Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
listlengths 1
91
| keywords
listlengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
APOBEC3G ( A3G ) is a host cytidine deaminase that , in the absence of Vif , restricts HIV-1 replication and reduces the amount of viral DNA that accumulates in cells . Initial studies determined that A3G induces extensive mutation of nascent HIV-1 cDNA during reverse transcription . It has been proposed that this triggers the degradation of the viral DNA , but there is now mounting evidence that this mechanism may not be correct . Here , we use a natural endogenous reverse transcriptase assay to show that , in cell-free virus particles , A3G is able to inhibit HIV-1 cDNA accumulation not only in the absence of hypermutation but also without the apparent need for any target cell factors . We find that although reverse transcription initiates in the presence of A3G , elongation of the cDNA product is impeded . These data support the model that A3G reduces HIV-1 cDNA levels by inhibiting synthesis rather than by inducing degradation .
APOBEC3G ( A3G ) is a potent anti-viral polynucleotide cytidine deaminase , initially identified as the cellular target of the HIV-1 viral infectivity factor ( Vif ) protein [1] . Since this discovery , many APOBEC proteins from various species have been shown to inhibit the replication of a diverse range of viruses and retrotransposons ( see [2]–[4] for reviews ) . However , the exact mechanism ( s ) by which APOBEC proteins elicit these effects is unresolved , enduringly controversial , and may differ among APOBEC family members . Studies on HIV-1 have revealed that in the absence of the Vif protein , human A3G from virus-producing cells is packaged into HIV-1 particles [5]–[9] . Two phenotypes are then observed when these particles infect new target cells: first , nascent viral reverse transcripts are extensively mutated [10]–[12] . Most of the mutations detected are G-to-A changes in the positive sense , coding strand , implying that deamination of cytidines must occur predominantly on the negative sense strand of cDNA . This , in turn , locates A3G to the site and time of reverse transcription . The fixation of such hypermutation in proviral sequences would presumably lead to the expression of inactive or truncated viral proteins , producing non-infectious virions . The second observed phenotype is a reduction in the accumulation of viral cDNA in target cells [11] , [13]–[20] . This could occur either by triggering degradation of reverse transcripts or by inhibiting DNA synthesis . We , and others , initially proposed a mechanism that linked hypermutation to DNA degradation [10] , [11] . Specifically , the hypothesis was that cellular enzymes known as uracil DNA glycosylases would recognise and remove uracil in the viral cDNA leaving abasic sites . These sites would then be cleaved by cellular apurinic/apyrimidinic endonucleases resulting in the degradation of single-stranded reverse transcripts . However , recent studies have challenged this theory . Several groups have now shown that knocking out or inhibiting the cellular uracil DNA glycosylases UNG2 and SMUG1 does not rescue the defect in viral cDNA accumulation [16] , [17] , [21] . Furthermore , there is mounting evidence to suggest that hypermutation can be dispensable for APOBEC-mediated antiviral activity ( reviewed in [3] ) . For example , various APOBEC proteins are able to inhibit MMTV , HBV , AAV or retrotransposons with little or no discernible editing activity [22]–[24]; A3G expressed in unstimulated CD4+ T-cells can inhibit incoming HIV-1 without inducing widespread hypermutation [25]; levels of mutations detected in HIV-1 do not correlate with the degree of viral inhibition or cDNA levels [13] , [21]; and finally , engineered catalytically inactive APOBEC mutants can still inhibit HIV-1 , MMTV , HBV or retrotransposons in cultured cell experiments [14] , [23] , [24] , [26] , [27] . In the absence of editing , therefore , what would be the trigger for viral cDNA degradation ? Alternatively , it is plausible that APOBEC proteins , which are known to bind both RNA and single stranded DNA [28]–[30] , may be able to prevent the synthesis of cDNA by interfering with the process of reverse transcription . In this regard , various conflicting reports have suggested that A3G is able to inhibit numerous steps during HIV-1 replication , including primer tRNA annealing , minus and plus strand transfer , primer tRNA processing and removal , DNA elongation , and proviral integration [11] , [13]–[20] , [31] , [32] . It is possible that minor blocks at any ( or all ) of these steps could , together , accumulate to produce the potent overall inhibition of HIV-1 infectivity . In addition , it has recently been reported that the observed block to HBV replication is not due to A3G-mediated DNA degradation , but instead due to an inhibition of HBV early minus strand DNA synthesis [33] , [34] . We have previously shown that A3G and A3F can elicit a pronounced block to early viral DNA accumulation and that this decrease correlates well with the block to viral infectivity although these proteins induce very different levels of hypermutation during HIV-1 infection [13] , [14] , [35] . We therefore decided to employ an alternative methodology to examine the effects of APOBEC proteins on the early steps of reverse transcription more closely , namely using using viral particles isolated from cell supernatants to study natural endogenous reverse transcription ( nERT ) . We show here that A3G-mediated inhibition of cDNA accumulation is independent of both the target cell and hypermutation . Using this assay system , we also show there is no defect in tRNAlys3 priming of reverse transcription in virions . However , a small decrease in the amount of cDNA can already be observed 16 bases after the start of reverse transcription , and this reduction amplifies with distance from the tRNAlys3 primer . The block to cDNA titrates with A3G concentration and can be induced by endogenous levels of A3G . Together with existing data , our results support the proposal that A3G inhibits the elongation of HIV-1 DNA by reverse transcriptase , probably by steric hindrance , rather than promoting the degradation of viral cDNA .
We have previously shown that human A3G and A3F inhibit the accumulation of early ( strong stop ) cDNA products during HIV-1 infection of SupT1 cells ( a T-cell line ) [13] , [14] . It has been proposed that this is due to specific degradation of viral cDNA by target cell enzymes [10] , [11] . We therefore wanted to test the effects of A3G on cDNA accumulation in different target cells . We synthesized vif-deficient ( Δvif ) HIV-1 in the presence or absence of A3G in 293T cells and used this virus to infect three different human T-cell lines or peripheral blood mononuclear cells ( PBMCs ) . In case the expression of endogenous A3G affected the accumulation of viral cDNA , we chose two so-called permissive T-cell lines that express little or no endogenous A3G , SupT1 and CEM-SS , and one cell line , CEM , known to express A3G and be non-permissive for HIV-1/Δvif replication [1] , [35] , [36] . PBMCs include CD4+ T-cells , the natural targets of HIV-1 infection , which also express A3G . Cells were harvested at different times after infection and the relative amounts of HIV-1 strong stop cDNA were measured by quantitative ( q ) PCR . As seen previously , in the absence of A3G , the level of reverse transcription products increased with time to a peak around 8 h and then declined ( Figure 1 , filled lines ) . The levels of transcripts detected in cells infected with virus produced in the presence of A3G were very low at all time points and only just detectable , regardless of which target cell culture had been infected ( Figure 1 , dotted lines ) . At the peak of accumulation , the amount of cDNA from these viruses ranged from ∼2–10% of the level of cDNA from control viruses made in the absence of A3G . There was no significant difference between PBMCs and T-cell lines , or between permissive and non-permissive cell types , indicating the inhibition of cDNA accumulation under these experimental conditions is independent of endogenous A3G in the target cell , and can occur in a range of different T-cells . As the A3G-mediated inhibition of viral cDNA accumulation seemed to be independent of the type of target cell infected , we wondered whether any target cell factors were required for this effect at all . Previous studies have tested the contribution of particular cellular proteins to the antiviral activity of A3G by knocking down individual proteins , specifically uracil DNA glycosylases [16] , [17] , [21] . By employing a nERT assay where cell-free viral particles are incubated in the presence of dNTPs and a membrane pore-forming substance called melittin in vitro [37] , [38] , we could test the involvement of all target cell proteins on A3G's influence on viral cDNA accumulation at once . Under these conditions , reverse transcription of the viral RNA proceeds using the natural tRNAlys3 primer and virion reverse transcriptase enzyme . We carried out nERT reactions on HIV-1/Δvif produced in 293T cells in the presence of increasing concentrations of A3G , and measured the amount of strong stop cDNA present at various times after the start of the reaction by qPCR ( Figure 2A ) . As seen with infections of target cells , the amount of viral cDNA increased with time in all reactions , peaking around 3 h after the addition of nucleotides . Strikingly , the amount of strong stop DNA that accumulated decreased with increasing A3G concentration . Virions made in the presence of the highest dose of A3G ( 1 µg transfected plasmid; Figure 2A , purple line ) only accumulated ∼5% of the amount detected in virions made in the absence of A3G ( Figure 2A , red line ) . This was very similar to the decreases in strong stop cDNA seen in T-cell cultures when viruses were made in the presence of 1 µg of A3G plasmid ( Figure 1 ) , suggesting that additional factors present in target cells are not required for this defect . The decrease in cDNA accumulation also notably correlated with decreasing viral infectivity as measured in a single cycle infection of TZM β-gal reporter cells ( Figure 2B ) . Similar results were also obtained using an HIV-1 vector system ( data not shown ) , confirming that this phenotype is not specific for the strain of HIV-1 used . Accordingly , because target cell proteins are dispensable for the A3G-mediated suppression of reverse transcript accumulation , we considered the nERT assay to be a simpler , more defined system with which to study this aspect of APOBEC protein function . To confirm that catalytically inactive APOBEC mutant proteins could also function independently of any target cell proteins , we synthesized HIV-1/Δvif in 293T cells in the presence of either control vector , wild-type ( WT ) A3G , WT A3F , or A3G/A3F variants with the catalytic glutamic acid residue substituted with a glutamine [14] . These viruses were then subjected to nERT followed by qPCR to detect strong stop cDNA . As above , reverse transcripts accumulated with time to a peak at 3 h ( Figure 2C ) . Again , the levels of cDNA were reduced in viruses made in the presence of APOBEC proteins . Mirroring our previous results in cells [14] , the catalytic A3G mutant E259Q inhibited cDNA accumulation to a lesser extent , than WT A3G; to ∼40% of control compared to <1% ( Figure 2C , compare purple line with orange line ) . However , the catalytic A3F mutant E251Q had a very similar effect on cDNA accumulation to WT A3F ( Figure 2C , compare green line with blue line ) . As seen with the A3G titration ( Figure 2B ) , the degree of inhibition of cDNA accumulation closely reflected the inhibition of viral infectivity in TZM cells ( Figure 2D ) . Considering panels A and B against panels C and D , it is remarkable that conditions that impart very similar comparative decreases in cDNA levels also reduce infectivity to similar extents: this point is illustrated by comparing the effects of 0 . 3 µg A3G ( blue in the top panels , 90% reduction in nERT , 99% decrease in infectivity ) with WT A3F ( blue in the bottom panels , 91% reduction in nERT , 99% decrease in infectivity ) . These findings are entirely consistent with our previous observations correlating loss of viral infection with diminished cDNA accumulation [13] , [14] , and indicate that APOBEC proteins are able to inhibit HIV-1 cDNA accumulation without requiring hypermutation or any target cell factors . Most studies on A3G use exogenous expression systems which lend themselves to a wide variety of manipulations and can be appropriately controlled . However , it is likely that A3G is frequently expressed to higher levels in these systems than the endogenous protein is in T-cells [39] . As both the accumulation of reverse transcripts and viral infectivity decrease with increasing A3G plasmid concentration ( Figure 2A and 2B ) , we wanted to compare the levels of A3G protein packaged into virions in these experiments with the level of endogenous A3G that is packaged . Unfortunately , we were unable to examine A3G incorporation in virions from H9 or Hut78 T-cell lines , as these cells release pelletable A3G into the medium that , in the context of infected cultures , co-purifies with viral particles ( data not shown ) . For this reason , we passaged WT or HIV-1/Δvif through another non-permissive T-cell line , CEM . While these cells do not constitutively secrete A3G , they express approximately 10-fold less A3G mRNA than H9 , Hut78 or PBMCs , yet still block spreading HIV-1 replication [36] . Figure 3A shows that transfecting increasing amounts of A3G plasmid into 293T cells leads to comparative increases in A3G protein expression ( lanes 4–8 ) . This also leads to corresponding increases in the amount of A3G packaged into viral particles ( Figure 3B , lanes 4–8 ) . The amount of A3G expressed in CEM cells is equivalent to 293T cells transfected with between 0 . 03 and 0 . 1 µg A3G plasmid ( Figure 3A , compare lane 1 with lanes 5 and 6 ) . The level , as expected , is reduced in CEM cells infected with WT HIV-1 ( lane 2 ) due to Vif expression inducing the degradation of A3G . Expression is not completely abolished as a proportion of these cells are presumably not infected at the time of sampling and therefore still express normal levels of A3G . As previously reported [5] , [7] , [9] , WT HIV-1 particles from CEM cells do not package detectable A3G ( Figure 3B , lane 2 ) . As with the cellular expression , the amount of endogenous A3G packaged into HIV-1/Δvif virions corresponds to that detected in viruses from 293T cells transfected with between 0 . 03 and 0 . 1 µg A3G plasmid ( Figure 3B , compare lane 3 with lanes 5 and 6 ) . This compares well with a previous report where HIV-1/Δvif virions from PBMCs packaged the equivalent amount of A3G as viruses made in 293T cells transfected with a 1∶5 molar ratio of A3G: proviral DNA [39] . In our experiment , transfecting 0 . 3 µg A3G plasmid also gives approximately a 1∶5 molar ratio , and CEM cells express ∼10-fold less A3G than PBMCs , corresponding to 0 . 03 µg A3G plasmid . When a nERT reaction was carried out on viruses from CEM cells , followed by qPCR for strong stop cDNA , the relative amount of viral cDNA detected in HIV-1/Δvif virions also corresponded to that in viruses from 293T cells transfected with between 0 . 03 and 0 . 1 µg A3G plasmid ( compare Figure 3C with Figure 2A ) . The reduction in cDNA levels is similar to that detected previously in CEM cells by non-PCR based methods [40] . This implies that endogenous A3G has a similar effect on cDNA accumulation as exogenously expressed A3G in 293T cells . In other words , the specific activity of the protein with respect to this attribute is similar irrespective of the cell type used for expression . Based on the 293T titration data from Figure 2B , this modest reduction in strong stop cDNA levels in Δvif viruses from CEM cells would be expected to lead to between an ∼55–90% reduction in viral infectivity . However , Δvif viruses from CEM cells were slightly less infectious than this , showing ∼95% reduction in infectivity compared to WT HIV-1 ( Figure 3D ) . This suggests that the antiviral properties of A3G may be accentuated when expressed in T-cells either through the action of cofactors provided by virus producing cells [41] or through direct effects on the protein itself [42] , [43] . Regarding the former possibility , a recent report showing that endogenous A3G has significantly lower cytidine deaminase activity than A3G produced in 293T cells would indicate that any additional activities are independent of hypermutation [36] . Several possible mechanisms could be responsible for the APOBEC-mediated decrease in the accumulation of HIV-1 strong stop cDNA . Whilst rapid degradation is still a formal possibility , we have shown that it would have to occur in the absence of hypermutation and be driven entirely by components packaged into the virion from the producer cell . The alternative explanation posits that A3G inhibits the synthesis of cDNA . This could be achieved by A3G inhibiting the annealing of the tRNAlys3 primer to the primer binding site , preventing reverse transcriptase from recognising the primer and initiating reverse transcription , inducing a specific block ( i . e . , at a particular site ) after initiation of reverse transcription but before the completion of strong stop , or by imparting a general decrease in the processivity of reverse transcriptase itself . To begin to distinguish between these possibilities , we decided to look at the initiation of reverse transcription by setting up a novel assay system . As before , HIV-1/Δvif viruses were made in 293T cells in the presence or absence of A3G and nERT reactions were performed . In these reactions , however , the mix of dNTPs was replaced with just biotinylated dCTP: cytidine being the first base added to the tRNAlys3 primer . If the tRNAlys3 primer was correctly bound to the genomic RNA , and reverse transcription could be initiated , the addition of dCTP would result in the tRNAlys3 primer becoming biotinylated . Total RNA from each time point was purified and an aliquot was incubated in streptavidin-coated qPCR plates . This allowed biotinylated dCMP-tRNA to bind specifically to the plate . A set of qPCR standards and an aliquot of total purified RNA for each sample was then added to the plate as controls , and a one-step quantitative reverse-transcriptase PCR was performed for tRNAlys3 . The results are shown in Figure 4A . The amount of total tRNAlys3 was constant for all samples ( data not shown ) , indicating each sample had received equal input virus and all RNA purification steps were similarly efficient . The amount of tRNAlys3 in the bound samples however , increased with time from trace amounts in the control samples to substantial levels at 30 min , continuing to increase slightly to 2 h ( data not shown ) . The ratio of biotinylated ( bound ) tRNAlys3 to total tRNAlys3 therefore also increased with time ( Figure 4A ) , implying biotin was being efficiently added to the tRNAlys3 primer during the nERT reaction . Importantly , although the presence of A3G significantly decreased viral infectivity ( Figure 4B ) , we were unable to detect any difference in the amount of tRNA biotinylation between viruses made in the presence or absence of A3G . This suggests that A3G does not inhibit either the placement of the tRNAlys3 primer or the initiation of reverse transcription from that primer . As the initiation of reverse transcription is normal in the presence of A3G , it is possible that A3G inhibits the ensuing elongation of reverse transcripts , either continuously or at a specific point ( s ) during early cDNA synthesis . Initially , we attempted to visualise early reverse transcription products on polyacrylamide gels after performing nERT reactions using 32P-labelled dNTPs . However , this was not sensitive enough to detect small cDNA fragments , other than strong stop DNA , even in the absence of A3G ( data not shown ) . Therefore , to investigate the effect on cDNA elongation , we designed qPCR primers and probes for monitoring different regions within the strong stop cDNA fragment . For the purpose of this analysis , we assumed that if a product amplified , then the template sequence must reach the 5′ end of the reverse primer for each primer/probe set ( Figure 5A ) . The length of the reverse transcript was therefore taken as the number of bases from the 3′ end of the anti-primer binding site ( anti-PBS ) to the 5′ end of the reverse qPCR primer ( Figure 5A ) . Our standard strong stop primers amplify a region from R into U5 , implying any reverse transcripts detected with this primer/probe set are at least 137 bases long . Our new primer/probe sets amplified reverse transcripts of 16 or 36 bases . As these primer/probe sets amplified templates that were part RNA , part DNA , a reverse transcription step was carried out before qPCR to ensure efficient amplification by DNA polymerase . Viruses were synthesized with or without A3G as before and nERT reactions were performed . Samples were taken after 2 h , added to cells-to-signal lysis buffer and an aliquot of the lysate was taken directly to a one-step quantitative reverse-transcriptase PCR . The amount of each cDNA detected in virions in the presence of A3G was calculated as a percentage of that detected in the absence of A3G . This was plotted against the length of the reverse transcript amplified by each primer/probe set ( Figure 5B ) . Data from independent preparations of pairs of viruses are plotted as individual points to illustrate the experimental variation and the mean for each primer/probe set is shown by a line . It is clear that the amount of cDNA detected in the presence of A3G decreased with increasing distance from the tRNAlys3 primer . There was only an ∼20% decrease in the amount of cDNA reaching 16 bases , increasing to an ∼35% decrease in the amount of cDNA reaching 36 bases , and an ∼85% decrease in cDNA 137 bases long . Thus , A3G appears to inhibit the elongation of reverse transcripts . Importantly , these effects on cDNA synthesis under nERT conditions are specific for the viral RNA template rather than directly inhibiting reverse transcriptase itself as reactions performed using viral lysates , exogenous poly-rA template and oligo-dT primer were unaffected by the presence of A3G in virions ( data not shown ) .
Members of the APOBEC family of cytidine deaminases inhibit HIV-1 infection . However , there are still many uncertainties and controversies as to exactly how they exert their anti-viral effects . Before the identification of A3G , it was known that vif-deficient HIV-1 produced less provirus in non-permissive cells [44]–[47] , and this has been confirmed in recent studies using A3G expression vectors [11] , [13]–[20] , [31] . There are two ways this defect could occur; either reverse transcripts could be synthesized but then degraded before they can form integrated provirus , or there could be a block to DNA synthesis . The former mechanism was initially favoured by many since the APOBEC mediated appearance of uracils in DNA could serve as the signal for recognition by cellular DNA repair enzymes and nucleases . However , anti-viral activity is not always associated with hypermutation , ( reviewed in [3] ) , and the enzymes responsible for recognising and removing uracils in DNA are apparently expendable for anti-viral function [16] , [17] , [21] . Moreover , deaminase defective variants of APOBEC proteins are still able to reduce the accumulation of viral cDNA [14] , suggesting that antiviral effects can be achieved by a non-editing mechanism ( s ) . While this effect was first noted with A3G , we have shown that it is more pronounced for A3F , as non-editing A3F mutants display antiviral phenotypes that closely match the wild-type protein over a range of concentrations [14] . Accordingly , since we have previously shown that the accumulation of reverse transcripts correlates well with viral infectivity , we set out to investigate this effect further . Initially , we measured cDNA accumulation in different target cells ( Figure 1 ) . Similar results were seen in both permissive and non-permissive cell types , indicating that the presence of endogenous A3G in the target cell causes no additional inhibition , even in activated PBMCs . Moreover , this implied either that any target cell factors involved in diminishing cDNA accumulation are present in all cell types studied or that these effects do not require any target cell factors . To examine this , we established a natural endogenous reverse transcription ( nERT ) assay and found that the extent of inhibition was highly reminiscent of that seen in target cell infections ( Figure 2A ) , revealing that no target cell factors are necessary for the block to cDNA accumulation . Whilst this does not refute a degradative mechanism for cDNA turnover , it does eliminate proteasomal degradation and implies that any endo- or exonucleases involved must be copurified or packaged into viral particles . As cDNA levels were also reduced by deaminase deficient APOBEC variants ( Figure 2C ) , an alternative trigger for degradation other than uracils in DNA would be necessary . Theoretically , A3G could cause the specific packaging of an as yet unidentified nuclease responsible for degrading nascent reverse transcripts . However , there is no direct evidence in the literature for A3G-induced degradation and , as we expand upon below , this is not our preferred explanation . The fact that the block to cDNA accumulation can be detected in nERT reactions allowed us to investigate this process in ways that are not possible in cells , but resemble HIV-1 infections more closely than reconstitution studies using purified components . For instance , we were able to monitor the initiation of reverse transcription in virions ( Figure 4 ) . Recently , the Kleiman group have reported that both A3G and A3F prevent tRNAlys3 primer annealing in vitro via an interaction with nucleocapsid [19] , [31] , [48] . However , annealing was not measured directly , and in a very thorough recent study , Iwatani et al . , found no effect on primer placement or initiation of the +1 cDNA , again in vitro using purified components [15] . By using a novel variation of the nERT assay to study reverse transcription initiation in viral particles , we have shown that A3G does not affect the addition of a biotinylated dCTP first base to the tRNAlys3 primer . We acknowledge that some reverse transcription may have initiated prior to incubation with biotinylated dCTP , but as we detect more than a two log increase in biotinylation after 30 min , we assert that this represents significant initiation in our experiment . Although there was no block to the initiation of reverse transcription , we could detect a significant reduction in the level of strong stop cDNA in cells ( Figure 1 ) and nERT reactions ( Figures 2 and 5 ) in the presence of A3G . This indicates either that there is an early block to reverse transcription or that cDNA is degraded very rapidly . To address whether reverse transcription was inhibited at a particular position or whether elongation was progressively impeded , we designed new qPCR primer/probe sets to measure reverse transcripts shorter than the 137 nucleotide standard strong stop product . The amounts of cDNA detected in the presence of A3G decreased with increasing distance from the tRNAlys3 primer ( Figure 5B ) , from an ∼20% reduction in cDNA reaching 16 bases to an ∼35% decrease at 36 bases and an ∼85% decrease at 137 bases . Some variation was seen between independent virus preparations ( indicated by individual points ) most likely reflecting minor differences in A3G expression and packaging . From these data , it therefore appears that there is no single abrupt point of termination to reverse transcription . The results of our study are in complete agreement with a recent report from Iwatani et al . who show that purified recombinant A3G is able to inhibit the elongation of exogenous cDNA by HIV-1 reverse transcriptase in vitro [15] . They hypothesise that A3G binding to HIV-1 RNA or single stranded DNA physically blocks RT movement along the template . If A3G bound genomic RNA at random points , this would imply that the likelihood of synthesizing a given product would decrease with increasing product length , as borne out here in Figure 5 . The fact that A3G has been shown to bind several different RNA molecules implies that binding is not particularly sequence specific [6] , [28] , [49]–[52] . Work on HBV has recently revealed that A3G is also able to inhibit the early steps in minus-strand DNA synthesis in this virus via a block to DNA strand elongation [33] . Other groups have reported a less dramatic decrease in early products than we see here , with escalating reductions at progressively later stages of replication [17] , [18] , [20] . Indeed , we have also published a greater decrease in the levels of later products compared to early transcripts [14] . The difference in the magnitude of the early effect between different groups has been attributed to differences in A3G expression levels , and we concur with this view since we show clearly that both the levels of early cDNA accumulation and infectivity titrate down with ascending A3G concentration ( Figure 2A and 2B ) . Whilst the levels of endogenous A3G expression and viral incorporation were low in CEM cells , the effect on early cDNA accumulation was consistent with that seen with exogenous A3G ( Figures 2 and 3 ) . We note , however , that the relative deficit in infectivity imparted by APOBEC proteins in CEM cells is greater than anticipated on the bases of the nERT assay . Unfortunately , we found the efficiency of strand transfer and second strand synthesis to be very low in nERT reactions , and therefore it was not possible to use this system to investigate the effect on later steps in reverse transcription with any degree of quantification or certainty ( data not shown ) . Possible underlying reasons are discussed above , and are a subject for future investigations . Importantly , a mode of action that involves direct binding of A3G to viral RNA would be expected to be sensitive to A3G concentration: if A3G can bind to multiple regions of the RNA genome with equal preference , then the probability of binding to a region within R or U5 , and therefore blocking strong stop cDNA synthesis , would increase with A3G concentration . Theoretically , a single molecule of A3G may be all that would be required to inhibit the synthesis of a full length reverse transcript , such that even low levels of endogenous A3G would be sufficient to prevent viral infection . Based on recent findings , we suggest that the relative contribution of editing and non-editing effects to the antiviral phenotype of A3G may depend upon its circumstances . If the inhibition of cDNA synthesis is inefficient , the resulting nascent transcripts may still be inactivated via hypermutation and loss of genetic integrity , and the production of such mutated viral sequences has been well documented in both cultured cells and in vivo [7] , [10]–[12] , [35] , [53]–[56] . Interestingly , the editing capability of endogenous A3G in cultured T-cells appears to be reduced compared to A3G produced in 293T cells [36] . In addition , viral cDNAs recovered from PBMCs infected with HIV-1/Δvif harbour lower levels of G-to-A mutations compared to cDNAs from H9 cells infected with HIV-1/Δvif , both in terms of the number of clones carrying mutations and the number of mutations per clone [57] . This might suggest a lesser role for hypermutation in vivo , although this issue requires further investigation . Nevertheless , the capacity to suppress HIV-1 infection via dual mechanisms presumably serves to enhance the potency of this class of antiviral proteins . Recently , two groups have shown that HIV-1/Δvif can replicate in stable cell lines expressing deaminase-defective A3G mutants but not wild-type A3G [58] , [59] . They have interpreted these results as proving that deamination is essential for A3G activity . We suggest that such observations should be interpreted with caution as we have shown that the A3G E259Q mutant also has a substantially reduced ability to inhibit cDNA accumulation ( Figure 2 and [14] ) . An alternative explanation for these findings is that disruption of the deaminase motif of A3G also affects an attribute necessary for inhibiting reverse transcription; since the C-terminal deaminase domain of A3G must engage nucleic acids for the purpose of editing , one possibility is that this defect could be in RNA binding . At present , it is unclear what effects such mutations have on the protein structure and function . In this study , we have shown that A3G-mediated inhibition of cDNA accumulation occurs independent of both the target cell and hypermutation . Although there is no defect in tRNAlys3 priming of reverse transcription , a small decrease in the amount of cDNA can already be observed at 16 bases , and this reduction increases with distance from the tRNAlys3 primer . These data bolster the argument against a degradative mechanism being responsible for the decreases in viral cDNA that are observed in the presence of A3G , and support the proposal that A3G inhibits the elongation of nascent HIV-1 cDNA by steric hindrance of reverse transcriptase through direct binding to viral genomic RNA .
All APOBEC proteins were expressed from pcDNA3 . 1 as previously described; for WT human A3G [1] , A3G E259Q [27] , WT A3F and A3F E251Q [14] . Expression vectors for wild type and Δvif HIV-1 ( pIIIB and pIIIB/Δvif , respectively ) , have been described before [35] , and contain a mutation at nucleotide 567 of the provirus to create a G to A substitution in the U5 region of the 5′-LTR , that would copy to the 3′-LTR during reverse transcription . All cell lines were maintained under standard conditions . Δvif HIV-1 stocks were typically prepared by co-transfection of 293T cells with proviral plasmid and A3G expression plasmid ( or empty pcDNA3 . 1 vector ) at a ratio of 3∶1 . For the A3G titration experiment shown in Figure 2 , 3 µg of Δvif provirus was co-transfected with varying amounts of A3G plasmid as indicated . For the experiment that included A3F and A3G/A3F mutants ( Figure 2 ) a ratio of 1∶1 provirus to APOBEC expression vector was used . The media was changed ∼18 h after transfection and replaced with DMEM containing 20 µl/ml RQ1-DNase ( Promega ) . After 8 h , the cells were washed and fresh media was added for a further 17 h , before virus containing supernatants were harvested . To study endogenous A3G , wild-type or Δvif HIV-1 pseudotyped with the G-protein from vesicular stomatitis virus was made in 293T cells and used to infect CEM cells by spin infection at 1200×g for 2 . 5 h at 20°C . The cells were extensively washed and allowed to grow for 24 h before the media was changed again , and virus containing supernatants were harvested 16 h following this . All viral titres were quantitated by enzyme-linked immunosorbent assay ( ELISA ) for p24CA content . Viral infectivities were determined in single-cycle assays by challenging 105 TZM-β-gal indicator cells [60] with viruses corresponding to 5 ng p24CA , and measuring the induced expression of β-galactosidase activity in cell lysates after ∼28 h , using the Galacto-Star system from Applied Biosystems . For quantitative PCR analysis of cDNA in target cells , viral preparations containing 30 ng of p24CA were added to 106 SupT1 , CEM-SS or CEM cells or PBMCs rotating at 4°C for 2 h . Cells were then washed , resuspended in fresh RPMI media , and incubated at 37°C for 0–24 h . Total DNA was purified using the DNeasy kit from Qiagen , and eluted in a total volume of 200 µl . After treatment with Dpn1 for 2 h at 37°C , 2 µl of DNA was analysed by real-time quantitative PCR . Viral preparations containing 10–25 ng of p24CA were pelleted by centrifugation for 1 h at 20 , 000×g at 4°C . Virions were resuspended in PBS , 2 . 5 mM MgCl2 , 15 µg/ml melittin and 1 mM dNTPs , and incubated at 37°C . Control reactions were incubated without any dNTPs for the same length of time as the longest time point . At the specified time points , aliquots were removed and equal volumes of PBS containing 40 µg/ml “carrier” salmon sperm DNA added . DNA was then purified using using the DNeasy kit from Qiagen , and eluted in a total volume of 200 µl . After treatment with Dpn1 for 2 h at 37°C , 2 µl of DNA was analysed by real-time quantitative PCR . For later experiments , an equal volume of 2× Cells-to-signal lysis buffer ( Ambion ) was added instead of PBS with carrier DNA , and samples were diluted 1∶10 in water before analysing 2 µl directly in real-time quantitative PCR . For this assay , a nERT reaction was performed essentially as above , with the exception of the dNTP addition: pelletted virions were resuspended in PBS , 2 . 5 mM MgCl2 , 15 µg/ml melittin and 0 . 5mM biotin-11-dCTP ( Jena Bioscience ) , and incubated at 37°C . Control reactions were incubated without any dNTPs for 2 h . At the specified time points , aliquots were removed , added to QIAzol lysis buffer ( Qiagen ) and frozen at −80°C . RNA was purified using the Qiagen miRNeasy kit and eluted in 30 µl of water . 2 µl 10× PBS was added to 18 µl RNA to make the final samples 1×PBS and the samples were heated at 95–100°C for 3 min and cooled instantly on ice to separate tRNA from genomic RNA . Samples were then added to streptavidin coated , 384-well , Strep Thermo-Fast plates ( Abgene ) and incubated at 37°C for 30 min . Plates were extensively washed and all liquid aspirated . Standards and aliquots of total purified RNA were added to the plates before the addition of Ag-path-ID One-Step RT-PCR Kit reagents ( Ambion ) and tRNAlys3 levels were measured by quantitative PCR analysis . Strong stop reverse transcription products were detected using primers that amplify the region between nucleotides 500 and 635 of the provirus: oHC64 ( 5′-TAACTAGGGAACCCACTGC ) and oHC65 ( 5′-GCTAGAGATTTTCCACACTG ) with probe oHC66 ( 5′-FAM-ACACAACAGACGGGCACACACTA-TAMRA ) . Reactions were performed in triplicate , in TaqMan Universal PCR master mix ( UNG-less ) using 900 nM of each primer , and 250 nM probe . After 10 min at 95°C , reactions were cycled through 15 sec at 95°C followed by 1 min at 60°C for 40 repeats , carried out on an ABI Prism model 7900HT ( Applied Biosystems ) . The Δvif-HIV-1 expression vector , pIIIB/Δvif , was diluted into purified SupT1 cellular DNA to create a series of control samples that were used to calculate relative cDNA copy numbers and confirm the linearity of the assay . tRNAlys3 was detected using primers tRNA-15for ( 5′-GTCGGTAGAGCATCAGACTTTTAATCT ) and Long PBS-G- ( C ) 7rev ( 5′-CCCCCCCGTGGCGCCCGAACAGGGACTTGAAC ) with MGB probe Probe-tRNA43for ( 5′-FAM-AGGGTCCAGGGTTC-MGB ) . An oligo with sequence: 5′-CCCCCCCGTGGCGCCCGAACAGGGACTTGAACCCTGGACCCTCAGATTAAAAGTCTGATGCTCTACCGAC was serially diluted in water to create a standard curve . Note that this primer/probe set was originally designed to detect tRNAlys3 with a 3′ extension . One-step quantitative reverse transcription PCR was performed using the Ag-path-ID One-Step RT-PCR Kit from Ambion using 20 µl reaction volumes and final primer and probe concentrations of 450 nM and 125 nM respectively . Cycling conditions were 45°C for 15 min then 95°C for 10 min , followed by 40 cycles of 15 sec at 95°C and 45 sec at 60°C . Earlier products of reverse transcription either 16 or 36 bases from the 3′ end of the tRNAlys3 primer were detected with set 2 or set 3 primer/probe sets respectively . Set 2 primer sequences were: tRNA-15for and PBSU5-rev , ( 5′-GGAAAATCTCTAGCAGTGGCG ) with MGB probe Probe-tRNA43for . Set 3 primer sequences were tRNA-41for ( 5′-TGAGGGTCCAGGGTTCAAGT ) and U5-rev-3 ( 5′-CAGACCCTTTTAGTCAGTGTGGAA ) with probe Probe-PBSU5f-3 ( 5′-FAM-CCTGTTCGGGCGCCACTGCT-BHQ1 ) . An oligo with sequence: 5′GTCGGTAGAGCATCAGACTTTTAATCTGAGGGTCCAGGGTTCAAGTCCCTGTTCGGGCGCCACTGCTAGAGATTTTCCACACTGACTAAAAGGGTCTGAGGGATCTCTA was serially diluted in water to create a standard curve . One-step quantitative reverse transcription PCR was performed using the Ag-path-ID One-Step RT-PCR Kit using 20 µl reaction volumes and final primer and probe concentrations of 400 nM and 120 nM respectively . Cycling conditions were as above for tRNAlys3 . All primer and probe sequences , as well as their respective amplicons , are also provided in Table S1 . Virions were concentrated by centrifugation through a 20% ( w/v ) sucrose cushion before immunoblot analysis . Human A3G protein , heat shock protein ( HSP ) 90 or viral p24CA proteins were detected in whole cell lysates of uninfected or HIV-1 infected CEM cells or transfected 293T cells , or in virion lysates , using a polyclonal rabbit serum to A3G , HSP 90α ̃β ( H-114 , Santa Cruz Biotechnology ) , or 24-2 , a monoclonal anti-CA antibody , respectively , secondary antibodies IRDye800CW Goat Anti-rabbit and IRDye800CW Anti-mouse ( LI-COR Biosciences UK Ltd ) and Li-cor Odyssey infrared imaging and quantitation .
|
APOBEC proteins are cell-encoded factors that inhibit the replication of numerous retroviruses , such as HIV-1 , and retrotransposons . In many cases , inhibition is clearly associated with cytidine-to-uridine editing of viral or transposon DNA . On the other hand , a number of studies with particular APOBEC protein/substrate combinations , or engineered proteins that are editing-deficient , have indicated that inhibitory mechanism ( s ) distinct from editing are also operative . Here , we have analyzed the effects of APOBEC3G , a potent HIV-1 inhibitor , on viral reverse transcription using cell-free viruses ( natural endogenous reverse transcriptase assays ) . We report that APOBEC3G inhibits viral DNA synthesis in a dose-dependent fashion , and does not require editing capabilities to do so . Because the addition of the first nucleotide to the tRNA primer is unaffected by A3G and the magnitude of inhibition increases as later reverse transcription intermediates are measured , we suggest that APOBEC3G acts by impeding the translocation of the reverse transcriptase enzyme along its RNA template , perhaps by binding directly to the RNA . These results provide novel insight into the biological activities of this class of host anti-viral proteins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/mechanisms",
"of",
"resistance",
"and",
"susceptibility,",
"including",
"host",
"genetics",
"virology/immunodeficiency",
"viruses",
"virology/host",
"antiviral",
"responses"
] |
2008
|
APOBEC3G Inhibits Elongation of HIV-1 Reverse Transcripts
|
Clostridium perfringens is a major cause of food poisoning ( FP ) in developed countries . C . perfringens isolates usually induce the gastrointestinal symptoms of this FP by producing an enterotoxin that is encoded by a chromosomal ( cpe ) gene . Those typical FP strains also produce spores that are extremely resistant to food preservation approaches such as heating and chemical preservatives . This resistance favors their survival and subsequent germination in improperly cooked , prepared , or stored foods . The current study identified a novel α/β-type small acid soluble protein , now named Ssp4 , and showed that sporulating cultures of FP isolates producing resistant spores consistently express a variant Ssp4 with an Asp substitution at residue 36 . In contrast , Gly was detected at Ssp4 residue 36 in C . perfringens strains producing sensitive spores . Studies with isogenic mutants and complementing strains demonstrated the importance of the Asp 36 Ssp4 variant for the exceptional heat and sodium nitrite resistance of spores made by most FP strains carrying a chromosomal cpe gene . Electrophoretic mobility shift assays and DNA binding studies showed that Ssp4 variants with an Asp at residue 36 bind more efficiently and tightly to DNA than do Ssp4 variants with Gly at residue 36 . Besides suggesting one possible mechanistic explanation for the highly resistant spore phenotype of most FP strains carrying a chromosomal cpe gene , these findings may facilitate eventual development of targeted strategies to increase killing of the resistant spores in foods . They also provide the first indication that SASP variants can be important contributors to intra-species ( and perhaps inter-species ) variations in bacterial spore resistance phenotypes . Finally , Ssp4 may contribute to spore resistance properties throughout the genus Clostridium since ssp4 genes also exist in the genomes of other clostridial species .
Clostridium perfringens is the 2nd most commonly-identified agent of bacterial food poisoning ( FP ) in the USA and UK , where ( respectively ) 250 , 000 or 85 , 000 cases of C . perfringens FP occur annually [1] , [2] , [3] . C . perfringens FP also currently ranks as the second or third leading cause of food-borne death in ( respectively ) the UK or USA [2] , [3] , mainly in the elderly or debilitated . Economic losses ( medical care and lost productivity ) from this single FP amount to several hundred million dollars per year [1] . The gastrointestinal symptoms of C . perfringens FP are caused by C . perfringens enterotoxin [4] . The enterotoxin gene ( cpe ) can be either chromosomal or plasmid-borne , but most FP isolates carry only a chromosomal cpe gene [5]–[8] . Those typical FP strains with a chromosomal cpe gene also produce spores that are extremely resistant to such common food hygiene approaches as cooking , holding foods at elevated or low temperatures , and the addition of chemical preservatives to foods [9]–[11] . For example , the spores of FP strains carrying a chromosomal cpe gene exhibit , on average , 60-fold higher decimal reduction values at 100°C ( D100 value , i . e . , the time a culture must be held at 100°C to reduce viability by 90% ) than either the spores of isolates carrying a plasmid-borne cpe gene or spores of cpe-negative C . perfringens isolates [9] . This exceptional spore resistance is thought to favor the survival of typical FP strains in improperly cooked , prepared , or stored foods , which represent the most common transmission vehicles for C . perfringens FP . No explanation has yet been offered for the resistance phenotype of spores made by typical FP strains . While α/β-type small acid soluble proteins ( SASPs ) have been associated with spore heat and nitrite resistance in both C . perfringens and Bacillus spp . [12] , a previous study reported that the three known C . perfringens α/β-type SASP genes ( ssp1 , ssp2 and ssp3 ) share identical sequences . Furthermore , these three ssp genes are expressed at similar levels in several C . perfringens isolates , including F4969 and SM101 , which ( respectively ) produce sensitive or resistant spores [13] , [14] . Therefore , the current study utilized the recently-sequenced genome of C . perfringens strain SM101 [15] to identify an additional ORF with homology to a novel α/β-type SASP . We now present evidence that variants of this novel C . perfringens SASP , which we are naming Ssp4 , are important for the resistant spore phenotype exhibited by most C . perfringens FP strains carrying a chromosomal cpe gene .
Since previous studies [13] , [14] had reported that the ORF sequences of the three known C . perfringens SASP-encoding genes are identical in several C . perfringens isolates ( including SM101 and F4969 ) , the current study first confirmed those prior findings by extending ssp1 , ssp2 , and ssp3 sequencing analyses to several additional C . perfringens isolates that had previously been well-characterized for their production of resistant or sensitive spores [9]–[11] , [13] , [14] . No sequence differences were detected in the three known ssp ORFs among FP strains 191-10 , NCTC8239 , NCTC10239 and SM101 , which each carry a chromosomal cpe gene and produce resistant spores , and nonfoodpoisoning ( NFP ) isolates NB16 , T34058 , F4969 , F5603 , 222 , ATCC3624 and ATCC13124 , which each produce sensitive spores . Therefore , a bioinformatic search [16] was performed on the recently-completed genome sequence of C . perfringens strain SM101 [15] , which is a transformable derivative of a FP strain and produces resistant spores . This search identified an SM101 ORF ( CPR_1870 ) that , at the initiation of this work , was annotated as a ribosomal protein but has since been re-annotated as possibly encoding a novel α/β-type SASP . This putative SASP-encoding ORF , which is being named ssp4 , is clearly distinct from the recognized ORFs encoding the three previously-identified SASP proteins of C . perfringens . The ORF is predicted to encode a 90 amino acid protein of ∼10 . 2 kDa sharing only 18 . 9% , 20 . 9% and 20 . 9% identity , respectively , with the ∼6 . 7 kDa Ssp1 , Ssp2 and Ssp3 proteins that are each comprised of 59 or 60 amino acids ( Fig . 1A ) . The sequence of this novel ssp4 ORF was found to be identical in all eleven initially-surveyed C . perfringens isolates , except for differences at two codons . Those sequence variations resulted in two different amino acids being consistently present at Ssp4 residue 36 and 72 between the four initially-surveyed FP isolates versus the seven surveyed NFP isolates ( Table 1 ) . The apparent correlation shown in Table 1 between ssp4 ORF sequence differences and spore sensitivity or resistance suggested that the newly-identified , putative ssp4 ORF might encode a key protein contributor to the resistant spore phenotype of typical FP strains . To test this hypothesis , we first evaluated by RT-PCR ( Fig . 2 ) whether the ssp4 ORF is expressed by two transformable C . perfringens isolates , i . e . , SM101 and F4969 , that ( respectively ) are known to produce resistant or sensitive spores ( [17] and Table 2 ) . Since expression of the ssp1 , ssp2 and ssp3 genes of C . perfringens is reportedly sporulation-associated [13] , this RT-PCR study also analyzed whether ssp4 expression , if any , occurs in exponentially growing vegetative cultures or sporulating cultures of SM101 and F4969 . Results from these RT-PCR studies ( Fig . 2 ) clearly demonstrated that ssp4 expression becomes detectable within 2 h after inoculation of either SM101 or F4969 into Duncan-Strong ( DS ) sporulation medium . Expression of the ssp4 gene then peaked between 4–6 hours post-inoculation in both SM101 and F4969 DS cultures . For comparison , the first visible forespores and phase-refractile spores of SM101 or F4969 appeared , respectively , within ∼6–8 h using these culture conditions . However , RT-PCR detected only weak ( if any ) ssp4 expression by vegetative cultures of either SM101 or F4969 growing in TGY medium ( Fig . 2 ) . When detected , this limited ssp4 expression in TGY cultures peaked during the log phase of exponential growth , i . e . , at ∼4 h post-inoculation , for both strains . In contrast to the poor ( if any ) ssp4 expression observed using RNA isolated from TGY cultures of F4969 or SM101 , RT-PCR detected strong expression of the plc gene using those same TGY RNA preparations , confirming that those RNA preparations were valid for detecting gene expression by TGY cultures . For completeness , plc expression was also demonstrated using RNA isolated from DS cultures of these two isolates ( Fig . 2 and data not shown ) . Consistent with the RT-PCR results of Fig . 2 , Western blot analysis demonstrated substantial Ssp4 production by DS cultures of both SM101 and F4969 , but detected only trace amounts ( if any ) of Ssp4 production in TGY cultures of those isolates ( data not shown ) . No forespores or spores were visible in either the SM101 or F4969 TGY cultures and no colonies grew after heat-shocking ( 70°C for 20 min ) of aliquots from these exponentially growing vegetative cultures . To directly evaluate whether Ssp4 is important for the resistant phenotype of FP spores , a targeted intron was then used to insertionally-inactivate the ssp4 gene in both SM101 and F4969 . For each resultant mutant , the presence of an intron-inactivated ssp4 ORF was demonstrated by PCR ( Fig . 3 ) , the presence of a single intron insertion in the ssp4 mutant was shown by Southern blotting ( Fig . 4 ) , and the disruption of ssp4 expression and Ssp4 production by the mutant was proven by RT-PCR and Western blot ( Fig . 5 ) . Phenotypic comparisons then demonstrated that the spores produced by the isogenic SM101 ssp4 null mutant ( SM101::ssp4 ) were considerably less heat- and sodium nitrite-resistant than wild-type SM101 spores ( Table 2 ) . These resistance differences are specifically attributable to inactivation of the ssp4 gene in SM101::ssp4 since complementing that mutant with the pJIR751 shuttle plasmid carrying the wild-type SM101 ssp4 gene ( creating SM101::ssp4 ( pCS ) ) substantially restored both spore heat resistance and sodium nitrite resistance . In contrast , only a small increase in spore heat- or sodium nitrite-resistance was detected if the SM101 ssp4 null mutant was complemented with the same shuttle plasmid carrying the wild-type F4969 ssp4 gene ( creating SM101::ssp4 ( pCF ) ) and no increased spore resistance to heat or sodium nitrite was observed if SM101::ssp4 was transformed with the shuttle plasmid alone ( creating SM101::ssp4 ( pJIR751 ) . Restored ssp4 expression and Ssp4 production by all SM101 complementing strains was demonstrated by , respectively , RT-PCR analyses and Western blotting ( Fig . 5 ) . Additionally , complementing a F4969 ssp4 null mutant ( F4969::ssp4 ) with the pJIR751 shuttle plasmid carrying the wild-type SM101 ssp4 gene ( to create F4969::ssp4 ( pCS ) ) produced spores that were substantially more heat- and sodium nitrite-resistant than the spores made by wild-type F4969 ( Table 2 ) . This effect was specific since those F4969::ssp4 ( pCS ) spores showed much greater resistance against heat or sodium nitrite than did spores made by F4969::ssp4 complemented with the wild-type F4969 ssp4 gene ( i . e . , F4969::ssp4 ( pCF ) ) , or spores made by the F4969 ssp4 null mutant transformed with the empty pJIR751 vector ( F4969::ssp4 ( pJIR751 ) . Restored ssp4 expression and Ssp4 production by all F4969 complementing strains was demonstrated by , respectively , RT-PCR analyses and Western blotting ( Fig . 5 ) . The F4969 and SM101 ssp4 null mutants were both stable over many passages and the complementing plasmids could be packaged inside spores since heat-shocking of complementing strains consistently produced erythromycin-resistant survivors . In addition , all mutants and complementing strains also exhibited similar vegetative growth rates and DS sporulation efficiencies as wild-type SM101 or F4969 ( not shown ) . Since α/β-type SASPs are thought to protect spores from heat or sodium nitrite by binding to DNA [12] , studies were performed to address whether the greater heat and sodium nitrite resistance of spores made by SM101 vs . F4969 might involve stronger DNA binding by the SM101 Ssp4 variant . These DNA binding experiments used highly-purified , recombinant , His6-tagged Ssp4 ( rSsp4 ) variants ( Fig . 6A ) . An electrophoretic mobility shift assay ( EMSA ) showed the purified SM101 rSsp4 is more effective than the purified F4969 rSsp4 at complexing with , and shifting migration of , C . perfringens DNA ( Fig . 6B ) . Also consistent with tighter DNA binding , SM101 rSsp4 remained bound to calf thymus DNA in the presence of NaCl concentrations that caused dissociation of F4969 rSsp4 from the same target DNA ( Fig . 6C ) . Since the Ssp4s of the seven Table 1 NFP isolates producing sensitive spores and the four initially-studied FP isolates producing resistant spores were identical except for amino acid substitutions at Ssp4 residues 36 ( where the four FP strains have an Asp instead of Gly ) and 72 ( where the four FP strains have an Asn instead of Lys ) , additional sequencing of the ssp4 gene ( upstream region and ORF ) was performed to test whether these same Ssp4 sequence differences hold true for two C . perfringens isolates carrying a chromosomal cpe gene ( data not shown ) that had been obtained during a recent Oklahoma food poisoning outbreak [18] . This analysis showed the ssp4 gene of Oklahoma FP isolate 01E809 is identical to the ssp4 gene of SM101 except the 01E809 ssp4 ORF encodes a Lys at Ssp4 residue 72 . In contrast , this sequencing revealed that the ssp4 ORF of Oklahoma FP isolate 01E803 is identical to the ssp4 ORF of C . perfringens isolates producing sensitive spores . Relative to the two Ssp4 variants made by the initially-studied four FP and seven NFP isolates , the ssp4 ORF of 01E809 naturally encodes a hybrid Ssp4 variant . Therefore , the spore resistance phenotypes of the two Oklahoma isolates were evaluated , which showed that 01E809 spores are similar in resistance to wild-type SM101 spores and much more resistant than 01E803 spores ( Table 2 ) . To directly assess whether the Ssp4 of 01E809 spores can mediate a resistant spore phenotype , the SM101 ssp4 null mutant was complemented with a shuttle plasmid carrying the 01E809 ssp4 gene . This complementation yielded spores with a strongly resistant phenotype ( Table 2 ) . Similarly , complementation of the F4969 ssp4 null mutant with the shuttle plasmid encoding 01E809 Ssp4 produced substantially more resistant spores than those of wild-type F4969 or the F4969::ssp4 mutant complemented with the F4969 ssp4 gene ( Table 2 ) . The Table 2 data indicated that substitution of an Asp for Gly at Ssp4 residue 36 is important for the resistant spore phenotype among the studied FP strains . Therefore , DNA binding assays were performed to evaluate whether the mechanism of this resistance might involve tighter DNA binding . EMSAs showed that a His6-tagged 01E809 rSsp4 variant resembles the SM101 rSsp4 variant ( and was more effective than the F4969 rSsp4 variant ) with respect to tight binding to C . perfringens DNA ( Fig . 6B ) . In addition , calf thymus DNA binding by purified 01E809 rSsp4 and SM101 rSsp4 were similarly NaCl-resistant but those rSsp4s were both more resistant to NaCl-induced dissociation from calf thymus DNA than was F4969 rSsp4 ( Fig . 6C ) .
This study has identified a first explanation for the exceptional spore resistance properties exhibited by most C . perfringens FP strains carrying a chromosomal cpe gene . We found that a novel SASP protein ( now named Ssp4 ) , which is preferentially expressed during sporulation , plays an important role in this spore resistance phenotype . Specifically , strains producing highly resistant spores have an Asp substitution ( in place of Gly ) at residue 36 of Ssp4 . As shown in Fig . 1 , residue 36 of Ssp4 is located in a conserved region present in all α/β-type SASPs [12] , including Ssp1 , Ssp2 and Ssp3 of C . perfringens . During spore outgrowth , this conserved SASP region is the site of cleavage by the GPR endoprotease , which exposes DNA for resumption of transcription and provides amino acids for protein synthesis and energy metabolism in the developing vegetative cell [12] . However , participation of this conserved SASP region in DNA protection and spore resistance properties has been less clear [12] . In particular , prior to the current study , the equivalent of Ssp4 residue 36 in α/β-type SASPs had not yet been clearly linked to spore resistance or DNA binding . Previous studies have shown that variations in SASP levels can impact spore resistance properties . For example , B . subtilis spores lacking ∼85% of their α/β type SASPs become more sensitive to DNA-damaging treatments [12] . Furthermore , antisense RNA-induced decreases in levels of the three previously known SASPs produced more heat-sensitive C . perfringens SM101 spores [17] . However , to our knowledge , the current findings provide the first indication that natural SASP variants can be important contributors to intra-species variations in spore resistance properties . Ssp4 may also contribute to spore resistance in other Clostridium spp . since bioinformatic searches identified the presence of Ssp4 ORF homologues in other genome-sequenced clostridial species ( Fig . 1B ) . At least five clostridial species , including several major human pathogens and industrially-relevant species , carry an ORF encoding a protein with >70% overall identity to C . perfringens Ssp4 [12] , [16] , [19] . Several additional clostridial species , including the increasingly important pathogen Clostridium difficile , carry an ORF encoding a protein with more limited , but still significant , identity to Ssp4 . Alignment of Ssp4-like proteins of several clostridial species ( Fig . 1B ) , or even aligning ( not shown ) all known SASPs made by sporulating bacteria [12] , indicated that the presence of an Asp at the equivalent of Ssp4 residue 36 is , thus far , unique to the C . perfringens FP strains that carry a chromosomal cpe gene and make resistant spores . However , it is notable there is some natural variability at Ssp4 residue 36 among the clostridia ( Table . 1 ) . Given this variability , it might be informative for future studies to compare the heat sensitivities of wild-type versus ssp4 null mutants in other genome-sequenced clostridial strains in order to further elucidate the contribution of Ssp4 ( and , possibly , intraspecies Ssp4 variants ) to spore phenotypes in other Clostridium species . The current study also revealed that several different amino acids can be present at residue 72 of C . perfringens Ssp4 . However , those residue 72 Ssp4 variations appear to be less important for resistance properties since both C . perfringens sensitive spores and the resistant spores made by strain 01E809 share a Lys at Ssp4 residue 72 . The presence of two different amino acids at Ssp4 residues 36 and 72 indicates that C . perfringens Ssp4 variants are more common than has been observed , to date , for the highly-conserved Ssp1 , Ssp2 and Ssp3 proteins of C . perfringens ( [14] and this study ) . We previously showed [20] that , at the time of retail purchase , ∼1–2% of raw meats , poultry and fish are contaminated with C . perfringens isolates carrying a chromosomal cpe gene . Every one of those recovered chromosomal cpe food isolates formed resistant spores , indicating that spore heat resistance is not selected from a C . perfringens population in foods during each cooking or nitrite exposure , but is instead already an intrinsic property of most C . perfringens isolates carrying a chromosomal cpe gene . Coupling that previous finding with the current observation that ( despite diverse geographic origins and isolation dates ) all of the currently surveyed FP isolates forming resistant spores share a ssp4 ORF encoding an Asp variant at residue 36 may suggest a common lineage for many typical FP isolates carrying a chromosomal cpe gene . Due to competitive advantage in the food poisoning environment from their spore resistance , these typical FP strains forming resistant spores now predominate in the FP environment . However , our study also provides the first indication that not all wild-type FP isolates carrying a chromosomal cpe gene produce resistant spores . This uncoupling of chromosomal cpe gene carriage from resistant spore production for isolate 01E803 is consistent with previous results demonstrating that an SM101 cpe null mutant still produces highly resistant spores [17] . The presence of different Ssp4 variants in 01E803 and 01E809 , two strains that otherwise appear closely-related ( if not clonal ) and originated from the same FP outbreak involving improperly cooked turkey [18] , may reflect a post-cooking reversion of the ssp4 gene in 01E803 to the Gly Ssp4 variant present in most C . perfringens . That revertant may have survived because spore heat resistance was no longer needed after cooking; presumably progeny of 01E803 would be less competitive in future FP events . Since there is no direct linkage between possession of a chromosomal cpe gene and formation of a resistant spore , it is possible that selective pressure in the food environment will eventually yield C . perfringens FP isolates carrying a plasmidborne cpe gene yet producing resistant spores involving Ssp4 variants ( a minority of food poisoning cases involve plasmid cpe isolates [21] , [22] ) . Additional studies will be necessary to fully elucidate how Ssp4 variants mediate different C . perfringens spore resistance properties , but the detection of DNA binding differences between different Ssp4 variants during the current work suggests one possible mechanism . Furthermore , while the current data clearly demonstrates that Ssp4 variants help to explain isolate-dependent C . perfringens spore sensitivity differences , Ssp4 is not the only SASP contributing to C . perfringens spore resistance properties . As mentioned earlier , studies from Sarker's group have shown that the three previously known SASPs are also necessary to obtain full resistance for spores made by typical FP strains [14] , [23] , [24] . Since various SASPs are thought to interact in vivo during DNA binding [12] , it is possible that the Ssp4 variants identified in this study may display even greater DNA binding differences in the presence of Ssp1 , 2 and 3 , than was detected by the in vitro DNA binding studies of Fig . 6 using only Ssp4 . If so , this magnified DNA binding effect might further explain the exceptional resistance properties of some C . perfringens spores . Our determination that the SM101 ssp4 null mutant still remains substantially more heat-resistant than wild-type F4969 , together with previous studies [13] , [14] , [23] showing similar expression levels of Ssp1 , 2 and 3 by SM101 and F4969 , may suggest that additional factors beyond the SASPs also contribute to the resistant phenotype of spores produced by many FP strains . Further studies are needed to fully understand all of the mechanisms contributing to the resistant spore phenotype of FP strains , as this knowledge may identify strategies for reducing the incidence of C . perfringens type A food poisoning .
C . perfringens type A isolates used in this study are described in Table 1 . FTG and TGY broth were used for growing vegetative cultures [9] . Brain heart infusion ( BHI ) agar was used for plate count analyses [9] . Modified Duncan-Strong ( MDS ) sporulation medium was used to induce sporulation of C . perfringens type A isolates [10] . E . coli DH5α was grown at 37°C in LB broth with shaking or on LB agar . Antibiotics were from Fisher Scientific Company . Primers B1F ( 5′-ATGAGCAAGACACCATTAAAAAA-3′ ) and B1R ( 5′-TTACTTTTCGTCA ACGTGAGG-3′ ) were designed from the ssp4 gene sequence of C . perfringens SM101 ( Gene bank accession number CPR_1870 ) [15] . For each Table 1 isolate , template DNA was obtained from colony lysates [20] . PCR reactions were performed using the following amplification conditions: 94°C for 2 min , 35 cycles of 94°C for 30 sec , 54°C for 30 sec , 72°C for 30 sec , following by a 10 min extension at 72°C . The products were then cloned into pCR®2 . 1-TOPO vectors ( Invitrogen ) and sequenced by the University of Pittsburgh Core DNA Sequencing Facility . Unique ssp4 ORF sequence were deposited in GenBank ( accession numbers EU287944 and EU287945 ) . Wild-type SM101 and F4969 were each grown in TGY for 0–10 h at 37°C . Every 2 h , a 3 ml aliquot of culture was removed and used for RNA extraction with the RiboPure™ Bacteria kit from Ambion , according to the manufacturer's instructions . RNA extractions from mutant or complementing strains used only aliquots from a 6 h TGY culture . RNA with an intron insertion was unstable ( data not shown ) , so only freshly isolated RNA was used for RT-PCR analyses . After 1 h of DNase treatment , RT-PCR reactions were then performed on the RNA samples using the AccessQuick RT-PCR system ( Promega ) . Briefly , 100 ng of each RNA sample were reverse transcribed to cDNA at 45°C for 1 h and then used as template for PCR with primers targeting ssp4 sequences ( as above ) or plc sequences ( cpaF-GCTAATGTTACTGCCGTTGA and cpaR-CCTCTGATACATCGTGTAAG ) . Control RT-PCR reactions were similarly performed , except for the omission of reverse transcriptase . As an additional control , a PCR amplifying ssp4 or plc sequences was performed using DNA extracted from each strain using the MasterPure Gram-Positive DNA Purification Kit ( Epicentre Biotechnologies ) . The ssp4 gene in isolates F4969 or SM101 was insertionally-inactived using a Clostridium-modified TargeTron gene knock-out system [25] . Using optimal intron insertion sites identified by the Sigma TargeTron algorithm ( www . sigma-genosys . com/targetron/ ) , an intron was targeted to insert , in the antisense orientation , between F4969 ssp4 ORF nucleotides 47/48 or , in the sense orientation , between SM101 ssp4 ORF nucleotides 136/137 . Primers used for targeting the intron to the F4969 ssp4 ORF were IBS47 ( 5′-AAAAAAGCTTATAATTAT CCTTAAATTCCTTATTAGTGCGCCCAGATAGGGTG-3′ ) ; EBS47-d ( 5′-CAGATTGTACA AATGTGGTGATAACAGATAAGTCTTATTAGATAACTTACCTTTCTTTGT-3′ ) ; and EBS47 ( 5′-TGAACGCAAGTTTCTAATTTCGGTTGAATTCCGATAGAGGAAAGTGTCT-3′ ) or SM101 ssp4 ORF are IBS136 ( 5′-AAAAAAGCTTATAATTATCCTTAATAAGATTGA TAGTGCGCCCAGATAGGGTG-3′ ) ; EBS136-d ( 5′-CAGATTGTACAAATGTGGTGATAAC AGATAAGTCTTGATAAATAACTTACCTTTCTTTGT-3′ ) ; EBS136 ( 5′-TGAACGCAAGTT TCTAATTTCGATTCTTATTCGATAGAGGAAAGTGTCT-3′ ) . The 350-bp PCR products were inserted into pJIR750ai [25] . The resultant plasmids , named pJIR750ssp4anti and pJIR750ssp4sense , were electroporated , respectively , into F4969 or SM101 . The transformation efficiency for SM101 was 1 . 5×10−6 or 5×104 transformants/µg plasmid DNA . For F4969 , the transformation efficiency was 4×10−5 or 1×106 transformants/µg DNA . Transformants selected on BHI agar plates containing 15 µg/ml of chloramphenicol were PCR-screened for an intron-disrupted ssp4 gene using primers B1F and B1R . A digoxigenin-labeled ssp4 probe was prepared using primers KO-IBS and KO-EBS1d [26]; that probe was employed for Southern blotting to confirm the presence of a single intron insertion in SM101::ssp4 and F4969::ssp4 . The ssp4 gene ( ORF and ∼250 bp of upstream region ) was PCR-amplified from SM101 , F4969 or 01E809 using primers Spro-F ( 5′- CCACGAATTCAATATCCCTCCTAAATATAATC-3′ ) and Spro-R ( 5′- TAGAGGATCCTTAAATCCCCCATATATTATTC-3′ ) . After digestion with EcoRI and BamHI , those products were separately cloned into the shuttle plasmid pJIR751 , creating pCS , pCF or pCO . The F4969 and SM101 ssp4 null mutants were then individually transformed by electroporation with pJIR751 , pCS , pCF or pCO and transformants were selected on BHI agar plates containing 30 µg/ml of erythromycin . The ssp4 ORFs of SM101 , F4969 or 01E809 were separately cloned into the E . coli expression vector pTrcHis A ( Invitrogen ) using primers SASPC-F ( 5′- CATGGGATCCATGAGCAAGA CACCATTAAA-3′ ) and SASPC-R ( 5′- CATCAAGCTTTTACTTTTCGTCAACGTGAGG ) . The resultant plasmids were then transformed into E . coli DH5α . Those transformants were grown at 37°C to an OD600 of 0 . 6 and then induced with 1 mM isopropyl-β-D-thiogalactopyranoside ( Sigma Aldrich ) , followed by continued growth at 37°C for an additional 4 h . rSsp4 was purified from lysates of the induced transformants with a Ni-NTA spin kit ( Qiagen ) using a modified native elution buffer ( 50 mM NaH2PO4 , 30 mM NaCl and 250 mM imidazole , pH 8 . 0 ) . rSsp4 purity was assessed by Coomassie blue R250 staining samples run on an SDS-PAGE gel and degradation was analyzed by Western blot analysis using a mouse monoclonal antibody against polyHistidine ( Sigma Aldrich ) . To produce Ssp4 antibodies , Rabbits were immunized with the highly-purified SM101 rSsp4 shown in Fig . 6A . This 28 day rapid immunization was performed by Pocono Rabbit Farm and Laboratory ( Canadensis , PA ) , an AAALAC-approved , USDA-licensed and OLAW-assured facility . Unless otherwise specified , Western blot analyses using the Ssp4 antiserum involved inoculating a 0 . 2 ml aliquot of an FTG culture of a wild-type parent , null mutant or complementing strain into 10 ml of DS medium . After overnight incubation at 37°C , the DS cultures were examined by phase-contrast microscopy to verify sporulation had occurred . The culture was then centrifuged and the pellet washed twice with PBS . The pellet was then resuspended in 0 . 5 ml of SDS sample buffer and boiled for 10 min . The boiled samples were then microfuged and 20 µl of the supernatant was subjected to Western blotting , as previously described [27] . A previously described protein: DNA binding assay [28] was modified by incubating purified His6-tagged rSsp4 ( 100 ng ) from F4969 , SM101 or 01E809 for 1 h at 37°C with 100 µg of either empty cellulose beads or cellulose beads containing bound double-stranded calf thymus DNA ( Sigma ) in binding buffer containing 10 mM Tris-maleate ( pH6 . 7 ) , 50 mM potassium acetate , and 10% glycerol . The beads were then washed three times with binding buffer before sequential washes with 0 . 25 M , 0 . 50 M , 0 . 75 M , and 1 . 0 M NaCl . After each NaCl wash , an aliquot of beads was removed and resuspended in SDS-PAGE sample buffer , boiled for 5 min; after centrifugation , the supernatant was analyzed by SDS-PAGE , followed by silver staining . A 3′- biotin-labeled probe consisting of a 55 bp sequence of the cpe gene was prepared using primers Label-D ( 5′-TTAGGAAATATTGATCAAGGTTCATTAATTGAAACTGGTGAAAG ATGTGTTTTAA-3′ ) and Label-R ( 5′-TTAAAACACATCTTTCACCAGTTTCAATTAATGA ACCTTGATCAATATTTCCTAA-3′ ) and a biotin 3′-end DNA labeling kit ( Pierce ) . This probe was then used in a modified version of a previously-described EMSA [29] , which involved incubating 1 µl of probe with 25 , 50 or 100 ng of purified SM101 , F4969 , or 01E809 His6-tagged rSsp4 protein at 37°C for 1 h , then fixing any rSsp4 bound to DNA by the addition of glutaraldehyde ( final concentration of 0 . 01% ( v/v ) ) for 15 min incubation at 37°C . Those mixtures were loaded onto a 6% polyacrylamide gel and electrophoresed in 0 . 5× TBE ( Tris-borate-EDTA ) buffer at 4°C for 1 h . DNA-protein complexes were transferred to a positive charge nylon membrane ( Roche Applied Science ) and detected with a LightShift Chemiluminescent EMSA kit ( Pierce ) . The resistance of C . perfringens spores to moist heat was determined as described previously [9] . To evaluate sodium nitrite ( nitrous acid ) resistance , we modified a previous assay [30] by incubating a 1 ml aliquot of pelleted spores in 100 µl of 100 mmol NaNO2 , 100 mmol Na acetate ( pH4 . 5 ) at room temperature for 60 min; aliquots were then diluted 10 fold in 500 mmol KPO4 ( pH8 . 5 ) . After mixing and centrifugation , the pellet was washed with 1 ml of sterile water and then resuspended in 1 ml of water . The spore suspension was heated at 75°C for 20 min to kill the remaining vegetative cells . These suspensions were then serially diluted from 10−2 to 10−7 with sterile water and plated on BHI agar plates , which were incubated anaerobically overnight at 37°C prior to colony counting . Vegetative growth of wild-type , mutant and complementary strains was determined as described previously [10] .
|
Spores made by pathogenic Bacillus and Clostridium spp . contribute to disease transmission . Clostridium perfringens food poisoning ( FP ) isolates typically produce spores with exceptional resistance to heat and sodium nitrite . This spore resistance probably facilitates FP strain survival in improperly cooked/held foods , contributing to C . perfringens FP outbreaks , which rank among the most common food-borne diseases in developed countries . Currently , the mechanistic basis of the resistant spore phenotype of FP strains is unknown . Here , we report the identification of a novel small acid soluble protein , named Ssp4 , and show that sporulating cultures of FP strains producing resistant spores express an Ssp4 variant with Asp at residue 36 , while sporulating cultures of C . perfringens strains producing sensitive spores express an Ssp4 with Gly at residue 36 . We now demonstrate that i ) the Ssp4 Asp variant is required for extreme spore resistance of FP strains and ii ) this protein may help protect FP strains via tighter DNA binding than the Ssp4 Gly variant . Our study provides important insights into the transmission of a common FP agent and may suggest strategies to interfere with resistant spores of FP strains . These findings may also have relevance for other pathogenic Clostridium spp . carrying an ssp4 gene .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/applied",
"microbiology",
"microbiology/microbial",
"physiology",
"and",
"metabolism",
"microbiology/medical",
"microbiology"
] |
2008
|
A Novel Small Acid Soluble Protein Variant Is Important for Spore Resistance of Most Clostridium perfringens Food Poisoning Isolates
|
When taking a bloodmeal from humans , tsetse flies can transmit the trypanosomes responsible for sleeping sickness , or human African trypanosomiasis . While it is commonly assumed that humans must enter the normal woodland habitat of the tsetse in order to have much chance of contacting the flies , recent studies suggested that important contact can occur due to tsetse entering buildings . Hence , we need to know more about tsetse in buildings , and to understand why , when and how they enter such places . Buildings studied were single storied and comprised a large house with a thatched roof and smaller houses with roofs of metal or asbestos . Each building was unoccupied except for the few minutes of its inspection every two hours , so focusing on the responses of tsetse to the house itself , rather than to humans inside . The composition , and physiological condition of catches of tsetse flies , Glossina morsitans morsitans and G . pallidipes , in the houses and the diurnal and seasonal pattern of catches , were intermediate between these aspects of the catches from artificial refuges and a host-like trap . Several times more tsetse were caught in the large house , as against the smaller structures . Doors and windows seemed about equally effective as entry points . Many of the tsetse in houses were old enough to be potential vectors of sleeping sickness , and some of the flies alighted on the humans that inspected the houses . Houses are attractive in themselves . Some of the tsetse attracted seem to be in a host-seeking phase of behavior and others appear to be looking for shelter from high temperatures outside . The risk of contracting sleeping sickness in houses varies according to house design .
Sleeping sickness , or human African trypanosomiasis , is caused by two species of trypanosome , i . e . , Trypanosoma brucei gambiense and T . b . rhodesiense , that are transmitted by tsetse flies ( Glossina spp . ) when taking blood from hosts [1] . It seems to have been assumed that the risk of humans being bitten by tsetse is by far the greatest when people enter the normal woodland habit of the flies . In keeping with this , almost all of the data available for the nature of the contact between humans and tsetse relate to humans in woodland , especially to people walking through it [2] . Such data indicate that the samples of tsetse caught from humans usually contain high proportions of males which appear to be seeking a mate rather than food [3] . Hence , while many tsetse can occur in the vicinity of humans , the risk of a human being bitten is usually very low . However , a recent investigation of the numbers of Glossina morsitans morsitans and G . pallidipes that actually attempted to feed on humans in various situations indicated that the risk of humans being bitten in woodland was less than the risk occurring when the humans were in or near houses and offices located in large clearings [4] . Moreover , the same work showed that the proportion of females among the tsetse probing humans in the buildings was consistently higher than among tsetse probing people in woodland settings away from buildings . The upshot is that buildings seem to be important , distinctive and neglected venues for the transmission of sleeping sickness , and this leads to many questions . Why are tsetse found in buildings ? Do they enter only at certain seasons and times of day ? Are some types of building more important than others ? How does the sex , species and age compositions of samples of tsetse from buildings compare with those from traps designed to catch host-seeking [5] or resting [6] tsetse ? Present work addressed such questions by studying the catches of G . m . morsitans and G . pallidipes in houses and at other baits in Zimbabwe . To focus on the attractiveness of the houses themselves , none of the houses studied was occupied by humans .
The procedures for sampling tsetse followed long-standing protocols practiced at Rekomitjie . All persons used as catchers or baits in the experiments were permanent pensionable employees of the Division of Tsetse Control , Government of Zimbabwe and given regular updates on the purpose and results of the studies . Before recruitment , the Division explains the nature of the work , the risks associated with tsetse , other disease vectors and wild animals , and warns of the social hardships attending life on a remote field station . Recruits sign a document indicating their informed consent to perform the work required . This document is held by the Division . All experiments were given ethical approval by the Division's Review Committee for Rekomitjie . All houses ( Fig . 1 ) were 20–30 years old and were situated near the centre of the 30 ha clearing of the station , that contained short grass and only a few trees and bushes . Semi-evergreen and deciduous woodland occurred outside the clearing . Each of the houses was unoccupied during the studies , having been vacant for at least a year previously . The walls of the houses were 25 cm thick , made of cement blocks with air cavities , and painted inside and out with white PVA . The roofs were of gabled thatch ( House 1 ) or consisted of corrugated and gently sloping sheets of asbestos ( House 2 ) or galvanized iron , henceforth called tin ( House 3 ) – the latter two “houses” were in fact unused kitchens about 3 m from large thatched houses , but they simulated the types of small building commonly used for field accommodation in central and southern Africa . For some studies the corrugated sheets were covered externally with a 15 cm layer of compressed grass to simulate thatching . Doors on all houses were windowless , hinged and wooden , 2 m tall and 0 . 8 m wide . Windows were of various width , extending between about 1 m to 2 m above floor level , steel framed and clear-glazed , with the exception of the large mosquito-netted windows along the veranda of House 1 . About half of the area of each glazed window could be opened . The netted windows were permanently closed . Four treatments of each type of house were made , involving changes to the windows and doors that opened to the outside: ( i ) windows and doors shut , ( ii ) only windows open , ( iii ) only a door open , and ( iv ) windows and a door open . Items opened were fully open . Any internal doors and windows were always open . Whereas House 1 had two exterior doors , only the one on the West front was ever opened . At all houses the four treatments on windows and the exterior door were operated for 24 h , starting just after 1700 h , with subsequent inspections of the house at 2 h intervals from 0700 h to 1700 h the next day . For each inspection , three hand-net catchers stopped just outside the door and closed it quickly . They then caught and discarded any flies seen around them; entered the house , re-closed the door and closed any open window rapidly . Thereafter , the men walked slowly through the house for a few minutes , catching and recording any fly that alighted on them . Afterwards , any flies in the house were captured , most being taken at the windows after being disturbed by swishing hand-nets and long sticks to disturb flies on the walls or roof . The whole inspection took about 5 min , after which the men left the house and reset the windows and doors to the treatment conditions of the day . While separate records were kept of flies caught from the house structures and from the men , the numbers from the men were always relatively small . The catches from the men were pooled with those from the house structures when the intention was to assess the overall number of tsetse in the houses . An Epsilon trap [5] , baited with artificial ox odor was employed to give samples of host-seeking tsetse [7] . The odor consisted of 200 mg/h of acetone , 1 mg/h of 4-methyl phenol , 0 . 5 mg/h of 1-octen-3-ol and 0 . 1 mg/h of 3-n-propyl phenol [7] , dispensed as described by [8] . Three Box refuges [6] provided samples of tsetse seeking a cool dark place to rest during hot weather . The trap and refuges were operated all day at 25–100 m from the houses , in a predominantly cross-wind direction from them , and were sited to maximize catches . This involved putting the trap in a sunny position [9] , and placing the refuges next to boles of shady trees [6] , although the absence of many such trees from the general surroundings of the refuges would have reduced their performance [6] . Tsetse were removed from the trap cage and the refuges a few minutes before the inspection of the houses . The removal of flies from a refuge involved quickly closing the entrance with netting sheet , and disturbing the flies inside so that they presented themselves to a cage at the end of a conical part of the sheet . Dry bulb temperatures were measured in a Stevenson screen near the centre of the station . Inside the houses , thermometers were at head height on walls not in direct sunlight . In Box refuges the thermometers were at the back of the insulated drum , i . e . , where most tsetse rested . Female tsetse were dissected to determine their ovarian category , which offers an index of age [10] . Flies that had ovulated at least once , i . e . , in ovarian categories ≥1 , had their uterus examined and classed as either empty , or containing an egg or a first to third instar larva ( L1–L3 ) . Females with no undigested blood , i . e . , those roughly equivalent to hunger stage IV for males [11] , were distinguished from those with blood , i . e . , stages I–III . With each house the four window/door treatments were allocated in randomized 4-day blocks of consecutive or nearly consecutive days , but the number of flies of each sex and species caught daily in the houses and at some of the other baits were often zero , making it impossible to perform reliable statistical analyses of mean daily catches . To avoid this problem , the analyses were performed on the combined catches of males and females of both species; the catches from the three refuges were pooled , and unless stated otherwise , the catches from the four window/door treatments were also pooled . Chi-squared tests were performed for the homogeneity of the distributions of catches between various categories , with pooling of categories in some cases to ensure expected values ≥5 . The term “significant” implies P<0 . 05 .
Catches were made from House 1 for four or five 4-day blocks per calendar month between Aug 2009 and Aug 2010 . The total catches ( Table 1 , House 1 ) indicated no gross effect of the door plus windows open , as against just the door or windows . Not surprisingly , when the windows and doors were closed , i . e . , for the Nil treatment , the catches were reduced greatly , by an average of 88% . Perhaps more surprisingly , the catches with this treatment were not zero . Some of the flies may have entered the house via the gaps of about 10 cm that occurred under the eaves . Others may have followed the observers un-noticed into the house – according with the observation that a relatively high proportion of the catch with the Nil treatment consisted of male G . m . morsitans , the sex and species that predominates grossly in samples from walking men [4] . In the case of the treatments with an open door , some of the flies following the men may have entered the house when the men arrived outside the door , and before the door was closed . Nevertheless , the compositions of catches with all of the house treatments did not show the huge bias normally expected in catches from men [4] , suggesting that the men caused no more than a few flies to enter . Hence , an intriguing point emerged: the house itself seemed attractive in its own right . The elements in the attractiveness of the house are suggested by considering the percent of G . pallidipes in catches from the various baits . The proportion in the trap was very high , at 91% , and significantly different ( P<0 . 001 ) from the 36% evident at the refuges . With the house treatments the percents were intermediate , at 61–81% ( average 76% ) . This suggested the hypothesis , henceforth termed the “mixed sample” hypothesis , that the catches from House 1 consisted of two segments , one comparable to refuge catches and the other comparable to trap catches . The implication is that House 1 functioned as both a trap and a refuge , attracting some flies that were host-seeking and others looking for shelter . It seemed that House 1 did indeed offer a good refuge since in the middle of the day , when screen temperatures were greatest , the temperatures in the house were about two degrees lower than screen temperatures – much like the Box refuges but in sharp contrast to the asbestos-roofed House 2 and particularly the tin-roofed House 3 ( Fig . 2 ) . In some of the months in which catches were made from House 1 , simultaneous catches were also made from the other houses . In the first experiment ( Table 2 , Expt 1 ) the mean catches from the small houses as a percent of those from the large thatched house were only 17% for the small asbestos-roofed House 2 and even lower at 13% for the small tin-roofed House 3 , i . e . , the hotter the house ( Fig . 2 ) the lower the catches . Moreover , the hotter the house , the lower the proportion of G . pallidipes in the total catches – the percents being 87% , 36% and 28% for Houses 1 , 2 and 3 , respectively . These proportions were significantly heterogeneous ( P<0 . 001 ) . In the next experiment the asbestos or tin roofs of the small houses were covered in grass , so that the temperatures in them became cooler and more like those of the thatched House 1 , with temperatures at 1100 h–1700 h in Houses 2 and 3 being less than screen temperatures by an average of 1 . 4°C ( 95% CL 1 . 3–1 . 6 ) and 0 . 9°C ( 0 . 8–1 . 1 ) , respectively . The mean catches at these houses then increased slightly to 14–36% of the House 1 catch . However , House 3 still gave the fewest tsetse , and the proportion of G . pallidipes in catches from Houses 1 and 2 was still lower than in House 1 ( P<0 . 001 ) ( Table 2 , Expt 2 ) . Having failed , above , to demonstrate any gross effect of temperature and roof type on the magnitude and composition of catches from houses , it was suspected that the distinctive samples from the different houses were associated with window type . In Houses 2 and 3 the windows consisted only of glass , whereas in House 1 much of the “window” space was netting , i . e . , on the veranda , so encouraging ventilation . Hence , the following study of window type was made . On some days in Aug–Sep 2010 the windows of Houses 1 and 2 were closed , so that exit via them was completely barred by glass . On other days the opening parts of the windows were fully open , but covered in netting , so that tsetse could not enter or leave via the windows . The doors were open for both of the window treatments , and the roofs were covered with grass . Catches were compared with simultaneous catches from House 1 with the door open and windows closed , i . e . , the way the small houses were operated . The total catches ( Table 3 ) showed that even with the all-glass windows , i . e . , the type of treatment used in previous months , the numbers of tsetse caught from the small houses relative to House 1 , and the proportions of G . pallidipes in catches from the small houses , were now increased substantially . This was associated with the onset of the hot-dry season , so perhaps the rising temperatures outside the house caused G . pallidipes to disregard those features of the small houses that previously reduced the availability to such houses . In any event , the main point of the experiment , i . e . , the investigation of any effect of window type on the formerly very low proportions of G . pallidipes from small houses , was somewhat undermined . Nevertheless , the results did show that , during the hotter weather at least , there was no gross effect of window type in the small houses , and that the total catches from the small houses were still less than from the large , and still contained relatively low proportions of G . pallidipes . The heterogeneity in the proportions of G . pallidipes in samples remained significant ( P<0 . 001 ) . In House 1 the total catches from the men consisted of 23 males and 16 female G . m . morsitans , and two males and one female G . pallidipes . In the smaller Houses 2 and 3 the figures were 24 , 3 , 2 and 0 , respectively . The percents of male G . m . morsitans in the samples was therefore 53% with House 1 and 83% with the other houses , and the difference was significant ( P<0 . 05 ) . The monthly catches at the trap and refuges ( Fig . 3 , A and B ) followed the patterns typically observed at Rekomitjie , with the refuge catches being by far the greatest in the hot-dry season of Sep–Nov and smallest in the cool-dry season of mid-year , and with the trap catches being more evenly distributed [6] . The pattern with the house catches was intermediate , giving support to the mixed sample hypothesis , above . The general patterns of the availability to traps and refuges was as usually found at Rekomitjie [6] . Thus , with both species of tsetse the catches from the traps ( Fig . 4 , A ) were greatest in the morning and late afternoon , but there were seasonal distinctions . The mid-day trough in trap catches was most pronounced in the hottest months of Sep–Nov ( Fig . 4 , A1 ) and least marked in the coolest months of May–Aug ( Fig . 4 , A3 ) . Moreover , while the morning peak of trap catches was greater than the afternoon peak in Sep–Nov , the afternoon peak became more pronounced as the weather cooled . The refuge catches ( Fig . 4 , B ) were concentrated in the middle of the day and early afternoon , and so differed markedly from trap catches . Again there were seasonal variations in that during Sep–Nov ( Fig . 4 , B1 ) the refuge catches started to rise earlier than in the cooler conditions of Dec–Aug ( Fig . 4 , B2 and B3 ) , presumably because during the hotter months the need to avoid high temperatures occurred sooner in the day . Catches from House 1 ( Fig . 4 , C ) differed from trap catches ( Fig . 4 , A ) in being large in the morning and/or the afternoon , i . e . , somewhat like trap catches . However , the house catches differed from trap catches in showing no trough in the late morning and early afternoon . In general , the diurnal pattern at the house seemed to be a hybrid of the patterns at the trap and refuge , as expected on the mixed sample hypothesis . Nevertheless , there was a slight departure from expectation in that the catches from the house were not as great as predicted at the 0700 h inspection , when the presence of many flies in traps should have been associated with many flies being caught at the house . This could be due to the fact that the trap was baited with odor , whereas the house was not . However , the more likely explanation is associated with the observation that some of the tsetse in the house were attacked by ants , as evidenced by the presence of half-eaten carcasses or wings , found mainly after the long overnight delay between the 1700 h inspection of one day and the 0700 h inspection on the next . The was little or no evidence of trap catches being attacked overnight . Few flies were caught at certain times of day with all baits , making it impossible to identify any clear diurnal variations in the reproductive condition of samples , so the data for all times of day were pooled . Such pooling led to no evidence of a seasonal change in the distributions of uterine contents of females of either species . However , the proportion of old flies was relatively low in the latter half of the dry season . For example , in Aug–Nov the percent of G . m . morsitans in ovarian categories ≥4 was 20% ( N = 56 ) in the houses and 24% ( 25 ) the traps , as against figures of 48% ( 42 ) and 30% ( 61 ) , respectively , in other months . For G . pallidipes the figures were 49% ( 166 ) and 47% ( 128 ) , respectively , in Aug–Nov and 65% ( 141 ) and 58% ( 499 ) , respectively , in other months . The seasonal heterogeneity in the proportion of old flies was significant ( P<0 . 01 to <0 . 05 ) in all cases except for G . m . morsitans from the trap . With the latter bait some of the catches of G . m . morsitans were small , making it difficult to find a significant difference Despite the seasonality in some aspects of the results , the pooled data for ovarian categories ( Fig . 5 ) and uterine contents ( Fig . 6 ) in the whole study period illustrate two matters that applied at all seasons . First , with each bait the samples of G . pallidipes were older than for G . m . morsitans and contained a lower proportion of flies with larvae as against eggs . Second , the samples of G . m . morsitans from all baits were older , and with higher proportions of larvae , than the samples taken from men during other work performed at Rekomitjie in parallel with the present investigations [4] . In that other work the catches of G . m . morsitans from the men in various situations inside and outside houses throughout the year showed only 18% ( N = 257 ) in ovarian categories 4–7 , and only 23% ( 189 ) of the flies in categories ≥1 carried larvae . These compositions are significantly different from the figures of 32% ( N = 98 , P<0 . 01 ) and 51% ( N = 85 , P<0 . 001 ) , respectively , for the present catches of G . m . morsitans from houses over the year ( Figs . 5 and 6 ) . Of the 19 female G . m . morsitans caught from men in houses in the present work , only five were dissected . Two were in category , 0 , one was in category 1 and two were in category 2 . All three flies in categories 1 and 2 had an egg in the uterus . Since the age structure and uterine contents of samples from the trap and refuges where closely similar ( Fig . 5 , A and B; Fig . 6 , A and B ) , the mixed sample hypothesis required , as observed , that the age structure and uterine contents of the house catches ( Fig . 5 , C; Fig . 6 , C ) were much the same as for the trap and refuge catches . For the refuge catches , most of which were in Sep–Nov , the percent of females with undigested blood was fairly high with each species , averaging 34 . 9% ( N = 109 ) for both species combined . For traps at all times of year , and for the houses in months other than Sep–Nov , the percent of the catches with blood was very low , averaging 2 . 7% ( N = 713 ) for the traps and 4 . 4% ( 252 ) for the houses . However , the percent in the catches from houses increased significantly ( P<0 . 05 ) to 10 . 5% ( N = 153 ) in Sep–Nov , consistent with the evidence ( Fig . 3 , C ) that many refuge-seeking flies entered the houses in these months .
We recorded the sex and species composition , age structure , pregnancy condition and hunger stage of samples of tsetse caught in various types of unoccupied houses at different times of day throughout the year , and compared these data with those of catches from artificial refuges and host-like traps nearby . In general , the character of catches from the houses was intermediate between those from the refuges and traps . Our results suggest that the structure of a house is itself attractive to tsetse , so that the flies enter even when no humans are inside , but that if humans then enter the house some of the tsetse already in it can go to the people . The methods of the present work offer valid indications for the numbers of tsetse that entered houses and then remained inside for up to two hours during the day , even if the numbers staying inside overnight and found at 0700 h may have been reduced by ant predation . However , the methods provide only crude measures of the numbers entering since , strictly speaking , the work showed only the numbers found in the houses at each inspection , rather than addressing the entry responses themselves . Thus , many flies may have entered the houses and left before the inspections were made . In particular , when many openings allowed tsetse to enter the house they might also have facilitated a quick exit , so it is hardly surprising that having both the door and windows open had no great effect on house catches . Moreover , the reason for the seasonally low proportions of G . pallidipes in catches from small houses might have been that many G . pallidipes entered the small houses at all seasons , but sometimes they left them rapidly , perhaps because the houses were insufficiently large and lofty to offer the right microclimates . These matters could have been investigated more critically by placing electrocuting grids [12] over the openings of the doors and windows , to catch flies at the instant of entry , but this would have precluded an important aspect of the present work , i . e . , assessment of the number of tsetse that remained in the houses for some while , so that they would have had a good opportunity to contact any humans that entered . Despite the above problems , the results do suggest that there were two main reasons why tsetse entered houses . First , in all months a house acts like a trap that attracts tsetse in the host-seeking phase of behavior that they exhibit in the early morning and/or late afternoon . Second , in hot weather other tsetse enter houses to find a cool shady refuge during the late morning and early afternoon . The indication that the flies identify the doors and windows as entrances to refuges fits with the fact that some natural refuges consist of openings into very large objects – for example , rot-holes in baobab trees and hollows in tall river-banks [6] . Why tsetse appear to mistake a large white house for a host is less clear , but then it is hardly clear why tsetse seem to regard a bright blue trap as a host . In any event , the fact that traps [13] and artificial refuges [6] of various color differ greatly in their efficacy suggest that house color could also be important . In particular , by analogy with various types of artificial refuges operated at different seasons and situations [6] , one could expect that a large dark-colored house in shady riparian woodland would attract many refuge-seeking tsetse in the hot season – far more than found in present houses . There is no direct evidence in present work to indicate what proportion of the overall catch at the houses was represented by refuge-seeking flies , especially given the caveat that the catches at the three Box refuges might have underestimated substantially the numbers of tsetse seeking refuge in the very much larger houses . However , taking together the data for catch compositions ( Table 1 ) , and for diurnal and seasonal patterns of catches ( Figs . 3 and 4 ) and hunger stage , the proportion of refuge-seeking flies seems to have been substantial – about a quarter to three-quarters on hot days . Despite the apparent importance of houses as refuges , many of the flies in houses at all times of the year appeared to have entered in direct search of food , and the blood reserve of many of these flies and some of the refuge seekers seemed so low that , had they been left in the houses , they might have sought food from resident humans there once the temperatures declined sufficiently in the evening to obviate any need for refuge . Thus , present results accord with the indication [4] that houses can be at least as important as other venues for contact between humans and hungry tsetse . In respect of the risk of being bitten by tsetse , it might seem fortunate that the samples of flies in houses contained relatively high proportions of old tsetse , and high percents of females and of G . pallidipes , and so were very different from those normally associated with probing on humans [4] . In particular , the fact that old flies usually avoid human hosts is important because only such flies can be effective vectors of sleeping sickness [14] . However , it is worrying that humans in houses can be in very close proximity to tsetse old enough to be potential vectors . The big question , therefore , is to what extent different types of building , and different patterns of human occupation , might induce a broader spectrum of the flies in the buildings to attack humans there . The fact that conditions inside buildings can change the normal behavior of tsetse is indicated by previous work [4] which found that female G . m . morsitans formed a relatively high proportion of the flies probing men in the mainly large buildings at Rekomitjie . Current data for the numbers of G . m . morsitans taken from men in the large House 1 accord with that result , although in the smaller houses the proportion of females in catches from men was low . Present work considered only three types of house , each of which was unoccupied by people except for the presence of the observers at the brief inspections during the day . The number of flies in a house , and their propensity to attack humans , might be expected to change if , for example , the humans remained in or near the house for many hours , if the flies in the house were not removed frequently during the day , if other animals were kept in or near the house to attract or distract flies [4] , and if domestic cooking generated wood smoke that can be repellent [15] . These matters are currently under investigation at Rekomitjie .
|
To explore the nature of houses as venues for the contact between humans and tsetse flies , and hence for the transmission of sleeping sickness , we studied the sex and species composition and physiological condition of samples of tsetse caught in various types of house throughout the day and at different seasons . These aspects of the catches were intermediate between those for traps which caught host-orientated flies and artificial refuges that sampled flies seeking a cool dark resting site . This suggested that some flies entered houses in search of food , and others entered for shelter . Windows seemed about as effective as doors as entry points . Several times more tsetse were found in a large thatched house , compared to smaller houses with asbestos or metal roofs . Many of the tsetse in houses were old enough to be potential vectors of sleeping sickness . Some of the tsetse inside alighted on people that inspected the houses .
|
[
"Abstract",
"Introduction",
"General",
"Methods",
"Experiments",
"and",
"Results",
"Discussion"
] |
[
"medicine",
"biology"
] |
2013
|
A Neglected Aspect of the Epidemiology of Sleeping Sickness: The Propensity of the Tsetse Fly Vector to Enter Houses
|
The study of the concerted action of hormones and transcription factors is fundamental to understand cell differentiation and pattern formation during organ development . The root apical meristem of Arabidopsis thaliana is a useful model to address this . It has a stem cell niche near its tip conformed of a quiescent organizer and stem or initial cells around it , then a proliferation domain followed by a transition domain , where cells diminish division rate before transiting to the elongation zone; here , cells grow anisotropically prior to their final differentiation towards the plant base . A minimal model of the gene regulatory network that underlies cell-fate specification and patterning at the root stem cell niche was proposed before . In this study , we update and couple such network with both the auxin and cytokinin hormone signaling pathways to address how they collectively give rise to attractors that correspond to the genetic and hormonal activity profiles that are characteristic of different cell types along A . thaliana root apical meristem . We used a Boolean model of the genetic-hormonal regulatory network to integrate known and predicted regulatory interactions into alternative models . Our analyses show that , after adding some putative missing interactions , the model includes the necessary and sufficient components and regulatory interactions to recover attractors characteristic of the root cell types , including the auxin and cytokinin activity profiles that correlate with different cellular behaviors along the root apical meristem . Furthermore , the model predicts the existence of activity configurations that could correspond to the transition domain . The model also provides a possible explanation for apparently paradoxical cellular behaviors in the root meristem . For example , how auxin may induce and at the same time inhibit WOX5 expression . According to the model proposed here the hormonal regulation of WOX5 might depend on the cell type . Our results illustrate how non-linear multi-stable qualitative network models can aid at understanding how transcriptional regulators and hormonal signaling pathways are dynamically coupled and may underlie both the acquisition of cell fate and the emergence of hormonal activity profiles that arise during complex organ development .
The root apical meristem ( RAM ) of A . thaliana is an important model for understanding the complex mechanisms underlying cell differentiation and morphogenesis during organ development of multicellular organisms [1–8] . The RAM of A . thaliana has a relatively simple cellular organization while it shares a general cellular structure and dynamics with stem cell niches ( SCN ) from both plants and animals [9 , 10] , suggesting an underlying generic system-level mechanism that we may unravel by studying plant meristems , particularly the RAM [8] . In this study we build upon previous studies to further understand such mechanism in the RAM . We particularly aim at exploring how transcriptional regulation is integrated with the auxin and cytokinin ( CK ) hormonal pathways to regulate the cellular decisions regarding cell fate and behavior at the RAM . The RAM comprises the SCN , the proliferation domain ( PD ) and the transition domain ( TD ) ( Fig 1A ) . The SCN is at the tip of the RAM and is formed by the quiescent center ( QC ) cells surrounded by the so-called initial cells [11] . The QC cells are stem cells that have very low proliferation rates [12–15] , while the initial cells are stem cells that divide at slightly higher rates and are specified as epidermis/lateral root cap , endodermis/cortex , pericycle/pro-vascular tissues and columella initial cells [11] . Upon division , the initial cells self-regenerate and produce a daughter cell that exits the SCN [16] . The progeny of the distal initial cells differentiate immediately into the root cap at the tip of the organ . In contrast , the progeny of the rest of the initial cells divide at higher rates in the PD towards the base of the plant . Eventually , these cells transit to the TD where they divide at slower rates and begin to endoreduplicate [17–19] . Afterwards , the cells leave the RAM , conform the Elongation Zone and finally the Differentiation Zone , where they acquire the morphological features of the differentiated tissues that constitute the radial structure of the root [18] . The cross-talk of hormone signaling and metabolism , coupled with the regulatory activity of transcription factors , microRNAs and mobile peptides involved in cell differentiation , is an important component of the system-level mechanisms underlying the organization and the maintenance of the RAM [8 , 20–22] . Experimental work has uncovered the function of some important genetic and hormonal components involved in RAM patterning [3 , 4 , 23–36] . While there are many regulatory mechanisms involved , the role of 1 ) the GRAS transcription factors and 2 ) the auxin and CK signaling pathways have been more thoroughly studied due to their importance defining the radial [30 , 37–39] and the apical-basal patterning of the RAM [23 , 26 , 40 , 41] , respectively . High-throughput experiments coupled with bioinformatic analysis have established the expression patterns of many genes in the RAM [42] , have identified the regulatory targets of main regulators of the RAM [43–47] , and have made it possible to infer the topology of global gene regulatory networks involved in RAM development [45–47] . But how such networks dynamically underlies the emergence of the expression patterns experimentally described , is not entirely understood . In this sense , systemic and dynamic approaches are recently starting to integrate available experimental data in order to postulate computational dynamical models of regulatory modules following a bottom-up approach , and provide integrative and formal frameworks to tackle the collective action of coupled hormonal and transcriptional regulatory mechanisms [1–4 , 7 , 48–51] . This integrative approach may lead to a systemic understanding of how documented patterns of expression emerge , and may also provide novel predictions that can be tested experimentally , leading to a recursive cycle between modeling and experiments . For instance , we previously proposed a minimal model of the gene regulatory network for SCN cell patterning [1 , 2] . This module integrates the activity of the GRAS transcription factors , the auxin signaling pathway and the PLETHORA genes , as well as a mobile peptide , among other key regulators of cell-fate in the root SCN . Despite the comprehensive experimental and theoretical work done so far , some important questions regarding the genetic-hormonal regulation at the RAM remain unanswered . For instance , it is known that auxin and CK have opposite roles in the RAM as both act upon the cell cycle and alter cell behavior: auxin promotes cell proliferation at the PD [40] , while CK promotes cell endoreduplication at the TD [35] . But the co-activity of both signaling pathways in the root cap where cells differentiate immediately [52–54] and the quiescence of the QC cells that have a maximum of auxin concentration [23 , 55] , show that it is not possible to establish linear relations between hormone activity and cellular behavior at the RAM . Instead , another possibility is that the relative concentration of auxin and CK at the RAM or the cellular context is what underlies cell behavior along the root apical-basal axis . Here , we explore this possibility . The distribution of auxin at the RAM ( Fig 1A ) emerge , in part , as a consequence of polar auxin transport mechanisms [7 , 48 , 49] , but a more complete system-level understanding of how hormone signaling and metabolism act in conjunction with other hormones and genetic regulatory networks involved in cell differentiation is still poorly understood . In fact , multiple regulatory interactions between the auxin and CK pathways and the function of transcription factors have been uncovered . For example , a GRAS and a HD-ZIP III transcription factors are known to regulate the expression of enzymes involved in CK metabolism in the RAM [4 , 44] and of components of the auxin/CK signaling pathways [34 , 43 , 56 , 57] . Additionally , WUSCHEL-RELATED HOMEOBOX-5 ( WOX5 ) , a key regulator of QC identity , promotes the accumulation of auxin [58 , 59] . In turn , auxin signaling both promotes and represses the expression of WOX5 [27 , 31 , 58] . These opposite responses of WOX5 to auxin could be explained in terms of auxin dosage; with auxin promoting the expression of WOX5 at intermediate levels , and reducing it at either high or low auxin concentrations [60] . But we still do not understand how such correlations emerge . The complex relations between auxin and CK activity and cellular behavior could be emerging from the concerted action of several genetic and hormonal regulators , which show different activity profiles in different regions of the RAM . A systemic understanding of how the genetic expression configurations of different cells at the RAM are established from the joint activity of auxin/CK and such transcriptional regulators can be studied with the approach of dynamic discrete regulatory networks to uncover a core system-level mechanism that integrates experimentally grounded interactions [8] . In this study we used a Boolean dynamic network to propose a minimal model of the genetic-hormonal regulatory network ( GHRN ) that integrates the previously reported cross-talk between the auxin and CK signaling pathways , with transcriptional regulators that have been shown to be important in RAM patterning . Alternative GHRN models were proposed to test the plausibility of two novel hypotheses concerning the regulatory mechanisms underlying cell-fate specification and context-dependent hormone responses at the RAM . Our results show that the hypothetical regulatory interactions we proposed are necessary in the context of the GHRN model to recover attractors with the activity profiles of the genetic components considered and the auxin/CK activity configurations observed for different cell types at the RAM and the root cap . The model presented here is useful to predict the existence of cell activity configurations at the TD that have not been characterized before , and to understand how cells might interpret and regulate auxin responses . Particularly , the model explains that the response of WOX5 to auxin might be context-dependent . According to our results , the multi-stability of the GHRN underlies the emergence of different cellular contexts , each with specific auxin responses . This result allowed us to identify a potentially generic system-level mechanism to explain the cells’ “competence” to respond to hormones as they acquire different fates . We assessed the robustness of the GHRN by making perturbations in its Boolean functions , and by making a continuous extension of the model . We also validated the GHRN model through the simulation of gain and loss of function mutations ( GOF and LOF , respectively ) . Based on the mutant simulations we detected that one of the proposed hypothetical interactions provides a mechanistic explanation to several mutants that do not express WOX5 and have a misspecification of the QC cells . We also identified the particular regulatory interaction between cell fate regulators and hormones that explains the co-activity of auxin/CK signaling in the root cap attractor . Therefore , this study extends our understanding of the system-level mechanisms underlying the emergence and maintenance of the cellular activity profiles at the RAM of A . thaliana . We achieve this with a newly uncovered minimal regulatory module with the necessary and sufficient set of components and interactions to recover the configurations of these , that have been documented experimentally . Additional components and hormone signaling pathways may now be added to the proposed framework in future modelling efforts .
To build the minimal GHRN of the RAM of A . thaliana , we extensively reviewed the available experimental information . We decided to include in the minimal GHRN model the genetic components and hormonal regulators that were well characterized at the functional level with detailed molecular genetic experimental studies in WT and various mutant conditions , and that their interactions with other important regulators of the RAM were well described and substantiated with various independent experimental approaches . Furthermore , we were particularly interested in adding solidly documented functional feedback regulatory loops to the network , because their combinations are crucial for the functioning and dynamics of the multi-stable system under study . Understanding this genetic-hormonal multi-stable network was our main aim in the present work . A vast number of putative regulatory interactions have been provided by genome-wide experiments in the context of the development of the RAM , based on various inference methods that generally consider the correlation on transcription levels of genes under various conditions or mutant backgrounds [43–47] . Such interactions could be explored in future extensions of the current model we are presenting in this paper . We summarize below the information used for the GHRN model and more details can be found in Table 1 . The GRAS transcription factors SHORTROOT ( SHR ) and SCARECROW ( SCR ) , and the BIRD transcription factors JACKDAW ( JKD ) /BALDIBIS ( BIB ) , MAGPIE ( MGP ) /NUTCRACKER ( NUC ) among others , regulate the radial patterning of the RAM and the specification of the QC cells [27 , 28 , 36–39 , 43 , 45 , 61 , 62] . The expression of SHR is regulated by many activators and repressors that in conjunction underlie its pattern of expression in the RAM . In conjunction they delimit its expression to the pro-vascular tissues [47] . SHR protein moves from its site of synthesis in the stele to the adjacent layer where it is retained by forming protein complexes with JKD/BIB and SCR [36 , 39] . These transcription factors regulate directly and indirectly each other expression . SHR , JKD and SCR are altogether necessary for the expression of SCR in the adjacent layer to the pro-vascular tissues [28 , 36 , 38 , 43 , 61] . SHR forms a protein complex with SCR that activates the expression of SCR [38 , 43 , 61] , but it absolutely needs SCR and JKD to be fully located in the nucleus to do so [36] . Then , even though JKD is not a direct regulator of SCR , it is required for its effective expression . Similarly , JKD activation requires SCR and SHR presence [28] , as both single mutants have a reduction in JKD expression . Interestingly , it has been shown that in the absence of SHR , JKD can repress the expression of SCR [45] , indicating that JKD might have a dual role on SCR depending on the activity state of SHR . SHR and SCR are also necessary for the effective expression of MGP and NUC as their expression is reduced in shr and scr mutants [28 , 43] . JKD and BIB are jointly expressed in the ground tissue , the cortex/endodermis initials and the QC [36 , 63]; and MGP/NUC are highly expressed in the ground tissue and the cortex/endodermis initials [28 , 43] . The collective activity of all these transcriptional regulators , SHR , SCR , JKD , BIB , MGP and NUC is necessary for the specification of the endodermal cell fate in the RAM [36] . SHR , SCR and JKD are also necessary to specify the QC cells , as mutants in any of them have a misspecification of these cells [24 , 27 , 28] . SCR and SHR also promote the expression of the microRNAs MIR165a/6b that are expressed in the endodermis [30] . MIR165a/6b diffuse from its site of synthesis to the neighboring tissues where it promotes the degradation of the mRNA of the HD-ZIP III transcription factor PHABULOSA ( PHB ) [30] , creating complementary MIR165a/6b and PHB activity domains that pattern the stele and the ground tissue of the RAM [30 , 64] . Low PHB levels are necessary to correctly establish the protoxylem , the pericycle and the ground tissue [[30 , 64] , while high levels specify the metaxylem [30] . In the particular case of MIR166b , it is also expressed in the QC cells but its role in this context has not been conclusively established experimentally [30] . Moreover , it is interesting to notice that PHB feedbacks to the BIRD transcription factors by repressing the expression of JKD [64] . Parallel to the GRAS/BIRD/PHB/MIR165a/6b mechanism , the hormone auxin is an important regulator of cell behavior in the RAM [23 , 40] . Auxin promotes the degradation of the Aux/IAA proteins that otherwise bind to and repress the transcriptional activity of the ARFs ( Auxin response factors ) [65 , 66] . ARFs have been classified as activators or repressors of gene expression depending on their protein domains and the effects in the expression of auxin responsive genes [67] . ARF activators interact with a great variety of Aux/IAA proteins in comparison with the ARF repressors [68 , 69] , but reports have shown that the interactions between the ARF repressors and the Aux/IAA proteins are necessary for certain auxin responses [70 , 71] . In the RAM , auxin distribution forms a gradient that correlates with the behavior of the cells: a concentration peak coincides with the position of the QC cells , intermediate levels with the PD and the root cap , and low levels with the position of the TD [23 , 52 , 55] ( Fig 1A ) . On the other hand , CK responses are relatively high in both the TD and the root cap , as observed by the effect of the activity of CK transcriptional reporters [54 , 72] , CK cell measurements of different cell types of the RAM [53] and local degradation of CK in the TD [26] . The activity of the CK signaling depends on a phosphorylation cascade to activate the ARR type-B transcription factors that regulate the expression of CK target genes [73] . In the TD , the ARR type-B regulators ARR1 , ARR2 and ARR12 promote the expression of SHY2 , an Aux/IAA protein that is key for the cross-talk between the Auxin and CK pathways in the transition from proliferation to endoreduplication at the TD of the RAM [29 , 35 , 74] . The expression of SHY2 is particularly high in the pro-vascular tissues of the TD [29] . WOX5 is a transcription factor fundamental for QC identity , and it is widely acknowledged that is regulated by two parallel pathways ( i . e . , the GRAS transcription factors and auxin signaling [24 , 27] ) . SHR and SCR are necessary for WOX5 expression , while auxin promotes the expression of WOX5 through the ARF activator ARF5 ( MONOPTEROS ) and represses it through the ARF repressor ARF10 [27 , 31] . Additionally , the expression of WOX5 is negatively regulated by the mobile peptide CLE40 [75 , 76] . ARF5 activity in the PD is important to maintain cell proliferation [40 , 77] . Multiple regulatory interactions have been reported among the mentioned regulators: SCR represses the expression of ARR1 [34 , 57] , WOX5 promotes the accumulation of auxin [58 , 59] , auxin signaling and SHR promote the degradation of CK [44 , 78] , CK strongly represses the expression of MIR165a/6b [4 , 79] , PHB promotes the biosynthesis of CK [4] and both ARF10 and ARF5 were predicted to be repressed by SHR in a bioinformatic study [43] . We integrated this reported evidence of the regulatory mechanisms that underlie cell patterning in the RAM into a minimal GHRN model ( Fig 2A ) . From hereon when we refer to hormones we mean auxin and CK that are the ones included in the GHRN model . It has been shown that JKD and BIB , as well as MGP and NUC have the same expression patterns and are putatively redundant in the RAM [36 , 43] . Thus , we decided to model the role of these regulators with the representative nodes JKD and MGP , respectively . Because there are 23 ARFs and 29 Aux/IAA proteins in A . thaliana , but there is only specific data about the role of SHY2 , ARF5 and ARF10 in the RAM , these genes were modeled independently while all other ARFs and Aux/IAAs were included in a single ARF and AUXIAA node , respectively . Regarding the role of the type-B ARR regulators of CK signaling , it is known that ARR1 , ARR2 and ARR12 redundantly promote the expression of SHY2 [29 , 35 , 74] , and we decided to model their role with the representative node ARR1 . Finally , we used MIR166 as a generic node to model the role of MIR165a/6b , as MIR166b is expressed in a broader domain than MIR165a [30] . The resulting network comprises the role of up to date experimental information about the hormonal and genetic regulation of cell fate and cellular behavior in the RAM . The model has two levels of complexity: the first one is revealed by the overall structure of direct/indirect interactions among the components of the network ( Fig 2 ) , and the second one consists of the formalization of the experimental information in the form of logical rules or tables of truth that describe how the activity of each node changes depending on the state of its regulators the previous time step . In the model the activity of the genetic components considers regulation at the transcriptional , post-transcriptional and protein activity levels; for hormones this entails metabolic regulation ( biosynthesis and degradation ) . For instance , SCR is a clear example of how we formulated the logical rule of a genetic component that is regulated at different scales ( see Model Assumptions ) . Briefly , its transcription is positively regulated by SHR and SCR [38 , 43 , 61]; JKD does not participate directly in the regulation of SCR but instead regulates the cellular localization of SHR [36] , making JKD’s activity an indirect but necessary condition for SCR activity . This conditional dependency made us include JKD as an activator in the logical rule of SCR , along with SCR and SHR . Hence , we are not explicitly considering the detailed biochemical mechanisms involved , but the overall structure and logic of the documented regulatory interactions are captured in the logical rules . The assumptions of the model are listed in the Methods ( including two representative examples of how experimental information was formalized in the logical functions ) and the logical functions of the nodes can be found in S1 Appendix . Given the regulators included in the GHRN and based on expression data from the literature , we expected to recover at least 8 attractors combining gene/protein activities and hormone presence , corresponding to the following cell types of the RAM: the QC , endodermis PD and TD , peripheral pro-vascular PD and TD , central pro-vascular PD and TD , and the root cap ( Fig 3A ) . In this context , a cell type is formalized as the activity configuration of the components considered in the model , that have been experimentally documented to correlate with different cells in the RAM . Notice that for the central pro-vascular TD and root cap attractors the value of some nodes ( indicated with * in Fig 3A ) could be either 1 or 0 . Hence , in these cases more than one attractor could represent the expected cell types . For the rest of the attractors we expected to find a unique attractor . With the logical functions we proposed , based on experimental information , we solved the system to study the dynamic and concerted action of the regulators considered . The GHRN model that only includes experimentally reported interactions did not recover the expected activity profiles of the cells of the RAM . Based on the complementary expression patterns of CLE40 and SHR , and of WOX5 and MGP , repressive interactions were previously proposed between each pair of regulators [1 , 2] ( Table 1 ) . The GHRN model with these hypothetical interactions still did not recover attractors with configurations that have been documented for different cell types at the RAM ( S2 Appendix ) . This is because WOX5 was not active in any attractor , so the model did not recover an attractor corresponding to the QC ( S2 Appendix ) . Moreover , ARF5 and ARF10 activities do not match what is observed experimentally: the attractors corresponding to the central and peripheral pro-vascular PD do not have ARF5 and ARF10 activity contrary with expression data ( Fig 4; S1 Fig; S2 Appendix ) . Therefore , the attractors recovered by the GHRN model are incorrect . To further verify that the known and previously proposed interactions are insufficient to describe the activity configurations of the cells of the RAM , we explored , systematically and exhaustively , if there exist any network that can recover a predefined set of attractors ( Fig 3A ) given the set of regulatory interactions ( Table 1 ) included in the GHRN model ( Methods ) [91–92] . Interestingly , we did not find any network that could recover the expected attractors , indicating that it is not possible to obtain the genetic and hormonal activity profiles experimentally described for the cells at the RAM of A . thaliana with the regulatory interactions that we integrated in the GHRN model . The inability of the GHRN model to recover the correct activity configurations described for the cells of the RAM , particularly the QC attractor and the activity profiles of ARF5 and ARF10 in the pro-vascular attractors , suggests that additional constraints concerning the regulation of these ARFs need to be taken into account . ARF10 expression is low in the QC and the ground tissues , while it is relatively higher in the developing xylem and the columella ( Fig 4; S1 Fig ) . On the other hand , ARF5 is expressed in the columella , the QC and the developing xylem , and its expression is lower in the ground tissues ( Fig 4; S1 Fig ) . The expression patterns of ARF5 and ARF10 in the RAM tissues could be underlying a differential regulation of WOX5 expression by auxin in different regions of the RAM . In fact , the expression analyses that showed opposite WOX5 responses to auxin were done using different portions of the root , enriched with different tissues of the RAM [31 , 58] . Since not much is known about the transcriptional regulation of these ARFs , we searched in the literature and found that JKD directly binds to the promoter of ARF10 but the effect of this interaction is unknown [45] . We also noticed that JKD and ARF10 have complementary expression patterns ( Fig 4 ) . Given this evidence and because SHR and JKD can form dimers to regulate gene expression [28 , 62] , we first hypothesized that the SHR-JKD dimer represses ARF10 expression . Then , we noticed that MGP has a complementary expression pattern to that of ARF5 in the adjacent layer to the pro-vascular tissues ( Fig 4 ) . Since SHR can also dimerize with MGP [28 , 62] , as a second hypothesis we proposed that this dimer negatively regulates ARF5 expression . These hypotheses imply that JKD and MGP might provide target specificity to the previously reported repression of ARF10 and ARF5 by SHR [43] . As mentioned previously , the current understanding of WOX5 expression is that two parallel pathways regulate it: the GRAS transcription factors and the auxin pathway . However , it is important to notice that if different protein complexes formed between SHR and other transcription factors regulate the components of the auxin signaling pathway that are involved in WOX5 regulation , as we are proposing here , it will imply that these pathways are not parallel . We integrated these hypotheses and the rest of the regulatory interactions ( Table 1 ) in the GHRN1 model ( Fig 2B ) . The logical rules and Boolean functions of the GHRN1 model can be found in S3 Appendix . We included the two hypotheses in the GHRN1 model because an exhaustive analysis of the dynamic possibilities [91 , 92] showed that by including separately the hypotheses it was not possible to recover attractors that correspond to the expected configurations of the cells at the RAM . On the contrary , the GHRN1 model recovered 11 identical fixed-point attractors ( Fig 3 ) independently of the updating regime employed ( Methods ) , indicating that these attractors are robust and emerge as a consequence of the topology and of the regulatory interactions integrated in the model , and not of the updating regime used . We will refer to these 11 fixed-point attractors as the original attractors of the model . We recovered 6 additional cyclic attractors when we solved with the synchronous updating regime; these cyclic attractors result from the coexistence of MIR166 and PHB , and of MGP and WOX5 ( S2 Appendix ) . The fact that the cyclic attractors appear only under the synchronous regime suggests that they are an artifact of this updating scheme , as was later confirmed with the continuous version of the model . The activity configurations of the 11 fixed-point attractors correspond to the expected ones of the cell types of the RAM that we aimed to describe ( Fig 3 ) , including the QC attractor and the correct activity profiles of ARF5 and ARF10 ( Fig 4 ) . The attractors that correspond to the endodermis , peripheral pro-vascular and central pro-vascular tissues were recovered by duplicate with the nodes Auxin and ARF either active or inactive ( Fig 3B ) . These attractors correspond to cells of the PD ( hereafter referred as the PD attractors ) when they were active , or TD ( TD attractors ) when they were inactive . The activity of ARF10 in the PD attractors agrees with what was expected; it is active in the central and peripheral pro-vascular PD attractors and inactive in the endodermis PD and the QC attractors ( Figs 3 and 4 ) . On the other hand , ARF5 is active in the QC , the peripheral and central pro-vascular PD attractors , but not in the Endodermis PD attractor , as expected ( Figs 3 and 4 ) . These ARFs have different roles in the regulation of cell behavior at the RAM , particularly in the regulation of WOX5 . As they are not expressed homogenously , this suggests that the regulatory interactions that underlie their expression patterns regulate how cells will respond to auxin . In the GHRN1 model the proposed regulators of ARF5 and ARF10 are key regulators of cell fate , indicating that the regulation of WOX5 by auxin might be tissue-specific . This is an interesting result because it constitutes a system-level mechanism implying that the regulatory network formed by hormones and transcriptional regulators might link the acquisition of cell identity with the differential capacity to respond to auxin in the RAM . The TD attractors are characterized by the activity of the node AUXIAA indicating the inactivity of the auxin signaling pathway . The model recovered two central pro-vascular TD attractors ( central pro-vascular TD1 and central pro-vascular TD2 ) that match the expected configurations ( Fig 3B ) . Of the attractors representing the TD of the RAM , the CK signaling pathway was found active only in these central pro-vascular TD1/2 attractors , agreeing with this tissue being the main site of CK signaling in the TD of the RAM [4 , 26] . The difference between these two central pro-vascular TD1/2 attractors is that the central pro-vascular TD2 attractor shows activity of CLE40 and no activity of SHR . Regarding the validity of this attractor , a translational reporter has shown that the signaling peptide CLE40 is present in the pro-vascular tissues of the TD [75] , and in silico visualization of root tip expression patterns indicates that SHR expression is dramatically decreased near the TD [42 , 85] . We recovered an additional central pro-vascular TD3 attractor that is similar to the central pro-vascular TD2 attractor but has activity of the nodes of both the CK and the Auxin pathways ( indicated with blue in Fig 3B ) . This activity configuration does not correspond to the activity configuration that has been described for this zone as it is expected to have only CK signaling activity . It is possible that the unexpected activity configuration of the central pro-vascular TD3 attractor could be representing a transitioning state of the central pro-vascular tissues between the PD and the TD of the RAM . In this sense , the activity of CLE40 and the decrease in SHR activity could be important signals preceding the end of cell proliferation in the RAM . Lastly , two attractors can be identified as root cap tissues . These attractors differ in the activity of the nodes Auxin , ARF , ARF5 and ARF10 ( Fig 3 ) . As auxin signaling is known to regulate the differentiation of the root cap through ARF10 [31] , we interpreted these attractors as cell types of the columella initial cells ( Root Cap 1 ) and differentiated root cap cells ( Root Cap 2 ) . Importantly , the model recovers the co-activity of the auxin and CK signaling pathways in the Root Cap 2 attractor , as indeed it is observed in the root cap of A . thaliana [52–54] . Next we tested what happens to the 11 original attractors if we only consider direct but not the indirect regulation in our model in some particular cases ( Fig 2 ) . That is , what happens if we do not consider JKD as a positive regulator of SCR ( as it does not regulate directly SCR , but promotes the nuclear localization of its activator , SHR ) and inversely , what happens if we do not consider SCR as a positive regulator of JKD ( for the same reason ) . We removed individually each of these indirect interactions and evaluated the impact of such removals in the original attractors . We found that in the first case , the model with a modified logical rule for SCR still recovered the 11 original attractors , plus a new attractor to which we could not attribute any biological meaning as no cell in the RAM has been identified with such activity configuration ( it had activity of CK , AUXIAA , SCR , SHR , PHB and MGP ) . Hence , the removed restriction is crucial to restrain the dynamics of the network to converge only to the expected attractors , but not strictly necessary to recover the 11 original attractors . In the second simulation we simplified the logical rule of JKD such that it is only regulated by SHR and PHB , and found that the model recovers 11 attractors , 9 of them with a perfect correspondence to the 11 original attractors . The two attractors that do not match the expected activity configurations were those corresponding to the peripheral pro-vascular tissues of the PD and the TD . In such cases JKD is active , which does not match what is experimentally observed; this gene expression is restricted to the ground tissues and the QC [63] . Moreover , in the peripheral pro-vasculature PD attractor ARF10 is not active while WOX5 is; this also contrasts with what has been documented for these tissues . Therefore , in this second simulation the constraint imposed by SCR is necessary to recover the correct activity pattern of JKD in the model . Finally , if we remove both interactions at the same time , we recover a cumulative effect of leaving out the two restrictions: we recovered 9 of the original attractors , two incorrect peripheral pro-vascular attractors , and an attractor with not known biological meaning . In summary , these three simulations show that the two regulatory interactions tested are fundamental for the correct in silico description of cell fate acquisition in the context of the GHRN1 model . Also , it further validates the formalism used in the GHRN1 Boolean network to integrate diverse experimental information , and this model itself , to describe the overall structure of interactions among the components considered . It also shows that the restrictions considered in the logical functions grounded on experimental data , are fundamental to understand the documented patterns of expression . In summary , this analysis shows that the minimal GHRN1 model that includes two novel hypotheses recovers the gene expression and hormone activity configurations described for different cell types of the RAM and the root cap of A . thaliana . It also strongly suggests that the regulatory effect of auxin over WOX5 activity depends on the ARFs that are present in each cell type of the RAM , and predicts attractors that might correspond to uncharacterized cell types that according to experimental data are found in the central pro-vascular tissues of the TD of the RAM . We performed two analyses to test the robustness of the GHRN1 model [93] . The first test estimates the frequency of recovering the original attractors in perturbed copies of the GHRN1 model; and the second test evaluates if the emergence of the attractors is independent of the formalism used to model the activity of the nodes ( discrete or continuous ) . As a means to validate the GHRN1 model , we simulated the constitutive activation ( GOF ) and inactivation or loss of function ( LOF ) of every node of the model . The attractors recovered by each mutated network were compared with the reported root mutant phenotypes , when data was available . The rest constitute novel predictions in the context of the restrictions considered in the model shown here ( see Model Assumptions ) . The recovered configurations in the mutant simulations were the same for the Boolean and the continuous version of the model ( Fig 6 ) , as could have been predicted from the robustness analyses and the exploration of the continuous version of the GHRN1 model . The attractors recovered in the GOF/LOF simulation can be found in S6 Appendix ( Methods ) . To make the comparison between the simulation results and experimental data , we gathered information from the literature about the expression patterns of the components of the GHRN1 in the corresponding mutant backgrounds . With this information we assessed if the attractors corresponded with what was observed experimentally given the components that we included in the model . A summary of the comparison between the in silico and the root phenotypes can be found in S7 Appendix . Below , we mentioned some of the simulations with particularly interesting results . Some mutant simulations lost attractors corresponding to cell types that conform the radial pattern of the RAM as has indeed been observed in several reports . For example , the GOF of MIR166 and PHB [30 , 64] , and the LOF of JKD [28 , 36] , SCR [37] , SHR [38] , MIR166 [64] and PHB [30 , 64] are among these cases that result in the loss of the endodermis , peripheral or central pro-vascular tissues ( Fig 6 and S7 Appendix ) . Defects in the specification of the QC cells have been described in some mutants , and the QC attractor was lost in the corresponding simulations . These simulations include the GOF of CLE40 [75 , 76] and ARF10 [31] , and the LOF of ARF5 [27] , SCR [27] , SHR [27] , JKD [28] and WOX5 [27] ( Fig 6 and S7 Appendix ) . It is noteworthy to highlight that the simulations of the LOF of SCR and SHR do not have a QC attractor , consistent with their previous notion as necessary regulators for WOX5 expression in the QC [27] . Even though SHR and SCR do not regulate WOX5 directly in the GHRN1 model , they do regulate directly/indirectly the activity of ARF10 and in the absence of any of them , ARF10 will be active in all the PD attractors . Therefore , these simulations indicate that even though SCR and SHR may not regulate WOX5 directly , they are necessary for its activity as experimentally reported in the RAM [27] . Furthermore , the model predicts that the lack of WOX5 expression in these mutants might be accompanied by a broader expression domain of ARF10 in the RAM . Three simulations are particularly interesting because the predicted negative regulatory interaction between SHR-JKD and ARF10 provides an explanation to their results . The first simulation is the GOF of PHB that does not have an attractor corresponding to the QC ( Fig 6A and S7 Appendix ) . It has been demonstrated that PHB represses WOX5 expression during embryonic development [98] and this simulation suggests that this could also be happening post-embryonically . Additionally , the model suggests that the repressive action of PHB over WOX5 is constrained by JKD . In the simulation , a GOF of PHB represses the activity of JKD activity [64] , which leads to an ectopic activation of ARF10 in all the PD attractors , akin to be active in the entire PD of the RAM ( S6 Appendix ) . Under these conditions WOX5 cannot be active due to the presence of ARF10 in all the PD attractors . Similarly , there is no QC attractor in the simulation of the LOF of MIR166 ( Fig 6B ) that is a repressor of PHB . Experimentally , the importance of MIR166 in the QC has not been conclusive [30] , and our model shows that it might be an important constraint to maintain PHB out of these cells as a necessary condition for WOX5 activity . Another interesting simulation is the LOF of JKD that has no QC attractor ( Fig 6B ) and in which ARF10 is expressed in all the PD attractors ( S6 Appendix ) . The jkd mutants have a misspecification of the QC cells [28] , which has been previously explained due to the decrease in SCR expression . Nevertheless , the jkd scr double mutants have a more severe phenotype than the scr single mutant [28] , suggesting that the role of jkd in the QC goes beyond the regulation of SCR expression . The simulation of the LOF of JKD indicates that in addition to JKD being necessary for SCR expression in the QC , it might also be necessary to repress ARF10 . Otherwise , WOX5 cannot be active because of the ectopic activity of ARF10 , as explained above . The role of the GRAS transcription factors and auxin pathways had been described as independent of each other at the RAM , but the GHRN1 model presented here shows that there are multiple regulatory interactions among them ( Fig 2 ) . Interestingly , in the simulation of the GOF of SHR the activity of the nodes representing the components of the auxin signaling pathway recovered the expected configurations of the PD and TD attractors ( S6 Appendix ) . Conversely , in the GOF simulation of Auxin or ARF the activity profiles of the GRAS transcription factors are unaffected ( S6 Appendix ) . These simulations show that , as it has been observed experimentally , these two pathways appear to be parallel even though multiple regulatory interactions exist among them in the GHRN1 . Some simulations could not be compared directly with experimental data because the chemical fields that underlie the RAM organization cannot be included in the Boolean formalism used here . For example , several mutants have quantitative alterations in the size of the RAM domains ( S7 Appendix ) . As the Boolean network model put forward here is a unicellular model , the comparison between the corresponding simulations and the experimental phenotypes is not completely feasible unless assuming that the loss of an attractor is equal to a partial decrease in the size of that domain . But in principle the simulation results are unicellular and discrete ( 1 or 0 ) and in these cases are not comparable with such experimental phenotypes . This is the case for the LOF/GOF simulations of auxin ( Fig 6 ) . Another example is the simulation of the LOF of ARF10 ( Fig 6B ) that predicts that WOX5 will be active in the central and peripheral pro-vascular PD attractors ( S6 Appendix ) . Experimentally , this is not observed as the expression of WOX5 is still confined to its regular position in the arf10 arf16 double mutant [99] . The expression domain of the QC-specific marker QC25 is slightly expanded in this genetic background [100] , but not as much as the simulation suggests . An aspect that is not considered by the Boolean network is the spatial distribution of auxin in the RAM , which is unperturbed in the arf10 arf16 double mutant [100] . The role of the auxin chemical field or uncharacterized redundancy in the auxin signaling pathway involved in repressing WOX5 could be the reason this phenotype is not observed in vivo . Therefore , it is not possible to make a quantitative comparison between some of the simulations and the experimental evidence . But , it remains a possibility to observe the expression of WOX5 in the pro-vascular tissues of the RAM by altering the auxin distribution in the arf10 arf16 double mutant . The GHRN1 model that we proposed is a useful framework that can be used to explore the specific role of particular regulatory interactions of a node under study . Thereby , we simulated a mutant where SHR does not inhibit CK , to evaluate the effect of this particular regulatory interaction in the model . In this simulation , the GHRN1 model still recovered 11 steady-state attractors ( Fig 6B ) . The only difference with the original attractors is that the root cap attractors do not have activity of the CK signaling pathway in this simulation ( S6 Appendix ) , showing that the repression of CK biosynthesis by SHR is necessary to explain the co-activity of auxin and CK signaling in the root cap . As the PD and TD attractors were recovered with the expected activity configurations ( Fig 6B ) , this result is consistent with the repression of CK by SHR not being involved in the regulation of the transition from proliferation to differentiation in the RAM [79] . Therefore , the cross-talk of hormones and transcription factors that we integrated in the GHRN1 model provides a possible explanation not only for the paradoxical effects of auxin responses in the RAM , but also for the emergence of the co-activity of auxin and CK pathways in the root cap . Overall the analysis of mutants showed that the GHRN1 model agrees in most cases with what has been reported experimentally ( S7 Appendix ) , including the simulation of the LOF mutants of SCR , SHR , JKD and the GOF of PHB that have a misspecification of the QC cells ( Fig 6 ) . Moreover , the model elucidated a potential new role of MIR166 in the maintenance of the QC cells , proving that the core system-level module that we uncovered is a valuable theoretical framework that can be used to predict and discern on the regulatory role of a component in the context of the rest of the interactions integrated in the model . Our analysis suggests that WOX5 is not active in any of these simulations because of the broader activity domain of ARF10 . These results provide further support to the hypothesis concerning ARF10 regulation proposed here , and show that the GHRN1 model contains the components and interactions necessary and sufficient to recover attractors that correspond to the genetic and auxin/CK activity profiles that have been described for different cell types in the RAM for WT and mutants of A . thaliana . Moreover , the simulation where SHR does not repress CK is another example where the GHRN1 model proved to be a useful framework to evaluate the role of particular interactions in the regulation of the RAM .
Signaling molecules regulate different aspects of multicellular development . Understanding how cells acquire positional information or interpret a signaling molecule is key for understanding how ordered patterns of development , growth and regeneration emerge in multicellular organisms . In this paper we explored with a dynamic model the interactions among transcriptional regulators and two hormones . This model enabled us to explain , among other things , the paradoxical effects of auxin over a key gene for RAM organization: WOX5 . Our model indicates that auxin readout might depend on which signaling components are present in a cell , which is a result of the differentiation process ( Fig 7 ) . The gene regulatory networks are inherently multi-stable , each state corresponding to a configuration characteristic of a cell type , and this feature is translated into the multiplicity of auxin responses . As long as there is redundancy and functional divergence in the components of a signaling pathway , it is possible that the correspondence between the readout of signaling molecules and cell differentiation that we proposed for the RAM of A . thaliana is a common feature in multicellular development . Such mechanism implies that as cells acquire a cell fate with a particular hormone/regulators configuration , they may also establish a differential capacity to respond to generic signaling molecules . The RAM constitutes a SCN with a generic cellular pattern mostly shared by niches of all multicellular organisms [9 , 10] . The model provided here bears novel hypotheses that can be tested experimentally and it thus contributes a useful theoretical framework to continue integrating additional genetic and chemical components and interactions that underlie cell differentiation and patterning in a SCN . Therefore , the GHRN1 model we proposed in this paper is a significant step forward toward understanding the complex interactions among genetic and hormone signaling pathways in multicellular development and patterning of stem cell niches .
Boolean networks allow the qualitative study of the dynamics of cell fate acquisition considering the concerted action of several main regulators [104–108] . In Boolean networks the nodes are modeled as discrete variables that have one of two possible activity states: 0 ( inactive ) and 1 ( active ) . The system is solved in discrete time steps , such that the state of a node will be influenced by the state of its regulators in a previous time step . Each node has a truth table ( also known as Boolean function [105–108] ) that explicitly states all the activity combinations of the state of its regulators ( input ) , and the state of the node in the next time step ( output ) . These truth tables/Boolean functions can be summarized into equivalent logical functions using the operators AND , OR and NOT and take the general form: xi ( t+1 ) =Fi ( x1 ( t ) , … , xk ( t ) ) ( 1 ) Where xi ( t + 1 ) is the state of a node xi at time t + 1 and x1 ( t ) , … , xk ( t ) are the states of its regulators at time t . For example , for a node x with regulators y and z and with the logical rule x ( t+1 ) = y ( t ) AND z ( t ) , its derived truth table/Boolean function is shown in Table 2 . The system can be solved synchronously if the state of all nodes is updated each time step , or asynchronously if the nodes have different updating schemes . The system can be explored exhaustively from all possible initial conditions ( 2^number of nodes ) by applying the logical functions iteratively until eventually the system reaches an activity configuration that is recovered periodically . Such configurations are known as attractors ( steady states ) and are interpreted as the phenotypes or cell types of the system under study [104] . Attractors can be fixed-point or cyclic depending if they reached one state or if they oscillate between two or more states . We solved the GHRN models using three updating schemes: synchronous , all nodes are updated each time step; random asynchronous , where at each time step a single node is randomly selected to be updated; and weighed asynchronous , where nodes were classified in two groups of fast or slow and updated accordingly . For the weighed asynchronous updating scheme , we classified the nodes as fast or slow updating nodes based on their molecular nature . In particular , we defined the signaling proteins and some elements known to move between cells as fast updating nodes , whereas transcriptional regulators were considered as slow updating nodes . For the GOF and LOF mutant simulations we fixed the state of the mutated node to 1 or 0 , respectively . This is equivalent to substituting all the output positions to 1/0 in the Boolean function of that node . For this analysis we used the random asynchronous updating scheme . For the WT simulations , to link an activity configuration to an expected attractor we expected a 100% similarity between the expected and recovered attractors ( considering that * means the one could be either 1 or 0 ) ; in the case of the mutant analysis , an attractor was associated to an expected cell type as long as the activity of 12 nodes was the same between the expected and recovered attractors ( 0 . 75% similarity ) , but making sure that the transcription factors known to underlie cell fate had the correct activity patterns . The analysis of the Boolean networks was performed with the R package , BoolNet [109] . We used Griffin for the exhaustive exploration of the alternative GHRNs models to prove that no model can be recovered without including the two hypotheses we proposed . Griffin is a software that transforms a set of constrains ( in our case the set of expected attractors and the topology of the network ) into a Boolean sentence . Using symbolic algorithms , Griffin finds assignments of the Boolean variables that make the sentence true . Each assignment contains a set of Boolean functions that recovers the expected attractors . Griffin software was published before , and more information of how it works can be found in [91] . An updated version of Griffin is available upon request . The robustness of the attractors of the Boolean networks was estimated by calculating the frequency of recovering the original attractors in perturbed copies of the network . The perturbed networks were constructed by changing an output value of the truth table/Boolean function from 0 to 1 ( bitflip ) , or vice versa . Notice that the effect of fixing the state of a node to 0 or 1 , as in the simulation of GOF and LOF mutants , is different from a bitflip in a position of the truth table/Boolean function . In the first instance , the activity of a node is fixed during throughout the simulation , whereas in the second case the logical function of the node is altered . In the first robustness analysis , we compared the robustness of the GHRN1 against random networks . The perturbations were performed at random 100 times for the GHRN1 network and for 1 , 000 random networks with the same topological features ( same number of nodes with the same number of input regulators ) . We solved each network from all possible initial conditions , and quantified if the original attractors were recovered in these perturbed networks . A significance threshold was set at 0 . 05 , meaning that the attractor conservation in the GHRN1 network is higher than >95% of the random networks . In the second robustness analysis , we repeated this analysis by systematically altering each position of all truth table/Boolean functions of the GHRN1 model . For the continuous model we approximated the Boolean functions to continuous sigmoidal functions following the protocol reported in [110] . The activity of each node in the continuous model is described by a differential equation of the form: dxidt=−e0 . 5h+e−hwi ( 1−e0 . 5h ) ( 1+e−h ( wi−0 . 5 ) ) −γixi ( 2 ) In this equation the first and second terms describe the production and degradation of the node , respectively . This equation adjusts the dynamic of each node to a sigmoidal function . The parameter h determines the strength of the interactions and controls if the activation curve of a node resembles a step function , a logistic function or a straight line; γ is the degradation rate and w is the continuous form of the logical functions using fuzzy logic . The w’s used in this analysis can be found in S4 Appendix . We analyzed 100 sets of parameters for each ODEs system . In each set the value of the parameters of each node were selected at random from the ranges ( 10–50 ) and ( 0 . 5–1 ) for h and γ , respectively . Each model was solved from 10 , 000 random initial conditions to recover the steady states at which they converged . The sets of parameters used for this analysis can be found in S5 Appendix . For the simulation of mutants , we fixed the value of w to 1 ( GOF ) or 0 ( LOF ) for the perturbed node . The parameters used to analyze the fate of the attractors of the Boolean network in the continuous version and for the mutant analysis were the same for all nodes: h = 50 , and γ = 1 . To analyze the steady states at which the system converges , we considered a node as active ( 1 ) when it had a value of >0 . 9; if its state was <0 . 1 then the node was inactive ( 0 ) . The expression maps of ARF5 , ARF10 and ARF16 in the RAM ( S1B Fig ) integrate information from transcriptional data of specific root tissues from [89] , transcriptional reporters from [87] , and in a few instances translational reporters from [83 , 100] . Boolean logic can qualitatively formalize many sorts of regulatory interactions and dependencies in the form of logical functions , to dynamically study their concerted action . The nodes of the Boolean network models presented here represent hormones , signaling proteins or genes . For hormones , if a node is active ( 1 ) it means that its cellular concentration is high enough to activate its signaling pathway . Otherwise , the node is inactive ( 0 ) . For genes , inactivity means that the gene is not expressed , or could be expressed but not active due to post-translational regulation . Thus , the activity of nodes that represent genes means that they are expressed and active at the protein level . Below , we provide two representative examples of how experimental information at different levels of regulation was formalized into Boolean functions . The first example is SCR . Functionally , SCR expression requires SHR and SCR itself , that form a protein complex that act as transcriptional activators [38 , 43 , 61] . Moreover , JKD and SCR form protein complexes with SHR and promote its nuclear localization [36 , 61] , otherwise it will be located in the cytoplasm . Then , JKD is a necessary constraint for SCR expression ( even though JKD does not regulate SCR expression directly ) given its regulatory role on SHR cellular localization . In the model , cellular compartments are not considered explicitly . Thus , we formalized these documented interactions and dependencies among the nodes in an abstract but valid way , as SCR ( t+1 ) = SCR ( t ) and SHR ( t ) and JKD ( t ) . Hence , we can study the overall structure of the documented regulatory interactions beyond biochemical details . Below , we show the truth table derived from the logical function of SCR ( Table 3 ) , to show how the output value of each row was decided based on experimental information . Another example is ARR1 . It has been reported that SCR represses the expression of ARR1 in the QC and the RAM [34 , 57] . Moreover , ARR1 at the protein level has to be activated by the phosphorylation cascade initiated by CK . Therefore , the logical rule of ARR1 is ARR1 ( t+1 ) = not SCR ( t ) and CK ( t ) . This logical rule considers that one condition for ARR1 to be active is that SCR has to be absent . Additionally , it is necessary to have activity of CK . The truth table of ARR1 is shown below ( Table 4 ) . These two examples ( SCR and ARR1 ) show how the activity of a node depends on various regulatory processes that may act at different levels of molecular regulation , and how this regulation can be integrated in the form of a logical function . This means that for a node to be active , several conditions likely to be acting in different regulatory processes need to be satisfied . In the model we consider only regulatory interactions that modify the activity of a node , and not regulatory interactions that modulate the magnitude of activity of a node . For example , we did not consider the activation of Aux/IAA expression by the ARFs because it does not compromise auxin responses [111] . Moreover , we did not consider the repression of PHB by CK , because it has been proposed not to compromise its activity but to modulate it quantitatively [4] . The logical functions of the nodes AUXIAA and ARF only consider post-translational regulation because the transcriptional regulators that control their expression are unknown for most of them . Thus , we assumed that these nodes have a basal transcription rate . MIR166 moves from its site of synthesis in the adjacent layer to the pro-vascular tissues , where it promotes the degradation of PHB . A mutual degradation between MIR166 and PHB has been suggested to form sharp boundaries of activity [50] . We followed this assumption such that the non-cell autonomous role of microRNA MIR166 was modeled considering that is active either in its site of synthesis ( where SHR and SCR are active ) or if PHB is not present . The same assumption regarding movement was made for SHR; it is active either in its site of synthesis ( SHR ) or where JKD and SCR are present as SHR move between cells and these proteins promote its nuclear retention outside its site of synthesis . For the second instances to be valid , at least one attractor must represent its site of synthesis . ARF10 and ARF16 expression patterns are different at the RAM ( S1A Fig ) suggesting that they are differentially regulated there . However , their expression patterns overlap and they redundantly promote the differentiation of the root cap [89 , 100] . Therefore , we considered them as a single node . For the validation analysis , the ARF10 GOF simulation was compared with a resistant line that overexpresses ARF16 ( Pro35S:mARF16 ) , and in the LOF analysis of ARF10 the comparison was made with the double mutant arf10 arf16 ( S7 Appendix ) .
|
In multicellular development , signaling molecules are essential for the organization of cells into complex differentiated tissues . It is widely acknowledged that tissue or cell context is instructive for the specificity of cell behavior responses , but the underlying system-level mechanisms remain unresolved . The dynamic analysis of multi-stable regulatory network models grounded on experimental information allows the characterization of necessary and sufficient restrictions to recover the steady state gene/hormone configurations that correlate with different cell types or behaviors . Therefore , it is possible to formally understand how the cellular context , that mediates or biases particular regulatory interactions , is established during development . To this end , we proposed a minimal network model that integrates the regulatory cross-talk among the auxin and cytokinin signaling pathways with the main studied transcriptional regulators operating during the establishment and organization of the A . thaliana root apical meristem . We uncovered a regulatory network that represents a system-level mechanism that underlies the acquisition of characteristic activity configurations that correlate with different cell types , and at the same time mediates the readout of a hormone . Our model hence suggests that when a cell acquires a particular cell fate it may also acquire a differential capacity to respond to a particular hormone . This coupling mechanism between cell differentiation and the specificity in the responses to a hormone could be a general system-level mechanism operating in all multicellular eukaryote organisms . The systemic mechanism proposed here could hence contribute at understanding how signaling molecules and gene regulatory networks information processing operate during development . To achieve this understanding , the root meristem proved to be a very useful system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"gene",
"regulation",
"regulatory",
"proteins",
"brassica",
"dna-binding",
"proteins",
"hormones",
"regulator",
"genes",
"plant",
"science",
"model",
"organisms",
"plant",
"hormones",
"membrane",
"receptor",
"signaling",
"hormone",
"receptor",
"signaling",
"transcription",
"factors",
"experimental",
"organism",
"systems",
"gene",
"types",
"plants",
"research",
"and",
"analysis",
"methods",
"arabidopsis",
"thaliana",
"transcriptional",
"control",
"proteins",
"gene",
"expression",
"biochemistry",
"plant",
"biochemistry",
"signal",
"transduction",
"eukaryota",
"plant",
"and",
"algal",
"models",
"cell",
"biology",
"genetics",
"biology",
"and",
"life",
"sciences",
"auxins",
"cell",
"signaling",
"organisms"
] |
2017
|
A dynamic genetic-hormonal regulatory network model explains multiple cellular behaviors of the root apical meristem of Arabidopsis thaliana
|
The regulatory mechanisms governing the cell cycle progression of hematopoietic stem cells ( HSCs ) are well characterized , but those responsible for the return of proliferating HSCs to a quiescent state remain largely unknown . Here , we present evidence that CD81 , a tetraspanin molecule acutely responsive to proliferative stress , is essential for the maintenance of long-term repopulating HSCs . Cd81−/− HSCs showed a marked engraftment defect when transplanted into secondary recipient mice and a significantly delayed return to quiescence when stimulated to proliferate with 5-fluorouracil ( 5FU ) . In addition , we found that CD81 proteins form a polarized patch when HSCs are returning to quiescence . Thus , we propose that the spatial distribution of CD81 during the HSC recovery phase drives proliferative HSC to quiescence , and is important to preserve the self-renewal properties . Here , we show that lack of CD81 leads to loss of HSC self-renewal , and the clustering of CD81 on HSC membrane results in deactivation of Akt , which subsequently leads to nuclear translocation of FoxO1a . Thus , CD81 functions as part of a previously undefined mechanism that prohibits excessive proliferation of HSCs exposed to environmental stress .
Hematopoietic stem cells ( HSCs ) , which represent around 1/104 to 1/105 bone marrow cells , can proliferate to replenish the hematopoietic system after its exposure to environmental stresses such as infection , ablative chemotherapy , or irradiation . The stem cell compartment is maintained by self-renewal , in which HSCs generate daughter cells that retain stem cell function after cell divisions . The delicate balance maintained between differentiation and self-renewal necessitates a constellation of intrinsic and extrinsic regulatory mechanisms that are still not well understood . The vast majority of HSCs possess a dormant phenotype ( G0 phase of the cell cycle ) [1] and slow cycling kinetics ( prolonged G1 phase ) [2] , enforced by regulatory mechanisms that inhibit cell cycle progression from a quiescent stage to active proliferation [3] . In the face of proliferative stimuli , however , these controls are overridden to allow the stem cells to perform their regenerative functions , posing an intriguing question: how do the self-renewing HSCs re-enter a quiescent state ? Although extrinsic signaling from cytokines has been proposed to facilitate HSC quiescence [4]–[6] , the mechanisms that intrinsically dictate cell cycle exit in HSCs remain largely undefined . We previously identified several cohorts of genes that are preferentially expressed in HSCs during proliferative and quiescent states [7] , and are thus candidates for playing a role in regulation of self-renewal . The gene encoding CD81 ( also called Tapa-1 ) , a cell surface transmembrane protein that belongs to the tetraspanin family , emerged from that study as one of the most consistently upregulated molecules in HSCs exposed to proliferative stress [7] . This protein is found in a variety of tissues and has been shown to regulate cell migration , adhesion , and fusion , as well as proliferation and pathogen entry [8] . It is widely expressed in the murine hematopoietic system [9] , where its roles include cell-cell interaction , lymphocyte activation , and leukocyte adhesion [10] . This functional versatility and the upregulated expression pattern of Cd81 in proliferating HSCs led us to hypothesize that this tetraspanin molecule has a specific function in HSC fate determination , particularly during active stem cell regeneration and proliferation . Serial competitive transplantations provide extreme proliferative stress for HSCs , in which the functional integrity of HSCs has to be delicately maintained and tightly controlled such that an imbalance of cell cycle progression ( reviewed in [3] ) or uncontrollable level of reactive oxygen species ( ROS ) [11] compromise the ability of HSCs to regenerate . Akt/Pkb , a serine/threonine kinase , is found to be a key regulator in maintaining HSC integrity . Constitutive activation of Akt in HSCs leads to HSC hyperproliferation and loss of HSC engraftment in primary competitive transplantation ( the first round of transplantation ) [12] , a similar phenotype to that of HSCs lacking Pten , a negative regulator in the Akt pathway [13] . HSCs lacking both Akt1 and Akt2 show an engraftment defect in the third competitive transplantation ( a third round of transplantation using donor-derived HSCs or bone marrow ) [14] . Interestingly , loss of molecules downstream of Akt , such as Atm , FoxOs , and p21 , results in similar phenotypes . Atm−/− HSCs failed to engraft long-term in competitive transplantation assays [15] . HSCs lacking FoxO3a show a significantly decreased engraftment in the secondary competitive transplantation [16] , while Foxo1/3/4−/− HSCs present a much more severe engraftment defect such that they fail to engraft in the primary transplantation [17] . In addition , HSCs lacking p21Cip1/Waf1 , a cyclin-dependent kinase inhibitor downstream of Akt , exhaust at the 4th transplantation [18] . Here we show , by using Cd81−/− HSCs in competitive transplantation assays and monoclonal antibody treatment to induce CD81 clustering on the HSC membrane , that CD81 paces the return of proliferating HSC to a quiescent state . We also demonstrate that CD81 mediates this effect by downregulating the activation state of Akt and subsequently promoting FoxO1a translocation into the nucleus , where it induces cell cycle suppression . Moreover , with the treatment of perifosine , an Akt inhibitor , the engraftment defect of Cd81−/− HSCs is corrected , indicating that CD81 is involved in maintaining HSC function integrity through Akt pathways . In addition , we show here the expression of p19 , a downstream CDK inhibitor of FoxO proteins [19] , is significantly lower during the regeneration of Cd81−/− HSCs . In addition , the expression of oxidative responsive genes is significantly decreased in quiescent Cd81−/− HSC post-regeneration , suggesting Cd81−/− HSCs are more susceptible to reactive oxygen species ( ROS ) , which may contribute to their compromised function .
In our previous study [7] , Cd81 mRNA was sharply upregulated in HSCs ( SPKLS , c-Kit+ Lin− Sca-1+ purified from the Side Population of mouse bone marrow cells [20] ) after treatment with 5-flurouracil ( 5FU ) , a cytotoxic drug that induces HSCs to proliferate . The entire side population ( SP ) compartment expanded markedly after 5FU induction ( Figure S1A ) , exhibiting a heterogeneous surface expression of CD48 , a known marker of HSC differentiation that is absent in unperturbed HSCs [21] , [22] , and CD81 . The presence or absence of CD81 and CD48 defined three subpopulations of the 5FU-stimulated , heterogeneous SP cells ( Figure S1B ) , and the pattern of expression provided an opportunity to further assess the apparent association between CD81 and proliferating HSCs . Interestingly , the CD81+CD48− subpopulation was preferentially distributed toward the lower SP ( Figure S1B ) , associated with the most primitive long-term HSC activity [20] , [21] , consistent with the hypothesis that CD81 plays a functional role in HSC self-renewal . To further test whether CD81 expression was associated with HSC activity after 5FU treatment , we compared the ability of the CD81+ or CD81− fractions of SP cells to reconstitute hematopoiesis , using competitive transplantation assays . At 5 , 13 , and 20 weeks post-transplantation into lethally irradiated mice , the CD81+CD48− donor cells showed significantly greater repopulating activity than the CD81−CD48+ or the CD81+CD48+ fractions ( Figure 1A; the number of CD81−CD48– cells is insignificant ) . Because CD48 expression is associated with differentiation [22] , this suggests that these markers delineate a transition between self-renewal and differentiation , with CD81 expression associated with sustained HSC activity . More interestingly , among the three subpopulations , CD81+CD48− are the only cells presenting a rapid return to quiescence ( Figure 1B ) , suggesting that HSCs tend to return to quiescence once sufficient progeny are generated . Further evidence for a role of CD81 in HSC self-renewal came from studies that monitored the expression of CD81 protein by HSCs ( SPKLS ) over the course of 5FU treatment , using an anti-CD81 monoclonal antibody , EAT2 . The proliferative response of HSCs to a single dose of 5FU ( 150 mg/kg ) has been established: HSCs begin to proliferate late on day 1 post-treatment and reach maximal proliferation on day 6 , returning to quiescence after day 7 [7] . The expression of CD81 correlated closely with the proliferation kinetics of the HSCs ( as defined both SPKLS and CD150+CD48−c-Kit+Sca-1+Lin− ) . In contrast to its low expression by unstimulated HSCs ( 5FU-Day 0 ) , the CD81 protein was detected in abundance on days 2 , 5 , and 8 post-treatment , with a return to background levels by day 11 ( Figure 1C ) . The timing of CD81 expression on proliferating HSCs suggested a role for this molecule in HSC self-renewal , a prediction we sought to test using HSCs purified from CD81 deficient mice ( Cd81−/− ) [23] and wild-type HSCs transplanted into lethally irradiated mice . In the primary competitive transplantation assays ( Figure 2A ) , the engraftment capacity of Cd81−/− HSCs did not differ appreciably from that of the wild-type cells and presented a comparable ability to give rise to blood lineages ( Figure S2A ) . To test whether Cd81−/− HSCs were able to generate HSCs in the transplant recipients ( self-renewal ) we purified HSCs from the primary recipients and examined their function . Despite their ability to differentiate in vitro , as determined by methycellulose assays ( Figure S2B ) , they engrafted secondary transplant recipients only marginally ( Figure 1B ) , indicating a defect in their ability to self-renew . In addition , Cd81−/− and wild-type HSCs showed nearly identical properties in homing assays performed over the first 24 hours of secondary transplantation ( Figure S2C ) , indicating that the hematopoietic repopulation defect shown by the Cd81−/− HSCs is independent of their response to chemokine-guided homing to stem cell niches . It is worth noting that the un-challenged Cd81−/− bone marrow not only presents with comparable stem cell composition and cellularity ( unpublished data ) , but the primary transplantation with whole bone marrow cells also exhibited no defect in engraftment ( unpublished data ) , indicating that the defect found in Cd81−/− HSC is specific to the HSC progeny that have gone through self-renewal . To elucidate the mechanism underlying defective engraftment by Cd81−/− HSCs , we considered that a hyperproliferation phenotype leading to depletion of the stem cell pool is often linked to ineffective secondary or serial transplantations [18] , [24] , [25] . As a molecule highly expressed primarily during the short window of HSC proliferation , we reasoned that CD81 may specifically exert its function only when cells encounter proliferative stress . We therefore challenged primary recipients of transplantation with wild-type or Cd81−/− HSC transplants with one dose of 5FU ( 150 mg/kg ) , and evaluated the proliferative status of the donor-derived HSCs . Under steady-state conditions in the absence of proliferative stress , similar fractions of Cd81−/− and wild-type HSCs were proliferating , determined by bromodeoxyuridine ( BrdU ) labeling assays ( Figure 3A ) . Comparable proportions of Cd81−/− and wild-type HSCs were also proliferating on day 4 after 5FU treatment , suggesting that CD81 does not participate in the early phase of HSC proliferation stimulated by the drug ( Figure 3B ) . Thereafter , the Cd81−/− cells showed only a modest decline in proliferative activation on day 8 after 5FU treatment , while their wild-type equivalents began a relatively rapid return to quiescence ( Figure 3B ) . Interestingly , Cd81−/− HSCs were able to return to a comparable level of quiescence in the later stage of recovery ( 5FU-Day12 ) , indicating that CD81 functions to facilitate the recovery of HSCs in the face of 5FU stimulation or other types of proliferation stress . Therefore , it is evident that the defective engraftment in the secondary transplantation is not an exhaustion of Cd81−/− HSCs through over-proliferation . Rather , it is a loss of functional integrity with signs of uncoordinated cell cycle progression . CD81 is a tetraspanin molecule that is believed to organize membrane domains for signaling molecules and therefore is involved in a variety of signaling pathways downstream of these molecules . We therefore sought to determine if CD81 forms discrete domains in proliferating HSCs . We identified a distinct clustering pattern of the molecule on day 8 after 5FU treatment ( Figure 3C , D ) , which was not observed for other membrane proteins , such as CD29 ( unpublished data ) . CD81 was identified as an anti-proliferation target from monoclonal antibody screenings [26] , [27] . It has been reported that engagement of CD81 by a high concentration of monoclonal antibody interferes with cell proliferation [28] . To test whether clustering of CD81 on the cell membranes facilitates quiescence in HSCs , we used an anti-CD81 monoclonal antibody , EAT2 [29] , to engage signaling downstream of this tetraspanin molecule . Notably , the antibody-treated proliferating HSCs ( 5FU-Day7 ) harbored CD81 protein clustered in patches on the cell membrane , while in the isotype control-treated HSCs , the CD81 proteins were scattered throughout the membrane ( Figure 4A ) . By measuring the area of clustering , we found that the EAT2 treatment promotes tighter clustering of CD81 protein on proliferating ( 5FU-Day7 ) HSC ( Figure 4B ) , a day earlier than CD81 naturally coalesces ( Figure 3C , D ) . Finally , binding of the anti-CD81 antibody triggers an early exit of HSCs from the cell cycle , as a significant fraction of HSCs treated with the EAT2 antibody return to quiescence ( Figure 4C , D ) . Thus , cross-linking CD81 molecules with monoclonal antibodies on proliferating HSCs ( 5FU-Day7 ) recapitulated what we observed on HSCs in later proliferating stages when returning to quiescence ( 5FU-Day8 ) , and the clustering pattern of CD81 on HSCs correlated with a quiescence phenotype . When brought into close proximity , tetraspanin molecules are thought to form microdomains that constrain other resident membrane proteins , ultimately affecting downstream signaling pathways [10] . We therefore investigated the downstream signaling cascade initiated by CD81 clustering in proliferating HSCs , first testing the activation state of ERK ( MapK1 ) , p38 ( MapK14 ) , JNK ( MapK8 ) , and Akt , which were reported to participate in signaling pathways downstream of CD81 after its stimulation by various means [30] , [31] . At 1 h after stimulation with EAT2 antibody , only Akt showed a significant reduction ( 20% ) in its phosphorylation state in response to antibody-induced CD81 clustering ( judged by the ratio of median fluorescence intensity of phospho-Akt in EAT2-treated HSC to that in isotype-control antibody-treated HSCs ) ( Figure 5A ) . More importantly , the expression of CyclinD1 , a cell cycle regulator downstream of Akt , was found to decrease by 30% ( Figure 5A ) . This suggests that EAT2-induced CD81 clustering on HSCs leads to a downregulation of Akt activity , which subsequently represses HSC proliferation via CyclinD1 reduction . The FoxO transcription factors , which are suppressed by Akt during cell proliferation , are essential for HSC self-renewal [32] . Activated Akt phosphorylates FoxO proteins , which are then restricted from entering the nucleus . A return of HSCs to quiescence should thus involve deactivation of Akt and nuclear entrance by FoxOs . To examine whether the Akt deactivation we observed resulted in nuclear localization of any FoxO proteins and suppression of HSC proliferation , we utilized Imagestream flow cytometry [33] to visualize the localization of FoxO1a and FoxO3a and to quantify the number of HSCs with FoxO nuclear localization during FACS analysis . We discovered that FoxO3a was localized in the nucleus regardless of antibody treatment ( Figure 5B ) , while FoxO1a translocated from the cytoplasm to the nucleus after anti-CD81 antibody stimulation ( Figure 5B ) . The nuclear localization of FoxO1a in EAT2-treated HSCs was significantly higher than that in the isotype control-treated HSCs ( Figure 5C ) . In addition , in the experiments that identify CD81 as a marker for regenerating HSCs that are returning to quiescence ( Figure S1A ) , we observed that the CD81-expressing HSCs ( CD81+CD48−Lin−SP cells ) correlate with a lower phospho-Akt level ( Figure 5D ) as well as a lower expression of CyclinD1 protein ( Figure 5E ) . More importantly , Cd81−/− HSCs express a higher level of CyclinD1 protein during HSC regeneration ( Figure 5F ) , indicating that HSCs utilize CD81 to manage their recovery from proliferative stimuli by down-regulating Akt activity , and subsequently the protein expression of CyclinD1 . We next investigated the activation level of Akt in the Cd81−/− HSCs by measuring the phospho-Akt level and found that the Cd81−/− HSCs possess a higher phospho-Akt on 5FU-Day8 when they present a hyper-proliferating phenotype ( 5FU-Day8 ) . The activity of Akt has been shown to be tightly associated with the proliferation state of HSCs such that constitutive activation of Akt leads to their hyper-proliferation [12] while HSCs lacking both Akt1 and Akt2 were more quiescent [14] . However , the consequences of both the gain and loss of Akt function was to perturb the regeneration power of HSCs such that constitutive activation of Akt resulted in HSC exhaustion [12] and Akt1/Akt2 loss resulted in defective hematopoiesis [14] . We therefore reasoned that Akt activity is tightly controlled during HSC self-renewal , and perturbing Akt activity during the recovery phase of HSC after stress may lead to altered HSC activity . Thus , we incubated recovering HSCs with perifosine , an Akt inhibitor , at the stage when Cd81−/− HSCs show higher proliferation and higher expression of CyclinD1 . At 30 min after the perifosine treatment ( 2 µg/ml ) , we found it returns the phospho-Akt to a comparable level with WT cells ( Figure 6A ) . We administered perifosine to recipients during their recovery phase of transplantation ( 50 mg/kg , 7 d post-transplantation ) when HSCs are presumably recovering and returning to homeostasis . Perifosine is an alkylphospholipid that is thought to incorporate into cell membranes , limit the accessibility of membrane signaling domains for Akt , and subsequently block Akt activation via phosphorylation [34] . Ten weeks later , when the transplant recipients were stably engrafted , the regenerated marrow was extracted and transplanted into secondary recipients . This single administration of perifosine rescues engraftment of donor HSC-derived progeny as functionally measured by their ability to contribute to peripheral blood regeneration in secondary transplant recipients ( Figure 6B ) . Moreover , while the regeneration of Cd81−/− HSCs and their progenitors are completely diminished in the untreated group ( Figure 6C and Figure S4A ) , those of perifosine-treated Cd81−/− HSCs give rise to comparable number of progeny ( Figure 6C and Figure S4B ) . Interestingly , the treatment of perifosine that corrects the level of phospho-Akt in Cd81−/− HSCs does not bring their proliferation state to a comparable level with WT HSCs ( unpublished data ) . This indicates that although perifosine may have been limiting Akt activity through blocking Akt from the signaling domains on membrane as we propose CD81 may be acting , the inhibitory kinetics are distinct from each other . Nevertheless , the dramatic rescue of the Cd81−/− phenotype in the secondary recipients suggests that CD81 functions at least in part via limiting Akt activity during HSC self-renewal . The correlation between the loss of a tightly regulated recovery rate and the engraftment defect of Cd81−/− HSCs after proliferation stimuli suggests that CD81 is essential to maintain the function integrity of regenerating HSCs . We thus sought to determine the expression of CDK ( cyclin dependent kinase ) inhibitors and oxidative stress genes in HSCs in homeostasis ( 5FU-Day0 ) and during regeneration ( 5FU-Day8 and 5FU-Day12 ) . While we found that the expression of the CDK4/CDK6 inhibitors , p15 , p16 , and p18 [35] , was not detectable from 250–300 cell equivalents at all time points ( unpublished data ) , the expression of p19Arf , a FoxO-dependent CDK inhibitor , is significantly lower in Cd81−/− HSCs at the early recovery phase ( 5FU-Day8 ) but comparable in quiescent cells ( Figure 7A ) . In addition , the expression of a CyclinE-CDK2 inhibitor , p21Waf/Cip1 [35] , in Cd81−/− HSCs was found lower only during homeostasis ( Figure 7C ) , suggesting an inherently imbalanced cell cycle control . However , the expression of another CyclinE-CDK2 inhibitor , p27Kip1 [35] , was found unaltered or undetermined ( Figure 7D ) . Additionally , the expression of Nr4a2 , a recently identified transcription factor regulating early G1 progression [36] , was also comparable in quiescent HSCs ( albeit undetectable in regenerating HSCs ) ( Figure 7B ) , indicating loss of CD81 only impacts part of the cell cycle regulatory circuitry . The significantly low level of p19Arf ( Figure 7A ) in regenerating Cd81−/− HSCs and the translocation of FoxO1a in response to the CD81 monoclonal antibody modulation ( Figure 5B and C ) suggest altered responses to oxidative stress in the Cd81−/− HSCs . Akt and FoxO proteins have been found to be essential to maintain HSC identity and function through mediating their resistance to oxidative stress [14] , [17] , ; while the expression of p19Arf was found to be upregulated in response to oxidative stress and was blocked in hematopoietic progenitors lacking Atm ( Ataxia Telangiectasia Mutated protein ) [11] . Thus , we sought to measure the expression of genes that respond to oxidative stresses , Atm and p130/Rbl2 . The expression of both genes was found to be low in homeostatic HSCs such that they are not detectable in 250 to 300 cell equivalents of RNA . However , at the later HSC regeneration stage ( 5FU-Day12 ) when cells have returned to quiescence ( Figure 3B ) , the expression of both genes becomes detectable and their expression level in Cd81−/− HSCs was significantly lower ( Figure 7E and F ) . To further investigate whether oxidative stress may contribute to the defective regeneration phenotypes of Cd81−/− HSCs , we purified donor-derived hematopoietic progenitor cells from the regenerating bone marrow ( 5FU-Day8 ) and analyzed the expression level of both Atm and p130/Rbl2 . Atm but not p130/Rbl2 was found to be at a significantly lower level in Cd81−/− hematopoietic progenitors ( Figure 7G ) . Taken together , these findings suggest the oxidative stress may contribute to compromise the function and integrity of Cd81−/− HSCs post-hematopoietic regeneration .
It is well accepted that regulation of cell cycle progression in HSCs is critical to maintain stem cell activity [3]; however , there is only scant understanding of the mechanisms by which proliferating HSCs return to quiescence . The results of our study delineate a novel mechanism in which CD81 , a stress-responsive membrane protein , is upregulated in HSCs exposed to proliferative stimuli and then acts to inhibit their otherwise unrestrained division . The severe self-renewal defect of Cd81−/− HSCs after robust proliferative stress such as transplantation suggests that the role of CD81 in HSC self-renewal is transient and constrained in time . The key feature of this mechanism seems to be the ability of CD81 molecules to form specialized microdomains on the HSC surface . These domains have been identified on other cell types , such as lymphocytes and astrocytes , where they provide scaffolds for signaling molecules and orchestrate the interactions of membrane-associated proteins with effector molecules to initiate signaling cascade that can either induce or inhibit cell proliferation [37] . Interestingly , we found that CD81 forms a distinct patch-like , polarized pattern when HSCs are returning to quiescence . The membrane distribution of CD81 protein can be manipulated to form clusters with high concentrations of monoclonal antibodies that accelerate the return to quiescence of proliferating HSCs . With this manipulation , only Akt , among several kinases tested including JNK , p38 , and ERK , showed a reduction in activation in response to CD81 clustering , suggesting Akt may be closely associated with the CD81 microdomains . Subsequently , we found translocation of FoxO proteins are affected in response to the clustering of CD81 . However , with no known ligands for CD81 , it is unclear how CD81 is brought together to form patches . As CD81 is thought to be able to interact with itself through its extracellular domain [38] , we thus speculate the clustering may be spontaneous when there is an upregulated expression level of CD81 on the cell membrane , such as we observe during HSC regeneration ( Figure 1C ) , which increases the local CD81 density . Additionally the study with Cd81−/− HSCs in 5FU-stimulated regeneration highlights a time point when HSCs start to return to quiescence after the 5FU proliferative stimuli ( 5FU-Day8 ) . At this time , Cd81−/− HSCs showed a hyper-proliferation phenotype with increased Akt activity , elevated expression of CyclinD1 , and decreased RNA expression of p19Arf , indicating that CD81 acts through Akt pathways during the proliferation phase of HSC regeneration . Moreover , at the stage when HSCs return to quiescence ( 5FU-Day12 ) , Cd81−/− HSCs show significantly decreased expression of oxidative response genes , Atm and p130/Rbl2 , suggesting that CD81 preserves the functional integrity of regenerating HSCs partly through impacting the oxidative stress response and possibly the intracellular level of ROS ( reactive oxygen species ) . Taken together , these data indicate that CD81 acts through Akt pathways to pace the cell cycle progression , the oxidative stress response , and therefore to preserve the functional integrity of HSCs . Furthermore , a single in vivo treatment of an alkylphospholipid , perifosine , to the recovering mice ( during the primary transplantation ) rescues the defective engraftment of Cd81−/− HSCs as tested in secondary transplantation . In addition to being an Akt inhibitor , perifosine is found to act through JNK-dependent mechanism and induce caspase-dependent apoptosis [39] . It has been proposed to condition the regiment to treat acute myelogenous leukemia [40] , [41] . However , in our study , we have not found an altered activity of JNK in Cd81−/− HSCs ( unpublished data ) . We thus suspect that the treatment of perifosine replaces the role of CD81 in the recovering HSCs as microdomains that block signal transduction , and effectively modulates Akt activity . Ultimately , to fully understand how CD81 microdomains communicate with the Akt pathway , it will be necessary to identify their interacting proteins in HSCs in different stages of proliferation . More importantly , our finding that perifosine increases HSCs engraftment may aid to improve the human bone marrow transplantation in treating leukemic patients . A variety of extrinsic signals provided by the HSC niche have been reported to critically affect the ability of HSCs to maintain stem cell integrity as well as their proliferation status [42] . However , it is unclear how any of these pathways dominantly govern the response of the HSCs to environmental cues . Our study suggests that when HSCs are under proliferative stress , the CD81 microdomains orchestrate the spatial distribution of signaling receptors on membrane as well as membrane-affiliated intracellular molecules on HSCs , and selectively transduce the extrinsic cues from the bone marrow niches . Future studies of the divergent roles of the CD81 microdomains on long-term repopulating HSCs may afford productive targets for the regulation of stem cell growth .
