in the presence of selection , i . e . when domain walls have deterministic biases ( nonzero v w i j ) [33] . Analytical results are rare because the moment hierarchy of this model does not close [2] as discussed in S1 Appendix . In this paper , we measure and predict three quantities relevant to the evolutionary dynamics of our four competing strains of E . coli in radial range expansions: the average fraction of each of our four strains , the two-point correlation functions between our strains , and the relative annihilation and coalescence probabilities per domain wall collision ( see Fig 1 ) , a quantity that has received theoretical attention [27–29] but has neither been explored experimentally nor investigated in the presence of selection . We measure these three quantities using an image analysis toolkit ( available on GitHub , complete with examples of how to use it [34] ) that extends experimental techniques for two-color ( two-allele ) range expansions [8 , 9 , 17 , 18 , 35 , 36] to an arbitrary number of competing strains . We next use an efficient radial simulation ( also on GitHub [34] ) of annihilating and coalescing random walkers with deterministic wall velocities to determine what sets the scale of the dynamics and to synthesize our experimental and theoretical results . We show that three key combinations of R0 , Dw and v w i j control the dynamics of our four strains . We conclude with suggestions for future studies . The details of our experimental , theoretical , and simulation methods are given in the last section .
We begin by reporting our measurements of the average fraction of each strain , the two-point correlation functions between strains , and the relative rates of annihilations and coalescences as a function of length expanded for our four competing strains of E . coli . As discussed in the Materials and Methods , we found that our eCFP and eYFP strains had the fastest expansion velocities followed by the black strain and finally the mCherry strain ( see Table 1 ) . We expected that our experimental measurements would reflect this hierarchy of speeds; faster expanding strains should have a larger fitness than slower expanding ones . To illustrate the presence of selection , we used neutral theory ( discussed in detail in S1 Appendix ) as a null expectation; selection caused deviations from the neutral predictions . To calibrate neutral theory to our experiments we fit R0 and Dw , two model parameters illustrated in Fig 1 , following the procedures discussed in the Materials and Methods . The fit values of R0 and Dw can be seen in Table 2 . In later sections , we show how to predict the average fraction , two-point correlation functions , and relative rates of annihilation and coalescences using our random-walk model and simulation . In this section , we introduce three key combinations of our random walk model’s input parameters R0 , Dw , and v w i j ( see Fig 1 ) that control the evolutionary dynamics of our four competing E . coli strains . Using simulation , we show that we can utilize these key combinations to collapse the simulated evolutionary dynamics ( focusing on the experimental quantities we measured above: the average fraction , two-point correlation function , and annihilation asymmetry ) of an arbitrary number of competing strains in a range expansion . A major goal of this paper is to test if the annihilating and coalescing random-walk model can predict the experimental evolutionary dynamics of our four competing strains ( alleles ) with different fitnesses ( radial expansion velocities ) . To the best of our knowledge , analytical results for the random-walk model are unavailable ( as discussed in S1 Appendix ) ; we consequently used our simulations to predict the dynamics . In this section we quantify the three key parameter combinations for our experimental expansions and then use them to predict the evolutionary dynamics of all four of our competing E . coli strains in an independent experiment . In the last section , we found that our simulation dynamics could be collapsed onto master curves for a fixed set of κ i j = R 0 / L s i j by rescaling the length expanded L by any single L s i j = D w / ( v w i j ) 2 and by rescaling ϕ by ϕ c = 8 D w / R 0 . These simulated master curves were invariant to the alteration of simulation parameters provided that the set of κij remained the same . This insight allowed us to develop a novel method of characterizing the experimental dynamics . Namely , we could experimentally determine L s i j , κij , and ϕc , collapse the experimental data the same way as the simulations ( i . e . L / L s i j , ϕ/ϕc ) , and compare the two to predict the dynamics of many competing alleles in a range expansion . As discussed below , this technique ultimately allowed for accurate predictions of the evolutionary dynamics of the four competing strains and , surprisingly , allowed us to make much more precise measurements of selective differences between strains . As mentioned above in the Experimental Results section , using the procedures outlined in the Materials and Methods , we had previously determined R0 = 3 . 50 ± 0 . 05 mm and Dw = 0 . 100 ± 0 . 005 mm ( Table 2 ) . In order to fit L s i j = D w / ( v w i j ) 2 and κ i j = R 0 / L s i j , however , we needed to measure v w i j . By tracking the growth of a more fit sector sweeping through a less fit strain ( see the Materials and methods ) , we found that each strain swept through mCherry with a wall velocity of v w i R = 0 . 06 ± 0 . 02 ( as seen in Table 2 ) ; we could not detect the wall velocity of the eYFP and eCFP sweeping through the black strain . In principle , the measured values of R0 , Dw , and v w i j should have allowed us to totally calibrate the three key parameter combinations . For example , ϕ c = 8 D w / R 0 = 0 . 48 ± 0 . 01 . The value of κ i j = R 0 / L s i j followed from the measurement of L s i j using the known value of R0 . Unfortunately , the final parameter L s i j = D w / ( v w i j ) 2 was more difficult to calibrate . Using v w i R = 0 . 06 ± 0 . 02 , we found that L s i R = 30 ± 20 m m; the error on this value was too large for it to be predictive in our simulations . Furthermore , as we were unable to accurately measure the wall velocity of the eCFP/eYFP strains sweeping through the black strain , we could not calculate the corresponding selection length scale . We therefore needed a new technique to determine L s i j . As our eCFP and eYFP strains were neutral within error , we treated our system as composed of one neutral ( N ) eCFP/eYFP strain , a red ( R ) mCherry strain , and a black ( B ) strain ( q = 3 colors ) . As the eCFP/eYFP expanded faster than the black followed by the mCherry strain , we needed to determine the values of L s N R , L s N B , and L s B R . To fit L s i j more precisely than that from our direct measurement of wall velocity , we competed pairwise combinations of strains in range expansions ( i . e . the eCFP/eYFP strain vs . mCherry ) and calculated the two-point correlation functions Fij ( L , ϕ ) at the maximum length expanded of L = 6 . 5 mm . As there were only two competing strains , there was only one Ls . To fit the value of Ls , we began by rescaling the experimental length expanded L by Ls and ϕ by ϕc ( Table 2 ) and calculated the resulting κ = R 0 / L s . Note that Ls simultaneously rescales the length expanded L by Ls and sets the value of κ = R 0 / L s , changing the shape of the collapsed correlation function . We then ran a simulation at the set value of κ ( the chosen simulated values of Ls and ϕc did not matter due to the collapse ) and then compared the collapsed experimental dynamics to our simulation . Fig 7 illustrates the fitting procedure by displaying the experimentally rescaled two-point correlation function FNR ( the solid red line ) at a length expanded of L = 6 . 5 mm between our eCFP/eYFP strain ( N ) and our mCherry strain ( R ) ( inoculated at fractions of 2/3 and 1/3 respectively ) and simulated universal correlation functions corresponding to different values of Ls ( dashed lines ) . To determine the best-fitting value of Ls , we calculated the sum of the squared displacements weighted by the inverse of the experimental standard error squared between experiment and simulation . The best-fitting Ls was determined by finding the value which minimized the weighted sum of squares . To estimate the error in our fit , we assigned each potential value of Ls a probability proportional to the inverse of the weighted sum of squares , normalized the probability distribution , and set the error in our fit of Ls to the confidence intervals of the probability distribution . Our fit values of L s i j and κij using this technique are listed in Table 3; the values of κij are also plotted in Fig 5 . Although this technique was about a factor of 5 more precise than using the measured wall velocities v w i R to determine L s i j , the upper bounds of the 95% confidence intervals were still very large as seen in Table 3; the potential values of Ls had a very large tail . To test that the resulting Ls and κ could accurately predict the experimental dynamics at all L and not just the L where the correlation functions were fit , we plotted the experimental average fraction and correlation functions ( solid lines , Fig 8 ) as we varied L and compared their values to those predicted by simulation ( dashed lines , Fig 8 ) . Fig 8 uses the same set of experimental data as that from Fig 7 . The simulation using the fit parameters always closely tracked the experimental values at all L , suggesting that our fitting technique was robust and could be used to describe the dynamics of our strains . Having determined L s i j and κij from pairwise competitions between strains , we tested if we could predict the average fraction , the two-point correlation functions , and the annihilation asymmetry when the four E . coli strains were grown together ( treating the eYFP and eCFP strains as neutral , so q = 3 ) in an independent experiment . We inoculated the four strains in equal proportions . Fig 9 shows experimental measurements of the average fractions and two-point correlation functions ( solid lines ) together with simulated predictions ( dashed lines ) that used the independently fit values; no additional fitting parameters were used . The predicted average fractions and correlation functions closely tracked the dynamics for L ≳ 3 mm . We attribute the deviations for L ≲ 3 mm to image analysis artifacts resulting from the presence of the black strain ( see the Image Analysis section in the Materials and methods ) . At the largest length expanded of L = 6 . 5 mm where artifacts were minimal , the experiments matched the predictions within error . All average correlation functions at this length expanded were successfully predicted by the simulations; we only display FNR for simplicity . In addition to predicting the average fractions and correlation functions , the simulation with our fit L s i j and κij predicted that the annihilation asymmetry would deviate only slightly from neutrality ( at most a change of 0 . 1 ) over the length expanded by our colonies in every experiment , agreeing with our findings ( Fig 4 ) . This can be readily observed in Fig 6 which displays a simulation of two neutral strains ( our eCFP and eYFP strains ) and a less fit strain ( our mCherry strain ) inoculated in equal fractions . If we rescale the maximum distance expanded by our colonies of Lmax = 6 . 5 mm by the smallest selection length ( this results in the largest possible change of ΔP ) of L s N R = 13 mm , Lmax/Ls ∼ 0 . 5 and ΔP increases from 0 ( neutrality ) to at most 0 . 1 . This small deviation from neutrality is within the uncertainty of our experimental measurement of ΔP . Evidently , certain quantities , like the average fraction and correlation functions , show signs of selection before others ( in this case , the annihilation asymmetry ) . The quantitative agreement between our model and our experiments suggests that the one-dimensional annihilating-coalescing random walk model can indeed be used to predict the dynamics of many competing strains with different fitnesses in a range expansion .
We investigated the evolutionary dynamics of four competing strains of E . coli in radial range expansions with differing selective advantages . We measured the average fraction Fi of each strain , the two-point correlation functions Fij between strains , and the annihilation asymmetry ΔP with our image analysis toolkit [34] . Our simulations , which model the expansions as a one-dimensional line of random walkers subject to deterministic drift on an inflating ring , showed us that these three quantities could be collapsed onto universal curves for a fixed set κ i j = R 0 / L s i j when the length expanded by the colony L was rescaled by any of the lengths L s i j = D w / ( v w i j ) 2 and the angular distance between strains ϕ was rescaled by ϕ c = 8 D w / R 0 . To test if the random walk model could predict experimental dynamics , we independently calculated experimental values of L s i j , κij , and ϕc and compared the dynamics between the two . The simulations accurately predicted the dynamics of the average fraction , correlation functions , and annihilation asymmetry when all four of our strains were present with no additional fitting parameters . The annihilation asymmetry ΔP is a quantity unique to range expansions with three or more strains and , to the best of our knowledge , has not been studied previously . Our results illustrate the importance of considering domain wall annihilation and coalescence when more than two strains are present and suggest that the annihilating-coalescing random-walk model can act as a useful predictive tool when describing the evolutionary dynamics of range expansions composed of an arbitrary number of competing alleles with different fitnesses . Along the way , we introduced a new technique that compared universal simulated correlation functions to experimental correlations to fit L s i j . The resulting values of L s i j were about a factor of 5 more precise than directly evaluating L s i j = D w / ( v w i j ) 2 with the wall velocities extracted from the growth of sectors . Given our fit L s i j , we evaluated v w i j using L s i j = D w / ( v w i j ) 2 and the known Dw; we compare these values to those extracted from single sectors in Table 4 . The wall velocities from both measurements agreed within error , but the wall velocities obtained from our correlation method were at least a factor of two more precise than tracking single sectors . Although the correlation technique dramatically increased the precision in our evaluation of L s i j , the resulting precision increase for v w i j was less pronounced as v w i j ∝ 1 / L s i j . Nevertheless , it is clear that the correlation technique can be used to precisely extract small differences in fitness between spatially competing strains . Our work illustrates that the annihilating-coalescing random walk model can predict the experimental dynamics of an arbitrary number of competing alleles with different fitnesses in microbial range expansions . It is possible that this model could predict the dynamics of range expansions occurring outside of the laboratory , especially if the expanding organisms’ underlying motion did not completely smear out the population’s spatial structure; the organismal motion could potentially be accounted for by increasing the domain wall diffusion coefficient Dw . To predict the dynamics of expansions , however , the annihilating-coalescing walk model relies on a key set of parameters: the set of L s i j , the set of κij , and ϕc . We found that the set of L s i j could not be predicted from the independent radial expansion velocities of our strains; standard techniques [17] using the relative ratio of expansion velocities to predict v w i j , and thus L s i j , yielded inconsistent results ( see S2 Appendix where we quantify the discrepancy and postulate why it occurred ) . As the set of L s i j is so fundamental to the evolutionary dynamics of range expansions , future work should investigate why relative radial expansion velocities could not be used to accurately predict v w i j and thus L s i j and whether this phenomenon is specific to E . coli range expansions or our specific strains . It would also be interesting to incorporate the reported super-diffusive motion of domain walls [8 , 9] into our simplified simulations and theoretical analysis . The random walk model’s ability to successfully predict the evolutionary dynamics of our experiments suggests that annihilating and coalescing genetic domain walls subject to diffusion and selection-induced displacement provide a useful conceptual framework from which to understand range expansion dynamics .