All mice were housed at the Baylor College of Medicine according to an AAALAC-approved protocol ( Animal Welfare Assurance Number A3823-1 ) . Mice care and treatment were approved by the Institutional Animal Care and Use Committee at the Baylor College of Medicine ( IACUC , protocol number AN-2234 ) . 5FU ( American Pharmaceutical Partners ) was injected intraperitoneally at 150 mg/kg in PBS prior to the assays . In transplantation assays , C57BL/6 mice carrying the CD45 . 1 allele were used as recipients . Cd81−/− mice generated in a 129 background were developed in the Geha lab [23] , and were generously provided by R . Kesterson at the University of Alabama , Birmingham , and backcrossed for at least four generations to C57BL/6 mice carrying the CD45 . 2 allele . HSCs used in this study were purified as previously described [43] , [44] based on the Hoechst33342 efflux phenotype , which defines a characteristic side population ( SP ) , consisting of hematopoietic stem cells . The hoechst33342-stained whole bone marrow cells were subjected to magnetic enrichment for lineage-negative cells ( autoMACS ) . Surface marker staining for Sca-1 , c-Kit , CD81 ( EAT2 ) , CD48 ( BCM1 ) , and lineage cocktail ( CD4 , CD8 , B220 , Gr-1 , Mac-1 , and Ter119 , BD Bioscience ) was performed as previously described [7] . Briefly , 2 ng/ml antibodies were used to stain cells at the concentration of 1×108 cells/ml . Because HSCs are known to upregulate Mac-1 expression during proliferation , the marker was excluded from the lineage cocktail of 5FU-treated bone marrow cells . HSCs were purified from the side population with a Mo-Flo instrument ( Beckman Coulter ) based on the immunophenotype-SP , c-Kit+ , Lin− , and Sca-1+ ( SPKLS ) unless otherwise specified . In the EAT2 antibody-treatment experiment series , HSCs were purified based on the expression of SP , Sca-1+ , and Lin- ( SPSL ) , for the whole bone marrow SPKLS and SPSL populations are highly overlapped . In the homing assays , hematopoietic progenitors are defined as c-Kit+Lin−Sca-1+ ( KLS ) . In the stem cell and progenitor assays , the surface markers used to define each compartment are as follows: LT-HSC ( c-Kit+Sca-1+Lin−Flk2−CD34− ) , ST-HSC ( c-Kit+Sca-1+Lin−Flk2−CD34+ ) , MPP ( c-Kit+Sca-1+Lin−Flk2+CD34+ ) , CLP ( Lin−IL7ra+c-Kit+Sca-1+ ) , CMP ( Lin−IL7ra−c-Kit+Sca-1−CD34+CD16/32− ) , MEP ( Lin−IL7ra−c-Kit+Sca-1−CD34−CD16/32− ) , and GMP ( Lin−IL7ra−c-Kit+Sca-1−CD34+CD16/32+ ) . FACScan and LSRII ( BD Biosciences ) were used for the analysis of engraftment and cell proliferation . Engraftment was determined by the proportion of CD45 . 1 and CD45 . 2 cells in peripheral blood; the multilineage engraftment was based on the detection of B220 ( B cells ) , CD4 and CD8 ( T cells ) , and Gr-1 and Mac-1 ( myeloid lineages ) [9] . In the monoclonal antibody treatment experiment , HSCs were sorted from wild type mice 7 d post-5FU treatment . Cells were then incubated for 30 min with either 20 µg/ml of CD81 antibody ( clone EAT2 , BD Biosciences , Cat No . 559518 ) or of an Armenian Hamster IgG , k isotype control ( BD Biosciences , Cat No . 553970 ) . Cells were then fixed in 4% paraformaldehyde for later analysis . In the perifosine treatment experiment , HSCs were sorted and cultured in StemSpan SFEM medium ( Stem Cell Technologies , Cat No . 09600 ) containing 10 ng/ml mouse SCF ( Invitrogen , Cat No . PMC2111 ) for 30 min , with or without the presence of 2 mg/ml perifosine ( Selleck Chemicals LLC , Cat No . S1037 ) . In the Cd81−/− transplantation assays , given numbers ( 50–300/experimental group ) of donor HSCs ( CD45 . 2 ) were purified , mixed with 2 to 2 . 5×105 freshly isolated whole bone marrow cells ( CD45 . 1 ) as competitors , and transplanted into individual lethally irradiated recipients ( CD45 . 1 ) . In the secondary transplantations , 300 donor-derived ( CD45 . 2 ) HSCs were transplanted with 2×105 CD45 . 1 competitors into lethally irradiated recipients ( CD45 . 1 ) . In the perifosine rescue experiments , 50 mg/kg of perifosine was injected into primary recipients ( CD45 . 1 ) 7 d via intra-peritoneal injection after the first transplantation with 1×106 whole bone marrow cell from either wild-type or Cd81−/− mice while Stempro34 ( Invitrogen , Cat No . 10639-011 ) plain media ( without nutrient supplement ) was injected into the control group . 10 wk after the perifosine injection , 500 donor-derived HSCs ( CD45 . 2+c-Kit+Sca-1+Lin−CD150+CD48− bone marrow cells ) were purified and transplanted into lethally irradiated secondary recipients ( CD45 . 1 ) with 3×105 whole bone marrow cells as competitors ( CD45 . 1 ) . To assess HSC proliferation under homeostasis , we performed BrdU labeling as previously described [45] . Three days before the analysis , mice were intraperitoneally injected with BrdU ( 5 mg/30 g of mouse body weight ) and given BrdU-containing drinking water 1 mg/ml ) for 3 d . Two or three mice per genotype were pooled for each experiment . SPKLS cells were isolated and mixed with 25 , 000 carrier cells ( B220+ or Gr1+ ) for BrdU intracellular staining ( BrdU Flow kit , BD Biosciences ) . In the 5FU-stimulated proliferation experiments , Ki-67 staining was employed to measure cell proliferation because BrdU staining is not compatible with 5FU treatment owing to excessive toxicity . The 5FU-treated HSCs were isolated and mixed with 25 , 000 carrier cells ( B220+ cells ) . Samples were then fixed with Cytoperm/CytoFix solution and permeablized with the Cytoperm solution ( BD Bioscience ) before the Ki-67 intracellular staining with Ki-67 ( BD Biosciences , Cat . No . 612472 ) . The background of each Ki-67 staining experiment varied , so that the positive and negative gates were determined with the isotype control in conjunction with the carrier cells in each sample . Single cell images were acquired by applied precision deconvolution microscopy . In the immunostaining experiments , HSCs were first purified from Mo-Flo ( Beckman Coulter ) and cytospun ( Cytopro , Wescor ) onto glass slides . Cells were fixed with 4% paraformaldehyde and subjected to immunostaining of surface markers . CD81 expression was detected with the monoclonal antibody EAT2 ( BD Biosciences , Cat . No . 559519 ) . For intracellular staining , HSCs were permeabilized by either ethanol or 0 . 1X Perm buffer IV ( BD Biosciences , Cat No . 560746 ) . HSCs were then labeled with either amine reactive Alexa488 carboxylic acid , succinimidyl ester ( Invitrogen , Cat . No . A-20100 ) , or Pacific Blue carboxylic acid , succinimidyl ester ( Invitrogen , Cat . No . P-10163 ) . HSCs were mixed with spleenocytes as carrier cells before staining . The intracellular staining of phospho-Akt ( p-Akt Thr308 , clone C31E5E , Cell Signaling Technology ) , phospho-ERK ( p-ERK Thr202/Tyr204 , clone D13 . 14 . 4E , Cell Signaling Technology ) , phospho-p38 ( p-p38 Thr180/Tyr182 , rabbit polyclonal , Cat . No . 9211 , Cell Signaling Technology ) , phospho-JNK ( p-SAPK/JNK Thr183/Tyr185 , rabbit polyclonal Cat . No . 9251 , Cell Signaling Technology ) , FoxO1a ( rabbit polyclonal , Cat . No . ab39670 , Abcam Inc . ) , and FoxO3a ( clone 75D8 , Cell Signaling Technology ) was quantified with an HRP-conjugated goat anti-rabbit antibody , followed by signaling amplification yielding Alexa647 fluorescence . Intracellular staining was then detected with an LSRII instrument ( BD Bioscience ) . In the experiments that compare the level of p-Akt between wild-type and Cd81−/− HSCs , cells were stained with Alexa647-conjugated antibody ( pAkt Thr308 , clone C31E5E , Cell Signaling Technology ) . The protein expression of CyclinD1 was detected with a FITC-conjugated CyclinD1 antibody ( Clone SP4 , Abcam Inc . ) . Differences in protein expression levels were assessed by comparing ratios of median fluorescence intensities . The localization of FoxOs in single HSCs was determined with ImageStream flow cytometry . The ImageStream is a multispectral imaging flow cytometer that generates high resolution images of cells at a rate of over 100 cells per second [33] . Spectral compensation and background correction was performed , and images were analyzed with the IDEAS image analysis software . To identify and compare HSCs that had been treated with different agents at the same acquisition , we bar-coded the HSCs with either Alexa488 or Pacific Blue carboxylic acid succinimidyl ester . Approximately 50 , 000 images per sample were collected to obtain the largest number of the ∼0 . 5% HSC cells present in each sample . For comparison of treatment group differences , we used the unpaired two-tailed Student t test . All error bars indicate standard errors of the mean ( SEM ) , while p values indicated with asterisks were considered significant at the p = 0 . 05 level or lower . To analyze the nuclear localization of FoxO transcription factors , we employed an established statistic model that calculates degrees of protein nuclear localization with Pearson’s correlation coefficient [33] . We assigned a positive nuclear correlation an arbitrary value 1 , although the positive correlations may range from 0 to 5 . HSC collection at each time point: 3 , 000 to 5 , 000 donor-derived HSCs ( CD45 . 2+SPKLS ) were collected on 5FU-Day0 , while 10 , 000 HSCs were collected on 5FU-Day8 and 5FU-Day12 . Total RNA was isolated from HSCs using RNeasy Mini Kit ( Qiagen , Cat . No . 74104 ) that provides in-column DNaseI treatment during column purification . First strand synthesis was performed with Superscript VILO cDNA synthesis kit that includes SuperscriptIII reverse transcriptase and random hexamers ( Invitrogen , Cat . No . 11754-050 ) . An equivalent of 250 to 330 cells were subjected to each Q-RT-PCR reaction . Q-RT-PCR was performed with pre-validated Taqman probe sets ( Applied Biosystems ) on a 7300 Real-Time PCR system for 55 cycles . A mouse internal GAPD control was included in every reaction for normalization . The threshold cycle was determined with software provided by the manufacturer , and expression was measured for each assay relative to the GAPD internal standard ( ΔCt ) . Assays were performed in triplicate ( technical replicates ) and each experiment was performed in at least two biological replicates . Relative expression between two cell populations was calculated by subtracting the ΔCt values ( ΔΔCt ) . Fold differences were calculated as 2̂ ( ΔΔCt ) when ΔΔCt > 0 or − ( 2̂ ( −ΔΔCt ) ) when ΔΔCt < 0 .
|
Hematopoietic stem cells ( HSCs ) remain dormant in the bone marrow until needed to replenish the hematopoietic system , at which point they are stimulated to proliferate extensively , undergoing both regeneration ( self-renewal ) and differentiation . Self-renewal is key to maintaining an adequate HSC reserve , and return to dormancy after such stimulation is critical , yet still poorly understood . In this study , we report that CD81 , a transmembrane organizing protein , is a novel regulator involved in HSC self-renewal . Transplanting HSCs into mice that are lethally irradiated to remove their native HSCs stimulates the transplanted HSCs to proliferate to replenish the hematopoietic system , allowing us to examine whether and how HSCs return to quiescence . HSCs lacking CD81 take longer to return to quiescence after such stimulation , resulting in reduced stem cell function . Conversely , forced CD81 membrane clustering , using an antibody , promotes early return of proliferating stem cells to quiescence and nuclear localization of FoxO1a , a key protein that mediates the cell cycle arrest . CD81 clustering also constrains Akt activity , which orchestrates multiple pathways such as cell proliferation and responses to reactive oxygen species . Treatment of Cd81-deficient HSCs with an Akt inhibitor , perifosine , which bypasses the requirement for CD81 in this process , rescues the delay defect of Cd81-deficient HSCs . Together , our data demonstrate that CD81 is critical to maintaining the functional integrity of HSCs during regeneration , and it is acting through Akt to influence its downstream pathways that govern cell cycle progression .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"molecular",
"cell",
"biology"
] |
2011
|
CD81 Is Essential for the Re-entry of Hematopoietic Stem Cells to Quiescence following Stress-Induced Proliferation Via Deactivation of the Akt Pathway
|
Plasmids are extrachromosomal DNA elements of microorganisms encoding beneficial genetic information . They were thought to be equally distributed to daughter cells during cell division . Here we use mathematical modeling to investigate the evolutionary stability of plasmid segregation for high-copy plasmids—plasmids that are present in up to several hundred copies per cell—carrying antibiotic resistance genes . Evolutionary stable strategies ( ESS ) are determined by numerical analysis of a plasmid-load structured population model . The theory predicts that the evolutionary stable segregation strategy of a cell depends on the plasmid copy number: For low and medium plasmid load , both daughters receive in average an equal share of plasmids , while in case of high plasmid load , one daughter obtains distinctively and systematically more plasmids . These findings are in good agreement with recent experimental results . We discuss the interpretation and practical consequences .
Plasmids are circular or linear pieces of DNA of different size ( several 1000 to 100 , 000 bp ) encoding beneficial genetic information for their microbial hosts replicated independently from the chromosome ( s ) [4] . In some cases , plasmids are essential for the growth and survival of the microorganisms under certain environmental conditions [5] . The copy number of plasmids per cell is dependent on the nature of the origin of replication ( ori ) and varies between 1 to 2 copies for low copy plasmids to 50 to 800 for high copy plasmids [6] . Often plasmids possess genes for enzymes or transporters mediating resistance to antibiotics [7] , sporulation , or germination [8] . In the present paper , we focus on high-copy plasmids . In contrast to low-copy plasmids that often incorporate a distinct active mechanism of segregation ensuring that during cell division each daughter inherits at least one copy [9–11] , high-copy plasmids are mainly segregated by random diffusion [12–14] . Only a few years ago equal distribution of plasmids between daughter cells during cell division was assumed . Now we know that at least some high-copy plasmids are distributed unequally between their production hosts [15] . The plasmids investigated in those experiments are of particular interest for biotechnological use . Unequal plasmid segregation may lead to the commercially important factor of cost-intensive feeding of non-productive microbial cells . Therefore , experimental and theoretical approaches are required to elucidate the underlying evolutionary and molecular mechanisms as a foundation for their directed improvement . In detail , recent experimental findings indicate that the segregation of high copy number plasmids depends on the copy number within the mother cell [11 , 15] . If the copy number is in a low or in a medium range , both daughters receive in average the same amount of plasmids . If the copy number of the mother is high , one daughter cell receives systematically more plasmids that the other . The consequences of unequal plasmid segregation have been addressed before [16 , 17] , but did not receive much attention; in these papers , evolutionary theory has not been taken into account . The aim of the present paper is exactly to focus on evolutionary forces in order to shed some light on segregation . In particular , we aim to identify evolutionary mechanisms that explain the experimental findings . There are several models for plasmid dynamics introduced in the literature , see e . g . [18–25] . The present model takes up ideas particularly developed in [18] and [24] , where a bacterial population structured by copy number is described . We use , as [24] , a copy-number dependent plasmid reproduction rate , and extend the model ( in a similar spirit as e . g . [26] ) by possibly unequal segregation and bacterial reproduction , both depending on the plasmid copy-number . In the supplementary information ( S1 Text , effect of horizontal plasmid transmission ) we furthermore investigate the effect of horizontal plasmid transfer . Moreover , in the natural environment of bacteria , antibiotics are present only from time to time . Most likely , evolutionary forces act in the setting of a fluctuating environment . An observation in recent years is the importance of fluctuating environment on phenotypic heterogeneity [27–32] . We extend our model by fluctuating environment ( see section “Long-term behavior” below ) . Based on that model , we use ideas of Adaptive Dynamics [33 , 34] to identify evolutionary stable segregation strategies ( ESSS ) . According to Fisher’s fundamental theorem , in the situation described above , the evolutionary stable trait maximizes the average fitness of the population , that is , the average growth rate of the bacterial community [35] . We numerically compute the average population growth rate for a given segregation strategy , and find , again by means of numeric analysis , the ESSS . It turns out that for a wide parameter range , the ESSS resembles qualitatively the observed segregation pattern . In order to understand this finding intuitively , we come up with a simplified model that is close to Chao’s theory for damage segregation [26] .
In order to develop a new mathematical model ( incorporating unequal plasmid segregation ) of a bacterial population carrying a high-copy plasmid , we consider a well-mixed , homogeneous recombinant bacterial population , structured by copy number of the corresponding recombinant plasmid ( find the details of the model below , after the discussion ) . The plasmids we have in mind protect their carrier against certain antibiotics , but result in specific metabolic costs [36] . In the natural environment , local antibiotic concentrations are usually low but functional . Under these conditions protection does not depend on the plasmid copy number—in principle , if one plasmid is present , a cell is protected—nevertheless , the metabolic burden increases with the copy number . We model the system on two different levels: ( 1 ) plasmid reproduction within bacteria , and ( 2 ) bacterial reproduction/division . With respect to the first point , it is well known that plasmid reproduction is tightly controlled [37–40] . The plasmid production rate is well approximated by logistic growth . That is , if we find z plasmids within a cell , the plasmid reproduction rate is given by b z ( 1 - z / z ^ ) . In the main part of the paper , we neglect horizontal plasmid transfer [41 , 42] but show in the SI ( S1 Text , effect of horizontal plasmid transmission ) that our results are stable w . r . t . this mechanism . To come to the second point , we take the effect of the metabolic burden induced by plasmids on the cell cycle into account . We assume that cells containing z plasmids divide at rate β 0 ( 1 - z / z ^ ) ( see section “parameters and results of the sensitivity analysis” below for a discussion of the rationale behind this choice ) . If cells divide , the plasmids are distributed to the daughter cells . Plasmid segregation is modeled by a stochastic process . If a cell containing z plasmids divides , we assume that the copy number inherited by one daughter is described by a binomial distribution with N = z and some probability pz . The other daughter receives the remaining plasmids ( Fig 1 ) . If pz = 1/2 , segregation is equal in both daughters and both receive in average the same amount of plasmids . If pz distinctively differs from 1/2 , one daughter systematically receives more plasmids . As pz depends on the number of plasmids z in the mother cell , the model allows for a plasmid-load dependent segregation characteristic . The asymptotic copy number distribution is shaped by two counter-acting mechanisms . The plasmid replication within cells ( assumed to follow a logistic dynamics with a carrying capacity/maximal copy number z ^ ) increases the plasmid load , while cell division decreases the copy number by distributing the plasmids on two daughter cells ( Fig 1 ) . According to Fisher’s fundamental theorem about selection , evolutionary forces maximize the average fitness of the population [35] . In the present simple setting , the average fitness of the population is given by the long-term population growth . A segregation characteristic maximizing this long-term population growth forms an evolutionary stable strategy ( ESS ) [33 , 34] . In mathematical terms , the long-term average growth rate is defined as the Lyapunov exponent of the model [43] . In a linear model with constant coefficients , the Lyapunov exponent agrees with the dominant eigenvalue [44] . Without horizontal plasmid transfer , the model is reducible . In contrast to irreducible models ( which have a unique non-negative exponentially growing solution ) , we have two different non-negative exponentially growing solutions: One describes the population without plasmids , and one the solution of the population bearing plasmids . We compute the Lyapunov coefficient for the population that carries plasmids numerically and used the method of steepest ascent to numerically determine the segregation strategy that maximizes the Lyapunov exponent . In doing so , we solely focus on the plasmid-bearing subpopulation . The plasmid-free subpopulation , however , might have a higher Lyapunov exponent , that is , a higher fitness . In this case , the plasmid-bearing population is outcompeted by the plasmid-free subpopulation , and the plasmid gets lost . This is , in particular , the case if antibiotics are never present in the environment . If antibiotics appear—at least from time to time ( switching environment , see section “Long-term behavior” ) —and superimpose a fitness disadvantage to plasmid-free cells that is high enough , the plasmid carrying cells have an advantage . Though plasmids come with a certain burden ( fitness disadvantage ) , the advantage due to the protection outweighs their disadvantages ( see Theorem 1 below ) . The plasmids will persist in the population , and Adaptive Dynamics predicts that evolutionary forces drive the segregation strategy towards the ESSS . Numerical analysis revealed that the strategy maximizing the average growth rate varies with the copy number ( Figs 2 ( a ) and 3 , left panel ) . For a small or medium plasmid copy number , pz = 1/2 is optimal . This is due to the fact that cells without plasmids will most likely be killed in episodes with antibiotics . pz = 1/2 maximizes the chance that both daughter cells receive at least some plasmids , and in this , the survival chance of both daughters is maximized . However , the optimal segregation characteristic also avoids plasmid copy numbers that seriously decrease the bacterial reproduction due to the metabolic burden . Therefore , in the case of high copy numbers , the cells choose an unequal segregation mechanism . One daughter is pushed back to copy numbers that on the one hand ensure protection , also after several further cell divisions , and on the other hand are small enough not to induce a large metabolic burden . This strategy necessarily produces sister cells with a high copy number and a high metabolic burden . Those sister cells are basically lost for generating population growth . Fig 3 ( left panel ) shows that the expected copy number of one daughter in the ESSS is relatively constant ( if the copy number of the mother exceeds 30 ) . Accordingly , the copy number of the second daughter increases linearly with the mothers’ copy number and becomes rather high . The ESSS optimizes the copy number of one daughter cell only . The resulting asymptotic copy number distribution is bimodal ( Fig 2 ( b ) ) . There is a large , active subpopulation with relatively few plasmids , while a second ( smaller ) subpopulation appears at the carrying capacity of plasmids within a cell . That subpopulation will be inactive due to the high metabolic burden . A third subpopulation is not shown in the figure , as we concentrate in the present section on the plasmid-bearing bacteria only: there also exists a subpopulation without plasmids , optimized for the antibiotic-free environment . This finding is rather stable w . r . t . the choice of parameters ( see below , section “Parameters and results of the sensitivity analysis” ) : Essentially , the plasmid growth rate b needs to be in a similar range or larger than the cell division rate β0 , and the plasmids need to come with a heavy metabolic burden if present in a large number . Other parameters and mechanisms as the plasmid carrying capacity z ^ or horizontal plasmid transfer only play a minor role . In former experiments , recombinant plasmids encoding resistance against the antibiotic tetracycline were further manipulated , such that they cause the production of recombinant green fluorescent protein GFP in the plasmid carrying cell ( find details in [15] ) . It was shown by a quantitative polymerase chain reaction and corresponding fluorescence in situ hybridization that the amount of fluorescence of a cell caused by GFP correlates with the plasmid copy number per cell [15] . Using time-lapse microscopy , the offspring of one single cell has been followed for several generations . In this way , it was possible to obtain information about the plasmid copy number in the mother as well as in both daughter cells [15] . These data , in turn , carry information about the plasmid-dependent segregation probabilities ( Fig 2 ) . In order to extract copy-number-dependent segregation strategies , daughter cells are grouped according to the fluorescence of the mother ( the lowest 10% , 10%-20% , etc . , in relation to the observed maximal fluorescence ) . Then , the relative amounts of fluorescence of the daughters are computed fluorescence of a daughter sum of fluorescence in both daughtes ≈ plasmids in a daughter plasmids in the mother and represented as a histogram . If f1 is the fluorescence for one daughter , and f2 for the other daughter , the histogram is based the fraction of fluorescence contained in both daughters , that is , on the data points f1/ ( f1 + f2 ) and f2/ ( f1 + f2 ) . Since f 1 f 1 + f 2 = 1 - f 2 f 1 + f 2 ⇒ f 1 f 1 + f 2 - 1 2 = 1 2 - f 2 f 1 + f 2 , the histograms are perfectly symmetrical about 0 . 5 . This histogram shows the segregation structure of the corresponding mother cells . We find that these histograms are concentrated at 1/2 for the lowest 40% ( Fig 2 ( c ) and 2 ( d ) ) . This finding corresponds to a segregation probability equal or at least close to pz = 1/2 . Above 40% , the histograms show a bimodal distribution ( Fig 2 ( g ) and 2 ( h ) ) , indicating that pz is distinctively below 1/2 . Another way to compare model and measurements is to address the average number of plasmids in the daughter cells in dependence on the mothers’ plasmid copy number ( Fig 3 , left panel for the result of the model ) . We compare this figure with the data ( Fig 3 , right panel ) , where mothers are ranked according to their fluorescence , a substitute for their plasmid copy number . The fluorescence ( ≈ plasmid copy number ) of both daughters are drawn over the mother’s rank . If the mothers’ rank is small , we find one single , linearly increasing branch formed by both daughters . At rank 100 , suddenly a bifurcation takes place , where the lower branch stays approximately at a constant level , while the upper branch steeply increases . Qualitatively , the two panels in Fig 3 agree very well . The theoretical ESSS describes the experimental outcome appropriately . In order to better understand the mechanism that yields the observed ESSS , we discuss an oversimplified , conceptual model . Hereby we take up ideas about damage inheritance [26] . Let us assume that a “newborn” cell incorporates z0 plasmids . We neglect the effect of plasmid accumulation during the lifetime on the division rate . In our conceptional model , the division rate is simply defined by β 0 ( 1 - z 0 / z ^ ) . The fitness f0 of the cell is identical with this division rate , f 0 = β 0 ( 1 - z 0 / z ^ ) . To make the argument as clear as possible we consider a deterministic model . The time T to the next division is given by the deterministic value T ( z 0 ) = 1 β 0 ( 1 - z 0 / z ^ ) . During this time , plasmids accumulate according to z ′ ( t ) = b z ( t ) ( 1 - z ( t ) / z ^ ) , z ( 0 ) = z0 . The plasmid copy number just before the next cell division amounts to F ( z0 ) = z ( T0 ( z0 ) ) , that is [44] F ( z 0 ) = z 0 z ^ e - ( b / β 0 ) / ( 1 - z 0 / z ^ ) ( z ^ - z 0 ) + z 0 ( see Fig 4 for a graph of F ( z0 ) ) . At time T ( z0 ) our cell divides , and has two progeny cells . In case of equal segregation , both daughters receive the same number of plasmids , and hence the fitness of both daughters is f 1 = β 0 ( 1 - 1 2 z ^ F ( z 0 ) ) . Note that F ( z 0 ) ≤ z ^ , such that the fitness is always positive . Unequal segregation can be expressed by an additional parameter a ∈ ( 0 , 1] , where one daughter recieves ( 1 + a ) F ( z0 ) /2 , and the other ( 1 − a ) F ( z0 ) /2 plasmids . In this case , the fitnesses of the daughters are given by f ± = β 0 ( 1 - 1 2 z ^ ( 1 ± a ) F ( z 0 ) ) . As the fitness is linear in the plasmid load , the average fitness f ¯ 1 = ( f + + f - ) / 2 in the first generation is not changed by asymmetric segregation , f ¯ 1 = f 1 . There seems to be no reason for the advantage of asymmetric segregation . However , plasmids come with a metabolic burden—though the average fitness is unchanged , the quality of the two daughters is different . One of the daughters starts her life with a smaller metabolic burden than her sister . This becomes visible in the second generation: Let z1 = F ( z0 ) , the copy numbers of the four progeny cells are given by 1 2 ( 1 ± a ) F ( ( 1 ± a ) z 1 / 2 ) . That is , the average fitness in the second generation reads f ¯ 2 = β 0 ( 1 - 1 2 z ^ 1 2 [ F ( ( 1 + a ) z 1 / 2 ) + F ( ( 1 - a ) z 1 / 2 ) ] ) . As F ( x ) is concave ( see Fig 4 ) , F ( z1/2 ) > [F ( ( 1 + a ) z1/2 ) + F ( ( 1 − a ) z1/2 ) ]/2 . The fitness in the second generation is larger for asymmetric than for symmetric segregation . Plasmids are dumped in one daughter ( seriously decreasing her fitness ) to allow the other daughter to reproduce efficiently . The argument so far considered plasmids exclusively as a burden , and thus we could use the ideas about damage distribution developed by Chao [26] . However , cells also benefit from plasmids as they protect against antibiotics . The fitness of a cell without plasmids is strongly decreased . If z0 is large , there is no risk that the plasmids are lost in the next few generations , also in case of ( moderate ) asymmetric segregation . This is different if z0 is small . We introduce a factor q ( z ) ∈ [0 , 1] that express the relative fitness reduction by plasmid loss for a cell ( or part of her progeny ) that starts with z plasmids . “Relative” does refer to a hypothetical case where also cells without any plasmids are perfectly protected against antibiotics . Obviously , q ( 0 ) ≈0 , and q ( z ) →1 if z becomes large . In our conceptual model , we define q ( z ) = z/ ( K+ z ) , where K is the copy number z necessary to achieve q ( z ) = 1/2 . Under these circumstances , we find for the average fitness in the first generation ( using the notation introduced above ) f ¯ 1 = 1 2 ( q ( ( 1 + a ) z 1 / 2 ) f + + q ( ( 1 - a ) z 1 / 2 ) f - ) . Note that q ( z ) is increasing , while its derivative is decreasing . That is , q ( ( 1 + a ) z1/2 ) increases slower in a than q ( ( 1 − a ) z1/2 ) decreases . Therefore , the stronger reduction of the larger fitness f− cannot be compensated by the weaker reduction of the smaller fitness f+ . It is not a good idea to push one cell towards plasmid loss , though this cell would have ( without the disadvantage at total plasmid loss ) the better fitness . More formally , a straightforward computation based on the monotonicity properties of q ( z ) and q′ ( z ) shows that the derivative of f ¯ 1 with respect to a is negative for a > 0 ( see S1 Text , monotonicity of the fitness in a ) , and hence the fitness is maximized in the symmetric case ( a = 0 ) . The beneficial effects of plasmids force the cell to use symmetric segregation if the copy number is small . In that discussion , we only considered the average fitness of one or two generations . The argument is only complete if we follow the average fitness over many generations . This task is accomplished in the full model , with results in line with our conceptual approach .
The present study concentrated on the question of how evolutionary forces influence plasmid segregation strategies for high copy plasmids . Therefore , a model for a bacterial population , carrying a high copy plasmid that may segregate unequal has been developed , and Adaptive Dynamics has been used to determine the ESSS . The plasmid we have in mind carries genes encoding for an antibiotic resistance . It turns out that in case of few plasmids , each daughter receives in average an equal share of plasmids ( equal segregation ) . For larger copy numbers , one daughter receives systematically more; the model predicts a distinctively unequal segregation strategy ( up to 20% plasmids for one , and 80% plasmids for the other daughter ) . This finding is rather stable w . r . t . variation of parameters , the augmentation of the model by horizontal plasmid transfer ( S1 Text , effect of horizontal plasmid transmission ) , or switching environment ( section “Long-term behavior” ) . The ESSS determined by numerical analysis is in line with earlier experimental observations [11 , 15] . We can understand the ESSS intuitively . In the case of few plasmids , the probability that each of the daughters receives at least one plasmids is maximized . For low-copy plasmids , there is a biochemical mechanism controlling the plasmid segregation [9–11] . In the present case of high copy plasmids , there is no such control [12–14] , such that the ESSS is the best the bacteria can do to prevent that a daughter does not inherit any plasmid and is unprotected . If , however , the copy number is high , plasmids become a burden and the bacterial reproduction rate is decreased [18] . In this situation , it is better to dump many plasmids into one daughter . Consequently , that daughter grows only slowly or even stops to divide , while the other daughter is in the preferred range of plasmid copy numbers: small enough to not be a metabolic burden , and high enough such that both of her daughter cells receive plasmids . And indeed , the numerical results reveal that the average copy number of one daughter is kept fairly constant , independently on the mothers’ copy number , while consequently the average copy number of the second daughter increases; also in the experimental data [15] , we could find back this structure ( Fig 3 , right panel ) . The molecular mechanisms for the realization of the ESSS surely represent a major metabolic burden . Most likely , the energetic costs for the DNA synthesis accompanying the accumulation of plasmids are high . Moreover , a competition between chromosomal and plasmid replication might occur . This might explain , that cells full with plasmids simply stop to divide , their fitness tends to zero . In contrast , we expect that the fitness costs for the formation of the molecular segregation apparatus itself will only play a minor role . It is interesting that our finding is in line with experimental results for waste proteins in cells [45] . Plasmids in high numbers represent mainly a burden for the cell , resembling cell damage . And indeed , the theory of cell damage suggest that unequal segregation increases the average fitness of the population [26 , 46] . Bacteria accumulate proteins that are not recycled but are waste . To prohibit poisoning by these old molecules , bacteria tend to dump them into one daughter . The explanation is similar to the explanation we developed to understand plasmid segregation: one daughter is sacrificed in order to allow the other daughter to reproduce at the maximal rate . We may interpret the ESSS as a special form of division of work . Though we do not find clearly separated subpopulations , we may nevertheless identify three different phenotypes: ( a ) one phenotype without plasmids , specialized to the antibiotics-free environment ( b ) one phenotype with few plasmids , specialized for the environment with antibiotics , and ( c ) the phenotype with many plasmids , serving as a rubbish-heap , and perhaps also as a protein source for the remaining population ( once these cells dissolve ) . In this point of view , the underlying evolutionary mechanism shaping the plasmid segregation strategy is the same as that leading to phenotypic heterogeneity as persister cells [30] , sporulation [47] and competence [48] .
We have a population of cells with a heterogeneous plasmid copy number distribution . There are advantageous and disadvantageous aspects of the plasmids for a cell . The advantage is the protection against antibiotics , while the disadvantage is decreased growth rate due to the metabolic burden . An overview of symbols and parameter values is given in Table 1 . The rationale for the choice of the parameters is explained below . We model two different levels: The linear system is not irreducible [44] . We have one exponentially growing solution without plasmids ( y = 0 ) , and one exponentially growing solution with plasmid-bearing cells ( y > 0 ) . Let us first consider the plasmid-free solution ( y = 0 ) . We may introduce the average growth rate in the switching environment λ 0 ( t ) = 1 t ∫ 0 t ( β ( 0 , α ( τ ) ) - μ ( 0 , α ( τ ) ) ) d τ and write this solution as x 0 ( t ) = x 0 e t λ 0 ( t ) , y ( t ) = 0 . ( 7 ) Now we turn to the second solution ( y > 0 ) . If plasmids are present , the linear ODE y′ = Ay ( which is irreducible ) tends to the dominating solution of that ODE . The spectral bound λ1 ( the eigenvalue with the largest real part ) of A is real , and has a positive eigenvector y ^ , such that A y ^ = λ 1 y ^ . As A depends on the segregation strategy ( p z ) z = 1 , … , z ^ , also this eigenvalue is a function of pz , λ1 = λ1 ( pz ) . In the long run , the solution for y ( t ) reads [44] y ( t ) = c e λ 1 t y ^ ( 8 ) for some positive constant c . Let us assume that y ( 0 ) = y ^ . The variation-of-constant formula yields x ( t ) = x 0 e t λ 0 ( t ) + ∫ 0 t e ∫ s t ( β ( 0 , α ( τ ) ) - μ ( 0 , α ( τ ) ) ) d τ B T y ^ e λ 1 s d s , y ( t ) = c e λ 1 t y ^ . ( 9 ) As an immediate consequence , we have the following theorem . Theorem 1: Let N ( t ) = x 0 ( t ) + ∑ z = 1 z ^ y z ( t ) . Assume that the limit lim t → ∞ λ 0 ( t ) = λ ^ 0 exists and λ ^ 0 > λ 1 , then ∑ z = 1 z ^ y z / N ( t ) → 0 and the plasmid is lost by the population . If λ ^ 0 < λ 1 , then y ( t ) / N ( t ) → c y ^ for some c > 0 , and we find a stable plasmid bearing subpopulation . In order to define the ESSS , we consider two competing subpopulations y and y ^ with different plasmid segregation characteristics pz resp . p ^ z . If λ 1 ( p z ) > λ 1 ( p ^ z ) , then y will grow faster then y ^ and out-compete y ^ . The evolutionary stable segregation strategy maximizes λ1 ( z ) . Definition 2: The plasmid segregation strategy p z * ∈ [ 0 , 0 . 5 ] z ^ that maximizes λ1 ( pz ) is called the ESSS . Note that the ESSS p z * is only sensible if the plasmid is not lost by the population , that is , if λ ^ 0 < λ 1 ( p z * ) . Under this condition , the population with plasmid segregation strategy p z * cannot be invaded by another segregation strategy . Adaptive Dynamics [34] predict that evolutionary forces drive the segregation strategy towards p z * . It is straightforward to extend this approach and to investigate a fluctuating environment ( see section “Long-term behavior” ) . It is not possible to analytically compute the ESSS . We use numerical analysis to find p z * by means of the steepest ascent method [51] . Let ∇ denote the gradient w . r . t . p z = ( p 1 , … , p z ^ ) . We introduce the artificial time s ( note that s has nothing in common with chronological time t , but basically measures the time we already spend using the optimization method ) , and consider pz = pz ( s ) , the segregation strategy as a function of this artificial time s . In order to maximize λ1 ( pz ) , we solve the ODE d d s p z ( s ) = ∇ λ 1 ( p z ( s ) ) . Assume that this ODE converges to p z * . Then , p z * is a stationary state of the ODE , and ∇ λ 1 ( p z * ) = 0 . That is , we obtain a candidate for a ( local ) maximum , minimum , or saddle point . We ensure that we have a local maximum by varying pz locally around p z * and inspecting λ1 ( pz ) . Next , we need to check if we have not only a local but a global maximum . This is , strictly spoken , impossible . In order to ensure that we have at least most likely a global maximum , we use different initial conditions and check that we always end up in the same maximum p z * . In order to determine the ESSS , we only need to consider the subpopulation y . The dynamics of this subpopulation is solely influenced by β 0 ( 1 - z / z ^ ) , and b ( 1 - z / z ^ ) , that is , by the parameters β0 , b , and z ^ . Since rescaling time does not change the dynamics , we may use without restriction β0 = 1/h . It turns out that the ESSS assumes the form observed in data ( Fig 3 ) if b is at least in the range of β0 or larger . We choose b = 1 . 2/h . For this parameter , the ESSS closely resembles the observed structure . Analysis of the experiment in [15] yields β0 ≈ b ≈ 1/h ( see [52] , Fig . 6 ) . Note that evolutionary forces most likely shaped the plasmid segregation strategy for the wild-type plasmid , while we use here a biotechnological tailored variant , that e . g . produces GFP . As the GFP production will influence the plasmid reproduction , it is rather possible ( but not proven ) that the wild-type form of the plasmid grows slightly faster than the variant used here . Sensitivity analysis ( S1 Text , sensitivity analysis ) indicates that the results are stable w . r . t . parameter variation in a reasonable range . The carrying capacity of the plasmids z ^ has almost no influence ( S1 Text , variation of the carrying capacity of plasmids ) . Neither the horizontal plasmid transfer ( S1 Text , effect of horizontal plasmid transmission ) nor a fluctuating environment does change the ESSS . Two main ingredients are necessary: First , the plasmids need to come with a reasonable metabolic burden . If the metabolic burden is only decreased by 20% , the segregation strategy of the ESSS is always equal ( S1 Text , scaling the metabolic burden ) . This observation is intuitive , as the burden forces unequal segregation to be favorable ( see also section “conceptual model” above ) . The second ingredient is the growth rate of plasmids b , that should be at least in the same range or faster than the ( maximal ) division rate of cells β0 ( S1 Text , variation of the reproduction rate of plasmids ) . We can also understand this observation intuitively: only if cells start to accumulate at the carrying capacity z ^ , the segregation strategy is adapted to prevent that all daughters eventually will be filled up with plasmids . Recalling Fig 1 , we find that division of cells counteracts plasmid aggregation . If cell division happens at a higher rate than plasmid division ( β0 > b ) , the cells do not tend to accumulate plasmids , and hence an equal segregation is the ESSS .
|
In the last years , it becomes more and more clear that heterogeneity in isogenic bacterial populations is rather the rule than the exception . This observation is interesting as it reveals the complex social life of bacteria , and also because of tremendous practical implications in medicine , biotechnology , and ecology . The central questions in this field are the identification of the underlying proximate causes ( molecular mechanisms ) on the one hand and on the other hand the identification of ultimate causes ( evolutionary forces ) that shape the social life of bacteria . We focus on plasmid dynamics , in particular on plasmid segregation . Recent experiments showed that plasmid segregation depends on the plasmid load . We identify possible evolutionary factors that shaped this process . It turns out that the ambivalence in the effect of plasmids—advantageous if present in low copy numbers , a metabolic burden if present in high copy numbers—is able to explain the experimental observations . The experimental findings can be interpreted as a variant of the principle of division of labor , as it is well known from e . g . persister cells or sporulation . Our model extends the theory of unequal segregation of damage to the ambivalent role of plasmids . Similarly , it is known that certain gene regulatory proteins are acting in a dose-dependent manner . Due to differences in their cellular concentrations and in their affinities to various target promoters , differential gene expression patterns are achieved . Consequently , tight concentration control is observed [1] . Another example is the strict copy-dependent utilization of autolysins during cell division . These enzymes carefully open the bacterial cell wall to allow for its extension [2] . Overproduction of these enzymes leads to cell lysis [3] . These molecules are in principle also candidates for a segregation characteristic similar to that of high copy plasmids described here .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] |
[
"antimicrobials",
"cell",
"physiology",
"medicine",
"and",
"health",
"sciences",
"evolutionary",
"biology",
"cell",
"cycle",
"and",
"cell",
"division",
"plasmids",
"cell",
"processes",
"drugs",
"microbiology",
"applied",
"mathematics",
"cell",
"metabolism",
"plasmid",
"construction",
"simulation",
"and",
"modeling",
"algorithms",
"evolutionary",
"computation",
"antibiotic",
"resistance",
"mathematics",
"antibiotics",
"genetic",
"elements",
"forms",
"of",
"dna",
"dna",
"construction",
"dna",
"pharmacology",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"antimicrobial",
"resistance",
"molecular",
"biology",
"evolutionary",
"genetics",
"evolutionary",
"algorithms",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"computational",
"techniques",
"genetics",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"mobile",
"genetic",
"elements"
] |
2019
|
Evolutionary model for the unequal segregation of high copy plasmids
|
Though evidence is mounting that a major function of sleep is to maintain brain plasticity and consolidate memory , little is known about the molecular pathways by which learning and sleep processes intercept . Anaplastic lymphoma kinase ( Alk ) , the gene encoding a tyrosine receptor kinase whose inadvertent activation is the cause of many cancers , is implicated in synapse formation and cognitive functions . In particular , Alk genetically interacts with Neurofibromatosis 1 ( Nf1 ) to regulate growth and associative learning in flies . We show that Alk mutants have increased sleep . Using a targeted RNAi screen we localized the negative effects of Alk on sleep to the mushroom body , a structure important for both sleep and memory . We also report that mutations in Nf1 produce a sexually dimorphic short sleep phenotype , and suppress the long sleep phenotype of Alk . Thus Alk and Nf1 interact in both learning and sleep regulation , highlighting a common pathway in these two processes .
Sleep behavior is conserved from worms and insects to fish and mammals [1] . Why animals spend a large amount of time seemingly doing nothing , passing on opportunities to forage , hunt or mate , and remaining vulnerable to dangers , is still a mystery . It is hypothesized that a major function of sleep is to maintain brain function , in particular , to ensure synaptic homeostasis of neurons and to consolidate memory [2 , 3] . Levels of synaptic proteins are associated with sleep/wake states in both flies and mammals [4 , 5] , and sleep deprivation impairs memory formation in a variety of species , including humans [6] , mice [7] , and Drosophila [8 , 9] . In addition , some molecules that regulate learning and memory turn out to be required for sleep/wake regulation [10] . However , only a handful of such molecules have been identified and for most it is not known if effects on the two processes are mechanistically linked . Anaplastic lymphoma kinase ( Alk ) , which encodes a member of the ALK/LTK ( leucocyte tyrosine kinase ) ) family of receptor tyrosine kinases ( RTKs ) , is proposed to play important roles in the nervous system based on its extensive expression in the CNS of both mammals and flies [11–14] . Its in vivo functions are mostly studied in the context of Drosophila development . Together with its secreted ligand Jelly Belly ( Jeb ) , ALK is essential for 1 ) gut muscle differentiation [15 , 16]; 2 ) retinal axon targeting in the optic lobe [17]; 3 ) growth and organ size regulation [14 , 18]; and 4 ) modulation of neuromuscular transmission and synapse growth at larval neuromuscular junctions ( NMJ ) [19] . However , there is also evidence for a role of Alk in brain plasticity in adult contexts . Adult-specific activation of Alk causes deficits in associative olfactory learning in Drosophila; concordantly , reducing neuronal Alk activity in adult flies enhances olfactory learning [14] . Similarly in mice , loss of ALK function enhances spatial memory and novel object recognition , and reduces anxiety and depression [20 , 21] . Effects of Alk on learning , at least in flies , are most likely mediated by Ras/ERK signaling . This is supported by an interaction with Nf1 , conserved ortholog of the human Neurofibromatosis type 1 ( NF1 ) disease gene , which encodes a GTPase-activating protein ( GAP ) that negatively regulates Ras/ERK signaling . Specifically , the learning deficit in Nf1 flies is rescued by down regulation of Alk [14] . Based on the role of Alk in neuronal plasticity and learning , we hypothesized that Alk may be involved in sleep regulation . We found that inactivation of ALK causes increased sleep . We probed for the neuronal circuit that underlies Alk’s involvement in sleep and found that inhibiting Alk in the mushroom body induces more sleep , suggesting Alk as a mechanistic link between learning and sleep . In addition , Alk interacts with Nf1 in the regulation of sleep just as it does in the context of learning . Nf1 also have circadian phenotypes [22] , but Alk is not required for circadian rhythms , nor does it interact with Nf1 in the circadian regulation of rest/activity rhythms . Thus , interactions between the two molecules are specific for sleep and learning .
Because ALK plays a crucial role in gut development , the null allele Alk1 is homozygous lethal at the early larval stage [23] . However , a temperature-sensitive allele , Alkts , fully complements Alk1 at 18°C , but fails to complement developmental Alk1 lethality at 29°C [19] . We were therefore able to raise Alkts/1 trans-heterozygous or Alkts homozygous flies to the adult stage at 18°C and prevent developmental phenotypes such as changes in body length ( S1 Fig ) . Alk mutants raised in this manner are presumably also spared other developmental defects seen with manipulations of ALK activity , such as altered NMJ structure and function [19] and mis-targeting of retinal neurons in the optic lobes [17] . We assayed sleep using the traditional single infrared beam interruption device ( Trikinetics , MA ) in adult Alkts/1 and Alkts female flies at the permissive temperature of 18°C and at the restrictive temperature 29°C . Because genetic background has a profound impact on sleep [24] , Alk mutants were backcrossed for five generations into a white ( w ) isogenic background , iso31 , a line generated specifically for use in behavioral experiments [25] . At 18°C , control iso31 and Alk flies had very similar sleep patterns . However , acute inhibition of Alk by switching the environmental temperature to 29°C drastically increased sleep in Alk flies as compared to iso31 . In iso31 flies , the shift to 29°C initially increased daytime sleep and decreased nighttime sleep , consistent with previous reports of increased siesta at higher temperatures [26]; overall sleep increased on the third day , but there was no net change in sleep over the three day period . Inhibition of Alk increased both day and night sleep . This temperature-sensitive sleep phenotype was reversed by lowering the temperature back to 18°C ( Fig 1A ) . Quantification shows that Alkts/1 flies slept ~51 . 06±10 . 09% more than the control iso31 flies during the high temperature shift ( 6 independent experiments , n = 87 for iso31 and n = 80 for Alkts/1 ) . Similarly , Alkts homozygous flies and flies that harbor an Alkts allele over a deficiency uncovering the Alk gene slept more than control flies at 29°C . There was no difference in total sleep amount between Alkts/1 , Alkts , or Alkts/Def flies , suggesting that the restrictive temperature completely abolished ALKts protein function ( Fig 1B ) . Importantly , we were able to rescue the sleep phenotype of Alk mutants by re-expressing Alk transgenically ( discussed below ) . Though sleep profiles and sleep metrics produced by the standard single infrared beam sleep monitors are reliable and have been published widely , they sometimes overestimate sleep as they miss fly movement away from the infrared beam; the degree of error varies from one genotype to another [27 , 28] . We therefore assayed sleep in a new multi-beam sleep monitor ( Trikinetics , MA ) , which provides an order-of-magnitude higher spatial resolution compared to the traditional single beam monitors . Measurements by multi-beam monitors validated our results from the single beam method , showing that Alkts/1 females had increased daytime and nighttime sleep at 29°C as compared to iso31 control flies ( Fig 1C and S2 Fig ) . However the long sleep phenotype was less pronounced when assayed with multi-beam monitors , with Alkts/1 female flies showing a 32 . 3±3 . 3% increase over iso31 ( 3 independent experiments , 47 iso31 flies and 48 Alkts/1flies ) . We also assayed sleep in Alk male flies with both monitor systems , and found that it was similarly increased ( S3 Fig ) . Henceforth we focused our analysis on female flies , typically the gender studied in Drosophila sleep experiments . To exclude the possibility that the apparent increase in sleep was due to locomotion impairment , we measured waking activity , calculated as the average number of beam crossings per waking minute . We found that it did not account for the long sleep phenotype as it was reduced in both long-sleeping Alkts/1 flies as well as normal-sleep Alk1/+ controls ( Fig 1D ) . We also assayed the mobility of Alk flies in an independent negative geotaxis assay that measures the ability of flies to climb vertically when startled [29] . The response of Alkts/1 flies was indistinguishable from that of iso31 flies ( Fig 1E ) , suggesting that Alkts/1 flies have no gross motor defects . To confirm that the increased inactivity in Alk mutants is genuine sleep , as opposed to quiet wake , we assayed their arousability to a mechanical stimulus at different times of day ( Fig 2 ) . Similar percentages of previously sleeping iso31 and Alkts flies were aroused by the stimulus at ZT6 and ZT20 , when most flies were sleeping . At ZT22 , more iso31 flies were aroused than Alkts flies , probably because iso31 flies were transitioning from sleep to wake at this time point . Notably , a higher percentage of the previously awake flies exhibited activity after the stimulus than sleeping flies at all three time points , suggesting that arousal threshold was indeed higher in sleeping flies . We conclude that Alk mutants are bona fide long sleepers , suggesting that ALK functions to inhibit sleep or promote wake . Following a period of sleep deprivation , flies , like other animals , show a homeostatic response in the form of increased sleep [30] . We wondered whether this homeostatic regulation was disrupted in Alk mutants . We found that after 6 hours of sleep deprivation by mechanical stimulation , both iso31 and Alkts flies increased sleep the following morning ( Fig 3A ) . However , sleep-deprived Alkts flies fell asleep faster than iso31 controls , suggesting sleep pressure was higher in Alkts flies ( Fig 3A and 3D ) . Similar to the expression pattern of the mouse ALK gene , the Drosophila Alk gene is extensively expressed in the developing and adult nervous system [12 , 14 , 23] . Its adult expression includes the mushroom body , the protocerebral bridge , the antennal lobes , the suboesophageal ganglion , the medial bundle and lateral horns [14] . To locate the sleep regulatory function of Alk in the brain , we carried out a brain mini-screen using a series of GAL4 lines to drive UAS-Alk RNA interference ( RNAi ) in brain circuits . Inducing Alk RNAi with a pan-neuronal driver , elav-GAL4 , reduced Alk mRNA level to ~35% of that of control flies and produced longer sleep , suggesting that ALK functions in neurons ( S4 Fig ) . We then screened over 40 GAL4 drivers with diverse neuronal expression patterns ( S1 Table and S5 Fig ) , including those with expression patterns in the known sleep/wake regulating regions [31–39] . 13 GAL4 lines significantly increased sleep relative to controls , when driving Alk RNAi , while c309-Gal4-driven expression of Alk RNAi decreased sleep ( Fig 4A ) . Of the identified sleep-promoting GAL4 lines , most are broadly expressed , overlapping in several regions of the brain , such as the mushroom body , the ellipsoid body , and the pars intercerebralis . However , inhibiting Alk specifically in the pars intercerebralis ( InSITE106 , Kurs58 and Dilp2-GAL4 ) , or the ellipsoid body ( c819 , c232 and c107 ) did not alter total sleep . The amounts of sleep increase as well as the sleep profiles were different between different GAL4 lines ( S6 Fig ) . Most lines showed increased daytime sleep as well as nighttime sleep . However , lines 1471 , c320 and 386Y mainly increased daytime sleep and 7Y mostly increased night-time . 386Y caused a delay in sleep at the beginning of the night while driving an increase at other times . c320 increased sleep immediately after lights-on in the morning . These sleep patterns were consistent across repeated experiments . We found that the long sleep phenotype of Alk mutants could be rescued by restoring functional ALK with targeted drivers that we identified through the RNAi screen . To circumvent the developmental effects of overexpressing Alk , we expressed Alk in adult Alkts mutants only during a period of restrictive temperature at 29° by combining GAL4s with a temperature-sensitive form of GAL80 ( tub-Gal80ts ) [40] . We tested 386Y , 7Y , 121Y , 1471 , 30Y and MJ63 , all of which induce longer sleep when driving Alk RNAi . We found that 386Y and 1471 fully rescued the long sleep phenotype of Alkts at high temperature , while 30y and121y produced a partial rescue ( Fig 4B ) . 7Y and MJ63 , however , did not rescue the long sleep of Alkts , and neither did any of the drivers that yielded no phenotype with Alk RNAi ( S7 Fig ) . We infer that while Alk expression is necessary , it may not be sufficient for sleep regulation in regions of 7Y and MJ63 expression . The rescue results further confirm that the long sleep phenotype is specific to the Alk gene and involves specific brain regions . Prompted by the extensive mushroom body expression of most driver hits in our screen , we focused on the function of ALK in the mushroom bodies ( MB ) . When Alk RNAi was excluded from the MB by combining GAL4s with a mushroom body-specific Gal80 transgene ( MB-Gal80 ) [41] ( Fig 5 ) , it eliminated the sleep increase induced by 386Y and 30Y , suggesting that Alk function is required in mushroom body neurons labeled by these two drivers . In fact , when 30Y was combined with MB-Gal80 , it decreased sleep to below those of controls . c309 , a driver that caused short sleep with Alk RNAi , also has extensive mushroom body expression . Driving Alk RNAi with c309/MB-Gal80 , however , decreased sleep further compared to Alk RNAi driven by c309 . These results indicate that the sleep-increasing effects of Alk deficiency occur mainly in the MBs and with 30Y and c309 they are countered by sleep-inhibiting influences of Alk knockdown outside the mushroom body . We thus tested several drivers that have localized expression in the mushroom body and little expression elsewhere , but did not observe any increase in sleep ( Fig 4 ) . Of these three drivers , 17D innervates the core of α/β lobes; D52H has sexually dimorphic expression with strong expression in the α/β and the main γ lobe in males but faint dorsal γ expression in the females , which is the gender we used for sleep assays; R71G10 preferentially innervates the γ lobe and R76D11 has strong expression in both α/β and γ; c305a , which innervates α′/β′ lobes in addition to cells in other brain regions [42 , 43] . Given that of the positive drivers , H24 , NP1131 and 1471 label Kenyon cells that project exclusively in the γ lobes , we hypothesize ALK functions to inhibit sleep in a subset of γ lobe neurons that are not targeted by R71G10 and R76D11 . Genetic interactions between Alk and Nf1 in growth and learning processes led us to investigate whether Nf1 is also required for sleep regulation . Interestingly , a prevalence of sleep disturbances has recently been reported in NF1 patients [44 , ] . We detected considerable variability in total sleep amount in Nf1 mutant flies across experiments . We tested three Nf1 alleles , Nf1P1 , Nf1P2 , and Nf1c00617 . Nf1P1 and Nf1P2 alleles are both assumed to be null because neither expresses NF1 protein and homozygous flies have similar defects in locomotor activity rhythms , body size and learning [22 , 46 , 47] . Although the average total sleep of male flies harboring any two Nf1 alleles was significantly less than that of control flies , we did not observe a consistent difference between Nf1 mutant and control female flies ( Fig 6 ) . While Nf1P2/c00617 female flies slept less than controls , sleep amounts in Nf1P1/P2 female flies were generally not different from those of control flies . Furthermore , Nf1P1 and Nf1P2 female flies did not consistently show sleep reduction as compared to iso31 controls in separate experiments . Similar discrepancies were found when sleep was assayed with the multi-beam sleep monitors . However , both Nf1 male and female flies exhibited nocturnal hyperactivity ( Fig 6A and 6C ) , which resulted in an increase in daytime sleep and a decrease in nighttime sleep . Nf1 mutants also showed consistent defects in sleep consolidation . Both daytime sleep and nighttime sleep were highly fragmented in Nf1 males , such that the average sleep bout duration was reduced and the number of sleep bouts increased ( S8 Fig ) . While reductions in average bout duration did not reach significance in females , increased numbers of bouts suggest deficits in sleep maintenance and consolidation . Rescuing Nf1 with pan-neuronal expression of a UAS-Nf1 transgene increased sleep of Nf1 mutants and in fact caused a long sleep phenotype compared to wild-type controls . This did not result from ectopic expression of the transgene as expressing the same UAS-Nf1 transgene in wild-type flies had no effect ( Fig 6B ) . Interestingly , sleep increase was seen with Nf1 rescue in both male and female flies , suggesting that effects of Nf1 on sleep are quite complex . We then investigated whether Alk and Nf1 interact to regulate sleep . We chose to test Alk with the Nf1P1/P2 allelic combination because Nf1P1/P2 females have normal amount of sleep and so any sleep suppression in the double mutants would not be confounded by additive effects of short-sleeping Nf1 mutants . To sensitize the assay , we compared sleep in Alkts , Alkts/1 or Nf1P1/P2 single mutants and Alkts;Nf1P1/P2 and Alkts/1;Nf1P1/P2 double mutants at three different temperatures that render different dosages of functional ALK . Interestingly , total amounts of sleep in Alk;Nf1 double mutant flies were less than those of flies deficient for Alk alone , and not different from Nf1 single mutants or iso31 control flies ( Fig 7 ) . We found that regardless of the severity of the Alk alleles , Nf1P1/P2 completely suppressed the long sleep phenotype . As another piece of evidence for genetic interaction , we found that Nf1P1/P2 also suppressed the long sleep phenotype caused by Alk pan-neural RNAi ( S9 Fig ) . These results suggest that Nf1 interacts with Alk in a sleep regulating circuit . Nf1 is part of the circadian output pathway that controls rest: activity rhythms [22] . As Alk was found to interact with Nf1 in sleep regulation , as well as in growth and learning , we asked whether Alk is required also for circadian rhythms and whether Alk and Nf1 interact in circadian pathways . We found that Alkts/1 trans-heterozygotes raised at 18°C maintained locomotor activity rhythms at the restrictive temperature of 29°C in constant darkness ( Fig 8 and Table 1 ) , indicating Alk is not required to maintain circadian activity . Indeed , the FFT values , a measure of rhythm strength , of Alkts/1 and Alkts/Def flies were higher at 29°C than at 18°C , suggesting that loss of Alk may actually improve rhythms rather than disrupt them . However , inhibiting Alk failed to rescue the circadian defects in Nf1 flies: Alkts/1; Nf1P1/P2 double mutant were arrhythmic , just like Nf1 single mutants . To exclude a requirement for Alk in the development of circadian circuits , we also tested Alkts homozygous flies raised at 25°C , at which temperature Alkts flies have moderate lethality [19] . The partial reduction in ALK function throughout development did not cause arrhythmia nor did it suppress arrhythmia in Nf1P1/P2 flies ( Table 1 ) . These results suggest that Alk does not function in the circadian output circuit regulated by Nf1 .
Many downstream signaling pathways have been proposed for ALK , among them Ras/ERK , JAK/STAT , PI3K and PLCγ signaling [11] . ERK activation through another tyrosine receptor kinase Epidermal growth factor receptor ( EGFR ) has been linked to increased sleep [36 , 51] , while here we show that Alk , a positive regulator of ERK , inhibits sleep . We note that ERK is a common signaling pathway targeted by many factors , and may have circuit- specific effects , with different effects on sleep in different brain regions . Indeed , neural populations that mediate effects of ERK on sleep have not been identified . The dose of ALK required for ERK activation might also differ in different circuits . Region-specific effects of Alk are supported by our GAL4 screen , in which down-regulation of Alk in some brain regions even decreased sleep . The overall effect , however , is to increase sleep , evident from the pan-neuronal knockdown . We found that the mushroom body , a site previously implicated in sleep regulation and learning , requires Alk to inhibit sleep . Interestingly , the expression patterns of Alk and Nf1 overlap extensively in the mushroom body [14] , suggesting that they may interact here to regulate both sleep and learning . However , it was previously shown that Alk activation in the mushroom body has no effect on learning [14] . The mushroom body expression in that study was defined with MB247 and c772 , both of which also had no effects on sleep when driving Alk RNAi ( Fig 4 ) . The spatial requirement for Nf1 in the context of learning has been disputed in previous studies with results both for and against a function in the mushroom body [14 , 52] . The discrepancies between these studies could result from: 1 ) varied expression of different drivers within lobes of the mushroom body , with some not even specific to the mushroom body; 2 ) variability in the effectiveness and specificity of MB-Gal80 in combination with different GAL4s . We confirmed that our MB-Gal80 manipulation eliminated all mushroom body expression and preserved most if not all other cells with 30Y , 386Y and c309 . Future work will further define the cell populations in which Alk and Nf1 interact to affect sleep . We observed a substantial sleep decrease in Nf1 male flies compared to control flies . However , sleep phenotypes in Nf1 female flies are inconsistent . It is unlikely that unknown mutations on the X chromosome cause the short-sleeping phenotype because our 7 generation outcrosses into the control iso31 background started with swapping X chromosomes in Nf1P1 and Nf1P2 male flies with those of iso31 flies . In support of a function in sleep regulation , restoring Nf1 expression in neurons of Nf1 mutants reverses the short sleep phenotype to long sleep in both males and females . This does not result from ectopic expression of the transgene as expressing the same UAS-Nf1 transgene in wild-type flies has no effect . We hypothesize that Nf1 promotes sleep in some brain regions and inhibits it in others , and sub-threshold levels of Nf1 , driven by the transgene in the mutant background , tilt the balance towards more sleep . As reported here , Alk also has differential effects on sleep in different brain regions , as does protein kinase A [33] , thus such effects are not unprecedented . We also note severe sleep fragmentation in Nf1 mutants , which suggests that they have trouble maintaining sleep . The sex-specific phenotypes of Nf1 mutants may reflect sexually dimorphic regulation of sleep . A recently published genome-wide association study of sleep in Drosophila reported that an overwhelming majority of single nucleotide polymorphisms ( SNPs ) exhibit some degree of sexual dimorphism: the effects of ~80% SNPs on sleep are not equal in the two sexes [53] . Interestingly , sex was found to be a major determinant of neuronal dysfunction in human NF1 patients and Nf1 knock-out mice , resulting in differential vision loss and learning deficits [54] . The sex-dimorphic sleep phenotype in Nf1 flies provides another model to study sex-dimorphic circuits involving Nf1 . Interestingly , a prevalence of sleep disturbances have recently been reported in NF1 patients [44 , 45] , suggesting that NF1 possibly play a conserved function in sleep regulation . An attractive hypothesis for a function of sleep is that plastic processes during wake lead to a net increase in synaptic strength and sleep is necessary for synaptic renormalization [3] . There is structural evidence in Drosophila to support this synaptic homeostasis hypothesis ( SHY ) : synapse size and number increase during wake and after sleep deprivation , and decrease after sleep [55] . However , little is known about the molecular mechanisms by which waking experience induces changes in plasticity and sleep . FMRP , the protein encoded by the Drosophila homolog of human fragile X mental retardation gene FMR1 , mediates some of the effects of sleep/wake on synapses [55 , 56] . Loss of Fmr1 is associated with synaptic overgrowth and strengthened neurotransmission and long sleep . Overexpressing Fmr1 results in dendritic and axonal underbranching and short sleep . More importantly , overexpression of Fmr1 in specific circuits eliminates the wake-induced increases in synapse number and branching in these circuits . Thus , up-regulation of FMR accomplishes a function normally associated with sleep . We hypothesize that Alk and Nf1 similarly play roles in synaptic homeostasis . They are attractive candidates for bridging sleep and plastic processes , because: 1 ) Alk is expressed extensively in the developing and adult CNS synapses [14 , 57] . In particular , both Alk and Nf1 are strongly expressed in the mushroom body , a major site of plasticity in the fly brain . 2 ) Functionally , postsynaptic hyperactivation of Alk negatively regulates NMJ size and elaboration [19] . In contrast , Nf1 is required presynaptically at the NMJ to suppress synapse branching [58] . 3 ) Alk and Nf1 affect learning in adults and they functionally interact with each other in this process [14] . It is tempting to speculate that in Alk mutants , sleep is increased to prune the excess synaptic growth predicted to occur in these mutants . Such a role for sleep is consistent with the SHY hypothesis . The SHY model would predict that Alk flies have higher sleep need , which is expected to enhance rebound after sleep deprivation . While our data show equivalent quantity of rebound in Alk mutants , we found that they fall asleep faster than control flies the morning after sleep deprivation ( Fig 3 ) , suggesting that they have higher sleep drive . Increased sleep need following deprivation could also be reflected in greater cognitive decline , but this has not yet been tested for Alk mutants . We note that Nf1 mutants have reduced sleep although their NMJ phenotypes also consist of overbranched synapses [58 , 59] . We postulate that their sleep need is not met and thus results in learning deficits . Clearly , more work is needed to test these hypotheses concerning the roles of Alk and Nf1 in sleep , learning , and memory circuits .
The following lines were used previously in the lab [33 , 37 , 60]: Elav-Gal4 , 201Y-GAL4 , c739-GAL4 , 238Y-GAL4 , c309-GAL4 , Kurs58-GAL4 , Pdf-Gal4 , H24-GAL4 , c507-GAL4 , 30Y-GAL4 , 50Y-GAL4 , MJ63-GAL4 , c232-GAL4 , 104Y-GAL4 , 17D-GAL4 , Dilp2-Gal4 , Tdc2-Gal4 , TH-Gal4 , Mai301-GAL4 , c767-GAL4 , 1471-GAL4 , ok107-GAL4 , c929-GAL4 , 53b-GAL4 , c320-GAL4 and UAS-GFP . NLS . elav-GAL4; Dcr2 ( 25750 ) , c305a-GAL4 ( 30829 ) , c107-GAL4 ( 30823 ) , c819-GAL4 ( 30849 ) , 121Y-GAL4 ( 30815 ) , 7Y-GAL4 ( 30812 ) , 36Y-GAL4 ( 30819 ) , 386Y-GAL4 ( 25410 ) , c584-GAL4 ( 30842 ) , DopR-GAL4 ( 19491 ) , Ddc-GAL4 ( 7009 ) , R71G10-GAL4 ( 39604 ) and R76D11-GAL4 ( 39927 ) were ordered from the Bloomington Drosophila Stock Center . NP1131-GAL4 ( 103898 ) , NP1004-GAL4 ( 112440 ) and NP2024-GAL4 ( 112749 ) were ordered from the Drosophila Genetic Resource Center . Tub-GAL80ts [40] , MB-Gal80 [41] , D52H-GAL4 [42] , c687-GAL4 [61] , 6B-GAL4 [62] , InSITE106-GAL4 [63] , Cha-GAL4 [64] , Kurs45-GAL4 [65] were gifts . Nf1P1 and Nf1P2 alleles were reported [22] and were outcrossed into an iso31 background for 7 generations . Alkts/CyO was a gift from Dr . J Weiss [13] and was outcrossed into an iso31 background . Alk1/CyO and UAS-Alk/CyO were gifts from Dr . R Palmer and both were outcrossed into an iso31 background [13 , 16] . The deficiency line uncovering the Alk gene , AlkDef ( 7888 ) /CyO , was ordered from Bloomington . Alk RNAi ( 11446 ) and UAS-Dcr2 ( 60008 ) were ordered from the Vienna Drosophila Resource Center . Reporter GFP expression driven by GAL4 lines was visualized through whole-mount brain immunofluorescence as previously described [60] . Rabbit anti-GFP ( Molecular Probes A-11122 ) 1:1000 and Alex Fluor 488 Goat anti-rabbit ( Molecular Probes A-11008 ) 1:500 were used . Sleep was monitored as described previously [37] . Flies were raised and kept on a 12h:12h light/dark ( LD ) cycle at 18°C or 25°C as stated . 3–7 day old flies were loaded into glass tubes containing 5% sucrose and 2% agar . Locomotor activity was monitored with the Drosophila Activity Monitoring System ( Trikinetics , Waltham MA ) , or when indicated with multi-beam monitors ( Trikinetics , Waltham MA ) that generate 17 infrared beams . Data were analyzed with Pysolo software [5] . For sleep assays with temperature shifts , total sleep amount was averaged for 3 d at the lower temperature before the shift and 3 d at the high temperature . For sleep deprivation experiments , flies were monitored for a baseline day and then sleep deprived on the second day for 6 hours from Zeitgeber Time ( ZT ) 18 to ZT24 during the night . Sleep was continually monitored for 2 recovery days . Mechanical sleep deprivation was accomplished using a Trikinetics vortexer mounting plate , with shaking of monitors for 2 seconds randomly within every 20 second window for 6 hours . The arousal threshold assay was described previously [66] . A 12oz rubber weight was dropped from 2-inch height onto a rack supporting large DAMS monitors at ZT20 . Flies with no activity 5 min before a stimulus and exhibited beam crossings within 5 min after the light pulse were recorded as “aroused” . Individual male flies were loaded into glass tubes containing 5% sucrose and 2% agar . Locomotor activity was monitored with the Drosophila Activity Monitoring System ( Trikinetics , Waltham MA ) , and analyzed with Clocklab software ( Actimetrics , Wilmette ) . To evaluate ALK’s role in maintaining adult rhythms , all genotypes were raised at 18°C to avoid inactivating ALK during development . 3–7 d old flies were then loaded into glass tubes and entrained for 3 d to a 12h:12h LD cycle , followed by 4 days in constant darkness at 18°C and then 4 days in constant darkness at 29°C . Rhythmicity analysis was performed for each 4 d period . In a separate experiment , iso31 , Nf1P1/P2 , Alkts and Alkts;Nf1P1/P2 flies were raised and tested at 25°C to evaluate whether inhibiting Alk during development affect rest-activity rhythm . A fly was considered rhythmic if it met 2 criteria: 1 ) displayed a rhythm with 95% confidence using χ2 periodogram analysis , and 2 ) a corresponding FFT value above 0 . 01 for the determined period length . The negative geotaxis assay was adapted from ( Barone MC and Bohmann D 2013 ) . Please see supplemental methods for details . Data were analyzed and plotted with SigmaPlot and GraphPrism software . One-way ANOVA analysis was done to reveal differences between genotypes in the same experiments and pairwise comparison between genotypes were done with post-hoc analysis as indicated in the figures . ns , not significant; ** , p<0 . 01; *** , p<0 . 001; **** , p<0 . 0001 .
|
Animal and human studies suggest that sleep has a profound impact on learning and memory . However , little is known about the molecular pathways linking these phenomena . We report that mutations in the Drosophila Anaplastic lymphoma kinase ( Alk ) gene , an ortholog of a human oncogene ALK , cause increased sleep . ALK is required for sleep suppression in the mushroom body , a structure important for both sleep and memory . ALK generally activates the Ras/ERK pathway , which is negatively regulated by Neurofibromin 1 ( NF1 ) . Mutations in Nf1 are the causes of the common neurological disorder Neurofibromatosis type 1 ( NF1 ) , which affects 1 in 3 , 000 live births . We find that male flies lacking the NF1 protein have reduced sleep , a phenotype opposite that of Alk flies . Interestingly , even though mutations in Nf1 don’t always cause short sleep in female flies , they suppress the sleep increase induced by ALK inactivation . Previous studies have shown that Alk and Nf1 play antagonistic roles in learning and that both genes regulate synaptic growth . Thus Alk and Nf1 interact to regulate both sleep and learning , suggesting that the two processes share a common pathway . Our results support a model in which changes in synaptic plasticity during sleep promote learning and memory .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Anaplastic Lymphoma Kinase Acts in the Drosophila Mushroom Body to Negatively Regulate Sleep
|
Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles . Moreover , deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes . In this study , we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures . The method is based on characterizing unique properties of electrostatic patches on the protein surface . Using an ensemble of general protein features and specific properties extracted from the electrostatic patches , we have trained a support vector machine ( SVM ) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids . Specifically , the method was applied on proteins possessing the RNA recognition motif ( RRM ) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions . Overall the method achieves 88% accuracy in classifying RNA-binding proteins , yet it cannot distinguish RNA from DNA binding proteins . Nevertheless , by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets , specifically , whether they bind a ribosomal RNA ( rRNA ) , a transfer RNA ( tRNA ) , or messenger RNA ( mRNA ) . Finally , we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs .
In recent years , there has been a growing appreciation for the importance of RNA and its interacting proteins . RNA-binding proteins ( RBPs ) function both in basic cellular processes and as key regulators of gene expression . Genome sequencing and analysis has identified many highly conserved noncoding RNAs [1] as well as numerous RBPs whose biological roles are still unknown . An increasing amount of new evidence on noncoding RNAs suggests that many other cellular processes may be mediated by RNA [2] . In most cases , RNA is found in complexes with proteins , either as large ribonucleoprotein complexes ( such as the ribosome ) or in more transient interactions ( such as the helicase-RNA interactions ) [3] . Identification of proteins involved in interaction with RNA is essential to unraveling the cellular processes in which these interactions are involved . RBPs are characterized by a modular structure and are composed of multiple repeats that are built from a small number of basic domains that are arranged in various ways in order to satisfy their diverse functional requirements [4] . The RBPs can be classified into different families based on their basic binding motifs , for example: the RNA recognition motif ( RRM ) , the KH domain , the double stranded RNA-binding domain ( dsRBD ) , and the zinc finger motif [5] . Based on the first draft of the human genome , it was estimated that there are more than a thousand RBPs with known RNA-binding motifs in the genome . These numbers are expected to increase dramatically when considering all proteins that have RNA-binding capacities [6] . In recent years , new RRMs , such as the PAZ domain and the PIWI motif , which are found in the RNA-induced silencing complex ( RISC ) , have been identified [7] , revealing distinct , novel modes of RNA recognition [8] . An increasing amount of evidence on noncoding RNAs suggest that new RNA-binding motifs are yet to be discovered [9] . For many years , computational methods for identifying RNA-binding function based on structural information were not practical , due to the great diversity of the proteins and lack of structural information about them . With the exponential increase in the number of proteins being identified by genomics and proteomics projects , and specifically by structural genomics initiatives , predicting RNA-binding function from structure is now feasible . Since it is impractical to perform a functional assay for every uncharacterized protein , scientists have been turning to sophisticated computational methods for assistance in annotating the huge volume of sequence and structural data being produced . To date , many techniques are available for automatic function prediction , including: homology-based methods , phylogenetic methods , sequence patterns , structural similarity , structural patterns , methods based on genomic context , and microarray expression data [10] . Among these , several computational methods have concentrated specifically on predicting DNA-binding proteins from three-dimensional ( 3D ) structures [11]–[16] . In addition , a couple of successful methods for prediction of RNA-binding function based on primary sequence were recently developed [17] , [18] . The structural work of the last decade has elucidated the structures of many major RNA-binding protein families . Furthermore , the structures of proteins in complex with their RNA targets have shed light on how RNA recognition takes place [5] . Recently , several bioinformatics approaches have been applied for identifying RNA-binding sites on RBPs [19]–[22] . Here we present a machine learning approach to classifying RBPs , in an attempt to identify new RBPs with unique binding motifs . The method is based on characterizing the structural and electrostatic properties of the proteins . The electrostatic properties are mainly calculated from patches on the protein surfaces that are automatically extracted using our PatchFinderPlus algorithm [11] , [23] . Combining an ensemble of features , we train an SVM system to distinguish RBPs from other non-nucleic-acid binding proteins that are characterized by large positive patches on their surfaces , with a very high accuracy of 88% . Applying a multiclass SVM , we show that we can successfully classify RBPs based on their RNA target ( tRNA , rRNA , or mRNA ) , although we could not distinguish DBPs from RBPs . Interestingly , when tested on a nonredundant set of proteins that possess the RNA recognition motif ( RRM ) , a typical RNA-binding motif known to be also involved in ssDNA binding and protein–protein interactions [24] , we could successfully distinguish between RRM motifs involved in RNA-binding and the atypical RRMs involved in protein interactions .
The tremendous increase in structural information on RBPs enabled us to generate a nonredundant dataset of protein structures on which we were able to perform a comprehensive analysis . In the first step , we extracted from the Protein Data Bank ( PDB ) all RBP structures solved either by X-ray crystallography or by NMR . The original list was cleaned for redundancy by removing all structures that had more than 25% identity ( for details see Materials and Methods ) . Further , the structures were annotated using the SCOP classification [25] and only protein chains including domains from unique families were retained in the final dataset . Overall , the final set included 76 nonredundant structures . As a control , we used a nonredundant database of 246 non-nucleic-acid binding protein chains ( NNBP ) , used previously for nucleic-acid binding ( NA-binding ) prediction [11] . In order to examine whether the calculated features can be used to distinguish the RBPs from other proteins ( specifically NNBPs that possess large positive patches ) , we applied a machine learning approach , namely , the support vector machine ( SVM ) . SVMs are supervised learning methods; they take as inputs a set of features , called feature vectors , to train a model and output a classification for a query based on the model . After being trained on a set of feature vectors whose expected outputs were already known , SVMs are able to classify new input vectors . Recently , SVMs have been increasingly used in addressing the problems of protein classification , including fold recognition [35] protein structural class prediction [36] , protein–protein interaction [37] , membrane protein type recognition [38] , [39] , and G-protein coupled receptors classification [40] . Furthermore , SVMs have been utilized to solve protein classification problems and were shown to complement other methods that are based on sequence similarity [41] . We applied an SVM classifier to distinguish between the nonredundant set of RBPs and the NNBPs , as well as between the RBPs and the subset of NNBPs with large positive patches . For training , we applied a normalized feature vector that included all 40 sequence and structural parameters that were extracted from both the electrostatic patches and from the whole protein . For testing , we applied a cross-validation ( leave one out ) test , where for each SVM run , one protein was extracted from the training and tested separately . To evaluate the SVM performance , we plotted the ROC curve ( receiver operating characteristic ) describing the relationship between the false positive rate ( FPR ) and the true positive rate ( TPR ) . The results of the SVM test are illustrated in Figure 4; overall we could successfully distinguish RBPs from NNBPs and from the subset of large-patch NNBPs with 88% and 86% accuracy , respectively ( details in Table 1 ) . The areas under the curve ( AUCs ) calculated for these experiments were 0 . 9 and 0 . 88 , for the full and subset , respectively . The high performance achieved for distinguishing RBPs from other protein with large patches is extremely encouraging , since by visual inspection of the physical and electrostatic properties of the proteins one cannot distinguish between the two functionally different groups . Furthermore , when calculating each parameter independently , many of the properties did not show significant differences between the RBPs and NNBPs with large positive patches; only by combining all parameters using an SVM could we clearly distinguish between the groups . These results imply that RBPs have unique properties that can distinguish them from proteins that do not bind nucleic acids . Importantly , the distinctive properties do not relate either to the fold of the protein or to its binding motif . To ensure that the good performance of the cross-validation test was not due to overfitting of the data , we tested an independent set of hypothetical proteins from the PDB database , which were solved by structural genomics projects and classified as RNA-binding proteins . To prevent circularity , the hypothetical proteins chosen for the test did not share more than 25% identity with any of the proteins in our training set , each representing a different fold and a different RNA-binding motif . Furthermore , since in many cases RNA binding is automatically predicted based on the existence of a known RNA-binding motif or sequence similarity , we included in the testing set only proteins that were verified experimentally to bind RNA ( detailed description of the test set is given in Table S3 ) . Overall we tested 13 proteins verified experimentally to bind RNA and 10 ( 78% ) were successfully predicted as RBPs . Interestingly , all three false negative results were annotated to be involved in tRNA binding . Since RBPs share many common characteristics with DBPs in terms of their electrostatics and structural features , clearly the most challenging goal would be to distinguish between these two groups . Several studies have demonstrated that RNA-protein recognition differs from DNA recognition in several aspects [22] , [42] , [43] . Since the RNA and the DNA adopt different helical parameters , dsDNA usually adopting a B-form while dsRNA adopts A-form helices frequently interrupted by internal loops and bulges [44] , it is expected that the electrostatic patches will differ between the two types of NA binding proteins . As a first step we examined whether the new feature set selected for predicting RBPs would be as efficient for predicting DBPs . To test this , we calculated the 40 features for the set of nonredundant DNA binding proteins and built an SVM classifier for DBPs vs . NNBP . As for the RBP classifier , here too we tested the DBPs against the set of nonredundant NNBPs applied in Stawiski et al . [11] , [45] . Overall the SVM performed similarly to the RBP vs . NNBP classifier , though with lower accuracy ( 85% ) . Interestingly , the current SVM results were slightly inferior to those previously reported with artificial neural network ( ANN ) classifiers [11] . These results are as expected , since the feature set we used in the current study was specifically designed for predicting RBPs and excluded the evolutionary information . Nevertheless , the relatively high performance achieved for predicting DBPs reinforces that the two sets of NA binding proteins have much in common . Next , we examined how well the SVM classifier discriminates between RBP and DBPs . Using the set of 40 features we were not able to distinguish RBPs from DBPs ( Table 1 ) . It is well established that certain RNA-binding motifs can also bind DNA and vice versa ( e . g . , [46] ) . Furthermore , it is anticipated that nucleic-acid binding proteins have several roles in gene expression pathways and thus potentially have the intrinsic ability to bind both DNA and RNA [47] . Nevertheless , after excluding from our training data all proteins that bind via motifs known to bind both DNA and RNA ( e . g . , C2H2 zinc finger ) and generating two unique data sets , single strand RBPs ( ssRBPs ) vs . double stranded DBPs ( dsDBPs ) , we still could not distinguish between the RPBs and DBPs based on the above parameters . When testing on 36 dsDNA vs . 40 ssRNA-binding proteins ( full list given in the Materials and Methods section ) , we classified only 19 as DNA-binding and 21 as RNA-binding , achieving a weak overall accuracy of 47% . This suggests that further refinement of nucleic-acid binding function will be required in order to build a classifier to distinguish exclusively RNA-binders from DNA binding proteins . To further study the role of the electrostatic properties in discriminating RBSs from NNBPs we excluded from the SVM classifier all features related to the protein parameter group ( features 19–25 in Dataset S1 ) . Though the SVM performance was evidently reduced upon eliminating these features ( Table 1 and Figure 4 ) , we still found that the electrostatic features were sufficient for distinguishing RBPs from NNBPs . Further , to test which of the calculated features contributes most to the RNA-binding prediction , we performed a Recursive Feature Elimination procedure ( RFE ) ( see Materials and Methods ) . When applying the RFE algorithm to our data , eliminating 50% of the features at each iteration , for the first three rounds of selection we did not observe notable changes in the AUC value . Only in the fourth iteration did the SVM performance decrease dramatically . The lists of the selected features that were retained in the third iteration ( both when testing RBPs vs . all proteins and the RBPs versus NNBPs with large-patches ) are shown in Table 2 . As expected , the majority of features ( 8/10 ) selected among the top ten properties in the RBPs vs . NNBPs classifier were electrostatic-related features . Interestingly , there was a large overlap between the top ten parameters that were selected with the RFE algorithm in both classifiers . These results reinforce that the differences between the RBPs and the NNBPs are related to the function of the RBP and not simply to the size of the patch . To further test the contribution of each one of the top ten parameters to the final SVM performance we conducted a backwards feature selection procedure and eliminated , in turn , each one of the parameters from the feature set and repeated the SVM testing ( using the same cross-validation approach ) . For each test , we calculated the ΔAUC , which is the difference between the AUC achieved when including the feature and the AUC after excluding the feature . When testing on the full dataset of RBPs vs . NNBPs , no notable reduction was observed after eliminating a single parameter from the top ten list . Generally , the ΔAUC analysis suggests that all features that were selected by the RFE contribute equally to the SVM performance . Nevertheless , as shown in Table 1 , when including only the top ten features in the RBP vs . NNBP classifier , the SVM achieved the same results as with the full parameter set . However , in the more challenging case of RBPs vs . the large patch NNBP set , all 40 features were needed to achieve the best performance ( both in terms of sensitivity and selectivity ) . Thus for achieving the best performance for RNA-binding classification in general , we consistently use the extended classifier . Although the 76 RBPs in our positive set were cleaned for redundancy both at the sequence and structural ( family ) level , within the structural groups we still had representatives of RBPs with a common binding motif ( e . g . , two proteins with an RRM motif ) . In order to be confident that the SVM results do not depend on having several proteins sharing the same binding motif within our dataset , we applied a motif-independent test . In this test we withheld , in turn , all proteins sharing a common binding motif and trained the SVM on the remaining proteins ( Table S4 ) . We then tested each member of the binding motif family on an SVM classifier from which that group had been completely withheld . As shown , the motif test performed exactly the same as the original test did , with very slight differences in the discriminating values obtained for each tested protein ( Table S5 ) . Interestingly , there was one motif group of tRNA-binding proteins which was completely misclassified ( seven out of seven proteins ) using both the RBP classifiers ( leave-one-out vs . leave-family-out ) . Overall the SVM results suggested that in the majority of cases RBPs can be uniquely characterized , independent of their binding motif . These results encouraged us to further test whether our method could discriminate RNA from non-RNA-binding proteins that possess a common binding motif . The RRM is one of the most abundant protein domains in eukaryotes . This motif is a classical RNA-binding motif , however it has been found to appear in a few ssDBPs , and most interestingly , in many proteins the RRM motif is involved in protein-protein interactions [24] . While the RRMs that mediate protein interactions commonly interact both with RNA and protein ( frequently the protein-protein interactions are between two RRMs ) , in unique cases the RRM is solely involved in protein–protein interactions [24] . To test whether our method can distinguish between these cases , we obtained from the PDB a nonredundant set of protein chains that possess an RRM domain ( Table S6 ) . The structures were extracted automatically from PDB using a 35% sequence identity cutoff . The existence of the RRM motif was further verified against the pfam database [48] . Further , we tested each of the 27 protein chains with our SVM classifier using all 40 features . Consistent with the motif-independent test , the proteins were tested against a classifier in which the two original proteins including an RRM were excluded from the training . Overall , amongst the 27 protein chains , 21 were classified as RBPs , with one marginal prediction and six chains classified as NNBPs ( Table S6 ) . Amid the six protein chains that were classified as NNBPs was the RRM domain of Y14 from the Y14-Magoh complex ( PDB code: 1rk8A ) , which has been confirmed experimentally to be involved only in protein-protein interactions [24] , [49] . In addition , the RRM1 domain of the SET1 histone methyltransferase ( PDB code: 2j8aA ) was classified as NNBP . The latter result is consistent with experimental studies which have shown that the RRM1 of the SET1 protein does not bind RNA in vitro , suggesting that the protein may be involved in RNA binding in vivo only via RRM–RRM interactions [50] . Three other chains that were predicted as NNBPs are the RRM of U2AF 35 ( PDB code: 1jmtA ) and the atypical RRMs ( U2AF-homology motif ) of U2AF65 and SFP45 ( PDB codes: 1opiA and 2pe8A , respectively ) ; all three were confirmed to be involved in protein–protein interactions in the spliceosome [51] . Interestingly the protein chain of the splicing factor SRp20 , including an RRM and a TAP binding motif ( PDB code: 2i2yA ) , was also classified as NNBP . It is plausible that these results are influenced by the existence of the TAP protein binding domain within the protein chain [52] . Notably , among the chains classified as RBPs , only in the case of elF3 ( PDB code: 2nlwA ) was our classification in contradiction to the experimental data , which suggests that the RRM motif does not bind RNA directly [53] . The elFj is part of a large multiprotein complex involved in initiation of translation in eukaryotes , binding the 40s ribosomal subunit . Recent studies have shown that the RRM of elFj interacts with elFb , which directly binds the ribosome [53] . Interestingly , we found the largest positive patch of the surface of elFj is on the opposite side of the RRM ( data not shown ) , suggesting that the protein might not be interacting with the rRNA via the RRM . Consistent with our previous result , the RRMs of UP1 , which binds RNA and ssDNA , was classified as RNA binding . Overall , our results suggest that we can distinguish between RRM motifs involved in nucleic acid binding from those that are involved in protein–protein interactions . However , since our current method can only distinguish RNA from non-NA binding , in the ambiguous cases where the protein is involved in both RNA and protein interactions ( either via the RRM motif or another motif ) , the SVM results may not be sufficient for prediction . To better understand which of the features used for the SVM training contributed to the ability of the classifier to distinguish the RNA from non-RNA-binding RRMs , we split the data into positive and negative predictions and applied the Mann–Whitney–Wilcoxon test on each one of the 40 parameters . Interestingly , the features that showed the most significant differences between the positive and negative groups were the features related to the electrostatic patches ( Table S7 ) . Figure 5 illustrates the largest positive patch in the U2B″–U2A′ complex ( PDB code: 1a9nA ) , including an RRM known to be involved both in RNA and protein interactions , in comparison to the largest electrostatic patch in the Y14 proteins ( PDB code: 1rk8A ) , including an RRM motif which is involved only in protein-protein interactions . In the U2B″–U2A′ complex , the large positive patch ( blue ) overlaps the RRM ( green ) , which interacts directly with the RNA , while in the Y14 complex the largest positive patch is relatively small and does not overlap with the RRM motif , which is involved in the interaction with the Magoh protein . A critical step in evaluating the strength of a classifier is to carefully examine the cases were it fails ( i . e . , the false negatives and the false positives ) . As mentioned earlier , when we analyzed the results of the SVM , we discovered that amongst the false negative results there were several tRNA-binding proteins . Previous structure analysis of the aminoacyl-tRNA synthetases demonstrated that these proteins bind tRNA via multiple domains , each of which independently recognizes different sites on the RNA [54] . In addition , it has been observed that the aminoacyl tRNA synthetases possess an unexpectedly negatively charged surface [29] . Other RBPs , such as the bacterial release factors that mimic tRNA also have highly negatively charged surfaces [55] . To further explore the unique properties of tRNA-binding proteins , we generated a set of 13 nonredundant tRNA-binding proteins that share not more than 25% sequence identity among them ( six of them were in our original dataset ) . Further , we built a new SVM classifier for the 13 tRNA-binding proteins against all RBPs ( excluding the tRNA-binding proteins ) . Applying a cross validation test , the SVM was able to separate the two data sets with very high accuracy ( AUC = 0 . 94 ) . Interestingly , when testing the misclassified proteins from the hypothetical test ( Table S3 ) against the tRNA vs . RBPs classifier , all three proteins were classified correctly as tRNA-binding . These results are consistent with previous studies on tRNA-binding proteins that showed a very different mode of binding to RNA relative to other RNA-binding proteins [56] , and are also consistent with recent sequence-based RNA-binding predictions , which demonstrated high prediction accuracy for tRNA-binding proteins [17] , [18] . To test which are the most significant features for distinguishing between the tRNA-binding proteins and all other RBPs , we calculated the Spearman correlation coefficient ( CC ) of each one of the 40 features . Figure 6 demonstrates the correlation values ( ρ ) for the 40 features ( numbered as in Dataset S1 ) . Interestingly , the features that showed the highest correlations were the molecular weight and surface accessibility of the whole protein ( colored in red ) ; both were significantly higher in the tRNA group ( p∼10−16 ) , suggesting that tRNA-binding proteins are generally larger than other RBPs in our data . In addition , the roughness of the large positive patch was significantly greater in the tRNA group , while the average surface accessibility was lower in the group of tRNA binders compared to other RBPs . Strikingly , as can be noticed on the right hand side ( blue bars ) of Figure 6 , all the ten features related to the “other patches” ( i . e . , the size of the negative , second and third patch , distances between the patches , etc . ) were among the top ranked features that showed a significant , high CC . These results emphasize that the tRNA-binding proteins have unique electrostatic properties that can be utilized for identifying novel proteins possibly involved in tRNA processing . Moreover , we noticed that the electrostatic properties distinguishing between the tRNA and the other RBPs are mainly related to the secondary patches and not to the largest positive patch . Following these observations , we were encouraged to test whether we could automatically distinguish between different RNA-binding strategies of known RNA-binding proteins . Previously , a multi-SVM approach was applied for classifying genes involved in different stages of the gene-expression pathway into subclasses based on microarray data [47] , [56] . To test whether a multiclass approach could be applied for classifying subsets of RBPs based on the type of RNA they bind , we built three new SVM classifiers , which were trained on experimentally verified RBPs: an rRNA-binding protein classifier , an mRNA-binding protein classifier and a tRNA-binding protein classifier ( see Materials and Methods ) . It is important to note that the groups were not split based on the RNA-binding motif and in several cases the same motif ( such as the KH motif or the zinc finger motif ) was found in different subsets . The 82 RBPs were tested subsequently on each of the three classifiers ( in each case , the tested protein was held out from the training set ) . Finally , a protein was assigned a value based on the classifier in which it achieved the highest positive discriminating value . The results of the multi-SVM test are shown in Figure 7 and summarized in Table 3 ( detailed results are given in Table S8 ) . As demonstrated in Table 3 , in all three subclasses the highest number of proteins was correctly assigned to the appropriate subgroup . As expected , the best results were obtained for the tRNA-binding proteins , where 13 of the 13 tRNA-binding proteins were clearly assigned as tRNA-binding . As can be observed in Figure 7C , the majority of tRNA-binding proteins also achieved a positive score in the mRNA classifier , though in all cases the scores were lower than for the tRNA classifier . Different studies have demonstrated that tRNA synthetases are also involved in mRNA-binding; for example , it was recently shown that the Glu-Pro tRNA synthetase has a role in blocking the synthesis of specific proteins by binding to the 3′ UTR of their mRNA [57] . In the rRNA-binding protein group , while the majority of the proteins ( 70% ) scored the highest in the correct rRNA classifier , some proteins were still misclassified . Among the 14 misclassified proteins , nine were classified as mRNA and five as tRNA ( Figure 7B and Table S8 ) . These results are consistent with the notion that ribosomal proteins have several other functions in the gene expression pathway [58] . Interestingly , included in the set of rRNA proteins that were misclassified as tRNA , was the ribotoxin restrictocin bound to the sarcin/ricin domain ( SRD ) from the large ribosome subunit ( PDB code 1jbr ) . This toxin disrupts elongation factor binding to the SRD domain that also binds tRNA [59] . Notably , our classification is purely based on structural information and does not rely on homology information , and thus it is expected to achieve lower performance compared to available sequence-based rRNA classification [17] . Finally , for the mRNA group we collected 23 nonredundant proteins: 13 proteins that bind mRNA at the different stages of the gene expression pathway ( transcription , splicing , polyadenylation , etc . ) and ten other proteins that bind mRNA such as hydrolases , export factors , viral mRNA , binding , etc . ( for details see Table S8 ) . Overall , amongst the 23 mRNA-binding proteins composed of different binding motifs , 73% of the proteins were assigned correctly ( Figure 7A ) . Among the false negatives , five were predicted as rRNA . Notably , the false negative mRNA-binding proteins did not belong to a certain binding motif or fold ( 2 KH , 1 RRM , 1 LRR , 1 PUF , and 1 Zinc Finger ) , again reinforcing that our classification is motif-independent . As noted , the basic assumption behind our algorithm was that the electrostatic patch is related to the nucleic acid binding interface . Thus it is expected that the success of the method would depend on the correlation between the patch residues ( identified automatically by our algorithm ) and the experimentally defined RNA-binding interfaces . We previously found that in DNA binding proteins the largest positive patch of the protein encompasses , on average 80% of the protein-DNA interface [11] . As demonstrated in Figure 1 , the positive patch of the RBPs does not always coincide with the real binding interface . Here we tested the correlation between the patch–interface overlap and the confidence of the RNA-binding classification , as derived from the SVM . Applying an SVM , each tested protein was assigned a discriminating value ( generally the distance of the protein from the hyper plane ) . As illustrated in Figure 8 , when applying a Spearman correlation coefficient , we found a significant positive correlation ( ρ = 0 . 64 , p<10−8 ) between the percent overlap of the positive electrostatic patch and RBP interface and the discriminating value obtained by the SVM . These results imply that the success of the method at classifying RBPs from NNBP strongly relies on the degree of overlap between the largest positive patch and the binding interface . The correlation between the patch-interface overlap and the SVM performance is also consistent with the feature selection results that showed that the majority of the features contributing to the performance were associated with the largest positive patch . In this study we applied a machine learning approach to classify RNA-binding function from the 3D structure of the protein . Using features extracted from the positive electrostatic patches on RNA and non-nucleic-acid binding proteins , we trained an SVM to classify RBPs . We show that our method successfully distinguishes , with relatively high accuracy ( 88% ) , the RBPs from other proteins that do not bind nucleic acids . Similar results were achieved both when applying a cross-validation ( leave one out ) approach and when testing an independent set of proteins solved by a structural genomics initiative and confirmed experimentally to bind RNA . However , our method was not able to distinguish between RNA and DNA binding proteins . Interestingly , although the RBPs were distinguished from non-nucleic acid binding proteins by a combination of properties , we show that the success of the classification strongly depends on the degree of overlap between the largest positive patch and the real binding interface . Furthermore , we could show that the results do not depend on the RNA-binding motif , and correct classification was also achieved when we withheld all proteins that share a similar binding motif . Overall , our method is applicable for classifying RBPs that are generally very diverse in terms of their structure , function , and RNA recognition motifs . Moreover , since the method does not rely on sequence or structure conservation , we suggest that it could be applied to identify novel nucleic acid binding proteins with unique binding motifs . One of the great challenges in classifying ligand binding proteins ( such as RBPs ) is to be able to identify to which ligand it will bind . For this purpose , we have applied a multiclass SVM classifier , which was trained on three different groups of known RBPs classified according to their RNA target: tRNA , rRNA , or mRNA . In the majority of cases , given that a protein is a RBP , we could assign it to a specific subgroup . Consistent with sequence-based predictions , we succeeded in correctly predicting all tRNA-binding proteins , whereas only 70–73% of rRNA and mRNA-binding proteins were assigned correctly . Overall , the results we obtained are very encouraging , reinforcing the idea that structural properties of proteins that are not directly related to the protein fold can give clues to the protein's interacting partner . It is important to note that subclassification of the RBPs to the three subgroups ( mRNA , rRNA , or tRNA ) using our multiclass approach is only possible given the prior knowledge that the protein binds RNA . Finally , consistent with other recent studies , our results suggest that electrostatic features of the protein surface can contribute to fine-tuning predictions of nucleic-acid binding proteins .
A nonredundant set of RBPs was constructed based on the RNA recognition motifs definition in Chen and Varani [5] . Additional proteins have been added to the data set based on manual data mining of the RCSB Protein Data Bank using the SCOP family definition [60] . From each SCOP family , only one representative protein was added to the dataset . From each protein included in our dataset , only the chain or chains containing the RNA-binding domain were analyzed . The chains involved in RNA binding were selected by manual inspection using the PyMOL viewer [61] . All selected chains were further cleaned for redundancy , including only proteins that share less than 25% sequence identity . In addition , the PISCES program [62] was applied to automatically select for proteins with resolution better than 3 . 5 Å , R-factor ≤0 . 3 , and a sequence length from 40 to 1000 amino acids . The NNBP data set was constructed from Hobohm and Sander's “pdb select” list of proteins [63] used previously in Stawiski et al . [11] , excluding all proteins involved in binding NAs . Similarly to the RBP set , the control data set was further cleaned by excluding sequences with more than 25% identity . The subset of large-patch NNBPs was selected from the control set by sorting the proteins by the size of the largest patch; the top 76 proteins were chosen: 1skf , 1a6oA , 1pbe , 1a17 , 1hcl , 1a7s , 1oaa , 1gox , 1ayl , 1uae , 1oyc , 1fnc , 1hcz , 1cpt , 1pda , 1lam , 1frb , 1ido , 1drw , 1fds , 1axn , 1gky , 1opr , 1lfo , 1ciy , 1fmk , 1csn , 1nsj , 1ndh , 1a8p , 1atg , 1bg2 , 1csh , 1lit , 1rcb , 1cot , 1lid , 1bdb , 1fit , 1pbv , 1br9 , 1ppn , 1a53 , 1czj , 1a8e , 1mai , 1dhr , 1lki , 1c52 , 1mrp , 1sbp , 1php , 1gnd , 1nfp , 1af7 , 1aj2 , 1alu , 1rhs , 1ddt , 1amf , 1ng1 , 1al3 , 1koe , 1mla , 1bhp , 1lbu , 1kte , 1nox , 1amm , 1a6m , 1phd , 1gen , 1b6a , 1gsa , 1ash , 1moq A nonredundant set of RBPs that bind ssRNA was constructed from the original dataset and includes the following 40 protein chains: 1a1tA , 1a9nB , 1aq3A , 1asyA , 1b23P , 1b34A , 1cx0A , 1ddlA , 1e8Ob , 1ec6A , 1f7uA , 1fjgB , 1fjgC , 1fjgF , 1fjgG , 1fjgI , 1fjgJ , 1fjgK , 1fjgL , 1fjgM , 1fjgN , 1fjgO , 1fjgP , 1fjgR , 1fjgS , 1fjgT , 1gtfA , 1h2cA , 1hq1A , 1i6uA , 1jidA , 1k8wA , 1knzA , 1kq2A , 1m8wA , 1mmsA , 1mzpA , 1rgoA , 1ropA , 2fmtA . The set of dsDNA binding proteins was selected from the DNA binding proteins dataset [11] . The 36 selected protein chains were: 1a02F , 1a31A , 1a3qA , 1a73A , 1aayA , 1am9A , 1b3tA , 1bdtA , 1bnkA , 1cktA , 1cmaA , 1d66A , 1ddnA , 1ecrA , 1fokA , 1hmiA , 1ignA , 1ihfA , 1lmb3 , 1mnmA , 1pdnC , 1pnrA , 1sknP , 1tc3C , 1trrA , 1tupA , 1wetA , 1xbrA , 2bopA , 2dgcA , 2hmiA , 2irfG , 2nllA , 3croL 3mhtA , 3pviA For the independent test set we extracted from PDB RNA-binding proteins that were classified as “hypothetical” or “structure genomics . ” The RNA-binding function was defined based on Gene Ontology ( GO ) terms , considering the molecular function level http://www . geneontology . org/ . In cases where GO annotation was not available , we included proteins that were defined as RNA-binding proteins in the primary citation . Further , the list was manually curated , including only proteins that were verified experimentally ( based on the literature ) to bind RNA . Importantly , proteins which were defined by GO as RBP based on the existence of an RNA-binding domain or on high sequence similarity to a known RBP were not included in the final list . The detailed list of the hypothetical proteins is given in Table S3 . Overall , 40 different input features were calculated; the features can be roughly classified into four major subgroups: The PatchFinder algorithm[11] was applied to extract all continuous positive patches on the proteins surface with a cutoff of >2kT/e [23] . The patches were sorted based on the number of grid points included within the patch , and the largest three patches were selected . The largest negative patch ( <−2kT/e ) was extracted as described in Stawiski et al . [11] . The distances between the patches were calculated from the center of mass of each patch . Protein features were calculated as described in [11] . In addition , the dipole and quadrupole moments were calculated using the Protein Dipole Moments Server [64] . Interface residues were calculated using the Intervor web server [65] . Intervor calculated macromolecular interface using the Voronoi cells approach . This approach was shown to be highly compatible with classical surface accessibility calculations [66] . The Voronoi cells represent a convex polyhedron that contains all points of space closer to that atom than to any other atom . Two atoms are in contact if their Voronoi cells have a facet in common [66] . The overlap between the patch and the interface was calculated as the number of patch residues included in the interface divided by the total number of residues in the interface . The F-test , Student's t-test ( assuming equal variance ) , Mann–Whitney–Wilcoxson , and the Spearman correlation coefficient ( CC ) were performed using the R Stats package [67] . To account for multiple testing , the P-value was adjusted using the Bonferroni correction . A standalone package , NAbind , for nucleic-acid binding prediction ( suitable for linux OS ) is available for download ( Dataset S2 ) .
|
Gene expression in all living organisms is regulated by a complex set of events at both transcriptional and posttranscriptional levels . RNA-binding proteins play a key role in posttranscriptional events including splicing , stability , transport , and translation . Nowadays , there is increasing evidence that many other cellular processes may be mediated by RNA . Identifying new proteins involved in interaction with RNA is thus essential to unraveling the cellular processes in which these interactions are involved . In the current study we present a successful computational approach for classifying RNA-binding proteins and distinguishing them from other proteins based on structural and electrostatic properties . We test the method on a unique protein domain , the RNA recognition motif ( RRM ) , which mediates both RNA and protein interactions . We show that we can discriminate RNA-binding RRMs from protein-binding RRMs . Further , we demonstrate that we can classify known RNA-binding proteins based on their RNA target ( mRNA , rRNA , or tRNA ) . Our method does not rely on any kind of evolutionary information and thus can be applied to identify RNA-binding proteins with novel modes of RNA recognition .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/bioinformatics",
"computational",
"biology/macromolecular",
"structure",
"analysis",
"molecular",
"biology/rna-protein",
"interactions"
] |
2008
|
Classifying RNA-Binding Proteins Based on Electrostatic Properties
|
Eukaryotic adaptation pathways operate within wide-ranging environmental conditions without stimulus saturation . Despite numerous differences in the adaptation mechanisms employed by bacteria and eukaryotes , all require energy consumption . Here , we present two minimal models showing that expenditure of energy by the cell is not essential for adaptation . Both models share important features with large eukaryotic cells: they employ small diffusible molecules and involve receptor subunits resembling highly conserved G-protein cascades . Analyzing the drawbacks of these models helps us understand the benefits of energy consumption , in terms of adjustability of response and adaptation times as well as separation of cell-external sensing and cell-internal signaling . Our work thus sheds new light on the evolution of adaptation mechanisms in complex systems .
The ability to adapt to different environmental conditions is a hallmark of any living organism , allowing sensory systems to adjust their sensitivity to changes in nutrient availability , stress , and other stimuli [1] , [2] . Adaptation mechanisms generally rely on biochemical feedback pathways , specific for the organism and type of stimulus [3] , [4] . Surprisingly , when comparing adaptation pathways in different organisms , one cannot help noticing the wide variety of pathway designs , ranging from remarkably simple in bacterial chemotaxis [5] to highly complex in eukaryotes , including Dictyostelium discoideum chemotaxis [6] , olfactory-transduction [7] , [8] and photo-transduction [9] . Since bacterial chemotaxis is known to work ( and adapt ) exquisitely well , it is unclear why eukaryotic organisms have such elaborated pathway designs , and what their advantages might be . In light of such complexity , the “physicist's approach” may help guide the identification of common principles among the different pathways . Recently , Lan et al . [10] analyzed a core adaptation pathway and concluded that adaptation always relies on energy consumption by the cell . However , whether it is generally impossible to adapt without consuming energy is still an open question , considering how adaptation mechanisms could have evolved in ancient protocells without sophisticated pathways . We address this issue by first comparing the sensory pathways of small bacterial and large eukaryotic cells , highlighting their similarities and differences . We then investigate the possibility of achieving adaptation with no energy consumption by means of a “protocell” based on equilibrium physics . The hypothesis of no cost of energy for the cell guides us in designing minimal adaptation mechanisms . By pondering the drawbacks of these mechanisms and the advantages brought by energy-consuming pathways , we unravel some of the complexity of sensory systems . These considerations may provide a path towards a general theory of adaptation in eukaryotic cells .
Constrained by their small size , bacteria are spatially highly organized [5] , [11] . Consider e . g . bacterial chemotaxis in Escherichia coli , known for amplifying weak signals by cooperative receptors and precise adaptation ( Fig . 1a ) . There is an apparent high level of local control in these processes: receptors are arranged hexagonally in clusters [12] and the adaptation enzymes CheR and CheB are tethered to the receptors to increase local enzyme concentration and specificity , and to reduce noise [13] . Adaptation in bacterial chemotaxis is a robust feature of the pathway , without “fine-tuning” of biochemical parameters [2] , achieved by integral feedback control [3] . Integral feedback control [10] , [14] and the related incoherent feedforward loop [15] are also found in eukaryotes . Despite these similarities , eukaryote's most striking feature is the staggering complexity , often relying on long , multistep signaling cascades in parallel [16] . Consider three different pathways: chemotaxis in Dictyostelium discoideum ( Fig . 1b ) , and photo- ( Fig . 1c ) and olfactory-transduction ( Fig . 1d ) in mammalian sensory systems . Perhaps most perplexing , the key signaling component for all of them are small , fast-diffusible second messengers , like for olfactory- and photo-transduction ( which is even toxic for the cell in large quantities [17] , [18] ) , and cAMP in Dictyostelium amoeba . Relying on these small signaling molecules could negatively affect the precision of the response . Due to their fast diffusion , affected targets may not only be the designated molecules , but large parts of the cells . Moreover , all of the eukaryotic examples mentioned rely on G-protein coupled receptors ( GPCR ) . The excitation of the receptor catalyzes the production of GTP and the dissociation of the G-protein . GTP then binds to the subunit and the subunits ( for Dictyostelium [6] , [19] ) or the subunit ( for photo- and olfactory-transduction [7] , [9] ) can activate the downstream processes . The activity of the guanosine triphosphatase ( GTPase ) hydrolizes the GTP which detaches from the subunit . Subsequently , the subunit can re-bind the subunits , thus reassembling the G-protein [20] . All bacterial and eukaryotic adaptation pathways share the consumption of energy by hydrolysis of fuel molecules , including S-adenosyl methionine ( SAM ) in bacterial chemotaxis , and cyclic adenosine and guanosine monophosphate ( cAMP and cGMP ) in eukaryotes [10] . These observations led to the conclusion that energy consumption is an essential ingredient in precise adaptation [10] ( see Fig . 1 , middle panel , for a definition ) . However , ancient protocells might have been able to respond and adapt to stimuli without this requirement , with molecular components added later by evolution , to produce the currently observed pathways . As it turns out , our protocell models , which only rely on equilibrium physics for the cellular components , may help unravel some of the signaling complexity in eukaryotic cells . Here , we present two minimal models in which a protocell exploits the non-equilibrium aspect of the changing external environment to respond and adapt to a stimulus without consuming ( dissipating ) energy itself . The first model contains only one component , represented by a receptor on the cellular membrane ( Fig . 2a , left ) . This receptor includes two sensing regions: the first binds extracellular ligand , the second mediates intracellular sensing and adaptation . As soon as the ( extracellular ) ligand arrives at the cell surface , the receptor binds and starts signaling . However , the stimulus molecules are also able to permeate the cellular membrane , e . g . via passive pores , and to consequently bind additionally to the intracellular region of the receptor . This second binding blocks the signaling activity of the receptor , thus precisely counteracting the activation due to the extracellular binding . In case the external stimulus cannot be used to mediate adaptation , e . g . for photo-transduction , a second model with two components is needed ( Fig . 2b , left ) . The first represents the receptor , which senses the extracellular stimulus and , upon stimulation , releases two intracellular subunits , and . The subunit is smaller and can diffuse faster than the subunit . The second component is a membrane-bound protein , able to respond to the binding of the and subunits . In particular , the binding of to this second protein causes an increase of its signaling activity , while the binding of exactly compensates for it , and hence turns signaling off . How do these minimal models compare with data from actual adaptation mechanisms ? The adaptation time , a measure of the speed of adaptation , can be defined as the time required for the response to return back to half of the displacement from the prestimulus value ( see Fig . 1 for a graphical explanation ) . Experimentally measured adaptation times vary from seconds to minutes: adaptation in bacterial chemotaxis by receptor methylation can take up to hundreds of seconds for very large stimuli [23] , [24] and similarly for cell-internal adenylyl cyclase ACA in Dictyostelium chemotaxis [25] ( although cGMP and activated RasG can be significantly faster [15] , [26] ) . In contrast , adaptation of the transmembrane currents in the olfactory- [27] and photo-transduction [28] pathways is faster ( a few seconds ) . Another important feature of adapting systems is fold-change detection ( FCD ) , which allows cells to interpret chemical gradients irrespective of scale [29] . Specifically , when applied to a step change in concentration , the output response should only depend on the fold change in the input; if the input is rescaled by a multiplicative factor , the output should remain exactly the same for every time point considered . This feature entails both exact adaptation ( that the system returns exactly to the prestimulus value ) and Weber's law ( that the smallest detectable stimulus is proportional to the background stimulus ) , but it is not implied by either or both of them . Bacterial chemotaxis , despite considerable energy consumption , indeed exhibits fold-change detection [30] . Olfactory- and photo-transduction do not adapt perfectly , and thus do not satisfy FCD . Fig . 6 shows the results for the adaptation time and FCD for the one-component system ( see also Figs . S5-S7 in Text S1; the two-component behaves very similarly , see Figs . S12 , S13 in Text S1 ) . The adaptation time in response to a positive step decreases with increasing size ( Fig . 6a ) , while the response to a negative step has the opposite behavior ( Fig . 6b ) . This can be explained within our intuitive , minimal model: since for extracellular binding ( and vice versa for the intracellular ) , in response to a positive step , ligand strongly binds to the off-state and weakly to the on-state of the extracellular domain of the receptor . As a result , the state of the receptor switches from on to off . When the ligand enters the cell , even a small concentration is enough to bind intracellularly the receptor in the on-state , turning it on . Therefore adaptation is fast , and the adaptation time decreases with increasing input steps due to the increased ligand gradient and flux . On the contrary , after a negative step , the state of the receptor is on with a high intracellular concentration of ligand , and thus for the receptor to switch off , the intracellular concentration has to decrease below the ( small ) intracellular . In this case , the larger the initial intracellular concentration the greater the time required to reach a small intracellular ligand concentration; the adaptation time consequently increases . The experimental adaptation times behave very differently ( see insets of Fig . 6a , b for a comparison with the slowest adaptation time course of the one-component model ) . In particular , in most of the experimental data , the adaptation time in response to a positive step tends to increase with increasing step size ( although activated RasG in Dictyostelium chemotaxis has the opposite trend [15] ) . In bacterial chemotaxis , this trend can be traced back to a maximal , saturated rate of receptor modification during adaptation [13] , [24] . Interestingly , the bacterial chemotaxis data we considered in response to a negative step exhibit a “stereotypical response” , with an adaptation time independent of the amplitude of the stimulus , reflecting a more complicated and highly activated demethylation reaction [22] , [31] . Finally , Fig . 6c , d shows that fold-change detection is almost perfectly satisfied by our minimal model , demonstrating that even a simple model is capable of producing sophisticated sensory features . To further compare our minimal adaptation models with data , we consider three additional characteristics of adaptation . Sensitivity represents the relative change of the output response with respect to a change in input stimulus ( see Fig . 1 ) . Both our one- and two-component models display small sensitivity values and dynamic ranges when compared with the experimental data available for bacterial chemotaxis and photo-transduction ( see Fig . 7a ) . This discrepancy can be understood considering that chemoreceptors in bacteria are known to cluster to increase their sensitivity . Additionally , receptor types with different ligand-binding strengths are known to extend the dynamic range of sensing [13] , [24] . These strategies could also be exploited in our equilibrium-physics models , but making clusters of different receptor types would nonetheless cost energy for the cell , even if this is paid for during cell growth and thus is not directly connected with sensing . The response time is the time interval between the onset of the stimulus step and the peak amplitude of the response ( see Fig . 1 ) . Fig . 7b shows that the response of the one-component model is instantaneous , and is therefore similar to the fast bacterial chemotaxis response , while the two-component model resembles the slower eukaryotic responses . Both in our one-component model and bacterial chemotaxis , the response is mainly determined by a “conformational” change in the receptor , which is very fast ( ns- ) [32] . In contrast , eukaryotic responses involve long cascades based on diffusion . Consistently , in both our simulations and the experimental data , the response time does not depend on the background stimulus , as this time is determined by the speed of cellular components . Considering the steady state after adaptation to a step change , we can distinguish the level of precision of adaptation . In particular , if we define the imprecision as presented in Fig . 1 , we notice that both our one- and two-component models are perfectly precise , i . e . the steady state of the system is independent of the stimulus strength , even for large stimuli ( Fig . 7c ) . This does not occur for bacterial chemotaxis , which is perfectly adapting for small background stimuli but loses precision with increasing stimulus strength . While our minimal models are precisely adapted when fully equilibrated , precision in bacterial chemotaxis is regulated by a non-equilibrium pathway with constraints , e . g . from the finite number of methylation sites . The same trend of imprecision is present in photo-transduction , which is not even fully precise at small background stimuli . This may be explained considering that the photo- and olfactory-transduction pathways ( see Fig . 1 of [27] ) represents only the first stage of a complex response , and consequently the output signal of these pathways undergoes further processing and error corrections . The Dictyostelium adenylyl cyclase activity shows a constant 33% imprecision independent of stimulus strengths ( Fig . 7c ) . Note however that cGMP ( Fig . 2A of [26] ) and activated RasG ( Fig . 2A of [15] ) exhibit near perfect adaptation ( data not shown in Fig . 7c ) . When considering adaptation in Dictyostelium chemotaxis , it is worth noting that cells do not respond to step changes but to spatial gradients . In particular , even if the models we are considering do not include cell motility , we can nevertheless study the response to those stimuli: approximating the cell by a round circle in a 2D plane with an initial homogeneous internal ligand concentration , we simulated the response of the one-component model when the external ligand concentration changes linearly in space across the cell length . Fig . 8a shows the spatial distribution of the attractant at different times due to slow diffusion across the membrane . The internal concentration and receptor-activity time courses at the cell rear ( minimal external concentration ) and at the cell front ( maximal external concentration ) for different receptor lengths are depicted in Fig . 8b and d , respectively . Also in spatial sensing the activity of the receptors adapts perfectly along the cell circumference . To quantify directional sensing we consider the dipole moment of the receptor activity , defined as the sum of the activity on the cell circumference weighted by the normalized position along the gradient: ( 9 ) where represents the adapted steady-state activity . The initial response of is strong but then vanishes completely with adaptation of the receptors ( see Fig . 8c , top ) . Although the response ceases , the internal gradient remains , thus representing the cell's degree of polarization ( Fig . 8c , bottom ) . The results of the two-component model are shown in Text S1 .
In this work we analyzed and compared adaptation pathways from very different organisms , ranging from bacteria to eukaryotes . All these pathways require energy in order to adapt [10] . Here , we showed that it is possible to build minimal adaptation mechanisms without the need of energy consumption by the cell , as possibly relevant for ancient protocells . Despite their extreme simplicity , our two minimal models can help elucidate some aspects of complex signaling pathways . Known transmembrane receptors in eukaryotes are grouped into ionotropic and metabotropic receptor types . Ionotropic receptors are characterized by a direct response , much as an ionic channel changing its conformation ( such as opening or closing ) in response to an extracellular stimulus . A direct activation of this kind is similar to the conformational change-based mechanism in the bacterial chemotaxis pathway . In contrast , metabotropic receptors are more sophisticated: triggering of the receptor activates a cascade , usually a G-protein , leading to a change of second messenger concentration . This often involves complex feedback mechanisms [6] . The influx/efflux of these second messengers , together with the current flowing through any ion channel activated by them , produce a change in membrane potential , which usually represents the output of the pathway . All the eukaryotic examples presented in this work fall into this category ( Fig . 1b–d ) . Our two minimal models directly relate to metabotropic signaling pathways . The one-component model functions by means of a small diffusible ligand , which can permeate through the membrane . This is somewhat comparable to the presence of cAMP both inside and outside starving Dictyostelium cells and to the inflow of in photo- and olfactory-transduction . In addition , in both our one-component model and the Dictyostelium pathway this ligand is responsible for both sensing and adaptation . cAMP is not only outside and inside the cell , but also follows the external stimulus ( Fig . 5B of [25] ) and thus can be considered mediating adaptation of the adenylyl cyclase ACA ( Fig . 4C in [25] ) . In contrast , the two-component model involves the detaching of the two subunits , and , from the receptor after its activation , precisely resembling the dissociation of the and the subunits in Dictyostelium , photo- and olfactory-transduction pathways . Similar to the cAR1 receptor and G-protein in Dictyostelium [20] , the first component of the two-component model does not adapt , leaving the adapting response to the second component , the latter resembling the time course of adenylyl cyclase ACA . The network motif effectively implemented in both minimal models is the incoherent feedforward loop ( see Fig . 2 , right panels ) , which is encountered in the Dictyostelium pathway as well [15] . This design principle can easily be identified through the presence of a slow inhibitory process , which is the transmembrane diffusion of the ligand in the one-component model and the diffusion of the subunit in the two-component model . An important difference between the biological signaling pathways and our minimal models is the source of energy dissipation . Unlike our minimal models , cells have to pay for their significant energy costs . Hence , what are the advantages which may have led to the evolution of biological non-equilibrium pathways ? A first drawback of our models is that they exhibit a low sensitivity and dynamic range ( Fig . 7a ) . However , as already mentioned , this could be amended by introducing receptor complexes of different receptor types [24] . A more serious constraint of our equilibrium models is that the response and adaptation times are determined by diffusion constants which cannot easily be adjusted by the cell . Furthermore , the one-component model requires the external stimulus to enter the cell , while modern energy-consuming pathways generally separate external sensing from internal signaling , thus avoiding that toxic chemicals enter the cell to mediate adaptation . Why do G-protein cascades employ small fast diffusible molecules with little spatial control to mediate adaptation ? A possibility is stimulus amplification since active G-protein subunits can further activate many downstream signaling molecules [33] , [34] . In addition , eukaryotic cells are often highly specialized , as in the case of olfactory receptor neurons and photoreceptors , and thus the low specificity of these small molecules is compensated by the high specificity of the cell types . Alterations in transmembrane potential also permits fast and reliable electrical transmission through excitation , typical of neurons . Some molecular species of our minimal models may represent “fossils” , remnants of ancient protocells in current adaptation pathways . For instance , the role of the “non activating” G-protein subunit remains unclear in eukaryotic signaling [19] . Others may have taken on new roles: GTP binding and hydrolysis may have introduced a “timer” into the pathways , promoting the reassociation of the G-protein complex and thus the termination of the downstream activation . The consequence may be a bursty , frequency modulated signaling , with the advantage of being more accurate for both sensing and encoding [35]–[37] . In conclusion , our simple schemes for perfect adaptation are energy efficient , but evolution may have replaced them by energy-consuming pathways to increase adjustability and control of the response and adaptation times for the cells' changing needs . Similar to kinetic proofreading , in which the probability of a correct output is increased through repeated cycles [38]–[40] , adaptation pathways could represent schemes in which cells improve the control and robustness of the response by exploiting energy expenditure for enhanced fitness .
For a process described by forward ( ) and reverse ( ) rates , the entropy production rate is given by [41] ( 10 ) with entropy in units of the Boltzmann constant . For our one-component model , following Fick's first law , and , with the diffusion constant of the ligand , and the membrane thickness , leading for the total cell of radius to ( 11 ) which is equal to 0 at steady state ( i . e . when ) , and only when a concentration gradient across the membrane is present . The energy dissipation rate corresponds to Eq . ( 11 ) multiplied by the temperature of the system . Equation ( 8 ) for precise adaptation [22] can be generalized to include both the forward and reverse reactions ( 12 ) where the first two terms represent the contribution of CheR , and the last two the contribution of CheB . The reverse rate constants , and , can be adjusted to keep the net fluxes and at steady state , such that the net actions of CheR and CheB are methylation and demethylation , respectively . We used parameters [22] , , [22] , . To describe imprecise adaptation in response to a stimulus of concentration , we consider ( 13 ) which does not dissipate energy when rate constants , , , fulfill equilibration conditions and . Here , we chose and ( this leads to equilibrium for ) . Since the dynamics of the methylation level does not depend on the activity , Eq . ( 13 ) leads to imprecise adaptation . Equations ( 12 ) and ( 13 ) can be considered two components of the same system . By combining them through a parameter , we can describe their relative contributions ( 14 ) Following Eq . ( 10 ) , summing over the different reactions and neglecting the contributions of phosphorylation of CheB and CheY , the corresponding entropy-production rate is given by ( 15 ) with the approximate number of receptors in a bacterium [42] . By moving from 0 to 1 , this system becomes gradually imprecise and approaches equilibrium ( see Fig . 4b ) . To simulate the response of the one- and two-component models we considered a two-dimensional cell and numerically solved the diffusion equation where the boundary conditions at a distance from the center are given by a gradient in the direction , with 0 mM corresponding to the minimal concentration at and 1 mM corresponding to the maximal concentration at . Both the creation of the mesh and the solution of the equation were obtained by means of the Partial Differential Equation Toolbox of MATLAB ( The MathWorks , Inc . , Natick , Massachusetts , United States ) .
|
Adaptation is a common feature in sensory systems , well familiar to us from light and dark adaptation of our visual system . Biological cells , ranging from bacteria to complex eukaryotes , including single-cell organisms and human sensory receptors , adopt different strategies to fulfill this property . However , all of them require substantial amounts of energy to adapt . Here , we compare the different biological strategies and design two minimal models which allow adaptation without requiring energy consumption . Schemes similar to the ones we proposed in our minimal models could have been adopted by ancient protocells , that have evolved into the pathways we now know and study . Analyzing our models can thus help elucidate the advantages brought to the cells by consumption of energy , including the bypassing of hard-wired cell parameters such as diffusion constants with increased control over time scales .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
|
Unraveling Adaptation in Eukaryotic Pathways: Lessons from Protocells
|
Oropouche Virus is the etiological agent of an arbovirus febrile disease that affects thousands of people and is widespread throughout Central and South American countries . Although isolated in 1950’s , still there is scarce information regarding the virus biology and its prevalence is likely underestimated . In order to identify and elucidate interactions with host cells factors and increase the understanding about the Oropouche Virus biology , we performed microRNA ( miRNA ) and target genes screening in human hepatocarcinoma cell line HuH-7 . Cellular miRNAs are short non-coding RNAs that regulates gene expression post-transcriptionally and play key roles in several steps of viral infections . The large scale RT-qPCR based screening found 13 differentially expressed miRNAs in Oropouche infected cells . Further validation confirmed that miR-217 and miR-576-3p were 5 . 5 fold up-regulated at early stages of virus infection ( 6 hours post-infection ) . Using bioinformatics and pathway enrichment analysis , we predicted the cellular targets genes for miR-217 and miR-576-3p . Differential expression analysis of RNA from 95 selected targets revealed genes involved in innate immunity modulation , viral release and neurological disorder outcomes . Further analysis revealed the gene of decapping protein 2 ( DCP2 ) , a previous known restriction factor for bunyaviruses transcription , as a miR-217 candidate target that is progressively down-regulated during Oropouche infection . Our analysis also showed that activators genes involved in innate immune response through IFN-β pathway , as STING ( Stimulator of Interferon Genes ) and TRAF3 ( TNF-Receptor Associated Factor 3 ) , were down-regulated as the infection progress . Inhibition of miR-217 or miR-576-3p restricts OROV replication , decreasing viral RNA ( up to 8 . 3 fold ) and virus titer ( 3 fold ) . Finally , we showed that virus escape IFN-β mediated immune response increasing the levels of cellular miR-576-3p resulting in a decreasing of its partners STING and TRAF3 . We concluded stating that the present study , the first for a Peribunyaviridae member , gives insights in its prospective pathways that could help to understand virus biology , interactions with host cells and pathogenesis , suggesting that the virus escapes the antiviral cellular pathways increasing the expression of cognates miRNAs .
Oropouche Virus ( OROV ) is the etiological agent of Oropouche fever , an arthropod-borne viral disease characterized by symptoms like fever , headache , myalgia , arthralgia , malaise , photophobia , nausea , vomiting , dizziness , skin rash , and in few cases encephalitis and meningitis [1–7] . It was first described in Trinidad in 1955 [8] and the first Brazilian strain was isolated from a dead pale-throated three-toed sloth ( Bradypus tridactylus ) near a highway construction campsite in Belém , Pará state , northern Brazil [9] . It is estimate that more than 500 , 000 people were infected in at least 30 outbreaks in South and Central America between 1961 and 2009 [8 , 10 , 11 , 12] , placing Oropouche fever as one of the most prevalent arboviral disease in some states of Brazil , after Dengue , Chikungunya and Yellow Fever . However , the virus pathogenesis is still obscure , and Oropouche fever is still considered a neglected disease . During urban outbreaks , the virus is mainly transmitted by its major transmission vector , the midge Culicoides paraensis [3 , 9 , 13] . Other insect species , like mosquitoes of the genus Aedes and Culex , might also be potential vectors [9] . OROV is classified in the order Bunyavirales , Peribunyaviridae family , Orthobunyavirus genus , as Bunyamwera Virus , La Crosse Virus and the recently discovered Schmallenberg Virus [14] . The order Bunyavirales is the largest virus order , containing several viruses implicated in the etiology of relevant human diseases , such as La Crosse Virus ( LACV ) and Oropouche Virus ( Orthobunyavirus ) , Rift Valley Fever Virus ( RVFV ) ( Phlebovirus ) , Crimean-Congo Fever Virus ( CCFV ) ( Orthonairovirus ) and the rodent-borne Hantaan Virus ( HTNV ) , Andes Virus ( ANDV ) and Sin Nombre Virus ( SNV ) ( Orthohantavirus ) . OROV has a tri-segmented negative strand RNA genome with a small segment ( S ) that encodes the nucleocapsid protein N and a non-structural protein NSs; a medium ( M ) segment that encodes the glycoproteins Gc and Gn and another non-structural protein , NSm , and a large ( L ) segment that encodes the viral RNA-dependent RNA polymerase ( RdRP ) [15] . Despite its relevance as a human pathogen and its high prevalence in South America , little is known about OROV replicative cycle , pathogenesis and virus-host interactions . A recent study demonstrated that the OROV entry in HeLa cells is dependent on clathrin-coated pits [16] . Another report showed the relevance of MAVS , IRF-3 and IRF-7 , components of the innate immune response , in restricting OROV infection in knockout mice models and non-myeloid cells [17] . Despite that , the virus pathogenesis and the cellular pathways regulated by OROV infection are not known in detail . Gene expression and post-transcriptional regulation is mediated by short non-coding RNAs ( microRNAs , miRNAs or miR ) that plays important roles during virus replication . MicroRNAs span between 19–22 nucleotides in length and their first description was made in nematodes [18 , 19] , though now they have been identified in several phyla of plants and animals [20] , and even in viral genomes [21] . In mammals , they can be generated from intronic and exonic regions of protein-coding genes or intergenic regions [22] . They can be found as single miRNA genes or in clusters that encodes long precursor molecules , the pri-miRNA , ranging from hundred to thousand nucleotides in length [23 , 24] . Pri-miRNAs begins to be edited in the nucleus by the enzyme Drosha into pre-miRNAs , shorter 70 nucleotides long molecules with hairpin structures [25 , 26] . Those pre-miRNAs are exported from the nucleus into the cytoplasm by proteins such as exportin 5 and RAN-GTP [27] , and are further processed by Dicer into a 22 nucleotides long double-stranded RNA ( commonly referred as miRNA:miRNA* ) [28 , 29] . The double-stranded RNA is loaded into an Argonaute-driven RNA induced silencing complex ( RISC ) , which selects one strand and binds to a target mRNA ( commonly in the 3’-untranslated region , or 3’-UTR region ) [30 , 31] by base complementarity . The miRNA interaction with its target mRNA induces gene silencing by degradation ( when full complementarity between the miRNA and the target sequence occurs ) [32] , or translational inhibition ( in case of partial complementarity ) [33 , 34] . Since the seed sequence ( the minimal complementarity site between miRNA and mRNA ) is usually 7–8 nucleotides long , a single miRNA could regulates expression of several genes , as well as a single gene could be regulated by many miRNAs [35 , 36] . MiRNAs have already been described influencing disease progression , pathogenicity and replicative cycle of several viruses , being either inhibitory or stimulatory of the infection [37 , 38] . The liver-specific miRNA-122 stimulates HCV translation , stabilizing and protecting the 5’-UTR of viral RNAs from degradation , leading to an accumulation of the same in the cytoplasm [39–42] . In resting CD4+ T lymphocytes , HIV-1 viral production is impaired by cellular miRNAs that contribute to establish the viral latency [43] . Another miRNA , miR-29a , targets HIV-1 RNA to accumulate in RNA processing bodies ( P-bodies ) , inhibiting virus infection through translation suppression [44] . Even different strains of the same virus can elicit different miRNA regulation responses , as demonstrated for the highly-pathogenic avian-derived Influenza A H7N7 strain and the low-pathogenic swine-derived Influenza A H1N1 strain [45] , suggesting that miRNA signature profiles could raise clues about pathogenicity variation . Concerning miRNA regulation by bunyaviruses , a study with pathogenic and non-pathogenic strains of hantaviruses demonstrated the variation on miRNA profile among the different specie-specific viruses and cell types [46] . Another study with the Hantavirus Respiratory Syndrome ( HPS ) -causing agent , Andes Virus ( ANDV ) , identified down-regulation of miR-126 expression , a miRNA that acts as regulator of SPRED1 [47] . Increased expression of SPRED1 was suggested to be one of the mechanisms that augment endothelial cells permeability , leading to HPS . A recent study with PBMC of patients presenting acute hemorrhagic fever caused by the Crimean-Congo Hemorrhagic Fever Virus ( CCHFV ) showed the deregulation of several miRNAs , some of them associated with innate immunity and viral escape mechanisms [48] . The only study with phleboviruses described the association between miR-142-3p and the endocytic vesicle protein VAMP3 , suggesting a control mechanism for the intracellular trafficking of Uukuniemi Virus ( UUKV ) [49] . Due to the scarcity of information regarding the regulation of bunyaviruses by miRNA and the increasing necessity of better understanding of virus-host interactions of relevant emerging pathogens , we aimed to evaluate and identify the cellular miRNA profile and target genes induced by OROV infection in vitro . We demonstrated that miRNAs miR-217 and miR-576-3p , differentially expressed during infection , could be regulating crucial pathways , like innate immunity response , mainly in upstream proteins of interferon-β induction pathway ( adaptor and kinase proteins , as well as transcription factors ) , protein shutoff and apoptosis .
Cell lines Vero ( ATCC , CCL-81 ) , U87-MG ( ATCC , HBT-14 ) and HeLa ( ATCC , CCL-2 ) were maintained in DMEM ( Gibco ) supplemented with 10% v/v Fetal Bovine Serum ( FBS ) ( Gibco ) and 1% v/v of penicillin-streptomycin ( 10 . 000 U/ml-10 . 000 μg/ml ) ( Gibco ) at 37°C and 5% CO2 . HuH-7 cells were maintained in DMEM without sodium pyruvate ( Gibco ) supplemented with 10% v/v HyClone serum ( GE Life Sciences ) , 1% v/v antibiotics , 1% 200 mM L-Glutamine ( Gibco ) and 1% v/v non-essential aminoacids ( Gibco ) at 37°C and 5% CO2 . Jurkat ( ATCC , TIB-152 ) and THP-1 ( ATCC , TIB-202 ) were maintained in RPMI-1640 medium ( Gibco ) supplemented with 10% v/v FBS , 1% v/v antibiotics and 1% v/v sodium bicarbonate ( Gibco ) at 37°C and 5% CO2 . OROV strain BeAn19991 was originally obtained from the Evandro Chagas Institute and propagated by serial passages in Vero cells by routine methods using DMEM . The OROV stock used in the present experiments was propagated in HeLa cells and titrated to 2 x 106 PFU/ml . Infections were performed at MOI 1 during 1 h at 37°C and 5% CO2 in medium without FBS , under biosafety level 3 conditions at a BSL-3 laboratory at Universidade Federal do Rio de Janeiro . Virus titration was performed by plaque assay in Vero cells plated at 3 x 105 cells/well in 12 well plates 1 day prior to infection . After 1 h incubation with the virus , cells were replenished by DMEM supplemented with 1% v/v FBS , 1% v/v antibiotics and 1% v/v carboxymethyl cellulose ( CMC ) ( Sigma-Aldrich ) , and incubated at 37°C and 5% CO2 during 4 days . Cells were fixed with 4% formaldehyde for 20 min at room temperature , washed in Phosphate Buffered Saline ( PBS ) ( Gibco ) and stained with 20% v/v ethanol-violet crystal solution for 15 min . In order to induce monocyte-to-macrophage differentiation , THP-1 cells were stimulated with 100 nM phorbol 12-myristate 13-acetate ( PMA ) ( Sigma-Aldrich ) in standard RPMI medium for 24 h or 3 days followed by 5 days incubation at RPMI medium without PMA . Fresh RPMI medium was provided to cells after treatment and before infections . THP-1 derived macrophages cells were infected as described above , at 24 h or 8 days post PMA treatment . Cells ( 105 cells/sample ) were fixed with 4% paraformaldehyde for 20 min and permeabilized in 1% v/v Triton X-100 PBS solution . Blocking was performed in 5% v/v Donkey Serum ( Sigma-Aldrich ) PBS solution for 1h at 37°C . OROV infected and uninfected cells were incubated with mouse polyclonal anti-OROV antibody at 1:300 dilution in blocking solution at 37°C for 30 min . Cells were then washed thrice in PBS and incubated with 2 μg/ml Donkey anti-mouse AlexaFluor 488 secondary antibody ( Thermo Fisher Scientific ) at 37°C for 30 min . After incubation with the secondary antibody , cells were washed and resuspended in PBS . Flow cytometry was performed in Accuri C6 cytometer ( BD Biosciences ) . At least 10 , 000 gated events were counted per experimental replica at FITC channel . HuH-7 were plated at 2 x 104 cells/well density on 96-well plate and incubated at 37°C and 5% CO2 for 12 h . After that , cells were infected as described above . Cell viability was evaluated by CellTiter-Blue ( Promega ) according to manufacturer’s instructions . The fluorescence was measured at SpectraMax Paradigm Multi-Mode Detection Platform ( Molecular Devices ) . Total cellular RNA for microarray and target mRNA RT-qPCR analysis was isolated using MirVana kit ( Thermo Fisher Scientific ) according to manufacturer’s instructions . RNA quantification and quality was assessed by 2100 Bioanalyzer using RNA 6000 Nano kit ( Agilent Technologies ) . Only samples with a RNA Integrity Number ( RIN ) ≥ 9 . 0 were used for microarray . Extraction of RNA for miRNA validation with specific primers was performed using PureLink RNA Mini Kit ( Thermo Fisher Scientific ) and quantification and integrity were assessed in NanoVue Spectrophotometer ( GE Life Sciences ) . All RNAs were treated with DNase ( TURBO DNA-free Kit , Thermo Fisher Scientific ) before RT-qPCR experiments to avoid DNA contamination . In order to evaluate the expression profile of miRNAs , an array using Taqman chemistry was performed as follows: 12 h after infection , six independent replicas of mock-infected or Oropouche infected ( 4 x 106 cells/replica at MOI 1 ) HuH-7 cells were trypsinized ( Trypsin 0 . 25% , Gibco ) and the total cellular RNA was extracted and quantified as described above . cDNA was generated using TaqMan MicroRNA Reverse Transcription Kit ( Thermo Fisher Scientific ) with 100 ng of RNA per sample according to manufacturer’s instructions . The cDNA was preamplified using Megaplex PreAmp Primers ( Thermo Fisher Scientific ) and Taqman PreAmp Master Mix ( Thermo Fisher Scientific ) as instructed by manufacturer . The qPCR reaction was performed using Taqman OpenArray Human MicroRNA Panels ( Thermo Fisher Scientific ) , Taqman OpenArray Real-Time Master Mix and the OpenArray Accufill system OpenArray real-time robotics ( Thermo Fisher Scientific ) . This platform is able to quantify 754 human inventoried miRNAs . R statistical language [50] was used for background correction and data exploratory analysis ( Rn intensity cumulative curve and High Resolution Melting—HRM graphs ) for each RT-qPCR reaction . For relative expression quantification , a four parameters sigmoidal curve adjustment was done using the qpcR functions in R language [51] . Quantification cycle ( Cq ) was determined as the relative cycle to second derivative maximum point of adjusted sigmoidal curve ( cpD2 ) . The amplification efficiency was determined at the exponential amplification region , at the mean point between relative cycles to the first derivative maximum point and second derivative maximum point of adjusted sigmoidal curve [expR = cpD2- ( cpD1-cpD2 ) ] , and calculated as the ratio between the expR corresponding cycle fluorescence and the prior cycle fluorescence . For each miRNA , the amplification efficiency was determined as the mean of efficiencies calculated for the corresponding miRNA . Endogenous small-nucleolar RNAs RNU 44 , RNU 48 and U6 RNA were candidates for normalization controls selected by the geNorm method [52] . As an alternative normalization method , the normalization factor was calculated by the geometric mean of all miRNA expressed in each sample [53] . For normalized expression comparison between two sample groups , we performed a non-parametric T-test with 1 , 000 permutations [54] . For three or more groups comparison we used a one-way non-parametric ANOVA with unrestricted permutation ( n = 1 , 000 ) followed by a non-parametric pairwise T-test mean comparison with permutation ( n = 1 , 000 ) followed by Bonferroni correction [54] . Results were presented as mean ± S . E . M ( standard error mean ) . Two-tailed p-values in sample groups’ comparison lower or equal to 0 . 01 , 0 . 05 or 0 . 1 were considered as highly significant , significant and suggestive , respectively . The relationship between sample profiles was investigated by Bayesian Infinite Mixtures Model cluster analysis [55] and represented by 2D heatmap with dendrograms ( bi-cluster ) . For the purpose of display in the heatmap , k-nearest neighbors method ( k = 5 ) was performed to predict the missing values in uninfected cells for miR-217 , miR-26a-2-3p and miR-92a-5p . After imputation of the missing values , a scaled ( Z-score ) normalization was performed ( subtracted miRNA mean divided by miRNA standard deviation ) . Reverse transcription was performed using miRNA 1st-Strand cDNA Synthesis Kit ( Agilent Technologies ) and qPCR reactions were made with High-Specificity miRNA QPCR Core Kit ( Agilent Technologies ) and forward specific primers for each miRNA investigated . Human U6 RNA forward primer ( Agilent Technologies ) was used as normalization control . All the experiments were done in four independent replicas for each time point and sample group . The qPCR reaction was performed in 7500 Real-Time PCR System ( Applied Biosystems ) . The cycling parameters were set for standard SYBR Green method according to manufacturer’s instructions as follow: 95°C– 10 min and 95°C– 10 sec , 60°C– 15 sec , 72°C– 20 sec for 40 cycles . The miRNA forward primers sequences are depicted in S1 Table . Statistical analysis was performed using non-parametrical Mann-Whitney tests . We only consider a putative target for differentially expressed miRNA ( miRNA:mRNA interaction ) the ones predicted in at least 3 out of 6 public databases as follows: TargetScan , ( available at http://www . targetscan . org/index . html ) miRTarget2 ( available at http://mirdb . org/miRDB/ ) , PicTar ( available at https://pictar . mdc-berlin . de/ ) , miRBase ( available at http://www . mirbase . org/ ) , TarBase ( available at http://carolina . imis . athena-innovation . gr/diana_tools/web/index . php ? r=tarbasev8%2Findex ) and miRanda V3 . 3a . Interaction network tree was designed using Cytoscape v3 . 2 . 1 software ( Cytoscape Consortium ) . Ontology enrichment analysis [56] was performed for the predicted targets of the differentially expressed miR-217 and miR-576-3p . The ontologies were enriched mainly to biological processes , molecular function , cellular components , and gene interaction/regulation pathways . Only genes predicted in at least 3 out of 6 databases were considered candidate targets . Gene Entrez id for the predict ontologies were used in the Gene Ontology Database ( GO , available at http://www . geneontology . org/ ) , KEGG ( available at http://www . genome . jp/kegg/ ) and REACTOME ( available at http://www . reactome . org/PathwayBrowser ) for this purpose . Only genes over represented in hypergeometric tests with p-value ≤ 0 . 001 were considered . HuH-7 cells were seeded ( 105 cells/replica ) in triplicate into 24 wells plate overnight . Negative control inhibitor , miR-217 inhibitor and miR-576-3p inhibitor ( Integrated DNA Technologies ) were transfected at a final concentration of 75 nM using 2 μl of Lipofectamine 2000 ( Thermo Fisher Scientific ) per replica . Green fluorescent short RNA siGLO ( Dharmacon , GE Life Sciences ) was used to assess transfection efficiency and establish the miRNA inhibitor concentration for inhibition experiments . 3 h post-transfection , cells were infected with OROV at MOI 1 and RNA were extracted for miRNA quantification ( 6 h post-infection ) or target gene and OROV RNA quantification ( 18 h post-infection ) by RT-qPCR . OROV segment S RNA was quantified using primers and probe [57] with Taqman 2x Universal PCR Master Mix ( Thermo Fisher Scientific ) and normalized by GAPDH using PrimeTime primers and probe mix ( Integrated DNA Technologies ) . Cells were seeded ( 106 cells/sample ) and infected with OROV at MOI 1 . The RNA was extracted at 12 h post infection and reverse transcription was performed using High-Capacity cDNA Reverse Transcription Kit ( Thermo Fisher Scientific ) and 1 μg of RNA . Quantitative PCR was done in six replica per condition using 50 ng/well of cDNA on Custom Taqman Array Fast plates ( 96 well ) ( Thermo Fisher Scientific ) using specific primers and probes and Taqman Fast Universal PCR Master Mix ( Thermo Fisher Scientific ) according to manufacturer’s instructions on 7500 Fast Real-Time PCR System ( Thermo Fisher Scientific ) . Statistical analysis was done as described for microarray using endogenous 18S , GAPDH , HPRT1 and GUSB as normalization genes . For target kinetics SYBR Green PCR Master Mix ( Applied Biosystems ) and pre-designed PrimeTime primers ( Integrated DNA Technologies ) were used according to manufacturer’s instruction ( for primers sequences see S1 Table ) .
In order to expand the knowledge on the range of OROV-permissive cells , blood and hepatocyte cell lines were used to evaluate in vitro infection ( Fig 1A ) . T CD4+ lymphocytes ( Jurkat ) , monocytes ( THP-1 ) and hepatocytes ( HuH-7 ) cell lineages were infected with OROV at MOI 1 and , at 12 h post infection , infectivity was assessed by immunofluorescence using specific antibodies against OROV proteins and virus-positive cells were counted by flow cytometry . At indicated time points , 21% of Jurkat cells were infected , while THP-1 presented no susceptibility to the OROV infection . THP-1 cells can be induced to differentiate into macrophage by PMA treatment , becoming permissive to some viral infections , as described elsewhere [58–61] . In order to assess if THP-1 cells differentiated into macrophages were permissive to OROV infection , THP-1 cells were treated with PMA for 24 h or for 3 days , followed by incubation in medium without PMA for 5 more days . Differentiation of THP-1 into macrophage-like phenotype was accompanied by microscopy and attachment . At 12 h post infection , 31% and 50% of THP-1 treated with PMA for 24 h or 8 days , respectively , were infected with OROV , suggesting an increasing permissiveness to OROV infection as the cells shift from monocyte to macrophage-like phenotypes ( Fig 1A ) . At the same MOI , HuH-7 cells showed to be more permissive to OROV infection , presenting 90% infected cells at 12 h post-infection ( Fig 1A ) . Based on this result with HuH-7 cells , and considering previous demonstrations that the liver is an important replication site during experimental OROV infection in hamster [62 , 63] and mouse [64] , we chose the hepatocyte cell line HuH-7 as our in vitro model for further experiments . To assess the most suitable conditions to ensure that most cells would be infected at indicated time points , HuH-7 cells were infected with different MOIs and the infectivity was measured by flow cytometry ( Fig 1B ) . We reached 30% of infectivity at MOI 0 . 1 with a plateau of 90% in higher concentrations of virus ( MOIs 1 , 5 and 10 ) , with no further increase of infectivity levels ( Fig 1B ) . To assure that cells were still viable for further experiments , we assessed the cytopathic effect at 6 , 12 , 18 and 24 h post infection with MOI of 1 using Cell Titer-Blue ( Fig 1C ) . We did not detect cell death associated to the OROV infection at least 18 h post infection . However , only 44% of cells were viable at 24 h post infection . We also quantified the virus titer generated in those cells by plaque assay and at 6 h post-infection , the titer in the supernatant was 6 x 103 PFU/ml ( Fig 1C , gray line ) . As the infection progressed , a peak of 8 . 6 x 105 PFU/ml could be detected at 18 h post infection , reaching a plateau with no further increase in viral titer at 24 h post infection ( Fig 1C , gray line ) . Based on these data , we proceeded using HuH-7 cells infected with MOI 1 in subsequent experiments to evaluate the virus-host interactions . MiRNAs can be informative of cellular targets modulated by virus infection . In order to identify candidate cellular pathways differentially expressed in OROV infected cells , we performed an exploratory screening of 754 human miRNAs through probe-based RT-qPCR . MiRNAs expression was evaluated in four uninfected ( control ) and five OROV infected biological replicas at 12 h post infection . We found thirteen miRNAs differentially expressed upon OROV infection in HuH-7 cells with statistical significance: twelve up-regulated after infection and only one down-regulated ( miR-450b-5p ) ( Fig 2 and Table 1 ) . The reproducibility of effects in miRNAs was indicated by small variance noted among biological replicas , as depicted in the heat map hierarchical dendrogram ( Fig 2 ) . The differential expression of the miRNAs in OROV infected cells was classified into three groups: up-regulated , down-regulated and infection-dependent miRNAs ( selectively expressed miRNAs ) . MiRNAs miR-324-3p ( 1 . 73x ) , miR-1227 ( 1 . 95x ) , miR-362-3p ( 1 . 85x ) , miR-99b-3p ( 2 . 21x ) , miR-19b-1-5p ( 4 . 11x ) , miR-628-3p ( 2 . 77x ) , miR-26a-1-3p ( 42 . 47x ) , miR-576-3p ( 2 . 49x ) and miR-27a-5p ( 108x ) were up-regulated , in OROV-infected cells relative to uninfected cells . MiR-450b-5p was down-regulated 4 . 65 times in infected cells compared to uninfected cells . The induction of miR-26a-2-3p and miR-217 were inconsistent and observed only in three out of five infected replicas . From the thirteen selected miRNAs from the screening , only miR-576-3p and miR-26a-1-3p sustained significance ( p ≤ 0 . 05 , p ≤ 0 . 01 , respectively ) after Bonferroni correction according to the method used in this study . Nonetheless , some miRNAs presented borderline limits of significance ( p = 0 . 0595 ) , namely , miR-1227 , miR-19b-1-5p and miR-450b-5p ( Table 1 ) . In order to validate the miRNAs that were significantly deregulated in the array ( miR-26a-1-3p and miR-576-3p ) and to verify the expression of the miRNAs only detected in infected cells in the expression profile array ( miR-217 , miR-26a-2-3p and miR-92a-1-5p ) , we designed specific primers for each miRNA and checked its expression by RT-qPCR ( Fig 3 ) . Our validation experiments showed the same tendency of the miRNAs panel with an increasing expression of both miR-217 ( Fig 3A ) and miR-576-3p ( Fig 3B ) during infection , reaching a peak of expression at 6 h post-infection ( about 5 . 5 fold increase for both miRNAs ) . The kinetics of expression of miR-217 suggests an early induction during infection compared to miR-576-3p , since miR-217 was up-regulated 2 . 26 times as early as 3 h post-infection while miR-576-3p was only up-regulated 1 . 44 at the same point . However , at later stages of infection , miR-217 expression was already closer to uninfected expression levels ( up-regulated only 1 . 7 at 12 h post-infection ) , whereas miR-576-3p was still up-regulated 2 . 83 times in infected cells , indicating a slightly different kinetics for those miRNAs . To confirm the robustness of our analysis , we further validated the expression of three other less stable star miRNAs: the highly significant miRNA miR-26a-1-3p and two miRNAs detected only upon infection , miR-26a-2-3p and miR-92a-1-5p ( Fig 3C ) . Those three miRNAs were up-regulated 5 . 3 , 4 . 5 and 6 . 3 fold , respectively , at 12 h post-infection in comparison with uninfected cells ( p ≤ 0 . 01 ) . Altogether , these results with specific primers to each miRNA corroborate with our large-scale panel data , identifying miRNAs that are modulated during OROV infection showing the same tendency with different approaches . Star miRNA nomenclature corresponds to passenger strands less favorable to processing by RISC with lower likelihood to regulate gene expression [65 , 66] . As most of those miRNAs are previously annotated as star miRNAs ( as example of miR-26a-1-3p previously annotated as miR-26a-1* ) and some prediction database algorithms use proved interaction as criteria for prediction , we only selected miR-217 and miR-576-3p , both mature strand miRNAs , for further target prediction analysis ( one detected only in infected cells and the other one up-regulated significantly upon infection in the array , respectively , and both validated ) . To investigate possible pathways regulated by miR-217 and miR-576-3p during OROV infection , we performed target prediction using TargetScan , miRTarget2 , PicTar , miRBase , TarBase and miRanda databases . Target genes predicted by at least 3 out of 6 of those databases were considered candidates . We predicted 195 cellular genes to interact with miR-217 , miR-576-3p , or both , using that criterion ( S2 Table ) . We used enrichment analysis with GO , KEGG and REACTOME to identify cellular pathways affected by the predicted targets identified with our selection criteria . Our analysis showed the enrichment of cellular pathways related to regulation of cell metabolic processes , cell cycle and differentiation , chromatin stability and RNA metabolism and expression , suggesting that OROV infection possibly affects cell basic processes and RNA-related regulation processes , as expected for a RNA virus ( Fig 4 ) . This can be confirmed by the increasing numbers of observed genes ( gray columns ) compared with the expected numbers ( black columns ) for each cellular pathway analyzed ( Fig 4 ) . All the analysis showed very significant statistical levels with p values < 0 , 0001 . We selected 95 target genes , which were either in the group of 195 predicted target genes ( 92 genes ) and/or either were already published as target genes for those miRNAs , to evaluate their expression through RT-PCR in OROV infected hepatocyte cells . The selection criterion was based on the function described in the literature or involvement in relevant biological pathways related to RNA viruses such as: intracellular trafficking , apoptosis , innate immunity , gene expression regulation , antiviral restriction factor , protein synthesis regulation and intracellular signaling . The predicted selected targets and their association with miRNAs are depicted in the Fig 5 interaction network ( see also S3 Table for a brief description of targets function ) . From the 95 selected genes tested by RT-qPCR analysis we showed only the 18 genes that were differentially expressed 12 h post infection in comparison with uninfected cells ( Fig 6A ) . The majority ( 16 genes ) were down-regulated , corroborating with the opposite up-regulation trend of the related miRNAs during infection . The gene expression of membrane anchor protein ADAM9 , the component of SCF ( SKP1-CUL1-F-box protein ) E3 ubiquitin-protein ligase complex F-box Only protein 11 ( FBXO11 ) , the TNF Receptor Associated Factor 3 ( TRAF3 ) , the Mitogen-Activated Protein Kinase 1 ( MAPK1 ) and the Mitochondrial Antiviral-Signaling protein ( MAVS ) , all had a trending of ( 0 . 05 ≤ p ≤ 0 . 1 ) down-regulation . They were 3 . 9 , 3 . 9 , 1 . 59 , 1 . 39 and 1 . 26 fold ( ADAM9 , FBXO11 , TRAF3 , MAPK1 and MAVS , respectively ) less expressed in infected cells 12 h post infection . On the other hand , the pro-inflammatory chemokine C-X-C motif Ligand 2 ( CXCL2 ) had a trending 3 . 5 fold up-regulation ( 0 . 05 ≤ p ≤ 0 . 1 ) . The Cytochrome C Oxidase assembly subunit 18 ( COX18 ) was the only significantly up-regulated transcript ( p ≤ 0 . 05 ) with a fold increase of 21 . 5 times . The significantly down-regulated transcripts include the Decapping Protein 2 ( DCP2 ) , Fibronectin Type III Domain Containing 3B ( FNDC3B ) protein , the chaperone protein Chaperonin Containing TCP1 Subunit 6B ( CCT6B ) and glutamate transporter Solute Carrier Family 1 Member 2 ( SLC1A2 ) ( 2 . 78 , 5 . 47 , 17 . 21 and 39 . 56 times in infected cells , respectively ) . The Neurofibromin 1 ( NF1 ) , the FYVE , RhoGEF And PH Domain Containing 4 ( FGD4 ) , the transcription factor Nuclear Factor I A ( NFIA ) , the Cardiotrophin-Like Cytokine Factor 1 ( CLCF1 ) , the Stimulator for Interferon Genes ( STING ) and the structural component of caveolae invaginations Caveolin 2 ( CAV2 ) were down-regulated ( 12 . 57 , 11 . 16 , 8 . 95 , 7 . 96 , 7 . 16 and 6 . 59 times , respectively ) with the same significance ( p ≤ 0 . 01 ) . In order to evaluate if target regulation could present a higher effect in a later point of the infection , we selected two predicted and published targets for miR-217 and three for miR-576-3p to assess their expression 24 h post infection ( Fig 6B ) . As it was demonstrated that apoptosis is regulated by OROV replication [67] , we selected the Mitogen-Activated Protein Kinase 1 ( MAPK1 ) for being a known miR-217 target that regulates apoptosis [68] . Although MAPK1 was not significantly deregulated at 12 h post infection it showed a 2 . 23 fold down-regulation at 24 h post infection ( p ≤ 0 . 05 ) . The three selected and unpredicted miR-576-3p targets , MAVS , TRAF3 and STING , are known to be important genes in the regulation of IFN-β response in viral infected cells [69] . MAVS and TRAF3 did not presented a significant down-regulation at 12 h post infection ( Fig 6A ) ; however , both presented significantly down-regulation at 24 h post infection ( 2 and 7 . 4 fold , respectively ) . STING , the only one of the three selected targets of miR-576-3p that already demonstrated a significant down-regulation at 12 h post infection , showed an even higher down-regulation at 24 h post infection ( 39 fold down-regulation at 24 h post infection compared to 7 . 16 at 12 h post infection ) . The Silent Information Regulator 1 ( SIRT1 ) , a histone deacetylase known to be involved in stress-responsive pathways as inflammation [70 , 71 , 72] , was a miR-217 target that did not show significant differential expression relative to uninfected cells 12 h post infection but presented a significant down-regulation at 24 h post infection ( 3 . 45 fold down-regulated ) , what reinforces that different target genes of the same miRNA have different dynamics of regulation . Although not proved as a miRNA target yet , we included DCP2 , the only selected miR-217 target already significantly down-regulated at 12 h post infection , because of its relevance as a restriction factor for other bunyavirus [73] . In our model , DCP2 kept a decreasing expression in infected cells , being 20 fold significantly down-regulated at 24 h post infection ( p ≤ 0 . 001 ) . Overall , we confirmed the modulation of target genes transcription in the opposite direction of its cognate miRNA , showing that miRNA screening is very informative to predict cellular host genes modulated by virus infection . The type I interferon response is an important canonical innate immunity response mechanism to viral infection . As STING , MAVS and TRAF3 were demonstrated to be key factors in regulation of that response [69] , we aimed to quantify the variation in IFN-β transcripts in response to the infection . The IFN-β mRNA levels increased until 12 h post-infection , when it began to drop abruptly , reaching lower levels at 24 post infection ( Fig 7A ) . Those results are consistent with interferon immune response being triggered at early stages of virus replicative cycle . Virus RNA secondary structures are recognized by RIG-I-like receptors ( RLR ) or toll like receptors members at early stages of virus replication . However , as the infection proceed , the virus induces the miR-576-3p expression promoting the down regulation of its target genes STING and TRAF3 ( Fig 7B and 7C ) ) . We hypothesize that OROV try to escape IFN-β response reducing the levels of STING and TRAF3 through miR-576-3p induction ( Fig 7D ) . In order to assess if miR-217 and miR-576-3p were playing a role in OROV infection , we aimed to evaluate their impact in OROV replication using specific anti-miRNAs ( Fig 8 ) . To accomplish that , HuH-7 cells were transfected with non-human negative control miRNA inhibitor , miR-217 inhibitor , miR-576-3p inhibitor or both miRNA inhibitors and infected with OROV 3h post-transfection . At least 70% of cells were efficiently transfected with negligible cytotoxicity at the concentration tested ( S1 Fig ) . Both miRNA presented a 3-fold decrease in the presence of its respective inhibitor in comparison with negative inhibitor control ( Fig 8A ) . Nonetheless , the predicted target genes for miR-217 and miR-576-3p , DCP2 and STING , respectively , recovered to similar levels to non-infected cells in the presence of miRNA inhibitors 18 h post-infection ( Fig 8B ) . DCP2 RNA levels were slightly above of those in non-infected cells ( up to 0 . 5 fold ) whereas STING mRNA levels did not recovered completely but presented a lower decrease compared to the positive control ( 1 . 9 and 7 . 4 fold decrease , respectively ) . To further confirm if the miRNA inhibition would influence OROV replication , we measured the intracellular viral RNA levels 18 h post-infection in the presence of miRNA inhibitors ( Fig 8C ) . Inhibition of miR-217 led to a 2 . 3 fold decrease in viral RNA replication , while inhibition of miR-576-3p led to a 7 . 7 fold decrease . The highest reduction was observed with inhibition of both miRNAs ( 8 . 3 fold ) , but was not significantly lower than miR-576-3p inhibition alone ( Fig 8C ) . Finally , reduced viral titers confirmed the diminished replication , as a 3-fold decrease was observed in the same time point using miR-217 and miR-576-3p inhibitors ( Fig 8D ) . Altogether , those data demonstrate that inhibition of miR-217 and miR-576-3p is a prospective approach to restrict OROV replication in HuH-7 cells .
In this study , we aimed to identify miRNAs and the target genes regulated in OROV infected hepatocyte cell lines . We demonstrated that miR-217 and miR-576-3p were up-regulated during infection and that their cognate targets were down-regulated . Gene targets related to apoptosis , type I interferon-mediated response and antiviral restriction factors were associated with those miRNAs , suggesting a post transcriptional modulation of those pathways by OROV infection , giving new insights about virus-host interactions . We initially investigate the susceptibility of human cell lines to the viral infection ( Fig 1 ) . Lymphocytes T CD4+ cells ( Jurkat ) demonstrated low permissiveness to OROV , though being susceptible to infection in vitro , as denoted by the 20% of infected cells ( Fig 1A ) . On the other hand , the monocyte cell line THP-1 was not infected in the same conditions . Activation with PMA leads to progressive differentiation of THP-1 into macrophage-like phenotype , as demonstrated elsewhere [58 , 59 , 60] . We observed an increase in THP-1 infected population under two different PMA treatment conditions , suggesting a higher susceptibility of those cells as they shift to macrophage phenotype . Indeed , a recent case report detected OROV in peripheral blood mononuclear cells of two patients [74] , sustaining the possibility of blood cells playing a role in OROV pathogenesis in humans . In mouse models , however , macrophages only sustained viral replication in immune-compromised individual with deletions in IFN genes [17] . The human hepatocyte cell line HuH-7 was highly permissive to OROV infection , corroborating with previous data that suggest a sustainable liver tropism for OROV [17 , 62 , 63] . HuH-7 presented a 90% rate of infected cells with no associated cytopathic effect until 18 h post-infection at MOI 1 ( Fig 1C ) , leading us to choose it as our cell model . We initially found 13 miRNAs differentially expressed in infected cells relative to uninfected cells ( Fig 2 , Table 1 ) . Some of them were induced while others were modulated upon infection , in agreement with the complexity of miRNA regulation network . MiR-217 was already described in cancer cells involved in tumor migration suppression [75 , 76] . Expression kinetics showed a peak at 6 h post-infection for this miRNA ( Fig 3A ) . Regarding the predicted target genes , ten of them were significantly down-regulated 12 h post-infection in RT-qPCR screening ( Fig 6A ) . SLC1A2 , also known as Excitatory Amino Acid Transporter 2 ( EAAT2 ) is a glutamate transporter in astrocytes . Lower expression of this transporter was associated with neuropathogenesis outcomes in HIV-1 [77] and Human Herpesvirus 6 ( HHV-6 ) infected cells [78] . In hepatocytes , an increased expression was associated to cholestasis outcome [79] . It is unclear how this transporter could affect OROV infection in hepatocytes , but considering the neurotropism of OROV infection in vivo , an investigation of its role in neuropathogenesis should be considered . Another possible cellular factor related to neuropathogenesis of OROV is NF1 . The regulation of NF1 by other miRNAs was already demonstrated in neurons and other tissues [80] , and is considered a mechanism of fine-tuning in neurological disorders such as neurofibromatosis . The transcription factor NFIA was recently demonstrated to be a novel factor that is negatively regulated by miR-373 [81] . As a consequence , IFN-β response is down-regulated , facilitating Porcine Reproductive and Respiratory Syndrome Virus ( PRRSV ) replication . The data suggest that both miR-217 and miR-576-3p could act synergistically to inhibit IFN-β antiviral response . CLCF1 is a member of Interleukin-6 ( IL-6 ) family and play a dual role as pro-inflammatory and anti-inflammatory cytokine . FNDC3B has a role in cell migration and invasiveness in hepatocellular carcinoma [82] and glioblastoma cells [83] . In the second case , it was shown that FNDC3B could be down-regulated by miR-129-5p . Likely , FNDC3B could be one of many targets which down-regulation leads to an apoptotic state in OROV infection . The chaperone protein CCT6B as well as its relevance in OROV infection remains elusive . The assembly factor COX18 is a key component for cytochrome oxidase complex works properly [84] . Although its regulation showed an opposite trend , we speculate that this phenomenon could be a collateral effect of the apoptosis state , with cells trying to increase cytochrome oxidase efficiency due to a leaking of cytochrome c to cytoplasm . We further assessed the expression in a later point of infection ( 24 h ) of three targets of miR-217 during OROV infection: DCP2 , MAPK1 and SIRT1 ( Fig 6B ) . DCP2 is a decapping protein involved in mRNA decay . It was recently demonstrated that bunyaviruses compete for the same cellular capped mRNAs that DCP2 targets for degradation in a process known as “cap-snatching” [73] . Bunyaviruses need to snatch capped cellular mRNAs in order to replicate the virus RNA genome; therefore , DCP2 is a direct competitor for bunyaviruses replication . Our results suggested that the down-regulation of DCP2 by miR-217 could explain OROV sustained replication . The kinase MAPK1 and protein SIRT1 both presented a significant lower transcription only 24 h post infection ( Fig 6B ) , when most living cells presumably are in apoptosis process . The miR-576-3p expression peaked at 6 h post-infection and presented a kinetic similar to miR-217 ( Fig 3B ) . The expression levels at 12 h post-infection were consistent in both quantitative assays ( e . g . miRNA array and validation RT-qPCR ) , corroborating our findings ( Table 1 and Fig 3B ) . At the same time point , two candidate targets , FGD4 and CAV2 were down-regulated , confirming the inverse trend of miR-576-3p ( Fig 6A ) . FGD4 is a protein involved in regulation of actin cytoskeleton and cell migration . Another miRNA , miR-155 , was associated to reduced FGD4 levels , resulting in impaired neutrophil migration in myelodysplastic syndromes [85] . CAV2 is a protein component of caveolae structures . A recent study demonstrated that the caveolae and , therefore , its components could act as restriction factor for Tiger Frog Virus ( TFV ) release in late steps of viral cycle [86] in another hepatocyte cell line , HepG2 . As OROV entry is mediated by clathrin-endocytosis [16] , we speculate that caveolae could be a restriction site for viral budding/release; therefore down-regulation of a structural component could favors viral release . MiR-576-3p was recently proposed as a key miRNA in feedback regulation of IFN-β pathway in response to viral infections [69] . Our results regarding down-regulation of STING and TRAF3 corroborated that hypothesis ( Fig 6 ) . Moreover , IFN-β transcription regulation correlated with miR-576-3p , STING and TRAF3 transcription dynamics ( Fig 7 ) , implying in a temporal feedback mechanism in response to OROV infection , as suggested for other viruses . Based on our data and in conclusions of other group [69] , we proposed the following dynamics in antiviral response ( depicted in Fig 7D ) : upon viral entry and uncoating , double-strand viral RNA triggers the IFN-β signaling pathway through STING , MAVS and TRAF3 action , leading Interferon Responsive Factor 3 ( IRF3 ) to activate INF β transcription . Concomitantly , miR-576-3p transcription is also activated by the transcription factor IRF3 and the miRNA accumulation increases until peak 6 h post-infection ( Fig 3B ) . When enough miR-576-3p accumulates in cytoplasm ( 6 h post-infection ) the target mRNA levels , mainly for TRAF3 and STING , begin to fall progressively ( Fig 7B and 7C ) ) . At 12 h post-infection , as result of the decrease of STING and TRAF3 mRNA levels , the IFN-β response begins to be relieved , starting a feedback mechanism that leads to a halt in antiviral response and sustaining viral replication . The miR-576-3p is a primate specific miRNA that was conserved along the evolution , presumably , to avoid tissue damage derived from an excessive inflammatory response due to an infection . Indeed , in mice , OROV infection can be successfully controlled by IFN pathway in immune competent individuals . On the other hand , immune compromised mice ( e . g . deleted for genes of IFN pathway ) have high mortality rates and fast disease progression with notable liver damage [17] . Our results suggested that , unlike mice , the presence of miR-576-3p in primates and repression of INF-ß rendered them more susceptible to OROV infection . Furthermore , the inhibition of miR-217 and miR-576-3p partially restricted viral replication , as demonstrated by a decreasing in both viral RNA and titer in the presence of miRNA inhibitors ( Fig 8 ) . Those results are in accordance with previous data for miR-576-3p inhibition in other viral infections [69] . We speculate that the restriction is a consequence of a longer sustained innate immune response due to lower suppression of IFN-β pathway signaling cascade in a miRNA inhibition scenery , since both miRNAs might regulate target genes of that pathway . Finally , as NSs protein has been demonstrated elsewhere to be a candidate viral protein that regulates host innate immune response in other bunyaviruses [87] . A recent study demonstrate that a mutant NSs-deleted OROV induces a strong IFN-α production in opposition to the virus with functional NSs [88] . However , sensitivity to IFN-α treatment was not related to the presence of NSs , as both viruses presented similar sensitivity . It was also demonstrated that OROV is more resistant to IFN-α in comparison to BUNV . Although NSs alone seems to be a candidate viral protein to modulate IFN pathway , we cannot exclude the role of other viral or cellular proteins , as well as viral secondary RNA structures in this conundrum . We focused our analysis on miR-217 and miR-576-3p given the aforementioned reasons; nonetheless , we cannot exclude the possibility that the other miRNAs identified could be playing a role in OROV infection , as we could validated some of them ( Fig 3C ) . As most of them were star miRNAs and could not be properly investigated by our methodology , a different approach would be necessary to further evaluate if they regulate target genes . Recently , new methodologies to investigate miRNA-mRNAs interactions have been proposed [89 , 90] and could be an alternative for future studies . We chose the hepatocyte cell line HuH-7 as our model for an initial , representative study . However , it is also possible that at different time points or using different cell models we could identify different miRNA signatures . It would be interesting to compare the miRNA signature among other permissive cells and using different OROV strains to investigate unique and common miRNA responses to the viral infection . Although the targets validated by RT-qPCR are a good indicative of regulation , those assumptions must be considered with caution , as only RNA down-regulation not necessarily reflects a decrease in protein expression coded by the RNA . Protein quantification ( e . g . western blot ) would be necessary to assure that the final products of those genes are indeed being regulated . We limited the present study to identify miRNAs and their targets regulated during OROV infection , however , the mechanism by which that regulation occurs remains elusive . A further functional study with expression and knockout of viral proteins could shed a light on the role of viral proteins in this mechanism . To our knowledge , this is the first study to identify candidate miRNAs that could modulate infection of a member of Orthobunyavirus genus , the most representative genus from Peribunyaviridae family . Taken together , the data obtained in this study hint at pathways that could impact OROV infection , replication and pathogenesis , and expand the knowledge of the complex interactions in bunyavirus infections .
|
Oropouche Virus causes typical arboviral febrile illness and is widely distributed in tropical region of Americas , mainly Amazon region , associated with cases of encephalitis . 500 , 000 people are estimated to be infected with Oropouche worldwide and some states in Brazil detected higher number of cases among other arboviruses such as Dengue and Chikungunya . As much as climate change , human migration and vector and host availability might increase the risk of virus transmission . Despite its estimated high prevalence in Central and South America populations , the literature concerning the main aspects of viral biology remain scarce and began to be investigated only in the last two decades . Nonetheless , little is known about virus-host cell interactions and pathogenesis . Virus infection regulates cellular pathways either promoting its replication or escaping from immune response through microRNAs . Knowing which microRNAs and target genes are modulated in infection could give us new insights to understand multiple aspects of infection . Here , we depicted candidate miRNAs , genes and pathways affected by Oropouche Virus infection in hepatocyte cells . We hope this work serve as guideline for prospective studies in order to assess the complexity regarding the orthobunyaviruses infections .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"liver",
"respiratory",
"infections",
"gene",
"regulation",
"immunology",
"rna",
"extraction",
"microbiology",
"pulmonology",
"micrornas",
"extraction",
"techniques",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"gene",
"expression",
"hepatocytes",
"viral",
"replication",
"immune",
"response",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"anatomy",
"virology",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"non-coding",
"rna"
] |
2018
|
MicroRNA and cellular targets profiling reveal miR-217 and miR-576-3p as proviral factors during Oropouche infection
|
African trypanosomiasis is a chronic debilitating disease affecting the health and economic well-being of many people in developing countries . The pathogenicity associated with this disease involves a persistent inflammatory response , whereby M1-type myeloid cells , including Ly6Chigh inflammatory monocytes , are centrally implicated . A comparative gene analysis between trypanosusceptible and trypanotolerant animals identified MIF ( macrophage migrating inhibitory factor ) as an important pathogenic candidate molecule . Using MIF-deficient mice and anti-MIF antibody treated mice , we show that MIF mediates the pathogenic inflammatory immune response and increases the recruitment of inflammatory monocytes and neutrophils to contribute to liver injury in Trypanosoma brucei infected mice . Moreover , neutrophil-derived MIF contributed more significantly than monocyte-derived MIF to increased pathogenic liver TNF production and liver injury during trypanosome infection . MIF deficient animals also featured limited anemia , coinciding with increased iron bio-availability , improved erythropoiesis and reduced RBC clearance during the chronic phase of infection . Our data suggest that MIF promotes the most prominent pathological features of experimental trypanosome infections ( i . e . anemia and liver injury ) , and prompt considering MIF as a novel target for treatment of trypanosomiasis-associated immunopathogenicity .
African trypanosomiasis is a parasitic disease of medical and veterinary importance that adversely affects the public health and economic development of sub-Saharan Africa . The causative agents , trypanosomes transmitted by the tsetse fly ( Glossina spp ) , are extracellular hemoflagellated protozoans that cause fatal diseases in mammals , commonly called sleeping sickness in humans ( HAT , Human African Trypanosomiasis ) or nagana in domestic livestock [1] . In the case of bovine trypanosomiasis , anemia is considered to be the most prominent pathogenicity feature and the major cause of death associated with the disease [2] . In fact , the main difference between trypanosusceptible and trypanotolerant animals relies in their capacity to control anemia development . The underlying mechanisms mediating trypanosome-associated anemia have been scrutinized in murine models [3] . The data collectively suggest that a strong pro-inflammatory/type I immune response , involving classically activated myeloid cells/macrophages ( M1 ) , is required for initial parasite growth control . Yet , if maintained , this response contributes to pathogenicity in general and anemia in particular in trypanosusceptible mice , resulting in reduced survival of the host . Hereby , myeloid cell hyperactivation was proposed to be involved in the extravascular destruction of red blood cells ( RBCs ) due to enhanced erythrophagocytosis by spleen and liver-associated M1 cells of the infected host [3] , [4] causing trypanosome-associated anemia . Such type I immune response driven anemia resembles anemia of inflammation , also termed anemia of chronic disease ( ACD ) , that is associated with chronic infections and sterile inflammations [5] , [6] . Uncontrolled inflammation associated with persistence of M1 cells is also a major cause of liver injury and cachexia observed in trypanosusceptible animals , whereby the anti-inflammatory cytokine IL-10 was found to be detrimental to prevent these pathogenic features [7] . Hence , therapies should aim at re-establishing the balance between pro- and anti-inflammatory signals during the disease to avoid tissue damage . In this context , the glycosylphosphatidylinositol ( GPI ) -anchor of the Variant Surface Glycoprotein ( VSG ) coat was identified as a major parasite-derived molecule with a M1-activating potential [8] . Interestingly , a GPI-based treatment was found to protect against infection-associated cachexia , liver damage , anemia , and to prolong survival by modulating the myeloid cell activation state , i . e . forcing a transition from M1 to M2 ( alternatively ) activated myeloid cells during the course of infection [9] . The possibility to render trypanosusceptible animals more tolerant by modulating the activation state of myeloid cells offers an attractive model to identify genes and gene-products involved in the pathogenicity of African trypanosomiasis . In this context , a comparative gene expression analysis revealed that the macrophage migration inhibitory factor ( MIF ) expression was significantly reduced in mice rendered trypanotolerant upon GPI treatment . This “early response” cytokine is expressed by numerous cell types , including myeloid cells , and plays a key role in innate and adaptive immunity [10] , [11] . MIF is a prominent inducer of systemic inflammation in many inflammatory diseases [12] , [13] . It functions by recruiting myeloid cells to the site of inflammation [14] , by inducing their differentiation towards M1 cells secreting TNF [15] and by suppressing p53-dependent apoptosis of inflammatory cells [16] . Since African trypanosomes trigger a persistent type I/M1 immune response in trypanosusceptible ( e . g . T . brucei brucei ( T . brucei ) ) infected mice , we evaluated the potential role of MIF in the development of infection-associated pathogenicity . More specifically , the effect of MIF on the infiltration of myeloid cells , liver damage and anemia development was investigated .