We used four E . coli strains ( labelled BW001 , BW002 , BW003 , and BW012 ) with a DH5α background and plasmids whose sequences coded for spectrally distinguishable fluorescent proteins . The unique colors were obtained by using the plasmid vector pTrc99a [39] and the open reading frame for the respective fluorescent proteins . Strains BW001 , BW002 , and BW003 expressed eCFP ( cyan/blue ) , Venus YFP ( yellow ) , and mCherry ( red ) respectively , and were identical to the E . coli strains eWM282 , eWM284 , and eWM40 used in Ref . [40] . Note that these three strains were isogenic and differed only by the open reading frames corresponding to their respective fluorescent proteins . The final strain , BW012 , was a mutated descendant of strain BW002 ( yellow ) that fluoresced at a decreased intensity , appearing black , while retaining its ampicillin resistance from the pTrc99a vector . Throughout this work , no additional mutations were introduced or observed . We therefore consider that these four strains correspond to four different alleles . Throughout the paper , we refer to the strains as eCFP , eYFP , mCherry , and black . To prepare saturated cultures , strains were inoculated in 10mL of 2xYT media and were shaken for approximately 16 hours at 37°C . After vortexing each saturated culture and obtaining their concentration via optical density ( OD-600 ) measurements , appropriate volumes ( e . g . , 1:1:1 mixtures of three strains ) were added to an Eppendorf tube with a final volume of 1mL . The Eppendorf tube was then vortexed to uniformly mix the strains . A volume of 2 μL was taken from the vortexed tube and placed on center of a 100 mm diameter Petri dish containing 35 mL of lysogeny broth ( LB ) , ampicillin at a concentration of 100 μg/mL , and 1 . 25% w/v bacto-agar . The carrier fluid in the resulting circular drop evaporated within 2-3 minutes , depositing a circular “homeland” of well-mixed bacteria onto the plate . After inoculation , plates were stored for 8 days upside down ( to avoid condensation ) in a Rubbermaid 7J77 box at 37°C with a beaker filled with water; the water acted as a humidifier and prevented the plates from drying out . The plates were occasionally removed from the box and imaged ( at roughly 24 hour intervals ) using the brightfield channel to determine the radius of the colony as a function of time . On the eighth day , the plates were imaged in both fluorescent and brightfield channels . The number of replicate plates used are stated next to the respective experimental results . If we noticed that a mutation had occurred during an expansion ( mutations usually presented themselves as unexpected large bulges at the front of a colony or as distortions in fluorescent intensity ) , we discounted the colony . We imaged our range expansions with a Zeiss SteREO Lumar . V12 stereoscope in four channels: eCFP , eYFP , mCherry ( fluorescent channels ) , and brightfield . In order to analyze a colony with a maximum radius of approximately 10 mm using a single image , we stitched four images together with an overlap of 20% using AxioVision 4 . 8 . 2 , the software accompanying the microscope . We blended the overlapping areas of the images to lessen the impact of background inhomogeneities . An example of a stitched image can be seen on the left side of Fig 10 . Stitching introduced small artifacts such as vertical lines near the center of our expansions; we verified that these did not affect our results . To extract the local fraction of each strain per pixel , we first created binary masks for each fluorescence channel indicating if the corresponding E . coli strain was present . We utilized the “Enhance Local Contrast” ( CLAHE ) algorithm [41] in Fiji [42] , an open-source image analysis platform , to help correct for inhomogeneities in background illumination . After applying the CLAHE algorithm , a combination of automatic thresholding and manual tracing yielded a binary mask of each channel , an example of which is shown in Fig 10; the image on the left is an overlay of an experimental range expansion’s fluorescent channels and the image on the right is the overlay of the corresponding binary masks . A small amount of manual tracing was required near the edges of our colonies because our fluorescent lamp provided uneven illumination; resulting dark regions could barely be identified above background noise . As we mainly used manual tracing near the edge of the colonies where the monoclonal sectors were well defined , we found that our procedure was very reproducible . To alleviate this problem , future work could utilize brighter strains or a more advanced imaging setup . We mapped the binary images to the local fraction of each E . coli strain in the following way: if N binary masks ( corresponding to N colors ) were “on” at a pixel , the local fraction of their corresponding channels was assigned to be 1/N . Although this assignment produces inaccuracies ( i . e . , if one strain occupied 90% of a pixel and the other occupied 10% , our algorithm would register both as 50% ) , domain boundaries were the only areas besides the homeland and the early stages of the range expansions where multiple strains were colocalized . The black strain was defined to be present at pixels reached by the range expansion in which no other strains were present . Although this definition introduced errors at radii close to the homeland with significant color overlap , the error became negligible at large radii as quantified in Supplementary S5 Fig . Once we determined the fraction of each strain at each pixel , we were able to extract quantities such as the total fraction of each strain in the colony and spatial correlations between strains at a given expansion radius . The mask in Fig 10 highlights that sector boundaries can be used to determine local strain abundance . Although it is possible to extract the position of every domain wall from each strains’ local fraction , it is challenging to actually track a single wall due to collisions between walls . To address this problem , we created a binary mask of the edges in our images and labelled the edges of each domain . Annihilations and coalescences were counted manually within Fiji [42]; automated measures were not accurate enough . It is worth pointing out that in this paper , we ignore the three-dimensional structure of our colonies and describe them by our two-dimensional images taken with the stereoscope . We justify this approximation because the initial diameter of our colonies is at least a factor of 10 larger than their height ( less than 1 mm as judged by a ruler ) , so they are effectively two-dimensional , and because the strain composition of our colonies does not vary with height inside the colony . We confirmed that strain composition does not vary with height by using a confocal microscope to probe the internal structure and also by taking a pipette tip , scratching it through a sector , growing the cells touched by the tip in overnight culture , and verifying that plated single colonies from the culture were the same color as the sector . We used the average expansion velocity of each strain for radii R > R0 as a proxy for selective advantage , similar to previous work [17 , 35] . In three independent sets of experiments using different batches of agar plates ( the main source of variability in our experiments ) , we measured the diameter of 12 expansions of each strain approximately every 24 hours following the protocol for range expansions with two or more strains . To account for biological variance , sets of four of the 12 colonies were created from independent single colonies; no statistical difference was seen between biological replicates . The diameters were determined by manually fitting a circle to a brightfield image of the expansion three times and averaging the measured diameters . Fig 11 shows the average radius increasing with time for each strain from one of our experiments . In every experiment , the eCFP and eYFP strains had the fastest expansion velocities ( the respective datapoints overlap in Fig 11 ) , followed by the black strain , and then finally the mCherry strain . The expansion velocity slowly decreased as a function of time; we attribute this to nutrient depletion in the plates . The radial expansion velocity of each strain was obtained by using linear regression to fit the radius versus time for radii greater than R0 . We calculated the average radial expansion velocity between the three sets of plates and reported its error as the standard error of the mean; see Table 1 . Additionally , we quantified the dimensionless selective advantage of each strain relative to the slowest growing mCherry strain following [17] via siR = ui/uR − 1 where the R indicates the mCherry strain ( red ) in each experiment . The selective advantages were consistent , within error , when we calculated the velocities ui and uR over different time intervals . We averaged siR across our three experiments and reported its error as the standard error of the mean as seen in Table 1 . The eCFP and eYFP strains had an average selective advantage of 9% , similar to the experiments of Weber et al . [35] which found , despite the fact that they used different E . coli strains and plasmids , that the expression of mCherry decreased the expansion velocity of their strains by approximately 15% in certain “fast growth” environmental conditions . Our black strain had an approximately 6% enhancement over the mCherry strain . Differences in radial expansion velocities of this magnitude have been used to study yeast S . cerevisiae and E . coli range expansions in the past [9 , 17] . To investigate the source of this fitness defect , we took the plasmids from our original strains , inserted them into a different set of clonal DH5α cells , and inoculated the new eCFP , eYFP , and mCherry strains in equal proportions in a range expansion . We saw that the average mCherry fraction decreased by 10% at a radius expanded of R = 10 mm , matching the results of Fig 2 , suggesting that the presence of the plasmids was responsible for the fitness defect . From Table 1 , it is clear that the variance in siR was large between different sets of agar plates . Although siR varied significantly , the order of expansion velocities between the strains was consistent; the eCFP and eYFP strains always expanded faster than the black strain which expanded faster than the red strain . Importantly and in stark contrast to siR , the demixing radius R0 , wall velocities v w i j , and diffusion coefficient Dw were very consistent between sets of plates ( measured below ) , resulting in consistent evolutionary dynamics between our competing strains . To test if the radial expansion velocity differences were related to the basal growth rates of our strains in liquid-culture , we competed all of our strains against mCherry in 10mL flasks of 2xYT growth media . We created three independent replicates of each pairwise competition ( 9 tubes in total ) in the flasks by inoculating 60% of mCherry and 40% of the other strain in mid-log phase . We passaged saturated E . coli samples into new media every 24 hours; we determined the population composition of each flask using a BD LSR Fortessa FACS machine when the cells were passaged . We competed the strains for 72 hours , corresponding to approximately 45 generations ( doubling times ) . We found that every strain grew faster than mCherry in every replicate in liquid culture as judged by a decrease in fraction of mCherry over time . Following previous work [43] , we used the decrease in mCherry fraction to determine the dimensionless “well-mixed” selective advantage s i R w m = g i / g R − 1 of the strain competing against it , where gi is the growth rate of strain i and gR is the growth rate of mCherry . We list the measured values of s i R w m in Table 1 . The radial expansion velocity fitness siR did not agree with the liquid-culture fitness s i R w m within error , in contrast to previous experiments with the yeast Saccharomyces cerevisiae [17] . However , every strain in liquid culture still grew faster than mCherry . Interestingly , in well-mixed culture , the black strain had the largest growth rate followed by eCFP and eYFP ( they had the same growth rate ) and then mCherry , disagreeing with the order of radial expansion velocities ( where black expanded slower than eCFP and eYFP ) . As the growth rate differences were small , it is possible that additional factors allowed the eCFP and eYFP strains to expand faster than black on agar . Our E . coli switched from log to stationary phase in the 24 hour cycle; the changing environment may have resulted in a different order of fitnesses compared to the agar plates as well . Future work should investigate how the eCFP and eYFP strains expanded faster than the black strain despite a smaller basal growth rate and should also investigate how such small growth rate differences in liquid culture resulted in such large differences in radial expansion velocities on solid agar . When calibrating our model to experiment , the precise value of R0 did not matter as long as each strain’s local fraction could be accurately measured at that radius . Therefore , to maximize the length over which we could quantify range expansion growth , we defined the local fixation radius R0 as the minimum radius where our image analysis package became accurate . For R < R0 , our package predicted equal fractions of each strain due to the overlap of each channel in the homeland ( see Fig 10 ) . Therefore , to determine R0 , we inoculated radial expansions with three strains in unequal proportions; we used 10% of two strains and 80% of another . The minimum radius where the fractions agreed with their inoculated values was R0 = 3 . 50 ± 0 . 05 mm as seen in Supplementary S6 Fig . We found that this value of R0 worked for all colonies . Past work has found that E . coli colony domain walls fluctuate diffusively in certain conditions [18] and super-diffusively in others [8] . In our expansions , the domain walls appeared to fluctuate super-diffusively ( as judged by tracking the position of domain walls and determining their variance vs . length expanded ) , but we were able to successfully fit the evolutionary dynamics using a diffusive theory . Creating a super-diffusive theory to describe the evolutionary dynamics of our system is beyond the scope of this paper . To obtain an effective diffusion constant Dw and to test if the diffusive approximation adequately described our experimental dynamics , we fit the neutral Voter model’s prediction of heterozygosity . The heterozygosity is the probability that two points separated by an angle of ϕ at a length expanded of L = R − R0 are occupied by different strains and is thus a measure of spatial genetic diversity . The neutral Voter model’s prediction of heterozygosity can be given in terms of the two-point correlations used in the main text or can be explicitly written as ( see S1 Appendix ) H ( ϕ , L ) = ∑ i ∑ j ≠ i F i j ( ϕ , L ) = H 0 erf [ ( 1 + R 0 / L ) | ϕ / ϕ c | ] . ( 11 ) H0 = H ( ϕ , L = 0 ) is the heterozygosity when L = 0 and ϕ c = 8 D w / R 0 is a characteristic angular correlation length ( one of the key combinations of model parameters from the main text ) . For q colors inoculated in equal fractions , H0 = 1 − 1/q . We fit H ( ϕ , L ) to our experimentally measured heterozygosity of two neutral strains ( eCFP and eYFP ) on three independent sets of agar plates each with 14 range expansions . We averaged the heterozygosity at each L as can be seen in Fig 12 ( error bars were omitted for readability; the same figure with error bars can be found as Supplementary S7 Fig ) . As we had previously measured R0 = 3 . 50 ± 0 . 05 mm , and H0 = 1/2 for two neutral strains inoculated at equal fractions , Dw is the single free parameter in eq ( 11 ) . We consequently fit Dw at each L with non-linear least-squares , averaged the Dw from the three independent experiments , and found Dw = 0 . 100 ± 0 . 005 mm; the reported error is the standard error of the mean between the experiments . The value of the diffusion constant is on the same order of magnitude as that from previous work [18] . Fig 12 shows the Voter model’s fit ( dashed lines ) together with the experimental heterozygosity ( solid lines ) for one set of plates using our values of Dw and R0 . The fit closely matches the experimental heterozygosity suggesting that a diffusive description of E . coli domain motion is justified . We use this value of Dw for all strains . In principle , Dw may depend on ij , the particular domain wall type . However , we checked that the measured value of Dw did not vary for our all ij ( all strain ) combinations by examining the variance in domain wall position versus length expanded; the variances agreed within error and were thus consistent with a constant Dw . The two-point correlation functions in the main-text were well fit by a constant Dw as well . Unlike the Voter model and our simulations , the experimental heterozygosity at zero separation H ( L , ϕ = 0 ) fails to vanish due to overlap between strains at domain boundaries; this effect is less pronounced at large radii because the effective angular width of boundaries decreased . The discrepancy between the theoretical and experimental heterozygosity is larger at small lengths expanded because the overlap between strains is larger; our image analysis is consequently less accurate . We used image analysis to directly quantify v w i j from the angular growth of more-fit sectors . Characteristic single sectors of each strain sweeping through the mCherry strain can be seen on the left side of Fig 13 . In radial expansions , more fit strains should , on average , sweep logarithmic spirals through less fit strains at large lengths expanded , as verified in yeast expansions [17] . It can be shown that the average angular width of a sector of strain i sweeping through strain j is given by ( see S1 Appendix for more details ) ⟨ ϕ - ϕ 0 ⟩ = 2 v w i j ln ( R / R 0 ) ( 12 ) where ϕ is the angular width at radius R and ϕ0 is the initial angular width of the domain at R0 . 2 v w i j can thus be extracted from the slope of a linear regression fit of 〈ϕ − ϕ0〉 vs . ln ( R/R0 ) as seen on the right side of Fig 13 . By tracking domain walls directly , we found that more fit strains ( eCFP , eYFP , black ) swept through the less fit mCherry strain with a wall velocity of v w i R = 0 . 06 ± 0 . 