As a first step towards evaluating the potential role of MIF during the course of T . brucei infection , we analysed its gene expression in different organs . As shown in Fig . 1A-C , MIF gene expression level in liver , spleen and bone marrow was characterized by two distinct phases , i . e . an initial increase during the acute phase of infection that returns back to the level of non-infected mice , followed by a second more progressive increase during the chronic phase of infection . Serum MIF protein levels followed the same kinetic as in the tested organs ( Fig . 1D ) . To evaluate the potential role of MIF in inflammation-associated pathogenicity occurring during T . brucei infection , two strategies targetting MIF production/activity were evaluated , ( i ) a comparison between wild type ( WT ) and MIF-deficient ( Mif−/− ) mice and ( ii ) a comparison between monoclonal anti-MIF IgG or isotype control antibody treated WT mice . As shown in Fig . 2A , first peak parasitemia and the further progression of parasitemia development were similar in WT and Mif−/− mice . However , there was a small , yet significant prolongation in median survival time in Mif−/− mice ( Fig . 2B ) . Next , we investigated whether infected WT and Mif−/− mice exhibited different cytokine immune responses . The results shown in Fig . 2C indicate that during the course of infection , both strains of mice mounted a prominent pro-inflammatory immune response as evidenced by the elevated serum levels of IFN-γ , TNF and IL-6 . Yet , these cytokine levels were lower in Mif−/− mice than in WT mice , especially during the chronic stage of infection . Conversely , serum IL-10 levels progressively increased during the chronic stage of infection ( day 18 p . i . till the end ) in Mif−/− mice whereas they were low/marginal in WT mice . Similar to Mif−/− mice , anti-MIF IgG treatment did not affect parasitemia development but increased the median survival time compared to control antibody treated mice ( Fig . S1A-B ) , suggesting a role for extracellular MIF in disease pathogenesis . As in Mif−/− mice , the pro-inflammatory cytokine production was decreased and IL-10 production was elevated during the chronic stage of infection upon anti-MIF IgG treatment of WT mice ( Fig . S1C ) . In a tsetse fly-mediated T . brucei infection model that mimics the natural route of infection , Mif deficiency did not affect parasitemia development but resulted in a prolonged survival ( Fig . S2A-B ) and a reduced pro-inflammatory cytokine profile ( mainly IFN-γ ) together with an increased IL-10 production during the chronic stage of infection ( Fig . S2C ) . A persistent pro-inflammatory immune response contributes to liver damage in the chronic stage of T . brucei infection [7] , [17] . Interestingly , at this stage ( day 25 p . i . ) , Mif−/− mice exhibited significantly reduced liver pathogenicity than WT mice , as evidenced by lower hepatomegaly and reduced ALT ( alanine aminotransferase ) levels ( Fig . 3A left and middle panel , respectively ) . The reduced serum AST ( aspartate aminotransferase ) levels further confirmed lower tissue pathogenicity in infected Mif−/− mice ( Fig . 3A right panel ) . We have documented that infiltration of CD11b+Ly6c+ myeloid cells in the chronic stage of T . brucei infection contributes to liver pathogenicity in WT mice [7] , [18] . Upon gating on CD45+ liver non-parenchymal cells ( see gating strategy Fig . S3A-C ) , we found that the infiltration of CD11b+Ly6c+ myeloid cells ( Fig . 3B , middle panel ) was reduced by 25% in infected Mif−/− mice compared to WT mice ( Fig . 3B , left panel ) . The CD11b+Ly6c+ cells ( Fig . 3B , middle panel ) were further subdivided into CD11b+Ly6chighLy6G− inflammatory monocytes and CD11b+Ly6cintLy6G+ neutrophils ( Fig . 3B , right panel ) . Both cell populations were significantly less represented in the liver of infected Mif−/− mice as compared to WT mice ( Fig . 3C , upper panels ) , correlating with a reduced gene expression level of the inflammatory monocyte chemoattractant CCL2 and of the neutrophil chemoattractants CXCL1 ( KC ) and CXCL5 ( LIX ) in total liver ( Fig . 3C , lower panels ) . These observations in Mif−/− mice were corroborated by anti-MIF IgG treatment of WT infected mice ( Fig . 4A-B ) . Neutrophils can represent an important source of MIF [19] and so far their contribution to African trypanosomiasis remains unknown . We addressed the possible involvement of neutrophils to T . brucei infection outcome in first instance by measuring the myeloperoxidase ( MPO ) activity as read-out of neutrophil activity . We observed that MPO levels increased more in WT than in Mif−/− mice during the course of infection ( Fig . S4 ) . Secondly , we performed adoptive transfer experiments whereby bone marrow-derived neutrophils ( i . e . CD11b+Ly6cintLy6G+ cells ) isolated from T . brucei infected ( day 24 p . i . ) WT or Mif−/− mice were transferred into infected Mif−/− mice . Using neutrophils from T . brucei infected ubiquitin-GFP mice we could demonstrate that these cells were still present within the liver of recipient mice 18 hours post-transfer ( Fig . S3D-E ) . Upon transfer of WT but not Mif−/− neutrophils , TNF levels in the liver cell culture supernatants as well as serum ALT/AST levels of Mif−/− recipient mice increased to the levels of infected WT mice ( Fig . 5 ) . MIF levels also increased in liver cells culture supernatants of Mif−/− recipient mice treated with WT neutrophils ( Fig . 5 ) , reflecting the contribution of neutrophils to MIF production . On the other hand , adoptive transfer of WT CD11b+Ly6chighLy6G− inflammatory monocytes in infected Mif−/− recipient mice resulted in a lower increase in TNF and MIF levels in the liver cell culture supernatants than transfer of WT neutrophils ( Fig . S3F-G; Fig . 5 ) . The serum ALT/AST levels increased to a similar level in Mif−/− recipient mice treated with WT monocytes or WT neutrophils ( Fig . 5 ) . Together , these data indicate that during T . brucei infection neutrophils can produce more MIF than monocytes and hereby are more important contributors than monocytes to liver pathogenic TNF production . Neutrophils and monocytes contribute to similar extent to liver injury in infected mice . However , while neutrophil-derived MIF is primarily responsible for liver injury , the pathogenic activity of monocytes is relatively less MIF-dependent . Besides liver damage , persistent inflammation during T . brucei infection causes anemia [5] , which is characterized by two distinct phases; ( i ) a rapid decline in RBC levels followed by partial recovery during the acute phase of infection and ( ii ) a more progressive decline in RBC levels during the chronic phase of infection ( Fig . 6A ) . During the acute phase of infection , the initial drop in RBC percentages starts at the same time ( day 5–10 p . i . ) in both mouse strains but was more severe in WT than Mif−/− mice . Indeed , Mif−/− mice lost about 30% of the total percentage of RBCs while WT mice lost about 50% as compared to non-infected mice ( Fig . 6A ) . Subsequently , a partial RBC recovery phase reaching about 70% of that of non-infected mice occurred ( day 10–14 p . i . ) , whereas in Mif−/− mice this recovery reached about 90% over a longer time period ( day 10–18 p . i . ) . During the chronic phase of infection ( following recovery ) the RBC levels declined progressively and remained lower in WT than Mif−/− mice . Furthermore , treatment of infected WT mice with anti-MIF IgG resulted in a better recovery of RBCs similar as in infected Mif−/− mice ( Fig . S5A ) . Finally , Mif−/− mice also showed reduced anemia upon tsetse fly-based infection ( Fig . S5B ) . Anemia development during persistent inflammation can result from iron accumulation within the mononuclear phagocyte system ( MPS ) leading to iron deprivation from erythropoiesis . Hence , the reduced pro-inflammatory immune response in Mif−/− mice during the chronic stage of T . brucei infection as compared to WT mice might impact on iron-homeostasis and hemoglobin levels . At day 18 p . i . , the time-point when the differences in anemia development and cytokine levels between WT and Mif−/− mice become apparent ( see Fig . 6A and Fig . 2C ) , hemoglobin and serum iron levels were less reduced in Mif−/− mice as compared to WT mice ( Fig . 6B ) . These differences were corroborated at the level of expression of genes implicated in iron homeostasis in the liver . In this context , it should be emphasized that under physiological conditions , tissue-associated myeloid cells , in particular liver myeloid cells , recover ferrous iron ( Fe2+ ) via engulfment of senescent RBCs and hemoglobin recycling . The Fe2+ iron from hemoglobin is extracted by myeloid cells via heme oxygenase-1 ( HO-1 ) , then transported into the cytosol by the divalent metal transporter 1 ( DMT-1/Nramp2 ) , from where it can be either exported from or stored inside the myeloid cells via ferroportin-1 ( FPN-1 ) or ferritin ( FHC ) , respectively , to prevent toxicity , depending on the iron demand of the host [6] . As shown in Fig . 6C , there was a lower liver HO-1 ( Hmox1 ) gene expression level in infected Mif−/− than WT mice ( suggesting lower iron extraction ) , while the DMT-1 ( Dmt1 ) gene expression levels were similar in both mouse groups ( suggesting similar iron transport ) . In addition , the FHC ( Fth1 ) gene expression levels were lower in infected Mif−/− mice ( Fig . 6C ) , indicating reduced iron accumulation in Mif−/− than WT mice . Finally , Fpn1 mRNA levels were higher in infected Mif−/− mice ( Fig . 6C ) , suggesting that iron export in these mice is less impaired as compared to WT mice . Together these data indicate that there is less iron extracted and retained in the MPS from infected Mif−/− than WT mice , which in turn allows increased iron availability for erythropoiesis . The persistent pro-inflammatory immune response in T . brucei infection ( see Fig . 2C ) , leading to iron accumulation within the MPS and iron deprivation ( see Fig . 6B-C ) could impair erythropoiesis . Therefore , the efficiency of the constitutive ( i . e . bone marrow ) and inflammation-induced ( i . e . spleen ) erythropoiesis was determined in WT and Mif−/− mice during the chronic stage of infection ( day 18 p . i . ) using three different approaches . First , as shown in Fig . 7A ( left panel ) we measured the relative abundance of immature ( Ter-119+CD71+ ) and mature ( Ter-119+CD71− ) RBCs as described by [20] . Infected Mif−/− mice had relatively more mature RBCs in the bone marrow and the spleen than WT mice ( Fig . 7A , right panels ) . Concomitantly , the reduction in the percentage of mature RBCs in the blood was less pronounced in infected Mif−/− mice than in WT mice . Secondly , we determined the expression levels of representative genes involved in RBC differentiation ( Gata1 , Epor , Tal1 , Maea and Gas6 ) in the bone marrow and spleen . As shown in Fig . 7B , the expression of most genes was not induced ( with the exception of Tal1 ) during infection in WT mice in both organs , while they increased in infected Mif−/− mice . Thirdly , we quantified the different stages of erythropoiesis ( from nucleated erythroblasts ( P1 ) till enucleated erythrocytes ( P5 ) ) using a recently described protocol [21] based on a CD44 versus FSC profile following gating on the Ter-119+ cells ( Fig . 8A ) . It appeared that compared to WT mice , infected Mif−/− mice had a more efficient RBC maturation mainly at the later stage of differentiation ( reflected by the increased percentage of P5 ) in the spleen and to a lesser extent in the bone marrow ( Fig . 8B-C , respectively ) . Together , these data demonstrate a higher level of erythropoiesis in the absence of MIF in T . brucei infected mice . An increased RBC elimination may also contribute to anemia in T . brucei infected mice . To address this question , we injected green fluorescent protein positive ( GFP+ ) RBCs i . v . at the start of the chronic phase of T . brucei infection ( day 12 p . i . ) in WT and Mif−/− mice and analyzed their clearance as the infection progressed . Non-infected WT and Mif−/− mice were treated similarly with GFP+ RBCs as controls . As shown in Fig . 8D , there was no difference in GFP+ RBC clearance between non-infected WT and Mif−/− mice . However , the GFP+ RBC clearance during infection was faster compared to non-infected mice . In addition , relatively more GFP+ RBCs remained in infected Mif−/− compared to WT mice ( Fig . 8D ) . These data suggest that a reduction in RBC elimination observed in Mif−/− mice could contribute to less severe anemia during T . brucei infection .
We investigated herein the role of MIF , an upstream regulator of the inflammatory response , in the immunopathogenicity of experimental African trypanosomiasis . We found that during T . brucei infection in trypanosusceptible wild type ( WT ) mice , the MIF gene expression profile in lymphoid tissues and the systemic serum protein level consists of two distinct waves; an upregulation during the acute phase of infection that declines after the first peak of parasitemia , followed by a second increase as the infection progresses to the chronic phase . Our results are in line with observations documenting that the Mif mRNA levels were increased in spleen cells during T . brucei infection in rats [22] . Using Mif−/− mice and neutralizing anti-MIF IgG treatment , we revealed that absence/blockade of MIF did not affect parasite growth . Yet , absence of MIF slightly increased survival time and reduced serum IFN-γ , TNF , and IL-6 concentrations while inducing IL-10 production in the chronic stage of infection . This observation most likely reflects a switch from a pro-inflammatory/pathogenic type I to an anti-inflammatory/anti-pathogenic type 2 response in the host . The higher TNF and IFN-γ response observed in WT mice compared to Mif−/− mice during the chronic stage of infection could result from the reported positive feedback loop between MIF , TNF and IFN-γ [23] . The switch from a pro-inflammatory to an anti-inflammatory immune response in Mif−/− or anti-MIF IgG treated WT mice during the chronic stage of infection was reflected by a reduction in pathogenicity , as evidenced by less liver damage and anemia , which are the main pathogenic manifestations of T . brucei infection [3] , [7] . CD11b+Ly6chighLy6G− inflammatory monocytes are reported to contribute to T . brucei-induced pathogenicity development through their production of TNF , whereby in the acute phase of infection their emigration from the bone marrow is CCL2/CCR2-dependent [18] , [24] . Although MIF can induce CCL2 [25] and trigger monocyte recruitment and subsequent arrest in tissue [14] , the bone marrow emigration and recruitment of inflammatory monocytes in tissues during the acute stage of infection was found independent of MIF [24] . We now report that MIF contributed to the recruitment of inflammatory monocytes into the liver in the chronic stage of T . brucei infection . The lower accumulation of inflammatory monocytes in Mif−/− mice at this stage of infection could result from the reduced expression of Ccl2 and/or increased production of IL-10 . Indeed , during T . brucei infection , IL-10 has been reported to limit the CCR2-mediated egress of monocytes from the bone marrow and to suppress their differentiation/maturation into TNF producing cells , thereby preventing tissue damage [18] , [24] . The more sustained MIF production during the chronic stage of infection compared to the acute stage could also account for the observed accumulation of inflammatory monocytes in WT infected mice . Our data further revealed that CD11b+Ly6cintLy6G+ neutrophils accumulated in the liver of WT mice during the chronic stage of T . brucei infection , coinciding with increased gene expression of the neutrophil-specific chemokines KC and LIX in total liver [26] . Yet , this increase was higher in WT than Mif−/− mice . Of note , besides indirect effects of MIF on monocyte and neutrophil recruitment mediated via induction of CCL2 and KC/LIX , respectively , we can't exclude that direct MIF effects account for the observed difference in cell recruitment/retention between WT and Mif−/− infected mice . Indeed , MIF features structural motives shared by canonical CXC+ ligands and is therefore considered a non-cognate ligand of the chemokine receptors CXCR2 and CXCR4 that , through interaction with these receptors , can be instrumental in inflammatory pathogenic leukocyte recruitment [14] . Moreover , MIF was also shown to bind CD74 ( Ia-associated invariant chain ) , a single-pass membrane-receptor , which in turn can form functional complexes with either CXCR2 or CXCR4 and subsequently mediates MIF-specific signaling [14] . Although neutrophilia has been documented before [27] , the role of neutrophils in the outcome and pathogenesis of African trypanosomiasis is poorly investigated . In this regard , we report that the serum myeloperoxidase activity , as indicator of neutrophil activity which can play a pro-inflammatory pathogenic role during chronic infections [28] , increased significantly during T . brucei infection in WT mice and to a lower extent in Mif−/− mice . An accumulation of neutrophils in the liver of T . brucei infected mice can be relevant . Indeed , neutrophils represent an important source of MIF that can be released upon activation or TNF-induced apoptosis [19] . Moreover , neutrophils can enhance hepatocyte death that release necrotic products into the circulation triggering a systemic inflammatory immune response [29] , which is the major cause of tissue pathogenicity in T . brucei infected mice . Accordingly , liver MIF protein levels increased drastically during the chronic phase of infection . Moreover , we observed a pathogenic role for WT neutrophils upon their adoptive transfer into infected Mif−/− recipient mice , which resulted in increased TNF production and increased ALT/AST levels . Since the transfer of neutrophils from Mif−/− mice into WT recipient mice did not affect TNF and liver injury , it seems that MIF production by neutrophils evidenced in this report plays a prominent , previously unappreciated role in trypanosomiasis-associated pathogenicity . On the other hand , the transfer of WT monocytes , that contribute to MIF production in acute T . brucei infection stage [24] , into Mif−/− mice recipient mice only resulted in a moderate increase in MIF and TNF production but induced similar level of tissue injury than the transfer of WT neutrophils . Hence , neutrophil-derived MIF is a more important contributor than monocyte-derived MIF to pathogenicity in infected mice . To our knowledge , this is the first time that the neutrophil is identified as the more prominent source of MIF in a disease situation . Although the pathogenic activity of monocytes is relatively less dependent from their MIF production and TNF induction than the one of neutrophils , monocytes could contribute to liver injury through production of other pathogenic type I immune response associated molecules such as Cxcl10 , Cxcl9 or Ccl3 [24] . Besides liver pathogenicity , a clinically significant anemia develops during African trypanosomiasis [3] , [30] . We observed reduced anemia and preserved serum iron levels during the course of T . brucei infection in Mif−/− mice . These data are in concordance with our previous results showing that reducing the inflammatory immune responses using a GPI-based treatment coincides with the alleviation of anemia and improved iron-homeostasis resulting in higher iron-bioavailability and increased erythropoiesis [31] . Similarly , key genes involved in iron homeostasis were differentially modulated in T . brucei infected WT and Mif−/− mice . Indeed , Hmox1 which is involved in RBC catabolism and iron extraction from hemoglobin , together with the iron storage molecule Fth1 , showed reduced expression in infected Mif−/− mice . Conversely , the iron cell exporter Fpn1 expression increased upon T . brucei infection . Given that TNF and IFN-γ can induce Hmox1 , that TNF and IL-6 are important inducers of Fth1 and that IFN-γ can suppress Fpn1 expression [6] , the reduced levels of TNF and IL-6 , as well as of IFN-γ may explain for the lower Hmox1 , Fth1 and higher Fpn1 expression levels in Mif−/− mice . The reduced inflammatory immune response coupled with restored iron-homeostasis in infected Mif−/− mice could alleviate the suppression of erythropoiesis that occurs during the chronic phase of T . brucei infection [32] . Our data indeed show that the absence of MIF correlated with improved erythropoiesis as evidenced by the higher percentage of mature RBCs and increased expression of genes involved in erythropoiesis ( Gata1 , Epor , Tal1 , Maea and Gas6 ) in both the bone marrow and spleen during the chronic phase of infection . In this context , elevated MIF levels in the spleen and bone marrow of P . chabaudi-infected mice were found to inhibit the early stages of erythropoiesis as well as hemoglobin production [33] . In the T . brucei African trypanosomiasis model , the suppressive effect of MIF was observed at the later stage of erythroid differentiation . The increased gene expression levels in T . brucei infected Mif−/− mice of Maea , which is crucial for the enucleation of reticulocytes [34] , and of Gas6 , which reduces TNF-mediated apoptosis of erythroid progenitor cells [35] , may contribute to the more efficient RBC maturation . The reduced anemia observed in T . brucei infected Mif−/− mice could also be attributed to the better RBC recovery/reduced RBC clearance in the chronic phase of infection , which in turn could result from reduced myeloid cell activation and/or lower IFN-γ as compared to WT infected mice . Collectively , our results suggest that in the chronic phase of T . brucei infection , MIF contributed to inflammation-associated pathogenicity by ( i ) sustaining a persistent pro-inflammatory type I immune response and ( ii ) maintaining/enhancing the recruitment of pathogenic monocytic cells and neutrophils in the liver . Hereby , neutrophil-derived MIF contributed significantly to enhanced TNF production and liver damage . In addition , MIF contributed to ( iii ) iron-accumulation in liver myeloid cells , suppressing erythropoiesis at later stages of erythroblast differentiation and ( iv ) enhanced RBC clearance . Despite reduced pathogenicity , Mif−/− mice exhibit only a moderate increase in survival compared to WT mice , inferring that MIF-independent mechanisms determine survival of infected mice . Yet , so far the exact cause of death in murine African trypanosomiasis is unknown and is suggested to be due to Systemic Inflammatory Response Syndrome ( SIRS ) , thus likely multifactorial [36] and going beyond the role of MIF . Interestingly , polymorphisms in the Mif gene have been shown to contribute to susceptibility in several inflammatory diseases ( reviewed in [37] ) . Moreover , low-expression MIF alleles , which occur more commonly in Africans , may offer protection from disease manifestations in trypanosomiasis endemic settings [38] . Therefore , interfering with MIF signaling could be a novel approach to limit inflammation-associated complications during T . brucei trypanosomiasis .
All experiments complied with the ECPVA guidelines ( CETS n° 123 ) and were approved by the VUB Ethical Committee ( Permit Number: 08-220-8 ) . T . brucei tsetse fly infections were approved by the Environmental administration of the Flemish government . Clonal pleomorphic T . brucei AnTat 1 . 1E parasites were a kind gift from N . Van Meirvenne ( Institute for Tropical Medicine , Belgium ) and stored at −80°C . Tsetse flies infected with non-clonal T . brucei AnTAR1 parasites were maintained at the Institute of Tropical Medicine . Wild type ( WT ) C57Bl/6 mice were obtained from Janvier . MIF deficient ( Mif−/− ) [39] and ubiquitin-GFP ( Jackson Laboratories ) C57Bl/6 mice were bred in our animal facility . Female mice ( 7–8 weeks old ) were infected with 5×103 AnTat1 . 1E trypanosomes ( intraperitonealy ( i . p . ) ) or using one individual tsetse fly with a mature salivary gland infection which was allowed to feed per mouse . Parasite and red blood cell ( RBC ) numbers in blood were determined via haemocytometer by tail-cut ( 2 . 5 µl blood in 500 µl RPMI ) . Anemia was expressed as percentage of RBCs remaining in infected mice compared to that of non-infected mice . MIF-neutralisation experiments were performed by i . p . injection of mice with 500 µg neutralizing anti-MIF IgG1 [40] or matching isotype control IgG ( University of Louvain , Experimental Immunology Unit ) every second day starting from day 1 post infection ( p . i . ) . Total serum iron was measured using the IRON FZ kit ( Chema Diagnostics ) as recommended by the suppliers . Serum MPO concentrations were measured as described by the suppliers ( R&D systems ) . Blood Hemoglobin levels were quantified colorimetrically . Briefly , 2 µl blood was diluted in 200 µl distilled water in a 96 well round bottom plate ( Falcon ) and incubated for 30 min . at 37°C . After centrifugation ( 600 g , 10 min . ) the supernatant was collected and the OD540nm measured in an Ultra Microplate reader ( ELx808 , Bio-Tek instruments . inc ) . The hemoglobin concentration was calculated using a hemoglobin standard ( Sigma ) . Livers from CO2 euthanized mice were perfused with 30 ml heparinized saline ( 10 units/ml; Leo Pharma ) containing 0 . 05% collagenase type II ( Clostridium histolyticum; Sigma-Aldrich ) , excised and rinsed in saline . Following liver mincing in 10 ml digestive media ( 0 . 05% collagenase type II in Hanks' Balanced Salt Solution ( HBSS ) without calcium or magnesium; Invitrogen ) and incubation at 37°C for 30 min , the digested tissue was homogenized and filtered ( 40 µm pore filter ) . The cell suspension was centrifuged ( 7 min , 300×g , 4°C ) and the pellet treated with erythrocyte-lysis buffer . Following centrifugation ( 7 min , 300×g , 4°C ) the pellet was resuspended in 2–5 ml RPMI/5%FCS and cells counted to bring them at 107 cells/ml for flow-cytometric analysis , RT-QPCR and cell culturing . For RT-QPCR analysis 107 cells were resuspended in 1 ml TRIzol ( Gibco-Invitrogen Life Technologies ) and stored at −80°C . Spleen and bone marrow ( tibia and femur ) cells were obtained by homogenizing the organs in 10 ml RPMI medium , passing the suspension through a 40 µm pore filter and centrifugation ( 7 min , 300×g , 4°C ) . After resuspending the pellet , cells were counted and brought at 107 cells/ml for flow cytometry ( RBC ) analysis . Remaining cells were pelleted ( 7 min , 300×g , 4°C ) and processed as described for the liver ( see above ) . Concentrations of MIF and TNF ( R&D Systems ) as well as IFN-γ , IL-6 and IL-10 ( Pharmingen ) in serum and/or culture medium were determined by ELISA as recommended by the suppliers . 1 µg total RNA prepared from 107 cells was reverse-transcribed using oligo ( dT ) and Superscript II Reverse Transcription ( Roche Molecular Systems ) following the manufacturer's recommendations . RT-QPCR conditions were as described in [41] . Primer sequences are reported in Table S1 and Ct values of the S12 household gene for naive and infected mice samples ( within the BM , spleen and liver ) is shown in Table S2 . Blood , spleen and bone marrow cells were analysed before RBC lysis . Briefly , total blood ( 2 . 5 µl diluted in 500 µl RPMI ) and spleen and bone marrow ( 100 µl from a stock solution of 107 cells/ml ) were incubated ( 20 min , 4°C ) with Fc-gamma blocking antibody ( 2 . 4G2 , BD Biosciences ) and further stained with phycoerythrin ( PE ) -conjugated anti-Ter-119 , fluorescent isothiocyanate ( FITC ) -conjugated anti-CD71 , Allophycocyanin ( APC ) -conjugated CD44 ( eBioscience , ImmunoSource ) , PE-Cy7 conjugated rat anti-CD11b ( BD Pharmingen ) and matching control antibodies . The cells were washed with PBS , measured on FACSCanto II ( BD Biosciences ) and the results analysed using FlowJo software by gating on Ter-119+ cells . The bone marrow , spleen and liver cells after RBC lysis were analyzed using APC-Cy7 conjugated CD45 ( BD Pharmingen ) , PE-Cy7 conjugated rat anti-CD11b ( BD Pharmingen ) , Alexa647-conjugated rat anti-Ly6c ( Serotec ) , PE-conjugated rat anti-Ly6G ( BD Pharmingen ) and matching control antibodies . 7AAD ( BD Pharmingen ) was used to exclude death cells . Neutrophils and monocytes were isolated from the bone marrow of infected mice ( day 24 p . i ) . Following isolation and surface staining as described above using PE-Cy7 conjugated rat anti-CD11b ( BD Pharmingen ) and Alexa647-conjugated rat anti-Ly6c ( Serotec ) , neutrophils were sorted based on their CD11b+Ly6cint surface expression and FSC/SSC profile using FACSAria II ( BD ) and subsequently stained with PE-conjugated rat anti-Ly6G ( BD Pharmingen ) to confirm their purity ( 95–99% ) . Monocytes were sorted based on their CD11b+Ly6chigh surface expression and FSC/SSC profile using FACSAria II ( BD ) . Next , 3 individual infected ( 24 days p . i . ) WT or Mif−/− mice were injected intravenously ( i . v . ) with 2×106 neutrophils , 2×106 monocytes or 200 µl RPMI ( control group ) . 18 hours after transfer cells were isolated from the liver of recipient mice and analyzed . Liver cells ( stock: 107/ml ) were diluted till 2×106 cells/ml in RPMI-1640 medium/10% FCS/non-essential amino acids/glutamate/penicillin/streptomycin . Next , 500 µl cell suspension/well were cultured ( 36 hours , 37°C , 5% CO2 ) in 48 well plates ( Nunc ) and the supernatant tested for TNF levels . Serum AST and ALT levels were determined as described by the suppliers ( Boehringer Mannheim Diagnostics ) . 200 µl heparinized blood from 7–8 weeks old ubiquitin-GFP C57Bl/6 mice was injected i . v . into 3–5 non-infected or infected ( 12 days p . i . ) mice . Every second day , RBC numbers were enumerated via haemocytometer ( see above ) and the remaining cells analysed via FACS ( see above ) using PE-conjugated Ter-119 antibody . Following gating on Ter-119+ cells , two distinct RBC populations could be identified ( GFP+ or - ) . To calculate the RBC clearance , the percentage of GFP+ RBCs present at day 1 post injection was referred to as 100% signal . All other time-points were compared with this day . The GraphPad Prism 4 . 0 software was used for statistical analyses ( Two-way ANOVA or student t-test ) . Values are expressed as mean ± SEM . Values of p≤0 . 05 are considered statistically significant .
|
Uncontrolled inflammation is a major contributor to pathogenicity development during many chronic parasitic infections , including African trypanosome infections . Hence , therapies should aim at re-establishing the balance between pro- and anti-inflammatory responses to reduce tissue damage . Our experiments uncovered that macrophage migration inhibitory factor ( MIF ) plays a pivotal role in trypanosomiasis-associated pathogenicity development . Hereby , MIF-deficient and neutralizing anti-MIF antibody-treated wild type ( WT ) T . brucei-infected mice exhibited decreased inflammatory responses , reduced liver damage and anemia ( i . e . the most prominent pathogenicity features ) compared to WT control mice . The reduced tissue damage coincided with reduced infiltration of pathogenic monocytic cells and neutrophils , whereby neutrophil-derived MIF contributed more significantly than monocyte-derived MIF to tissue damage . MIF also promoted anemia development by suppressing red blood cell production and enhancing their clearance . The clinical significance of these findings follows from human genetic data indicating that low-expression ( protective ) MIF alleles are enriched in Africans . The current findings therefore offer promise for human translation and open the possibility of assessing MIF levels or MIF genotype as an indication of an individual's risk for severe trypanosomiasis . Furthermore , given the unmet medical need of African trypanosomiasis affecting millions of people , these findings highlight MIF as a potential new therapeutic target for treatment of trypanosomiasis-associated pathogenicity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"parasitology",
"biology",
"and",
"life",
"sciences",
"immunology"
] |
2014
|
MIF Contributes to Trypanosoma brucei Associated Immunopathogenicity Development
|
Sexual transmission of Ebola virus disease ( EVD ) 6 months after onset of symptoms has been recently documented , and Ebola virus RNA has been detected in semen of survivors up to 9 months after onset of symptoms . As countries affected by the 2013–2015 epidemic in West Africa , by far the largest to date , are declared free of Ebola virus disease ( EVD ) , it remains unclear what threat is posed by rare sexual transmission events that could arise from survivors . We devised a compartmental mathematical model that includes sexual transmission from convalescent survivors: a SEICR ( susceptible-exposed-infectious-convalescent-recovered ) transmission model . We fitted the model to weekly incidence of EVD cases from the 2014–2015 epidemic in Sierra Leone . Sensitivity analyses and Monte Carlo simulations showed that a 0 . 1% per sex act transmission probability and a 3-month convalescent period ( the two key unknown parameters of sexual transmission ) create very few additional cases , but would extend the epidemic by 83 days [95% CI: 68–98 days] ( p < 0 . 0001 ) on average . Strikingly , a 6-month convalescent period extended the average epidemic by 540 days ( 95% CI: 508–572 days ) , doubling the current length , despite an insignificant rise in the number of new cases generated . Our results show that reductions in the per sex act transmission probability via abstinence and condom use should reduce the number of sporadic sexual transmission events , but will not significantly reduce the epidemic size and may only minimally shorten the length of time the public health community must maintain response preparedness . While the number of infectious survivors is expected to greatly decline over the coming months , our results show that transmission events may still be expected for quite some time as each event results in a new potential cluster of non-sexual transmission . Precise measurement of the convalescent period is thus important for planning ongoing surveillance efforts .
Recent reports suggesting the potential for sexual transmission of Ebola virus from convalescent survivors have raised a number of important questions regarding its impact on the final phase of the epidemic in West Africa [1 , 2] . Even once the worst hit countries of Guinea , Liberia , and Sierra Leone are declared free of Ebola virus disease ( EVD ) , rare cases may still arise from the large number of remaining survivors . Importantly , sexual transmission is dependent on the frequency of infections rather than the density of available hosts , allowing chains of transmission to persist at low susceptible densities where non-sexual transmission would typically fail to occur [3] . Perhaps the most crucial element for bringing the epidemic to an end is maintaining vigilance in the community by preventing—or quickly responding to—new chains of transmission . Thus , it is important to investigate the potential impact of convalescent sexual transmission on the transmission dynamics in general , and on the tail of the epidemic in particular , to understand how long that vigilance might remain critical . Follow-up studies on survivors of the 1995 outbreak in the Democratic Republic of Congo [4] and the 2000 [5] and 2007 [6] outbreaks in Uganda have raised awareness of what is now being termed “post-Ebola syndrome” ( post-Ebola sequelae ) –debilitating illnesses from myalgia to uveitis—which can persist for at least 21 months after the onset of symptoms . Though the virus is no longer detected in the blood after acute EVD symptoms disappear , active ( replicating ) virus has been documented in ocular fluid , rectal fluids , vaginal fluids , and semen [1 , 4 , 7 , 8] . Transmission to sexual partners was never confirmed in earlier outbreaks , but was suspected to have occurred in at least one instance [4] . Similarly , cases of sexual transmission of other hemorrhagic fever infections , notably by the closely related Marburg virus , have been suspected in the past [9 , 10] . Studies from the West African outbreak , showing viremia in semen 4–6 months after onset of symptoms in 65% of men tested ( 7–9 months in 26% ) [1] and presenting molecular evidence of sexual transmission from a survivor 179 days after onset of symptoms [2] , suggest that sexual transmission from convalescent men can and does occur . Sexual transmission of Ebola virus from convalescent survivors is likely a rare event , but researchers have warned that it should be considered in epidemiological models that are used to predict the trajectory of an outbreak [11] . Without aiming to make a predictive model but rather to understand what aspects of the epidemic may be affected by inclusion of sexual transmission , we devised a novel formulation of the mathematical model for EVD transmission: SEICR ( susceptible-exposed-infectious-convalescent-recovered ) , which includes a component for convalescent sexual transmission from convalescent survivors who maintain active Ebola virus replication . We illustrated the model by fitting it to weekly EVD incidence from Sierra Leone , the largest population of recovering survivors from the current West Africa epidemic . We performed sensitivity analysis to understand the influence of key unknown parameters , such as the duration of the convalescent period and the transmission probability per sexual contact . Considering the stochastic nature of such rare sexual transmission events , we also performed Monte Carlo simulations to explore the impact of sexual transmission on the epidemic tail in Sierra Leone .
We extended a SEIR ( susceptible-exposed-infected-recovered ) modeling framework , which has been extensively used to describe EVD transmission [12–14] , by adding a component for sexual transmission from convalescent survivors who maintain active Ebola virus replication ( Fig 1 ) . The resulting SEICR model has five states: susceptible , S , exposed , E , symptomatic and infectious , I , convalescent , C , fully recovered and immune , R , and dead , D . The model is represented by the following set of ordinary differential equations ( ODEs ) : dSdt = β ( t ) S I−βS p C SNdEdt = β ( t ) S I+βS p C SN − σEdIdt = σE − γ IdCdt = ( 1−f ) γ I − α CdRdt = α CdDdt = f γ I ( 1 ) where N = S + E + I + C + R denotes the total population size . We assumed the non-sexual transmission rate , β ( t ) , to be initially constant ( β0 ) before it starts to decay exponentially due to the effect of control interventions and behavior change after timeτ: β ( t ) = β1 + ( β0 –β1 ) e-k ( t-τ ) [12] . The sexual transmission parameter , βs , can be described as the product of two parameters ( βs = ηq ) that we will consider separately: η is the per sex act transmission probability of Ebola virus from convalescent men , and q is the daily rate at which they engage in sexual intercourse . The number of convalescent individuals are scaled by p , which is the proportion of convalescent survivors who are sexually active men . 1/σ and 1/γ represent the average durations of incubation and symptomatic infection , respectively . f is the case fatality rate . The average duration after which convalescent patients recover completely and shed no further replicating Ebola virus from their body is given by 1/α . We assumed that sexual transmission is frequency-dependent [3 , 15 , 16] , i . e . , the probability that the sexual partner of a convalescent man is susceptible is given by S/N . The basic reproductive number , R0 , for the SEICR model can be calculated using the next-generation matrix method [17 , 18] and is given by R0=βS0γ+ ( 1−f ) p βs α , where S0 is the initial number of susceptible individuals ( see S1 Appendix ) . When α goes to infinity or either βs = 0 or p = 0 , the equation reduces to the basic reproductive number in absence of sexual transmission: R0 , N=βS0γ . Thus , the second term represents the contribution of sexual transmission from convalescent patients to the overall R0: R0 , C= ( 1−f ) p βsα . Since the number of sexual transmission events was likely to be small and little information is currently available on the nature of each transmission event , we fitted only the non-sexual ( SEIR ) deterministic EVD transmission model to weekly incidence of confirmed and probable cases in Sierra Leone as reported in the WHO patient database [19] ( S1 Fig ) . The data set was extended with weekly incidence from the situation report for the most recent weeks when no data was available in the patient database . In order to account for variability in the accuracy of reporting , we assumed that the number of reported cases follows a negative binomial distribution with mean as predicted by the model and dispersion parameter ϕ [20] . We derived maximum likelihood estimates ( MLE ) of the following model parameters [14 , 21]: the baseline transmission rate β0 , the time τ at which transmission starts to drop , the rate k at which transmission decays , and the dispersion parameter ϕ . For the fitting procedure , we assumed that there were no sexual transmission events , i . e . , we set βS to zero . The basic reproductive number in absence of sexual transmission is R0 , N = β0N0/γ , and the reproductive number in presence of partially effective control interventions is R1 , N = β1N0/γ , with N0 being the population size of Sierra Leone . We explored value ranges for sexual transmission parameters ( Table 1 ) based on information from the current epidemic [22] and studies of human immunodeficiency virus [23 , 24] . Remaining parameters were based on published values from the literature ( Table 1 ) . We solved the system of ODEs numerically using the function ‘ode’ from the ‘deSolve’ package in the R software environment for statistical computing [29] . We compared the following response variables of the model: the epidemic peak number of exposed , E , acute , I , and convalescents , C , cases; the cumulative number of EVD cases , deaths , and recoveries; the date at the epidemic peak; the daily and cumulative incidence of sexual transmission; and the date at which the last symptomatic case either died or entered into convalescence ( “day of last case” ) . We defined the day of last case as the time when the number of symptomatic and infectious individuals , I , dropped below 0 . 5 . We considered the following parameters for the sensitivity analysis: the per sex act transmission probability of Ebola virus from convalescent men ( η ) , the proportion of convalescent survivors who are sexually active men ( p ) , the rate at which they engage in sexual intercourse ( q ) , and the rate at which convalescent patients recover completely and shed no further replicating Ebola virus from their body ( α ) . The sensitivity of the response variables to changes in R0 was explored simultaneously as a comparison . We generated 1000 parameter combinations from the uniform ranges , log-transformed [0 . 5x – 2x] for the parameter values for η , p , q , and α , given in Table 1 via Latin hypercube sampling using the Huntington and Lyrintzis correlation correction method ( function ‘lhs’ from R package ‘pse’ ) [30] . We then calculated partial rank correlation coefficients ( PRCCs ) using 50 bootstrap replicates [31] . We performed stochastic simulations of the SEICR model with and without sexual transmission using Gillespie’s algorithm [32] . We specifically investigated the following response variables from the simulations: the cumulative number of EVD cases , the size and date of the epidemic’s peak incidence ( daily number of new symptomatic infections ) , and the date of last case ( last day that symptomatic infections , I , fell below 1 ) . Summary statistics were based on the results of 1000 simulation runs for each transmission scenario . We calculated the average of the peak and total cumulative number of EVD cases by including all simulations runs , i . e . , also the simulations that rapidly go extinct . In contrast , the average dates of the epidemic peak and last case were based on the simulated epidemic trajectories over which 50 or more cases were accumulated .
Assuming a conservative baseline scenario ( η = 0 . 001 and 1/ α = 3 months; Table 1 ) , the reproductive number of a convalescent infection , R0 , C , is 0 . 0024 . This corresponds to only 0 . 12% of the overall R0 of 2 . 0224 . Increasing the convalescent period from 3 to 6 months , the contribution of R0 , C ( 0 . 0051 ) to the overall R0 rises to 0 . 25% . The equation for R0 , C ( see Methods ) illustrates that doubling the per sex act transmission probability has the same impact as doubling the convalescent period . It is important to note that the relative contribution of sexual transmission to the overall reproductive number rises as the effective reproductive number drops during the epidemic due to the effects of control interventions and decreasing density of susceptible hosts ( see S2 Fig ) . The two key unknown parameters of sexual transmission are the per sex act transmission probability , η , and the rate at which convalescent survivors fully recover , α . Both parameters were found to have very small effects on the peak number of infected or exposed patients ( Figs 2A , 3A , 4A , 4B and 4C; S2 and S3 Figs ) . The duration of the convalescent period has a large impact on the peak number of convalescent individuals , while η does not ( compare Fig 2A and Fig 3A ) . The total number of recovered individuals is reached more slowly the longer the convalescent period ( Fig 2B ) , which is not an effect caused by η ( Fig 3b ) . While the convalescent periods ( 1/ α = [3–9 months] ) and the values of η ( η = [0 . 0005–0 . 002] ) we explored create very few extra cases ( Figs 2B and 3B ) , sensitivity analyses revealed that a higher per sex act transmission probability , η , a higher proportion of sexually active convalescent individuals , p , or a higher frequency of sex acts , q , have larger impacts on the total number of cases than would proportional increases in the convalescent period ( see S3A Fig ) . Sensitivity analyses also revealed that these sexual transmission parameters could produce a small delay in the epidemic peak , more so than would changes in the convalescent period ( see S3B Fig ) . The number of sexual transmission events expected from the baseline scenario ( η = 0 . 001 and 1/ α = 3 months ) is 31 . 2 , the majority of which will occur around the peak of the epidemic ( Figs 2C and 3D ) and thus likely go undetected . Doubling either η or 1/ α results in nearly equal increases in the incidence and cumulative number of sexual transmission events ( Figs 2C , 2D , 3C and 3D ) , with either leading to roughly double the number of sexually transmitted cases over the course of the whole epidemic ( > 60 cases ) . It should be noted that the total number of cases increases more than by simply the number of sexual transmission events , because each sexual transmission event results in a new potential cluster of non-sexual transmission . The day of last case is affected more by the convalescent period than the per sex act transmission probability ( represented by vertical lines in Figs 2A and 3A ) , a result confirmed by the sensitivity analysis ( see S3A Fig ) . The tail of the epidemic will depend on a small number of events that are likely to be affected by stochastic processes , thus we used Monte Carlo simulations to explore this behaviour . We performed stochastic simulations of the EVD transmission model to investigate the epidemic dynamics when the number of new cases becomes small , i . e , during the tail of the epidemic . Comparing model simulations while assuming a convalescent period of 3 months to those without sexual transmission confirmed the deterministic results that sexual transmission from convalescent survivors does not lead to a significant increase in the cumulative number of infected cases ( non-STI: 11 , 092 +/- 627 cases; STI: 10 , 944 +/- 642 cases; Wilcox rank sum test: W = 491990 , p = 0 . 53 ) , nor the size ( non-STI: 77 +/- 4 . 1 new cases per day; STI: 75 +/- 4 . 2 new cases per day; W = 493710 , p = 0 . 62 ) or timing ( non-STI: day 187 +/- 0 . 9; STI: day 187 +/- 0 . 9; t = 0 . 19 , df = 1017 . 4 , p = 0 . 85 ) of the epidemic peak incidence ( Fig 4A , 4B and 4C ) . This conservative period of potential sexual transmission , which has recently been shown to extend well beyond 3 months in at least 65% of patients [1] , lengthened the average date on which the last active case could be detected by nearly three months ( non-STI: 548 +/- 4 . 0 days; STI: 630 +/- 6 . 6 days; difference: 83 days [95% CI: 68–98 days] , t = -10 . 8 , df = 867 . 97 , p < 0 . 0001; Fig 4D , 4E , 4G and 4H ) . The width of the tail ( s . d . = 151 days ) was such that 23 . 4% of the 529 simulated epidemics that accumulated at least 50 cases still experienced symptomatic individuals 730 days ( two years ) after the start of the epidemic ( Fig 4H ) . Strikingly , when the convalescent period was extended from 3 months to 6 months , the projected length of the epidemic increased to a mean of 1088 days ( +/- 15 . 5 ) , with 84 . 0% of the 538 sustained epidemics taking over two years to end ( Fig 4F and 4I ) . However , the average number of new cases produced remained small ( 11 , 869 +/- 663 cases; W = 482790 , p = 0 . 18 ) . Importantly , there is greater variance in the tail of the epidemic when sexual transmission is considered , and this uncertainty grows with the length of the convalescent period ( Fig 4G , 4H and 4I ) . To understand the differential impacts of the convalescent period ( 1/ α ) and the per sex act transmission probability ( η ) on the epidemic tail , the effect of two-fold reductions in η on the average duration of the epidemic under both 3 and 6 month convalescent periods were compared ( S1 Table ) . Cutting the per sex act transmission probability in half led to statistically significant reductions in the length of the epidemic , but this was 3-fold less effective than equivalent changes in the convalescent period ( S4 Fig and S1 Table ) , corroborating the results of the deterministic sensitivity analyses ( above ) . We also note that reducing η did not greatly reduce the enormous variance observed with the longer convalescent period .
Our study shows that the length of the convalescent period will determine whether or not sexual transmission of Ebola virus from recovering patients will have a profound effect on the length of time it will take for the epidemic to completely fade . For Sierra Leone , we found that an average convalescent period of 3 months , and a per sex act transmission probability of 0 . 1% , could extend the EVD epidemic in Sierra Leone by an average of 83 days ( 95% CI: 68–98 days ) . Such a scenario would be consistent with the occurrence of a small number of sexual transmission events during the end-phase of the epidemic . However , assuming an average convalescent period of 6 months led to simulated epidemics whose tails were much more variable , and much longer , despite a lack of significant increase in the total number of cases . So far , the reported cases of sexual transmission of EVD remain rare [1 , 2] . Hence , the per sex act transmission probability of Ebola virus from male convalescent survivors is unlikely to be higher than 0 . 1% , and might well be below this value . Our sensitivity analysis indicates that the duration of the EVD epidemic is heavily influenced by the period during which convalescent men can transmit sexually , calling for a better understanding of the persistence and duration of infectivity of Ebola virus RNA in convalescent patients . We extended an accepted modeling framework that has been widely used to describe the epidemic trajectories of EVD outbreaks and epidemics [12–14] . To our knowledge , this is the first study using mathematical modeling to assess the potential impact of sexual transmission of Ebola virus on the epidemic in West Africa . In addition , given the generality of the model , this is also the first model that investigates the impact of including a secondary transmission route from convalescent individuals . Similar compartmental models have been formalized to account for more realistic infectious periods , including both infectious relapse [33 , 34] and progression through classes of varying stages of infectivity [35 , 36] . However , none of these models included a change in transmission mode between the primary and subsequent infectious classes . This model , then , may also have implications for other pathogens with this kind of secondary transmission route ( e . g . some adenoviruses [37]; see [38] for examples across mammal species ) including other neglected tropical diseases , such as African sleeping sickness [39] , other hemorrhagic fevers that display pathologies similar to EVD [9 , 10] , and the most recent emergence of Zika virus [40 , 41] . In the absence of a better understanding of sexual transmission of EVD , mathematical modeling currently remains the only tool to explore its potential impact on the epidemic trajectory . There are several extensions to this work that future models should consider including in order to make accurate predictions . These include: transmission by other bodily fluids ( e . g . , vaginal secretions [4 , 7] ) , asymptomatic infection [42 , 43] , considering spatial aspects of both social and sexual transmission [44] , and heterogeneity in sexual behavior [3 , 45] . Sexual behaviors , for instance , are often specific to a given culture , and may change drastically in response to such a devastating epidemic that can destroy entire communities and create stigma , disrupting existing social and sexual contact networks [46 , 47]; the lack of these details particularly in developing countries presents a challenge for parameterizing a more complex model . We assumed the duration of convalescence to be exponentially distributed . Eggo et al . [48] fitted a series of unimodal distributions to the data on Ebola virus RNA detection in semen recently reported by Deen et al . [1] and found that the convalescence period could be best described by a gamma distribution . Furthermore , Deen et al . [1] found that the cycle-threshold values decreased over time , indicating that the Ebola virus load in semen and the viral infectivity might also decrease during the convalescence period . It is also critical to measure the length of time viral particles persisting in the seminal fluid remain infectious . Molecular techniques to detect intermediate ( replicative ) positive-sense RNA stages of the virus , infection of human cell lines in tissue culture , or tests in animal models are some typical methods . Retrospective studies using phylodynamics could also prove helpful for estimating this type of parameter [25 , 49] . Uncertainty in the data is not limited to sexual transmission; we fitted our model to weekly incidence of confirmed and probable cases in Sierra Leone , but did not take into account potential underreporting as others have done recently [36] . In addition , the incidence data to which we fit our model will , for the most part , be driven by direct transmission of the virus and thus , to better parameterize and estimate the risk of sexual transmission , we would need data with greater resolution ( e . g . knowledge of which cases were caused by sexual transmission events ) . Another caveat is that EVD is known to exhibit superspreading characteristics [50 , 51] , and superspreading events could lead to explosive regrowth of the epidemic after the occurrence of a new case through sexual transmission [50] . And finally , like other negative-sense single-stranded RNA ( ( - ) ssRNA ) viruses [52] , the species currently circulating in West Africa has been estimated to have high substitution rates [26 , 53 , 54] . This rapid evolution detected throughout the current outbreak zone suggests that within- or between-host adaptation of the virus leading to pro-longed persistence in the seminal fluids is possible . However , without significant attenuation of EVD’s high mortality and morbidity virulence , evolution of sexual transmission becoming the primary route of spread is highly unlikely , as the subsequent infections that arise from a sexual transmission event will be caused primarily through transmission during the acute non-sexual transmission phase of the infection . Awareness of the potential for sexual transmission has led to WHO issuing recommendations that ask convalescent men to abstain from sexual activity as much as possible and to use condoms for up to 6 months after the onset of symptoms [28] . Condom use and social awareness of the risks of sexual transmission during convalescence should reduce the per sex act transmission probability ( η ) and the frequency of sex acts ( q ) , respectively . Our results suggest that while such interventions should reduce the number of sporadic sexual transmission events , they will not necessarily reduce the overall number of cases nor the length of time during which the public health community must stay vigilant in responding to these sporadic cases because they will not affect the time during which convalescent survivors can shed infectious virus ( 1/α ) . This is especially poignant since adherence to these recommendations will never be 100% , particularly after the threat from symptomatic individuals passes . Thus , our results suggest that the current requirement for declaring a region free from EVD ( 42 days following either death or a second negative RT-PCR test of the blood from the last known patient ) , may be too short . Sierra Leone was first declared free from EVD on 7 November 2015 [55] , but the case of a young woman who died from Ebola in January [56] highlights the need for the 90-day period of enhanced surveillance after the declaration has been made . The WHO report that 10 such “flare-ups” , or cases with no apparent link to the original acute symptomatic transmission chains , have been identified throughout the region , and are suspected to have resulted from contact with infectious survivors [57] . However , none of these events have caused a major resurgence of new cases . This is likely primarily due to the continuing vigilance , awareness , and resources provided by public health infrastructures , and is reflected by the basic reproduction number in presence of control interventions , R1 , N < 1 ( Table 1 ) . A relaxation of current response and surveillance efforts could allow a rare sexual transmission event to propagate a new epidemic . As more data about the convalescent survivors of EVD becomes available , this and future mathematical modeling studies will help to better understand the potential epidemiological consequences of sexual transmission on EVD epidemics . Precise estimates of key parameters are important for providing convalescent survivors with sound advice that balances protection of the community with the harm that could come from unnecessary stigmatization [46 , 58 , 59] .
|
Researchers have recently raised suspicion that the Ebola virus can be transmitted sexually from survivors after recovering from the life-threatening acute phase characteristic of Ebola virus disease ( EVD ) . However , the nature of the impact sexual transmission from convalescent survivors may have on disease dynamics remains unknown . Mathematical models are useful for translating empirical uncertainty into a range of possible outcomes . We formalized an epidemiological model that accounts for a secondary route of transmission of EVD through sexual contact with otherwise healthy survivors . We found that while very few additional cases are expected , a 3-month period of convalescent infectivity could extend the 2014–2015 Sierra Leone epidemic by nearly 3 months , and a 6-month convalescent period could double the current length by extending it an additional 18 months . Our results reveal that measures to reduce sexual contact between survivors and susceptible individuals are not likely to have a major impact on the length of time affected public health communities must remain vigilant , and highlight the need for ongoing surveillance efforts .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"infectious",
"disease",
"epidemiology",
"pathogens",
"spatial",
"epidemiology",
"microbiology",
"geographical",
"locations",
"tropical",
"diseases",
"ebola",
"hemorrhagic",
"fever",
"viruses",
"filoviruses",
"mathematics",
"rna",
"viruses",
"statistics",
"(mathematics)",
"sexually",
"transmitted",
"diseases",
"neglected",
"tropical",
"diseases",
"africa",
"research",
"and",
"analysis",
"methods",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"semen",
"medical",
"microbiology",
"epidemiology",
"mathematical",
"and",
"statistical",
"techniques",
"microbial",
"pathogens",
"sierra",
"leone",
"monte",
"carlo",
"method",
"people",
"and",
"places",
"ebola",
"virus",
"anatomy",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"viral",
"diseases",
"statistical",
"methods",
"hemorrhagic",
"fever",
"viruses",
"organisms"
] |
2016
|
Potential Impact of Sexual Transmission on Ebola Virus Epidemiology: Sierra Leone as a Case Study
|
Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour . Models are tuned through repeated simulation until they match the observed behaviour . More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups . Here , we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns ( Paratya australiensis ) . We show that these exhibit a stereotypical ‘phase transition’ , whereby an increase in density leads to the onset of collective motion in one direction . We fit models to this data , which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions , integrating their experiences over time when deciding to change behaviour . We show that the mean-field model fits the large scale behaviour of the system , but does not capture the observed locality of interactions . Traditional self-propelled particle models fail to capture the fine scale dynamics of the system . The most sophisticated model , the non-Markovian model , provides a good match to the data at both the fine scale and in terms of reproducing global dynamics , while maintaining a biologically plausible perceptual range . We conclude that prawns’ movements are influenced by not just the current direction of nearby conspecifics , but also those encountered in the recent past . Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should , if they are to be realistic at multiple biological scales , include memory of previous interactions and other non-Markovian effects .
The most striking features of the collective motion of animal groups are the large-scale patterns produced by flocks , schools and other groups . These patterns can extend over scales that exceed the interaction ranges of the individuals within the group [1]–[4] . For most flocking animals , the rules dictating the interactions between individuals , which ultimately generate the behaviour of the whole group , are still not known in any detail . Many ‘self-propelled’ particle models have been proposed for collective motion , each based on a relatively simple set of interaction rules between individuals moving in one , two or three dimensions [2] , [5]–[8] . Typically these models implement a simple form of behavioural convergence , such as aligning the focal individual's velocity in the average direction of its neighbours or attraction towards the position of those neighbours . Generally such rules are explicitly kept as simple as possible while remaining realistic , with the aim of explaining as much as possible of collective motion from the simplest constituent parts . Each of the models in the literature is capable of reproducing key aspects of the large-scale behaviour of one or more biological systems of interest . Together these models help explain what aspects of inter-individual interactions are most important for creating emergent patterns of coherent group motion . With this proliferation of putative interaction rules has come the recognition that some patterns of group behaviour are common to many models , and that different models can have large areas of overlapping behaviour depending on the choice of parameters [4] . Common patterns of collective behaviour are also observed empirically across a diverse range of animal and biological systems . For example , a form of phase transition from disorder to order has been described in species as diverse as fish [9] , ants [10] , locusts [11] , down to cells [12] and bacteria [13] . In all these systems , as density of these species is increased there is a sudden transition from random disordered motion to ordered motion with the group collectively moving in the same direction . These studies indicate that a great deal can be understood about collective behaviour without reduction to the precise rules of interaction . In many contexts however the rules of interaction are of more interest than the group behaviour they lead to . For example , when comparing the evolution of social behavior across different species , it is important to know if the same rules evolved independently in multiple instances , or whether each species evolved a different solution to the problem of behaving coherently as a group [1] . Recently researchers in the field have become interested in using tracking data from real systems on the fine scale to infer what precise rules of motion each individual uses and how they interact with the other individuals in the group [14]–[19] . This is an important trend in the field of collective motion as we move from a theoretical basis , centred around simulation studies , to a more data-driven approach . The most frequent approach to inferring these rules has been to find correlations between important measurable aspects of the behaviour of a focal individual and its neighbours . For example , Ballerini et al . [14] looked at how a focal individual's neighbours were distributed in space relative to the position of the focal individual itself in a group of starlings . Significant anisotropy in the position of the nearest neighbour , averaged over all individuals , was regarded as evidence for an interaction between each bird and that neighbour . More recently Katz et al . [18] and Herbert-Read et al . [19] investigated how the change in velocity of each individual in groups of fish was correlated to the positions and velocities of the neighbouring fish surrounding the focal individual . This provides evidence not only for the existence of an interaction between neighbours but also estimates the rules that determine that interaction . In these studies the rules of interaction are presented non-parametrically and cannot be immediately translated into a specific self-propelled particle model . Nor are these models validated in terms of the global schooling patterns produced by the fish . An alternative model-based approach that does fit self-propelled particle and similar models to data is proposed by Eriksson et al . [16] and Mann [17] . Under this approach , the recorded fine-scale movements of individuals are used to fit the parameters of , and select between , these models in terms of relative likelihood or quality-of-fit . This approach has the advantage of providing a parametric ‘best-fit’ model and can provide a quantitative estimate the relative probability of alternative hypotheses regarding interactions . What all previous empirical studies have lacked is a simultaneous verification of a model at both the individual and collective level . Either fine scale individual-level behaviour is observed without explicit fitting of a model [18] , [19] or global properties , such as direction switches [11] , [20] , speed distributions [21] , [22] or group decision outcome [23] have been compared between model and data . Verification at multiple scales is the necessary next step now that inference based on fine-scale data is becoming the norm . Just as simulations of large-scale phenomena can appear consistent with observations of group behaviour without closely matching the local rules of interaction , so can fine-scale inferred rules be inconsistent with large-scale phenomena if these rules of inferred from too limited a set of possible models or from correlations between the wrong behavioural measurements . The closest that any study so far has come to finding consistency between scales has been Lukeman et al . [15] . In their study the local spatial distribution of neighbouring individuals in a group of scoter ducks was used to propose parametric rules of interaction , with some parameters measured from the fine-scale observables , but with others left free to be fitted using large-scale data . We suggest that if group behaviour emerges from individual interactions , then the form of these interactions should be inferable solely from fine-scale data without additional fitting at the large-scale . An inability to replicate the group behaviour using a selected model demonstrates that the model space has been insufficiently explored . When faced with alternative hypothesised interaction rules , model-based parametric inference provides the best means of quantitatively selecting between them . In this paper we study the collective motion of small groups of the glass prawn , Paratya australiensis . Paratya australiensis is an atyid prawn which is widepsread throughout Australia [24] . Although typically found in large feeding aggregations , it does not appear to form social aggregations and has not been reported to exhibit collective behaviour patterns in the wild . We conduct a standard ‘phase transition’ experiment [9] , [11] , [12] , studying how density affects collective alignment of the prawns . We complement this approach by using Bayesian inference to perform model selection based on empirical data at a detailed individual level . We select between models by calculating the probability of the fine scale motions using a Bayesian framework specifically to allow fair comparison between competing models of varying complexity . Comparison of the marginal likelihood , the probability of the data conditioned on the model , integrating over the uncertain parameter values , is a well developed and robust means of model selection that forms the core of the Bayesian methodology [25]–[28] and which has been applied to compare models in the biological sciences , particularly neuroscience [29] . Bayesian methods are also well established in animal behaviour through consideration of optimal decision making in the presence of conflicting information , both environmental [30] and social [31] , [32] . In adopting this approach , we reject the dichotomy of model inference based on either fine scale behaviour of the individuals or the motion of the group . Instead we use reproduction of the large scale dynamics through simulation as a necessary but not sufficient condition of the correct model .
Next we investigated a series of interaction models as to their ability to reproduce the fine scale interactions of the prawns . We predict the probability , , that a focal prawn will change its orientation , given one of a number of potential models . The direction changes are determined by the data from the six-prawn treatment . This treatment provides the best balance between the number of data points , density of direction changes , clear large scale behaviour and tracking accuracy . Each model specifies the probability that a focal prawn will change its direction in the next time step conditioned on the relative positions and directions of the other individuals in the arena . We use a logistic mapping to ensure probabilities remain between zero and one , so each model uses the relevant variables to determine a latent ‘turning-intensity’ , , such that , ( 2 ) where is a function of the relative positions and directions of the other prawns , both now and potentially in the recent past , and the model parameters . The models are , in increasing degree of complexity , as follows . Firstly to consider models that do not include zones-of-interaction – non-spatial models . We establish a baseline with a Null model . This simply posits that direction changes occur at random , at the rate established from the single prawn data , and the prawns do not interact in any way that changes this direction-changing probability . Therefore is given simply by a baseline constant , , which is determined by the rate of direction changing in single prawns . ( 3 ) We also consider two models where the interaction is independent of absolute spatial separation . The Mean Field ( MF ) model includes interactions between all prawns regardless of position , such that their relative directions alter the probability of changing direction . Since the number of prawns in the experiment is fixed , the probability for a direction change is influenced by the number of individuals moving in the opposite direction ( negative prawns ) , . Each negative prawn increases the turning intensity by an amount , ( 4 ) A Topological ( T ) model restricts these interactions to a limited number of nearest-neighbours , , the individuals closest to the focal prawn . The turning intensity is now influenced by the number of negative prawns , within the set of nearest-neighbours . ( 5 ) Secondly we consider a class of Spatial models ( S1–S4 ) . These models closely resemble the classic one-dimensional self-propelled particle models from the literature [5] . The focal prawn interacts with neighbours within a spatial zone-of-interaction , . The number and directions of individuals within this interaction zone determine the probability of changing direction . A number of further variations are possible; interactions can be limited to prawns ahead of the focal prawn and/or to prawns travelling in the opposite direction to the focal prawn . We consider four variations , indicated in Table 1 . The general form for this model is given by , ( 6 ) where and are the number of negative and positive ( travelling in the same direction ) prawns within the interaction zone , and and parameterise the influence of each individual on the turning intensity . . Interactions can occur with negative prawns only , , or with both negative and positive oriented prawns , . The spatial interaction zone is either a symmetrical area centred on the focal prawn , of width radians around the ring ( spatial symmetric models in Table 1 ) , or is only directed radians ahead of the focal prawn ( spatial forward models ) . Visual inspection of the movements of the prawns suggests that interactions often follow a particular pattern . Two prawns , travelling in the opposite directions , collide . After the prawns have passed each other one of the prawns may subsequently decide to change direction . Self-propelled particle and other models of collective motion do not capture this type interaction . Such interactions are non-Markovian , i . e . the change in direction is not just the result of the environment now , but of the past environment as well . We proposed a third class of models ( D1–D4 ) , simple non-Markovian extensions of the basic spatial models , where each prawn would ‘remember’ the other individuals it encountered , with those memories fading at an unknown rate after the interaction was complete . As such the prawn would integrate those interactions over time , building up experiences which would alter its chance of changing direction . Mathematically this means that the turning intensity is now auto-regressive , depending on its own value at the previous time step as well as the current positions and directions of the neighbouring individuals . We introduce a decay parameter , , which determines how quickly the turning intensity returns to normal after an interaction with a neighbour has occurred . The same variations of interaction are allowed as for the spatial models , giving a general form for the non-Markovian turning intensity as , ( 7 ) where now indicates the turning intensity at time , which depends on the value of the turning intensity at the previous time step , . The number of prawns still in the interaction zone from time is indicated by , while the number of new arrivals in the interaction zone is given by . Hence raised ( or lowered ) turning intensities persist over time , with a duration controlled by the value of . After the focal prawn changes direction the turning intensity is reset to the baseline , , at the next time step . Table 1 specifies the interaction zone structure for each of eleven alternative models , grouped according to the description given above . For each model we calculate the marginal likelihood of the data , conditioned on the interaction model ( see Materials and Methods ) . The marginal likelihood is the appropriate measure for performing model selection , especially between models of varying complexity . More complex models , by which we mean models with a larger number of free parameters , are penalised relative to simpler models when integrating over the parameter space , since less probability can be assigned to any particular parameter value a priori . The marginal likelihood indicates how likely a particular model is , rather than a model and an chosen optimal parameter value ( see , for example , Mackay [33] Chapter 28 and other standard texts for discussions on this topic ) . The marginal likelihoods of each model are shown in Figure 3A . We also measure the consistency between the large scale results of our experiments and the results predicted by simulation of each model , using the parameter values in Table 1 . We set a consistency condition that any model that accurately approximates the true interactions must fulfil . We measure the large scale quality-of-fit between the model simulations and the experiments using the Kullback-Leibler divergence [34] between the distribution of simulated and experimental outcomes and performing a G-test for quality-of-fit [35] ( see Materials and Methods ) . The p-value associated with this quality-of-fit for each model is shown in Figure 3B , showing which models are deemed to be consistent with experiments ( those with ) . Large scale results from the simulation of each model are shown individually in Figures S2 , S3 , S4 , S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 in the supplementary materials . The Null model , in which prawns do not interact , performs significantly worse than the mean-field model . Figure 4 shows that the mean-field reproduces both the global alignment of the prawn groups , with an increase in polarisation with time and group size . These results show that the prawns interactions involve matching their directions to that of others , producing alignment . Are local spatial interactions important in reproducing observed direction changes ? We note first that a topological interaction zone , where the focal prawn interacts with its nearest neighbours , has a marginal likelihood slightly lower than the mean field model . The topological model is ‘punished’ for having more parameters than the mean-field model , since the most probable value of the topological interaction range encompasses all neighbours . However , interactions between prawns are local . Figure 5 shows how the probability of changing direction depends on the position of the nearest opposite facing neighbour . An opposite facing neighbour within approximately radians of a focal prawn strongly increases the chance that the focal prawn will change direction . This observation suggests that a local interaction spatial model should outperform the mean field model , and we can use the approximate observed range of interaction ( radians ) to inform our prior probability on the interaction zone for models that include one . However , Figure 3A shows that with this limit on the interaction zone , the spatial models ( S1–S4 ) all have a marginal likelihood lower than the mean field model . Simulating these models with most-probable parameters inferred from our analysis of the data ( see Table 1 ) shows that these fit poorly on the large scale too , having a relatively large divergence between the simulated outcomes and the observed large scale alignment patterns and are therefore showing significant differences in the quality-of-fit test ( Figure 3B ) . Both Figure 5 and our biological reasoning insist that locality must be maintained in interactions between individual animals . Therefore the poor performance of these spatial models indicates that they are an incomplete description of the true behaviour of the prawns . The models incorporating a non-Markovian delayed response together with a spatial interaction zone ( models D1–D4 ) all outperformed the most probable Markovian spatial model on both the fine and large scales ( Figure 3 ) . Model D3 is the best performing model on both scales , and is the only model with a greater marginal likelihood than the Mean Field model . This then is the best model we can infer from our selection of possibilities . Figure 6 shows that simulations of model D3 produce collective alignment of the prawns and consistently stronger and faster alignment for larger group sizes , fulfilling our large-scale consistency requirement for a realistic model . The inferred value of the memory parameter associated with this model ( see Table 1 ) puts the half-life of these memories at approximately one second . Combined with the average angular speed of the prawns ( radians/s ) this means that prawns can be separated by a full half of the arena while still exerting a considerable influence on each other's behaviour . This potentially explains the strong performance of the mean field model in explaining the fine scale interactions between individuals .
A number of physical [36]–[38] , technological [39] and biological systems , including animals [9]–[11] , [40] , tissue cells [12] , microorganisms [13] , [41] are known to increase their collective order with density . Glass prawns are one additional example of such a system , which is particularly interesting since they are not known as gregarious or social species . By confining the prawns to a ring we facilitated their interactions and in doing so generated collective motion . This adds further support to the idea that collective motion is a universal phenomenon independent of the underlying interaction rules [4] , [11] , [42] . While we do not expect that prawns often find themselves confined in rings in a natural setting , they and other non-social animals do aggregate in response to environmental features such as food and shelter . Such environmental aggregations can , above a certain density , result in an apparently ‘social’ collective motion . The true value of this study , however , is found not in the addition of one more species to this growing list , but in demonstrating a rigorous methodology for selecting an optimal and multi-scale consistent model for the interactions between individuals in a group . We have used a combination of techniques to identify the optimal model for our experiments: Bayesian model selection , validation against global properties and consistency with biological reasoning . We applied Bayesian model selection to identify the model that best predicts the fine-scale interactions between prawns . This approach allows us to perform model selection in the presence of many competing hypotheses of varying complexity , while avoiding over fitting [17] . This indicated the selection of a non-Markovian model with a persistent ‘memory’ effect . We find that interactions are governed by a perceptual range which is symmetric about the focal individual which is somewhat greater than the average body length of the prawns ( approximately radians ) . Reproduction of the large-scale dynamics is frequently used to validate mathematical models of biological systems , but presents only a necessary and not a sufficient condition for model validation . Indeed , all of the models we have assessed in this work can , with the appropriate parameters , generate aligned motion consistent with experiment . The fact that our mean-field model reproduces global dynamics , but fails at a fine-scale level is not particularly surprising . Mean-field models are not designed to reproduce spatially local dynamics [1] . More illuminating , however , is the failure of Markovian spatial models to reproduce the fine-scale dynamics when the locality of interactions between individuals is imposed . Models S1 , S2 , S3 , S4 are variants of the standard one dimensional Vicsek self-propelled particle model [43] , which has previously been validated against the global alignment patterns of marching locusts [11] . For the prawns these models perform poorly on both capturing the fine scale dynamics of interactions and in reproducing the large scale alignment patterns seen in the data . This inconsistency allowed us to reject standard self-propelled particle models as a good model of the data . To identify a better model we first visually inspected the interactions between the prawns . These observations suggested a ‘memory effect’ , whereby a prawn would remain influenced by individuals beyond the moment of interaction . The resulting models are able reproduce the fine scale and large scale dynamics of the prawns , while also maintaining the biologically-intuitive locality of interactions between individuals . More generally , we would expect other examples of animal motion to be non-Markovian , with individuals taking time to react to others , to complete their own actions and also potentially reacting through memory of past situations . In this context , it is important to consider the limitations of recent studies identifying rules of interaction of fish [18] , [19] . These studies concentrated on quantifying local interactions , but do not try to reproduce global properties . It may be that non-Markovian and other effects are needed to produce these properties . In what circumstances can we expect non-Markovian effects to play an important role in collective behaviour ? Inference based on a Markovian model must account for behavioural changes of a focal individual in terms of their current environment . As such the crucial factor is how much the local environment changes between when the animal receives information and when it responds . Large changes in the local environment can be caused by long response times or by rapid movements of other animals relative to the focal individual . Where behavioural changes are strongly discontinuous , such as the binary one-dimensional movement in this study , non-Markovian effects may become especially important . This is because the focal individual may have to execute a number of small changes ( such as stopping and turning through a several small angles ) in order to register as having changed its direction of motion . Over the course of making many adjustments the environment can change dramatically from the moment that the change was initiated . We have compared the models on the large scale by evaluating the quality-of-fit between the distribution of large scale outcomes predicted by model simulations with that seen in experiments . The model we select from the fine scale analysis is also evaluated as the best on this large scale analysis , and produces simulation results that are qualitatively consistent with experiment ( see Figure 6 ) . Because the same model is selected from both analyses we have not been forced to weight the relative importance of each . In future it may be necessary to decide on an appropriate weighting of these different criteria where they disagree on the optimal model . The research presented here provides a first step towards the use of multi-scale inference in the study of collective animal behaviour and in other multi-level complex systems .
The frame-by-frame movements of the prawns are imperfect representations of the true orientation , since a prawn will often stop or even drift slightly backwards without physically turning around . A Hidden Markov Model ( HMM ) allows the underlying orientation of the prawns to be discovered from the noisy frame-by-frame movements by demanding a higher degree of ‘evidence’ for a direction change , in essence only identifying direction changes when the prawn makes a sustained movement in the new direction . This gives a better estimate of the true orientation than given by the instantaneous velocity alone . We constructed a two-state HMM [44] for the observed changes in position of the prawn , as shown in Figure 7 . The two states represent clockwise ( CW ) or anti-clockwise ( anti-CW ) orientation . In a CW oriented state it is assumed that the prawn will normally move in CW direction over the course of one frame , but because the prawns movements are noisy it may move in the reverse direction over short time periods while remaining oriented CW . We model the distribution of these movements as a Gaussian distribution . We further assume a symmetrical model , such that the distribution of movements in the CW state is anti-symmetric to the distribution of movements in the anti-CW state . Thus a movement of zero is equally probable in either state . We use the Baum-Welch algorithm [44] , [45] to learn the transition probability and the mean and standard deviation of the Gaussian observation probability distribution , using data from single-prawn experiments . We then apply this learnt model to identify the most probable state sequence for each of the prawns in the three- , six- and twelve-prawn experiments , using the Viterbi algorithm [44] , [46] . We further reduce the number of artifactual detected direction changes by removing any instances where a prawn changes direction twice within one second , since inspection suggests these events are caused by tracking errors . A given model , describes the probability of a change of direction for the focal prawn at time , conditioned on the current , and potentially past , positions of the other prawns , and and the parameters of the model . The likelihood for a given parameter set of the model is the probability of the data , , conditioned on the parameters and the model and is the product over both time steps and focal prawns of the probability for the observed outcome - either a change of direction or no change . Let equal one when prawn in experiment changes direction at time , and is zero otherwise , then , ( 8 ) where and indicate the number of experiments and the number of prawns in each experiment respectively . The marginal likelihood of the model is given by integration over the space , , of unknown parameters , ( 9 ) The prior distribution of the parameters , is chosen to represent the available knowledge about the parameters and is split into independent parts . We use the empirical observations in Figure 5 to inform the prior distribution on the interaction range and possible interaction strengths . The prior distribution over the number of interacting neighbours in the topological model is set to the entire possible range for the analysed six-prawn experiments , and the prior distribution for the memory factor is naturally between 0 ( no memory ) and 1 ( permanent memory ) . The prior for the same parameter over different models is the same to allow fair comparison . ( 10 ) where indicates a continuous uniform distribution , indicates a discrete uniform distribution and is the Dirac delta function . Numerical integration over the appropriate parameters was performed using annealed importance sampling [47] , with 1000 parameter samples . We select the most probable parameter values , for each model as those which maximise the posterior probability distribution , ( 11 ) where the posterior probability distribution is given in terms of the likelihood , prior distribution and model evidence defined above ( 12 ) In practice we evaluate the posterior probability for each parameter sample generated within the annealed importance sampling algorithm [47] and select the most probable for each model . Given the most probable parameter values ( maximum a posteri ) for a given model inferred from the fine scale data via equation 12 , simulations of that model can be performed to assess the likely large scale results of the interactions the model encodes . To perform these simulations we treat individual prawns as particles moving on a circular ring . Each particle is initially set to have either CW or CCW motion at random . At each time step each particle , taken in a random order , moves around the ring in its direction of motion , moving a distance sampled from a distribution matched to the mean and variance of the experimentally observed motions ( radians/s ) . After this motion , the distance between all the particles is calculated , and for each particle a decision is made whether to change the direction of motion , based on the rules encoded by the model being simulated . The time step used is s , which is matched to the time spacing in the analysed data . It is observed in model simulations that the rate at which the group aligns is highly dependent on the speed of individuals , which we have not attempted to model accurately . However , the final state after 360 seconds of simulation ( the length of the experiments ) is not sensitive to this factor . Therefore we evaluate the quality-of-fit between the model and experimental data by examine the distribution of final states in the experiments and simulations – that is , how many individuals are travelling clockwise when the experiment or simulation ends . We average this over the final 10 seconds of the experiment or simulation to increase the accuracy of this judgement . The quality-of-fit for the model is given by the Kullback-Leibler ( KL ) divergence [34] , from the experimental distribution of outcomes , to the simulated distribution , . This is a canonical measure of how well one distribution ( the simulated outcomes ) approximates another ( the experimental outcomes ) . If is the proportion of experiments where prawns are travelling clockwise , and similarly the proportion of simulations where particles are travelling clockwise , then the divergence is given by ( 13 ) where is the total number of prawns in the experiment or simulation . We calculate this divergence between experiment and simulation for scenarios with 3 , 6 and 12 prawns to check for consistency over varying group size . The statistical significance of these divergences can be calculated using the G-statistic , , where is the number of experiments , and the KL divergence is evaluated using the natural logarithm . The null hypothesis that the experimental results come from the simulated distribution implies a -distribution for the G-statistic [35] . This article is a revised version of a paper of the same title [48] that was previously published in PLOS Computational Biology and was subsequently retracted when a computational error was discovered .
|
The collective movement of animals in a group is an impressive phenomenon whereby large scale spatio-temporal patterns emerge from simple interactions between individuals . Theoretically , much of our understanding of animal group motion comes from models inspired by statistical physics . In these models , animals are treated as moving ( self-propelled ) particles that interact with each other according to simple rules . Recently , researchers have shown greater interest in using experimental data to verify which rules are actually implemented by a particular animal species . In our study , we present a rigorous selection between alternative models inspired by the literature for a system of glass prawns . We find that the classic theoretical models do not accurately predict either the fine scale or large scale behaviour of the system . Instead , individual animals appear to be interacting even when completely separated from each other . To resolve this we introduce a new class of models wherein prawns ‘remember‚ their previous interactions , integrating their experiences over time when deciding to change behaviour . These show that the fine scale and large scale behaviour of the prawns is consistent with interactions only between individuals who are close together .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"statistical",
"mechanics",
"applied",
"mathematics",
"bayes",
"theorem",
"probability",
"distribution",
"animal",
"behavior",
"mathematics",
"theoretical",
"ecology",
"stochastic",
"processes",
"zoology",
"complex",
"systems",
"theoretical",
"biology",
"probability",
"density",
"biology",
"probability",
"theory",
"behavioral",
"ecology",
"physics",
"ecology",
"computational",
"biology",
"markov",
"model"
] |
2012
|
Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection
|
Salmonella enterica serovar Enteritidis is a cause of both poultry- and egg-associated enterocolitis globally and bloodstream-invasive nontyphoidal Salmonella ( iNTS ) disease in sub-Saharan Africa ( sSA ) . Distinct , multi-drug resistant genotypes associated with iNTS disease in sSA have recently been described , often requiring treatment with fluoroquinolone antibiotics . In industrialised countries , antimicrobial use in poultry production has led to frequent fluoroquinolone resistance amongst globally prevalent enterocolitis-associated lineages . Twenty seven S . Enteritidis isolates from patients with iNTS disease and two poultry isolates , collected between 2007 and 2015 in the Ashanti region of Ghana , were whole-genome sequenced . These isolates , notable for a high rate of diminished ciprofloxacin susceptibility ( DCS ) , were placed in the phyletic context of 1 , 067 sequences from the Public Health England ( PHE ) S . Enteritidis genome database to understand whether DCS was associated with African or globally-circulating clades of S . Enteritidis . Analysis showed four of the major S . Enteritidis clades were represented , two global and two African . All thirteen DCS isolates , containing a single gyrA mutation at codon 87 , belonged to a global PT4-like clade responsible for epidemics of poultry-associated enterocolitis . Apart from two DCS isolates , which clustered with PHE isolates associated with travel to Spain and Brazil , the remaining DCS isolates , including one poultry isolate , belonged to two monophyletic clusters in which gyrA 87 mutations appear to have developed within the region . Extensive phylogenetic diversity is evident amongst iNTS disease-associated S . Enteritidis in Ghana . Antimicrobial resistance profiles differed by clade , highlighting the challenges of devising empirical sepsis guidelines . The detection of fluoroquinolone resistance in phyletically-related poultry and human isolates is of major concern and surveillance and control measures within the region’s burgeoning poultry industry are required to protect a human population at high risk of iNTS disease .
Nontyphoidal Salmonella ( NTS ) are a leading cause of bloodstream infection in sub-Saharan Africa ( sSA ) , a clinical syndrome referred to as invasive nontyphoidal Salmonella ( iNTS ) disease . iNTS disease in sSA is most commonly caused by Salmonella enterica serovars Salmonella Typhimurium or Salmonella Enteritidis , and predominantly affects children <3 years and adults with advanced HIV infection , with a case fatality rate of 19% [1] . The most recent global estimate for iNTS disease was 3 . 4 million cases in 2010 , the majority of which ( 58% ) were in sSA [2 , 3] . The lack of a human vaccine against NTS places a primary importance on interruption of transmission and the availability of effective antibiotics to reduce iNTS-related morbidity and mortality . The strains circulating in sSA are , however , frequently multidrug resistant ( MDR; defined in this study as resistant to three or more antimicrobial classes ) , leaving third-generation cephalosporins and fluoroquinolones as the key agents in treating iNTS disease [4–6] . The emergence of both fluoroquinolone resistance mutations and extended spectrum beta-lactamase production amongst NTS in recent years could therefore lead to cases that are untreatable with currently available antibiotics in resource-poor settings [4 , 7] . Whilst the prevalence of fluoroquinolone resistance amongst African salmonellae is still relatively low in comparison to other regions , such as Asia , this is expected to change as these agents become more widely available [4 , 7–12] . There have been several reports of novel lineages of NTS associated with invasive disease in Africa [5 , 6 , 13 , 14] . A study of S . Enteritidis provided evidence for the recent emergence of two novel clades of this serovar , one geographically restricted to West Africa and the other to Central/East Africa [13] . Both clades differ substantially from the clade causing the global epidemic of poultry- and enterocolitis-associated S . Enteritidis . In common with S . Typhimurium multi-locus sequence type ST313 [5] , the novel clades of S . Enteritidis both have distinct prophage repertoires , harbour an expanded multidrug-resistance plasmid and exhibit genomic degradation similar to that seen in host-restricted typhoidal salmonellae , which have a more invasive pathotype . Although Salmonella enterica in Europe has historically remained susceptible to most antibiotics , resistance to ciprofloxacin is now significant ( 13 . 3% amongst human NTS isolates in 2015 ) and these figures are largely driven by S . Enteritidis [15] and consumption of poultry products contaminated with resistant strains [16 , 17] . Similarly , nalidixic acid ( NA ) resistance ( a marker for diminished ciprofloxacin susceptibility , DCS ) , conferred by single point mutations in the gyrA gene at codons 83 or 87 , has been found in as many as 50% of human S . Enteritidis isolates in European studies [16 , 18 , 19] . We recently reported a high prevalence of DCS amongst iNTS disease-associated isolates collected in the Ashanti region of Ghana between 2007 and 2012 , particularly amongst S . Enteritidis , with 10/19 ( 53% ) having reduced susceptibility compared to only 2% ( 3/129 ) of S . Typhimurium [20] . The underlying epidemiology of DCS amongst NTS strains in Africa remains poorly described . The increasing global trade in poultry and the high levels of ciprofloxacin resistance amongst NTS strains in food animals in high-income countries [15 , 21 , 22] support a possible role for importation of resistant strains . In particular , Ghana imports large volumes of live birds and poultry meat from abroad ( https://comtrade . un . org ) . Alternatively , less regulated use of antibiotics in animal husbandry combined with a growth in intensive farming practises may promote the local emergence of resistance . S . Enteritidis displays niche plasticity , with distinct clades that enable it to become a prominent cause of gastroenteritis globally in association with the industrial production of eggs and poultry , and of multidrug-resistant , bloodstream-invasive infection in Africa [13] . Ghana has both a human population at risk of iNTS disease and an expanding poultry industry . We therefore performed whole genome sequencing of iNTS disease-associated S . Enteritidis isolates from Ghana found to have a high rate of the DCS phenotype , as well as poultry isolates from the same region , in order to understand whether DCS was associated with African or global clades of S . Enteritidis , as control measures are likely to be different .
Blood cultures were collected as part of hospital surveillance studies for bloodstream infections conducted between September 2007 and April 2015 at the Inpatient Department of Agogo Presbyterian Hospital and the Outpatient Department of St Michael’s Hospital , Pramso , both located in the Ashanti Region in central Ghana [10 , 20 , 23 , 24] . All children ≥30 days and ≤15 years of age presenting with either a temperature of ≥37 . 5°C or reported fever within the past 72 hours were enrolled in the study . One to three milliliters of blood was taken from each child following local antisepsis protocols and inoculated into a paediatric blood culture bottle ( BACTEC Peds Plus/F , Becton Dickinson ) and processed using a BACTEC 9050 blood culture system ( Becton Dickinson ) . During 2010 , recruitment took place and blood cultures were performed on the adult ward of the Agogo Presbyterian Hospital . Both local and imported poultry meat was purchased between May to December 2015 from retailers and open markets within Kumasi , the capital of the Ashanti region . 15g of each meat sample was immediately placed in sterile homogeniser bags and transported refrigerated to the laboratory . Ethical approval was granted by The Committee on Human Research , Publications and Ethics , School of Medical Science , Kwame Nkrumah University of Science and Technology in Kumasi , Ghana ( CHRPE/AP/427/13; CHRPE/101/09 ) , the International Vaccine Institute , South Korea ( IVI IRB#2008–002 , 2011–001 ) and the Ethics Committee of the Medical Association Hamburg , Germany ( PV4592 ) . Written informed consent was obtained from adults or the parents or guardian of study children prior to enrolment . Methods for the identification of Salmonella from blood culture have been described previously [20 , 24] . In brief , positive blood cultures were subcultured on Columbia blood agar , chocolate agar , and MacConkey agar ( Oxoid , UK ) . Salmonella isolates were identified biochemically by API 20E tests ( bioMérieux , France ) and serotyped following the White–Kauffmann–Le Minor scheme . Ground meat samples were incubated overnight in Selenite broth ( Oxoid ) and subsequently cultured on Xylose Lysine Deoxycholate agar ( Oxoid ) . Identification was performed as for blood culture isolates . Ciprofloxacin minimum inhibitory concentrations ( MICs ) were determined by Etest ( Oxoid ) according to the European Committee for Antimicrobial Susceptibility Testing ( EUCAST ) guidelines [25] . Isolates were classed as ciprofloxacin susceptible ( MIC ≤0 . 06 μg/mL ) , intermediate/diminished susceptibility ( MIC >0 . 06 and <1 μg/mL ) or resistant ( MIC ≥1 μg/mL ) . DNA was extracted using the QIAamp DNA Mini-kit ( Qiagen , Germany ) according to the manufacturer’s instructions . Sequencing of extracted DNA from isolates 1–21 was performed by the Wellcome Trust Sanger Institute using the Nextera XT library preparation kit on the Illumina HiSeq 2000 ( Illumina , USA ) yielding 100bp paired-end reads . Extracted DNA from isolates 22–29 was sequenced by the Public Health England ( PHE ) Genome Sequencing Unit using the Nextera XT library preparation kit on the Illumina HiSeq 2500 ( Illumina , USA ) run in fast mode according to the manufacturer’s instructions , which yielded 2x 100bp paired end reads ( see S1 Table for individual isolate details and accession numbers ) . Multi-locus sequence typing ( MLST ) analysis was performed using MOST [26] . Identification of antimicrobial resistance determinants ( ARD ) was performed as previously described [27] . Quality trimmed Illumina reads were mapped to the Salmonella enterica Enteritidis reference genome P125109 ( GenBank:AM933172 ) using BWA-MEM [28] . Single nucleotide polymorphisms ( SNPs ) were then identified using GATK2 [29] in unified genotyper mode . Core genome positions that had a high quality SNP ( >90% consensus , minimum depth 10x , GQ> = 30 ) in at least one strain were extracted and RaxML v8 . 17 [30] used to derive the maximum likelihood phylogeny of the isolates under the GTRCAT model of evolution . Support for the maximum likelihood phylogeny was assessed via 100 bootstrap replicates . Single linkage SNP clustering of the isolates within the PHE S . Enteritidis ( eBurst Group 4 ) database , consisting primarily of clinical isolates from routine surveillance in the UK , but also sequences obtained from public databases , was performed as previously described [31] . FASTQ reads from all sequences in this study can be found at the PHE Pathogens BioProject at the National Center for Biotechnology Information ( Accession PRJNA248792 ) . The temporal signal for a given phylogenetic tree was determined using TempEst v1 . 5 . 1 [32] , using the tree and tabulated sample dates as input data . The presence and absence of the MDR virulence plasmid , pSEN-BT ( GenBank accession: LN879484 ) , and a reference plasmid , pSENT ( GenBank accession: HG970000 ) , not associated with MDR , was determined by mapping ( >90% consensus , minimum depth 10x , GQ> = 30 ) short reads from each isolate to the two plasmids using BWA-MEM [28] and plotting the depth coverage . A coverage threshold of > 90% was used to score the presence or absence of the virulence and reference plasmids . MDR was defined in this study as resistance to ≥ 3 antimicrobial classes .
Twenty nine S . Enteritidis isolates were collected between 2007 and 2015; twenty five from children ( median age 24 months , IQR 12–36 months ) , two from adults and two from poultry meat in 2015 ( S1 Table ) . One poultry isolate originated from fresh slaughtered local meat while the other originated from imported ( USA ) frozen meat . Of the 27 individual patients only one had a suspected admission diagnosis of gastroenteritis recorded ( Table 1 ) . The majority ( 21/27 , 77 . 8% ) had a non-focal febrile illness , such as malaria or sepsis , recorded . All Ghanaian isolates had Sequence Types ( STs ) within S . Enteritidis eBurst Group 4; 25/29 were ST11 , and the remaining four were distinct single locus variants of ST11 . When the isolates were placed in the context of the PHE surveillance isolates and a recently published global collection [13] , a considerable amount of strain diversity was revealed ( Fig 1 ) . Fifteen isolates ( 14/27 human and 1/2 poultry ) belonged to a ‘global epidemic clade’ containing isolates of multiple phage types ( PT ) , including PT4 and PT1 [13] , which have been linked to the global human epidemic of poultry-associated enterocolitis [33] . Six isolates ( five human and one poultry ) belonged to a lineage associated with shell egg outbreaks and egg-producing industries in North America [34] . Five human isolates belonged to the recently described West African clade and one human isolate clustered within the Central/East African clade , both associated with iNTS disease . The remaining two human isolates represented newly identified diversity in the S . Enteritidis population structure . Thirteen isolates contained a single gyrA mutation at codon 87 , corresponding with a DCS phenotype by Etest ( MIC >0 . 06 and <1 μg/mL ) , and all belonged to the global epidemic clade . The majority of global epidemic clade isolates ( 13/15 , 86 . 7% ) contained the gyrA mutation , including the poultry isolate from this clade , which was obtained from a locally slaughtered bird . No other fluoroquinolone resistance determinants , including plasmid-mediated qnr genes , were detected amongst any of the isolates . Apart from the gyrA 87 mutation , the most commonly identified resistance determinants were as follows: eleven isolates contained sulphonamide ( most commonly sul-2 ) , ten isolates streptomycin ( strA and strB ) and nine isolates tetracycline ( most commonly tet ( A ) ) resistance genes ( S1 Table ) . Eleven isolates were MDR ( 10/27 human and 1/2 poultry ) . These comprised 7/15 ( 46 . 7% ) of global epidemic clade isolates and 4/5 ( 80% ) of West African clade isolates . The previously described MDR phenotype in African S . Enteritidis is conferred by an expanded MDR pSENT virulence plasmid ( pSEN-BT ) [13] of incFII/incFIB type . In this study , West African clade isolates harboured a different expanded MDR pSENT virulence plasmid of incompatibility type incI1 . The majority ( 10/15 ) of the global epidemic clade isolates , including the poultry isolate ( SRR7072859 ) , clustered into a monophyletic clade with substantial diversity ( max SNP distance of 92 ) ( Ghana Clade 1 in Figs 1 and 2 ) . The clade has a strong temporal correlation ( root-to-tip R2 = 0 . 8 ) suggesting this clone has been present in Ghana for over twenty years . Clade 1 also contained ten isolates from human clinical cases from the UK , of which five reported recent travel to Ghana , as well as two isolates from human clinical cases from France . The French isolates were obtained in 2016 from stool samples from two children with gastroenteritis , returning from Ghana’s neighbouring countries , Togo and Cote D’Ivoire . Within Clade 1 9/10 Ghanaian isolates , 4/10 UK isolates and 2/2 French isolates harboured the same gyrA 87:D-G mutation . The tree topology reveals that this gyrA mutation likely evolved on two independent occasions within this region of West Africa ( supported by 100% bootstrap values for these bifurcating nodes ) . Similarly , within this clade there have been multiple acquisitions of resistance determinants to sulphonamides , streptomycin and tetracycline . A smaller monophyletic cluster within the global epidemic clade comprised three Ghanaian isolates and four UK clinical isolates ( Ghana Clade 2 in Figs 1 and 3 ) . Metadata associated with three of the UK isolates had travel to Ghana recorded . Six out of seven isolates contained the gyrA 87:D-N mutation and an IS6-flanked integron containing catA-1 , tet ( A ) , strA , strB , sul-2 , sul-1 and dfrA-1 genes . Within Clade 2 , both emergence of the gyrA mutation and acquisition of the MDR locus has apparently occurred within the region ( supported by 100% bootstrap values ) . One isolate ( ERR1010141 ) had also acquired β-lactamase TEM-123 . A single Ghanaian isolate ( ERR1010036 ) , containing the gyrA 87:D-Y mutation , was found to be related ( 36 SNPs to closest isolate ) to a large sub-clade of PHE isolates strongly associated with travel to Spain , all of which contained the same gyrA mutation ( Fig 4 ) . The remaining Ghanaian global epidemic clade isolate ( ERR1010101 ) clustered with a PHE isolate associated with travel to Brazil ( 22 SNPs to PHE isolate ) , and both contained a gyrA 87:D-N mutation . No other resistance determinants were present in these two Ghanaian isolates . Six out of the twenty nine Ghanaian isolates , including one poultry isolate , clustered within a lineage historically associated with North American poultry and shell egg outbreaks [34] ( Fig 1 ) . Related PHE isolates were overwhelmingly associated with travel to the Caribbean while the Ghanaian poultry isolate was obtained from frozen meat imported from the USA . No antimicrobial resistance ( AMR ) determinants were identified in the six isolates . Half ( 4/8 , 50% ) of the isolates obtained in the last two years of sampling ( 2013–2015 ) belonged to this lineage .
We report extensive diversity amongst S . Enteritidis isolated from bloodstream infections in the Ashanti region of Ghana , with four recently described , major clades [13] represented and distinct clusters identifiable within a global poultry- and enterocolitis-associated epidemic clade . In this study , the majority ( 15/29 , 52% ) of isolates belonged to this global epidemic clade , whereas in other studies , African clades of S . Enteritidis have been the predominant cause of iNTS disease [12] . Despite the strong association between African clades and invasive disease [13] , the considerable contribution of global clades to iNTS disease in this study is perhaps unsurprising given the importance of host risk factors in iNTS disease [35] and the recent expansion in intensive poultry farming in Ghana . It is also consistent with the experience in Malawi where both global and Central/ East African strains cause significant iNTS disease [13] . The diversity of AMR profiles between different clades of Salmonella and across sSA [this study , 12 , 13] highlights the challenges of implementing WHO IMCI/IMAI guidelines for the empirical management of sepsis and the importance of improving access to diagnostic microbiology facilities in low-income countries . In this study , almost half the global epidemic clade isolates were MDR ( 7/15 , 47% compared to 4/5 , 80% of West African clade isolates ) . The MDR phenotype has been shown to be strongly associated with the expansion of African NTS lineages and sequential iNTS epidemics , as well as the re-emergence of S . Typhi , in sSA [4 , 6 , 36] . Furthermore , MDR strains have been found to be strongly associated with NTS bacteremia compared to NTS diarrhoea [37] . The region may therefore be at risk of an iNTS epidemic due to MDR global strains . Thirteen out of twenty nine ( 44 . 8% ) isolates in this study contained a fluoroquinolone resistance mutation , specifically a gyrA 87 mutation , and all belonged to the global epidemic clade . This clade is transmitted through eggs and poultry , in settings with established industrialised poultry farming where fluoroquinolone resistance rates of around 10% are frequently observed amongst S . Enteritidis [15 , 22] . Currently , Ghana is both rapidly expanding its domestic poultry industry [38 , 39] and importing a significant quantity of live fowl and poultry meat from abroad ( https://comtrade . un . org ) . Our analysis of global epidemic clade isolates in this study provides evidence of both local emergence of gyrA 87 mutations and importation of DCS S . Enteritidis . A DCS isolate carrying the gyrA 87:D-Y mutation was related to a large sub-clade associated with travel to Spain in the PHE collection , raising the possibility that this strain was imported from Europe . 2 . 3 million kg of poultry meat was imported to Ghana from Spain in 2012 ( https://comtrade . un . org ) . A further Ghanaian isolate , related to a PHE isolate with reported travel to Brazil , may also represent importation of the gyrA mutation . Brazil , along with the US and EU , exports large quantities of day old chicks and hatching eggs to Ghana [39] . The majority of DCS isolates however , appeared to originate from strains in which the mutation has likely emerged locally . The isolate from locally slaughtered poultry , containing the gyrA 87:D-G mutation , is related to a large monophyletic cluster of Ghanaian human bloodstream isolates . Within this cluster , the gyrA 87:D-G mutation appears to have evolved independently at least twice . These results are consistent with the emergence of the mutation in response to fluoroquinolone use in domestic poultry industries . Interestingly , two French isolates within the cluster were associated with travel to Togo and Cote D’Ivoire , which may represent trade in poultry between Ghana and its neighbours or wider circulation of the strain . A second cluster of Ghanaian isolates demonstrates gyrA 87:D-N mutation emergence and MDR locus acquisition within the region . A recent cross-sectional survey found over 10% of poultry farms in the Ashanti region of Ghana , where the largest poultry farms in the country are located [39] , reported fluoroquinolone use [40] . Indeed , the most common resistance determinants detected in the current study ( other than the gyrA gene ) , conferring resistance to tetracyclines , sulphonamides and streptomycin , reflect the most commonly used antimicrobials in the Ghanaian poultry industry [40 , 41] . The global epidemic isolate from local poultry ( both DCS and MDR ) may therefore have originated from an intensive production background . The second poultry isolate ( antimicrobial susceptible ) , which clustered within a lineage associated with North American poultry and shell egg outbreaks , was obtained from imported meat from the US , where broiler flock vaccination coverage is low [42] . Selection pressure arising from antimicrobial use within the region’s expanding poultry industry may be promoting the emergence of multidrug-resistant NTS disease-causing strains . The global epidemic of poultry- and egg-associated S . Enteritidis that began in the 1970s , and resulted in the collapse of national egg-producing industries , was eventually brought under control in European countries in the 2000s through a raft of farming hygiene measures as well as vaccination of poultry flocks [43] . The growing Ghanaian poultry industry may be facing the same challenges that the European industry faced during the 1980s and 1990s . Our study is limited by its single site nature and analysis of a greater number of isolates from multiple sites would allow for a fuller understanding of S . Enteritidis dynamics in the region . Poultry sampling was conducted over a short period after completion of the hospital surveillance studies and only two isolates were obtained . However , both isolates clustered within monophyletic clades containing significant numbers of our bloodstream isolates , thus providing a link between poultry production and trade and human iNTS disease in the region . Amongst global epidemic clade strains , a high rate of DCS may be developing in response to drug selection pressure within the region . Indeed , DCS strains that are likely circulating in Ghana’s expanding poultry industry appear to be responsible for a significant proportion of the S . Enteritidis invasive disease burden . Importantly , NTS disease and AMR patterns linked to poultry production are currently vaccine preventable problems , through a poultry rather than human vaccine . Surveillance and control measures within the burgeoning poultry industry , as well as a ‘one health’ approach , are required to protect a human population at high risk of iNTS disease .
|
Salmonella enterica serovar Enteritidis is both a prominent global cause of zoonotic gastroenteritis , in association with the industrial production of eggs and poultry , and of bloodstream-invasive infection in sub-Saharan Africa , a clinical syndrome referred to as invasive nontyphoidal Salmonella ( iNTS ) disease . African epidemic iNTS strains are frequently multi-drug resistant , leaving fluoroquinolone antibiotics as key agents in reducing iNTS-associated morbidity and mortality . We analysed the genomes of S . Enteritidis collected in Ghana from patients with iNTS disease to investigate the emergence of fluoroquinolone resistance in the region . Extensive phylogenetic diversity was present , however , fluoroquinolone resistance was confined to a single clade causing global epidemics of poultry-associated gastroenteritis . This resistance has predominantly emerged locally , rather than being imported . We found that antimicrobial resistance patterns differed by S . Enteritidis clade , highlighting the challenges of devising empirical sepsis treatment guidelines in the absence of diagnostic microbiology facilities to monitor changes in resistance profiles . Furthermore , we detected fluoroquinolone resistance in closely related poultry and human isolates , suggesting a role of antimicrobial use within the growing local poultry industry in driving the emergence of resistance and a need for surveillance measures within this industry to protect a human population at high risk of iNTS disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"taxonomy",
"livestock",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"geographical",
"locations",
"microbiology",
"vertebrates",
"diet",
"animals",
"animal",
"products",
"bacterial",
"diseases",
"phylogenetics",
"data",
"management",
"nutrition",
"meat",
"enterobacteriaceae",
"phylogenetic",
"analysis",
"pharmacology",
"bacteria",
"africa",
"bacterial",
"pathogens",
"infectious",
"diseases",
"food",
"antimicrobial",
"resistance",
"computer",
"and",
"information",
"sciences",
"birds",
"medical",
"microbiology",
"microbial",
"pathogens",
"salmonella",
"enterica",
"evolutionary",
"systematics",
"salmonella",
"agriculture",
"poultry",
"people",
"and",
"places",
"ghana",
"eukaryota",
"blood",
"anatomy",
"physiology",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"amniotes",
"organisms"
] |
2019
|
Emergence of phylogenetically diverse and fluoroquinolone resistant Salmonella Enteritidis as a cause of invasive nontyphoidal Salmonella disease in Ghana
|
Leprosy control is achieved through a fine-tuning of TH1 and TH2 immune response pattern balance . Given the increasing epidemiological overlay of HIV and M . leprae infections , immune response in co-infected patients consists in an important contemporary issue . Here we describe for the first time the innate lymphoid cells compartment in peripheral blood of leprosy and HIV/M . leprae co-infected patients , and show that co-infection increases group 2 innate lymphoid whilst decreasing group 1 innate lymphoid cells frequencies and function .
Leprosy is a chronic infectious disease caused by the intracellular bacillus Mycobacterium leprae . Although usually asymptomatic , some subjects develop the clinical form of disease , exhibiting skin and peripheral nerves lesions [1] . In 2013 , the Brazilian Healthy Department , registered 31 . 988 new cases of leprosy , with 84% of cure [2] . However , Menezes et al . , describe an increasing number of HIV-1/M . leprae co-infected patient admitted in a center of leprosy treatment in Rio de Janeiro state [3] . In a referral HIV and leprosy center in Amazonas state , Talhari et al . , observed in 13-year follow-up study higher leprosy prevalence among HIV-positive individuals [4] . Normally , clinical forms of the disease can be divided under the light of TH1/TH2 paradigm; being tuberculoid , or paucibacillary , leprosy linked to a TH1 response pattern , and lepromatous , or multibacillary , leprosy linked to a TH2 response profile . Lately , TH17 cells were shown to play a role in disease pathogenesis , especially in lesion formation and M . leprae control [5] . Moreover , it was suggested that TH17 cells contribute to pathogen clearance , when TH1 or TH2 polarization is ineffective [6] . As in other chronic and latent infections , the control of pathogen load is highly dependent on the immunological fitness of the host . Therefore , immunosuppressive factors contribute to disruption of host-pathogen homeostasis , and may account to aggravation of disease and enhancement of M . leprae transmission [7] . HIV-1 infection is the most common acquired immunodeficiency . In HIV-1/Mycobacterium tuberculosis co-infection , the suppression of immune system caused by HIV was shown to accelerate disease progression [8] . Likewise , in HIV-1/M . leprae co-infected patients our group observed an imbalance towards TH2 responses , with lower CD4:CD8 ratios and higher IL-4 levels when compared to healthy controls [9] . Furthermore , patients co-infected with HIV-1 and M . leprae exhibited lower frequency of plasmacytoid dendritic cells ( pDCs ) and Natural Killer T ( NKT ) cells when compared to healthy controls [9 , 10] , indicating that innate immunity cells are also affected during co-infection . In recent years , innate lymphoid cells ( ILCs ) have emerged as important effector cells of innate immunity , responsible for bridging innate and adaptive responses . Recently , ILCs have been divided in three major groups , regarding their effector function and cytokine production profile [11] . These groups correlate well with T helper lymphocyte subsets . Hence , Group 1 ILCs ( here referred to as ILC1 ) are characterized by the production of “TH1” cytokines , such as IFN-γ and TNF-α . Natural Killer ( NK ) cells are comprised in this group , and the ontogeny of other IFN-γ-producing ILCs is still controversial [11 , 12] . Group 2 ILCs ( ILC2 ) is an important innate source of “TH2” cytokines , such as IL-4 , IL-5 and IL-13 . These cells respond to IL-25 and IL-33 and are associated with protection in helminthes infection and allergic asthma [11 , 12] . Finally , Group 3 ILCs ( here referred to as ILC3 ) is composed of the well known Lymphoid-tissue inducer ( LTi ) cells and NCR+ and colitogenic NCR- non-LTi cells . These cells produce IL-17A and/or IL-22 and are involved in different bacterial infections [11 , 12] . There are only a few studies regarding the effects of HIV or SIV infection on ILCs subsets . In two macaque studies , SIV infection led to a disturbance of IL-17-producing ILC3 cells [13 , 14] . However , in HIV-infected patients IL-22-producing ILC3 cells were still fully functional [15] . Interestingly , there are no studies on ILCs in leprosy and , consequently , in HIV/M . leprae co-infection . Thus , in the present study we evaluated the frequency and function of all three ILCs subsets in the peripheral blood of M . leprae and HIV mono-infected patients and HIV/M . leprae co-infected patients .
Volunteers were recruited at the Federal University of São Paulo and the Federal University of Pará , Brazil . Written informed consent , approved by the Institutional Review Board , ( Comitê de Ética em Pesquisa Humana da Universidade Federal de São Paulo/UNIFESP and Comitê de Ética da Universidade Federal do Pará ) were obtained from all volunteers , according to the Brazilian Ministry of Health Guidelines and the Declaration of Helsinki . Leprosy patients were treated according to World Health Organization Guidelines [16] , and co-infected patients were treated with the appropriate multidrug therapy ( MDT ) for paucibacillary ( PB ) and multibacillary ( MB ) leprosy . The initial treatment for patients with HIV mono- and co-infection o was defined using modified criteria adopted by the Brazilian Ministry of Health at the time of sample collection that includes patients with a CD4+ T cell count of < 350 cells/μL or any AIDS-defining clinical condition [17] . These guidelines have been recently updated [18] . The HIV mono-infected and co-infected patients received highly active antiretroviral therapy ( HAART ) and multidrug therapy ( MDT ) . Patients with immune reconstitution inflammatory syndrome , with leprosy reactions and under systemic corticosteroid and/or thalidomide were not included in the present study , to avoid potential interference in the immune parameters as described in a previous studies . The study subjects were divided into four groups: 16 healthy controls ( Healthy ) and 11 HIV seropositive patients ( HIV ) , most of whom had CD4+ T cell counts of less than 400 cells/μL , 7 patients infected with M . leprae ( Leprosy ) , and 10 co-infected patients with M . leprae and HIV co-infection ( Dual ) , recruited at Leprosy Outpatient Clinics at both sites . In this the major presentations of leprosy were multibacillary form rather than paucibacillary form . For all analysis we grouped the leprosy mono-infected and co-infected patients ( leprosy per se ) ( S1 Table ) . To characterize and define ILCs subsets immune staining of surface molecules was performed using anti-Lin ( CD11c , CD16 , CD3 and CD19 ) , CD127 ( clones: HIL-7R-M21 and R34 . 34 ) , CD25 ( clone: M-A251 ) , CD45 ( clone: 2D1 ) , CRTH2 ( clone: BM16 ) , CD161 ( clone: DX12 ) , NKp44 ( clone: Z231 ) , CD56 ( clone: B159 ) antibodies by flow cytometry . The amine Aqua dye ( Invitrogen , Carlsbad , CA , USA ) was used to exclude dead cells in all samples . Briefly , peripheral blood mononuclear cells ( PBMC ) were collected from all subjects and frozen in liquid nitrogen until usage . After thawing , cells were stained with aforementioned antibodies and acquired on a LSR Fortessa flow cytometer ( BD Biosciences ) [9] . For the cytokine production measurement , PBMC were thawed and incubated in the presence of 100 ng/ml phorbol 12-myristate 13-acetate ( PMA–Sigma ) and 500 ng/mL ionomycin ( Sigma ) . After 1 h at 37°C and 5%CO2 , brefeldin A ( 5mg/ml ) was added ( BD Biosciences ) . After incubation for 16 h , cells were washed , and incubated with monoclonal antibodies for surface . Cells were washed and fixed/permeabilized using fixation/permeabilization reagents from Life Technologies in accordance with manufacturer’s instructions . Cells were then washed and incubated with anti-IL-4 ( clone: MP4-25D2 ) , IL-13 ( clone: JES10-5A2 ) , TNF-α ( clone: MAb11 ) , and IL-17 ( clone: SCPL1362 ) and acquired in a LSR Fortessa flow cytometer ( BD Biosciences ) . Fluorescence minus one ( FMO ) was used as gating strategy for surface panels . For intracellular analysis , was used the unstimulated cells for each panels . All samples were acquired using FACSDiva software ( BD Biosciences ) , and then analyzed with FlowJo software version 9 . 7 . 8 ( Tree Star ) . Fluorescence voltages were determined using matched unstained cells . Compensation was carried out using CompBeads ( BD Biosciences ) single stained with all fluorochromes used in the experiments . Samples were acquired to reach at least 1 , 000 , 000 events . Groups were compared using non-parametric models; data were reported with median and 25–75% interquartile range . p values were considered significant if below 0 . 05 . Results are expressed in medians and interquartile ranges ( IQR ) .
Study subjects were divided into four distinct groups , 16 healthy controls ( Healthy ) , 11 HIV-infected patients ( HIV ) , 7 patients infected with M . leprae ( Leprosy ) and 10 co-infected patients with M . leprae and HIV co-infection ( Dual ) . Demographic details can be found at S1 Table . The median age of all participants was 36 years and no difference in age distribution was found between groups . Most of the subjects were male ( 70 . 21% ) . All patients were properly treated with HAART and/or MDT therapy . Group 2 innate lymphoid cells were defined as Lin-CD45+CD161+CD25+CRTH2+ cells ( Fig 1A ) . Consistent with previous data from our group , HIV-1/M . leprae co-infection generates a TH2-polarized environment [9] , here demonstrated by an increase in ILC2 cells frequencies when compared co-infected HIV-1/M . leprae patients ( dual ) with HIV-1 mono-infected patients and healthy controls ( 50 . 00 , IQR 42 . 1–67 . 30 vs . 9 . 30 , IQR 4 . 80–14 . 70 , p<0 . 001 , and 50 . 00 , IQR 42 . 1–67 . 30 vs . 22 . 40 , IQR 8 . 20–33 . 80 , p<0 . 01 respectively ) ( Fig 1B ) . Additionally , M . leprae mono-infected patients exhibited higher frequencies of ILC2 cells when compared to HIV-1 mono-infected patients ( 39 . 10 , IQR 33 . 30–45 . 30 vs . 9 . 33 , IQR 4 . 88–14 . 70; p<0 . 01 ) ( Fig 1B ) . When compared to healthy controls , M . leprae mono-infected patients showed a trend , although not significant , increase in the frequency of ILC2 cells ( 39 . 10 , IQR 33 . 30–45 . 30 vs . 22 . 75 , IQR 9 . 07–37 . 80 , p>0 . 05 , respectively ) . Altogether , these results indicate that in HIV-1/M . leprae co-infected patients it is observed an higher frequencies of ILC2 compartment when compared to healthy controls and HIV-1 mono-infected patients . ILC2 cells have been shown to secrete different TH2 cytokines . Here we analyzed the production of both IL-4 and IL-13 by Lin-CD45+CD25+ cells . As expected , these cells produced both IL-4 ( Fig 1C ) and IL-13 ( Fig 1D ) , consistent with ILC2 phenotype . Surprisingly , ILC2 cells from HIV-1/M . leprae co-infected patients exhibited lower frequencies of IL-4- ( Fig 1C ) and IL-13-producing cells ( Fig 1D ) when compared to cells from M . leprae mono-infected patients ( 0 . 245 , IQR 0 . 217–0 . 342 vs . 0 . 075 , IQR 0 . 048–0 . 237; p<0 . 05 and 0 . 224 , IQR 0 . 172–0 . 408 vs . 0 . 043 , IQR 0 . 009–0 . 179; p<0 . 05 , respectively ) . These findings demonstrate that HIV-1/M . leprae co-infection although promoting an increase in ILC2 cells frequency does not increase their functional capacity when compared to other groups . Moreover co-infection scenario impairs ILC2 ability to produce TH2 cytokines , in comparison to M . leprae mono-infected patients . Additionally to ILC2 subset , we evaluated the frequencies of ILC1 and ILC3 populations on the peripheral blood of all subjects . ILC1 cells were defined as Lin-CD45+CD56+TNF-α+ cells and ILC3 subset was defined as Lin-CD45+CD56+IL-17+ cells ( S1 Fig and Fig 2A ) . Conversely to the increase of ILC2 cells frequencies in HIV/M . leprae co-infected patients , we found a decrease in ILC1 cells frequencies when compared to healthy controls ( Fig 2B ) ( 4 . 52 , IQR 0 . 89–14 . 08 vs . 35 . 50 , IQR 26 . 25–46 . 25; p<0 . 01 ) . No differences were found in ILC3 cells frequencies between groups ( Fig 2C ) .
To our knowledge , this is the first study to investigate and describe ILCs subsets in leprosy and HIV-1/M . leprae co-infected patients . Moreover , this is the first time that a complete profile of the three different ILCs subsets was carried out in HIV-1 infected patients , as the only study regarding this question focused on ILC3 cells [15] . One limitation of this study , however , is the relatively small sample size , which may have affected the ability to identify more subtle changes between groups . Nevertheless , we were able to identify major changes in ILCs compartment caused by HIV-1/M . leprae co-infection . To define the three major groups of ILCs we chose to use a functional criterion ( i . e . cytokine secretion ) in order to avoid misconception of ILCs subpopulations [12] . Consequently , when using the ILC1 term we are referring to both NK cells and TH1 ( i . e . , TNF-α ) cytokines-producing ILCs [11] . Similarly , here we used the ILC3 term to comprise LTi cells , NCR+ and NCR- cells , based on their ability to produce IL-17 [11] . Finally , ILC2 cells were defined both by their IL-4 and IL-13 secretory capacities and surface molecules , because this subset is composed by only one known population of cells [11] . Here we found that patients with HIV-1 and M . leprae co-infection showed an increase in ILC2 cells and a decrease in ILC1 cells frequencies in peripheral blood , when compared to healthy subjects . This data is in line with previous findings from our group , which showed a skewing in cellular immune response towards a TH2 bias [9] . Thus , our data add new evidences to the hypothesis that HIV-1 in patients with an ongoing infection with M . leprae promotes a disturbance in TH1 and TH2 balance , sustaining a TH2 environment . Even though not conclusive , our results indicate that HIV-1 infection is responsible for enhancing this TH2 shift . However , it is not clear if this skewing in immune response is a viral escape mechanism or an attempt from the immune system to control the virus . Although higher in frequencies , ILC2 cells from HIV-1/M . leprae co-infected patients produce less IL-4 and IL-13 , when compared to M . leprae mono-infected patients . Additionally , circulating ILC2 cells in HIV-1 mono-infected patients are present in similar frequencies and with comparable functional capacity of healthy subjects , suggesting that HIV-1 is not responsible for inducing a relevant TH2 response , per se . Therefore , suggesting that HIV-1 infection impairs TH2 response , through decrease of functional response , and the resulting increase in ILC2 frequencies is an attempt from the immune system to rescue this phenotype . Concurrently with the increase of circulating ILC2 frequencies it was observed a decrease of circulating ILC1 frequencies in co-infected patients when compared to their healthy counterparts , further supporting this notion of an unbalancing of TH1/TH2 responses . Of note , to better understand the immunological landscape in HIV-1/M . leprae co-infection it would be crucial to dissect the immune response of ILCs and other immune cells located in the site of M . leprae infection ( i . e . in the skin and nerves ) . We found no differences in the frequencies of ILC3 subset from HIV-1/M . leprae co-infected patients when compared to healthy subjects and mono-infected patients , as opposed to previous findings that showed IL-17-producing ILCs cells depletion in SIV infected macaques [14] . Nonetheless , in the study led by Xu , cells were obtained from mucosal tissues . Moreover , these findings were obtained in an experimental macaque model [14] . Conversely , Fernandes and colleagues looked on IL-22-producing ILC3 cells population on mucosal surfaces of HIV-1 infected individual and found no differences in their frequencies when compared to healthy controls [15] . Although functionally different ILC3 subsets , the conditions in this study are the most comparable to ours , adding strength to our findings that HIV-1 does not alter ILC3 cells population . Finally , in this study we added new information to the growing body of data that shows that ILCs have effector functions that parallels T helper cells subsets during infection . Additionally , our main finding that HIV-1 infection has a major effect on ILC2 cells demonstrate that these cells might have a role during viral infections , and not only on asthma and helminthes infections [11] .
|
Mycobacterium leprae is a clinical relevant pathogen that can lead to leprosy upon infection . This chronic infectious disease is characterized by the appearance of skin and peripheral nerve lesions . Normally , a healthy immune system is able to control the infection and impede the generation of lesions . However , immunosuppressive conditions may reduce the fitness of host’s immune system , with consequences of disease progression and pathogen spreading . HIV is the major cause of pathogen-associated immunodeficiency , and during the past decades , an epidemiological overlay of M . leprae and HIV infections spectra has been observed . Thus , the increase of co-infection cases raised the interest of the scientific community in this subject . Accordingly , our group has previously shown that co-infected patients presented a shift in the type of cellular immune response . Nevertheless , in the past few years a new group of innate lymphoid cells ( ILCs ) was shown to play an important role in the initiation and maintenance of effective immune responses . Here we describe for the first time the circulating ILCs subsets in leprosy , HIV and M . leprae/HIV co-infected patients . Additionally , we found that the co-infection scenario leads to a shift in different ILCs population , concordant with those observed previously for T lymphocytes and innate immunity cells .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
New Players in the Same Old Game: Disturbance of Group 2 Innate Lymphoid Cells in HIV-1 and Mycobacterium leprae Co-infected Patients
|
Hosts encounter an ever-changing array of pathogens , so there is continual selection for novel ways to resist infection . A powerful way to understand how hosts evolve resistance is to identify the genes that cause variation in susceptibility to infection . Using high-resolution genetic mapping we have identified a naturally occurring polymorphism in a gene called Ge-1 that makes Drosophila melanogaster highly resistant to its natural pathogen Drosophila melanogaster sigma virus ( DMelSV ) . By modifying the sequence of the gene in transgenic flies , we identified a 26 amino acid deletion in the serine-rich linker region of Ge-1 that is causing the resistance . Knocking down the expression of the susceptible allele leads to a decrease in viral titre in infected flies , indicating that Ge-1 is an existing restriction factor whose antiviral effects have been increased by the deletion . Ge-1 plays a central role in RNA degradation and the formation of processing bodies ( P bodies ) . A key effector in antiviral immunity , the RNAi induced silencing complex ( RISC ) , localises to P bodies , but we found that Ge-1-based resistance is not dependent on the small interfering RNA ( siRNA ) pathway . However , we found that Decapping protein 1 ( DCP1 ) protects flies against sigma virus . This protein interacts with Ge-1 and commits mRNA for degradation by removing the 5’ cap , suggesting that resistance may rely on this RNA degradation pathway . The serine-rich linker domain of Ge-1 has experienced strong selection during the evolution of Drosophila , suggesting that this gene may be under long-term selection by viruses . These findings demonstrate that studying naturally occurring polymorphisms that increase resistance to infections enables us to identify novel forms of antiviral defence , and support a pattern of major effect polymorphisms controlling resistance to viruses in Drosophila .
Hosts and their pathogens are engaged in a never-ending arms race , where the evolution of new defences in turn selects for pathogens that can overcome those defences . In order to understand how hosts are evolving resistance , it is necessary to investigate the genes that cause variation in the susceptibility to infection in the wild . Resistance can evolve not only by altering the host immune defences , but also by changing host factors that are hijacked by the pathogen for its own benefit . For example , bacteria commonly evolve resistance to phages by modifying surface receptors needed to bind and enter cells [1] . Therefore studying natural variation can identify novel forms of host defence that are not apparent from classical immunology . Viruses are important pathogens of insects , but the antiviral defences of insects are still comparatively poorly understood . The most important immune defence against RNA viruses is RNAi , whereby double-stranded viral RNA is cleaved into short RNAs by Dicer 2 , and these then guide the degradation of viral RNA by Argonaut 2 [2] . In response , many insect viruses have evolved Viral Suppressors of RNAi ( VSRs ) that block RNAi in a variety of different ways [2–5] . Several other pathways and the endosymbiotic bacterium Wolbachia have been implicated in the antiviral immunity in Drosophila [6–16] , but these are mostly relatively poorly understood . In insect populations there is extensive genetic variation in susceptibility to viral infection . In Drosophila genome-wide association studies have shown that much of this genetic variation can be explained by a small number of major effect polymorphisms [17 , 18] . Interestingly , these major effect polymorphisms were only seen when natural coevolved viral pathogens of Drosophila were used ( Drosophila melanogaster sigma virus ( DMelSV ) and Drosophila C virus ( DCV ) , but not when flies were infected with viruses from other species . Haplotypes carrying the resistant alleles of two genes–ref ( 2 ) P and CHKov1 –carry very little genetic variation , indicating that they have been driven to a high frequency by natural selection [17 , 19–21] . It has been estimated that ref ( 2 ) P has experienced a selective sweep within the last 1000–7000 years , while CHKov1 swept to a higher frequency within the last few hundred years ( although the allele may be much older ) [20 , 22 , 23] . Therefore , it appears that selection for resistance to the sigma virus is driving major-effect resistance alleles through populations , and the genetic variation observed in nature is the result of these transient polymorphisms . In Drosophila melanogaster three of these major-effect polymorphic resistance genes have been identified at the molecular level , although their mode of action remains uncertain . The first of the genes to be cloned is known as ref ( 2 ) P or p62 and confers resistance to sigma virus . Resistance arose through a mutation from a Gln-Asn to a single Gly in the PB1 domain ( a protein interaction module ) of the protein [19 , 21 , 22 , 24] . P62 is an adaptor protein which , among other functions , selectively targets polyubiquitinated substrates for degradation by autophagy . In mammals it targets bacteria for degradation by autophagy [25] [26] , and autophagy is known to protect flies from infection of vesicular stomatitis virus ( VSV ) , a relative of sigma viruses [11] . Therefore p62 may contribute to sigma virus defence through its role in autophagy . The second gene to have been identified was CHKov1 [17] . Three alleles of CHKov1 genes occur in natural populations of D . melanogaster , each conferring a different level of protection to DMelSV infection . The ancestral allele has the most susceptible phenotype , and a transposable element ( Doc1420 ) insertion into the protein coding sequence of CHKov1 has dramatically increased flies’ resistance to DMelSV . The insertion results in truncation of mRNA , leading to four different transcripts [20] . This allele is the most common allele in North American populations , being found at a frequency of 0 . 82 in North Carolina . The third allele is the most resistant and is the result of two duplications , with rearrangement of three copies of both the truncated CHKov1 allele and CHKov2 ( one of which is truncated ) [17] . The mechanism by which CHKov genes confer protection is unclear . The third gene to have been identified is pastrel , which provides resistance to DCV [18 , 27] . DMelSV infects up to 18% of flies in natural populations [28–30] . It is a single-stranded , negative-sense RNA virus from the Rhabdovirus family . It is only transmitted vertically from parent to offspring , and is therefore a host-specific pathogen of D . melanogaster . Compared to some viruses that cause high levels of mortality , infection by sigma virus appears relatively benign . However it can reduce egg viability and infected adults may be less likely to survive to overwinter than uninfected ones [31] . Both field and laboratory studies have estimated that sigma virus infected flies suffer a reduction in fitness of approximately 20–30% [32 , 33] . Therefore as sigma viruses are common and costly parasites of D . melanogaster , there is selection for flies to evolve resistance . Aside from the two polymorphic genes known to affect sigma virus–P62 and CHKov1 –three other naturally polymorphic genes that alter the replication or transmission of sigma virus have been mapped to fairly large regions of the Drosophila genome: ref ( 1 ) H , ref ( 2 ) M and ref ( 3 ) O[34] . Among them ref ( 2 ) M , a naturally polymorphic resistance gene , was mapped to a region of the left arm of the second chromosome between two visible markers in 1978[34] . There is little study of this gene since . Here we combine linkage mapping , association studies and reverse genetics to map this gene and identify the polymorphism causing resistance .
The susceptibility of D . melanogaster to DMelSV is affected by several naturally polymorphic genes that have been roughly mapped in the genome but have not been identified at the molecular level [34 , 35] . One of these genes , which has previously been called ref ( 2 ) M , has been approximately mapped to a region of chromosome 2 [34] . Starting with two fly stocks known to carry different alleles of this gene: EME ( resistant ) [34] and 22a ( susceptible ) [17] , we found that its effect on the DMelSV was dramatic—10 days after injecting the virus , 94% of flies carrying the susceptible allele showed symptoms of infection after exposure to CO2 compared to only 3% of flies with the resistant allele ( Fig 1A ) . To map the location of this gene to a smaller region , we crossed the resistant and susceptible fly lines , and then used balancer chromosomes to generate stocks that carried a homozygous recombinant 2nd chromosome . As we knew the approximate location of the gene , we only retained 31 lines that had recombined between two molecular markers at 36 and 52 cM on the left arm of chromosome 2 . Further molecular markers were then scored in this region , and these lines were then injected with DMelSV and tested for infection using the CO2 assay . These recombinant lines had a bimodal distribution of infection rates , with most lines having infection rates of either 0% or greater than 40% . There was a region of approximately 735kb where there was a perfect correspondence between the genotype of the fly and infection rate ( Fig 1B; Wilcoxon Rank Sum Test: W = 225 . 5 , P = 8 . 4x10-6 ) . To further refine the location of the gene , we repeated this experiment to generate informative recombinants in the candidate region . To reduce any effects of the genetic background , in this experiment we crossed a susceptible line 22a to one of the resistant recombinants from the previous experiment . Out of 2112 individuals , 133 were recombinants in a 6cM interval containing this region . Initially 28 of these were used to create lines homozygous for the recombinant chromosomes . Again individuals from each line were injected with the virus and genotyped , which reduced the region to 298kb ( Fig 1C; Wilcoxon Rank Sum Test: W = 176 , P = 3 . 8x10-5 ) . We then returned to the remaining recombinant lines , and used these to generate 8 homozygous recombinants in this reduced region . Again these lines ( along with 7 lines from before ) were genotyped and phenotyped . This new information allowed the identification of an 89kb region containing the candidate gene ( Fig 1D; Wilcoxon Rank Sum Test: W = 56 , P = 0 . 0013 ) . Generating recombinants in an 89kb region using this approach was not feasible , thus we turned to transposable elements carrying visible markers to select new recombinants . We chose two transposable element lines , each homozygous for an EP element that flanked our region of interest . These elements were combined on a single chromosome to generate a ‘2EP’ line [36] which was susceptible to the DMelSV ( 100% infected ) . As these elements carry visible eye-colour markers , recombinants between our resistant chromosome and the 2EP line can be detected from their light orange eye colour ( the non-recombinant resistant and susceptible flies have white or dark orange eyes respectively ) . This approach was used to generate 12 recombinant lines . Again the lines were assayed for resistance to DMelSV and genotyped for several markers across the 89kb region , which reduced the region to just under 8kb ( 2L: 11094733–11102848 , BDGP5; Fig 2A , Wilcoxon Rank Sum Test: W = 32 , P = 0 . 0063 ) . This region contains a whole gene called Ge-1 and the flanking non-coding regions upstream of the genes CG4705 and Reps . Ge-1 has been shown involved in RNA degradation and the formation of processing bodies ( P bodies ) in animals and plants [37–40] . It acts as a bridge between Decapping protein 1 and Decapping protein 2 , which remove the 5’ cap from mRNA ( “decapping” ) . This results in the RNA molecule being rapidly degraded by exonucleases [39] . In all of the experiments above we have used the symptom of paralysis after CO2 exposure to map resistance . To check that Ge-1 is affecting the viral load rather than CO2 sensitivity itself , we used quantitative PCR to measure viral titres in 20 of the resistant and 16 of the susceptible recombinant lines after they had been injected with the DMelSV . We found that six days after the injection , there was approximately a 471–fold lower viral load in resistant lines compared to susceptible lines ( Fig 2B; F1 , 76 = 137 . 5 , p<2 . 2e-16 ) , and after 12 days this rose to a 1168–fold difference ( Fig 2B; F1 , 97 = 125 . 8 , p<2 . 2e-16 ) . To identify the mutations that could be responsible for resistance , we sequenced this entire region ( 2L: 11094733–11102848 ) in the original resistant and susceptible line . The two sequences differed by 47 SNPs and 1 indel . The indel is 78bp long in the 5th exon of Ge-1 ( 2L: 11097925–11098002 ) , and reduces the length of the serine-rich linker region of the protein by 26 amino acids , with the resistant allele being the shorter of the two ( Fig 2C ) . The homolog of this gene in D . simulans encodes the full length protein , suggesting that a deletion mutation has occurred in this region on the resistant chromosome . The SNPs are spread across the 3’ UTR of Reps and CG4705 and all of Ge-1 , with only two found in intergenic regions ( S1 Table ) . To examine whether the difference seen in viral resistance could be due to a change in the expression of Ge-1 , we used quantitative PCR to measure its expression in 20 resistant and 16 susceptible recombinant lines that were used in the viral load measurement . We found that there was no significant difference in gene expression in the resistant and susceptible lines both six days and twelve days after they had been injected with the virus ( Fig 2D; day6: F1 , 76 = 0 . 0189 , p = 0 . 891; day12: F1 , 97 = 6 . 109 , p = 0 . 015 ) . We estimated the frequency of the 26 amino acid deletion in natural populations from the USA and Africa . We scored the presence or absence of the deletion by PCR in the 189 inbred lines from North Carolina that comprise the DGRP panel [41] . We found that only two of the lines contained the deletion . We also looked for the deleted allele in genome sequences from several African populations ( 319 alleles of Ge-1 ) [42] , and none of the lines contained the deletion . In a separate experiment , we have injected all of the DGRP lines with the DMelSV and measured the proportion of flies that were infected 13 days later using the CO2 assay [17] . We found that the two lines with the deletion lines were both very resistant to the DMelSV ( 4% of flies in line 153 and 8% in line 361 were infected after injection , compared to an average of 40 . 6% ) , but this difference was not significant ( S1 Fig; MCMCglmm: p = 0 . 092 ) . We also tested all of the other polymorphisms in the 8kb region for an association with resistance and found that only one SNP , located in the 3’ UTR of Reps , was significant ( MCMCglmm: p<0 . 001 ) . In order to test if Ge-1 or Reps is involved in sigma virus resistance in D . melanogaster , we used RNAi to knock down the expression of the two genes . These flies did not have the Ge-1 deletion . As we found that knock-downs in flies reared at 25°C are lethal , the flies were reared at a low temperature ( 18°C ) where Gal4 drivers are normally inefficient before being transferred to a higher temperature ( 25°C or 29°C ) . Ge-1-RNAi flies showed a higher proportion of flies infected after exposure to CO2 than the control ( Fig 3A; Generalized Linear Model: |z| = 3 . 391 , P<0 . 001 ) while Reps-RNAi flies had a similar proportion of flies infected to the control ( Generalized Linear Model: |z| = 0 . 951 , P = 0 . 34 ) . We also measured the viral titres to see whether the resistance is due to reduction in viral replication in flies . We found that the viral load in the Ge-1-RNAi line was ~8-fold higher than in the control ( |t| = 2 . 336 , P = 0 . 02 ) while Reps-RNAi flies had a similar viral load to the control ( Fig 3B blues bars; |t| = 0 . 490 , P = 0 . 627 ) . We repeated the RNAi test and kept injected flies at 29°C . This time the viral titre in Ge-1-RNAi line was ~5-fold higher than in the control ( |t| = 3 . 281 , P = 0 . 002 ) while the Reps-RNAi flies had similar viral loads to the control ( Fig 3B red bars; |t| = 0 . 282 , P = 0 . 779 ) . The knockdowns in all these flies were likely inefficient , as qPCR on Reps and Ge-1 RNA levels was not significantly different from the controls . To test whether the 26 amino acid deletion in Ge-1 is causing flies to be resistant to DMelSV , we generated transgenic flies that only differ by this deletion . To do this we first took a BAC clone of the region that lacked the deletion , and used recombineering [43] to seamlessly delete the 26 amino acids in E . coli . We then inserted two forms of the BAC into identical positions on the 3rd chromosome using the phiC31 integrase system [44] , to generate flies that express the two alleles of the gene under the control of the same natural promoter . In total we constructed four independently transformed transgenic lines , one with the deletion ( CH322-Ge-1Δ78C ) and three without ( CH322-Ge-1+A , CH322-Ge-1+B and CH322-Ge-1+C ) . These transgenic lines were then crossed to a Ge-1 null mutant , dGe-1Δ5/Cyo , so that only the transgene is expressed . The dGe-1Δ5 mutation is homozygous lethal [37] , and the insertion of either allele of Ge-1 on the 3rd chromosome complemented this lethal effect , allowing dGe-1Δ5 homozygotes to be generated ( dGe-1Δ5;CH322-Ge-1Δ78C , dGe-1Δ5;CH322-Ge-1+A , dGe-1Δ5;CH322-Ge-1+B and dGe-1Δ5;CH322-Ge-1+C ) . Transgenic flies carrying the deletion in Ge-1 are highly resistant to DMelSV . Following injection with DMelSV , only 1 . 6% of flies from the line with the deletion showed the symptoms of paralysis and death after exposure to CO2 compared to 69 . 6% of the flies in the three lines without the deletion ( Fig 4A; Generalized Linear Mixed Model: |z| = 7 . 350 , P<<0 . 001 ) . Similarly , viral titres were 512-fold higher in the transgenic line with the deletion than the three lines without the deletion ( Fig 4B; F1 , 56 = 156 . 9 , P<<0 . 001 ) . In order to test whether Ge-1 expression differs in transgenic lines , we measured Ge-1 expression in dGe-1Δ5;CH322-Ge-1Δ78 , dGe-1Δ5;CH322-Ge-1+ ( mix of A , B and C ) flies . We didn’t detect any significant difference in Ge-1 expression in these two genotypes ( S1 Fig; F1 , 37 = 0 . 008 , P = 0 . 92 ) . In addition , we measured Ge-1 expression in DMelSV injected flies and controls that were injected with Ringer’s solution . In both transgenic lines , we found no significant difference between virus injected and Ringer’s injected flies ( S1 Fig; Ge-1+: F1 , 18 = 2 . 32 , P = 0 . 15; Ge-1Δ78C:F1 , 17 = 0 . 403 , P = 0 . 53 ) . These results indicate that Ge-1 expression does not differ in the resistant and susceptible transgenic flies and DMelSV infection does not induce Ge-1 expression . The RNAi effector protein Argonaute 2 , which is a key antiviral defence in Drosophila , localises to some P bodies and this localisation depends on Ge-1 [37 , 38 , 40] . To test whether Ge-1 resistance to sigma virus is mediated by RNAi , we crossed the mutant allele Ago251B into lines carrying the susceptible and resistant alleles of Ge-1 . This allele of Ago2 deletes the first two exons and is known to abolish its slicer activity [45] , but it does still produce the shortest Ago2 transcript and this may have some function [46] . In the CO2 sensitivity assay , the two alleles of Ge-1 significantly affected susceptibility regardless of whether there was a functional allele of Ago2 ( Fig 5A; Wilcoxon Rank Sum Test with Ago2 mutant: |Z| = 4 . 9 , P = 8 . 99e-7; Wilcoxon Rank Sum Test with Ago2 wild-type: |Z| = 5 , P = 4 . 7e-7 ) . The same pattern was found when we measured viral titre in these lines–Ge-1 had a large effect on viral titre ( Fig 5B; Ge-1: F1 , 37 = 100 . 82 , P<0 . 0001 ) , but this was not affected by Ago2 ( interaction Ge-1*Ago2: F1 , 37 = 0 . 3 , P = 0 . 59 ) . The Ago251B mutants did have higher titres of DMelSV ( Fig 5B; Ago2: F1 , 37 = 35 . 69 , P<0 . 0001 ) . Therefore , siRNA pathway does defend flies against DMelSV but Ge-1 does not rely on the siRNA pathway to provide resistance . Ge-1 is essential for forming P bodies , so we investigated the role of eight other P-body components in DMelSV resistance . As mutants tend to be lethal , we used RNAi to knock down the expression of these genes in adult flies . We found that knocking down the expression of Decapping protein 1 ( DCP1 ) resulted in increased viral load ( Fig 5C; Ge-1 susceptible background: Wilcoxon Rank Sum Test: W = 27 , P = 0 . 009; Ge-1 heterozygous background: Wilcoxon Rank Sum Test: W = 87 , P = 0 . 02 ) . The effect of knocking down DCP1 was greater in Ge-1 susceptible flies than in flies that were heterozygous for the two Ge-1 alleles ( Fig 5C; interaction between Ge-1 and DCP1: |t| = 2 . 15 , P = 0 . 03 ) . However , we would interpret this interaction cautiously , as the Ge-1 susceptible and heterozygous flies did not differ in their viral load , perhaps due to genetic background effects or dominance ( Fig 5C ) . Knocking down the other genes did not have a significant effect on DMelSV titres ( S2 Fig; Edc3 , DCP2 , DCP1 , GW182 , pcm , me31B , Part-1 and stau; note the efficiency of these knockdowns was not checked ) . We tested whether Ge-1 resistance was specific to DMelSV by infecting Ge-1 transgenic flies with Drosophila A virus ( DAV ) and Drosophila C virus ( DCV ) . Both DAV and DCV are natural pathogens of D . melanogaster , and DAV infects flies in nature and laboratories widely [30 , 47] . Transgenic flies carrying resistant ( Ge-1Δ78C ) or susceptible allele ( Ge-1+ ) of Ge-1 have similar viral titres after DAV infection ( Fig 6A; F1 , 16 = 1 . 685 , P = 0 . 21 ) and after DCV infection ( Fig 6B; F1 , 13 = 1 . 871 , P = 0 . 19 ) . This indicates that this polymorphism in Ge-1 does not have an effect on DAV or DCV infections and its antiviral function is likely to be specific to sigma virus . As changes in Ge-1 can make flies resistant to viral infection , it is possible that it has been the target of sustained selection by viruses during evolution . To investigate this we used a McDonald-Kreitman Test to detect beneficial amino acid changes that have been fixed by natural selection during the evolution of Ge-1 [48] . In both D . melanogaster and D . simulans we estimate that over 80% of the amino acid substitutions were fixed by natural selection ( α in Table 1 ) and therefore had a beneficial phenotypic effect . Virus resistance is caused by a change to the serine-rich linker , and 13 of the 22 non-synonymous changes between D . melanogaster and D . simulans are located in this region ( stars in Fig 2B ) . When we repeated the test on just the serine-rich linker we found evidence of selection on this domain in both the D . melanogaster and D . simulans lineages and no evidence of selection in the remainder of the protein ( Table 1 ) .
We have identified a naturally occurring polymorphism in a gene called Ge-1 that makes D . melanogaster highly resistant to the naturally occurring rhabdovirus DMelSV . When we knocked-down the expression of the susceptible allele of Ge-1 by RNAi the flies became even more susceptible to infection , indicating that Ge-1 is an existing restriction factor whose antiviral effects have been increased by the deletion . By modifying the sequence of the gene in transgenic flies , we identified a 26 amino acid deletion in the serine-rich linker region of Ge-1 that is causing the resistance . This polymorphism has no effect on resistance to DAV or DCV , indicating resistance is likely to be specific to sigma or related viruses . Ge-1 acts as a bridge bringing together DCP1 and Decapping Protein 2 ( DCP2 ) , which can then remove the 5’ cap from mRNA leading to its degradation [38] . We found that DCP1 also restricts sigma virus infection in flies , suggesting that this pathway may underlie resistance . The serine-rich linker of Ge-1 has experienced strong selection during its evolution , suggesting that it may be involved in an ongoing arms race with viruses . Ge-1 plays a central role in RNA degradation and the formation of processing bodies ( P bodies ) in animals and plants [37–40] . It is required for the removal of the 5’ cap from mRNA , which results in the subsequent degradation of the RNA molecule by an exonuclease in the 5’ to 3’ direction [49] . It is thought to act as a bridge between two of the key molecules involved the decapping process , with its C-terminal domain interacting with the protein DCP2 and its N-terminal domain interacting with DCP1 [39] . The C-terminal domain of Ge-1 also results in the localization of the protein to P bodies , which are sites in the cytoplasm where many of the enzymes involved in RNA degradation are localized . Ge-1 not only localises to P bodies , but it is also required for P-body stability and formation , probably due to its role as a scaffold protein [38 , 39] . P bodies can play both pro- and anti-viral roles [50] . In some cases viruses disrupt P body formation , presumably to prevent viral RNA being degraded . For example , P body components act as restriction factors for influenza A virus , and the viral NS1 protein in turn disrupts the formation of P bodies [51] . Similar interactions also occur between insects and their viruses . In Drosophila the dicistrovirus CrPV disrupts P bodies [52] . Furthermore , several P body components involved in mRNA decapping have an antiviral effect against the bunyavirus Rift Valley fever virus ( RVFV ) [53] . This is thought to be because bunyaviruses “cap-snatch” their 5’ cap from host mRNAs , and the decapping process reduces the availability of 5’ caps for viral replication [53] . As Rhabdoviruses do not cap-snatch , this mechanism cannot explain our results . Other viruses have co-opted P bodies for their own benefit . This is the case for West Nile virus , which recruits various P body components to its replication centres , and knocking down the expression of these proteins results in lower viral titres [54] . There are two possible ways in which Ge-1 could confer resistance . First , it could be used in some unknown way by DMelSV during the viral replication cycle , and the resistant allele of the gene may interfere with this process . Second , it could play an existing antiviral function that is made more efficient . We found that knocking down the expression of the susceptible allele of Ge-1 further increased the susceptibility of flies , indicating that the polymorphism that we identified is increasing the effectiveness of an existing restriction factor . We demonstrated that a 26 amino acid deletion in Ge-1 is the cause of increased resistance to DMelSV by modifying the gene in transgenic flies . We ensured the gene was under the control of its natural promoter by modifying BAC clones of this region of the Drosophila genome , and these BACs were then inserted into a fly line that carried a null allele of Ge-1 . This deletion removes 26 amino acids from the middle of the protein , which is a flexible linker that lies between two structured domains . It does not alter the sequence of the domains that interact with DCP1 and DCP2 , or the region required for P body formation [38] . It is therefore possible that the deletion may affect the protein’s role as a scaffold by changing the conformation of the protein complex . We investigated two possible ways in which Ge-1 might be affecting the susceptibility of flies . The primary antiviral defence of insects is RNAi , and the RNAi-Induced Silencing Complex ( RISC ) localises to P bodies [55 , 56] . However , our results show that Ge-1-based resistance does not require Ago2 , and therefore is independent of RNAi . Therefore , a likely hypothesis is that resistance relies on the destruction of viral genomic or messenger RNA in an RNAi-independent way in P bodies . Consistent with this we found that knocking down the expression of DCP1 increases DMelSV titres in flies . As the polymorphism affecting resistance is in the serine-rich linker that forms a bridge between DCP1 and DCP2 , it seems likely that Ge-1-based resistance involves the decapping complex . By looking at the Ge-1 sequence in D . simulans , we found the ancestral state of Ge-1 is the susceptible allele . This fits with an evolutionary arms race in which hosts are continually evolving new defences against pathogens . Consistent with this , we found that the protein sequence of Ge-1 has been subject to long term positive selection . While our analysis demonstrates that many of the amino acid substitutions that have occurred in the D . melanogaster lineage provided a selective advantage to flies , further work would be needed to demonstrate this was linked to the antiviral function of Ge-1 ( especially given the mutation that gave rise to resistance was a deletion not a substitution ) . Nonetheless , studies of other genes involved in antiviral immunity , such as those in the siRNA pathway , have found that they are also evolving rapidly under positive selection [57 , 58] . Therefore , selection by viruses may frequently be an important force in the molecular evolution of genes involved in antiviral defence . The resistant allele of Ge-1 is currently rare , being found in just 1% of the North American flies we tested and none of the African flies . There are multiple possibilities that may contribute to it being a rare polymorphism . First , it could be a relatively new mutation which has not had time to spread among D . melanogaster populations . Second , we examined the frequency of the polymorphism in North America and Africa but the resistant allele was isolated from France and it may be more common there . Finally , it could be costly . For example , if it disrupts mRNA degradation it might have many pleiotropic effects on other traits . This could maintain the allele at a low frequency if there is only a net benefit to being resistant at certain times or places where the viral prevalence is high . Alternatively , costs may maintain the allele at low frequency due to heterozygote advantage or negatively frequency dependant selection . Our results show that studying naturally occurring polymorphisms that increase resistance to infection provides a valuable alternative to studying immunity through artificial mutations that reduce resistance . Not only can this approach identify novel forms of antiviral defence , but it also allows us to understand how resistance to infection evolves . This is important as evolved defences may not always involve the classical immune response .
Susceptible ( 22a ) and resistant ( EME ) fly lines were provided by Didier Contamine . The 2nd chromosome in the EME stock carries the resistant allele of the gene ref ( 2 ) M [34] , and the other two chromosomes are from a susceptible ebony stock . To map the resistance genes we crossed the resistant and susceptible parental stocks and created lines that carried homozygous recombinant chromosomes . The female F1 progeny of a cross between EME and 22a were crossed to a balancer stock SM5 , Cy/Pm . In the next generation we selected individual SM5 , Cy/+ males and crossed these back to the balancer . A few days after setting up this cross we removed the male parents from the vial and genotyped them using molecular markers flanking the region that we knew contained the resistance gene ( S2 Table ) . This allowed us to discard all the lines that had not recombined in this region and only retain the informative recombinants . In the next generation we crossed sibling SM5 , Cy/+ flies , and then selected for homozygous recombinants in the subsequent generation . Using this approach we initially generated 33 lines that had recombined between insertion/deletion ( indel ) markers at 36cM and 52cM on the 2nd chromosome . We then repeated the experiment to produce recombinants between markers at 42cM and 47cM . To select recombinants in even smaller regions we used phenotypic markers flanking the region of interest rather than molecular markers . First , mapping lines were generated by crossing two P-element lines that flanked the region of interest to create a chromosome carrying both P-elements . These elements both carried the mini-white gene , and flies that carry a single heterozygous element have lighter coloured eyes than flies carrying two heterozygous elements [36] . The lines w*;P{EP}2377 and w*;P{EP}2478 were combined to the same chromosome to generate the 2nd chromosome mapping line ( 2EP-2 ) . This was then crossed to one of the resistant recombinant lines ( M34 ) that had been generated in the experiment described above . 12 homozygous 2nd chromosome recombinant lines were generated using the balancer SM5 , Cy/+ and the crossing scheme described above . DNA was extracted using either Chelex resin ( Sigma-Aldrich ) [59] or a Tissue Genomic DNA kit ( Metabion , Munich ) using manufacturer protocols . Genotyping of each location was done using microsatellites , Indels , SNP-specific primers or via sequencing ( S2 Table ) . The PCR reaction consisted of incubation at 95°C ( 5min ) , followed by 30 cycles of 95°C ( 30 sec ) , 55°C ( 20 sec ) and 68°C ( 1 min per kb ) , then 68°C ( 8min ) . Short PCR products were run on 2% agarose gels , while larger products were run on 1% agarose gels to score length differences ( indels and microsattelites ) . PCR products for sequencing were cleaned up by incubating with the restriction enzyme ExoI and Shrimp Alkaline Phosphotase ( SAP ) at 37°C for 1hr , followed by 15 min incubation at 72°C to deactivate the enzymes . The sequencing reaction consisted of 25 cycles of 95°C ( 30 sec ) , 50°C ( 20 sec ) and 60°C ( 4 min ) using BigDye reagents ( ABI ) . Sequencing was carried out at either Source Bioscience Life Sciences ( Cambridge ) or The Genepool ( Edinburgh ) . DNA for sequencing was extracted using either Tissue Genomic DNA kit ( Metabion , Munich ) or DNeasy 96 Blood & Tissue Kit ( Qiagen ) . The 8 kb region identified on chromosome 2 ( 2L: 11094733–11102848 , BDGP5 ) was sequenced for lines EME and 22a . Primer pairs were designed to amplify these regions in overlapping fragments ( S2 Table ) , and the sequencing was performed as described above . Diagnostic PCR primers were designed to genotype flies for a deletion in Ge-1 . The forward primer Ge-1 Indel 1F ( 5’ AGCGTCAAGCTTTTCCTTCA 3’ ) and the reverse primer Ge-1 Indel 1R ( 5’ CACCAGCGGTCAGGATAGAT 3’ ) were used to establish presence or absence of the 78bp deletion in Ge-1 . The Hap23 strain of DMelSV was extracted from an infected line of D . melanogaster ( Om ) using the protocol described in Magwire et al . 2011[17] . Female D . melanogaster were injected in the abdomen with sigma virus either by blowing virus through a glass needle connected with a hose until slight extension of the proboscis was observed ( only for recombinant mapping experiment ) or by using Nanoject ( Drummond Scientific ) to inject 69nl virus suspension ( all other experiments ) . Injected flies were tipped onto new media every two days before they were tested for infection . Flies infected with the DMelSV become paralysed and die on exposure to CO2 . To test for this symptom of infection , the flies were exposed to CO2 for 15 minutes at 12°C 10–16 days post infection . The exact gassing date for each experiment was carefully picked by pilot CO2 exposure . Flies were given 2 hours to recover from the CO2 and then the number of dead or paralyzed individuals was counted as well as the total number of individuals in each vial . In the recombinant mapping experiment , four replicate vials each containing approximately 20 flies were used in each experiment except for the first round of recombinant assay ( one replicate ) and were used in the CO2 sensitivity assay . In the 25°C RNAi experiment , 29–40 replicate vials for each cross were injected with virus . 14–25 were used in the CO2 sensitivity assay and the remaining 15 vials were used to measure viral titre ( see details below ) . In the 29°C RNAi experiment , 24 copies of each cross were injected with sigma virus and all of them were used to measure viral titre . In the transgenic fly experiment and the Ago2 dependency experiment , 30–32 replicate vials each containing approximately 15 flies were injected with virus . 15–18 vials per line were assayed for resistance to sigma virus by measuring CO2 sensitivity and the remaining 15 vials were used to measure viral titre . RNA was extracted from 8–15 individuals from each line at day 6 and/or day 12 post sigma virus injection using TRIzol ( Invitrogen Corp , San Diego ) following the manufacturer’s instructions . In recombinant mapping experiment , viral RNA load was measure using SensiFAST SYBR & Fluorescein Kit on cDNA template . RNA was reverse transcribed into cDNA using MMLV ( Invitrogen ) or GoScrip Reverse Transcriptase ( Promega ) and random hexamers ( Sigma ) . Viral load was determined using quantitative PCR using fluorescein and the primers DmelSV_F1 ( 5’ TTCAATTTTGTACGCGGAATC 3’ ) and DmelSV_R1 ( 5’ TGATCAAACCGCTAGCTTCA 3’ ) , which amplify a region of the viral genome spanning one gene and the 5’ linker ( and therefore amplify genomic RNA but not mRNA ) in mapping experiments [17] . Ge-1 expression was measured using the forward primer qGe-1_F2 ( 5’ TCTTTGTGTCTCGAGCATGG 3’ ) and the reverse primer qGe-1R3 ( 5’ GAGCAAGCAATTTCTGGATACTT 3’ ) . Expression of Actin 5C was used as a reference using the primers qActin5c_for2 ( 5’ GAGCGCGGTTACTCTTTCAC 3’ ) and qActin5c_rev2 ( 5’ aagcctccattcccaagaac 3’ ) [17] . In RNAi and transgenic experiments , viral load was measured using a QuantiTect Virus+ROX Vial Kit ( QIAGEN ) on RNA template . Dual-labelled probe [FAM] TGTGCCAAGTCTGTAATCCTGCTA [NFQ-MGB] and primers DMelSV_F ( 5’ CCGACTACAAATGCTATATG 3’ ) , DMelSV_R ( 5’ CAGGTATTAGAGGCTTCTTA 3’ ) were used to amplify DMelSV genomic RNA . The amount of viral RNA and gene expression were standardised to twohousekeeping genes , Ef1alpha100E or RPL32 , using the ΔΔCt ( critical threshold ) method ( see below ) . Ef1alpha100E was amplified using probe [FAM] ATCGGAACCGTACCAGTAGG [BHQ3] and primers Ef1a100E_FW ( 5’ GGACGTCTACAAGATC 3’ ) and Ef1a100E_RV ( 5’ TCTCCACAGACTTTAC 3’ ) . RPL32 was amplified using probe [VIC] ACAACAGAGTGCGTCGCCGCTTCAAGG [NFQ-MGB] and primers RPL32_FW ( 5’ TGCTAAGCTGTCGCACAAATGG 3’ ) and RPL32_RV ( 5’ TGCGCTTGTTCGATCCGTAAC 3’ ) . We performed two or three technical replicates of each PCR and used the mean of these in subsequent analyses . We calculated the PCR efficiency by using a dilution series . Using this approach we found that the actin PCR is 93% efficient , the virus PCR using fluorescein is 96% efficient , the Ge-1 PCR is 102% efficient , the virus PCR using probe is 98% and the RPL32 PCR is 96% . Fly strains UAS-Ge-1-RNAi ( y , w1118;P{KK102275}VIE-260B , KK 106687 ) , UAS-Reps-RNAi ( y , w1118;P{KK101677}VIE-260B , KK 110704 ) as well as a control strain y , w1118;P{attP , y+ , w3-} ( 60100 ) were bought from VDRC Stock Center [60] . A ubiquitously expressed Gal4 driver under the control of the daughterless promoter w*;P{GAL4-da . G32}UH1 was crossed to the UAS strains and the control strain to induce the knock down effect . Four replicates were set up for each cross and flies were reared in cornmeal bottles with live yeast at 18°C where Gal4 drivers are inefficient ( efficient knock-downs are lethal ) . Two- to three-day old mated F1 females were injected with DMelSV for each cross and injected flies were kept at 25°C to induce an efficient knock-down effect before assaying for resistance . We later repeated this experiment and kept the injected flies at 29°C for a more efficient knock-down effect . The Ge-1-RNAi construct is 345 bp long and targets exon 9 of Ge-1 . The Reps-RNAi construct is 360 bp long and targets exon 4 of Reps . Since Ge-1 is essential for forming P bodies , we also tested the effect of knocking down other P-body genes by RNAi on DMelSV susceptibility [60] . We knocked down 8 P-body genes: Edc3 ( CG6311 , w1118;P{GD11886}v30149 , GD30149 ) , DCP2 ( CG6169 , P{KK101790}VIE-260B , KK105130 ) , DCP1 ( CG11183 , P{KK101204}VIE-260B , KK105638 ) , GW182 ( CG31992 , P{KK101472}VIE-260B , KK103581 ) , pcm ( CG3291 , P{KK108511}VIE-260B , KK105739 ) , me31B ( CG4916 , w1118; P{GD11470}v49378 , GD49378 ) , Part-1 ( CG5208 , P{KK104961}VIE-260B , KK100872 ) and stau ( CG5753 , P{KK108121}VIE-260B , KK106645 ) in Ge-1 susceptible background and Ge-1 heterozygous background separately . Daughterless Gal4 driver was crossed to Ge-1 resistant and susceptible recombinants to generate two Gal4 drivers with resistant or susceptible Ge-1 backgrounds . UAS lines were then crossed to two daughterless Gal4 drivers and kept at 18°C where Gal4 drivers are inefficient ( efficient knock-downs are lethal ) . Because UAS-RNAi lines contain susceptible Ge-1 allele , so the crosses result in flies with susceptible Ge-1 background or heterozygous Ge-1 background . Two- to five-day old mated F1 females were injected with DMelSV for each cross and injected flies were kept at 25°C to induce an efficient knock-down effect before assaying for resistance . Infected flies were transferred to new food every three days and were homogenized at day12 post infection for RNA extraction . Note that the design of the crosses means that there are 4 different genetic backgrounds ( the GD and KK RNAi lines crossed to the two different Gal4 driver lines ) . Within these four genetic backgrounds , the flies should be genetically identical except for the RNAi construct . Drosophila P[acman] Bacteria Artificial Chromosomes ( BACs ) clone CHORI-322-120M19 covering fly genome 2L: 11090119–11111928 was obtained from BACPAC Resources Centre ( BPRC ) [61] . This BAC contains the susceptible allele of Ge-1 which doesn’t have the deletion . Recombineering was carried out to replace the susceptible allele of Ge-1 in the BAC with a resistant allele containing the deletion through homologous recombination [43] . DNA was extracted from the resistant stain EME to use as a template for amplifying the resistant allele of Ge-1 . A fragment containing a region of Ge-1 with the deletion was amplified using the primers Ge-1_newrc_F1 ( 5’ TATCTCCTGCACCTCTCGAC 3’ ) and Ge-1_newrc_R1 ( 5’ CTGCCTGCACGAG TGGAA 3’ ) . The GalK targeting cassette was amplified from bacterial strain pgalK . Phusion High Fidelity polymerase ( NEB ) and the primers: Ge-1_galK_F ( 5’ CGTCCTGTGTGGCCATTATCTCCTGCACCTCTCGACTCGGACTCGAACTGCCGCT-CCTGTTGACAATTAATCATCCGCA 3’ ) , Ge-1_galK_R ( 5’ GGAAGCCACATGATGGCAAAAA-AGTCTGCTCTCTCTTTTTCCTCGACAATATCAACAAGTCAGCACTGTCCTGCTCCTT 3’ ) were used in PCRs . The sequences in italics were homologous sequences to the Ge-1 gene . To transform flies with the BAC clone , the BAC carrying the resistant allele of Ge-1 was injected into embryos of a fly strain containing the 3rd chromosome attp site: M ( eGFP , vas-int , dmRFP ) ZH-2A;;M ( attP ) ZH-86Fb [62] . The original BAC was also injected into the same strain as a control . Since the BAC contains a mini-white gene , flies emerged from injected embryos were crossed to a white-eye double balancer w-;If/Cyo;TM6B/MRKS and successful transformants were picked by their red-eye phenotype . Balanced transformants were crossed to their siblings and generated homozygotes with balanced 2nd chromosome . Ago2 mutant Ago251B was kindly provided by Dr . Maria Carla Saleh ( Institute Pasteur ) . Two recombinants 47 ( resistant ) and 16 ( susceptible ) generated above were chosen to cross to an Ago2 mutant strain Ago251B and generated two homozygous lines: 47; Ago251B and 16; Ago251B . 30 vials containing 15 two- to five-day old females were infected with sigma virus for each line . The same number of flies were infected for strain 47 and strain 16 as controls . Infected females were kept at 25°C and maintained on cornmeal food . We also infected the transgenic flies carrying resistant allele ( Ge-1Δ78C ) and susceptible allele ( Ge-1+ ) of Ge-1 with two other viruses extracted from D . melanogaster: DAV and DCV ( TCID50 = 5x108 ) [63] . For line Ge-1Δ78C , 10 vials of 15 mated females were pricked with either DAV or DCV and kept in 25°C . 10 vials of infected females of Ge-1+ ( Ge-1+A , Ge-1+B and Ge-1+C ) were also pricked with viruses and kept in 25°C . DAV infected flies were collected 3 days post infection and DCV infected flies were collected 2 days post infection for RNA extraction . Our data from the infection experiments consists of numbers of infected and uninfected flies , which we treat either as a proportion analysed with non-parametric statistics or as a binomial response in a generalized linear mixed model . The parameters of the model were estimated using the R library MCMCglmm [64] , which uses Bayesian Markov chain Monte Carlo ( MCMC ) techniques . To test for an association between resistant genes and resistance to sigma virus in DGRP , we fit the model: vi , j=logit−1 ( XiTβ+αj+εi , j ) Where νi , j is the probability of flies in vial i from line j being infected . β is a vector of the fixed effects of ref ( 2 ) P genotype , doc1420 of CHKov1 genotype ( two genes known to affect sigma virus resistance [17 , 19] ) , and XiT is a row vector relating the fixed effects to vial i . αj is a random effect of line j . The residual , εi , j , allows over-dispersion due to unaccounted for heterogeneity between vials in the probability of infection . We tested for the effects of a 78bp deletion in Ge-1 and SNPs by including this as an additional fixed effect in β . For each fly line in which we measured viral titres or gene expression by quantitative RT-PCR , we first calculated ΔCt as the difference between the cycle thresholds of the gene of interest and the endogenous control . In recombinant mapping and transgenic lines’ assays , the viral titre or gene expression in resistant flies relative to susceptible flies was calculated as 2-ΔΔCt , where ΔΔCt = ΔCtresistant—ΔCtsusceptible , where ΔCtresistant and ΔCtsusceptible , are the means of the ΔCt values of the resistant and susceptible lines . In RNAi , the viral titre or gene expression in RNAi lines relative to the control was calculated as 2-ΔΔCt , where ΔΔCt = ΔCtRNAi—ΔCtcontrol , where ΔCtRNAi and ΔCtcontrol , are the means of the ΔCt values of RNAi lines and the control . To assess whether these differences were statistically significant , we compare ΔCt in the resistant lines ( RNAi lines ) and the susceptible lines ( control ) by fitting the ΔCt values in a linear model . In P-body genes RNAi experiment , the viral titre data was not normally distributed . We first Box-Cox transformed the data using R function “boxcox” from package “MASS” [65] . Then we fitted the transformed data in a linear model to test whether ΔΔCt of RNAi lines were significantly different from controls . To test for positive selection , we used a McDonald and Kreitman ( MK ) test [48] applied to 197 D . melanogaster sequences from Zambian lines from Drosophila Population Genomics Project 3 ( DPGP3 ) [42] and 6 D . simulans sequences [66] . Substitutions were polarised along the D . melanogaster and D . simulans lineages . Consensus sequences of 197 D . melanogaster samples were downloaded from http://www . dpgp . org/ . Ge-1 sequences ( 2L: 11095353–11100866 ) were pulled out from all lines using the scripts “breaker . pl” and “dataslice . pl” written by the authors ( Masking package available from http://www . dpgp . org/ ) . Script “breker . pl” inserts line breaks every 1000 bp in all files and script “dataslice . pl” returns locus-specific FastA files when given a subset of individuals and locus information . Coding sequences of Ge-1 from all the lines were manually aligned using BioEdit . Vertical multiple alignment ( vma ) files of 6 D . simulans lines were downloaded from the DPGP website . Vma files were converted into FastA files by script “VMA2FASTA” provided by the authors . Ge-1 coding sequences were pulled out and manually aligned . Drosophila yakuba and Drosophila erecta sequences were used to infer the sequence of the most recent common ancestor of Ge-1 in D . melanogaster and D . simulans . For any polymorphic codon , if the ancestral state is ambiguous ( ie . both the D . melanogaster and D . simulans nucleotides were present in the outgroup species ) , we simply excluded it from the analysis . Standard MK test was carried out using McDonald and Kreitman Test ( MKT ) software [67] . We excluded polymorphic sites with a frequency less than 10% . Polarized 2 × 2 contingency tables were used to calculate α , which can be used as an estimate of the proportion of variants fixed under selection [68] . Statistical significance of the 2 × 2 contingency tables was determined by carrying out using a χ2 test .
|
Hosts and their pathogens are engaged in a never-ending arms race , and hosts must continually evolve new defences to protect themselves from infection . In the fruit fly Drosophila melanogaster we show that virus resistance can evolve through a single mutation . In flies that are highly resistant to a naturally occurring virus called sigma virus we identified a deletion in the protein-coding region of a gene called Ge-1 . We experimentally confirmed that this was the cause of resistance by deleting this region in transgenic flies . Furthermore , we show that even the susceptible allele of Ge-1 helps protect flies against the virus , suggesting that this mutation has made an existing antiviral defence more effective . Ge-1 plays a central role in RNA degradation in regions of the cytoplasm called P bodies , and our results suggest that this pathway has been recruited during evolution to protect D . melanogaster against sigma virus . The protein domain that contains the deletion has experienced strong selection during its evolution , suggesting that it may be involved in an ongoing arms race with viruses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2016
|
A Polymorphism in the Processing Body Component Ge-1 Controls Resistance to a Naturally Occurring Rhabdovirus in Drosophila
|
Resistance to allopurinol in zoonotic canine leishmaniasis has been recently shown to be associated with disease relapse in naturally-infected dogs . However , information regarding the formation of resistance and its dynamics is lacking . This study describes the successful in-vitro induction of allopurinol resistance in Leishmania infantum cultured under increasing drug pressure . Allopurinol susceptibility and growth rate of induced parasites were monitored over 23 weeks and parasite clones were tested at selected time points and compared to their parental lines , both as promastigotes and as amastigotes . Allopurinol resistance was formed in strains from two parasite stocks producing a 20-fold rise in IC50 along three distinct growth phases . In addition , characteristic differential clustering of single nucleotide polymorphisms ( SNP ) was found in drug sensitive and resistant parasite clones . Results confirm that genetic polymorphism , as well as clonal heterogeneity , contribute to in-vitro resistance to allopurinol , which is likely to occur in natural infection .
Visceral leishmaniasis caused by Leishmania infantum is a life threatening disease , affecting humans in Europe , Asia , North Africa and Latin America , as well as domestic dogs which are the main reservoir for this infection [1 , 2] . We recently reported the detection of disease relapse in infected dogs associated with allopurinol resistant parasite strains [3] . Allopurinol is the main drug used for long-term control of the canine disease , and since resistant parasites may enhance transmission to humans and other dogs [4] , this finding is alarming . In this study , we aimed to improve our understanding of the formation of resistance to allopurinol by following an in-vitro model of resistance induction in susceptible isolates under increasing drug pressure and examining the susceptibility to allopurinol of several clones from the same time point , in both promastigote and amastigote stages .
Two allopurinol susceptible L . infantum isolates , obtained prior to drug treatment from dogs at time of first diagnosis of clinical disease , were used in the study; MCAN/IL/2011/NT4 and MCAN/IL/2011/NT5 . Both dogs were males; presented weight loss , skin lesions , enlarged lymph nodes , mild anemia and elevated serum globulin levels . Dog NT4 was also azotemic . Isolation , culture procedure and IC50 testing were done as previously described [3] . Briefly , allopurinol susceptibility was determined using a promastigote viability test , following 72 h incubation in increasing drug concentrations . Each test was repeated twice . Resistance was induced in cultures designated NT4 . L and NT5 . L in a stepwise manner; beginning with the original isolates and every 2–6 days thereafter , 5*106 promastigotes were transferred into 5 mL of complete M-199 medium containing increasing allopurinol concentrations , starting at 100 μg/mL with 50 μg/mL increments . Thus , each step was defined as the period between two successive subcultures . Average growth rate for each step was calculated as the increase in parasite concentration during the step divided by its length in days ( average step growth rate–ASGR ) . Once parasites were able to grow at 900 μg/mL allopurinol , they were maintained in it for at least two additional months . Allopurinol IC50 of cultures was tested every 7–14 days and culture samples were cryopreserved . Controls of each isolate cultured without allopurinol , designated NT4 and NT5 , were maintained and tested in parallel . All culture medium components manufacturers and lot numbers were kept constant for the duration of the experiments . Drug induced cultures from selected time points were thawed and single clones were isolated using the hanging drop method , adapted from Evans and Smith [5] . Briefly , 0 . 5 μL samples were taken of each culture adjusted to contain 2*103 parasites per mL . Samples were inspected microscopically , and those containing individual promastigotes were subcultured in 200 μL of culture medium until a stable clonal culture was established . IC50 was established for each revived frozen culture , as well as for 5–10 of its clones . Allopurinol susceptibility was also studied in intracellular amastigotes developed for each induced strain and its clones at one time point , as previously described [3] . Briefly , DH-82 cells were infected with promastigotes from thawed samples of the drug induced strains and 5 respective clonal cultures . Infected cells were treated with either 0 or 300 μg/mL allopurinol for 72h , followed by counting of intracellular parasites per 100 DH-82 cells on Giemsa stained preparations , and calculation of the percent inhibition caused by the drug . Drug-free control cultures were also thawed and tested at the specific time points . Six clonal strains derived from cultures NT4 . L and NT5 . L as described above , presenting low ( n = 2 ) and high ( n = 4 ) allopurinol IC50 values were chosen for whole genome sequencing ( WGS ) . These included clone 1 of NT4 . L on day 28 ( NT4 . L . s , see S1 Table ) and clones 3 and 4 from day 104 ( NT4 . L . r1 and NT4 . L . r2 , respectively ) ; clone 2 of NT5 . L from day 28 ( NT5 . L . s ) and clones 1 and 5 of day 86 ( NT5 . L . r1 and NT5 . L . r2 , respectively ) . DNA for WGS was extracted from 2*108 mid log-phase promastigotes of each of the six strains described above . Following centrifugation at 1500 rpm for 10 minutes , supernatant was discarded and promastigotes were suspended in 250μL phosphate buffered saline . DNA was then extracted using the Illustra blood genomicPrep Mini Spin KIT ( GE Healthcare , UK ) according to manufacturer’s instructions and included RNAse treatment ( RNAse A , Sigma-Aldrich , St . Louis , MO ) . Quantity and quality of DNA was tested using the NanoDrop 2000 ( Thermo Scientific , Wilmington , DE ) , followed by visualization in 1% agarose gel with ethidium bromide . Fragmentation was done using the Covaris shearing ( Covaris S2 , Covaris , Woburn , MA ) set to the size target at 400bp for library preparation . Libraries were made using the TruSeq DNA kit ( Genomic DNA Sample Prep Kit , FC-102-1004 , Illumina , San Diego , CA ) . Sequencing was done using 100 bases paired ends reads , on an Ilumina HiSeq2000 platform , with the TruSeq SBS Kit ( TruSeq SBS v3 , FC-401-3001 , Illumina , San Diego , CA ) and TruSeq PE Cluster Kit ( TruSeq PE Cluster Kit v3 , PE-401-3001 , Illumina , San Diego , CA ) , at the DNA LandMarks Laboratory ( St . -Jean-sur-Richelieu , Canada ) . Raw reads were subjected to a cleaning procedure using the FASTX Toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/index . html , version 0 . 0 . 13 . 2 ) . Trimming read end nucleotides with quality scores under 30 was done using the fastq_quality_trimmer , and removal of read pairs was done if reads in the read pair had less than 70% base pairs with quality score under or equal to 23 , using the fastq_quality_filter . The L . infantum JPCM5 genome with chromosomes 1–36 was used as a reference genome ( European nucleotide archive , BioProject PRJNA12658 , FR796433—FR796468 ) . Cleaned paired-end reads , obtained after processing and cleaning of the 6 samples were mapped to the reference genome using the Bowtie2 program version 2 . 0 . 0 with default parameters [6] . SNP analysis was done using the Picard ( http://broadinstitute . github . io/picard/ ) and GATK UnifiedGenotyper ( version 2 . 5–2 ) [7] programs . The SNP’s calling was done in reference to the L . infantum JPCM5 genome . The degree of similarity between SNP of the six induced resistant strains was studied by a maximum likelihood analysis with bootstrapping ( N = 100 ) , using the PhyML 3 . 0 software [8] . Comparing IC50 values between induced and control cultures for matching time points was done using the t-test . Tukey HSD and Wilcoxon tests were used to compare IC50 values within each drug-cultured isolate at different time points . The Tukey HSD test was used to compare IC50 and percent inhibition values within strains and respective clones on each time point . Correlations between promastigote IC50 values and amastigote percent inhibition values for respective clones were described for NT4 . L and NT5 . L using a linear , logarithmic or polynomial trend lines . The animal care protocol used in this study was approved by the Hebrew University’s Institutional Animal Care and Use Committee ( IACUC ) ; approval no . MD-08-11476-2 , following the USA NIH guidelines .
A starting allopurinol concentration of 100 μg/mL was chosen because the IC50 values of the parent isolates were 105 and 93 μg/mL for NT4 and NT5 , respectively [3] . During the experiment , drug concentration in the culture medium quadrupled by day 22 and maximal drug level tested was 900 μg/mL allopurinol from days 60–71 and on , about 10 folds higher than the initial IC50 values of NT4 . L and NT5 . L , and close to the average IC50 value found for resistant clinical isolates previously [3] ( Fig 1A and 1B ) . Allopurinol susceptibility kinetics showed three distinguishable phases in both isolates . In the initial phase ( up to day 28 in NT5 . L and day 60 in NT4 . L ) , IC50 values had not changed significantly compared to the initial value , and were comparable to those measured for the controls at most points . In the second phase , a significant 4 folds increase in IC50 was seen , only in drug exposed cultures of both isolates ( t-test , P <0 . 05 ) . Peak IC50 values recorded were 2225 μg/mL at day 93 for NT5 . L and 2209 μg/mL at day 94 for NT4 . L . In the third phase , a decline of over 2 folds in IC50 compared to peak values was measured for both drug-cultured isolates ( Tukey HSD and Wilcoxon tests , P <0 . 05 ) . Control cultures NT4 and NT5 , not exposed to drug pressure , fluctuated in IC50 over time , however peak values did not exceed 405 μg/mL . Using ASGR values , two distinct phases were detected in both induced cultures , marked by a sharp change in ASGR . In the first phase , growth rate was decreased compared to the original isolate , and slower growth lasted approximately to day 39 for NT5 . L ( allopurinol concentration 550 μg/mL ) or day 60 for NT4 . L ( allopurinol concentration 700 μg/mL ) . In the second phase , growth rate significantly increased by 2 folds or more ( Fig 1C and 1D , t-test , P <0 . 001 ) . Difference in frequencies of steps length between the two growth phases was tested , and no significant difference was found between the two phases ( χ2 test , P = 0 . 3945 ) , confirming that promastigotes growth rates were indeed increased in phase two for both cultures . Frozen drug-cultured isolate samples from different time points were revived , clones were isolated and their promastigote IC50 compared with that of the respective parent sample ( Fig 2A and 2B ) . IC50 values of clones presented a 2–4 fold variation at all time points , with significant differences detected between clones ( Tukey HSD test , P <0 . 05 , S1 Table ) . Interestingly , values of parent cultures were within the 95% confidence intervals created by values of their respective clones . As found for promastigotes , testing of intracellular amastigotes also demonstrated in most cases a significant difference between susceptible control strains and resistant induced strains and clones ( Fig 2C , S2 Table ) . Inter-clonal variation demonstrated in amastigotes was smaller than seen in promastigotes . However , this can be in part due to limitation of the assay that prevents using drug concentrations of over 300μg/mL allopurinol . R square values for correlation between promastigote IC50 values and amastigote percent inhibition values ranged between 0 . 86–0 . 97 for NT4 . L and 0 . 7–0 . 79 for NT5 . L , for linear and polynomial model , respectively . WGS resulted in cleaned paired-end reads of 17–38*106 reads per sample , high assembly rate of 98 . 13–98 . 52% and coverage of x99-x218 . Maximum likelihood analysis including all SNP’s found ( including 9 , 969 positions , S3 Table ) resulted in the resistant and susceptible clonal strains dividing into two distinct clusters ( Fig 3 ) .