02 . We could not accurately measure the wall velocity of the eCFP and eYFP strains sweeping through the black strain . The wall velocity was significantly smaller than expected from the basal independent expansion velocities of our strains ( Table 1 ) ; potential explanations for this phenomenon are discussed in S2 Appendix . The magnitude of the velocities were consistent between experiments ( using 40 single sectors on three sets of plates ) but were too imprecise to be predictive in our models . Lattice simulations of range expansions , especially radial ones , can suffer from artifacts arising from the preferred directions of the lattice . It is possible to use an amorphous Bennett model lattice [44] to mitigate some of these effects [32] . Instead , we developed a simple off-lattice method that treats the domain walls as annihilating and coalescing random walkers moving along the edge of an inflating ring . The basic idea of the simulation is illustrated in Fig 14 . We incorporate both the random , diffusive motion of the domain walls as well as deterministic movement due to selection . The radial expansion procedure is most easily understood by first considering a linear range expansion simulation for which the simulation steps are as follows: Our simulation’s diffusion coefficient per length expanded , characterizing the random motion of the domain walls , can be shown to be Dw = a/2 when rij is small while its wall velocity per length expanded , characterizing the deterministic displacement of domain walls due to selection , can be shown to be v w i j = r i j ≤ 1 . Our algorithm , thus far simulating only linear expansions , can easily be extended to simulate radial geometries . To incorporate the radially inflating perimeter , we note that a domain wall at a radius R will jump an angular distance of δϕ = a/R , as shown in Fig 14 . As the radius of our experimental expansions increases approximately linearly with generation time , we describe its radius as R = R0 + at . We thus account for inflation by using a time-varying angular jump length of δ ϕ ( t ) = a R 0 + a t . ( 13 ) If there are N0 individuals at the frontier , R0 is given by R0 = N0a/ ( 2π ) . This modification of the domain wall step size δϕ is the only difference between the radial and linear cases ! In contrast to algorithms that follow the position and state of every organism at the front of a colony , our algorithm only tracks the positions of domain walls and is consequently much faster per generation as the sectors coarsen , allowing for simulations of larger colonies . Fig 15 displays a radial and linear simulation with three neutral colors and a fourth red color with a selective disadvantage comparable to our experiments . We check that our simulation correctly reproduces the behavior of a single more fit domain wall sweeping through a less fit strain as we vary simulation parameters in Supplementary S8 Fig . Our implementation of this algorithm and examples of how to use it are available on GitHub [34] . | Population expansions occur naturally during the spread of invasive species and have played a role in our evolutionary history when humans migrated out of Africa . We use a colony of non-motile bacteria expanding into unoccupied , nutrient-rich territory on an agar plate as a model system to explore how an expanding population’s spatial structure impacts its evolutionary dynamics . Spatial structure is present in expanding microbial colonies because daughter cells migrate only a small distance away from their mothers each generation . Generally , the constituents of expansions occurring in nature and in the lab have different genetic compositions ( genotypes , or alleles if a single gene differs ) , each instilling different fitnesses , which compete to proliferate at the frontier . Here , we show that a random-walk model can accurately predict the dynamics of four expanding strains of E . coli with different fitnesses; each strain represents a competing allele . Our results can be extended to describe any number of competing genotypes with different fitnesses in a naturally occurring expansion as long as the underlying motility of the organisms does not cause our model to break down . Our model can also be used to precisely measure small selective differences between spatially competing genotypes in controlled laboratory settings . | [
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| 2017 | Genetic drift and selection in many-allele range expansions |
Schistosome infection begins with the penetration of cercariae through healthy unbroken host skin . This process leads to the transformation of the free-living larvae into obligate parasites called schistosomula . This irreversible transformation , which occurs in as little as two hours , involves casting the cercaria tail and complete remodelling of the surface membrane . At this stage , parasites are vulnerable to host immune attack and oxidative stress . Consequently , the mechanisms by which the parasite recognises and swiftly adapts to the human host are still the subject of many studies , especially in the context of development of intervention strategies against schistosomiasis infection . Because obtaining enough material from in vivo infections is not always feasible for such studies , the transformation process is often mimicked in the laboratory by application of shear pressure to a cercarial sample resulting in mechanically transformed ( MT ) schistosomula . These parasites share remarkable morphological and biochemical similarity to the naturally transformed counterparts and have been considered a good proxy for parasites undergoing natural infection . Relying on this equivalency , MT schistosomula have been used almost exclusively in high-throughput studies of gene expression , identification of drug targets and identification of effective drugs against schistosomes . However , the transcriptional equivalency between skin-transformed ( ST ) and MT schistosomula has never been proven . In our approach to compare these two types of schistosomula preparations and to explore differences in gene expression triggered by the presence of a skin barrier , we performed RNA-seq transcriptome profiling of ST and MT schistosomula at 24 hours post transformation . We report that these two very distinct schistosomula preparations differ only in the expression of 38 genes ( out of ∼11 , 000 ) , providing convincing evidence to resolve the skin vs . mechanical long-lasting controversy .
Schistosomiasis is a parasitic disease caused by platyhelminths of the genus Schistosoma . It has been estimated that ∼200 million people are infected and ∼200 , 000 die due to schistosomiasis-related pathologies [1] . Without a vaccine , mechanisms of prophylaxis rely primarily on reduction of the number of infected individuals through mass-administration of the only available drug praziquantel . However , the number of infected people has changed little over the last decades [1] . What is more , reduced susceptibility of Schistosoma mansoni worms to praziquantel has been reported in the field [2] , [3] and resistance to the drug can be induced under experimental conditions [4] , [5] , raising the possibility that a similar situation could be also seen in the field . Consequently , the development of new mechanisms of intervention is a priority . In this context , it is important that the process of infection is well characterised . The infectious agents for the human host , the cercariae , are microscopic free-living larvae released by infected fresh water snail hosts . Cercariae infect their mammalian host during water contact by trespassing across the skin barrier . This process is characterised by rapid morphologic , metabolic and physiological changes [6]–[9] that results in obligate parasitic schistosomula in as little as 2 hours [10] . The most prominent aspects of this transformation are the loss of the cercarial tail and a series of changes in the parasite's surface . During skin penetration , the outermost layer in the parasite's surface , the glycocalyx , gets thinner by the action of secretions from the parasite's own acetabular glands [11] , which are emptied during the process of transformation [12] . The remains of the glycocalyx are shed together with transient microvilli structures that form and disappear during this transformation process [13] . At the same time , pre-packed multi-laminated vesicles originating from the body of the parasite make their way to the surface where they release their contents; these contribute to the generation of the new double-bilayer membrane , characteristic of the intra-mammalian stage of the parasite [14] . Increasing research on the schistosomulum stage required the development of efficient , reproducible and rapid ways to generate large quantities of biological material . Various effectors are known to elicit the artificial transformation of cercariae into schistosomula , for example , cell growth media at 37°C [15] , [16] or just low osmolarity phosphate buffer saline solution [17] seem to be enough to trigger the cercariae to schistosomula transformation . The presence of certain skin lipids , yet is not essential [17] , also plays a role in the process of cercariae transformation and penetration [18] , [19] probably by triggering the release of acetabular glands contents [20] . The most popular method for obtaining artificially transformed schistosomula uses a mechanical transformation ( MT ) protocol that includes some sort of shear force ( centrifugation [21]–[23] , passages through an emulsifying needle [24] , or shaking [15] ) applied to freshly shed cercariae followed by separation of cercariae heads from tails ( usually by centrifugation in a density gradient ) and posterior incubation of the cercariae heads/schistosomula in culture media at 37°C . Parasites obtained using this protocol show no major morphological or biochemical differences with those recovered from natural infections [10] , [15]; making the MT the method of choice for obtaining large quantities of schistosomula . However , at the level of the whole transcriptome , equivalency of MT schistosomula to those obtained from natural infections has not been established; even though these artificial parasite preparations have been used almost exclusively in the identification of potential vaccine proteins and in high-throughput studies of gene expression [25]–[29] , identification of drug targets [27] and screening of a compound library [30] . Artificial induction of stress or mechanical damage may induce gene expression signals that are not responding to the natural process of infection . Moreover , failure to induce physiologically important transcription events , triggered by host-skin specific signals , could lead to exploitable vulnerabilities being missed . Our work presented here uses high throughput transcriptome sequencing technology , known as RNA-seq [31] , in combination with the latest genome assembly available for S . mansoni [32] to compare the profile of genes expressed in MT and ST schistosomula .
S . mansoni ( NMRI strain of Puerto Rican origin ) cercariae were shed from infected Biomphalaria glabrata snails by exposing them to the light for 1 . 5 hours . MT schistosomula were obtained using an optimised version of the protocol used by Brink et al . , [10] . Optimisation steps of the protocol were implemented in the tail detachment step ( shake cercariae vigorously for approximately 30 seconds in a vortex mixer before passing these through a 21G syringe needle approximately 13–15 times ) and the separation of heads/schistosomula and tails ( by placing the heads plus tails suspension on 10 ml of ice-cold 70% Percoll ( Sigma , UK ) and 90 mM NaCl solution in DMEM in 15 ml conical tubes ) by centrifugation at no more than 1000 g for 10 minutes at 4°C . Skin-transformed ( ST ) schistosomula were obtained using a modified version of the protocol published by Clegg et al . , [33] . TO ( Tuck Ordinary ) mice ( Harland , UK ) were killed with an overdose of anaesthetics followed by cervical dislocation according to Home Office regulations . Hair was removed from the abdominal and dorsal skin areas using clippers and skin was later excided from the animal using dissecting scissors . Each animal provided an area of skin of approximately 7 . 5 cm2; which was divided into two halves . Gel-like dermal tissue was removed by rubbing the skin gently ( for approximately 5 minutes ) with sterilized gauze soaked in supplemented DMEM ( Dulbecco's Modified Eagle's medium supplemented with 100 U/L penicillin , 0 . 1 mg/L streptomycin and 10 mM L-glutamine ) . The transformation apparatus is presented in Figure 1A . The lower compartment of the assembly was filled with supplemented DMEM containing 2% fetal calf serum ( FCS ) and one half of prepared skin was mounted covering the opening of the tube with the dermal side facing downwards . The upper compartment was placed above the lower compartment with a rubber O-ring in between . All pieces were kept in place by holding both tubes with a metal clip ( Figure 1B ) . The skin surface was washed three times with aquarium water and assemblies were checked for leaks . All assemblies were placed in a water bath pre-warmed at 37°C; the bottom compartment of the assembly was constantly kept at this temperature ( Figure 1C ) . Experiments were carried out in a room with controlled temperature of 28°C . Approximately 12 , 000–14 , 000 freshly shed cercariae kept in aquarium water were placed in each assembly and these were left in the water bath for 3 hours . Schistosomula preparations were individually checked for contamination with tails . Samples with more than 4% tails/cercariae contamination were discarded . MT and ST schistosomula preparations were placed in individual tubes , washed 3 times in supplemented DMEM and incubated at 37°C and 5% CO2 for a total of 24 hours in growth media ( supplemented DMEM , 10% FCS , 1% Hepes buffer ) . Schistosomula preparations were observed under the microscope using a Leica DM 1 L inverted microscope ( Leica , Milton Keynes , Bucks , UK ) at 10× or 40× . Video recordings were taken using a Dino-Lite AM-423X camera and DinoXcope software ( Version 1 . 7 . 3 ) . The criterion used for evaluating parasites is the one used in Mansour et al . , [34] . After the incubation period was completed , parasites were transferred to 15 ml conical tubes and centrifuged at 1 , 000 g for 5 minutes , supernatant was discarded and schistosomula were suspended in 1 ml of TRIzol reagent ( Invitrogen , UK ) and stored at −80°C until RNA extraction . Total RNA from parasite material was extracted using TRIzol ( Invitrogen , UK ) according to manufacturer specifications . After extraction , RNA quality was assessed using an Agilent RNA 6000 Nano - Bioanalyzer and quantified using a NanoDrop ND-1000 UV-Vis spectrophotometer . RNA-seq libraries were prepared as previously described [31] and sequenced as 76-base paired reads using the Illumina Genome Analyzer IIx platform . Raw sequence data were submitted to ArrayExpress ( http://www . ebi . ac . uk/arrayexpress/ ) under accession number E-MTAB-451 . RNA-seq reads were aligned to the latest S . mansoni reference genome ( version 5 . 0 , [32] ) using TopHat [35] ( version 1 . 3 . 1 ) with default parameters except for minimum and maximum intron sizes which were set to 10 and 30 , 000 bp respectively . Other parameters that were specified included the type of library sequenced ( set to standard cDNA Illumina library; –library-type fr-unstranded ) and the mate pair distance ( or insert size; -r option ) , which was calculated individually for each library . Only uniquely mapping reads were kept for further analyses . The number of reads aligned to each transcript was calculated using BEDTools [36] and used to calculate RPKM ( reads per kilobase per million of reads mapped ) values [31] for each transcript . A threshold RPKM value was calculated as described in Protasio et al . , [32] and transcripts with expression <2 RPKM were removed from the dataset resulting in the reduction of the total number of transcripts from 11 , 778 to 9 , 291 ( 2 , 487 transcripts had negligible expression in both samples ) . Differential expression of transcripts was performed using EdgeR [37] . P-values were adjusted for multiple testing [38] and the threshold for significance set at adjusted p-value≤0 . 05 . A complete list with fold change values and associated adjusted p-values obtained from EdgeR are provided in Supplementary Table S1 . Relative expression of a subset of genes found differentially expressed between the ST and MT schistosomula were assayed using real time quantitative PCR ( RT-qPCR ) . Primers for these genes were designed using Primer3 software [39] and ordered from Sigma , UK ( primer sequences are available upon request ) . First strand cDNA was synthesised from 1 ug of original total RNA samples ( MT2 and ST2 – Table 1 ) using SuperscriptII ( Invitrogen , UK ) according to manufacturers instruction . All RT-qPCR reactions were performed in a Mx3005P QPCR System ( Agilent Technologies ) and using KAPA SYBR FAST qPCR Kit ( Kapa Biosystems ) . PCR efficiencies for each primer pair were calculated using 10-fold dilutions of MT2 cDNA . Relative gene expression for a given gene was quantified relative to the expression of a reference gene ( rRNA18S ) . Cycle thresholds ( Ct ) for each reaction where obtained using the MxPRO QPCR Software ( Agilent Technologies ) and used in the Pfaffl equation [40] to calculate the fold change expression of a target gene between samples . Fold change values reported are the mean of four replicates . In order to compare RT-qPCR and RNA-seq derived fold change values , RNA-seq standard deviation ( SD ) was calculated using the method described by Busby et al . , [41] . AlamarBlue incorporates a colour indicator of metabolic activity of the mitochondrial function [42] and has previously been used to assess the viability of schistosomula [34] . In order to assess metabolic activity of MT and ST parasites , 250 24-hours-old-schistosomula obtained from MT and ST methods were incubated in AlamarBlue ( Invitrogen , UK ) for either 3 or 24 hours prior to measurement . Eleven and 12 samples were assayed for MT and ST respectively . Absorbance was measured at 570 nm ( with reference at 600 nm ) using a microplate reader BioTek PowerWave HT ( BioTek Instruments Inc . , Winooski , VT , USA ) ; data collection was performed using the software Gen5 ( BioTek Instruments Inc . , Winooski , VT , USA ) . Raw absorbance data is presented in Supplementary Table S2 . Student's t-test was used to evaluate the significance between mean absorbance . All animal procedures were performed in accordance with the UK Animals ( Scientific Procedures ) Act 1986 and as authorised on personal and project licences issued by the UK Home Office .