Leishmania infantum resistance to allopurinol may pose a combined veterinary and public health threat . Drug resistance can result in infected dogs having an uncontrolled high parasite load and being parasitemic for longer periods , increasing both the impact of the disease on the canine host and the potential for transmission via sandflies to humans [9 , 10] . Valuable molecular and biochemical information can be obtained by analyzing resistant field isolates . The in-vitro generation of drug resistance is a useful complementary tool for elucidating the mechanisms of resistance formation , especially when a genetic basis is suspected and sought [11–15] . As a first step in exploring resistance to allopurinol we constructed an in-vitro promastigote model that allowed monitoring the progression of resistance development under drug pressure over time , with less variables and complexity compared to an intracellular amastigote based model . This same approach , when applied in studies of antimonials [16] and miltefosine [17] , resulted in the identification of genetic changes found also in resistant amastigote strains [18 , 19] . In the present study , we applied drug pressure of up to 10 times higher ( 900μg/mL ) than the initial IC50 of the two induced promastigote cultures , a drug level compared to the average IC50 level found previously for resistant clinical isolates ( 996±372μg/mL ) [3] . This experimental setup has induced or selected for a considerable increase in allopurinol resistance , resulting in IC50 levels of up to 20 folds higher than initial level , comparable to levels measured for allopurinol-resistant parasites isolated from dogs that experienced clinical disease relapse [3] . Three distinct stages were discerned by monitoring growth rates and IC50 values during the induction of resistance . Initially , following introduction of drug pressure parasite growth rates decreased . This was due either to adaptation to the culture medium or the additional stress put on by the drug . Although an increase in IC50 values accompanied the decreased growth rate , it was found to be non-significant and shared by both control and test cultures . Therefore , this increase may also represent an adaptation to the culture medium and to the purine sources in particular , affecting the uptake or metabolism of the purine analog allopurinol [20–22] . Following this period of adaptation the growth rates of the isolates cultured with drug at least doubled in parallel to a significant increase in their IC50 . Since the peak IC50 values were measured slightly after maximum drug concentration was reached , this IC50 increase was most likely due to genetic adaptations , where the drug pressure selected variants that carried advantageous polymorphisms [15 , 23 , 24] . The existence of an inherent basis for resistance is supported also by the relatively high correlation found between drug susceptibilities of promastigotes and amastigotes of respective strains . The dynamics of resistance formation seen here fits the suggested model for appearance of pathogen drug resistance in infectious diseases following treatment , which includes emergence , establishment , increase and equilibrium of mutations promoting growth and survival [25] . Noteworthy is the significant 2–3 folds decline in IC50 values following the peak values seen in both NT4 . L and NT5 . L . This decline occurred when the drug concentration in the medium was maintained constant at maximal levels , leaving IC50 values still significantly higher compared to most time points during the adaptation phase . This phenomenon might reflect the result of intra-clonal mechanisms such as negative sign epistasis between mutations or may be caused by inter-clonal interaction in mixed cultures in response to prolonged exposure to high drug concentrations , such as clonal interference [25 , 26] . In support of the latter explanation , the IC50 values of clones generated from a culture under drug pressure at individual time points revealed significant heterogeneity , both when promastigotes or amastigotes were tested . As a rule , clonality is well recognized in Leishmania and was suggested to play a role in its evolution [15] . Albeit the heterogeneity in IC50 values within cultured populations , both cultures ( NT4 . L , NT5 . L ) demonstrated very similar patterns during the development of allopurinol resistance , both with respect to IC50 values and growth rates . This suggests that the process of adaptation to drug pressure may have been similar in both independent cultures . In conclusion , this study describes the successful induction of allopurinol resistance in L . infantum , under drug pressure . The model may facilitate studies on the mechanisms , pathways and genetics of allopurinol resistance in parasite populations , as well as identification and monitoring of resistance in clinical isolates .
|
Visceral leishmaniasis caused by the parasite Leishmania infantum is a neglected tropical disease transmitted from animal hosts to humans by sand fly bites . This potentially fatal disease affects thousands of people annually and threatens millions who live in disease risk areas . Domestic dogs are considered as the main reservoir of this parasite which can also cause a severe chronic canine disease . Allopurinol is the main drug used for long term treatment of this disease but it often does not eliminate infection in dogs . We have recently demonstrated that allopurinol resistant parasites can be isolated from naturally infected dogs that have developed clinical recurrence of disease during allopurinol treatment . In this study we aimed to see if resistance can be induced in susceptible parasite strains isolated from sick dogs by growing them in increasing drug concentrations under laboratory conditions . The changes in allopurinol susceptibility were measured and the impact of drug on parasite growth was monitored over 23 weeks . Induction of resistance was successful producing parasites 20-folds less susceptible to the drug . The pattern of change in drug susceptibility suggests that a genetic change is responsible for the increased resistance which is likely to mimic the formation of resistance in dogs .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"microbiology",
"vertebrates",
"cloning",
"parasitic",
"diseases",
"animals",
"protozoan",
"life",
"cycles",
"mammals",
"dogs",
"parasitic",
"protozoans",
"developmental",
"biology",
"protozoans",
"leishmania",
"genome",
"analysis",
"molecular",
"biology",
"techniques",
"promastigotes",
"research",
"and",
"analysis",
"methods",
"genomic",
"libraries",
"genomics",
"life",
"cycles",
"molecular",
"biology",
"amastigotes",
"leishmania",
"infantum",
"eukaryota",
"genetics",
"biology",
"and",
"life",
"sciences",
"protozoology",
"computational",
"biology",
"amniotes",
"organisms"
] |
2017
|
Induction of allopurinol resistance in Leishmania infantum isolated from dogs
|
A published study used a stochastic branching process to derive equations for the mean and variance of the probability of , and time to , extinction in population of tsetse flies ( Glossina spp ) as a function of adult and pupal mortality , and the probabilities that a female is inseminated by a fertile male . The original derivation was partially heuristic and provided no proofs for inductive results . We provide these proofs , together with a more compact way of reaching the same results . We also show that , while the published equations hold good for the case where tsetse produce male and female offspring in equal proportion , a different solution is required for the more general case where the probability ( β ) that an offspring is female lies anywhere in the interval ( 0 , 1 ) . We confirm previous results obtained for the special case where β = 0 . 5 and show that extinction probability is at a minimum for β > 0 . 5 by an amount that increases with increasing adult female mortality . Sensitivity analysis showed that the extinction probability was affected most by changes in adult female mortality , followed by the rate of production of pupae . Because females only produce a single offspring approximately every 10 days , imposing a death rate of greater than about 3 . 5% per day will ensure the eradication of any tsetse population . These mortality levels can be achieved for some species using insecticide-treated targets or cattle—providing thereby a simple , effective and cost-effective method of controlling and eradicating tsetse , and also human and animal trypanosomiasis . Our results are of further interest in the modern situation where increases in temperature are seeing the real possibility that tsetse will go extinct in some areas , without the need for intervention , but have an increased chance of surviving in other areas where they were previously unsustainable due to low temperatures .
Both sexes of tsetse ( Glossina spp . ) feed only on blood and are vectors of human and animal trypanosomiasis in Africa . They are also very unusual biologically . During each reproductive event , the mature adult female tsetse ovulates a single egg that is retained in the uterus until it hatches . The resulting larva develops through three instars , nourished via a milk gland , resulting ultimately in a third instar larva that may weigh as much as , or even slightly more than , its mother . This reproductive mechanism is termed adenotrophic viviparity . The mature late-third-instar larva is typically deposited on soft soil , into which it burrows rapidly , pupating immediately and remaining underground , without feeding further , until it develops into a young adult fly . Given the large amount of energy and raw material required to produce the large pupa , the female only produces one pupa every 7–12 days: and the resulting pupa takes 3–7 weeks to develop into an adult fly—the rates for these processes depending on temperature . The teneral ( i . e . unfed ) adult emerging from the puparial case has the full linear dimensions of the mature adult , but has a poorly developed flight musculature , and lower levels of fat reserves than mature adults . The first 2-3 blood-meals must be used to build flight muscle and fat levels before the female can start producing her own pupae . Given the implicitly low birth rate , it is clear that tsetse populations can only survive if they are able to keep their mortality at low levels . In the laboratory , male and female G . m . morsitans can survive for up to 241 and 208 days , respectively [7] . In the field , the flies are seldom that long-lived and females survive for longer than males , sometimes surviving at least 130 days [8] . There is a marked loss , with increasing age , in female reproductive potential in laboratory populations , but there is little suggestion of such an effect in field flies [9] . Similarly , whereas trypanosome infection can result in increased mortality in tsetse > 50 days old , the evidence for such an effect in field flies is not as convincing . For present modelling purposes we have , accordingly , ignored any effect of trypanosome infection on tsetse survival .
A female tsetse fly generally mates only once; it is thus crucial to include in our model the probability that a female tsetse fly is inseminated by a fertile male . We will also assume that the probability that a deposited pupa is male or female can be anywhere in the open interval ( 0 , 1 ) . Note that , at both endpoints , extinction occurs with probability 1 . 0 , because the population will consist only of one gender of fly . The probability p1 , 1 that a female survives one pregnancy and produces one surviving female offspring is calculated as follows: First , we know that a female tsetse fly is inseminated by a fertile male with a probability ϵ , then survives with probability λ ( ν+τ ) up to the time she produces her first pupa , which itself has a probability β of being female . This pupa survives the pupal period with a probability φP , and the mother finally dies with a probability ( 1 − λτ ) during the next pregnancy . Thus , combining all these factors , we obtain the probability that a female tsetse fly produces one surviving daughter after surviving one pregnancy as p 1 , 1 = ϵ λ ( ν + τ ) β φ P ( 1 − λ τ ) . ( 1 ) In general , the probability that a female tsetse fly produces k surviving daughters , after surviving n pregnancies , is given by p n , k = ϵ λ ( ν + n τ ) ( 1 − λ τ ) ( n k ) β n φ k P ( 1 β − φ P ) n − k , ( 2 ) for n > 0 , 1 ≤ k ≤ n , and where ( n k ) are the binomial coefficients . Proof: Let An be the event ‘a mother deposits exactly n pupae’ , and Bn , k be the event ‘n pupae produces exactly k female adults’ . We can then define M n = P ( A n ) = ϵ λ ν + n τ ( 1 − λ τ ) . q n , k = P ( B n , k / A n ) . It is clear that p n , k = P ( A n ⋂ B n , k ) = P ( A n ) . P ( B n , k / A n ) = M n . q n , k . ( 3 ) We notice that Mn refers to the mother’s survival and qn , k refers to the pupae survival . So we can base our proof by concentrating on the pupal survival since the product of the two gives the result of interest . It was actually observed that Eq ( 2 ) can be proved without resorting to induction . Notice that for each pupa there are two possibilities; either it becomes an adult female or it does not . The probability that it becomes an adult female is βφP , and the probability that it does not is then clearly ( 1 − βφP ) . Since the probabilities are the same for all pupae , and these outcomes for different pupae are independent , the probability that there are k adult females from n pupae is given by a binomial distribution as q n , k = ( n k ) ( β φ P ) k ( 1 − β φ P ) n − k = ( n k ) β k φ P k β n − k ( 1 β − φ P ) n − k = ( n k ) β n φ P k ( 1 β − φ P ) n − k . Thus , from Eq ( 3 ) , we obtain the expression for pn , k as p n , k = M n . q n , k = ϵ λ ( ν + n τ ) ( 1 − λ τ ) ( n k ) β n φ P k ( 1 β − φ P ) n − k . ( 4 ) Note that this reduces to the governing equation in [3] when β = 0 . 5 . Remarks: The heuristic explanation for Eq ( 2 ) in [3] is misleading because it terms a number greater than 1 a probability . Nonetheless , the formula is correct for the case considered , and is also correct more generally with the adjustment of that term , as the proof shows . The governing equation in [3] works only when β = 0 . 5 . After making the correction , it can be observed that Eq ( 4 ) works for all values of β . Summing Eq ( 2 ) over n leads to the probability ( pk ) that a female tsetse fly produces k surviving female offspring before she dies . Thus p k = ∑ n = k ∞ ϵ λ ( ν + n τ ) ( 1 − λ τ ) ( n k ) β n φ k P ( 1 β − φ P ) ) n − k ( 5 ) = ϵ λ ν ( 1 − λ τ ) φ k P ∑ n = k ∞ ( n k ) ( λ τ β ) n ( 1 β − φ P ) ) n − k . ( 6 ) Evaluating the sum gives p k = ϵ λ ν + k τ ( 1 − λ τ ) β k φ k P ( 1 − β λ τ ( 1 β − φ P ) ) ) k + 1 k > 0 . ( 7 ) The probability that a female tsetse fly produces at least one surviving daughter before she dies can be obtained by summing Eq ( 7 ) over k > 0 , to obtain p ( k > 0 ) = ϵ λ ν + τ β φ P 1 − λ τ ( 1 − β φ P ) ) . ( 8 ) ( See S1 Text for detailed proofs of Eqs ( 7 ) and ( 8 ) ) . Thus , the probability that a female tsetse fly does not produce any surviving female offspring before she dies is given by p 0 = 1 − p ( k > 0 ) = 1 − ϵ λ ν + τ β φ P 1 − λ τ ( 1 − β φ P ) ) . ( 9 ) Assuming that we start with one female tsetse fly in the initial generation , which produces k surviving offspring , we can write the moment generating function for the next generation as ϕ ( θ ) = ∑ k = 0 ∞ p k θ k = p 0 + ∑ k = 1 ∞ p k θ k . Substituting for p0 and pk and putting the terms not involving k outside the summation sign we get ϕ ( θ ) = A + B C ( 1 − θ ) A + B ( 1 − θ ) , ( 10 ) where A = 1 − λτ , B = βλτφP and C = 1 − ϵλν . The extinction probability can be found by solving the quadratic equation ϕ ( θ ) = θ , and it is the smallest non-negative root [10 , 11] . Thus the extinction probability is: θ = B C + A + B − ( B C + A + B ) 2 − 4 B ( A + B C ) 2 B , ( 11 ) where B ≠ 0 . This is the probability that a female tsetse population , resulting from an initial population of one adult female fly , goes to extinction . If the initial population consists of N such flies , then , assuming the independence of the probability of extinction of each female line , the probability of extinction is θN . We will use the method of moments to find the mean and variance of the expected number of offspring produced . From these variables we can then derive the mean and variance of the female tsetse population at a given generation n . By definition , the mth moment of pk is given by M m = ∑ k = 0 ∞ k m p k . When m = 1 , we obtain the first moment as M 1 = ϵ λ ν + τ β φ P ( 1 − λ τ ) . ( 12 ) And when m = 2 , we obtain the second moment as M 2 = ϵ λ ν + τ β φ P ( 1 − λ τ ( 1 − 2 β φ P ) ) ( 1 − λ τ ) 2 . ( 13 ) ( See S1 Text for the proofs of Eqs ( 12 ) and ( 13 ) ) . The mean , or expected number of surviving daughters of female tsetse fly is μ = ϵ λ ν + τ β φ P ( 1 − λ τ ) , and the variance is given by σ 2 = ϵ λ ν + τ β φ P ( 1 − λ τ ( 1 − 2 β φ P ) ) ( 1 − λ τ ) 2 − ( ϵ λ ν + τ β φ P ( 1 − λ τ ) ) 2 , where M ( n ) = μ n . ( 14 ) and V ( n ) = { n σ 2 , μ = 1 ( 1 − μ n ) σ 2 μ n − 1 1 − μ , μ ≠ 1 . ( 15 ) M ( n ) and V ( n ) are the mean and variance of the size of each generation ( Xn ) , respectively with the assumption X0 = 1 . Eqs ( 14 ) and ( 15 ) can be shown easily by induction . From the general framework developed by Lange [10 , 11] for the probability of extinction of a branching process . We have θ n = ∑ k = 0 ∞ p k ( θ n − 1 ) k , n = 1 , 2 , 3 , … ( 16 ) where θn is the probability of extinction at the nth generation and k is the number of offspring . Eq ( 16 ) can be rewritten in terms of a moment generating function as ϕ ( θ n − 1 ) = ∑ k = 0 ∞ p k ( θ n − 1 ) k = θ n . ( 17 ) Thus , from ( 17 ) , extinction probabilities can be calculated by starting with θ0 = 0 , θ1 = ϕ ( θ0 ) , θ2 = ϕ ( θ1 ) , and continuing iteratively through the generations to obtain θ n = ϕ ( θ n − 1 ) . ( 18 ) We also derived the first moments of T , based on the general formula obtained by Feller in [12] as E ( T j ) = ∑ n = 0 ∞ [ ( n + 1 ) j − n j ] ( 1 − θ n ) , ( 19 ) where ( 1-θn ) = P ( T > n ) and T is the extinction time . The first two moments of T are: E ( T ) = ∑ n = 0 ∞ ( 1 − θ n ) , ( 20 ) and E ( T 2 ) = ∑ n = 0 ∞ ( 2 n + 1 ) ( 1 − θ n ) . ( 21 ) Thus , using Eqs ( 10 ) and ( 18 ) and taking θ0 = 0 , we can calculate the values of θn by iteration . The first two , for example , are: θ 1=ϕ ( θ 0 ) = ϕ ( 0 ) = A + B C A + B , ( 22 ) θ 2=ϕ ( θ 1 ) = ϕ ( A + B C A + B ) = A + B C ( 1 − A + B C A + B ) A + B ( 1 − A + B C A + B ) . ( 23 ) In a situation where there are N surviving females , with N > 1 , Eqs ( 20 ) and ( 21 ) can be generalised . The probability of extinction at or before generation n is θn . If we have N surviving females , then the probability that they all become extinct at generation n is ( θn ) N . Thus , E ( T ) = ∑ n = 0 ∞ ( 1 − ( θ n ) N ) , ( 24 ) and E ( T 2 ) = ∑ n = 0 ∞ ( 2 n + 1 ) ( 1 − ( θ n ) N ) . ( 25 ) To estimate the mean and variance of the time to extinction for a population of N female tsetse flies , all that needs to be done is to estimate θn for a population consisting of a single fly , raise each of the values to power N , and obtain the appropriate sums .
We produced MATLAB code to solve Eq ( 11 ) and generate the extinction probabilities for given values of parameters A , B and C . Our results were closely similar to those previously published [3] , as illustrated in S1 Fig—S5 Fig in the S1 Text . For example , for a pupal duration ( P ) of 27 days , a time to first ovulation ( ν ) of 7 days , an inter-larval period ( τ ) of 9 days , a probability of β = 0 . 5 that a deposited pupa will be female and where all females are inseminated by a fertile male ( ϵ = 1 ) , the extinction probability for a population consisting of a single inseminated female fly increased linearly with adult female mortality rate ( ψ ) , at a rate which increased with increasing pupal mortality rate ( χ ) ( S1A Fig ) . If the pupal mortality is high enough , then the probability of extinction is high even if the adult mortality is low . For example if χ = 0 . 03 per day , then there is a greater than 40% chance that extinction will happen , even if the adult mortality rate is only 0 . 01 per day . Even when there was zero pupal mortality , however , extinction was certain when adult mortality rate approached levels of 0 . 04 per day . When the pioneer population consisted of more than a single inseminated female , the extinction probability was of course generally lower ( S1B Fig ) . If the pupal mortality rate was even 0 . 005 per day , however , all populations eventually went extinct , with probability 1 , as long as adult mortality rate exceeded about 0 . 032 per day . In situations where , for example , sterile male tsetse are released into a wild population or where a population is extremely low , females may fail to mate with a fertile male and ϵ will then fall below 1 . 0 . When the starting population was a single inseminated female , and with other input parameters as defined above , the extinction probability decreased approximately linearly with increasing values of ϵ ( S2A Fig ) . Increasing the assumed value of the adult mortality rate ( ψ ) simply shifted the whole graph of extinction probability towards a value of 1 . 0 , without changing the rate of increase of extinction probability with ϵ . When the pioneer population was greater than 1 , the relationship with ϵ was no longer linear ( S2B Fig ) and , even when the starting population was only 16 inseminated females , the extinction probability was still effectively zero when the probability of fertile insemination fell to 50% . No population could avoid extinction , however , when ϵ was less than about 10% Extinction is of course certain if a population consists only of one sex , but the probability of extinction goes to 1 . 0 more rapidly as the probability ( β ) , that a deposited pupa is female , goes to 0 ( all male population ) than as it goes to 1 ( all female population , Fig 1 ) . For adult female mortality rates very close to zero , the extinction probability goes to 1 . 0 as β goes to zero: but , for higher adult death rates the limit is reached for values of β > 0 . For example , when λ = 0 . 98 , extinction is already certain once the female proportion among pupae drops to 30% . The minimum extinction probability always occurs for a value of β > 0 . 5 , by an amount that increases as adult female mortality increases . We derived the general equation for the expected number of generations to extinction for independent lines of N females in Eq ( 24 ) . Eqs ( 22 ) and ( 23 ) give the first two iterations of the probability of extinction . MATLAB code was written to solve Eq ( 24 ) iteratively and thus find the expected number of generations to extinction . S3 Fig shows that the expected number of generations to extinction decreases with any increase in pupal mortality . S4 ( A ) and S4 ( B ) Fig show that , in the event that eradication is attempted through the release of sterile males , in order to reduce the probability that females are inseminated by fertile males , the eradication process will be much hastened if the mortality of the wild female population is also increased . S5 Fig gives the result of the expected number of generations to extinction against the probability of insemination . From the graph , we can see that the lower the probability of insemination by a fertile male , the smaller the number of generations to extinction .
All of the results presented here have been calculated on the assumption that , for each scenario , all rates of mortality and reproduction are constant over time . In reality , in the field , temperatures change with time and , since tsetse are poikilotherms , all of the mortality rates and developmental rates associated with reproduction also change continuously with time . The calculation of extinction probabilities is greatly complicated where temperatures are changing with time , and consideration of such situations is beyond the scope of the current study . Extreme weather events , such as prolonged spells of very hot weather , as have been experienced in recent years in the Zambezi Valley of Zimbabwe , may push tsetse populations close to extinction . We are currently investigating the circumstances under which it is possible to calculate extinction probabilities in such situations . Where we cannot obtain analytical solutions of the type derived here , when all model parameters are time-invariant , we will use simulation methods to investigate the problem . A full consideration of the issue of cost comparison between control methods , and the cost savings associated with eradication versus control , is an important , but complex , issue requiring full and careful consideration that is beyond the scope of the current presentation . Our modelling is restricted to the calculation of extinction probabilities of populations that are closed to in and out migration . We have not attempted to extend the modelling to more complex situations where metapopulations are made up of population patches with variable inter-patch connectivity . Preliminary work suggests that extinction probabilities will be reduced in the latter situations [32] .
|
We derive equations for the mean and variance of the probability of , and time to , extinction in population of tsetse flies ( Glossina spp ) , the vectors of trypanosomiasis in sub-Saharan Africa . In so doing we provide the complete proofs for all results , which were not provided in a previously published study . We also generalise the derivation to allow for the probability that an offspring is female to lie anywhere in the interval ( 0 , 1 ) . The probability of extinction was most sensitive to changes in adult female mortality . The unusual tsetse life cycle , with very low reproductive rates , means that populations can be eradicated as long as adult female mortality is raised to levels greater than about 3 . 5% per day . Simple bait methods of tsetse control , such as insecticide-treated targets and cattle , can therefore provide simple , affordable and effective means of eradicating tsetse populations . The results are of further interest in the modern situation where increases in temperature are seeing the real possibility that tsetse will go extinct in some areas , but have an increased chance of surviving in others where they were previously unsustainable due to low temperatures .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[] |
2019
|
Improved estimates for extinction probabilities and times to extinction for populations of tsetse (Glossina spp)
|
Transition metal ions ( Zn ( II ) , Cu ( II ) / ( I ) , Fe ( III ) / ( II ) , Mn ( II ) ) are essential for life and participate in a wide range of biological functions . Cellular Zn ( II ) levels must be high enough to ensure that it can perform its essential roles . Yet , since Zn ( II ) binds to ligands with high avidity , excess Zn ( II ) can lead to protein mismetallation . The major targets of mismetallation , and the underlying causes of Zn ( II ) intoxication , are not well understood . Here , we use a forward genetic selection to identify targets of Zn ( II ) toxicity . In wild-type cells , in which Zn ( II ) efflux prevents intoxication of the cytoplasm , extracellular Zn ( II ) inhibits the electron transport chain due to the inactivation of the major aerobic cytochrome oxidase . This toxicity can be ameliorated by depression of an alternate oxidase or by mutations that restrict access of Zn ( II ) to the cell surface . Conversely , efflux deficient cells are sensitive to low levels of Zn ( II ) that do not inhibit the respiratory chain . Under these conditions , intracellular Zn ( II ) accumulates and leads to heme toxicity . Heme accumulation results from dysregulation of the regulon controlled by PerR , a metal-dependent repressor of peroxide stress genes . When metallated with Fe ( II ) or Mn ( II ) , PerR represses both heme biosynthesis ( hemAXCDBL operon ) and the abundant heme protein catalase ( katA ) . Metallation of PerR with Zn ( II ) disrupts this coordination , resulting in depression of heme biosynthesis but continued repression of catalase . Our results support a model in which excess heme partitions to the membrane and undergoes redox cycling catalyzed by reduced menaquinone thereby resulting in oxidative stress .
Approximately 30% of proteins require a metal cofactor . Unlike iron ( Fe ( II ) ) , which can generate cell damaging hydroxyl radicals in the presence of hydrogen peroxide ( Fenton reaction ) , Zn ( II ) is not redox reactive . As a result , Zn ( II ) is favored as a structural cofactor that facilitates folding of a large number of proteins , and is also widely used as a Lewis acid catalyst . Total cellular Zn ( II ) ( the Zn ( II ) quota ) must be high enough to perform these essential roles . Yet , since Zn ( II ) binds much more avidly to common protein ligands than Fe ( II ) or Mn ( II ) ( an observation codified in the Irving-Williams series; [1] ) excess Zn ( II ) may result in mismetallation of proteins requiring these other metals . Thus , cellular Zn ( II ) is highly regulated at multiple levels: in Bacillus subtilis the total intracellular concentration at equilibrium is ~0 . 8 mM , and much of this is sequestered in metalloproteins . A subset of intracellular Zn ( II ) comprises a labile pool which buffers the thermodynamically free Zn ( II ) concentration in the picomolar range ( < 1 Zn ( II ) per cell ) [2 , 3] , thereby ensuring that only physiologically relevant Zn ( II ) metalloproteins are normally metallated by Zn ( II ) . The narrow range of free Zn ( II ) in Bacillus subtilis is set by the transcription repressors Zur , the sensor of Zn ( II ) limitation , and CzrA , the sensor of Zn ( II ) excess [4–6] . B . subtilis contains one high affinity uptake system ( znuABC ) and two efflux systems ( cadA and czcD ) . Under conditions of Zn ( II ) sufficiency , Zn ( II ) -binds to Zur [7] , which represses transcription of the Zn ( II ) uptake systems . Upon Zn ( II ) deficiency , transcription is depressed and Zn ( II ) is imported into the cell [4] . When Zn ( II ) is in excess , CzrA binds Zn ( II ) and is inactivated [8] , leading to depression of CadA and CzcD and Zn ( II ) efflux [5] . These metalloregulators sense the labile Zn ( II ) pool consisting of Zn ( II ) bound to small molecules , proteins and other macromolecules in a kinetically accessible form . In B . subtilis and related low G+C Firmicutes , the abundant LMW thiol , bacillithiol ( BSH ) , serves as a major buffer of the labile Zn ( II ) pool [3] . These buffering systems maintain labile Zn ( II ) concentrations high enough for metallation of Zn ( II ) containing proteins , but low enough to reduce mismetallation . The specific targets of zinc intoxication are not well defined . In this study , we take advantage of the well characterized Zn ( II ) homeostasis mechanisms in the model Gram-positive bacterium , B . subtilis , and use a forward genetic approach to investigate the underlying causes of Zn ( II ) intoxication . Our results suggest that in wild type cells , which are competent for export of Zn ( II ) from the cytosol , Zn ( II ) intoxication results from inactivation of the electron transport chain due to inhibition of the major aerobic cytochrome aa3 oxidase . Zn ( II ) resistant suppressors arise that either reduce access of Zn ( II ) to the cell surface or increase expression of the alternative anaerobic cytochrome bd oxidase due to inactivation of Rex , a NAD+/NADH sensing transcription factor . Conversely , in a Zn ( II ) efflux deficient mutant ( cadA czcD ) , Zn ( II ) intoxication results from mismetallation of cytosolic proteins . Here , we identify heme accumulation as a major consequence of intracellular Zn ( II ) intoxication , which in turn results from mismetallation and consequent dysregulation of the PerR regulon .
To identify potential targets of Zn ( II ) toxicity and genes involved in Zn ( II ) resistance , we isolated Zn ( II ) resistant mutants . A mariner transposon library was generated in wild-type cells and plated on a Petri plate containing LB medium and a continuous Zn ( II ) gradient ( 0–5 mM ) . Colonies able to grow in the highest Zn ( II ) concentrations were isolated and the location of the transposon insertion was identified . We isolated multiple independent transposon insertions in rex , ykuI , and the fla-che operon ( Table 1 ) . We backcrossed the transposon insertions into the parental strain by chromosomal DNA transformation . These reconstructed strains , as well as targeted gene deletions , phenocopied the originally isolated Zn ( II ) resistant transposon mutants , suggesting that the observed Zn ( II ) resistance is linked to the transposon insertion rather than a second site mutation . YkuI is a c-di-GMP binding protein [9] known to affect production of extracellular matrix ( ECM ) in B . cereus [10] . The fla-che operon contains genes encoding components of the flagella and chemotaxis machinery , as well as the alternative sigma factor , σD [11] . ECM production is inversely controlled with respect to flagellar motility in B . subtilis [12 , 13] . We therefore hypothesized that the ykuI and fla-che disruptions prevent Zn ( II ) intoxication by increasing production of ECM which can prevent access of Zn ( II ) to the cell , rather than by altering a target of mismetallation . In contrast , Rex is a regulator of anaerobic metabolism and is not known to affect ECM production . To test whether the ykuI and the fla-che transposon mutants serve to restrict access of Zn ( II ) to the cell , we monitored intracellular Zn ( II ) levels after Zn ( II ) shock in each of the isolated suppressors ( S1 Fig ) . We reasoned that mutations that restrict access of Zn ( II ) to the cell , and thereby reduce uptake , would not accumulate Zn ( II ) . Conversely , those that allow the cell to circumvent metabolic pathways intoxicated by Zn ( II ) would still accumulate Zn ( II ) upon Zn ( II ) shock . In contrast with wild-type , strains with transposon insertions in ykuI and the fla-che operon did not accumulate intracellular Zn ( II ) upon shock , whereas those with insertions in rex did ( S1 Fig ) . These results support the notion that ykuI and fla-che operon insertions restrict access of Zn ( II ) to the cell , presumably by increasing ECM production . Since our goal in this study is to define mechanisms of Zn ( II ) intoxication , we focus here on the role of rex in Zn ( II ) resistance . Rex is a DNA-binding transcription repressor that senses the intracellular NAD/NADH+ ratio and regulates genes involved in growth under anaerobic conditions in many Gram positive bacteria such as S . coelicolor and B . subtilis , including the cytochrome bd terminal oxidase ( cydABCD ) , lactate dehydrogenase ( lctP-ldh ) , and a putative nitrate transporter ( ywcJ ) [14–16] . Since a transposon insertion in rex leads to increased Zn ( II ) resistance , we hypothesized that derepression of one or more members of the Rex regulon contributes to Zn ( II ) resistance . We constructed mutants in which each Rex-regulated gene was individually deleted in a wild-type or Δrex background . Deletion of cydABCD resulted in a Zn ( II ) sensitive phenotype in a wild-type background ( Fig 1A ) , while there was no Zn ( II ) phenotype associated with deletion of any other member of the Rex regulon . Additionally , deletion of cydABCD in a Δrex background completely reversed the Zn ( II ) resistance phenotype of the Δrex mutant , consistent with the idea that derepression of cydABCD confers Zn ( II ) resistance ( Fig 1B ) . Furthermore , expression of the Rex regulon is derepressed under conditions of Zn ( II ) intoxication as measured by qRTPCR of cydA and ldh expression ( Fig 2 ) . B . subtilis encodes three terminal oxidases , cytochrome caa3 , aa3 , and bd [17] . Cytochrome caa3 and aa3 are heme-copper oxidases , whereas the relatively less efficient cytochrome bd oxidase does not utilize copper . The major cytochrome oxidase used during exponential growth is cytochrome aa3 [18] . Interestingly , expression of either cytochrome aa3 or cytochrome bd is required for viability [18] . Thus , during Zn ( II ) intoxication , the expression of the relatively Zn ( II ) insensitive cytochrome bd terminal oxidase may be required since the major aerobic system , cytochrome aa3 , is inhibited by Zn ( II ) ( Fig 1C ) . This is consistent with prior findings in Escherichia coli and Streptomyces coelicolor that suggest a similar extracellular target of Zn ( II ) intoxication [14 , 19] . Since the ability of Zn ( II ) to inhibit cytochrome oxidases is well established , we next decided to repeat our selection of Zn ( II ) resistant mutants using an efflux deficient strain in which Zn ( II ) intoxication presumably occurs by mismetallation of cytosolic targets . We selected spontaneous Zn ( II ) resistant mutants in a Zn ( II ) efflux mutant background that lacks the genes encoding the CadA and CzcD Zn ( II ) efflux pumps . Since the cadA czcD mutant displays Zn ( II ) toxicity at concentrations well below the MIC for wild-type , we reasoned that suppressors isolated from this background would reveal intracellular targets of Zn ( II ) intoxication . Using whole-genome resequencing , seven independently isolated strains were found to contain nonsense or frameshift mutations in aroB and aroC ( Table 1 ) . No additional mutations were identified in the suppressed strains , suggesting that the Zn ( II ) resistant phenotype is linked to the inactivation of aroB or aroC . Consistent with this prediction , strains in which aroB or aroC were inactivated by insertion of an antibiotic resistant cassette phenocopied the evolved Zn ( II ) resistant mutants ( S2 Fig ) . Interestingly , aroB and aroC were not recovered as suppressors of Zn ( II ) intoxication in the wild-type background ( Table 1 ) . This indicates the presence of distinct extracellular and intracellular targets of Zn ( II ) intoxication . Indeed , each suppressor mutation conferred Zn ( II ) resistance only in the genetic background in which it was isolated ( Fig 3 ) . For example , deletion of rex ( isolated in wild-type ) did not confer Zn ( II ) resistance to the Zn ( II ) efflux mutant , and mutation of aroB ( isolated in the Zn ( II ) efflux mutant background ) did not confer resistance to wild-type ( Fig 3 ) . The aroB and aroC genes are involved in the biosynthesis of chorismate , a precursor for aromatic amino acid biosynthesis . Since our selection was performed on rich media , it is unlikely that the cells are limited for amino acids . However , chorismate is also a precursor for menaquinone biosynthesis [20] . We therefore hypothesized that Zn ( II ) resistance may have resulted from a decrease in cellular pools of menaquinone . In B . subtilis , null mutations affecting late steps in menaquinone biosynthesis fail to form colonies on LB medium [21] , whereas the aroB and aroC null mutants grow well . This suggests that LB medium provides a precursor ( perhaps chorismate ) that can be used to maintain some level of menaquinone synthesis , and likely accounts for the failure to recover insertions affecting later steps in this pathway . Transposon insertions in aroB and aroC , as well as other menaquinone biosynthesis genes , were previously identified in S . aureus in a selection for heme resistance [22] . The authors suggested a model in which heme toxicity results when superoxide is generated by the redox cycling of membrane-associated heme and reduced quinone molecules [22] . We therefore hypothesized that Zn ( II ) intoxication in B . subtilis may also be related to menaquinone and perhaps to heme . To determine if the relevant effect of the aroB and aroC mutations is a reduction of menaquinone levels , we measured Zn ( II ) sensitivity in media supplemented with menaquinone or its precursor 1 , 4-dihydroxy-2-napthoate ( DHNA ) . We observed that external menaquinone or DHNA supplementation reverses the Zn ( II ) resistance phenotype of the cadA czcD aroB mutant . However , menaquinone or DHNA supplementation does not affect a cadA czcD hemA mutant , which is defective in heme biosynthesis ( Fig 4 ) . Overall , these data support the hypothesis that an aroB mutation leads to Zn ( II ) resistance by reducing cellular menaquinone levels . If the role of menaquinone is to act as an electron donor to membrane-localized heme , as proposed for S . aureus [22] , we reasoned that a loss of heme synthesis would also lead to Zn ( II ) resistance . Indeed , a cadA czcD hemA mutant is as Zn ( II ) resistant as a cadA czcD aroB mutant ( Fig 4 ) . This strain forms small colonies and is slow growing , thus it not surprising that mutations affecting heme biosynthesis were not isolated in our initial Zn ( II ) resistance selection . Importantly , the effects of the aroB and hemA mutations are not additive ( compare the czcD cadA aroB hemA quadruple mutant with the triple mutants ) , and this epistasis implies that they act in the same genetic pathway . To determine if heme accumulates under conditions of Zn ( II ) intoxication , we used a fluorescence based assay to monitor heme levels . The level of heme increases more than two-fold in the cadA czcD mutant background , but not in wild-type ( Fig 5A ) . Although B . subtilis can utilize heme as an Fe ( II ) source , it is not known to encode heme uptake or efflux systems . However , B . subtilis does encode two heme monooxygenases , HmoA and HmoB , that bind and degrade heme in vitro , although a physiological role for these proteins has not yet been demonstrated [23] . HmoB belongs to the well-characterized IsdG family and is not regulated by Fe ( II ) , whereas the Fur-regulated HmoA protein represents a poorly characterized subgroup of monooxygenases found in several pathogenic bacteria [23 , 24] . Since Zn ( II ) intoxication leads to heme accumulation , we reasoned that HmoA and HmoB may contribute to Zn ( II ) tolerance . We tested the Zn ( II ) sensitivity of cadA czcD mutant strains where hmoA and hmoB were deleted individually or in combination . While deletion of either gene does not have a significant effect , simultaneous deletion of both heme monoxygenases reveals an increased Zn ( II ) sensitivity in a cadA czcD mutant background ( Fig 5B ) . Interestingly , expression of either hmoA or hmoB from an inducible promoter is able to complement Zn ( II ) sensitivity of the cadA czcD hmoA hmoB mutant . These results suggest that HmoA and HmoB are both active and that they are functionally redundant in vivo . Collectively , these data support the hypothesis that intracellular Zn ( II ) intoxication is due to the toxic redox cycling of excess intracellular heme , as suggested previously in S . aureus [22] . Next , we sought to find a link between elevated intracellular Zn ( II ) levels and the observed increase in intracellular heme . The heme biosynthesis genes are encoded by the hemAXCDBL operon . This operon is repressed by PerR , a metal-cofactored , DNA-binding repressor that serves as a sensor of peroxide stress [25] . In addition , PerR also regulates the expression of catalase ( katA ) [26] , the major vegetative catalase in B . subtilis and an abundant heme binding protein . Coordinate regulation of catalase and heme biosynthesis by PerR ensures sufficient heme availability for catalase function under conditions of oxidative stress . PerR contains both a structural Zn ( II ) binding site and a regulatory metal binding site . PerR represses transcription when its regulatory site is associated with either Mn ( II ) or Fe ( II ) , but only the Fe ( II ) bound form responds to H2O2 [27–29] . Since PerR requires a bound regulatory metal ion in order to bind DNA , it also senses conditions of Fe ( II ) and Mn ( II ) depletion . In vitro data suggest that Zn ( II ) can also populate the PerR metal sensing site [30] , and it may thereby affect the expression of PerR regulated genes . This raises the possibility that under conditions of intracellular Zn ( II ) intoxication , PerR may become mismetallated with Zn ( II ) , leading to dysregulation of its target genes and to intracellular heme accumulation . To investigate this hypothesis , we monitored expression of the hemA and katA genes by qRT-PCR . Upon Zn ( II ) intoxication , hemA mRNA levels increased whereas katA mRNA levels decreased in the cadA czcD mutant , whereas they were relatively unaffected in a wild-type background ( Fig 2 ) . Additionally , catalase activity is also decreased under these same conditions ( Fig 5C ) . This discoordinate regulation contrasts sharply with the documented effects of Fe ( II ) and Mn ( II ) , which both lead to coordinate regulation of these two operons to allow for sufficient heme production to support catalase activity [31] . These data suggest that the PerR regulon , particularly genes involved in heme biosynthesis and usage , is dysregulated upon Zn ( II ) intoxication . To test if this effect is due to direct mismetallation of PerR with Zn ( II ) , we used a fluorescence anisotropy assay to monitor DNA-binding activity . Although PerR binds DNA when bound with either Mn ( II ) or Fe ( II ) , we routinely use Mn ( II ) to allow measurement of DNA-binding activity under aerobic conditions [32 , 33] . As expected , Mn ( II ) -cofactored PerR binds tightly to 6-carboxyfluorescein-labeled DNA fragments containing binding sites for PerR as judged by an increase in the fluorescence anisotropy signal [34] . Here , we used DNA containing the known PerR operators for the hemA operon ( encoding heme biosynthesis function ) , katA ( encoding the major vegetative catalase ) , and mrgA ( encoding a mini-ferritin that sequesters Fe ( II ) under oxidative stress conditions ) . The mrgA operator is the first identified [35] and the best characterized Per box [36] . Upon titration with Zn ( II ) , a decrease in anisotropy was observed indicative of PerR dissociation from the hemA and mrgA operator sites ( Fig 6A ) . Interestingly , addition of Zn ( II ) did not lead to full dissociation of PerR from the katA operator site . Collectively , these data suggest that elevated intracellular Zn ( II ) can lead to the mismetallation of the Fe ( II ) /Mn ( II ) sensing transcription factor PerR resulting in a loss of coordination in synthesis of heme and catalase , a major heme-containing protein ( Fig 6B ) .
Metal ion homeostasis relies on the precise control of metal uptake and the complementary action of metal efflux pumps , which prevent intracellular intoxication . Exposure to excess Zn ( II ) , either environmentally or imposed by the innate immune system during infection , can result in growth inhibition [37] . Because of the high efficiency of efflux , Zn ( II ) often inhibits bacterial growth by binding to extracytoplasmic targets . Two major extracellular targets have been identified previously . In S . pneumoniae , Zn ( II ) exerts its toxic effects by preventing the uptake of the essential metal Mn ( II ) by binding to the Mn ( II ) solute binding protein , PsaA [38] , which delivers Mn ( II ) to the specific PsaBCD ATP binding cassette ( ABC ) transporter [39] . Inhibition of PsaA by Zn ( II ) leaves S . pneumoniae more sensitive to oxidative stress and more susceptible to killing by the host immune response . Previous studies , and now this work , suggest that a major extracellular target of Zn ( II ) toxicity in bacteria and mammalian mitochondria is the major aerobic cytochrome oxidase [19 , 40] . Cytochrome oxidases are key enzymes in aerobic respiration , responsible for establishment of the proton gradient required for ATP synthesis . Measurement of Zn ( II ) inhibition of the Rhodobacter sphaeroides and mitochondrial cytochrome c oxidase indicates an extracellular Zn ( II ) binding site with Ki of 2–5 μM [1 , 41–43] . We and others have shown that the expression of the alternate anaerobic cytochrome oxidase ( cydBD ) is upregulated under conditions where the major cytochrome oxidase is inhibited by anaerobic conditions [2 , 14] , excess Zn ( II ) [4 , 5 , 14] , or excess sulfide [4 , 44] . Additionally , expression of the cydBD cytochrome oxidase is important for survival within a host for S . aureus [5 , 45] , M . tuberculosis [46] and E . coli [47] . Proteomic studies of S . aureus cells internalized by macrophages reveal an increased level of the anaerobic cydBD cytochrome oxidase [48] , suggestive of an important role in survival in the host . It is notable that in the case of Zn ( II ) , mutation of rex and derepression of the cydBD cytochrome oxidase significantly increased Zn ( II ) tolerance as judged by a zone-of-inhibition assay ( Fig 1 ) . Since excess Zn ( II ) leads to induction of the Rex regulon ( Fig 2 ) , we infer that induction of the cydBD operon in the presence of Zn ( II ) does not confer a similar level of Zn ( II ) tolerance . This suggests that assembly of the CydBD system may be impaired in the presence of excess Zn ( II ) , and what would be an appropriate adaptive response to inhibition of the major aerobic oxidase ( s ) is , in this case , inadequate . Bacteria form biofilms and increase the production of extracellular matrix ( ECM ) in response to a variety of environmental stresses [49] . In both Xylella fastidiosa and E . coli toxic levels of Zn ( II ) trigger increased ECM production [50 , 51] . Here , we isolated transposon insertions in ykuI and the fla-che operon that likely had a similar effect . YkuI is a c-di-GMP binding protein previously implicated in regulation of ECM [9] , and mutations affecting flagellar motility have been shown to increase poly-γ-glutamate , a component of the ECM , in B . subtilis [12 , 13] . It remains to be seen if biofilm formation and increased ECM synthesis is be a normal physiological response to Zn ( II ) stress in B . subtilis . The importance of intracellular Zn ( II ) homeostasis suggests the presence of an intracellular target for Zn ( II ) toxicity . In Group A Streptococcus , key glycolytic enzymes are mismetallated by Zn ( II ) and production of capsule polysaccharides is inhibited [52] . In E . coli , intracellular Zn ( II ) toxicity under conditions of oxidative stress results from mismetallation of the iron sulfur clusters of dehydratases , which are critical to key metabolic processes [53 , 54] . We sought to identify additional proteins that could be mismetallated by Zn ( II ) and the underlying mechanisms of Zn ( II ) intoxication in B . subtilis . By isolation of suppressors of zinc toxicity in a Zn ( II ) efflux mutant , we identified PerR as a target of mismetallation by Zn ( II ) . PerR has been implicated in Zn ( II ) homeostasis in S . pneumoniae since it regulates expression of the P1B4-type ATPase encoded by pmtA [55] . In a perR null mutant , overexpression of PmtA leads to induction of a Zn ( II ) starvation response , implying that this transporter may export Zn ( II ) . However , PmtA is not known to be induced by Zn ( II ) stress , and its physiological role is most likely related to peroxide resistance ( as evidenced by its regulation by PerR ) . Indeed , recent studies of PmtA orthologs in B . subtilis [56] , L . monocytogenes [57] , and M . tuberculosis [58] ( PfeT , FrvA , and CtpD , respectively ) suggest that the primary substrate of these P1B4-type ATPases is Fe ( II ) , and this is also a likely role for PmtA consistent with the key role this protein plays in peroxide resistance [55] . It is presently unclear why Zn ( II ) inhibits binding of PerR to some operons , but not others . Prior work suggests that many Fur family proteins may utilize the cooperative binding to effect repression [32 , 59] . For example , Corynebacterium diptheriae DtxR can bind DNA cooperatively as a tetramer [60] , and a similar model has been proposed for E . coli Zur [61] . We note that Zn ( II ) leads to complete dissociation of PerR from the hemA operon regulatory site , but not from katA ( Fig 6 ) . The regulatory region of hemA contains multiple PerR binding sites [28] , whereas the katA regulatory region includes one very strong consensus site [26] . Therefore , we can speculate that perhaps Zn ( II ) -metallated PerR is defective in cooperative binding , but can still bind strong consensus sites as a dimer . Regardless of the molecular details , Zn ( II ) clearly leads to dysregulation of the PerR regulon and an increase in intracellular heme levels . Our work supports the model of heme toxicity proposed by the Skaar lab which suggests that heme toxicity is caused by the generation of superoxide as membrane associated heme and reduced quinones form a redox cycle [22] . Previous studies support the idea that mismetallation of metalloregulatory proteins can have dire consequences . The Fe ( II ) sensing transcription factor Fur is known to be mismetallated by Mn ( II ) under Mn ( II ) intoxication conditions or when Fur levels increase [62] . Recently , Cd ( II ) intoxication in S . pneumoniae was linked to dysregulation of Zn ( II ) homeostasis , resulting from inhibition of Zn ( II ) uptake gene expression and activation of Zn ( II ) efflux gene expression [63] . However , direct interaction of Cd ( II ) with the Zn ( II ) sensing transcription factor AdcR and SczA has not yet been shown . Additionally , as a shown in Group A Streptococcus , under conditions of Mn ( II ) intoxication , the Fe ( II ) /Mn ( II ) sensing transcription factor PerR is in its Mn ( II ) -cofactored form [64] . As a result , the PerR regulon is unable to be induced by H202 , resulting in increased sensitivity to oxidative stress . Similarly , excess Mn ( II ) blocks catalase de-repression and prevents the increase in H2O2 tolerance that is typical of the entry of B . subtilis cells into stationary phase [26] . Understanding the mechanisms of Zn ( II ) intoxication may contribute to the development of novel antibacterial treatments . Only recently has the role of host-mediated Zn ( II ) toxicity as an antimicrobial mechanism been widely appreciated: regulated Zn ( II ) trafficking to the phagosome appears to play an active role in antimicrobial responses in several systems . Phagosomal Zn ( II ) levels rise dramatically upon Mycobacterium tuberculosis [65] or Streptococcus pyogenes [66] infection , and Zn ( II ) containing vesicles have been observed in macrophages upon Salmonella enterica Typhimurium infection [67] . Furthermore , many pathogenic bacteria require Zn ( II ) efflux pumps to avoid killing by macrophages [65 , 66 , 68] . Such evidence suggests that high levels of Zn ( II ) may exert a direct bactericidal effect within macrophages .
Strains used in this study are listed in S1 Table . Bacteria were grown in the media described in the following sections . When necessary , antibiotics were used at the following concentrations: chloramphenicol ( 10 μg ml-1 ) , kanamycin ( 15 μg ml-1 ) , spectinomycin ( 100 μg ml-1 ) , and tetracycline ( 5 μg ml-1 ) . Gene deletions were constructed using long flanking homology PCR as previously described [69] . Chromosomal DNA transformation was performed as described . Strains were grown in LB at 37°C with vigorous shaking to an OD600~0 . 4 . A 100 μl aliquot of these cultures was added to 4 ml of LB soft agar ( 0 . 7% agar ) and poured on to prewarmed LB agar plates . The plates were then allowed to solidify for 10 minutes at room temperature in a laminar flow hood . Filter disks ( 6 mm ) were placed on top of the agar and 10 μl of Zn ( II ) ( 50 mM ) was added to the disks and allowed to absorb for 10 minutes . The plates were then incubated at 37°C for 16–18 hours . The diameter of the zone of inhibition was measured . The data shown represent the values ( diameter of the zone of inhibition minus diameter of the filter disk ) and standard deviation of three biological replicates . For quantification of the total Zn ( II ) quota , cells were grown in 5 ml LB medium in the presence or absence of 200 μM ZnCl2 to mid-log phase . For fractionation experiments , 25 ml of cells were grown in presence or absence of 200 μM Zn ( II ) . All samples were prepared as described previously . Briefly , samples were washed once with buffer 1 ( 1X PBS buffer , 0 . 1 M EDTA ) then twice with buffer 2 ( 1X chelex-treated PBS buffer ) . Cell pellets were resuspended in 400 μl buffer 3 ( 1X chelex-treated PBS buffer , 75 mM NaN3 , 1% Triton X-100 ) and incubated at 37°C for 90 min for cell lysis . Lysed samples were centrifuged and subject to Bradford assay to quantify the total protein content . Then , samples were mixed with 600 μl buffer 4 ( 5% HNO3 , 0 . 1% ( v/v ) Triton X-100 ) and heated in a 95°C sand bath for 30 min . Samples were centrifuged and supernatants were diluted in 1% HNO3 . Levels of intracellular Zn were analyzed by Perkin-Elmer ELAN DRC II ICP-MS . Gallium was used as an internal standard . The total concentration of metal ions is expressed as μg ion per gram of protein . The data shown represent the average and standard deviation of three biological replicates . Cells were grown at 37°C in LB medium with rigorous shaking till OD600 ~0 . 4 . 1 ml aliquots were treated with ZnCl for 10 min . Total RNA from both treated and untreated samples were extracted RNeasy Mini Kit following the manufacturer's instructions ( Qiagen Sciences , Germantown , MD ) . RNA samples were then treated with Turbo-DNA free DNase ( Ambion ) and precipitated with ethanol overnight . RNA samples were re-dissolved in RNase-free water and quantified by NanoDrop spectrophotometer . 2 μg total RNA from each sample was used for cDNA synthesis with TaqMan reverse transcription reagents ( Applied Biosystems ) . qPCR was then carried out using iQ SYBR green supermix in an Applied Biosystems 7300 Real Time PCR System . 23S rRNA was used as an internal control and fold-changes between treated and untreated samples were plotted . Cells were grown on LB agar plates overnight . A colony was picked from the fresh plate and placed in a drop of dilute hydrogen peroxide . Strains resulting in the formation of bubbles were determined to possess catalase activity . For quantitative determinations of catalase activity , cells were assayed for the rate of H2O2 decomposition spectrophotometrically ( 240 nm ) as described previously [70] . pET16b plasmids carrying PerR between the NcoI and BamHI restriction sites were used for overexpression in Escherichia coli . A single colony was inoculated into 10 ml of LB and grown overnight at 37°C . Cells were then diluted into 1 L of LB medium containing 0 . 5% glucose . 1 mM IPTG was added to the culture when OD600 reached ∼0 . 6 and cells were harvested after 2 h of induction by centrifugation . After resuspension and sonication in buffer A [20 mM Tris , pH 8 . 0 , 100 mM NaCl , 5% ( v/v ) glycerol] with 10 mM EDTA ( ethylenediaminetetraacetic acid ) , the supernatant were loaded onto a heparin column pre-equilibrated with the same buffer . Proteins were eluted with a linear gradient of NaCl from 0 . 1 to 1 M and fractions containing PerR were combined and concentrated to load onto a Superdex 200 size exclusion column . The recovered PerR eluted was further purified using a Mono-Q column with a linear gradient of 0 . 1–1 M NaCl in buffer A containing 10 mM EDTA . Fractions containing PerR were concentrated and dialyzed extensively against buffer A to remove EDTA . The purified PerR was then aliquoted and frozen at −80°C . The purified PerR was determined to be ∼85% active , respectively , based on the amount of protein required to stoichiometrically bind 100 nM DNA . A 6-carboxyfluorescein ( 6-FAM ) —labeled DNA was used and fluorescence anisotropy ( FA ) was measured with λex = 492 nm and λem = 520 nm . For Zn ( II ) binding , increasing amounts of ZnCl2 were mixed with 120μl buffer A containing 100 nM labeled DNA , 100 nM active PerR dimer , and 10 μM MnCl2 . FA measurements of each sample were performed immediately after transferring to a quartz cuvette . The data were fit using a nonlinear regression model with GraphPad Prism . Strains were grown overnight in LB medium and then subcultured with 1:100 ratio in fresh LB medium to an OD600 of 0 . 4 . OD600 was recorded and aliquots of 5 ml of cell culture were harvested . Cell pellets were resuspended in 1 ml 50 mM Tris-HCl buffer ( pH 7 . 4 ) containing 100 μg ml−1 of lysozyme and were incubated at 37°C for 30 min to lyse the cells . Cell debris was removed by centrifugation . Heme was extracted from 500 μl of the clear lysates using 500 μl acidic acetone ( 20% ( v/v ) 1 . 6 M HCl ) . Precipitate was removed by centrifugation and the supernatant was analyzed by fluorescence spectroscopy . The fluorescence emission of heme was scanned from 400 nm to 500 nm with excitation at 380 nm as described . Fluorescence intensity at 450 nm ( peak ) was normalized and plotted .
|
Zinc ( Zn ( II ) ) is often considered to be a “first among equals” in metal ion homeostasis . Zn ( II ) is critically important to the proper function of many cellular processes , yet is toxic at high levels . The molecular basis for Zn ( II ) intoxication is poorly understood . Using a forward genetic approach in B . subtilis , we demonstrate that elevated levels of external Zn ( II ) inhibit the electron transport chain , whereas intracellular Zn ( II ) intoxication is due to dysregulation of heme biosynthesis . Since the host immune system utilizes both Zn ( II ) sequestration and toxicity as a means of responding to pathogens , these findings contribute to our understanding of host-microbe interactions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"heme",
"b",
"vitamins",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"chemical",
"compounds",
"gene",
"regulation",
"pathogens",
"bacillus",
"microbiology",
"organic",
"compounds",
"toxicology",
"toxicity",
"prokaryotic",
"models",
"experimental",
"organism",
"systems",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"public",
"and",
"occupational",
"health",
"proteins",
"medical",
"microbiology",
"vitamin",
"k",
"zinc",
"microbial",
"pathogens",
"gene",
"expression",
"chemistry",
"vitamins",
"mental",
"health",
"and",
"psychiatry",
"regulons",
"biochemistry",
"chemical",
"elements",
"organic",
"chemistry",
"post-translational",
"modification",
"bacillus",
"subtilis",
"genetics",
"intoxication",
"biology",
"and",
"life",
"sciences",
"biosynthesis",
"physical",
"sciences",
"substance-related",
"disorders",
"organisms"
] |
2016
|
Intracellular Zn(II) Intoxication Leads to Dysregulation of the PerR Regulon Resulting in Heme Toxicity in Bacillus subtilis
|
As obligate blood-feeding arthropods , ticks transmit pathogens to humans and domestic animals more often than other arthropod vectors . Livestock farming plays a vital role in the rural economy of Pakistan , and tick infestation causes serious problems with it . However , research on tick species diversity and tick-borne pathogens has rarely been conducted in Pakistan . In this study , a systematic investigation of the tick species infesting livestock in different ecological regions of Pakistan was conducted to determine the microbiome and pathobiome diversity in the indigenous ticks . A total of 3 , 866 tick specimens were morphologically identified as 19 different tick species representing three important hard ticks , Rhipicephalus , Haemaphysalis and Hyalomma , and two soft ticks , Ornithodorus and Argas . The bacterial diversity across these tick species was assessed by bacterial 16S rRNA gene sequencing using a 454-sequencing platform on 10 of the different tick species infesting livestock . The notable genera detected include Ralstonia , Clostridium , Staphylococcus , Rickettsia , Lactococcus , Lactobacillus , Corynebacterium , Enterobacter , and Enterococcus . A survey of Spotted fever group rickettsia from 514 samples from the 13 different tick species generated rickettsial-specific amplicons in 10% ( 54 ) of total ticks tested . Only three tick species Rhipicephalus microplus , Hyalomma anatolicum , and H . dromedarii had evidence of infection with “Candidatus Rickettsia amblyommii” a result further verified using a rompB gene-specific quantitative PCR ( qPCR ) assay . The Hyalomma ticks also tested positive for the piroplasm , Theileria annulata , using a qPCR assay . This study provides information about tick diversity in Pakistan , and pathogenic bacteria in different tick species . Our results showed evidence for Candidatus R . amblyommii infection in Rhipicephalus microplus , H . anatolicum , and H . dromedarii ticks , which also carried T . annulata .
Pakistan , a predominantly farming nation , has an agriculture sector representing 20 . 9% of the country’s total gross domestic product and employs 43 . 4% of the country’s total workforce . According to the 2013/14 Pakistan Livestock Census [1] , the livestock sector within the agricultural economy doubled from 25 . 3% in 1996 to 55% . The gross value of the livestock increased from $7 . 22 billion ( Rs . 756 . 3 billion ) in 2012/ 13 to $7 . 41 billion ( Rs . 776 . 5 billion ) in 2013/14 , an increase of 2 . 7% as compared to the previous year . Gross production of milk in Pakistan increased from 47 , 895 million tons in 2011/12 to 50 , 990 million tons in 2013–14 . Among the 8 . 4 million dairy-producing households , 51% own a herd of one to four animals , and 28% maintain five to ten animals [1] . Buffaloes and cows are the major milk-producing animals and 80% of the milk in Pakistan is produced by rural smallholders and commercial producers . The role of the livestock sector in the rural economy is crucial , as 30–35 million people in the rural population rely on this sector for their livelihoods . Ticks and tick-borne diseases cause an estimated US $ 13 . 9 to 18 . 7 billion loss and an annual shortfall of approximately 3 billion pieces of hide and skin in cattle alone [2 , 3] . Ticks are known for their negative impact on livestock and human health through infestation and are capable of transmitting a wide range of pathogens including protozoans , viruses , and bacteria such as the spirochetes and rickettsiae . Rhipicephalus , Haemaphysalis ( hereafter referred to as Ha . in species names ) , Hyalomma ( hereafter referred to as Hy . in species names ) and Ornithodoros , which are widely distributed throughout Pakistan , are the main tick genera infesting humans and animals [4 , 5] . A study in 1960 reported the presence of Haemaphysalis cornupunctata and Ha . kashmirensis in Pakistan [4] . Hyalomma and Rhipicephalus tick species pose major threats to livestock production in Pakistan . The cattle tick Rhipicephalus microplus is a competent vector of Babesia bovis , B . bigemina , and Anaplasma marginale , which cause tick fever in Pakistan and the rest of the world [6] . Hyalomma species are known vectors of Theileria annulata , a malaria like disease of animals [7] . Despite the pressing need for more information on the epidemiology of tick-borne zoonosis in Pakistan , there is a paucity of such data . It has been reported that tick species simultaneously harbor a variety of pathogenic species and endosymbionts , and the communities of such organisms are known as pathobiomes and microbiomes , respectively [8–10] . The pathobiome is defined as pathogenic bacteria , virus or fungi within the community of the bacteria or biotic environment which itself can be described as subset of overall bacterial community ( microbiome ) which possesses or gain pathogenicity during the interaction within bacterial community . Previous microbial community descriptions have relied heavily on in vitro culture-based identification tools; however , the metagenomic approach offers a convenient alternative for obtaining microbial profiles . Specifically , pyrosequencing of partially amplified 16S rRNA sequences has been used for studying the bacterial composition and diversity associated with many diverse biological organisms including Ixodes ricinus , R . microplus , Amblyomma americanum , A . maculatum , and A . tuberculatum , and neotropical tick species [11–15] . In fact , even though humans are considered “accidental hosts” of ticks , the rickettsial diseases transmitted by various arthropod vectors affect an estimated one billion people worldwide [16 , 17] . In Pakistan , an early study using serological assays reported the presence of rickettsial agents in ticks [5 , 18] . However , antigen conservation among the various rickettsial species makes it difficult to accurately identify rickettsial species using antibodies [19] . Limited information is available on the diversity of tick species that infest ruminants , their associated microbial diversity , and tick-borne pathogens in Pakistan . Therefore , the aim of this study was to survey the range of tick species and bacterial diversity in these ticks to facilitate better understanding of these species in Pakistan . To the best of our knowledge , this is the first detailed molecular study on tick species infesting livestock in Pakistan . We also investigated the presence of pathogenic rickettsial infections and the presence of the protozoan T . annulata in the tick species we collected .
This study was carried out in accordance with the Manual for the Use of Animals of the Pakistan Veterinary Association . This protocol was approved by the Institutional Animal Care and Use Committees at each respective Pakistan-based institution ( The University of Agriculture , Faisalabad , Sindh Agriculture University , Tando Jam , and Lasbela University of Agriculture , Lasbela ) . A total of 3 , 866 ticks belonging to 19 species were collected from a variety of ruminant species from different geographic regions of Pakistan ( S1 Table ) . The livestock pocket area of different provinces of Pakistan ( Fig 1A ) were visited in 2011–12 and tick infestation in livestock farm ( Cattle , Buffalo , Sheep , Goat , Camel , Poultry ) or domestic animals ( Cat and dog ) were assessed by veterinarian from University of Agriculture , Faisalabad; Sindh Agriculture University , and Lasbela University of Agriculture , Lasbela . Ticks were collected based on the livestock or domestic animal host ( S1 Table ) to understand tick species specific to host and further survey pathogenic bacteria or Theileria in ticks . This study was solely focused on the ticks , and tick-associated pathogens . Ticks attached to the animals were carefully removed using fine tweezers and then surface sterilized by rinsing them in distilled water followed by 100% ethanol to remove any surface bacteria and/or any host tissue . Ticks were stored in 70% ethanol and shipped from Pakistan to the University of Southern Mississippi for further analysis using the U . S . Department of Agriculture's Animal and Plant Health Inspection Service ( permit # 11122050 ) . Tick identification was performed by an expert taxonomist ( Dmitry A . Apanaskevich ) at the United States National Tick Collection ( USNTC ) according to the criteria used in previously published reports [4 , 20–22] . All stages were examined on an Olympus SZX16 stereoscopic microscope and reference specimens from this study have been deposited in the USNTC at Georgia Southern University , USA . Individual tick samples were cut into small pieces using a sterile scalpel and then homogenized in 200 μL of phosphate-buffered saline ( pH 7 . 4 ) with a sterile micro-pestle . The individual tick homogenates were further disrupted by passage through a 27-guage needle attached to a 1 mL sterile syringe [23] . Genomic DNA was extracted from each individual whole tick homogenate using a DNeasy blood and tissue kit ( Qiagen , Valencia , CA , USA ) following the manufacturer’s protocol . The concentrations of the extracted genomic DNA samples were quantified using a Nanodrop ND-100 instrument . The extracted genomic DNA samples were stored at −20°C until further use . Prior to sequencing , the samples were pooled to survey the sequence diversity of the microbial communities because of the technical and financial burden of conducting a more complete analysis , despite the loss of statistical data . Fifteen sample pools were constructed from a variable number of ticks originating from ten different species and four different hosts ( S2 Table ) . A total of 514 individual ticks of different species were screened for PCR identification of the spotted fever Rickettsia group ( SFGR ) . Additionally , 387 Hyalomma ticks were screened for the presence of Theileria species infections . Pyrosequencing analysis of tick DNA and analysis of the downstream sequencing data was performed as previously described [13] . Briefly , tick DNA samples were used for bacterial tag-encoded titanium amplicon pyrosequencing ( bTETAP ) [24] . The output used for analysis had an average read length of approximately 450-bp with the sequencing extending from the 27F 5′- GAG TTT GAT CNT GGC TCA G-3′ to 519R 5′-GTN TTA CNG CGG CKG CTG-3′ primers in relation to Escherichia coli 16S , extending across V1 and into the V3 ribosomal region ( Research and Testing Laboratory , Lubbock , TX ) . A single-step 30-cycle PCR with HotStarTaq plus master mix kit ( Qiagen ) was used under the following conditions: 94°C for 3 min , followed by 32 cycles of 94°C for 30 s , 60°C for 40 s , and 72°C for 1 min , and a final elongation step at 72°C for 5 min . Following PCR , all amplicon products from the different samples were mixed to an equal concentration and purified using Agecourt Ampure beads ( Agencourt Bioscience Corporation , MA , USA ) . Samples were sequenced utilizing Roche 454 FLX titanium instruments and reagents and following the manufacturer’s guidelines . The sequences were curated to obtain Q25 sequence data , which was processed using a proprietary analysis pipeline ( www . mrdnalab . com ) and the QIIME pipeline ( www . qiime . org ) . All the sequences were trimmed to remove barcodes , primers , and short sequences under 200-bp in length . Sequences with ambiguous base calls and homopolymer runs exceeding 6-bp in length were deleted [25–27] . The taxonomic levels for the operational taxonomic unit ( OTU ) classifications were performed using the Basic Local Alignment Search Tool ( BLASTn ) program at the National Center for Biotechnology Information ( NCBI , https://www . ncbi . nlm . nih . gov/ ) against the curated GreenGenes database [28] in QIIME 1 . 9 ( http://qiime . org/ ) [29] . The taxonomic levels of the bacterial classes , family and genera were profiled across the tick species . All the raw sequences obtained were submitted to GenBank under the Pakistani Tick Microbiome Bioproject ( PRJNA279069 ) . SFGR infections were detected using rickettsial outer membrane protein A ( rompA ) gene-specific primers in a nested PCR assay [23] . Briefly , RR190-70 ( 5′-ATGGCGAATATTTCTCCAAAA-3′ ) and RR190-701 ( 5′-GTTCCGTTAATGGCAGCATCT-3′ ) primers were used for primary PCR , while 190-FN1 ( 5′-AAGCAATACAACAAGGTC-3′ ) and 190-RN1 ( 5′-TGACAGTTATTATACCTC-3′ ) primers were used for nested PCR . In the primary reaction , 2 . 5 μL of DNA template ( ∼62 . 5 ng ) was added to 12 . 5 μL of 2× PCR Master Mix ( Promega , Madison , WI ) , 8 μL of nuclease-free water , and 1 μL of each primer ( 10μM ) . In the nested reaction , 12 . 5 μL of 2× PCR Master Mix , 8 μL of nuclease-free water , 1 μL of each nested primer ( 10μM ) , and 2 . 5 μL of the primary PCR reaction were used . PCRs were performed in a MyCycler Thermal Cycler ( Bio-Rad Laboratories , USA ) as follows: 1 cycle at 95°C for 3 min , 35 cycles of 95°C for 20 s , 46°C for 30 s , and 63°C for 60 s , and 1 cycle at 72°C for 7 min . The amplicons were separated on a 2% agarose gel containing ethidium bromide and then observed using a UV transilluminator . After electrophoresis , PCR products of 540-bp in length were excised from the agarose gel , and the DNA was extracted using a QIAquick DNA gel extraction kit ( Qiagen ) . The purified DNA samples were sent to Eurofins MWG Operon ( Huntsville , AL ) for sequencing . The partial sequences obtained were checked against the NCBI BLAST program for rickettsial identification and the unique sequences were deposited in GenBank under accession numbers JX441089–JX441113 and KC245100–KC245101 . Candidatus R . amblyommii was identified and quantified by targeting the gene encoding the rickettsial outer membrane protein B ( rompB ) in a quantitative PCR ( qPCR ) assay [30] . Briefly , Candidatus R . amblyommii genomic DNA ( GenBank accession FJ455415 , a gift from the Viral and Rickettsial Diseases Department at the Naval Medical Research Center , Silver spring , MD ) was used to amplify rompB using the rompB gene-specific primers Ra477F ( 5'-GGTGCTGCGGCTTCTACATTAG-3' ) , Ra618R ( 5'-CTGAAACTTGAATAAATCCATTAGTAACAT-3' ) , and the Candidatus R . amblyommii specific-probe Ra532 ( FAM-CGCGATCTCCTCTTACACTTGGACAGAATGCTTATCGCG-BHQ-1 ) . The reaction mixture contained 0 . 5 μM of each primer , 0 . 4 μM of the probe , and 3 mM magnesium chloride in 12 . 5 μl of 2× TaqMan PCR master mix ( Promega ) . The reaction mix was subjected to thermal cycling ( CFX96 Real-time Detection System , BioRad Laboratories , CA ) at 95°C for 2 min followed by 45 two-step cycles of 94°C for 5 s and 60°C for 30 s . The Candidatus R . amblyommii copy number was estimated using the standard curve generated from predetermined rompB DNA concentrations . Piroplasma spp . were PCR-detected using Theileria genus-specific primers that amplify the 18S ribosomal rRNA gene ( Forward: 5′-GGT AAT TCC AGC TCC AAT AG-3′ and Reverse 5′-ACC AAC AAA ATA GAA CCA AAG TC-3′ ) . The PCR mixture contained 25–35 ng of genomic DNA from the ticks , 400 nM of each primer , and PCR master mix ( Biolab Inc . ) . The reaction mix was subjected to thermal cycling at 94°C for 3 min followed by 39 cycles of 94°C for 20 s , 48°C for 60 s , and 68°C for 30 s , and a final extension step at 68°C for 2 min . The amplicons obtained were isolated and purified using a gel purification kit ( Qiagen ) , and the purified products were sequenced by Eurofins . The partial sequences obtained were subjected to the NCBI BLAST program for species identification of the piroplasma sequences . T . annulata was quantified using a method described previously [31] . Briefly , T . annulata 18S rRNA gene-specific primers ( Tann18SF: 5′-AGACCTTAACCTGCTAAATAGG-3′ and Tann18SR: 5′-CATCACAGACCTGTTATTGC-3′ , 200 nM each ) and 150 nM of the specific probe ( FAM 5′-AAG[+T]TT[+C]TA[+C]TG[+T]CCCGTT-3′ BHQ1 ) were used in a 25 μl PCR mixture containing 2× One Taq PCR master mix ( BioLabs , USA ) . The mixture was subjected to qPCR on a CFX96 instrument ( BioRad Inc . ) using cycling conditions of 50°C for 2 min , 95°C for 10 min and 40 cycles of 95°C for 15 s and 60°C for 1 min . Samples were analyzed in triplicate along with the three non-template controls on each plate . T . annulata quantification was performed using the standard curve derived from the cycle threshold values obtained from known 18S rRNA PCR concentrations . All the ticks were collected from livestock animals across the Pakistan and collected ticks were stored in 70% ethanol by veterinarian and students from University of Agriculture , Faisalabad; Sindh Agriculture University; and Lasbela University of Agriculture , Lasbela and shipped to University of Southern Mississippi . The tick vials were labelled with host species and geographical region of collection including the date and name of collector . Each tick was identified by taxonomist ( Dmitry A . Apanaskevich ) at the United States National tick collection ( USNTC ) and separated based on identified tick species from each original vial . Part of the identified specimen were deposited in the collection housed at USNTC . All the identified ticks were used for subsequent microbial and pathogenic bacterial identification and quantifications . All the data were generated at the University of Southern Mississippi and all the sequences generated by 16S rRNA and spotted fever group rickettsia detection were deposited in respective public repositories .
During the ecological survey of the ruminants in Pakistan , a total of 3 , 866 ticks belonging to 19 species were collected ( S1 Table ) . These ticks included males ( n = 1 , 330 ) , females ( n = 2 , 066 ) , larvae ( n = 570 ) , and nymphs ( n = 413 ) ( S1 Table ) . Two soft tick species ( Argas persicus and Ornithodoros tholozani ) and 17 hard tick species ( Hy . bispinosa , Ha . cornupunctata , Ha . montgomeryi , Ha . sulcata , Ha . kashmerensis , Hy . anatolicum , Hy . dromedarii , Hy . isaaci , Hy . kumara , Hy . scupense , Hy . turanicum , Hy . hussaini , R . microplus , R . haemaphysaloides , R . sanguineus , R . turanicus , and R . annulatus ) were found ( Fig 1 ) . However , the following four tick species comprised over 80% of the total samples: Hy . anatolicum ( n = 1 , 203 ) , Ha . bispinosa ( n = 853 ) , Ha . montgomeryi ( n = 641 ) , and R . microplus ( n = 416 ) ( Fig 1 ) . Map of Pakistan is prepared from Information management unit , Food and Agriculture Organization of the United Nations , Pakistan . After curation , we obtained 58 , 194 sequences from 15 samples ( average 3 , 879 sequences per sample ) and these formed 544 unique OTUs . Profiling of the bacteria sampled from the various livestock species identified , in decreasing order of abundance , six main classes: Bacilli , ɤ-Proteobacteria , β-proteobacteria , Clostridia , α-proteobacteria and Actinobacteria ( S3 Table ) . There were 30 bacterial families representing over 2% of the sequence reads obtained from the tick species . Overall , Oxalobacteraceae , Staphylococcaceae , Clostridiaceae , Enterobacteriaceae , Coxiellaceae , Rickettsiaceae , Streptococcaceae , and Lactobacillaceae were the predominant families ( S1 Fig , S4 Table ) . In the R . microplus ticks collected from cattle ( group 1 ) , Enterobacteriaceae was the most prevalent bacterial family . However , Rickettsiaceae , Oxalobacteraceae , and Micrococcaceae were abundant in the R . turanicus ticks infesting goats ( group 2 ) ( S1 Fig , S4 Table ) . In group 3 , Ha . cornupunctata from sheep , and in group 4 Ha . cornupunctata from goats , contained Oxalobacteraceae , Enterobacteriaceae , Staphylococcaceae , but no Rickettsiaceae . Ha . kashmerensis from goats ( group 5 ) , Ha . montgomeryi from goats ( group 6 ) and Ha . montgomeryi from buffaloes ( group 7 ) were the dominant tick species for Rickettsiaceae along with Staphylococcaceae and Clostridiaceae , respectively ( S1 Fig , S4 Table ) . In the Ha . montgomeryi ticks infesting cattle ( group 8 ) , Enterobacteriaceae , Oxalobacteraceae , and Staphylococcaceae were the dominant families , whereas Staphylococcaceae and Streptococcaceae were dominant in Ha . bispinosa from goats ( group 9 ) ( S1 Fig , S4 Table ) . Clostridiaceae solely dominated Ha . bispinosa removed from buffaloes ( group 10 ) , but Hy . anatolicum removed from cattle ( group 11 ) and buffaloes ( group 12 ) was dominated by Staphylococcaceae , Oxalobacteraceae , Burkholderiaceae , and Pseudomonades . Coxiellaceae solely dominated Hy . scupense infesting goats ( group 13 ) . Similarly , Lactobacillaceae and Staphylococcaceae bacterial families were dominant in Hy . isaaci blood-fed on cattle , whereas Oxalobacteraceae was found solely in the soft tick , O . tholozani from buffaloes ( S1 Fig , S4 Table ) . The dominant bacterial genus was Ralstonia . It was present in all the tick species , comprising up to 97% of the total number of sequences for Ha . cornupunctata collected from sheep ( group 3 ) , but as low as 0 . 3% in Ha . bispinosa collected from buffaloes ( group 10 ) ( Fig 2 ) . The Clostridium genus was most prevalent ( >80% ) in Ha . montgomeryi from goats ( group 6 ) , buffaloes ( group 7 ) , and in Ha . bispinosa from goats ( group 9 ) and buffaloes ( group 10 ) ( Fig 2 ) . Corynebacterium was dominant in Ha . bispinosa and Hy . anatolicum from buffaloes ( group 12 ) . Staphylococcus was most abundant in R . microplus from cattle ( group 1 ) , in Ha . cornupunctata ( group 4 ) and Ha . kashmerensis from goats ( group 5 ) and in Hy . anatolicum from buffaloes ( group 12 ) ( Fig 2 ) . The Rickettsia genus was dominant ( 3–40% ) in R . turanicus ( group 2 ) and Ha . cornupunctata from goats . Similarly , Rickettsia was dominant in Ha . montgomeryi from goats ( group 6 ) and buffaloes ( group 7 ) , and in Hy . anatolicum from buffaloes ( group 12 ) . Interestingly , we did not observe Rickettsia in any other tick species ( Fig 2 , S5 Table ) . Coxiella was the dominant genus in Hy . scupense collected from goats ( group 13 , while Francisella was present in Hy . anatolicum ( group 12 ) ( Fig 2 ) . A total of 514 ticks were individually screened and 54 ( 54/514 , 10% ) rickettsial fragments were identified ( Table 1 ) based on the partial rickettsial ompA sequences . Twenty-one tick samples were identified as “Candidatus Rickettsia amblyommii” which is an infection rate of 4% ( 21/514 ) of the total number of ticks tested ( Table 1 ) . Among the tested ticks , DNA isolated from Hy . isaaci , R . turanicus and R . sanguineus was not PCR-amplifiable for SFGR ( Table 1 ) . The Candidatus R . amblyommii-infected tick DNAs were further verified by qPCR by specific amplification of the rickettsial rompB gene and the copy numbers ranged from 40–10 , 497 ( Table 2 ) . The copy numbers for Candidatus R . amblyommii in the Hy . anatolicum ticks removed from a variety of ruminants varied from as low as 40 , to a maximum of over 10 , 000 ( Table 2 ) . Similarly , Hy . dromoderii was infected with Candidatus R . amblyommii via blood feeding on goats and camels ( Table 2 ) . In total , 387 randomly selected Hyalomma ticks were individually tested for infection with the pathogenic protozoan , Theileria , using Theileria 18S rRNA gene-specific primers . Table 3 shows the results for PCR amplification of DNA from 22 Hy . anatolicum and Hy . dromedarii ticks . DNA sequencing of the Theileria-specific 18S rRNA PCR amplicons revealed Theileria- or Babesia-like sequences based on the closest homology ( Table 3 ) . The T . annulata-specific qPCR assay using a specific probe was used to genetically identify T . annulata in the screened tick samples and , surprisingly , 19 out of 22 were positive with an infection rate varying from 100–3887 copies/μL ( Table 3 ) . The co-occurrence/co-infection of tick pathogens , Theileria and Babesia were reported in this study in Hyalomma anatolicum and Hyalomma dromedarii ticks ( Table 3 ) . The primers which amplify both piroplasma species was selected to decipher presence of both in ticks using PCR methods . The piroplasma species were targeted in 17 Hy . anatolicum species , and nine of these ticks detected Babesia by PCR assay . A further testing of these ticks revealed Theileria annulata amplicons as tested by qPCR specific assay suggesting possible co-occurrence or co-infection of Babesia and Theileria species . Intriguingly , only two Hy . dromerdarii ticks tested for piroplasma infection , and one showed the co-infection both piroplasma species .
In this study , we surveyed ticks from farm and domestic livestock holder’s animals in different ecological locations across Pakistan . We identified 19 different tick species representing three important hard ticks , Hyalomma , Rhipicephalus and Haemaphysalis , and two soft tick species , Argas and Ornithodorus . Bacterial pathogens in the ticks were assessed using next generation sequencing , which successfully profiled the bacteria from the different tick species , and we focused on validating the Rickettsial agents present in the ticks using this technique . The Rickettsial agent , Candidatus R . amblyommii , was identified and quantified . Hyalomma ticks were screened for the presence of the causative agent of bovine theileriosis , T . annulata , the levels of which within the ticks were also quantified .
|
Ticks are known for their negative impact on animal and human health through infestation and are capable of transmitting a wide range of pathogens including protozoan , viruses , and bacteria such as the spirochetes and rickettsiae . Ticks are widely distributed in different ecological , and geographical regions of Pakistan . Tick-borne diseases , such as bovine babesiosis and theileriosis have been reported in Pakistan . Crimean-Congo hemorrhagic fever ( CCHF ) , a tick-borne viral disease in Pakistan , affects individuals who have close contact with livestock , including those working in slaughterhouses , veterinary practices , and hospitals . In Pakistan , CCHF is vectored by Hyalomma tick species , and therefore a significant threat could be posed to human health . However , there is a paucity of data on tick-borne zoonoses in Pakistan . In the current study , tick species infesting livestock across the Pakistan were catalogued , and molecular approaches were utilized to gain an insight into the microbiome associated with tick species . The results of our study revealed 19 tick species infesting livestock , and rich diversity of microbial communities associated with the ticks . Spotted Fever Group rickettsia , and Theileria annulata specific quantitative PCR assay confirmed the infection in Hyalomma tick species . To the best of our knowledge , this is the first detailed molecular study on tick species infesting livestock in Pakistan , and in depth vector competence studies should be conducted .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"livestock",
"medicine",
"and",
"health",
"sciences",
"parasite",
"groups",
"ixodes",
"pathology",
"and",
"laboratory",
"medicine",
"ruminants",
"pathogens",
"geographical",
"locations",
"microbiology",
"vertebrates",
"buffaloes",
"animals",
"mammals",
"parasitology",
"apicomplexa",
"bovines",
"molecular",
"biology",
"techniques",
"ticks",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"pakistan",
"artificial",
"gene",
"amplification",
"and",
"extension",
"medical",
"microbiology",
"microbial",
"pathogens",
"molecular",
"biology",
"disease",
"vectors",
"goats",
"agriculture",
"arthropoda",
"people",
"and",
"places",
"arachnida",
"asia",
"polymerase",
"chain",
"reaction",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"amniotes",
"theileria",
"organisms"
] |
2017
|
A study of ticks and tick-borne livestock pathogens in Pakistan
|
In plants , each male meiotic product undergoes mitosis , and then one of the resulting cells divides again , yielding a three-celled pollen grain comprised of a vegetative cell and two sperm cells . Several genes have been found to act in this process , and DUO1 ( DUO POLLEN 1 ) , a transcription factor , plays a key role in sperm cell formation by activating expression of several germline genes . But how DUO1 itself is activated and how sperm cell formation is initiated remain unknown . To expand our understanding of sperm cell formation , we characterized an ARID ( AT-Rich Interacting Domain ) -containing protein , ARID1 , that is specifically required for sperm cell formation in Arabidopsis . ARID1 localizes within nuclear bodies that are transiently present in the generative cell from which sperm cells arise , coincident with the timing of DUO1 activation . An arid1 mutant and antisense arid1 plants had an increased incidence of pollen with only a single sperm-like cell and exhibited reduced fertility as well as reduced expression of DUO1 . In vitro and in vivo evidence showed that ARID1 binds to the DUO1 promoter . Lastly , we found that ARID1 physically associates with histone deacetylase 8 and that histone acetylation , which in wild type is evident only in sperm , expanded to the vegetative cell nucleus in the arid1 mutant . This study identifies a novel component required for sperm cell formation in plants and uncovers a direct positive regulatory role of ARID1 on DUO1 through association with histone acetylation .
In contrast to animals , where meiotic products directly become gametes , the germline in plants is established by mitotic divisions after meiosis is completed . The male germline arises by an asymmetric mitotic division of each meiotic product . The resulting vegetative and generative cells of the bicellular pollen grain have distinct fates . The larger vegetative cell is arrested at the G1 phase of the cell cycle , while the smaller generative cell divides mitotically to produce the two male gametes or sperm cells [1] . The vegetative cell forms a pollen tube to convey the sperm cells to the female gametes . Several genes have been implicated in sperm cell formation [2]–[8] . Among these , DUO POLLEN1 ( DUO1 ) encodes a male germ cell–specific R2R3 Myb transcription factor that is necessary for twin sperm cell formation [3] . Although many genes have been implicated in sperm cell formation , the molecular mechanism as to how the generative cell initiates the second mitosis and divides into two sperm cells remains unclear . By monitoring the expression of genes associated with germ cell specification , a previous study [9] demonstrated that DUO1 controls germline expression of a cyclin , CYCB1 , and dictates correct differentiation of the male germline by promoting expression of MGH3 ( MALE GAMETE-SPECIFIC HISTONE H3 ) [2] , GCS1 ( GENERATIVE CELL SPECIFIC 1 ) [5] , and GEX2 ( GAMETE EXPRESSED 2 ) [10] . Subsequently , they profiled DUO1 targets at a genome-wide level [11] . In contrast to the myriad DUO1 targets , how DUO1 transcription is regulated is less understood . Only microRNA159 ( miR159 ) was found to inhibit DUO1 expression [12] . miR159 is greatly reduced but not absent at the bicellular stage [8] , but DUO1 is gradually activated from the early bicellular stage to the middle bicellular stage [9] , indicating that DUO1 activation is not due only to the decrease of miR159 at the bicellular stage and that other factors are required for DUO1 activation during sperm cell formation . However , such factors were unknown . In animals , ARID ( AT-Rich Interacting Domain ) proteins exhibit a range of cellular functions , including participation in epigenetic regulation of gene expression during cell differentiation and development [13] . ARID is an ancient DNA-binding domain that is conserved throughout higher eukaryotes . In animals , ARID proteins are grouped in several subfamilies based on the presence of additional conserved domains [13] . In mice , ARID4A and ARID4B are required for male fertility [14] , [15] and are associated with the histone deacetylase complex [16] , [17] . JARID2 , an important developmental regulator during cell cycle regulation [18] , [19] , is also a component of the Polycomb-repressive complex 2 ( PRC2 ) either as an activator [20] , [21] or a repressor [22] , [23] in mammals . Thus , considering the importance of the cell cycle during sperm cell formation in plants and the increasing knowledge about the role of ARID domain-containing proteins both in gene regulation and germline development , we wanted to know whether plant ARID proteins might be involved in the regulation of sperm cell formation . The Arabidopsis genome encodes 10 ARID proteins that have been grouped into four subfamilies based on the presence of domains located at the C-terminus [24] . However , no function of these ARID proteins has been reported . In lotus , an ARID is required for early nodule development [24] . Here , we show that ARID1 , an Arabidopsis pollen-specific ARID protein containing an ELM2 ( EGL-27 and MTA1 homology ) domain , is required for sperm cell formation , as plants with disrupted ARID1 function had an increased incidence of pollen with single sperm and exhibited reduced fertility . Furthermore , DUO1 expression was reduced in arid1 mutants and Chromatin Immunoprecipitation analysis and in vitro DNA binding assays demonstrated that ARID1 binds to the DUO1 promoter . Microscopic analysis showed that ARID1 is located in developmentally dynamic nuclear bodies during pollen development , and ARID1 obviously accumulated in the generative cell , which implies that the presence of ARID1 is correlated with the initiation of the second mitosis . We showed that ARID1 physically interacts with histone deacetylase 8 ( HDA8 ) in vitro and in vivo , and an immunofluorescence assay showed that the histone acetylation signal expanded to the vegetative nucleus in arid1 pollen . Furthermore , an obviously reduced level of histone acetylation was observed at DUO1 , whereas the level of histone methylation was not altered . These results imply that ARID1 is required for sperm cell formation by positively regulating DUO1 and for maintaining the levels of histone acetylation in pollen by associating with the histone modification machinery .
Microarray data [25] , [26] and our RT-PCR results ( Figure 1A ) showed that one ARID , At2g46040 , here named ARID1 , was expressed in a pollen-specific manner . In addition to the ARID domain , ARID1 contains an ELM2 domain at the C-terminus . In animals , ELM2 domains mediate histone modifications by interacting with histone deacetylase [27] , [28] . The combination of ARID and ELM2 domains in a single protein is plant-specific . Given the importance of cell cycle regulation by ARID proteins in animals [19] , and that a mouse mutant in a gene encoding an ARID protein is male infertile [15] , we suspected that disrupting ARID1 function might lead to disorganized cell divisions during pollen development . The arid1-1 mutant has a T-DNA insertion in the only intron of ARID1 ( Figure S1A ) . The insertion did not abolish expression , as a truncated transcript upstream of the inserted location was detected ( Figure S1A ) , suggesting that arid1-1 might be a weak allele . Plants homozygous for arid1-1 had short siliques and reduced seed set ( Figure 1B and 1C ) . We identified homozygous plants by genotyping a F2 population of arid1-1 backcrossed with wild type plants . As only 10 of the 96 F2 plants were homozygous , we hypothesized that there might be a transmission problem . Reciprocal crosses with wild type plants showed that transmission through the female was normal , but was perturbed through the male ( Table 1 ) . To investigate whether the arid1-1 phenotype was caused by the T-DNA insertion , we constructed two ARID1 transgenes by engineering GFP or RFP tags at the C-terminus to a genomic fragment of ARID1 , driven by its native promoter . ARID1-GFP or ARID1-RFP completely complemented the reduced seed set phenotype ( Figure 1B and 1C ) . Because arid1-1 appeared to be a weak allele , we explored whether a more complete loss of arid1 function would have similar or more severe phenotypes . We therefore generated a binary construct expressing antisense ARID1 under the control of the native ARID1 promoter . The seed set of 48 independent transgenic lines was examined: 42 plants showed reduced seed set , ranging from 15% to 95% ( Figure 1D ) . Transcript analysis of representative antisense lines confirmed that the phenotype of reduced seed set correlated with reduced transcript levels of ARID1 ( Figure 1D ) . Because immature seeds from antisense lines with severely reduced seed set finally shriveled , we performed further analyses using arid1-1 . To dissect which developmental stage was defective in the arid1-1 mutant and antisense lines , we stained with DAPI to examine pollen development , used in vitro pollen germination assays to observe pollen germination and pollen tube growth , and performed ovule clearing analysis followed by DAB ( Decolorized Aniline Blue ) staining to evaluate pollen tube guidance . There were no detectable defects in pollen germination and pollen tube growth ( Figure S1B ) or pollen tube guidance ( Figure S1C ) , but both the arid1-1 mutant and antisense lines showed increased incidence of single sperm-like cells in mature pollen ( Figure 1E and Figure S1D ) . The stronger phenotypes observed in the antisense ARID1 transgenic plants suggest that the ARID domain present in truncated proteins in arid1-1 was partly functional . Because antisense ARID1 transgenic plants with strongly reduced or undetectable expression of ARID1 were sterile , we performed all further analyses in the arid1-1 mutant background . The defect in sperm cell formation was rescued when we introduced the ARID1-RFP construct into the arid1-1 mutant ( Figure 1E ) . Taken together , our phenotypic analysis indicates that ARID1 is required for sperm cell formation . The single sperm-like phenotype of arid1-1 was similar to the phenotypes of mutants such as duo1 [3] , cdka1;1 [4] , fbl17 [6] , and duo3 [7] . We therefore used qPCR to examine whether the expression of these genes was disturbed in arid1-1 . Of these , only DUO1 mRNA levels were reduced in arid1-1 ( Figure 2A ) , suggesting that ARID1 positively regulates DUO1 at the transcriptional level , either directly or indirectly . DUO1 expression was also reduced in the antisense ARID1 plants with reduced seed set ( Figure S2A ) . We also crossed a DUO1-RFP reporter into arid1-1 and saw that the DUO1-RFP signal was slightly reduced , in both bicellular pollen ( Figure 2B , upper panels , yellow arrows ) and mature pollen ( Figure 2B , lower panels , yellow arrows ) . Since DUO1 is one of the targets of miR159 [12] , we examined MIR159 expression in the arid1-1 mutant by qPCR , but found no change in miR159 levels ( Figure S2B ) . As we recently showed that Anaphase Promoting Complex 8 ( APC8 ) is involved in CYCB1 regulation at both the transcriptional level and protein degradation level [8] , we crossed APC8-YFP and CYCB1-GFP into arid1-1 . CYCB1 is mainly present during early pollen developmental stages but not in mature pollen [8] , [9] . Overall CYCB1 expression in arid1-1 was not altered , as assessed by qPCR ( Figure 2B ) . However , the weak accumulation of CYCB1 in the generative cell of wild type bicellular pollen was undetectable in arid1-1 ( Figure 2C , red arrows ) , although no apparent change of CYCB1 accumulation in the vegetative cell was seen in arid1-1 ( Figure 2C , white arrows ) . However , the APC8-YFP level was not affected in arid1-1 ( Figure S2C ) . Similarly , we detected no effect on the expression of other known genes implicated in sperm cell function ( Figure S2D ) , including HTR10 [2] , GEX2 [10] and GEX1 [29] . Taken together , these results suggest that ARID1 might promote DUO1 expression directly , but independently , of miR159 . To test how ARID1 affects DUO1 expression , we performed a ChIP assay to examine whether ARID1 , as a transcription factor , binds DUO1 directly . We tested ARID1 occupancy at the genomic region of DUO1 , including the ∼1 . 4 kb promoter region upstream of the ATG and the ∼300 bp UTR region downstream of the stop codon in ARID1-GFP transgenic plants ( wild type plants as the negative control ) , using an antibody against GFP . We sub-divided the genomic region of DUO1 into ten subfragments of around 300 bp each ( Figure 3A ) and used EIF4A1 as a negative control . An obvious ARID1 occupancy over most of the DUO1 genomic region was detected in ARID1-GFP transgenic plants , with the peak of enrichment located between the ∼600–300 bp promoter region upstream of the ATG ( Figure 3B , upper panel ) . In contrast , there was not much difference between wild type and ARID1-GFP plants for EIF4A1 enrichment ( Figure 3B , upper panel ) , and enrichment from the no antibody control was negligible ( Figure 3C , lower panel ) . To confirm the results from the ChIP assay , we performed an in vitro DNA binding assay . We expressed ARID1 with an in vitro transcription/translation system ( Figure 3C , the band shown in the “input” lanes ) , and validated that ARID1 directly binds the DUO1 promoter region ( Figure 3C ) . Therefore , both in vitro and in vivo evidence showed that ARID1 directly binds the DUO1 promoter . Together with the observation of reduced DUO1 expression in the arid1 mutant , we therefore conclude that ARID1 acts as an activator of DUO1 , which is important for the initiation of the second mitosis for sperm cell formation . Microarray analyses and our RT-PCR results indicated that ARID1 is pollen-specific . In order to substantiate the RT-PCR analyses , we constructed a promoter-reporter construct of proARID1:NLS-GFP . The GFP reporter was undetectable in root and shoot apical meristems but a weak GFP signal was detected in the vegetative nucleus in tricellular pollen ( TP ) ( Figure S3A ) . Microarray analysis [26] indicated that ARID1 was more highly expressed ( 11 fold ) in sperm than in pollen . Several genes with >7X higher expression in sperm than in pollen have been shown to be sperm-specific [2] , [5] , [10] , [30] . We had therefore anticipated that ARID1 expression would be sperm-specific , so the ARID1 promoter:GFP reporter results were unexpected . We then examined the subcellular localization of pARID1:ARID1-GFP or -RFP fusion proteins in pollen . Microscopic analysis showed that ARID1-GFP localized to a single body in mature pollen ( MP ) ( Figure S3B , white arrowheads ) . We occasionally ( 1% , n>500 ) observed a second ARID1-GFP body ( Figure S3B , red arrowheads ) . To confirm the location of the ARID1-GFP body , we crossed HTR10-RFP , a sperm-specific marker [2] into ARID1-GFP transgenic plants . The ARID1-GFP body did not co-localize with HTR10 ( Figure S3C , lower panel ) , but did co-localize with the vegetative nucleus in DAPI-stained mature pollen ( Figure S3C , upper panel ) . We therefore concluded that ARID1-GFP was only present in the vegetative nucleus in mature pollen . Considering that ARID1 can bind to the DUO1 promoter directly to promote DUO1 expression in both bicellular pollen and mature pollen ( Figure 2 , Figure 3 , and Figure S2A ) , and DUO1 is specifically expressed from the early bicellular stage to mature pollen [3] , we hypothesized that ARID1 might overlap with DUO1 , in a cell type-specific pattern . We therefore examined the localization of ARID1 nuclear bodies at different developmental stages in ARID1-GFP and ARID1-RFP transgenic plants . To avoid interference due to the partial overlap of excitation wavelengths for DAPI and GFP , we mainly used ARID1-RFP transgenic plants for these observations . As in mature pollen ( Figure S3B and S3C ) , the ARID1-RFP body was a single nuclear body in unicellular microspores ( UM ) ( Figure 4A ) . Unexpectedly , increased numbers of nuclear bodies were observed in bicellular pollen ( Figure S4 ) . We categorized the ARID1-RFP nuclear bodies in bicellular pollen ( BP ) into four distinct patterns with similar incidences ( Figure 4B ) : all foci within the vegetative nucleus; multiple foci in both the vegetative nucleus and the generative nucleus; several foci in the vegetative nucleus but a single dot in the generative nucleus; and much larger foci in the vegetative nucleus but a less intense single dot in the generative nucleus ( yellow arrows indicate signals in the generative cell ) . Additional representative examples are shown in Figure S4C . Although the overall signal intensity in vegetative nuclei was much stronger than that in the generative cell , these images show that ARID1-RFP was in both the vegetative cell and generative cell at the bicellular stage , which is obviously different from the distribution in UM and MP . In TP , three patterns of ARID1-RFP bodies were identified ( Figure 4C ) : most pollen ( >60% ) had a single ARID1-RFP body , as in mature pollen , but the rest had multiple foci in the vegetative nucleus or a single weakly fluorescent ARID1-RFP body in sperm nuclei ( yellow arrows ) . Only a single nuclear body in the vegetative nucleus was observed in mature pollen ( Figure S3B , S3C , and MP , Figure 4D ) . To further substantiate the biological significance of the presence of ARID1 in the generative cell , we constructed transgenic plants with ARID1 driven by LAT52 ( LAT52:ARID1 ) , a vegetative cell-specific promoter , and HTR10 ( HTR10:ARID1 ) , a generative cell and sperm cell-specific promoter , respectively . We observed reduced seed set ( Figure S5A ) and increased DUO1 expression ( Figure S5B ) in >10 independent T1 HTR10:ARID1 plants but not in LAT52:ARID1 plants , indicating that accumulation of ARID1 in the generative cell is biologically relevant during pollen development . Taken together , these data indicate that the subcellular distribution of ARID1-RFP nuclear bodies is variable during pollen development . The transient localization of ARID1-RFP in the generative nucleus is consistent with the idea that ARID1 might be required for initiation or progression of the second mitosis , by promoting DUO1 activation . A human ELM2 domain-containing protein , MI-ER , directly interacts with Histone Deacetylase 1 ( HDAC1 ) [27] , [28] . To determine whether ARID1 might be associated with plant histone deacetylase complexes , we performed yeast two hybrid experiments . We detected an interaction ( Figure 5A ) between ARID1 and HDA8 ( Histone Deacetylase 8 ) , which is highly expressed in the vegetative nucleus but not in the generative nucleus or sperm cell nuclei ( Figure S6A ) . Moreover , we found that the ELM2 domain of ARID1 was important for the interaction with HDA8 ( Figure 5A ) , as was shown for an ELM2 protein with a histone deacetylase in human cells [28] . To confirm the yeast results , a recombinant HDA8-GST protein ( Figure 5C ) was used for pulldown assays , with GST as a negative control . ARID1-GFP was pulled down by HDA8-GST but not by GST ( Figure 5B ) . To confirm this association in vivo , we performed Co-IP experiments and showed that HDA8-YFP was co-immunoprecipitated with antibodies that recognize the ARID1-Myc fusion protein ( Figure 5D ) . Since ARID1 interacts with HDA8 , we predicted that the in vivo histone acetylation level might be altered in the arid1-1 mutant . We therefore performed immunofluorescence with antibodies specific to H3K9 acetylation . In wild type pollen , the signal was only detected in the two sperm nuclei ( Figure 6A , upper panel ) , but in the arid1-1 mutant , the immunofluorescence signal was also detected in the vegetative nucleus ( Figure 6A , lower panel ) . The immunofluorescence signal with the Histone 3 antibody , used as a control , showed no difference between wild type and arid1-1 ( Figure 6A ) . These results indicate that ARID1 is required to restrict histone acetylation to sperm cells . For genes encoding proteins , increased expression is usually accompanied with increased active marks in euchromatin , that is histone H3 lysine 9 acetylation ( H3K9Ac ) and histone H3 lysine 4 trimethylation ( H3K4me3 ) . Antibodies to a constitutive histone ( H3 ) reacted similarly with the control gene EIF4A1 and with DUO1 , in both wild type and the arid1 mutant ( Figure 6B ) . However , in the arid1 mutant the H3K9Ac level was obviously reduced at DUO1 but not at EIF4A1 ( Figure 6B ) . Furthermore , in the arid1 mutant there was no detectable difference in the level of H3K4me3 at DUO1 or EIF4A1 ( Figure 6B ) , suggesting that it is specifically histone acetylation that contributes to the reduced DUO1 expression . In addition , we detected slight de-repression of transposable elements ( TEs ) in the arid1-1 mutant ( Figure S6B ) , accompanied with increased histone acetylation at these loci ( Figure S6C ) , indicating that ARID1-mediated histone deacetylation activity also contributes to the silencing of TEs .
Sperm cell formation is regulated by several genes [31] . Among these genes , DUO1 plays a key role in the initiation of the second mitosis and acts as a switch to turn on the expression of other germline-related genes [9] . No positive element has been reported to regulate DUO1 expression but , in addition to the negative regulation mediated by miR159 [12] , a putative repressive GRSF ( Germline-Restrictive Silencing Factor ) binding site was noted in the DUO1 promoter [32] . However , mutagenesis of the putative GRSF binding site did not affect germline-specific expression of DUO1 [9] and the ∼150 bp proximal DUO1 promoter sequences that excluded the putative GRSF binding site were sufficient for germline-specific expression [9] . These results suggest that activation of the DUO1 promoter may depend on transcription factors that bind to the proximal region of the promoter and that are inherited and/or segregated during asymmetric division of the microspore . Here we provide evidence for such a positive transcription factor , ARID1 . ChIP analysis and a DNA binding assay showed that ARID1 directly binds to the ∼300–600 bp promoter region adjacent but distal to the 150 bp proximal region of the promoter ( Figure 3 ) . We surmise that the discrepancy from the observations in the previous study was based on whether or not the expression driven by the 150 bp proximal promoter was sperm cell-specific [9] , but ignored the difference in expression level in the generative cell between the intact promoter and the 150 bp proximal promoter . Thus we suggest that ARID1 binding to the DUO1 promoter facilitates the activation of DUO1 . To support the biological significance of this binding , we showed that disruption of ARID1 resulted in reduced DUO1 expression in germline cells ( Figure 2 ) . Although we did not observe decreased expression of three DUO1 direct targets ( HTR10 , GCS1 , and GEX2 ) , DUO1-mediated CYCB1 accumulation in the generative cell was affected in the arid1 mutant ( Figure 2C ) . The unaffected expression of GCS1 and GEX2 in the arid1 mutant might be explained by redundancy with DUO3 , since it also promotes expression of GCS1 and GEX2 , but not of CYCB1 [7] , and the expression of DUO3 was not affected in arid1 ( Figure 2A ) . Together with the normal sperm cell formation in htr10 , possibly due to redundancy with other HTR members , we suggest that defective sperm cell formation in arid1 results only from the disrupted function of the DUO1-CYCB1 module in germ cell division and not the function of DUO1-GCS1/GEX2 in germ cell specification , both of which contribute to severely defective sperm cell formation in duo1 . Furthermore , that only the DUO1-CYCB1 module was disrupted , and not the DUO1-GCS1/GEX2/HTR10 module , possibly explains the much weaker phenotype in arid1-1 , since specification of germ cells in arid1-1 might be maintained by the remnant DUO1 and/or DUO3-mediated activation of GCS1/GEX2/HTR10 . We presume that DUO1-CYCB1-mediated generative cell division is prerequisite for sperm cell formation , and that unaffected DUO3 should partially suppress the phenotype of single sperm cell-like pollen in arid1 , if DUO1 and/or DUO3-GCS1/GEX2/HTR10-mediated germ cell specification is parallel with DUO1-CYCB1-mediated germ cell division . In addition , unlike the DUO1 expression pattern , ARID1 is initially expressed in the microspore nucleus and subsequently expanded or segregated into the generative nucleus during the first asymmetric division ( Figure 4 and Figure S4 ) , indicating that DUO1 activation is an active process mediated by ARID1 from the vegetative cell , and not passively accomplished due to the completion of the first asymmetric division . We propose a model ( Figure 7 ) to explain how DUO1 could be coordinately and sequentially regulated by the negative regulator miR159 and by the positive regulator ARID1 . In the vegetative cell , MIR159 is transcribed abundantly during the unicellular stage , and so might play a major role in blocking DUO1 expression . As pollen development proceeds , in spite of the gradually decreasing but still detectable repressive role of miR159 in bicellular pollen , ARID1 , inherited from the microspore , partitions into the generative cell to bind to DUO1 and gradually promote DUO1 activation . We hypothesize that other factors together with AIRD1 are potentially involved in DUO1 activation , as duo1 is 100% penetrant and because the generative cell fails to divide in all duo1 pollen grains . In contrast , disruption of ARID1 only caused partial defects in sperm cell formation . Due to the absence of DUO1 accumulation in the vegetative nucleus of arid1-1 , we hypothesize that unknown factors ( other than ARID1-associated histone modification machinery ) might take over the major role of miR159 restricting DUO1 expression in the vegetative cell nucleus in the weak arid1-1 mutant ( Figure 2 ) . In parallel , ARID1 might promote sperm cell formation by altering the epigenetic status in both the vegetative cell and the generative cell; ARID1 physically associates with histone deacetylases , which could affect expression of the unknown gene ( s ) involved in sperm cell formation . Our data showed that ARID1 is necessary for the balance of histone acetylation between the vegetative cell and sperm cells of mature pollen ( Figure 6A ) , indicating that this characteristic could be carried over from bicellular pollen . Moreover , a recent study showed that increased histone acetylation in cultured microspores led to the switch from gametophytic division to sporophytic division [33] , further indicating that histone acetylation is critical for cell cycle progression during sperm cell formation . Given the permanent presence of ARID1 in the vegetative cell and its short stay in the germline cell ( Figure 4 ) , we speculate that ARID1 might carry information that strengthens communication between two distinct cell types , facilitating initiation of the second mitosis or the process of sperm cell formation , by acting on related genes , in addition to the key regulator , DUO1 . Our results indicated that ARID1-mediated DUO1 activation is important for sperm cell formation . It will be interesting to discover whether ARID1 regulates other genes and more broadly to deduce the functions of other plant ARID proteins , in order to understand the role of the conserved ARID domain . In animals , ARID proteins have been implicated in a variety of biological processes including embryonic development , cell lineage , gene regulation and cell cycle control . ARID1 is perhaps analogous to ARID4A and ARID4B in animals , which associate with the histone deacetylase complex [16] , [17] and are involved in male fertility control by acting in the Retinoblastoma ( RB ) pathway [15] . ARID4A is a RB-binding protein , and it is well established that the RB pathway controls cell cycle progression in a variety of organisms [34] , including plants [35] , [36] . A plant homologue of RB , RBR ( Rb-related ) , plays a pivotal role in male gametophyte patterning by regulating cell division and cell fate [37] , as both the vegetative cell and generative cell over-proliferated in rbr mutants [37] . As this phenotype was not seen in the arid1 mutant or in arid1 antisense plants and as ARID1 does not contain a pRB-binding motif , LXCXE [16] , it is unlikely that ARID1 binds RBR . However , there are 10 ARID proteins in Arabidopsis [24]and several other ARIDs are expressed in pollen [26] , [38] , so it is possible that other ARIDs are involved in the RB pathway to regulate sperm cell formation . In addition to its transient presence in the germline cell , ARID1 is in the vegetative nucleus from the microspore to mature pollen stage ( Figure 4 ) . What does ARID1 do in the vegetative cell ? There are two possibilities . First , ARID1 , as a transcription factor , might regulate expression of unknown genes in the vegetative cell . This regulation perhaps includes transcriptional activation , such as for DUO1 , and transcriptional repression . Together with the association of ARID1 with histone modification machinery , we suggest that ARID1 is required to regulate the chromatin environment around DUO1 so that it can achieve its maximal/optimal expression . Second , ARID1 in the vegetative cell might be required for maintaining the level of transposable element ( TE ) de-repression , as de-repression of many TEs occurs in the vegetative cell [39] , although the biological significance of TE de-repression remains unclear . We demonstrated greater TE de-repression in arid1-1 pollen ( Figure S6B ) , which was accompanied with increased histone acetylation at those de-repressed TE loci ( Figure S6C ) . Therefore , we propose that the function of ARID1 in the vegetative nucleus is to modulate the overt de-repression of TEs by association with the histone modification machinery . A dual role for ARIDs , either as transcriptional activators or transcriptional repressors , has been reported in animals [21] , [23] . Moreover , the ARID1 nuclear body ( Figure 4 ) might be a processing center for ARID1-mediated histone modifications . We showed [40] that Cajal bodies , which are processing centers for RNA-directed DNA Methylation ( RdDM ) , were similarly developmentally variable during sperm formation
The arid1-1 T-DNA insertion mutant ( SALK_047099 ) was obtained from the ABRC ( www . arabidopsis . org ) . Seeds of HTR10-RFP and DUO1-RFP were kindly provided by Fred Berger and David Twell , respectively . For the construction of ARID1-GFP , ARID1-RFP , and ARID1-Myc plasmids , ARID1 was amplified from wild type ( Columbia-0 ) genomic DNA with the primer pair ARID1F1 and ARIDR1 , cloned into pENTR-D/TOPO , and then transferred into the plant expression destination vector pMDC107 to construct ARID1-GFP , into pMDC163 ( GUS was replaced by a mRFP fusion tag ) to construct ARID1-RFP , and into pEarleyGate303 to construct ARID1-Myc . For ARID1 promoter analysis , a 1 . 5-kb fragment upstream of the ATG was amplified using the primer pair ARID1F1/R4 and was subcloned into pENTR-D/TOPO and then transferred into the plant expression vector pGII-NLS3XGFP . For constructing the antisense ARID1 plasmid , cDNA was obtained using the primer pair ARID1F5/R5 , cloned into pENTR-D/TOPO , then transferred into the plant expression vector pB7WG2 ( digested with SacI and SpeI to replace the 35S promoter with the native ARID1 promoter and then digested with KpnI and ApaI to insert the LAT52-GFP cassette ) . To construct the LAT52:ARID1 and HTR10:ARID1 plasmids , cDNA was obtained using the primer pair ARID1F6/R1 , cloned into pENTR-D/TOPO , then transferred into the plant expression vector pB7WG2 ( digested with SacI and SpeI to replace the 35S promoter with the LAT52 promoter or the HTR10 promoter , respectively , then digested with KpnI and ApaI to insert the LAT52-GFP cassette ) . To construct ARID1 for the in vitro transcription and translation system , we inserted ARID1 full length cDNA , amplified with the primer pairs ARID1F2/R6 into the pCMVTNT vector ( Promega ) . For yeast two hybrid experiments , cDNAs of ARID1 and ARID1-C were amplified from pollen RNA using RT-PCR and the primer pairs ARID1F2/R2 and ARID1F4/R2 , then cloned into the pGBT9 yeast expression vector; the cDNA of HDA8 was amplified using the primer pair HDA8F1/R1 , then cloned into the pGAD10 yeast expression vector . For GST pulldown experiments , the cDNA of HDA8 was amplified using primers HDA8F1/R1 and cloned into the pGEX-2TK vector . For the HDA8-YFP construct , HDA8 was amplified from wild type genomic DNA with the primer pair HDA8F2/R2 , cloned into pENTR-D/TOPO , and then transferred into the plant expression destination vector pGWB40 . Primer sequences are listed in Table S1 . The yeast two hybrid assays were performed according to the protocol available on the Clontech website , using strain AH109 . Yeast transformation was performed using yeast transformation buffer ( 0 . 1 M LiAc , 40% PEG3350 in TE ) . Transformants were plated and selected on synthetic complete medium that lacked the specified amino acids . Positive colonies were inoculated and spotted with a 100-fold dilution onto synthetic complete medium lacking leucine , tryptophan , and histidine and containing 10 mM 3-amino-1 , 2 , 4-triazole . The plates were incubated for 2–4 d at 28°C for interaction analysis . ChIP was performed according to [8] . ARID1 occupancy at the DUO1 genomic region was determined by ChIP using a GFP antibody ( Cat . 632460 , Clontech , 1∶200 ) and inflorescences from wild type and ARID1-GFP transgenic plants , respectively . The occupancy of histone modification marks at DUO1 was determined by ChIP using Histone 3 antibody ( Cat . 06-755 , Upstate , 1∶50 ) , Histone Lysine 9 acetylation antibody ( Cat . 17-10241 , Upstate , 1∶200 ) , and Histone Lysine 4 trimethylation antibody ( Cat . ab8580 , abCam , 1∶200 ) and inflorescences from wild type and the arid1-1 mutant , respectively . DNA present in the immunoprecipitates was quantified by qPCR or PCR relative to total input DNA . The results shown were consistent in two biological replicates . The primer sets used for the PCR are listed in Table S1 . DNA binding assays were performed as described [41] with the following modifications . Briefly , biotinylated DNA fragments corresponding to 3 , 4 , 5 , and 10 ( Figure 3 ) were generated by PCR using primer pairs DUO1_F3/R3 , DUO1_F4/R4 , DUO1_F5/R5 , and DUO1_F10/R10 , with labeling by 5′biotin at F3 , F4 , F5 , and F10 , respectively . Then 100 pmol of the biotinylated DNA fragments were incubated with 50 ul prewashed Streptavidin Agoraose Resin ( Thermo , Cat . 20349 ) in IP100 buffer ( 100 mM potassium glutamate , 50 mM Tris-HCl pH 7 . 6 , 2 mM MgCl2 , 0 . 5% NP40 ) for 2 h at room temperature with slight rotation , and washed five times in IP100 buffer . In parallel , ARID1 was subjected to TNT T7 in vitro transcription/translation with the TNT Coupled Wheat Germ Extract System ( Promega , Cat . L4140 ) . 25 ul freshly translated ARID1 protein was added to DNA-bound beads in the IP buffer plus complete protease inhibitor cocktail , and the mixture was rotated at 4°C for 2 h . Beads were washed eight times with IP100 buffer , then proteins were stripped off the beads by boiling with 2XSDS buffer and then subjected to SDS-PAGE . The ARID1 protein bound by biotinylated DNA was detected by immunoblotting with a 1∶200 dilution of anti-ARID1 ( The peptide “SMVADEDAVDYSKT” was conjugated to KLH and used to raise rabbit polyclonal antibodies ( GL Biochem ) ) . The pulldown assay was carried out as described previously [42] . GST and GST-HDA8 were expressed in E . coli BL21 . Cells were disrupted by sonication and the proteins were purified by glutathione Sepharose 4B affinity chromatography . 600 µl of protein extract from inflorescences of ARID1-GFP plants was applied to the beads-protein mixture ( 30 µg total protein ) and incubated for 2 h at 4°C on a rotating wheel . The beads were washed 5 times with IP lysis buffer . The bound ( pellet ) and unbound ( supernatant ) proteins were detected by immunoblotting with anti-GFP ( Cat . 632480 , Clontech , 1∶2000 dilution ) and anti-Hsc70 ( Cat . SPA-818 , Stressgen , 1∶10000 dilution ) antibodies . 1/6 of the pellet fractions , and 1% and 0 . 1% of the supernatant fractions for anti-GFP and anti-Hsc70 IPs were used , respectively . The immunoprecipitation assay was carried out as described [42] . One gram of inflorescences from wild type or ARID1-Myc; HDA8-YFP doubly transgenic plants were ground in liquid nitrogen and homogenized in 2 ml of protein lysis buffer ( 50 mM Tris–HCl at pH 7 . 5 , 150 mM NaCl , 0 . 2% NP-40 , 2 mM DTT , 10% glycerol , complete protease inhibitor cocktail ) . The lysates were incubated at 4°C for 50 min on a rotating wheel and centrifuged at 16000 g to pellet debris , and then supernatants were pre-cleared for 20 min with protein G agarose beads . Equivalent lysate was mixed with Myc antibodies ( Cat . SC-70463 , Santa Cruz , 1∶200 ) pre-coupled to protein G agarose beads or to beads alone , respectively . After incubation for 2 h at 4°C , the immune complexes were washed with lysis buffer . Proteins from 1/6 of the “No Ab” ( no antibody ) IP and 1/6 of the anti-myc IP were analyzed by immunoblotting using anti-Myc , anti-GFP , and anti-Hsc70 . Proteins from 1/100 of the input were used for the anti-Myc and anti-GFP blots , while proteins from 1/1000 of the input were used for the anti-Hsc70 blot . Inflorescences were fixed for 30 minutes in methanol∶acetone ( 4∶1 , v/v ) at room temperature , and anthers were dissected to release tricellular pollen onto a slide covered with liquid pollen germination medium [43] . The slides were allowed to dry for about 20 min at room temperature and then were covered with a thin layer of agarose/gelatin/sucrose ( 0 . 94% low melting agarose/0 . 84% gelatin/0 . 3% w/v sucrose ) for 10 minutes at 37°C . The slides were soaked in blocking buffer ( PBS with 5% BSA ) for 1 hour at 37°C , and then incubated with H3 antibody ( 1∶50 dilution , Cat . 06-755 , Upstate ) or H3K9ac antibody ( 1∶200 dilution , Cat . 1710241 , Upstate ) , respectively , overnight at 4°C in a dark moist chamber . After washed with blocking buffer , pollen was incubated in blocking buffer containing Alexa Fluor 488 goat anti-rabbit antibody ( 1∶200 dilution , Cat . 711-545-152 , Jackson ) for 6 h at room temperature . Slides were washed in PBS for five times , and observed with an Axiovert microscope under the GFP channel . Images were acquired using an AxioCamRM camera and AxioVision 4 . 8 . 1 software and processed using Adobe Photoshop CS2 ( Adobe ) .
|
For all eukaryotes , gamete formation is an essential aspect of sexual reproduction . Unlike in animals , where meiotic products directly become gametes , the germline in plants is established by two consecutive mitotic divisions after meiosis is completed . The first mitosis is asymmetric , forming a larger vegetative cell and a smaller generative cell . The smaller generative cell then divides to produce two sperm cells . Current knowledge indicates DUO1 ( DUO POLLEN 1 ) , a transcription factor , plays a key role in this process by controlling expression of other germline genes . But how DUO1 is activated in the generative cell is unknown . To better understand the mechanisms that govern sperm cell formation and activate DUO1 expression , we characterized , ARID1 , encoding an ARID ( AT-Rich Interacting Domain ) -containing protein . We show that ARID1 is required for DUO1 activation and sperm cell formation in Arabidopsis . Furthermore , ARID1 physically associates with a histone deacetylase , facilitating the maintenance of histone acetylation between the vegetative nucleus and sperm nuclei . Thus , our study shows that a pollen-specific ARID protein plays an important role during sperm cell formation in a dual manner: as a transcription factor to activate DUO1 and as a potential component of the histone modification machinery to maintain epigenetic status in pollen .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"pollen",
"developmental",
"biology",
"plant",
"anatomy",
"plant",
"science",
"cell",
"biology",
"fertilization",
"gene",
"expression",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
"cell",
"biology",
"flowering",
"plants",
"plants",
"organisms"
] |
2014
|
An ARID Domain-Containing Protein within Nuclear Bodies Is Required for Sperm Cell Formation in Arabidopsis thaliana
|
The life cycle of the mammalian pathogen Trypanosoma brucei involves commuting between two markedly different environments: the homeothermic mammalian host and the poikilothermic invertebrate vector . The ability to resist temperature and other stresses is essential for trypanosome survival . Trypanosome gene expression is mainly post-transcriptional , but must nevertheless be adjusted in response to environmental cues , including host-specific physical and chemical stresses . We investigate here the control of ZC3H11 , a CCCH zinc finger protein which stabilizes stress response mRNAs . ZC3H11 protein levels increase at least 10-fold when trypanosomes are stressed by heat shock , proteasome inhibitors , ethanol , arsenite , and low doses of puromycin , but not by various other stresses . We found that increases in protein stability and translation efficiency both contribute to ZC3H11 accumulation . ZC3H11 is an in vitro substrate for casein kinase 1 isoform 2 ( CK1 . 2 ) , and results from CK1 . 2 depletion and other experiments suggest that phosphorylation of ZC3H11 can promote its instability in vivo . Results from sucrose density centrifugation indicate that under normal culture conditions translation initiation on the ZC3H11 mRNA is repressed , but after suitable stresses the ZC3H11 mRNA moves to heavy polysomes . The ZC3H11 3'-UTR is sufficient for translation suppression and a region of 71 nucleotides is required for the regulation . Since the control works in both bloodstream forms , where ZC3H11 translation is repressed at 37°C , and in procyclic forms , where ZC3H11 translation is activated at 37°C , we predict that this regulatory RNA sequence is targeted by repressive trans acting factor that is released upon stress .
The African trypanosome Trypanosoma brucei is responsible for sleeping sickness in humans and nagana in livestock . Bloodstream-form trypanosomes , which are found in mammalian blood and tissue fluids , are exposed to temperatures ranging from about 36°C to 40°C ( fever ) . Trypanosomes are transmitted by Tsetse flies , where they replicate as procyclic forms in the midgut , progressing to epimastigotes , then metacyclic forms in the salivary glands . Within Tsetse , the temperature may fluctuate between 20°C and 43°C [1] . In addition , availability of nutrients in the two hosts is different . Trypanosomes , like other kinetoplastids , manage these changes almost exclusively through post-transcriptional mechanisms . Transcription is polycistronic and individual mRNAs are generated by processing: this precludes transcription control at the level of individual open reading frames . In contrast , there is extensive evidence for regulation of mRNA stability [2] and translation [3 , 4] . This regulation is often determined by sequences in the 3'-untranslated regions ( 3'-UTRs ) of mRNAs , and mediated by RNA binding proteins [5–8] . It has long been known that kinetoplastids , like other organisms , respond to temperature stress by inducing synthesis of heat shock proteins ( HSPs ) , and shutting down synthesis of other proteins . The response of T . brucei to heat stress includes repression of transcription [9] , mRNA processing [10–12] and translation [13] . Heat shock and other stresses also cause the formation of stress granules that contain translationally silenced mRNAs [13 , 14] . Several studies in kinetoplastids have demonstrated roles of 3'-UTRs in determining the stability and translation of HSP mRNAs [13 , 15–19] . We recently found an RNA binding protein called ZC3H11 , which is required for preferential retention of the transcripts upon heat shock and for survival of the parasites after heat shock [20] . This protein binds to mRNAs encoding major cytoplasmic HSPs including HSP70 , HSP83 , HSP100 , HSP110 , HSP20 , DNAJ1 , DNAJ2 , and FKBP . Each of these mRNAs contains multiple repeats of the AU-rich element ( UAU ) n within their 3'-UTRs . These repeats are bound by ZC3H11 and required for the response of the mRNA to heat shock [20] . ZC3H11 can act through recruitment of a complex containing three essential proteins: MKT1 , LSM12 and PBP1 , which in turn recruit poly ( A ) binding proteins ( PABP ) to the 3'-UTR , with consequent mRNA stabilization [21] . ZC3H11 is almost undetectable in trypanosomes grown at normal temperature , whether they are bloodstream forms at 37°C or procyclic forms at 27°C . Upon heat shock , however , ZC3H11 protein becomes readily detectable . It migrates much slower than expected on denaturing polyacrylamide gels , which is partially due to heavy phosphorylation [20] . In this paper , we aimed to understand the mechanism by which the abundance of ZC3H11 increases after heat shock . We present evidence for changes in mRNA translation , protein modification and protein stability , and identify a region within the 3'-UTR of the ZC3H11 mRNA that is responsible for heat-regulated translation .
To analyse expression of native ZC3H11 , we made a polyclonal antiserum to the N-terminal 119 amino acids ( S1A–S1D Fig ) . This antiserum specifically detects ZC3H11 in cytoskeleton-free detergent extracts ( S1B Fig ) . Fig 1A shows native , untagged ZC3H11 expression after a variety of stresses . The predicted molecular weight of ZC3H11 is 40 kDa . Under normal culture conditions ( lane 1 ) , it is barely detectable . As expected , heat shock at 37°C resulted in the appearance of a band that migrated above 50 kDa ( Fig 1 , lane 2 and S1 Fig ) . In our previous experiments , we found that V5-tagged ZC3H11 ( molecular weight about 42 kDa ) migrated at about 60 kDa , and phosphatase treatment reduced this to about 50 kDa . The aberrantly slow migration of ZC3H11 is thus due partly to phosphorylation , and partly to some intrinsic characteristics of the protein sequence . Lane 3 shows the effect of a one-hour heat shock at 41°C , which is the temperature that was used in previous publications [13 , 20] . Despite the severity of this treatment , trypanosomes are able to resume RNA synthesis almost immediately upon returning to 27°C [13] and they also resume growth [20] . After the 41°C heat shock , the ZC3H11 band that was recognised by the antibody migrated faster in polyacrylamide gels; this was previously seen with the V5-tagged protein , and could be due either to dephosphorylation , or to protein degradation . By quantitative immunoblotting ( S1E and S1F Fig ) we estimated that procyclic cells grown at 27°C contain no more than 2×103 ZC3H11 molecules per cell , increasing to 15–20×103 molecules per cell at 37 or 41°C . As seen for in situ tagged protein [20] , ZC3H11 levels were also increased by low doses of puromycin , and by the proteasome inhibitors MG132 and lactacystin [20] ( Fig 1A , lanes 4–7 ) . In order to determine the specificity of ZC3H11 induction we subjected procyclic forms to variety of other stresses: ethanol , sodium arsenite , altered pH , and ER stress from dithiothreitol ( which causes protein misfolding in the endoplasmic reticulum by reducing disulfide bonds ) or tunicamycin ( which causes accumulation of unfolded glycoproteins in the endoplasmic reticulum ) . We also tested two inhibitors of ribosome translocation , hygromycin and G418 , and the effects of cold shock at 4°C and 16°C . Just three of these new stresses were found to increase the ZC3H11 levels: ethanol ( Fig 1A , lanes 8–10 ) , sodium arsenite ( Fig 1A , lanes 11–13 ) and pH 5 . 5 ( Fig 1A , lane 15 ) . The response to acid , but not alkaline , pH makes sense since the Tsetse midgut has a pH range of about 8–10 . 5 [22] . Inhibitors of translation elongation ( S2A Fig , lanes 1–12 ) and cold shock ( S2A Fig , lanes 23–25 ) did not increase ZC3H11 levels . It was notable that the endoplasmic reticulum stress inducers also had no effect ( S2A Fig , lanes 13–22 ) . Endoplasmic reticulum stress is known to trigger a signal transduction cascade that leads to a shut-off of spliced leader transcription or "spliced leader silencing" [23 , 24] . The ZC3H11 stress response is clearly separate from this: instead , the common feature of the ZC3H11-inducers is that they are likely to cause cytosolic accumulation either of incompletely folded proteins , or , for puromycin , of protein fragments which may not be able to attain native conformations . To find out whether HSP70 mRNA levels are affected by the new stresses , and also whether such responses depend on ZC3H11 , we tested cells with ZC3H11 RNAi ( Fig 1B and 1C ) . The amount of total mRNA can be assessed by hybridizing Northern blots with the spliced leader ( SL ) sequence that is found at the 5'-end of every trypanosome mRNA ( Fig 1B ) . This gives a smear with two regions of more concentrated signal around about 2kb and 1kb . Heat shock inhibits transcription , so after an hour , there is a substantial decrease in the amount of total mRNA; but the HSP70 mRNA escapes destruction ( Fig 1B , lanes 1 , 2 and 8 ) . With the exception of arsenite ( Fig 1B , lanes 6 and 12 ) , the other stressors had no effect on total mRNA ( Fig 1B , lanes 3–6 ) , suggesting that they did not inhibiti transcription; all but arsenite caused slight increases in HSP70 mRNA abundance ( Fig 1B , lanes 3–5 and 9–11 ) . RNAi targeting ZC3H11 did not affect the abundance of HSP70 mRNAs at 27°C ( Fig 1B , lanes 7 ) but prevented its persistence at 41°C ( Fig 1B , lanes 8 ) or after arsenite ( Fig 1B , lanes 12 ) . ZC3H11 RNAi also prevented HSP70 mRNA accumulation after puromycin , MG132 and ethanol treatment ( Fig 1B , lanes 9–11 ) . Heat shock was the only stress that led to an increase in ZC3H11 protein mobility ( Fig 1A , lane 3 , and 1C , lanes 2 and 7 ) . Puromycin treatment in bloodstream forms also led to accumulation of only the slower-migrating form of ZC3H11 [20] . There was no change in the abundance of ZC3H11 mRNA after heat shock [20] . To elevate the protein level , two options remained: increased mRNA translation , and increased protein stability . To address the latter possibility , we measured the half-life of ZC3H11 . Since detection of the protein in unstressed cells was very unreliable , we compared cells that were incubated at either 37°C or 41°C , or with arsenite at 27°C . To measure the half-life we added cycloheximide , and followed the disappearance of the protein at the same temperatures by Western blotting . We quantified both detected species of ZC3H11 ( Fig 2A , 2E and 2F ) . A minor cross-reacting band at about 50kD was very occasionally seen in unstressed control and ZC3H11 RNAi samples ( S1B Fig , lines 5–8 ) but was more commonly absent ( Fig 2A , lane 15 ) . At 37°C , the faster-migrating ZC3H11 protein ( upper band ) was slightly more stable than the slower-migrating protein ( lower band ) ( Fig 2A , lanes 1–7 , and 2E ) ; at 41°C the lower band predominated and both bands were more stable than at 37°C ( Fig 2A , lanes 8–14 , and 2F; Table 1 ) . Arsenite also strongly stabilized ZC3H11 ( Fig 2E ) ; only traces of the lower band were detected ( Fig 2A , lanes 16–22; Table 1 ) . To rule out the possibility that the effects of temperature on stability were due to cycloheximide treatment , we measured degradation of ZC3H11 at 37°C and 41°C by pulse labelling with [35S]-methionine followed by a chase ( Fig 2B ) . The half-life estimate for cells at 37°C was similar than that seen using cycloheximide ( Table 1 ) , with mostly the upper band present but some smearing ( Fig 2B , lanes 2–7 ) , whereas for 41°C there was a smear between roughly equal amounts of the upper and lower bands ( Fig 2B , lanes 8–13 ) , and the half-life estimate was shorter than after cycloheximide ( Fig 2G and Table 1 ) . The major difference between the two assays is that pulse-labelling examines only protein that was made in the previous 20 minutes , whereas the cycloheximide assay examines the complete pool of old and new protein . It is possible that the cycloheximide experiment detects a stable pool of ZC3H11 that has aggregated at 41°C , while the new soluble protein that is detected by pulse labelling is more accessible to degradation . The amount of ZC3H11 detected at 27°C was insufficient for half-life measurement by pulse labelling ( Fig 2B , lane 1 ) . We also examined cells in which one ZC3H11 locus had been tagged at the 5' end with sequence encoding a V5 tag . V5-ZC3H11 migrated exclusively as the upper band after incubation at 37°C ( Fig 2C , lanes 1–7 ) and gradually shifted to the more prominent lower band after incubation at 41°C ( Fig 2C , lanes 8–14 ) . Half-lives were similar to those measured for the untagged protein ( Fig 2H and Table 1 ) . Arsenite again resulted in stabilization ( Fig 2C , lanes 15–21 , and 2H; Table 1 ) . To control for tag effects , a sequence encoding a myc tag , and a truncated actin ( ACT ) 3'-UTR , was inserted , by homologous recombination , at the 3'-end of the ZC3H11 open reading frame . This results in an mRNA which encodes ZC3H11 with a C-terminal myc tag , and has an ACT 3'-UTR instead of the normal one . Interestingly , C-terminally myc-tagged ZC3H11 was detectable at 27°C ( Fig 2D , lanes 1 ) , but had lower abundance after incubation at 41°C ( Fig 2D , upper panel lane 15 ) than at 37°C ( Fig 2D , upper panel lane 7 ) . After cycloheximide treatment , ZC3H11-myc had a half-life of about 10 min at 27°C , and was more stable at 37°C ( Fig 2I ) . In contrast to previous results , a further temperature increase to 41°C had no effect on the apparent half-life; but the signal may have been too low for accurate measurement ( Fig 2D ) . Arsenite ( Fig 2D , lower panel lanes 7–12 ) and MG132 ( Fig 2D , lower panel lanes 13–19 ) stabilized ZC3H11-myc ( Fig 2J , Table 1 ) Taken together , these results indicated that the increase in ZC3H11 abundance after heat shock treatment was at least partially due to an increase in protein stability . This effect was seen for both upper and lower bands , but the lower one was more stable than the upper one at all tested temperatures . Arsenite stabilised the slower-migrating ( phosphorylated ) species of ZC3H11 . Since degradation was inhibited by MG132 , the most likely effector is the proteasome . The results from the myc tagging also suggested that the ZC3H11 3'-UTR might play a role in temperature-dependent repression of ZC3H11 synthesis . We next wondered whether phosphorylation might play a role in ZC3H11 regulation . Although phosphorylation had previously been demonstrated unambiguously , we were unable to detect any phosphorylated peptides by mass spectrometry . It is possible that they are so negatively charged that they do not enter the mass analyser . This failure meant that we could not do a mutational analysis . As an alternative , we therefore looked for kinases and phosphatases that co-purified with tandem affinity purified ZC3H11 or MKT1 [21] . These were mitogen-activated protein kinase 2 ( Tb927 . 10 . 5140 ) , which co-purified with ZC3H11; and a protein phosphatase ( Tb927 . 5 . 1660 ) , an unclassified protein kinase ( Tb927 . 5 . 2820 ) and casein kinase 1 isoform 2 ( CK1 . 2 , Tb927 . 5 . 800 ) , which co-purified with MKT1 . RNAi targeting the first two kinases and the phosphatase had no effect on ZC3H11 expression ( S2B Fig ) . As previously demonstrated [25] , targeting Tb927 . 5 . 2820 or Tb927 . 5 . 1660 did not affect trypanosome proliferation or morphology , whereas Tb927 . 10 . 5140 was essential . Depletion of CK1 . 2 inhibited cell growth ( Fig 3A ) . The effect was beginning to be visible after 2 days , when the level had only decreased to about 40% of normal ( Fig 3A , inset ) . CK1 . 2 is also essential in bloodstream form T . brucei [26] . CK1 . 2 depletion caused a decrease in the relative abundance of the slower-migrating ( phosphorylated ) ZC3H11 species , irrespective of the temperature ( Fig 3B , lanes 5 , 7 and 9 ) or the stress applied ( Fig 3C , lanes 8 , 10 and 12 ) . After heat shock , dephosphorylated ZC3H11 persisted in the CK1 . 2-depleted cells ( Fig 3B , lanes 7 and 9 , and 3C , lane 8 ) ; it was not seen after the other stresses ( Fig 3C , lanes 10 and 12 ) . After a 41°C heat shock , the half-life of the dephosphorylated ZC3H11 was the same with or without CK1 . 2 RNAi ( Fig 3D ) . We concluded that phosphorylation may play a role in destabilizing ZC3H11 . The upper band also seemed to have become more stable , although separating them for measurement was rather difficult . We could not mutate the in vivo phosphorylation sites of ZC3H11 , since they are unknown . We therefore could not directly assess the contribution of phosphorylation to ZC3H11 instability . Equally , we could not tell whether the effects of CK1 . 2 depletion were direct or indirect . We could , however , at least test whether ZC3H11 could serve as a substrate for CK1 . 2 . For this , recombinant N-terminal fragments of Z3H11 ( first 104 , 119 or 136a . a . ) purified from E . coli via a His10-tag , were incubated with immunoprecipitated V5-CK1 . 2 ( Fig 4A ) in the presence of [γ-32P]-ATP . The products were resolved by SDS-PAGE ( Fig 4B ) and visualized by autoradiography ( Fig 4C ) . All three ZC3H11 fragments were phosphorylated by CK1 . 2 , but to different extents ( Fig 4C , lanes 6–8 ) . The 119a . a . fragment gave the strongest signal ( Fig 4C , lane 7 ) but the amount of the purified 136a . a . fragment loaded was lower , because it was only partially soluble and the preparation contained contaminants ( Fig 4B , lanes 4 and 8 ) . These results show that CK1 . 2 could be directly responsible for ZC3H11 phosphorylation , but an indirect role is equally possible . We considered testing the effect of CK1 . 2 RNAi on the heat shock response , but concluded that the results would not be meaningful because CK1 . 2 RNAi compromises cell viability . We next asked why ZC3H11 phosphorylation was decreased by severe heat shock . To find out whether CK1 . 2 was inactivated , we wanted to compare the in vitro activities of V5-CK1 . 2 obtained from cells at 27°C or after one hour at 41°C . When we did the V5-CK1 . 2 pull-downs from extracts from cells treated at 41°C , it appeared that only some of the V5-tagged enzyme was accessible to the anti-V5 antibodies ( Fig 4D , compare lanes 5 & 6 with lanes 2& 3 ) . To investigate the reason for this , we analyzed the soluble supernatant fraction ( input for immunoprecipitation ) and cell debris fraction ( pellet ) . CK1 . 2 was normally partially in the pellet ( Fig 4E , compare lanes 2 and 3 ) , but after incubation at 41°C , the proportion in the pellet reproducibly increased ( Fig 4E , compare lanes 5 and 6 with lanes 2 and 3 ) . The lowered pull-down efficiency could therefore be due to masking of the V5 tag within aggregates . To compensate for this for the kinase assay , we therefore took amounts of purified material that were predicted to contain roughly the same amounts of enzyme rather than the same cell equivalents ( Fig 4D , lane 7 ) . ( Note that since the assay uses freshly prepared protein we could not test the CK1 . 2 content in advance . ) Phosphorylation of the 119-residue ZC3H11 fragment was substantially reduced when the V5-CK1 . 2 purified from heat-shocked cells was used ( Fig 4F , compare lanes 2 and 4 ) . In fact the residual activity was not much greater than the background that we had previously seen from cells that did not express V5-CK1 . 2 ( Fig 4C , lane 3 ) . The result was not much affected if the kinase assay was performed at 41°C ( Fig 4F , compare lanes 2 and 6 ) , suggesting that some of the loss in CK1 . 2 activity depended on the cellular environment . These results suggested that the decrease in ZC3H11 phosphorylation upon heat shock could be due to inactivation of CK1 . 2 . Although heat shock clearly affected ZC3H11 stability , the 2–3 fold change in half-life seemed unlikely to be sufficient for the major differences in steady-state protein that were observed . Moreover , we knew that replacing the 3'-UTR of the ZC3H11 resulted in increased protein abundance at 27°C , and diminished abundance at 41°C ( Fig 2D ) . We therefore turned our attention to ZC3H11 protein synthesis: we examined the association of the ZC3H11 mRNA with polysomes by sucrose gradient centrifugation . Startlingly , at 27°C the ZC3H11 mRNA was concentrated in the low-density region of the gradient , co-migrating with the 40S small ribosomal subunits ( Fig 5A , 27°C control , fraction 3 ) . After a mild heat shock of 37°C for 1 hour , as expected , the overall distribution of ribosomes had shifted , with an increase in 80S at the expense of polysomes ( Fig 5A , 37°C ) . The same shift was also seen at 41°C ( S3A Fig ) , and with puromycin , arsenite and MG132 , but not with lactacystin or ethanol ( Fig 5A ) . After 37°C heat shock , puromycin , arsenite or MG132 treatment the ZC3H11 mRNA had shifted almost completely to the polysomal fractions ( Fig 5A , fractions 6–9 ) . This suggests that the increase in ZC3H11 protein after heat shock , puromycin , arsenite and MG132 treatment is partly caused by increased protein synthesis . In contrast , the increases in ZC3H11 after lactacystin and ethanol are probably mainly due to protein stabilization , since these two treatments had only very minor effects on ZC3H11 translation ( Fig 5A ) . Inclusion of translation inhibitors can cause accumulation of 80S ribosomes near the start codon [27] . Although we cannot really imagine how this could cause association of ZC3H11 mRNA with a 40S fraction , we nevertheless felt that it was essential to make sure that the same behaviour was seen without cycloheximide . As expected , without cycloheximide , the 80S peak was higher and polysomes were slightly decreased ( S3B Fig ) . ZC3H11 mRNA , however , stayed in the 40S fraction . The silenced ZC3H11 mRNA appeared to be migrating with the 40S ribosomal subunit . We decided to examine this behaviour in more detail . We used a 10%-30% sucrose gradient , which gives better resolution of ribosomal subunits and monosomes than a 17 . 5%-50% gradient , and took more fractions ( S3C Fig ) . ZC3H11 mRNA still migrated at 40S . One possible explanation is that the ZC3H11 mRNA might be associated with 40S ribosomal subunits , which would either be cap-associated , scanning the 5'-UTR or "stuck" at the start codon . This seemed somewhat unlikely since initiation complexes usually migrate at 48S . Alternatively migration at this position could be due to many proteins binding along the mRNA . We attempted to distinguish these possibilities by digesting the ZC3H11 RNA with RNase H prior to gradient analysis ( S3C Fig ) . We cut the RNA into three pieces containing the 5' end , to look for 40S association with the cap; a region around the start codon , to look for a block in 60S joining; and the remaining 2 . 4 kb which should not be associated with a small subunit . If just one mRNA contained a bound 40S subunit , it should migrate lower in the gradient than the others . Instead , each piece migrated at a density that was less than 40S , but higher than the main protein peak . This result suggests that the migration of the intact mRNA at 40S was not due to specific association of a charged 40S subunit with the cap , 5' UTR , or start codon . However , we cannot rule out the possibility that bound 40S subunits dissociated during the RNase H digestion . There is some evidence that expression of the HSP83 mRNA in Leishmania mexicana is regulated by changes in the secondary structure of a sequence in the 3'-UTR [16] . The fact that ZC3H11 translational regulation was seen in procyclic trypanosomes after a variety of stresses at 27°C , however , argued against any role for changes in mRNA secondary structure . Most compellingly , in bloodstream forms growing at 37°C , ZC3H11 mRNA was largely in the 40S fraction ( Fig 5B , left panel ) . Meanwhile in bloodstream forms at 42°C , when most translation had been suppressed , ZC3H11 mRNA was in the polysome fraction ( Fig 5B , right-hand panel ) . This unambiguously demonstrated that the increase in ZC3H11 translation was a response to abnormally increased temperature , rather than to the temperature per se . To find out whether the ZC3H11 coding region plays any role in translation control , we replaced one ZC3H11 open reading frame with a gene encoding neomycin phosphotransferase ( NPT1 ) with an N-terminal myc tag ( Fig 6A ) . ( This leaves the other allele intact . ) Both UTRs were preserved after this knock-in procedure . After one hour of mild heat shock at 37°C ( Fig 6B , lane 2 ) , the myc-NPT1 protein level was increased 2-fold in comparison to the control , while mRNA levels were slightly decreased ( Fig 6B , lane 5 ) . Polysome profiling revealed that myc-NPT1 mRNA completely mimicked the behaviour of ZC3H11 mRNA ( Fig 6C ) . The ZC3H11 open reading frame was therefore not required for translational regulation . Quantification ( Fig 6D ) revealed that at 27°C , less than 35% of the ZC3H11 or myc-NPT1 mRNA was in the heavy polysomes ( fractions 5–10 ) while more than 20% migrated at 40S ( fraction 3 ) . After a 37°C heat shock , 60–65% of both ZC3H11 and myc-NPT1 mRNAs was in heavy polysomes ( Fig 6D ) . The distribution of β-tubulin mRNA ( TUBB ) along the gradient did not change in response to heat shock , but HSP70 mRNA had accumulated in heavy polysome fractions . To find out whether the ZC3H11 3'-UTR alone could confer a response to heat shock , we made a reporter plasmid in which the chloramphenicol acetyltransferase ( CAT ) ORF was flanked by the EP 5'-UTR ( from a gene encoding EP procyclin ) and the ZC3H11 3'-UTR ( Fig 7 , left-hand panel ) . The construct was designed for integration into the tubulin locus , which will result in transcription by RNA polymerase II . Procyclic cells containing the reporter were either incubated at 27°C , or stressed at 37°C for 24 hours , then CAT protein activity and CAT mRNA levels were measured ( Fig 7 , right-hand panel ) . All results were normalized to those from a control CAT construct with a truncated actin 3'-UTR ( ∆ACT ) . In addition , translation efficiencies were estimated by polysome profiling ( Fig 8 ) . In comparison to the control reporter , the full-length ZC3H11 3'-UTR construct showed approximately 3-fold decrease in CAT activity at 27°C , which was more than doubled after heat stress ( Fig 7 , construct #1 ) . In contrast , CAT mRNA levels were comparable with those from the ∆ACT control and did not change much at 37°C ( Fig 7 , construct #1 ) . The control CAT reporter was in the polysomes at both temperatures ( Fig 8 , top panel ) whereas the CAT reporter with the ZC3H11 3'-UTR was , like native ZC3H11 , concentrated in the 40S fraction at 27°C and in the polysomes at 37°C ( Fig 8 , construct #1 ) . The reporter was thus mimicking the behaviour of the native ZC3H11 mRNA . In order to localise sequences that were required for translation control , we now started to test different segments of the ZC3H113'-UTR . Results for CAT activity and mRNA are shown in Figs 7 and S4A , and corresponding polysome gradients are in Figs 8 , S4B and S4C . Fragment #1 ( nt 1–645 relative to the stop codon , construct #2 ) , and fragment #3 ( nt 1133–1504 , construct #4 ) were not able to give regulation , but a reporter with fragment #2 ( nt 626–1150 , construct #3 ) gave the same pattern as the full-length 3'-UTR . Several stem-loop structures were predicted within this segment ( S5A Fig ) . Deletion of a predicted stem-loop at nt 894–924 ( S5A Fig ) , shown as red bar in Fig 7 , caused 6-fold increase of CAT activity and 2 . 6 fold increase of the CAT mRNA levels at 27°C ( Fig 7 , construct #5 ) , eliminating the response to elevated temperature and causing a complete loss of translational repression at 27°C ( Fig 8 , construct #5 ) . Deletions of two other predicted stem-loops ( 945–982 or 1003–1076 ) resulted in strong decrease in the reporter mRNA levels , but both mRNAs still showed translational activation at 37°C ( S4B and S4C Fig ) . The 894–924 segment has a GU-rich sequence—GUUGUUGUUGUUG—at positions 908-920 . Deletion of this sequence ( construct #6 ) or mutation of the Gs to Cs ( CUUCUUCUUCUUC , construct #7 ) , were both predicted to eliminate the stem-loop as shown by Mfold ( S5B Fig ) . Both of these mutations gave only a marginal attenuation of the translational block at 27°C ( Figs 7 and 8 ) . Our results so far indicated that the sequence from 894–924 was necessary for temperature-dependent translational repression . We next investigated whether this sequence was also sufficient to give regulation . Insertion of the sequence into the control reporter , between the CAT ORF and the ACT 3'-UTR had no effect , and placing it between two copies of the ACT 3'-UTR ( 100nt each ) again gave no regulation . However , incorporation of nts 827–1003 ( construct #9 ) or 873–944 ( construct #10 ) between 2 copies of fragment #1 led to translational repression similar to that from the full-length fragment #2 . Thus the 71nt from positions 873–944 in the ZC3H11 3'-UTR were only sufficient for temperature-dependent translational regulation in a particular context . We do not know whether it is the sequence of fragment #1 , or simply the distance from the poly ( A ) tail or termination codon , that is important for the function of the 71nt element . Association of ZC3H11 mRNA in small cytosolic granules [14] might conceivably result in migration in the 40S-80S range . To test this we employed the novel method for granule enrichment described in [28] . This method exploits the trypanosome sub-pellicular microtubule corset as a natural sieve to trap structures larger than about 24 nm . After cell lysis under conditions that maintain the microtubules , all macromolecules in lower-diameter structures are released during 3 wash steps ( Fig 9A , SN1-3 ) ; these washes contain most of the ribosomal RNA and 60–80% of total mRNA ( Fig 9A and 9B ) . The microtubules are then disrupted by high salt; and the released material is separated into a small-granule fraction ( S4 ) and a large granule pellet fraction ( G in Fig 9 ) . At 27°C , only about 5% of the β-tubulin ( TUBB ) mRNA was in the small and large granule fractions , but after a 41°C heat shock , the amount in small granules had doubled and 30% was in the large granule fraction ( Fig 9 ) . This result suggests that the procedure is able to enrich heat shock granules . The accumulation of TUBB mRNA in granules was specific: only 10% of HSP70 mRNA was in granules after heat shock ( Fig 9 ) , which is expected since the HSP70 mRNA retains active translation . The fraction of total mRNA in granules after heat shock was lower than for tubulin , which might be because after heat shock , the actively translated HSP mRNAs are preferentially stabilized whereas other mRNAs are lost . There was no evidence that ZC3H11 mRNA is in granules larger than 24nm at 27°C: less than 5% was in the small granules and none was in large granules ( Fig 9 ) . After heat shock almost 10% of the ZC3H11 mRNA was enriched in small and large granule fractions—slightly more than for HSP70 , but considerably less than for TUBB . We also checked whether the ZC3H11 3'-UTR can influence the subcellular location of a reporter mRNA by single-molecule in situ hybridisation . The locations of CAT mRNAs with either the full ZC3H11 3'-UTR , or the proximal 645nt fragment #1 , which does not give regulation , were compared with that of total mRNA ( detected with an SL probe ) . At 27°C the SL probe gave a very even cytosolic signal and both CAT mRNAs were scattered throughout the cytosol ( Fig 10 ) . After heat shock , some concentration of the total mRNA was clearly evident but the localisations of the two CAT-ZC3H11 mRNAs were unchanged ( Fig 10 ) . The results must be treated with some reservation because it is possible that CAT mRNAs in granules were not very accessible to the rather large probes . Similarly , if there are hundreds of very small suppressive granules , we would not have seen clustering of the CAT mRNAs , since there were less than 50 CAT molecules per cell . Despite these caveats , our results yielded no evidence for a role for granules in regulation by the ZC3H11 3'-UTR .
The amount of ZC3H11 was increased by stresses that cause the accumulation either of protein fragments , or of incompletely folded proteins in the cytosol . ZC3H11 protein abundance was , in contrast , not affected by inducers of ER stress . This result corresponds to the role of ZC3H11 in stabilizing mRNAs that encode proteins involved in refolding cytosolic proteins [20] . The pattern of induction suggests that the existence of ( partially ) unfolded proteins might be the stimulus that causes ZC3H11 accumulation . However , there is a conundrum , since ZC3H11 expression is increased at 37°C in procyclic forms , but repressed at the same temperature in bloodstream forms . Putting procyclic forms at 37°C is most unlikely to cause mass protein denaturation , since the bulk of the procyclic proteome is identical to that of bloodstream forms . It is however possible that some procyclic-specific proteins are heat-sensitive . How meaningful is the increase in ZC3H11 expression ? The total number of mRNA target molecules per procyclic trypanosome is not greater than 500 [2 , 20] , and at 27°C there were about 2×103 ZC3H11 molecules per cell—a 4:1 ratio . However , the ( AUU ) repeats in the target mRNAs should be able to bind several ZC3H11 molecules . It is therefore possible that at 27°C , not all binding sites are occupied . In contrast , full occupancy would be mathematically possible after heat shock , when ZC3H11 levels have increased almost 10-fold . Gel-shift experiments with a recombinant ZC3H11 fragment gave a dissociation constant of about 30nM , which is similar to the concentration of ZC3H11 after heat shock . Thus the difference in concentration is indeed meaningful . It is also possible that phosphorylation of ZC3H11 affects RNA binding . Under normal growth conditions ZC3H11 protein is rapidly degraded . Degradation is probably by the proteasome , since lactacystin increases the amount of ZC3H11 ( Fig 1A , lane 7 ) without affecting ZC3H11 mRNA translation ( Fig 5A ) . MG132 inhibits ZC3H11 degradation ( Fig 2D ) as well as activating ZC3H11 translation ( Fig 5A ) . Both of these agents inhibit the proteasome , although lactacystin is thought to be more specific [29] . We do not know why only one of them induced ZC3H11 translation , but it is possible that at the doses used , lactacystin gave either less complete proteasome inhibition , or had fewer side-effects than MG132 . After appropriate stresses , degradation of ZC3H11 is inhibited . As noted above , it is unlikely that this is solely because the proteolytic system is overloaded with stress-insulted proteins , at least in procyclic forms at 37°C . A more specific effect such as a change in ZC3H11 modification seems more probable . After treatment with MG132 and puromycin , phosphorylated ZC3H11 accumulated ( Fig 3 ) . In contrast , severe heat shock caused a very strong increase in dephosphorylated ZC3H11 , which was more stable than the phosphorylated version . The dephosphorylated version is likely to be functional , since we know that unmodified ZC3H11 can bind to RNA and can interact with MKT1 , PBP1 and itself [21] . To analyse the role of ZC3H11 phosphorylation , we looked for candidate kinases . Of the three kinases that co-purified with affinity tagged ZC3H11 , we found evidence for possible involvement of one: CK1 . 2 . Two lines of evidence implicate CK1 . 2 in ZC3H11 phosphorylation . The first is that depletion of CK1 . 2 results in accumulation of faster-migrating ZC3H11 , consistent with partial loss of phosphorylation ( Fig 3 ) . The second line of evidence is that CK1 . 2 can phosphorylate N-terminal fragments of ZC3H11 in vitro ( Fig 4 ) . We therefore speculate that the accumulation of dephosphorylated ZC3H11 after severe heat shock could be due to inactivation of CK1 . 2 . Other kinases could however also be involved: depletion of CK1 . 2 is ultimately lethal , and could easily lead to the loss of other kinases . Bloodstream-form trypanosomes lacking the MAP kinase kinase homologue , MKK1 , were impaired in the ability to grow at 39°C [30] , and procyclic cells lacking the MAP kinase homologue MAPK4 were unable to survive at 37°C [31] . Under normal culture conditions , translation of ZC3H11 mRNA is repressed . The 5'-UTR and coding sequences of the ZC3H11 mRNA were not required , but a 71nt region within the 3'-UTR was necessary for stress-dependent translation repression . The activity of this sequence was context-dependent , since it did not work when placed next to the termination codon , or between two truncated ( 100nt ) copies of the ACT 3'-UTR , but it was functional when inserted between two 625nt copies of fragment #1 . The 71nt sequence may have to be located at a specific distance from the termination codon or poly ( A ) tail , or its secondary structure might be influenced by surrounding sequence . The regulatory element might be bound by a repressive RNA-binding protein . Preliminary attempts to identify such a protein through affinity purification with a streptavidin aptamer [32] failed . We have , however , already identified a number of RNA-binding proteins that are capable of repressing expression of a bound reporter mRNA in bloodstream forms [33 , 34] , and we have a panel of bloodstream-or procyclic-form trypanosome lines containing inducible RNAi constructs . We tested the effects of inducing RNAi against DRBD2 , DRBD7 , PUF3 , RBP9 , RBP31 , ZC3H8 , ZC3H13 , ZC3H22 , ZC3H32 , ZC3H35 , ZC3H39 , 4E-IP and Tb927 . 11 . 14220 . None of these RNAi's resulted in an increase in ZC3H11 protein ( S2C Fig ) , but this is inconclusive because the RNAi might not have reduced the target protein sufficiently to have an effect . Eukaryotic translation initiates when the 43S complex—which contains the 40S subunit , various translation factors , and charged tRNA—is recruited to the 5'-end of an mRNA by the eIF4E/G complex , making a 48S complex . The tRNA-ribosome-factor complex then scans to the initiation codon before large subunit joining [35] . There are three general mechanisms by which translation of ZC3H11 mRNA may be inhibited . The first two require that the 48S complex is associated with the RNA , which initially seemed possible since the non-translated ZC3H11 mRNA migrates at about 40S on sucrose gradients . Sequence-specific stalling of the subunit within the 5'-UTR ( Fig 11A ) can be ruled out , since neither the 5'-UTR nor the coding region was required for translational repression . An alternative would be that the 40S-subunit-containing complex is stalled at the start codon ( Fig 11B ) . General translation inhibition by Reaper works this way [36] , and sequence-specific control of the same type has been suggested for three mRNAs , although the evidence was somewhat circumstantial [37–39] . The most commonly described mechanism of sequence-specific translation regulation in eukaryotes is , however , a complete failure of 43S recruitment [35] ( Fig 11C ) . We favour this option , because the ZC3H11 usually migrated at , but not below , 40 S , and we found no evidence for association of a 40-48S complex with any particular region of the mRNA ( S3C Fig ) . Since a single RNA binding protein is likely to occupy between 4 and 20 residues [40–42] , the 3 . 5kb ZC3H11 mRNA could be associated with more than 50 RNA-binding proteins , resulting in migration at a similar position to ribosomal subunits . Trypanosomes have several homologues of the cap-binding translation initiation complex components eIF4E and eIF4Gs [43–45] . In T . brucei , eIF4E1 represses expression when tethered to a reporter , as does its binding partner 4E-IP [33 , 34] , but RNAi targeting these did not increase ZC3H11 expression ( S2C Fig ) . We nevertheless speculate that under non-stressed conditions , a protein or proteins associated with the ZC3H11 3'-UTR recruits a translationally inactive 4E-containing complex to the ZC3H11 5' cap . Upon heat shock , normal eIF4E4/G3-dependent translation is shut down , but translation of the ZC3H11 mRNA escapes , either through activation/modification of its existing cap-bound complex , or through exchange for a new , active one ( Fig 11D ) . Our experiments have revealed two mechanisms by which ZC3H11 levels are regulated: protein degradation and translation initiation . Neither form of control is well understood in kinetoplastids . Firstly , under normal conditions , ZC3H11 is rapidly degraded by the proteasome . Although the proteasome itself has been well characterized [46 , 47] , and various proteins have been shown to be ubiquitinated prior to degradation [48–50] , we know almost nothing about how the ubiquitination machinery recognizes appropriate targets . Secondly , ZC3H11 mRNA translation is tightly controlled . Despite evidence for translational regulation of hundreds of mRNAs [3 , 4] , we know little about what the different translation initiation complexes do or how they are regulated . Extensive further work in both these areas will be required in order to understand ZC3H11 regulation .
The antisera against ZC3H11 were generated by Charles River Laboratories , Kisslegg , Germany . The company is responsible for compliance with all relevant regulations regarding animal welfare . Details of all plasmids and oligonucleotides are provided in S1 Table . Site-directed mutagenesis method was used to generate deletions and G→C mutations within the plasmids with wild-type ZC3H11 3'-UTR sequence . The culture conditions were as described in [51] . Procyclic trypanosomes were grown in MEM-Pros medium at 27°C ( unless stated otherwise ) at densities lower than 6×106 cells/ml . Bloodstream forms were grown in HMI-9 medium . Nearly all experiments were done with Lister 427 monomorphic procyclic form parasites expressing the Tet-repressor , except one case ( Fig 5B ) where bloodstream forms were used . Stable cell lines were created with constitutive expression of CAT reporter mRNAs , or with sequences encoding the V5 or myc tag in frame with open reading frames of interest . Additional lines had tetracycline-inducible expression of dsRNA or tagged proteins . For these , expression was induced using 200ng/ml tetracycline . All plasmids used are listed in S1 Table . Fragments of the ZC3H11 open reading frame ( first 104a . a . , 119a . a . , 136a . a . ) were cloned into pQEA38vector after His10-tag , between KpnI and HindIII sites . Bacteria ( E . coli strain Rosetta pLysS , Novagen ) were grown at room temperature to an OD600 of 0 . 6 , induced with 0 . 25mM isopropyl β-D-1-thiogalactopyranoside and incubated at the same temperature for five hours before harvesting . Recombinant proteins were purified with Ni-NTA Agarose ( QIAGEN ) following the manufacturers’ instructions [20] . Buffer in protein samples was exchanged to PBS . Rabbits were immunized with His10-ZC3H11 ( 119a . a . ) according to standard procedures ( Charles River Laboratories , Kisslegg , Germany ) . Polyclonal antibodies were affinity-purified from crude anti-serum using His10-ZC3H11 ( 119a . a . ) fragment coupled to CNBR-activated Sepharose ( GE Healthcare ) . Cytoskeleton-free extracts were obtained as previously described [52] . Cells were harvested by centrifugation ( 850g , 8min , 20°C ) , washed with cold phosphate-buffered saline and lysed in extraction buffer ( 1% ( vol/vol ) IGEPAL in 0 . 1M PIPES , 2mMEGTA , 1mM MgSO4 , 0 . 1mM EDTA , pH6 . 9 , supplemented with 10μg/ml leupeptin and tablet/10ml of PhosSTOP Phosphatase Inhibitor Cocktail , Roche ) . After centrifugation ( 3400g , 10min , 4°C ) , supernatant was taken and resuspended in 2× Laemmli buffer . 1×108 procyclic cells with or without a 1 hour heat shock were treated with 100μg/ml cycloheximide 5min prior to starting the indicated time course , and 1×107 cells were collected at the indicated time points . The endogenous ZC3H11 protein was detected by Western blotting in cytoskeleton-free extracts using anti-ZC3H11 antibodies . Alternatively , cells were subjected to [35S]-methionine pulse labelling as described in [53 , 54] . The ZC3H11-myc protein expressed from modified endogenous ZC3H11 locus was detected in total lysate using anti-myc antibodies . Quantification was done using MultiGauge or Adobe Photoshop Software . A band that cross-reacted with the anti-ZC3H11 antibody , or Ponceau S staining , were used as loading controls . Immunoprecipitation assays were done as previously described [21] . 1×108 procyclic trypanosomes expressing V5-CK1 . 2 were harvested by centrifugation ( 850g , 8min , 20°C ) , washed with 1ml of cold phosphate-buffered saline and lysed in hypotonic buffer ( 10mM NaCl , 10mMTris-Cl pH7 . 5 , 10μg/ml leupeptin , 0 . 1%NP40 ) by passing 20–30 times through a 21G needle . After pelleting insoluble debris by centrifugation ( 17000g , 10min , 4°C ) and adjusting to 150mM NaCl , the clarified lysate was used for immunoprecipitation with 200μl of anti-V5-coupled beads ( Bethyl Laboratories ) for 2 hours at 4°C . The beads were washed then 5 times at 4°C with IPP150 ( 10mM Tris pH7 . 5; 150mM NaCl; 0 . 1% IGEPAL ) and divided into 4×50μl aliquots for in vitro kinase assay . Samples for western blots were taken during the procedure . Proteins were detected by western blotting according to standard protocols . For detection of the endogenous ZC3H11 protein only cytoskeleton-free extracts were used . Antibodies used were to the ZC3H11 ( rabbit , 1:10000 , this paper ) , to V5 tag ( AbD seroTec , 1:1000 ) , the myc tag ( Santa Cruz Laboratories , 1:1000 ) , aldolase ( rabbit , 1:50000[54] ) . Detection was done using ECL solutions ( GE Healthcare ) . Chloramphenicol acetyltransferase activity was measured in a kinetic assay involving partition of 14C-buturyl chloramphenicol from the aqueous to the organic phase of scintillation fluid [55] . Total protein concentration was measured by Bradford method . 3–5×108 procyclic cells were treated with cycloheximide ( 100μg/ml ) for 5minutes , harvested at room temperature by centrifugation ( 850g , 8min , 20°C ) , washed once in 1ml of ice-cold PBS and lysed in 300μl of lysis buffer ( 20mM Tris pH7 . 5 , 20mM KCl , 2mM MgCl2 , 1mM DTT , 1200u RNasin ( Promega ) , 10μg/ml leupeptin , 100μg/ml cycloheximide , 0 . 2% ( vol/vol ) IGEPAL ) by passing 20–30 times through a 21G needle . After pelleting insoluble debris by centrifugation ( 17000g , 10min , 4°C ) and adjusting to 120mM KCl , the clarified lysate was layered onto a 17 . 5–50% sucrose gradient ( 4ml ) and centrifuged at 4°C for 2 hours at 40000 rpm in Beckman SW60 rotor . Monitoring of absorbance profiles at 254nm and gradients fractionation was done with a Teledyne Isco Foxy Jr . system . A human β-globin in vitro transcript was added to each of the collected fractions as a spike-in control . Total RNA was extracted using peqGOLDTrifast ( Peqlab ) . Isolated RNA ( typically 20μg of total RNA or RNA purified from entire gradient fraction ) was resolved on formaldehyde agarose gel , blotted to nylon membranes and detected by hybridization with radioactive probes for CAT , ZC3H11 , NPT1 , HSP70 ( Tb927 . 11 . 11330 ) and β-tubulin ( Tb927 . 1 . 2370 ) mRNAs . Total mRNA was detected using an oligonucleotide antisense to mini-exon . Quantification was done using MultiGauge Software . Signal from 7SL RNA was used to measure loading . Kinase assays using purified V5-CK1 . 2 were performed as described [56] . Phosphorylation reactions ( 10μl total volume ) using the immunoprecipitated V5-CK1 . 2 contained 10μCi of [γ-32P]-ATP in1× NEBuffer for Protein Kinases ( 50mM Tris-HCl pH8 . 0 , 10mM MgCl2 , 0 . 1mM EDTA , 2mM DTT , 0 . 2% ( vol/vol ) IGEPAL ) and 5μg of BSA ( control ) or recombinant His10-ZC3H11 N-terminal fragments ( first 104 , 119 or 136 amino acids ) . Reactions were allowed to proceed for 20min at room temperature . Proteins were resuspended in 2×Laemmli buffer , resolved by SDS-PAGE , stained with Coomassie blue and the incorporation of radioactive phosphate into recombinant ZC3H11 fragments was detected by autoradiography . RNase H cleavage was done in clarified lysate from 4×108 procyclic cells obtained in the same way as for polysome fractionation . Lysate was divided into two portions ( ~200μl each ) and mix of two oligonucleotides antisense to the ZC3H11 mRNA was added to the final concentration 2μM . One oligonucleotide annealed 81 nucleotides upstream the start codon ( cz5636 ) , second annealed 295 nucleotides downstream the start codon ( cz4596 ) . Both tubes were incubated at 37°C with slow cooling to room temperature during 20min . 10u of RNase H ( Thermo Fisher Scientific ) were added to one of the tubes and incubated for another 20min at 37°C . Then both control and RNase H treated lysates were layered onto a 10–30% sucrose gradient ( 4ml ) and centrifuged at 4°C for 2 hours at 40000 rpm in Beckman SW60 rotor . RNA isolated from 15 collected fractions was probed with 5'-end radiolabeled oligonucleotides antisense to ZC3H11 mRNA in order to detect three fragments of cleavage: 5'-UTR fragment ( 216nt + splice leader , probe cz5635 ) , 5'-ORF fragment ( 372nt , probe cz5891 ) and 3'-ORF/3'UTR fragment ( 2314nt + poly ( A ) tail , cz5824 ) . Granules from normal and heat-shocked procyclic cells were enriched as described previously [28] . 5×108 control or heat-shocked ( 1 hour at 41°C ) procyclic cells were harvested at room temperature by centrifugation ( 1500g , 10min ) , washed in 1ml of PBS and lysed in 200μl of ice-cold buffer A ( 20mM Tris-HCl pH 7 . 6 , 2mM MgCl2; 0 . 25M sucrose , 1mM DTT , 10% glycerol , 1% TritonX100 , 800u RNasin ( Promega ) , 1 tablet Complete Protease InhibitorCocktail EDTA free ( Roche ) /10ml buffer ) by pipetting . Lysis was confirmed microscopically . The lysate was clarified ( 20000g , 10min ) and the supernatant ( SN1 ) was transferred to fresh tube with 750μl of peqGOLDTrifast FL ( Peqlab ) . All remaining supernatant was removed after one short centrifugation ( 3min , 20 . 000g ) . The pellet was resuspended again in 200μl of buffer A by passing 30–40 times through a 21G syringe , vortexed and centrifuged ( 20000g , 5min ) . The supernatant ( SN2 ) was taken and the pellet was resuspended in 200μl buffer A as above . Whole procedure was repeated one more time to obtain the supernatant SN3 . Then the pellet was resuspended one more time in 200μl buffer A as above and microtubules were disrupted by the addition of 12 μl 5M NaCl ( 283mM final conc . ) , the samples were passed through 21G syringe , incubated on ice for 30 minutes with vortexing every 5 minutes and centrifuged ( 20000g , 10min ) . The supernatant ( SN4 ) was removed up and the pellet was washed once in 200μl of buffer A without resuspension ( 20000g , 10min ) and finally resuspended in 750μl of Trifast FL . Another 5×107 control or heat-shocked procyclic cells were taken to obtain total RNA . 5×107 procyclic cells ( control or heat-shocked for 1 hour at 41°C ) expressing CAT reporter with full-length or fragment #1 ( 1-625nt ) ZC3H11 3'-UTR were washed in PBS , pelleted ( 1400g , 10min ) , resuspended in 1ml PBS , fixed by the addition of 1ml 8% paraformaldehyde in PBS for 10min and pelleted again after the addition of 13ml PBS . The cells were resuspended in 1ml PBS and allowed to settle on a baked superfrost microscopy slide ( within hydrophobic circles ) for 15min . Affymetrix FISH was done with the QuantiGene ViewRNA ISH Cell Assay kit ( Affymetrix ) , according to the manufacturer's instructions , but with protease digestion for 30min at the highest suggested concentration ( 1:500 ) [28] . This treatment increases the number of visualised mRNA molecules , but also causes cell loss and disrupted cell morphology [28] . The CAT ORF Affymetrix probe sets were used in a 1:100 dilution of the original stock and DNA was stained with the DAPI . Z-stack images ( 100 stacks at 100nm distance ) were taken with a custom build TILL Photonics iMic microscope equipped with a sensicam camera ( PCO ) , deconvolved using Huygens Essential software and are , unless otherwise stated , presented as Z-projections ( method sumsliced ) produced by ImageJ software .
|
Like other organisms , the mammalian pathogen Trypanosoma brucei is able to sense environmental changes and to change its gene expression accordingly . In contrast with other organisms , however , trypanosomes and related kinetoplastids effect these changes almost exclusively by controlling the translation of mRNAs into protein , and by adjusting the rate at which the mRNAs are degraded . ZC3H11 is an RNA binding protein , which stabilizes mRNAs that encode chaperones . Chaperones are needed to refold proteins after stress . Under normal growth conditions ZC3H11 protein is very unstable , and in addition , not much of the protein is made . Although ZC3H11 mRNA is present under normal , unstressed conditions , most of it is not translated . However , when the cells were stressed by elevated temperature , arsenite , ethanol , puromycin or proteasome inhibitors the amount of ZC3H11 rose almost 10-fold . This was caused by a combination of increased protein stability and enhanced translation of the mRNA . We found that a 71 nucleotide segment of the 3'-untranslated region of the ZC3H11 mRNA was responsible for the regulated translational blockage . We also obtained evidence that casein kinase 1 isoform 2 might phosphorylate ZC3H11 , and that phosphorylation can promote ZC3H11 protein degradation . Overall , our results show that the increase in the ZC3H11 level after stress occurs because of changes in protein synthesis , phosphorylation , and stability .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"phosphorylation",
"rna-binding",
"proteins",
"cellular",
"stress",
"responses",
"rna",
"interference",
"messenger",
"rna",
"cell",
"processes",
"polyribosomes",
"immunoprecipitation",
"epigenetics",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"heat",
"shock",
"response",
"genetic",
"interference",
"proteins",
"gene",
"expression",
"precipitation",
"techniques",
"ribosomes",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"post-translational",
"modification",
"protein",
"translation",
"genetics",
"biology",
"and",
"life",
"sciences"
] |
2016
|
Regulating a Post-Transcriptional Regulator: Protein Phosphorylation, Degradation and Translational Blockage in Control of the Trypanosome Stress-Response RNA-Binding Protein ZC3H11
|
The fungus Paracoccidioides lutzii was recently included as a new causative species of paracoccidioidomycosis ( PCM ) and most cases have been reported from Brazil . According to available epidemiological information , P . lutzii is concentrated in the Middle-West region in Brazil , mainly in the state of Mato Grosso . However , clinical and laboratorial data available on patients infected with P . lutzii remain extremely limited . This work describes the clinical manifestations of 34 patients suffering from PCM caused by P . lutzii , treated along 5 years ( 2011–2017 ) at a reference service center for systemic mycoses in Mato Grosso , Brazil . Adult rural workers ( men ) , aged between 28 and 67 predominated . All patients had the chronic form of the disease , and the oral mucosa ( n = 19; 55 . 9% ) , lymph nodes ( n = 23; 67 . 7% ) , skin ( n = 16; 47 . 1% ) and lung ( n = 28; 82 . 4% ) were the most affected sites . Alcohol intake ( n = 19; 55 . 9% ) and smoking ( n = 29; 85 . 3% ) were frequent habits among the patients . No patient suffered from any other life-threatening disease , such as tuberculosis , cancer or other inflammatory or infectious parasitic diseases . The positivity in culture examination ( 97 . 1% ) was higher than that found for the direct mycological examination ( 88 . 2% ) . Particularly , one patient presented fungemia at diagnosis , which lead to his death . The time elapsed between the initial symptoms and the initiation of treatment of PCM caused by P . lutzii was 19 . 7 ( 31 . 5 ) months , with most patients diagnosed 7 months after the symptoms’ onset . Compared with the classical clinical-epidemiological profile of PCM caused by P . brasiliensis , the results of this descriptive study did not show significant clinical or epidemiological differences that could be attributed to the species P . lutzii . Future studies may confirm or refute the existence of clinical differences between the two fungal species .
Paracoccidioidomycosis ( PCM ) is the most prevalent deep mycosis in Latin America , being endemic only in Brazil , Colombia and Venezuela . In Brazil , the state of Mato Grosso ( Middle-West region ) , has a large number of cases , and the recently described new species P . lutzii [1] was recovered from the clinical isolates of patients from this geographic location . Paracoccidioides lutzii and P . brasiliensis are thermal dimorphic fungi , which grow at room temperature as mycelia and as yeasts with bipolar or multipolar buds at a temperature of 35 to 37°C ( parasitic form ) . Estimates of annual incidence in Brazil vary from 0 . 71 cases to 3 . 70 cases per 100 thousand inhabitants [2] . According to information from the Ministry of Health , 3 , 181 cases of PCM deaths were recorded in Brazil between 1980 and 1995 , resulting in a PCM mortality rate of 1 . 45 cases per million inhabitants ( 2 . 59 for the Southern region , 2 . 35 for the Central-West region , 1 . 81 for the Southeast , 1 . 08 for the North , and 0 . 20 for the Northeast ) [3] . In Brazil , PCM is the 8th cause of mortality among the parasitic infectious diseases . Even so , it is still included in the group of neglected diseases , and there is no requirement for compulsory notification despite the severity of the disease and the fact that it is considered a public health problem [4] . The incidence of hospital admissions for PCM in Brazil is 7 . 99/1000 inhabitants , surpassing other endemic mycosis such as histoplasmosis and coccidioidomycosis [5] . The state of Mato Grosso is known as the country's granary , being the largest producer of soy , corn , cotton together with cattle breeding . This productivity is achieved due to the intense modernization of farming techniques . For this reason , most of the cases of patients affected by PCM are directly related to the agricultural activities carried out in rural properties of different territorial extensions . On the other hand , agricultural machine operators also constitute a target audience for PCM acquisition . Recently , 65 isolates of P . brasiliensis were analyzed through nuclear and mitochondrial DNA , as well as the morphology of conidia and yeasts; in this study , the authors propose a new classification for the P . brasiliensis complex and the taxonomic recognition of the four genetic groups as P . brasiliensis ( S1 ) , P . americana ( PS2 ) P . restrepiensis ( PS3 ) , P . venezuelensis ( PS4 ) , suggesting that they be considered as distinct species [6] . Humans and the nine-banded armadillo ( Dasypus novemcinctus ) are the accidental hosts of Paracoccidioides spp . and are usually infected in rural and peri-urban environments . Despite the consensus that the fungus’ habitat is the soil , few studies were able to demonstrate the isolation from this micro niche , existing many gaps concerning the knowledge on the still unresolved eco-epidemiology of PCM . Recently , P . brasiliensis and P . lutzii were detected in soil samples from three different locations in Brazil using molecular methods [7]; nevertheless little is known about the pathogenicity , virulence of strains , and more detailed aspects relating to the eco-epidemiology of the new species P . lutzii . In 2018 , Hrycyk et al . [8] confirmed that while armadillos are highly infected by P . brasiliensis , including multiple infections by distinct genotypes or species ( P . brasiliensis and P . americana ) in the same animal , the same does not hold true for P . lutzii , which in turn seems to present less capacity for mycelial growth and conidial production , when developing in a soil-related condition , but this deserves further investigation . Respiratory infection occurs via inhalation of conidia present in nature , which later reach the pulmonary alveoli . Usually , the infection is controlled by the cellular immune response , but scars can remain with latency of yeast cells . Thus , there is usually asymptomatic infection or nonspecific symptoms , or even some individuals showing the progression of infection to disease [4] . When the disease develops , the classical clinical forms are known as acute or subacute ( "juvenile" ) , prevalent in children and young adults , in which there is inadequate Th2 cell type response to control the fungal infection . The chronic form represents 80 to 95% of the cases , affects individuals in the productive age ( after the third decade of life ) , usually affecting the lungs , upper region including lesions in the oral mucosa , nasal mucosa , skin in places adjacent to the mouth and nose , and cervical lymph nodes . The incubation period of the disease is uncertain and may develop after many years after the individual's initial contact with the fungus [2 , 4] . Paracoccidioides brasiliensis is composed of a cluster of molecular siblings recognized as S1 ( S1a and S1b ) , PS2 , PS3 , and PS4 [9 , 10] . The phylogenetic species S1a and S1b are widespread and predominantly found in lower South America , especially in the southeast and South of Brazil , Argentina , and Paraguay [10] . PS2 has a sporadic distribution and has been less frequently reported , with human cases only being reported thus far in Venezuela and the southeast of Brazil . The PS3 and PS4 species are , to date , exclusively endemic to Colombia and Venezuela , respectively [11] . Phylogenetic analyses demonstrated that P . lutzii represents a highly divergent lineage monophyletically separated from P . brasiliensis . Paracoccidioides lutzii is often found in the Middle-West region [1] and North [12] of Brazil , and most of the genetically evaluated clinical isolates were from the state of Mato Grosso . Regarding morphology , conidia of P . lutzii are elongated ( 2–22 μm ) , while that of P . brasiliensis measure from 2 up to 5 μm [13 , 14] . To date , the main difference related to P . brasiliensis and P . lutzii lies in the serological diagnosis , where there is a need to employ local antigenic preparations in serological techniques such as ELISA , immunodiffusion and latex [15 , 16] . The taxonomic description of a new species has raised the curiosity of physicians due to the possible clinical implications . Furthermore , characteristics of the in vivo susceptibility of P . lutzii to drugs conventionally used in the history of PCM also raise the interest of the professionals that manage patients affected by PCM . The objective of this work was to describe the first results concerning the epidemiological and clinical characteristics of patients affected by P . lutzii from the Middle-West ( Mato Grosso ) of Brazil and reflect on possible similarities or differences between these characteristics and the classical profile of the disease caused by P . brasiliensis .