Both MT and ST transformation protocols were subjected to optimization . Results comparing the standard and optimised MT protocols are shown in Figure 2A and 2B respectively . For the MT , optimised conditions resulted in increased numbers of schistosomula and a lower percentage of damaged or non-viable individuals . Reduction of the number of syringe passages resulted in increased number of viable parasites while a higher percentage of Percoll resulted in less contaminating tails ( ∼1% ) . For the ST , we found that schistosomula preparations virtually free from tail contamination ( ∼1% to 4% ) could be obtained by placing no more than 14 , 000 cercariae in the upper compartment of the transformation apparatus . However , tail contamination was more frequent in the ST preparations than in MT and samples dedicated for RNA-seq libraries had to be carefully selected . Contrary to schistosomula resulting from MT , non-viable parasites were hardly ever observed in ST preparations . Under the light microscope , schistosomula obtained from both protocols were indistinguishable from each other ( Supplementary Video S1 and Video S2 ) and both schistosomula preparations progressed to later stages in the life cycle ( up to two weeks post transformation ) when cultured in vitro ( data not shown ) . In terms of recovery , MT yields ∼90% of the applied cercariae , while the ST yields only ∼10% . An overview of the sequencing and alignment results obtained for the sequenced samples is presented in Table 1; where samples 1 and 2 ( for both MT and ST ) represent independent schistosomula transformations ( replicates ) . In the case of ST samples , and due to the limited number of schistosomula obtained in each experiment , approximately 3 experiments were pooled to provide enough biological material . Our dataset included 11 , 778 annotated transcripts and we found that 9 , 291 showed expression above background in at least one of the samples . Correlation analysis of the 24-hour old MT and ST schistosomula transcriptome samples showed high values for both Pearson's product and Spearman's rank coefficients ( 0 . 98 and 0 . 99 , respectively; Figure 3A ) . Using the software package EdgeR [37] , we found only 38 differentially expressed transcripts ( adjusted p-value<0 . 05 ) between MT and ST schistosomula . Of these , 28 transcripts showed higher relative expression in the ST parasites ( Table 2 ) while 10 transcripts showed higher relative expression in the MT ( Table 3 ) . A graphical representation of differentially expressed transcripts is shown in Figure 3B . RT-qPCR validation was performed for 33 of the 38 genes found differentially expressed between ST and MT schistosomula ( Figure 4 ) . With 95% confidence interval , the fold change values obtained from both methods overlapped in 14 cases ( Smp_029780 , Smp_057860 , Smp_124000 , Smp_132670 , Smp_172770 , Smp_211020 , Smp_212760 , Smp_900010 , Smp_900020 , Smp_900030 , Smp_900050 , Smp_900070 , Smp_900080 , Smp_900090 ) . Moreover , fold change values obtained from these two different methods are highly correlated ( Pearson's correlation 0 . 89 , p-value 3 . 85E−12 ) and only in 4 cases ( Smp_028850 , Smp_067800 , Smp_155320 , Smp_001070 ) the direction of the fold change disagrees between the two methods . Table 2 shows a list of genes more highly expressed in skin-transformed parasites . We found that all 12 mitochondrial genes ( Smp_900000–Smp_900110 ) are found in this list . In order to investigate whether the higher expression of the mitochondrial genes had any consequences on metabolic activity we used the AlamarBlue ( AB ) assay . AB is a good indicator of mitochondrial activity through the measurement of redox species generated by the respiratory electron chain [42] . Incubation of 24-hour old schistosomula for 3 hours in AB showed no significant difference between MT and ST parasites ( Figure 5 – blue boxplots ) . Increasing the incubation time to 24 hours showed an incremental increase in the absorbance ( compared to blank wells ) and a significant difference ( t-test , p-value<0 . 01 ) between MT and ST schistosomula ( Figure 5 – green boxplots ) suggesting that these two populations of parasites have not only different rates of mitochondrial metabolism but that ST parasites are more metabolically active than their MT counterparts after 24 hours of in vitro culture . The remainder of genes found relatively more expressed in the ST sample included examples that could be associated with the infection process . Two of these genes , for instance , are involved in calcium sensing ( Smp_151600 ) or binding ( Smp_132670 ) , functions that have been associated with schistosomula adaptation to the mammalian host [43] . It is possible that such mechanisms are induced by contact with the host skin explaining the reduced expression of such transcripts in MT parasites . Proteases and proteases inhibitors are among the transcripts that were more expressed in MT schistosomula . Proteases have a recognised role in schistosomes; for example , adult worms use a set of aspartic proteases called cathepsins for the purpose of feeding [44] while cercariae use elastase and other proteases during the process of skin invasion [45] , [46] . We found a gene encoding a secreted serine protease from the trypsin family ( Smp_002150 ) , with expression in MT parasites double that of ST parasites . RNA-seq data ( Supplementary Table S3 in Ref . [32] ) showed that the expression of this gene is developmentally up regulated after the transformation in schistosomula , showing an impressive 30-fold increase between 3-hour and 24-hour post-transformation parasites . Alongside the serine protease we found two protease inhibitors that were also differentially expressed . Protease inhibitors can neutralise the action of host- and/or parasite-derived proteases . Smp_089670 encodes a 1 , 800 amino acids polypeptide with high similarity to an alpha-macroglobulin . Macroglobulin-type inhibitors entrap their target proteases limiting the range of substrates they can act upon; hence , they have a regulatory role rather than strictly inhibitory effect [47] . Macroglobulins can also inhibit coagulation [48] , perhaps indicating that the secretion of the S . mansoni alpha-macroglobulin functions as a facilitator of schistosomula migration through the broken tissue/vessels during the skin stage . The second protease inhibitor was a kunitz-type serine protease inhibitor ( Smp_147730 ) . These types of inhibitors have been postulated to have an important role in the host-parasite interaction in Echinococcus granulosus infections [49] . Interestingly , both protease inhibitors were significantly up regulated during transformation from cercariae to schistosomula ( Supplementary Table S3 in Ref . [32] ) suggesting that their expression is developmentally regulated . Microexons ( <36 bp , in multiples of 3 bases ) typically form a small part of some genes in most eukaryotes [50] but for a few genes in S . mansoni , microexons comprise the majority ( ∼75% ) of the sequence length and these genes have therefore been termed microexon genes ( MEGs ) [51] , [52] . Each MEG has the potential to generate an enormous repertoire of splicing variants through exon skipping because missed exons do not cause frame-shifts . The particular gene structure of MEGs therefore provides an easy mechanism to generate protein variation and seems to be both time and tissue specific [52] . Two MEGs from two different families appeared more expressed in MT compared to ST schistosomula at 24 hours after transformation ( Table 3 ) . In the case of Smp_180340 , a MEG-2 member , RNA-seq coverage was poor and not specific to the exons in both samples; probably indicating unprocessed transcripts and was not considered for further analysis . For Smp_124000 , RNA-seq data from schistosomula samples agreed with the current annotation of the gene ( Figure 6 ) . Intriguingly however , the isoforms expressed in MT and ST differed from each other , with three exons that were expressed in the ST being absent from the MT schistosomula sample ( Figure 6 ) . Because the RNA-seq experiments assayed the transcriptional status of large numbers of parasites simultaneously , we can therefore rule out – at least in this example – that exon skipping is simply a stochastic process .