This study was submitted to and approved ( CAAE: 17177613 . 6 . 0000 . 5541 ) by the Federal University of Mato Grosso ( UFMT ) and protocol number 1796–10 by the Federal University of São Paulo ( UNIFESP ) . All adult subjects provided informed written consent and the study was approved by ethical committee under number 288 . 250/CEP/HUJM/UFMT . A descriptive study was carried out on 34 confirmed PCM cases caused by P . lutzii ( Fig 1 ) , that is , those with compatible clinical manifestations and positive fungal culture for Paracoccidioides spp . from different clinical materials and which were later confirmed by genotyping as P . lutzii . The patients in the study were enrolled at a reference service center of systemic mycoses of the Júlio Muller University Hospital–Federal University of Mato Grosso ( UFMT / HUJM ) , Cuiabá , Mato Grosso—Central-West region of Brazil . The Paracoccidioides spp . isolates were obtained from various clinical material ( sputum , cervical lymph aspiration , blood , oral mucosa scraping , scraping of the larynx , scraping of the nasal mucosa , fragment of skin biopsy ) . The clinical materials were cultivated in Sabouraud Dextrose Agar ( DIFCO ) and incubated at a temperature of 35º C in a BOD incubator ( Eletrolab ) for a period of up to 20 days . Colonies with cerebriform appearance and creamy color , typical of the yeast forms , were isolated with subsequent confirmation of micromorphological characteristics of Paracoccidioides spp . Isolates morphologically identified as Paracoccidioides spp . were subjected to molecular characterization using either HSP70 amplification [1] or via TUB1-RFLP [17] . DNA was extracted and purified from fungal colonies with the Fast DNA kit protocol ( MP Biomedicals ) . The primer pair HSPMMT1 ( 5’-AAC CAA CCC CCT CTG TCT TG-3’ ) and PLMMT1 ( 5’-GAA ATG GGT GGC AGT ATG GG-3’ ) targeting an exclusive indel region of P . lutzii were used for PCR [1] . Isolates Pb01 and B339 were used as controls of P . lutzii ( positive ) and P . brasiliensis ( negative ) respectively . In addition , for TUB1-RFLP , the protocol described by Roberto et al . [17] was used . TUB1 fragments were amplified using the primer pair α-TubF ( 5′-CTG GGA GGT ATG ATA ACA CTG C-3′ ) and α-TubR ( 5′-CGT CGG GCT ATT CAG ATT TAA G-3′ ) [18] following a double digestion with BclI and MspI restriction endonucleases . The reaction contained 13 μL H2O , 3 μL TUB1-PCR product , 2 μL 10× fast digest buffer , and 1 μL each of the BclI ( 10 U/μL; Thermo Scientific ) and MspI ( 10 U/μL; Thermo Scientific ) restriction endonucleases . The digestion mixture was incubated at 37°C for 2 hours . The digested products were electrophoresed on 2 . 5% ( w/v ) agarose gels for 120 min at 100V in the presence of GelRedTM ( Biotium , USA ) . We included a lane loaded with 50bp DNA Step Ladder ( Promega , USA ) . Molecular identification was performed at the Medical and Molecular Mycology Laboratory ( UNIFESP/EPM ) . The bands generated by PCR or TUB1-RFLP were visualized using the L-Pix Touch ( Loccus Biotecnologia , São Paulo , Brazil ) imaging system under UV illumination . Epidemiological and clinical data were collected from medical records of P . lutzii PCM treated patients between 2011 and 2017 . The categorical variables were summarized by percentages and 95% confidence interval , and the numeric variables by mean and standard deviations . All analyses were performed by Stata Statistical Software version 12 . 0 ( College Station , Texas , USA ) .
Altogether 34 patients with confirmed diagnosis of PCM were evaluated , 33 men ( n = 33; 97 . 1% ) and only one woman ( 2 . 9% ) , with a mean ( SD ) age of 46 . 7 ( 9 . 3 ) years of age . Most of the patients ( 75 . 7% ) resided in the north and central regions of the state of Mato Grosso ( Fig 1 ) ; 73 . 5% in rural areas and 26 . 5% in urban areas . The occupations of farmer ( 53 . 6% ) and rural truck driver ( 32 . 1% ) were the most frequent . Smoking ( 85 . 3% ) and alcohol intake ( 55 . 9% ) were very frequent among patients ( Table 1 ) . None of them suffered from other life-threatening diseases . The species P . lutzii was identified by TUB1-RFLP in all 34 patients described , 30 ( 88 . 2% ) being new cases and 4 ( 11 . 8% ) relapsed cases of the disease . The PCM in this series of cases was multifocal in 88 . 2% ( n = 30 ) and unifocal in 11 . 8% ( n = 4 ) . All patients had the chronic clinical form of the disease , with pulmonary involvement in 82 . 4% , lymph nodes ( Fig 2A and 2B ) in 67 . 7% , oral ( Fig 2C ) in 55 . 9% , cutaneous in 47 . 1% , laryngeal in 32 . 8% , nasal in 11 . 8% , bone ( Fig 2D ) in 11 . 8% and 2 . 9% in adrenal glands . One of these patients had symptoms of fungemia by P . lutzii . No patient presented central nervous system or genital involvement . The average time ( SD ) elapsed between the initial symptoms and the initiation of treatment of PCM by P . lutzii was 19 . 7 ( 31 . 5 ) months , with most patients diagnosed 7 months after the symptoms’ onset ( Table 2 ) . The diagnosis of PCM was initially confirmed by culture in 97 . 1% ( n = 33 ) of cases , direct mycological examination ( DME ) in 88 . 2% ( n = 30 ) , histopathological examination in 35 . 3% ( n = 12 ) . Clinical specimens used for the mycological exams were ganglionic secretion ( n = 14 ) , scraped oral mucosa lesion ( n = 13 ) , sputum samples ( n = 3 ) , skin biopsy ( n = 3 ) and blood ( n = 1 ) ( Table 1 ) . The decision on the treatment of patients was based on the II Brazilian Consensus of Paracoccidioidomycosis [4] , using sulfamethoxazole + trimethoprim in 88 . 2% of the patients . Out of these , 7 ( 23 . 3% ) also used itraconazole and another 2 ( 6 . 9% ) amphotericin B deoxycholate . The initial hematological and biochemical evaluation of the patients did not present any relevant changes ( Table 2 ) .
In the present study on 34 patients with confirmed infection by P . lutzii , there were no clinical or epidemiological differences that could be attributed to the P . lutzii species . The epidemiological profile of PCM has been revealing remarkable changes in frequency , demographic characteristics and geographical distribution . More than a decade ago studies published by our research group showed differences between isolates of Paracoccidioides spp . Initial investigations were conducted looking for correlations between clinical forms of the disease , geographical origin of same , susceptibility to antifungal drugs and epidemiological findings [19 , 20] . In 2009 , Batista et al . [21] , showed significant differences in serological test results using double radial immunodiffusion for diagnosis of PCM when sera from patients from the Middle-West and Southeast regions of Brazil were evaluated . The exoantigens obtained from isolates from patients from these geographical regions affected by PCM presented strong evidence of antigenic variation among the isolates [15 , 22] . It was also observed through the RAPD technique that clinical isolates from different anatomical sites ( arm and face ) of a same patient presented genetic differences [23] . All evidence collected related to possible antigenic differences whenever exoantigens from different geographic locations [21] were used by different researchers who obtained results from the use of different molecular techniques seeking correlation with virulence of isolates [24] and clinical forms of the disease [20] , was important for the proposal of a new species: P . lutzii [1] . However , the vast literature related to clinical , demographic and epidemiological aspects of P . brasiliensis as a single etiologic agent of the disease until 2009 , highlights classical presentations fairly known by medical professionals . For the physician , it is important to assess the epidemiological , clinical , diagnostic and therapeutic impact on the disease of different species of Paracoccidioides , i . e . , whether there are indeed differences regarding clinical manifestations between the two species: P . lutzii and P . brasiliensis , possibly attributed to the antigenic differences of clinical isolates [15] , or even to the virulence of the strains [24 , 25] . Taking into account the acute/subacute forms according to Ferreira [26] , a multisystemic involvement of the disease is observed; the presence of lymphadenomegaly , cutaneous lesions , hepatosplenomegaly or abdominal masses . Jaundice , ascites , and peripheral edema may also be present . The latter justify the investigation of hypoalbuminemia . Signs of adrenal involvement , as well as neurological involvement , are rare in this clinical form . Digestive complaints , such as abdominal pain , chronic malabsorptive diarrhea and vomiting , are also quite frequent . Fever and weight loss complete the clinical picture , presence of growth or pain in the bone region requires the identification of bone lesions . According to Mendes [27] and Valle et al . [28] , the chronic form is assessed through signs and symptoms related to the pulmonary , tegumentary and laryngeal involvement ( cough , dyspnea , mucopurulent expectoration , ulcerated skin lesions and nasopharyngeal mucosa , odynophagia , dysphagia and dysphonia ) ; lymphatic ( adenomegaly ) ; adrenal [29 , 30] ( asthenia , weight loss , hypotension , darkening of skin , abdominal pain ) . Relating to the central nervous system , according to Pereira et al . [31] and Almeida et al . [32] the following may be observed: headache , motor deficit , convulsive syndrome , changes in behavior and/or level of consciousness . Regarding the digestive impairment , diarrhea and sometimes malabsorption syndrome are reported [33] . In this study , all patients evaluated presented the chronic form of the disease , where pathognomonic signs and symptoms of this form were recognized , mainly showing pulmonary , lymphatic , oral and cutaneous impairment . There was no clinical evidence in this sample of patients evaluated ( n = 34 ) that could be highlighted , considering the etiology of PCM caused by P . lutzii . One case of fungemia was observed [34] , but it is not possible to infer that P . lutzii is more virulent than P . brasiliensis because of this finding . Moreover , out of the 34 cases evaluated with etiology of PCM by P . lutzii , only two were classified as severe chronic form , the majority ( n = 32 ) being classified as moderate clinical form . Considering the proposed species ( P . brasiliensis S1a , S1b , PS2 , PS3 , PS4 and P . lutzii ) , Macedo et al . [35] described an autochthonous clinical case in the southeast of Brazil ( Rio de Janeiro ) , classified as P . brasiliensis PS2 . These authors reported that few cases with this molecular taxonomy have been recorded in the literature when compared with S1 and PS3 , and that among the cases registered pointing PS2 as the etiologic agent a higher frequency of the chronic form of the disease was observed . This finding was also observed in 34 patients affected by PCM caused by P . lutzii assessed in this study . Associated habits ( smoking and drinking ) were also frequent , as well as the frequency in male individuals in the productive age . These characteristics coincide with those described in the literature for classical PCM caused by P . brasiliensis ( smoking ( >20 cigarettes/day for >20 years ) and alcohol intake ( >50g/day ) . They are also often associated with the mycosis ) [36] . Regarding the duration of symptoms in months for patients affected by P . lutzii in this series of cases , two groups were the most frequent: 13 patients allocated in the range of 1 to 6 months , and 12 patients ranged higher than 12 months , corroborating the classical data already published for P . brasiliensis . In terms of distribution by regions in the state of Mato Grosso , the North ( n = 14 ) and Central South ( n = 11 ) regions were responsible for the largest number of cases . The concentration of the highest number of cases in the Northern region can be explained by environmental factors due to the opening of new agricultural frontiers with forest felling , especially in the Amazon—Mato Grosso region [37] . In addition , the occurrence of different species of Paracoccidioides may also be contributing to the change in the epidemiological pattern [37] . The suspected diagnosis of PCM occurs through clinical and epidemiological data , but the confirmation is done primarily by the identification of the etiologic agent in fresh tissue examinations , cultures and histopathologic preparations , which are considered the gold standard in the definition of the disease , being known as direct techniques in the diagnosis of PCM . Indirect techniques are represented by the presence of antibodies and circulating antigens in the serum of patients with PCM . A very interesting result was found in this study , with higher positivity for the culture identification ( 97 . 1% ) when compared with that found by direct mycological examination ( 88 . 2% ) . Generally speaking , it is not possible , so far , to establish important clinical differences that can be attributed to P . lutzii or P . brasiliensis complex . In 2017 , our research group evaluated , in another study , a total of 554 patients who were treated at the same hospital during the study period ( 1998 to 2014 ) , 527 had confirmed PCM diagnosis . Out of 527 patients , 244 ( 46 . 3% ) patients ( mean age , 48 . 4 [10 . 9] years; range , 14–83 years ) , classified as the chronic form of PCM . All patients were living in rural areas , and most performed activities related to agriculture [38] . These data show that the acute form of PCM is less frequent in the state of Mato Grosso , central region of Brazil , a geographical region where a higher frequency of P . lutzii has been observed so far . This is the first study that presents a series of cases of P . lutzii , identified by molecular methods and correlating them with the clinical and epidemiological profile of affected patients . The actual incidence of each phylogenetic species and its involvement in clinical practice should include other studies in different regions of Brazil and Latin America to compare the forms of PCM and clinical manifestations with the genetic profile of these entities . Only a few studies are found to date in the literature offering the molecular identification of clinical isolates and their association with clinical characteristics of patients affected by PCM . For comparison purposes , considering clinical findings and molecular characterization , Macedo et al . [39] recently carried out phylogenetic analysis of 54 Paracoccidioides spp . clinical strains from Rio de Janeiro , Brazil where P . brasiliensis ( n = 48 ) and P . americana ( n = 6 ) were identified as the causative agents of PCM . Considering the clinical classification , the authors reported that 41 strains were identified as P . brasiliensis , 23 corresponded to the chronic form , and 16 were acute . In Mato Grosso , all 34 clinical cases infected by P . lutzii corresponded to the chronic form of PCM . In relation to the affected organs , for both P . lutzii ( Table 2 ) and P . brasiliensis [39] , lungs and lymph nodes were the most affected . Regarding the severity of the disease , 7 were classified as mild , 18 ( moderate ) , and 16 ( severe ) in the case of P . brasiliensis [39] , in contrast to 32 cases ( moderate form ) PCM caused by P . lutzii , and only 2 cases were classified as severe . The number of clinical cases evaluated by Macedo et al [39] concerning P . americana is very small . Thus , it is difficult to make any inference or comparison considering clinical manifestations . This has proven to be a limitation for the study [39] . Based on the clinical findings regarding P . lutzii and P . brasiliensis complex , there is no evidence that allows us to point out significant clinical differences between species . For this reason , we believe that studies with a greater number of isolates should be conducted to confirm or refute the hypothesis that there are clinical differences related to the different species . However , the genetic susceptibility of the host should always be an important parameter to be considered , as well as the virulence of the strain , regardless of the species that causes PCM .
|
Paracoccidioidomycosis ( PCM ) is an endemic mycosis in Latin America with high incidence in Brazil . The fungi Paracoccidioides brasiliensis ( including genetic groups S1 , PS2 , PS3 and PS4 ) and Paracoccidioides lutzii are the etiological agents , but little is known about the clinical manifestations of PCM caused by P . lutzii . Regarding eco-epidemiological aspects , the habitat is believed to be the soil due to the predominance of the disease among rural workers and other individuals who work in contact with the land . Paracoccidioides spp . has been isolated from aerosol samples , armadillos and dog food , but more data are needed to better understand the ecology of this fungus . The Middle-West region of Brazil presents the highest number of cases of P . lutzii infection . It is important to note that this species presents particularities regarding the serological diagnosis in patients . Thus , this study aims to verify possible clinical-epidemiological differences in 34 patients from this geographical region . Our results do not point out significant clinical or epidemiological differences between the two species causing PCM . In Brazil , the Ministry of Health has made an effort to include this disease in the list of compulsory notification diseases in order to implement a health policy aimed at an early detection , diagnosis and treatment .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"respiratory",
"infections",
"pathogens",
"tropical",
"diseases",
"microbiology",
"geographical",
"locations",
"pulmonology",
"fungi",
"lymph",
"nodes",
"signs",
"and",
"symptoms",
"lymphatic",
"system",
"paracoccidioidomycosis",
"paracoccidioides",
"brasiliensis",
"neglected",
"tropical",
"diseases",
"fungal",
"diseases",
"fungal",
"pathogens",
"paracoccidioides",
"infectious",
"diseases",
"mycology",
"south",
"america",
"medical",
"microbiology",
"microbial",
"pathogens",
"lesions",
"brazil",
"people",
"and",
"places",
"eukaryota",
"diagnostic",
"medicine",
"anatomy",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2019
|
Clinical and epidemiological features of paracoccidioidomycosis due to Paracoccidioides lutzii
|
Cellular receptors can act as molecular switches , regulating the sensitivity of microbial proteins to conformational changes that promote cellular entry . The activities of these receptor-based switches are only partially understood . In this paper , we sought to understand the mechanism that underlies the activity of the ANTXR2 anthrax toxin receptor-based switch that binds to domains 2 and 4 of the protective antigen ( PA ) toxin subunit . Receptor-binding restricts structural changes within the heptameric PA prepore that are required for pore conversion to an acidic endosomal compartment . The transfer cross-saturation ( TCS ) NMR approach was used to monitor changes in the heptameric PA-receptor contacts at different steps during prepore-to-pore conversion . These studies demonstrated that receptor contact with PA domain 2 is weakened prior to pore conversion , defining a novel intermediate in this pathway . Importantly , ANTXR2 remained bound to PA domain 4 following pore conversion , suggesting that the bound receptor might influence the structure and/or function of the newly formed pore . These studies provide new insights into the function of a receptor-based molecular switch that controls anthrax toxin entry into cells .
Cellular receptors can act as molecular switches that initiate conformational changes in microbial proteins required for cellular entry . Examples of such switches include an anthrax toxin receptor ( described in detail below ) as well as those for a number of viruses including HIV-1 and other retroviruses [1] , [2] , [3] , measles virus [4] , and herpesviruses [5] . The mechanisms by which these receptor-based switches function to promote cellular entry are only partially understood . In this report we set out to define the mechanism by which a receptor-based switch regulates anthrax toxin prepore-to-pore conversion . Anthrax toxin , the key virulence factor secreted by Bacillus anthracis , is a bacterial AB toxin composed of three independent , plasmid-encoded polypeptide chains: the receptor-binding ( B ) moiety , protective antigen ( PA ) , and two different enzymatic ( A ) moieties , lethal factor ( LF ) and edema factor ( EF ) [6] , [7] , [8] . The first step in cellular intoxication involves binding of an 83 kD form of PA ( PA83 ) to specific cell surface receptors . Although several PA receptors have been defined [9] , [10] , [11] , anthrax toxin receptor type 2 ( ANTXR2 ) ( also known as capillary morphogenesis protein 2; CMG2 ) , is the most physiologically relevant receptor [12] , [13] , [14] . ANTXR2 is a type 1 transmembrane protein and its extracellular von Willebrand factor type A ( VWA ) domain is the site of PA-binding [15] , [16] . Following receptor binding , PA83 is cleaved to a 63kD form ( PA63 ) that spontaneously oligomerizes to form either a heptameric , or an octameric , PA63 prepore structure [17] , [18] . Oligomeric PA63-receptor complexes are then taken into cells primarily by a clathrin-dependent endocytic mechanism and delivered to an acidic endosomal compartment where low pH triggers formation of a PA63 pore within an endosomal membrane [19] , [20] . LF and EF are then translocated through the pore and delivered to the cytosol where they promote intoxication [21] . X-ray structural analysis of monomeric and heptameric PA-ANTXR2 VWA-domain complexes revealed that the receptor acts as a molecular switch or clamp that inhibits prepore-to-pore conversion at neutral pH [15] , [16] . Specifically , the receptor VWA-domain interacts with the base regions of PA domains 2 and 4 , thereby sterically hindering the movement of the PA 2β3-2β4 loop region necessary for pore formation [15] , [16] . Those findings led to a model in which release of the receptor contact with PA domain 2 at an acidic endosomal pH is necessary to permit the conformational changes required for PA pore formation [15] , [16] . Consistent with this idea , the pH threshold of the receptor-regulated toxin pore formation can be dictated by specific amino acid residues located at the PA domain 2-binding region of the ANTXR2 VWA-domain [22] . Presently , it is not clear if PA domain 2-receptor contacts are released at a step that occurs prior to , or is coincident with , prepore-to-pore conversion . Furthermore , it is not clear if the receptor remains attached following pore conversion and , if so , how it remains attached . Evidence supporting dissociation has come from co-immunoprecipitation experiments [23] and from previous NMR studies [24] , [25] . On the other hand , evidence in favor of receptor attachment has come from other co-immunoprecipitation studies [19] , [26] , from NMR binding studies performed with a fragment ( Domain 4 ) of PA [27] , and from the finding that the presence of a receptor seems to influence voltage-dependent inactivation and small molecule inhibition properties of the newly formed pore [28] . Based upon structural considerations , it has also been argued that the receptor may remain bound to serve as a structural support for the pore [16] , [29] . To clarify these issues , we have employed NMR techniques to monitor changes in the PA63 heptamer-ANTXR2 VWA domain contacts as a function of pH . Initially we attempted to examine the interaction between the ANTXR2 VWA domain and PA63 using chemical shift perturbation ( CSP ) by titrating in substoichiometric amounts of unlabeled PA63 into a 1H-15N labeled ANTXR2 sample . Based on results with other systems [30] , we anticipated that titrating in PA63 might allow us to monitor chemical shift changes as a function of receptor binding and/or cause selective broadening of specific peaks associated with residues at the PA binding interface . If so , this would allow us to monitor specific receptor residues bound to PA63 under different pH conditions . Additionally , if shift perturbation of crosspeaks were detected using saturating conditions of the binding partner ( PA63 ) , this would help to approximate the fractional population of bound species versus the free species at equilibrium [30] . However , when low stoichiometric concentrations of PA63 were titrated into the ANTXR2-VWA domain sample , extensive line broadening and the disappearance of cross-peaks in the 1H-15N TROSY-HSQC was observed at a ratio of 1∶0 . 25 ANTXR2-VWA domain to PA63 . This was likely due to the large size of the PA complex , indicating a larger effective correlation time ( τc ) , restricted local motion , and complete binding at the concentrations used . Therefore , we hypothesized that the method of transferred cross-saturation ( TCS ) may be well suited to investigate these interactions since this approach has previously been used to identify contact residues of protein ligands in large protein complexes [31] . To investigate the function of the ANTXR2-based switch , TCS was employed to monitor changes that occur in PA63 heptamer-ANTXR2 VWA-domain contacts as a function of pH . In this approach , an unlabeled protein is added at substoichiometric amounts to a deuterated , 15N-labeled protein , in this case , PA63 and the ANTXR2 VWA-domain , respectively . The aliphatic proton resonances of the unlabeled protein are then saturated with a brief radiofrequency pulse and this saturation is transferred selectively to contact residues of the 2H , 15N-labeled protein by spin diffusion . Consequently , the intensity of amide cross-peaks representing labeled residues that lie at the protein-protein interaction surface are selectively reduced by cross-relaxation [32] , [33] . Here we have used this technique to obtain evidence for a new toxin-receptor intermediate in the pathway leading to pore formation and show that the receptor remains attached to PA domain 4 following low pH-dependent conversion . Additionally , chemical shift perturbations associated with receptor residues located near the PA domain 4 binding region revealed moderate conformational changes that occur during the attachment and detachment of PA from the receptor .
The deuterated , 15N-labeled ANTXR2 VWA-domain was produced as a GST-fusion protein from bacterial cells . In order to limit spin diffusion in the 15N-labeled protein , it was extensively deuterated by growing the cells in 100% D2O minimal media using 2H-glucose as the sole carbon source [32] . The labeled VWA-domain was purified to homogeneity as described under Materials and Methods and was unfolded to protonate the residues within the protein core and refolded to increase the number of cross-peaks in the [15N , 1H] TROSY-HSQC spectrum . The integrity of the refolded protein was confirmed by circular dichroism ( CD ) analysis performed at either pH 8 . 0 , 6 . 0 , or 5 . 0 and in each case the protein displayed alpha-helical properties ( Supplementary Figure S1 ) . The refolded protein also functioned as an efficient receptor decoy in a toxin neutralization assay ( Supplementary Figure S2 ) . Moreover , Transverse Relaxation Optimized Spectroscopy-Heteronuclear Single-Quantum Coherence ( TROSY-HSQC ) spectrum analysis indicated that the protein was correctly refolded when compared to a control [15N , 1H] TROSY-HSQC spectrum of the ANTXR2 VWA domain that had not been previously denatured ( Figure 1A ) . Assignments for the backbone resonances of the ANTXR2 VWA-domain were obtained using data from the following experiments: [1H-15N] TROSY-HSQC , 3D TROSY-HNCO , 3D TROSY-HN ( CA ) CO , 3D TROSY-HNCACB , 3D TROSY-HNCA , and a 3D 15N-edited NOESY-HSQC . NMR data were processed using NMRPipe and analyzed using Sparky and CARA software packages [34] , [35] , [36] . A representative example of this data analysis is shown in Supplementary Figure S3 . Using this approach 87% of the backbone residues of the ANTXR2 VWA-domain , including the PA contact residues , were assigned ( Figure 1A and B ) . The principle of the TCS approach used to monitor PA63-ANTXR2 VWA-domain interactions is outlined in Figure 2A . In order to observe saturation transfer , the concentration of the binding partner must be kept sufficiently low to effectively allow for fast exchange so that amide cross-peaks are not broadened following its addition . The efficiency of TCS depends on the sample conditions as well as the binding constants between the receptor and PA . According to Shimada et al , TCS is applicable for a system where a large pB , or fraction of bound ligands is preferred for high saturation efficiency , if koff >0 . 1 s−1 , or if koff ≥10 s−1 , a pB ≥0 . 1 is preferred [37] . Therefore , for the TCS experiments , the concentrations of the two protein partners were optimized by performing titration experiments at pH 8 . 0 , and a ( 10∶1 ) molar ratio of the ANTXR2 VWA-domain to PA63 was chosen for all cross saturation experiments , because at this concentration there were no signs of peak broadening . Three separate sets of interleaved experiments were subsequently performed on the ANTXR2 VWA: ( PA63 ) 7 complex in buffers of pH 8 . 0 , 6 . 0 , and 5 . 0 . Saturation transfer was achieved by applying a selective radiofrequency pulse at 0 . 8 ppm , prior to the [15N , 1H] TROSY-HSQC . A pH of 8 . 0 was chosen for the initial analysis because it closely approximated those used previously for X-ray structural analysis of PA-receptor complexes . i . e . pH 7 . 5 [16] and pH 8 . 25 , [15] . These studies revealed that the majority of the labeled residues in the ANTXR2 VWA-domain were not saturated by a radiofrequency pulse , i . e . those with similar signal intensities under conditions of no saturation ( black peaks ) or saturation ( red peaks ) ( Figure 2B ) . However , a subset of the amide cross-peaks were saturated ( black-only peaks ) in the overlayed spectra ( Figure 2B ) and a number of those cross-peaks corresponded to contact residues with PA domains 2 or 4 . For simplicity the saturation data was represented as 1D cross sections of the corresponding cross-peaks in the HSQC spectra ( Figure 3A ) . The degree of saturation of each residue was calculated by dividing the observed peak intensity of the saturated spectrum ( Is ) by the observed peak intensity of the control spectrum ( Io ) ( unsaturated ) . In these studies an ( Is/Io ) value of <0 . 75 is considered significant and one of <0 . 5 highly significant , as in [37] . Based upon these criteria all of the PA domain 2 and 4 contact residues that could be unambiguously assigned were saturated under these conditions ( Figure 3B ) . Taken together , this study verified that the TCS method can be used to specifically monitor contacts between the ANTXR2 VWA-domain and PA domains 2 and 4 in the heptameric toxin-receptor complex . To characterize the changes in PA-receptor contacts that occur after incubating the complex under mildly acidic conditions , the TCS experiment was repeated at pH 6 . 0 . That condition is approximately 1 . 0 pH unit above that needed to trigger toxin prepore to pore conversion when the PA heptamer is bound to the ANTXR2 VWA-domain [23] , [28] ( Supplementary Figure S4 ) . However , not all of the PA-contact residues that were observed at pH 8 were visible in the saturated and unsaturated spectra obtained at pH 6 . This finding is probably due to a structural change upon loss of contact with the PA yielding to an increased H/D exchange or/and slow conformational exchange dynamics . Analysis of the data clearly showed that the PA domain 4 contact residues that were resolved remained strongly saturated at pH 6 ( residues G53 , S54 , N57 , V115 , E117 , T118 , H121 , E122 , and G123; ( Figure 4A and 4B ) . By striking contrast , PA domain 2 contact residues were much less saturated at pH 6 . 0 ( Figure 4B ) . Taken together , these data are consistent with a model in which the receptor remains bound to PA domain 4 but its interactions with PA domain 2 are significantly weakened or are lost prior to prepore-to-pore conversion . When bound to ANTXR2 , the PA63 prepore is triggered to form a pore species at pH values that are less than or equal to pH 5 . 2 [23] , [28] ( Supplementary Figure S4 ) . Therefore , to determine if the receptor remains attached to PA following pore formation , the TCS experiment was performed under both saturating and non-saturating conditions at pH 5 . 1 . Consistent with the results obtained at pH 6 . 0 , the receptor residues that contact PA domain 2 were not saturated at pH 5 . 1 , with the possible exception of residue A159 ( Figure 5A and 5B ) . More importantly however , virtually all of the domain 4 contact residues that could be resolved at this pH value were saturated at pH 5 . 1 ( Figure 5B ) . These data are consistent with a model in which the receptor contacts with PA domain 2 are lost during anthrax toxin prepore to pore conversion but the receptor remains bound to PA domain 4 . The [1H , 15N] TROSY-HSQC data also revealed chemical shift perturbations of certain receptor residues , including PA domain 4 contact-residues that were associated with ( PA63 ) 7 binding at pH 8 . 0 ( Figure 6A ) . Small shift changes due to the isotope effect of being a highly deuterated protein were also taken into consideration as well as the pH effects [38] . Specifically , peaks associated with residues in and around helix 1 of the ANTXR2 VWA domain , that were involved in binding PA domain 4 , significantly changed their position in the presence of PA63 . These residues include G53 , W59 , and N57 ( Figure 6B ) . A similar observation was made with residues Y46 and F47 , which lie within the hydrophobic core of the ANTXR2 VWA domain , as well as with G135 , which lies on the opposite face of the receptor VWA domain ( Figure 6B ) . These latter effects are likely due to an allosteric or structural change in the receptor domain following PA binding . Strikingly , the peaks associated with all of these residues reverted back to their “unbound” configuration when the ANTXR2 VWA/PA63 complex was incubated at pH 5 . 1 ( Figure 6A ) , even though the receptor remains bound to PA domain 4 under this condition . The only exception was residue Y46 which was not resolved at pH 5 . 1 but moved back towards its “unbound” configuration at pH 6 , ( Figure 6A ) .
In this study we have used the TCS NMR approach to monitor how the ANTXR2-based receptor switch regulates anthrax toxin prepore-to-pore conversion . We showed that this is a robust method for identifying the receptor contacts with PA domains 2 and 4 , in the prepore configuration at pH 8 . 0 . We also obtained evidence at pH 6 . 0 for a new toxin-receptor intermediate in the pathway leading to pore formation , one in which the receptor remains bound to PA domain 4 but contacts with PA domain 2 have been significantly weakened . That intermediate would presumably exist within a mildly acidic early endosomal compartment during endocytic trafficking of toxin-receptor complexes [39] . Furthermore , we demonstrated that the ANTXR2 VWA-domain remains attached to PA domain 4 after triggering PA pore formation at pH 5 . 1 , consistent with a more strongly acidic late endosomal pH [39] . Subtle structural changes , associated with reversion back to an unbound configuration , were also detected in residues located near the PA domain 4-binding site following pore conversion . This effect was also seen in the opposite face of the protein with residue G135 , and with two hydrophobic residues within the core , Y46 and F47 . It is known that chemical shifts of those nuclei that lie within close proximity of the binding partner can be substantially perturbed in the presence of that partner . However chemical shift perturbations ( CSP ) can also arise from allosteric effects as well as extended conformational changes that may occur in the target protein upon protein partner binding [40] , [41] . These latter effects most likely account for the chemical shift perturbations seen with residues Y46 , F47 , and G135 , which lie distal from the PA63 binding site of the receptor ( Figure 6B ) . Taken together , these studies have led to a revised model of the changes in toxin-receptor contacts during pore formation ( Figure 7 ) and support the idea that the bound receptor may influence the structural and/or functional properties of the toxin pore . It is unlikely that the TCS effects that were observed could be attributed to non-specific aggregation of the PA63 heptamer-receptor complex at the different pH values tested since these effects were almost exclusively restricted to the toxin-binding face of the receptor . Indeed , inspection of 1D slices of the TCS experiments for selected crosspeaks did not indicate a broadening of lineshapes at several residues at pH 8 or pH 5 , as would have be expected if there was aggregation ( data not shown ) . Additionally , to further assess protein aggregation at pH 8 and pH 5 , a wavelength scan ( from 280–360 nm ) was performed on the ANTXR2 VWA domain in complex with PA63 at the same 10∶1 ratio used for the TCS NMR experiments , since protein aggregation can be monitored at 340 nm [42] , [43] . These studies revealed no substantial increase of absorbance at 340 nm between pH 8 and pH 5 ( Supplementary Table S1 ) , and visual inspection of the sample yielded no noticeable turbidity in the supernatant , under any of the conditions tested . Furthermore , there was no substantial difference in the absorbance at 280 nm seen with either the pH 8 . 0 or pH 5 . 0 samples before or after a 24 hour incubation at 37°C ( Supplementary Table S2 ) . Therefore , we concluded that the TCS NMR studies were not compromised by any non-specific aggregation of the PA63 heptamer-receptor complex at pH values ranging from pH 5–8 . Previous NMR studies had been interpreted as being consistent with receptor release from the newly formed toxin pore . In contrast to the current report , these studies included the detergent , octyl-glucoside and a higher salt concentration in the samples , and a lower temperature was used for the NMR experiments ( 293K versus 310K ) . In one of these studies , strongest methyl resonance of carbon-13 ( SMRC ) NMR analysis was employed , which analyzes the first dimension of a 1H- 13C heteronuclear single quantum coherence experiment ( HSQC ) . That approach indicated that contacts between the PA pore and the ANTXR2 VWA-domain were lost at acidic pH [25] . Similar experiments were performed with a 13C-labeled 2 fluorohistidine labeled ANTXR2 VWA domain [25] . In both studies , a single peak was monitored upon PA binding , the 1H methyl resonance of 13C labeled ANTXR2 VWA domain , and was focused on the 1H methyl resonance ( 0 . 8 ppm ) in the presence or absence of PA63 . In the absence of PA63 , a strong signal for the ANTXR2 VWA domain was seen as a sharp peak at 0 . 8 ppm , but in the presence of the toxin subunit , the peak signal was diminished because of line broadening , and there was substantial loss of peak height due to PA63 binding . These studies were conducted at both pH 8 . 0 and 5 . 0 . Although the peak heights were increased at the lower pH value indicating PA63 dissociation , they did not return to the levels seen in the unbound state [25] . Therefore , we suggest that receptor dissociation might not have been complete when these studies were performed at pH 5 . 0 . Consistent with the findings reporting the present report , another NMR study performed by the same group demonstrated that a recombinant fragment of PA ( PA domain 4 ) remained bound to the ANTXR2 VWA-domain at pH 5 . 0 [27] , although in that case it was not possible to relate these effects to the process of prepore-to-pore conversion . Previous co-immunoprecipitation experiments led to conflicting conclusions about whether the receptor remains attached to the newly formed anthrax toxin pore complex . In one study , a PA-antiserum , that did not disrupt the PA63 prepore-receptor interaction , was used to demonstrate that both ANTXR1 and ANTXR2 co-precipitated with the PA63 prepore , but not with the PA63 pore [23] . In another study , an antiserum that recognized an epitope tag engineered into the cytoplasmic tail domains of both receptors co-precipitated both PA63 prepore and pore complexes [19] , [26] . In light of the current report , it seems most likely that the latter study is correct and that these discrepant findings are probably due to the PA antiserum disrupting the weakened PA63-receptor interaction following pore formation . That effect would not be seen with antibodies binding to the cytoplasmic tails of the receptors . Therefore , these previous results obtained by co-immunoprecipitation of membrane-associated proteins are consistent with the conclusions of this report , i . e . both lines of evidence support receptor-association following prepore-to-pore conversion . The bound receptor might influence the structural and/or functional integrity of the PA pore complex . The structure of the PA pore resembles the mushroom shaped structure of S . aureus α-hemolysin [44] , [45] . However , the dimensions of these structures are drastically different . Crystallographic studies of the α-hemolysin pore revealed a mushroom structure with a 100 Å diameter cap and a stem region of 52 Å [44] . By contrast , electron micrograph studies of the PA pore stabilized with GroEL revealed a 125 Å diameter cap and a stem region that was almost as long ( 100 Å ) [45] . Santelli and colleagues hypothesized that the receptor might occupy the predicted 75 Å gap between the pore cap structure and the membrane , thereby stabilizing the pore [16] . The results of the present study , which demonstrates that receptor remains bound to PA domain 4 after pore formation is triggered , provide direct support for a possible pore-stabilizing role for the receptor . A pore-stabilizing role for the receptor is also consistent with results from a previous voltage patch clamp study of ion conductance by the PA pore in whole cells versus artificial membranes . That study indicated that the receptor might influence pore structure since it was associated with altered voltage-dependent inactivation properties of the pore and with altered sensitivity to inhibition by the small molecule inhibitor , TBA [28] . Also consistent with such a role , it has been reported that disulfide-bond formation in the extracellular immunoglobulin-like region of ANTXR2 , which lies between the membrane and the VWA-domain of the receptor , can influence anthrax toxin pore function [46] . Future studies will aim to uncover how the receptor-PA domain 4 contacts influence the structure and or function of the anthrax toxin pore .
The VWA-domain of ANTXR2 ( residues Ser38 to Cys218 ) was produced from a pGEX-4T-1 vector ( Amersham Pharmacia ) and was expressed as a GST fusion protein [47] in Escherichia coli C43 ( DE3 ) cells ( OverExpress ) . The RIL plasmid of BL21-CodonPlus-RIL cells ( Stratagene ) was also co-expressed in the C43 ( DE3 ) cells due to rare codons within the VWA-domain protein-encoding region . Isotopically enriched 15N , 15N/13C/2H , 15N/13C , and 15N/2H ANTXR2 VWA-domain samples were prepared for NMR studies from 4 liters of E . coli culture grown in standard M9 minimal media with 15NH4Cl at 0 . 1% ( wt/vol ) , with and without 13C6-glucose or 2H/13C-glucose ( 0 . 4% ( wt/vol ) . Unlabeled samples of the ANTXR2 VWA-domain were produced in standard Terrific Broth . The cell cultures were grown with carbenicillin ( 50 ug/ml ) , chloramphenicol ( 34 ug/ml ) , and spectinomycin ( 50 ug/ml ) for plasmid selection . For the transfer cross saturation ( TCS ) experiments , the ANTXR2 VWA-domain was produced in 100% D2O based M9 minimal media supplemented with 15NH4Cl ( 0 . 1% wt/vol ) , 2H/13C6-glucose ( 0 . 4% wt/vol ) and MEM Vitamin B solution ( Sigma ) . Growth of the C43 ( DE3 ) cells in 100% D2O required acclimating the cells in 5 mls of standard M9 minimal media and slowly acclimating the cells to a 20% increase in D2O levels every 12–24 hours until growth was sustained in 100% D2O-containing medium . A 5 ml sample of cells grown in 100% M9 media was then used to inoculate 1L of 100% D2O M9 media , which was then used for standard isotopic labeling procedures . Once the cell populations had reached an OD600 of 0 . 75 , ANTXR2 VWA-domain expression was induced with 0 . 5 mM isopropyl β-d-thiogalactopyranoside ( IPTG ) for 6–8 hours at 37°C . The bacterial cells were then harvested by centrifugation at 8000× g in a JA-10 rotor and resuspended into 50–75 ml of lysis buffer ( 50 mM Tris pH 7 . 5; 150 mM NaCl; 1 mg/ml lysozyme , 100 units DNAse ) . The cells were then lysed by three cycles of sonication ( 0 . 5 sec pulses/20 seconds per cycle using a 550 Sonic Dismembrator ( Fisher Scientific ) ) and protease inhibitor cocktail II tablets ( Roche ) were added to the lysate . The lysate was cleared by centrifugation at 12 , 000× g in a JA-20 rotor for 1 hour at 4°C and the supernatant was filtered with a 45 µm filter ( vacuum filtration device ( Nalgene ) ) . The supernatant was circulated over a 5 ml GSTrap HiTrap FF column ( Amersham Pharmacia ) using a peristaltic pump ( LKB Pump P1 , Amersham Pharmacia ) . The resin was then washed with Buffer A ( 50 mM Tris HCl pH 8 . 0; 150 mM NaCl ) and incubated with 5 mls of thrombin cleavage buffer ( 50 mM Tris HCl pH 8 . 0; 150 mM; 5 mM CaCl2; 500 units thrombin ( Sigma ) ) for 12–16 hours overnight at room temperature . The labeled protein samples were eluted with Buffer A and cleared of thrombin using a HiTrap Benzamidine FF column ( Amersham Pharmacia ) . The protein was further concentrated using a filtered centrifugal device ( Vivaspin 15R , Sartorius ) . A lack of several backbone amide resonances in the [15N , 1H] TROSY-HSQC of the ANTXR2 VWA-domain was observed and attributed to slow back exchange of the amides from deuterons to protons , when the protein expressing E . coli were grown in a D2O based media . Because this phenomenon resulted in the loss of several probes , the deuterium-labeled ANTXR2 VWA-domain had to be unfolded to protonate the deuterated residues that were buried within the core of the folded protein . ANTXR2 VWA-domain was unfolded at a concentration of 1 mg/ml and protein unfolding was performed for 1 hour at 4°C in unfolding buffer ( 3M guanidine HCl; 50 mM Tris-HCl pH 8 . 0; 150 mM NaCl ) . It was then added drop-wise with stirring into refolding buffer ( 50 mM Tris-HCl; 2 mM MgCl2; 150 mM NaCl; 10% vol/vol glycerol ) at 4°C , and kept under agitation for one hour . The refolded protein sample was then dialyzed against NMR buffer ( 50 mM Tris-HCl pH 8 . 0; 150 mM NaCl ) and concentrated using a filtered centrifugal device ( Vivaspin 15R , Sartorius ) . The integrity of the refolded protein was demonstrated by a [15N , 1H] TROSY-HSQC which was comparable to a control spectrum of a non-denatured 1H-15N ANTXR2 VWA-domain , and through an in vitro toxin neutralization assay as described elsewhere [48] . PA83 was expressed from a pET22b+ vector ( Novagen ) [49] in Rosetta 2 cells ( Novagen ) due to rare codon usage and grown at 37°C in Luria Broth containing carbenicillin ( 50 ug/ml ) and chloramphenicol ( 34 ug/ml ) . Cells were grown to an OD600 of 1 . 0 , and PA83 expression was induced by addition of 0 . 5 mM IPTG for 6 h at 25°C . Periplasmic proteins were obtained by osmotic shock by first resuspending pelleted cells in 1L of Buffer B ( 20% sucrose; 5 mM EDTA; 50 mM Tris-HCl ( pH 8 . 0 ) ) with stirring at room temperature for ten minutes . The cells were then harvested at 8000× g for 15 minutes at 4°C in a JA-10 rotor and the pellet was resuspended with stirring in a cold 5 mM MgSO4 solution at 4°C for 15 minutes . This sample was centrifuged again with the same harvesting conditions , protease inhibitor tablets ( Roche ) were added and the resulting supernatant containing the desired protein was brought up to 50 mM Tris HCl at pH 8 . 0 with a 1M stock solution of Tris HCl pH 8 . 0 . The supernatant was then circulated over an anion-exchange HiTrap QFF column ( Amersham Pharmacia ) and purified with a gradient of 0M to 1M NaCl in buffer A ( 50 mM Tris-HCl; pH 8 . 0 ) using an AKTA-FPLC system ( Amersham Pharmacia ) . Column fractions containing PA83 were then concentrated and applied to a Hi Load Superdex 26/60 gel filtration column ( Amersham Pharmacia ) and eluted using gel filtration buffer ( 50 mM Tris-HCl; 150 mM NaCl; pH 8 . 0 ) . PA83 was purified to 90% homogeneity , as judged by a Coomassie stained SDS-PAGE gel and concentrated using centrifugal filter devices ( Vivaspin 15R , Sartorius ) . This protocol was modified from Miller et al 1999 in order to produce a large scale prep for NMR studies [49] . To generate PA63 by trypsin cleavage [49] , the purified PA83 sample was concentrated down to 1 . 5 ml ( final concentration 5 mg/ml ) for treatment with trypsin-conjugated magnetic beads . Prior to that incubation , 1 ml of the magnetic beads slurry ( Mag-Trypsin , Clontech ) was washed in gel filtration buffer and then separated from the wash using a microfuge magnetic stand ( Promega ) . The washed beads were then mixed with the purified PA83 for 45 minutes at room temperature with constant agitation ( Nutator ) . The trypsin beads were then removed using the magnetic stand and the generated PA63 heptamer was purified by gel filtration using a Hi Load Superdex 26/60 gel filtration column ( Amersham Pharmacia ) and samples were eluted with gel filtration buffer ( 50 mM Tris-HCl; 150 mM NaCl; pH 8 . 0 ) . Fractions containing the heptamer were then concentrated by a filtered centrifugal device ( Vivaspin 15R , Sartorius ) . The composition of the PA63 heptamer was confirmed by static light scattering/refractive index measurements coupled with size exclusion chromatography ( data not shown ) . The ANTXR2 VWA-domain was purified as described , and the samples were concentrated to 10 mg/mL and stored at 4°C . The concentrated samples were diluted into either Buffer A ( 50 mM Tris-HCl , pH 8 . 0; 5 mM DTT , 150 mM NaCl , 2 . 5 mM MgCl2 ) or into Buffer B ( 50 mM sodium phosphate buffer , pH 5 . 0 or 6 . 0; 150 mM NaCl; 2 . 5 mM MgCl2 ) to a final concentration of 25 µM , Supplementary Figures S1 and S2 , respectively . The solution was then placed into a 0 . 1-cm path-length quartz cell ( Hellma , Forest Hills , NY ) . Spectra were acquired using a BioLogic MOS-450 ( Molecular Kinetics , Pullman , WA ) . All measurements were done at 25°C . Spectra were recorded at a wavelength range of 190–260 nm . Three independent experiments were performed with each sample . Raw data were manipulated by smoothing and subtraction of buffer spectra , according to the manufacturer's instructions . 300 µM of ANTXR2 VWA domain was incubated at 37°C for 48 hours in buffers ranging from pH 8 , 7 , 6 , and 5 . Buffer A ( 50 mM Tris-HCl , 150 mM NaCl , 2 . 5 mM MgCl2 ) was used for the pH 8 and pH 7 samples . Buffer B ( 50 mM phosphate buffer; 150 mM NaCl , 2 . 5 mM MgCl2 ) was used for and the pH 6 and pH 5 . 1 samples . The samples were subjected to a wavelength scan using a Beckman DU 530 Life Science UV/VIS spectrophotometer . The apparent optical density , which is proportional to turbidity , was then analyzed at 340 nm . All NMR experiments were recorded at 310K on a Bruker 700-MHz spectrometer equipped with four radiofrequency channels and a triple-resonance cryoprobe with a shielded z-gradient coil . Measurements were performed on either 15N , 15N/13C , or 15N/13C/2H , 350 µM labeled ANTXR2 VWA-domain in NMR buffer ( 50 mM Tris-HCl ( pH 8 . 0 ) ; 150 mM NaCl; 0 . 01% NaN3; 10/90 D2O/H2O ) , if not stated otherwise . Assignments for the backbone resonances were obtained using data from the following experiments: [1H , 15N] TROSY-HSQC , 3D TROSY-HNCO , 3D TROSY-HN ( CA ) CO , 3D TROSY-HNCACB , 3D TROSY-HNCA , and a 3D 15N-edited NOESY-HSQC . NMR data were processed using NMRPipe and analyzed using the Sparky and CARA software packages [34] , [35] . Transferred cross-saturation experiments were performed with deuterated , 15N-labeled ANTXR2 VWA-domain in a buffer containing 85-90% D2O . The final NMR sample contained 350 µM VWA-domain and 35 µM PA63 ( ratio 10∶1 ) in NMR buffer ( 50 mM deuterated Tris-HCl pH 8 . 0 buffer; 5 mM DTT; 150 mM NaCl; 2 . 5 mM MgCl2; 85% D2O/H2O ) or low pH NMR buffer ( 50 mM sodium phosphate buffer pH 6 . 0 or 5 . 1; 150 mM NaCl; 2 . 5 mM MgCl2; 85% D2O ) Experiments were performed at 310K . Selective saturation of the protein was achieved by applying a train of Gaussian shaped pulses prior to the [1H-15N] TROSY-HSQC experiment with the saturation frequency set to 0 . 8 ppm [32] , [50] . The experiment was performed in an interleaved manner with a phase sensitive Echo/Antiecho gradient selection . Experiments were performed similarly with 120 scans , 0 . 5 sec saturation durations , and a relaxation delay of 2 . 0 s . The experiments were performed with 2048× 256 complex points in the 1H and 15N dimensions with spectral widths of 10000 and 2270 Hz , respectively . The spectra were transformed to 2048× 256 complex points using zero-filling .
|
The bacterium that causes anthrax produces a toxin called anthrax toxin that is largely responsible for causing disease symptoms . The first step in anthrax intoxication involves binding of the toxin to a specific protein , called a receptor , on the cell surface . Receptor-binding acts like a switch to prevent the toxin from forming a pore in a cell membrane until the toxin-receptor complex is taken up into cells and delivered to a specific location ( called an endosome ) where it is exposed to an “acid bath” . This acidic environment promotes structural changes in the toxin leading to pore formation in the endosomal membrane . In this report , we have studied how the receptor regulates pore formation by following the associated changes in toxin-receptor contacts . These studies have defined a new toxin-receptor intermediate in the pathway leading to pore conversion and demonstrate that the receptor remains bound after pore conversion . Our results provide important new insights into how the receptor regulates anthrax toxin pore formation , information that could be useful for designing new therapeutic strategies to treat this disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"protein",
"interactions",
"proteins",
"biology"
] |
2011
|
A Receptor-based Switch that Regulates Anthrax Toxin Pore Formation
|
Networks of interacting transcription factors are central to the regulation of cellular responses to abiotic stress . Although the architecture of many such networks has been mapped , their dynamic function remains unclear . Here we address this challenge in archaea , microorganisms possessing transcription factors that resemble those of both eukaryotes and bacteria . Using genome-wide DNA binding location analysis integrated with gene expression and cell physiological data , we demonstrate that a bacterial-type transcription factor ( TF ) , called RosR , and five TFIIB proteins , homologs of eukaryotic TFs , combinatorially regulate over 100 target genes important for the response to extremely high levels of peroxide . These genes include 20 other transcription factors and oxidative damage repair genes . RosR promoter occupancy is surprisingly dynamic , with the pattern of target gene expression during the transition from rapid growth to stress correlating strongly with the pattern of dynamic binding . We conclude that a hierarchical regulatory network orchestrated by TFs of hybrid lineage enables dynamic response and survival under extreme stress in archaea . This raises questions regarding the evolutionary trajectory of gene networks in response to stress .
All organisms encounter reactive oxygen species ( ROS ) originating from biotic and abiotic sources . ROS are produced at relatively low levels as natural byproducts of aerobic respiration , Fenton reactions , or other biotic sources [1] , [2] . In contrast , abiotic sources include environmental toxins such as solar UV radiation , pollutants , and excessive metals , which damage macromolecules [3] . In each case , oxidants must be neutralized and macromolecular damage repaired at the cellular level to enable survival . Enzymes such as superoxide dismutase and thioredoxin reductase are induced to neutralize oxidants and restore redox balance in the cell [4] . The production of these oxidant response proteins is typically transient and precisely controlled to enable rapid restoration of homeostasis following oxidant clearance and damage repair [5] . Such regulation is accomplished by a diversity of strategies throughout the microbial world . For instance , complexes of transcription factor ( TF ) proteins coordinate ROS-induced cell cycle block with production of repair enzymes in yeast [6] . In bacteria , TFs [7] , [8] or their bound cofactors [9] , [10] are directly and reversibly oxidized in the presence of ROS , altering DNA binding specificity to induce repair enzyme-coding genes [5] , [11] . Relative to the other domains of life , the function of TFs that control the oxidant response in archaea remain understudied . To our knowledge , only a few transcription factors have been characterized to date [12]–[16] . Generally , components of archaeal transcription complexes are hybrid between the bacterial and eukaryal domains . For example , the basal transcriptional machinery in archaea , like that of eukaryotes , consists of transcription factor II B ( Tfb ) , a TATA binding protein ( TBP ) , and an RNA-Pol II-like polymerase [17] . The proteins that modulate transcription ( e . g . stress-responsive TFs ) typically resemble those of bacteria at the amino acid sequence level [18] . This class of TFs , like those of bacteria , can sense stressors or metabolites directly [14] , [19] , [20] . Recent evidence also suggests that these “bacterial-like” TFs can bind together on DNA combinatorially to expand their repertoire of gene regulation [21] , [22] . Machine-learning efforts to reconstruct gene regulatory networks in archaea also suggest combinatorial regulation [16] , [23] , [24] . More generally , it remains an open question how networks of transcription factors interact dynamically to enact genome-scale regulation during stress response across the domains of life . Here we use the salt-loving archaeon H . salinarum as a model , both to characterize the genome-wide binding dynamics of an ROS-responsive transcription factor , and to analyze regulatory network function during ROS stress in archaea . This hypersaline adapted archaeal model organism encounters high levels of abiotic oxidants in its natural salt lake environment , where intense solar radiation and desiccation are frequent [25] . Halophilic archaea use several complementary strategies to protect against , respond to , and repair damage induced by ROS . These include the natural protective capacity of cytoplasmic salt inclusions [26] , multiple copies of repair enzymes [27] , and an extensive transcription regulatory network that has been hypothesized to respond to oxidative damage [16] . However , this network was computationally inferred from gene expression data . To experimentally characterize TFs with putative involvement in this network , our previous work identified the winged helix-turn-helix DNA-binding TF RosR . This TF dynamically regulates expression of more than 300 genes in response to oxidative stress in H . salinarum [13] . RosR is required for survival of oxidants from multiple sources ( e . g . H2O2 and paraquat ) . Genes directly and indirectly controlled by RosR in response to oxidant encode macromolecular repair functions . In the current study , we ask which of these genes are direct targets of RosR regulation . Integrated analysis of genome-wide binding location time course data with gene expression data demonstrates that RosR binds and regulates over 100 target genes . These encode molecular repair functions and a surprisingly high number of other TFs . RosR binds many of these sites in the absence of stress . Upon exposure to H2O2 , RosR disengages from DNA at most loci . However , at other loci , RosR-DNA binding is dynamic following peroxide exposure , with locus-specific differences in TF occupancy over time . RosR binding is mediated via a 20-bp palindromic cis-regulatory binding sequence . Integration of data generated here in the context of other existing systems biology datasets reveals extensive combinatorial binding of RosR with multiple Tfb proteins throughout the regulon . We conclude that RosR is a master regulator of a hierarchy of TFs that performs global , dynamic physiological readjustment in response to oxidative stress .
Previous work demonstrated that the RosR transcription factor is required for the differential expression of genes in response to ROS [13] . To differentiate direct from indirect targets of RosR transcriptional regulation , we mapped DNA binding locations genome-wide in the presence and absence of H2O2 over time ( see Methods ) . A total of 189 regions ( 252 genes , including operons and divergently transcribed genes ) were significantly enriched for RosR binding throughout the genome in the absence of stress , with fewer sites bound over time upon exposure to H2O2 ( Fig . 1A , S2 Table ) . Upon clustering , four major RosR-DNA binding profiles were detected: ( 1 ) nearly one-third of sites ( 88 genes ) is significantly enriched for RosR binding under standard , non-stress conditions ( Fig . 1B , middle and Fig . 2A , Cluster 1 ) . Binding enrichment at these loci fell below the statistical threshold upon the addition of H2O2 and remained low for the duration of the time course . ( 2 ) At other sites ( 90 genes ) , RosR binding was initially lost in the presence of H2O2 , but binding recovered within 60 minutes ( Fig . 1B , right and Fig . 2A , Cluster 2 ) . ( 3 ) RosR binding to fewer sites ( 29 genes ) was detectable above statistical threshold only after the addition of H2O2 and RosR remained bound to these sites for the duration of the time course ( Fig . 1B , left and Fig . 2A , Cluster 3 ) . ( 4 ) At the remainder of observed sites ( 45 genes ) , binding was more dynamic , with variability in binding enrichment throughout the time course ( Fig . 2A , Cluster 4 ) . Similar dynamic categories were observed for each of the two other genomic elements ( megaplasmids ) of the H . salinarum genome ( S2 Table ) . Dynamic binding patterns for representative loci were validated by ChIP-qPCR as shown in Fig . 2B ( cluster 2 Spearman correlation = 0 . 4; cluster 3 Cs = 0 . 8 ) . RosR binding ability in the absence of stress ( clusters 1 and 4 at the 0 time point ) was previously validated by ChIP-qPCR [13] . Together , these experiments suggest that RosR-DNA binding distributions are dynamic and reproducible genome-wide over time in response to oxidant treatment . To determine if RosR-DNA binding results in functional consequences in gene expression , we asked whether genes nearby binding loci were also differentially expressed over time . ChIP-chip binding profiles were compared to previously published gene expression data from H . salinarum Δura3 parent vs ΔrosR exposed to oxidative stress over time ( 0 , 10 , 20 , and 60 min relative to H2O2 addition; [13] ) . Of the 252 genes ( including operon members ) within 250 bp of a binding locus , 51 exhibit differential expression in response to H2O2 and/or deletion of rosR when all time points are considered together [13] . To uncover additional putative functional binding events , the correlation of RosR-DNA binding with gene expression was calculated for all 252 genes associated with binding loci . Patterns of RosR binding occupancy nearby 70 genes are strongly correlated with expression profiles ( “GE-ChIP correlation” , Cs≥0 . 6 , Fig . 3A , left graphs ) . Binding time course patterns at 52 other sites were anticorrelated with gene expression profiles ( Cs≤−0 . 6 , Fig . 3A , right graphs ) . The remaining sites were uncorrelated , which suggests that these sites represent non-specific DNA interactions and/or that other factors may be required for significant change in gene expression at these sites [28] , [29] . The four clusters observed for binding profiles alone were also detected for genes exhibiting strongly correlated or anticorrelated gene expression and binding patterns ( Figs . 2 and 3A ) . Across the distribution of strong GE-ChIP correlations and anticorrelations , deletion of rosR significantly alters the relationship between binding and gene expression , with a trend toward uncorrelated gene expression and binding relationships in this strain ( Fig . 3B ) . Because the time scale of TF-DNA binding is faster than that of transcript synthesis ( <1 minute vs >5 minutes , respectively; [30] ) , binding and expression would appear simultaneous with the resolution of the time course experiments herein ( Fig . 3A ) . Therefore , we reasoned that the relationships between gene expression and binding profiles detected are consistent with RosR activity , with activated genes exhibiting correlated binding and expression , and repressed genes showing anticorrelated binding and expression . Together , these results suggest that: ( a ) dynamic binding events are strongly associated with a change in gene expression before and/or after oxidant exposure; and ( b ) RosR is required for direct and dynamic activation or repression of over 100 genes in response to oxidative stress in H . salinarum . A key component of gene network function is the specific cis-regulatory binding sequence for a TF . To provide further support for RosR direct activation and repression of these target genes , we next sought to determine this binding sequence consensus for RosR . In previous work , a putative cis-regulatory sequence was computationally predicted from promoters of genes differentially expressed in response to deletion of rosR ( direct and indirect RosR target genes; [13] ) . This sequence consisted of a 7 bp inverted repeat palindrome with the consensus TCGnCGA . To gain additional refinement in these predictions , the cis-regulatory sequence search was repeated using only direct RosR targets detected here by binding location analysis ( S3 Table ) . The resultant consensus motif contained a 20 bp imperfect palindrome sequence TCGnCGACGAGnTCGnCGAC ( Fig . 4A , p<3 . 5×10−12 ) , which was detected nearby 37 of RosR-bound loci ( ∼15%; p<10−37 ) , but not detectable elsewhere in the genome ( S5 Table ) . Some loci contain more than one motif . Of these 37 loci with motifs detected , 40% also exhibited strong ChIP-GE associations ( Cs≥|0 . 6|; Fig . 3 ) . On average , motifs were located within 18 bp of ORF start sites ( Fig . 4B ) . To validate the function of this computationally predicted binding site experimentally , the native genomic promoter ( TATA box and putative cis-regulatory sequence ) of VNG2094G ( trh4 , a TF-coding gene ) was fused to GFP . Promoter activity was assayed in the ΔrosR vs parent strain in the absence of stress , when RosR binding activity was evident in ChIP-chip experiments for these promoters . Ptrh4 activity is significantly higher in the ΔrosR strain relative to the parent and the empty vector background control ( Fig . 4C ) . This suggests that the predicted cis-regulatory sequence is required for RosR-mediated repression of this promoter , consistent with the genome-wide data ( Figs . 1–4 , S2 Table ) . Together , these data suggest that ( a ) the computationally predicted motif is biologically relevant; ( b ) RosR binds to the predicted cis-regulatory sequence in vivo to regulate gene expression; and ( c ) this cis-regulatory sequence carries significant importance in the function of the RosR regulatory network . To gain additional insight into RosR function in the cell , we calculated statistical enrichment in archaeal clusters of orthologous genes functional ontology categories [31] for RosR target genes ( those bound in binding location assays ) . These genes are significantly enriched for stress response functions ( e . g . genes encoding heat shock proteins hsp4 and hsp5 , peroxidase perA ) , translation ( e . g . genes encoding ribosomal protein ) , DNA replication , cell growth and division , and transcription ( e . g . RNA polymerase subunits , TFIIB family member tfbB , LRP family homolog trh4; Table 1 ) . In general , the direction of regulation corresponds with the function of these gene products . For example , genes associated with translation ( e . g . eif2B ) are downregulated upon ROS exposure , whereas stress response genes ( e . g . perA , hsp5 ) are upregulated ( S2 Table; [16] ) . This analysis confirms previous results implicating RosR in the regulation of genes whose products serve stress repair functions [13] , but also expands the RosR regulon . The functional enrichment analysis revealed novel RosR targets , notably 21 genes encoding TFs and 4 other putative regulators involved in signal transduction and DNA binding ( Table 1 , Tables S2 and S3 ) . Cis-regulatory sequences were detected in the vicinity of the translation start site for 14 of these TF-coding genes , including rosR itself ( Table 2 , Fig . 5 , S5 Table ) . This could explain why direct RosR binding was not detected for many genes affected by deleting rosR [13] ( i . e . RosR binding not detected here ) . For example , nearly 25% of RosR indirect gene regulation appears to be mediated through TfbB , whose encoding gene is among the TFs directly regulated by RosR ( Fig . 6; [32] ) . Dynamic ChIP-chip profiles for seven of the 14 TF-coding genes with cis-regulatory sequences nearby were anticorrelated with their gene expression profiles ( Table 2 ) . Closer inspection of binding and gene expression profiles revealed that these seven TFs are repressed by RosR during optimum growth in the absence of stress but de-repressed in response to H2O2 ( Fig . 5A ) . These sites were bound again within 60 minutes . Temporally coherent binding profiles resulted in two waves of time-resolved expression of TF-coding genes , with the majority of RosR-regulated TFs expressed in the late wave ( Fig . 5A , S2 Table ) . Taken together , these results suggest that RosR regulates a hierarchy of TFs , the majority of which are transiently de-repressed in a RosR-dependent manner during oxidative stress . We reasoned that such TF-TF regulation might contribute to H . salinarum survival of extreme oxidative stress . To test this , we generated strains deleted in-frame of two of the TF-coding genes regulated by RosR ( VNG0194H and hrg ) . Relative to the isogenic parent strain , both TF knockout strains are significantly impaired for growth in response to oxidative stress induced by addition of H2O2 to the cultures ( Fig . 5B ) . These phenotypes are significantly complemented when the corresponding wild type copy of the TF gene is supplied in trans on a plasmid . These phenotypes are similar to that previously observed for the ΔrosR mutant strain ( Fig . 5B; [13] ) . Together , these results implicate new TFs in oxidative stress survival in H . salinarum , suggest important physiological consequences for RosR regulation of other TFs , and validate hypotheses generated from systems-level datasets . RosR regulates many genes encoding TFs , a subset of which is required for oxidant survival . However , we reasoned that RosR might not be the only regulator of these TFs , since the phenotyping results described above are inconsistent with a classical epistatic relationship with TFs downstream of RosR in a linear regulatory cascade . To identify candidates for such co-regulation , RosR binding positions were compared to those for Tfb proteins from previously published high-resolution genome-wide DNA binding location experiments [32] , [33] . Similar to RosR , Tfb binding sites are detected under standard , non-stress conditions , providing comparable physiological conditions . At 82 of each of the 252 RosR-bound loci , we also detected binding for five of seven H . salinarum Tfb proteins ( TfbA , B , D , F , G , S4 Table ) . A single Tfb bound together with RosR at just over half of these loci ( Fig . 6A ) . In contrast , 2 or more Tfbs co-bound at the same locus with RosR at 40 loci . At least four Tfbs together with RosR occupied 10 of these 40 loci ( Fig . 6A , 6B ) . Whether the different Tfb proteins bind simultaneously or one at a time together with RosR remains unclear . While TfbA was underrepresented for co-binding with RosR , TfbG alone was significantly enriched for co-binding with RosR . At other loci , TfbF and TfbG together were enriched for co-binding with RosR ( Fig . 6B ) . Also among the total 82 co-bound loci were 12 of the 21 RosR-regulated TF-coding genes ( S4 Table , Fig . 6C ) . Previous studies suggest that sequence-specific TFs in archaea activate gene expression by binding upstream of the transcription pre-initiation complex [PIC , includes TATA-binding protein ( TBP ) and TFIIB ( Tfb ) ] . In contrast , most repressor TFs inhibit gene expression by binding downstream of the PIC [34]–[37] . To test this model and the mechanism of RosR gene regulation , the RosR-to-Tfb binding locus distance was calculated for the 82 RosR sites where Tfb binding was also detected ( see Methods ) . These distances were compared to RosR activity using the GE-ChIP correlation as a proxy . Interestingly , the distance between RosR and Tfb binding loci was strongly and significantly anticorrelated with RosR activity . That is , if RosR binding upstream of Tfb is considered as a negative distance , then positive GE-ChIP correlation , or activation , is observed and vice versa . When these sites are binned into distance cut-offs ( absolute value of 5 bp ) , a peak association is detected at distances of 65–75 bp ( Fig . 6D ) . This relationship is abrogated in the ΔrosR mutant background ( Fig . 6D , light grey trace ) and is significantly different from random distributions across the distance scale ( Fig . 6D , dark grey dotted trace ) . Together , this integrated analysis of RosR and general transcription factor networks: ( a ) suggests extensive and unexpected combinatorial control of gene expression between Tfb proteins and RosR; ( b ) provides further support for the biological significance of the GE-ChIP dynamic correlations ( Fig . 3 ) ; and ( c ) supports the hypothesis that the relative binding position and distance between Tfb proteins and sequence-specific transcription factors dictates the activation or repression of target genes . We next assessed how predictions of statistically inferred gene regulatory network models ( “environmental gene regulatory influence network ( EGRIN ) ”; [16] , [23] ) compared to the RosR regulatory network determined from the experiments described here . Of the 252 experimentally observed direct RosR-gene interactions , 15% were predicted from EGRIN ( p<5 . 68×10−3; see Methods for p-value calculation and S2 Table for a list of genes with validated predictions ) . Further , the correspondence between predicted and observed target gene lists subject to combinatorial control by RosR-TfbB or RosR-TfbG was significant ( p<2 . 05×10−4 for TfbB; p<2 . 16×10−7 for TfbG ) . In contrast , predictions from the model did not match experimental observations regarding combinatorial control by RosR-TfbD and RosR-TfbF pairs ( S4 Table ) . Of all RosR regulated genes that were both predicted and observed , genes encoding TFs and functions in transcription are most highly enriched ( arCOG category enrichment p<1 . 77×10−5; see also Fig . 6C ) . This analysis suggests that network topological predictions from the EGRIN model are accurate for RosR regulatory influences , especially for those genes that encode functions in transcriptional regulation .
Data and analyses presented here suggest that H . salinarum RosR is a bifunctional regulator that directly controls a large hierarchy of transcription factors in combination with Tfb proteins to enable extreme oxidative stress survival . The majority of these sites are bound in the absence of stress , with RosR released from DNA in the presence of oxidant . A subset of loci exhibits the opposite binding pattern . We show that RosR binds to a ∼20 bp imperfect palindrome cis-regulatory sequence and directly activates or represses genes encoding functions in transcription , macromolecular repair and central cellular physiology . We demonstrate that RosR regulates genes encoding TFs that are also required for oxidative stress survival . Such regulation is conducted in concert with Tfb proteins . We conclude that RosR plays an important role in a large transcriptional network that enables a rapid response to extreme oxidative stress followed by re-establishment of homeostasis . The function of gene products in the RosR regulon reported here reflects the observations from our previous work [13] . Here we expand this regulon , differentiating between direct and indirect control of gene expression by RosR , including new gene targets whose products are involved in central cellular functions such as translation , transcription , and DNA replication . RosR regulation of specific genes encoding such functions is also accurately predicted from a computationally inferred gene regulatory network for H . salinarum [23] ( S2 and S4 Tables S2; Fig . 6D ) . However , the RosR cis-regulatory binding sequence we detected and validated here was not predicted from the model , nor was combinatorial control of gene expression by RosR and Tfbs D and F , possibly because the inference model predicts regulatory interactions primarily based on gene expression [23] . Recent evidence suggests that such predictions can be improved by the incorporation of TF-DNA binding data ( e . g . ChIP-seq or ChIP-chip , [38] ) . Therefore , the current work also pinpoints specific areas for model refinement . The integrated genome-wide analysis presented here suggests hypotheses for the RosR biochemical mechanism . Dynamic TF-DNA binding analysis suggests a differential preference in RosR promoter occupancy , as some promoters are re-bound while homeostasis is restored , whereas a small subset of other sites are bound only in the presence of peroxide ( Fig . 2 , Cluster 3 ) . Binding to slightly different cis-regulatory sequences could enable promoter binding under both conditions , similar to transcription factors that use Fe-S clusters as cofactors in bacteria [39] . However , we observed only one significant motif in our computational analysis ( S5 Table ) , suggesting that other co-factors may be involved ( e . g . Tfb proteins , Fig . 6 ) . It remains unclear how and whether RosR itself senses oxidant , since no cysteines are present in the protein . Further biochemical studies are required . In contrast to RosR targets in Cluster 3 , a significant fraction of sites are bound in the absence of H2O2 and re-occupied by RosR within 60 minutes of oxidant exposure ( Fig . 2 , Cluster 2 ) . Clearance of oxidant from the cell by detoxification enzymes ( e . g . perA , sod2 ) may enable RosR to re-bind . For example , ΔperA mutants experience high intracellular H2O2 concentrations during mid-log phase growth , whereas H2O2 is cleared from the H . salinarum parent strain within the time frame tested here [16] . The gene encoding PerA is a direct target of RosR regulation ( S2 and S3 Tables ) . Dynamic patterns of differential promoter occupancy observed in yeast suggest that the probability of productive gene expression correlates with longer TF-DNA dwell times [40] . The addition of stress in the experiments reported here links these dynamic events to environmental perturbation . For example , TF-coding genes are found almost exclusively in dynamic binding cluster 2 , which are re-bound at the earliest time point following ROS exposure ( S2 Table , Fig . 2 ) . Binding at these sites correlates well with gene expression dynamics and TF knockout strains are more sensitive to H2O2 challenge than the parent strain ( Table 2 , Fig . 5 ) . The pattern of binding in cluster 2 is therefore consistent with an immediate need for TFs to work with RosR to restore homeostasis following stress exposure . Taken together , these dynamic genome-wide data point to a non-canonical mechanism for RosR regulation in response to oxidant . Integrated analysis of several genome-wide binding location and gene expression datasets for TFIIB homologs [32] , [33] with those presented here suggests a surprising degree of RosR-Tfb combinatorial control of gene expression in response to oxidant ( Fig . 6 ) . RosR combinatorial control contrasts with the H . salinarum nutritional regulator TrmB , which regulates far fewer TFs ( only 4 for TrmB vs . 21 for RosR ) and binds together with only one other Tfb protein at its target promoters [36] . Similarly , H . salinarum iron regulators Idr1 and Idr2 only regulate one other TF each [22] . Further regulatory interactions were observed between TFs , including TfbB regulation of RosR , setting up a potential feedback loop ( Fig . 6D; [32] ) . Taken together , these data are consistent with the hypothesis that the regulatory reach of RosR under oxidative stress conditions is extended significantly via TF-TF network interactions . Systems-level studies suggest that extensive TF-TF interactions may be a conserved feature of transcriptional regulation of stress response across the domains of life . For example , hierarchical regulation in response to oxidant has been observed in Escherichia coli , where SoxS regulates at least four other TF-coding genes ( fur , marA , marR , rob; [41] ) , some of which in turn regulate other TF-coding genes . However , RosR control of more than 20 other TF-coding genes is closer to the order of the global nutritional regulator , CRP , which controls the expression of at least 50 other TF-coding genes . Such extensive inter-TF regulation in H . salinarum is also reminiscent of multi-TF regulatory networks in yeast that coordinate the cell cycle with DNA damage repair [6] . Thus , RosR appears to possess unique functional features , resembling a eukaryotic-like TF in global activation of gene expression ( Fig . 1 ) , control of a large network of TFs ( Figs . 5 , 6 , Table 2 ) , and extensive coordinate control of gene expression ( Fig . 6; [33] ) . However , some features of RosR also resemble a bacterial-type TF , with its DNA binding sequence specificity ( Fig . 4 ) , repression of gene expression ( Fig . 4C ) , and stress-specific alteration of its binding activity ( Fig . 1 ) .
Strains of Halobacterium salinarum NRC-1 used in this study are listed in S1 Table . Cultures were routinely grown in complex medium ( CM; 250 g/L NaCl , 20 g/L MgSO4 7H2O , 3 g/L sodium citrate , 2 g/L KCl , 10 g/L peptone ) . Δura3 , the parent strain , and transcription factor deletion strain derivatives thereof , were grown in CM supplemented with 0 . 05 mg/mL uracil to complement the auxotrophy . In-frame gene deletion strains ( Δura3ΔVNG0194H , Δura3Δhrg ) were constructed using the pop-in/pop-out gene deletion strategy described previously [42] . Δura3ΔrosR , referred to throughout as ΔrosR for brevity , was constructed previously [13] . H . salinarum strains harboring plasmids were cultured in CM supplemented with 20 µg/mL mevinolin for plasmid maintenance . H2O2 was added to mid-logarithmic phase cultures to 25 mM or at inoculation at 5 or 6 mM to test oxidative stress response as displayed in the figures . Time course profiles of processed ChIP-chip binding data were grouped using Spearman correlated complete linkage hierarchical clustering to identify various dynamic binding patterns . To determine the dynamic relationship between binding and gene expression , each gene in each dynamic binding cluster was correlated to expression data under the same culturing conditions as ChIP-chip from a previous study [13] ( mid-logarithmic phase cultures exposed to 25 mM H2O2 at 0 , 10 , 20 , and 60 min; GEO accession GSE33980 ) . These correlations are referred to throughout as “GE-ChIP correlations” . GE-ChIP correlations were calculated separately for each of the ΔrosR deletion and isogenic parent backgrounds as an additional metric for the impact of RosR binding on gene expression . Significance of the difference in GE-ChIP correlations between the parent and ΔrosR strains was calculated using Student's t-test . Genes with strong GE-ChIP correlations ( Cs≥0 . 6 ) were interpreted as directly activated by RosR , whereas anticorrelations ( Cs≤−0 . 6 ) were interpreted as repressed . Statistical overrepresentation in archaeal clusters of orthologous genes ( arCOG ) functional categories [31] for RosR-bound genes was calculated for using the hypergeometric test . Enriched categories are listed in Table 1 . Detailed arCOG annotations , GE-ChIP correlation values , and significance of correlations for each of the 252 genes nearby RosR binding sites are listed in S3 Table . The code repository containing the pipeline used for binding location data analysis and correspondence to gene expression can be accessed at github . com/amyschmid/rosr-chip-utils . To detect RosR-Tfb combinatorial control , or “co-binding” , high resolution ChIP-chip binding data for TfbA and TfbF [33] , [44] and ChIP-seq binding data for TfbB , G , and D [32] were analyzed . Genes located within 250 bp of a Tfb protein binding site with ChIP enrichment significance of p<0 . 01 were selected using the R bioconductor MeDiChI package [44] . Sites meeting the following criteria were considered to be co-bound by RosR and a Tfb protein: ( a ) both RosR and Tfb binding sites were detected within 250 bp of the same gene; ( b ) RosR and Tfb binding positions were at most 250 bp away from each other . Venn diagram was constructed using the VennDiagram package in R [48] and RosR-Tfb gene regulatory network shown in Fig . 6D was constructed using BioTapestry [49] . Distances from RosR to Tfb binding sites for each of the co-bound genes are listed in S4 Table . The relationship between Tfb-to-RosR binding site distances with RosR GE-ChIP activity values was calculated using Spearman correlation . These correlations were calculated separately for each strain background ( parent and ΔrosR ) . Significance of these correlations was computed from by comparing 10 , 000-fold resampled data to actual data ( S4 Table ) at each distance cutoff in 50 bp sliding windows . The negative log10 transform of resultant p-values are reported . Simulated data was generated from the random normal distribution with the same mean , standard deviation , and number of samples in the actual data set ( S4 Table ) . All other p-values of significance listed in the text , including comparisons to EGRIN predictions , combinatorial control , arCOG functional enrichments , etc . , were calculated using the hypergeometric test against the genome-wide background distribution unless indicated otherwise . To validate RosR binding patterns from ChIP-chip time course experiments , representative binding sites from dynamic binding pattern groups were selected . Chromatin immunoprecipitation ( ChIP ) samples were prepared over the time course described above and subjected to quantitative real-time PCR analysis ( qPCR ) using SYBR green as previously described [22] , [50] . Primers used are listed in S1 Table . H . salinarum Δura3 parent , TF deletion strains Δura3ΔVNG0194 and Δura3Δhrg ( deletion of VNG0917G ) , and the complementation strains ( see S1 Table for strain details ) were pre-grown in CM containing 0 . 05 mg/mL uracil ( and 20 µg/mL mevinolin for complementation strains ) , then tested for growth phenotypes in high throughput as previously described [13] . Strains were diluted to OD600 ∼0 . 1 and H2O2 was added to final concentrations of 0 , 5 , or 6 mM . Absorbance at an optical density of 600 nm was measured every 30 minutes using the Bioscreen C ( Growth Curves USA , Piscataway , NJ ) . Growth rates were calculated from the slope of the log2 transformed data during logarithmic growth . Reported in the figures are ratios of the growth rates of each strain under H2O2 stress relative to the same strain's growth rate without stress . All growth data are provided in S6 Table . Regions of the H . salinarum genome sequence 250 bp upstream and downstream of each of the 189 ChIP-chip binding loci ( nearby 252 genes including operons , S2 Table ) were searched for a cis-regulatory consensus binding motif for RosR using MEME [51] . The output of the search was constrained to three motifs , any number of repeats per sequence , forward or reverse strand , and maximum motif width of 20 bp . Palindromic motifs were not enforced . Similar cis-regulatory sequences were detected using varying subsets of the input sequences . Motif significance was determined using the Wilcoxon signed rank test comparing randomized input sequences to actual sequences . Resultant significance of the top-scoring motif is reported in the text . Details regarding motif genomic positions , E-value of significance of similarity to consensus , and sequence are listed in S5 Table . To validate the predicted cis-regulatory binding sequence , a 200-bp region containing the putative cis-sequence and TATA box of VNG2094G was cloned into the pMTF1044GFP plasmid [36] , [52] by Gibson assembly [53] using primers listed in S1 Table . The maximum cloned DNA fragment size was kept to 200 bp to reduce signal from other cryptic promoter elements . H . salinarum Δura3 parent and ΔrosR strains transformed with the fusion vector were grown to mid-logarithmic phase ( OD600 ∼0 . 3–0 . 6 ) in the absence of stress in 50 mL CM . Samples were collected , washed and fixed as previously described [54] except for fixing temperature ( 4°C ) . Resultant samples were measured for fluorescence in an FLx800 fluorimeter ( BioTek ) . Δura3 harboring the empty vector ( i . e . GFP-encoding gene with no promoter ) or vector containing GFP-encoding gene driven by the strong constitutive Pfdx promoter [33] were used as negative and positive controls , respectively ( S1 Table ) . For each strain , at least five biological replicate cultures with 2 to 4 technical replicates each were tested . Resultant raw fluorescence values were normalized to the cell density of each culture . The mean of these normalized values and standard error of the mean are presented in the figures .
|
Complex circuits of genes rather than a single gene underlie many important processes such as disease , development , and cellular damage repair . Although the wiring of many of these circuits has been mapped , how circuits operate in real time to carry out their functions is poorly understood . Here we address these questions by investigating the function of a gene circuit that responds to reactive oxygen species damage in archaea , microorganisms that represent the third domain of life . Members of this domain of life are excellent models for investigating the function and evolution of gene circuits . Components of archaeal regulatory machinery driving gene circuits resemble those of both bacteria and eukaryotes . Here we demonstrate that regulatory proteins of hybrid ancestry collaborate to control the expression of over 100 genes whose products repair cellular damage . Among these are other regulatory proteins , setting up a stepwise hierarchical circuit that controls damage repair . Regulation is dynamic , with gene targets showing immediate response to damage and restoring normal cellular functions soon thereafter . This study demonstrates how strong environmental forces such as stress may have shaped the wiring and dynamic function of gene circuits , raising important questions regarding how circuits originated over evolutionary time .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"systems",
"biology",
"genetics",
"biology",
"and",
"life",
"sciences",
"microbiology",
"computational",
"biology",
"molecular",
"biology"
] |
2015
|
A Regulatory Hierarchy Controls the Dynamic Transcriptional Response to Extreme Oxidative Stress in Archaea
|
The hemolytic phospholipase C ( PlcHR ) expressed by Pseudomonas aeruginosa is the original member of a Phosphoesterase Superfamily , which includes phosphorylcholine-specific phospholipases C ( PC-PLC ) produced by frank and opportunistic pathogens . PlcHR , but not all its family members , is also a potent sphingomyelinase ( SMase ) . Data presented herein indicate that picomolar ( pM ) concentrations of PlcHR are selectively lethal to endothelial cells ( EC ) . An RGD motif of PlcHR contributes to this selectivity . Peptides containing an RGD motif ( i . e . , GRGDS ) , but not control peptides ( i . e . , GDGRS ) , block the effects of PlcHR on calcium signaling and cytotoxicity to EC . Moreover , RGD variants of PlcHR ( e . g . , RGE , KGD ) are significantly reduced in their binding and toxicity , but retain the enzymatic activity of the wild type PlcHR . PlcHR also inhibits several EC-dependent in vitro assays ( i . e . , EC migration , EC invasion , and EC tubule formation ) , which represent key processes involved in angiogenesis ( i . e . , formation of new blood vessels from existing vasculature ) . Finally , the impact of PlcHR in an in vivo model of angiogenesis in transgenic zebrafish , and ones treated with an antisense morpholino to knock down a key blood cell regulator , were evaluated because in vitro assays cannot fully represent the complex processes of angiogenesis . As little as 2 ng/embryo of PlcHR was lethal to ∼50% of EGFP-labeled EC at 6 h after injection of embryos at 48 hpf ( hours post-fertilization ) . An active site mutant of PlcHR ( Thr178Ala ) exhibited 120-fold reduced inhibitory activity in the EC invasion assay , and 20 ng/embryo elicited no detectable inhibitory activity in the zebrafish model . Taken together , these observations are pertinent to the distinctive vasculitis and poor wound healing associated with P . aeruginosa sepsis and suggest that the potent antiangiogenic properties of PlcHR are worthy of further investigation for the treatment of diseases where angiogenesis contributes pathological conditions ( e . g . , vascularization of tumors , diabetic retinopathy ) .
The diverse roles that phospholipase C ( PLCs ) and sphingomyelinase ( SMase ) play in biology and medicine are extraordinary . Both types of phosphodiesterases and their substrate products ( e . g . ceramide , diacylglycerol ) have proven to be far more multifaceted than initially perceived , and their impact on a wide range of basic cellular processes in eukaryotes , including oncogenesis , apoptosis , and inflammation has been increasingly appreciated [1]–[3] . For example , Teichgräber et al . [4] recently reported that ceramide ( CM ) accumulation in the lungs of Cftr-deficient mice , resulting from the activation of an endogenous acidic SMase ( ASMase ) , mediated a harsh inflammatory response . This led to wide spread apoptosis in pulmonary epithelial cells and an increased susceptibility to severe Pseudomonas aeruginosa infections . More recently , it was reported that a defective ASMase pathway in cystic fibrosis ( CF ) is perhaps a key contributor to the unabated IL-8 response during P . aeruginosa infections and to the failure of compromised hosts to eradicate bacterial colonization [5] . Likewise , there are sundry noteworthy functions for prokaryotic PLCs and SMases [6] , [7] . One major class of bacterial PLCs is the Zn-dependent enzymes , which include α-toxin of Clostridium perfringens and PlcB of Listeria monocytogenes . Zn-dependent refers to the three Zn2+ atoms present in their active sites that are required for catalytic activity . Although α-toxin and PlcB both belong to this class of PLCs , their roles in pathogenesis are quite distinct . The α-toxin is markedly cytotoxic to eukaryotic cells and contributes to the severe myonecrosis associated with gas gangrene . By contrast , PlcB is important for the escape of this facultative intracellular pathogen from phagocytic vacuoles into the cytoplasm of macrophages , and other cell types infected with L . monocytogenes [8] . Some of the above bacterial phosphodiesterases act on phosphatidylcholine ( PC ) , as well as sphingomyelin ( SM ) . The α-toxin of C . perfringens and PlcB or L . monocytogenes hydrolyse either PC or SM to generate DAG or CM and phosphorylcholine , while a Bacillus cereus PC-PLC from the same zinc-dependent class of enzymes , has no activity whatsoever on SM [9] . The SMase activity of α-toxin however , is more critical to its role in pathogenesis than its PC-PLC activity [10]–[12] . A second major class of prokaryotic phosphorylcholine preferring PLCs , some of which are also SMases , is part of a large Phosphoesterase/PLC Superfamily [13] . These include bacterial and fungal PLCs , in addition to prokaryotic and eukaryotic phosphatases . The PLC members of this family are not Zn-dependent enzymes; rather divalent metals including Zn and nickel readily inhibit their activity [13] . For this and other reasons , Zn-dependent and Zn-independent bacterial PLCs each use distinct catalytic mechanisms to hydrolyze the phosphodiester bond between the phosphoryl head group of a choline containing phospholipid or sphingolipid , and their DAG or CM moieties [14] . The hemolytic phospholipase C ( PlcH ) of Pseudomonas aeruginosa , the focus of the present report , is the founding member of the large Zn-independent PLC family . Frank bacterial pathogens ( i . e . Mycobacterium tuberculosis , Bordetella pertussis & Francisella tularensis ) , opportunistic bacteria ( P . aeruginosa , Acinetobacter baumannii ) , fungal pathogens ( Aspergillus fumigatus ) , as well as the Class B Select Agents Burkholderia pseudomallei & mallei , express homologous proteins belonging to the phosphodiesterase ( i . e . PLC ) part of the family [13] . Some pathogens ( e . g . Mycobacterium tuberculosis , B . pseudomallei ) carry three , or as many as four genes , encoding enzymes belonging to this class of PLCs [15]–[18] . All P . aeruginosa strains thus far sequenced ( 7 ) have two genes encoding members of this family , PlcH and PlcN [13] . In one way or another , many of these PLCs have now been associated with the virulence of the particular organism that produces them , but their contributions at the cellular level remain obscure . PlcH , which is both a PC-PLC and a SMase , is a significant virulence determinant of P . aeruginosa in an array of infection models . Ostroff et al . showed that a PlcH deletion mutant had a 10 , 000-fold increase in its LD50 in a mouse thermal injury model , compared to that of its wild type parent [19] . Rahme et al . [20] confirmed the attenuated phenotype of a PlcH mutant in the same type of mouse infection model , but their mutant was constructed using a different parental P . aeruginosa strain ( i . e . PA-14 ) . They also reported that this PlcH mutant was attenuated in a plant ( i . e . Arabidopsis thaliana ) infection model . PlcH can be a noteworthy virulence factor in other non-mammalian hosts , as well . Hogan and Kolter reported that a PlcH mutant was attenuated in a P . aeruginosa infection of the mycelial phase , but not the yeast phase , of Candida albicans [21] . This particular mutant was one of the more attenuated ones in an assortment of other types of P . aeruginosa mutants they tested in this model . Early on , PlcH was predominantly characterized for its hemolytic activity and enzymatic properties [13] , [22] . More recent studies suggest that the SMase activity of PlcHR is much more critical than its PC-PLC activity , with regard to its ability to cause a phenomena known as “hot-cold hemolysis” as well as hemolysis at 37°C [13] , [22] , [23] . The present study however , began as an effort to assess its potential toxicity to a variety of nucleated mammalian ( i . e . non-erythrocytic ) cell lines , with the objective of discerning additional clues about its possible role at the cellular level in P . aeruginosa infections . In the present study , we report that PlcHR is a potent bacterial toxin , with selectivity for endothelial cells ( EC ) that is , at least in part , mediated through its Arg-Gly-Asp ( RGD ) motif . Based on its selective cytotoxicity for EC , PlcHR is a potent antiangiogenic agent in assorted assays ( e . g . EC invasion assays , tube formation ) that evaluate different stages of angiogenesis in vitro , and PlcHR inhibits angiogenesis in an in vivo model in zebrafish . These and other data , implicate PlcH in the pathogenesis of “vasculitis” , and the poor , angiogenesis-dependent , wound healing , typically observed in sepsis associated with this opportunistic pathogen [24] , [25] . From a different perspective , it is possible that potent antiangiogenic bacterial toxins , such as Anthrax toxin and PlcHR , might also provide novel ways to mitigate the pathological consequences of abnormal angiogenesis ( e . g . tumor vascularization , diabetic retinopathies ) .
PlcHR , a complex heterodimer , consisting of PlcH and PlcR , was expressed in P . aeruginosa and purified as previously described [13] , [22] , [23] . PlcHR is hemolytic to human and sheep erythrocytes , and had been previously reported to be moderately cytotoxic to neutrophils and macrophage [22] , [23] , [26] , [27] . Yet , no one had conducted a systematic evaluation of its cytotoxic potential for other types of nucleated cells . Accordingly , the cytotoxic effects of PlcHR toward an assortment of other types of eukaryotic cells was undertaken using the following cell lines: a 1° human lung cell line from a CF patient [28]; HeLa ( epithelial ) ; L929 mouse fibroblasts; J774 macrophage; Chinese hamster ovary cells ( CHO ) ; and Human Umbilical Vascular Endothelial Cells ( HUVEC ) . As shown in Figure 1 and Figure S1 , remarkably low concentrations ( <10 ng/ml ) of PlcHR were distinctly cytotoxic to HUVEC and CHO cells . By contrast , much higher concentrations of PlcHR ( i . e . >4 µg/ml ) were only slightly cytotoxic to any of the other cell types examined including , 1° CF lung epithelial cells and J774 macrophage , that were evaluated at 6 hrs after the application of varying amounts of PlcHR . An additional epithelial cell line , A549 lung cells was also tested , but they were even more resistant to the cytotoxic effects of PlcHR than the 1° CF lung epithelial cells were ( data not shown ) . As shown in Figure 2 , PlcHR does not seem to cause lysis of EC even after 1 hour ( Figure 2B ) , as would be expected if it had been simply hydrolyzing their membranes through its PLC/SMase activity . Moreover , because the outer leaflets of the membranes of all of these cells contain equimolar concentrations of PC and SM [29] , these data suggest that the enzymatic activities of PlcHR , per se , are not sufficient to account for its EC and CHO cell selectivity , compared to the other types of cells that were tested ( Figure 1 and Figure S1 ) . An association between calcium and the hemolytic activity of PlcHR has been previously noted [13] , and associations between the calcium responses of eukaryotic cells to other bacterial PLCs have been reported [7] . Based on those observations , the effect of PlcHR on intracellular calcium levels in EC was investigated . Only 5 ng/ml of PlcHR induced a significant increase in intracellular calcium levels in EC , which peaked at 7 minutes ( Table 1 ) . The rise in calcium levels could originate from the influx of extracellular calcium or via the release of calcium from stores in the endoplasmic reticulum ( ER ) . The two possible pathways by which intracellular calcium increases can be separated by pretreatment of EC with the calcium channel blocker SK&F96365 for 15 min ( inhibits entry of extracellular calcium via calcium channels ) or by pretreatment with thapsigargin for 45 min ( depletes intracellular calcium stores in the endoplasmic reticulum [30] , [31] ) . The 45 minute pretreatment of EC with thapsigargin ( 1 µM ) , before application of 5 ng/ml of PlcHR ( Table 1 ) , significantly blocked the subsequent rise in intracellular calcium levels , that is normally observed in EC treated only with PlcHR ( 5 ng/ml ) ( Table 1 ) . Intracellular calcium levels at 7 minutes in EC treated with thapsigargin and PlcHR was 55±6 nM , while intracellular calcium levels at 7 minutes in EC treated only with PlcHR was 568±58 nM . In contrast , there were no significant differences in the intracellular calcium levels between cells that were pre-treated with 25 µM of SK&F96365 , and then subsequently treated with PlcHR ( 435±43 nM ) , compared to those treated only with PlcHR ( 429±48 nM ) ( Table 1 ) . When PlcH alone was compared to PlcHR a more rapid ( >2 fold ) release of intracellular calcium in EC was observed , but their maximums were ultimately virtually the same ( data not shown ) . Also , >100 ng/ml of PlcHR failed to induce any increase in intracellular levels of calcium in J774 macrophage ( data not shown ) , which are resistant to the cytotoxicity of PlcHR ( Figure 1 ) . These data indicate that the types of cells that are susceptible or resistant to calcium signaling by PlcHR reflect the type of cells that are susceptible or resistant to its cytotoxicity . Because PlcHR induces the release of calcium from the ER in EC , rather than through calcium channels , it is particularly relevant to note that calcium release from the ER , will in turn , activate downstream effectors of apoptosis ( e . g . calcineurin , calpains , endonucleases ) [32] . Finally , with regard to the PC-PLC and SMase activity of PlcHR , it is important to note that increased levels of ceramide ( a SMase product ) , but not diacylglycerol ( a PC-PLC product ) , can induce the release of calcium from ER stores [33] . The aspartate-specific cysteinyl proteases or caspases are a set of mediators implicated in apoptosis . The activation of caspase-3 in mammalian cells is a hallmark of apoptosis [34] . While the generation of DAG from PC would be expected to induce EC proliferation , hydrolysis of membrane SM by PlcHR , would cause a relatively increase in CM , leading to programmed cell death ( i . e . apoptosis ) [33] . We therefore chose to examine whether PlcHR cytotoxicity could induce increased levels of caspase-3 activity , which is one of the effector caspases that ultimately carry out apoptosis . As shown in Figures 3 , S2A and S2B , treatment of EC with pM concentrations of PlcHR resulted in activation of caspase-3 . In experiments shown in Figure 3 , both caspase-3 activation and LDH release were measured at 16 hours post treatment with increasing concentrations of PlcHR . Figure 3 shows that caspase-3 activity increases as the concentration of PlcHR increases until it peaks at 6 . 25 ng/ml PlcHR . The level of caspase-3 activity induced with 6 . 25 ng/ml PlcHR is similar to cells treated with the apoptosis control compound camptothecin ( Figure 3 ) . Beyond 6 . 25 ng/ml PlcHR , caspase-3 activity begins to decrease , but the release of LDH continues to increase until ultimately at 100 ng/ml PlcHR there is very little caspase-3 activity and LDH release has reached its maximum . The addition of the pan-caspase inhibitor Z-VAD-FMK completely inhibited PlcHR activation of caspase-3 and reduced the level of LDH release ( Figure S2B ) , indicating that a significant portion of the cells releasing LDH are actually dying by apoptosis Finally , a time course for activation of caspase-3 was performed to determine when caspase-3 was activated by treatment with PlcHR . As shown is Figure S2A treatment of EC with as little as 2 . 5 ng/ml of PlcHR activated caspase-3 between 3 and 6 hours . Caspase-3 activation was never detected in resistant cells ( e . g . Hela ) when they were treated with >100 ng/ml of PlcHR ( data not shown ) . PlcH has an RGD motif ( Figure S3 ) , which is present in only one other PLC ( i . e . one of the three PlcH homologs expressed by Burkholderia thailandensis ) of the now >50 other members of this class of enzymes . RGD motifs are associated with cell-cell and cell-matrix interactions and they play a major role in host cell recognition by assorted viruses , as well as bacterial virulence factors ( e . g . invasin , toxins ) through their interactions with cell surface integrin receptors [35] . Yet , the context of an RGD motif in a protein is likewise critical to its ability to bind integrins [36] , and variants of this motif ( i . e . RGE , KGE ) may still be functional for binding integrin receptors [37] , [38] . Several approaches were taken to assess whether the RGD motif of PlcH is associated with its selectivity for EC or CHO cells . PlcHR and free PlcH caused a significant increase in the level of intracellular calcium release that peaked at 7 minutes ( Tables 1 and 2 ) . However , when a 5-mer peptide with an RGD motif ( i . e . GRGDS ) was added at the same time as PlcHR , or free PlcH , there was no evidence of release of calcium from ER stores over that seen in untreated cells . By contrast , when a 5-mer peptide with a scrambled RGD sequence ( i . e . GDGRS ) was added to EC in conjunction with PlcH , an increase in the intracellular calcium release reaching a maximum at 7 min , comparable to that seen with PlcHR alone , was observed ( Tables 1 and 2 ) . The ability of these peptides to attenuate the cytoxicity of PlcHR was also evaluated . Similar effects of these peptides were seen ( Table 3 ) . The RGD containing peptide ( GRGDS ) at approximately a 50-fold molar excess of peptide to PlcHR significantly dampened the lethal impact of PlcHR , while the scrambled peptide ( GDGRS ) at the same concentration demonstrated no significant effect on PlcHR toxicity in this assay . Additionally , several RGD variants of PlcHR were constructed . Various plcH genes encoding PlcH with altered RGD motifs were expressed , along with the wild type plcR gene , and the resulting PlcHR-RGD mutant proteins were purified . Each contain variant RGD motifs ( e . g . RGE , RAD , RQD ) that are found in other PlcH homologs in the NCBI database ( Figure 4 ) . Several RGD variants were then evaluated for their enzymatic activity ( Figure 4A ) , as well as , their binding ( Figure 4B ) and cytotoxicity to EC or CHO cells ( Figure 4A & 4B ) . In support of the hypothesis that the RGD motif of PlcHR is a critical factor in its ability to kill EC or CHO cells , some variants ( i . e . RGE , KGE ) , still retained wild type PLC activity , but were significantly decreased in their binding or lethality to EC or CHO cells by comparison to the wild type PlcHR ( Figure 4A & 4B ) . Finally , we found that a resistant cell line ( i . e . L929 fibroblasts ) bound significantly less toxin ( 16% of total PlcHR added ) compared to EC ( 42% of total PlcHR added ) and that binding of PlcHR to EC was saturable . That is , there was no increased binding despite higher concentrations of PlcHR added to these cells ( Stonehouse , M . doctoral thesis University of Colorado Denver ) . Based on the selective and potent toxicity of PlcHR toward EC , it was of interest , particularly in the context of vasular disease associated with P . aeruginosa sepsis ( see Discussion below ) , to examine whether this EC selective toxin inhibits the more complex processes associated with angiogenesis ( i . e . formation of new vessels from the existing vasculature ) . There are assorted in vitro angiogenesis assays ( e . g . migration and invasion assays , tube formation ) to approximate individual mechanisms involved in the formation of new blood vessels . See reference [39] for a detailed description of the angiogenesis assays described below . The EC migration assay is based on the movement of cells through a fluorescence blocking , microporous inert filter coated with human fibronectin . The pores of the membrane are not occluded , which allows the EC to attach to the membrane and freely migrate toward the angiogenic stimulus , vascular endothelial growth factor ( VEGF ) , in a chamber below . A fluorescent plate reader is used to quantify EC , labeled with a fluorescent dye , which migrated through the filter . In the EC migration assay ( Figure 5A ) only 3 ng/ml of PlcHR was required to inhibit EC migration by 50% , ( IC50 ) while the IC50 for an unrelated PLC/SMase , C . perfringens α-toxin ( generously provided by Graeme Clarke and Richard Titball ) was 45 ng/ml ( Figure 5A ) . The EC invasion assay is performed in a similar manner however , the filter in this assay is coated with a Matrigel Matrix , which is a biologically active basement membrane preparation derived from the Engelbreth-Holm-Swarm mouse tumor . This coating blocks the passage of non-invasive cells while allowing the passage of activated EC . Because the membrane blocks any fluorescence coming from the upper surface of the membrane , only fluorescence from cells that have invaded through the basement membrane are detected . A fluorescence plate reader is used to quantify the labeled cells , as with the migration assay . With the EC invasion ( Figure 5B ) assay the IC50 of native PlcHR was 10 ng/ml , but >100 ng/ml of a heated PlcHR preparation ( 10 min @ 95°C ) showed no inhibition in the EC invasion assay ( Figure 5B ) . The in vitro tube , or tubule , assay is regarded as one that represents the later stages of angiogenesis and is considered to be a model for in vivo capillary development . This method has been extensively employed to evaluate novel compounds for their antiangiogenic properties . In this assay EC differentiate into capillary-like structures ( i . e . tubes ) , which contain a lumen surface surrounded by cells , which display cell membranes that are connected to one another by junctional complexes , indicative of in vivo-capillary formation . Only 4 ng/ml of PlcHR completely disrupted tube formation when it was applied during the formation of the tubes ( Figure 6A and Table S1 ) . However , if tubes were allowed to form before PlcHR was applied , it took ∼8-fold higher concentrations of PlcHR ( i . e . 32 ng/ml ) to completely disrupt tube formation ( Figure 6B and Table S1 ) . In both cases , 64 ng/ml of a heated preparation of PlcHR had no effect on tube formation ( data not shown ) . It should be noted that when the media containing PlcHR2 was exchanged with fresh media after the initial 24 hour incubation , tubes formed within 24 hours indicating that PlcHR2 was not necessarily killing the endothelial cells but was inhibiting the tube formation process ( data not shown ) . These and other data ( Stonehouse and Vasil unpublished observations ) suggest that proliferating EC might be more sensitive to PlcHR than EC already established in a vascular structure . We sought to provide evidence that the cytotoxicity of PlcHR is dependent on its enzymatic activity . Although presently , there is no crystal structure data for PlcHR , there is a member of the Phosphoesterase/PLC Superfamily of proteins , which , based on sequence similarities to other members , is situated just at the evolutionary border between the known PLC members , and those , which are only phosphatases [13] , [14] . This protein , AcpA ( with 23% identity to two thirds of the amino terminus of PlcH ) , is a significant virulence factor of Francisella tularensis . AcpA appears to play a role in his pathogen's intracellular trafficking in macrophages [40] . AcpA is a periplasmic protein with acid phosphatase activity that can efficiently use an assortment of biologically significant phosphorylated compounds as substrates ( e . g . tyrosine-PO4 and ATP ) [41] . Although AcpA is able to cleave the synthetic PLC substrate , ρ-nitrophenyl-phosphorylcholine ( NPPC ) , in which a nitrophenyl group replaces the diacylglycerol ( DAG ) moiety of phospholipids ( i . e . PC ) , AcpA cannot cleave the phosphodiester bond between the head group and the DAG or SM moiety of authentic phospholipids or sphingolipids ( e . g . PC or SM ) [14] , [41] . Despite its questionable role as a legitimate PLC , analysis of the structure of AcpA has provided valuable insight into the location of the active site of PlcH [14] especially since it is the only member of the entire Phosphaesterase/PLC superfamily for which a crystal structure is available . The molecular architecture of AcpA was determined through an analysis of crystals formed in the presence of the competitive phosphatase inhibitor orthovanadate . Vanadate is also an inhibitor of PlcH activity ( Vasil , M . and Stonehouse , M . unpublished observations ) and it is an analog of the phosphate ion that would be present in an AcpA substrate ( e . g . tyrosine-PO4 ) or as a phosphate that links the head group ( e . g . choline ) of a phospholipid or sphingolipid ( PC or SM ) to diacylglycerol or ceramide . Because a phosphatase and a PLC both cleave a phosphoester bond , specifically a phosphodiester bond in the case of PlcH , the arrangement of the orthovanadate in the AcpA structure provided some significant information about the amino acids in the active site of AcpA , as well as PlcH . Five of the ten AcpA active site residues are identically conserved in PlcH ( Figure 7 ) . Additionally , Thr-178 of PlcH aligns with the nucleophile Ser-175 of AcpA ( Figure 7 ) . Prior to the publication of the AcpA structural data , a Thr178Ala mutant of PlcH was constructed , expressed and purified by the same methods used for native PlcHR ( see Materials and Methods ) . The enzyme kinetics of the mutant and the wild type PlcHR were evaluated using NPPC as the substrate [13] providing the following results: PlcHR wild type; Vmax ( µmol·min−1·mg−1 ) = 147±6; Km ( µM ) = 19±2 . 9; kcat ( s−1 ) = 192: PlcHR Thr178Ala; Vmax ( µmol·min−1·mg−1 ) = 4 . 85±1; Km ( µM ) = 200±57; kcat ( s−1 ) = 6 . The structural AcpA data , along with the enzymology data above , suggest that Thr178 is an active site residue in PlcH . With regard to its biological activity , >500 ng/ml of the Thr178Ala mutant showed less than 5% cytotoxicity to EC in the LDH release assay and its IC50 in the EC invasion assay was 1200 ng , as compared to an IC50 of 10 ng/ml for the wild type PlcHR ( data not shown ) . Finally , the in vivo antiangiogenic activity of the Thr-178-Ala mutant was severely attenuated in the zebrafish model and 20 ng of the Thr178Ala mutant failed to cause any of the phenotypes observed in this model with only 2 ng of wild type PlcHR ( Table 4 ) . The zebrafish embryonic vasculature is highly accessible to the study of endothelial cell function , from the earliest differentiation of angioblasts ( vasculogenesis ) to the formation of new vessels from the existing vasculature ( angiogenesis ) . Accordingly , zebrafish embryos were used to evaluate the effects of PlcHR on EC in vivo . PlcHR or the active site mutant , PlcHR-Thr178Ala , were injected directly into the circulation of embryos at 48 hours post fertilization ( hpf ) following an established protocol ( Figure 8 ) [42] . Zebrafish embryos are transparent and have functional cardiovascular system by this time [43] . Transgenic zebrafish embryos expressing enhanced green fluorescent protein ( EGFP ) in endothelial cells , ( Tg ( fli1:EGFP ) [44] , [45] were utilized to observe PlcHR action on these cell types directly at the various times after the injection of these proteins ( hpi – hours post injection ) . At 2 ng of PlcHR 6hpi , the embryos had significantly impaired circulation and pericardial edema ( Figure 8C and C′ ) which , became more pronounced by 24 hpi ( Figure 8D and D′ ) as intersegmental vessels ( IVS , white arrows ) . Higher magnification of these IVS ( Figures 8E–8H ) revealed that their lumens collapsed before endothelial cell regression . Interestingly , PlcHR induced dosage-dependent defects were best observed at 1–2 ng ( Table 4 ) . At doses of 1 ng and lower , embryos exhibited decreased circulation and blood pooling in the venous plexus within 15 min following injection; however , they recovered by 24 hours post injection ( hpi ) , to look comparable to wild type embryos ( Table 4 ) . At 2 ng of PlcHR , the same defects became progressively more severe until all embryos had no blood circulation , pericardial edema , and altered heart morphology , by 24 hpi ( Table 4 ) . At doses higher than 2 ng , the same defects occurred earlier and rapidly became more severe . At 10 ng PlcHR , necrosis developed in the heart , followed by the entire body within one hour . In contrast , no vascular or other phenotypic changes were observed in embryos injected with PlcHR-Thr178Ala , at doses up to 20 ng ( Figures 8 , 9 , 10 , 11 and 12 ) . We used the 2 ng dose for subsequent experiments focusing on PlcHR's endothelial effects . To examine PlcHR effects on endothelial cell death , we used a transgenic line with endothelial nuclear expression of EGFP ( Tg ( fli:nucEGFP ) y7 [46] ) . For this assay , endothelial cells within the 8 ISVs immediately anterior to the cloacae were counted [42] . At 2 ng PlcHR , we noted an ∼50% decrease in endothelial cell nuclei number in these vessels by 6 hpi . Nuclei counts continued to drop up to 24 hpi . Acridine orange staining was used to verify that the observed decrease in endothelial cell nuclei numbers was due to cell death ( Figure 9 ) . PlcHR is also hemolytic to some , but not all mammalian and avian erythrocytes that have been examined . For example , PlcHR is hemolytic to Human , Sheep and Rabbit erythrocytes , but Horse , Ox and Cow erythrocytes are resistant to the hemolytic effects of PlcHR ( Vasil , M and Vasil , A . unpublished observations ) . In our experiments we observed that blood cells ceased circulating and decreased in number over a 24 h time course at doses of 2 ng and higher ( Figures 10 , 11 ) . To test whether PlcHR any of the effects on the cardiovascular system or endothelial cells were due to its action on blood cells , we used an antisense morpholino knockdown of GATA-1 , an important blood cell regulator , using an established morpholino [47] . Morpholino injected embryos , called morphants , have dramatically reduced circulating red blood cells as compared with control , wild type embryos ( Figures 10 , 11 ) . We injected either PlcHR or PlcHR-Thr178Ala and visually monitored embryos for effects at regular intervals over a 24 h period . We found that endothelial cells were still responding to PlcHR or PlcHR-Thr178Ala in a similar manner , in wildtype and morphant embryos ( Figures 10 , 11 ) . Finally , we examined whether EC death plays a prominent role in circulatory collapse and recovery following a 1 ng dose of PlcHR . This was done by injecting ( Tg ( Fli:EGFP ) and ( Tg ( Fli:nucEGFP ) embryos [41] with 1 or 2 ng of PlcHR or the Thr178Ala catalytic mutant ( Figure 12 ) . Acridine orange staining detected minimal cell death following the injection of 1 ng of PlcHR , ISV's collapsed , but did not regress as in the 2 ng dose ( Figure 12 C′ ) and the EC nuclei did not decrease ( Figure 12 A″ , B″ ) . By contrast , the 2 ng dose of PlcHR generated increased cell death , vessel regression and reduced EC nuclei ( Figure 12 panels C , C′ and C″ ) . In all cases , 2 ng of the active site mutant of PlcHR , Thr178Ala , showed no effects .
Experimental data presented in this report have defined an EC-selective role for a novel extracellular PC-PLC/SMase toxin ( i . e . PlcHR ) of P . aeruginosa , using mammalian , EC-based in vitro angiogenesis assays and an in vivo vertebrate model ( i . e . zebrafish ) . Even though there are other bacterial toxins , such as the Cytolethal Distending Toxins ( CDTs ) , that are directly lethal to EC , and inhibit in vitro angiogenic processes ( i . e . tube formation ) , they do not exhibit the degree of selectivity , we have observed with PlcHR [48] . CDTs are just as likely to be cytotoxic to an assortment of other cell types including: epithelial cells ( HeLa , Hep-2 ) , fibroblasts ( human lung , human gingival ) and immune effector cells ( e . g . human macrophage , Jurket T-cells ) . Anthrax Lethal Toxin ( ALT ) has also been shown to: induce EC apoptosis , inhibit EC tube formation , and block the vascularization tumors in mice [49]–[51] . However , a more recent assessment of the effects of ALT in the context of an intact animal model , zebrafish , indicates that ALT does not cause significant EC cell death , while inhibiting angiogenesis [42] . Such data , indicate that , in vitro , as well as , in vivo models are necessary to fully and precisely elucidate the actual biological function of all antiangiogenic factors ( e . g . ALT , PlcHR ) . PlcH was initially called the “heat labile hemolysin” of P . aeruginosa . Early on it was shown to: ( i ) have modest cytotoxic effects on macrophage cell lines ( unpublished observations ) ( ii ) incite a strong chemokine response in mice and human granulocytes and ( iii ) profoundly suppress a PMA-stimulated neutrophil respiratory burst , at concentrations as low as 0 . 1 pg/ml [13] , [23] , [26] . However , no especially potent cytotoxic activity had been previously associated with PlcHR . What is more , its PC-PLC/SMase activity was presumed to be sufficient for its hemolytic or cytotoxic properties , through its ability to hydrolyse either PC or SM in the outer leaflet of eukaryotic cell membranes [13] . If that had indeed been the case , then based on the data presented herein , it would be problematical to reconcile that perception with the fact that not all cell lines examined ( i . e . epithelial , fibroblasts , endothelial ) are equally susceptible to PlcHR . While they are asymmetrically distributed , the relative PC and SM content ( i . e . ∼1∶1 ) in the outer leaflet of all mammalian cell lines tested in this study are nearly the same [29] , and PlcHR is equally active on either type of phospholipid in micellar form ( i . e . PC and SM ) in vitro . However data in this report , provide plausible explanation for EC selectivity . Because RGD motifs of some microbial proteins ( e . g . toxins , viral ) bind to specific integrin receptors , it is not unreasonable to presume that PlcHR , through its RGD motif , interacts with specific integrin receptors on EC or CHO cells , which are not expressed by the other cells examined ( HeLa , 1°CF lung epithelia ) . Its toxicity to EC however , still depends on its enzymatic activity , as clearly demonstrated by the lack of cytotoxicity of the Thr178Ala active site mutant to EC . Extraordinarily high concentrations ( >1 µg/ml ) of this mutant were required to exhibit any inhibition in the EC-dependent invasion assay and a 10-fold higher dose ( i . e . 20 ng/embryo ) of the mutant , compared to that used for the wild type PlcHR , produced no detectable phenotype in the zebrafish model . With regard to the SMase activity of PlcHR , is of interest to note that a cyclic RGD ( GRGDFL ) peptide which blocks the function of αvβ3/αvβ5 integrin receptors , is currently being used in Phase III clinical trials as an antiangiogenic , antitumor , therapeutic agent named Cilengitide [52]–[54] . Cilengitide has been shown cause an increase in ceramide levels and induce EC apoptosis via a SMase-dependent mechanism [55] , [56] . Perhaps , PlcHR , through its RGD motif and SMase activity that could very likely increase ceramide levels in EC , mimics the mechanism by which this antiangiogenic , antitumor therapeutic works . Since only picomolar ( pM ) concentrations of PlcHR are directly toxic to EC , then its mechanism of action is very likely associated with signaling events downstream of the hydrolysis of membrane PC or SM [33] . Although the generation of DAG from PC would be expected to induce EC proliferation , hydrolysis of membrane SM , by PlcHR , would cause a relatively increase in CM , likely leading to programmed cell death ( i . e . apoptosis ) . In this regard treatment of EC with less than 2 . 5 ng/ml of PlcHR induced a significant increase in Caspase-3 activity , which was inhibitable by the pancaspase inhibitor Z-VAD-FMK ( Figure 3 and Figure S2A and Figure S2B ) . Additionally , the type of calcium signaling observed with PlcHR ( i . e . release of calcium from ER stores ) is clearly associated with a SMase activity and not with PC-PLC activity [33] . On the other hand , it is still an enigma as to how PlcHR could raise the relative level of CM without affecting DAG levels , because it should be able to do both , . It may ultimately be possible to separate these activities , since PlcHR is hemolytic to sheep erythrocytes , which only contain sphingomyelin in the outer leaflet of their membranes . Likewise , it is of interest to note that the gene immediately 5′ to the plcHR operon encodes an extracellular ceramidase that could further contribute to EC death by producing sphingosine , a pro-apoptotic derivative of ceramide . Other scenarios are also possible , but further experimentation will be required to fully elucidate the mechanisms PlcHR lethality to EC . Using the transparent zebrafish model , we also demonstrated the selectivity of PlcHR for endothelial cells , where dose-dependent cytotoxic effects can be monitored in real-time , independent of any of its unknown effects . In addition , the failure of the PlcHR-Thr178Ala to generate , in this animal model , any phenotypic changes at concentrations 10× higher those used for the wild type protein , persuasively corroborates our cellular assays ( e . g . EC invasion assay ) with this mutant . We have successfully defined a unique set of endothelial phenotypes and we have established a zebrafish model to further elucidate the mechanisms of the antiangiogenic effects of PlcHR . With regard to the use of this model in the context of P . aeruginosa infections there are now three recent reports describing the use of zebrafish to examine the virulence of P . aeruginosa [57]–[59] . A conclusion of one of these studies it that P . aeruginosa infection in zebrafish seems to best model an acute bacteremic infection associated with thermal injury or neutropenia . However , those observations do not necessarily exclude PlcHR from participating in the pathogenesis of chronic P . aeruginosa pulmonary infections . A recent study noted that the function of the cystic fibrosis transmembrane regulator ( CFTR ) is required for stress-induced apoptosis in lung endothelial cells by maintaining adequate CM activation [60] . These investigators suggested that aberrant endothelial cell death could disturb normal lung vascular homeostasis , thereby contributing to abnormal angiogenesis and to the chronic inflammation characteristically observed in CF patients . It would therefore not be surprising if the SMase of PlcHR produced by P . aeruginosa also contributes to apoptosis in lung endothelial cells and further exacerbates chronic inflammation seen in CF patients . PlcHR is a complex heterodimeric protein , with several distinctive features , which presently have no known functions ( Figure S3 ) . For example , nearly all of the PLC members of this large family carry duplicate , C-terminal domains of unknown function , comprising nearly one third of their lengths . Additionally , even though it is understood that PlcR is a chaperone required for the secretion of PlcH through the inner membrane via the twin arginine translocase ( TAT ) secretory system and through the outer membrane via the Xcp secretory system of P . aeruginosa [61]–[63] , its contributions to the properties of PlcHR investigated in this report , are not entirely certain . It is very likely that the sensitivity and tractability of the zebrafish assay , along with site-specific mutagenesis of these intriguing regions of PlcH , will reveal insights into their biological roles ( e . g . trafficking in a whole animal ) that might not otherwise be revealed through conventional in vitro assays ( e . g . enzymatic , cytotoxicity to cultured EC ) . Moreover , the versatility of the zebrafish model for chemical biology , genetics and targeted gene knockdown by morpholinos [64] suggests that future studies on host components ( e . g . specific integrin receptors , signaling mechanisms downstream of CM ) required for PlcHR's endothelial toxicity could be investigated using this inexpensive vertebrate model . For example , a downstream derivative of CM and sphingosine , sphingosine-1-phosphate is currently being investigated as a therapeutic based on its antiapoptotic properties [65] . Data presented herein , provide some additional insights into the role that PlcHR might play in particular types of infection caused by P . aeruginosa . During septicemia , this opportunist has a proclivity to invade blood vessels , thereby inciting vasculitis and causing vascular necrosis . However , bacterial invasion of the vasculature is not necessarily found near or in the areas of necrosis , and organisms are not found in all necrotic vessel walls . It has been suggested however , that the toxic products expressed by P . aeruginosa ( e . g . PlcHR , Exotoxin A ) most likely play a role in this type of organism-distal vascular pathology [64] . The effects of PlcHR on EC , in vitro angiogenesis , and in zebrafish are therefore entirely consistent with this aspect of the pathogenesis of P . aeruginosa sepsis . Finally , Anthrax toxin and some of its variants have been examined for their ability , through their antiangiogenic properties , to block the vascularization and growth of tumors implanted in animal models [66] , [67] . Accordingly , it is not unreasonable to suggest that it would be worthwhile to further investigate the ability of PlcHR to inhibit angiogenesis , related to tumor growth , as well as in eye diseases where angiogenesis directly contributes to pathogenesis ( e . g . macular degeneration ) . While , there may be concerns about the use of bacterial toxins as therapeutic agents , some of which are based on their immunogenicity , this concern may be mitigated for therapeutic applications in the eye , which is considered to be an immunologically privileged site [68] .
PC-PLC and SMase assays were performed as previously described [13] . Production and purification of PlcHR and PlcH from P . aeruginosa were performed as previously described [13] . The Thr178Ala plcH mutant was identified from an alanine scanning mutagenesis experiment to identify enzymatically deficient PlcH . A region containing the segment of the plcH gene that encodes the Thr178Ala mutation was then used to replace the corresponding plcH wild type sequence in the P . aeruginosa PlcHR expression plasmid with a T7 promoter [13] . The mutant plcH gene along with the wild type plcR gene were expressed in the same P . aeruginosa T7 expression system that is used for expression of wild type PlcHR [13] . The mutant ( i . e . Thr178Ala ) was purified using the same protocol for wild type PlcHR [13] except that fractions recovered during purification , which contained the Thr178Ala PlcHR mutant were detected using monoclonal antibodies against PlcH and PlcR [13] . RGD mutants were constructed by site-specific mutagenesis methods as previously described . In each case the mutant PlcH gene was expressed in the P . aeruginosa T7 expression system along with the wild type plcR gene . In all cases in this report where endothelial cells ( EC ) are used they are: Human Umbilical Vascular Endothelial Cells ( HUVEC ) . They were acquired from BD biosciences ( San Jose , CA ) . All primary cell lines ( e . g . HUVEC , CF lung epithelial ) were always used at less than 8 passages . Assays were performed with the designated cell lines according to manufacturer's recommendation using the CytoTox 96 Assay Kit ( Promega Corporation ) . Percent activity was determined by performing a total lysis control in which cells were lysed with 1% triton X-100 . Percent lysis was determined via the formula . Percent lysis = [ ( experimental−spontaneous control ) / ( Total lysis−spontaneous control ) ]×100 To measure binding of PlcHR to CHO cells ( Figure 5B ) were grown in 24 well tissue culture dishes until they reached 80–90% confluence . The media was replaced with 300 µl of media containing 7 . 5 ng/ml wild type or a mutant PlcHR . The cells were incubated for 2 h at 37°C with 5% CO2 . Control samples consisted of 7 . 5 ng/ml mutant or wild type PlcHR2 incubated in 300 µl media alone . To determine the percent of activity ( enzyme ) bound by the cells , the PLC activity in the supernatants from the wild type or mutant PlcHR samples incubated with cells was compared to the control samples . PLC activity was detected using the synthetic substrate ρ-nitrophenyl-phosphorylcholine as previously described [13] , which detects PlcHR activity , but it does not react at all with CHO cells that are not exposed to PlcHR . The difference in recovered activity between the controls and the samples with cells was then used to calculate the percent of activity bound to the cells . Percent activity bound = [ ( control activity - activity with cells ) /control activity]*100 . Lysing the cells with 1% Triton X-100 recovered all activity . Caspase-3 activity was assayed with the colorimetric CaspACE Assay system ( Promega , Madison , WI ) . The colorimetric substrate ( Ac-DEVD-pNA ) is hydrolyzed by activated caspase-3 releasing pNA producing a yellow color that is quantified at an absorbance of 405 nm . Camptothecin , a topoisomerase I inhibitor , was used as a positive control for activation of caspase-3 and induction of apoptosis . For inhibition of apoptosis ZVAD- FMK was added to samples at a final concentration of 50 µM . HUVEC were cultivated in 6 well tissue culture dishes to 80–90% confluency at which time the media was exchanged with 2 ml of fresh media containing PlcHR2 or other compounds to be examined . The cultures were allowed to incubate at 37°C in 5% CO2 for 3 to 16 hours . The cells were harvested by trypsin/EDTA treatment , washed with ice cold PBS and suspended in lysis buffer at a concentration of 1×108 cells/ml . To prepare lysates cells were freeze-thawed four times and sonicated twice for 15 seconds at level 10 in a Sonic Dismembrator Model 100 ( Fisher Scientific , Hampton , NH ) . The lysates were incubated on ice for 15 minutes before the cell lysate supernatant was harvested by centrifuge at 16 , 100×g for 20 minutes at 4°C . Caspase-3 activity in the cell lysates was assayed for by the manufactures' recommended protocol . EC migration assays , EC invasion assays and EC tube formation assays were performed according to manufacturer's recommendations using the BD Biocoat Angiogenesis System ( BD Biosciences ) with primary cultures of HUVEC . All animal protocols were approved by the Institutional Animal Care and Use Committee of Children's Hospital Boston . Breeding fish , wild type ( AB strain ) , or transgenic lines , were maintained at 28 . 5°C on a 14 h light/10 h dark cycle . Embryos were collected by natural spawning , and raised in 10% Hank's saline at 32°C . Microinjections into the zebrafish vasculature at 48 hpf were carried out as described by Bolcome et al . ( 2008 ) . PlcHR or PlcHR-Thr178Ala were diluted immediately before , and kept at 4°C until injection . Injected amounts are indicated in the figure legends for each experiment . Phenol red ( 0 . 05% ) was added to each condition for visibility during microinjection . Volumes of 40 nl or less were delivered into the common cardinal vein of embryos anesthetized with tricaine ( Sigma ) at 48 hpf using a gas driven microinjector ( Medical Systems Corp . ) . After injection , embryos were transferred into fresh medium for recovery , maintained at 32°C , and scored for toxin action at time points indicated in the text . Dechorionated embryos were placed in 5 mL of a 50 µg/mL solution of acridine orange ( acridinium chloride hemi-[zinc chloride] , Sigma ) in 10% Hank's saline solution at room temperature . After 30 minutes of staining while protected from light , embryos were washed three times for 10 minutes with 10% Hank's saline . Following the third wash , embryos were mounted on glass slides for fluorescence microscopy .
|
Pseudomonas aeruginosa is a major bacterial opportunistic pathogen responsible for acute ( e . g . , sepsis ) and chronic infections ( e . g . , pulmonary ) . While it expresses assorted extracellular toxins that in one way or another contribute to pathogenesis , the precise cellular and molecular mechanisms by which these factors act is largely unknown . During septicemia , P . aeruginosa frequently causes vasculitis and thrombosis . Endothelial cells line the entire vascular system of mammals and have sundry key functions in infectious diseases , including thrombosis and inflammation . Endothelial cells are essential to the formation of new blood vessels ( angiogenesis ) needed for proper wound healing . This report describes in vitro and in vivo experiments demonstrating that an extracellular toxin , PlcHR , at very low ( picomolar ) concentrations , is highly lethal to endothelial cells and inhibits angiogenesis in vivo . Data herein also suggest that PlcHR is selectively toxic to endothelial cells through its ability to bind a cell receptor ( s ) . These observations may be particularly relevant to the mechanisms by which P . aeruginosa causes vascular lesions and inhibits the healing of wounds during septic infections . Finally , the potent antiangiogenic attribute of PlcHR could be useful in the treatment of noninfectious diseases where angiogenesis contributes their pathogenesis , including the vascularization of tumors and the eye ( e . g . , diabetic retinopathy ) .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/nosocomial",
"and",
"healthcare-associated",
"infections",
"cell",
"biology/cell",
"signaling",
"biochemistry/protein",
"chemistry",
"genetics",
"and",
"genomics/disease",
"models",
"infectious",
"diseases/bacterial",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2009
|
A Complex Extracellular Sphingomyelinase of Pseudomonas aeruginosa Inhibits Angiogenesis by Selective Cytotoxicity to Endothelial Cells
|
ClinicalTrials . gov NCT03173742 .
The control of Neglected Tropical Diseases ( NTDs ) has in ivermectin ( IVM ) the most significant tool among all the drugs used for morbidity control and interruption of transmision . Due to its impact on onchocerciasis and lymphatic filariasis ( LF ) , this macrocyclic lactone has been used in millions of individuals mainly through the Mectizan Donation Program , achieving goals of breaking transmission in several countries and putting those landmark achievements in the horizon of several other countries [1–3] . The very basic approach to the use of IVM consists in its distribution to entire communities through annual or biannual mass drug administration ( MDA ) campaigns provided its excellent safety profile [4] , whose only significant severe adverse reaction has been determined by its use in Loa loa infected individuals due to the life-threatening adverse events in this group [5] . The large experience on the use of IVM in the veterinary world , where it was first introduced in 1981 and the growing perception of its capabilities in human disease since its introduction in 1985 has widened its indications to a growing number of infectious diseases . The wide spectrum of action of IVM includes treatment of Strongyloides stercoralis , Gnathostoma spp , Mansonella streptocerca and ectoparasites such as head lice or scabies [1 , 6] . Moreover , it has been evaluated in co-administration with albendazol for the treatment of soil transmitted helminthiasis ( STH ) showing an increased efficacy compared to albendazole stand-alone against Trichuris trichiura [7 , 8] , therefore increasing the feasibility of achieving transmission interruption , as shown in modelling exercises [9] . Due to its alternative mechanism of action , the addition of IVM to a benzimidazole based regimen lowers the threat of emergence of drug resistance [10] , as suggested in modelling studies conducted in veterinary medicine [11] . Furthermore , the recent finding that the endectocide effect of IVM reduces the survival of Anopheles mosquitoes that feed on an IVM treated person after a single standard oral dose , supports the integration of IVM-based interventions for the control of multiple tropical infectious diseases [12 , 13] . Along with its favorable pharmacodynamic aspects , the pharmacokinetic characteristics of IVM also appear to be opening possibilities to expand its use and access . It has rapid oral absorption , high liposolubility and is widely distributed in the body [14] . Following a standard oral dose in healthy humans , IVM reaches peak plasma levels at 3 . 4 to 5 hours; and plasma half-life has been reported to be 12 to 66 hours [15 , 16] . It is metabolized in the liver through the cytochrome P450 system and excreted almost exclusively in feces [14 , 17] . Despite its widespread use , there are relatively few studies on the pharmacokinetics of IVM in humans [18] and a full understanding of the relationship between drug levels and activity is also missing , including the mechanisms related to remnant activity beyond time points when significant drug levels are measured , as has been demonstrated in veterinary and vector-borne diseases studies [19 , 20] Regardless of its safety profile and pharmacokinetic features , IVM is prescribed for all its indications in weight ( or height ) based regimens , which difficult its administration in MDA interventions and introduces the risk of under-dosing [21] . A fixed and high dose regimen which takes advantage of the wide therapeutic index of IVM is an attractive alternative for improving the distribution and therefore potentially increasing coverage rates of treatment campaigns as has been the case for primaquine in the treatment of malaria [22] . Moreover , a safe and efficacious fixed-dose IVM in formulations different than the traditional 3 and 6 mg tablets would be required if co-formulated with other anthelmintics such as albendazole . The aim of this study was to evaluate the safety and pharmacokinetic profile of two fixed doses of IVM using a newly developed 18 mg tablet formulation compared to a standard dosing regimen at 150–200 μg/kg in healthy adult volunteers .
This study was designed to evaluate the safety and pharmacokinetic profile of 3 dosing regimens of IVM in 54 healthy adult volunteers stratified in 3 weight groups in an open-label , randomized , crossover phase I clinical trial performed under fasting conditions . The study was single dose , three-period , comprising 3 experimental phases of treatment with different doses of IVM . Each experimental period lasted from at least 12 h prior to drug administration to + 168 h post-dose ( 7 days ) . The study was carried out at the Centre d’Investigació de Medicaments ( CIM-Sant Pau ) of the Hospital de la Santa Creu i Sant Pau , in Barcelona , Spain and registered at clinicaltrials . gov ( REF NCT03173742 ) . The drugs administered were IVM 18 mg , manufactured by Laboratorios Liconsa S . A . , Spain and IVM Revectina 6 mg , manufactured by Abbott Laboratórios do Brasil Ltda , Brazil [23] , both as immediate-released tablets . Three groups of 18 healthy volunteers with different weights participated in this trial . All participants in each group received three sequential treatments with 240 mL of mineral water . Subjects in Group 1 , weighing from 51 to 65 kg , in Group 2 , weighing from 66 to 79 kg and in Group 3 , weighing ≥ 80 kg received: i ) one tablet of IVM 18 mg ( FD18 ) , ii ) two tablets of IVM 18 mg , 36 mg in total ( FD36 ) and iii ) IVM Revectina 200 μg/kg in 6mg tablets ( Weight-adjusted reference treatment: WA-ref ) using the following sliding scale: 50 to 64 . 9 kg 2 tabs , 65 to 79 . 9 kg 2 ½ tabs , 80 to 94 . 9 kg 3 tabs , 95 to 109 . 9 kg 3 ½ tabs and 110 to 115 kg 4 tabs . A fourteen-day washout period between each dosing was used ( Fig 1 ) . The rationale for the dosing groups was based on the prescription of 2 experimental fixed doses of IVM that provide an amount of drug per kg of body weight that was up to over 700 μg/kg with a minimum of 150 or 300 μg/kg for individuals of up to 120 kg ( Fig 2 ) . At the beginning of the study , the subjects were allocated to a randomization number following a procedure of consecutive assignment following a randomization list generated using Windows SPSS software ( IBM Corp . Released 2013 . IBM SPSS Statistics for Windows , version 22 . 0 . Armonk , NY: IBM Corp ) in a balanced way ( an equal number of subjects in each treatment sequence ) . From the coefficients of variation of the truncated AUC obtained ( 49 . 75% ) from previous pharmacokinetic studies with ivermectin [15 , 16 , 24] performed in healthy volunteers , by applying the formula of Sanford Bolton [25] considering an error of type 1 or α of 0 . 05 and an error of type 2 or β of 0 . 20 , the sample size obtained was 50 volunteers . However , we stratified the volunteers by body weight into 3 groups , ( N of 18 volunteers per group , N = 54 ) . Nevertheless , the proposed sample size of 18 volunteers per group was within the range cited in several publications about the pharmacokinetic evaluation of IVM [15 , 16 , 24] and it was considered adequate for the analysis of the main objective of the study . In order to avoid the possible confounding effect of the sex factor , an imbalance of more than 60% , in the male of female ratio ( rounding off to the nearest whole number ) was not allowed . Participants were male and female subjects aged between 18 and 45 years meeting the inclusion criteria: i ) No abnormal findings in medical history and physical examination , ii ) Normal laboratory tests ( hematology evaluations , blood chemistry and urinalysis ) , vital signs ( systolic and diastolic blood pressure , heart rate and temperature ) and ECG record , iii ) Not having participated in another clinical trial during the 3 months before starting the current trial and iv ) Not having donated blood during the 8 weeks prior to starting the current trial . Female volunteers had to use reliable contraceptive measures not containing hormones . Participants were asked to abstain from drinking alcoholic , xanthine-containing beverages , St John’s Wort , vitamins , herbal remedies and chewing-gum from 48 hours prior to the beginning of the study until study completion . They also agreed to abstain from beverages or food containing grapefruit for 14 days prior to the first study drug administration until study completion . Participants with prior history of alcohol consumption or use or abuse of recreational drugs , consumption of stimulating drinks ( > 5 cups of coffee , tea , chocolate or cola drinks per day ) , smokers or ex-smokers that gave up smoking less than 1 year prior to the study , with history of allergy , idiosyncrasy or serious adverse events and hypersensitivity to drugs or excipients included in drugs , positive serology for hepatitis B , C , or for HIV , and those who took any other medication or medicinal plants in a 15-day period prior to the trial , with history or clinical evidence of chronic diseases , having surgery during the previous 6 months , pregnant or lactating women were excluded . None of the volunteers was at risk of being affected by Loa loa or other filarial infections . During the three treatment periods , urine pregnancy test for female subjects and urine screening for abuse drugs ( ethanol , cannabis , cocaine , amphetamines , opiates , benzodiazepines ) were performed within the 12 hours before each treatment administration and they were not discharged until +24 h post-medication . Then they returned to the CIM-Sant Pau at +36 h , +48 h , +72 h , +120 h , and +168 h post-dose , for blood extractions . In each period after fasting overnight for 10 h , subjects received the assigned treatment described above . Fasting continued until +4 h postmedication at which time a standard breakfast was served , followed by a standard lunch at +7 h and a standard dinner at +10 h postmedication . During the experimental phase in each period , volunteers were allowed to drink water ad libitum and eat solids from 4 hours after dosing . Nineteen venous samples of 6 mL ( 2 and 4 mL for IVMB1a and IVMB1b respectively ) were collected into EDTA K2 plastic tubes at baseline and at +0 . 5 h , +1 h , +2 h , +3 h , +3 . 5 h +4h , +5 h , +6 h , +8 h , +10 h , +12 h , +16 h , +24 h through a cannula placed in the arm of the volunteer and at +36 h , +48 h , +72 h , +120 h , and +168 h post-drug administration by direct venipuncture . Blood samples were centrifuged within 60 minutes after extraction for 10 minutes at 1900 g and at 4°C and the resulting plasma samples were separated into two aliquots of 0 . 4 and 0 . 3 mL for IVMB1a and 1 . 0 and 0 . 4 mL for IVMB1b respectively that were stored at -20°C ± 5°C until assayed . Bioanalytical determinations were performed by Anapharm Europe S . L . using a HPLC/MS/MS technique following a full validated method coded 13ANE-2242V and 13ANE-2243V for IVMB1a and IVMB1b respectively according to Guideline on bioanalytical method validation [26] , with a limit of quantification of 0 . 4 ng/mL and 40 pg/mL for IVMB1a and IVMB1b respectively and following the Guide for validation of analytical and bioanalytical methods [27] . Analytical work was performed according to Good Laboratory Practices ( GLP ) . The calibration curve ranged from 0 . 40 to 40 . 00 ng/mL for IVMB1a and from 39 . 80 to 3980 . 00 pg/mL for IVMB1b . IVMB1a was extracted by a liquid-liquid procedure with tert-butyl methyl ether , whereas IVMB1b was extracted with a protein precipitation procedure with formic acid 1% prepared in acetonitrile and a subsequent liquid-liquid extraction with dichloromethane . The internal standard for IVMB1a and IVMB1b was doramectin . Within-run accuracy ( at 0 . 40 , 1 . 20 , 100 . 00 , 150 . 00 , 200 . 00 and 2000 . 00 ng/mL for IVMB1a and at 44 . 80 , 134 . 40 , 2800 . 00 , 4200 . 00 , 5600 . 00 and 56000 . 00 pg/mL IVMB1b ) ranged from 100 . 57–109 . 83% to 103 . 01–110 . 37% for IVMB1a and IVMB1b , respectively . Between-run precision was not higher than 5 . 58% and 10 . 63% for IVMB1a and IVMB1b , respectively . The Per-Protocol population , ( defined as all randomized subjects who met the entry criteria , received all study medication , completed the study and did not present protocol violations ) was used for the pharmacokinetic analysis . Pharmacokinetic parameters were estimated from the sum of IVMB1a and IVMB1b plasma concentrations–time data by non-compartmental analysis using Profesional WinNonlin-Pro version 2 . 1 ( Pharsight Corporation , Saint Louis , MO ) . Missing samples were treated as non-reportable concentration . In the case of volunteers having plasma concentrations at baseline greater that 5% of Cmax ( in treatment periods 2 and/or 3 ) , we performed a sensitivity analysis by adding or substracting this individual from the analysis in order to evaluate the impact of that volunteer in the pharmacokinetic profile . Cmax was obtained directly from the plasma concentration–time data . The area under the plasma concentration–time curve ( AUC ) to the last time with measurable concentration exceeding the limit of quantification ( Ct ) of the drug ( AUC0t ) was estimated by applying log/linear trapezoidal rule . The terminal plasma elimination half-life ( t1/2 ) was calculated as t1/2 = 0 . 693/ke , where ke represents the first-order elimination rate constant associated with the terminal ( log linear ) portion of the curve , estimated via linear regression of time versus log concentration . The apparent volume of distribution ( V/F ) of IVM was calculated as V/F = D/ ( ke*AUC0∞ ) , where D is dose and F is bioavailability and AUC extrapolated to infinity ( AUC0∞ ) was determined by adding the extrapolated area ( Ct/ke ) to the AUC0t . Total oral clearance ( Cl/F ) was calculated as D/AUC0t . The safety population defined as all randomized subjects who took at least one dose of the study medication was used for safety analyses . Vital signs ( systolic/diastolic blood pressure in decubitus and heart rate ) were recorded on each treatment day at baseline and at +0 . 5 h , + 1h , +4 h , +10 h , +24 h , +48 h , +120 h , and +168 h post-dose; hematology evaluations , blood chemistry , and urianalyses were performed at the screening visit in all subjects and at the end of each of the three periods . Moreover , a complete physical examination and ECG were assessed at the screening visit and at the end of each the three periods . All adverse events observed either by the investigator or reported by the subjects themselves during the clinical study were also recorded and evaluated by the investigator for severity and relationship to the study drug . The severity of each AE was graded according to the following categories: mild , moderate , or severe . The study adhered to the updated Declaration of Helsinki [28] and was conducted according to rules of Good Clinical Practice [29] . Prior to initiation , the study protocol was approved by an independent ethics committee ( Clinical Research Ethics Committee of the Hospital de la Santa Creu i Sant Pau , in Barcelona , Spain ) and the national competent authority ( Spanish Agency for Medicines and Health Care Products , AEMPS , Spain ) . All subjects provided written informed consent to participate in the study after the nature and purpose of the study was fully explained to them and received stipends for their collaboration . Descriptive statistics were calculated for all pharmacokinetic parameters as well as a comparison between treatments were performed for all pharmacokinetic parameters . A comparative analysis of bioavailability was applied for the parameters determining exposition in extent [AUC0t] and rate of absorption [Cmax] without dose correction . Three four-way analysis of variance ( ANOVA ) for the crossover design were used to assess the effect of dosage , periods , sequences , and subjects-within-sequences on the same parameters . For comparison of the safety and tolerability of IVM between fixed doses ( FD18 vs FD36 ) and between fixed doses and WA-ref adjusted by body weight for each group , an ANOVA and posterior paired analysis with contrast was performed for the parameters obtained in the vital signs , ECG , hematology evaluations , blood chemistry and urianalyses . Additionally , for pooling of all groups , an ANOVA of 2 factors ( ANOVA group and treatment arm ) and posterior paired analysis with contrast was performed for the same parameters . The incidence of treatment-emergent AEs ( TEAEs ) was classified by system organ class and preferred term according to the Medical Dictionary of Regulatory Activities ( MedDRA version 20 . 0 ) , the relationship to the study drug , and the severity for each dose . Plasma concentrations over 16 ng/ml are generally considered antimosquitocidal for individuals receiving IVM [13] . The minimum , maximum , and median time during which participants had concentrations superior to 16 ng/ml were calculated and compared by means of a Friedman test an a posterior Wilcoxon test , overall and for each weight group . Additionally , we explored the statistical association of weight and BMI with the main PK parameters ( AUC0t , Cmax , V/F , Cl/F and t1/2 ) by means of an ANCOVA incorporating weight or BMI as a covariates . All the analysis were carried out by mean of Windows SPSS software ( IBM Corp . Released 2013 . IBM SPSS Statistics for Windows , version 22 . 0 . Armonk , NY: IBM Corp ) .
Study population initially included 27 female and 30 male caucasian volunteers without clinical evidence of laboratory , ECG or vital sign abnormalities . None of the randomized subjects were smokers . A description of the most important demographic parameters and other baseline data is shown in Table 1 . Fifty-seven volunteers were included in the study and received at least one dose of study drug . After recruitment , one volunteer withdrawn due to personal reasons and two were excluded ( one for for a slightly prolonged partial thromboplastin time and the other by protocol deviation in treatment administration ) and did not complete the study . A total of 54 volunteers completed the study ( Fig 3 ) . All subjects receiving at least one dose of study drug were included in the safety analysis ( n = 57 ) . No abnormal result or significant differences were found between biochemistry at baseline and after the administration of IVM in any of the three study arms . A slight decrease in Haemoglobin ( Hb ) levels was observed after administration of IVM in the three study arms . Hb decreased from 142 . 80 ± 13 . 8 g/L at the screening to 137 . 1 ± 13 . 55 g/L at the end of the study in the WA-ref ( p<0 . 001 ) , to 136 . 5 ± 14 . 51 g/L for FD18 ( p<0 . 001 ) and to 135 . 4 ± 12 . 97 g/L for FD36 ( p<0 . 001 ) . However , no signs or symptoms of anemia were detected in any of the study participants . The main electrocardiographic parameters were not affected by the administration of IVM . Systemic blood pressure measurements were not affected by treatment administration . A total of 33 treatment emergent adverse events were reported by 22 subjects who received at least one dose of the study medication . Eleven adverse events were reported by 10 subjects after receiving WA-ref , 9 were reported by 9 subjects after receiving FD18 and 13 were reported by 13 subjects after FD36 ( Table 2 ) . No significant association was found between the distribution of adverse events and the three treatments arms ( p = 0 . 695 ) . The most frequent adverse event described by study participants was headache ( 6 . 02% of the study subjects ) , followed by dysmenorrhea ( 5 . 54% ) , throat pain ( 1 . 80% ) and diarrhea ( 1 . 80% ) . Of the 33 adverse events reported , 10 were graded as mild and 23 were graded as moderate . The type and distribution of adverse events by study group are shown in Table 3 . Fifteen adverse events were considered possibly related to the investigational products and 18 not related to the investigational products . It was necessary to administer concomitant medication on 14 subjects due to appearance of adverse events ( Table 2 ) . The initial analysis of plasma concentrations for IVM showed that one participant presented baseline levels of IVMB1a above 5% of Cmax in period 2 , 4 participants presented baseline levels above 5% of Cmax of IVMB1b in period 2 and 2 participants presented baseline levels above 5% of Cmax of IVMB1b in period 3 . A sensitivity analysis was conducted to evaluate if the absence of these 5 subjects with the pre-dose value greater than 5% of Cmax value had implications in the main PK parameters . Since the results were not significantly altered , we present the main results of the study including all 54 participants . The pharmacokinetic parameters of IVM in the three study groups are shown in Table 4 . Fig 4 shows the mean IVM plasma concentrations in the three treatment arms . The parameters related with drug exposure ( AUC0t and Cmax ) showed a high interindividual coefficient of variation ( CV ) ( CV = 37 . 4% and CV = 32 . 5% ) and intraindividual variability ( CV = 39 . 6% and CV = 33 . 2% ) respectively . When comparing the systemic bioavailability ( AUC0t and Cmax ) of WA-ref with the other two study groups using fixed doses , we observed an overall increase in AUC0t of 2 . 9% for FD18 and a 74 . 44% increase for FD36 . These higher values were observed in Cmax as well , showing a similar increase of 4 . 7% of the systemic bioavailability for FD18 and a 80 . 69% increase for FD36 . The analysis of the relationship ( ANCOVA ) between IVM PK parameters with BMI and weight indicates that individuals with high BMI and weight present higher V/F and t1/2 . However , no significant association was found between weight and BMI with Cmax and AUC0t ( Table 5 ) . The median time ( hours ) which the participants presented plasma IVM levels above those described as the lethal concentration 50 ( LC50 ) against Anopheles gambiae s . s . ( >16 ng/ml ) was 8 h for WA-ref , 8 h for FD18 and 14 h for FD36 ( p<0 . 001 ) .
We report in this study safety and pharmacokinetic results of an alternative dosing regimen for IVM . These results are of particular relevance for public health interventions based on preventive chemotherapy through MDA of anthelmintics as those used for onchocerciasis and LF and under study for other NTDs like STH and scabies , as well as malaria [9 , 30 , 31] . This study provides the first pharmacokinetic and safety data of a formulation of IVM in 18 mg tablets , which adds further logistical advantages for fixed dosing as proposed in this study and sets the pharmaceutical conditions for an eventual co-formulation with other anthelmintics like albendazole for the control of LF , Trichuris trichiura and S . stercoralis in areas where these species are found among the prevalent STH . Safety data was consistent with previous studies regarding the lack of significant adverse events even at the highest doses uses in this study ( 36 mg ) which in the lowest weight group ( 51 to 65 kg ) providing doses of up to 700 mcg/kg [16 , 32 , 33] . Changes in Hb observed through the study , even in the control group treated with usual dosing , might be explained by the frequent blood draws although further studies might be needed . However , these hematologic results were not found in other smaller studies using IVM at doses up to 2000 mcg/kg in healthy volunteers or 800 mcg/kg in individuals infected with O . volvulus [16 , 34] . Although IVM was very well tolerated , 14 participants received treatment to control adverse events that were mostly to improve mild headache , common in participants of phase I trials after deprivation of caffeine and other substances . At the same time that fixed and higher doses of IVM proved to have an excellent safety profile in our study , higher systemic exposure ( AUC0t increased approximately 65% for FD36 ) of plasma IVM were achieved overall among study participants . Although no efficacy evaluation has been done , similar or higher efficacy is expected against the common pathogens targeted by IVM , while keeping a good safety profile and facilitating delivery of the drug . As expected , higher levels of AUC0t and Cmax were found in participants of group 1 , compared to the heavier group of participants in group 3 ( FD36 ) . These differences in systemic exposure among participants having different weights might have implications on the efficacy of ivermectin , potentially achieving higher cure rates in those patients with lower weight . Values of AUC0t and Cmax at the three dose studied are consistent with linear behavior previously reported by Guzzo et al . with single doses of up to 120 mg [16] . Pharmacokinetic parameters related with drug exposure in magnitude ( AUC0t , AUC0∞ ) and rate ( Cmax ) showed a high inter and intra individual variability , as reported by other authors [15 , 16 , 24 , 35] . The absorption parameter tmax is comparable to that reported in other studies in healthy volunteers [15 , 16 , 35] . Disposition parameters ( V/F and Cl/F ) are rarely reported in IVM pharmacokinetic studies in healthy volunteers . However , the values obtained in our study for Cl are in the range from what has been previously reported from 12 to 30 L/h [36][37] . It has been recently suggested that an increased drug variability associated with suboptimal drug concentrations may have implications on the development of IVM resistance [11] . However , this data is based in modeling studies in veterinary medicine , and still more studies are needed to confirm this hypothesis . The strategy proposed in this study is not only based in a fixed-doses of IVM but also is based in the use of a high dose , ranging from 200 mcg/kg for those weighting 90 kg to 360 mcg/kg for those weighting 50 kg . Thus , we ensure that patients do not receive lower doses of the drug , and most of them receive a superior dose of IVM . Most notably , elimination half life was long enough to still be detectable at significant levels ( 5% of the Cmax ) after the 14-day wash-out period in 5 cases , which although proved not to affect the analysis , highlights the persistence of IVM in plasma in some participants . The elimination half life ranged between 60 to 100 hours in the different weight groups , with increasing values in the individuals with higher BMI and weight probably reflecting the high liposolubility of IVM with longer retention times proportional to the presence of more adipose tissue; an explanation also consistent with the finding of longer half lifes in females than in males reported by other authors [38][14] . Our findings reveal longer half lifes of IVM than other studies reporting values from 12 to 28 h in healthy volunteers , [36] [16][23] However , this could be expIained due to the fact that other authors reported studies with shorter follow-up periods and detectable IVM levels at the latest timepoints ( 56 and 60 hours ) [36][16] The use of IVM for the different indications for which it has a demonstrated clinical usefulness has dosing strategies that in all instances are based on weight based dosing . Although dose finding experiments have identified the appropriate dosing like in onchocerciasis , where doses higher than 150 mcg/kg appear to have no increments in efficacy ( neither toxicity ) [32] , there are no target plasma drug levels or adequate markers of efficacy . A potential exception is being attempted on the benefits of IVM as a mosquitocidal drug for the control of malaria , where a lethal concentration 50 ( LC50 ) against Anopheles gambiae s . s . has been estimated at 15 . 9 ng/ml [13] . In our study , those concentrations were maintained for 8 hours in the WA-ref and FD18 and for 14 hours in the FD36 group . Strategies are currently being evaluated to mantain IVM concentrations in human blood at mosquitocidal levels [39] . Our proposal of using high and fixed dose of IVM could be helpful to prolong IVM concentrations at levels that could have impact on Anopheles mosquitoes , although probably combined with other strategies , such as increasing the drug administration to multiple-days regimens [40] The limitations of this study include the healthy , non-infected status of the volunteers; although this limitation might not be relevant based on a previous study showing no differences in PK parameters between O . volvulus infected individuals and controls [35] . Whether the same applies for individuals infected with gut-dwelling parasites is currently unknown . Another limitation is the use of a different IVM for the reference group rather than the widely used Mectizan donated by Merck , which is used in the large majority of MDA programs . However , the Abbott labs IVM used in this study is the reference IVM product in Brazil [23] . In conclusion , the administration of IVM in a fixed dosing strategy with 18 mg or 36 mg is as safe as the reference product adjusted by weight , adding a potential benefit due to the increased systemic exposure to the drug particularly in low weight adult individuals . Moreover , the fixed dose regimen offers a logistical advantage for the deployment of large MDA interventions aiming at the control and interruption of transmission of NTDs , and facilitates the co-administration with other antihelmintics prescribed this way , like albendazole or mebendazole . Further studies evaluating these concepts in pediatric populations and infected individuals , as well as clinical trials with efficacy endpoints are warranted .
|
Current efforts for the control of poverty-related diseases provide drug treatments through mass drug administration ( MDA ) as a key component . Ivermectin is an antiparasitary drug which has been used to fight some of these diseases , and millions of treatments have been distributed with a favorable toxicity profile . The dosing strategy of ivermectin is based on weight , which in view of the safety characteristics of ivermectin might not be necessary , while a fix dosing strategy might improve logistics and access to the drug to those who need it . This study was conducted in healthy adult volunteers in which we compared 3 treatment regimens: the weight-based reference standard versus 2 experimental regimens of fix-dose 18 and 36 mg using 18 mg tablets . All 54 volunteers received the 3 treatments sequentially . The results confirmed that the fixed-dose regimen ( both 18 mg and 36 mg ) are as safe as the standard dosage and could justify the use of fix dosing regimens rather than the current weight based strategy .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"pharmacologic",
"analysis",
"body",
"weight",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"clinical",
"research",
"design",
"diet",
"research",
"design",
"physiological",
"parameters",
"nutrition",
"pharmaceutics",
"drug",
"administration",
"pharmacology",
"research",
"and",
"analysis",
"methods",
"elimination",
"half-life",
"calculation",
"adverse",
"events",
"blood",
"plasma",
"pharmacokinetics",
"tea",
"blood",
"anatomy",
"pharmacokinetic",
"analysis",
"physiology",
"beverages",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy"
] |
2018
|
Safety and pharmacokinetic profile of fixed-dose ivermectin with an innovative 18mg tablet in healthy adult volunteers
|
The murine model of experimental cerebral malaria ( ECM ) has been utilised extensively in recent years to study the pathogenesis of human cerebral malaria ( HCM ) . However , it has been proposed that the aetiologies of ECM and HCM are distinct , and , consequently , no useful mechanistic insights into the pathogenesis of HCM can be obtained from studying the ECM model . Therefore , in order to determine the similarities and differences in the pathology of ECM and HCM , we have performed the first spatial and quantitative histopathological assessment of the ECM syndrome . We demonstrate that the accumulation of parasitised red blood cells ( pRBCs ) in brain capillaries is a specific feature of ECM that is not observed during mild murine malaria infections . Critically , we show that individual pRBCs appear to occlude murine brain capillaries during ECM . As pRBC-mediated congestion of brain microvessels is a hallmark of HCM , this suggests that the impact of parasite accumulation on cerebral blood flow may ultimately be similar in mice and humans during ECM and HCM , respectively . Additionally , we demonstrate that cerebrovascular CD8+ T-cells appear to co-localise with accumulated pRBCs , an event that corresponds with development of widespread vascular leakage . As in HCM , we show that vascular leakage is not dependent on extensive vascular destruction . Instead , we show that vascular leakage is associated with alterations in transcellular and paracellular transport mechanisms . Finally , as in HCM , we observed axonal injury and demyelination in ECM adjacent to diverse vasculopathies . Collectively , our data therefore shows that , despite very different presentation , and apparently distinct mechanisms , of parasite accumulation , there appear to be a number of comparable features of cerebral pathology in mice and in humans during ECM and HCM , respectively . Thus , when used appropriately , the ECM model may be useful for studying specific pathological features of HCM .
Cerebral Malaria ( CM ) , one of the most severe complications of Plasmodium falciparum ( Pf ) infection , is defined clinically by an unrousable coma in the presence of Pf parasitemia , with no other known cause of neuropathology [1] . Although the syndrome only occurs in 1% of Pf infections , it has a high fatality rate ( 15–20% of cases ) , with death typically occurring despite administration of established anti-malarial drug regimens [1 , 2] . Moreover , whilst CM induced-encephalopathy has historically been considered acute and reversible , recent follow-up studies in individuals post-CM have determined that a significant percentage ( 10–26% ) exhibit long-term neurological sequelae [3] . Individuals with limited prior exposure to parasite are disproportionally susceptible to the syndrome [4]; as a result , the majority of fatal CM cases consist of young children in endemic regions of Africa [5] . Indeed , with an estimated 2–3 million cases of the syndrome annually , CM-associated mortality and neuro-disability imposes a substantial social and economic burden on this region [6 , 7] . Consequently , there remains an urgent need to understand the pathogenesis of CM , to facilitate the development of more efficacious anti-malarial drugs and/or adjunct therapies for the condition . Neuropathological studies from fatal CM cases have detailed dense sequestration of parasitised erythrocytes ( pRBCs ) within the cerebral micro-vasculature as a canonical feature of the syndrome [8–11] . Indeed , pRBC sequestration in cerebral capillaries and venules is quantitatively greater in HCM patients , than in individuals who succumb to non-cerebral malarial complications [9 , 10] . It is believed that pRBC congestion of vessels may impair tissue perfusion by perturbing cerebral flow , and/or lead to local immune-mediated injury via secondary host response ( s ) to parasite products [11 , 12] . However , accumulating evidence indicates that CM is a relatively complex neuropathology , with pRBC sequestration typically occurring concomitant with significant intravascular accumulation of mononuclear cells , intracerebral haemorrhage , enhanced blood-brain barrier ( BBB ) permeability and oedema [5] . Moreover , pathology is not restricted to the cerebral vasculature during CM , and axonal injury and demyelination have also been documented [5 , 13] . Nevertheless , despite our knowledge of the pathology of fatal CM , restricted access to post-mortem samples for histopathological study has prevented correlation of pathological features with onset of clinical symptoms . Thus , the importance and/or relative contributions of the above observed pathological events to pathogenesis of CM remains incompletely understood . Inaccessibility of the human brain pre-mortem has led to the development and study of the experimental mouse model of cerebral malaria ( ECM ) [14–20] . Susceptible mice infected with Plasmodium berghei ( Pb ) ANKA present with similar graded and sequential signs of disease as humans affected with CM ( HCM ) ; including ataxia , paralysis , coma and , if untreated , death [21] . Furthermore , mice treated with anti-malarial drugs at the point of neurological dysfunction demonstrate comparable levels of mortality and long-term cognitive dysfunction [22 , 23] . Such similarities in clinical presentation and long-term consequence between ECM & HCM , suggest the pathophysiological processes underlying the two conditions may be comparable . Indeed , parasite accumulation has been observed in the brains of mice that developed fatal malaria-induced cerebral pathology compared with those that developed asymptomatic infections [24] . Concurrent to parasite accumulation , haemorrhage and BBB disruption have also been observed in the brains of mice experiencing ECM; with the latter perceived as a key feature of the syndrome [25 , 26] . Additionally , brain-accumulating CD8+ T-Cells have been shown to play a critical role in ECM pathogenesis by promoting BBB disruption via perforin and Granzyme B dependent mechanisms [27–29] , potentially following interaction with brain endothelial cells cross-presenting parasite antigen [30 , 31] . Despite the extensive use of the ECM model , there remains significant debate regarding its validity to study HCM [21 , 32–35] . In particular , the importance of pRBC sequestration within the brain for the development of ECM has been questioned [36] . Indeed , it is currently unknown whether true pRBC sequestration occurs during ECM , or if pRBCs simply accumulate within intracerebral haemorrhages and/or leukocyte occluded brain vessels [37] . This lack of understanding is because intracerebral parasite accumulation during ECM has previously been studied using spatially insensitive techniques such as RT-PCR or whole body luminescent imaging , rather than through detailed histopathological assessment , such as performed during HCM [24 , 36–39] . Importantly , the lack of detailed histopathological knowledge of the ECM syndrome means we also currently do not know the spatial relationship between pRBCs and other pathological parameters involved in ECM development , such as CD8+ T-cells , haemorrhage and oedema , or how these pathological events affect different brain regions . Consequently , at present the pathology of ECM has been incompletely characterised and , as such , it is not possible to definitively conclude whether it is a valid model to study all , some , or none of the pathological features of HCM . In this study , to improve our knowledge of the pathology of ECM , we have performed a systematic and quantitative histopathological investigation of ECM using comparable methodologies as utilised in the study of HCM . Crucially , we show that intracapillary parasite accumulation throughout the brain is a canonical feature of ECM , and that a single mature , pRBC seems sufficient to occlude narrow murine capillaries , and thus cause localised haemostasis . Parasite accumulation also appears to mediate the subsequent local intravascular recruitment of low numbers of CD8+ T-cells that , together with parasite , is associated with widespread BBB disruption . Strikingly , BBB disruption appears to occur due to vascular junction remodelling and increased levels of caveolae , rather than through extensive endothelial cell apoptosis . Finally , we detected axonal and myelin injury adjacent to multiple neurovascular pathogenic parameters associated with ECM , indicating two potential common pathways for neurological impairment to occur during malaria-induced cerebral pathology . Collectively , our data indicates that the mechanisms underlying the response of the brain to local parasite accumulation are conserved between humans and mice , and , therefore , supports the use of the ECM model to understand the pathogenesis of HCM .
We sought to characterise the pathological features specifically associated with ECM , compared with those that simply occur during uncomplicated malaria infection . Therefore , we utilised two closely related murine plasmodium strains with contrasting infection outcomes . Consistent with our previous studies [26 , 40 , 41] , C57BL/6 mice infected with Pb ANKA typically developed signs of late stage ECM; including ataxia , convulsions , paralysis and/or coma , on day 7 ( p . i . ) ( Fig 1A ) . In contrast , mice infected with Pb NK65 , despite exhibiting comparable parasitemia ( with the notable exception of day 7 p . i . ) ( Fig 1B ) and weight loss ( Fig 1C ) as mice infected with Pb ANKA , survived the critical window for developing ECM ( days 6–12 p . i . ) without exhibiting neurological symptoms . Pb NK65 infected mice instead developed hyperparasitemia and succumbed to infection on day 25 ( Fig 1A ) . Thus , this comparative model provides a tractable way to identify host and parasitological events that specifically contribute to the development and progression of malaria-induced cerebral pathology . Although analyses using RT-PCR and luciferase-expressing parasites have shown that ECM is associated with accumulation of parasites in the brain [24 , 37–39] , the compartmentalisation of the pRBCs in the brain during ECM is not known . Consequently , whether parasite sequestration , and subsequent microvascular obstruction , occurs within the brain during ECM development is , at present , unclear . Therefore , we performed a detailed analysis of parasite accumulation in the well-perfused brains of mice infected with Pb ANKA and Pb NK65 . Utilising GFP-tagged parasites , which enabled us to perform high resolution histopathological analyses of pRBC location within the intact brain architecture , we observed significantly higher accumulation of pRBCs in all assessed brain regions ( S1 Fig ) during Pb ANKA infection compared with Pb NK65 infection ( we did not detect any innate signal through the GFP channel in the brains of uninfected mice ) ( Fig 2A and 2B & S2 Fig ) . We noted differences in the size of parasite GFP signal , and confirmed these differences in expression related to parasite maturity using whole brain homogenate ( S3 Fig ) . To exclude the possibility that differences in GFP expression by Pb ANKA and Pb NK65 parasites were responsible for the observed differences in parasite accumulation , we additionally confirmed that significantly greater intracerebral parasite accumulation occurs during Pb ANKA infection compared to Pb NK65 infection by utilising Pb anti-sera ( S4 Fig ) . Whilst GFP expression is constrained to live parasite , Pb anti-sera visualised all parasite material , and thus the degree of Pb anti-sera immunoreactivity was much higher relative to anti-GFP staining from the same samples ( S4 Fig ) . Importantly , we noted that the >90% of intracerebral pRBCs during ECM were intracapillary , rather than associated with intravascular accumulations of leukocytes and/or haemorrhage ( Fig 2C and 2D ) . Combined , these results demonstrate that global parasite accumulation within cerebral capillaries is a specific event associated with ECM . As pRBC accumulation is principally compartmentalised within the microvasculature during ECM , we theorised that this phenomenon is likely dependent on a form of sequestration . Accordingly , we utilised transmission electron microscopy ( TEM ) to characterise the precise nature of the interaction between pRBCs and cerebral endothelial cells ( ECs ) in the brains of mice infected with Pb ANKA . Consistent with our immunofluorescence staining , we observed pRBCs within the capillaries of perfused brains from Pb ANKA mice with late-stage ECM ( Fig 3A ) . Moreover , we detected the occasional electron-dense spot on the surface of some pRBCs adjacent to the EC membrane ( Fig 3AI ) ; such events were not evident on the surface of uninfected erythrocytes ( Fig 3B ) . Whilst we noted greater numbers of luminal RBCs compared to luminal pRBCs , importantly , longitudinal sections of capillaries invariably demonstrated that such RBCs accumulated specifically behind vessel-spanning pRBCs ( Fig 3C ) . We further assessed the level and presentation of intravascular pRBC accumulation during ECM by smearing the well-perfused brain tissue of mice infected with Pb ANKA . Brain smears preserve lengthy microvessels , and thus have historically been used in HCM for assessing sequestration [42 , 43] . We observed several intracapillary trophozoites and schizonts in Romanowsky and H&E stained brain smears ( Fig 3D and 3E ) . Critically , and in agreement with our TEM data , a single pRBC appeared sufficient to occlude a capillary . Consequently , uninfected erythrocytes and/or immature pRBCs could be observed in varying degrees of accumulation behind individual , mature pRBCs within , apparently obstructed , capillaries ( Fig 3F–3K ) . We did not identify any cytoadherent pRBCs in venules or other larger calibre vessels . Conversely , we noted that a number of these larger vessels were distended and enriched with leukocytes , predominantly monocyte/macrophages , often dense with parasitic material ( Fig 3L ) . We did not observe any extravascular pRBCs , though parenchymal macrophages ( potentially microglial cells ) enriched with parasitic material were observed occasionally ( Fig 3M ) . These observations were validated in H&E stained sections of perfused brain tissue derived from Pb ANKA infected mice; whereby a number of microvessels were seen to be congested with erythrocytes , of which only a minority were parasitised ( Fig 3N , 3NII , 3O & 3OIII ) . Critically , these results indicate that pRBC accumulation during ECM is dependent , or co-dependent , on parasite strain intrinsic capacities to deform within , and/or cytoadhere to , the cerebral microvasculature . Moreover , our results show that a single , pRBC appears sufficient to occlude and cause haemostasis within narrow murine cerebral capillaries . Critically , these observations imply that , despite the lower cerebral parasite biomass noted in ECM compared to HCM , the haemorheological consequences of pRBC accumulation may be similar in both . We next utilised our comparative model to perform a detailed quantitative examination of CD8+ T-cells . Whilst CD8+ T-cells are known to play a critical role in the development of ECM [27] , we , and others , are still investigating their role in HCM . Due to an inability to utilise CD8 mAbs in fixed murine tissue [44] , T-cells were labelled with CD3 . Importantly , the majority of CD3+ T-cells in the brain are also CD8+ during ECM ( S5 Fig ) . T-cells were observed in the cerebral vessels ( identified by tomato lectin ) of mice infected with either strain of Pb , and absent from the cerebral vessels of uninfected mice ( Fig 4A , S6 Fig ) . Furthermore , whilst the number of T-cells was quantitatively greater in all brain regions from mice infected with Pb ANKA compared to Pb NK65 , total T-cell numbers were low ( Fig 4B ) . Indeed , despite ECM being a CD8+ T-cell dependent syndrome , T-cells were , on average , rarer than pRBCs in all corresponding brain regions from mice infected with Pb ANKA ( Figs 2B and 4B ) . T-cells were predominantly found luminal or abluminal to the cerebral microvasculature , or as part of dense intravascular leukocyte accumulations in larger-calibre vessels during ECM ( Fig 4C and 4D ) . Of note , the majority of leukocyte packed vessels contained lectin-labelled monocytes or macrophages , rather than CD3+ T-cells ( S7 Fig ) . There was no evidence of extravasation of T-cells into the brain parenchyma , and the few T-cells observed within the parenchyma were associated with intracerebral haemorrhage ( Fig 4C and 4D ) . As pRBCs and/or parasite material were predominantly found in the same intracerebral compartments as T-cells ( Figs 2C and 2D and 4C and 4D ) , we therefore hypothesised that the two might co-localise within the same subset of vessels . To test this hypothesis , we stained brain smears sampled from mice exhibiting fulminant ECM by H&E , and examined the association between morphologically-identified lymphocytes and parasite . We noted that while arrested pRBCs were often independent of lymphocytes , intracapillary or perivascular lymphocytes were invariably proximal to pRBCs ( Fig 4E , S7 Fig ) . Our results indicate that , in the context of local pRBC accumulation , very few intracerebral T-cells are required for the development of ECM . Moreover , the co-localisation of T-cells with pRBCs implies that pRBCs may promote CD8+ T-cell accumulation . We next employed our comparative model to characterise the neurovascular-pathological events downstream of cerebral T-cell and pRBC accumulation during Pb ANKA infection . H&E staining demonstrated that intracerebral haemorrhage , a commonly described neuropathological feature in HCM and ECM , was quantitatively greater in all brain regions ( with the notable exception of the olfactory bulbs ) during Pb ANKA infection compared to Pb NK65 infection ( Fig 5A and 5B , S8 Fig ) . Intracerebral haemorrhage was not observed in uninfected brains ( S8 Fig ) . Large amorphous haemorrhages , perivascular bleeding and/or petechiae were all observed in the brains of mice during late-stage ECM ( Fig 5C–5F ) . Whilst thrombosed vessels , with and without extravasated erythrocytes , were evident ( Fig 5G and 5H ) , we saw no evidence of ring haemorrhages; a typical feature of HCM . As opposed to HCM , where haemorrhage occurs predominantly in the white matter [5] , haemorrhaging was observed equivalently in the grey and white matter during ECM ( Fig 5I and 5J ) . Our results , therefore , indicate that the frequency of haemorrhaging is increased during ECM , compared to uncomplicated malaria . However , the relative rarity of haemorrhage , in comparison to other pathological events , suggests it may not be the predominant cause of mortality during ECM . Cerebral oedema resulting from enhanced BBB permeability is a common feature of HCM [5 , 45 , 46] , and is thought to be a critical element of ECM pathogenesis [18 , 26 , 47] . However , to date , there has been no attempt to quantitatively assess the nature or presentation of vascular permeability during ECM . We assessed BBB permeability during P . berghei infection by staining for endogenous IgG , a serum protein ordinarily excluded from the cerebral parenchyma by an intact BBB [48] . We observed noticeably higher levels of IgG immunoreactivity , and , correspondingly , significantly increased numbers of permeable vessels , in the brains of mice infected with Pb ANKA compared to those infected with Pb NK65 ( Fig 6A and 6B & S9 Fig ) . Positive IgG staining was not observed in uninfected brains ( S10 Fig ) . Permeable vessels were characterised by a “halo” of IgG ( Fig 6C ) , or dense extravascular depositions of IgG and/or IgG immunoreactive astrocytes ( Fig 6D ) . In some brain regions ( in particular the brainstem ) from mice infected with Pb ANKA , but not Pb NK65 , dense areas of IgG immunoreactive neurons were observed ( Fig 6E ) . Although such neuronal staining has been defined as a historical marker of cerebral oedema [49 , 50] , as it was not possible to relate this parenchymal staining to particular blood vessels we did not quantify this pathological feature in our analysis . Furthermore , we excluded from our analysis any vessels that exclusively exhibited intravascular IgG staining ( Fig 6F ) , as this identified occluded , not permeable , vessels . Extravasation of IgG was typically associated with haemorrhage ( Fig 6G ) , larger calibre leukocyte-occluded vessels ( Fig 6H ) , and/or intracapillary pRBCs ( Fig 6I ) . Interestingly , scattered permeable vessels devoid of pRBCs , leukocytes or haemorrhage were also observed , however , these were typically in the vicinity of vessels exhibiting a specific pathological feature ( as described above ) ( Fig 6J ) . Consistent with our IgG staining , we also observed clear histological evidence of cerebral oedema during ECM ( Fig 6K and 6L ) , suggesting the severity and/or prevalence leakage must be substantial . Combined , these results , in agreement with the literature [26 , 51] , show that BBB permeability is widespread within the brain during ECM , and that vascular leakage and subsequent oedema is significantly greater during Pb ANKA than during Pb NK65 infection . Importantly , our data also shows that whilst BBB permeability during ECM is typically associated with parasitised microvessels , intravascular accumulations of leukocytes or haemorrhage , permeable vessels devoid of any such associated vascular pathology are also present . Whilst this could indicate that the pathological event triggering the vascular leakage cleared subsequent to analysis , it may also suggest that soluble mediators expressed by the vascular bed at distinct pathological sites may induce diffuse BBB permeability during ECM . We next examined the mechanistic basis for intracerebral vascular permeability during ECM . In particular , as it has been proposed that CD8+ T-cells promote cytolysis of cross-presenting endothelial cells [30 , 31] , we assessed the level of cellular apoptosis in the brains of mice infected Pb ANKA or Pb NK65 . Cellular apoptosis ( detected by expression of cleaved Caspase 3 ( CC3 ) ) was rarely observed in the brains of mice infected with Pb ANKA , was even less frequent during Pb NK65 infection , and was not observed in the brains of uninfected mice ( Fig 7A & S11 Fig ) . The majority of the apoptotic events during ECM were associated with the vasculature , and were predominantly endothelial cells , leukocytes , or , more infrequently , astrocytes ( Fig 7B ) . Atypical parenchymal cellular apoptosis did not appear to be neuronal , based on morphological criteria ( Fig 7B ) . Notably , cerebral oedema ( characterised by uncondensed parenchyma and/or perivascular dilation ) was observed proximal to vessels with and without evidence of apoptotic ECs ( Fig 7C ) , suggesting leakage is not dependent on EC loss . Haemorrhages were associated with disrupted vessel staining , but not EC apoptosis; indicating non-apoptotic mechanisms are likely responsible for haemorrhage ( Fig 7D ) . We saw no evidence of conterminous vascular degeneration adjacent to haemorrhage , i . e . endothelial cell apoptosis within the afflicted vascular bed; though we did observe apoptotic leukocytes focal to the lesion ( Fig 7D ) . Importantly , the area and number of vessels was unaltered during Pb ANKA and Pb NK65 infection ( S12 Fig ) . This implies that vascular loss is a limited and stochastic event associated exclusively with haemorrhage , and not a central contributor to the cerebral oedema seen during ECM . Combined , these results indicate that programmed cell death of ECs is highly unlikely to be the major mechanism provoking the widespread vascular leakage that occurs during ECM . As widespread BBB disruption during ECM did not appear to be associated with a loss of cerebral ECs , we subsequently sought to examine whether alterations in transcellular and/or paracellular transport mechanisms could account for enhanced brain vessel permeability . Utilising TEM , we observed extensive pseudopodia , or cytoplasmic extensions , in a number of vessels during ECM . Pseudopodia were seen only rarely on cerebral ECs during Pb NK65 infection and not seen in uninfected samples ( Fig 8A & S13 Fig ) . Caveolae were abundant in cerebral ECs in the brains of mice infected with Pb ANKA compared to Pb NK65 ( Fig 8AI and 8AII ) . In some vessels aggregations of caveolae appeared to form transendothelial pores specifically during ECM ( Fig 8B ) . In addition , large clefts in the cerebral microvascular tight junctions were observed during ECM , but were only rarely seen in uncomplicated malaria infection ( Fig 8C and 8D ) . Interestingly , clefts in tight junctions and accumulations of caveolae were observed proximal to cerebral oedema ( Fig 8E and 8F ) . This suggests that alterations to the transcellular and/or paracellular permeability of the brain microvasculature may be responsible for the vascular leakage observed during ECM . Additionally , we noted an apparent thickening of the basement membrane and luminal contraction consistent with vasospasm in a number of the cerebral microvessels of mice infected with Pb ANKA compared to Pb NK65 ( Fig 8A ) . However , due to the natural range of capillary diameter within the rodent brain [52] , definitive assessment of vasospasm was not possible in our analysis . Whilst it is not entirely clear how the cerebral vascular pathology that characterises HCM influences parenchymal brain function to induce coma and death , it has been shown that axonal injury ( AI ) and myelin loss are common neuropathological features of the syndrome [5 , 13] . Conversely , there is currently no histopathological data defining the neurological abnormalities that occur during ECM . Using our comparative model , we observed significant evidence of AI specifically during ECM , as shown by β-APP , a protein that accumulates at sites of AI ( Fig 9A ) . Several patterns of β-APP staining were evident: labelling of single axons; diffuse regions; more intense regions; and scattered , intensely-immunoreactive neuronal cell bodies ( Fig 9B ) . AI was noted adjacent to specific vascular pathological features during ECM , including: parasitised capillaries; leukocyte-packed vessels; and haemorrhage ( Fig 9C and 9D ) . In contrast , we observed that the neuronal architecture ( defined by NeuN staining ) was broadly unaltered during both Pb ANKA and Pb NK65 infection ( S14 Fig ) . Moreover , and consistent with our data in Fig 7 , we saw no evidence of apoptotic neurons in the brains of Pb ANKA or Pb NK65 infected mice ( S14 Fig ) . However , we did note neuronal lesions proximal to some haemorrhages in the olfactory bulbs of mice infected with both Pb ANKA and Pb NK65 ( Fig 9E ) . In addition to AI , there was evidence of extensive myelin pathology specifically in the brains of mice infected with Pb ANKA ( Fig 9F ) . We observed discrete regions of gross demyelination , and myelin pallor and fragmentation associated with parasitised capillaries , leukocyte-packed vessels and haemorrhage ( Fig 9G and 9H ) . Collectively , these results suggest the nature of cerebral parenchymal damage is comparable in HCM and ECM , and provide a logical explanation for the clinical similarity in the transient and long-term neurological dysfunction that occurs during and post HCM and ECM . To further improve our understanding of the pathogenesis of ECM , we examined the spatial nature of the defined pathological features within individual ECM-affected brains . Although the magnitude of parasite accumulation varied between cases of ECM , we observed broad trends in regional parasite accumulation within individual cases of ECM ( Fig 10A ) , with parasite load typically greater in the Olb , Ctx , TH , MB and CBX than the other regions ( Fig 10A ) . The number of haemorrhages was typically highest in the Olb ( dramatically in some cases ) , but was of low level and variable in other regions between brains ( Fig 10A ) . Conversely , the spatial nature of T-cell accumulation and permeable vessels was highly consistent in all cases of ECM , with the pathological features showing strong regional overlap ( Fig 10A ) . Thus , although ECM is clearly a graded syndrome where the magnitude of pathological events varies from cases to case , the pathology is not stochastic with specific brain regions consistently more severely affected than others . This implies that architectural or physiological properties may predispose specific brain regions to malaria-induced cerebral pathology . Given the conserved and equivalent regionalisation , we examined the co-dependent relationship ( s ) between the identified pathological processes and their relative ( individually and in combination ) contribution in promoting ECM . As expected , there was a significant correlation between regional parasite and T-cell load , which is in agreement with the observation that parasites and T-cells co-localise within the tissue ( S15 Fig & Fig 4E ) . Interestingly , the degree of vascularity did not correlate significantly with the number of parasites , haemorrhages or permeable vessels within brain regions , suggesting that vessel quality , rather than quantity , is more critical in determining regional pathological burden during ECM ( S15 Fig ) . Importantly , through using generalised linear modelling , we found that combinations of histopathological parameters were , generally , better predictors of vascular permeability ( which our results indicate is the major pathological event during ECM ) within a brain region , compared to any single histopathological parameter ( Fig 10B & S1 Table ) . For example , parasite burden , T-cell load and degree of vascularity combined were a better predictor of the number of permeable vessels within a brain region , than any of these factors in isolation ( Fig 10B ) . Collectively , these data support the assertion that ECM is a multifactorial neuropathology that does not develop in response to a singular , dominant pathological event within any region of the brain . However , parasite accumulation within the brain appears to be a proximal event important for intracerebral T-cell accumulation , localisation and function , which ultimately provokes vascular dysfunction .