The process of cercarial invasion and early stages of schistosomula migration are relevant for the development of intervention strategies against schistosomiasis . The skin schistosomula stage represents the first encounter of the parasite with the mammalian host and it is regarded as a vulnerable stage for parasite killing [53]–[55] . Hence , schistosomula have been the target of many studies that focus on both the adaptation of the parasite to its host and the identification of drug targets and vaccine development . The study of changes in gene expression across different stages of host invasion can be used to investigate parasite adaptation to the host . With one exception , where in vivo recovered and in vitro cultured S . japonicum schistosomula were compared [56] , all high-throughput gene expression studies have used MT schistosomula at different developmental stages by just prolonging the in vitro incubation time [25] , [27] , [28] , [32] , [57] . Since MT schistosomula are only proxies for natural infections , the differences between these and more naturally transformed parasites needs to be established . For instance , misleading artefactual parasite responses induced by stress or damage need to be identified as well as potentially important parasite responses that are only induced during the rapid natural transformation of free-living cercariae into obligatory parasitic schistosomula . Our work used the latest RNA-seq technology to investigate the differences in the gene expression of MT and ST schistosomula at 24-hour post-transformation . We found that these samples differ only in the expression of 38 transcripts ( out of 9 , 291 expressed transcripts; adjusted p-value<0 . 05 ) . In order to validate our approach , we performed RT-qPCR on 33 out of the 38 genes found differentially expressed and found that , at least in this experiment , RNA-seq seems to over estimate the fold change values of differentially expressed genes . However , a high correlation value of 0 . 89 was found between the two methods ( similar values have been reported elsewhere [58] ) suggesting that the RNA-seq is a valid and reliable method for high-throughput identification of differentially expressed genes . Increased transcript coverage ( greater sequencing depth ) as well as the addition of more biological replicates may result in better measurement by providing greater statistical power . Transcripts encoded in the mitochondrial genome ( mitochondrial genes ) were found more highly expressed in ST parasites resulting in higher metabolic rates in ST parasites . Previously Brink et al . , [10] suggested that ST parasites are a selection of the most “fit” cercariae . We suggest that MT preparations may contain a mixture of fit and less-fit parasites and therefore not all of them are expected to develop at their maximum metabolic rate resulting in an averaged reduced metabolic activity for the MT schistosomula population . MT parasites showed higher expression of a protease and two protease inhibitors . Proteases have been linked to host tissue invasion [59] , [60] and have been shown expressed in parasites recovered from in vivo infections [56] as well as in purely MT schistosomula when compared to cercariae [28] . Therefore it is not surprising that they are present in our schistosomula samples . However , the unexpected finding is that these are more expressed in the MT rather than in the ST sample . If proteases were linked to the process of invasion , it would be expected that these be triggered by the presence of components of the skin , elements that are absent during the MT transformation . Further research will be needed to understand what triggers the expression of these genes . Two members of the microexon gene family were found overexpressed in the MT parasites . What is more , the transcript variants found here are different from the ones previously reported confirming that different splice variants from the same loci are expressed at different time points of the life cycle . Interestingly , one of the variants expressed in 24 hours old schistosomula has a different exon profile in the two preparations , suggesting that cues from the environment might be triggering splicing variation . The role and regulation of alternate splicing in MEG may become clearer as the functions of microexon genes are further elucidated . Due to the different treatments received by MT and ST schistosomula , including the low temperature and mechanical stress endured by MT schistosomula , we had hypothesised that stress-associated transcripts ( e . g . stress/apoptotic pathways ) would be differentially expressed . Surprisingly , we could not identify clear markers of stress , possibly because we had optimised our MT protocol to yield a minimum proportion of damaged parasites . Other MT protocols involving , for example a greater number of passages through a syringe-needle or a different source of mechanical stress may give different results . Since MT schistosomula are not exposed to skin lipids that are known to play a role in transformation [18] , [19] and induce the release of contents from the acetabular glands [20] , we had anticipated observing differences related to the presence of lipids in the ST . However , we could not identify transcripts related to the binding ( fatty-acid binding proteins ) or to the transport of fatty acids and therefore conclude that the effect elicited by the presence of skin lipids is independent from transcriptional regulation at that time and is more likely related to machinery that the parasite may already have in place prior to its encounter with host skin . Previously , gene expression changes using in vivo recovered ( IVS ) and mechanically transformed schistosomula ( MTS ) in S . japonicum at 3 days after transformation has been published [56] . The authors showed that IVS parasites show higher expression of transcripts encoding protaglandins , glutathione-S-transferase Sm28GST , paramyosin , stress related proteins and transcripts related to markers of anti-inflammatory and immunomodulatory processes . In the case of MTS parasites , the authors report higher expression of transcripts involved in glucose transport , fatty acids transport and haemoglobin digestion . None of the genes found differentially expressed in our analysis could be associated with the functions described in the S . japonicum study; probably due to the differences in the experimental design of both studies ( i . e . , time points at which differential expression is assessed ) . Our study represents a snapshot of the schistosomula transcriptome after transformation . It is possible that a greater effect of the different treatments applied to both populations might be seen at shorter times after transformation . Ideally , a time-course experiment comprising more than one time point comparison between ST and MT schistosomula should have been performed . Nevertheless , should differences in gene expression exist at earlier time points , they disappear at 24 hours after transformation and are unlikely to have consequences on the gene expression profile of the parasites . Finally , we emphasise that in view of the great differences in the transformation processes analysed here , the number of genes found differentially expressed between ST and MT at 24-hours post-transformation is unexpectedly small , suggesting that changes in gene expression induced upon transformation might be independent from the methodology employed to transformed parasites , at least in the two methods here studied . In this work , we provide further evidence that transformation might be triggered by more robust signals , such as the change in osmotic pressure [17] and/or temperature [15] between the water and the host environments . We recommend that , except for the reported genes , these samples should be considered as transcriptionally equivalent . Our work contributes to the validation of gene expression studies that have used MT schistosomula and provides further evidence that the MT is a good proxy for natural skin-transformation . | Schistosomiasis is an endemic parasitic disease affecting ∼200 million people in the most socioeconomically deprived regions of the world . Human infection occurs during water contact where free-living larvae called cercariae penetrate host skin and become parasitic organisms called schistosomula . This stage represents the first encounter of the parasites with the host and is also regarded as one of the most vulnerable stages of the parasite's life cycle . Therefore , schistosomula are the focus of many studies , many of which look at changes in the expression of genes as a way of understanding the process of infection , identifying potential drug targets and vaccine candidates . Because collecting enough parasitic material from natural infections is not possible for certain types of studies ( for example , gene expression studies ) , a mechanical transformation of the cercariae into schistosomula is often used instead and assumed as a good proxy for the natural transformation process . However , the equivalency of gene expression profiles between naturally transformed parasites and the mechanically transformed counterparts has never been studied . In this report , we analyse differences in gene expression patterns between these two different parasite preparations and provide enough data to resolve a long-lasting controversy . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
]
| [
"genome",
"expression",
"analysis",
"gene",
"expression",
"genetics",
"biology",
"genomics",
"microbiology",
"host-pathogen",
"interaction",
"genetics",
"and",
"genomics",
"parasitology"
]
| 2013 | Comparative Study of Transcriptome Profiles of Mechanical- and Skin-Transformed Schistosoma mansoni Schistosomula |
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