In this study we have utilised detailed histopathological investigations , analogous to those used in the study of HCM , to definitively assess the relative merit of the ECM model for the study of HCM . Critically , by contrasting cerebral pathology observed during ECM with that during uncomplicated malaria infection , we have also substantially resolved the specific intracerebral events associated with the development of the ECM syndrome . We demonstrated that the global accumulation of pRBCs within the capillaries of the murine brain is a specific and cardinal feature of ECM . The compartmentalisation of pRBCs predominantly within the cerebral microvasculature during ECM , rather than pooled within haemorrhage or entrapped by intravascular leukocyte aggregations , indicates that intracerebral pRBCs likely play a causal role in the late-stages of the murine syndrome . Indeed , the comparable efficacy of anti-malarial drug treatment in reversing ECM and HCM strongly implies that intracerebral pRBCs play an active role in the late-stages of both mouse and human malaria-induced cerebral pathology [2 , 22] . However , our results also highlight clear differences in the presentation and magnitude of parasite accumulation during ECM compared with HCM . We found that intracerebral pRBCs were typically observed individually and irregularly distributed within brain capillaries during ECM . In contrast , a number of studies have shown that Pf parasitised erythrocytes are densely packed and congest significant lengths of the microvasculature during HCM [8–11 , 43 , 53] . Nevertheless , despite these differences , our results imply that some of the consequences of intracerebral pRBC accumulation may be the same in mice and humans . We have shown that in ECM , as in HCM , pRBC-dependent occlusion of brain capillaries and haemostasis are associated features of disease . In ECM , the width of murine cerebral capillaries necessitates the single-file passage of extensively deformed tubular erythrocytes . Consequently , the arrest of a single pRBC appears to be sufficient to occlude a murine brain capillary , and thus cause localised haemostasis . Conversely , in HCM , the available histopathological evidence suggests that mechanical obstruction of brain capillaries and resultant haemostasis depends on the incremental accumulation of large numbers of cytoadherent pRBCs [8–11 , 43 , 53] ( Fig 11 ) . Indeed , a process whereby uninfected erythrocytes are initially able to squeeze past cytoadhered pRBCs , until a critical threshold of pRBCs is reached within a vessel and mechanical obstruction occurs , is the only logical explanation for the high intracerebral pRBC sequestration indexes observed in HCM ( i . e . in one study it was shown that that , on average , 66 . 5% of intracerebral RBCs were parasitised , compared to 1 . 4% in the peripheral blood [42] ) . Thus , although the presentation of pRBC accumulation in the cerebrovasculature may be different in ECM and HCM , our results provide a rational potential explanation for the comparable alterations in blood flow observed in vivo during murine and human malaria-induced cerebral pathology [54–59] . Whether the differential natures of pRBC-mediated vascular occlusion during ECM and HCM depend upon established differences in murine and human capillary diameter ( average 3um vs 6 . 4um ) [60–62] , or upon the degree to which murine and human cerebral capillaries can mechanically dilate , requires further investigation . The major question , therefore , is how do Pb ANKA parasites accumulate within the brain during ECM ? Whilst we did not observe any knob formation on Pb infected erythrocytes ( and thus no knob-based cytoadhesion as is observed with Pf [9 , 11 , 63 , 64] ) , we did observe the occasional electron dense spot on the surface of some parasitised erythrocytes adjacent to the cerebral vasculature during ECM . Such events appear comparable to the knob-independent forms of sequestration demonstrated by Pf infected RBCs in vitro [65] , and may reflect the in vitro capacity of Pb ANKA infected RBCs to bind VCAM-1 expressed by brain ECs [66] . However , electron dense spots were not observed consistently on pRBCs within perfused brain microvessels , implying other mechanisms must also contribute to intracapillary pRBC accumulation during ECM . The equivalent propensity of Pb ANKA and Pb NK65 to parasitise larger reticulocytes suggests pRBC size does not determine the capability of different Pb strains to immobilise within cerebral capillaries [67] . However , it may be that some strain intrinsic qualities relating to the rheological properties of pRBCs , including deformability , specify the intracerebrovascular accumulation capacity of Pb infected erythrocytes . Indeed , Pasini et al have previously demonstrated differences in the repertoire of proteins expressed by ECM-inducing and non-ECM inducing Pb strains [68] . Crucially , although the differential expression of these proteins did not directly alter the cytoadherent capabilities of the parasites , it was not assessed whether the repertoire of protein expression influenced pRBC rheology . However , we also noted , consistent with a previous study [55] , that vasospasm appeared to be a specific feature of Pb ANKA infection . A narrowing of the vascular lumen would exacerbate any rheological impairment , suggesting variant host responses to different Pb strains may also contribute to haemostasis during ECM . The conclusion that mature Pb ANKA infected RBCs become mechanically trapped within narrow murine brain capillaries during ECM , rather than accumulating as a result of strong cytoadherence , is supported by our failure to detect independent pRBC accumulation in venules or other large calibre vessels . Indeed , we previously failed to observe long-lasting pRBC adherence within the wider pial vessels using intravital microscopy [26] . Moreover , as opposed to observations in HCM [69] , we occasionally observed extravascular pRBCs in the perivascular spaces and within haemorrhages ( an observation that was relatively more common in the meninges [26] ) , suggesting Pb ANKA pRBCs are not tightly adhered to the brain endothelium and thus freely liberated from vessels upon necrotic EC loss . Further work will be required to identify the precise factors that dictate the capacity of specific Pb strains to accumulate within the cerebrovasculature during infection to cause ECM . Nevertheless , although our results suggest that ECM is a good model to understand the downstream effects of intracerebral pRBC accumulation and resultant haemostasis , they also imply that it is not a good system to investigate the consequences of direct pRBC cytoadherence to brain ECs . However , relevantly , the importance of direct ( parasite sequestration-dependent ) compared with indirect ( inflammation-driven ) activation and dysfunction of human brain ECs in the development of HCM is yet to be definitively identified [70] . Although our data indicates that pRBC-mediated occlusion of the cerebrovasculature appears to occur during ECM , as is observed during HCM , microvascular obstruction alone cannot explain the full repertoire and nature of murine and human malaria-induced cerebral pathology [71] . Beyond ischemia , which does not satisfactorily explain our neuropathological findings , nor the rapidly reversible nature of ECM and HCM , it is unclear how parasitised erythrocytes and subsequent microrheological alterations promote coma and death . Murine studies propose the cross-presentation of merozoite-derived parasitic material by cerebral ECs licences cerebrovascular CD8+ T-cells to promote vascular leakage during ECM [30 , 31] . Accordingly , we observed significantly greater numbers of intracerebral CD8+ T-cells during Pb ANKA infection compared to Pb NK65 infection . CD8+ T-cells were located within the intra- or perivascular space and , interestingly , were typically proximal to pRBCs or parasite material during ECM . Indeed , there was a strong correlation between parasite and T-cell load within the different brain regions . Thus , our data , in the context of the current literature , suggests intracerebral pRBC accumulation and subsequent microvascular obstruction fulfils three roles vital to the pathogenesis underlying ECM: 1 ) to provide parasite antigen for cross-presentation; 2 ) to promote haemostasis , thus ensuring the necessary microenvironment in which cerebral ECs are able to obtain merozoites for cross-presentation , which , under physiological flow conditions , would normally be rapidly cleared; and 3 ) to instigate signals important for the cerebrovascular localisation of CD8+ T-cells . Notably , despite the importance of CD8+ T-cells in promoting ECM [27] , they were observed relatively rarely within the brains of mice infected with Pb ANKA; being less frequent than pRBCs and substantially less populous than macrophages and monocytes . The reasons for the differences in intracerebral macrophage/monocyte and CD8+ T-cell numbers , considering they are similar located in the cerebrovasculature and likely depend on the same EC-derived ligands/integrins , are not clear , but may depend upon temporal differences in recruitment [72] . Nevertheless , the general rarity of CD8+ T-cells within the brain implies that very few are required to promote ECM and , furthermore , may explain the failure , thus far , to consistently locate this cell population in HCM histopathological studies [5 , 9] . CD8+ T-cell-dependent vascular leakage is currently considered critical to the development of ECM [26 , 51] . Consistent with this , we observed greater evidence of cerebral oedema during Pb ANKA infection compared to Pb NK65 infection . The causes of oedema during ECM appeared multifactorial in origin as , in accordance with HCM histopathological study [5] , we detected increased permeability around haemorrhages , parasitised capillaries , intravascular leukocyte accumulations , and some scattered microvessels devoid of any specific vascular-pathological feature . Indeed , we observed significant correlation between the number of permeable vessels within a brain region and parasite , T-cell or haemorrhage load . These observations additionally support the hypothesis that extensive cerebral oedema may promote the fatal cerebral swelling that occurs during human and murine malaria-induced encephalopathy [25 , 26 , 51 , 73 , 74] . Our data also provides significant information on the mechanism through which CD8+ T-cells cause vascular leakage during ECM . We showed that , whilst haemorrhage was evidently secondary to EC damage , the majority of vascular leakage occurred independently of EC loss or apoptosis . This suggests , consistent with our previous study [26] , and despite the importance of CD8+ T-cell cytolytic functions in promoting BBB dysfunction during ECM [28 , 29] , that vascular leakage occurs without EC loss during murine malaria-induced cerebral pathology . Instead of vascular loss , we demonstrated that significant vascular remodelling occurs specifically during ECM . Clefts within the microvascular tight junctions and increased levels of caveolae were noted within ECs adjacent to cerebral oedema . Moreover , pronounced cytoplasmic extensions , or pseudopodia , were evident in several brain vessels . Such alterations in EC morphology are traditionally viewed as hallmarks of angiogenesis [75] . Consequently , our observations support a model whereby CD8+ T-cells , via Granzyme B and perforin [28 , 29] , promote a vascular stress response in ECs , resulting in the production of angiogenic factors which , although protective against cellular apoptosis , cause lethal alterations to the paracellular and/or transcellular permeability of the cerebrovasculature during ECM ( Fig 12 ) . Supporting this hypothesis , in a variation of Theiler’s murine encephalomyelitis model of MS , CD8+ T-cells have been shown to promote the production of VEGF in a perforin-dependent fashion , causing vascular leakage without cerebral EC loss [76] . Importantly , this scenario affords a rational explanation as to why rapid recovery from ECM and restoration of vascular integrity can occur after anti-malarial drug treatment , which would not be possible if vascular leakage were determined by extensive and irreversible EC loss . Intriguingly , not only has vascular leakage in the absence of EC loss been observed in HCM histopathological studies [5] , but so has disruption of intercellular tight junctions [12 , 77] . Our data , in conjunction with the literature , imply the mechanisms underlying vascular dysfunction may be conserved between humans and mice during malaria-induced cerebral pathology . These observations further underline the requirement for highly resolved histopathological studies to be performed to specifically examine the potential importance of CD8+ T cells in the pathogenesis of HCM . Therefore , the final and critical questions are 1 ) how does the neurovascular pathology characterising ECM influence parenchymal brain function to induce coma and death , and 2 ) are the pathways to neurological dysfunction conserved between ECM and HCM ? We demonstrated that axonal injury ( AI ) is a significant and specific feature of ECM , as evidenced by positive β-APP staining , a protein that accumulates at sites axonal damage [78] . Axons extend significant distances , and are thus dependent on a huge number of microvessels for the provision of oxygen and glucose to permit their metabolically expensive functions [13] . Indeed , AI has been shown to occur in response to both hypoxia and hypoglycaemia [79 , 80] . Accordingly , we observed AI proximal to erythrocyte congested vessels , suggesting microvascular obstruction likely accounts for much of the AI observed during ECM . However , we also observed AI adjacent to haemorrhage and intravascular leukocyte accumulation , indicating that axonal dysfunction is potentially a common mechanism through which multiple pathological parameters of ECM impair neurological function . Interestingly , β-APP accumulation within axonal tracts may represent reversible axonal damage , consistent with the rapid neurological recovery observed in ECM after anti-malarial drug treatment [81] . In addition to AI , we frequently observed myelin damage during ECM , with areas of myelin pallor and vacuolation seen proximal to erythrocyte-congested vessels , and physical loss of the myelin sheath detected adjacent to haemorrhage . The progressive accumulation of myelin and axonal damage would explain the graded and sequential neurological dysfunction observed during ECM , including ataxia , fitting and reduced responsivity [82] . Coma may ensue due to a culmination of axonal and myelin pathology ( potentially as a programmed neuroprotective response to lower cerebral metabolic demand ) . Importantly , the nature of AI and myelin damage observed in ECM is highly similar to that reported in HCM [5 , 13] . Moreover , AI and myelin damage are colocalised to specific , shared vascular-pathological features , including haemorrhage and pRBC occluded microvessels , in both ECM and HCM [5 , 13] Combined , this implies that the mechanisms responsible for the reversible and/or permanent neurological dysfunction observed during and after an episode of malaria-induced encephalopathy , are very likely conserved between mice and humans . In summary , the results in this manuscript show , in significant detail and for the first time , that mature pRBCs specifically accumulate within the cerebral capillaries during ECM . Although the mechanism and presentation of pRBC accumulation during ECM is significantly different to that observed during HCM , there appears to be overlap in the pathological impact of parasite-induced haemostasis during both syndromes . It is , however , clear that ECM does not appear to be a good model to study the impact and role of pRBC cytoadherence to cerebral blood vessels in the development of malaria-induced cerebral pathology . Nevertheless , in spite of this , a number of pathological features of HCM are observable and appear comparable in nature during ECM , including; BBB disruption , AI and myelin damage . The critical role of CD8+ T cells in initiating the ECM syndrome , when roles for the cells have yet to be revealed in HCM , remains a divisive point . Based upon our data , showing significant similarities in vascular and parenchymal pathology in ECM and HCM , and the fact that so few cerebrovascular CD8+ T cells can dominantly drive cerebral pathology during ECM , detailed investigations of the role of CD8+ T cells during HCM are warranted . Nonetheless , if , after detailed investigation , it is ultimately found that CD8+ T cells cannot be involved in the development and progression of HCM , the observation that many of the pathological features of ECM are similarly found in HCM indicates that convergent signals may ultimately drive the same severe pathological manifestations during ECM and HCM . Thus , when used appropriately ( i . e . not solely relying on KO mouse studies to examine pathogenesis or employing treatments before ECM develops ) , our collective results support the utilisation of the ECM model to understand the pathological events secondary to pRBC accumulation in HCM . In addition , careful utilisation of the ECM model may be useful for the identification of novel adjunct therapies for the repair and resolution of the vascular and parenchymal damage that occurs similarly within the established ECM and HCM syndromes .
All animal work was approved following local ethical review by the University of Manchester Animal Procedures and Ethics Committees and was performed in strict accordance with the U . K Home Office Animals ( Scientific Procedures ) Act 1986 ( approved H . O . Project License 70/7293 ) . Female and male 8–10 week old C57BL/6 mice were purchased from Charles River and maintained in individually ventilated cages at the University of Manchester . Cryopreserved P . berghei ANKA GFP [83] and P . berghei NK65 GFP [84] parasites were thawed and passaged once through C57BL/6 mice before being used to infect experimental animals . Animals were infected via intravenous injection of 1x104 parasitised red blood cells ( pRBCs ) . Peripheral parasite burdens of infected mice were followed from day 3 post infection ( p . i . ) by microscopic examination of giemsa stained thin blood smears and weight loss was monitored . The development of ECM was assessed using a well-established clinical scale [41]: 1 = no signs; 2 = ruffled fur and/or abnormal posture; 3 = lethargy; 4 = reduced responsiveness to stimulation and/or ataxia and/or respiratory distress/hyperventilation; 5 = prostration and/or paralysis and/or convulsions[41] . Stages 4–5 were classified as ECM . P . berghei ANKA infected mice were euthanised ( exposure to a rising concentration of CO2 ) when they reached stage 5 ( typically day 7 p . i . ) and P . berghei NK65 mice were culled at the equivalent time point . After termination , the hepatic portal vein was severed and mice were transcardially perfused with 10mls of 0 . 1M ice cold phosphate buffered saline ( PBS ) followed by 10mls of ice cold 4% paraformaldehyde ( PFA ) . Brains were dissected out and post-fixed in PFA/20% sucrose for 16-24h at 4°C . Brains were subsequently cryoprotected in PBS/20% sucrose for 48h , snap-frozen in powdered dry ice and stored at -80°C . Brains were serially sectioned at a thickness of 30μm on a freezing sledge microtome ( Bright Instruments , Cambridge , UK ) . Series of coronal sections encompassing 10 spatially-defined anatomical regions of the brain ( S1 ) were stored in cryoprotectant solution ( 30% ethylene glycol , 20% glycerol in PBS ) in the individual wells of a 24 well tissue culture plate ( Corning , NY , US ) at -20°C until use . Brain sections were stained with direct and indirect immunofluorescent technique for the following: i ) anti-GFP ( 1:400 rabbit anti-GFP Alexa Fluor 488 conjugated clone A-21311 Life Technologies ) for detection of GFP-tagged parasites; ii ) CD31 ( 1:200 rat monoclonal antibody [mAb] clone MEC 13 . 3 BD Pharmingen ) for visualisation of vasculature; iii ) CD3 ( 1:100 rat mAb clone CD3-12 AbD Serotec ) for assessment of cerebral T lymphocyte accumulation; iv ) Lycopersicon esculentum ( tomato ) lectin ( 1:100 [reconstituted 1mg/ml in PBS] biotin conjugated clone L0651 Sigma-Aldrich ) for visualisation of vasculature and activated cerebral macrophage populations; v ) cleaved caspase 3 ( 1:200 rabbit mAb clone Asp175 5A1E Cell Signalling ) for assessment of cellular apoptosis; vi ) NeuN ( 1:200 mouse mAb biotin conjugated clone A60 Chemicon/Merck Millipore ) for demonstration of neuronal architecture; and vii ) β-Amyloid Precursor Protein ( β-APP 1:200 rabbit polyclonal antibody [pAb] clone CT695 Zymed/Thermo Fisher Scientific ) for detection of axonal injury . The nature of the immunofluorescent staining protocol varied depending on the antibody/epitope pairing . Some stains ( i and ii ) were performed utilising free-floating protocols . The remaining stains were performed after floating sections were washed in PBS and mounted in distilled water onto Superfrost Plus slides ( VWR ) , before drying vertically overnight at 37°C . All sections were re-hydrated in several changes of PBS before being subjected to heat-mediated antigen retrieval in either Sodium Citrate pH9 buffer ( i and ii ) , Sodium Citrate pH6 buffer ( iv , v , vi and vii ) or Tris EDTA pH9 buffer ( iii , iv and v ) pre-heated , respectively , to 80°C , 95°C and 99°C in a water-bath . Sections were subsequently heated for 30 minutes and then allowed to cool at room temperature for 20 minutes . With respect to β-APP ( vii ) , slides were additionally treated with 90% formic acid in distilled water for 15 minutes . All sections were then washed in several changes of wash buffer ( 0 . 1M Tris-HCL pH7 . 5 , 0 . 15M NaCl , 0 . 05% Tween in distilled water ) before being blocked for 1 . 5 hours at room temperature in block buffer ( 0 . 1M Tris-HCL pH7 . 5 , 0 . 15M NaCl , 1% Bovine Serum Albumin [BSA] 0 . 3% Triton X in distilled water ) . Block was removed and sections were incubated with primary antibody diluted appropriately in block buffer; either at room temperature for 3 hours ( iv and v ) or 12–20 hours at 4°C ( i , ii , iii , iv , v , vi and vii ) . Sections were rinsed several times in wash buffer , and , for fluorescent detection , sections were incubated for 1 . 5 hours at room temperature in excess quantities of secondary antibodies ( goat-anti rat 546 , goat anti-rat 647 , goat anti-rabbit 546 , streptavidin 546 , and streptavidin 647 Life Technologies/Thermo Fisher Scientific ) diluted in block buffer . An intermediate incubation utilising a biotinylated goat anti-rabbit antibody ( Vector ) was undertaken to amplify signal for adequate fluorescent visualisation of β-APP ( vii ) , whilst a Tyramide Signal Amplification kit ( Thermo Fisher Scientific ) was utilised as per manufacturer’s instructions in order to enhance detection of CD3 ( iii ) . Sections were finally washed in several changes of wash buffer and counterstained in DAPI ( Sigma-Aldrich ) . Sections were sequentially rinsed in PBS and distilled water , dried overnight in the dark at room temperature and then coverslipped in ProLong Diamond anti-fade Mountant ( Life Technologies ) . Antiserum to Pb ANKA and Pb NK65 infected erythrocytes was prepared as previously described [85] . In brief , mice underwent three rounds of infection and drug cure before whole serum was extracted and IgG purified on Protein-G ( HiTrap ) . Mounted brain sections were blocked with rat serum prior to incubation with anti-Pb ANKA or Pb NK65 IgG for 1 hr at room temperature . Following incubation with anti-PbA IgG , slides were visualised using FITC rat anti-mouse antibody ( clone 11-4011-85: E-Bioscience ) . To assess BBB permeability , brain sections were stained via indirect immunoperoxidase technique for endogenous Immunoglobulin G ( IgG 1:500 Horse pAb biotin conjugated clone BA-2000 Vector ) . Free-floating sections were washed in PBS and mounted on to Superfrost Plus slides ( VWR ) in distilled water , then allowed to dry vertically overnight at 37°C . Sections were rehydrated in several changes of PBS and subjected to heat-mediated antigen retrieval in preheated Sodium Citrate pH6 buffer at 95°C for 30 minutes , and then allowed to cool at room temperature for 20 minutes . Slides were rinsed twice in PBS and then endogenous peroxidase activity was blocked by incubation in 3% H2O2 in distilled water at room temperature for 30 minutes . Sections were washed in several changes of PBS and then incubated for 3 hours at room temperature with primary antibody appropriately diluted in PBS , 0 . 3% Triton X and 0 . 1% BSA . Slides were washed thoroughly in PBS and 0 . 1% tween and then incubated for 1 . 5 hours at room temperature in ABC solution , as per manufacturer’s instructions ( Vector ) . Colour was developed via a 5 minute incubation in diaminobenzidine tetrahydrochloride ( DAB , Merck Millipore ) . Sections were counterstained with haematoxylin ( Vector ) , dehydrated through alcohol , cleared in two changes of xylene and coverslipped using DPX mounting agent ( Sigma-Aldrich ) . Brain sections were stained via haematoxylin and eosin ( H&E ) to assess the degree of haemorrhage , parasite sequestration , oedema and white matter disruption . In brief: free-floating sections were washed in PBS and mounted on Superfrost Plus slides ( VWR ) in distilled water , then allowed to dry vertically overnight at 37°C . Sections were stained using a Thermo Shandon Linstain GLX ( Rankin Biomed , US ) automated staining machine . Slides were coverslipped using DPX mounting agent ( Sigma-Aldrich ) . Animals were terminated via exposure to a rising concentration of CO2 . The hepatic portal vein was severed and mice transcardially perfused with 10mls PBS . Brains were removed and anatomically comparable regions of cortical and cerebellar grey matter , measuring no greater than 1mm in diameter , were excised . Smears were generated between two microscope slides as previously described [43] . For H&E staining , slides were immediately wet-fixed in acetic alcohol at 4°C , and staining performed as previously described for cytological specimens [86] . For Quik-Diff staining , slides were air-dried before subsequent fixation and staining , performed as per manufactures instructions ( Baxter ) . Animals were terminated via exposure to a rising concentration of CO2 . The hepatic portal vein was severed and mice transcardially perfused with 10mls PBS . Brains were removed and a single-cell suspension was generated by homogenising tissue through a 70μm cell strainer ( BD Falcon ) in 5ml of ice-cold PBS . 10μl of cells were pipetted onto a microscope slide and cover-slipped . Images were collected on a Zeiss Axioskop upright microscope or Olympus BX51 upright microscope using a 20x objective and captured using a Coolsnap ES camera ( photometrics ) through MetaVue software ( Molecular Devices ) . Images were then analysed and processed utilising either ImageJ or Image-Pro Premier software ( Media Cybernetics ) . Under isoflurane anaesthesia , mice were sequentially perfused intracardially with PBS and fixative ( 2% PFA and 2 . 5% glutaraldehyde ) at 10ml/minute for 7 minutes . Brains were removed and post-fixed for a further 4 hours before anatomically comparable regions of cortical and cerebellar grey matter , measuring approximately 1mm in width , were excised and post-fixed for a further 20 hours . Tissue was then additionally fixed on ice for 1 hour with 1 . 5% potassium ferrocyanide and 2% osmium tetroxide ( weight/vol ) in 0 . 1M cacodylate buffer . This was followed by incubations with 1% thiocarbohydrazide for 20 minutes at room temperature , 2% osmium tetroxide for 30 minutes at room temperature and 1% uranyl acetate at 4°C overnight . The next day , samples were stained with freshly prepared Walton’s lead aspartate ( 0 . 02M in lead nitrate and 0 . 03M in aspartic acid , pH 5 . 5 ) for 30 minutes and embedded in Epon 812 ( hard forumalation ) epoxy resin ( Electron Microscopy Science , UK ) . Resin-embedded samples were subsequently cut at a thickness of 70nm using an ultramicrotome ( Leica ) . Sections were mounted on formvar-coated grids and viewed on an FEI Tecnai 12 Biotwin Transmission Electron Microscope . To assess endothelial cell morphology in each specimen , images of the first 15 capillaries identified were collected digitally using a Gatan Orius SC1000 camera . To examine the interaction between the endothelium and sequestered pRBCs , entire grids were examined at low power to identify regions of interest and then imaged digitally at a high power ( Gatan Orius SC1000 camera ) . Images were analysed and processed utilising ImageJ . Ten spatially-defined anatomical regions of the brain , determined by the Allen reference atlas for the C57BL/6 brain [87] , were selected for examination . For quantitative purposes 10 random fields of view per region were captured . Distribution , number and/or area of GFP+ parasites , CD3+ T-cells , haemorrhages , extravascular IgG+ permeable vessels and CD31+ vessels were counted manually in a blinded fashion or via ImagePro Premier’s smart segmentation technology in a semi-automated fashion as previously described [26] . Data are expressed as number or area of objects/mm2 in a given brain region , or , alternatively , as % of a given distribution within the total number of objects within a given brain region . All statistical analyses were performed using GraphPad PRISM ( GraphPad Software ) . Comparison between two groups was made using unpaired t tests with Welch’s correction . Comparison between multiple groups was made using a one-way ANOVA with Tukey’s test for multiple comparisons . Correlation between different variables within individual brain regions was determined using Spearman-Rank test . Generalised linear models were fitted to the data using the lm function of the R statistical language . Linear models were fitted in turn to each measured variable and combinations thereof , within individual brain regions . The quality of different models was compared by computing the R2 value .
|
Cerebral malaria ( HCM ) is the most severe complication of malaria infection . Despite this , we have an incomplete understanding of the cause ( pathogenesis ) of the syndrome . To improve our understanding of HCM pathogenesis , animal models of the syndrome have been developed . The most commonly used model is the murine experimental cerebral malaria ( ECM ) model . However , to date , there has not been a detailed investigation of the pathology of ECM using the same methodological approaches ( histopathology ) employed in the study of HCM . Thus , it has been unclear whether ECM is a valid model for HCM . In this histopathological study , we show that , as in HCM , cerebrovascular parasite accumulation is an important feature of ECM . However , unlike HCM , we did not observe large numbers of parasitised red blood cells ( pRBCs ) attached to the walls of cerebral blood vessels during ECM; instead individual pRBCs were trapped in narrow murine brain capillaries . Nevertheless , despite this , we showed that cerebrovascular parasites were still associated with disturbed blood flow , vascular leakage and impaired neuronal function in ECM , in a similar fashion to that reported in HCM . Therefore , our results define the specific aspects of HCM pathology that can potentially be studied within the ECM model .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"cell",
"death",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"cardiovascular",
"anatomy",
"immunology",
"tropical",
"diseases",
"cell",
"processes",
"parasitic",
"diseases",
"capillaries",
"signs",
"and",
"symptoms",
"red",
"blood",
"cells",
"cytotoxic",
"t",
"cells",
"white",
"blood",
"cells",
"animal",
"cells",
"blood",
"vessels",
"t",
"cells",
"cerebral",
"malaria",
"diagnostic",
"medicine",
"cell",
"biology",
"anatomy",
"apoptosis",
"hemorrhage",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"malaria",
"vascular",
"medicine"
] |
2017
|
A quantitative brain map of experimental cerebral malaria pathology
|
Plasmodesmata provide the cytoplasmic conduits for cell-to-cell communication throughout plant tissues and participate in a diverse set of non–cell-autonomous functions . Despite their central role in growth and development and defence , resolving their modus operandi remains a major challenge in plant biology . Features of protein sequences and/or structure that determine protein targeting to plasmodesmata were previously unknown . We identify here a novel family of plasmodesmata-located proteins ( called PDLP1 ) whose members have the features of type I membrane receptor-like proteins . We focus our studies on the first identified type member ( namely At5g43980 , or PDLP1a ) and show that , following its altered expression , it is effective in modulating cell-to-cell trafficking . PDLP1a is targeted to plasmodesmata via the secretory pathway in a Brefeldin A–sensitive and COPII-dependent manner , and resides at plasmodesmata with its C-terminus in the cytoplasmic domain and its N-terminus in the apoplast . Using a deletion analysis , we show that the single transmembrane domain ( TMD ) of PDLP1a contains all the information necessary for intracellular targeting of this type I membrane protein to plasmodesmata , such that the TMD can be used to target heterologous proteins to this location . These studies identify a new family of plasmodesmal proteins that affect cell-to-cell communication . They exhibit a mode of intracellular trafficking and targeting novel for plant biology and provide technological opportunities for targeting different proteins to plasmodesmata to aid in plasmodesmal characterisation .
Plasmodesmata are channels that cross the cell wall and establish symplastic continuity throughout most of the plant . Their importance has been highlighted by the range of diverse non–cell-autonomous functions that depend on intercellular macromolecular communication through plasmodesmata . Hence , a range of transcription factors in the shoot apical meristem and at the root tip have functional roles in cells other than those in which they were produced [1 , 2] . Similarly , some small RNAs generated as part of the RNA silencing pathway can act non–cell-autonomously [3] . Last , plant virus pathogens , which are restricted to the symplast , must use plasmodesmata to invade neighbouring cells [4] , and very recently [5] it has been proposed that biotrophic fungi may exploit plasmodesmata during tissue invasion . All of these macromolecules and organisms are above the experimentally defined normal size-exclusion limits for plasmodesmata , which points to highly regulated plasmodesmal processes for their recognition and transport between cells . This is exemplified by plant viruses , all of which encode so-called movement proteins ( MPs ) that interact with and modify the properties of these structures to allow the passage of virus particles or other forms of ribonucleoprotein complexes [4] . The organisational complexity of plasmodesmata remains a matter of speculation constrained by the interpretation of electron microscopical images [6] . Briefly , they comprise plasma membrane ( PM ) -lined channels that cross the cell wall to join adjacent cells symplastically . They contain an axial-appressed membrane element derived from the endoplasmic reticulum ( ER ) , called the desmotubule , and may contain proteinaceous spoke-like structures that cross the cytoplasmic sleeve between the PM and desmotubule ( Figure 1A ) . Despite the crucial role of plasmodesmata in growth and development , plant defence , and pathogenesis , almost nothing is known about the integral components of plasmodesmata or plasmodesmal biogenesis . From the identification of proteins interacting with viral MPs , coimmunolocalisation studies applied to candidate proteins , and proteomics approaches , a number of proteins have been identified in association with plasmodesmata [7] . These include cytoskeletal elements ( e . g . , actin and myosin VIII ) , proteins found in the ER ( e . g . , calreticulin ) ( refs in [7] ) , a casein kinase 1 that phosphorylates tobacco mosaic virus ( TMV ) MP [8] , a β-1 , 3-glucanase [9] , and class 1 reversibly glycosylated proteins ( C1RGPs ) [10] . The lack of identification of the constituent components of plasmodesmata has remained an outstanding challenge in plant biology and has hindered a fuller understanding of the non–cell-autonomous control of plant development and the processes of tissue invasion by pathogens . A fundamental question relating to the formation and function of plasmodesmata is how proteins are recruited to this unique subcellular environment . Previous studies have indicated the importance of components of the cytoskeleton [11–15] and the post-Golgi vesicle trafficking systems [10 , 16] , although in no case have the specific protein sequences that define the molecular address for delivery to plasmodesmata been identified . We report here the use of Arabidopsis thaliana suspension cultures as a source of membrane proteins located in the cell wall to identify a novel family of proteins functioning within plasmodesmata to affect cell-to-cell communication , and identify the key principles by which specific subcellular targeting of these proteins is achieved .
A survey of the subcellular targeting of cell wall–associated membrane proteins derived from highly purified Arabidopsis cell walls [17] was carried out . Translational N- and/or C-terminal fusions with green fluorescent protein ( GFP ) were expressed transiently using the cauliflower mosaic virus ( CaMV ) 35S promoter in Nicotiana benthamiana leaves and onion epidermal monolayers , and transgenically in Arabidopsis plants . From this survey , we identified the protein derived from At5g43980 as a plasmodesmal protein . We refer to this newly identified protein as plasmodesmata-located protein 1a ( PDLP1a ) . Critically , expression in transgenic Arabidopsis using either the CaMV 35S promoter or the native promoter for At5g43980 showed that the fusion protein was located as punctate spots on the cell wall ( Figure 1B to 1E ) and that this fluorescence was retained on the wall after plasmolysis ( Figure 1D and 1E ) . This pattern of fluorescence was most notable in leaf spongy mesophyll cells , where the punctate spots were present on adjoining walls , but absent from the nonadjoining walls ( Figure 1E ) . Similar patterns of protein accumulation were observed following transient expression in the heterologous species N . benthamiana and onion ( Figure S1 ) . The pattern of localisation was most distinctive when PDLP1a was expressed from its own promoter , when the protein was targeted exclusively to plasmodesmata . ( Figure S2A shows a confocal stack through pPDLP1a::PDLP1a:GFP–expressing Arabidopsis epidermal cells , and unique association with plasmodesmata . ) Further evidence that these punctate sites were plasmodesmata was obtained by demonstrating colocalisation with callose ( Figure 2A–2C ) and with TMV MP:GFP ( Figure 2D–2F ) . Callose distribution was revealed by staining with the fluorescent dye aniline blue , and was especially informative in cases where patterns of both callose and PDLP1a distribution identified the characteristic organisation of groups of plasmodesmata in pit fields ( Figure S2B ) . TMV MP is one of the best-characterised viral MPs , which shows strong targeting to plasmodesmata in newly infected cells and in transgenic plants [4] . In transgenic plants expressing TMV MP , the protein characteristically is localised within complex plasmodesmata that are a feature of photosynthetic source tissues rather than in the simple plasmodesmata of sink tissues [18] . Sequence analysis showed PDLP1a to have a domain structure ( Figure 3A ) conserved in a small family of plant-specific proteins , including representatives from Arabidopsis , rice , Medicago , and Phaseolus . Briefly , the proteins are predicted to be type I membrane proteins with molecular mass ranging from 30 . 2 to 35 . 3 kDa and comprising an N-terminal signal peptide , a large region containing two similar domains annotated as domains of unknown function 26 ( DUF26 ) , a single transmembrane domain ( TMD ) , and a short C-terminal tail . DUF26 domains have a conserved C-X8-C-X2-C motif , which is distinct from the Cys-rich regions found in S-locus glycoproteins [19] . The PDLP1 families in Arabidopsis and rice comprise two clades from eight members . C-terminal fusions with GFP were constructed for all eight PDLP1 proteins and analysed for their subcellular targeting after expression from the CaMV 35S promoter; all showed targeting to plasmodesmata ( representative proteins from At2g01660 and At2g33330 are illustrated in Figure 3B ) . In addition , there are three other groups of DUF26-related proteins . These include a group of proteins that contain a signal peptide for secretion , but lack the TMD domain ( e . g . , protein from At5g48540 , which was secreted to form large , unresolved bodies in the apoplast ) , a second group where the TMD and short C-terminus are replaced with a glycosylphosphoinositol ( GPI ) anchor domain ( e . g . , protein from At1g63580 , which was localized to the PM ) , and a third group of predicted receptor-like kinases ( e . g . , protein from At4g23140 ) where the short C-terminus is replaced with a serine/threonine kinase domain . In this last case , the protein was also targeted to the PM , although in this case , the distribution was less uniform than seen for the GPI-anchored protein from At1g63580 ( Figure 3B ) . By analogy with the wider members of the 2xDUF26 class of proteins , including some kinases that have DUF26 domains located extracellularly and signal through a TMD to the cytoplasmic kinase module [20] , we predicted that PDLP1a should be orientated with its short C-terminal tail in the cytoplasm and the 2xDUF26 domain in the apoplast . To confirm this prediction , we employed a bimolecular fluorescence complementation ( BiFC ) strategy in which half of yellow fluorescent protein ( YFP ) fused to the test protein was complemented with the corresponding half of YFP targeted to the cytoplasm [21] . Hence , when either half of YFP ( YN or YC , Figure 4A ) was fused to the C-terminal tail of PDLP1a and cotransiently expressed in N . benthamiana with the alternate unfused half-YFP , BiFC fluorescence in plasmodesmata was observed ( Figure 4B ) . In contrast , when PDLP1a:YC was coexpressed with the alternate unfused half carrying signals for targeting to and retention in the ER ( YN-ER; [21] ) , no BiFC was observed ( Figure 4B ) , confirming our predictions that PDLP1a was orientated with the C-terminus in the cytoplasm ( Figure 4C ) . Since PDLP1 is a type I membrane protein with a single TMD , the N-terminal portion of the protein is by default located facing the apoplast . The implication from the location of PDLP1 in plasmodesmata is that the protein contributes functionally to symplastic communication from cell to cell . One means of assessing this property is to measure the diffusional capacity of monomeric untargeted GFP following bombardment into single cells of leaf tissues [22] in Arabidopsis lines where the expression of PDLP1 genes has been changed . Wild-type Arabidopsis leaves cobombarded with 35S::GFP and 35S::RFPer ( red fluorescent protein [RFP] targeted to the ER , therefore unavailable for symplastic diffusion , and used to mark target cells; Figure S3 ) showed the primary bombarded cells and the diffusion of free GFP to neighbouring cells where lateral diffusion could be used as a quantitative measure of plasmodesmal trafficking potential . Insertional mutant lines ( knock-out lines; KOs ) for six of the eight PDLP1 genes are available from public collections . None of these lines showed any obvious growth or developmental phenotype . GFP diffusion in leaves of these lines was not significantly different from that in wild-type Arabidopsis leaves ( unpublished data ) . However , scrutiny of the public microarray expression data ( http://www . genevestigator . ethz . ch ) showed that the tissue-specific pattern of expression of these genes differed widely with respect to expression in leaf tissues . To accommodate potential problems of functional redundancy within the family , various combinations of KOs were made by crossing . We concentrated on crosses between members of clade 1 ( Figure 3B ) that included examples of genes expressed relatively highly in leaf tissues . KO combinations of At5g43980 and At1g04520 , At5g43980 and At2g33330 , and At2g33330 and At1g04520 were tested in our GFP bombardment assay ( Figure 5A ) . Whereas the homozygous progeny of At5g43980×At1g04520 showed no significant change in the spread of GFP ( unpublished data ) , the progeny of the other crosses ( At5g43980×At2g33330 and At2g33330×At1g04520 ) showed significantly increased trafficking ability ( p ≤ 0 . 05 and ≤ 0 . 01 , respectively ) . The corollary to increased trafficking with reduced PDLP1 was tested by assessing GFP spread in transgenic plants overexpressing PDLP1a from the CaMV 35S promoter . Transgenic plants expressing genes for either PDLP1a:GFP or haemaglutinin-tagged PDLP1a ( PDLP1a:HA ) showed a reduced-growth phenotype that correlated with transgene copy number , i . e . , plants with homozygous single insertions were more dwarfed than heterozygous plants ( Figure 5B ) and this dwarfing phenotype positively correlated with protein accumulation , assessed using anti-HA antibody ( Figure 5B ) . To minimise the contributory effect of the dwarf phenotype and to avoid the complication of transgene silencing of GFP after bombardment , GFP diffusion assays were carried out on heterozygous plants of the PDLP1a:HA line . Measurements of lateral diffusion from sites of bombardment in these plants showed that cell-to-cell trafficking of GFP was highly significantly impaired ( p ≤ 0 . 0001; Figure 5B ) . Trafficking of proteins to the plasmodesmata has variously been shown to exploit ( e . g . , C1RGP [10] ) or bypass ( e . g . , TMV MP; [23 , 24] ) the secretory pathway . Chemical or protein inhibitors were used to investigate the role of the secretory pathway in PDLP1a trafficking to plasmodesmata . Brefeldin A ( BFA ) , an inhibitor of specific ADP ribosylation factor ( ARF ) GTPase exchange factors ( GEFs ) , arrests vesicle trafficking at various points along the secretory pathway [25] , whereas Sar1[H74L] , a GTPase-defective mutant of Sar1p , very specifically affects COPII-mediated ER-to-Golgi transport [26] . Both of these inhibitors provided a qualitative assessment of the roles of the ER and COPII pathways in PDLP1a targeting to plasmodesmata . Coexpression of PDLP1a:GFP and ManI:RFP , encoding a well-described secretory marker whose translocation to the Golgi apparatus depends on a functional secretory pathway [27] , resulted in the identification of PDLP1a:GFP at plasmodesmata , and distinct and separate Golgi labelling with ManI:RFP ( Figure 6A to 6C ) . Addition of BFA led to the complete loss of Golgi stacks and the concomitant formation of large ER–Golgi hybrid bodies/BFA compartment in which both PDLP1a:GFP and ManI:RFP accumulated ( Figure 6D to 6F , stars ) . In addition to this location , PDLP1a:GFP could still occasionally be seen within plasmodesmata after treatment ( compare Figure 6A and 6D , arrowheads ) . This was likely due to a ( dose-dependent ) incomplete inhibition of secretion or to pre-existing PDLP1a:GFP molecules within plasmodesmata before addition of BFA . Since BFA induces a range of effects on treated cells due to the inhibition of multiple Arf-GEFs [25] , additional experiments were performed using a dominant-negative mutant of the Ras-like small GTPase , Sar1 ( Sar1[H74L] ) , to specifically block ER export; the wild-type protein was used as the control . As expected , coexpression of wild-type Sar1:RFP with PDLP1a:GFP had no effect on PDLP1a:GFP steady-state accumulation within plasmodesmata ( unpublished data ) . In contrast , coexpression with the GTPase-impaired mutant Sar1[H74L] led to the retention of PDLP1a:GFP in the ER as deduced from the perinuclear labelling ( Figure 6G ) and the visualisation of a typical polygonal network in cortical sections ( Figure 6H ) . Similar to BFA treatment , incorporation of PDLP1a into plasmodesmata was not completely abolished in all cells . This could be as a consequence of partial inhibition of secretion following expression of Sar1[H74L] , because inhibition of secretion is directly related to the steady-state accumulation of Sar1[H74L] [28] , or prior accumulation of PDLP1a:GFP before Sar1[H74L] expression . Such variability is inherent in experiments using transient expression following agro-infiltration . Most importantly , however , induced retention of PDLP1a:GFP in the ER or in the BFA compartment was never observed under conditions where secretion was unperturbed . It is therefore concluded that PDLP1a utilises the secretory pathway for delivery to plasmodesmata . Bioinformatic analysis of the PDLP1 family clade 1 identified a common TMD of 21 amino acids upstream of a short but variable length C-terminal tail ( Figure 7A ) . To assess the importance of the C-terminal tail in directing PDLP1a to plasmodesmata , this region was deleted ( Figure 7B ) and the resulting C-terminal fusion to GFP tested for targeting to plasmodesmata . Transient and transgenic expression showed that the protein lacking the C-terminal tail was still targeted to plasmodesmata ( Figure 7C ) . That PDLP1a was targeted to plasmodesmata in the absence of the C-terminal tail led us to ask whether the TMD alone would be sufficient . To assess this , two constructs were tested . The citrine variant of YFP ( this has higher stability in the acidic extracellular environment ) [29] was fused between the PDLP1a N-terminal signal peptide and the TMD plus C-terminal tail , or the TMD alone ( Figure 8A and 8B ) . In both cases , the YFP open reading frame was flanked with flexible linker peptides [30] , minimizing protein folding interference between the YFP and PDLP1a domains . Transgenic or transient expression of the latter fusion protein showed unique targeting to plasmodesmata , which was unaffected by plasmolysis ( Figure 8C and 8D , arrows ) . For the former construct including the C-terminal tail , similar targeting was observed except that YFP fluorescence was also visible at discrete unidentified locations in the cytoplasm ( Figure 7C ) . Therefore , our experiments have uniquely identified a short TMD sequence with the capacity to target proteins to plasmodesmata . The specification of plasmodesmal targeting by the TMD alone identifies either a default pathway based upon some physical property of the TMD ( e . g . , TMD length ) , or that the TMD includes specific targeting signals . To test these hypotheses , we focussed on the C-terminal three amino acids of the TMD . It has been proposed [31] that a TMD-length rule applies to some proteins translocated along the secretory pathway to correspond with the increasing thickness of the membranes along the path . Hence , frequently , ER proteins have shorter TMDs than do PM proteins [31 , 32] . In agreement with this model , a construct with a further deletion of three amino acids at the C-terminus of the TMD , shortening it to 18 amino acids ( Figure 7B ) , was no longer targeted to plasmodesmata , but was retained within the ER ( Figure 7D ) . The three C-terminal amino acids in the PDLP1a TMD comprise the hydrophobic amino acids LVL . Alanine-substitution mutants were made in which individual or combinations of amino acids were replaced with alanine to test whether these three amino acids , in part , specified plasmodesmal targeting; alanine substitution preserved the length of the hydrophobic TMD . Single or combined mutations revealed only two targeting phenotypes . Changes that flanked the central V residue continued to be targeted to plasmodesmata . Any changes that included the central V→ A change resulted in retention of PDLP1a in the ER ( Table 1 ) .
Until now , progress on the identification of the protein constituents of plasmodesmata has been remarkably slow . Being embedded within the relatively rigid cell wall structure has hindered plasmodesmal purification using conventional biochemical approaches and delayed progress on understanding these molecular channels that lie at the heart of cellular communication in plants . Our approach , based upon a bioinformatic analysis of a partial Arabidopsis cell wall proteome [17] , has identified PDLP1 proteins as a family of novel plasmodesmal constituents . Other plasmodesmata-associated proteins have been identified ( cited earlier ) , but in the majority of cases , these appear to have multiple subcellular locations that could reflect various cellular roles . By drawing parallels with the nuclear complex , which could be considered to have analogous functions in the translocation of macromolecules , plasmodesmata should have many tens of proteins involved in their intrinsic structure and function and which may be uniquely located in that environment . PDLP1a expressed from its own promoter appears to uniquely accumulate in plasmodesmata and , hence , pPDLP1a::Pldp1a:GFP transgenic plants provide a valuable marker line for the location and frequency of plasmodesmata . In contrast to TMV MP , which only accumulates in complex plasmodesmata that are a feature of photosynthetic source tissues , PDLP1a:GFP accumulates more widely . It was identified from rapidly dividing suspension cells that contain only simple primary plasmodesmata [33] and identifies plasmodesmata in both very young ( photosynthetic sink; unpublished data ) and mature leaf tissues . The PDLP1 family resides within a larger group of proteins that share the 2xDUF26 domain configuration in common . Some of these have features of receptor-like kinases that are induced in response to treatments with pathogens or signalling molecules associated with pathogen attack ( e . g . , reactive oxygen species and salicylic acid [20 , 34 , 35] ) . These kinases have DUF26 domains located extracellularly and signal through a TMD to the cytoplasmic kinase module [20] . In accord with the predictions resulting from bioinformatic analysis suggesting that PDLP1a is a type I membrane protein , we showed that the short C-terminal tail of PDLP1a resides within the cytoplasm , leaving the 2xDUF26 domain in the apoplast . It is intriguing that such receptor-like molecules at the PM should be uniquely targeted to plasmodesmata and suggests that extracellular signals could be transduced directly into the environs of the plasmodesma to control molecular trafficking between cells . That PDLP1 proteins have the potential to modulate cell-to-cell trafficking was clearly established by altering their expression and measuring the effect in a GFP diffusion assay . This assay has been used widely as a measure of plasmodesmal function [22] . Lines overexpressing PDLP1a showed a dramatic reduction in GFP movement . Interestingly , the homozygous transgenic lines showing a large ectopic accumulation of PDLP1 also showed a reduced-growth phenotype indicative of the damaging effect of reduced cell-to-cell communication . The logical corollary of the reduced trafficking would be increased trafficking when PDLP1 proteins were reduced in their expression . However , this makes a number of assumptions: First , that in normally growing plants , the PDLP1 protein is constitutively active , and second , that the effects on single genes would not be masked due to redundancy with other members of the multigene family . The diverse expression patterns of the PDLP1 proteins are distinctive but show extensive overlap across different tissues . Hence , we were not surprised to find that single insertional KO lines showed no visible phenotype and no change in GFP movement . However , by concentrating on clade 1 of the PDLP1 family , within which there are members expressed relatively highly in leaves ( the test tissues for our GFP assay ) , we showed that combined KO lines did exhibit the reverse phenotype ( i . e . , more GFP movement ) . Three double KO combinations were tested , but only two showed increased GFP trafficking . We assume that the effect of these combined mutations is dependent upon the relative contribution each gene makes in wild-type plants and that extending the combinations further ( e . g . , triple and quadruple KOs ) would increase the likelihood of an altered response and , possibly , ultimately some negative impact upon plant performance . Uncovering the contribution of the PDLP1 proteins to plasmodesmal function will add important information to our very limited understanding of plasmodesmal control . Little has been known of the mechanisms , the chaperones , or the addresses that specify plasmodesmal targeting . For the three best-characterised plasmodesmal-associated proteins , TMV MP , β-1 , 3-glucanase , and the C1RGPs , the intracellular route to plasmodesmata is different . TMV MP is a membrane-embedded protein [36] , but in its targeting to plasmodesmata , is BFA-insensitive [24] and likely involves the actin cytoskeleton [24] . In contrast , targeting of the C1RGPs , which are soluble and not membrane proteins [37] , depends on active secretion [10] although they lack an N-terminal signal peptide . The RGPs have no established function , and the determinants for plasmodesmal targeting remain to be identified . The plasmodesmal β-1 , 3-glucanase is a GPI-anchored protein [9] that is presumably secreted . It is intriguing that this protein partitions between a general PM location and plasmodesmata , but is not retained at the latter site after plasmolysis [9] . This implies that unlike PDLP1a , the β-1 , 3-glucanase moves to plasmodesmata via the PM and sustains only a superficial physical association with plasmodesmata . The PDLP1 family of proteins have structural features typical of secreted proteins and are targeted to plasmodesmata via the secretory pathway . Hence , PDLP1a accumulates in the BFA compartment after treatment and shows sensitivity to disruption of the COPII-mediated ER-export by the GTP-locked form of Sar1 . In addition , it is well established that the destination along the secretory pathway for single-pass membrane proteins is influenced markedly by the length of the hydrophobic domains [31 , 32] . Thus , lengthening the TMD of the plant vacuolar sorting receptor BP-80 from 19 to 22 amino acids resulted in the escape of the protein from the Golgi and its accumulation in the PM [31] . Conversely , reducing the length of the TMD of another type I protein from 23 to 20 or to 17 residues led to the retention of the protein in the Golgi or the ER , respectively [31] . The same rule is likely also to apply to PDLP1a and other members of the family . With its predicted 21 amino acids , the TMD is probably sufficient to promote exit from the ER and the trafficking of the protein to the Golgi or even to the PM , a hypothesis further substantiated by the ER retention of the PDLP1aΔC+:GFP mutant with a shortened TMD . We exclude a contribution from the PDLP1a signal peptide since across the eight members of the family , the signal peptides show no amino acid homology except that they fulfil the hydrophobic and approximate length requirements of signal peptides . It seems very unlikely , however , that the length of the TMD on its own would be sufficient to specify plasmodesmal targeting . Rather , our results where we have mutated the TMD while retaining its predicted length of 21 amino acids strongly support the presence of a sorting signal that would be recognized by a receptor system and machinery ( specific vesicles; intermediate compartments; and trafficking regulatory proteins ) for delivery to the correct destination . Sorting signals of intrinsic membrane proteins generally reside within the extramembranous domains ( exposed toward the cytoplasm or the ER lumen ) [38 , 39] . In the absence of such exposed determinants , as demonstrated by plasmodesmal targeting of the chimeric SP:Citrine:TMD ( Figure 8 ) that completely lack the N- and C-terminal extramembranous domains of PDLP1a , such a sorting signal must reside within the TMD and its recognition must take place within the lipid bilayer . The most likely scenario would be for this to take place through lateral interaction with other TM proteins . Further experiments are needed to address the existence of such sorting signal mechanisms for the targeting of PDLP1-type and other proteins to plasmodesmata . Also it will be important to determine whether sorting occurs during secretion , i . e . , within the Golgi complex or trans-Golgi network where most proteins are sorted , or only once PDLP1a has reached the PM . Although we understand little at present about the structure and function of plasmodesmata , that PDLP1a TMD is sufficient to confer plasmodesmal targeting opens up the possibility of targeting other novel functions to this unique subcellular compartment with the potential to selectively control the passage of macromolecules or for the more general control of molecular trafficking .
Gateway technology ( Invitrogen ) was used to generate all the clones in this publication . The primer sequences used for cloning and mutagenesis are available upon request . Gene sequences were amplified by PCR using Phusion DNA polymerase ( NEB ) . The resulting DNA fragments were purified and transferred by recombination into the entry vector pDONR207 ( Invitrogen ) using BP clonase II ( Invitrogen ) following the manufacturer's conditions . The sequence of the resulting pDONR clone was verified by automated sequencing . The PDLP1a sequence was transferred by recombination to the indicated binary destination vector using LR clonase II ( Invitrogen ) following the manufacturer's conditions . PDLP1 coding sequences ( CDS ) were amplified from a pool of cDNA made from Arabidopsis thaliana Col-0 RNA and recombined into pDONR207 before transfer by recombination to the binary destination vector pB7FW2 . 0 [40] to give 35S::PDLP1:GFP . Subsequent recombination into pEarleygate301 [41] generated 35S::PDLPa . HA . PDLP1a regulatory sequences were amplified from 1 . 5 kbp upstream of the ATG of PDLP1a ( promoter ) and to the end of the CDS . PDLP1a and eGFP were combined using overlap PCR with attB adaptor primers and was recombined into pDONR207 and then to the binary destination vector pEarleygate301 to give pPDLP1a::PDLP1a:GFP . PCR mutagenesis of PDLP1a was used to create deletions of the TMD and cytoplasmic tail . All cloning was again carried out using Gateway technology with the entry vector pDONR207 and destination vector pB7FWG2 . 0 . Genes for GPI-anchor proteins specify N- and C-terminal targeting signals , which are cleaved to generate the mature protein . Hence , At1g63580 was cloned with a modified [42] citrine variant of YFP [29] inserted as a translational fusion between the N-terminal secretion signal and the CDS using overlap PCR . Similar constructs were made to fuse PDLP1a N-terminal secretion signal to YFP and the TMD or TMD+C-terminal of PDLP1a . For BiFC , overlap PCR was used to fuse the CDSs for two nonfluorescent YFP fragments , YFP1–154 ( YN ) and YFP155–239 ( YC ) from pRT-YN and pRT-YC [21] to the C-terminus of the PDLP1a CDS , giving 35S::PDLP1a:YN and 35S::PDLP1a:YC . Reporter constructs for bombardment were eGFP and RFPer , cloned into pK7WG2 [40] . For the latter , overlap PCR was used to add the secretion signal sequence from Bip2 ( At5g42040 ) and the KDEL ER-anchor sequence to the N- and C- termini , respectively . Particle bombardment followed published methods [43 , 44] . A total of 5 μg of each DNA plasmid was mixed and precipitated onto 1-μm gold particles ( Bio-Rad ) . Fully expanded Arabidopsis leaves on plates ( MS medium + 0 . 8% agar ) were bombarded twice . GFP diffusion , counted as the numbers of secondary cells surrounding the primary target site ( shown by RFPer ) , was analysed 24 h post-bombardment by confocal microscopy . Statistical nonparametric Mann-Whitney analysis was performed using Graph Prism software ( GraphPad Software ) . Binary clones were transformed into Agrobacterium tumefaciens GV3101 using electroporation , and infiltrated into leaf tissue under gentle pressure using a syringe barrel , or used for plant transformation . For leaf infiltration , bacteria were grown overnight in LB plus the appropriate antibiotics , collected , and then resuspended in 3 ml of 10 mM MgCl2 containing 100 μM acetosyringone . After a minimum of 2 h at room temperature , the culture was diluted to an optical density at 600 nm ( OD600 ) of 0 . 2–0 . 5 . Arabidopsis flower dip transformation was carried out according to [45] . Transgenic seedlings were identified by spraying the germinated T0 seedlings with Challenge ( Bialophos herbicide ) and screening the T0 survivors using a confocal microscope . Two leaves on a young plant at the four-leaf stage were pressure infiltrated with the bacterial culture and left for 48–60 h before visualising using the confocal microscope . For the membrane topology experiment , bacteria carrying pLH::YN , pLH::YC , pLH::YNER ( gifts from A . Zamyatnin ) , 35S::PDLP1a:YN , 35S::PDLP1a:YC , or pBIN::HcPro ( a viral silencing suppressor included to enhance expression of the -YN and -YC fusions [21] ) were mixed in the appropriate -YC and -YN combinations together with pBIN::HcPro , and infiltrated into N . benthamiana leaves . Leaves were analyzed at 5 d post-infiltration by confocal microscopy . ( The 35S::PDLP1a:YN/pLH::YCER combination has been excluded from the reported data since , as indicated in Zamyatnin et al . , [21] , it gave rise to high background fluorescence; unpublished data ) . The epidermis was dissected from the onion and placed on plates containing MS medium + 6% agar . Biolistic bombardment using the BioRad gene gun delivered gold particles coated in 35S::PDLP1a:GFP . The onion epidermis was incubated in a growth room for 48–60 h before being visualised using a confocal microscope . Transformed plant tissue was infiltrated with 30% glycerol and viewed immediately . Aniline blue fluorochrome ( Biosupplies ) was used at 0 . 1 mg/ml and infiltrated into the transformed plant tissue before being analysed by confocal microscopy . Plant tissue was imaged at room temperature using a Leica TCS SP2 inverted confocal microscope with an Argon ion laser . GFP was excited at 488 nm and the emitted light captured at 505 to 555 nm; light emitted at 630–680 nm recorded chlorophyll autofluorescence . YFP was excited at 514 nm , and the emitted light was captured at 525–650 nm . RFP was excited using 543 nm and captured at 590–630 nm . Aniline blue fluorochrome was excited with a 405-nm laser ( 30% strength ) and the emitted light captured between 460–500 nm . Images were captured digitally and handled using the Leica LCS software . Alternatively ( Figure 6; inhibitor experiments ) , protein location was similarly assessed using a Zeiss LSM510 confocal microscope with equivalent settings . Inhibitor studies were carried out following transient expression of 35S::PDLP1a:GFP in 4-wk-old N . benthamiana plants . To differentiate the location of 35S::PDLP1a:GFP from Golgi , plants were coinfiltrated with a construct expressing ManI:TdTomato , named ManI:RFP throughout the text . To implicate the secretory pathway in PDLP1a targeting to plasmodesmata , leaves were infiltrated with BFA ( 50 μg/ml in water ) 40 h after infiltration with the expression constructs and examined after a further 12 h . Water controls were run in parallel . To determine the role of COPII-mediated ER-export in PDLP1a targeting , 35S::PDLP1a:GFP was coinfiltrated with 35S::Sar1:RFP or the mutant 35S::Sar1[H74L]:RFP . To create 35S::Sar1[H74L] , NtSar1 [46] was first cloned into pDONR Zeo ( Invitrogen ) using GATEWAY cloning to generate pDONR Sar1 that was used as a template for site-directed mutagenesis . In-frame C-terminal fusions between wild-type or mutated Sar1 and mRFP were generated by recombination using pH7RWG2 vector [40] . Web-based bioinformatic tools were used in the identification and analysis of PDLP1 homologues . The SMART database ( http://smart . embl-heidelberg . de/ ) was used to identify proteins with a similar domain structure to PDLP1a , and their similarity compared using BLAST . The homologs were aligned using Clustal ( http://www . ebi . ac . uk/clustalw/ ) , and the alignment output from Clustal was put into Treetop Phylogenetic tree prediction ( http://www . genebee . msu . su/services/phtree_reduced . html ) using the Phylip format . The distances that came out of this prediction were put into Phylodendron phylogenetic tree printer ( http://iubio . bio . indiana . edu/treeapp/treeprint-sample1 . html ) to draw the phylogenetic tree .
|
In plants , cylindrical , microscopic channels called plasmodesmata provide intracellular connections between cells for communication and material transport , and are important for many aspects of plant growth and defence . We identify a novel family of plasmodesmata-located proteins ( called PDLP1 ) with features of type I membrane receptor-like proteins . In line with the potential for this protein to regulate molecular movement from cell to cell , we show that altered expression of the protein changes the efficiency of protein diffusion from plasmodesmata . We have also analysed the manner in which PDLP1 is transported to plasmodesmata . We show that the single transmembrane domain ( TMD ) of the protein contains all the information necessary for targeting to plasmodesmata and that proper targeting depends upon specific interactions with other factors within the membrane . Notably , a single amino acid close to the C-terminus of the TMD is critical for determining the intracellular destination . Further , by fusing the TMD to yellow fluorescent protein , we establish that the TMD can be used to target heterologous proteins to plasmodesmata .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology"
] |
2008
|
Specific Targeting of a Plasmodesmal Protein Affecting Cell-to-Cell Communication
|
The Drosophila eye is a mosaic that results from the stochastic distribution of two ommatidial subtypes . Pale and yellow ommatidia can be distinguished by the expression of distinct rhodopsins and other pigments in their inner photoreceptors ( R7 and R8 ) , which are implicated in color vision . The pale subtype contains ultraviolet ( UV ) -absorbing Rh3 in R7 and blue-absorbing Rh5 in R8 . The yellow subtype contains UV-absorbing Rh4 in R7 and green-absorbing Rh6 in R8 . The exclusive expression of one rhodopsin per photoreceptor is a widespread phenomenon , although exceptions exist . The mechanisms leading to the exclusive expression or to co-expression of sensory receptors are currently not known . We describe a new class of ommatidia that co-express rh3 and rh4 in R7 , but maintain normal exclusion between rh5 and rh6 in R8 . These ommatidia , which are localized in the dorsal eye , result from the expansion of rh3 into the yellow-R7 subtype . Genes from the Iroquois Complex ( Iro-C ) are necessary and sufficient to induce co-expression in yR7 . Iro-C genes allow photoreceptors to break the “one receptor–one neuron” rule , leading to a novel subtype of broad-spectrum UV- and green-sensitive ommatidia .
The primary role of sensory organs is to probe the environment and to transmit precisely this information to the brain for processing . The visual and olfactory systems are composed of sensory epithelia with thousands of sensory receptor cells , each specifically expressing a single sensory receptor gene out of a much larger repertoire [1–6] . This “one receptor–one neuron” rule allows specific detection of sensory information at the periphery . Together , the architecture of the visual or olfactory organs , the correct specification of the sensory neurons , and the expression of specific sensory receptor molecules are crucial for the acquisition of sensory information . Sensory organs have thus adapted for optimal detection of specific stimuli and often exhibit spatial regionalization within the sensory organ itself . This regionalization also extends into topographic maps in the brain ( retinotopy of the visual system , chemotopy in the olfactory system ) [7] . The Drosophila compound eye is composed of approximately 750 simple eyes called ommatidia . Each ommatidium contains eight photoreceptor cells named R1–R8 . The light-gathering structures ( rhabdomeres ) of outer photoreceptors ( R1–R6 ) form an asymmetric trapezoid whose center is occupied by the rhabdomeres of the inner photoreceptors , where the R7 rhabdomere sits on top of that of R8 [8] . The last step in photoreceptor differentiation is the selective expression of one of the photosensitive pigments , the rhodopsins . The expression of a given rhodopsin , along with additional filtering or sensitizing pigments , dictates the color sensitivity of a photoreceptor . Five rhodopsins are expressed in the compound eye . They respect the general rule of “one receptor–one neuron”— R1–R6 cells express Rh1 [2] . The rhodopsins are similar in function to the vertebrate rods in that they are sensitive to a broad range of wavelengths . They are involved in motion detection . Inner photoreceptors ( R7 and R8 ) mediate color vision [9 , 10] , and are thus comparable to vertebrate cones [11 , 12] . These photoreceptors express the remaining four rhodopsins , which have a restricted spectrum of absorption ranging from ultraviolet ( UV ) in R7 to blue or green in R8 [1 , 3 , 4 , 13–15] . Although the eye appears to be composed of morphologically identical ommatidia , the main part of the retina consists of a mosaic of two stochastically distributed subtypes of ommatidia: pale type ( p ) contains a UV-absorbing Rh3 in R7 and a blue-absorbing Rh5 in R8; yellow type ( y ) contains a different UV-absorbing Rh4 in R7 and green-absorbing Rh6 in R8 [1 , 14 , 16] . A filtering pigment , “yellow” sharpens the sensitivity of yR7 and filters out the blue light reaching the green-sensitive underlying yR8 [17 , 18] . y ommatidia represent ∼70% of ommatidia in flies ranging from Musca to Drosophila . These ommatidia can now be defined more accurately based on their Rh content . The Drosophila homolog of the vertebrate dioxin receptor spineless ( ss ) is responsible for the specification of the retinal mosaic [19] . ss expression in ∼70% of R7 cells in pupae commits them to the yR7 fate and to express rh4 . The cells that do not express ss become pR7 , express rh3 , and instruct pR8 to express rh5 . By default , the remaining yR8 express rh6 [1 , 20] . Thus , 30% of ommatidia ( p ) appear to be more involved in the discrimination of shorter wavelengths , whereas the remaining 70% ( y ) should be more appropriate for the discrimination of longer wavelengths . The p and y ommatidia appear to be randomly distributed . The Drosophila eye , like in many insects , has also developed a particularly striking example of sensory system specialization in the dorsal rim area ( DRA ) . DRA ommatidia develop in the dorsal-most row of the eye and have distinct morphological characteristics that enable them to be used to detect the electric vector ( e-vector ) of light polarization [21 , 22] . Because polarized light comes from UV-rich sunlight scattered by the atmosphere , this row of ommatidia is limited to the dorsal edge of the eye and must therefore be specified by positional cues [22 , 23] . Regionalization of tissues often starts very early during organogenesis and often involves conserved molecular mechanisms that are important for patterning tissues as different as Drosophila sensory systems or vertebrate limb buds . In the Drosophila eye imaginal disc , dorso-ventral compartmentalization involves the differential expression of genes of the Iroquois Complex ( Iro-C ) . Iro-C genes encode conserved homeodomain transcription factors from the TALE class [24]—araucan ( ara ) , caupolican ( caup ) , and mirror ( mirr ) —and their genomic organization as a cluster of three genes is conserved from flies to mammals [25 , 26] . In Drosophila , ara and caup have almost identical patterns of expression [27] , whereas mirr is more divergent . Among other functions , these three genes have been implicated in very early stages of eye-antennal disc development as “dorsal selectors” that are required for the correct specification of dorsal head structures and for the formation of the dorsal compartment of the eye [28–30] . During larval development , the Iro-C genes are expressed in dorsal nondifferentiated cells of the eye imaginal disc and are then down-regulated once neurogenesis has begun . This expression distinguishes different cell fates on either side of the dorso-ventral boundary and is necessary to establish the organizer center at the equator ( reviewed in [26] ) . Although expression of Iro-C genes fades away after the morphogenetic furrow , their expression reappears in the adult . Iro-C genes are necessary to specify the DRA: ommatidia near the edge of the disc are exposed to wingless signaling and become DRA ommatidia only when they are located dorsally [22 , 23] . Here we describe a new function for Iro-C genes in photoreceptor development: they define a subtype of ommatidia that is restricted to the dorsal region of the eye in which the “one receptor–one neuron” rule is broken . These ommatidia are positioned in the dorsal part of the retina and co-express the two genes encoding UV-absorbing Rhs—rh3 and rh4—in R7 cells . This co-expression results from the induction of rh3 in yR7 cells while pR7 are normal . Therefore , the mutual exclusion pathway that prevents co-expression of sensory receptors appears to be disabled by the activity of the Iro-C genes , allowing the expression of two sensory receptors in a single cell .
It is widely accepted that individual Drosophila photoreceptors express a single rhodopsin gene: rh1 in R1–R6 , rh3 or rh4 in R7 [15 , 31] ( Figure 1A ) , and rh5 or rh6 in R8 [1 , 3] . However , careful examination of antibody stainings on whole-mounted retinas revealed a surprising exception to this rule: a fraction of R7 cells co-expresses both rh3 and rh4 ( Figure 1A and 1B ) in a region that starts near the dorsal edge of the eye , outside the DRA , and extends toward the equator , spanning approximately one-third of the eye at its maximum point ( Figure 1A ) . This phenomenon is also clearly observed in cross-sections of the eye ( Figure 1C ) . In the ventral region of the eye , Rh3 and Rh4 proteins are present at a high level in R7 cells and are never found in the same cell ( Figure 1C , “V” ) . In contrast , all R7 cells located in the dorsal eye contain Rh3 , either alone or in combination with Rh4 ( p and y subtypes , respectively , see below ) ( Figure 1C , “D” ) . In R7 cells co-expressing rh3 and rh4 , the level of Rh3 protein is lower than in non–co-expressing cells ( Figure 1A and 1B ) . Together , these data suggest that a subset of dorsal ommatidia induce rh3 expression in rh4-expressing yR7 cells ( Figure 1B ) . Rh3 and Rh4 colocalization was observed using different combinations of primary antibodies , indicating that this is not an artifact of a particular pair of antibodies ( unpublished data ) , and co-expression is present in all wild-type backgrounds tested to date ( yw and all other Gal4 and upstream activating sequence ( UAS ) lines used in this study ) , suggesting that this is a conserved feature of the Drosophila eye . In our previous studies , we had detected expression of an rh3 promoter fusion to a green fluorescent protein ( GFP ) reporter [32] in most ommatidia located in the dorsal eye . To distinguish whether the mutual exclusion or co-expression of rhodopsins in one cell results from transcriptional or post-transcriptional regulation , we performed double in situ hybridization to visualize rh3 and rh4 mRNA . In the ventral and central regions of the eye , rh3 and rh4 mRNA are present at high levels in a mutually exclusive manner ( Figure 1D , “V” ) . However , in the dorsal eye , all R7 cells contain rh3 mRNA , either alone or in combination with rh4 mRNA ( Figure 1D , “D” ) . Moreover , staining of rh3-lacZ reporter constructs consistently reveals expanded , weak rh3 transcription in all ommatidia in the dorsal eye , whereas restricted expression to p ommatidia is observed in the remaining part of the retina ( unpublished data and [33] ) . Together , these data indicate that there is localized transcriptional control of rh3 and rh4 that allows their co-expression in the dorsal retina . We quantified the frequency of R7 cells co-expressing UV-opsins . In line with previous observations , antibody stainings on dissociated ommatidia identified the three previously described subtypes of ommatidia [1 , 3]: DRA ommatidia that contain Rh3 in both R7 and R8 ( Figure 2A ) , p ommatidia that contain Rh3 and Rh5 ( Figure 2B ) , and y ommatidia that contain Rh4 and Rh6 ( Figure 2C ) [16] . In addition , a small proportion ( 5 . 7%; 6/106 ) of all ommatidia ( dorsal or ventral ) express Rh3 in R7 ( without Rh4 ) associated with Rh6 in R8 . These likely correspond to the previously described rare Rh3/Rh6 “odd coupled” ommatidia where the signal from pR7 fails to induce rh5 in R8 ( unpublished data and [19 , 20 , 22] ) . However , we also identified a fourth subtype of R7 cells that contain both Rh3 and Rh4 in R7 cells ( Figure 2D ) . These represent ∼10% of all ommatidia and are always coupled with Rh6-expressing R8 cells ( Figure 2D ) . Stainings with anti-Rh4 , anti-Rh5 , and anti-Rh6 antibodies never revealed expression of Rh4 and Rh5 in the same ommatidium ( 0/200 ) . yR7 cells contain a pigment that gave rise to their name ( “yellow” ) that is visible under the confocal microscope after neutralization of the cornea [17] . To further confirm that these co-expressing cells are yR7 , we imaged the eyes of flies by confocal microscopy to visualize the “yellow” pigment as well as red fluorescent protein ( RFP ) controlled by the rh3 promoter ( rh3>RFP ) . As expected , “yellow” and rh3>RFP do not overlap in the ventral eye , because “yellow” marks yR7 cells and rh3>RFP labels pR7 cells . However , in the dorsal eye , “yellow” overlaps with rh3>RFP ( Figure 2E ) . We have thus identified a class of dorsal ommatidia that express both rh3 and rh4 in R7 , and rh6 in R8 . These ommatidia represent a subset of y ommatidia that also express rh3 in addition to the endogenous rh4 . Ommatidia containing Rh3/Rh5 make up ∼30% of all ommatidia as evaluated by quantification of dissociated ommatidia ( there is no Rh4/Rh5 coupling , and Rh5-positive ommatidia represent ∼30% [178/636] of all ommatidia ) . The remaining ∼70% of ommatidia express rh6 ( 458/636 ) . Stainings with anti-Rh3 and anti-Rh4 antibodies revealed that ∼30% of R7 express only rh3 ( 85/273 ) , ∼60% express only rh4 ( 158/273 ) , and ∼10% co-express rh3 and rh4 ( 30/273 ) . Thus , the ∼70% rh6-expressing ommatidia can be divided into two subtypes: ∼60% of all ommatidia express rh4/rh6 and ∼10% express ( rh3 + rh4 ) /rh6 , representing y ommatidia in the dorsal region of the eye . Iro-C genes control dorsal identity during early eye development; therefore , we analyzed their expression in an effort to identify the determinants of this “dorsal” identity [28 , 30] . As mentioned earlier , these genes are expressed transiently during early larval stages of eye disc development ( Figure 3A ) . ara and caup ( but not mirr ) are re-expressed in the adult in the dorsal retina . To perform a more detailed analysis of the Iro-C gene expression pattern , we used reporter lines ( Iro-C-nuZ or Iro-C-Gal4 ) , that are insertions in the Iro-C complex and are believed to reflect the expression of both ara and caup [21 , 27] . At 24 h after puparium formation ( APF ) , the Iro-C-Gal4 reporter is highly expressed in all photoreceptors in the dorsal eye ( Figure 3B ) . The level of expression gradually decreases toward the equator due to fewer and fewer cells per cluster that express the reporter . Ultimately , only R7 cells , identified with the R7-specific marker Prospero ( Pros ) , express the reporter ( Figure 3C ) . In the adult , the expression of the reporter persists in outer photoreceptors , as well as in R7 and R8 as previously shown [22] . This expression pattern correlates with the distribution of y ommatidia that co-express rh3 and rh4 in R7 and express rh6 in R8 ( Figure 3E and 3F ) ( see below for discussion ) . Thus , in the adult retina , the Iro-C genes ara and caup are specifically expressed in the region of the eye where there is co-expression of rhodopsins . The similarity between the expression profile of the Iro-C genes ara and caup in the region of the eye where rh3 and rh4 are co-expressed suggested that these transcription factors regulate this newly defined subset of ommatidia . To test this hypothesis , we induced clones of cells that were mutant for Iro-C by using a deficiency that covers ara and caup and deletes most of the regulatory sequences of mirr [27 , 34] . Ventral clones are easily recovered but , as expected , they do not have a visible phenotype . While small dorsal clones do not produce a strong morphological phenotype , large clones often lead to the formation of ectopic eye tissue near the dorsal head cuticle , presumably because they create a new organizer between Iro-C + and Iro-C – cells ( unpublished data ) [28 , 30] . In the few dorsal mutant clones recovered , R7 cells co-express rh3 and rh4 in the surrounding heterozygous tissue , whereas in mutant tissue , R7 cells contain only Rh3 or Rh4 ( Figure 3G ) . Thus , similar to the adult ventral eye where Iro-C is not expressed , dorsal Iro-C mutant R7 cells exclusively contain either Rh3 or Rh4 . Therefore , Iro-C expression in the dorsal eye appears to be required for rhodopsin co-expression in R7 cells of dorsal y ommatidia . To study whether the ara and caup genes are sufficient to induce rhodopsin co-expression , we performed a series of mis-expression experiments . We observed essentially the same phenotype when over-expressing ara and/or caup , with the only difference being that the over-expression of both genes produces a more severe morphological phenotype than the expression of either one of them alone . We only show experiments using UAS-caup , but the same set of data is presented for ara in Figure S1 . Because mirr is not expressed at this stage , we did not investigate its mis-expression phenotype . To mis-express ara and caup genes , we used the long glass multiple reporter-Gal4 ( lGMR-Gal4 ) driver whose expression is restricted to all photoreceptors . lGMR expression starts during larval stages , after photoreceptors are specified at the morphogenetic furrow and is maintained throughout photoreceptor development and adulthood [19 , 35] . Over-expression of caup or ara at 25 °C leads to strong morphological defects in the eye , likely due to the prolonged expression of Iro-C genes when they are normally down-regulated during photoreceptor development . However , lowering Gal4 activity by raising flies at 18 °C induces robust lGMR>caup–dependent co-expression of rh3 and rh4 specifically in all yR7 cells ( Figure 4A ) , whether ventral or dorsal . Importantly , caup-induced expansion of rh3 in yR7 cells does not repress rh4 expression . lGMR>caup over-expression does not induce ectopic expression of rh3 in outer ( R1–R6 ) or in R8 photoreceptors , and co-expression of rh5 and rh6 is not observed . However , lGMR>caup does increase to various degrees the proportion of rh6-expressing R8 cells with a corresponding decrease in rh5-expressing cells ( Figure 4B ) . This expansion of Rh6 in R8 cells produces mis-coupling between R7 and R8 cells , resulting in an increase in ommatidia containing Rh3 in R7 and Rh6 in R8 . Our interpretation is that , because lGMR>Iro-C produces morphological defects in the eye , the communication between R7 and R8 might be disrupted . In the absence of a signal from R7 to R8 , most R8 cells express the default rh6 ( as in sevenless mutant eyes ) [1 , 20] . We have previously shown that the decision between p and y fates is made during early pupation , when ss is activated in yR7 precursors , after lGMR-Gal4 starts to be expressed and long before rhodopsins are expressed [19] . To test whether Iro-C genes can cell-autonomously induce rhodopsin co-expression after the p versus y decision is made , ara and caup genes were expressed using a promoter that is expressed at late stages in development . PanR7-Gal4 is a combination of rh3 and rh4 promoters that is expressed in every R7 cell and in DRA R8 cells [19] , starting at late pupal stages when rhodopsin expression starts [36] . Over-expression of caup using this late driver induces co-expression of rh3 and rh4 in the majority of R7 cells ( Figure 4C ) , which are likely yR7 cells . To test whether a very late signal can induce co-expression in yR7 , we expressed Iro-C genes using a rh4-Gal4 driver , which is only expressed in yR7 cells . This should allow R7 to be normally specified as yR7 and turn on rh4 , which would then supply the Iro-C signal . Again , mis-expression of caup using this driver induces expression of rh3 in most rh4-expressing cells ( Figure 4E ) . The phenotype is stronger in the central or more-dorsal areas than in ventral regions where this driver is not able to transform all yR7 cells , because it might lack the strength of the PanR7 driver . Together , these results suggest that there is an endogenous sub-threshold level of Iro-C in the dorsal eye close to the equator that is not sufficient to induce co-expression of rh3 and rh4 in a wild-type situation . The PanR7- and rh4-Gal4 drivers must only add limited amount of ara or caup , or provide it late , such that not all yR7 cells co-express . Neither PanR7- nor rh4-Gal4 drivers induce phenotypes in R8 cells ( 31 . 7% and 33% of rh5 expression , respectively ) ( Figure 4D and 4F ) , suggesting that , as expected , the early decision between p and y fates is not affected . In addition , the expression of caup only in R8 with rh5- and rh6-Gal4 drivers does not produce a visible phenotype ( Figure S1G ) . Therefore , the presence of the Caup or Ara transcription factors in yR7 cells , even very late in development , instructs them to co-express rh3 and rh4 .
The co-expression of rhodopsins in R7 is restricted to the dorsal eye , which faces the sky . The biological significance of these particular ommatidia in Drosophila is not known . The Drosophila “dorsal y” ommatidia that contain both UV-Rh3 and UV-Rh4 in R7 and green-absorbing Rh6 in R8 provide a unique configuration to measure the ratio between UV and long wavelengths: They contain two UV opsins in R7 , providing broad UV sensitivity that is expanded toward shorter wavelengths by Rh3 , along with a blue-filtering pigment that prevents short wavelengths to penetrate the R8 layer containing the green-absorbing Rh6 [8] . These ommatidia might be used to discriminate between the “solar” and “antisolar” halves of the sky , necessary to navigate in the correct direction [40] . Although the exclusion of sensory receptors is a general rule , co-expression to achieve a novel sensitivity might be used in special cases when the expression of a single receptor is not sufficient to confer high enough sensitivity . Although the mouse retina is dominated by rods , it also contains cone cells . The majority of these cone cells co-express both S ( blue ) and M ( green ) opsins [41] . Presumably , mice live in a dark environment and are mostly color blind; the co-expression might be useful for optimal utilization of cones . The eye of butterflies also displays co-expression of two rhodopsins in several of their photoreceptors , perhaps to expand the spectrum of sensitivity of photoreceptors in species that do not have a rhodopsin with broad absorption spectrum such as Rh1 , which is unique to Diptera [42–44] . Vertebrate olfactory neurons also express only one olfactory receptor gene per olfactory receptor neuron , and a direct feedback from the expressed receptor molecule has been proposed to ensure that this rule is stringently applied [45–47] . However , it cannot be excluded that two olfactory receptor genes are co-expressed , because their large number prevents comprehensive expression studies . Indeed , in Drosophila , a striking example of co-expression of two chemosensory receptors that mediate sensitivity to CO2 was recently described for the olfactory system [48] . The expression of each receptor is not sufficient to confer olfactory CO2-chemosensation on its own , but their combined expression does . Therefore , the addition of multiple receptors might not only increase the receptive spectrum of cells , but might also confer sensitivity to new stimuli . In the CO2 sensitivity case , the co-expression is crucial for the fly to detect a repellent smell that indicates danger . Thus , precise regulation of receptor co-expression must be achieved . The spatial specialization induced by Iro-C genes in the fly retina is not the only example where regionalized specification occurs within sensory systems . For example , in the “love spot” of the housefly Musca , the antero-dorsal region of the male eye has presumably lost color vision , because R7 cells are transformed into motion detecting outer photoreceptors that express Rh1 [49] . The human eye also has geographic specialization: the center of the eye ( fovea ) contains exclusively cones that are involved both in acute and color vision in bright light . The periphery of the eye is mostly composed of rods and is involved in dim light vision ( reviewed in [50] ) . The mouse olfactory system also exhibits specialization where the main olfactory epithelium that is responsible for detection of general odorants is separated from the vomeronasal organ that is involved in pheromone detection [51] . Drosophila also has two olfactory organs , the antenna and the maxillary palps , which express different sets of olfactory receptors and are likely involved in the detection of different types of odors [52] . Iro-C genes may not only be responsible for relieving the “one receptor–one neuron” constraint in the Drosophila eye , but may also allow receptor co-expression elsewhere . For instance , members of the orthologous family , the Irx genes , are expressed in mouse photoreceptors where opsin co-expression is observed [53–55] . Although the terminal differentiation of bipolar cells is affected in mice with mutant Irx5 [54] , it will be of interest to study cone opsin expression in this and other Irx mutants to test whether these genes are also involved in the co-expression of opsins . Mouse olfactory neurons do not express Irx5 or Irx6 [53] , and they do not express more than one olfactory receptor gene [56] . In contrast , recent comprehensive studies in the Drosophila antenna and maxillary palp have identified a subgroup of olfactory receptor neurons that co-express two divergent receptors [57 , 58] . Interestingly , cells that co-express different olfactory receptor genes are the only neurons that express Iro-C genes in the maxillary palp ( EOM , AC , and CD; unpublished observations ) . Unfortunately , the loss of Iro-C function in this tissue leads to re-specification of these neurons toward other non-neuronal fates ( EOM , AC , and CD; unpublished observations ) , preventing us from further testing the involvement of Iro-C genes in the lack of exclusion . Genes directly controlled by Iro-C transcription factors are still elusive . Binding sites for Mirr that presumably mediate repression of fringe in the dorsal eye disc were recently described [59] . The identification of target genes of the Iro/Irx family might shed some light on the regulation of the pathway that maintains mutual exclusion of sensory receptors .
Flies were raised on standard corn meal–molasses–agar medium and grown at room temperature ( 24 ± 1 °C ) unless otherwise noted . y1w67 flies were used as control for Rhodopsin expression . As the red color of adult eyes interferes with fluorescent immunostainings , the eyes were rendered white by using an RNAi construct against the white gene [60] when a white marker gene was introduced in the genetic background by P-element transgenes . lGMR-Gal4 was produced by a pentamerized Glass binding site [22] , UAS-ara and caup were gifts from J . Modolell . irorF209-PZ and Df ( 3L ) iroDFM3 were obtained from the Bloomington Stock Center . Iro-C-Gal4 was created by replacing the P element in irorF209-PZ with one containing Gal4 . rh3- , rh4 , and PanR7-Gal4 drivers were described in [19] . To visualize “yellow” and rh3 expression with a reporter , we used flies containing rh3-LexA and lexAop-RFP . Clones were generated using the standard FLP/FRT technique . Antibodies and dilutions used were as follows: mouse anti-Rh3 and anti-Rh4 ( 1:100 ) and mouse anti-Rh5 ( 1:50 ) ( gift from S Britt , University of Colorado ) ; rabbit anti-Rh4 ( 1:400 ) ( gift from C . Zuker , University of California San Diego ) ; rabbit anti-Rh6 ( 1:5000 ) ; rabbit anti-βGal ( 1:5000 ) ( Cappel ) ; mouse anti-βGal ( 1:500 ) ( Promega ) ; mouse anti-pros ( 1:50 ) ( Developmental Studies Hybridoma Bank ) ; rat anti-ElaV ( 1:10 ) ( DSHB ) ; and rabbit anti-GFP ( 1:800 ) ( Biogenesis ) . Chicken anti-Rh3 was from [61] . All secondary antibodies were Alexa-conjugated ( 1:800 ) ( Molecular Probes ) . Throughout the paper , Rh3 and Rh4 were stained using antibodies generated in mouse and rabbit , respectively , because they are significantly better that the other two . Antibody stainings for larval and pupal retinas were essentially the same except for the collection of tissue . The protocols merge after the fixation step . Cerebral complexes of late third instar larvae were dissected in phosphate buffered saline ( PBS ) ( 1× ) and fixed in PBS + 4% paraformaldehyde for 20 min at room temperature ( RT ) . Pupal cases were collected at 24 h after puparium formation at 25 °C and the head was dissected in ice cold PBS ( 1x ) . Several eye-brain complexes were extracted by gentle pipetting and collected in PBS ( 1× ) on ice . After 20 min fixation using PBS ( 1× ) + 4% formaldehyde at RT , the samples were washed four times with PBS + 0 . 1% Triton-X-100 ( PBT ) . The first antibody was added overnight at 4 °C . After four washes with PBT , the secondary antibody was added for at least 2 h at RT . After another four washes in PBT , each retina was separated from the brain by using two tungsten needles and then mounted flat in Vectashield ( Vector Laboratories ) . 10-μm horizontal eye sections were produced using a cryostat ( Zeiss ) and deposited on Superfrost PLUS slides ( Fisher ) . The slides were then fixed 15 min in PBS ( 1× ) + 4% formaldehyde . After four washes with PBT , the first antibody was added overnight at 4 °C . After four washes with PBT , the secondary antibody was added for at least 2 h at RT . After four washes with PBT , the slides were mounted in Aquamount . Adult retinas were dissected out and after a rinse with PBS ( 1× ) , they were fixed for 15 min with 4% formaldehyde at RT . After three washes in PBT , the retinas were incubated with the primary antibodies diluted in BNT ( PBS , 0 . 1% BSA , 0 . 1% Tween-20 , 250 mM NaCl ) overnight at 4 °C . After two rinses and a 30 min wash with PBT , the retinas were incubated with secondary antibodies for 2–4 hours at RT . Two quick rinses with PBT were followed by an overnight wash at 4 °C . Retinas were cleaned of any remaining cuticle and mounted in Vectashield . The retina of 3–5 dissected eyes were removed from the cornea and dissociated on a slide using dissection needles in a drop of PBS . After the samples dried at RT , they were fixed with 4% formaldehyde and staining was carried out as for frozen sections . Adult retinas were dissected as described for antibody stainings . Dissected retinas were mounted on Superfrost PLUS slides ( Fisher ) and dried for 2 h at 65 °C . After fixation for 15 min with 4% paraformaldehyde , the slides were washed in PBS , treated with Proteinase K for 5 min at 37 °C and refixed for 10 min . Following a short PBS wash , the slides were treated with 0 . 2 M HCl for 10 min , washed in PBS , and acetylated with 0 . 1 M triethanolamine . Retinas were hybridized overnight at 65 °C with 100 μl hybridization buffer ( 50% formamide , 5× SSC , 5× Denhardt's , 250 μg/ml yeast tRNA , 500 μg/ml herring sperm DNA , 50 μg/ml heparin , 2 . 5 mM EDTA , 0 . 1% Tween-20 , 0 . 25% CHAPS ) containing a digoxygenin-labeled rh3 probe and a fluorescein-labeled rh4 probe . After a series of washes in 5× SSC; 50% formamide , 2× SSC; 2× SSC; 0 . 2× SSC , and 0 . 1× SSC ( 5 , 30 , 20 , 20 , and 20 min , respectively ) , the rh3 probe was detected using HNPP/Fast Red ( Roche ) and the rh4 probe was detected using the TSA Biotin System ( Perkin Elmer ) and streptavidin-Alexa488 according to the manufacturers suggestions . Anesthetized flies were fixed to a Petri dish using nail polish . Then , flies were submerged in water and visualized using a 20× water immersion lens . To visualize “yellow” , FITC settings were used [31] .
|
Most sensory systems follow the rule “one receptor molecule per receptor cell . ” For example , photoreceptors in the fly eye and cones in the human eye each express only one light-sensitive rhodopsin . Rhodopsins are G-coupled protein receptors , a class of ancient signaling molecules that mediate not just vision but also the sense of smell , the inflammatory response , and other physiological processes . However , the mechanisms that regulate mutual exclusion of receptor genes in the visual and olfactory systems are poorly understood . Each ommatidium in the fly eye consists of eight photoreceptors ( R1–R8 ) ; six of which mediate broad-spectrum motion vision ( R1–R6 ) and two that mediate color vision ( R7 and R8 ) . We identified a new class of photoreceptors in the fly retina that violates the one rhodopsin–one receptor rule . This subset of ommatidia , located in the dorsal third of the eye , co-expresses two ultraviolet-sensitive rhodospins ( rh3 and rh4 ) in R7 , while maintaining discrimination between green and blue opsins in R8 . We took advantage of the genetic tools offered by the fruit fly to show that this co-expression depends on the Iroquois Complex ( Iro-C ) genes that are both necessary and sufficient to allow the two ultraviolet-sensitive rhosopsins to be expressed in the same R7 cell . These results shed new light on the mechanisms regulating co-expression of rhodopsins in the eye , and may well have implications for regulating co-expression in olfactory receptors and other G-protein coupled systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience"
] |
2008
|
Iroquois Complex Genes Induce Co-Expression of rhodopsins in Drosophila
|
Epoxyeicosatrienoic acids ( EETs ) confer vasoactive and cardioprotective functions . Genetic analysis of the contributions of these short-lived mediators to pathophysiology has been confounded to date by the allelic expansion in rodents of the portion of the genome syntenic to human CYP2J2 , a gene encoding one of the principle cytochrome P450 epoxygenases responsible for the formation of EETs in humans . Mice have eight potentially functional genes that could direct the synthesis of epoxygenases with properties similar to those of CYP2J2 . As an initial step towards understanding the role of the murine Cyp2j locus , we have created mice bearing a 626-kb deletion spanning the entire region syntenic to CYP2J2 , using a combination of homologous and site-directed recombination strategies . A mouse strain in which the locus deletion was complemented by transgenic delivery of BAC sequences encoding human CYP2J2 was also created . Systemic and pulmonary hemodynamic measurements did not differ in wild-type , null , and complemented mice at baseline . However , hypoxic pulmonary vasoconstriction ( HPV ) during left mainstem bronchus occlusion was impaired and associated with reduced systemic oxygenation in null mice , but not in null mice bearing the human transgene . Administration of an epoxygenase inhibitor to wild-type mice also impaired HPV . These findings demonstrate that Cyp2j gene products regulate the pulmonary vascular response to hypoxia .
Human cytochrome P450 2J2 ( CYP2J2 ) is abundant in cardiovascular tissues and pulmonary endothelium [1] and metabolizes arachidonic acid ( AA ) to epoxyeicosatrienoic acids ( EETs ) and hydroxyeicosatetraenoic acids ( HETEs ) , short-lived mediators that have potent vascular protective properties [2]–[4] . The mouse chromosomal locus syntenic to human CYP2J2 contains 10 genes ( 8 presumed genes and 2 pseudogenes ) spanning 626 kb on chromosome 4 . Gene clusters in the mouse that are syntenic to a single human gene are not uncommon , but their study is rarely straightforward . Mutant mice with short gene deletions can be generated through the conventional gene targeting strategies [5] , but the deleted region rarely exceeds ten kilobases in most applications of the existing technology . Bacterial artificial chromosomes ( BACs ) , which can have lengths up to ∼250 kb , have been used for gene targeting [6]–[8] , but even in these cases the length of the BAC creates a formal upper limit for the size of the deletion . Deletion of a large DNA region has been accomplished by sequential introduction of loxP sites followed by the expression of Cre recombinase in embryonic stem cells [9]–[11] . However , it is difficult to distinguish loxP sites integrated into the same autosome from those integrated into separate autosomes , and Cre-mediated recombination has relatively low efficiency when the distance between loxP sites is great [9]–[11] . Here we describe a method to join BACs using prokaryotic integrases to create a deletion replica in E . coli that is subsequently used to target the murine locus . CYP2J2 products elicit a variety of effects including fibrinolysis , vasodilation , and inhibition of inflammation [12]–[14] . However , a definitive identification of the contributions of Cyp2j genes in the cardiovascular system has remained challenging due to the expansion of the locus in mice . Murine Cyp2j isoforms may act as epoxygenases and hydroxylases to metabolize AA into EETs and HETEs [15] . It has been shown that the pulmonary vasoconstrictor response to alveolar hypoxia is ablated in mice deficient for cytosolic phospholipase A2α ( cPLA2α ) , an enzyme that lies upstream of Cyp2j and is responsible for liberation of AA from esterified forms of phospholipids in the cell membrane [16] . There are four pathways downstream of cPLA2α mediating the metabolism of AA , including the cyclooxygenase ( COX ) , lipoxygenase ( LO ) , epoxygenase , and ω-hydroxylase pathways . It has been shown that inhibition of COX or 5-LO pathways does not impair hypoxic pulmonary vasoconstriction ( HPV ) [17] , [18] . Previous studies have demonstrated that products of CYP epoxygenases and hydroxylases can produce pulmonary vasoconstriction and vasodilation , respectively . However , it is unknown which cytochrome P450 is the major contributor to the regulation of HPV - a mechanism unique to the pulmonary vasculature , that diverts blood flow away from poorly ventilated lung regions , thereby preserving oxygenation of systemic blood [4] , [16] , [19] . The pulmonary vasoconstrictor response to alveolar hypoxia is crucial for maintaining arterial oxygenation during acute respiratory failure and lung injury . Due to the diversity of potential metabolites and the challenges associated with their measurement , stemming from their propensity for rapid metabolism and multiple functionally-relevant isomeric forms , it has been challenging to precisely identify which gene family and which eicosanoids are the most relevant modulators of HPV . In this study , we describe a strategy to engineer large DNA fragments in bacteria and mammalian cells . We performed large scale ablation and human allelic complementation of the Cyp2j locus in mice using E . coli genetic techniques and bacterial artificial chromosome technology . Phenotypic characterization of the resulting Cyp2j-null and complemented mice showed that disruption of mouse Cyp2j genes did not alter pulmonary and systemic hemodynamic parameters at baseline . However , the increase in left lung pulmonary vascular resistance induced by selective left lung hypoxia was impaired in Cyp2j-null mice , but not in complemented mice , demonstrating that the Cyp2j genes contribute to hypoxic pulmonary vasoconstriction .
Because no single BAC has been reported to span the entire mouse Cyp2j locus , two BACs that contain the termini of the Cyp2j gene cluster were separately modified to permit joining by site-specific recombination in E . coli ( Figure 1A , 1B ) . Homologous arms were amplified from the BAC clones and subcloned into one targeting vector containing a kanamycin resistance element and the TP901-1 integrase attB site , and into another targeting vector containing an ampicillin resistance element and a TP901-1 attP site . Following homologous recombination , the selectable markers and integrase sites were integrated into the two BACs , forming MT5′BAC and MT3′BAC ( Figure 1B ) , as identified using four PCR amplifications ( using primers P1 to P8; Figure S1A ) . The PCR products were sequenced to confirm that the correct recombination products had formed . The MT5′BAC and the MT3′BAC containing the termini of the mouse Cyp2j locus were then fused in E . coli by site-specific recombination . A plasmid expressing TP901 integrase under control of the araBAD promoter was introduced into a bacterial strain harboring the BAC containing the kanamycin resistance element and the TP901 attB site . TP901 expression was induced prior to creation of electrocompetent cells , and the modified BAC bearing ampicillin resistance was introduced . Following selection for resistance to both kanamycin and ampicillin the fused BAC resulting from integrase was identified by PCR . Two pairs of primers ( P9 to P12 shown in Figure 1B ) were used to identify the integration events ( PCR results shown in Figure S1B ) . PCR products were sequenced to confirm the desired TP901 attL and attR sites had formed ( representative sequences shown in Figure S1B ) . The correctly fused BAC ( FS BAC ) was digested with SpeI and BamHI to confirm that no unwanted rearrangements had occurred ( Figure S1C ) . The fused BAC was electroporated into mouse ES cells and geneticin-resistant clones were screened by multiplex ligation-dependent probe amplification ( MLPA ) using five wild-type probes—5wt , 3wt , wtM1 , wtM2 and wtM3 , located within the region targeted for deletion of the Cyp2j gene locus , and three mutant probes—5 m , 3 m and neo—located within the engineered Cyp2j gene locus and recognizing vector sequences and the selectable marker ( Figure 2A ) . Two internal control probes , HP1 and ITGB3 , that detect genes located on chromosome 8 and 11 , respectively , were used to normalize signal intensities from probes for Cyp2j wild-type and mutant genes . The desired clones showed the expected pattern , in which the wild-type signal intensity ( 5wt , 3wt , wtM1 , wtM2 and wtM3 ) was decreased approximately by half , indicating disruption of one allele ( Figure 2B , C ) . The areas of mutant signal intensities , including 5m , 3m , and neo , reflect the integrated copy numbers . Clones showing the lowest mutant signal intensity among the screened clones were considered likely to be single copy integrations ( representative data shown in Figure 2B ) . The selectable markers and vector sequences were removed from two ES clones , 1C04 and 4G02 , using R4 integrase . A plasmid expressing the integrase under the control of the chicken actin-CMV hybrid promoter was transfected into the two ES cell lines . The action of the R4 integrase resulted in excision of the sequences located between the R4 attB and attP sites , as illustrated in Figure S2 . The recombination between attB and attP sites gives rise to a chromosomal R4 attL site ( sequences shown in Figure S2 ) and a circularized attR remnant that has no mechanism for persistence during cell division . The deletion events were initially identified by PCR using primers P13 and P14 . Thirty of 43 clones for 1C04 and 29 of 37 clones for 4G02 showed the expected 561 bp PCR fragment ( data not shown ) . Successful R4 integrase-mediated recombination was confirmed by sequencing the PCR products to detect the presence of the R4 attL site and by MLPA to confirm loss of the geneticin resistance allele ( data not shown ) . Four clones of mouse Cyp2j target ES cells were microinjected into C57BL/6 blastocysts . Four chimeric mice were born from 4 clones of which one , from B6-white ES cells , showed germ line transmission: among 20 litters , 20 pups from 148 offspring ( 13% ) were derived from ES cells . Heterozygous mice were mated to generate homozygous mutant mice ( Cyp2j−/− ) and wild-type littermates ( Cyp2j+/+ ) . Genotyping by MLPA showed the absence of all internal regions located in the deletion region of the homozygotes ( Figure S3A ) . Mouse genotypes were also tested by PCR , as shown in Figure S3B . Human CYP2J2 and the eight mouse Cyp2js share 66–83% similarity in protein sequence and 55–88% sequence identity for mRNA sequence ( Figure S5 ) . RT-MLPA [20] , a technology which allows detection and quantitation of nucleic acids having single nucleotide differences , as well as measurement of the expression of multiple genes in a single tube , was used to examine the expression of the eight Cyp2j genes in wild-type mice . Expression of 3 internal control genes , Tbp , Hprt , and Gapdh , was used to normalize the data . Each Cyp2j gene has a distinct expression pattern , as shown in Figure 4A . Kidney , liver , and gastrointestinal tissues are the major sites of Cyp2j isoform gene expression . Expression of six Cyp2j genes ( 2j5 , 2j6 , 2j8 , 2j11 , and 2j13 ) is detectable in liver and kidney . Cyp2j7 is expressed at low levels in the liver . Cyp2j13 is highly expressed in the kidney . Cyp2j9 shows expression in small intestine , liver , and brain . Cyp2j6 is broadly expressed: small intestine > stomach > thyroid > liver > large intestine > kidney > brain . Only low levels of expression of 2j5 , 2j6 , 2j9 , 2j11 , and 2j13 were detected in lung . RT-MLPA was also applied to RNA prepared from liver and kidney of Cyp2j−/− mice . No transcripts from Cyp2j genes were detected ( Figure 4B ) . To evaluate potential species differences in lineage-dependent expression , CYP2J2 gene expression was examined by quantitative reverse-transcriptase PCR ( RT-PCR ) using commercial pooled human cDNA preparations . The human gene is highly expressed in liver , and the abundance of transcripts in heart exceeds that in lung ( Figure 4C ) . In contrast , in RNA prepared from Cyp2j−/−-Tg mice , CYP2J2 mRNA levels were substantially higher in lung than in heart in Cyp2j−/−-Tg mice but not in Cyp2j+/+-Tg mice ( Figure 4D ) , an observation that was verified in mice derived from two independent founders ( data not shown ) . To investigate whether Cyp2j deficiency affects systemic hemodynamic measurements , the blood pressure ( BP ) and heart rate ( HR ) were measured in conscious male and female Cyp2j+/+ and Cyp2j−/− mice by tail cuff plethysmography . Systemic blood pressure and HR did not differ between genotypes ( Table 1 ) . Invasive hemodynamic measurements in anesthetized Cyp2j+/+ and Cyp2j−/− mice of both sexes also did not reveal differences in HR , BP , cardiac output , systemic vascular resistance , or left ventricular systolic or diastolic function ( Table 2 ) . To assess the contribution of Cyp2j to HPV , we measured changes in the left pulmonary vascular resistance ( LPVR ) in response to left mainstem bronchial occlusion ( LMBO ) in Cyp2j+/+ and Cyp2j−/− mice . We used dynamic measurements of pulmonary pressure and flow in the left pulmonary artery during transient inferior vena cava occlusion to estimate the LPVR [16] . Before LMBO , LPVR was similar in Cyp2j+/+ ( 80±5 mmHg⋅ml⋅min⋅g−1 ) and Cyp2j−/− mice ( 88±6 mmHg⋅ml⋅min⋅g−1 ) . In Cyp2j+/+ mice , LMBO decreased the left pulmonary arterial blood flow ( QLPA ) without changing the pulmonary arterial pressure ( PAP ) , doubling the LPVR ( Figure 5A , Table S3 ) . In contrast , LMBO did not change LPVR in Cyp2j−/− mice ( Figure 5A , Table S3 ) , consistent with the absence of HPV . To estimate the impact of impaired HPV on systemic arterial oxygenation , we measured arterial blood gas tensions 5 minutes after LMBO , while the right lung was ventilated at an inspired oxygen fraction ( FIO2 ) of 1 . Arterial oxygen partial pressure ( PaO2 ) was higher in Cyp2j+/+ than in Cyp2j−/− mice ( 247±36 vs . 153±9 mmHg , respectively; P<0 . 05; Table S3 ) . However , there was no difference in blood pHa , the arterial partial pressure of carbon dioxide , or the concentration of HCO3− ( data not shown ) . Systemic oxygenation during LMBO was further assessed using an intra-arterial PaO2 probe in a subset of Cyp2j+/+ and Cyp2j−/− mice . No difference in PaO2 before LMBO was detected between Cyp2j+/+ and Cyp2j−/− mice ( Figure 5B ) . After LMBO , PaO2 decreased in both genotypes to its new steady state within 2 min; however , Cyp2j−/− mice had a lower PaO2 than did Cyp2j+/+ mice during LMBO ( Figure 5B ) . These observations confirm the presence of increased intrapulmonary shunting during LMBO in Cyp2j−/− mice and are consistent with absent HPV . Since CYP2J2 is the only human gene homologous or paralogous to multiple murine Cyp2j genes , we investigated whether or not complementation with CYP2J2 could restore HPV in Cyp2j−/− mice . At baseline , hemodynamic parameters did not differ between Cyp2j+/+ and Cyp2j−/−-Tg mice ( Table S3 ) . LMBO increased LPVR in Cyp2j+/+ and Cyp2j−/−-Tg mice to a similar extent ( Figure 5C ) , indicating that HPV is preserved in Cyp2j−/−-Tg mice . Furthermore , during LMBO , arterial oxygen partial pressure ( PaO2 ) did not differ between Cyp2j−/−-Tg and Cyp2j+/+ mice ( Table S3 ) , consistent with preserved HPV . These results suggest that presence of the single human CYP2J isoform in mice , in which the entire Cyp2j locus is deleted , is sufficient to permit pulmonary vasoconstriction . To exclude the possibility that the lifelong Cyp2j deficiency might lead to unanticipated compensatory mechanisms that could impair HPV in mice , we assessed HPV in Cyp2j+/+ mice treated with the epoxygenase inhibitor , N-methylsulfonyl-6- ( 2-propargyloxyphenyl ) hexanamide ( MS-PPOH ) . The LMBO-induced increase in LPVR was markedly attenuated in a dose-dependent manner when mice were studied 90 minutes after treatment with MS-PPOH ( Figure 5D ) . The PaO2 during LMBO was lower in MS-PPOH-treated than in vehicle-treated mice . These results further confirm that cytochrome P450 epoxygenase enzymatic activity contributes to HPV in mice . To examine the possibility that HPV is impaired in Cyp2j−/− mice due to an alteration in the balance of vasoconstrictors and vasodilators , we studied the effects of enhancing pulmonary vascular tone by inhibiting nitric oxide ( NO ) production on HPV in Cyp2j−/− mice . At 30 minutes after administration of NG-nitro-L-arginine methylester ( L-NAME , an inhibitor of NO synthases ) , before LMBO , hemodynamic parameters did not differ between Cyp2j+/+and Cyp2j−/− mice ( Table S3 ) . During LMBO , inhibition of NO synthesis with L-NAME augmented the increase in LPVR in Cyp2j+/+ mice and restored the ability of LMBO to increase LPVR in Cyp2j−/− mice ( Figure 5E , Table S3 ) . These findings demonstrate that the Cyp2j−/− mice retain the mechanisms necessary for the pulmonary vascular response to hypoxia and that HPV can be restored in these mice by enhancing vasoconstriction . The plasma and urine EET levels did not differ between Cyp2j+/+and Cyp2j−/− mice ( data not shown ) . Levels of 11 , 12- and 14 , 15-EET , as reflected by the difference of 11 , 12- and 14 , 15-DHET levels before and after hydrolysis of EETs ( measured by ELISA ) were evaluated in bronchoalveolar lavage fluid ( BALF ) from Cyp2j+/+ , Cyp2j−/− and Cyp2j−/−-Tg mice . BALF EET levels did not differ among the genotypes ( Figure 6A , B ) . Moreover , the generation of EETs and DHETs by pulmonary microsomes from Cyp2j+/+ , Cyp2j−/− and Cyp2j−/−-Tg mice did not differ ( Figure 6C ) . In addition to members of the Cyp2j subfamily , members of the Cyp2c family of cytochrome P450 enzymes , including Cyp 2c44 , 2c38 , and 2c29 , are able to metabolize AA to EETs in endothelial cells [21] , [22] . Expression of these three Cyp2c family members in lung and heart of Cyp2j+/+ , Cyp2j−/− and Cyp2j−/−-Tg mice was measured using quantitative RT-PCR . Pulmonary expression of the Cyp2c genes did not differ among genotypes , but deletion of the Cyp2j locus led to increased expression of the three Cyp2c genes in the heart ( Figure 7 ) .
Allelic expansion in the rodent genome is a commonly encountered phenomenon that has the potential to reduce the utility of rodent models for understanding human gene function . At a minimum , the presence of multiple functional murine paralogs confounds the extrapolation to the human context of the results of single gene ablations in mice . An alternative to single gene analysis is the inactivation of an entire locus syntenic to the human gene of interest . For the most part , the genetic tools to undertake such inactivations have been relatively underdeveloped . In this report , we demonstrate the feasibility of joining multiple bacterial artificial chromosomes using site-specific recombination to form a deletion replica that can be used to induce genomic rearrangement in mice . Previous approaches for the deletion of large DNA fragments required two targeting vectors harboring loxP sites [9]–[11] . These approaches require sequential gene targeting . The fused BAC targeting approach represents a powerful and efficient method for developing genetically-modified mice for the purpose of characterizing the function of gene clusters or studying genetic diseases associated with large chromosomal DNA deletions . Using this technology , we generated Cyp2j-null mice in which the 626-kb Cyp2j locus is deleted , as well as mice carrying a transgene specifying the human CYP2J2 allele in context of a Cyp2j-null allele . It has previously been shown that overexpression of human CYP2J2 has cardiovascular protective effects in mice [13] , [14] , [23] . CYP2J synthesizes EETs in vascular endothelial cells [12] , [13] . Epoxygenase-derived EETs hyperpolarize vascular smooth muscle cells in kidney , brain , and heart , resulting in vasorelaxation [24]–[27] . Previously Athiracul et al . reported that female mice deficient in the Cyp2j5 gene on a 129/SvEv background exhibit increased systemic blood pressure [28] . We therefore expected that mice lacking the entire Cyp2j gene family would show systemic vascular effects . However , deletion of the Cyp2j locus did not affect baseline systemic hemodynamic parameters or left ventricular contractile function in either sex . The variance with previous observations might be attributable to differences in strain background or to the actions of other Cyp2j enzymes in Cyp2j5−/− mice which are not present in the Cyp2j−/− mice . Effects of Cyp2j5 deletion on pulmonary vascular function have not been reported for Cyp2j5−/− mice . In the pulmonary circulation , EETs enhance vasoconstrictor tone [3] , [19] , [29] , [30] , likely via activation of TRPC6 channels in vascular smooth muscle cells [30] . Epoxygenase-derived EETs are reported to contribute to the pulmonary vascular response to hypoxia [30] . Moreover , 11 , 12- and 14 , 15-EET levels were recently reported to be increased in isolated-perfused murine lungs following exposure to hypoxia ( FIO2 0 . 01 ) for 10 minutes [31] . Previous studies of the roles of epoxygenases in the regulation of HPV have relied on chemically-synthesized EETs , pharmacological activators or inhibitors of cytochrome P450 enzymes , or lineage-restricted overexpression of CYPs [3] , [30] , which cannot distinguish between the contributions of CYP2J and CYP2C . We did not detect an effect of deleting the Cyp2j locus on pulmonary vascular tone at baseline , but the pulmonary vasoconstrictor response to hypoxia was absent in Cyp2j−/− mice . There are several possible mechanisms by which the Cyp2j subfamily might regulate HPV . It is probable that Cyp2j−/− mice have reduced ability to generate pulmonary vasoconstricting EETs . Alternatively , it is possible that the deletion of Cyp2j subfamily shunts arachidonic acid into other pathways leading to the increased synthesis of cyclooxygenase , lipoxygenase , and ω-hydroxylase products . Some of these products , such as prostacyclin or 20-HETE are known to be pulmonary vasodilators [32] , [33] and could impair HPV . We were unable to detect differences in EET levels in the plasma , urine , and BALF of wild-type and Cyp2j−/− mice . Moreover , we did not observe differences in the generation of EETs and DHETs by microsomes extracted from the lungs of wild-type , Cyp2j−/− , or Cyp2j−/−-Tg mice . Previous studies have shown that multiple cytochrome P450 enzymes , including CYP1A , CYP2B , CYP2C , CYP2D , CYP2G , CYP2J , CYP2N , and CYP4A subfamilies , are capable of EET biosynthesis in vitro [34] . It is conceivable that EET production by CYP isoforms other than Cyp2j could obscure the impact of Cyp2j deficiency on pulmonary and systemic EET generation . In lung tissues , immunohistochemistry studies detected CYP2C proteins exclusively in the serous cells of bronchial glands , whereas CYP2J proteins were detected in a variety of cell types including pulmonary vascular smooth muscle and endothelial cells [1] , [35] . We observed that Cyp2c genes were expressed in the lungs of wild-type and Cyp2j−/− mice with or without the human CYP2J2 transgene . In addition to actions mediated by their enzymatic activity , Cyp2j isoforms could function in signaling circuits via other mechanisms , for example , serving as scaffold proteins or mediators in signal transduction complexes that regulate HPV via mechanisms not dependent on epoxygenases or hydroxylases . The proposal that the catalytic activity of Cyp2j family members regulates HPV is supported by the finding of the present study that administration of an epoxygenase inhibitor , MS-PPOH , to wild-type mice impaired HPV in a dose-dependent manner . Transgenic introduction of human CYP2J2 into Cyp2j-deficient mice did not affect baseline hemodynamic parameters but restored the pulmonary vasoconstrictor response to LMBO . These results suggest that the human CYP2J2 functions in a manner similar to one or more of the murine Cyp2j isoforms in the regulation of pulmonary vascular tone by hypoxia . It is of note that the expression of human CYP2J2 transgene was greater in the lungs of Cyp2j−/− mice than in wild-type mice , suggesting the existence of a feedback loop designed to maintain expression of Cyp2j . Administration of the NO synthase inhibitor , L-NAME , restored HPV in Cyp2j-null mice , indicating that Cyp2j-deficient mice retain the ability to constrict their pulmonary vasculature in response to alveolar hypoxia . This result is in agreement with observations of Ichinose et al . , who reported that L-NAME restores HPV in cPla2-deficient mice [16] . These observations suggest that HPV is highly sensitive to the balance of vasoconstrictors and vasodilators in the lung . Enhancing vasoconstrictor tone or reducing vasodilation restores HPV in a variety of settings [36] . Taken together , these findings suggest that , although EET biosynthesis potentially increases in response to hypoxia [31] can enhance HPV , cPLA2/CYP2J signaling is not required for the pulmonary vasculature to sense and respond to regional hypoxia . After genes duplicate , they often diverge in ways that can lead to new functions that were not exhibited by the parental gene . Mice have evolved eight Cyp2j genes and two pseudogenes . The results of this study have shown that the expression profile for each Cyp2j gene is distinct . CYP genes may also become specialized with respect to substrate specificity or product distribution . Cyp2j5 shares the highest nucleic acid sequence similarity with the human CYP2J2 gene , whereas Cyp2j6 and Cyp2j9 have the highest similarity with the sequence of the human protein ( Fig . S5 ) . It is conceivable that one or more of the mouse Cyp2j isoforms may have functions that differ from the single human CYP2J2 enzyme . However , our observations that both human and mouse CYP2J enzymes contribute to HPV suggest that the function of human CYP2J2 and one or more mouse Cyp2j isoforms has been conserved as the two genomes diverged during evolution . In conclusion , ablation of a large gene family through fused BAC-mediated homologous recombination in ES cells has generated mice in which the 626-kb murine Cyp2j gene cluster was deleted . The single human ortholog/paralog CYP2J2 was introduced transgenically to complement the deleted mouse locus . Surprisingly , genetically modulating Cyp2j activity did not affect baseline vascular function . However deletion of the Cyp2j gene locus resulted in a compromise of the pulmonary vasoconstrictor response to hypoxia .
Mouse BAC clones RP23-24J24 and RP23-70M4 and the human BAC clone RP11-163O24 were obtained from the Children's Hospital Oakland Research Institute . Primers P1 through P22 , mouse genotyping primers , and RT-PCR primer sequences are shown in Table S1 . The MLPA probe sequences are shown in Table S2 . A codon-optimized TP901 integrase was designed by Dr . Changhong Pang and placed in an E . coli expression vector ( pacycTP901_ermb ) under the control of the araBAD promoter . A codon-optimized R4 integrase was inserted in the mammalian expression vector pEAK15 under the control of the chicken actin-CMV hybrid promoter . All the animal studies conform to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health and were approved by the Subcommittee on Research Animal Care of the Massachusetts General Hospital . The non-selective nitric oxide synthase ( NOS ) inhibitor , NG-nitro-L-arginine methylester ( L-NAME ) ; kanamycin; and ampicillin were purchased from Sigma-Aldrich , St . Louis , MO . The selective epoxygenase inhibitor , N-methylsulfonyl-6- ( 2-propargyloxyphenyl ) hexanamide ( MS-PPOH ) , was purchased from Cayman Chemical , Ann Arbor , MI . Ganciclovir and blasticidin were purchased from Novagen . Hygromycin and geneticin were obtained from Invitrogen . The mouse Cyp2j and human CYP2J2 gene structures are shown in Figure 1A . Target vectors were endowed with four restriction enzyme sites to allow insertion of the recombination homology arms ( Figure 1B ) . Arms , 200–2000 bp in length , were amplified from BAC DNA and subcloned into the desired target vector . The target vector was cut with PI-SceI , and a fragment containing the homology arms and the selection cassette was recovered by gel purification and electroporated into competent cells containing the target BAC clone and a recombinase expression vector ( pacycredabsce_ermb ) bearing the bacteriophage λ redα and β genes under the control of the araBAD promoter [6] . Electrocompetent cells were prepared by growing the BAC strain bearing the recombinase expression vector in LB medium containing 0 . 1% arabinose . Candidate recombinant clones were identified by growth on selective medium ( kanamycin or ampicillin and chloramphenicol ) and screened by PCR using primers flanking the arms ( P1–8 ) ( Figure 1B ) . The authenticity of candidate clones was confirmed by sequencing of the resulting PCR products . DNA from a verified clone was electroporated into DH10B competent cells and individual colonies were re-streaked on different selection plates to confirm the removal of the recombinase plasmid . The resultant recombinant BACs MT5′BAC and MT3′BAC were digested with restriction enzymes chosen to distinguish the recombinant from the original BAC sequences ( data not shown ) . A similar process was followed to trim the sequences flanking human CYP2J2 gene from a human BAC ( Figure 3A ) . Homologous arms were amplified from the wild-type BAC and subcloned into two target vectors containing two different selectable markers , conferring trimethoprim and ampicillin resistance . A plasmid expressing TP901 integrase under araBAD promoter control was introduced into bacteria harboring the recombinant BAC bearing the TP901 attB site . Electrocompetent cells were prepared from cells propagated in 0 . 1% arabinose . The recombinant BAC bearing the TP901 attP site and expressing ampicillin resistance was electroporated into cells that were then spread on plates containing kanamycin , ampicillin , and chloramphenicol . The fused BAC clones were screened by PCR , and PCR products were sequenced to confirm the formation of TP901 attL and attR sites . DNA was prepared from a correctly fused clone and electroporated into DH10B competent cells to remove the TP901 expression plasmid . DNA from the fused BAC was digested with diagnostic restriction enzymes to confirm the structural integrity of the fused BAC . ES cell lines were cultured with irradiated fibroblast feeder cells in Knock-Out Dulbecco's Modified Eagle's Medium ( KO-DMEM ) supplemented with 15% Fetal Bovine Serum and 1000 units Leukemia Inhibitory Factor ( LIF ) per mL . BAC DNA ( 5–20 µg ) was digested with NotI or PI-SceI in 30–50 µL volume overnight and then electroporated into 107 ES cells at 0 . 25 kV , 960 µF with a Bio-Rad Gene Pulser . Transformants were selected for 8–10 days with 250 µg/mL geneticin ( Invitrogen ) . A transfection mixture containing a plasmid capable of expressing R4 integrase and Lipofectamine 2000 ( Invitrogen ) was prepared according to the manufacturer's instructions and incubated for 20–30 min at room temperature . Target ES cells at 80–90% confluence were trypsinized using 0 . 1% trypsin in 10 mM EDTA and resuspended in fresh ES cell culture medium with low ( 2% ) serum at 3×105 cells per mL . The ES cell suspension ( 10 mL ) was mixed with the transfection mixture and replated on a 10 cm feeder plate . After 24 h , the negative selective agent , ganciclovir ( 2 . 5 µM ) , was added . Transformant colonies were visible after 8–10 days of culture . MLPA was performed as described previously [37] . Fragment analysis was carried out on an ABI 3730XL DNA analyzer . ES cells were injected into C57/BL6 mouse blastocysts to generate chimeric mice . Chimeras from ES cell clones derived from the B6-white ES cell line were mated with wild-type B6-white mice ( B6 ( Cg ) -Tyrc-2J/J , Jackson Laboratory , Bar Harbor , Maine , USA ) to test germ line transmission identifiable by coat color difference . Heterozygous mice ( Cyp2j+/− ) were identified by MLPA . Cyp2j−/− and littermate-matched wild-type ( Cyp2j+/+ ) mice were obtained by mating of pairs of Cyp2j+/− mice . The recombinant human CYP2J2 BAC DNA was linearized by PI-SceI digestion . The purified DNA was microinjected into pronuclear zygotes from ( C57BL/6×DBA ) F1 mice and embryos were transplanted into recipients for the generation of transgenic mice . The resultant transgenic mice ( Cyp2j−/−-Tg ) were backcrossed with B6-white Cyp2j−/− mice for more than 6 generations prior to molecular and physiological characterization . The RT-MLPA probes used in this study are shown in Table S2 . The RT-MLPA procedure was performed as described previously [20] . Total RNA was extracted and purified using an RNeasy kit ( Qiagen ) from mouse tissues ( 6–8 week-old ) . The primers used are detailed in Table S1 . Reverse transcription and real-time quantitative PCR ( qPCR ) reactions were prepared with SuperScript II Reverse Transcriptase ( Invitrogen ) and iQ SYBR Green Supermix ( Bio-Rad ) and run in triplicate on a Bio-Rad iQ5 . The cDNA panels of human adult tissue were obtained from Clontech . We studied mice of both sexes with an age range of 2–5 months , weighing 20–30 g . Animals in each experimental group were matched for body weight . Systolic blood pressure ( SBP ) and heart rate ( HR ) were measured using a non-invasive blood pressure system ( XBP 1000 , Kent Scientific , Torrington , Conn ) in awake Cyp2j+/+ and Cyp2j−/− mice , as described previously [38] . Invasive hemodynamic measurements were performed in anesthetized Cyp2j+/+ and Cyp2j−/− mice , as described previously [39] . Briefly , mice were anesthetized by intraperitoneal injection of ketamine ( 120 mg·kg−1 ) , fentanyl ( 0 . 05 mg·kg−1 ) , and pancuronium ( 2 mg·kg−1 ) . After intubation , animals were mechanically ventilated inspired oxygen fraction ( FIO2 ) 1 . 0 , tidal volume 10 µL·g−1 , respiratory rate 120 breaths·min−1 , and a fluid-filled catheter was inserted into the left carotid artery for infusion of saline ( 0 . 5 µL·g−1·min−1 ) . A second fluid-filled catheter was inserted into the right jugular vein for measurement of central venous pressure ( CVP ) . A thoracotomy was performed , and a pressure-volume conductance catheter ( Size 1F , Model PVR-1030 , Millar Instruments , Inc . , Houston , TX ) was inserted via the apex into the left ventricle . Systemic vascular resistance ( SVR ) was calculated based on mean arterial pressure ( MAP ) , CVP , and cardiac output ( CO ) using following formula: SVR = ( [MAP-CVP]·CO−1 ) . The following parameters were derived from left ventricular pressure-volume curves: LVESP , left ventricular end-systolic pressure; LVEDP , left ventricular end-diastolic pressure; EF , ejection fraction; SV , stroke volume; dP/dtmax , maximum rate of developed left ventricular pressure; dP/dtmin , minimum rate of developed left ventricular pressure; τ time constant of isovolumic relaxation; SW , stroke work; Ea , arterial elastance . To assess HPV , left lung pulmonary vascular resistance ( LPVR ) was estimated before and during left mainstem bronchial occlusion ( LMBO ) in Cyp2j+/+ , Cyp2j−/− , and Cyp2j−/−-Tg mice ( n = 10 , 10 , and 5 , respectively ) , using methods described previously [16] . Briefly , mice were anesthetized , mechanically ventilated at FIO2 of 1 . 0 , and then subjected to a thoracotomy . An arterial line was inserted into the right carotid artery , a custom-made catheter was placed into the main pulmonary artery , and a flow probe was positioned around the left pulmonary artery . MAP , pulmonary arterial pressure ( PAP ) , and left pulmonary arterial blood flow ( QLPA ) were continuously measured and recorded before and during LMBO . To estimate the LPVR , the inferior vena cava was partially occluded to transiently reduce CO until QLPA was reduced by approximately 50% . LPVR was calculated from the slope of the PAP/QLPA relationship . The increase in LPVR induced by LMBO ( ΔLPVR ) was obtained by calculating the change in the mean value of the PAP/QLPA slopes in each mouse . Five minutes after LMBO , arterial blood was sampled from the right carotid artery . Blood gas tension analyses were measured by using an ABL800 FLEX analyzer ( Radiometer America , Inc . , Westlake , USA ) . To further assess the impact of Cyp2j deficiency on systemic oxygenation during LMBO in 4 Cyp2j+/+ and 3 Cyp2j−/− mice , a flexible polarographic Clark-type oxygen micro probe ( 0 . 5 mm OD; LICOX CC1 . R , GMS , Kiel-Mielkendorf , Germany ) was advanced into the aortic arch via the right carotid artery . Arterial oxygen partial pressure ( PaO2 ) was measured in real time and recorded continuously . The PaO2 electrodes were calibrated before and after each experiment in air at ambient pressure according to the manufacturer's instructions . Wild-type ( C57BL/6-W ( B6 ( Cg ) -Tyrc-2J/J ) ) mice received MS-PPOH ( 30 or 60 mg·kg−1 dissolved in 50 µL dimethyl sulfoxide ( DMSO ) ) or vehicle alone ( equal volume of DMSO ) ( n = 5 per group ) via tail vein 90 minutes before measurement of ΔLPVR . The dose and timing of administration were chosen based on data published previously [3] . ΔLPVR was measured 30 minutes after intravenous administration of L-NAME , dissolved in 50 µL vehicle ( normal saline ) at a dose of 100 mg·kg−1 in Cyp2j+/+ mice ( n = 5 ) and Cyp2j−/− mice ( n = 6 ) . The dose was chosen based on results from a previous study [40] . Collection of BALF was performed as previously described [41] . To assess in vivo 11 , 12- and 14 , 15-EETs production , an enzyme-linked immunosorbent assay ( ELISA ) kit ( Detroit R&D , Inc . , Detroit , MI ) was used to determine concentrations of the stable 11 , 12- and 14 , 15-EETs metabolites 11 , 12- and 14 , 15-dihydroxyeicosatrienoic acid ( 11 , 12-DHET and 14 , 15-DHET ) in BALF of Cyp2j+/+ , Cyp2j−/− and Cyp2j−/−-Tg mice ( n = 3 per group ) . 11 , 12 and 14 , 15-DHET were quantified by ELISA according to the manufacturer's instructions and normalized by protein content in BALF samples . The difference of the quantity of DHET before and after EET hydrolysis represents EET quantity in the samples . Microsomal fractions were prepared from both lungs of Cyp2j+/+ , Cyp2j−/− , and Cyp2j−/−-Tg mice ( n = 4 per group ) as described previously [42] , [43] . To determine the eicosanoids generated , samples containing microsomal fractions were incubated with 1 µg of arachidonic acid and 1mM NADPH for 1 hour at 37C in a shaking water bath . Reactions were terminated by adding 2 volume of HPLC grade methanol and stored at −80C prior to analysis to each sample . The generated eicosanoid profiles were determined by LC-MS-MS , as previously described [44] . All data are expressed as means ± SEM . P values<0 . 05 were considered statistically significant . Statistical analyses were performed using Prism 5 software ( GraphPad Software Inc . , La Jolla , CA ) . For hemodynamic experiments , a two-way ANOVA with repeated measures was used to compare differences between groups . However , when the interaction P value between time and condition was significant , a one-way ANOVA with post hoc Bonferroni tests ( two-tailed ) for normally distributed data or a Kruskal-Wallis test ( two-tailed ) with a post hoc Dunn's test for data that was not normally distributed was used . Measurements within the same experimental group were compared by a paired t-test . If the normality test failed , Mann-Whitney rank sum test was applied .
|
In mice and humans , the CYP2J class of cytochrome P450 epoxygenases metabolizes arachidonic acid ( AA ) to epoxyeicosatrienoic acids ( EETs ) , short-lived mediators with effects on both the pulmonary and systemic vasculature . Genetic dissection of CYP2J function to date has been complicated by allelic expansion in the rodent genome . In this study , the mouse chromosomal locus syntenic to human CYP2J2 , containing eight presumed genes and two pseudogenes , was deleted via generation of a recombinant template created by homologous and site-specific recombination steps that joined two precursor bacterial artificial chromosomes ( BACs ) . The Cyp2j null mice were subsequently complemented by transgenic delivery of BAC sequences encoding human CYP2J2 . Hypoxic pulmonary vasoconstriction ( HPV ) and systemic oxygenation during regional alveolar hypoxia were unexpectedly found to be impaired in null mice , but not in null mice bearing the transgenic human allele , suggesting that Cyp2j products contribute to the pulmonary vascular response to hypoxia .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Deletion of the Murine Cytochrome P450 Cyp2j Locus by Fused BAC-Mediated Recombination Identifies a Role for Cyp2j in the Pulmonary Vascular Response to Hypoxia
|
The neural precursor cell expressed developmentally down-regulated gene 4–2 , Nedd4-2 , is an epilepsy-associated gene with at least three missense mutations identified in epileptic patients . Nedd4-2 encodes a ubiquitin E3 ligase that has high affinity toward binding and ubiquitinating membrane proteins . It is currently unknown how Nedd4-2 mediates neuronal circuit activity and how its dysfunction leads to seizures or epilepsies . In this study , we provide evidence to show that Nedd4-2 mediates neuronal activity and seizure susceptibility through ubiquitination of GluA1 subunit of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor , ( AMPAR ) . Using a mouse model , termed Nedd4-2andi , in which one of the major forms of Nedd4-2 in the brain is selectively deficient , we found that the spontaneous neuronal activity in Nedd4-2andi cortical neuron cultures , measured by a multiunit extracellular electrophysiology system , was basally elevated , less responsive to AMPAR activation , and much more sensitive to AMPAR blockade when compared with wild-type cultures . When performing kainic acid-induced seizures in vivo , we showed that elevated seizure susceptibility in Nedd4-2andi mice was normalized when GluA1 is genetically reduced . Furthermore , when studying epilepsy-associated missense mutations of Nedd4-2 , we found that all three mutations disrupt the ubiquitination of GluA1 and fail to reduce surface GluA1 and spontaneous neuronal activity when compared with wild-type Nedd4-2 . Collectively , our data suggest that impaired GluA1 ubiquitination contributes to Nedd4-2-dependent neuronal hyperactivity and seizures . Our findings provide critical information to the future development of therapeutic strategies for patients who carry mutations of Nedd4-2 .
A hyperactive brain circuit is a common abnormality observed in patients with various neurological and psychiatric disorders , including epilepsies ( 1 ) . Evidence from human genetic studies implicates genes encoding ion channels or their regulators in the etiology of those pathophysiological conditions [1 , 2] . Characterizing those genes and their function in regulation of brain circuit activity is likely to reveal novel therapeutic targets for these diseases . One of those genes is the neural precursor cell expressed developmentally downregulated gene 4-like ( Nedd4-2 ) [3] . Nedd4-2 , is an epilepsy-associated gene containing at least three missense mutations identified through genomic mutation screening in patients with epilepsy [3–6] . Nedd4-2 encodes a ubiquitin E3 ligase that belongs to the Nedd4 family of ubiquitin E3 ligases [7] but is the only member encoded by an epilepsy-associated gene [3] . Because of an N-terminal lipid-binding domain , Nedd4-2 has high affinity toward binding and ubiquitinating membrane proteins [8] . Several neuronal membrane substrates of Nedd4-2 have been identified , such as voltage-gated sodium channel Nav1 . 6 [9] , voltage-gated potassium channels Kv7/KCNQ [10–12] , neurotrophin receptor TrkA [13 , 14] and the GluA1 subunit of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor ( AMPAR ) [15] . Our previous work has demonstrated elevated seizure susceptibility in mice when Nedd4-2 is knocked down [16] . However , the mechanisms by which the dysfunction of Nedd4-2 contributes to epileptogenesis are unclear . Presumably wild-type ( WT ) Nedd4-2 mediates or represses circuit activity by ubiquitinating one or more of its substrates while the epilepsy-associated mutants fail to do so and lead to seizures and/or epilepsies . To test this possibility , we needed to identify the relevant substrate of Nedd4-2 in regulation of neuronal excitability and characterize the effect of epilepsy-associated mutations on substrate recognition . The AMPAR is a major subtype of ionotropic glutamate receptors and is the most commonly found receptor in the mammalian nervous system [17 , 18] . AMPARs are assembled as homo-or hetero-tetramers and are comprised of combinations of GluA1–GluA4 subunits [19] . Each subunit has a non-conserved C-terminal , an intracellular domain that harbors regulatory elements subject to various post-translational modifications such as ubiquitination . All four AMPAR subunits can be ubiquitinated , but only GluA1 ubiquitination has been specifically described upon different activity stimulations [20 , 21] . Studies have shown that GluA1 ubiquitination contributes to its internalization [22 , 23] . This internalization , part of AMPAR trafficking mechanisms [24] , is critical for synaptic depression as well as homeostatic regulation of synaptic strength [25–28] . Because GluA1-GluA2 is the predominant AMPAR heteromer [29] , and GluA1 is required for successful trafficking and targeting of GluA2 [30] , we hypothesized that Nedd4-2 is required for limiting GluA1 surface expression and functionality of AMPAR . Because GluA1 levels affect neuronal activity [31] , and dysregulation of AMPARs has been shown to be linked to epilepsy [32 , 33] , Nedd4-2 may play a role in affecting neuronal activity , seizures , and/or epilepsy through fine-tuning of AMPARs . In our current study , we provide in vitro and in vivo evidence to demonstrate GluA1- and AMPAR-dependent elevation of neuronal activity and seizure susceptibility induced by functional insufficiency of Nedd4-2 . To our knowledge , our findings provide the first mechanism underlying Nedd4-2-associated circuit hyperactivity and seizures and open up a new avenue for the development of therapeutic strategies to potentially treat epileptic patients who carry Nedd4-2 mutations .
It is unknown how dysregulation of Nedd4-2 is involved in seizures or epilepsies . To answer this question , we employed a mouse model , Nedd4-2andi , in which the long-form ( isoform 1 ) of Nedd4-2 is selectively deleted due to a spontaneous mutation in exon-2 ( Fig 1A ) . Because Nedd4-2 knockout mice are not viable [34] , Nedd4-2andi serves as an ideal , in vivo knockdown model to study Nedd4-2 . To assess the question of whether Nedd4-2 mediates neuronal activity , we employed a multielectrode array ( MEA ) recording system ( S1 Fig ) to record extracellular spontaneous spikes of electrical activity in primary cortical neuron cultures prepared from WT or Nedd4-2andi mice . As shown in Fig 1B , the frequency of spontaneous spikes was significantly elevated in Nedd4-2andi cultures in comparison to WT cultures . The average spontaneous spike amplitude did not differ between WT and Nedd4-2andi cultures ( Fig 1C ) . These data indicate that spontaneous neuronal activity is basally elevated in Nedd4-2andi cortical cultures . To determine whether elevated spontaneous neuronal activity in Nedd4-2andi cultures is accompanied by altered synaptic transmission , we performed whole-cell patch-clamp recording to obtain miniature excitatory post-synaptic current ( mEPSC ) from WT or Nedd4-2andi cortical neuron cultures . As shown in Fig 1D , Nedd4-2andi neurons exhibit elevation of both mEPSC amplitude and frequency when compared to WT neurons . These data suggest that Nedd4-2 likely mediates both pre- and post-synaptic properties , and the elevation of spontaneous neuronal activity observed in Nedd4-2andi cortical cultures ( Fig 1C ) is likely contributed by multiple factors . We previously identified the GluA1 subunit of AMPAR as a substrate of Nedd4-2 [15] . We therefore asked whether AMPAR mediates spontaneous neuronal activity and whether it is responsible for the hyperactivity observed in Nedd4-2andi cultures . An AMPAR agonist , AMPA ( 1 μM ) , was applied to determine how spontaneous neuronal activity was affected when AMPAR was activated . MEA recordings from WT cultures before and after AMPA treatment for 15 min indicated elevated spontaneous spike frequency ( Fig 2A1 and 2A3 ) suggesting that spontaneous neuronal activity can be modulated by AMPARs . The same treatment produced significant , but smaller , effects on Nedd4-2andi cultures ( Fig 2A2 and 2A3; Significant interaction between treatment and genotype was detected , p<0 . 05 . ) . The average of spontaneous spike amplitude ( Fig 2B ) and electrode burst activity did not differ after AMPA treatment for either genotype ( S2A Fig ) . These data suggest that Nedd4-2 contributes to the elevation of spontaneous neuronal activity , particularly spontaneous spike frequency , when the AMPAR is activated . We then asked whether Nedd4-2andi cultures respond differently when AMPARs are pharmacologically inhibited . We employed a specific AMPAR antagonist , NBQX ( 2 μM ) , and again recorded the spontaneous neuronal activity before and after a 15-min treatment . As shown in Fig 3A , NBQX slightly , but not significantly , reduced spontaneous spike frequency in WT cultures , while NBQX significantly reduced spike frequency in Nedd4-2andi cultures ( Significant interaction between treatment and genotype was detected , p<0 . 05 . ) . Again , average spontaneous spike amplitude did not differ between either treatments or genotypes ( Fig 3B ) . Interestingly , we observed elevated burst activity without changes in interburst interval ( IBI ) after NBQX treatment ( S2B Fig ) , which was also observed in another study [35] . Although we suspect the lack of changes in IBI is potentially because NBQX was applied acutely , we did not pursue further experiments with prolonged treatments since our data suggest that NBQX-induced burst activity is independent of Nedd4-2 ( S2B1 and S2B2 Fig ) . We previously showed elevated synchrony of spontaneous neuronal activity in Nedd4-2andi cultures [16] . Elevation of synchronized activity indicates potentially elevated network activity . To determine whether AMPAR is involved in this phenomenon , we analyzed the synchrony index in WT and Nedd4-2andi cultures treated with either AMPA or NBQX . As shown , although AMPA treatment elicits some effect toward elevation of synchrony , no difference was observed between genotypes . NBQX , on the other hand , produces no effect on synchrony in either WT or Nedd4-2andi cultures ( S3 Fig ) . These results suggest that , although prolonged stimulation of AMPAR might further elevate the synchrony of neuronal activity , the effect is unlikely to be Nedd4-2-dependent . Furthermore , AMPAR is also unlikely to be responsible for basally elevated synchrony when Nedd4-2 is compromised [16] since NBQX exerts no effect . Therefore , whether and how Nedd4-2 mediates synchrony of spontaneous neuronal activity or other network activity , such as network spikes and bursts , through other substrates would be an important future direction . In summary , we showed that Nedd4-2andi cultures were less sensitive to AMPAR activation but very sensitive to AMPAR blockade with regard to spontaneous spike frequency . These results suggest that altered GluA1/AMPAR signaling in Nedd4-2andi mice contributes to basally elevate spontaneous neuronal activity . We have previously demonstrated that Nedd4-2andi mice exhibit greater seizure susceptibility induced by systematic administration of kainic acid , a potent agonist for kainate-class ionotropic glutamate receptors that is widely used to induce seizures in animal models [16 , 36] . Greater sensitivity to AMPAR blockade in reducing spontaneous neuronal activity , as seen in Fig 3 , led to our hypothesis that elevated GluA1 level contributes to elevated seizure susceptibility in Nedd4-2andi mice . To test this hypothesis , WT or Nedd4-2andi mice were crossed with GluA1 knockout mice to obtain the following four genotypes: 1 ) Nedd4-2wt/wt GluA1+/+; , 2 ) Nedd4-2wt/wt GluA1+/- , 3 ) Nedd4-2andi/andi GluA1+/+ , and 4 ) Nedd4-2andi/andi GluA1+/- . As shown in Fig 4A , GluA1 levels in Nedd4-2wt/wt GluA1+/- and Nedd4-2andi/andi GluA1+/- mice were reduced by 31% and 46% , respectively , when compared to their control littermates ( Nedd4-2wt/wt GluA1+/+ and Nedd4-2andi/andi GluA1+/+ , respectively ) . Most importantly , the GluA1 level in Nedd4-2andi/andi GluA1+/- mice was similar to Nedd4-2wt/wt GluA1+/+ mice ( Fig 4A ) . We then determined seizure susceptibility in these mice by intraperitoneal injections of kainic acid . Four-week-old mice were injected with kainic acid ( 15 , 30 , or 60 mg/kg ) as done in our previous study [16] . Behavioral seizures were monitored and scored during a 1-hr observation period . As shown , Nedd4-2andi mice ( Nedd4-2andi/andi GluA1+/+; Fig 4C , left panel ) had enhanced seizure response in comparison to WT mice ( Nedd4-2wt/wt GluA1+/+; Fig 4B , left panel ) . Reducing GluA1 levels in Nedd4-2andi mice ( Nedd4-2andi/andi GluA1+/-; Fig 4C , right panel ) significantly reduced seizure response in comparison to control Nedd4-2andi mice ( Nedd4-2andi/andi GluA1+/+; Fig 4C , left panel ) . Furthermore , reducing GluA1 level in Nedd4-2andi mice produced a seizure response similar to WT mice ( Nedd4-2wt/wt GluA1+/+; Fig 4B , left panel ) . A slight , but not significant , reduction of seizure response was also observed when GluA1 level was reduced in WT mice ( Nedd4-2wt/wt GluA1+/-; Fig 4B , right panel ) . In conclusion , our results indicate that genetically reducing GluA1 level is able to correct elevated seizure susceptibility caused by insufficient function of Nedd4-2 in Nedd4-2andi mice . Our data suggest that impaired GluA1 ubiquitination may be responsible for Nedd4-2-mediated seizure and/or epilepsies . To test this hypothesis , we sought to characterize the functional consequence of GluA1 ubiquitination by Nedd4-2 . We first attempted to map the Nedd4-2-ubiquitinated residues of GluA1 as ubiquitination at different residues may affect the function of GluA1 differently [20 , 21] . We employed human embryonic kidney ( HEK ) cells because they do not express a detectable level of GluA1 or Nedd4-2 endogenously ( S4 Fig ) . There are four lysine residues ( K813 , K819 , K822 , and K868 ) located on the carboxyl-terminal , intracellular domain of GluA1 ( Fig 5A ) [21–23] . As reported previously , mutating all four residues completely abolishes GluA1 ubiquitination; this rules out other lysine residues as targets [21] . Accordingly , WT Nedd4-2 was then co-transfected with WT GluA1 or mutant GluA1s in which each lysine was replaced by arginine ( R ) at each individual site ( K813R , K819R , K822R and K868R ) or all four lysine residues together were mutated to R ( 4KR ) . As shown in Fig 5B , the GluA1 with either K868R or all four lysine residues mutated to arginine ( 4KR ) showed significantly reduced ubiquitination when co-expressed with Nedd4-2 . A trend toward reduced ubiquitination is also observed when GluA1 carries K822R , suggesting a potential alternative residue for Nedd4-2-mediated ubiquitination . In summary , these results suggest that K868 is the most critical residue ubiquitinated by Nedd4-2 . GluA1 ubiquitination at K868 has been shown to affect its surface expression [22 , 23] . To determine whether Nedd4-2 mediates surface expression of GluA1 , we labeled surface proteins with biotin in WT or Nedd4-2andi cortical neuron cultures followed by purification of biotinylated proteins with streptavidin beads . As shown in Fig 5C , Nedd4-2andi cultures indeed exhibited elevated surface GluA1 when compared with WT cultures . N-cadherin serves as a control and did not differ between WT or Nedd4-2andi cultures . These results confirm the role of Nedd4-2 in limiting GluA1 surface expression . Because elevated surface GluA1 level has been linked to enhanced seizure susceptibility [37 , 38] , our findings further support our hypothesis that altered GluA1 ubiquitination contributes to Nedd4-2-associated seizures and/or epilepsies . There are three epilepsy-associated missense mutations of Nedd4-2 ( S233L , E271A , and H515P ) identified in patients with epilepsies [4 , 5] . Based on our findings , we aimed to test the hypothesis that one or more of these mutations could disrupt GluA1 ubiquitination . To avoid potential inference from other neuronal E3 ligases for GluA1 [39 , 40] , we applied reconstitutive systems to determine GluA1 ubiquitination using either HEK cells or in vitro ubiquitination . When using HEK cells co-transfected with GluA1 and Nedd4-2 , we found that GluA1 is less ubiquitinated when co-expressed with any of the Nedd4-2 mutants in comparison to WT Nedd4-2 ( Fig 6A and S5 Fig ) . When in vitro ubiquitination was performed using recombinant full-length GluA1 with WT or any of the expressed Nedd4-2 mutants ( partially purified from HEK cells ) , we also found that in comparison to WT Nedd4-2 , all three mutant Nedd4-2s exhibited reduced ability to ubiquitinate GluA1 in vitro ( Fig 6B ) . Previously we showed that Nedd4-2-mediated-GluA1 ubiquitination leads to degradation of GluA1 [15] . To determine whether mutant Nedd4-2s fail to degrade GluA1 , HEK cells were co-transfected with GluA1 and WT or mutant Nedd4-2 . Using cycloheximide ( 100 μg/ml ) to inhibit protein translation and follow protein degradation , significant GluA1 down-regulation is only observed when co-expressed with WT Nedd4-2 but not with any of the mutant Nedd4-2s ( Fig 6C ) . Slightly enhanced levels of GluA1 or Nedd4-2 after cycloheximide treatment were observed in some groups after normalization with the internal control Tubulin . This is due to a lower turnover rate of GluA1 or Nedd4-2 than that of Tubulin when cells were transfected with mutant Nedd4-2s . To strengthen the idea that GluA1 degradation is altered when co-expressed with mutant Nedd4-2s , HEK cells co-transfected with GluA1 and WT or mutant Nedd4-2 were treated with proteasome inhibitor MG132 ( 10 μM ) ( S6 Fig ) . GluA1 significantly accumulates when co-expressed with WT Nedd4-2 , but not with any of the mutant Nedd4-2s , after MG132 treatment . Altogether , our data suggest that all three missense mutations disrupt Nedd4-2-mediated GluA1 ubiquitination and degradation . When expressing WT or mutant Nedd4-2s in HEK cells , it was observed that the mutant Nedd4-2s exhibited increased basal levels and reduced degradation when compared with WT Nedd4-2 ( Fig 6C3 and S6 Fig ) , suggesting enhanced stability . Because Nedd4-2 can self-ubiquitinate , the reduced down-regulation of both GluA1 and Nedd4-2 suggests that these missense mutations most likely affect the general ubiquitination process mediated by Nedd4-2 [41 , 42] . Furthermore , all three mutations are located on or near one of the three protein-protein interaction domains ( WW domain ) in Nedd4-2 ( S7 Fig ) , suggesting potentially altered interaction with its interacting proteins . To test this possibility , we studied the adaptor protein 14-3-3 , which directly interacts with Nedd4-2 and has been shown to mediate Nedd4-2’s substrate recognition [43–45] . We first aimed to determine whether 14-3-3 mediates Nedd4-2-mediated GluA1 ubiquitination . In vitro ubiquitination using recombinant GluA1 and Nedd4-2 yielded some GluA1 ubiquitination ( Fig 7A1 , lane 1 ) . Remarkably , in the presence of recombinant 14-3-3ε , one of the 14-3-3 isoforms known to interact with Nedd4-2 [46] , GluA1 ubiquitination was significantly enhanced ( Fig 7A1 , lane 2 ) . To validate the role of 14-3-3 , a peptide-based general 14-3-3 inhibitor , R18 trifluoroacetate ( R18; 0 . 025 mg/ml ) , which is known to disrupt the interaction between 14-3-3 and its binding partners , was used [47–49] . As predicted , R18 reduced GluA1 ubiquitination to a level similar to Nedd4-2 alone ( Fig 7A1 , lane 4 ) . For controls , the same reactions in the absence of either Nedd4-2 or ubiquitin showed nearly undetectable GluA1 ubiquitination ( Fig 7A1 , lanes 5–8 ) . These data suggest that , while Nedd4-2 is capable of ubiquitinating GluA1 in the absence of 14-3-3 , 14-3-3 significantly facilitates this ubiquitination . We then asked whether reduced GluA1 ubiquitination by epilepsy-associated missense mutations of Nedd4-2 occurred through altered interactions with 14-3-3 . WT or mutant Nedd4-2 was transfected into HEK cells . Co-immunoprecipitation showed that all three mutants have reduced interactions with endogenous 14-3-3 in HEK cells ( Fig 7B ) . Similar results were obtained when using recombinant 14-3-3ε to immunoprecipitate WT or mutant Nedd4-2 expressed in HEK cells ( S8 Fig ) . To determine whether the Nedd4-2 mutants fail to respond to 14-3-3 when ubiquitinating GluA1 , in vitro ubiquitination using recombinant GluA1 and WT or mutant Nedd4-2 partially purified from HEK cells was performed . While WT Nedd4-2 strongly ubiquitinated GluA1 and responded to additional 14-3-3ε with further GluA1 ubiquitination , all of the mutant Nedd4-2s failed to do so ( Fig 7C ) . Because the level of 14-3-3 does not seem to be regulated by Nedd4-2 ( S9 Fig ) , our results suggest that the epilepsy-associated missense mutations of Nedd4-2 disrupt GluA1 ubiquitination , at least partially through reduced interaction with 14-3-3 . Because the epilepsy-associated missense mutations of Nedd4-2 disrupt GluA1 ubiquitination and degradation , we hypothesize that these mutations fail to mediate surface GluA1 and spontaneous neuronal activity . To this end , we performed surface protein biotinylation to obtain and measure surface GluA1 from WT cortical neuron cultures lentivirally transduced with WT or mutant Nedd4-2 for 5 days . Surprisingly , no significant effect was observed ( S10 Fig ) . We suspect that the level of Nedd4-2 during early development might reach a threshold in WT cultures , and therefore expression of additional Nedd4-2 fails to elicit significant effects . Furthermore , because mutant Nedd4-2s exhibit significantly reduced affinity toward interacting with 14-3-3 and therefore GluA1 ( Figs 6 and 7 ) , they might not be dominant-negative , at least in the context of GluA1 ubiquitination . The endogenous Nedd4-2 in WT cultures potentially dominates even in the presence of mutant Nedd4-2s . Therefore , to determine the effects of mutant Nedd4-2s without the interference from endogenous Nedd4-2 , we repeated this experiment in Nedd4-2andi cortical neuron cultures . Using Nedd4-2andi cultures possesses the advantage of studying the behavior of mutant Nedd4-2s while minimizing the concern of overexpression . As shown in Fig 8A , WT Nedd4-2 significantly reduced total and surface GluA1 in Nedd4-2andi cultures while all three mutant Nedd4-2s showed no effect . When expressing WT Nedd4-2 or any of the mutant Nedd4-2s in Nedd4-2andi cortical neuron cultures for 5 days , we found that the cultures transduced with WT Nedd4-2 showed significantly lower spontaneous spike frequency when compared with untransduced cultures or cultures transduced with any of the mutant Nedd4-2s ( Fig 8B1 and 8B2 ) . The average spontaneous spike amplitude did not differ between transduced and untransduced cultures . Altogether , our data showed that the three epilepsy-associated missense mutations of Nedd4-2 disrupt the ability to regulate surface GluA1 and spontaneous neuronal activity .
In this study , we present evidence to show that neuronal hyperactivity in vitro and increased seizure susceptibility in vivo associated with Nedd4-2 dysfunction are modulated by altered GluA1 and AMPAR signaling . These findings are further supported by the data showing that three epilepsy-associated missense mutations of Nedd4-2 partially , but significantly , disrupted GluA1 ubiquitination through reduced interaction with the adaptor protein 14-3-3 . All mutant Nedd4-2s retain partial function toward ubiquitinating GluA1 , reflecting the fact that the mutations were located on or near protein-protein interaction domains but not the lipid-binding or catalytic HECT ( Homologous to the E6-AP Carboxyl Terminus ) domain . Nevertheless , this is the first report that demonstrates a mechanism to explain Nedd4-2-dependent epilepsy in patients . Although the mutations of Nedd4-2 increase its stability , which is different from the in vivo knockdown mouse model we used ( Nedd4-2andi mice ) , we showed that the reduction of Nedd4-2 and the mutations each reduce the ability of Nedd4-2 to ubiquitinate GluA1 . Furthermore , because our data showed that genetic reduction of GluA1 normalized the seizure response in Nedd4-2andi mice , it suggests that inhibition of AMPARs might be a suitable treatment plan for Nedd4-2-associated epilepsy . One of the antagonists of AMPAR , Perampanel , has been approved to clinically reduce partial-onset seizures with or without secondary generalized seizures in epileptic patients [50–52] . Such medication might therefore be specifically useful for epilepsy patients who carry mutations in Nedd4-2 . A future study on Perampanel will be very important to determine whether and to what the extent Nedd4-2-associated seizures and/or epilepsy can be ameliorated . We used Nedd4-2andi mice , in which the long form of Nedd4-2 is disrupted , to study Nedd4-2 . Because Nedd4-2 knockout mice exhibit perinatal lethality [34] , Nedd4-2andi mice serve as an ideal model to study Nedd4-2 in vivo . However , another question is thus raised regarding the differential contribution of long versus short form of Nedd4-2 to the regulation of spontaneous neuronal activity and brain circuit excitability . The short form of Nedd4-2 , which lacks an N-terminal C2 domain ( S7 Fig ) , has also been identified in humans [53 , 54] . Indirect evidence has suggested that the C2 domain mediates membrane-targeting of Nedd4-2 [8] . The C2-containing ( long ) and C2-lacking ( short ) isoforms therefore target different intracellular locations and substrate pools [8] . Currently , it is unclear whether the short form of Nedd4-2 exhibits similar affinity toward binding to and ubiquitinating GluA1 . If it does , the question arises as to whether epilepsy-associated mutations affect the function of short form Nedd4-2 in a similar manner as to the long form of Nedd4-2 . If it does not , the second question is whether the short form serves as a dominant-negative Nedd4-2 to sequester interacting or signaling molecules to affect the functions of the long form of Nedd4-2 . Because single-nucleotide polymorphisms ( SNPs ) in human Nedd4-2 lead to differential expression of these isoforms , examining the functional differences of these isoforms may increase our understanding of neuronal plasticity and associated seizure susceptibility in different populations [53 , 54] . In the adult brain , the AMPAR has been shown to mediate the majority of excitatory synaptic transmission with GluA1 being one of the major subunits [55] . Activity-mediated changes in the numbers and properties of GluA1/AMPAR are essential for excitatory synapse development and synaptic plasticity . Ubiquitination of GluA1 has been linked to AMPAR surface expression and trafficking , which subsequently may affect many synaptic plasticity mechanisms , such as homeostatic synaptic scaling and synaptic depression [21–23 , 56 , 57] . We previously demonstrated that Nedd4-2 mediates GluA1 ubiquitination upon chronic neuronal activity stimulation , suggesting a potential role of Nedd4-2 in homeostatic synaptic downscaling [15] . Whether Nedd4-2 participates in other synaptic plasticity mechanisms is unknown . One speculation would be that because Nedd4-2 functions to limit the amount of surface GluA1 as seen in Fig 5C , neuronal activity that mediates depression or elimination of excitatory synapses might induce Nedd4-2-mediated GluA1 ubiquitination . We recently found that the expression of Nedd4-2 is modulated by another ubiquitin E3 ligase murine double minute-2 ( Mdm2 ) and its downstream effector tumor suppressor p53 [15] . Mdm2 is known to be crucial for activity-dependent synapse elimination [58] , which is crucial for brain circuit development and maturation . Activation of Mdm2-p53 signaling and Nedd4-2 expression might therefore contribute to elimination of excitatory synapses . Activation of Mdm2-p53 signaling and Nedd4-2 expression might therefore contribute to elimination of excitatory synapses . Further studies are required to delineate the broader effects of Nedd4-2 . In addition to GluA1 , other neuronal substrates of Nedd4-2 potentially involved in neuronal activity regulation are voltage-gated sodium channels Nav1 . 6 and voltage-gated potassium channels Kv7 . 3/KCNQ3 [9–12] . These two substrates are both crucial to modulating action potential firing and intrinsic excitability . Although our data showed that GluA1 mediates Nedd4-2-associated neuronal hyperactivity and seizures in mice , it does not rule out the potential contributions of Nav1 . 6 and Kv7 . 3/KCNQ3 in Nedd4-2-associated brain circuit excitability . Our data also suggest that presynaptic defects are potentially involved in the neuronal deficits associated with Nedd4-2 ( Fig 1D ) . Multiple substrates of Nedd4 family members are known to mediate presynaptic vesicle release and activity , including α-synuclein [59–61] and tyrosine kinase A receptors [13 , 14 , 62] . Altered ubiquitination of these substrates when Nedd4-2’s function is compromised could contribute to aberrant synaptic transmission . The ubiquitination status , expression level , and subcellular distribution of Nedd4-2’s other substrates are pending further investigation to obtain the full picture of synaptic abnormality and excitability caused by pathogenic functions of mutant Nedd4-2s . As we described previously , future studies are expected to elucidate broader effects of Nedd4-2 and provide better understanding of this important , yet underdeveloped , molecule in the central nervous system .
All experiments using animal data followed the guidelines of Animal Care and Use provided by the Illinois Institutional Animal Care and Use Committee ( IACUC ) and the guidelines of the Euthanasia of Animals provided by the American Veterinary Medical Association ( AVMA ) to minimize animal suffering and the number of animals used . This study was performed under an approved IACUC animal protocol of University of Illinois at Urbana-Champaign ( #14139 to N . -P . Tsai . ) The Nedd4-2andi mice , GluA1 knockout mice and WT control mice were obtained from The Jackson Laboratory . Primary cortical neuron cultures were made from p0-p1 mice as described previously [58] and maintained in NeuralQ basal medium ( Sigma ) supplemented with 1X B27 supplement ( Invitrogen ) , 1X GlutaMax ( final concentration at 2 mM; Invitrogen ) , and Cytosine β-D-arabinofuranoside ( AraC , final concentration at 2 μM; Sigma ) . The medium was changed 50% on DIV 2 and every 3 days thereafter . Dimethyl sulfoxide ( DMSO ) was from Fisher Scientific . AMPA was from Cayman Chemical and NBQX was from Alomone Labs . Recombinant GluA1 and 14-3-3ε were from Origene . Recombinant Nedd4-2 was from Abnova . R18 was from Sigma-Aldrich . Cycloheximide , poly-D-lysine and Protein A/G beads were from Santa Cruz Biotechnology . The antibodies used in this study were purchased from Santa Cruz Biotechnology ( anti-α-Tubulin ) , Cell Signaling ( anti-Nedd4-2 , anti-pan-14-3-3 , anti-N-cadherin and anti-Ubiquitin ) , Millipore ( anti-GluA1 ) , Abcam ( anti-MAP2 ) , Thermo Scientific ( anti-HA ) and GenScript Corporation ( anti-Gapdh ) . The epilepsy-associated mutations were generated using site-directed mutagenesis reagent ( Agilent ) to introduce mutations into pCI-HA-Nedd4-2 [15] . The primers used are as below . S233L: 5’-GGACGTGTCCTCGGAGTTGGACAATAACATCAGAC-3’ , 5’-GTCTGATGTTATTGTCCAACTCCGAGGACACGTCC-3’; E271A: 5’- GGGCGGGGATGTCCCCGCGCCTTGGGAGACCATTTC-3’ , 5’- GAAATGGTCTCCCAAGGCGCGGGGACATCCCCGCCC-3’; H515P: 5’- CGTTTGAAATTTCCAGTACCTATGCGGTCAAAGACATC-3’ , 5’- GATGTCTTTGACCGCATAGGTACTGGAAATTTCAAACG-3’ . After washing cultured cells with PBS three times , 0 . 1 mg LLC NHS-LC-BIOTIN ( from Apexbio Technology ) was added to cultures for 30 min . at room temperature . At the end of the reaction , cultures were washed with PBS three times . The cell were harvested and lysed in an IP buffer ( 50 mM Tris , pH 7 . 4 , 120 mM NaCl , 0 . 5% Nonidet P-40 ) followed by purification using Magnetic Streptavidin Beads ( from Cell Signaling ) . HEK cells were transfected using Lipofectamine 3000 ( Invitrogen ) . Primary neuron cultures were transduced using lentivirus . WT and mutant Nedd4-2s were sub-cloned into Lenti-CMV-GFP-2A-Puro Vector ( from Applied Biological Materials ) . Lentivirus was produced in HEK cells as described previously [58] . For immunoprecipitation ( IP ) , cell lysates were obtained by sonicating pelleted cells in IP buffer . Eighty μg of total protein or protein mixtures after in vitro ubiquitination was incubated 2 hours at 4°C with 0 . 5 μg primary antibodies . Protein A/G agarose beads were added for another hour followed by washing with IP buffer three times . For western blotting , after SDS-PAGE , the gel was transferred onto a polyvinylidene fluoride membrane . After blocking with 1% Bovine Serum Albumin in TBST buffer ( 20 mM Tris pH 7 . 5 , 150 mM NaCl , 0 . 1% Tween-20 ) , the membrane was incubated with primary antibody overnight at 4°C , followed by three 10-min washings with TBST buffer . The membrane was then incubated with an HRP-conjugated secondary antibody ( from Santa Cruz Biotechnology ) for 1 hour at room temperature , followed by another three 10-min washings . Finally , the membrane was developed with an ECL Chemiluminescent Reagent [15] . All the western blot results were semi-quantitatively normalized to the control groups before statistical analysis . HA-Ub ( Boston Biochem ) , Ubiquitin Activating Enzyme ( UBE1 ) ( Boston Biochem ) and UbcH5b/UBE2D2 ( Boston Biochem ) were obtained . Recombinant WT Nedd4-2 was obtained from Abcam . When HA-tagged WT or mutant Nedd4-2s were produced in HEK cells , 250 μg of total protein lysates were subjected to immunoprecipitation with anti-HA antibody to partially purify HA-tagged Nedd4-2s . Recombinant GluA1 ( Origene ) was used as substrate for in vitro ubiquitination with recombinant Nedd4-2 ( Fig 7A ) or Nedd4-2s obtained from transfected HEK cells ( Figs 6B and 7C ) following a protocol previously described [63] . Recombinant 14-3-3ε was obtained from Origene . Each MEA plate was coated with poly-D-lysine for 30 minutes and plated with 2x105 cells counted using a hemocytometer . Recordings were done at DIV 13–14 ( Figs 1 , 2 and 3 ) or DIV 9 and DIV 14 ( Fig 8 ) in the same culture medium using an Axion Muse 64-channel system in single well MEAs ( M64-GL1-30Pt200 , Axion Biosystems ) inside a 5% CO2 , 37°C incubator . Field potentials ( voltage ) at each electrode relative to the ground electrode were recorded with a sampling rate of 25 kHz . Right before a recording , if an electrode channel displays excessive “noise” ( >10 μV ) that channel is grounded for the entirety of the recording to avoid interference with other channels [64] . After 30 min of baseline recording , the MEA was treated with the drugs specified and recorded for another 30 min . Due to changes in network activity caused by physical movement of the MEA , only the last 15 min of each recording were used in data analyses . AxIS software ( Axion Biosystems ) was used for the extraction of spikes ( i . e . action potentials ) from the raw electrical signal obtained from the Axion Muse system . After filtering , a threshold of ±7 standard deviations was independently set for each channel; activity exceeding this threshold was counted as a spike . Only MEAs with more than 2 , 000 spikes during the last 15 minutes of recording were included for data analysis [16 , 65] . The total spikes obtained from each MEA culture was normalized to the number of electrodes , as described in a previous study [66] . The settings for burst detection in each electrode were a minimum of 5 spikes with a maximum inter-spike interval of 0 . 1 sec as described previously [16] . The burst duration , number of spikes per burst , and interburst interval were analyzed by AxIS software . Synchrony index was also computed through AxIS software , based on a published algorithm [67] as we conducted previously [16] , by taking the cross-correlation between any two spike trains , removing the portions of the cross-correlogram that are contributed by the auto-correlations of each spike train , and reducing the distribution to a single metric . To ensure consistency when acquiring MEA data , all the experiment procedures , including the animal dissection , cell counting and plating , medium changing , and recordings are conducted by the same individual in each experiment . Throughout culture maturation and before recording , each MEA is visually inspected under the microscope and any MEA with poor growth or a patchy network is excluded . Recordings of each experiment were alternate between genotypes . For all before and after drug treatment comparisons , to minimize the variability between cultures , the recording from each MEA culture after treatment was compared to the baseline recording from that same culture . Immunocytochemistry was done as previously described [68] . In brief , primary neurons grown on poly-D-lysine coated coverslips were fixed at DIV 14 with ice-cold buffer ( 4% paraformaldehyde and 5% sucrose in PBS ) . After washing and permeabilization with an additional incubation with 0 . 5% Triton X-100 in PBS for 5 min , an incubation with anti-MAP2 antibody was performed for 4 hours . After washing three times with PBS , fluorescence-conjugated secondary antibodies were applied to the cells at room temperature for 1 hour . After additionally washing the cells three times with PBS , the coverslips were mounted and observed on Zeiss LSM 700 Confocal Microscope . Male mice at age 4-weeks old were intraperitoneally injected with kainic acid , prepared in saline solution ( Hannas Pharmaceutical ) , at doses of 15 , 30 , or 60 mg/kg . The total injection volume was kept close to 0 . 15 ml . After injection , mice were closely observed in real time for 1 hour . The intensity of seizure was assessed by Racine’s scoring system [69] . To clearly determine seizure susceptibility , only stage 4 ( rearing ) and stage 5 ( rearing and falling ) were considered positive for seizures , as previously performed [16 , 70] . Mice showing stage 4 seizure and above are counted as 1 while mice showing stage 3 seizure or under are counted as 0 for analysis . ANOVA with post-hoc Tukey HSD ( Honest Significant Differences ) test was used for multiple comparisons between treatments or genotypes . Student’s t-test was used for analyzing spontaneous neuronal activity in Fig 1B and 1C , and mEPSC data in Fig 1D . One-sample t-test was used when experimental groups were normalized to control groups , such as western blotting in Fig 5C . Each “n” indicates an independent culture . Differences are considered significant at the level of p < 0 . 05 .
|
Many patients with neurological disorders suffer from an imbalance in neuronal and circuit excitability and present with seizure or epilepsy as the common comorbidity . Human genetic studies have identified many epilepsy-associated genes , but the pathways by which those genes are connected to brain circuit excitability are largely unknown . Our study focused on one of the epilepsy-associated genes , Nedd4-2 , and aimed to dissect the molecular mechanism underlying Nedd4-2-associated epilepsy . Nedd4-2 encodes a ubiquitin E3 ligase . Several neuronal ion channels have been identified as its substrates , including the GluA1 subunit of AMPAR . Our results first demonstrate up-regulation of spontaneous neuronal activity and seizure susceptibility when Nedd4-2 is reduced in a mouse model . These deficits can be corrected when GluA1/AMPAR is pharmacologically or genetically inhibited . In addition , we found that three epilepsy-associated missense mutations of Nedd4-2 inhibit the ubiquitination of GluA1 and fail to reduce GluA1 surface expression or spontaneous neuronal activity when compared to wild-type Nedd4-2 . These findings suggest the reduction of GluA1 ubiquitination as a crucial deficit underlying insufficient function of Nedd4-2 and provide critical information to the development of therapies for patients who carry mutations of Nedd4-2 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"hek",
"293",
"cells",
"membrane",
"potential",
"biological",
"cultures",
"electrophysiology",
"neuroscience",
"animal",
"models",
"mutation",
"model",
"organisms",
"immunoprecipitation",
"experimental",
"organism",
"systems",
"research",
"and",
"analysis",
"methods",
"missense",
"mutation",
"epilepsy",
"animal",
"cells",
"proteins",
"ubiquitination",
"mouse",
"models",
"cell",
"lines",
"precipitation",
"techniques",
"biochemistry",
"cellular",
"neuroscience",
"cell",
"biology",
"post-translational",
"modification",
"physiology",
"neurons",
"genetics",
"neurology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"neurophysiology"
] |
2017
|
Epilepsy-associated gene Nedd4-2 mediates neuronal activity and seizure susceptibility through AMPA receptors
|
Ecological speciation is the process by which reproductively isolated populations emerge as a consequence of divergent natural or ecologically-mediated sexual selection . Most genomic studies of ecological speciation have investigated allopatric populations , making it difficult to infer reproductive isolation . The few studies on sympatric ecotypes have focused on advanced stages of the speciation process after thousands of generations of divergence . As a consequence , we still do not know what genomic signatures of the early onset of ecological speciation look like . Here , we examined genomic differentiation among migratory lake and resident stream ecotypes of threespine stickleback reproducing in sympatry in one stream , and in parapatry in another stream . Importantly , these ecotypes started diverging less than 150 years ago . We obtained 34 , 756 SNPs with restriction-site associated DNA sequencing and identified genomic islands of differentiation using a Hidden Markov Model approach . Consistent with incipient ecological speciation , we found significant genomic differentiation between ecotypes both in sympatry and parapatry . Of 19 islands of differentiation resisting gene flow in sympatry , all were also differentiated in parapatry and were thus likely driven by divergent selection among habitats . These islands clustered in quantitative trait loci controlling divergent traits among the ecotypes , many of them concentrated in one region with low to intermediate recombination . Our findings suggest that adaptive genomic differentiation at many genetic loci can arise and persist in sympatry at the very early stage of ecotype divergence , and that the genomic architecture of adaptation may facilitate this .
Several empirical studies have looked for such patterns in divergently adapted ecotypes , incipient species and incompletely isolated species with varying degrees of reproductive isolation [23–30] . Most of them have revealed heterogeneous genomic differentiation across genomes with islands of differentiation among ecotypes or species [8 , 23 , 24 , 26–29 , 31–36] . While some studies found mainly many smaller islands of differentiation [24 , 26 , 28–30 , 32 , 33 , 35 , 36] , others found few large islands [8 , 27] , and in some cases islands were associated with genomic regions of reduced recombination , e . g . inside inversions [8 , 26 , 37] . Most authors have interpreted these patterns as evidence for ongoing differential gene flow among incipient species , concluding that speciation with gene flow might be common [e . g . 8 , 24 , 27 , 28 , 34 , 38] . However , this conclusion has been challenged as some of the observed patterns of genomic differentiation might equally be explained by speciation without gene flow [39 , 40] . Indeed , when allopatric populations have no gene flow , heterogeneous differentiation across the genome is also expected due to local adaptation , background selection and drift in each population interacting with variation in recombination and mutation rates [39–41] . Therefore , sympatric species that began to speciate in allopatry before they established sympatry can also show this pattern . In order to find genomic signatures of speciation with gene flow , it is therefore crucial to distinguish between different possible causes of heterogeneous genomic divergence . One way to address this is to investigate pairs of populations with independent evidence for current gene flow and where a phase of geographical isolation can be ruled out . This is difficult for ecotypes or species for which divergence started several thousands to millions of generations ago [42] , as in most current speciation genomic studies . Instead , a focus on the very beginning of the ecological speciation process , when recently emerged ecotypes have diverged for tens to a few hundreds of generations without geographical isolation , does minimize uncertainty about past and current gene flow . It has the caveat , though , that it is impossible to know whether the ecotypes will continue to evolve towards distinct species and ultimately build diversity at macroevolutionary scales [1] . We here study very recently diverged ecotypes of the threespine stickleback ( Gasterosteus aculeatus complex ) that resemble older ecotypes and reproductively isolated species of this complex that are well-studied elsewhere in the world [43] . Threespine stickleback are a popular model for ecological speciation research because ecotypes have repeatedly evolved many times across the Northern hemisphere , by adapting to different habitats and evolving various degrees of reproductive isolation [43] . While most stickleback ecotypes and species pairs started diverging soon after the retreat of the Pleistocene glaciers ~12 , 000 years ago [43] ( but see [44 , 45] ) , stickleback were introduced into the Lake Constance region only less than 150 years ago [46] . This date comes from the examination of detailed records on the fish of the Lake Constance region , reaching back several hundred years in time [47–50] , and from ichthyologic analyses of the distribution and natural history of stickleback in that region , which all show that stickleback did not exist in the catchment until late in the 19th century [51 , 52] . A recent analysis suggested that stickleback had been present in the Lake Constance region for at least 2 , 000 to 4 , 000 years and had colonized Lake Constance from the upper Danube [53] . This is at odds with historical data that unequivocally document the absence of stickleback from the middle and upper Danube until the 19th century , when stickleback were introduced both into the upper Danube and into the Lake Constance system [46–52 , 54] . Mitochondrial phylogeographic analyses further suggest that the Lake Constance stickleback population originates from a North Eastern European lineage inhabiting the Southern Baltic Sea catchments [46 , 55] . It is only around the middle of the 20th century that stickleback have become common in Lake Constance and inflowing rivers [51] . Despite the recent colonization of Lake Constance , distinct lake and stream ecotypes have already evolved in this system ( cf . Fig 1B , [46 , 56] ) . Present day ecotypes differ in predator defense morphology , feeding-related morphology , male nuptial coloration , ecology , growth , and life history [56–59] . Stream stickleback are resident breeders in little streams around Lake Constance , they grow to a smaller adult size , reproduce earlier , die younger , and have shorter spines and smaller bony lateral plates than the lake ecotype [56–59] . Different from all previously studied lake-stream stickleback pairs , however , the lake stickleback that we study in Lake Constance are potamodromous , meaning that in spring they migrate into streams to breed in full sympatry with stream resident stickleback . Adults return to the lake after the breeding season as well as juveniles , where they spend most of their lives before returning to streams only as breeding adults . The adults of these potamodromous lake stickleback have a more pelagic diet than the stream resident fish , differ in feeding-related morphology , including longer gill rakers and a more torpedo-shaped body typical for pelagic fish , have longer spines , and are infested by more and a wider diversity of parasites [56–60] . Whether one of these ecotypes or a population of generalists was initially introduced to the Lake Constance system is unknown . Historically , stickleback were first recorded in isolated stream habitats [48 , 51] . From there they could have colonized the lake and adapted to this novel habitat before they entered other effluent streams and underwent renewed bouts of adaptation , now again to stream habitats . Ancestral stream stickleback may also have colonized other streams by long distance dispersal through the lake , before they adapted to and colonized the lake environment . Alternatively , as the stickleback populations from Lake Constance and the Eastern effluent streams are closely related to stickleback from catchments South of the Baltic Sea , where freshwater stickleback resemble typical marine stickleback in body armor [61 , 62] , these fish may have been preadapted to living in large lakes with many gape-limited predators and might have adapted to stream habitats only subsequently . Finally , given the presence of other distinct lineages of stickleback in Switzerland and Germany immediately West of Lake Constance [46] , it is possible that different sections of the Lake Constance catchment have been colonized independently by different stickleback lineages as is suggested by some phenotypic and genetic data . For instance , mtDNA haplotypes from the distinct Rhine and Rhone lineages of stickleback are abundant in inlet streams of the Northern , Western and South-Western shores of Lake Constance , alongside Baltic haplotypes [56] . Admixture with these Western European populations , which were isolated from Eastern lineages for several ten thousand years in ancient freshwater refugia [63 , 64] is also suggested by the presence of many fish with reduced body armor in the more Western effluents of Lake Constance [53 , 56] . In contrast , lake and stream stickleback populations from the South-Eastern effluents of Lake Constance ( Fig 1A ) that we studied here , have the Baltic mitochondrial haplotype , are predominately fully plated ( S7B Fig , [46 , 59] ) , are very closely related in microsatellite and AFLP markers [46 , 55] and show little if any genomic introgression from Rhine and Rhone stickleback populations [55] . Yet they have evolved phenotypically distinctly different lake and stream ecotypes [58 , 59 , 65] . Here we study genomic differentiation among these young lake and stream ecotypes in two streams , each containing breeding populations of both resident stream and potamodromous lake ecotypes ( Fig 1A ) . In one long stream , the breeding sites of the ecotypes are separated by many kilometers of less suitable habitat , which likely exceeds within-generation migration abilities of lake stickleback [cf . 56 , 66] , such that this ecotype pair can be considered to breed in effective allopatry or , more conservatively , in distant parapatry . Parapatry or allopatry is typical of all lake and stream stickleback ecotypes studied to date [43 , 66–69] , including previous work on Lake Constance [46 , 56 , 59] , and also of many marine and freshwater ecotypes [43] . In the other , shorter stream , migratory lake stickleback breed alongside resident stream stickleback in full sympatry ( Fig 1A ) at the same time of the year ( S1 Fig ) and lake fish outnumber stream fish in large parts of the stream , providing ample opportunity for interbreeding , and thus potentially allowing high levels of gene flow between ecotypes . We took advantage of the migratory behavior of the lake ecotype and we sampled stickleback at different sites along this stream early in the breeding season , just after the spawning run of the lake ecotype had started and before the most upstream site was reached by lake stickleback . We were thus able to collect both ecotypes separately at the opposite ends of the stream gradient , and also at the same sites in the middle sections of the stream ( Fig 1A ) . Previous population genomic studies of parapatric stickleback ecotypes have shown the presence of parallel genome-wide differentiation between marine and many independently derived freshwater ecotypes from around the Northern hemisphere [24–26 , 45] . In contrast , almost no genomic parallelism has been found in previous studies that compared parapatric , non-migratory lake and stream ecotypes from different river systems [32 , 36 , 70] . A recent natural experiment demonstrated that repeated marine-freshwater differentiation can emerge after only a few decades of adaptation in allopatry [45] . However , whether genomic divergence can emerge in sympatry ( or close parapatry ) on such a short timescale or be maintained in sympatry after just a few decades of divergence is unknown . The only known sympatric stickleback ecotypes , seven cases of largely reproductively isolated limnetic and benthic lake stickleback species from lakes in British Columbia [43 , 71] , have diverged for a much longer time , several thousand years [25 , 72] , and now show parallel genomic differentiation in sympatry that likely originated from double colonization of these lakes from the same marine source populations [25] . A case of sympatrically breeding lake and stream stickleback ecotypes has not been studied before and should thus , in comparison with a ‘standard’ parapatric contrast that we also investigated , provide insight into the effects of strong versus weak gene flow on the population genomics of ecotype divergence . We identify several regions in the genome that carry divergence islands which are robust to gene flow , suggesting that our sympatrically breeding ecotypes are indeed incipient species and not phenotypically plastic life history morphs . We ask if predictions from ecological speciation with gene flow models hold when we compare lake-stream ecotype pairs in different geographical settings . For instance , to the extent that speciation is constrained by gene flow , we expect lower average genomic differentiation , a smaller number of islands of differentiation and less heterogeneity in genomic differentiation in the sympatric than in the parapatric contrast . Furthermore , we predict that parallel divergent selection across multiple habitat transitions ( i . e . between the lake and these two streams ) , acting on similar initial standing genetic variation present in the colonizing lineage , should lead to an overlap between the genomic islands of differentiation in both streams . Independent of what phenotype was ancestral and in what direction colonization of habitats happened ( i . e . a transition first from a stream to a lake population followed by transition back from the lake to other streams , or multiple transitions from a lake population to different stream populations ) , such parallel genomic islands should reveal genomic regions under habitat-driven divergent selection . Our findings shed light on the interactions of divergent selection , gene flow , standing genetic variation and genomic organization at the earliest stage of ecological speciation .
We sequenced restriction-site associated DNA ( RAD ) tags of 91 threespine stickleback collected at six sites along the two streams flowing into Lake Constance and at their inlets into the lake ( Fig 1A , Table 1 ) . After filtering for high-quality genotypes ( see Materials and Methods ) , we obtained a genotype dataset of 3 , 183 , 890 bp nuclear DNA sequence containing 34 , 756 bi-allelic SNPs , including 15 , 092 SNPs with minor allele frequency greater than 1% at an average sequencing depth per individual ranging between 43 and 148x . We noticed increased mean FIS estimates in populations L1 , S1 and S2 ( Fig 2B ) , suggesting an excess of homozygotes in these populations . This could be due to real inbreeding , but it is more likely caused by the presence of PCR duplicates ( see Material and Methods ) leading to an excess of apparently homozygous genotypes , a well-known feature of single-end RAD tag sequencing [73 , 74] mimicking inbreeding , and thus effectively reducing the number of sampled chromosomes [75] . We accounted for this excess of homozygotes by allowing for inbreeding in the estimation of F-statistics , and by explicitly incorporating FIS estimates in the detection of outlier loci ( see Material and Methods ) . Furthermore , instead of using genotypes , we used one randomly picked allele per individual and site for Bayesian clustering , PCA and nucleotide diversity analyses . Subsets of the genotype datasets outlined above thus included a 3 , 183 , 890 site allele dataset with one allele per individual and site as well as a SNP allele dataset containing 24 , 784 SNPs with minor allele frequency above 1% . The first and second axes of a principal component analysis ( PCA , Fig 2A ) separate the migratory lake and stream resident populations . The parapatric stream site S2 separates from the geographically nearest lake site along PC1 ( ANOVA , F1 , 89 = 581 . 5 , p < 0 . 001 ) , whereas PC2 separates individuals of the other , shorter stream from the sympatrically breeding migratory lake fish ( Fig 2A ) . In particular fish from the most upstream site S1 in this shorter stream were most distinct on PC2 ( ANOVA , F1 , 89 = 106 . 9 , p < 0 . 001 ) from the fish caught further downstream and those caught in the lake inlet ( Fig 2A ) . These patterns translated into significant mean pairwise FST between the most upstream site in the sympatric stream S1 and the downstream stream sites as well as the lake inlet site L1 , and also between the parapatric stream site S2 and its corresponding lake site L2 ( Fig 2B ) . Stickleback from both upstream stream sites were also significantly differentiated from each other , while there was no significant differentiation either between the two lake sites or between these lake sites and the downstream sites in the sympatric stream ( S1a and S1b , Fig 2B ) , suggesting that the migratory lake stickleback form a single population . The genetic resemblance of most S1a and S1b individuals to lake stickleback ( Fig 2A ) is in line with field observations: individuals collected at S1a and S1b were phenotypically mostly lake ecotypes caught during their upstream breeding migration , whereas resident stream ecotypes were relatively rare at these sites and were most common at site S1 . Assignment of individuals by a Bayesian clustering algorithm implemented in STRUCTURE supported this presence of predominantly lake ecotypes but also revealed some stream ecotypes at sites S1b and S1a ( S2 and S3 Figs ) . This analysis also showed that some intermediate individuals occur at L1 , S1a , S1b and S1 , indicative of ongoing gene flow . In the stream where breeding is sympatric ( L1 vs . S1 ) , we found a large region on chromosome VII and three smaller regions on chromosomes X and XI that show elevated differentiation between lake and stream ecotypes , while there was very little differentiation across the rest of the genome ( mean pairwise FST in 2 Mb windows close to zero , Fig 3C ) . In contrast , our comparison of parapatric ecotypes ( L2 vs . S2 ) revealed more genomic regions with elevated pairwise FST ( Fig 3D ) , including the large region of elevated differentiation on chromosome VII that appeared in the sympatric lake-stream pair too , but was neither present in lake-lake nor stream-stream comparisons ( Fig 3A and 3B ) . We measured heterogeneity in genome-wide differentiation by computing the coefficient of variation ( CV ) for pairwise FST in non-overlapping 2 Mb windows across the genome ( see Materials and Methods ) . As expected , we found lower heterogeneity in genome-wide differentiation between lake and stream stickleback where breeding is sympatric ( median CVS1vsL1 = 3 . 38 ) than where they breed in distant parapatry ( median CVS2vsL2 = 4 . 03 ) . A heterogeneous pattern of genome-wide differentiation was also found when the two most upstream stream sites were compared against each other ( Fig 3B ) , whereas almost no genome-wide differentiation was seen between the two lake sites ( Fig 3A ) . We defined ‘genomic islands of differentiation’ as genomic regions with an accumulation of unusually strongly differentiated SNPs ( outlier loci; [76] ) showing high differentiation measured over all populations grouped hierarchically ( ‘hierarchical FST’ , see Materials and Methods ) . We identified 1 , 251 SNPs ( 3 . 6% ) as outliers in our dataset at the 5% alpha level and 242 SNPs ( 0 . 7% ) at the 1% alpha level , close to what would be expected by chance . Importantly , however , these outliers were not randomly distributed across the genome and instead more clustered than expected even after accounting for variation in recombination rate ( Ripley’s K function using genetic distances , S5 Fig ) . To infer the location and extent of ‘genomic islands of differentiation’ , we followed a Hidden Markov Model ( HMM ) approach that assigns each SNP to one of three differentiation states , ‘genomic background’ , regions of ‘exceptionally low’ and ‘exceptionally high’ differentiation ( [76] , see Materials and Methods ) . We identified 37 genomic regions of ‘exceptionally high’ differentiation considered here as ‘genomic islands of differentiation’ ( Fig 4B ) . No regions of ‘exceptionally low’ differentiation remained significant after correcting for multiple tests ( see Materials and Methods ) . These 37 genomic islands of differentiation were spread across 11 of the 20 autosomes , with a concentration on chromosome VII ( Fig 4B ) . Each island consisted of 1 to 26 SNPs , spanning up to 990 kb in size ( S1 Table ) . The presence of islands of differentiation was overall negatively associated with recombination rates ( Fig 4C , S2 Table ) . This association was mostly driven by the accumulation of islands on chromosome VII , clustering in a genomic region showing low to intermediate levels of recombination ( S6 Fig ) and further islands falling into such regions on chromosomes IV , IX and XV ( S2 Table , Fig 4 ) . However , if the same test was repeated for each chromosome , the strength of this association varied and was even positive on chromosome II ( S2 Table ) , where a genomic island falls into a high recombination region ( Fig 4C ) . Moreover , some of the strongest localized reductions of recombination in the stickleback genome such as on chromosome I [77] are not differentiated among the studied populations ( Fig 4C ) . Thus , genomic islands of differentiation identified in our study are not exclusively bound to low recombination regions . We observed parallel allele frequency changes between the lake ecotype population and both resident stream ecotype populations from the two streams in 19 of the 37 genomic islands of differentiation ( Figs 4B and 5 ) . Importantly , very few of these 19 islands were differentiated between the two stream ecotype populations or among lake ecotypes sampled at sites L1 and L2 ( Fig 3 ) . These ‘islands of parallel differentiation’ are thus prime candidates for harboring genes involved in ecological speciation . Interestingly , 12 of the 19 parallel islands clustered in a 10 . 5 Mb stretch on chromosome VII with low to intermediate recombination ( S6 Fig ) , and the highest levels of pairwise differentiation were observed in this region ( Fig 3C ) . Furthermore , one other parallel island found on chromosome I is located in a region that has previously been described as an inversion segregating between marine and freshwater stickleback [25] . The remaining six parallel islands were each found on different chromosomes ( III , IV , IX , XII and XIII , Figs 4B and 5 , S1 Table ) . All but one parallel islands contained multiple SNPs differentiated among ecotypes breeding in sympatry ( S1 Table ) . These 19 parallel islands appear to be rather robust to gene flow given the significant allele frequency differentials observed among the sympatric ecotypes . On the other hand , islands of non-parallel differentiation seem mainly driven by large frequency differentials only in the parapatric ecotype comparison ( L2 vs . S2 ) , which were not differentiated between ecotypes breeding in sympatry ( S1 Table ) . Overall , parallel islands that are robust to gene flow were associated with regions of low to intermediate recombination rate , also including a single case within a known inversion polymorphism region [26] , while the association between presence of islands with non-parallel differentiation and recombination was much weaker and the sign of this association varied across chromosomes ( S2 Table ) . Islands with non-parallel differentiation showed on average slightly but not significantly lower diversity than both the genomic background and that found in parallel islands ( Fig 6 ) , which is compatible with the action of background selection , with a past selective sweep pre-dating the population splits or with multiple local selective sweeps leading to non-parallel differentiation between sampling sites . Parallel islands showed on average slightly higher levels of nucleotide diversity than the genomic background and diversity levels did not differ between sampling sites ( Fig 6 ) . The observed increase in diversity is compatible with selection on standing genetic variation and notably the highest diversity among parallel islands is found in chromosome VII islands 7 . 6 and 7 . 2 , consistently across all sampling sites ( Fig 6 ) . The only parallel islands with reduced diversity show the same reduction in all populations ( islands 13 . 1 , 12 . 3 and 7 . 12 , Fig 6 ) , possibly due to background selection , a past sweep or multiple sweeps in each population with the same alleles favored in the respective habitat . We thus have little evidence for hard selective sweeps in parallel islands , although incomplete sweeps may not have led to a reduction in diversity yet . Rather , our data suggests that selection on standing genetic variation was acting in both stream and lake environments , or that sweeps have not been completed in either environment , as we observe similar levels of elevated diversity in both habitats . We classified the two alleles of SNPs showing parallel allele frequency changes between the lake ecotype and both populations of stream ecotypes either as lake-like or stream-like according to their major frequency ( Figs 5 and S8 ) . A PCA based on these SNPs only ( Fig 7 ) recovered the distribution of ecotypes over sampling sites: most individuals from sympatric stream sites S1a and S1b showed a lake-like genomic signature , but one and three of ten individuals at sites S1a and S1b respectively did show a stream-like genomic signature ( Fig 7 ) . As expected , a stream-like genomic signature was shown by a majority of the fish at site S1 , with only four of twenty individuals displaying lake-like genotypes ( Fig 7 ) . None of the 20 fish at site S2 showed a lake-like genomic signature , and none of the 30 fish at lake sites L1 and L2 showed stream-like genomic signatures . We observed increased levels of linkage disequilibrium ( LD ) among almost all chromosome VII islands at site S1a and to a lesser extent at S1b and S1 , while stickleback from the lake sites L1 , L2 and the parapatric stream site S2 revealed two haplotype blocks on chromosome VII ( S9 Fig ) . These patterns of LD are in line with the presence of both ecotypes in sympatry at sites S1a , S1b and S1 . There was overall little LD between islands located on different chromosomes , except for islands 1 . 4 , 4 . 1 showing some LD with islands on chromosome VII at sites S1a and S1b , and islands 9 . 4 and 13 . 1 showing elevated LD with each other and with chromosome VII islands at sites S1 and S1b ( S9 Fig ) , again in agreement with the presence of both ecotypes in sympatry at sites S1a , S1b and S1 , and gene flow between them . Similarly , lake populations L1 and L2 displayed elevated LD between islands 12 . 3 , 12 . 5 and islands on chromosome VII . The 19 parallel islands robust to gene flow overlap with 207 quantitative trait loci ( QTLs ) that have been previously identified in other stickleback populations ( Fig 4A , S3 Table , [78–103] ) . Ten of these QTLs are major effect QTLs located on chromosomes IV and VII , while the other QTLs are reported to have minor to moderate effect sizes ( Figs 4A and S6 and S3 Tables ) . We grouped QTLs into 32 phenotypic traits and tested if the 19 parallel islands clustered inside any of these traits more than expected by chance . For this , we permuted the positions of the 19 parallel islands across the genome , both on the physical and on the genetic map to account for recombination rate variation biasing confidence intervals of QTLs ( see Materials and Methods ) . This test identified a significant clustering of parallel islands inside QTLs for 11 of 32 traits ( Figs 4A , S10 and S11 ) . We checked if our lake and stream ecotypes were phenotypically divergent in these traits [56–59 , 65] . Six of these 11 traits with clustering of parallel islands concerned divergent traits: male breeding coloration and most defense morphology related traits such as first and second dorsal spine , pelvic spine , pelvic girdle morphology and lateral plate width ( Fig 4A ) . Three of the remaining five traits with clustering of parallel islands inside their QTLs have not been studied yet among Lake Constance ecotypes ( S11 Fig ) , while the last two traits , jaw morphology and lateral plate number , are not divergent among Lake Constance ecotypes studied here ( Figs 4A and S7B ) . 21 traits did not show significant clustering of parallel islands inside their QTLs , while many of them are still overlapping with parallel islands , including traits divergent among Lake Constance ecotypes such as head shape , body size , lateral plate height , body depth and body shape [56–59 , 65] . However , two of these traits without clustering , body depth and body shape , have been shown to be controlled largely by phenotypic plasticity in response to the environment among these Lake Constance ecotypes [65] . We searched the 19 parallel islands for genes that might be candidate targets of divergent selection between ecotypes . They contained 243 Ensembl predicted genes , including 208 genes with a known ortholog in human or zebrafish ( S4 Table , [104 , 105] ) . No enrichment of gene ontology terms was found in this gene set . However , a few of these genes might be candidate targets for divergent selection between habitats or life histories because they are involved in the development of traits that are divergent among ecotypes . For instance , beta-1 , 3-glucuronyltransferase 3 ( b3gat3 ) , positioned in island 7 . 6 , is involved in cartilage and gill structure morphogenesis in zebrafish [106–109]; phospholipase C beta 3 ( plcb3 ) , in island 7 . 6 , is involved in cartilage and viscerocranium morphogenesis , influencing gill raker and pharyngeal jaw development [110–113] . Similarly , integrin alpha 5 ( itga5 , island 12 . 5 ) is involved in pharyngeal arch , head and eye development [104 , 114 , 115] and claudin 7a ( cldn7a , island 7 . 9 , [116] ) and phosphatidylinositol 4-kinase type 2 beta ( pi4k2b , island 9 . 4 , [117] ) are involved in head development . In addition , ring finger protein 41 ( rnf41 , island 12 . 5 ) is involved in melanocyte differentiation [118] , thus potentially influencing pigmentation and camouflage . Fras1 related extracellular matrix 1a ( frem1a , island 7 . 12 ) is involved in morphogenesis of pectoral , caudal , anal and dorsal fin as well as pharyngeal jaw [110 , 119] , and meiosis 1 associated protein ( M1AP , island 7 . 7 ) is involved in spermatogenesis , thus possibly a target of sexual selection [104] . H6 family homeobox 4 ( hmx4 , island 7 . 2 ) is involved in retinal cone development and retinoic acid biosynthesis and might thus be relevant to vision and thus possibly to adaptation to deeper water habitats in the lake versus shallow stream habitats and also mate choice [120 , 121] . While we lack full sequences of any gene in the stickleback genome , our RAD-sequencing data contained two non-synonymous SNPs in the genes plcb3 and M1AP , that both show high and parallel lake-stream differentiation . A pairwise FST = 0 . 50 in the sympatric ( L1 vs . S1 ) and FST = 0 . 43 in the parapatric comparison was estimated for the non-synonymous SNP within plcb3 and FST = 0 . 35 and FST = 0 . 57 for sympatric and parapatric comparisons respectively for the non-synonymous SNP in M1AP .
We characterized genomic differentiation among very young lake and stream stickleback ecotypes , breeding in sympatry and in distant parapatry in two different streams , to understand processes acting at what might be the onset of ecological speciation . Our first and perhaps most salient result is that ecotypes are genetically differentiated at multiple places in the genome , both in sympatry and in parapatry . Hence we can rule out that these very young ecotypes are maintained by adaptive phenotypic plasticity only . Instead , significant genomic differentiation has arisen within less than 150 generations of evolution since the arrival of stickleback in Lake Constance . Because differentiation is found not just in parapatry but also in sympatry , our results are consistent with the incipient stage of ecological speciation . In the following we will discuss the evidence and attempt inferences of evolutionary mechanisms from our genomic data . Different from previous lake-stream stickleback studies , we investigated pairs of resident stream and potamodromous lake ecotype , the latter breeding in streams but spending most of its adult life in the lake . Combined with the migratory behavior of the latter , our sampling of both ecotypes from a short and a long stream gradient allowed us to compare phenotypically and ecologically very similar pairs of ecotypes where breeding is sympatric in one but parapatric in the other pair ( Fig 1 ) . Of the 37 genomic islands of differentiation identified in this system , 19 islands distributed across eleven chromosomes showed differentiation between the ecotypes breeding in sympatry . These islands thus persist in the face of gene flow ( S1 Table ) . In contrast , where ecotypes breed in distant parapatry , all 37 genomic islands ( Fig 3 , S1 Table ) show differentiation among the ecotypes , including the 19 islands also differentiated among the sympatrically breeding ecotypes . Both the heterogeneity of genome-wide differentiation and the average level of differentiation are higher in the parapatric comparison where there is much less opportunity for gene flow , in keeping with models of ecological speciation with gene flow [3–6] . Remarkably , all genomic islands with differentiation in sympatry thus showed differentiation in parapatry too , with the same alleles favored in the same ecotype ( Fig 5 ) . Some of these parallel islands , islands 1 . 3 , 7 . 9 , 7 . 10 and 7 . 13 ( S3 Tab . ) , overlap with SNPs identified as divergent between the lake ecotype and stream ecotype populations North , West and South-West of Lake Constance [53] . While genetic drift , background selection , or local adaptation could all have created islands in a single contrast , islands that are repeatedly divergent between the lake ecotype and two stream ecotype populations , with the same alleles favored in the same habitat and with divergence persisting in the face of gene flow , suggest that habitat- and/or life-history-associated divergent selection have led to their emergence . A striking feature of these islands of parallel differentiation that are found both in sympatry and in parapatry in Lake Constance stickleback is that they overlap with many QTLs and cluster in some QTLs for traits that are clearly differentiated between these ecotypes ( Fig 4A ) [56–59 , 65] . Although most QTLs have been identified in different populations , possibly with other causative mutations , the same genes might be involved in controlling the traits that differ among Lake Constance stickleback ecotypes . Many ecologically relevant traits controlling e . g . defense morphology and head shape are among these overlapping traits , as well as two traits , body size and male coloration , that are relevant to mate choice and thus possibly to pre-zygotic reproductive isolation . Body size often differs between migratory and resident stream fish life history morphs , not just in stickleback [122] . Lake Constance migratory lake fish are much larger than the stream residents [56 , 58] and body size is known to often mediate assortative mating in stickleback [123] . In addition , we identified a number of candidate genes within the islands of parallel differentiation that may underlie phenotypes under natural and sexual selection that diverge between the Lake Constance ecotypes . Phenotypic plasticity in some traits [65] might be responsible for additional differences between ecotypes and may also have reduced the power to detect associations between some of the phenotypic differences and genomic differences . The associations between islands of parallel differentiation and QTLs for divergent traits we observed support the view that divergent selection between migratory and resident life histories and lake and stream habitats underlies the genomic divergence persisting in sympatry . That the genomic basis of various ecologically relevant traits is often highly clustered on a few chromosomes in stickleback [96] may have facilitated the simultaneous divergent evolution of multiple phenotypic traits: several feeding and defense morphology trait QTLs as well as male coloration QTLs are clustered on chromosomes IV and VII ( Fig 4A ) . Divergent selection on any gene in these regions could then possibly have led to phenotypic divergence in several other traits , given sufficient standing genetic variation and linkage disequilibrium in that genomic region . Furthermore , given that both adaptation and reproductive isolation traits are located in these regions , divergent selection in these genomic regions may serve as a nucleus for ecological speciation . Most of the genomic islands of parallel differentiation are found in a region of low to intermediate recombination on chromosome VII , which shows the highest level of pairwise differentiation in sympatry ( Fig 3C ) and in parapatry in our populations ( Fig 3D ) and also among the lake population and two stream populations North and West of Lake Constance [53] . Furthermore , the parallel island 1 . 3 ( S3 Table ) , also found divergent between three stream populations North , West and South-West of Lake Constance and the lake ecotype [53] , overlaps with a region known to be polymorphic for an inversion that differentiates marine and freshwater stickleback [26] , suggesting that this inversion could potentially be polymorphic and suppressing recombination in this pair too [53] . These observations are consistent with models and evidence that the recombination landscape may influence adaptation and ecological speciation the face of gene flow [6 , 15 , 26] . Nevertheless , genomic islands of differentiation in our sympatric stickleback ecotypes are not exclusive to regions of low recombination ( S2 Table ) , suggesting that recombination rate variation alone cannot explain the overall differentiation patterns we observe . Rather , the interaction of life history-driven and/or habitat-driven divergent selection with recombination rate variation and gene flow seem to determine patterns of genomic differentiation . Furthermore , that several unlinked genomic regions beyond chromosome VII diverge in parallel suggests that either many genomic targets are under correlated divergent selection , that partial reproductive isolation has evolved or that a combination of both is maintaining the genomic differences between these ecotypes in sympatry , a situation that is thought to characterize the beginnings of ecological speciation [2] . This observation is consistent with the hypothesis that genomic islands with large and pleiotropic effects may act as seeds for ecological speciation with gene flow , when selection favors linkage disequilibrium between such a region and genes elsewhere in the genome [1] . Heterogeneous genomic divergence with islands of differentiation is also expected under scenarios of divergence without gene flow [14 , 40] , but this could only occur if complete reproductive isolation had already evolved among now sympatrically breeding lake and stream stickleback . This seems rather unlikely: first , there is no evidence that any pair of stickleback ecotypes studied before has reached complete reproductive isolation after less than many thousand generations of divergence . Second , our results suggest ongoing gene flow as we observe that the geographical opportunity for gene flow is negatively related to the number of islands that show differentiation in the ecotype pairs ( S1 Table ) and to the magnitude of pairwise differentiation within islands ( Fig 3 ) . Furthermore , genetically intermediate individuals between lake and stream ecotypes occur where they breed in sympatry , as suggested by genome-wide variation ( Figs 2A and S2 ) and by patterns of variation and LD in genomic islands of parallel differentiation ( S8 and S9 Figs ) . Although genomic changes associated with habitat-dependent adaptation in stickleback have been extensively studied [24–26 , 36 , 53 , 70 , 88 , 124–132] , genomic differentiation that persists among sympatrically breeding stickleback species has only been demonstrated in a few small lakes at the Pacific Coast of British Columbia [25] , perhaps the most classical cases of ecological speciation [133–135] . This repeated evolution of sympatric limnetic and benthic stickleback species has occurred over the past ~12 , 000 years and is thought to have included an allopatric phase , after which these lakes were colonized a second time from the ocean [25 , 72] . Despite the very different evolutionary histories and divergence times of the Canadian limnetic-benthic stickleback species pairs and the ecotype pairs from Lake Constance , the number of chromosomes containing genomic islands with parallel differentiation is remarkably similar between the two systems ( Constance eight , versus Canadian limnetic-benthic ten chromosomes ) and the number of such islands is even higher among Lake Constance ecotypes ( 19 versus 15 islands , but note that different methodologies to define islands were used in [25] ) . The number of divergent regions among sympatric Lake Constance ecotypes is also higher than that found among parapatric lake and stream ecotypes from several catchments on the Haida Gwaii archipelago , Canada [70] . The latter lake and stream ecotypes also evolved from a marine ancestor over the past ~12 , 000 years since the retreat of the ice sheets , or potentially even earlier and survived in ice age freshwater refugia [136–138] . The similarity in number of diverging chromosomes among these systems is surprising , as older , more diverged and more strongly reproductively isolated ecotypes are expected to accumulate divergence across much of the genome with time , due to background selection , selection unrelated to speciation itself ( including divergent selection between species ) and due to drift . However , the stream and lake ecotypes that we studied emerged in only 150 years [46] , suggesting that genomic regions differing between older ecotypes or species might already have been involved at the onset of ecological speciation . Given the short time that was available for evolutionary divergence and the observed patterns of diversity in parallel islands , the adaptive variation differentiating Lake Constance ecotypes must have originated from older , standing genetic variation present in the colonizing linage from the Southern Baltic Sea catchments . Despite high numbers of repeatedly diverging genomic regions among Lake Constance ecotypes , there is limited overlap in identity with such regions identified in lake-stream stickleback ecotype pairs from Canada , Alaska , Northern Germany and elsewhere [36 , 70 , 130] , or with divergent regions identified among freshwater-marine ecotypes [24 , 26 , 45 , 139] or limnetic-benthic species [25 , 93] . Of the 19 genomic islands of parallel differentiation we identified in our study , only seven regions have been previously found as outlier regions between ecotypes or species outside the Lake Constance system ( S3 Table ) . Most strikingly , island 1 . 3 has been identified as divergent between allopatric marine and freshwater stickleback populations [24–26 , 45 , 125 , 127] and between lake and stream ecotypes in Northern Germany ( S3 Table , [36] ) , and has been shown to be an inversion for which alternative haplotypes are favored in one or the other environment [26 , 53] . Three other shared outlier regions on chromosome VII have previously been identified as outliers: Island 7 . 14 on chromosome VII is divergent between fully sympatric limnetic and benthic stickleback in one of three studied lakes in British Columbia , Canada [25] , in eight out of nine parapatric lake-stream pairs on Haida Gwaii and Vancouver Island , Canada [70 , 130] , as well as in an allopatric marine-freshwater comparison from Northern Scandinavia [127] . Island 7 . 11 on chromosome VII , is differentiated between multiple allopatric marine and freshwater populations from across the Northern hemisphere [26] and island 7 . 7 between parapatric lake and stream ecotype from Alaska [36] . Finally , islands 3 . 1 , 12 . 3 and 12 . 5 are divergent between multiple marine and freshwater populations [24 , 125 , 127] and islands 12 . 3 and 12 . 5 both between lake and stream ecotypes from Alaska [36] and among Norwegian freshwater populations [125] ( S3 Table ) . There is a discrepancy between the widespread genomic parallelism among marine-freshwater ecotypes that have been studied around the Northern Hemisphere [24 , 26 , 45] and limited shared divergence among lake-stream ecotypes from different lakes [25 , 32 , 36 , 70 , 130] . One reason for this discrepancy could be the more diverse and complex evolutionary histories of stickleback populations living and diverging within freshwater bodies . Marine stickleback have larger effective population sizes resulting in large standing genetic variation , much of which is broadly shared among marine stickleback populations [43] . In contrast , standing genetic variation is smaller and less widely shared among isolated and geographically disjunct freshwater populations . The combination of these factors may explain the lack of parallelism in phenotypic [66 , 130 , 140–142] and genomic divergence [32 , 36 , 70 , 130] , as well as the large phenotypic diversity [46 , 57 , 59 , 143–145] observed among lake ecotype stickleback and among stream ecotype stickleback from different systems . Contrary to the reported lack of phenotypic and genomic parallel evolution between lake-stream stickleback ecotype pairs from other regions of the world [25 , 32 , 36 , 70 , 130] , we find patterns of parallel differentiation at the genomic level between a lake ecotype and stream ecotype populations in two streams of the recently colonized Lake Constance system . Multiple scenarios of colonization and ecotype formation could plausibly explain the observed parallel genomic differentiation . First , if lake-adapted stickleback were originally introduced , multiple streams may have been colonized independently and repeated recruitment of adaptive alleles could have occurred from the same initial standing genetic variation , resulting in parallel genomic differentiation . This ‘lake first’-scenario would be a true ‘parallel evolution’ scenario [146] and the marine-like phenotypic composition of Southern Baltic Sea catchment stickleback that colonized the Lake Constance system may be in favor of this scenario . Second , if stream-adapted stickleback were introduced into the system , ecotypic differentiation may have evolved once at the habitat transition to the lake . Under this ‘stream first’-scenario , colonization of other streams may have occurred after the evolution of a lake ecotype , either ( a ) through long-distance migration of stream genotypes through the lake to other streams or ( b ) through repeated adaptation from standing genetic variation retained in the lake ecotype . The former would require fortuitous , simultaneous long-distance dispersal of several stream-adapted stickleback to a new stream , possibly aided by active habitat selection [147] , and would not be considered a case of parallel evolution . The latter would need allele combinations or haplotypes favored in the original stream ecotype to be added to the standing genetic variation of the lake ecotype via gene flow and then be recruited from the standing variation into newly evolving populations of stream ecotype in other streams . This mechanism , also referred to as ‘transporter hypothesis’ , would be considered parallel evolution [146] and was proposed to explain the widespread genomic and phenotypic parallelism among marine and freshwater stickleback [148] . Long-distance dispersal and transporter mechanisms are not exclusive and a combination of dispersal between streams and transport of adaptive variants via standing variation in the lake population are possible . Third , a generalist could have been introduced into the system and rapidly expanded its range to both stream and lake environments , followed by adaptation to these habitats . Adaptation may have involved standing genetic variation spreading with the initial expansion or ‘transported’ to replicate stream habitats later , both ideas compatible with parallel evolution . A fourth scenario could be secondary contact between already divergent lake and stream ecotypes that independently colonized the lake and effluent streams , leading to parallel patterns of differentiation between lake and stream populations but no in-situ parallel evolution . We think that the ‘generalist’ scenario is the most likely scenario , given the patterns of genomic variation observed in the populations studied here: genomic diversity levels in parallel genomic islands suggest that selection on standing variation occurred in both lake and stream ecotypes ( Fig 6 ) . ‘Lake first’ and ‘stream first’ scenarios may however lead to very similar genomic patterns of variation due to selection on standing genetic variation and thus are plausible alternatives we cannot reject . In contrast , we exclude a secondary contact scenario between already differentiated lake and stream stickleback . Such a model cannot explain that our two stream ecotype populations are genetically as distinct from each other as either is from the lake ecotype ( Figs 2B and S2 ) . Furthermore , phylogeographic reconstructions and population genetic analysis also clearly reveal our lake and stream ecotypes as closely related sister groups to the exclusion of other Swiss and central European populations [46 , 59] and suggest they have received only very little if any gene flow from outside the system [55] . This does not rule out that some of the standing variation on which selection acted could have arrived in the gene pool through contributions from outside , a hypothesis we are currently investigating . In contrast , we think that a secondary contact scenario likely applies to stream and lake populations from the North , West and South-West of Lake Constance: Mitochondrial haplotypes from Rhine ( South-West ) and Rhone ( North ) lineages are numerous in the streams of those regions , whereas the adjacent lake populations are nearly exclusively composed of Baltic Sea catchment haplotypes [56] . Similarly , fish with reduced body armor occur at high proportions in these more Western streams but not in the adjacent lake [53 , 56] , whereas this contrast is completely lacking in the South-Eastern sections of the lake that we studied here and reduced armor is rare in Southern Baltic Sea catchment stickleback with the same haplotype as Lake Constance stickleback [46 , 55 , 61] . By studying sympatric ecotypes with ongoing gene flow , we show that adaptive genomic differentiation , reminiscent of incipient speciation , has arisen in a very short period of time ( 150 years or ~100 generations ) . Genomic and phenotypic divergence between a migratory lake ecotype and two populations of resident stream ecotypes possibly involved the re-use of standing genetic variation and resulted in the persistence of stream ecotype populations even where there is ample opportunity for gene flow between ecotypes in sympatry . We propose that the high levels of differentiation observed between ecotypes despite existing gene flow was facilitated by genomic properties such as reduced recombination and the genomic co-localization of genes controlling several phenotypic traits relevant to adaptation and mate choice .
We sampled adult stickleback in spring 2007/09 and 2012/13 from six sites in two streams draining into Lake Constance and the lake shores close to the stream inlets ( Fig 1A , Table 1 ) . From each site , 10–21 individuals from the same year ( except for site S2 , for which fish from 2007 and 2009 were combined ) with both sexes equally represented were randomly picked for genomic analyses . Stickleback were caught using minnow traps and hand nets and subsequently anesthetized and euthanized in clove oil solution , in accordance with granted permits issued by the fishery authorities of the canton St . Gallen . Fish collection followed the Swiss veterinary legislation in concordance with the federal food safety and veterinary office ( FSVO ) and the cantonal veterinary office in St . Gallen ( Veterinäramt Kanton St . Gallen ) . In addition to morphological , ecological and life history traits described earlier from the Lake Constance system [46 , 56 , 57 , 59 , 65] , we quantified a previously unexplored morphological trait , lateral plate cover , that we observed to diverge among lake and stream ecotypes . We measured the height of the first 28 lateral plates after the pelvic girdle in all fully-plated stickleback following [94] and body depth at the first dorsal spine ( ‘BD1’ , following [59] ) from sites L1 , L2 , S1 and S2 using ImageJ v1 . 49 [149] . We performed a PCA on size-corrected plate heights , i . e . residuals from linear regressions of plate height against body depth at the first dorsal spine , and used an ANOVA to test for differences in PC1 between lake stickleback from L1 / L2 and stream stickleback from S1 / S2 ( S7A Fig ) . Furthermore , we identified the plate morph of each fish by counting lateral plates following [150] and tested for differences between lake stickleback from L1 / L2 and stream stickleback from S1 / S2 ( S7B Fig ) . We prepared three RAD libraries following Baird et al . [151] with slight modifications: We used 400 ng genomic DNA per sample and digested each for 12 hours with four units SbfI-HF ( New England Biolabs ) . We multiplexed 98 , 77 resp . 49 individuals per library , after the ligation step using P1 adapters ( sensu [151]; synthesized by Microsynth ) with custom six base pair barcodes with a minimal distance of two bases between any barcodes . The first two libraries were sheared using a Sonorex Super RK 102 P sonicator ( Bandelin ) for 2 minutes . The third library was sheared on an S220 series Adaptive Focused Acoustic ( AFA ) ultra-sonicator ( Covaris ) with the manufacturer’s settings for a 400 bp mean fragment size . Sheared fragments between 300–500 bp were size-selected on a 1 . 25% agarose gel . We carried out the enrichment step in four aliquots with 50 μl reaction volumes each , and combined these prior to the final size selection step . All three libraries were single-end sequenced on an Illumina HiSeq 2000 platform , yielding 136 , 200 and 166 million 100 bp long reads , respectively . We sequenced each library on a single lane together with 7–20% bacteriophage PhiX genomic DNA ( Illumina Inc . ) to increase complexity at the first 10 sequenced base pairs . Sequencing was performed at the Center of Integrative Genomics ( CIG ) , University of Lausanne and at the Next Generation Sequencing ( NGS ) Platform , University of Bern , Switzerland . We filtered raw sequencing reads from each lane and library for an intact SbfI restriction sites , de-multiplexed and barcode-trimmed them using the FASTX toolkit v . 0 . 0 . 13 ( http://hannonlab . cshl . edu/fastx_toolkit/ ) and custom python scripts . We aligned reads for each individual and library against the October 2013 re-assembly version of the threespine stickleback reference genome [26 , 77] using end-to-end alignment in Bowtie 2 v2 . 0 . 0 with default parameters [152] . SAMtools v0 . 1 . 19 [153] was used to convert alignments to binary format . We recalibrated base quality scores of aligned stickleback reads using empirical error rate estimations derived from bacteriophage PhiX reads . Raw sequencing reads from each lane were aligned against the PhiX 174 reference genome ( accession: NC_001422; [154] ) , known variation was masked and PhiX-alignments were used to create a base quality score recalibration table for each lane and library combination using BaseRecalibrator from GATK v . 2 . 7 [155] . We obtained between 0 . 9–2 . 5 billion base pairs of PhiX-reads per lane , sufficient to ensure good recalibration results . Using the GATK-tool PrintReads and PhiX-based recalibration tables , we then recalibrated base quality scores in stickleback alignments from the respective lanes . We used the GATK tool UnifiedGenotyper to call variants and genotypes in a combined fashion for all individuals , using the following parameters: minimal phred-scaled base quality score threshold of 20 , genotype likelihood model calling both SNPs and insertions/deletions ( indels ) and assumed contamination rate of 3% . Using custom python scripts and vcftools v0 . 1 . 12 [156] , all genotypes with quality < 30 or depth < 10 were set to missing . Variants with quality < 30 or > 50% missing genotypes per sampling site , monomorphic sites , SNPs with > 2 alleles , indels and SNPs 10 bp around indels as well as SNPs from the sex chromosome XIX were removed from the dataset , the latter due to mapping and calling uncertainty in males . RAD-sequencing datasets contain PCR duplicate reads for a locus and individual , a well-known caveat of this technology [73–75 , 157 , 158] , that cannot be identified in single-end sequencing data and can cause a bias towards calling homozygote genotypes when one allele of a heterozygote was by chance over-amplified [75] . We therefore additionally removed all sites that showed an excess of homozygotes , as measured by a significant deviation from Hardy-Weinberg equilibrium ( p < 0 . 01 ) within any of the six populations using Arlequin v3 . 5 . 1 . 4 [159] . We noticed a higher prevalence of PCR duplicates in the first two libraries containing populations S1 , L1 and S2 , likely due to different shearing device used in the library preparation step . This is visible in elevated mean FIS in these populations ( see results section , Fig 2B ) . To reduce noise introduced by these PCR duplicates , we therefore randomly picked one allele per high-quality filtered genotype and used this ‘allele dataset’ in some of the analyses , while the high-quality filtered genotype dataset was used in analyses where we could account for an excess of homozygotes , i . e . for inbreeding . We used PGDSpider v2 . 0 . 5 . 0 [160] for conversion from VCF format to other formats . We partitioned genomic variation in the allele dataset into principal components using adegenet [161] , for sites with a minor allele frequency > 1% . We also performed Bayesian clustering assignment of individuals into one to five clusters using STRUCTURE v2 . 34 . 10 [162] , using the allele dataset with sites of greater than 1% minor allele frequency , following [163] . We ran 10 replicates assuming one to five clusters with 100 , 000 steps burn-in and 200 , 000 sampling steps and checked convergence of replicates visually . We identified the most likely number of clusters by the highest delta K statistics among the tested clusters [164] . We studied the genome-wide distribution of genetic differentiation by computing for each SNP FST estimates between pairs of sampling sites ( ‘pairwise FST’ , Fig 3 ) and among all sampling sites grouped hierarchically ( ‘hierarchical FST’ , S12A Fig ) . We used pairwise FST to characterize levels and heterogeneity of differentiation across the genome between pairs of populations , but we identified genomic islands of differentiation based on hierarchical FST in order to maximize the power to detect outlier SNPs , which were used to identify genomic islands of differentiation . SNP-level F-statistics ( FST , FIT and FIS ) were estimated in a locus-by-locus AMOVA in Arlequin v3 . 5 . 1 . 4 [159] . We characterized heterogeneity in genome-wide differentiation by calculating the mean , 95%-quantile and standard deviation of pairwise FST’s in non-overlapping , 2 Mb-wide adjacent windows across the genome containing at least 20 SNPs . We defined heterogeneity in differentiation as the absolute coefficient of variation of these pairwise mean window FST’s . Single SNP hierarchical FST was estimated in a locus-by-locus AMOVA analysis in Arlequin , with populations grouped into three groups ( stream 1 , stream 2 , lake ) while maintaining the six sampling sites as separate populations . The grouping was based on genetic similarity between the sampling sites , assessed from genomic PCA ( Fig 2A ) , mean weighted pairwise FST results ( Fig 2B ) and Bayesian clustering of individuals ( S2 Fig ) . The first two , stream-like groups thus contained sites S1 and S2 respectively , and the third , lake-like group sites L1 , L2 , S1a and S1b ( S2 Fig ) . In order to detect loci putatively under selection , we performed an outlier analysis based on a hierarchical island model [165] . This approach identified outlier SNPs by comparing observed hierarchical FST and heterozygosity values against a null distribution from a hierarchical island model , derived from 500 , 000 simulations of 10 groups with 100 demes each , as implemented in a modified version ( v3 . 5 . 2 . 3 ) of Arlequin [165] ( S12 Fig ) . Significantly positive population-specific FIS , potentially due to RAD sequencing PCR duplicates and leading to an apparent excess of homozygotes , were taken into account in the simulations used to build the joint null distribution of heterozygosity and FST . In brief , for each simulated diploid individual the population-specific FIS coefficient was used as the probability that the two gene copies present on homologous chromosomes were identical by descent or not . This procedure amounts at reducing the sample size by a factor 1-FIS in the simulations , and thus to correctly take into account measured levels of inbreeding , which could either be due to true inbreeding or to PCR duplicates of a single chromosome . Our choices of group and deme size for simulating null distributions followed the recommendations of [165] , who showed that reliable outlier probability estimation is obtained from simulations performed with numbers of groups and numbers of demes per group that exceed the actual ( but unknown ) numbers . We also ran the outlier analysis with different group / deme size combinations ( 3 groups / 4 demes , 3 / 10 , 5 / 10 , 50 / 10 , 50 / 50 ) and found highly congruent outlier probabilities for each SNP ( correlation coefficient r > 0 . 9999 ) . We tested if outlier loci were randomly distributed on each chromosome by calculating Ripley’s K function following the approach by Flaxman et al . [7] accounting for recombination rate bias by using SNP positions on a genetic map ( see section ‘genetic distances and recombination rates‘ below ) , with one modification: The null distribution of Ripley’s K was simulated by 10 , 000 times sampling n SNPs among all the SNPs in our dataset for the respective chromosome , not by drawing them from random positions in the genome [7] , with n being the number of outliers on a chromosome . This was to avoid a bias in estimating expected values for Ripley’s K due to the non-random location of RAD-sequencing derived SNPs biased towards G/C-rich regions in the genome [151] . We identified ‘genomic islands of differentiation’ following the approach of Hofer et al . [76] ( S12 Fig ) . The HMM is based on three underlying and unobserved states , corresponding to ‘genomic background’ ( assumed to be neutral under a hierarchical island model ) , regions of ‘exceptionally low’ differentiation , and regions of ‘exceptionally high’ differentiation . We refer to exceptionally high differentiation regions as ‘genomic islands of differentiation’ . All three types of regions can consist of single SNPs or of several consecutive SNPs , depending on how outlier loci are clustered in the genome . The most likely state for each SNP is inferred from the HMM , based on its observed probability to be an outlier from the hierarchical FST analysis outlined above [76] . Subsequently , we retained only exceptional regions after multiple-testing correction with a false discovery rate of 0 . 001 for outlier loci [76] . Our approach differs in two aspects from [76] . First , we used only SNPs with minor allele frequencies > 1% . This minor allele frequency cutoff was not necessary for the data used by Hofer et al . [76] , because they used ascertained SNPs . We found very low frequency allele SNPs to disrupt the detection of high differentiation levels , because they can never reach high differentiation and are thus less informative [166] , even though they are naturally very abundant in unascertained sequence data . Second , we ran the HMM method for the concatenated SNP dataset instead of modeling every chromosome separately . This increased information for parameter estimation and did not affect the identification of islands of differentiation ( i . e . no spurious islands of differentiation extending across chromosomes were identified ) . Among genomic islands of differentiation identified by the HMM , we distinguished between islands showing parallel differentiation between both lake and stream stickleback breeding in sympatry and lake and stream stickleback breeding in parapatry and between islands of differentiation without parallel differentiation . We inferred parallel differentiation for each SNP by comparing allele counts between lake site L1 and the stream endpoint S1 as well as between lake site L2 and stream site S2 . A parallel differentiation SNP had to show ( a ) parallel allele frequency change between habitats , i . e . the same allele had to be found at higher frequency in the same habitat in both comparisons and ( b ) the allele frequencies had to be significantly different in both lake-stream comparisons as assessed by a significant pairwise FST estimated in an AMOVA accounting for inbreeding levels as described above . We defined islands of parallel differentiation as islands containing at least one parallel differentiation SNP and computed a PCA with only those SNPs as described above . For all pairs of parallel differentiation SNPs , we estimated the extent of linkage disequilibrium within each sampling site from the absolute of the correlation coefficient between pairs of loci ( |r| ) based on genotype counts using PLINK v1 . 07 [167] . For all genomic islands of differentiation , we counted the number of SNPs showing significantly different allele frequencies in sympatry ( L1 vs . S1 ) and in parapatry ( L2 vs . S2 ) also assessed by a significant pairwise FST between these populations ( S1 Table ) . Nucleotide diversity in each population was calculated using one allele per high-quality genotype with quality > 30 , depth > 10 and maximal 50% missing data , excluding sites located within 10 bp from indels or sites on the sex chromosome XIX . These filtered sites were partitioned into windows of variable size containing at least 2 , 500 sequenced sites , without splitting single RAD sequence reads , resulting in a mean window size of 324 , 800 bp ( median 302 , 900 bp , range 58 , 960–1 , 036 , 000 bp ) . Arlequin v3 . 5 . 2 . 3 [165] was used to calculate nucleotide diversity for each window in each population . Windows were checked for the presence of parallel and non-parallel islands and labelled as ‘genomic background’ , ‘parallel island’ and ‘non-parallel island’ windows accordingly ( Fig 6 ) . Within each population , we tested for differences in mean nucleotide diversity between parallel island , non-parallel island and genomic background windows using t-tests and Bonferroni-based multiple comparison adjusted p-values . We derived genetic distances and recombination rates from a previously published recombination map based on a cross between threespine stickleback from Lake Constance and Lake Geneva , Switzerland [77] . Position along the genetic map for each SNP was estimated by linear interpolation of genetic vs . physical positions as published in [77] . We estimated the regional recombination rate around each SNP in our dataset by smoothing the genetic vs . physical map [77] with cubic splines and a spline parameter of 0 . 7 for each chromosome and calculating the smoothed curve’s first derivate [168] . We used non-parametric tests to find correlations between recombination rate and the presence of islands of differentiation ( Kruskal-Wallis test ) , hierarchical , and pairwise differentiation ( FST , Spearman-rank correlations ) and assessed significance with a Bonferroni-corrected alpha level of 0 . 05 . We studied the overlap of islands of parallel differentiation and previously identified QTL , candidate genes , expression outliers and outlier regions: We assembled a database of previously identified QTL in threespine stickleback from the literature published up to mid-2015 [78–103] . If reported , 95% confidence intervals were directly taken from the literature or the markers in the genetic map of the study adjacent to the ‘peak LOD score minus 1 . 5’ boundaries on both sides of the LOD peak were used as 95% confidence intervals . In studies where only the highest-scoring markers were reported , we used the marker ± 1 Mb as approximate QTL confidence intervals . Physical positions of QTL and confidence interval estimates were transformed into October 2013 stickleback re-assembly coordinates [77] using the UCSC tool liftOver [169] and corresponding positions along the genetic map were calculated as for SNPs ( see above ) . We then tested if QTLs grouped into 32 traits and genomic islands of parallel differentiation overlap , using a buffer of ±10 kbp on both sides of genomic islands to alleviate effects of sparse SNP sampling by RAD sequencing ( also applied in all following overlap analyses ) . We tested if overlaps were expected by chance by permuting the physical and genetic positions of these islands 100 , 000 times randomly across the genome , re-calculating overlaps and deriving empirical null distributions and p-values for the observed number of overlaps with a Bonferroni-corrected alpha level of 0 . 05 , based on the repeated testing for overlaps with 32 traits . We further examined gene content of genomic islands of differentiation and their overlap with previously identified candidate genes for divergent adaptation [88 , 126 , 128 , 132] and expression outliers [129 , 131] , for which full gene lengths and a buffer of ± 10 kbp sequence on both sides of each gene were used . The set of overlapping genes was tested for enrichment in gene ontology ( GO ) terms for the GO categories ‘biological processes’ and ‘molecular functions’ using the STRING v9 . 1 database [170] , applying a Bonferroni-corrected alpha level of 0 . 05 . Finally , we overlapped genomic islands of parallel differentiation from our study with previously identified outlier markers [25 , 53 , 70 , 124 , 125 , 127 , 130] or outlier regions [24 , 26 , 36] , of which physical locations were publicly available . We used either the exact outlier region if reported [26 , 36] , an approximation of an outlier region based on its reported content ± 100 kbp sequence on both sides [24] , the ± 100 kbp region surrounding a reported outlier marker for high-density SNP data [53 , 70] or the ± 1 Mb region surrounding an outlier marker for low-density microsatellite datasets [25 , 124 , 125 , 127 , 130] for comparison with our genomic islands of differentiation . Statistical analyses and plotting was done using R v3 . 0 . 1 [171] . Data analysis was conducted using the bioinformatics infrastructure of the Genetic Diversity Centre ( GDC ) , ETH Zurich/Eawag .
|
Ecological speciation can be defined as the evolution of new , reproductively isolated , species driven by natural selection and ecologically-mediated sexual selection . Its genomic signature has mainly been studied in ecotypes and emerging species that started diverging hundreds to thousands of generations ago , while little is known about the very early stages of species divergence . To fill this knowledge gap , we studied whether and how threespine stickleback , which have adapted either to lake or to stream environments in less than 150 years , differ across their genomes . We found several segments of the genome to be clearly divergent between lake and stream ecotypes , even when both forms breed side by side in the same area . Strikingly , this genomic differentiation was mainly concentrated in one region with low to intermediate recombination rates and clustered around genes controlling ecotype-specific phenotypic traits . Our findings suggest that genomic differentiation can arise despite gene flow already very early at the onset of speciation , and that its occurrence may be facilitated by the genomic organization of genes that control traits involved in adaptation and reproductive isolation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"ecology",
"and",
"environmental",
"sciences",
"population",
"genetics",
"vertebrates",
"animals",
"osteichthyes",
"aquatic",
"environments",
"habitats",
"speciation",
"bodies",
"of",
"water",
"dna",
"recombination",
"dna",
"population",
"biology",
"fishes",
"chromosome",
"biology",
"lakes",
"marine",
"and",
"aquatic",
"sciences",
"sticklebacks",
"biochemistry",
"freshwater",
"environments",
"cell",
"biology",
"nucleic",
"acids",
"heredity",
"earth",
"sciences",
"genetics",
"biology",
"and",
"life",
"sciences",
"gene",
"flow",
"genomics",
"evolutionary",
"biology",
"evolutionary",
"processes",
"organisms",
"chromosomes"
] |
2016
|
Genomics of Rapid Incipient Speciation in Sympatric Threespine Stickleback
|
Several low-grade persistent viral infections induce and sustain very large numbers of virus-specific effector T cells . This was first described as a response to cytomegalovirus ( CMV ) , a herpesvirus that establishes a life-long persistent/latent infection , and sustains the largest known effector T cell populations in healthy people . These T cells remain functional and traffic systemically , which has led to the recent exploration of CMV as a persistent vaccine vector . However , the maintenance of this remarkable response is not understood . Current models propose that reservoirs of viral antigen and/or latently infected cells in lymph nodes stimulate T cell proliferation and effector differentiation , followed by migration of progeny to non-lymphoid tissues where they control CMV reactivation . We tested this model using murine CMV ( MCMV ) , a natural mouse pathogen and homologue of human CMV ( HCMV ) . While T cells within draining lymph nodes divided at a higher rate than cells elsewhere , antigen-dependent proliferation of MCMV-specific effector T cells was observed systemically . Strikingly , inhibition of T cell egress from lymph nodes failed to eliminate systemic T cell division , and did not prevent the maintenance of the inflationary populations . In fact , we found that the vast majority of inflationary cells , including most cells undergoing antigen-driven division , had not migrated into the parenchyma of non-lymphoid tissues but were instead exposed to the blood supply . Indeed , the immunodominance and effector phenotype of inflationary cells , both of which are primary hallmarks of memory inflation , were largely confined to blood-localized T cells . Together these results support a new model of MCMV-driven memory inflation in which most immune surveillance occurs in circulation , and in which most inflationary effector T cells are produced in response to viral antigen presented by cells that are accessible to the blood supply .
Cytomegaloviruses ( CMVs ) are ubiquitous , β-herpesviruses that establish lifelong infections in their hosts . CMV causes an acute systemic viral infection , followed by latency in many cells throughout the body . Cells of the myeloid lineage and endothelial cells from many organs have been shown to harbor CMV[1]–[17] . However , the sites of viral latency have not been fully defined , largely because it is extremely difficult to detect the virus during latency . Latent CMV is thought to reactivate in a stochastic manner throughout the body[18] , [19] . Thus , keeping CMV asymptomatic requires a robust immune surveillance effort by NK cells and virus-specific CD4 and CD8 T cells[20] . For this reason , immune compromised individuals are at great risk of CMV reactivation[21] . Importantly , CMV-specific CD8 T cells directly suppress viral gene expression during this latent/persistent phase of infection[22] and can , in isolation , control CMV replication[23]–[26] . Because of this ongoing immune surveillance effort , the hallmark of the immune response against CMV is CD8 T cell “memory inflation” , a phenomenon in which T cells specific for certain CMV epitopes stabilize at very high levels in the blood of hosts[27]–[31] . Approximately 5% of all CD8 T cells in the average adult are specific for CMV[32] , making these T cell populations the largest to be described in the circulation of healthy adults . Although first described in the context of CMV infections , it is now clear that several low-level persistent viral infections can induce and sustain very large numbers of virus-specific T cells[33]–[39] . In all cases , the majority of these inflationary CD8 T cells have a phenotype that is characteristic of effector cell differentiation [29] , [40]–[43] which is consistent with repeated antigen exposure ( KLRG-1pos , CD127low , CD62Llow ) [44] , [45] . However , the homeostasis of these unusual responses is still poorly understood . Because CMVs are highly species-specific , human CMV ( HCMV ) cannot be used in any animal model . Fortunately , the natural mouse pathogen murine CMV ( MCMV ) , establishes a remarkably similar host-pathogen balance and promotes robust memory inflation ( reviewed in [46] ) , making this an excellent model . The robust CD8 T cell response elicited by CMV has also led to its exploration as a vaccine vector against heterologous infections and cancer[47]–[49] . Surprisingly , we have shown that a spread-defective vaccine strain of MCMV ( ΔgL-MCMV ) was able to induce memory inflation when administered systemically [50] , potentially alleviating safety concerns that would arise with a spread-competent vaccine . Importantly , spread-defective MCMV did not induce memory inflation when administered as a footpad injection , indicating that the route of infection ( and therefore the site of latency ) is of critical importance for memory inflation . A better understanding of where T cells interact with persisting virus is necessary for the effective use of CMV as a vaccine strain . As might be expected , sustaining effector-phenotype inflationary T cells depends on viral antigen . In humans , the number and phenotype of HCMV-specific T cells directly correlates with peak viral loads[51] . In mice , we and others have shown that division of MCMV-specific inflationary T cells at steady-state occurs only in the presence of antigen [43] , [52] . However , we have also shown that steady-state division of inflationary T cells occurs rarely , even in the presence of antigen , and inflationary T cells die with a half-life of approximately 2 months[43] . This half-life is remarkably similar to the half-life of HCMV-specific T cells found in people [53] . We interpreted these data to suggest that inflationary effectors must be continuously replaced from a subset of more proliferative cells . Notably , a minor subset of inflationary T cells retains a memory phenotype ( KLRG-1neg , CD127pos ) , and these cells seem to be much more proliferative ( [52] and unpublished data ) . Recent work has shown that memory inflation in mice depends on the presentation of viral antigen by non-hematopoietic cells[52] , [54] . Moreover , the Oxenius lab found that MCMV-specific T cells in lymph nodes had an elevated rate of division at steady-state , and that these T cells were much more likely to retain a memory phenotype than cells elsewhere in the body[52] . Together these data have led to the hypothesis that reservoirs of viral antigen and/or latently infected non-hematopoietic cells in lymph nodes are responsible for stimulating lymph node-localized memory T cells . These memory T cells are then postulated to divide , producing new effector progeny that leave the lymph node and transit through the blood as they migrate into non-lymphoid tissues for immune surveillance . We tested this model and found instead , that antigen-dependent division and maintenance of inflationary effector T cells occurred systemically and did not depend on T cell egress from lymph nodes . Rather , we found that the vast majority of inflationary T cells , including those undergoing antigen-driven division , were exposed to the blood supply at steady state . Strikingly , the two defining features of memory inflation - inflated T cell numbers and an effector phenotype - were evident primarily within the blood-exposed inflationary T cells . Together , these data suggest a new model of memory inflation in which effector T cell populations are produced and maintained hematogenously .
The hallmark of the MCMV-specific CD8 T cell response is memory inflation , in which CD8s specific for some epitopes accumulate and remain at high levels for life . In B6 mice , these “inflationary” T cells target peptides derived from the M38 , IE3 and m139 proteins ( Figure 1A and [28] ) . In contrast , CD8s specific for epitopes derived from M45 and M57 contract after the acute phase of the infection in a way that resembles a conventional memory response ( Figure 1A and [28] ) . The inflationary CD8s reach high frequencies in the blood , spleen , lungs , and liver of infected mice at late times post infection and the majority have an effector-like phenotype ( KLRG1pos , CD127low , Figure 1B–C for M38 , and Figure S2A–B for IE3 ) . In contrast , the frequencies of inflationary T cells remain low in the lymph nodes where the majority retain a memory-like phenotype ( KLRG1neg , CD127high , Figure 1B–C and Figure S2A–B ) . Our previous work showed that the inflationary populations in the blood turned over with a half-life of 45–60 days during the latent/persistent stages of infection , even in the presence of viral antigen [43] . Consistent with this , we labeled the inflationary T cells with a brief BrdU pulse and found that the labeled cells - those that divided during the pulse period - decayed over time ( Figure 1D ) , while the total frequency of inflationary CD8s in the blood remained stable during the same time period ( Figure S2C ) . Importantly , the loss of BrdU labeled inflationary cells from the blood paralleled the loss of labeled cells in the spleen , liver and lungs , suggesting that the observed loss of labeled inflationary cells from the blood is not due to migration and accumulation in latently infected tissues ( Figure S2D ) . BrdU-labeled effector phenotype cells were lost much more quickly than non-effector phenotype CD8s of the same specificity ( Figure 1E and Figure S2F ) , even though the phenotype of the overall inflationary populations remained stable over this time ( Figure S2E–F ) . We and others have shown that MCMV-specific inflationary T cells only undergo extensive proliferation in the presence of antigen[43] , [52] . However , it has proven difficult to find viral transcripts at late times post infection , even using an extremely sensitive nested PCR assay ( Figure S2G and not shown ) [55] . To demonstrate the role that viral antigen plays in the production of inflationary effectors , mice were seeded with OT-Is and infected with MCMV expressing the SIINFEKL peptide from ovalbumin ( MCMV-SL8 ) , which induces inflation of SL8-specific T cells including OT-Is[56] . After more than 3 months of infection , CD8 T cells were isolated from the spleens of these mice and adoptively transferred into mice that had been previously infected with MCMV either expressing or lacking SIINFEKL ( Figure 1F ) . Expression of Ki67 by OT-I effector-phenotype cells ( KLRG-1pos , CD127low ) was only evident in the presence of antigen ( Figure 1G ) , whereas memory phenotype ( KLRG-1neg , CD127high ) OT-Is underwent homeostatic division in both sets of recipient mice . These data show that inflationary effector T cells underwent constant turnover during MCMV infection and that division of inflationary effectors could be used as a read-out of T cell encounter with antigen , even at late times post infection when viral transcripts were undetectable . Current models propose that , to sustain such large effector T cell populations , reservoirs of viral antigen and/or latently infected cells in lymph nodes stimulate T cell proliferation , followed by migration of effector T cell progeny through the blood to non-lymphoid tissues . We assessed cell division during chronic infection by measuring Ki67 expression with or without BrdU incorporation over a short 16 hour time period ( gating strategy Figure S1 ) . In agreement with previous work , we found that the division of inflationary cells was elevated in the lymph nodes , although there was a high degree of mouse-to-mouse variability ( Figure 2A and Figure S3A ) . However , this was only evident in the mediastinal lymph nodes ( MLN ) , which drain an i . p . infection ( [57] and Figure S2G ) and not the cervical lymph nodes ( CLN ) . In contrast , non-inflationary CD8s showed no increase in division in the MLN , suggesting an antigen-specific phenomenon ( Figure 2A , third panel ) . It is important to note however , that the absolute number of inflationary T cells dividing outside of the lymph nodes was much higher than the number dividing within the lymph nodes ( Figure 2B ) . Interestingly , there was a slight , but significant increase in the frequency of dividing inflationary T cells in the liver ( Figure 2A and Figure S3A ) , which is noteworthy because liver sinusoidal endothelial cells are one of the few identified cellular sites of viral latency[58] . In all cases , the dividing inflationary cells in the blood , spleen , liver and lung were primarily effector phenotype , indicating that these dividing cells had recently responded to viral antigen ( Figure 2C , M38- and IE3-specific T cells ) . In the lymph nodes , dividing inflationary cells were less likely to express an effector phenotype than cells in other organs ( Figure 2C ) , but were still skewed away from a memory phenotype ( compare Figure 2C to Fig 1C ) . Notably , a similar anatomical distribution and pattern of division was seen within OT-Is driven to inflate by MCMV-SL8 infection ( not shown ) , indicating that a single T cell clone can display the breadth of phenotype and anatomical distribution induced by memory inflation . In contrast , non-inflationary cells undergoing division in all sites were mostly memory phenotype ( Figure 2C , M45-specific T cells ) . After an i . p . injection of MCMV , the mediastinal lymph nodes , spleen and liver constitute the first sites of viral infection[57] . We hypothesized that a single-cycle virus , which would be restricted to these first sites , would induce a far more restricted pattern of antigen-dependent effector T cell division . To test this , we used a spread defective ΔgL-MCMV , which induces memory inflation after i . p . inoculation[50] . Interestingly , the pattern of antigen-driven division ( Figure 2D and Figure S3B–-C ) mirrored that seen after wild-type MCMV infection , with the exception that IE3-specific CD8s had an unusually high rate of division in the blood ( compare Figure 2D to 2A ) . The fact that dividing effector cells were evident in all of the organs was unexpected given that the spread defective ΔgL-MCMV is limited to cells encountered in the first round of infection . Together , the results from both wild-type and ΔgL-MCMV infections lead to two possible interpretations: ( i ) that T cells stimulated in lymph nodes expand markedly ( ∼100 to 1000 fold ) and migrate into non-lymphoid sites within a short period of time ( less than 16 hours ) or while continuing to go through the cell cycle , or ( ii ) that many T cells respond to viral antigen and divide outside of the lymph nodes . To directly test whether the division of inflationary T cells depends on antigen recognition within the lymph nodes , mice infected with MCMV for more than three months were treated with FTY720 , a drug that blocks lymphocyte egress from the lymph nodes[59] and may force the retention of cells within the parenchyma of tissues[60] . Within one week of treatment , naïve CD8 T cells were significantly reduced in the blood of all mice as expected ( Figure 3A ) , leaving mostly CD44hi , CD62Llo cells in circulation . In contrast , the impact of FTY720 on MCMV-specific inflationary CD8 T cells - the vast majority of which are CD44hi CD62Llo ( Figure S4A ) - was minimal . In the blood , the numbers of inflationary CD8 T cells were reduced in some but not all mice ( Figure 3B ) and a small , but significant reduction in inflationary CD8 T cell number was evident in the spleen ( Figure 3C ) . However , there were no other significant changes in the numbers of inflationary T cells elsewhere in the animal ( Figure 3C ) . Even more remarkably , the pattern of inflationary T cell division throughout the animal was largely unchanged and dividing cells were still detected at all sites , albeit with slightly reduced frequencies in the blood , CLN and spleen ( Figure 3D ) . Importantly , the phenotype of the total inflationary population and of the dividing inflationary cells in the blood and other organs was not changed by FTY720 treatment ( Figure 3E and not shown ) . The previous data suggest that antigen stimulation within the lymph nodes is not responsible for the majority of the dividing inflationary T cells associated with other organs at any given time . However , there was some reduction in the number of antigen specific cells in the blood and spleen , and a slight reduction in the frequency of dividing cells in some sites upon treatment with FTY720 ( Figure 3 ) . These results raise the possibility that inflationary effector T cells might transit through non-lymphoid tissues , and return to the blood after draining back to lymph nodes via lymphatics . Such a migration pattern has been described for effector memory T cells [61] . To test whether prolonged FTY720 treatment would compound the effects observed after one week , mice were treated with FTY720 in the drinking water for five weeks . As expected , naïve cells in the blood declined significantly after one week of treatment , continued declining over the next two weeks , and remained low thereafter ( Figure 4A ) . As shown above , the number of inflationary cells per milliliter of blood was reduced after one week of treatment in some , but not all mice ( Figure 4B ) . Strikingly however those numbers rebounded , and by the last time point there was no difference in the number of inflationary T cells in the blood , liver or lungs ( Figure 4B and 4E ) . Since inflationary effector T cells are produced in an antigen-dependent manner and have shorter half-life than the rest of the inflationary population , any effect of FTY720 on memory inflation should manifest first as a preferential loss of the effector subset . However , we found the opposite to be true . The frequency of effectors among the inflationary populations in the blood increased during prolonged FTY720 treatment ( Figure 4C ) , which could suggest that the memory-phenotype T cells in circulation were being slowly sequestered within lymph nodes . Importantly , there was no change in the frequency of dividing inflationary cells in the blood at any time point during FTY720 treatment ( Figure 4D ) . Comparable results were obtained for IE3-specific T cells ( Figure S4B–D ) . As in the blood , prolonged FTY720 treatment had no effect on the numbers of inflationary T cells associated with the organs or in the proportion of inflationary T cells undergoing division ( Figures 4E and 4F ) . Importantly , the splenic inflationary T cells , which were reduced after one week of treatment , were not progressively lost with prolonged FTY720 treatment ( compare Figures 3C to 4E ) . Finally , the phenotype of the dividing cells was largely unaltered at any time point in the blood or in any organ at the end of the experiment ( Figure 4G and not shown ) . Although these data do not exclude a role for lymph nodes in the circulation of the inflationary T cell pool , they show that the antigen-dependent maintenance of MCMV-specific effector T cells does not depend on migration through or antigen recognition within the lymph nodes . Surprisingly , our data indicate that the maintenance of inflationary effectors does not depend on egress from lymph nodes or the recirculation of inflationary T cells through tissues and back to the blood via lymphatics . This is in contradiction to the current model , which predicts that immune-surveillance against the latent virus depends on the constant migration of inflationary cells from lymphoid to non-lymphoid organs . This led us to ask whether the inflationary cells observed in association with the organs after perfusion ( Figures 1–4 ) had actually migrated into the parenchyma of those organs . To test this , mice infected for more than three months were injected intravenously ( i . v . ) with fluorescently-labeled anti-CD8α antibody , and sacrificed three minutes later . As in all experiments above , mice were perfused until there was no visible evidence of blood in the target organs . Organs were harvested after perfusion and the harvested cells were co-stained with a CD8β-specific antibody and tetramer . Recent work has shown that this approach can distinguish cells exposed to the blood supply ( labeled by the i . v . -injected CD8α-specific antibody ) from those that have migrated into the parenchyma of a tissue ( labeled only with the CD8β-specific antibody added post-harvest ) [62]–[64] . In agreement with previous work , we found that this technique labeled CD8α T cells in the blood , the vasculature of the lungs and lymph nodes , the red pulp of the spleen , and the sinusoids of the liver , which have fenestrated endothelium and are permeable to the blood-borne antibody ( Figure 5A top; Figure S5 and [62] , [64] ) . In contrast , CD8s in the white pulp of the spleen , as well as those outside of the lymph node and lung vasculature were unlabeled ( Figure S5 and [62] , [64] ) . Strikingly , despite the perfusion , nearly all of the MCMV-specific inflationary cells extracted with the lung and the liver were labeled by the i . v . injected antibody , indicating that these cells were exposed to the blood supply ( M38-specific T cells Figure 5A middle , IE3-specific T cells Figure S6D ) . In the spleen , inflationary T cells were skewed toward the red-pulp , while in lymph nodes , a minority of the inflationary T cells were labeled , as expected . Inflationary T cells in all organs were more likely to be exposed to the blood when compared with the CD8 T cell population as a whole ( Figure 5A and 5B ) and dividing effector cells were overwhelmingly skewed towards the i . v . labeled fraction in the liver , lung and spleen ( Figure 5A bottom row and 5B ) . Analyses of dividing inflationary T cells in the vasculature of the mediastinal lymph nodes was difficult due to the low cell numbers and high degree of mouse-to-mouse variability . However , in animals with an adequate number of i . v . labeled cells for analysis , a substantial fraction of dividing MCMV-specific T cells were i . v . labeled ( Figure 5A bottom row ) , suggesting that the elevated frequency of T cell division evident in the MLN might be , at least in part , the result of T cells responding to antigen in the vasculature . Together , these data show that inflationary T cells associated with the lung and liver are almost all perfusion-resistant T cells residing in the vasculature , while in the spleen , inflationary T cells preferentially localize to the red-pulp . These results suggest a new model of memory inflation in which the vast majority of MCMV-specific T cells that are responding to the virus are exposed to the blood supply , even within secondary lymphoid organs The two primary hallmarks of memory inflation are the numerical dominance of inflating populations and the effector-skewed phenotype of inflationary T cells , both of which are thought to result from repeated antigen stimulation . Thus , we next asked whether these hallmarks were preferentially associated with the blood- or tissue-localized T cell fractions . For this , we included analyses of inflationary T cells associated with the kidney , since the lungs have been the only non-lymphoid organ with a closed circulatory system analyzed to this point . Of note , approximately half of the inflationary T cells associated with the perfused kidney were exposed to the blood supply ( Figure S6A ) . In all organs , cells labeled with the i . v . antibody exhibited the effector-skewed phenotype that is typical of inflationary T cells ( M38: Figure 6 , IE3: Figure S6B-C ) . In contrast , unlabeled inflationary cells in all organs were much less likely to express an effector phenotype and were far more likely to exhibit a memory phenotype ( Figure 6B–C , Figure S6B–C ) . Importantly , identical results were obtained with OT-Is undergoing inflation in response to MCMV-SL8 ( not shown ) , indicating that such diversity can be produced by a single T cell clone . Remarkably , the immunodominance of inflationary T cells also differed between the i . v . -labeled and unlabeled T cells . In the i . v . -labeled fractions of the liver , kidney , lung and spleen , M38-specific T cells were approximately 8 to 16-fold more numerous than non-inflating M45-specific T cells ( Figure 7A–B ) . In contrast , in the unlabeled compartment of the same organs , inflationary M38-specific T cells were only 1 . 3 to 3-fold more numerous than M45-specific T cells ( Figure 7A–B ) . In fact , M38-specific T cells were subdominant to M45-specific cells in the parenchyma of the lungs in half of the mice , and in the lymph nodes and white pulp of the spleen of most mice ( Figure 7B ) . Importantly , identical results were obtained by comparing the inflating IE3-specific T cell population with the non-inflating M57-specific population ( Figure 7B and Figure S6D ) . Together , these data show that the numerical dominance and the effector phenotype of inflationary T cells , both primary hallmarks of memory inflation and repeated antigen encounter , are almost entirely restricted to T cells exposed to the blood supply . Using the absolute numbers of recovered T cells , we assessed T cell localization to the vasculature and parenchyma of the spleen , lymph nodes , lungs , liver and kidney as well as other potential sites of T cell migration including the salivary gland , mammary gland and female reproductive tract . When compared to cells within the lung , liver and spleen , T cells associated with the lymph nodes , kidney and mucosal organs were substantially less likely to be labeled by the i . v . staining ( Figure S6A ) . However there were many fewer MCMV-specific cells present at those sites ( Figure 7A and Figure S6D ) . As a result , the vast majority of inflationary cells in an animal were exposed to the blood ( Figure 7C ) . So as not to count circulating cells twice , this analysis does not include cells recovered in the blood itself , which we estimate to be approximately comparable to the number of cells in the spleen ( compare Figures 3B and 4B with Figure 7A ) . Thus , the total fraction of inflationary T cells exposed to the blood is likely to be even greater than that reported here . The preference for blood localization of memory inflation was even more apparent when we analyzed inflationary T cells with an effector phenotype , greater than 90% of which were exposed to the blood ( Figure S7A ) . In marked contrast , approximately half of the non-inflationary cells ( Figure 7C ) and half of inflationary cells with a memory phenotype were unlabeled by the i . v . antibody ( Figure S7B ) . For both inflationary and non-inflationary T cells , cells that were protected from the i . v . antibody staining were primarily found in the white pulp of the spleen and the salivary gland ( Figure 7C ) . Collectively , these data support a new model of MCMV-driven memory inflation in which the majority of inflationary T cells in the body respond to antigen while remaining exposed to the blood supply . In other words , these data suggest that memory inflation is primarily a blood-localized phenomenon .
Memory inflation is a unique immune response that provides continuous immune surveillance against a lifelong infection without inducing T cell exhaustion[27] , [29] , [31] , [42] , [43] , [65] , [66] . However the mechanism that supports memory inflation has not been well defined . Our previous data support the model that memory inflation during MCMV infection is maintained by systemic antigen-dependent production of short-lived effectors[43] . Here , we show that T cell division originating in the lymph nodes , as well as the migration of T cells through lymph nodes , was completely dispensable for the long-term maintenance of these effector populations . Furthermore , our data show that the bulk of inflationary CD8s that appear to be “in” organs are part of a circulating population with access to the blood , with only a small minority residing within non-lymphoid organs at any given time during the late stages of infection . Indeed , the immunodominance profile and effector phenotype that are characteristic of memory inflation were primarily evident in the blood-localized compartment and not within cells that were shielded from the blood supply . These data lead us to propose a new model of memory inflation in which the effector T cell populations are primarily produced by exposure to antigen that is accessible to the blood supply and are subsequently maintained in circulation . One of the major setbacks to understanding the maintenance of effector T cells during memory inflation has been the difficulty in defining the sites of ongoing antigen stimulation . It is clear that CD8 T cells suppress viral gene expression during latent infection and that exposure to cognate antigen throughout infection results in T cell division and effector differentiation[22] , [43] , [52] . We used division of inflationary effectors , which is absolutely antigen-dependent ( Figure 1G ) , to better define the localization and trafficking pattern of T cells produced in response to antigen stimulation . The previous model of memory inflation for both MCMV and adenovirus proposed that antigen depots in the lymph nodes are responsible for stimulating T cells that subsequently migrate into non-lymphoid tissues[33] , [52] . Our data do not refute the idea that some antigen recognition occurs in lymph nodes . Indeed , we also observed a clear trend towards more frequent division of inflationary CD8s , but not non-inflationary CD8s , within mediastinal lymph nodes ( the draining lymph nodes after i . p . infection , e . g . Figure 2A ) , clearly suggesting an antigen-dependent process . However , we also observed division of effector T cells associated with all studied organs , where they outnumbered the dividing T cells in the MLN by 100 to 1000-fold ( Figure 2C ) . Most significantly , the proportion of cells dividing at each site was hardly altered by pre-treatment with FTY720 , a drug that potently inhibits T cell egress from lymph nodes ( Figure 3 and 4 ) . Thus , memory inflation in circulation can be maintained without input from the lymph nodes or cells recirculating through non-lymphoid tissues . Our observation that some , but not all mice exhibited transient decreases in the number and frequency of dividing cells in the blood after FTY720 treatment , might suggest that the lymph nodes contribute T cells to the circulating pool , but that this contribution is dispensable . Alternatively , since very few latently infected cells express viral transcripts at any given time[18] , [67] , the contribution of any individual site to memory inflation would be expected to vary from mouse-to-mouse and from time-to-time , depending where viral reactivation happened to occur . It is important to note that our FTY720 results do not rule out a role for the spleen in driving memory inflation . In fact , we think it likely that the spleen makes a major contribution to memory inflation due to the large number of inflationary cells found there and the fact that stroma of the spleen is a known site of MCMV latency and reactivation[4] . Because the spleen has an open blood supply , FTY720 would not be expected to prevent the continous circulation of cells through the red pulp as they move from the marginal sinuses to the venous sinuses . On the other hand , it has been suggested that T cell egress from the white pulp ( but not the red pulp ) is S1PR1-dependent and modulated by FTY720 [68]–[70] . However , trafficking of T cells out of the white pulp hasn't been fully characterized . Determining the precise roles of antigen presentation by specific cells in the spleen and other organs awaits further study . Our results , which rule out lymph nodes as a necessary source of memory inflation , raise the question of what other sites might be important for driving inflation . Inflationary effector T cells , including those undergoing antigen-driven cell division , were almost entirely exposed to the blood supply , and we have previously shown that their accumulation depends on antigen recognition[56] , [71] . Thus , we propose that most cells latently infected with MCMV may also be exposed to the blood supply or accessible to T cells that are in circulation . Although our understanding of the cell types that harbor latent MCMV is incomplete , endothelial cells were previously defined as a major site of MCMV latency[1] and liver sinusoidal endothelial cells ( LSECs ) were recently identified as a cellular site of both latency and reactivation[58] . Interestingly , LSECs are in direct contact with blood circulating T cells , and can stimulate CD8 T cells[72] , and the sinusoids are narrow enough that T cells must crawl through them[73] . Moreover , antigen-presentation by non-hematopoietic cells is critical to sustain inflationary T cell populations[52] , [54] . Thus , it is appealing to hypothesize that antigen presentation by sinusoidal endothelial cells is a main driver of memory inflation . Reactivating virus has also been described in the stroma of the spleen[4] ( also containing the sinusoids through which circulating T cells must pass ) , the lungs[19] , [67] , and the kidney[74] although the specific cell types harboring virus in these organs remain undefined . In addition , CMV-specific T cells express the CX3CR1 and CXCR3 receptors , which attract them to activated endothelial cells expressing fractalkine and IP-10[65] , [75] . Thus , it is possible that antigen recognition and the resultant production of effector T cells are almost entirely contained within the vasculature and sinusoids of multiple organs during MCMV infection . It is tempting to speculate that antigen recognition within circulation is a critical factor in the induction of memory inflation in general . Previous work showed that HSV-1 induced memory inflation when administered systemically , but not after a local infection[34] , [76] . Likewise , we showed that a spread-defective vaccine strain of MCMV had to be administered systemically to induce memory inflation[50] . Although we cannot rule out a role for latently infected cells in other compartments , it is possible that memory inflation is so apparent during CMV-infections because it is primarily happening as a result of immune surveillance in the blood . In support of this , two of the defining features of memory inflation - the effector phenotype of the inflationary cells and the immunodominance of CD8s with inflationary specificities - were primarily confined to cells exposed to the blood supply ( Figures 5–7 ) . It should be noted that non-inflationary T cells are not thought to recognize viral antigen often , if at all , during the persistent/latent phase of MCMV infection [43] , [77] , [78] . These non-inflators were co-dominant within the white pulp of the spleen , as well as the parenchyma of the kidney , lung and lymph nodes . The splenic white pulp is known to attract memory-phenotype T cells[79] and CMV-specific T cells within lymph nodes lack any obvious memory inflation [52] , [80] . However , non-lymphoid organs were thought to be the targets of the inflationary effector T cells transiting through the blood . Therefore it was surprising that the hallmarks of memory inflation were largely absent from the T cell populations protected from the i . v . antibody staining within non-lymphoid organs . It is important to note that inflationary CD8s are thought to participate in ongoing immune surveillance[22] , [66] . Thus , our results raise interesting questions about how T cells suppress viral reactivation throughout the animal . The results discussed above strongly suggest that the vast majority of immune surveillance occurs in circulation or in sites exposed to the blood supply . However , there are also likely to be latently infected cells that are not exposed to the blood supply , implying that the relatively small number of inflationary cells found within the parenchyma of non-lymphoid tissues must be sufficient to control viral latency at those sites . An alternative explanation is that inflationary effector T cells are continuously migrating into non-lymphoid tissues and dying rapidly upon arrival , thereby reducing their steady-state numbers . However , in pulse/chase experiments with BrdU , we failed to find evidence that BrdU-labeled inflationary cells in the blood migrated into the parenchyma of any tissue ( i . e . became preferentially protected from i . v . antibody over time ) or were lost more rapidly within tissue parenchyma compared to the blood ( not shown ) . Further experiments will be needed to address the virus-T cell dynamics at sites that are not accessible to the blood supply . In humans , CMV infection and immunity parallel what is seen in MCMV infected mice in many ways . HCMV-specific CD8s accumulate in the blood but not the lymph nodes and turnover with similar kinetics as the inflationary populations in mice [53] , [80] , suggesting that a similar mechanism may sustain memory inflation in both hosts . Moreover , similar cell types and organs are infected by both viruses and it is apparent that both myeloid and non-hematopoietic cells can harbor latent HCMV and MCMV DNA[1]–[17] . There is also a host of data suggesting that endothelial cells are at least one major non-hematopoietic site of viral latency in both humans and mice . Indeed , MCMV DNA has been detected in the endothelial cells of multiple organs[1] and HCMV DNA has been found in the vessel walls of major arteries ( reviewed in [3] ) . This , combined with our work presented here , would suggest a major role for antigen-presentation by endothelial cells in both humans and mice . Collectively , our work suggests a new model of CMV-driven memory inflation in which immune surveillance mostly occurs in circulation , and a large proportion of newly produced effector T cells have responded to viral antigen on latently infected cells that are accessible to the blood supply . Our work may have important implications for the development of CMV-based vaccine vectors since additional measures may be needed to ensure that blood-borne T cells migrate out of the vasculature and into the desired target tissues . However , it will be exciting to learn whether endothelial cells are the primary source of viral antigen that sustains inflationary T cells and whether other infections that sustain large numbers of effector T cells ( e . g . parvoviruses and adenoviruses[33]–[39] ) also depend on antigen recognition within circulation .
All animal work was performed in accordance with NIH guidelines and the Animal Welfare Act . The Thomas Jefferson University Office of Animal Resources has full accreditation from the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . The experiments were approved by the Institutional Biosafety Committee and the Institutional Animal Care and Use Committee at Thomas Jefferson . Mice were purchased from Jackson Laboratory and bred in house for use in all experiments . C57BL/6 mice were used for all direct infections . For adoptive transfer experiments , CD45 . 1 congenic mice ( B6 . SJL-Ptprca epcb/BoyJ ) and OT-Is on a B6 background ( C57BL/6-Tg ( TcraTcrb ) 1100Mjb/J ) were used ( see below ) . All mice were infected i . p . with 2×105 plaque forming units ( pfu ) of virus and were considered chronically infected after 3 months . Experiments were carried out with the K181 strain of MCMV ( kindly provided by Ed Mocarski ) except where indicated . All viruses were grown and titered on M2-10B4 cells as described[81] , except for the ΔgL virus ( Figure 2 ) which was produced on gL-3T3 cells as described previously[50] . For experiments shown in Figure 1F–G , mice were seeded with small numbers of congenic OT-Is and subsequently infected with MCMV-SL8-015 , as previously described[56] . After more than 3 months post infection , splenocytes containing OT-Is were harvested and transferred into congenic recipients that had been infected ( >3 months previously ) with either wild-type MCMV ( lacking the cognate antigen ) or MCMV expressing the cognate SIINFEKL peptide ( either MCMV-SL8-015 or K181-OVA ) . Mice received ∼2×107 total splenocytes . For the long-term BrdU pulse ( Figure 1 ) , mice were injected i . p . with 1mg of BrdU ( Sigma ) then subsequently provided with 0 . 8 mg/ml BrdU in their drinking water for 3 or 7 days . For the short-term BrdU pulse ( Figure 2 , 3 and 4 and Figure S2 ) , mice were injected i . p . with 1 mg of BrdU . BrdU incorporation was assayed using the BD Biosciences Flow kit followed by FACS analysis . For short term FTY720 treatment ( Figure 3 ) , mice were injected i . p . with FTY720 ( Cayman Chemical Company ) at a dose of 1 mg/kg body weight on days 0 , 2 , 4 and 6 . Cells were analyzed one day after the final FTY720 injection . For long term FTY720 treatment ( Figure 4 ) , mice were treated with FTY720 in their drinking water for 5 weeks at a concentration of 3 . 3 µg/ml . Water containing FTY720 was replaced every other day . For analyses of T cells in the blood of living mice , peripheral blood was harvested from the retro-orbital sinus . Alternatively , blood was harvested from the chest cavity at sacrifice after cutting the pulmonary vein . For isolation of lymphocytes from organs , mice were sacrificed , the pulmonary vein was cut , blood was harvested and then mice were immediately perfused with approximately 20 ml PBS containing 1 U/ml heparin . Perfusion invariably resulted in visible evidence that blood was removed from all tested organs . Lymphocytes in the spleen and lymph nodes were isolated by passing through a 70 µm cells strainer to achieve a single cell suspension . Protocols to isolate non-lymphoid organ localized lymphocytes were adapted from Zhang et al [82] and Mega et al [83] . In brief , the liver , lungs , salivary gland , mammary gland , kidney and female reproductive tract were either minced with scissors or dissociated with the gentle MACS dissociator ( Miltenyi Biotec ) . Livers were incubated at 37°C for 1 hour in digestion media containing 0 . 5 mg/ml collagenase type IV ( Sigma ) , 5 mM CaCl2 , 50 µg/ml DNase I ( Roche ) , and 10% FBS in RPMI 1640 with L-glutamine ( Cellgro ) . Lungs , salivary glands , mammary glands , kidneys and female reproductive tracts were incubated at 37°C for 1-1 . 5 hours in digestion media containing 1 mg/ml collagenase type IV , 5 mM CaCl2 , 50 µg/ml DNase , and 10% FBS in RPMI . To isolate lymphocytes , liver , kidney and female reproductive tract homogenates were suspended in 40% Percoll ( Sigma ) and overlayed on top of a 70% Percoll layer , ( each prepared in RPMI without serum ) . Salivary glands were suspended in 40% Percoll and overlayed on top of a 75% Percoll layer . Suspensions were centrifuged at 600×g for 25 minutes . Lung and mammary gland homogenates were suspended in 40% Percoll and centrifuged directly at 600×g for 25 minutes . Lymphocytes were isolated from the 70/40 interface , the 75/40 interface or the pellet respectively . MHC-tetramers loaded with peptides derived from M38 , IE3 , M57 and M45 were produced at the NIH tetramer core facility ( http://tetramer . yerkes . emory . edu/ ) and used to identify Ag-specific T cells as described previously[43] . Phenotypic analysis was performed with the following antibodies: CD8α ( clone 53–6 . 7 ) , CD44 ( clone IM7 ) , CD62L ( clone MEL-14 ) , CD127 ( clone A7R34 ) , KLRG1 ( clone 2F1 ) , Ki67 ( clone B56 ) , and BrdU ( clone 3D4 ) . For identifying Ki67-positive and BrdU-labeled cells , lymphocytes were fixed and permeabilized using the BrdU Flow Kit from BD Biosciences using the recommended protocol . For adoptive transfers , OT-Is were distinguished from host cells by staining for congenic markers CD45 . 1 ( clone A20 ) and CD45 . 2 ( clone 104 ) and the TCR Vα2 chain ( clone B20 . 1 ) . All antibodies were purchased from Biolegend or BD Biosciences . Cells were analyzed on an LSR II flow cytometer ( BD Biosciences ) and using FlowJo software ( TreeStar , Ashland , OR , USA ) . Intravenous antibody injection was used to distinguish between vasculature-localized and parenchyma-localized CD8 T cells as described previously[62]–[64] . Briefly , mice were injected i . v . with 3 µg Brilliant Violet 421-labeled anti-CD8α antibody ( clone 53–6 . 7 ) and sacrificed 3 minutes later . After perfusion , harvested organs were digested with collagenase as described above in the presence of 60 µg/ml unlabeled CD8α antibody . Isolated cells were stained with labeled anti-CD8β antibody ( clone 53–5 . 8 ) and other phenotypic markers . To confirm the sites of i . v . staining , mice were injected with APC labeled CD8α as described above . Isolated spleen , liver , lung and mediastinal lymph nodes were frozen in OCT and sectioned using a cryostat . Sections were fixed with cold acetone for 10 minutes and then stained with antibodies specific for B220 ( clone RA3-6B2 ) , F4/80 ( clone BM8 ) , CD31 ( clone 390 ) , CD45 . 2 ( clone 104 ) and CD8β ( clone YTS156 . 7 . 7 ) and co-stained with DAPI ( Prolong Gold antifade – Life Technologies ) . All antibodies were purchased from Biolegend . Images were generated with the LSM 510 Meta confocal laser scanning microscope ( Carl Zeiss ) and the LSM image browser software ( Carl Zeiss ) .
|
Herpesviruses persist for the life of the host and must be continuously controlled by a robust immune surveillance effort . In the case of the cytomegalovirus ( CMV ) , this ongoing immune surveillance promotes the accumulation of CMV-specific T cells in a process known as “memory inflation” . We and others have proposed that the ability to induce memory inflation may be an important benefit of CMV-based vaccine vectors that persist within the host and continuously boost the immune response . However , it has been difficult to determine where T cells are encountering CMV in the body , leading to many unanswered questions about the maintenance of this remarkable response . Previous models proposed that T cells encountered viral antigen within lymph nodes and then migrated to other tissues to prevent CMV reactivation . However , we found that the majority of T cells stimulated by CMV were present in circulation , where they could be sustained without the input from T cells localized to lymph nodes . In fact , two of the defining features of memory inflation - inflated numbers and an effector phenotype - were restricted to cells that were exposed to the blood . Thus , we propose that memory inflation during CMV infection is largely the result of immune surveillance that occurs in circulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"animal",
"models",
"of",
"infection",
"white",
"blood",
"cells",
"immune",
"cells",
"cell",
"biology",
"animal",
"cells",
"immunity",
"viral",
"persistence",
"and",
"latency",
"virology",
"t",
"cells",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"immunology",
"microbiology",
"acquired",
"immune",
"system",
"immune",
"response",
"immune",
"system"
] |
2014
|
Systemic Hematogenous Maintenance of Memory Inflation by MCMV Infection
|
Our ability to identify genes that participate in cell growth and division is limited because their loss often leads to lethality . A solution to this is to isolate conditional mutants where the phenotype is visible under restrictive conditions . Here , we capitalize on the haploid growth-phase of the moss Physcomitrella patens to identify conditional loss-of-growth ( CLoG ) mutants with impaired growth at high temperature . We used whole-genome sequencing of pooled segregants to pinpoint the lesion of one of these mutants ( clog1 ) and validated the identified mutation by rescuing the conditional phenotype by homologous recombination . We found that CLoG1 is a novel and ancient gene conserved in plants . At the restrictive temperature , clog1 plants have smaller cells but can complete cell division , indicating an important role of CLoG1 in cell growth , but not an essential role in cell division . Fluorescent protein fusions of CLoG1 indicate it is localized to microtubules with a bias towards depolymerizing microtubule ends . Silencing CLoG1 decreases microtubule dynamics , suggesting that CLoG1 plays a critical role in regulating microtubule dynamics . By discovering a novel gene critical for plant growth , our work demonstrates that P . patens is an excellent genetic system to study genes with a fundamental role in plant cell growth .
Early adopters of P . patens as a genetic model plant identified its haploid genetics as a valuable attribute for genetic analysis . Mutants displaying a variety of defects , including metabolic and hormonal deficiencies as well as morphological and physiological alterations , were easily isolated using simple mutagenesis [1–3] . Despite the success in isolating mutants , identification of the causal mutations was not readily achieved until recently with the advance of whole-genome sequencing and the availability of polymorphic strains [4] . Similar to other systems , mapping can be rapidly achieved by pooling the mutant DNA from segregants resulting from crosses between polymorphic strains and sequencing the segregants’ genomes , providing an immediate map to identify the location of a mutation with high accuracy [4–7] . Although the predominant haploid growth phase of P . patens is valuable for genetic screening , identifying mutations in essential genes , including genes important for cell growth and division , can be complicated . To overcome these limitations it is possible to isolate conditional mutants , which has been an effective approach to study genes that are essential for growth and viability in a number of organisms [8–13] . Temperature-sensitive ( TS ) conditional mutants display phenotypic defects under restrictive temperatures . TS mutants have not been widely used in plants , but some important studies–show their great potential for investigating plant genes important for growth [14–17] and microtubule dynamics in Arabidopsis thaliana [18–20] . Among many essential cellular structures , the microtubule cytoskeleton plays a prominent role in organizing plant cell growth and division . Subcellular arrays , such as the mitotic spindle and the phragmoplast , are critical for proper chromosome segregation and cytokinesis , respectively [21 , 22]; while the cortical microtubule array is involved in cellulose deposition and the delivery of other cell wall components [23 , 24] . For the microtubule cytoskeleton to function , it is necessary that the interaction of motors , bundling proteins , severing proteins , and end binding proteins are regulated in a dynamic fashion [25–27] . Many of these microtubule-associated proteins are conserved in plants and shown to have similar function to their animal and fungal homologues [28] . Nevertheless , it has also been shown that the plant microtubule cytoskeleton has unique forms of regulation and associated proteins not found in cells of other organisms [29] . Due to its complexity , our understanding of the composition and regulation of the plant microtubule cytoskeleton still requires additional investigation . Here , to identify genes important for plant cell growth and division , we aimed to isolate TS mutants from P . patens and identify the causal mutation using pooled segregant analysis and next-generation whole-genome sequencing . We used ultra-violet ( UV ) light-induced mutagenesis and screened mutants with impaired growth by separating them by size . We isolated several mutant plants that grow normally at room temperature ( 20–25°C ) but had reduced growth at 32°C . We selected one mutant with reduced cellular growth for detailed characterization and identified the mutation responsible for the TS phenotype . Highlighting the potential importance of our approach , the gene we identified was previously uncharacterized , but is conserved in slime molds , algae , and plants . Interestingly , this novel protein localizes to the microtubule cytoskeleton in P . patens , and tracks depolymerizing microtubules ends . RNA-based loss-of-function analysis suggests a possible role in the regulation of microtubule dynamics . Our TS mutant screen allowed us to discover a novel protein conserved throughout evolution and important for plant cell growth .
To isolate temperature-sensitive ( TS ) mutants , we first identified a temperature span that allows wild type P . patens plants to grow to a similar extent and have similar morphology . By comparing the plant area and morphology , we found wild type plants grow similarly between 20°C and 32°C ( Fig 1 ) . To identify TS mutants , we selected our standard culturing temperature of 25°C as the permissive temperature , and the maximum temperature of 32°C as the restrictive temperature . To isolate mutant plants , we irradiated protoplasts with UV light to induce mutations , optimizing the amount of irradiation to obtain approximately 90–95% killing frequency [30] . Protoplasts regenerated their cell walls for four days at 25°C and then were cultured for one week at 32°C to induce potential TS defects . Following this regeneration and culture period , we isolated plants smaller than 200 μm in diameter by filtering all the regenerated plants through a sieve . We grew these small plants at 32°C for an additional week to discard the background non-TS plants that grow under these conditions . We manually selected small plants that fail to grow at 32°C and distinguished TS plants from non-TS mutant plants by their ability to resume growth upon transfer to 25°C ( S1 Fig and Materials and Methods ) . To confirm temperature-sensitivity , we compared the growth of each putative TS mutant against wild type plants by expanding the isolated mutants onto two agar plates and culturing the plants at 25ºC and 32ºC . From three initial screens , where approximately 5 , 000 mutant plants were screened in each , we obtained an average yield of six TS mutant plants per screen . We named these genes CLoG for Conditional Loss of Growth and selected the mutant plant clog1 , which expresses a strong TS phenotype , for additional analysis . To obtain quantitative growth and morphological information , we performed growth assays on the clog1 mutant [31] . Protoplasts were regenerated for four days , transferred to growth medium , and assayed for growth at 20°C , 25°C , and 32°C . Three days after transfer to growth medium , we stained the cell walls of the regenerating plants with calcoflour , and imaged them with epifluorescence microscopy [31] . The mutant and wild type plants grew similarly at 20°C and 25°C and only clog1 exhibited an inhibition of plant growth at 32°C ( Fig 1 ) . We measured plant area and solidity ( area/convex hull area ) , using total area to assess growth rate and solidity to assess the extent of polarization and branching of protonemata filaments [31] . These data show that clog1 is a TS mutant for growth , demonstrating that by using a simple sieving and temperature selection screening system ( S1 Fig ) we can isolate TS mutant plants of P . patens with altered growth at the restrictive temperature . To further characterize the cellular basis of the reduced plant growth , we measured cell size and investigated possible cell division defects . We analyzed plants at the same stage and temperature as indicated above using three-dimensional reconstruction ( see Materials and Methods section ) . To evaluate cell size , we stained the cell walls , and to evaluate the presence of multinucleated cells , we generated clog1 cell lines expressing a GFP-GUS fusion with a nuclear localization signal [32] . The apical and sub-apical cells of the longest filaments showed a significant reduction in length , which was accompanied by an increase in width ( Fig 2A–2C ) . The change in width was not compensatory , because the final volume of the clog1 cells at the restrictive temperature was smaller than in control cells . These results suggest a role for CLoG1 protein in cell polarization and growth . With regard to cell division , we did not observe multinucleated cells in clog1 plants grown at the restrictive temperature ( Fig 2A ) , indicating that CLoG1 is not essential for completing cell division . A critical limitation that has hindered the establishment of P . patens as a forward genetic system is the inability to map and subsequently identify a mutated allele . Here we chose genome sequencing of pooled segregants as the strategy to identify the causal mutation for the clog1 TS mutant [6 , 7] . By only selecting segregants that display the TS phenotype , the causal mutation remains with the segregants while other parts of the genome undergo random chromosomal crossover and recombination during meiosis . Therefore , genomic recombination rates should decrease in frequency for regions closer to the causal mutation . We generated a mapping population by outcrossing clog1 plants ( Gransden strain ) to a polymorphic Villersexel strain [33] , which expresses soluble mCherry ( Vx::mCherry ) . We identified the crossed sporophytes by mCherry fluorescence of the capsule on a non-fluorescent TS mutant gametophyte [33] . Mapping using whole-genome sequencing is most successful with a large enough mapping population . To determine the appropriate size of our mapping population , we designed a Monte Carlo simulation exploring the relationship between the size of the mapping population ( number of segregants ) and the size of the mapping interval—the region potentially containing the causal mutation ( for details see Materials and Methods section ) . The simulation was based on an approximately 450Mbp genome consisting of 27 chromosomes ( Physcomitrella patens v3 . 0 early release ) and a recombination frequency per chromosome of zero , one , or two[34] . As seen in S2 Fig , the magnitude of the decrease in median mapping interval size became smaller when the mapping population size was increased from 20 to 30 and even smaller when the population size increased from 40 to 90 . Based on the simulation results , and given good sequencing quality with enough depth ( 10x coverage ) , the causal mutation should reliably be mapped onto one chromosome within 1–3 Mbp . This conclusion is based on a mapping population of 24 F1 clog1 segregants and an approximately 450 Mbp genome consisting of 27 chromosomes . To identify and pool the segregants of clog1 , we screened outcrossed plants at 25°C and 32°C for loss of growth at 32°C . After screening 120 F1 segregants , 24 were selected that exhibited a robust TS phenotype similar to that of clog1 at 32°C ( Materials and Methods ) . It is important to note that the precise segregation ratio was difficult to estimate because , to reduce any possible background , we discarded any plants that could not be clearly assigned a TS phenotype . To identify the approximate location of the clog1 mutation , we extracted , pooled , and sequenced genomic DNA from the 24 F1 progeny of the clog1-Vx::mCherry cross ( Materials and Methods ) . We identified single nucleotide polymorphisms ( SNPs ) as markers that defined differences between the Villersexel and Gransden genomes to measure genomic recombination in pooled segregants . The reference ( Gransden ) allele frequency at marker positions was used to map the clog1 mutation . We expected that the reference allele frequency would be highest in regions close to the causal mutation and would be approximately 0 . 5 in the rest of the genome assuming that random recombination occurs . We also calculated marker densities ( 1 marker every 200 /bp ) and average read depth ( 8X ) to assist in assessment of reference allele frequencies at different chromosomal positions ( Materials and Methods ) . We mapped the suspected causal mutation and the gene where it is located after aligning the reads to P . patens genome assembly V1 . 2 [35] , as this was the only assembly for which the genome annotation file was publicly available . We selected a total of 2 , 292 , 625 SNP markers by comparing the genome sequences of the P . patens Gransden and Villersexel strains , at an average of one marker per 207 bp . At the marker positions , we detected 1 , 722 , 037 SNPs ( 75 . 1% of all markers ) in the pooled segregants’ genome sequence . We used a MATLAB routine to visualize reference allele frequencies , marker densities , and average read depth across all 27 chromosomes ( Materials and Methods ) . We conducted these calculations for every non-overlapped 40 Kbp window . We found that chromosome 24 was the only chromosome whose reference allele frequency reached one at a particular position ( Fig 3 ) , approximately 4 . 6 Mbp into chromosome 24 ( the green line in Fig 3A ) . On both sides of this peak , the reference allele frequencies gradually increase from 0 . 5 on the right side and 0 . 6 on the left side to 1 . 0 . We did not observe a similar pattern on any of the other chromosomes , where the reference allele frequencies mostly fluctuated around 0 . 5 ( Fig 3B shows chromosome 12 as a representative ) . Additionally , the marker densities and average read depth of all chromosomes fluctuated around 200 markers per 40 Kbp window ( one marker per 200 bps ) and 8X coverage respectively ( Fig 3 ) . This is very close to the average marker density of one marker per 207 bp and the genome coverage of 9 . 2X determined from the alignment of the pooled segregants’ genome , indicating that markers were generally evenly distributed across the 27 chromosomes and that most regions of every chromosome were supported by eight reads . Taken together these data identified the mapping interval for the causal mutation of clog1 as a 1 Mbp segment ( located at 4 . 1–5 . 1 Mbp ) centered at the peak of reference allele frequency ( 4 . 6 Mbp ) on chromosome 24 . With such a large mapping interval ( 30–40 genes ) it is not possible to identify a single gene . Instead , we reasoned that the causal mutation is most likely a non-synonymous SNP in the open reading frame of a gene . There are two main reasons for this: first , point mutations are one of the most common signature mutations of UV mutagenesis [36] , and second , the causal mutation is likely to cause an amino acid change ( missense mutation ) in a functional protein because the protein conformational change and resulting growth defect only take place at high temperature . Therefore , we filtered for non-marker and non-synonymous SNPs within the mapping interval on chromosome 24 , which was covered by scaffolds 73 , 274 , and 387 of the V1 . 2 genome assembly ( Materials and Methods ) . We found one mutation in scaffold 387 that fulfills these requirements . The mutation is located at position 4 , 325 , 703 of chromosome 24 ( Physcomitrella patens v3 . 0 early release ) ( orange line in Fig 3A ) , which is approximately 270 Kbp from the peak of the reference allele frequency . This mutation mapped to gene Pp1s387_7V6 [Pp3c24_6470 , Genbank MG754010] and was predicted to cause an amino acid change at position 874 from a leucine to a phenylalanine; the length of the ORF is 3 , 822 bp ( 1 , 274 amino acids ) . The function of Pp3c24_6470 is currently unknown and there is only one copy of this gene in P . patens [35] . Additionally , no conserved domain of known function has been identified in the protein encoded by this gene . We identified homologous proteins in amoeboid protists ( Dctyostelium fasciculatum , Dyctostelium discoideum , and Polysphondylium pallidum ) , green algae , and land plants . The Panther Classification System classifies CLoG1 in the unnamed gene family PTHR34958 . Interestingly , with only a few exceptions , the gene is present as a single copy in most of the species analyzed ( S1 Table ) . Phylogenetic analysis of the amino acid sequences groups the proteins homologous to CLoG1 with the expected clades of protists , bryophyte , monocots , etc . ( S3 Fig ) . Amino acid composition shows an abundance of leucine residues ( ~12% ) , secondary structure prediction shows the propensity for the presence of alpha-helices and no coiled-coil formation ( S4 Fig ) . To evaluate the most conserved regions of the protein we aligned the amino acid sequences of two amoeboid protists , two green algae , and two land plants . Interestingly , the alga sequences are significantly longer that their plant or protist counterparts . We selected six highly conserved regions containing 20% or more identical residues between all six species . These regions are indicated on the P . patens sequence in S4 Fig and the alignments shown in S5 Fig . Besides the presence of abundant leucine residues in all these regions , no other obvious sequence motifs were observed . The longest conserved regions are located at the N and C termini of the molecule . To confirm that the mutation identified was responsible for the TS clog1 phenotype , we used homologous recombination to replace a 2kb genomic region flanking the putative clog1 point mutation with a wild type genomic fragment . We used PCR to amplify the 2kb fragment and transformed the PCR product into clog1 mutant plants . We co-transformed a circular plasmid with hygromycin resistance to help select for transformed plants . To confirm the replacement , via homologous recombination , of the mutant allele with the wild type allele , we sequenced the amplified region in twenty transformed plants and identified one plant with the wild type sequence . We verified that this plant was of the mutant’s genetic background—and not a result of contamination with wild type DNA , by sequencing a second mutant locus in chromosome 24 which exhibited the mutant’s genetic background . Using a growth assay , we confirmed genetic reversion of the conditional loss-of-growth phenotype ( Fig 4 ) . Together , these results strongly support that we identified the causal mutation responsible for clog1 . To gain insight into the intracellular function of CLoG1 , we generated CLoG1 proteins fused to monomeric enhanced green fluorescent protein ( mEGFP ) and determined their subcellular localization . To evaluate whether the fluorescent protein fusions are functional , we took advantage of a well-established transient RNAi and complementation assay [37 , 38] . We generated an RNAi construct that targets the 5´UTR of CLoG1 . Importantly , we found that plants transformed with the CLoG1-UTR RNAi construct show , at room temperature , a similar loss-of-growth phenotype to clog1 mutants grown at the restrictive temperature ( Fig 5 ) . By co-transforming CLoG1-UTR RNAi with a construct that constitutively expresses the CLoG1 open reading frame , we observed complete rescue of the RNAi phenotype ( Fig 5 ) . We also observed that either C-terminal and N-terminal fusions of CLoG1 to mEGFP fully complemented the CLoG1-RNAi plants , demonstrating that both fusion proteins are functional ( Fig 5 ) . Finally , clog1 plants expressing a C-terminal fusion of CLoG1 to mEGFP complement the TS phenotype , further confirming that the fusion protein is functional and demonstrating that the clog1 allele is recessive ( S6 Fig ) . We attempted to generate lines with the endogenous locus tagged with mEGFP , which we have shown is functional . Unfortunately , the resulting fluorescence levels in the properly tagged lines were too low to detect above background using our confocal microscope system . Low levels of CLoG1 protein are consistent with a low level of transcript expression deduced from the transcriptome atlas of P . patens [39] . Therefore , to observe the intracellular localization of CLoG1-mEGFP , we generated stable lines in the wild type background where CLoG1-mEGFP was driven by a constitutive promoter [37] . For imaging , we selected plants with normal growth . Strikingly , we found that CLoG1 localizes to filamentous structures that resemble the microtubule cytoskeleton [40–42]; we could identify filaments immediately below the plasma membrane , as well as in the cytoplasm ( Fig 6A and 6B ) . To identify if CLoG1 localizes with specific sub-structures of the microtubule cytoskeleton , we expressed mCherry-labeled alpha-tubulin , which integrates into dynamic microtubules , in the CLoG1 C-terminal mEGFP fusion line . In growing apical caulonema cells , CLoG1-mEGFP appeared to only localize to sections of microtubules , sometimes forming punctate structures ( Fig 6A and 6B , and S1 Movie ) . By analyzing the double-labeled line , we found that the CLoG1-mEGFP accumulation at the apical region of growing cells , at the zone where microtubules overlap at the tip of growing cells . This zone of microtubule overlap has been previously described and shown to be important for polarized growth [43] . Analysis of dividing cells showed an accumulation bias of CLoG1-mEGFP signal toward the spindle poles during anaphase ( Fig 6C , S7 Fig , and S2 Movie ) . It is well established that tubulin subunits in mitotic spindles undergo flux with subunit depolymerization at their poles [44 , 45] . This accumulation pattern suggested that CLoG1 might be tracking depolymerizing microtubules . To investigate this possibility , we analyzed individual cortical microtubules using high-resolution confocal microscopy of subapical cells , where the cytosolic signal of CLoG1-mEGFP is lower than in apical cells , resulting in an increase of the signal to noise ratio . This analysis confirmed that CLoG1-mEGFP localization on microtubules is punctate , with a bias towards depolymerizing ends ( S3 Movie ) . Kymographs of single depolymerizing microtubules confirmed the accumulation with depolymerizing ends ( Fig 7 and S4–S6 Movies ) . The fluorescent protein signal can be detected as a spot on both , slowly ( Fig 7A and 7C and S4 Movie ) and rapidly ( Fig 7B and 7C and S5 Movie ) depolymerizing microtubules . This suggests that CLoG1 can track the plus and minus ends of microtubules during depolymerization . In fact , this double localization is clearly observable in time-lapse movies where both ends of a single microtubule depolymerize simultaneously ( Fig 8A and S6 and S7 Movies ) . We never observed accumulation of CLoG1 on growing microtubules , but in some occasions , we were able to observe accumulation on microtubule ends after they stop polymerizing and start depolymerizing ( undergoing catastrophe ) ( Fig 8B and S8 and S9 Movies ) . CLoG1-mEGFP localizes to microtubules and tracks their depolymerizing ends; therefore , we hypothesized that microtubule dynamics may be altered with reduced levels of CLoG1 protein . To observe microtubule dynamics , we transiently silenced CLoG1 with the CLoG1-UTR-targeting RNAi construct in the background of a line stably expressing mCherry-αTubulin as well as the nuclear silencing marker ( NLS-GFP-GUS ) [46] . P . patens protonema have two microtubule populations- cortical and cytoplasmic . We acquired single-plane confocal images near the cortical microtubules of seven-day-old plants to get minimal background images for analysis . To quantify the cortical microtubule dynamics , we measured the correlation coefficient across frames for each movie , as described previously for actin [46 , 47] and microtubules [41] . In cells with silenced CLoG1 , the correlation coefficient did not decay as rapidly as in control cells , and thus these plants have slower microtubule dynamics ( Fig 9 ) . The detailed changes in dynamics from individual microtubules were not further analyzed due to difficulty generating a large data set and due to the small cell size of the CLoG1-RNAi cells .
Here we established a strategy to isolate and map loss-of-growth mutations in the moss P . patens , which enabled identification of a novel and ancient gene important for cell growth . CLoG1 is conserved in all plants and encodes a protein that localizes to the microtubule cytoskeleton and can track depolymerizing microtubule ends . We accomplished these advances by performing a conditional mutant screen , which is a powerful tool used in fungal and invertebrate systems , to identify genes important for cell growth . We then used segregation analysis and next-generation whole-genome sequencing to map the causal mutation . Taking advantage of efficient homologous recombination in moss , we used genetic complementation to validate the identity of the mutant gene . Finally , we used transient RNAi and functional complementation with mEGFP fusions to demonstrate that the novel protein localizes to microtubules , tracks the depolymerizing ends and may play a role in increasing microtubule dynamics . Understanding the precise role that CLoG1 plays in the cell will require further analysis; nevertheless , based on the loss-of-growth phenotype observed and its intracellular localization , we hypothesize that it may play a role in the regulation of microtubule organization during cell division and cell expansion [26 , 48 , 49] . Our measurements of growing cells shows reduced cell length and increased in cell diameter; these observations are consistent with CLoG1 participation in tip growth and the polarization machinery . Because we did not observe multinucleated cells in the clog1 cells at the restrictive temperature , we do not expect CLoG1 to play an essential role in cell division , but the number of cells per plant appears to be reduced in the clog1 plants grown at the restrictive temperature , suggesting that the timing of cell division may be slower in clog1 cells . Concerning the effects on microtubule dynamics , the following two possible scenarios could result in the slower dynamics we observed in the CLoG1-RNAi lines: a decrease in the rate of polymerization or depolymerization , or a decrease in the frequency of rescue or catastrophe . Because CLoG1 does not appear to localize to polymerizing ends , we suggest it may increase depolymerization rates . However , it may also play a subtler role by affecting the frequency of catastrophe or rescue events . Detailed analyses of CLoG1 localization with microtubules in the wild type and mutant backgrounds as well as in vitro studies should help to distinguish these possibilities . Consistent with a defect in microtubule dynamics , our localization studies show that CLoG1 accumulates on microtubules . While we expressed the CLoG1-mEGFP protein fusion from a constitutive promoter in the wild type background , it is possible that some of the observed localization is due to over-expression . Thus , additional studies using lines tagged at the endogenous locus with tandem mEGFP tags to boost the fluorescent signal are needed . Nevertheless , it is interesting to note that the observed tracking of depolymerizing plus and minus ends is similar to that of microtubule depolymerizing kinesins , such as the kinesin 13 family [50]; a possible hypothesis is that CLoG1 may associate or regulate kinesin-based end depolymerization . Many microtubule end-binding proteins have been reported previously , including proteins associated with depolymerizing ends [51] , but with the exception of kinesins mentioned above , these proteins associate only with one end of the depolymerizing microtubule . Hence , elucidating the mechanism for association of CLoG1 to both depolymerizing ends is likely to reveal a novel and important system for regulating microtubule dynamics during cell division and cell growth during interphase . The identification of CLoG1 as a component of this system will facilitate its analysis by providing a handle for future research . Our study reveals the great potential of P . patens in forward genetics that was envisioned by the pioneers of this system [1 , 2] , and similar to Arabidopsis , it guarantees to provide novel insights into many plant biology problems when combined with more sophisticated genetic screening strategies [52] . We anticipate that the combination of haploid genetics , simple development , and reduced genetic complexity will continue to strengthen the role of P . patens as a model land plant to study many aspects of plant growth and development [53 , 54] .
Except during crossing , all plants used in this study were proliferated on solid PpNH4 plates [55] at the designated temperature ( 15°C , 20°C , 25°C , or 32°C ) under a cycle of 16 h light ( 90 μmol m-2 s-1 ) and 8 h dark . Plant tissue was ground with a homogenizer ( Power Gen 125 , Fisher Scientific ) and transferred onto solid PpNH4 plates overlaid with cellophane . One week-old moss was harvested and incubated with a cell wall digestive solution ( 0 . 5% ( w/v ) driselase in 8% ( w/v ) mannitol ) for 1 h in order to remove the cell wall . The protoplasts were sieved through 70 μm mesh to remove debris and then centrifuged , after which the pellet of protoplasts was re-suspended in 10 mL 8% ( w/v ) mannitol and washed twice more . The genetic screen performed here ( S1 Fig ) was based on one used for identifying conditional morphological mutations in Neurospora crassa [12] and conditions for UV-induced mutagenesis were adapted from a protocol for isolation of gravitropic moss mutants in Ceratodon purpureus [30] . Wild type Gransden P . patens protoplasts were suspended in 1 to 2 mL liquid PpNH4 containing 8% ( w/v ) mannitol and 10mM CaCl2 and their concentration was calculated by counting the number of cells in the suspension using a hemocytometer . The protoplasts were distributed onto 90 mm petri dishes containing solid protoplast regeneration medium bottom [PRMB] overlaid with cellophane . Approximately 500 , 000 protoplasts were distributed onto each plate , which were then irradiated using UV light ( Fisher Scientific FB-UVXL-1000 UV Crosslinker ) . After regeneration , this resulted in approximately 500 plants per plate . The plates were then incubated at 25°C for four days followed by 32°C for a week . After this , mutant plants were re-suspended in 12 mL sterile liquid PpNH4 medium and selected by sieving through a 200 μm nylon mesh . Selected mutants were re-plated at 32°C for another week . Plants with mutant phenotypes were identified by eye , picked with tweezers to a fresh PpNH4 plate , cultured at 25°C on a PpNH4 plate until the plant reached ~5 mm in diameter , and tested for temperature-sensitivity by grinding and expanding each line on two PpNH4 plates: one at 25°C and one at 32°C . The growth assay in this study is a modified version of the method described previously [31] . Protoplasts of the control and TS mutants were suspended in 1 to 2 mL 8% ( w/v ) mannitol and the cells from this suspension were counted using a hemocytometer . Each mutant’s protoplasts were re-suspended in 2 mL melted PRMT medium [55] and kept at 47°C in concentrations of 25 , 000 and 50 , 000 cells/mL . This medium was distributed onto 90 mm PRMB plates overlaid with cellophane , on which the protoplasts were regenerated at 25°C for 4 d . After this , the cellophane from each plate was cut into three equal pieces , each of which was transferred to a different PpNH4 plate ( on Day 0 ) ; the three pieces were incubated at 20°C , 25°C , and 32°C . On day three , microscope slides were prepared by adding 30 μL of 10 μg/mL calcofluor ( Fluorescent Brightener 28 , Sigma ) diluted in distilled water onto a glass slide and inverting a coverslip-sized square of the sample–the cellophane with PRMT agar on top–onto the calcofluor . The cellophane was removed from the agar , another 20 μL calcofluor was added , and a coverslip was placed on top and sealed with a 1:1:1 mixture of melted vaseline:lanoline:paraffin . Imaging was performed with a 10X objective using a Zeiss Axiovert 200M microscope fitted with a CoolSNAP fx CDD camera . Zeiss Axiovision software was used to create an overlapping grid pattern of 200 to 300 pictures and an ImageJ macro [31] was used to measure parameters of the plants in these pictures , such as plant area and solidity . This procedure was repeated three times to reach the sample size indicated in the figure legend ( Fig 1 ) . Plant area was normalized to the average area of the wild type plants at either 25°C or 20°C . For statistical comparisons , the variance between experiments and groups has been previously shown to be similar [37]; the area was further normalized by obtaining the natural logarithm , because of the log normal distribution of plant areas [37] . To determine if there was a significant difference in plant area and solidity between the mutants and controls when grown at 20°C , 25°C , and 32°C , OriginPro 8 . 1 was used to conduct one-way ANOVA-Tukey tests to reject equivalence of means . From these tests , adjusted p-values for comparing ln ( area ) and solidity for 20°C vs . 25°C , 20°C vs . 32°C , and 25°C vs . 32°C were obtained for the mutant line and wild type at day three following transfer to growth medium . If the adjusted p-values were smaller than 0 . 05 , it was assumed that the difference in ln ( area ) or solidity was statistically significant . To evaluate stable complementation analysis , the growth assay described above was slightly modified . Images were acquired using the MosaicX module from AxioVision that allows for tile-based acquisition and stitching . Composite images were constructed by 10x10 individual images . A single composite image was generated for each condition and analyzed as indicated above . Plants from three experiments were used for the analysis . Statistical comparisons ( one way ANOVA-Tukey ) and plotting were performed using RStudio . The clog1 line and the corresponding wild type lines were transformed with the NLS-GFP-GUS construct previously described [32] and lines expressing similar levels of nuclear GFP signal were selected . For cell size analysis , protoplasts were plated and regenerated in the same conditions as for the growth assay ( see above ) and analyzed three days after transfer to growth medium . Cell walls were stained with 30 μL of 10 μg/mL calcofluor and the cells were visualized with a Zeiss Observer , 10x lens , DAPI and FTIC fluoresce filters , and equipped with an Apotome for three-dimensional sectioning . Z-stacks were projected by maximal intensity in blue and green channels and pseudo-colored green ( wall ) and red ( nuclei ) . Length and thickness were determined using the ImageJ measuring tool . In total 40 cells were measured from 3 independent experiments . Statistical comparisons ( one way ANOVA-Tukey ) and plotting were performed using RStudio . The method used for crossing moss was adapted from standard protocols for identification of hybrid Physcomitrella patens sporophytes [33] . TS mutants and fluorescently-labeled P . patens Villersexel ( Vx::mCherry ) were proliferated and harvested for crossing at one week old . A special solid medium , BCD medium with low nitrogen [33] , was used to help sporophyte development . Deep petri dishes were prepared using 90 mL of this melted medium . Plant tissue of all the mutants and the polymorphic Villersexel strain was ground with a homogenizer ( Power Gen 125 , Fisher Scientific ) , and the ground tissue of each TS mutant was mixed with the same amount of ground tissue of Vx::mCherry . The mixed moss tissue was grown at 25°C for 3 weeks , after which the plates were cultivated at 15°C . After 2 weeks , sterile distilled water was added to each plate to just submerge the tissue and the water was removed after one day . The same procedure was repeated after 3 weeks of culture at 15°C , and sporophytes were picked when capsules turned brown . To identify crossed sporophytes , tissue was observed using a fluorescence stereo microscope ( Zeiss ) with green light excitation and red light emission filters . The sporophytes of plants with fluorescent capsules on non-fluorescent gametophytes were chosen . One to three sporophytes were harvested in a sterile 1 . 5 mL microcentrifuge tube and then sterilized following published protocols [56] . To germinate the spores , the capsules were gently crushed with the pipette tip and mixed to produce a spore suspension , and approximately 400 μL of this suspension was distributed evenly onto germination solid medium in 90 mm petri dishes . The germination medium recipe is available from PHYSCObase’s spore germination protocol ( moss . nibb . ac . jp ) . When plants were large enough , they were picked onto PpNH4 agar plates . To screen for F1 segregants that retained the TS phenotype , each segregant and a control plant were proliferated on two PpNH4 agar plates each and incubated at 25°C and 32°C for one week . Imaging was performed on a stereomicroscope under white light at a magnification of 64X and because this selection was qualitative , only segregants that could be clearly identified as temperature-sensitive were pooled . We confirmed the presence of the clog1 mutation , mapped and identified as indicated above , by amplifying and sequencing the region of the locus predicted . Primers CLoG1-mut ( F ) and CLoG1-mut ( R ) ( S5 Table ) were used to amplify the DNA region containing the mutation from wild type ( Gransden ) and clog1 plants; primers CLoG1-inF and CLoG1-inR were used for sequencing the PCR product . To confirm the causal nature of this mutation via genetic rescue , plant DNA was isolated from wild type P . patens ( Gransden ) using the PowerPlant Pro DNA Isolation Kit ( Mo Bio ) . DNA was amplified using two rounds of PCR reactions with the primers CLoG1-mut ( F ) and CLoG1-mut ( R ) , and the PCR product purified ( NucleoSpin Extract II kit Machery-Nagel ) ; the total PCR product yield was ~30 μg . This wild type-derived PCR product , together with pTH-Ubi-3XmEGFP ( used for transient selection of hygromycin-resistant plants ) , were transformed into clog1 mutants following standard transformation procedures [55] . Nineteen plants resulting from the transformation were expanded and DNA was extracted as above . PCR was performed for each sample extracted from these plants using the external primers CLoG1-outF and CLoG1-outR . These primers were selected external to the previous set to amplify the putative CLoG1 locus and to avoid amplification of any unintentional insertion site of the previous targeting PCR product . PCR products amplified with primers CLoG1-outF and CLoG1-outR were gel-purified and sequenced using primers CLoG1-inF and CLoG1-inR . To confirm that that the background of the mutant line ( clog1 ) was present in the rescued plant , and that we were not analyzing accidentally a wild type plant , we identified and amplified a mutation found elsewhere in chromosome 24 of clog1 but not in the wild type plants . This mutation was identified using MATLAB code designed for comparing differences in nucleotides between the sequenced pool of DNA and the wild type genomes ( code available upon request ) . We identified one mutation present in the clog1 background but absent in the wild type genomes ( Gransden and Villersexel ) . To avoid any interference from the rescue DNA , the mutation was located at position 10 , 809 , 758 of chromosome 24 , which is several Kb from the putative casual mutation . Primers mutCLoG1-2F and mutCLoG1-2R were designed to bind between positions 10 , 809 , 479 and 10 , 810 , 028 generating a 550 bp PCR product . Following amplification and purification , the PCR product was sequenced using the same primers used for amplification . The phenotype of rescued and mutant plants was compared using the growth assay indicated above . The procedure was repeated three times to reach the sample size indicated in the figure legend ( Fig 4 ) . Protein sequences for CLoG1 protein homologues were identified by BLAST ( default settings ) in the Phytozome web portal ( phytozome . jgi . doe . gov ) or the ENTREZ web portal ( blast . ncbi . nlm . nih . gov ) . In most cases only a single gene locus was identified . This is consistent with results from the Panther Classification System for gene families ( www . pantherdb . org ) . All sequences analyzed and the corresponding accession numbers are listed in S6 Table . Multiple alignment and gene construction were done using the Geneious R7 software . Multiple alignment was done with the ClustalW algorithm using default settings . A maximum likelihood tree was obtained with the PHYML plugin [64] , using the Le Gascuel substitution model and bootstrap for branch support ( 100 bootstraps ) . A consensus tree was generated where only branches having more than 50% support are shown . Hydrophobicity and secondary structure plots were also determined using the Geneious default settings . To identify highly conserved regions in CLoG1 , an alignment of two amoeboid protists , two algae , and two land plant proteins sequences was performed and an identity graph displaying a 30 residue window was used to visually identify conserved regions . To observe a silencing phenotype of CLoG1 , a silencing construct was designed to target a 500 bp region of the 5´ untranslated region ( 5´UTR ) of CLoG1 . This region was PCR amplified from wild type ( Gransden ) cDNA using primers CLoG1UTRi500bpF and CLoG1UTRiR ( S5 Table ) and cloned into pENTR/D-TOPO . After sequencing , we used an LR clonase reaction ( Invitrogen ) to transfer the amplified region into the silencing vector pUGGi [65] . The resulting construct was named CLoG1-UTRi . Thirty μg of CLoG1-UTRi silencing construct was transformed into a line stably expressing GFP-GUS with a nuclear localization sequence ( NLS-4 ) [65] , as previously described [37 , 38 , 47 , 58 , 66] . Briefly , antibiotic resistant plants are visually selected for the loss of nuclear GFP signal , which indicates they are actively undergoing gene silencing of the target gene ( in this case CLoG1 ) . The plants are photographed using the chlorophyll out fluorescence and their area and solidity ( convex hull area/area ) are calculated for statistical comparison . Phenotypes were observed and measured on 7-day-old plants . To rescue the silencing of CLoG1 , the wild type CLoG1 coding sequence was PCR-amplified from cDNA using primers CLoG1-full-cds-F and CLoG1-full-cds-R . The PCR product was then cloned into pENTR/D-TOPO , and the coding sequence was inserted into an expression vector , pTHUBI-gate [37] , via an LR clonase reaction ( Invitrogen ) . The resulting construct was named pTHUbi-CLoG1cds . The mEGFP:CLoG1 fusion constructs were created using Invitrogen Multisite Gateway Pro 2 . 0 kit . For the C-terminal fusions , CLoG1 cDNA was PCR amplified using primers attB1CLoG1F and attB5rCLoGR , to produce attB1 and attB5r-flanked CLoG1 cDNA . The flanked PCR fragment was cloned into pDONR P1-P5r vector via a BP clonase reaction ( Invitrogen ) . This entry clone was sub-cloned , along with an entry clone containing mEGFP flanked with attB5 and attB2 , into the pTHUBI-gate destination vector using an LR clonase reaction ( Invitrogen ) . For the N-terminal fusions , the expression clone was constructed using the same method above; however , the CLoG1 cDNA PCR fragment was flanked with attB5 and attB2 while the mEGFP was flanked with attB1 and attB5r . The primers used for the CLoG1 cDNA PCR were attB5CLoG1F . Thirty μg of CLoG1-UTRi and 2 . 5–15 μg of pTHUbi-CLoG1cds or the pTHUbi-mEGFP:CLoG1 fusions were co-transformed into NLS-4 protoplasts , and phenotypes were observed and measured on 7-day-old plants using the growth assay described above , but using liquid plating medium instead of solid plating medium . This procedure was repeated at least three times to reach the sample size indicated in the figure legend ( Fig 5 ) . The pTHUBI constructs containing C and N terminal mEGFP fusions of CLoG1 were linearized using the SwaI enzyme and transformed into wild type moss using PEG-mediated transformation , and stable lines were generated following standard protocols [55] . Plants expressing mEGFP signal were screened by laser scanning confocal microscopy ( Leica SP5 ) . Cell lines expressing the lowest detectable level of expression and showing normal morphology were selected for high resolution microscopy . To observe growing cells , the plants were cultured on a thin layer of agar prepared on a coverglass of a Mattek dish [67] . Under these conditions caulonemal cells grow for several days and can be observed with high numerical aperture ( NA ) optics . To image the cells , we used the 63X 1 . 4 NA lens of the SP5 system ( Leica ) upgraded with a hybrid detector . Images were acquired at 0 . 68–1 sec . intervals . Images were background subtracted with a radius of 30 and contrast enhanced by histogram stretching ( normalization ) allowing a 0 . 4% of saturated pixels using ImageJ ( Fiji distribution ) . A single moss line expressing a CLoG1 with a C-terminal fusion of mEGFP was transformed with the plasmid pTZUbi-mCherry-tubulin that expresses mCherry-labeled alpha-tubulin and selected with Zeocin [40] . Lines expressing the tubulin reporter were selected and analyzed by confocal microscopy . For high-resolution time series acquisition , the confocal pinhole was closed to 0 . 4–0 . 5 airy units . Scanning rate was set to 200 Hz , the acquisition format set to 512x246 pixels , with a zoom of 6 and a pixel size of 80 . 2 nm . To maximize signal acquisition and reduce background a hybrid detector was used to acquire the CLoG1-mEGFP signal . The green channel ( CLoG1-mEGFP ) was background subtracted ( radius of 20 ) and normalized ( 0 . 4% saturation ) , the red channel ( microtubules ) was background subtracted ( radius 50 ) , filtered with unsharp mask ( radius 10 , mask weigh 0 . 5 ) and Gaussian blur ( sigma 1 ) , and normalized ( 0 . 4% saturation ) . Equivalent settings were used to image spindle and phragmoplast formation . For kymographic analysis of microtubule ends , we use the “Multi Kymograph” function of the ImageJ ( Fiji distribution ) . An in-house macro ( available upon request ) was used to track the microtubule ends from kymographs and determine their depolymerization rates based on the angles formed . To estimate the mean velocities for fast and slow depolymerizing ends we used a two Gaussians mixture model from the mixtools package ( normalmixEM procedure ) from R ( RStudio ) , which is based on the iterative expectation maximization ( EM ) algorithm . CLoG1 UTR RNAi plasmid was transformed into a NLS4/mCh-Tubulin line as described above . Both test and control transformants were selected for with hygromycin selection . Seven-day-old plants were visually screened for active silencing ( lack of nuclear GFP ) via fluorescent stereomicroscope ( Nikon SMZ1500 ) , and regions containing plants were marked for confocal imaging . Silenced plants were transferred to an agar pad mounted slide for confocal imaging . Cortical mCh-Tubulin was excited with a 561nm laser , and emission was collected with a 570+nm bandpass . ImageJ was used for post-acquisition processing . The correlation coefficient analysis was performed in MatLab as previously described [47] . Traces for individual cells were compared , and outliers were removed from the final average . An outlier was defined as a cell with a trace that was outside 1 . 5 times the interquartile range more than 50% of the trace .
|
Genes important for cell growth are difficult to identify because their disruption often results in the death of the organism . A solution to this problem is to isolate temperature-sensitive mutants where growth is blocked only at high temperatures . Here , we used the moss Physcomitrella patens , a simple model plant , to isolate temperature-sensitive mutants with reduced growth . We used whole-genome sequencing to identify the gene disrupted in one of these mutants ( clog1 ) . We found that CLoG1 is a previously uncharacterized gene present in algae and plants . Localization studies of CLoG1 protein in living cells showed CLoG1 concentrates on microtubules and tracks depolymerizing ends . Loss-of-function analysis suggests a possible role in controlling microtubule dynamics . Our approach establishes the moss P . patens as a valuable model-organism to investigate genes important for cell growth in plants .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"microtubules",
"microtubule",
"dynamics",
"cell",
"processes",
"organisms",
"mutation",
"chromosome",
"mapping",
"microtubule",
"polymerization",
"molecular",
"biology",
"techniques",
"plants",
"cellular",
"structures",
"and",
"organelles",
"nonvascular",
"plants",
"cytoskeleton",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"mosses",
"sequence",
"alignment",
"bioinformatics",
"artificial",
"gene",
"amplification",
"and",
"extension",
"gene",
"mapping",
"molecular",
"biology",
"point",
"mutation",
"eukaryota",
"cell",
"biology",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"polymerase",
"chain",
"reaction"
] |
2018
|
Conditional genetic screen in Physcomitrella patens reveals a novel microtubule depolymerizing-end-tracking protein
|
Although a combination of genomic and epigenetic alterations are implicated in the multistep transformation of normal squamous esophageal epithelium to Barrett esophagus , dysplasia , and adenocarcinoma , the combinatorial effect of these changes is unknown . By integrating genome-wide DNA methylation , copy number , and transcriptomic datasets obtained from endoscopic biopsies of neoplastic progression within the same individual , we are uniquely able to define the molecular events associated progression of Barrett esophagus . We find that the previously reported global hypomethylation phenomenon in cancer has its origins at the earliest stages of epithelial carcinogenesis . Promoter hypomethylation synergizes with gene amplification and leads to significant upregulation of a chr4q21 chemokine cluster and other transcripts during Barrett neoplasia . In contrast , gene-specific hypermethylation is observed at a restricted number of loci and , in combination with hemi-allelic deletions , leads to downregulatation of selected transcripts during multistep progression . We also observe that epigenetic regulation during epithelial carcinogenesis is not restricted to traditionally defined “CpG islands , ” but may also occur through a mechanism of differential methylation outside of these regions . Finally , validation of novel upregulated targets ( CXCL1 and 3 , GATA6 , and DMBT1 ) in a larger independent panel of samples confirms the utility of integrative analysis in cancer biomarker discovery .
The incidence of esophageal adenocarcinoma ( EAC ) is increasing at an alarming pace in the United States ( >600% increase since 1975 ) [1] . Since most patients with EAC present at diagnosis with an advanced disease stage , the 5-year survival rate is a dismal 13% [2] , underscoring the pressing need for early diagnostic biomarkers , as well as for improved therapeutic strategies , in this malignancy . Distinct pathological stages of specialized columnar epithelium ( Barrett metaplasia ) and low- followed by high-grade dysplasia precede adenocarcinoma [3] , [4] . Barrett esophagus ( BE ) is defined as a change in the esophageal epithelium that can be recognized grossly by a distinct salmon pink color at endoscopy , and confirmed by the presence of specialized columnar epithelium on biopsy . The prevalence of BE is not precisely known , but it has been estimated to range between 1–10% of the general population [5] . The incidence of EAC in patients with BE is increased 100-fold above that of the general population [6] . Thus , BE , with or without associated epithelial dysplasia , provides a unique opportunity for risk stratification and secondary prevention of EAC . Expression profiling of BE and EAC using genome-wide approaches has identified many of the transcriptomic alterations occurring during esophageal neoplastic progression [7]-[10] . In several instances , it has also been possible to identify the proximate genomic or epigenetic mechanism ( for example , intragenic deletion , truncating mutation , copy number aberration , or promoter methylation , respectively ) contributing to the altered expression [11]-[16] . This strategy has unequivocally yielded a rich seedbed of candidate biomarkers for diagnosis , as well as for prognostication , of BE neoplasia [17]-[20] . Nonetheless , there remains a notable lacuna in globally integrating transcriptomic abnormalities during Barrett progression with the corresponding changes occurring at the level of the genome and epigenome . We reasoned that a multi-platform integrated approach would not only enable the elucidation of novel biomarkers , but also clarify the genomic and/or epigenetic mechanisms driving transcript abnormalities during carcinogenesis . Such integration of global datasets has begun to emerge in solid tumors [21] , [22] , but to the best of our knowledge this has not been performed in precursor lesions , especially in the context of multistep progression occurring in a single individual . Herein , we provide an unbiased and comprehensive approach for integrating large-scale genomic , epigenetic , and transcriptomic datasets , obtained using tissue from patients undergoing endoscopic mucosal biopsy for BE . In contrast to numerous prior studies using two platforms ( for example , combined copy number and expression analysis ) where a “hit” on the second allele is typically inferred , the multi-platform analysis performed here can directly confirm or refute the Knudsonian paradigm for a given altered transcript . Our studies have identified striking epigenomic alterations , and in specific , widespread hypomethylation , which occurs at the earliest stages of epithelial carcinogenesis . This approach is in direct contrast to most single- or limited-locus studies that have focused on epigenetic silencing of candidate tumor suppressor genes by hypermethylation of the promoter . In addition , we have identified clustered transcripts , such as the chemokine ligands CXCL1 and 3 , that are markedly upregulated via simultaneous biallelic alteration by hypomethylation and gene amplification , respectively , and whose protein products have the potential to serve as serum biomarkers of neoplastic progression in BE .
Eight histologically validated endoscopic mucosal biopsies representing normal squamous mucosa and the various histopathological stages of Barrett esophagus progression were obtained from three patients undergoing repeat endoscopy for dysplasia ( Figure 1A , Figure S1 ) ; two additional unmatched gastric cardia biopsies were obtained as controls for the intended profiling studies . Prospective as well as retrospective surveillance studies have convincingly established that nondysplastic BE and low-grade dysplasia ( LGD ) both have a significantly lower risk of progression to EAC , when compared to high-grade dysplasia ( HGD ) ; in fact , for the purposes of therapeutic decision-making between continued surveillance versus local ablation , stratification typically occurs based on the diagnosis of HGD in BE mucosa . Therefore , with the objective of pair-wise comparison , samples of non-dysplastic BE and LGD were categorized as “low” , while the two HGD samples and one EAC were categorized as “high” . Nucleic acids extracted from cryostat-embedded sections of the ten samples were utilized for three concurrent microarray-based assays: 1 ) gene expression profiling , 2 ) array comparative genomic hybridization ( aCGH ) , and 3 ) genome-wide cytosine methylation , thus simultaneously querying the transcriptome , genome , and epigenome , respectively . Methylation profiling was performed using the HpaII tiny fragment Enrichment by Ligation-mediated PCR ( HELP ) assay , which compares HpaII ( methylation-sensitive ) and MspI ( methylation insensitive ) genomic representations to identify hypo- and hypermethylated loci in the genome . [23]-[25] . The HELP HpaII / MspI ratios were validated at over 60 independent loci by mass spectrometry-based high-throughput quantitative methylation PCR analysis ( Sequenom EpiTYPER ) ; based on this quantitative approach , a HpaII/MspI ratio of 0 . 3 corresponded to 50% cytosine methylation ( Figure S2 ) and was used as a threshold for defining hypo- or hypermethylation . Unsupervised hierarchical clustering analyses demonstrated that , at the level of the transcriptome , squamous mucosa clustered discretely from “glandular” epithelium ( including gastric cardiac as well as all stages of BE progression: Figure 1B ) ; in contrast , at the level of the epigenome , “normal” mucosa ( including both squamous and cardiac subtypes ) clustered discretely from all “abnormal” ( i . e . , BE ) epithelia ( Figure 1C ) . These results suggest some degree of commonality of epigenetic profiles between otherwise normal gastrointestinal tissues , despite obvious morphological differences . A pair-wise comparison of transcriptomic profiles between normal esophageal squamous and gastric cardiac mucosal samples revealed large numbers of significantly differentially expressed transcripts , consistent with the distinct histogenesis and biologies of these normal mucosal subtypes ( Figure 1D , left ) ; in contrast , global cytosine methylation profiles between the two mucosal locations were considerably more overlapping , with significant differences in either hypo- or hypermethylation restricted to fewer loci ( Figure 1E , left ) . In pair-wise comparisons of gene expression during BE progression , we found large numbers of significantly differentially expressed transcripts between the early lesions of Barrett metaplasia and LGD ( both classified as “Low” ) versus normal squamous mucosa , confirming the previous observation [7] that even non-dysplastic Barrett epithelium may harbor profound transcriptomic aberrations , some comparable to EAC ( Figure 1D , middle ) . Significant gene expression differences between “high and “low” BE lesions were more attenuated and restricted to only a handful of loci ( Figure 1D , right ) . While these gene expression data were confirmatory of published results , methylation profiling revealed an unexpected dimension to epigenetic dysregulation during BE progression . Contrary to the hypermethylation reported in previous single-locus studies , we identified significant hypomethylation occurring at a large number of loci genome-wide during the transition of squamous mucosa to Barrett epithelium ( 1160 hypomethylated versus 114 hypermethylated loci . , Figure 1E , middle , ) ; since this epigenetic “shift” is not observed in the comparison of normal esophageal squamous versus cardiac mucosal samples , we believe these methylation alterations may be reflective of the actual BE disease process , rather than simply due to acquisition of columnar histology . A second , smaller wave of hypomethylation was observed when comparing “high” versus “low” BE categories ( Figure 1E , right ) . This progressive hypomethylation was seen in methylation profiles of samples from the same patients ( Figure 1F ) . Validation at the whole-genome level by the Luminometric methylation assay ( LUMA ) revealed a significantly large increase in unmethylated CpGs in “low” BE samples versus matched normal squamous mucosa ( Figure 1G ) . These results demonstrate that the previously reported global hypomethylation observed in human cancers [26] can initiate at a very early stage of neoplastic transformation , such as in the non-invasive precursors of EAC . In addition to this panoramic view of epigenetic shifts , we also assessed the nature of the HpaII sites showing altered methylation during BE progression . As our microarray design includes both canonical CpG islands and additional CG dinucleotide loci within gene promoters , we sought to test whether CpG islands , long considered the principal target of epigenetic dysregulation in cancer [27] , were disproportionately affected . We compared the proportions of loci at which differential methylation was occurring within CpG islands versus other CG dinucleotide loci represented on the microarray . We determined that the majority of HpaII loci exhibiting differential methylation during BE progression lay , paradoxically , outside of canonical CpG islands ( Figure 2A ) . To further validate this observation at a representative locus , we chose the example of Deleted in Malignant Brain Tumor 1 ( DMBT1 ) , whose gene promoter lacks a defined CpG island , yet demonstrates progressive hypomethylation accompanied by significant transcript upregulation during BE progression ( Figure 2B ) . Histological examination of DMBT1 protein expression in a large archival cohort of 120 BE samples and 54 normal controls revealed significant ( P<0 . 05 ) upregulation of this protein early during BE neoplasia ( Figure 2C , 2D ) , thus validating the results obtained from our array-based analysis . These results suggest an epigenetic regulatory function for CG dinucleotide elements in the genome that do not meet the threshold for canonical CpG islands , which are strictly defined on base compositional criteria [28] . Subsequently , in order to generate a multi-component genetic model of BE progression , we developed an in silico algorithm for integrating data from these three high-resolution platforms ( i . e . , gene expression , HELP , and aCGH ) , using genomic coordinates for the respective probes from each of the platform arrays ( Figure 3A ) . This algorithm , which we call Multi-dimensional Integration of Genomic data from Human Tissues ( MIGHT ) , provides a composite three-dimensional graphical output of gene sets demonstrating significant alterations in pair-wise unbiased comparisons across the different array platforms ( Figure 3B , 3C ) . By integrating differences in transcript expression with both methylation status and copy number at a given locus , the MIGHT algorithm not only identifies significant transcriptomic alterations during neoplastic progression , but also elucidates the relative contributions of genomic and epigenetic factors toward such deregulation . The advantage of an integrative approach in developing an accurate “patient-specific” multi-component genetic progression model is illustrated in Figure 4 . A chromosome 9p21 hemizygous deletion was identified by aCGH analysis of LGD and HGD biopsies obtained from this individual , which was absent in the matched normal esophageal squamous epithelium , consistent with a somatic monoallelic loss , as confirmed by FISH analysis ( Figure 4B ) . This region harbors two closely approximated tumor suppressor genes: CDKN2A/p16 and CDKN2B/p15 . Prior copy number and other studies have implicated CDKN2A as the target of inactivation at this locus in BE [15] , [17] , [29] Nevertheless , in this particular example , microarray data demonstrated that the relative fold reduction in gene expression was considerably greater for CDKN2B ( ∼100-fold downregulation ) than for CDKN2A ( ∼4-fold ) ; moreover , this finding was independently validated by qRT-PCR , which confirmed the complete absence of CDKN2B transcripts in the dysplastic biopsy samples , while the expression of CDKN2A , albeit significantly reduced , was still detectable . Analysis of the third component ( HELP analysis ) clarified that the CDKN2B promoter in the retained allele underwent progressive hypermethylation during BE progression , while the CDKN2A promoter maintained its methylation status quo ( Figure 4A , bottom ) . This finding suggests the importance of methylation of the remaining allele in regulating expression , and provides direct experimental evidence for genetic and epigenetic hits acting concurrently and synergistically to downregulate tumor suppressor genes during oncogenesis through bi-allelic inactivation ( Figure S3 ) . Finally , in addition to the value of integrated datasets in understanding mechanisms of transcript disruption ( as illustrated above ) , we explored the utility of the MIGHT algorithm as a tool for biomarker discovery in Barrett progression . In particular , we focused on genes that were significantly overexpressed in pair-wise comparisons , by hypomethylation and / or genomic amplification , and validated selected examples of patient-specific aberrations in larger sample sets . Using the MIGHT platform , we determined that many genes not previously implicated in esophageal carcinogenesis were significantly upregulated during stepwise progression to cancer ( Figure 2 and Tables S1 , S2 , S3 , S4 ) . These were upregulated by either loss of methylation , or gene amplification , or both occurring together . Genes previously known to be important during metaplastic transformation of squamous to columnar epithelium were also included in the list of most significantly upregulated transcripts ( for example , villin and mucin genes ) , confirming the biological validity of our assays . Transcripts corresponding to a family of chemokine ligands were among the most significantly upregulated , and the corresponding gene cluster is present on the 4q21 chromosomal segment that was amplified in all patient samples during the process of Barrett neoplasia ( Figure 5A , 5B ) Integrative analysis revealed that in addition to amplification , the CXCL1 and CXCL3 gene promoters were also hypomethylated during transformation , and their relative increase in transcript expression was many fold greater ( 4–6 fold by array , 10–20 fold by qRT-PCR ) than that of IL-8 , a chemokine gene which was part of the 4q21 amplicon but whose promoter was not affected by loss of methylation ( Figure 5A , Bottom ) . These observations were validated by qRT-PCR in larger independent set of primary samples ( Figure 5C ) , and confirmed a greater than 30-fold mean increase in CXCL1 and CXCL3 levels when compared to IL-8 transcripts at each histological grade of BE progression , thus demonstrating the combinatorial affect of both genetic and epigenetic alterations on dysregulation of gene expression during carcinogenesis . In light of the significant upregulation of chromosome 4q21 chemokine cluster transcripts in BE and EAC , and the likely secretion of their protein products into the circulation , we evaluated the potential of using this chemokine familyas serum biomarkers of EAC . Serum samples were collected from an independent cohort of patients with EAC and were compared to samples from patients with gastroesophageal reflux disease ( GERD ) symptoms without demonstrable BE on histology . Levels of chemokine ligands , IL-8 ( IL8 ) , IP-10 ( CXCL10 ) , Eotaxin ( CCL11 ) , MCP-1 ( CCL2 ) and MCP-4 ( CCL13 ) were determined in serum samples by a multiplexed assay on an ultra-sensitive chemiluminiscence detection platform . We observed that both chemokines that were a part of the amplified 4q21 segment ( IL-8 and CXCL10 ) were significantly elevated in the EAC serum samples compared to the controls ( P<0 . 05 ) , while none of chemokines outside of this amplicon demonstrated a significant difference between cancer and control specimens ( Figure 5D ) . These results validate the feasibility of identifying candidate biomarkers by integrative analysis in a limited number of patient samples , and extrapolating their utilization to larger , independent patient cohorts . To further functionally validate the utility of our integrative discovery platform , we focused on a transcription factor , GATA6 , which was significantly overexpressed early in Barrett metaplasia , and was predicted by MIGHT to be amplified without concomitant alterations in methylation ( Figure 3 ) . Assessment of the aCGH data and FISH on primary tissues readily validated the copy number alterations at the GATA6 locus during esophageal carcinogenesis ( Figure 6A , 6B ) . Thereafter , using independent cohorts of snap-frozen and archival BE samples , respectively , we observed significant upregulation of GATA6 transcript expression ( >500 fold mean increase in LGD , HGD and EAC samples , Figure 6C , 6D ) and of the Gata6 protein levels ( No Gata6 staining observed in 54 normal controls when compared to mean 60% positivity in a total of 201 LGD , HGD and EAC samples; Figure 6F–6I ) , confirming the results from the genomic analysis . To validate an oncogenic role in esophageal carcinogenesis , we used an esophageal adenocarcinoma-derived cell line , OE33 [30] and observed significant overexpression of Gata6 protein in these cells ( Figure 6J ) . GATA6 was successfully knocked down using lentiviral short hairpin RNAs in OE33 cells ( Figure 6K ) . Loss of Gata6 function did not affect proliferation , ( Figure 6L ) but resulted in significantly decreased anchorage independent growth of OE33 cells and also led to decrease in invasion and migration ( Figure 6M–6P ) , thus providing a putative functional association between GATA6 amplification and disease progression in BE .
Esophageal cancer is the cancer with the fastest-growing prevalence in the United States [1] and arises from the metaplastic transformation of normal squamous mucosa , through the intermediate stages of dysplasia , culminating in cancer . Newer insights into the pathogenesis of this process are critically needed for prevention and early diagnosis of these lesions . Although alterations in DNA methylation have been described during esophageal carcinogenesis , studies performed thus far have focused on the aberrant hypermethylation of CpG islands located within promoters of selected tumor suppressors such as CDKN2A/p16 , HPP , RUNX3 , REPRIMO , amongst others . In contrast , we have determined that hypomethylation , rather than hypermethylation , is the more pervasive epigenetic alteration that occurs during Barrett progression . Additionally , we determined that global cytosine hypomethylation occurs very early during multistep carcinogenesis , observed within the first discernible metaplastic lesions within the native squamous esophagus . Even though global hypomethylation was reported in the pioneering epigenetic studies in cancer [31] , most investigators have subsequently focused on hypermethylation in CpG islands within selected gene promoters . Hypomethylation has been hypothesized to lead to carcinogenesis by encouraging genomic instability [32] as well as by aberrant activation of oncogenes [33] . Additionally , as illustrated in the example of DMBT1 , hypomethylation alone can lead to transcriptional upregulation during multistep progression to high-grade dysplasia and cancer . Furthermore , our data identify CG dinucelotide loci that can be targeted by differential methylation during neoplastic progression and are located outside of canonical CpG islands . Importantly , these CG alterations are not merely stochastic in nature , but appear to have bona fide regulatory influence on transcript expression . Recent work has similarly shown that cytosines present outside of CpG islands can be aberrantly methylated/ hypomethylated in cancer , and assays that cover these loci are critical to discovering the full landscape of altered methylome of malignancies [34] , [35] . Since carcinogenesis is multifactorial and gene inactivation and activation can be influenced by either genetic or epigenetic mechanisms , we performed an integrative analysis to dissect the relative contributions of these alterations during this process . Our study provides direct experimental evidence of deletions and promoter methylation acting in concert to silence tumor suppressors in a bi-allelic manner , as first postulated by Knudson's two-hit paradigm . We expand on this paradigm by demonstrating that gene amplifications and hypomethylation can function in concert to upregulate gene expression of various genes . Of note , the use of an integrated approach facilitated by the MIGHT algorithm provides accurate in silico insights into mechanisms of deregulation , particularly when closely spaced gene clusters harbor discrepant alterations in transcript level , as exemplified by CDKN2A and CDKN2B , or the chemokine family in the 4q21 amplicon . In each of these instances , a subsequent validation step ( such as FISH analysis for deletion , or MassArray for promoter methylation , respectively ) confirmed the suggested mechanism of transcript deregulation implicated by MIGHT , underscoring the robustness of the analysis platform . Finally , we demonstrate that multiplatform high resolution integrative analysis of limited number of well annotated samples ( N = three patients ) can lead to findings that can be extrapolated to larger independent sample cohorts . We have illustrated this paradigm using multiple examples throughout the text , such as with the 4q21 chemokine cluster , DMBT1 , and GATA6 . In each of these examples , we validated the findings elucidated in the “index” patients in cohorts of either snap frozen or paraffin embedded BE and EAC tissues , In the case of the 4q21 chemokine cluster , we extended the validation one-step further , using serum samples to confirm significantly elevated circulating levels of two of the cytokines in EAC patients compared to controls . Notably , other chemokine ligands not included within the 4q21 amplicon failed to demonstrate any significant differences between cancer and control specimens , reiterating the biological relevance of the in silico MIGHT data . Chemokine ligands have recently been shown to be secreted by malignant cells and have been shown to participate in neoplastic progression of melanoma , breast , cervical and colorectal cancers [36]-[38] . CXCL1 , CXCL3 and IL-8 bind to the CXCR2 receptor that has important roles in oncogene-induced senescence . It has been suggested that these chemokines potentiate tumor progression especially with cells with p53 inactivation , an event seen commonly in esophageal neoplasms [39] . These chemokines have also been implicated in tumor associated angiogenesis [37] and are a part of growing evidence of inflammatory mediators implicated in tumor growth and progression [40] . Our data reveals mechanisms associated with their upregulation in EAC and also demonstrates that this upregulation may occur early during neoplastic transformation , potentially allowing the development of a serum-based assay to screen subjects with BE for neoplastic progression . Overall , our studies suggest that widespread changes in DNA methylation , especially hypomethylation , as well as genomic copy number alterations , can occur early during the multistep process of esophageal carcinogenesis , and may act in concert to deregulate the expression of important potential cancer-related pathogenic genes .
Specimens were obtained from patients who underwent endoscopic surveillance . All patients were diagnosed with Barrett's esophagus . After signed informed consent approved by the Johns Hopkins University IRB , endoscopic biopsies were collected and snap frozen in liquid Nitrogen , de-linked from direct patient identifiers and stored at −80 °C . DNA and RNA was extracted from the same biopsy samples . All specimens were obtained from the surgical pathology files of the Johns Hopkins Hospital , Memorial Sloan-Kettering Cancer Center and Karmanos Cancer Center . Tissue microarrays ( TMA ) were generated from formalin-fixed paraffin-embedded archival tissues from 92 patients with Barrett's Esophagus and included esophageal squamous epithelium ( 60 cases ) , low-grade and high-grade dysplasia ( 19 and 38 cases ) , and adenocarcinoma ( 80 cases ) . Four 1 . 8 mm tissue cores represented each case and included two cores from the neoplastic compartment in order to account for potential tumor heterogeneity , and two cores from adjacent normal esophageal parenchyma as an internal control . Additionally for GATA6 IHC , 120 endoscopic mucosal resection ( EMR ) specimens from 67 patients with BE were analyzed , including 31 cases of low grade dysplasia , 40 cases of high grade dysplasia and 10 cases of adenocarcinoma . All specimens were obtained from the surgical pathology files of the Johns Hopkins Hospital . Rabbit polyclonal GATA6 ( H-92 ) antibody ( Santa Cruz Inc , sc-9055 ) was used at 1:500 dilution and visualized using the PowerVision+ Poly-HRP IHC kit ( Immunovision Technologies ) following the standard protocol for immunohistochemistry ( IHC ) described previously [41] . Immunohistochemical labeling was assessed in an outcome-blinded fashion by two of the authors ( J . C . R and A . M ) on a compound microscope . Intensity of labeling was evaluated as previously published [42] , [43] . The UCSC genome browser was used to select for the BAC clones spanning the 18q11 . 2 region: RP11-18K7 ( GATA6 , SpectrumOrange ) ; and RP11-49H23 ( 18q22 . 2 , Control SpectrumGreen ) . For the CXCL1-3 gene cluster , BAC clones covering the 4q21 . 1 - 4q21 . 2 region were RP11-94K4 ( SpectrumOrange ) and RP11-259E13 ( 4q11-q12 , Control SpectrumGreen ) . The BAC clones were obtained from the Children's Hospital Oakland Research Institute in Oakland , USA . We also used commercial LSI p16/CEP probes ( Vysis ) for spanning the genetic loci for p16 and p15 ( SpectrumOrange ) and the alpha satellite sequences specific to chromosome 9 ( SpectrumGreen ) . Barrett's associated adenocarcinoma cell lines OE33 ( European Collection of Cell Cultures , Wiltshire , UK ) and JH-EsoAd1 , recently described by our group [44] , were maintained in RPMI-1640 and supplemented with 10% or 20% FBS respectively and 100 U/mL penicillin , 100 mg/mL streptomycin . GATA6-expressing OE33 cells were seeded into 24-well plates at 9×104 cells per well concentration , and infected with either scrambled pLKO . 1 ( 18 bp stuffer ) lentiviral vector or with lentivirus expressing GATA6 shRNA ( Open Biosystems , Huntsville , AL ) . Stable clones were selected by adding 5 µg/ml of puromycin to the cell culture media . Quantitative reverse transcription PCR ( qRT-PCR ) analysis was used to select the best short hairpin constructs for GATA6 mRNA knockdown . Downstream experiments were performed with GATA6_sh3 and compared with scrambled control . One microgram of total RNA , isolated with the RNAgents kit ( Promega , Madison , WI ) , were reversed transcribed by using the Superscript II First Strand kit ( Invitrogen ) as per manufacturer's protocol . 1 µL of cDNA was amplified in a 25 µl volume containing 12 . 5 µL of 2× SYBRGreen PCR Master Mix ( Applied Biosystems ) and 0 . 5 µM of each primer . Reactions were performed in triplicate using a 7300 Real Time PCR machine ( Applied Biosystems , CA , USA ) using PCR conditions and data analysis as described earlier[41] . The melting curve was constructed for each primer to ensure reaction specificity . Following PCR , the threshold cycle ( CT ) was obtained and relative quantities were determined by normalization with the housekeeping gene SDHA . Data are presented as mean and S . E . M . and were compared using a Student's t-test ( or Mann-Whitney U-test , as appropriate ) . A five-parameter logistic equation was used to calculate the curve fit in the non-linear asymmetric regression . Calculations were done with Graphpad Prims 4 . 0 . Cell viability assays using the The CellTiter 96AQueousOne Solution Cell Proliferation Assay ( Promega , Madison , WI ) were performed on control-transfected and shRNA-expressing OE33 cells , as described previously [42] . At each time point evaluation , 20 µl/well of the Cell Titer 96 solution was added and incubated for 1 hour . Plates were read on a Wallac-1420 Plate reader at OD of 490 nm ( PerkinElmer , Boston , MA ) . All experiments were set up in triplicate to determine means and standard deviations . The Boyden chamber migration- and invasion assays were carried out on OE33 cells . For the invasion assay , 5×104 cells were suspended in medium containing 0 . 2% FBS and plated in the inner chamber of a matrigel-coated 8-µm polypropylene filter inserts ( BD Matrigel Matrix , BD Falcon ) . The bottom chamber contained normal growth media . After 24 h , the cells remaining in the insert were removed with a cotton swab , and the cells on the bottom of the filter were fixed and migrated cells were counted under the microscope . All experiments were set up in triplicate . Boyden chamber migration assay was carried out following the procedure described for the invasion assay except that the cells were plated on uncoated 8-µm pore polypropylene filter inserts in the Boyden chambers . Anchorage-independent growth was assessed by colony formation assays in soft-agar , as previously described [41] . Briefly , the soft agar assays were set up in 6-well plates , each well containing a bottom layer of 1% agarose ( Invitrogen ) , a middle layer of 0 . 7% agarose including 5×103 cells and a top layer comprising of medium only . Subsequently , the plates were kept in a tissue culture incubator maintained at 37°C and 5% CO for 14 days to allow for colony growth , with top medium being changed on a weekly basis . The assay was terminated at day 14 , when plates were stained with 0 . 005% crystal violet ( Sigma-Aldrich ) solution . Colony counting was performed for each triplicate condition using an automated ChemiDoc XRS instru-ment ( Bio-Rad , Hercules , CA ) . Total RNA , was isolated using the RNAgents kit ( Promega , Madison , WI ) as per the manufacturer's instructions and RNA integrity was corroborated with the Bioanalizer 2100 . One microgram of RNA was reverse transcribed as published previously [9] and cDNA was submitted to Roche NimbleGen Systems , Inc . ( Madison , WI ) for labeling and hybridization onto the NimbleGen array ( 2006-10-26_Human_60mer_1in2 ) containing at least 10 ( 60mer ) probes designed for 37 , 364 genes from GenBank build 35 . Arrays were scanned using a GenePix 4000B scanner ( Axon Instruments ) and microarray quality controls were done as previously described [9] . Gene expression microarray data will be submitted to the GEO database for public access . Raw data reports ( . pair files ) were combined and analyzed with the RMA 7 ( Robust Multi-array Analysis ) package from NimbleScan 2 . 3 software , including the algorithm for background correction ( data based background correction ) and normalization ( quantile normalization ) . After analysis , results of 36 , 846 unique nimblegene probes are reported as log2 values . Accession numbers from which NimbleGen probes were designed were linked to GenBank accessions from where Entrez IDs and additional annotations were isolated . The HELP assay was performed as published previously[23] , [24] , in summary , genomic DNA was isolated from cell lysates ( 0 . 1 M Tris-HCL pH 8 . 0; 10 mM EDTA pH 8 . 0 and 1% SDS ) , digested overnight at 50C on Proteinase K , follow by several organic extractions ( TE-saturated phenol and Phenol-Chloroform-Isoamyl alcohol ) , DNA was purified and concentrated by ethanol precipitation and resuspended in LOTE pH 7 . 5 ( 3 mM Tris-Hcl and 0 . 2 mM EDTA ) . Intact DNA of high molecular weight was corroborated , by electrophoresis on 1% agarose gel , in all cases . DNA was digested with the restriction enzyme HpaII ( methylation-sensitive ) and MspI ( methylation insensitive ) . Adapters are ligated to the fragments created by digestion that are subsequently used for ligation-mediated PCR amplification . The two digestion products are differentially labeled with two fluorophores and submitted to Roche NimbleGen Systems , Inc . ( Madison , WI ) for hybridization onto a human HG17 custom-designed oligonucleotide array ( 2006-10-26_HG17_HELP_Promoter ) . The focused array design stand for 25 , 626 HpaII-amplifiable fragments ( HAF ) defined by those fragments where two HpaII sites are located 200–2000 bp apart with at least some unique sequence between them and selected those located at gene promoters and imprinted regions . Each HAF was represented on the microarray by at least 14 oligonucleotides , each 50 nucleotides in length and randomly distributed across the microarray slide . HELP microarray data will be submitted to the GEO database for public access . Raw data reports ( . pair files ) went through an analytical pipeline of array performance and quality assessment previously described [45] . 10 and included removal of failed probes , summarization of the remaining probes belonging to the 20%-trimmed mean for each HAF . The functions in this pipeline also allows for quantile normalization that solves the fragment size-dependency of the MspI , HpaII and HpaII/MspI ratio distributions seen in this assay . The R statistical package ( http://www . r-project . org/ ) was used to calculate the normalized log2 HpaII/MspI ratios allowing us to semi-quantitatively categorize each loci as methylated or hypomethylated based quantitative methylation detected using MassArray Epityping as a validation approach described below . In all instances , HpaII-amplifiable fragments ( HAF ) sequence were linked to gene promoters ( defined as 2000 bp upstream from transcription start site ) by using genomic coordinates from National Center for Biotechnology Information Build 35 . DNA was hybridized to a oligonucleotide array CGH ( NimbleGen Systems ) , consisting of 388 , 560 isothermal probes ( length 45–75 bp ) covering unique genomic regions ( median probe spacing of 6 kb ) . Hybridizations were performed at NimbleGen Facility and a reference sample of Human Male Genomic DNA was used as a normal control . Intensity data underwent through a qspline fit normalization algorithm 11 that was used to compensate for inherent differences in signal between the two dyes . To avoid false positive calls due to local variation in signal intensity , a second pass filter was employed . This filter discards change points if their means are not at least 1 . 5 SD from each other . Log2-ratio values of the probe signal intensities ( Cy3/Cy5 ) were calculated and 120 , 000 bp windows-average were generated to visualize copy number changes using the circular binary segmentation algorithm 12 contained on the Roche NimbleScan software . Oligonucleotide array data will be submitted to the GEO database for public access . In all instances , DNA sequence coordinates are from National Center for Biotechnology Information Build 35 . An analytical pipeline was developed to integrate all platforms . In summary , Gene expression ( GE ) , HELP and aCGH probes were linked to genes base on the basis of genomic coordinates isolated from NCBI build35 . Probe to gene links containing data from all three platforms , after proper normalization described above , were considered for 3D integration . TTEST pairwise comparisons of GE and HELP were performed between main histological groups ( squamous vs dysplasia and dysplasia vs adenocarcinoma ) and fold change differences with their p-values were calculated . The last two variables for each pairwise were plotted simultaneously for each gene on a k-space where the x axis represents fold-change differences ( log2 ) in GE and the y axis represents fold-change differences ( log2 ) in methylation . The “z” axis is used to represent the significance level in –log10 p-value for both platforms . Color-coding for significant cut-offs was defined for those genes over 2 . 5 fold change in GE with a p-value < 0 . 05 , with green representing downregulated and red upregulated genes . The same criterion was used to detect significant changes in methylation . A darker shade of color was added to the red and green labels to define the hypermethylated genes ( on top of the graph ) and lighter shade of red and green was used to define significantly hypomethylated genes . Copy number alterations were defined by two fold criteria: the gene center approach and the genomic segmentation approach . In the gene center approach , the aCGH probes contained in the intragenic region or promoter region ( when intergenic area was not available ) , were averaged . Computational based approach to find accumulative changes on 50% of DNA content in all three histological stages for each patient was considered gain or loss depending if the progressive trend was positive or negative respectively . A similar approach was also taken for the Circular binary segmentation ( CBS ) analysis , this time visual inspection at genomic windows of 120 , 000 bp was applied and genomic segments isolated and classified as gain or losses . Genomic windows were linked to genes and gene copy number changes for each patient was defined as those that agreed on last two criteria . Finally , on the integrative figure , copy number changes were depicted by larger circles . Genes classified as amplified and deleted had a 3 fold more larger size compared to their normal counterparts . Quantitative high-throughput DNA methylation analysis to validate HELP results was carried out by MALDI-TOF mass spectrometry using Sequenom's EpiTYPER platform ( Sequenom . San Diego , CA ) . The same DNA aliquots used for HELP were bisulfite-converted; PCR amplified and follow by an in vitro RNA transcription and a base specific cleavage . MALDI TOF MS analysis of the cleavage product was performed as described originally [24] , [25] . MassArray primers were designed and are available upon request . Five serum samples from patients with gastroesophageal reflux disease ( GERD ) symptoms and normal esophageal histology , and five serum samples from esophageal adenocarcinoma patients were collected for analysis . Frozen endoscopic tissues from the same patients were also collected and used for tissue lysates . To determine cytokine levels in the serum , Meso Scale Discovery System multiplex assay were used for human Eotaxin ( CCL11 ) , IL-8 ( IL8 ) , IP-10 ( CXCL10 ) , MCP-1 ( CCL2 ) and MCP-4 ( CCL13 ) according to the manufacturer's instructions . Genomic DNA ( 200–500 ng ) was cleaved with HpaII + EcoRI or MspI + EcoRI in two separate 20 µl reactions containing 33 mM Tris-acetate , 10 mM Mg-acetate , 66 mM K-acetate pH 7 . 9 , 0 . 1 mg/ml BSA and 5 units of each restriction enzymes . The reactions were set up in a 96-well format and incubated at 37°C for 4 h . Then 20 µl annealing buffer ( 20 mM Tris-acetate , 2 mM Mg-acetate pH 7 . 6 ) was added to the cleavage reactions , and samples were placed in a PSQ96™MA system ( Biotage AB , Uppsala , Sweden ) . The instrument was programmed to add dNTPs in four consecutive steps including Step 1: dATP ( the derivative dATPαS is used since it will not react directly with Luciferase and prevents non-specific signals ) ; Step 2: mixture of dGTP + dCTP; Step 3: dTTP; and Step 4: mixture of dGTP + dCTP . Peak heights were calculated using the PSQ96™MA software . The HpaII/EcoRI and MspI/EcoRI ratios were calculated as ( dGTP + dCTP ) /dATP for the respective reactions . The HpaII/MspI ratio was defined as ( HpaII/EcoRI ) / ( MspI/EcoRI ) [46] .
|
The incidence of esophageal adenocarcinoma ( EA ) is increasing at an alarming pace in the United States . Distinct pathological stages of Barrett's metaplasia and low- and high-grade dysplasia can be seen preceding malignant transformation . These precursor lesions provide a unique in vivo model for deepening our understanding the early steps in human neoplasia . By integrating genome-wide DNA methylation , copy number , and transcriptomic datasets obtained from endoscopic biopsies of neoplastic progression within the same individual , we are uniquely able to define the molecular events associated progression of Barrett esophagus . We show that the predominant change during this process is loss of DNA methylation . We show that this global hypomethylation occurs very early during the process and is seen even in preinvasive lesions . This loss of DNA methylation drives carcinogenesis by cooperating with gene amplifications in upregulating proteins during this process . Finally we uncovered proteins that upregulated by loss of methylation or gene amplification ( CXCL1 and 3 , GATA6 , and DMBT1 ) and show their relevance by validating their levels in larger independent panel of samples , thus confirming the utility of integrative analysis in cancer biomarker discovery .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"gastroenterology",
"and",
"hepatology/gastrointestinal",
"cancers",
"genetics",
"and",
"genomics/epigenetics",
"gastroenterology",
"and",
"hepatology/esophagus",
"oncology/gastrointestinal",
"cancers"
] |
2011
|
Widespread Hypomethylation Occurs Early and Synergizes with Gene Amplification during Esophageal Carcinogenesis
|
Recent studies have identified broadband phenomena in the electric potentials produced by the brain . We report the finding of power-law scaling in these signals using subdural electrocorticographic recordings from the surface of human cortex . The power spectral density ( PSD ) of the electric potential has the power-law form from 80 to 500 Hz . This scaling index , , is conserved across subjects , area in the cortex , and local neural activity levels . The shape of the PSD does not change with increases in local cortical activity , but the amplitude , , increases . We observe a “knee” in the spectra at , implying the existence of a characteristic time scale . Below , we explore two-power-law forms of the PSD , and demonstrate that there are activity-related fluctuations in the amplitude of a power-law process lying beneath the rhythms . Finally , we illustrate through simulation how , small-scale , simplified neuronal models could lead to these power-law observations . This suggests a new paradigm of non-oscillatory “asynchronous , ” scale-free , changes in cortical potentials , corresponding to changes in mean population-averaged firing rate , to complement the prevalent “synchronous” rhythm-based paradigm .
Neuronal electrical activity may be measured at many scales , from individual ion channels [1] to the largest scale measurement of electroencephalographic ( EEG ) potentials entirely outside the head [2] . Synaptic current produces a change in the local electric field , and it is believed that large scale field potentials reveal primarily the aggregate synaptic activity from large neuronal populations [3] , [4] . Our particular experiments measure these potentials at the brain surface , using arrays of platinum electrocorticographic ( ECoG ) electrodes ( Figure 1 ) . Interaction properties between synapses , when averaged across the entire ensemble , may be revealed by the potential auto-correlation function: ( 1 ) which is an average over the entire time interval of the recording . The Fourier Transform of is the power spectral density ( PSD ) , and reveals to what degree the potential at one point in time is correlated with the potential at a later point in time . Because of this , characteristic phenomena in the cortical potential PSD have interpretable implications for the interaction properties between elements within neuronal populations . For example , since Adolf Beck first described in the 1890s how simple behavioral change produced widespread amplitude changes in rhythmic properties of the electric potential timeseries [5] , findings of peaked phenomena in the PSD have pointed to oscillatory activity that is synchronized across the neuronal population [6]–[12] , and have been linked to known large-scale brain phenomena like cortical-subcortical feedback loops which change during behavior [8] , [13]–[16] . Simple behaviors produce robust change in the oscillations: Opening the eyes decreases the occipital -rhythm ( 8–12Hz ) amplitude [17] , [18] , and movement decreases the lateral frontoparietal and ( 18–25Hz ) rhythm amplitudes [19]–[23] . Other studies have attributed band-specific processes in the so-called “high-” range ( 60–150Hz ) to local cortical processing [24] , [25] , with specific timescales linked to the particular choice of frequency range . In lateral brain regions , we observed a lack of distinct peaks in the cortical potential PSD beyond Hz , and hypothesized the existence of broadband changes across all frequencies which were obscured at low frequencies by covariant fluctuations in the // rhythms [22] , [26] . While previous studies had hypothesized that background power-laws existed in the PSD [27]–[30] , we hypothesized the existence of behaviorally-associated changes in a power-law process of the form , and named attempts to capture it the “-band/index , ” at the higher frequencies where it is most plainly observed [22] , [26] , [31] , [32] . Our early studies , sampled at 1kHz , had PSDs that truncated above 250Hz . Although we observed structure in these PSDs , and hypothesized the existence of a power-law , we needed data with a higher sampling rate to establish it firmly . The purpose of this study was to sample higher , at 10 kHz and determine , as accurately as possible , whether there is indeed such a power-law in the human cortical potential power spectrum , and how it might change with cortical activity . Results from this higher sampling rate ( 4 subjects ) might then allow us to return , informed , to the large group of lower sampling rate data ( 16 subjects ) , and re-examine it with knowledge of how it must behave at higher frequencies . Here , we identify and characterize a scale-free process in the ECoG potential PSD , revealed by a power-law . The existence of such a power-law process points to phenomena with no special timescale , where the neuronal population beneath is not synchronously oscillating . We demonstrate through very basic simulation how such spectra might arise from simple processes , and how observed broadband power-law changes in the PSD might simply reflect a change in population mean firing rate .
We measured the surface potential between pairs of surface electrocorticographic electrodes separated by one centimeter from each other on the lateral brain surface of 20 human subjects . From these potentials , we calculated power spectral densities ( PSDs ) averaged over several minutes of data . As detailed in the methodology section below , each PSD was examined for the presence and character of a power-law form . The strongest empiric finding from this study was the robust fit of a power-law form , with , for frequencies above 80Hz . We performed a stringent fitting protocol in the frequency range 80Hz580Hz of the averaged electrode pair PSDs of 4 subjects , and found extremely tight fits to the form and in each case . This was obtained by fitting 10 kHz sampled data from subjects 1–4 , during a simple fixation task . Figure 2A–D shows the PSD , averaged over electrode pairs . The inserts illustrate the robust quality of the power-law form , where the jitter of data around the fit is more than one decade down from the signal . The exponent and the parameters and in the form were estimated via a set of log-log least-squares linear fits of the power spectral density , with a range-shrinking scheme to ensure that local fits within the fitting range produced the same fit as the global form . The fitting range chosen for each was a lower bound of 80 Hz ( in order to stay above a “knee” at 75Hz ) and the highest frequency where spectra could be resolved from the noise floor in each subject ( 579 , 530 , 534 , 559 Hz for subjects 1–4 ) , excluding harmonics of 60 Hz . The combined spectra fit values of were 3 . 97 , 3 . 94 , 3 . 97 , and 4 . 02 for subjects 1–4 , respectively . The error estimates ( of order 0 . 1 or less for each subject ) were based on robustness against range shrinking as well as the deviations of the best fit with respect to the actual data across the entire frequency range ( see insets in Figure 2A–D ) . To test for universality , we also performed the same type of fits to each individual electrode pair spectrum for subjects 1–4 , from 80 Hz to 400 Hz . The histogram of these individual fits is shown in Figure 2E . The mean of the individual fits was = 4 . 01 , with SD = 0 . 13 ( N = 151 ) , in strong agreement with the averaged spectra . Individual electrode pair channel PSDs , fit between 80Hz400Hz , produced the same result as the average spectrum , without systematic variation by brain area . This power-law scaling extends over four decades in power and , because is large , over one decade in frequency . The noise around this straight line is minimal , and is robust against range shrinking ( that is , the fit exponent is unchanged if a smaller frequency range interval , within the total range , is chosen for fitting ) . This quantitative level of power-law scaling is rarely seen in experimental data [33] . Preliminary calculations showed a clear crossover ( “knee” ) in the PSD at 75Hz , below which the PSD takes a different form ( see Figures 2 and 3 ) . A natural question to ask is whether there is a different power-law form at lower frequencies , with exponent . Previous power-law estimates in the cortical potential focused on this lower frequency range [34]–[37] , and most naively fit scale-free exponents directly to spectra known to contain scale-dependent phenomena ( oscillatory brain rhythms peaked at specific frequencies ) . We wished to avoid the confounding influence of these rhythms , but , unfortunately , the & rhythms were strongly pronounced in most cortical channel pairs of the data recorded at 10kHz ( subjects 1–4 , clearly visible in Figure 2 ) . They obscured whatever asynchronous ( scale-free , non-peaked ) phenomena might be present underneath at frequencies below , and there were simply not enough channels without them to be meaningfully examined in the 10kHz sampled data . Therefore , we returned to our large set of initial 1kHz sampled data , and circumvented these rhythms with two approaches: the first was simple avoidance , by selection of channels where the rhythms were absent during the fixation task; the second was to use data from an experimental setting ( finger movement ) which caused the rhythms and the underlying broad-band change to vary differently , and the rhythms could be removed . In order to evaluate what the exponent in such a lower power-law would be ( below 80Hz ) , we first naively fit the resting spectra of a large ensemble of fixation data ( subjects 5–20 ) sampled at 1kHz . Only channel pairs which lacked & rhythms , and for which the noise floor was relatively small compared with the power , were selected and fit . This naïve fit of a low frequency power law yielded values of = 2 . 460 . 32 ( meanSD , N = 91 ) ( Figure 3 ) . We then modified the power spectra of this 1kHz fixation data , dividing by the product of 2 Lorentzian-like form factors: ( 2 ) based upon the knee observed in the spectra of the 10 kHz data , subject to the constraint that ( because the 10kHz fit implied that this must be the case for large ) , and calculated the values of and for which the modified spectra had a slope closest to zero , on the frequency interval 15–195Hz . These post-modification fits yielded = 2 . 010 . 18 ( SD , N = 91 ) , and was 77Hz14Hz , as shown in Figure 3 . It should be noted that this likely represents a true range , where the “knee” at may vary by location and individual . The change in the shape of the PSD during different levels of neuronal population activity reveals different dynamics within the population . A shift in the exponent , , would suggest a change in the correlation between neurons , whereas a shift in the coefficient , , would suggest an overall increase or decrease in population activity . In a recent manuscript [38] , we demonstrated how motor-behavior-related variation in the and bands allow them to be removed from the measured PSD in primary motor cortex . We repeated the same process as that on pair-wise re-referenced electrode channels and , removed the oscillatory ( peaked ) phenomena from the PSDs . Activity-related changes in individual channel pairs were examined by dividing the active , movement , spectra ( “” ) by the inactive , rest , spectra ( “” ) , element-wise ( ) . Calculating the vs . slope removes the common shape ( including the effect of ) , and reveals whether there is a shift in the slope , , during activity . This shift in exponent , when fit from 25–195Hz , was insignificant: 0 . 030 . 09 , ( SD , N = 25 , , by paired t-test; subjects 16–20 , 5 electrode pairs from motor cortex each ) . Because there was no significant shift in the exponent , then active/inactive power ratio ( between the amplitudes , Figure 4 ) was obtained for each channel simply by averaging across frequencies in each channel . The geometric mean of these ratios was with a variation ( standard deviation ) of order 0 . 31 ( maximum 2 . 47 , minimum 1 . 29 , N = 25 , ) . A simple simulation using Poisson-distributed pre-synaptic action potentials to a single neuron beneath one of our electrodes ( neurons beneath each [39] ) reproduced the spectra that we see , at different levels of cortical activity with simulated rates of 15 , 30 , and 60 ( Figure 5; “” = action potential ) . Using a modified leaky integrate-and-fire model ( without a firing component ) , consisting of an exponentially decaying post-synaptic current , temporal integration , and passive current efflux to estimate the time-dependence of a dendritic current-dipole field , a spectrum with the power-law form emerged . A factor , contributed by exponentially decaying synaptic current , directly follows previous work by Bedard et al [27] . The value of for high frequencies ( 80–500Hz ) in this simulated data was 4 . 0 at all three levels of cortical activity ( linear fit on log-log axes ) . The ratio of the coefficient , , was 4 . 03 for 60 vs . 15 , and 1 . 96 for 30 vs . 15 . Because the PSD of the superposition of many such uncorrelated model neurons will have the same shape as one , the model in a single neuron will generalize to an entire neuronal population .
Although traditional EEG studies have been limited to measurements of frequencies below 100Hz , the timing of fundamental neuronal processes suggests that information content should be present at much higher frequencies . Propagation time of a spike along an axon , synaptic neurotransmitter diffusion time , or the recursion time of reciprocally coupled neurons are all near or below 10 ms [39]–[42] . Synchronizations and correlations associated with these should exist at least up to 1kHz . The human brain is arguably the most complex and largest network available to observe scale free behavior in a natural setting , and at the 5mm2 scale of our electrodes , we have observed robust power-law scaling . Power-laws represent scale free behavior – the finding of which typically evokes discussion of scale free networks , complexity , avalanches , and self-organized criticality ( SOC ) [43]–[45] , and if our measured value of were distinct from an integer , we might have discussed SOC at the cortical surface . SOC is a process where very complex global phenomena arise in a population of interacting elements due to very simple properties of the individual element , and simple rules that dictate how pairs of elements interact with one another [43] . The global complexity is generally not immediately apparent from the simple properties and rules . A popular example of this is the emergence of earthquakes of different sizes and the frequencies on which they occur [46] , which have a power-law relationship . The form of this earthquake distribution that is observed in nature can also be derived from a simple model where blocks of matter are held together by springs , but slip against one another with friction [47] , [48] – global complexity emerges from basic interaction , and can be characterized by a power law . One might hypothesize that the interaction between neurons in a population , producing sophisticated computation , would exhibit SOC , and be revealed by a power law . Our experimental finding of power law scaling in the brain surface electric potential does not suggest SOC . Non-integer exponents in power-law relationships imply self-organized criticality in the population of constituent elements , but multiple-of-two integers , such as our finding of , point towards simple , noise-like , analytic functions , i . e . to a non-singular , non-fractal , non-complexity explanation of the shape of the power spectrum ( such as a diffusive process or filtered noise ) . Perhaps SOC behavior ( if it exists ) is only expressed in the PSD in more subtle ways , within the uncertainty , or at finer spatial scales , in the cortical surface potential . While not present in this study , evidence for complex neural correlations ( and different exponents ) may emerge in different experimental settings , such as power-fluctuations in the -rhythm [49] , in the magnitude of spatially-correlated cascades of activity in the LFP [50] , or the gain of neuronal firing in response to cyclical driving potentials [51] . In order to examine the PSD structure at lower frequencies , the 91 channels from subjects 5–20 without & rhythms were first blindly fit linearly on log-log axes up to the knee in the spectrum , from 15–80Hz , producing an estimate of a low-frequency power-law with exponent . Based upon the knee at , and the blind fit in Figure 3a , we examined a more complex parameterized form of the PSD which accounts for the knee , could be fit across a larger range ( 15–195Hz ) , and goes to for large ( i . e . of the form of equation 2 ) . Note that the discovered factor of might represent a form factor where the lower boundary of our fitting range is above , so a lower knee with a flattening of the spectrum is not appreciated . There may , in fact , be a lower “flattening” of the spectrum , below 20Hz , that is masked by the and ( ubiquitous ) / rhythms ( see , for example , plots in [27] , [28] ) . As described by Sigeti and Horsthemke , these types of “2+2” spectra can emerge from noise-like processes which have two simple correlation times [52] . There are many such combinations of two simple known neuronal processes , such as temporal integration in dendrites or soma , exponentially decaying membrane currents , low-pass RC filtering by tissue , or local network connectivity which , when modeled , will produce precisely this form ( one such is illustrated in Figure 5 ) . In a previous paper [22] , we hypothesized that observed high frequency changes called “high-” [53] or [31] were reflective of broad-band , power-law shifts , and were obscured by the & rhythms at lower frequencies in motor cortex . Indeed , the intersection of these two phenomena , “” , was subsequently shown to lie at ( meanSD ) ( range 32–57 Hz ) during hand motor movement [26] . When we made this hypothesis , it was uncertain whether this shift might reflect a change in the exponent , , or the coefficient , , of a power-law of the form . In a more recent manuscript , we performed a decomposition technique which removed the & rhythms [38] , revealing broadband increase beneath . Figure 4 shows that when this method is applied , and the residual broadband spectra are modified with a Lorentzian form , both active and inactive spectra are approximately linear on a log-log plot with slope −2 . In other words , they can be reasonably described by a power-law with exponent . When individual channel pairs were examined independently for active and inactive spectra , there was no difference in fit exponent: the shape of the PSD was unchanged , but the overall amplitude was . This implies that , at the spatial scale of our electrodes , after spatially and temporally averaging , the structure and complexity of the large-scale neural networks do not change during computation , but the overall amount of activity does . The active/inactive power ratio between the amplitudes ( 1 . 76 ) provides a sense of the dynamic range of this network in the behaving brain as it shifts between ‘idling’ and ‘computing’ regimes . An important caveat to these findings is that the PSDs which we fit were averaged over long periods of time ( minutes of fixation or seconds of movement/rest ) . If the same is done over very small windows , there are deviations from the form of the averaged PSD . It is within these small windows that computations take place , and the “instantaneous PSD” will not have the power-law shape at all times . Without reoccurring synchronized oscillatory processes , however , it averages to the power-law shape over time . To gain intuition about what may produce these signals , we performed a simple simulation from the perspective of a single neuron beneath one of our electrodes ( neurons immediately underneath each electrode [39] ) , and take into account only three factors: Poisson-distributed input action potentials , exponentially decaying post-synaptic currents , and ohmic current in the dendrite , produced a time-dependent signal with a PSD of the same shape that we measure , and with the same change during increase in activity . While our particular choice of model was one of many potential models , we believe that any simulation of the ECoG PSD should rely on very simple factors , ubiquitous in the cortical neuronal population , because the effect must be conserved after averaging across neurons . Although this simulation was largely oblivious both to the details of dendritic and overall neuronal processing ( between neurons , etc ) , and to many factors which must influence the creation of dendritic current dipoles , it does exhibit two things that we would like to stress . The first is that the knee we observe in the spectra likely corresponds to the timescale of a very simple process , like post-synaptic potential current of particular timescale , which occurs throughout the cortical surface . The second is that changes in the amplitude , , of the power-law reflect changes in Poisson-distributed ( after coarse graining ) input action potentials beneath each of the electrodes . Indeed , we have recently shown that the capture of this broad-band , here demonstrated to obey a power-law , reveals local cortical activity with high temporal precision [38] . The values that the simulation obtains ( factor of 2/4 increase in with a doubling/quadrupling of the action potential rate ) suggest that the difference we observe experimentally during finger movement might represent roughly a doubling of mean input action potential rate for the population of neurons . Collectively , these findings have important implications for understanding the electric potential at the cortical surface , with the necessary caveat that the effects seen reflect an average of neurons . Because of the connection between the autocorrelation in the potential and the PSD ( equation 1 ) , we can try to connect the form we observe in the PSD to correlation in the physiologic processes which produce it . At this coarse level , there is a special frequency at roughly 75Hz , and this must be accounted for . This may be due to an exponentially decaying temporal correlation of 2–4ms from post-synaptic current , tissue low-pass filter , protein dissociation , or some other . Perhaps this timescale corresponds to a recurrent process of 11–16ms such as characteristic reciprocal connectivity in local neuronal circuits , or to the “conduction time” of single neurons – how much time it takes for a coordinated pre-synaptic super-threshold pulse to produce an action potential at the axon hillock . If there is a lower “knee” below our fitting range , and masked by the theta rhythm , that implies a second timescale with physiologic importance of its own which must be accounted for . Each of these must also correspond to a factor of above the associated characteristic frequency . Our simple simulation follows that of Bedard et . al . [27] for the first factor , with exponentially decaying post-synaptic current accounting for and one factor of , and charge accumulation in the dendrite producing the second factor of , with the dendritic current leakage producing a second native frequency well below any fitting range we examined . Fluctuations in firing rate produce overall increases and decreases in the PSD , without a change in the frequency dependence of the PSD . Our experimental results , in contrast , differ significantly from the Bedard et . al . paper [27] . They reported a to transition in their PSD measurements . As we do , they attribute a factor of to Poisson-distributed spikes and the shape of the post-synaptic current . They attribute the remaining to passive tissue filtering , which has since been contradicted experimentally by Logothetis et . al . [54] . To the eye , the PSD from the Bedard et . al , study appears as if it may have been better fit by an to shape like the one found in this study . By extension of their logic to our finding , our power-of-two structure may point away from the presence of tissue attenuation . Activity-related narrow-band PSD increases , correlating with fMRI [55] , have been demonstrated in the “high-” ( 40–100Hz ) frequency range of the LFP [56]–[60] and the MEG [61] , [62] . In each case , these are peaked phenomena in the PSD , reflecting a coherent , oscillatory process , which increases with activity , and is specific to visual cortex . This is a very different phenomenon from the power-law increase that we demonstrate here . In fact , Siegel and Konig , in 2003 , explicitly distinguished between a peaked , lower , phenomena at 44–53 Hz , and a different , broad-spectral increase , beginning at 45 Hz extending well beyond 100Hz to the upper limit of their recording from cat visual cortex [57] . Henrie and Shapley , as well as Liu and Newsome , made the same distinction , with similar effect , in visual areas of the non-human primate [58] , [60] . Extracted broadband changes across the entire human ECoG spectrum , after removing the low-frequency rhythms , were recently demonstrated to capture the timing of individual finger movements with very high fidelity , and explicitly better than band-filtered high-frequency changes [38] . Even more recently , broadband LFP changes were demonstrated to correlate more highly with mean firing rate than any particular frequency band in single unit recordings from human cortex [63] . We suggest that what these manuscripts identify as broadband change , distinct from oscillations , and what others have called “high-” when referring to broad spectral increases [24] , [25] , [42] , [53] , are primarily shifts in the noise-like process identified here , captured at frequencies above the range of band-limited oscillations . This power law process likely reflects the mean input spike rate to neuronal populations , without a preferred timescale . True -oscillation , however , is likely due to population synchronization by fast-spiking inhibitory interneurons [64] , [65] , reflected by peaked elements in the PSD , and possibly specific to visual cortex ( note that none of the data in this manuscript was recorded from occipital visual areas ) . When one is sitting on the seashore , it is possible to hear individual waves breaking , first on the left , and then on the right , correlated by their relation to shape of the shore . As one walks away , however , the correlation between individual waves is lost because many are heard at once , from progressively larger stretches of the beach . The combination of our empirical and modeling findings point to a similar picture , where the internal correlations between neuronal events are lost by averaging over large spatial areas , but the changes that we measure do inform us about the overall number of events taking place in the population . We would like to propose that the popular “high-” range , where it has been postulated that synchronous , rhythmic , action potential activity produces changes , is often a reflection of changes in asynchronous activity instead , and revealed by this power-law process . This shift in thinking , to noise-like non-oscillatory changes , is a fundamentally new addition to the way people think about changes in the cortical potential spectrum . Whereas changes in characteristic brain rhythms are thought to reflect synchronized populations that coherently oscillate across large cortical regions , power-law scaling likely reflects asynchronous , averaged , input to the local neural population .
All patients participated in a purely voluntary manner , after providing informed consent , under a protocol approved by the Institutional Review Board of the University of Washington Twenty epileptic human subjects had subdural electrode arrays placed on the brain surface of the lateral frontal , temporal , and parietal cortical areas for the localization of seizure foci . These arrays were composed of circular platinum electrodes with 2 . 3mm diameter exposed , at 1 cm inter-electrode distance ( center-to-center ) , embedded in silastic . Electrodes lying on top of vasculature , near seizure foci , or with aberrantly high noise floors were excluded from the study . All kept data were recorded away from seizure times . Potentials at each electrode were recorded at 10 kHz ( subjects 1–4 ) or 1 kHz ( subjects 5–20 ) . The first type of experiment , fixation , was performed ( subjects 1 to 20 ) by the subjects fixating with their eyes open on an “X” , on the wall 3m away , for several minutes . The second type of experiment ( subjects 16 to 20 ) consisted of simple repeated finger movement ( visually cued ) . The brain surface potentials from the array were first re-referenced in terms of neighboring differential pair channels ( bipolar re-referencing for all nearest neighbors ) , which significantly reduced the overall noise in the signal . We empirically determined the amplitude attenuation function of the amplifiers independently using an external function generator . For the 10kHz data , a “reasonable range” of empirical amplifier noise floors was determined experimentally by measuring the potential across an equivalent conformation of resistors . Because there was a range of potential floor values for each electrode , depending upon the day , temperature , room , etc , the specific noise floor subtracted from each calculated spectra was determined within the empiric range using a recursive , self-consistent method . For the 10kHz sampled , fixation task data , the value of the exponent in the power-law relation was obtained by fitting a straight line to the experimentally measured PSD , , on vs . axes , after correcting for amplifier imposed artifacts ( Figures 1–5 ) . An infamous mistake in this procedure is to apply global least squares fit , and leave it at that . On a log-log plot , that assigns too much weight to the highest density of datapoints , at high frequency , where the low power and high relative influence of the noise floor make the data noisiest . In reality , a fit should be stable throughout the fitting range , and we employed a technique which is robust against range shrinking to a sub-range within the total fitting range . We determined local fits for the exponent by performing least-squares linear fits to the power spectrum ( on log frequency by log Power axes ) to obtain local slopes over varying frequency intervals , ( harmonics of 60Hz were explicitly excluded ) . The most appropriate value of globally is the one that is most stable across many values of and , for a given value of the noise floor , . Because variation in the noise floor exists and confounds the quantitative analysis , the appropriate value in a given channel pair for a given experiment is a self-consistent , recursive , 3 parameter fit to the form , over the entire frequency range , treating , , and as free fitting parameters ( is constrained within the empiric range of experimentally measured noise floors ) . In each iteration , is determined as the average exponent from a distribution of fit exponents , each calculated from a different sub-interval . Lower fit values ranged from and higher fits ranged from . The smallest value of , 80Hz , was chosen so that it would be sufficiently above an apparent “knee” in the PSD at . The highest value of was dictated by the noise floor ( beyond which the amplitude of the signal was far below the amplitude of the noise ) . After rejecting electrode pair channels which had notable and peaks in the PSD or a high noise floor ( leaving 91 channels ) , the 1kHz fixation data from subjects 5–20 was fit to a power law form below . They were corrected for frequency-dependent amplifier attenuation , but not for noise floor , since the contribution of the noise floor to the spectra was not pronounced in the fit range chosen , or the channel was rejected if an excessive noise floor was observed . First , a naïve linear least-squares fit was performed between 15–80Hz on the plot of the PSD the on vs . axes for each electrode-pair channel independently . We then divided the PSD through at each frequency by the form of equation 2 , and , based upon the fit of 10kHz data , set the constraint ( i . e . for , the form goes to , so ) . We then iteratively fit and , until both converged on stable values . Each iteration consisted of two steps . The PSD is first multiplied by a factor of , and the residual is fit on a log-log plot between 15–195Hz to determine a new value of . Then , the PSD is divided by the full form of equation 2 , for all values of 15–195Hz in 0 . 25Hz increments , and the residual is fit on a log-log plot; the value of for which the slope of the fit is closest to zero is chosen as the new . These two steps were iterated until both and converged to stable values . A finger movement task was used first as a tool to first remove synchronous rhythms from the PSD and then examine changes in the PSD during increases in brain activity . A method developed in a recent manuscript characterized how differing covariance between frequencies during different tasks allows underlying motifs to be isolated from the PSD [38]; we removed the motifs corresponding to the low frequency / rhythms during a finger movement task . Because this was shown to remove most , but not all of those rhythms , we avoided the center frequency of the beta rhythm , and performed fits to the data above 25Hz; without this method , we would not be able to address shifts in the power law process below 60Hz , where the beta rhythm causes an intersection between the movement and rest spectra [26] . The residual movement PSD was then divided by the rest PSD at each frequency . A least-squares linear fit was then performed on the plot of vs . . The slope of this fit for each channel pair reveals whether there was a shift in the exponent , , of the power-law shape spectrum in “active” cortex . Because the shift in slope was found to be zero , then the frequency-averaged ratio reveals the relative overall shift in the coefficient , . The significance of this shift in was estimated with a t-test of the distribution of vs . zero ( across electrode pair channels ) . While there are many potential models that are mathematically consistent with the form of the spectrum that we found experimentally , we performed a simplified simulation of only one such model . We model one pyramidal neuron with 6000 synapses , and only 3 simple processes to produce time dependence: Poisson-distribution arrival times of pre-synaptic action potentials ( , timeseries denoted below ) ; stereotyped , transient , exponentially decaying post-synaptic current , with ( consistent with experiment [40] , below ) , and each synapse has random peak current on the interval from −1 to 1 ( arbitrary units , below ) . These are summated , and integrated over time ( representing accumulated charge [66] ) . The leakage current of this charge through the dendritic membrane , produced by a transmembrane potential , is what we simulate as the time-dependence of the dendritic dipole ( with time constant [67] ) . ( 3 ) ( 4 ) Where we denote a series of delta functions reflecting the spike arrival times at synapse as , the shape of the post-synaptic response as ( total length ) , a random number on the interval from −1 to 1 , as , the decay timescale for dendritic current efflux as , and the convolution operation ( and is zero padded at the edges ) . is the simulated time dependence of our surface potential measurements , from which we calculate our simulated spectrum . As noted by Sigeti and Horsthemke [52] , “2+2” spectra , such as the one we have observed from cortical surface spectra , can result from linear systems described by: ( 5 ) ( 6 ) Where is a Gaussian white noise variable , and the resulting power spectrum of , , has characteristic corner frequencies at and . Note that equation 6 is the same as equation 4 , but that we explicitly construct with a convolution that allows us to connect the expression to known physiology in an intuitive way ( but and ultimately do have the same properties , where the decay timescale of our is ) . Our representation is intended to make the connection between the simulation and simple small-scale physiology more intuitive . The timing of this ohmic transmembrane current produced by accumulated charge gradient across the dendritic membrane is modeled as the time dependence producing the macroscale PSD . 6000 synaptic inputs , with input firing rates of 15 , 30 , and 60 were simulated for 2 minutes of 10kHz data [39] . Recent in-vivo simultaneous transmembrane and local field potential recordings have demonstrated a strong correlation between these two [68] , suggesting that models like this , based upon a relationship between post-synaptic potentials and field potential , may provide useful insight .
|
For a very long time , the measurement of the large scale potentials produced by the brain from outside of the head , using electroencephalography and magnetoencephalography , and from inside the head , using electrocorticography , has fixated on changes in specific rhythms and frequency ranges . This fixation presupposes physiologic changes where neuronal populations synchronously oscillate at specific timescales . Here , we demonstrate that there are phenomena which obey a broadband , power-law form extending across the entire frequency domain , with no special timescale . It is shown that , with local brain activity , there is an increase in power across all frequencies , and the power-law shape is conserved . Furthermore , we illustrate through simple simulation how fluctuations in this phenomenon may be linked to increases and decreases in “noise-like” patterns of activity in neuronal populations . Although power-laws have been postulated to exist in background electrical brain activity , the view that local activity can be captured by fluctuations in a broadband power-law in the power spectrum of electric potential timeseries represents a fundamentally new way of thinking about changes in the electric potential produced by the brain , and provides insight into what types of neuronal processes might produce these potentials .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/motor",
"systems",
"neuroscience",
"neuroscience/theoretical",
"neuroscience",
"physics/condensed",
"matter"
] |
2009
|
Power-Law Scaling in the Brain Surface Electric Potential
|
To protect germ cells from genomic instability , surveillance mechanisms ensure meiosis occurs properly . In mammals , spermatocytes that display recombination defects experience a so-called recombination-dependent arrest at the pachytene stage , which relies on the MRE11 complex—ATM—CHK2 pathway responding to unrepaired DNA double-strand breaks ( DSBs ) . Here , we asked if p53 family members—targets of ATM and CHK2—participate in this arrest . We bred double-mutant mice combining a mutation of a member of the p53 family ( p53 , TAp63 , or p73 ) with a Trip13 mutation . Trip13 deficiency triggers a recombination-dependent response that arrests spermatocytes in pachynema before they have incorporated the testis-specific histone variant H1t into their chromatin . We find that deficiency for either p53 or TAp63 , but not p73 , allowed spermatocytes to progress further into meiotic prophase despite the presence of numerous unrepaired DSBs . Even so , the double mutant spermatocytes apoptosed at late pachynema because of sex body deficiency; thus p53 and TAp63 are dispensable for arrest caused by sex body defects . These data affirm that recombination-dependent and sex body-deficient arrests occur via genetically separable mechanisms .
The mammalian p53 family includes p53 [1 , 2] , p63 [3] and p73 [4] , which are transcription factors encoded by three highly conserved genes [5 , 6] . Each member has three major domains: an amino-terminal transactivation ( TA ) domain , a central DNA binding domain , and a carboxy-terminal oligomerization domain [5 , 7] . Alternative promoters express two isoforms of p63 and p73 . The transactivating isoforms contain the TA domain and the ΔN isoforms lack it [7 , 8] . Generally , the TA isoforms tend to have tumor suppressor activities , while the ΔN isoforms act as dominant-negative inhibitors that can bind DNA but do not promote transcription [5 , 7 , 8] . Splicing variation at the 3′ end of the mRNAs generates additional isoforms [5 , 8 , 9] . The interplay between the family members , their isoforms , their differential tissue expression , and their ability to oligomerize yields a high complexity and diverse biological functions including prominent roles in the DNA damage response ( DDR ) [10–12] . In somatic cells responding to DSBs , the MRE11–RAD50–NBS1 ( MRN ) complex senses DSBs and activates ATM kinase [13] . ATM then phosphorylates a large set of downstream targets involved in DNA repair , cell cycle progression , and apoptosis , including CHK2 [14 , 15] and p53 among others [16–18] . Once phosphorylated , p53 is stabilized and mediates cell cycle arrest until DNA damage is repaired [19–21] . If DNA damage persists , p53 levels increase and induce pro-apoptotic genes [22 , 23] . Similarly to p53 [24] , major cellular functions of p63 and p73 include regulating DNA repair , cell cycle progression , and programmed cell death [7 , 25 , 26] . One endogenous source of DSBs occurs early in the first meiotic prophase , when SPO11 protein introduces numerous DSBs that are subsequently repaired by homologous recombination to promote chromosome pairing and synapsis [27 , 28] . Since errors at this point can cause genomic instability and introduce germ line mutations , mechanisms exist to detect defects in recombination and other processes during prophase , and if necessary delay cell cycle progression and/or promote programmed cell death [29–31] . These mechanisms are generically referred to as the pachytene checkpoint , and in male mouse meiosis can be divided into two main arrest pathways . One responds to defective repair of DSBs ( referred to as recombination-dependent arrest ) [29 , 32] . The other responds to failure in transcriptional silencing of the non-homologous portions of the sex chromosomes ( sex body-deficient arrest ) [33 , 34] . Genetic pathways responsible for these quality control systems remain incompletely understood . These arrest mechanisms can be distinguished cytologically using the incorporation of the testis-specific histone variant H1t [29] , which in wild-type mice accumulates on chromatin from mid-pachynema onwards ( Fig 1A ) [35] . Mutant spermatocytes with persistent unrepaired DSBs ( e . g . , in Dmc1-/- ) arrest at pachynema before expressing H1t , whereas H1t accumulates in mutant spermatocytes that do not form DSBs but have sex body defects ( e . g . , Spo11-/- ) [29 , 36] . This difference suggests that defects in DSB repair provoke arrest at an earlier substage of pachynema than is seen for defects in sex body formation . Nonetheless , both recombination-dependent arrest and sex body-deficient arrest cause apoptosis at stage IV of the seminiferous epithelial cycle , corresponding to mid-pachynema in wild type [29 , 36] . Recent studies have identified some of the proteins involved in recombination-dependent arrest in mouse spermatocytes and oocytes , using a hypomorphic gene-trap mutant allele of the Trip13 gene , Trip13mod/mod ( also known Trip13Gt/Gt ) [36 , 37] . TRIP13 is an AAA+ ATPase that is required for the completion of meiotic recombination , but is dispensable for homologous chromosome synapsis [38 , 39] . In spermatocytes , recombination-dependent arrest in response to Trip13 mutation depends on components of the somatic DDR , namely , the MRN complex , ATM , and CHK2 [36] . Most Trip13mod/mod spermatocytes arrest before incorporating H1t and display hallmarks of unrepaired DSBs , but removing or reducing ATM activity or eliminating CHK2 allows Trip13-mutant spermatocytes to progress further and incorporate H1t [36] . CHK2 is also necessary for eliminating Trip13mod/mod oocytes with persistent unrepaired DSBs , via activation of p53 and TAp63 [37] . Moreover , TAp63 is necessary for elimination of irradiated oocytes [40] . These findings in oocytes raise the possibility that p53 family members may also participate in similar arrest responses in spermatocytes . Notably , all p53 family members , p53 , p63 and p73 , are expressed in mammalian spermatocytes [41 , 42] . Moreover , this hypothesis is consistent with findings that p53 is activated in mouse spermatocytes in response to SPO11-generated DSBs [43] and is involved in arresting Arf-/- spermatocytes , which present high levels of γH2AX at pachynema [44] . Hence , we investigated whether p53 family members mediate recombination-dependent arrest in mouse spermatocytes .
If a p53 family member is required for recombination-dependent arrest , then removing that protein should allow Trip13-deficient spermatocytes to progress further into meiotic prophase . We tested this prediction by combining p53 family member mutations with Trip13 mutation ( Trip13mod/mod p53-/- , Trip13mod/mod TAp63-/- , and Trip13mod/mod p73-/- ) and analyzing the mutants’ meiotic phenotypes . For p63 , we used a mutation that specifically eliminates its TA forms [45] because p63-null mice die perinatally [46] . First , as we previously demonstrated for Trip13mod/mod Chk2-/- mutants [36] , we asked if the p53-family double mutants yielded more cells expressing H1t compared to the Trip13mod/mod single mutant . To do this , we stained squashed spermatocyte preparations for SYCP3 ( a component of the axial element of the synaptonemal complex ) [47] and H1t . As expected [36] , only 21 . 6% of spermatocytes were H1t-positive in Trip13mod/mod single mutants , compared with 52 . 7% in wild type ( Fig 1B ) . The reduction in Trip13mod/mod single mutants is a consequence of recombination-dependent arrest , with most of the H1t-positive cells representing a relatively recombination-proficient subset of cells ( “escapers” ) attributable to the partial penetrance of the Trip13 hypomorphic recombination defect [36 , 38 , 39] . In contrast , we recovered a significantly greater fraction of H1t-positive spermatocytes in the p53 and TAp63 double mutants compared with the Trip13mod/mod single mutant ( 48 . 4% of Trip13mod/mod p53-/- and 51 . 0% of Trip13mod/mod TAp63-/- spermatocytes; p≤0 . 001 , one-way ANOVA test for both comparisons ) . The p73 double mutant had 16 . 4% of H1t-positive spermatocytes , comparable to Trip13mod/mod ( p>0 . 05 , one-way ANOVA test , Fig 1B ) . These results suggest that in the absence of p53 or TAp63 , but not p73 , Trip13 mutant spermatocytes are able to bypass the recombination-dependent arrest . If this interpretation is correct , we would expect that p53 or TAp63 deficiency allows Trip13-mutant spermatocytes to progress despite the presence of unrepaired DSBs . We therefore stained spermatocyte spreads for γH2AX , the phosphorylated form of the histone variant H2AX that marks DSBs [48] , and counted the number of γH2AX patches present in each cell ( Fig 1 , S1 and S2 Figs ) . Previously , we showed that Trip13mod/mod spermatocytes accumulate numerous patches of γH2AX on fully synapsed chromosomes in early pachynema , indicative of persistent unrepaired DSBs [36 , 38] ( Fig 1K , left graph ) . Many of these patches persisted in H1t-positive cells , but two subpopulations of the mid/late pachytene Trip13mod/mod spermatocytes were apparent: one with low numbers of γH2AX patches , similar to wild type , and another retaining numerous patches ( Fig 1K , middle graph ) . However , those cells that reached diplonema did so with normal ( very low ) numbers of γH2AX patches ( Fig 1K , right graph ) . As previously argued [36] , we infer that stochastic cell-to-cell differences in the number of unrepaired DSBs translate into different arrest responses: cells with numerous DSBs experience recombination-dependent arrest early in pachynema ( H1t-negative ) , cells with an intermediate number progress to an H1t-positive stage before apoptosing ( possibly because of the DSBs , sex body defects , or both; see below ) , and the most repair-proficient cells progress still further to diplonema . Before the point of recombination-dependent arrest ( early pachynema , H1t-negative cells ) , the numbers of γH2AX patches in the p53 and p73 double mutants were not statistically significantly different from the Trip13mod/mod single mutant ( p = 0 . 091 and p = 0 . 086 , t test , respectively ) ( Fig 1K , left graph ) . γH2AX patches were slightly decreased in the TAp63 double mutant ( p = 0 . 027 , t test ) , but still much higher than in wild type . We infer that the absence of the p53 family members does not greatly ameliorate the DSB repair defect caused by TRIP13 deficiency . For the purpose of assessing arrest status of cells with unrepaired DSBs , the key stages are mid/late pachynema ( H1t-positive ) and diplonema . As predicted for defects in recombination-dependent arrest , the p53 and TAp63 double mutants had a higher average number of γH2AX patches in H1t-positive cells than was observed in the Trip13mod/mod single mutant ( p≤0 . 0001 , t test; Fig 1C , 1D , 1G , 1H and 1K and S1A and S1B Fig ) . This increase reflects less of an enrichment for the γH2AX-low subpopulation plus an increased occurrence of cells with γH2AX patches at or above the high end of the range seen in the single mutant ( Fig 1K , middle graph ) . Both double mutants also displayed more γH2AX patches in diplotene cells ( p = 0 . 00001 for p53 , and p = 0 . 00029 for TAp63 , negative binomial regression; Fig 1E , 1F , 1I , 1J and 1K ( right graph ) and S1C and S1D Fig ) . Similar results were obtained when RAD51 was used as a marker for recombination events: the p53 double mutant had similar numbers of RAD51 foci as the Trip13mod/mod single mutant at early pachynema , but had elevated numbers of RAD51 foci at mid/late pachynema and diplonema ( S3 Fig ) . Overall , these data indicate that spermatocytes with substantial numbers of unrepaired DSBs are more likely to progress to the H1t-positive stage and beyond if p53 or TAp63 are missing , similar to our previous findings for absence of CHK2 [36] . In contrast , the p73 double mutant was similar to the Trip13mod/mod single mutant , both in mid/late pachynema ( p = 0 . 49 , t test; Fig 1K , middle graph , and S1E and S1F Fig ) and in diplonema ( p = 0 . 65 , negative binomial regression; Fig 1K , right graph , and S1G and S1H Fig ) . This finding corroborates the conclusion that , even though p73 is expressed in mouse spermatocytes [42] , it is dispensable for recombination-dependent arrest . To determine if absence of p53 or TAp63 affects apoptosis of TRIP13-deficient spermatocytes , we performed TUNEL staining of double mutant spermatocytes ( Fig 2 and S4 Fig ) . As expected from prior results [36] , most ( 80 . 1% ) TUNEL-positive spermatocytes were H1t-negative in the Trip13mod/mod single mutant ( Fig 2A–2D and 2I ) . The p73 double mutant was similar , with 79 . 2% of apoptotic spermatocytes being H1t-negative ( p = 0 . 92 compared to Trip13mod/mod , one-way ANOVA test; Fig 2I and S4E–S4H Fig ) . In striking contrast , the majority of apoptotic spermatocytes in the p53 and TAp63 double mutants were H1t-positive ( only 27 . 6% H1t-negative for p53 , p≤0 . 0001; and 14 . 2% for TAp63 , p = 0 . 001; one-way ANOVA test ) ( Fig 2E–2H and 2I and S4A–S4D Fig ) . These results indicate that both p53 and TAp63 , but not p73 , promote the elimination of most spermatocytes with numerous unrepaired DSBs in early pachynema . To gain insight into how p53 and TAp63 may collaborate to promote recombination-dependent arrest , we analyzed the expression of p53 and p63 proteins in Trip13mod/mod single mutants and in the double mutants by immunofluorescent staining of testis sections ( Figs 3 and 4 ) . Each cross section of a seminiferous tubule can be classified as one of twelve epithelial stages ( I to XII ) on the basis of the developmental stages of germ cells present in the section [49 , 50] . Epithelial staging can also be deduced for mutants that experience spermatogenic arrest , although precise stage assignments are not always possible [50] . Similar to a recent report [51] , a p53 antibody stained only occasional cells within testis sections from wild type , with most germ cells showing little or no detectable staining at any stage of spermatogenesis ( Fig 3A ) . In the Trip13mod/mod single mutant , spermatocytes judged to be in leptonema through zygonema similarly had little if any detectable signal , but around one third of tubules at stages II through IV contained numerous pachtyene spermatocytes with strong anti-p53 antibody staining ( Fig 3B ) . This signal was highest in the cytoplasm but was also apparent within nuclei ( insets in Fig 3B ) . Particularly brightly stained cells were also apparent in more luminal positions in tubules of stages IX through XII or stage I; these are likely to be the escapers that have progressed further through meiotic prophase . The TAp63 double mutant showed p53 staining patterns indistinguishable from the Trip13mod/mod single mutant ( Fig 3C ) . Thus , p53 protein levels are up-regulated during pachynema in response to defects caused by TRIP13 deficiency , and this up-regulation is independent of TAp63 . In wild-type spermatocytes , the p63 antibody stained spermatocyte nuclei in a focal pattern , beginning faintly in early pachynema ( in stage II through IV tubules ) and increasing in intensity in later pachynema ( e . g . , in stage IX–X ) ( Fig 4A ) . The signal remained bright but was largely excluded from chromatin in metaphase I spermatocytes ( stage XII ) and was again bright on chromatin in round spermatids ( Fig 4A ) . These patterns match precisely with a prior report [42] . In the Trip13mod/mod single mutant , p63 staining became prominent at earlier stages , with a subset of early prophase cells showing detectable p63 foci ( i . e . , at or before the beginning of pachynema; stages IX through XII or stage I ) and with similar staining as in wild type in pachytene spermatocytes in stage II–IV tubules ( Fig 4B ) . Presumptive escapers also stained brightly ( e . g . , in stages IX through XII ) . The Trip13 p53 double mutant showed p63 staining patterns equivalent from the Trip13mod/mod single mutant ( Fig 4C ) . These findings indicate that p63 levels are up-regulated prematurely in the absence of TRIP13 , and this up-regulation is independent of p53 status . These results are consistent with both p53 and TAp63 being involved in responses to unrepaired DSBs in the Trip13mod/mod mutant , and thus being involved in implementing recombination-dependent spermatocyte arrest . A simple explanation for why both proteins are required for arrest could have been that they were mutually dependent for their expression . However , the independence of their up-regulation in the Trip13mod/mod mutant argues strongly against this possibility . Possible models to explain why up-regulation of one without the other is not sufficient to trigger arrest are provided in the Discussion . Most of the lengths of the X and Y chromosomes are non-homologous and thus remain unsynapsed during meiotic prophase . The unsynapsed sex chromosomes undergo transcriptional silencing , termed meiotic sex chromosome inactivation ( MSCI ) , which results in formation of the heterochromatic sex body [34] . MSCI is crucial for meiotic progression , since expression of sex chromosome genes during pachynema is deleterious for spermatocytes [33] . We previously reported that Trip13mod/mod Chk2-/- spermatocytes , although escaping recombination-dependent arrest , nonetheless arrested at epithelial stage IV due to the defects in sex body function that are another consequence of TRIP13 deficiency [36] . Because removing p53 or TAp63 allowed Trip13 mutants to progress further into meiosis but did not prevent eventual apoptosis of spermatocytes ( Fig 2 ) , we characterized this cell death in more detail . TUNEL staining on histological sections ( Fig 5A–5C ) demonstrated that the occurrence of apoptosis in the p53 and TAp63 double mutants was similar to the Trip13mod/mod single mutant ( Fig 5D ) . Importantly , by histological analysis , p53 and TAp63 double mutants both showed arrest at epithelial stage IV ( Fig 5E–5H ) , as do Trip13mod/mod single mutants and Trip13mod/mod Chk2-/- double mutants [36] . The p73 double mutant also displayed arrest at stage IV ( Fig 5I ) . To corroborate that stage IV arrest of p53 and TAp63 double mutant spermatocytes can be linked to MSCI failure , we analyzed the morphology and functionality of the sex body . First , we stained spermatocyte spreads for several sex body markers: γH2AX , ATR ( the kinase principally responsible for H2AX phosphorylation in the sex body [52] ) , and SUMO-1 ( which accumulates on the X and Y chromatin at pachynema in an ATR-dependent manner [52] ) ( see Fig 6A–6I for representative images and S1 Table for quantification ) . For each marker , the p53 and TAp63 double mutants displayed sex body abnormalities that were qualitatively and quantitatively indistinguishable from the Trip13mod/mod single mutant . Specifically , we found abnormally elongated sex bodies in approximately two-thirds of pachytene spermatocytes ( compare sex bodies in Fig 6B and 6C and S5A Fig to the round and intensely stained sex body in Fig 6A ) . Whereas ATR in wild-type pachynema usually covered the unsynapsed X and Y chromosome axes and expanded over their chromatin ( Fig 6D ) , less than 20% of pachytene cells from double mutants or the Trip13mod/mod single mutant displayed this chromatin-expanded pattern ( S1 Table ) . Instead most cells showed a more discontinuous localization of ATR along the X and Y axes ( Fig 6E and 6F and S5B Fig ) as well as only modest SUMO-1 staining ( Fig 6H and 6I and S5C Fig ) , unlike the intense signal typical of sex bodies in wild type ( Fig 6G ) . These results confirm that Trip13 mutants have defects in sex body formation , consistent with previous results [36] , and demonstrate that these defects are not ameliorated by absence of either p53 or TAp63 . Next , we assessed MSCI by performing RNA fluorescence in situ hybridization ( RNA-FISH ) analysis of the X-linked genes Scml2 ( located next to the pseudoautosomal region ) and Zfx ( more interstitially located ) [53] . Both genes are normally silenced by MSCI throughout pachynema . To identify early pachytene cells , slides were also immunostained for TOPBP1 ( DNA topoisomerase 2 binding-protein 1 ) , which associates with the unsynapsed sex chromosomes at pachynema [54] . Similar to our prior findings for the Trip13mod/mod single mutant [36] , a greater fraction of double mutant spermatocytes expressed one or the other X-linked gene than in wild type ( Fig 6J–6M and S5D–S5I Fig; further quantification in S2 Table ) . These results match what we previously observed for Trip13mod/mod Chk2-/- mutants [36] . Thus , alleviating recombination-dependent arrest in TRIP13-deficient spermatocytes by removing CHK2 , p53 , or TAp63 does not overcome the separate pachytene arrest pathway tied to MSCI failure . To further confirm that p53 family members are dispensable for sex body-deficient arrest , we asked if p53 or TAp63 deficiency can ameliorate the arrest caused by absence of Spo11 . Because they lack DSBs , Spo11-/- spermatocytes display synapsis defects that lead to failure to form a proper sex body , thus allowing expression of sex chromosome genes and activation of the sex body-deficient arrest [55 , 56] . We generated Spo11-/- p53-/- and Spo11-/- TAp63-/- double mutants and analyzed testis weights , which can distinguish between mutants arresting at different meiotic stages . As predicted if p53 or TAp63 are not required for sex body-deficient arrest , testis weights were indistinguishable for Spo11-/- p53-/- or Spo11-/- TAp63-/- double mutants and Spo11-/- single mutants ( p = 0 . 22 , t test ) ( S6 Fig ) . Furthermore , histological analysis of double mutant testis sections revealed the existence of stage IV arrest in Spo11-/- p53-/- , similarly to what is found in Spo11-/- mice ( Fig 7A and 7B ) . We further analyzed meiotic prophase progression cytologically in these two mutants . Spo11 mutant spermatocytes fail to complete synapsis , thus the most advanced spermatocyte that can be observed is a zygotene-like cell [55 , 56] . We showed previously that the Spo11-/- spermatocytes that apoptose are a subset of these zygotene-like cells that displayed a pseudo sex body [36] , i . e . , such spermatocytes are the most advanced germ cells present in Spo11 mutant testis . Therefore , to determine if p53 mutation affected the ability of Spo11 mutant spermatocytes to progress , we analyzed the presence of zygotene-like spermatocytes containing a pseudo sex body . The proportion of these cells in Spo11-/- and in Spo11-/- p53-/- mice was indistinguishable ( 61 . 0% ± 3 . 5% vs . 63 . 0% ± 7 . 8% , respectively; p = 0 . 77 , one-way ANOVA test; Fig 7C–7E ) . We conclude that absence of p53 does not improve meiotic progression of Spo11 mutant spermatocytes , thus supporting the conclusion that p53 family members are not involved in the arrest responding to sex body failure in mammals .
In this study , we provide new insights about the functionality of p53 family members in mouse meiotic surveillance mechanisms . Deficient recombination and/or sex body formation drives spermatocytes to arrest at pachynema and trigger programmed cell death , resulting in infertility . We previously reported that the MRN-ATM-CHK2 signaling pathway participates in the activation of recombination-dependent arrest in mouse spermatocytes ( Fig 8 and [36] ) . Moreover , it was described that in females , the activation of CHK2 , p53 , and TAp63 is required to eliminate defective oocytes with persistent unrepaired DSBs [37] . Concordantly , results presented here show that p53 and TAp63 also participate in recombination-dependent arrest in spermatocytes ( Fig 8 ) . However , the third p53 family member , p73 , does not participate in this arrest . By contrast , we report that p53 family members are not required for sex body formation dependent arrest . Our data shed light on the importance of the p53 family in pachytene arrest in male mouse meiosis and resolve previous contradictory reports . An initial study observed that deletion of p53 partially rescued the phenotype of mutant spermatocytes that failed to repair DSBs , suggesting the involvement of p53 in recombination-dependent pachytene stage arrest [57] . However , two subsequent studies did not support this conclusion: one study analyzed the same mutants but could find no signs of rescue [58] , while another study reported that p53 ablation had no impact on the meiotic progression of Sycp3-/- spermatocytes , which fail to form proper chromosome axes and also accumulate unrepaired DSBs [59] . Thus , both later studies discounted a role for p53 in the so-called “pachytene checkpoint” . These discrepancies can be explained by developments in the intervening years , combined with results presented here . For instance , while the first study used H1t as a marker of meiotic progression ( as we have done here ) , the subsequent studies did not . Furthermore , at the time these studies were conducted it was not known that there are two genetically independent mechanisms that trigger pachytene arrest in spermatocytes [29] . This is important because alleviating recombination-dependent arrest has no impact on surveillance of the sex body [36] . Consequently , cytological markers like H1t must be used to accurately define the arrest stage . Our data clearly provides evidence that the mammalian meiotic surveillance mechanism uses both p53 and TAp63 to activate recombination-dependent arrest at pachynema . Our analysis also demonstrates that p73 , despite being present in mouse spermatocytes , is dispensable for recombination-dependent arrest . A previous report proposed that p73 controls a p53-independent apoptotic response of mouse spermatogonia to irradiation [42] . Thus , it appears that , while p73 may be important to maintain genome integrity of premeiotic germ cells , p53 and TAp63 assume this role in spermatocytes . Because Trip13mod/mod p53 and Trip13mod/mod TAp63 double mutant spermatocytes phenotypically resemble Trip13mod/mod Chk2-/- [36] , we infer that p53 and TAp63 act downstream of CHK2 and that they are non-redundant for activation of recombination-dependent arrest . Furthermore , the observation of highly similar phenotypes in both Trip13mod/mod p53 and Trip13mod/mod TAp63 double mutants leads us to suggest that p53 and TAp63 are equivalently necessary to properly activate this arrest . p53 and TAp63 are intimately connected: they can promote transcription of the same genes [3] and can associate with each other through direct interaction [60] . There are several ways in which p53 and TAp63 could promote this arrest . For example , both proteins might form a heterotetramer that is required to activate the recombination-dependent response . Alternatively , p53 and TAp63 might act as homotetramers that work independently but additively to activate the same ( or partially overlapping ) set of target genes; in this model , activity of both is required in order to pass a certain response threshold . This latter option seems particularly appropriate for spermatocytes because they normally accumulate a certain level of unrepaired DSBs at pachynema . Specifically , the X chromosome accumulates DSBs [61] and because most of the X chromosome has no homologous partner , most of these DSBs remain unrepaired until late pachynema or later [62] . Thus , since all spermatocytes at pachynema could potentially activate this recombination-dependent arrest if it had a low threshold , it is plausible that requiring independent function of both p53 and TAp63 could make the threshold for arrest high enough to allow most wild-type spermatocyte progression . Interestingly , studies in yeast have established that sub-critical levels of DNA damage response are important for promoting proper recombination outcomes and coordinating meiotic progression with completion of recombination [63–65] . It is possible that regulation of TAp63 and/or p53 plays analogous roles in mouse meiosis . More studies will help us elucidate the nature of the interaction among p53 and TAp63 in order to control meiotic prophase progression . Requirement for both p53 and TAp63 contrasts with what has been observed in females , where p53 and TAp63 appear to be substantially redundant . For example , whereas Chk2 mutation completely rescued infertility in Trip13 mutant females , deletion of p53 only mildly increased the number of Trip13-deficient oocytes . Furthermore , absence of TAp63 is required in addition to heterozygosity for p53 in order to suppress the arrest observed in Trip13 mutant oocytes [37] . These gender differences may reflect constraints tied to other sexual dimorphisms in mammalian gametogenesis . One key difference between the sexes is that meiosis in the testis is a continually renewed process that starts at the onset of sexual maturity and lasts through most or all of adulthood , whereas meiosis in the ovaries occurs in one wave during fetal development with oocytes then arresting at the end of meiotic prophase ( around two days post-partum in mouse ) . Arrested oocytes do not resume meiosis until adulthood . These resting oocytes are also sensitive to DNA damage caused by ionizing radiation [37] . Importantly , it has been proposed that the same checkpoint mechanism that monitors meiotic DSB repair in fetal oocytes controls DNA integrity in the resting oocytes [37] . Thus , the fact that the recombination-dependent arrest machinery has to control genome integrity in resting oocytes may have resulted in a female-specific balance between p53 and TAp63 for responding to DNA damage during meiotic prophase . Our observations that Trip13 mutants present sex body defects resulting in inefficient MSCI and subsequent pachytene arrest corroborate our earlier conclusion that TRIP13 participates in the formation of the sex body [36] . Our results support a model whereby TRIP13 is required to properly load ATR onto unsynapsed X and Y axes to extend its signal to the chromatin , allowing proper H2AX phosphorylation and SUMO-1 loading . Additionally , because Trip13mod/mod p53-/- , Trip13mod/mod TAp63-/- , Trip13mod/mod p73-/- , Spo11-/- p53-/- and Spo11-/- TAp63-/- double mutants all display arrest at epithelial stage IV , these results show that p53 family members are not required for sex body-deficient arrest . Therefore , these findings further strengthen the conclusion that the recombination-dependent and the sex body-deficient arrests are genetically separable mechanisms that respond to different offenses [36] . A next question would be to investigate components downstream of p53 and TAp63 in recombination-compromised spermatocytes . After induction of DNA damage in somatic cells , p53 induces transcription of p21 [66] and pro-apoptotic genes such as Bax [67] , Puma [68] and Noxa [69] . Remarkably , irradiated oocytes also require TAp63 to induce expression of Puma and Noxa , and irradiated Puma-/- and Puma-/- Noxa-/- mice are protected from oocyte lost [70] . Therefore , future studies could be directed to determinate if this role of PUMA and NOXA , or other p53 family targets , is also conserved in DNA repair-compromised spermatocytes .
We used mice carrying Trip13 , p53 , TAp63 , p73 , Spo11 , Spo11 β-only , Dmc1 and Atm mutations that were previously generated and described elsewhere [38 , 45 , 55 , 61 , 71–74] . All lines were maintained in a C57Bl/6-129/Sv mixed background . Experiments were performed using at least two animals ( unless otherwise mentioned ) and comparing them to wild-type littermates ( when possible ) or from animals from other litters from the same matings or closely related parents ( See S3 Table for the relation between the control animals used in each experiment ) . Genotyping was performed by PCR analysis from tail extracted DNA as previously reported [38] . Experiments complied with U . S . and E . U . regulations and were approved by the Ethics Committee of the UAB and Catalan Government and by the MSKCC Institutional Animal Care and Use Committee . Testes from 2 to 4 months old adult mice were collected and processed for histology or cytology . For histology , testes were fixed overnight at 4°C with either Bouin’s solution or 4% paraformaldehyde , then embedded in paraffin and sectioned . For histological staging [49] , testis sections fixed with Bouin’s solution were stained with Periodic Acid-Shiff ( PAS ) and Hematoxylin . Testis sections fixed with paraformaldehyde were used for apoptosis analysis . TUNEL staining was performed using the In situ cell death detection kit ( Roche Diagnostics ) following the manufacturer’s protocol . Immunostaining was performed after antigen retrieval protocols were applied on testis sections . Mouse monoclonal antibody against p53 ( Cell signaling , 1:200 ) and a mouse monoclonal antibody against p63 ( Abcam , 1:50 ) were used . The secondary antibody was raised in goat and conjugated with Cy3 ( Jackson Immunoresearch Europe ) . Squashed nuclear spermatocyte preparations , which preserve nuclear chromosome structure , were made as previously described [75] . Surface-spread nuclei were prepared as described previously [76] . Briefly , cell suspensions from frozen testes were minced in PBS , spermatocytes were treated with 1% lipsol and fixed with 0 . 15% Triton X-100 and 1% paraformaldehyde for two hours in a humid chamber at 4°C , air-dried , and washed with 0 . 4% Photo-flo ( Kodak ) . Before the staining , squashes or spreads were blocked with PTBG ( 0 . 2% BSA , 0 . 2% gelatin , 0 . 05% Tween-20 in PBS ) . Immunofluorescence staining was performed using standard methods [76] . Primary antibodies used were: mouse and rabbit anti-SYCP3 ( Abcam , 1:400 ) ; mouse anti-γH2AX ( Millipore , 1:400 ) ; guinea-pig anti-H1t ( kind gift from M . A . Handel , Jackson Laboratory , 1:500 ) ; rabbit anti-RAD51 ( Calbiochem , 1:100 ) , rabbit anti-ATR ( Calbiochem , 1:100 ) and mouse anti-SUMO-1 ( Invitrogen , 1:100 ) . Secondary antibodies used were all raised in goat and conjugated with FITC , Cy3 or Cy5 ( Jackson Immunoresearch Europe ) . TUNEL staining was performed on immunofluorescent-stained slides as previously reported [36] , and then slides were mounted with Vectashield ( Vector Laboratories ) reagent containing DAPI . Images were captured using a Zeiss Axioskop fluorescence microscope with a ProgRes C10 camera using ProgRes Pro 2 . 7 . 7 software and processed with Photoshop . RNA FISH was performed with digoxigenin-labeled probes as described [36 , 53 , 77] . BAC DNA probes used for this study were: Scml2 , RP24-204O18 ( CHORI BACPAC library ) and Zfx , bMQ-372M23 ( Mouse bMQ BAC library ) . BAC-containing bacteria were grown in LB-chloramphenicol culture overnight at 37°C . A standard miniprep method was used to isolate BAC DNA . Approximately 2 μg of BAC DNA was labelled using DIG-Nick Translation Mix ( Roche ) and precipitated with Cot-1 DNA ( Invitrogen ) and salmon sperm DNA ( Stratagene ) . Frozen testis cells were permeabilized with CSK buffer ( 100 mM NaCl , 300 mM sucrose , 3 mM MgCl2 , 10 mM PIPES , 0 . 5% Triton X-100 , 2 mM vanadyl ribonucleoside ( New England Biolabs ) ) , fixed with 4% paraformaldehyde , and dehydrated through an ice-cold ethanol series . DNA-BAC probes were denatured at 80°C , pre-hybridized at 37°C , and incubated with the sample overnight at 37°C . After stringency washes , digoxigenin was detected with anti-digoxigenin-FITC ( 1:10 , Millipore ) . RNA FISH slides were then stained for immunofluorescence with anti-TOPBP1 ( 1:50 , Abcam ) and anti-γH2AX ( 1:100 , Millipore ) . Samples were analyzed on an Olympus IX70 inverted microscope and computer-assisted ( DeltaVision ) CCD camera ( Photometrics ) was used to capture images ( processed with ImageJ and Photoshop software ) . Student's t tests and one-way ANOVA tests were performed using GraphPad Prism software and/or GraphPad QuickCalcs online resource ( http://www . graphpad . com/quickcalcs/ ) . For comparing counts of γH2AX patches between genotypes at pachynema , we used t tests for simplicity since the data reasonably approximated a normal distribution . However , when we compared counts for diplotene cells , we used negative binomial regression because the count distributions were highly skewed for some genotypes and contained many zero values for all samples . Regression was calculated using the glm . nb function from the MASS package ( version 7 . 3–33 ) in R ( version 3 . 1 . 1 ) .
|
Meiosis is a specialized cell division that generates haploid gametes by halving chromosome content through two consecutive rounds of chromosome segregation . At the onset of the first meiotic division , SPO11 protein introduces double-strand breaks ( DSBs ) throughout the genome . These DSBs are repaired through homologous recombination , which promotes pairing and synapsis of the homologous chromosomes . Some DSBs will become repaired as crossovers , providing a physical connection between the homologous chromosomes which promotes correct chromosome segregation . In fact , recombination defects can lead to formation of aneuploid gametes , one of the major causes of miscarriages and chromosome abnormalities in humans . To protect germ cells from genomic instability and to produce balanced gametes , surveillance mechanisms ensure that meiosis occurs properly . It is known that in the presence of unrepaired DSBs a control mechanism promotes a spermatogenic block at the pachytene stage . Here we describe that , downstream MRE11-ATM-CHK2 pathway , p53 and TAp63 are the effectors responsible for activating recombination-dependent arrest in mouse spermatocytes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"cell",
"death",
"spermatocytes",
"nuclear",
"staining",
"cell",
"processes",
"germ",
"cells",
"oocytes",
"dna",
"sperm",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"animal",
"cells",
"proteins",
"recombinant",
"proteins",
"biochemistry",
"dapi",
"staining",
"cell",
"staining",
"cell",
"biology",
"nucleic",
"acids",
"ova",
"apoptosis",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"dna",
"repair",
"dna",
"recombination"
] |
2017
|
p53 and TAp63 participate in the recombination-dependent pachytene arrest in mouse spermatocytes
|
Toxoplasma gondii is the most common protozoan parasitic infection in man . Gamma interferon ( IFNγ ) activates haematopoietic and non-haematopoietic cells to kill the parasite and mediate host resistance . IFNγ-driven host resistance pathways and parasitic virulence factors are well described in mice , but a detailed understanding of pathways that kill Toxoplasma in human cells is lacking . Here we show , that contrary to the widely held belief that the Toxoplasma vacuole is non-fusogenic , in an immune-stimulated environment , the vacuole of type II Toxoplasma in human cells is able to fuse with the host endo-lysosomal machinery leading to parasite death by acidification . Similar to murine cells , we find that type II , but not type I Toxoplasma vacuoles are targeted by K63-linked ubiquitin in an IFNγ-dependent manner in non-haematopoetic primary-like human endothelial cells . Host defence proteins p62 and NDP52 are subsequently recruited to the type II vacuole in distinct , overlapping microdomains with a loss of IFNγ-dependent restriction in p62 knocked down cells . Autophagy proteins Atg16L1 , GABARAP and LC3B are recruited to <10% of parasite vacuoles and show no parasite strain preference , which is consistent with inhibition and enhancement of autophagy showing no effect on parasite replication . We demonstrate that this differs from HeLa human epithelial cells , where type II Toxoplasma are restricted by non-canonical autophagy leading to growth stunting that is independent of lysosomal acidification . In contrast to mouse cells , human vacuoles do not break . In HUVEC , the ubiquitinated vacuoles are targeted for destruction in acidified LAMP1-positive endo-lysosomal compartments . Consequently , parasite death can be prevented by inhibiting host ubiquitination and endosomal acidification . Thus , K63-linked ubiquitin recognition leading to vacuolar endo-lysosomal fusion and acidification is an important , novel virulence-driven Toxoplasma human host defence pathway .
Host cells invaded by intracellular pathogens have to mount a rapid recognition and cell-autonomous defence program to curb the replication of the intruder [1] . The cytokine gamma interferon ( IFNγ ) can stimulate cell-autonomous defence in immune or non-immune cells and is produced early during infection with many intracellular pathogens , including the protozoan parasite Toxoplasma gondii [2] . Worldwide , human Toxoplasma infections are estimated at 30% and the parasite can infect all warm-blooded animals . Human infections are mostly asymptomatic , but the parasite establishes a lifelong chronic infection in the form of cysts in brain and muscle tissue . Ocular disease is a complication for both the immunocompetent and immunocompromised , while serious illness and death are possibilities in the immunocompromised and the developing foetus of pregnant women . Toxoplasma strains in North America and Europe are mostly of the types I , II and III , with type I strains classified as highly virulent with an LD100 of 1 parasite in mice , and type II and III strains being less virulent in mice , with an LD50 greater than 1000 parasites/mouse [3 , 4] . Toxoplasma gondii actively invades any nucleated host cell where it resides and replicates within a nonfusogenic parasitophorous vacuole ( PV ) [5–7] and without immune pressure resists acidification [5 , 8] . Many defence mechanisms against Toxoplasma have been identified in macrophages of mice and man . For both organisms , CD40 ligation stimulates autophagy and fusion of the PV with lysosomes [9] and activation of the purinergic receptor P2X7R leads to killing of the parasite [10] . Interferon-induced production of nitric oxide plays a role in chronic infection in mice [11] , but not in human macrophages [12] . In mice , Atg5 has previously been shown to be important in the murine host immune response to Toxoplasma [13 , 14] . IFNγ not only stimulates macrophages but non-immune cells , and in chimeric mice , gamma interferon receptor is critical in both haematopoetic and nonhaematopoetic cell types [15] . In mice , the most important interferon-inducible effector mechanisms are the IRGs and p65 guanylate-binding proteins ( GBPs ) , both of which localise to the PV and disrupt the membrane of the vacuole . The autophagy proteins Atg7 , Atg3 , and the Atg12-Atg5-Atg16L1 complex were reported to target IRGs and GBPs to the PV of Toxoplasma for disruption in mouse cells [16] . Similarly , Atg7 and Atg16L1 deficient MEFs were impaired in the recruitment of IRGs and GBPs to the PV [17] . Unlike mice , humans lack IFNγ-inducible versions of IRGs and do not recruit GBP1 to the vacuole [18] . However , we have reported that GBP1 mediates an early restriction of Toxoplasma that is not dependent upon vacuolar localisation [18] . Of interest , it has long been appreciated that in human IFNγ-stimulated fibroblasts , both type I as well as type II parasite replication is controlled [19–21] . This has been attributed to nutrient starvation driven by the IFNγ-inducible indoleamine 2 , 3-dioxygenase ( IDO1 ) which degrades tryptophan for which Toxoplasma is auxotrophic [20] . While we have previously found that restriction of Toxoplasma is solely dependent on IDO1 in HeLa cells , in fibroblasts , interferon-dependent restriction is only partially mediated by IDO1 [22] . In fibroblasts , it is not overtly dependent on autophagy as assessed by Atg5 knockdown [22] , nor is the common Toxoplasma mouse virulence factor Rop18 involved [21] . Additionally , IDO1-mediated restriction occurs in fibroblasts but not in endothelial or epithelial cells [23 , 24] . Human endothelial cells have been shown to restrict Toxoplasma in an autophagy- and lysosome-independent manner [25] and human fibroblast-like cells ( HAP1 ) equally do not restrict Toxoplasma by autophagy [17] . A recent report has attributed the restriction of type II and III Toxoplasma in HeLa cells to a non-canonical acidification-independent autophagy pathway , requiring p62 and NDP52 [26] . It is thus apparent that multiple mechanisms of interferon-induced cell-autonomous resistance must exist in different human non-haematopoetic cells , but the nature of these is unclear . Other intracellular pathogens such as the bacteria Salmonella typhimurium are cleared by autophagy after an initial cellular recognition event that marks the bacteria or the bacterial vacuole with host cellular ubiquitin . Ubiquitination recruits autophagy adaptor proteins NDP52 , p62 , and optineurin , which in turn bind to LC3 that recruits the autophagic double membrane [27 , 28] leading to bacterial killing by acidification [29] . For control of Shigella flexneri infection , however , p62 and NDP52 recruitment has been shown to be inter-dependent and reliant on septin caging and actin polymerization . Other cytosolic bacteria such as the Listeria monocytogenes ActA mutant , that are susceptible to ubiquitination , p62 recruitment and septin caging , do not display the same dependence on septin and actin , suggesting p62 and NDP52 can direct different pathways of selective autophagy [30] . The hallmark event in all these pathways is the ubiquitination , with different ubiquitin linkages dictating different cellular responses [31] . Here we report the cell-autonomous killing of type II Toxoplasma gondii in human non-haematopoetic cells by fusion with the host’s endo-lysosomal system . We show that , in contrast to mouse cells , the PV in IFNγ-stimulated human cells does not rupture , but rather becomes LAMP1- and LysoTracker-positive indicating fusion with the endo-lysosomal system . Consequently , the parasites are acidified and die within the PV . IFNγ-driven K63-linked ubiquitination of the PV is a prerequisite of parasite death with the subsequent recruitment of NDP52 and p62 in overlapping microdomains . We do not find an overt dependence on autophagy , as down-regulation of the autophagy pathway by Atg16L1 knock-down does not rescue parasite growth . However , consistent with their role in parasite killing , inhibition of host ubiquitination , and vacuolar/endosomal acidification , enhance parasite viability under IFNγ-stimulated conditions .
Human endothelial cells are known to restrict Toxoplasma type I growth upon stimulation with gamma interferon [23] . In order to explore the kinetics and strain-dependence of IFNγ-mediated restriction in HUVEC , we enumerated how many parasites were contained per vacuole at 6h and 24h post-infection for both type I and type II parasites . We found that both parasite strains were already restricted in their replication at 6h with increasing significance at 24h ( Fig 1A ) . Ubiquitin recognition of intracellular bacteria has been shown to be the trigger for critical host effector mechanisms mediating cell-autonomous pathogen restriction [32] . We therefore asked if cellular ubiquitin is recruited to the vicinity of Toxoplasma in the presence and absence of IFNγ . Total ubiquitin was assessed by immunofluorescence using the ubiquitin antibody , FK2 , which recognizes K29 , K48 and K63-linked mono- and poly-ubiquitin chains . A representative confocal microscopy picture of total ubiquitin recruited to the PV of type II Toxoplasma is shown in Fig 1B . In HUVEC , stimulated with IFNγ and infected with type II Toxoplasma , 30% of PVs were positive for ubiquitin ( Fig 1C ) . Less than 5% PV ubiquitin recruitment was seen upon infection with type I Toxoplasma or in the absence of IFNγ . To determine whether the ubiquitin was localised to the vacuole or the parasite , a 3D surface intensity plot was constructed on a confocal plane of the intracellular parasite , and z-stack projections recorded through all confocal planes ( Fig 1B and S1 and S2 Figs ) . A clear separation of fluorescence intensity was observed between the vacuole ( red ) and parasite ( green ) in cells stained for total ubiquitin ( Fig 1B ) . Total ubiquitin staining appeared homogenous throughout the surface of PV containing type II parasites ( Fig 1B and S1 and S2 Figs ) . In murine cells , the virulence factors Rop16 and Rop18 have been shown to modulate STAT signalling , cytokine production and IRG and GBP recruitment [33–40] . In contrast , in HFFs , ROP18 does not impact the host’s cell-autonomous killing ability with parasitic ROP5 expression levels being a minimal defence determinant [21] . We thus asked if the murine virulence factors ROP16 and ROP18 could prevent ubiquitin recognition of the type II PV in HUVEC . Expressing virulence factors ROP16 or ROP18 in type II or III parasites had no impact on the levels of ubiquitin decoration of the PVs ( S3 Fig ) . As the type of ubiquitin linkage determines the different fates of target proteins [31] , we next examined if the ubiquitin recruited to the PV was of a particular linkage . Antibodies directed against M1 linear , K48 and K63-linked ubiquitin were tested . Both K63-linked and M1-linear ubiquitin stained the PV of type II Toxoplasma , similarly coating the full circumference of the vacuole ( Fig 1D ) . Quantitation of the PVs coated with M1 linear , K48 and K63-linked ubiquitin clearly indicated that the majority of ubiquitin localising to the PV was in the form of K63-linked poly-ubiquitin chains with 25–30% of type II PVs staining positive ( Fig 1C ) . A significant IFNγ-dependent increase in PVs staining for M1 linear ubiquitin was observed ( Fig 1C ) , with K48-linked ubiquitin recruitment not significantly different from background levels of total ubiquitin staining . These results demonstrate that during infection with type II , as opposed to type I Toxoplasma , mainly K63-linked ubiquitin chains are recruited to the PV in an IFNγ-dependent fashion We observed significant recruitment of K63-ubiquitin chains to type II Toxoplasma PVs ( Fig 1C ) . We tested the ability of the ubiquitin-binding proteins NDP52 and p62 to recognise type I and II Toxoplasma PVs in IFNγ-stimulated HUVEC . We found that both NDP52 and p62 localise to 20–30% of the type II PVs , 2 . 5h p . i . in IFNγ-stimulated cells ( Fig 2A and 2B ) . Neither protein was recruited significantly to the PVs of type I parasites consistent with their lack of ubiquitin deposition ( Fig 2A and 2B ) . Unlike the staining observed for ubiquitin , NDP52 and p62 staining was not continuous but occurred in patches around the PV , with NDP52 and p62 frequently occupying distinct domains ( S4–S6 Figs ) . We extended our studies to super-resolution Structured Illumination Microscopy ( SIM ) , which revealed that although p62 and NDP52 were most often found in separate microdomains , they did occasionally overlap ( Fig 2C and S5 Fig ) . Due to the ubiquitin-binding capacity of both p62 and NDP52 , we assessed the recruitment kinetics of ubiquitin , p62 and NDP52 to the PV . A time-course of ubiquitin recruitment to the vacuole suggested that recruitment was rapid with 15% of type II PVs ubiquitin positive by 30 min post infection ( p . i . ) , which increased to a plateau ~30% at 1h p . i . ( Fig 2D ) . p62 was recruited to approximately the same levels as ubiquitin by 2h p . i . , although with slower kinetics than ubiquitin . NDP52 recruitment also reached its plateau at 2h p . i . , but accumulated on only half the number of PVs as ubiquitin and p62 , with concurrent lower levels before 2h p . i . ( Fig 2D ) . We thus hypothesised that ubiquitin is recruited first , with p62 and NDP52 being recruited subsequently , by virtue of their ubiquitin-binding domains . To test this hypothesis we inhibited ubiquitination by chemically blocking the E1 conjugating enzyme . This led to a significant decrease in ubiquitin-coated PVs , a corresponding significant decrease in p62 recruitment and a trend for less NDP52 around type II PVs ( Fig 2E ) . siRNA against p62 in HUVEC led to a loss of IFNγ-dependent restriction of type II Toxoplasma , indicating that p62 functions in the control of the intracellular parasite in an IFNγ-dependent manner ( Fig 2F ) . Knock down of p62 was confirmed by immunoblotting ( S7 Fig ) . To assess whether type II PV was targeted for autophagic clearance , HUVEC left untreated , or primed with IFNγ were infected with type I or type II Toxoplasma and subsequently stained for LC3B , GABARAP or Atg16L1 . A representative confocal image of each autophagy protein recruited to the type II PV is shown in Fig 3A , 3B and 3C . Although some IFNγ-dependent recruitment was observed , targeting of these autophagy molecules to PVs was found in ≤10% of vacuoles in IFNγ-primed HUVEC and did not exhibit parasite strain dependence up to 6h p . i . ( Fig 3A , 3B and 3C ) . EM images were taken of both type I and type II Toxoplasma infections in IFNγ-stimulated HUVEC at 4h p . i . and showed no autophagosomes in the vicinity of the PVs , but rather close apposition of rough endoplasmic reticulum ( Fig 3D and S8A Fig ) . This differs from the observations made for infection of mouse cells in which type II PVs are ruptured and targeted by autophagy [41–45] . To confirm our results , we stained PVs for galectin 8 , a protein that has been shown to recognise exposed host glycans on damaged Salmonella-containing vacuoles and damaged lysosomes leading to the recruitment of NDP52 and autophagic destruction of the bacteria [46] . Galectin 8 was found to coat less than 7% of type II Toxoplasma in IFNγ-stimulated HUVEC while hardly recognising type I PVs or type II PVs in unstimulated cells ( S8B Fig ) . Representative confocal microscopy images of galectin 8 recruitment to type II PVs are shown in S8C Fig at 2 . 5h p . i . . We thus concluded that the PV of neither type I nor type II Toxoplasma grossly breaks as is the case in IFNγ-stimulated murine cells . Of course one cannot exclude minor leakage of the PV , as potentially hinted at by the low level ( ~6% ) galectin 8 staining we observed . Despite our inability to find obvious autophagic membranes around Toxoplasma PVs by EM and the strain-independent low coating of PVs , we wanted to ascertain if autophagy played a functional role in type II parasite killing . We thus inhibited autophagy by knocking down Atg16L1 and confirmed levels of knock down by immunoblot ( S9A Fig ) . Parasite replication was assessed after 18h and found to be not significantly different for the Atg16L1 knock down cells when compared with control siRNA-treated cells ( Fig 3E ) . In order to establish the level of parasite clearance in Atg16L1 knock down compared to control siRNA-treated cells , the percentage infected cells at 18h post infection in HUVEC was determined . No significant difference was recorded from control cells ( Fig 3F ) . As a further corroboration of the minimal effect of autophagy on the IFNγ-dependent control of Toxoplasma in HUVEC , we stimulated autophagy in HUVEC by pre-incubating cells for 24h with 100nM rapamycin concurrent with IFNγ-stimulation . Cells were then washed and infected with type I or II Toxoplasma for 18h and replication determined . Addition of rapamycin had no significant effect on parasite replication for either type I or type II Toxoplasma ( Fig 3G ) . Confirmation of the increase in autophagy in HUVEC treated with rapamycin was made by immunoblotting treated cells for p62 and LC3B ( S9B Fig ) , with bafilomycin A1 used post rapamycin treatment to allow visualisation of p62 and LC3B II which would otherwise be degraded by lysosomal enzymes . As expected for stimulation of autophagy , a decrease in p62 levels was observed on rapamycin treatment , as was an increase in conversion of LC3B I to LC3B II ( S9B Fig ) . We conclude from these experiments that autophagy is neither the only nor the dominant route for elimination of type II parasites in HUVEC . This is despite the ubiquitin binding proteins p62 and NDP52 both having LC3 binding domains and having been associated with autophagy regulatory functions in bacterial infections in HeLa cells [27 , 30 , 47 , 48] . We next assessed which host cellular destruction pathway plays an important role in eliminating type II Toxoplasma from IFNγ-stimulated HUVEC . We postulated that the type II Toxoplasma PV fuses with the cellular endocytic pathway rather than routes the parasite to destruction by autophagy . To determine whether Toxoplasma PVs intersected the endo-lysosomal system , we performed LAMP1 staining at 2 . 5h , 4h and 6h p . i . with type I and type II Toxoplasma . We found that 10–20% of type II Toxoplasma PVs in IFNγ-stimulated HUVEC were LAMP-1 positive throughout all time points , while less than 7% LAMP-1 localisation to PVs was observed in cells infected with type I Toxoplasma or type II Toxoplasma in the absence of IFNγ ( Fig 4A ) . Additionally , we observed the accumulation of the late endosome protein Rab7 to the PV at 2 . 5h p . i . , consistent with an endo-lysosomal route for destruction of type II Toxoplasma in IFNγ-stimulated HUVEC ( S10A and S10B Fig ) . The parasite in S10B Fig has lost fluorescence due to acid-lability of eGFP or parasite degradation , with only the Hoechst staining visible . As an additional measure for vacuolar acidification , we added LysoTracker red to cells infected with type II parasites at 2h p . i . and incubated the cultures for a further 1 hour before fixation and microscopy analysis . LysoTracker staining was observed over parasites that appeared to be ‘sick’ or dying ( Fig 4B ) . We noted that it was indeed the vacuoles coated with ubiquitin that frequently contained either “unhealthy” parasites or DNA ( Hoechst ) positive parasites that had lost their fluorescence . This could be due to parasite death and subsequent loss of the parasite cytoplasmic fluorescence , or acidification of the vacuole leading to loss of acid-labile eGFP fluorescence . Thus we examined if ubiquitin positive PVs correlate with ones that acidify . We found that a fraction of PVs that contain K63-linked ubiquitin , are also positive for LAMP1 and LysoTracker ( Fig 4C and S11 and S12 Figs ) . EM images at 4h p . i . , revealed a set of type II parasites that are being digested in an endosome/lysosome compartment ( S13 Fig ) . Approximately 45 percent of the EM images of type II infected IFNγ-stimulated HUVEC showed these enlarged lysosome structures and it was noted that more of these structures were present in type II compared with type I infected cells . Note that at 2 . 5h p . i not all ubiquitin-positive vacuoles stain with LysoTracker or are LAMP-1 positive ( Figs 2D and 4A ) . This suggests that ubiquitination precedes acidification of the vacuole . What is the fate of ubiquitin-coated type II Toxoplasma PVs that acquire cellular markers of acidification ? In order to answer this , we blocked host-driven ubiquitination with the E1 inhibitor UBEI-41 or neutralised lysosomes with NH4Cl . Both treatments markedly rescued the ability of type II Toxoplasma to replicate in the presence of IFNγ ( Fig 4D ) . Furthermore , parasite clearance , as measured by the percentage of infected cells at 18h p . i . , demonstrated that in the presence of IFNγ , parasites showed significantly better survival when cells were pre-treated with E1 inhibitor ( Fig 4E ) . As an additional measure of parasite clearance , we used a FACS-based assay to count the percentage of infected cells . Again , in the presence of IFNγ , the cells pre-treated with the E1 inhibitor showed increased Toxoplasma viability compared with untreated cells ( Fig 4F ) . A representative image of HUVECs infected with type II Toxoplasma and either treated or not with the ubiquitination inhibitor shows more parasites per field of view in treated cells ( Fig 4G ) . Additionally , it is noticeable that general LysoTracker staining denoting acid compartments are reduced in cells blocked in their ubiquitination capacity . It has been reported that IFNγ-dependent ubiquitination of type II and III Toxoplasma PVs route the parasite for growth stunting by non-canonical autophagy [26] . Importantly , this pathway does not rely on acidification of the PV . In this previous study , the markers of host defence on the parasite PVs in HeLa were determined at 6h p . i . ( ubiquitin , p62 , NDP52 , LC3B and LAMP1 ) , while the restrictive effect on parasite replication was assessed and found to have an effect at 24h p . i . . For the present study in HUVEC , we have conducted time courses from 2–6h p . i . for the markers of host defence , while also finding that in a window of 6–24h p . i . , restriction of the parasite’s replicative capacity was dependent on the ubiquitin host defence system . We first confirmed that ubiquitin is indeed recruited to the type II PV of Toxoplasma in HeLa cells in an IFNγ-dependent manner ( Fig 5A ) . Additionally , inhibiting the E1 with UBEI-41 significantly reduced the ubiquitin coating ( Fig 5A ) . Next , we ascertained that at 24h p . i . , replication of both type I and II parasites was restricted in HeLa cells by IFNγ ( Fig 5B ) . Inhibiting host-driven ubiquitination significantly rescued the replicative capacity of type II parasites , while having no effect on type I parasites ( Fig 5B ) . Interestingly , when assessing the IFNγ-dependent restrictive capacity of HeLa cells on type I and II Toxoplasma earlier than 24h p . i . , namely at 18h p . i . and 6h p . i . , we only detected a slight restriction of both parasite types at 18h and no restriction at 6h post-infection ( Fig 5B ) . Inhibiting host ubiquitination also did not rescue the slight IFNγ-driven restriction of type II Toxoplasma replication at 18h p . i . ( Fig 5B ) . These earlier time points of host restriction were not previously assessed in HeLa cells , however , they present first evidence that the pathways of IFNγ-mediated ubiquitin-driven restriction of type II Toxoplasma are indeed different in primary-like HUVEC versus HeLa cells . While HUVEC did not exhibit a distinct IFNγ-dependent , Toxoplasma strain-dependent coating of the PV with the autophagy markers LC3B , GABARAP and Atg16L1 , HeLa cells were shown to facilitate the recruitment of LC3B to type II Toxoplasma in a IFNγ-dependent fashion [26] . We confirmed that indeed at 6h p . i . in IFNγ-stimulated HeLa cells , LC3B was found on type II Toxoplasma PVs at 15–20% ( Fig 5C ) . To extend this finding , we analysed LC3B recruitment to the PV earlier than 6h p . i . and could detect it on type II Toxoplasma PVs at 2 . 5h and 4h p . i . ( Fig 5C ) . This again highlights that primary-like HUVEC and HeLa cells use different routes to inhibit type II Toxoplasma after priming with IFNγ . A major hallmark of IFNγ-driven ubiquitin-mediated elimination of type II Toxoplasma in HUVEC that we have identified in this study is the acidification of the PV and subsequent destruction of the parasite . Ubiquitin-targeted PVs in HeLa cells were found not to acidify , but instead restrict type II Toxoplasma through growth stunting . We confirmed that indeed in HeLa cells , LAMP1 is not recruited to Toxoplasma PVs at 2 . 5h , 4h and only marginally ( ~5% ) to type II at 6h p . i . ( Fig 5D ) . Unlike HUVEC and in agreement with this finding , the neutralisation of lysosomes with NH4Cl does not enhance Toxoplasma replication in HeLa cells ( Fig 5E ) . Thus we concluded that HeLa cells do not route type II Toxoplasma into an acidification-dependent restriction pathway as we observe in HUVEC ( S1 Table ) .
IFNγ is the major cytokine controlling both acute and chronic phase Toxoplasma infection in vivo . In this report , we define ubiquitin-driven vacuolar fusion with the endo-lysosomal system as a novel host cell-autonomous restriction mechanism of type II , but not type I Toxoplasma in IFNγ-primed human primary-like endothelial cells . IFNγ can restrict both type I and type II Toxoplasma , but is more effective against type II parasites measured at 24h p . i . and significant from 6h p . i ( Fig 1A ) . Other mechanisms that restrict type I and type II parasites in endothelial cells must therefore exist . Induction of indoleamine dioxygenase ( IDO1 ) by IFNγ appears not to be relevant for type II Toxoplasma restriction in HUVEC , since supplementation of cultures with tryptophan has no effect on replication ( S14 Fig ) . We have shown that in IFNγ-primed HUVEC type II and not type I Toxoplasma PVs undergo ubiquitination and subsequent acidification leading to parasite killing . We set out to determine the functional consequences of inhibiting either PV ubiquitination or acidification on parasite replication . To assess the role of ubiquitination we used UBEI-41 , an inhibitor of the cellular E1 ubiquitin-activating enzyme UBA1 . When IFNγ-primed HUVEC infected with type II Toxoplasma were treated with UBEI-41 , a reduction in IFNγ-dependent vacuolar ubiquitination was observed and the effect of IFNγ on ubiquitin coating of the PV became insignificant ( Fig 2E ) . Concordantly , decreased PV ubiquitination rescued the ability of the type II Toxoplasma to replicate in IFNγ-primed HUVEC ( Fig 4D ) and led to increased parasite survival ( Fig 4E and 4F ) . Consistent with our observation that ubiquitination of PVs precedes acidification , inhibiting ubiquitination with UBEI-41 led to less acidification of type II PVs in HUVEC , and significantly more parasites ( Fig 4G ) . This prompted us to directly assess the role of acidification in parasite killing . For this , vacuolar acidification was buffered through the addition of NH4Cl . As observed upon inhibition of ubiquitination , inhibition of vacuole acidification rescued the type II parasite’s ability to survive and grow in IFNγ-stimulated HUVEC ( Fig 4D ) . These results indicate that ubiquitination and acidification of type II Toxoplasma vacuoles is required for parasite death in IFNγ-stimulated HUVEC . They further demonstrate that ubiquitination is a prerequisite for PV endosome/lysosome fusion and subsequent PV acidification . Contrary to mouse non-haematopoetic cells , the type II PV in IFNγ-stimulated human cells does not exhibit ruffling and major breakage up to 4h p . i . ( Fig 3D , S8A Fig and [26] ) . This is likely due to the absence of IFNγ-inducible p47 GTPases ( IRGs ) from the human genome , as it is these proteins that are crucial for mediating vacuolar breakage [44] . It is also the two murine IFNγ-sensitive IRGs Irgm1/3 that are responsible for mediating recruitment of ubiquitin , p62 and the E3 ligase Traf6 to type II Toxoplasma PVs [49] . Whether the non-IFNγ-inducible human IRGM plays a role in the vacuolar acidification pathway or other human host resistance mechanisms remains to be investigated . The K63-linked , ubiquitin-mediated endo-lysosomal fusion and acidic killing mechanism we observe in human endothelial cells is however strictly dependent on IFNγ . This leads us to speculate that the determining factors for the new host defence mechanism are IFNγ-inducible and may possibly be one or more E3 ubiquitin ligases . It is intriguing to speculate however that there may be minor disruptions of the PV membrane of type II Toxoplasma , as we do observe almost 7% of these PVs in IFNγ-stimulated cells to stain positive for galectin 8 albeit this being only a trend with IFNγ and not statistically significant ( S8B Fig ) . Galectin 8 has previously been used as a marker for broken Salmonella vacuoles by detecting host vacuolar glycans exposed to the cytoplasm [46] . The significance of minor PVM damage in this scenario remains to be investigated . Furthermore , galectin 8 has been reported to activate antibacterial autophagy by recruiting NDP52 to broken pathogen vacuoles [46] , but from our observations , the percentage of NDP52 positive vacuoles is more than double the number of galectin 8 positive vacuoles , implying that NDP52 has a different role in type II Toxoplasma infections of HUVEC . Autophagic clearance initiated by ubiquitination and subsequent lysosomal fusion and killing of intracellular bacteria is a well-described phenomenon . Bacterial pathogens are recognised constitutively in human cells , while we find that type II Toxoplasma is only targeted by ubiquitin in IFNγ-stimulated cells . Endothelial cells as well as fibroblasts have previously been determined not to deploy autophagy to kill Toxoplasma and it had remained unclear how the parasite is eliminated [17 , 25] . In line with these previous studies we also do not find autophagy to be the major host resistance pathway , as knock down or induction of autophagy had absolutely no effect on the parasite’s replicative capacity as well as the percent infected host cells . Additionally , we could demonstrate that all three major autophagy markers LC3B , GABARAP and Atg16L1 were recruited to PVs in lower amounts , but without a strain-dependent pattern . However , as with bacterial clearance , NDP52 and p62 are also present at the PV of type II Toxoplasma , and interestingly in distinct microdomains . This may be due to interaction with different ubiquitin linkage partners . p62 is known to bind both K48 and K63 linked ubiquitin , but with a preference for K63 linkages [50–52] . NDP52 has been reported to occupy domains on cytosolic salmonella that are distinct from p62 but overlapping with optineurin which was shown to bind linear polyubiquitin chains , suggesting a likely binding of NDP52 to linear ubiquitin [28] . Interestingly , we observe broadly similar percentages of K63 ubiquitin and p62 positive vacuoles ( 25–30% ) and equally , levels of M1 linear ubiquitin mirror the percentage NDP52 staining ( 15–20% ) on type II vacuoles . The IFNγ-driven restriction of type II Toxoplasma is removed when p62 is knocked down in HUVEC , implying that this ubiquitin binding protein is likely mediating its effect via a signalling mechanism rather than through autophagy . It is possible that NDP52 functions in a similar manner since the only Atg8 protein it is able to bind is LC3C [53] and we could not detect LC3C binding to vacuoles of type I or type II Toxoplasma in IFNγ-stimulated HUVEC , indicating the lack of an autophagic role for NDP52 . Future work will seek to detail the autophagy-independent function of these host proteins at the PV membrane . Contrasting our study in human primary-like endothelial cells , another study has found that in IFNγ-stimulated epithelial HeLa cells ubiquitination of type II and III and not type I Toxoplasma vacuoles leads to parasite growth stunting via non-canonical autophagy without acidification [26] . We have confirmed the findings that ubiquitination of the PV in HeLa cells is also Toxoplasma strain-specific and IFNγ-driven ( Fig 5A ) . Moreover , we confirmed that the autophagy marker LC3B is recruited to these PVs ( Fig 5C ) , while little acidification is detected ( Fig 5D ) . We have summarised our findings in HUVEC and HeLa compared to the previous study in HeLa in S1 Table . To extend these observations in HeLa cells and to compare the kinetics of Toxoplasma restriction to HUVEC used in this study , we have additionally performed Toxoplasma replication assays in HeLa cells at 6 , 18 and 24h p . i . , with and without the inhibition of ubiquitination . We could clearly show that while in HUVEC restriction of type II Toxoplasma was already observable at 6h p . i . , in HeLa cells this was not apparent until 24h p . i . ( Fig 1A versus 5B ) . Nevertheless , for both cell types , the type II Toxoplasma restriction was dependent on ubiquitination and could be reduced by inhibiting the host ubiquitin pathway ( Figs 4D , 4E and 4F versus 5B ) . We additionally neutralised lysosomes with NH4Cl in HeLa cells and could demonstrate that concurrent with the lack of acidic markers and in contrast to our findings in HUVEC , this did not rescue Toxoplasma replication in HeLa cells ( Figs 4D versus 5E ) . We concluded that the previously described host restriction pathway in HeLa cells is , as the authors describe , a “growth stunting” of the type II parasites via an autophagy pathway . This likely explains why the impact on parasite replication takes much longer to be apparent in HeLa than in HUVEC . It remains to be determined which ubiquitin linkage ( s ) HeLa cells deploy to the PV and if and how the parasite is eventually killed and eliminated . The two different human cell types deploy the same initial defence molecules to similar quantities ( ubiquitin , p62 , NDP52 ) and then diverge in their ultimate strategy on how to destroy the parasite . While HUVEC present with a lysosomal acidification of the vacuole , HeLa deploy a higher quantity of autophagy markers to the PV and stunt the growth of the parasite . We pursued the lack of significant autophagy in HUVEC by assessing recruitment of additional Atg8 proteins , LC3C and GABARAP . We did not observe any staining with LC3C . The GABARAP antibody we employed detects all GABARAP subforms and amounts to about 5% on the PVs . Thus , if LC3B and GABARAP decorate distinct vacuoles , the sum of their recruitment would be around 10% , still far less than observed in HeLa . Accordingly , we still believe the main host effector mechanism that restricts Toxoplasma downstream of Ubiquitin/p62/NDP52 in HUVEC and HeLa are different . However , given that we find residual levels of LAMP1 on HeLa PVs and LC3B , GABARAP and Atg16L1 on HUVEC PVs , with some IFNγ-dependent significance , it is conceivable that the ubiquitin downstream defence mechanisms in these two cell types are simply present at varying quantities . Hence , in HUVEC there may be a basal IFNγ-dependent autophagy in both type I and II Toxoplasma , but insufficient to clear the parasites effectively , as we were unable to restore parasite viability by Atg16L1 knock down . In this context it is important to note that HeLa cells have been shown to have an unusually high level of basal autophagy [55 , 56] . In contrast , HUVEC ubiquitinate and send type II Toxoplasma to acidic destruction by means of the PV becoming endo-lysosomal . This process is much faster and results in parasite death and degradation . K63-ubiquitin recognition renders the Toxoplasma vacuole fusogenic with the endocytic pathway and to our knowledge presents the first physiologically relevant observation of Toxoplasma vacuolar acidification . A model proposing our observations in HUVEC is made in Fig 6 . An important question for the future is , does ubiquitin-driven acidification only exist in primary-like endothelial cells or is it also deployed in human macrophages ? Our discovery of a human host acidification dependent destruction pathway opens the door to determining the parasitic virulence factors used by Toxoplasma to evade recognition in human cells . These combined efforts might uncover novel host and pathogen targets for the development of anti-Toxoplasma compounds .
Human Umbilical Vein Endothelial cells , HUVECs , ( Promocell C12203 ) , were maintained in M199 medium ( Life Technologies ) supplemented with 30μg/ml Endothelial cell growth supplement ( ECGS ) ( Upstate 02–102 ) , 10units/ml heparin ( Sigma H-3149 ) and 20% FBS ( Life Technologies ) . Cells were grown on plates , pre-coated with 1% ( w/v ) porcine gelatin ( Sigma G1890 ) and cultured at 37°C in 5% CO2 . HUVEC were not used beyond passage 6 . HeLa ( ECACC , Sigma ) and human foreskin fibroblasts , HFFs ( ATCC ) , were cultured in DMEM with GlutaMAX ( Life Technologies ) supplemented with 10% FBS ( Life Technologies ) , at 37°C in 5% CO2 . HUVEC and HeLa were stimulated for 18–24h in complete medium at 37°C with addition of 50units/ml human IFNγ ( R&D Systems ) . Toxoplasma gondii expressing luciferase/eGFP or tdTomato ( Prugniaud type II , CEP type III and RH type I ) were maintained in vitro by serial passage on monolayers of HFF cells , cultured in DMEM with GlutaMAX ( Life Technologies ) supplemented with 1% FBS ( Life Technologies ) , at 37°C in 5% CO2 . Parasite virulence mutants Prugniaud ROP16I and CEP ROP18I were a gift from J P Saeij , MIT , MA . Toxoplasma were prepared from freshly 25G syringe-lysed HFF cultures in 1% FBS , adding to experimental cells at a multiplicity of infection ( MOI ) of 2–5:1 for type II and III strains and 0 . 5–1:1 for type I strain . The cell cultures with added Toxoplasma were then centrifuged at 1000rpm for 5 min to synchronise the infection , prior to culturing at 37°C , 5% CO2 for the required time . Ubiquitin E1 inhibitor UBEI-41 was from Biogenova . Cells were pre-treated with 50μM UBEI-41 for 2h and washed 3 times in medium prior to infection . NH4Cl and L-tryptophan were from Sigma-Aldrich . Acidification of parasite vacuoles was monitored using LysoTracker-red DND99 Molecular Probes ( L7528 ThermoFisher ) ; the dye has good retention post fixation with aldehydes and so was suitable for fixed immunofluorescence . LysoTracker-red was added at a concentration of 50nM , 1 hour prior to fixation . Induction of autophagy was achieved using rapamycin ( Sigma , R8781 ) and inhibition of autophagy by bafilomycin A1 ( Sigma , B1793 ) . Rapamycin was added to cultures at a concentration of 100nM 24h prior to infection and the cells washed in 3x in medium before adding parasites . Bafilomycin A1 was added to cultures at a concentration of 400nM after rapamycin treatment and 2h prior to lysing cells . siRNA for Atg16L1 ( mix of 3 Silencer Select Ambion #4392420: s30069 , s30070 , s30071 ) and siRNA control ( AM4635 ) were from ThermoFisher . Sequences for p62 siRNA ( p62: GCAUUGAAGUUGAUAUCGAU[dT][dT] , p62_as: AUCGAUAUCAACUUCAAUGC[dT][dT] [29] were synthesised by Sigma . Cells were transfected with siRNAs ( 50–100pmol ) by nucleofection , Lonza ( HUVEC old formulation VPB-1492 ) for 24h then IFNγ-stimulated and used after a further 24h . Efficiency of knock down was monitored by immunoblotting lysates of transfected cells . Rabbit polyclonal antibodies were α-p62 ( Cliniscience , PM045 ) , α-NDP52 ( AbCam , ab68588 ) , α-LC3B ( AbCam ab48394 ) , α-GABARAP ( Abgent , AP1821a ) . Rabbit monoclonal antibodies were α-LC3B ( Cell Signalling , 3868P for immunoblotting ) , α-ubiquitin Lys-63 specific , Apu3 ( Merck Millipore , 05–1308 ) , α-ubiquitin Lys-48 specific , Apu2 ( Merck Millipore , 05–1307 ) , α-ubiquitin M1 linear-specific , 1E3 ( Merck Millipore , 199 ) , α-Atg16L1 D6D5 ( Cell Signalling , 8089 ) , α-Rab7 D95F2 ( Cell Signalling , 9367 ) . Mouse monoclonal antibodies were α-ubiquitin FK2 ( Enzo Life Sciences , PW8810 ) , α-p62 ( abcam , 56416 ) , α-LAMP-1 H4A3 ( Abcam ab25630 ) , α-beta actin ( Sigma , A2228 ) . Goat polyclonal antibodies were α-galectin 8 ( R&D systems , AF1305 ) . Secondary antibodies used were Alexa Fluor 488- , or Alexa Fluor 568- , Alexa Fluor 647-conjugated chicken/goat α-rabbit , chicken/goat α-mouse or donkey α-goat ( Molecular Probes ) . Adherent cells were washed 2x with ice cold PBS before scraping in ice cold cell lysis buffer ( 25 mM Tris HCl pH 7 . 4 , 5mM MgCl2 , 150mM NaCl , 1% Triton X-100 with protease inhibitor cocktail III , EDTA free; Calbiochem ) . Lysates were run on SDS PAGE 10μg/lane , blotted and blocked overnight in 5% nonfat milk , 0 . 05% Tween 20 in PBS with 0 . 02% sodium azide . Blots were incubated for 1h RT with primary antibody diluted in PBS with 0 . 05% Tween 20 ( PBS Tween ) and 1% milk . After 3x washes in PBS Tween , blots were incubated in second antibody-HRP 1h RT washed 3x PBS Tween and developed using Immobilon Western Chemiluminescent HRP substrate ( Merck Millipore , WBKLS0500 ) . Infected cells were washed twice in PBS and lifted with 2X trypsin ( Life Technologies ) , before quenching in DMEM 10%FBS . The cell pellet was washed in PBS before staining with a fixable live/dead near infrared stain ( Thermofisher ) at 1/2000 in PBS for 20min on ice . PBS was added to quench the reaction before the suspension was centrifuged at 1250 rpm for 5 min at 4°C . The cell pellet was then fixed in 3% paraformaldehyde in PBS , for 20min on ice . PBS was added to quench the reaction before centrifugation at 1250 rpm for 5min at 4°C . The cells were resuspended in PBS 1%BSA 2 . 5mM EDTA and analyzed on a BD LSR-II FACS . Results were analyzed using FlowJo V . 10 . 1 software . Numerical data was plotted using Graph Pad Prism and presented with error bars as standard deviation . Significance of results was determined by 2way ANOVA or unpaired t-test .
|
Toxoplasma gondii is an intracellular parasite that can invade nucleated cells of any warm-blooded animal into a compartment known as a parasitophorous vacuole ( PV ) . The production of gamma interferon ( IFNγ ) drives the restriction and killing of Toxoplasma . It is not fully known how the parasite inside the PV is eliminated in human cells , although its fate depends on the cell type into which it invades . In IFNγ-stimulated epithelial HeLa cells for instance growth of type II parasites is restricted 24h post-infection by employing the cellular autophagy pathway . Distinctly , we show here that in human endothelial cells the parasite is destroyed by fusion of the PV with the cell’s endo-lysosomal pathway as early as 6h post-infection . This process , which is at odds with the normally non-fusogenic nature of the PV , is dependent on IFNγ . Parasite death follows Lysine63-linked ubiquitination of the PV and is specific to type II Toxoplasma . Our results demonstrate for the first time that vacuolar acidification leading to parasite death is central to controlling infection by Toxoplasma in human endothelial cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cell",
"death",
"medicine",
"and",
"health",
"sciences",
"autophagic",
"cell",
"death",
"vacuoles",
"parasite",
"replication",
"pathology",
"and",
"laboratory",
"medicine",
"hela",
"cells",
"cell",
"processes",
"biological",
"cultures",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"parasitology",
"protozoans",
"toxoplasma",
"cell",
"cultures",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"proteins",
"ubiquitination",
"pathogenesis",
"cell",
"lines",
"biochemistry",
"cell",
"biology",
"post-translational",
"modification",
"host-pathogen",
"interactions",
"biology",
"and",
"life",
"sciences",
"cultured",
"tumor",
"cells",
"organisms"
] |
2016
|
K63-Linked Ubiquitination Targets Toxoplasma gondii for Endo-lysosomal Destruction in IFNγ-Stimulated Human Cells
|
The emergence and re-emergence of pathogens remains a major public health concern . Unfortunately , when and where pathogens will ( re- ) emerge is notoriously difficult to predict , as the erratic nature of those events is reinforced by the stochastic nature of pathogen evolution during the early phase of an epidemic . For instance , mutations allowing pathogens to escape host resistance may boost pathogen spread and promote emergence . Yet , the ecological factors that govern such evolutionary emergence remain elusive because of the lack of ecological realism of current theoretical frameworks and the difficulty of experimentally testing their predictions . Here , we develop a theoretical model to explore the effects of the heterogeneity of the host population on the probability of pathogen emergence , with or without pathogen evolution . We show that evolutionary emergence and the spread of escape mutations in the pathogen population is more likely to occur when the host population contains an intermediate proportion of resistant hosts . We also show that the probability of pathogen emergence rapidly declines with the diversity of resistance in the host population . Experimental tests using lytic bacteriophages infecting their bacterial hosts containing Clustered Regularly Interspaced Short Palindromic Repeat and CRISPR-associated ( CRISPR-Cas ) immune defenses confirm these theoretical predictions . These results suggest effective strategies for cross-species spillover and for the management of emerging infectious diseases .
Understanding the factors that govern the ability of pathogens to invade a new host population is of paramount importance to design better surveillance systems and control policies . Mathematical epidemiology can provide key insights into these dynamics [1–4] . For instance , simple deterministic models identified critical vaccination thresholds , above which pathogens are driven extinct , which informed policy guidelines for vaccination campaigns [1] . However , chance events and rapid pathogen evolution can also play a critical role in determining the outcome of disease dynamics [2 , 4–6] . For example , recent experimental studies indicated that the dramatic size of the 2013–2016 Ebola epidemic can at least be partially explained by the acquisition of genetic mutations that increased transmissibility to humans [7 , 8] . Stochastic models of epidemiology can help to understand the emergence of evolving pathogen populations [5 , 9–13] . These models , however , often make the unrealistic assumption that the pathogen is spreading in a well-mixed and homogeneous host population , in which all hosts are equally susceptible . Although a handful of theoretical studies have shown that host heterogeneity could have an important impact on pathogen emergence , these models either relied on phenomenological or numerical approaches [12 , 13] , or assumed that the hosts only differ in their number of contacts but not their susceptibility to pathogens [11] . Here , we extend this line of inquiry by ( i ) building a mechanistic model of pathogen emergence in a diverse host population , in which only some hosts are resistant to the pathogen , ( ii ) deriving analytical expressions for the probability of evolutionary emergence of the pathogen , and ( iii ) providing the first experimental test of theoretical predictions on pathogen evolutionary emergence using a bacteria–phage interaction . We demonstrate that realistic increases in the diversity of host resistance alleles strongly reduce the probability of evolutionary emergence of novel pathogens , hence suggesting new strategies to manage the emergence of diseases . Crucially , using bacteria with distinct Clustered Regularly Interspaced Short Palindromic Repeat ( CRISPR ) immunity and their lytic viruses ( bacteriophages ) [14–17] , we experimentally explore the effect of host population heterogeneity on the emergence and evolution of pathogens . The experimental validation of our theoretical predictions with this microbial system confirms the ability of our mathematical model to capture the complexity of the interplay between the epidemiology and evolution of emerging pathogens in this model system .
In order to predict how the composition of host populations impacts the probability of pathogen emergence , we developed a branching process model [5 , 9–13] . We aimed to capture host–pathogen interactions in which different groups of individuals within a host population each carry unique resistance alleles that recognize different pathogen epitopes , and pathogens can evade recognition by acquiring “escape” mutations in the corresponding epitopes . In this model , we assume that the host population contains a fraction ( 1 − fR ) of individuals that are fully susceptible to the pathogen , while the remaining fraction fR of the population is resistant and composed of a mixture of n host types in equal frequencies , each of which has a different resistance allele . The efficacy of resistance is assumed to be perfect ( we relax this assumption in section S1 . 2 of S1 Text ) . Therefore , a pathogen with i escape mutations ( i between 0 and n ) can infect a fraction ( 1 − fR ) + fRi/n of the total host population . We further assume that a host infected with a pathogen that does not carry escape mutations transmits at rate b and dies at rate d . Host resistance prevents infection without affecting b or d . Whereas escape mutations allow the pathogen to infect a larger fraction of the host population , they also carry a fitness cost , c which causes pathogens with i escape mutations to reproduce at rate bi = b ( 1 − c ) i . The probability of acquiring an escape mutation is a function of n , the number of resistance alleles in the population , as well as i , the number of escape mutations already encoded by the pathogen . The probability that a pathogen with i escape mutations will acquire an additional one equals ui , n = 1 − ( 1 − μ ) n−i , where μ is the pathogen mutation rate per target site ( a target site is a region of the pathogen genome where a point mutation or a deletion may allow escape from recognition by host immunity ) . This simplifies to ui , n ≈ μ ( n − i ) when the pathogen mutation rate is assumed to be small ( note how the rate of escape mutations increases with ( n − i ) ) . For the sake of simplicity , we assume that escape mutations cannot revert to the ancestral types . These reversions are expected to have a negligible effect on the probability of evolutionary emergence when the target site mutation rate remains small [11] . To account for the effect of spatial structure , we assume that when a pathogen is released from an infected host , it will land with probability ϕ on the same type of host ( i . e . , a host susceptible to this pathogen ) and with probability ( 1 − ϕ ) on a random host from the population , which may or not be of the same type . The expected number of secondary infections caused by a pathogen with i escape mutations in an uninfected host population is given by its basic reproduction ratio: Ri , n=bidFi , n ( 1 ) where Fi , n = ( ϕ + ( 1 − ϕ ) ( fR i/n + ( 1 − fR ) ) ) is the effective fraction of hosts that can be infected by the focal pathogen . A pathogen with n escape mutations has a basic reproduction ratio equal to R0 ( 1 − c ) n , where R0 = b/d refers to the basic reproduction ratio of the pathogen with 0 escape mutations in a fully susceptible host population . Note , however , that a pathogen with 0 escape mutations introduced in a diverse host population has a basic reproduction ratio equal to R0 , n ≤ R0 . The key question we wish to address with this model is how the composition and structure of the host population determines the ultimate fate of a pathogen ( i . e . , extinction versus emergence , see S1 and S2 Figs ) . We detailed in the Materials and methods section the calculation of the probability of emergence , Pi , n , which is the probability that an inoculum of V0 pathogens with i escape mutations will not go extinct when introduced in a host population with n different resistance alleles . To understand the role of pathogen evolution in this process , we also derive the probability of evolutionary emergence , which quantifies the importance of escape mutations to pathogen emergence . Next , we wanted to experimentally explore the validity of the above predictions . While this is challenging given the paucity of suitable empirical systems that are amenable to experimental manipulations in a timely fashion , we explored whether this could be achieved by studying the evolutionary emergence of “escape” phages against bacteria with a CRISPR–CRISPR-associated ( Cas ) system . This immune defense provides full protection against a phage infection by adding phage-derived sequences ( known as “spacers” ) in a CRISPR locus carried by the bacterial host chromosome [14] . This empirical system allowed us to overcome three important technical challenges ( see details of the experimental protocols in the Materials and methods section ) . First , the stochastic nature of extinction requires a large number of replicate populations to measure a probability of emergence , which is possible using bacteria and phages in 96-well plates . Second , by mixing bacteria with different and unique CRISPR resistance alleles , we could manipulate the fraction of resistant hosts and the diversity in resistance alleles without affecting other traits of the host [18] . Third , unlike most other empirical systems , the mechanism of phage adaptation to CRISPR-based immunity is well known: lytic phages “escape” CRISPR resistance through mutation of their target sequence ( the “protospacer” ) [13 , 15 , 18 , 19 , 20] . In order to validate the model using this empirical system , we used eight CRISPR-resistant clones ( also referred as bacteriophage-insensitive mutants [BIMs] ) of the gram-negative Pseudomonas aeruginosa strain UCBPP-PA14 , each of which carried a single and distinct spacer targeting the lytic phage DMS3vir . Each of these spacers provides full resistance to infection . For each of these eight CRISPR-resistant clones , the rate at which the phage acquires escape mutations was found to be approximately equal to 2 . 8*10−7 mutations/locus/replication , as determined using Luria-Delbrück experiments ( see section S2 . 1 . 6 in S1 Text and S9 Fig ) . Using one of these BIMs , we first tested the theoretical prediction that the probability of emergence increases with the size of the virus inoculum ( V0 ) . To this end , 96 replicate populations , each composed of an equal mix of sensitive bacteria and a CRISPR-resistant clone , were exposed with five different inoculum sizes of the phage ( corresponding to a mean V0 of approximately 0 . 3 , 3 , 30 , 300 , and 3 , 000 phages ) . After 24 hours , we measured the fraction of phage-infected bacterial populations in which emergence had occurred . Consistent with the model predictions , we observed that the larger the phage inoculum size , the higher the probability of pathogen emergence ( Fig 3 , dashed line ) . In addition , we measured the fraction of viral populations in which the phages had evolved to escape CRISPR resistance . Again , in accordance with the theory , we found that larger phage inocula were associated with an increased evolution of phage escape mutations ( Fig 3 , full line , Kendall , z = 3 . 416 , tau = 0 . 784 , p < 0 . 001 ) . Furthermore , we obtained very similar results using a different empirical system consisting of the lytic phage 2972 and its gram-positive bacterial host Streptococcus thermophilus DGCC7710 . In this experiment , 96 populations composed of sensitive bacteria and a CRISPR-resistant clone were infected with three different inoculum sizes of the phage . As above , we found that a larger phage inoculum led to both a higher probability of emergence and a higher probability of evolutionary emergence ( S10 Fig ) . Next , we tested the theoretical prediction that the probability of pathogen evolutionary emergence is highest in populations with an intermediate fraction of resistant hosts ( Fig 4 ) . For each of the eight BIMs , we generated populations composed of sensitive bacteria and a variable proportion of CRISPR-resistant bacteria , ranging from 0% to 100% in 10% increments . These populations were subsequently infected with V0 = 300 phages , and the fractions of emergence and evolutionary emergence were measured . As expected , pathogen/phage emergence dropped when the proportion of host/bacteria resistance reached a threshold level ( S11 Fig ) . Interestingly , examination of phage evolution among emerging phage populations also confirmed that the probability of observing escape mutations is maximized for intermediate proportions of host resistance ( Fig 4 ) . Again , we obtained very consistent results with phage 2972 and S . thermophilus ( S10 Fig ) . We noticed substantial variation among CRISPR-resistant hosts in the observed frequencies of escape phage evolution ( Fig 4 ) . Variations in phage mutation rates are unlikely to explain this variability because , as pointed out above , we failed to detect significant variations in the rate of escape mutations to the different CRISPR-resistant hosts ( see S9 Fig ) . Variations in the fitness cost associated with these mutations could , however , explain the observed variations in the final frequency of escape mutations ( see S5 Fig ) . Finally , we experimentally explored the effect of resistance allele diversity on evolutionary emergence for a fixed proportion of host resistance ( fR = 0 . 5 ) . To this end , we generated bacterial populations that were composed of sensitive bacteria and an equal mix of one , two , four , or eight CRISPR-resistant clones . In this case , as expected , an inoculum size of 300 phages always led to pathogen emergence , but increasing host diversity had a strong negative effect on the ability of the phage to evolve to escape host resistance ( Fig 5 ) . We also found higher probabilities of observing multiple escape mutations in the low diversity treatment ( Kendall , z = −4 . 8771 , Tau = −0 . 3259 , p = 1 . 07*10−6 ) , which further supports the prediction that host diversity hampers the evolution of the phage population .
The emergence and re-emergence of pathogens has far-reaching negative impacts on wildlife , agriculture , and public health . Unfortunately , pathogen emergence events are notoriously difficult to predict and we need good biological models to experimentally explore the interplay between epidemiology and evolution taking place at the early stages of an epidemic . Here , we used a combination of diverse theoretical and experimental analyses to examine how the composition of a host population impacts the probability of pathogen emergence and evolution . Our theory is tailored to the biology of CRISPR–phage interactions , and subsequent validation using this experimental system demonstrates the predictive power of this theoretical framework . However , we suggest that this framework may be suitable for predicting pathogen emergence whenever hosts recognize specific pathogen epitopes and resistance can be overcome by epitope mutations . For instance , the specificity of the host–parasite interaction driven by CRISPR immunity ( S12 Fig ) is akin to the classical gene-for-gene system described in plant pathosystems [21] . However , host immunity may not always be perfect , which will impact both the dynamics and the evolution of the pathogen population [22–24] . To further generalize our findings , we derived the probability of pathogen emergence when immunity is imperfect ( see section S1 . 2 in S1 Text ) . Note , however , that this should be considered separately from the more complex epidemiological dynamics that occur when the probability of a successful infection depends on the pathogen dose or when the pathogen causes immunosuppression , both of which can cause emergence to become dependent on the pathogen population density [25 , 26] . Our framework provides several insights on emergence and re-emergence in both the presence and absence of pathogen evolution . For instance , this model captures how the composition and diversity of the host population impacts the emergence of a nonevolving pathogen . In this context , a larger proportion of resistant hosts decreases pathogen emergence , but this effect is weaker in spatially structured populations in which transmission is more likely to occur between the same host types , which allows for pathogen persistence in sensitive subpopulations . This effect is akin to the effect of the spatial distribution of suitable habitats on extinction thresholds [27–30] and consistent with earlier work that shows that host composition and spatial structure impact the growth rate of bacteriophage ϕ6 [31] . In the context of an evolving pathogen , our theory helps to explain the general observation that evolutionary emergence and the spread of escape mutations is maximal for an intermediate proportion of resistant hosts in the population [32] . Specifically , this is because increasing host resistance in the population has two opposite effects: ( i ) the influx of new mutations decreases because the ancestral pathogen cannot replicate on resistant hosts , and ( ii ) selection for escape mutations increases . Second , our model predicts that diversity in host resistance alleles decreases the probability of evolutionary emergence . Even though larger host diversity increases the number of adaptive mutations for the pathogen ( i . e . , a larger number of targets of selection ) , each mutation is associated with a smaller fitness advantage ( i . e . , a smaller increase in the fraction of the host population that can be infected ) . The theory presented here therefore helps to explain previous empirical data on the impact of host CRISPR diversity on the evolution of escape phages [18] . The link between host biodiversity and infectious diseases has attracted substantial attention recently [33–43] . Several studies support the “dilution effect” hypothesis , which postulates that host diversity limits disease spread [39 , 40 , 43] . For example , host diversity may limit the spread of a pathogen by increasing the fraction of bad-quality hosts in the population [43] . Indeed , increasing the fraction of resistant hosts ( but not the diversity of resistance alleles ) decreases the basic reproduction ratio of the wild-type pathogen [44 , 45] . In addition , host diversity per se may also limit disease spread , and several studies have shown the negative effect of host diversity on the deterministic growth rate of the pathogen under specific patterns of host–parasite specificity [35 , 46 , 47] . Notwithstanding these important insights , what sets our theoretical model apart is its ability to understand the factors that impact the initial pathogen emergence , rather than the downstream spread of a pathogen once it has already emerged . Studying this requires stochastic models , which are critical to model the probability of rare events , for example , pathogen spillover across species , including at the human-animal interface [48 , 49 , 4 , 50] , the emergence of drug resistance [51 , 52] , the evolution of vaccine resistance [53] , and the reversion of live vaccines [54–58] . In all these public health issues , understanding pathogen emergence requires models accounting for the stochastic nature of epidemiological and evolutionary dynamics . The present study focuses on the effect of the diversity of host resistance when each resistant host carries a single resistance allele ( i . e . , a single spacer in CRISPR ) . Our joint theoretical and experimental approach could be readily extended to evaluate the impact of the accumulation of multiple resistance alleles in a single host genotype rather than mixing multiple genotypes with a single resistance allele in the host population . The impact of such alternative strategies on the durability of resistance and on disease spread is particularly relevant in agriculture [59 , 60] . Our work provides a theoretical framework to study these different issues , and our experimental model system can be used to evaluate the ability of different control strategies to limit pathogen adaptation and emergence .
We detail the derivation of the probability of pathogen emergence presented in the main text ( the main parameters of the model are listed in S1 Table ) . We are interested in the ultimate fate ( extinction or not ) of a single pathogen with i escape mutations dropped into a very large host population with a proportion fR of resistant hosts . This resistant population is composed of an equal frequency of n different resistance genotypes . This free infectious particle first has to infect a host to avoid extinction , and the probability of ultimate extinction of this pathogen is Qi , n= ( 1-fR ) qi , n+fR ( inqi , n+n-in ) ( 5 ) where qi , n is the probability of ultimate extinction of the pathogen when it is currently infecting a host . Next , we focus on the probability qi , n ( t ) at time t that a pathogen with i mutations in an infected host will ultimately go extinct . In a small interval of time , dt , four different events may take place . First , the pathogen may transmit to a new host without additional escape mutations . Second , after a mutation event , the pathogen may transmit a pathogen with i + 1 escape mutations to a new host . Third , the infected host ( and the pathogen in the host ) may die . Fourth , nothing may happen during this interval of time dt . Collecting these different terms allows us to write down recursions for the probability qi , n ( t ) , at time t , as a function of the probability qi , n ( t + dt ) and qi+1 , n ( t + dt ) , at time t + dt: qi , n ( t ) =Ai , ndtqi , n ( t+dt ) qi , n ( t+dt ) ︸reproductionwithoutmutation+Bi , ndtqi , n ( t+dt ) qi+1 , n ( t+dt ) ︸reproductionwithmutation+ddt︸death+qi , n ( t+dt ) ( 1−Ai , ndt−Bi , ndt−ddt ) ︸noevent ( 6 ) with: Ai , n = bi ( 1 − ui , n ) Fi , n Bi , n = biui , nFi+1 , n Fi , n = ( ϕ + ( 1 − ϕ ) ( fR i/n + ( 1 − fR ) ) ) . The above calculation is based on the assumption that the pathogen never reaches a high prevalence and that the composition of the host population remains constant ( i . e . , Fi , n is assumed to remain constant ) . In other words , the probabilities qi , n ( t ) are assumed to be invariant with time . We can thus set qi , n ( t ) = qi , n ( t + dt ) to obtain a recursion equation that allows us to derive qi , n from qi+1 , n . The first term of this recursion gives the probability of extinction , qn , n that a pathogen with n escape mutations ( a pathogen fully adapted to the novel host population ) will go extinct . The heterogeneity of the environment has no impact on a fully adapted pathogen , and its probability of extinction is simply the extinction probability of the birth–death process: qn , n=1/ ( R0 ( 1-c ) n ) ( 7 ) Next , to derive qn−1 , n from qn , n , we need the recursion equation for qi , n . However , we have to distinguish two different scenarios . First , if Ai , n = 0 , for example , the case of a cell infected by a fully maladapted pathogen ( i . e . , i = 0 ) in a well-mixed population with no susceptible hosts ( i . e . , ϕ = 0 , fR = 1 ) , we find: qi , n=dd+Bi , n ( 1-qi+1 , n ) ( 8 ) Second , in the more general scenario , in which Ai , n > 0 , we have qi , n=Ci , n--4dAi , n+Ci , n22Ai , n ( 9 ) with: Ci , n = Ai , n + Bi , n ( 1 − qi+1 , n ) + d . Knowing qn , n and the above recursion equations , we can derive qn−1 , n and next qn−2 , n… until we get q0 , n . We are particularly interested in q0 , n and Q0 , n because these quantities measure the probability of extinction of a pathogen with no escape mutations ( in an infected host or as an infectious particle , respectively ) . Ultimately , we obtain the probability of emergence of an inoculum of V0 propagules of pathogen with no escape mutations ( when n = 1 , this yields Eq 2 in the main text ) : P0 , n=1- ( Q0 , n ) V0 ( 10 ) We show in Fig 2 how the diversity of host resistance affects the probability of pathogen emergence through a reduction of evolutionary emergence . In S4 Fig , we illustrate the interaction between host diversity and spatial structure in pathogen emergence . We show that more spatial structure decreases the impact of host diversity on evolutionary emergence and increases the overall probability of pathogen emergence . To study the impact of the host population composition on the probability of evolutionary emergence , we used two different microbial systems: ( i ) the gram-negative P . aeruginosa and its lytic phage DMS3vir , and ( ii ) the gram-positive S . thermophilus and its lytic phage 2972 . All the resistant bacteria ( i . e . , BIMs ) derived from the phage-sensitive wild-type strains P . aeruginosa UCBPP PA14 and S . thermophilus DGCC7710 rely on CRISPR-Cas immunity for complete resistance against the corresponding phage [14 , 61] . For all treatments , we performed 96 replicate infections of the corresponding host populations . We manipulated the composition of the host populations by mixing overnight cultures of sensitive bacteria and BIMs in the proportions indicated in the text , figures , and figure legends . Each replicate population was inoculated 1:100 into fresh growth media and infected with a quantity V0 of phages ( the inoculum size ) , as indicated in the text , figures , and figure legends . After 23 hours , we monitored within each population ( i ) the occurrence of phage epidemics ( i . e . , an emergence ) and ( ii ) the presence of escape mutants ( i . e . , an evolutionary emergence ) . A detailed description of these experiments is provided in section S2 of S1 Text .
|
The probability that an epidemic will break out is highly dependent on the ability of the pathogen to acquire new adaptive mutations and to induce evolutionary emergence . Forecasting pathogen emergence thus requires a good understanding of the interplay between the epidemiology and evolution taking place at the onset of an outbreak . Here , we provide a comprehensive theoretical framework to analyze the impact of host population heterogeneity on the probability of pathogen evolutionary emergence . We use this model to predict the impact of the fraction of susceptible hosts , the inoculum size of the pathogen , and the diversity of host resistance on pathogen emergence . Our experiments using lytic bacteriophages and CRISPR-resistant bacteria support our theoretical predictions and demonstrate that manipulating the diversity of resistance alleles in a host population may be an effective way to limit the emergence of new pathogens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"genome",
"engineering",
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"bacteriophages",
"pathogens",
"synthetic",
"biology",
"synthetic",
"bioengineering",
"crispr",
"viruses",
"mutation",
"evolutionary",
"emergence",
"synthetic",
"genomics",
"bioengineering",
"synthetic",
"genome",
"editing",
"ecological",
"metrics",
"pathogenesis",
"evolutionary",
"immunology",
"species",
"diversity",
"point",
"mutation",
"ecology",
"host-pathogen",
"interactions",
"genetics",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"evolutionary",
"processes",
"organisms"
] |
2018
|
Evolutionary emergence of infectious diseases in heterogeneous host populations
|
Nonalcoholic fatty liver disease ( NAFLD ) clusters in families , but the only known common genetic variants influencing risk are near PNPLA3 . We sought to identify additional genetic variants influencing NAFLD using genome-wide association ( GWA ) analysis of computed tomography ( CT ) measured hepatic steatosis , a non-invasive measure of NAFLD , in large population based samples . Using variance components methods , we show that CT hepatic steatosis is heritable ( ∼26%–27% ) in family-based Amish , Family Heart , and Framingham Heart Studies ( n = 880 to 3 , 070 ) . By carrying out a fixed-effects meta-analysis of genome-wide association ( GWA ) results between CT hepatic steatosis and ∼2 . 4 million imputed or genotyped SNPs in 7 , 176 individuals from the Old Order Amish , Age , Gene/Environment Susceptibility-Reykjavik study ( AGES ) , Family Heart , and Framingham Heart Studies , we identify variants associated at genome-wide significant levels ( p<5×10−8 ) in or near PNPLA3 , NCAN , and PPP1R3B . We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network ( NASH CRN ) . In comparisons with 1 , 405 healthy controls from the Myocardial Genetics Consortium ( MIGen ) , we observe significant associations with histologic NAFLD at variants in or near NCAN , GCKR , LYPLAL1 , and PNPLA3 , but not PPP1R3B . Variants at these five loci exhibit distinct patterns of association with serum lipids , as well as glycemic and anthropometric traits . We identify common genetic variants influencing CT–assessed steatosis and risk of NAFLD . Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits , suggesting genetic heterogeneity in the pathways influencing these traits .
NAFLD includes a spectrum of disease ranging from fatty infiltration of the liver ( steatosis ) to histologic evidence of inflammation ( nonalcoholic steatohepatitis or NASH ) , to fibrosis or cirrhosis , without a history of excessive alcohol ingestion [1] , [2] . NAFLD can lead to liver failure and is accompanied by substantial morbidity and mortality , with few known effective treatments [3] . Obesity is a primary risk factor for NAFLD , but not all obese individuals are affected [4] . Familial clustering of the disease has been identified [5]–[7] , suggesting that NAFLD may be influenced by genetic variants . However , thus far only one genetic locus has been found to reproducibly associate with magnetic resonance measured steatosis [8] , [9] . Liver attenuation measured using computed tomography ( CT ) is a quantitative measure that is inversely related to the amount of fat in the liver [10]–[12] . It is highly correlated ( r = 0 . 92 ) with the macrovesicular hepatic steatosis and thus is a non invasive measure of NAFLD [12] . The purpose of the present study was to determine the heritability of CT measured hepatic steatosis and to search for associated genetic variants in a meta-analysis of 7 , 176 individuals of European descent from the Framingham Heart Study ( FRAM ) , the Old Order Amish Study ( Amish ) , the Family Heart Study ( FamHS ) , and the Age , Gene/Environment Susceptibility-Reykjavik study ( AGES ) , which together comprise the GOLD ( Genetics of Obesity-related Liver Disease ) consortium ( See Table S1 ) . To validate top associating variants for risk of histologically verified NAFLD , we utilized cases from the NASH Clinical Research Network ( NASH CRN ) that were genetically matched to healthy controls from the Myocardial Genetics Consortium ( MIGen ) consortium ( See Table S1 ) . We then further tested genome wide significant or replicating SNPs for associations with histologic NAFLD using the same cases from the NASH Clinical Research Network ( NASH CRN ) versus a different set of controls from the Illumina Control Database ( iCONT ) ( See Table S1 ) . Further , we report the association of these SNPs with other metabolic traits using data from the Global Lipids Genetics [13] , GIANT [14] , DIAGRAM [15] , and MAGIC [16] Consortia , as well as investigate cis gene expression variation ( eQTLs ) in liver , subcutaneous and visceral fat from bariatric surgery patients from Massachusetts General Hospital [17] ( Figure 1 ) .
We estimated the heritability of CT hepatic steatosis in three family-based cohorts . We found that the heritability of CT hepatic steatosis was 0 . 27 ( standard error , SE 0 . 08 ) , 0 . 27 ( SE = 0 . 04 ) , and 0 . 26 ( SE 0 . 04 ) in the Amish , FamHS , and FRAM cohorts respectively ( n = 880–3 , 070 ) ( See Materials and Methods and Table 1 ) . These data suggest that CT hepatic steatosis , like other measures of fat has a genetic basis and that a search for influential genetic variants is warranted . To identify specific genetic loci associated with CT hepatic steatosis , genome-wide association analyses were carried out in each of the four studies ( See Materials and Methods and Tables S2 , S3 ) and the results combined using a fixed effects meta-analysis ( N = 7 , 176 in total ) . Variants at three loci emerged as being associated with CT hepatic steatosis at genome-wide significance levels ( p<5×10−8; Table 2 , Figure 2A ) . These included rs738409 in PNPLA3 ( p = 4 . 3×10−34 ) , a locus previously reported as associated with magnetic resonance spectroscopy measured steatosis , [8] and two additional novel loci: rs4240624 near PPP1R3B ( rs4240624 , p = 3 . 68×10−18 ) and rs2228603 near NCAN ( rs2228603 , p = 1 . 22×10−18 ) . The alleles associated with increasing CT hepatic steatosis ranged in frequency from 0 . 07 to 0 . 92 and together account for 4 . 4% of the variance in hepatic steatosis ( Table 2; range 0 . 79–2 . 41% ) . After removing these genome-wide significant loci , a quantile-quantile plot of the results demonstrated an excess of low p-values compared to expectations under the null ( Figure 2B ) , suggesting that additional variants among those with moderately low p-values may also be associated with this trait . Except for variants near PNPLA3 , we did not observe any variants in the region of any of the previously reported liver function test associated regions [18] . We could not assess whether the recently reported NAFLD associated variants near APOC3 [19] associate with CT hepatic steatosis as they were not genotyped on the Affymetrix or Illumina platforms used by our studies and these variants do not have proxies that we could use in HapMap to impute them . To determine whether SNPs with evidence of association with CT hepatic steatosis are also associated with histologic NAFLD , we genotyped 46 SNPs ( independent SNPs with p<5×10-3 , with independence defined as pairwise r2<0 . 1; See Table S4 for SNP details in GOLD and each cohort ) in 592 subjects with biopsy-proven NAFLD from the NASH CRN ( See Table S1 ) . Using ancestry-informative genetic markers [20] , we had previously matched these cases to 1 , 405 healthy controls [21] from the MIGen study [22] that had undergone GWAS genotyping and imputation ( See Table S1 ) . Forty-five of the 46 SNPs passed genotyping and imputation quality control in the NASH CRN and MIGen data sets respectively ( See Table S3 ) and were tested for association with histologic NAFLD in this sample . Two of the three variants with genome-wide significant associations to CT hepatic steatosis were also significantly associated with histologic NAFLD ( corresponding to a false discovery rate ( FDR ) p<0 . 001 ) : rs738409 in PNPLA3 ( OR = 3 . 26 , p = 3 . 6×10−43 ) as we and others have recently reported [21] , [23] and rs2228603 in NCAN ( OR = 1 . 65 , p = 5 . 29×10−5 ) which is a novel finding ( Table 2; See Table S5 ) . The rs4240624 variant near PPP1R3B was not associated with histologic NAFLD in this sample ( OR = 0 . 93 , p = 0 . 29 ) . Of the 43 remaining SNPs showing suggestive association with CT hepatic steatosis , rs780094 in GCKR ( OR = 1 . 45 , p = 2 . 59×10−8 ) and rs12137855 near LYPLAL1 ( OR = 1 . 37 , p = 4 . 12×10−5 ) were also significantly associated with histologic NAFLD ( Table 2; See Table S5 ) . To confirm that the effects on histologic NAFLD observed in the NASH CRN/MIGen analyses were not due to the characteristics of the controls , we performed a separate analysis of the NASH CRN cases with an alternate set of controls from the Illumina Control database ( iCONT; http://www . illumina . com/science/icontroldb . ilmn ) . We found that the effects and p values of rs738409 in PNPLA3 ( OR = 3 . 24 , p = 2 . 16×10−64 ) , rs2228603 in NCAN ( OR = 1 . 90 , p = 6 . 82×10−10 ) , rs4240624 near PPP1R3B ( OR = 0 . 86 , p = 0 . 15 ) , rs780094 in GCKR ( OR = 1 . 18 , p = 0 . 01 ) , and rs12137855 near LYPLAL1 ( OR = 1 . 21 , p = 0 . 03 ) were similar to the effects seen in MIGen establishing that these results are not dependent on the choice of control sample ( See Table S6 ) . Furthermore , assessment of imputation accuracy with the SNPs in these control sets indicates that imputed genotypes at the associated SNPs are likely to be highly accurate ( see Tables S7 , S8 ) . The variants with the lowest p-values of association with CT hepatic steatosis at the PNPLA3 ( rs738408 ) , NCAN ( rs2228603 ) , and GCKR ( rs780094 ) loci are in high LD with or are themselves non-synonymous variants in PNPLA3 ( rs738409; I148M , R2 = 1 ) , NCAN ( rs2228603; P91S , same as hepatic steatosis SNP ) , and GCKR ( rs1260326; P446L; R2 = 0 . 93 ) ( Figure 3 ) . The variants with the lowest p-values of association with CT hepatic steatosis at LYPLAL1 and PPP1R3B lie downstream and upstream of the coding regions of these genes ( Figure 3 ) . In epidemiologic studies NAFLD is associated with increased central obesity , higher low density lipoprotein ( LDL ) - cholesterol and lower high density lipoprotein ( HDL ) -cholesterol levels , impaired fasting glucose , increased risk of diabetes and increased insulin resistance . [24]In addition , variants in or near GCKR , NCAN , and PPP1R3B have been previously associated with lipid levels , GCKR with glycemic traits and LYPLAL1 with abdominal obesity [16] , [25]–[29] . Therefore , we examined the associations of each of the CT hepatic steatosis-associated variants with serum LDL-cholesterol , HDL-cholesterol , triglycerides ( TG ) , 2 hour glucose levels , 2 hour glucose levels controlled for body mass index ( BMI ) , fasting glucose , homeostatic model for beta call function ( HOMA-B ) , homeostatic model for insulin resistance ( HOMA-IR ) , fasting insulin , BMI , waist to hip ratio ( WHR ) controlled for BMI , and diabetes in the largest analyses of these traits available from the Global Lipids Genetics [13] , GIANT [14] , DIAGRAM [15] , and MAGIC [16] Consortia ( see Table 2 , Table S9 ) Interestingly , we observed several distinct patterns of association . The allele associated with increasing CT hepatic steatosis at NCAN was associated with lower triglycerides and plasma LDL-cholesterol levels . By contrast , the hepatic steatosis-increasing allele at GCKR was associated with higher levels of plasma LDL-cholesterol and triglycerides , lower fasting glucose , lower fasting insulin , lower HOMA-IR , but increased 2 hour glucose , increased 2 hour glucose controlled for BMI , and WHR controlled for BMI . The hepatic steatosis increasing allele at PPP1R3B was associated with increased HDL- and LDL-cholesterol levels and decreased fasting glucose . ( Table 2 , Figure 4 ) . The variants near PNPLA3 and LYPLAL1 were not associated with any of the traits tested ( See Table 2 , Table S9 and Figure 4 ) . For PNPLA3 ( rs738408 ) , NCAN ( rs2228603 ) , and GCKR ( rs780094 ) the variants with the lowest p-values of association with CT hepatic steatosis are either themselves missense SNPs or in high LD with missense SNPs . Thus , the most parsimonious model of how they may act is by directly affecting protein structure or function . However , the variants with the lowest p-values of association with CT hepatic steatosis near LYPLAL1 and PPP1R3B fall in non-coding regions and thus for these ( as well as the other three loci above ) we tested whether they have effects on the expression of nearby genes in liver and adipose tissue from a sample of bariatric surgery patients [17] ( See Table S10 ) . We found that that the hepatic steatosis increasing variant ( rs4240624 ) at the PPP1R3B locus increased liver mRNA expression of PPP1R3B and AW673036_RC and decreased expression of AK055863 . The hepatic steatosis increasing variant ( rs780094 ) at the GCKR locus increased expression of C2orf16 mRNA in liver . In these cases the eQTL with the lowest p-value of affecting these transcripts in the region was the same or highly correlated with the allele that had the lowest p-value of association with CT hepatic steatosis consistent with the possibility that these SNPs may function by affecting expression of nearby genes . For all other cases , the eQTL with the lowest p-value of affecting transcript expression at the locus was not eliminated by controlling for the variant that had the lowest p- value of association with CT hepatic steatosis and thus in these cases , the data do not support an expression effect as mediating the association with steatosis . Because alteration of PPP1R3B expression has been shown to affect serum lipid levels [13] one possibility is that changes in expression of this gene could mediate its effect on hepatic steatosis . For GCKR , the variant with the lowest p-value of association with CT hepatic steatosis is in high LD with a missense variant in GCKR which has been shown to affect GCKR function [30] . Thus , at GCKR an alternate model of action of how the CT hepatic steatosis associated variant affects hepatic steatosis is via altering GCKR function rather than via altering expression of C2orf16 . Further functional work will be needed to prove that these variants exert their effects on hepatic steatosis via these possible mechanisms .
We have identified variants in three novel loci ( NCAN , GCKR , and LYPLAL1 ) and one previously reported locus ( PNPLA3 ) that are associated with both increasing CT hepatic steatosis and histologic NAFLD . PPP1R3B is associated with CT steatosis but not histologic NAFLD that includes individuals mostly with inflammation and fibrosis . These variants all have distinct patterns of effects on NAFLD and metabolic traits . We have shown that CT hepatic steatosis is heritable and that GWA meta-analysis led to the identification of variants associated not only with CT hepatic steatosis but , also , with more severe NASH/fibrosis mostly present in the NASH CRN sample . Because CT hepatic steatosis measurements can be obtained noninvasively , much larger sample sizes can be accumulated , thereby increasing power to identify variants that associate with NAFLD compared with only studying individuals that have histology diagnosed disease . Follow-up association testing in samples with histologic phenotypes remains useful however . We did observe one variant near PPP1R3B that was associated with CT–assessed liver attenuation but not histology-proven NAFLD . Possible reasons for why the variant near PPP1R3B is associated with CT liver steatosis but not histology-proven NAFLD include 1 . It influences steatosis only , not progression to NASH/fibrosis: 2 . its association with CT fat may be a false positive: 3 . the NASH CRN/MIGen sample is underpowered to see an effect on histologic NAFLD: or 4 . the variant is associated with something other than fat reflected in the CT scan ( eg . glycogen content ) . Further work is needed to differentiate among these possibilities . We show that some of the variants that are associated with increased CT hepatic steatosis have distinct patterns of effects on metabolic traits that , when taken together , give us insight into their functional clustering . For example , unlike the other three loci , variants in or near PNPLA3 and LYPLAL1 do not affect any of the other metabolic traits and interestingly PNPLA3 and LYPLAL1-related proteins have been predicted to play a role in consecutive steps in triglyceride breakdown [31] , [32] . Thus these could increase hepatic steatosis by preventing breakdown of triglycerides , as recently shown for PNPLA3 ( I148M ) [33] . The apparent discordance between the strong effect on hepatic steatosis and modest , if any , effect on serum lipid levels suggests that these genes , if they are involved in lipid metabolism , exert their effects within the liver in ways that are not well reflected in serum measurements . Thus , similarities in the pattern of pleiotropic effects on other traits may provide insights into the functional clustering of the genes that these variants effect . Unlike PNPLA3 and LYPLAL1 , variants near NCAN ( which encodes for an adhesion molecule [34] ) , PPP1R3B ( which encodes for a protein that regulates glycogen breakdown [35] ) , and GCKR ( which , through inhibition of glucokinase , regulates glucose storage/disposal and provides substrates for de novo lipogenesis [30] ) , are associated with distinct changes in serum and liver lipids as well as glycemic traits . Indeed , these data may provide new insights into how obesity can lead to metabolic complications in some but not all individuals- some but not all of these individuals carry variants that predispose them both to liver fat deposition and to metabolic dysregulation . Further , our data show that the alleles of SNPs that associate with increased liver steatosis are also associated with a diverse pattern of metabolic phenotypes including different combinations of increased or decreased serum LDL-cholesterol , increased serum HDL-cholesterol , increased serum TG , decreased serum fasting glucose and insulin , decreased insulin resistance , and increased WHR adjusted for BMI . In addition , some hepatic steatosis-associated variants are not strongly associated with any of these metabolic traits ( PNPLA3 and LYPLAL1 ) . These results indicate that hepatic steatosis is likely to be influenced by different metabolic pathways , based on these various patterns of association . Thus it may be possible to resolve genetic heterogeneity in the etiology of hepatic steatosis , which may present unique opportunities for personalized therapies . Compared with earlier efforts , this study is well-powered , using more than 7 , 176 individuals for discovery of variants that affect NAFLD . Thus , noninvasive measures of hepatic steatosis such as CT scanning can provide valuable information for use in population- and family-based studies aimed at identifying genetic risk factors for NAFLD . Although the identities of nearby genes and effects on lipid levels provide important clues , functional studies will be needed to further understand the mechanisms by which these risk factors influence the development and progression of NAFLD . Overall however , our work gives us new insights into the biology and genetics of NAFLD and opens up avenues for biological , diagnostic , and therapeutic research for this condition in humans .
All work done in this paper was approved by local institutional review boards or equivalent committees . Each of the participating studies had the overarching objective of investigating cardiovascular disease and its risk factors . The studies are population based and 3 of the 4 are family studies . Genome-wide SNP data were available in each case , and the platforms and quality control measures are described in Tables S2 and S3 . To define independently associated SNPs , the LD was required to be R2<0 . 10 and the SNPs located at least 1 megabase from each other . From among these , the SNP with the strongest association was chosen for follow up ( P<0 . 0001 ) . Two iPlex pools consisting of 46 SNPs were designed and were successfully genotyped in the NASH CRN samples . Of these , only 45 were imputed well in MIGen , and only these SNPs were analyzed . Variants with a false discovery rate of q <0 . 05 were considered associated with NAFLD . Study: The NASH CRN samples were collected from eight different centers in the U . S . as previously described [2] , [49] . Adults from both the Database and the PIVENS trial ( Pioglitazone versus Vitamin E versus Placebo for the Treatment of Nondiabetic Patients with Nonalcoholic Steatohepatitis ) were used for analysis . Briefly , individuals from the Database were part of an observational study of nonalcoholic fatty liver disease . Inclusion criteria included age >18 , histologic diagnosis for NAFLD , or histologic diagnosis for cryptogenic cirrhosis or suspected NAFLD on the basis imaging studies suggestive of NAFLD , or clinical evidence of cryptogenic cirrhosis . No subjects reported regular excessive use of alcohol within two years prior to the initial screening period . Exclusion criteria included histologic evidence of liver disease besides nonalcoholic liver disease , known HIV positivity , and conditions that would interfere with study follow up . Individuals in the PIVENS database were part of a multicenter placebo controlled study with three parallel groups examining the effects of pioglitazone vs . vitamin E vs . placebo on NAFLD . Inclusion and exclusion criteria were as described previously [2] , [49] . For this analysis , we excluded individuals who did not describe their race as being white and non-Hispanic . There were 678 adults who matched these criteria . Finally , individuals without histology available for central review were excluded , leaving 592 adults for the current study . Histologic diagnoses were determined in the NASH CRN by central review by NASH CRN hepatopathologists using previously published criteria [2] , [49] . Predominantly macrovesicular steatosis was scored from grade 0–3 . Inflammation was graded from 0–3 and cytologic ballooning from 0–2 . The fibrosis stage was assessed from a Masson trichrome stain and classified from 0–4 according to the NASH CRN criteria . Individuals could contribute to more than one of these outcomes . The NASH CRN samples were genotyped and analyzed as described in Tables S2 and S3 . MIGen controls were matched to the NASH CRN samples for genetic background . As previously described , the MIGen samples were collected from various centers in the US and Europe by the Myocardial Infarction Genetics Consortium ( MIGen ) [22] as controls for individuals with early onset MI . The genetic ancestry the MIGen samples was explored by using the program Eigenstrat [46]; the first principal component was the most significant and correlated with the commonly observed Northwest- Southeast axis within Europe [20] and genetic ancestry along this principal component is correlated with reported country of origin in the MIGen sample [22] . From this analysis , 120 unlinked SNPs were chosen from the MIGen genotype data that were most strongly correlated with the first principal component . These SNPs were genotyped in the NASH CRN samples to enable matching of MIGen controls to the NASH CRN [20] cases for genetic background . PLINK [50] was used to match individuals based on identity by state ( IBS ) distance using a pairwise population concordance test statistic of >1×10−3 for matching . The SNPs selected for validation were tested in this case-control sample using logistic regression controlling for age , age2 , gender , and the first 5 principal components as covariates in PLINK [50] . We report the p-values , odds ratios and confidence intervals . We obtained 3 , 294 population based control samples with genotypes from Illumina ( see http://www . illumina . com/science/icontroldb . ilmn ) . These individuals were used as controls in various case control analyses . Individuals were removed as described in Table S4 and 3 , 212 individuals were then used as controls for the NASH CRN/iCONT analyses . The 592 individuals from the NASH CRN described above were used as cases and 3 , 212 individuals from the iCONT database were used as controls . Genome wide significant or replicating SNPs were tested in this case-control sample using logistic regression controlling for gender in PLINK [50] . We report the p-values , odds ratios and confidence intervals . To assess the concordance of imputed SNPs in the MIGen and iCONT samples we obtained the genotyped SNPs from the HapMap3 TSI ( Tuscans from Italy ) sample . Using only the SNPs present on the Affymetrix 6 . 0 platform ( used to genotype MIGen ) or only the SNPs present on the Illumina platform ( used to genotype iCONT samples ) and the LD information from HapMap2 we imputed the remainder of the SNPs using MACH ( 1 . 0 . 16 ) and compared the imputed calls to the actual genotypes stratified by imputation quality score ( R2 hat ) . To obtain data on whether CT hepatic steatosis SNPs affect other metabolic traits we obtained data from four consortia that had the largest and most powered analyses of these traits . Association results for HDL- , LDL- cholesterol levels and triglycerides ( TG ) were obtained from publicly available data of the GLOBAL Lipids Genetics Consortium ( http://www . sph . umich . edu/csg/abecasis/public/Teslovich et al . 2010 ) [13] . Association results for fasting insulin , glucose , 2 hr-glucose , HOMA-IR and HOMA-B were obtained from the MAGIC Investigators . Association results for risk of type 2 diabetes were obtained from the DIAGRAM consortium [15] . Association results for risk of BMI and waist to hip ratio controlled for BMI were obtained from the GIANT consortium [14] . We used a conservative nominal p<0 . 0008 corresponding to a bonferroni correction of 12 phenotypes tested for 5 SNPs to determine significance . The expression QTL analyses in liver , subcutaneous and omental fat tissue have been described in detail previously [17] . Tissue were obtained from patients who underwent bariatric surgery , and RNA expression assessed using a custom Agilent 44 , 000 feature microarray composed of 39 , 280 oligonucleotide probes targeting transcripts representing 34 , 266 known and predicted genes . Patients were also genotyped on the Illumina 650Y SNP genotyping arrays . SNPs were tested for cis-associations with transcripts within a 1 Mb region , assuming an additive effect of the CT hepatic steatosis increasing allele adjusting for age , race , gender , and surgery year using linear regression . Cis-associations between each SNP and the adjusted gene expression data were tested , and only associations with a nominal p-value <3 . 5×10−5 corresponding to a bonferroni correction for 284 gene transcripts x 5 SNPs tested are shown in Table S10 . Conditional analyses were performed by conditioning the CT hepatic steatosis associated SNP on the most significant cis-associated SNP for that particular gene transcript and vice versa .
|
NAFLD is a spectrum of disease that ranges from steatosis to steatohepatitis ( nonalcoholic steatohepatitis or NASH: inflammation around the fat ) to fibrosis/cirrhosis . Hepatic steatosis can be measured non-invasively using computed tomography ( CT ) whereas NASH/fibrosis is assessed histologically . The genetic underpinnings of NAFLD remain to be determined . Here we estimate that 26%–27% of the variation in CT measured hepatic steatosis is heritable or genetic . We identify three variants near PNPLAL3 , NCAN , and PPP1R3B that associate with CT hepatic steatosis and show that variants in or near NCAN , GCKR , LYPLAL1 , and PNPLA3 , but not PPP1R3B , associate with histologic lobular inflammation/fibrosis . Variants in or near NCAN , GCKR , and PPP1R3B associate with altered serum lipid levels , whereas those in or near LYPLAL1 and PNPLA3 do not . Variants near GCKR and PPP1R3B also affect glycemic traits . Thus , we show that NAFLD is genetically influenced and expand the number of common genetic variants that associate with this trait . Our findings suggest that development of hepatic steatosis , NASH/fibrosis , or abnormalities in metabolic traits are probably influenced by different metabolic pathways that may represent distinct therapeutic targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/complex",
"traits",
"diabetes",
"and",
"endocrinology/obesity",
"gastroenterology",
"and",
"hepatology/hepatology"
] |
2011
|
Genome-Wide Association Analysis Identifies Variants Associated with Nonalcoholic Fatty Liver Disease That Have Distinct Effects on Metabolic Traits
|
Persistent activity has been reported in many brain areas and is hypothesized to mediate working memory and emotional brain states and to rely upon network or biophysical feedback . Here , we demonstrate a novel mechanism by which persistent neuronal activity can be generated without feedback , relying instead on the slow removal of Na+ from neurons following bursts of activity . We show that mitral cells in the accessory olfactory bulb ( AOB ) , which plays a major role in mammalian social behavior , may respond to a brief sensory stimulation with persistent firing . By combining electrical recordings , Ca2+ and Na+ imaging , and realistic computational modeling , we explored the mechanisms underlying the persistent activity in AOB mitral cells . We found that the exceptionally slow inward current that underlies this activity is governed by prolonged dynamics of intracellular Na+ ( [Na+]i ) , which affects neuronal electrical activity via several pathways . Specifically , elevated dendritic [Na+]i reverses the Na+-Ca2+ exchanger activity , thus modifying the [Ca2+]i set-point . This process , which relies on ubiquitous membrane mechanisms , is likely to play a role in other neuronal types in various brain regions .
The accessory olfactory system , also known as the vomeronasal system , mediates chemical communication between conspecifics of most mammalian and reptilian species during social interactions [1] . Inputs to this chemosensory system originate from the sensory neurons of the vomeronasal organ ( VNO ) that synapse on the mitral cells of the accessory olfactory bulb ( AOB ) , which provide the output of the bulb [2] . Previously , we have shown that AOB mitral cells in vitro respond to brief afferent nerve stimulation with persistent firing activity lasting several minutes [3] . Persistent activity , defined as the ability of neurons to remain active in the absence of external inputs , was documented in many brain areas . Such activity enables the brain to maintain an internal state without continuous external input . It has been suggested that persistent activity is a neuronal correlate of working memory [4] , and that it can mediate neuronal integration over long time scales [5] . The time scale of persistent activity ( >1 min ) is much longer than that of most biophysical mechanisms ( typically 0 . 5–100 ms ) . Most attempts to explain how the extremely prolonged time scales of persistent activity emerge from such rapid biophysical processes have involved feedback mechanisms [6] . Such feedback can be implemented with recurrent excitation at the network level [7–9] , or alternatively , by biochemical pathways at the cellular level . An example of the latter is the mechanism proposed to underlie persistent activity in the entorhinal cortex [10 , 11] and hippocampal CA1 pyramidal neurons [12 , 13] . The mechanism involves an interaction between Ca2+ influx during spiking and a calcium-activated non-selective ( CAN ) cation conductance that depolarizes the cell . However , theoretical models of prolonged spiking based on feedback mechanisms are hard to construct in a way that is robust to small parameter changes , immune to noise and continuously graded [10 , 14–16] . Persistent activity in AOB mitral cells was shown to depend upon Ca2+ influx and CAN conductance . However , this intrinsic cellular mechanism does not depend on a feedback cycle involving ongoing neural activity , as persistent firing readily resumes after a temporal firing cessation [3] . In the present study we combined electrophysiological , imaging , and computational approaches to explore the mechanisms underlying persistent firing in AOB mitral cells . We describe a novel mechanism involving interplay between homeostatic processes controlling intracellular Na+ and Ca2+ concentrations . This novel mechanism , which does not rely upon feedback , is both resistant to noise and allows multiple stable firing states .
Prolonged firing activity of AOB mitral cells was demonstrated in behaving mice during social investigation of conspecifics [17] . It has remained unclear whether this sustained activity reflects the continuous detection of the stimulus or network properties . In order to explore this issue , we examined AOB responses in anesthetized mice following well-controlled chemosensory stimulus application to the VNO ( Fig 1A and 1B , S1 Fig ) [18] . While response dynamics often matched those attributed to the vomeronasal pump [18 , 19] , in other cases , elevated firing rates remained high well beyond this time scale , sometimes even after the stimulus was flushed from the nasal cavity and the VNO . Under a highly strict statistical criterion ( see Data Analysis in Materials and Methods ) , reliable cases of persistent activity were found in about one percent ( n = 7 ) of the recorded units , and were associated with a particular stimulus , while other stimuli elicited only transient response in the same cells ( Fig 1A and 1B ) . This stimulus selectivity is consistent with a requirement for a high level of activation to trigger the prolonged firing ( see below ) . Similarly , prolonged single unit AOB spiking activity could be readily elicited in anesthetized mice by direct stimulation of the vomeronasal nerve with a metal electrode ( Fig 1C ) , further confirming that the sustained responses are independent of VNO dynamics . Finally , In agreement with our previous study [3] , persistent firing could be elicited in AOB mitral cells in brain slices . An example is shown in Fig 1D ( top ) , where a 4 s train of action potentials is followed by a prolonged period of persistent spiking at a rate of 1–3 Hz lasting for over a minute . The reproducibility of this firing epoch is demonstrated by the mean rate response for the three recorded cells ( Fig 1D , bottom ) . Altogether , these results and the results of our previous studies [3 , 20] prove that AOB mitral cells are capable of persistent firing responses , both in vitro and in vivo to either electrical or chemical sensory stimulation . Conducting the in vitro protocol described above while shifting the membrane potential to −60 mV ( Fig 2A ) blocked the persistent firing and unmasked a prolonged depolarization with a similar time course as the firing activity ( compare to Fig 1D ) . To analyze the currents underlying the prolonged depolarization , the hybrid-clamp methodology was used ( see Materials and Methods ) . Cells were voltage-clamped to −80 mV and trains of action potentials at various frequencies were delivered during a 4 s long current-clamp period . The evoked inward current ( Fig 2B ) comprised an initial , rapidly decaying phase ( transient phase , enlarged in Fig 2C ) , followed by a second , prolonged phase ( persistent phase ) , that peaked after >10 s ( Fig 2B , arrows ) and slowly decayed with a more prolonged time course ( >30 s ) . Notably , the charge transfer during each of the phases monotonically increased with the stimulus frequency ( Fig 2D ) . Thus , the prolonged inward current underlying persistent firing in AOB mitral cells seems to involve transient and persistent components that are proportional to the firing frequency during the stimulation . The complex dynamics of the prolonged inward current suggest that multiple biophysical mechanisms are involved . To isolate the participating processes , we abolished the inward current , previously shown to be mediated by Ca2+-dependent , CAN conductance [3] . Removal of Ca2+ from the extracellular solution , as well as blocking the increase in [Ca2+]i by adding 5 mM BAPTA to the pipette solution ( S2 Fig ) , abolished the prolonged inward current . Under these conditions , the stimulating train was followed by an outward current of 18±3 pA that monotonically decayed with a single time constant ( τ = 5±1 s , n = 11 cells , Fig 2E , green trace ) . Subtracting the outward current from the control condition current ( Fig 2E , blue trace ) yields a net inward current ( Fig 2E , black trace ) , which is likely due to the CAN conductance . Similar results were previously obtained by blocking N/R type voltage-sensitive Ca2+ channels [3] . As shown in Fig 2F , the outward current measured in the absence of Ca2+ ions was independent of membrane potential , suggesting that it is not mediated by ionic conductance . The most likely candidate for a voltage-insensitive outward current is an ionic pump current , such as the one produced by the plasma membrane Na+-K+ pump ( Na+-K+ ATPase ) [21] . As shown in Fig 2G ( green ) , blocking the Na+-K+ pump using ouabain unmasks a strong net inward current peaking immediately after the spike train . The difference between the currents before and after ouabain application is a net outward current resembling the one measured in the absence of Ca2+ ions ( Fig 2G , black ) . Thus , the outward current is most likely mediated by the Na+-K+ pump . Overall , these data suggest that the complex dynamics of the prolonged inward current reflect the sum of two opposing currents—a voltage-independent outward current ( Na+-K+ pump ) decaying over a few seconds and a prolonged Ca2+-dependent inward current ( ICAN ) that remains active for minutes . To study the spatio-temporal relationship between [Ca2+]i and ICAN , we correlated [Ca2+]i indicator fluorescence in various cellular compartments with the simultaneously recorded somatic inward current . To that end , AOB mitral cells were filled with a Ca2+ indicator using the patch pipette ( inset in Fig 3 ) . Then , tuft fluorescence ( Fig 3A ) and the corresponding inward currents ( Fig 3B ) were simultaneously monitored as trains of action potentials at various frequencies were delivered via the patch pipette to activate the neurons . The transient increase in [Ca2+]i in the dendritic tuft was followed by an extremely prolonged decay lasting longer than the interval between stimuli , resulting in summation of [Ca2+]i levels over consecutive trains ( Fig 3A ) . Similarly , the prolonged inward current also persisted longer than the interval between stimuli , resulting in progressive increase in inward current as well ( Fig 3B ) . To analyze the relationship between dendritic [Ca2+]i and the inward current , the current amplitude at each time point was plotted against the simultaneously measured fluorescence level . Fig 3C shows this analysis , applied to the data shown in Fig 3A and 3B . As apparent , the inward current shows a clear sigmoidal dependence on the fluorescence signal , suggesting that the [Ca2+]i in the dendritic tuft tightly correlates with the slow dynamics of inward current ( see S3E and S3F Fig for more examples ) . In contrast to tuft fluorescence , somatic fluorescence does not correlate with the magnitude of the inward current ( Fig 3D , S3A and S3D Fig ) . The close relationship between tuft [Ca2+]i and the magnitude of the inward current suggests that the prolonged current reflects the extended elevation of tuft [Ca2+]i . Indeed , close examination of tuft fluorescence levels ( Fig 3E ) revealed that the decay of [Ca2+]i in the tuft followed two distinct time scales: fast initial decay ( τ = 1 . 9 s , mean of three cells ) followed by very slow decay ( τ = 47 . 0 s ) . This slow process suggests that [Ca2+]i is in a quasi-stable state , the level of which is determined by the stimulus frequency . Consistent with this , increasing the stimulation frequency from 15 Hz to 30 Hz almost doubled the quasi-stable state level ( Fig 3E , blue and green traces ) . Overall , these results suggest that the inward current underlying persistent firing of AOB mitral cells is mediated by dendritic Ca2+-dependent ionic conductance ( CAN ) and that its slow dynamics likely reflect a complex interaction between several ionic extrusion mechanisms . The result described above , in which the tuft [Ca2+]i decays to a quasi-stable state determined by the stimulation frequency , suggests that the quasi-stable state is generated by slowly changing , activity-dependent quantity . One such quantity may be the tuft [Na+]i , which affects the Ca2+ dynamics by interacting with ionic transport mechanisms such as the Na+-Ca2+ exchanger . This exchanger , which is the major mechanism for control of large excess Ca2+ [22] , uses the Na+ electrochemical gradient to extrude Ca2+ . Thus , increase in [Na+]i which leads to a decreased Na+ gradient , reduce or even reverse the Ca2+ flux through the exchanger [23] . We examined this possibility in a simple abstract dynamical model , with a minimal number of parameters ( Fig 4A; see S1 Text for a description of the model equations ) . In this model , [Ca2+]i and [Na+]i increase at a rate proportional to an abstract “voltage” quantity , given that the “voltage” is above a certain threshold . [Na+]i decays exponentially to zero over time , while [Ca2+]i decays to a level linearly determined by [Na+]i ( the quasi-stable state ) . The “voltage” is a sum of three components: externally applied current , inward Ca2+-dependent current , and outward ( negative ) Na+-dependent “pump” current . Fig 4B and 4D shows the results of running this model with a pulse of externally applied current ( black bar ) . As apparent , the “voltage” ( Fig 4B ) behavior qualitatively resembles the experimental observations ( compare to Figs 1D and 2A ) . This voltage trajectory is due to the changes in [Na+]i and [Ca2+]i ( Fig 4C ) and the corresponding currents ( Fig 4D ) . Thus , the feasibility of the mechanism suggested above is confirmed by this simple abstract model . In order to further test this hypothetical mechanism and produce quantitative predictions , we incorporated the principles of this mechanism into a realistic conductance-based model . A realistic conductance-based model ( see Materials and Methods ) , was constructed using the detailed morphology of a single typical mitral cell ( Fig 4E and S4 Fig ) for which the electrophysiological properties were characterized . The model assumes that active conductances reside in the apical dendrites and dendritic tufts , as well as in the soma and axon initial segment [24] , so that [Na+]i increase in these compartments following firing . A novel feature of our model is the incorporation of compartmental [Na+]i as state variables along with longitudinal ionic diffusion . Accordingly , [Na+]i not only sets the local Na+ reversal potential but also affects localized ionic extrusion mechanisms ( Na+-K+ pumps , Na+-Ca2+ exchangers ) . The Ca2+ influx , buffering and extrusion mechanisms ( including a simulated Ca2+ indicator ) , as well as a Ca2+ dependent conductance , were introduced in the dendritic tufts . The spatial distribution of membranal mechanisms in the model is shown in Fig 4E ( See Materials and Methods for a link to the model source code and S1 Text for a full description of the model equations and parameters ) . Using such a model , one can calculate the temporal dynamics of [Na+]i and [Ca2+]I in various cellular compartments . An evolutionary multi-objective algorithm [25 , 26] was used to find the biophysical parameters that best fit our electrophysiological observations . An initial evolutionary process was employed to find the best fit to the following measured parameters: the response to a hyperpolarizing current pulse ( Fig 5A ) , the shape of the action potential ( Fig 5B ) , the modulation of the spike amplitude during a strong ( 350 pA ) current injection ( Fig 5G ) , and the I-f curve ( Fig 5H ) . As shown , the model accurately reproduces the behavior of the real cell with respect to these objectives . Notably , the spike amplitude modulation during depolarizing current injection simulated by the model precisely fitted the experimental observations , despite the fact that only a 350 pA current injection was used as an objective ( Fig 5E–5G ) . Importantly , the spike amplitude modulation in the model was the result of [Na+]i accumulation during the spike train and would not be reproduced when [Na+]i accumulation was prevented ( S5 Fig ) . The goal of the next evolutionary process was to find the parameters that reproduce the tuft Ca2+ indicator fluorescence and the prolonged somatic inward current ( see Materials and Methods ) . Trains of action potentials with frequencies of 1 , 15 , and 30 Hz were used to activate the model neuron . The simulated dendritic tuft fluorescence and the accompanying prolonged inward current were then compared to the experimental observations . At a frequency of 1 Hz ( Fig 6A ) , the simulation ( red line ) perfectly reproduced the observed fluorescence signal ( blue line ) . At higher frequencies ( 15 Hz and 30 Hz , Fig 6B ) , both measured ( blue and green lines ) and simulated ( orange and red lines ) fluorescence levels , rapidly increased to saturation levels during the stimulation ( Fig 6B , top green bar ) . The rapid increase was followed by an equally rapid decline to a low quasi-stable level that strongly depended on the stimulation frequency ( Fig 6B ) . Moreover , the simulated prolonged inward current also closely fit the experimentally measured current ( Fig 6C ) . In order to assess the sensitivity of the model to changes in its parameters , we created a population of 1 , 200 model neurons . In each model , each parameter ( except the channels' half-activation voltage parameters ) was randomly selected from a uniform distribution that spanned between -10% and +10% relative to the original value . We then examined , in each of the models , the predicted prolonged inward currents evoked by 30Hz spike train . The properties of the resulting currents distribute normally ( see example histograms for the maximum current in S6A Fig and the residual current after 1 min for the train end in S6B Fig ) . As apparent from the 80% bounds of the distribution ( S6C Fig ) , this change in parameters did not cause a large deviation from the fit of the model to the experimental data . A critical validation of the model is its ability to reproduce the persistent firing recorded in AOB mitral cells . Indeed , a train of simulated spikes evoked long lasting persistent activity ( Fig 6D ) which resembled the experimental observations ( Fig 1D ) . Adding Gaussian current noise introduced variability to the responses that upon averaging reproduced the PSTH observed in vitro ( compare Figs 6E to 1D ) . Another critical validation is the ability of the model to predict the time course of dendritic [Na+]i , and particularly its rise during stimulation and very slow subsequent decay that maintains a quasi-stable state for the tuft [Ca2+]i . To that end , we used two fluorescent Na+ indicators , sodium-binding benzofuran isophthalate ( SBFI ) and Sodium Green , to image the dendritic Na+ dynamics following a stimulus train [27] . As shown in Fig 6F , the averaged observed dynamics ( thick green line ) indeed match the predicted dynamics ( red line ) ( For dF/F signal not normalized by standard deviation and similar results using the Sodium Green indicator , see S7 Fig ) As shown in the presence of tetrodotoxin ( TTX , blue lines ) , the signal does relate to opening of voltage-gated Na+ channels . Notably , similar dynamics were previously observed experimentally in cortical pyramidal neurons [27] . Thus , we conclude that our mitral cell model adequately reproduces the experimental observations . We used this model to examine possible mechanisms underlying the prolonged current responses of AOB mitral cells . We first examined the time course of [Na+]i in two cellular compartments: the axon initial segment ( AIS ) and the dendritic tuft , while the model neuron was activated by a 4 s train of 15 or 30 Hz ( Fig 7A , orange and red traces , respectively ) . The model predicts that during the stimulus , [Na+]i at the AIS ( dashed lines ) would reach a very high level ( 50 and 63 mM for 15 and 30 Hz stimulation , respectively ) , followed by a relatively rapid decline ( τ = 3 . 85 s ) to baseline levels . The large increase in AIS [Na+]i is due to the high density of voltage-gated Na+ channels and the limited volume of the compartment . The relatively fast recovery results from the activity of the Na+-K+ pump and the fast diffusion to the compartments adjacent to the AIS ( axon and soma ) that serve as diffusion sinks . In contrast to the AIS , tuft [Na+]i increased only to a moderate level ( 15 and 17 mM for 15 and 30 Hz stimulation—solid orange and red traces in Fig 7A , respectively ) , followed by extremely slow exponential recovery over a time course of minutes ( τ = 130 s and 115 s for 15 and 30 Hz stimulation , respectively ) . This slow time course , also observed in real mitral cells using Na+ fluorescent indicators ( Fig 6F ) , stems from the slow diffusion in the thin dendritic process and the low density of Na+-K+ pumps in this compartment . The prolonged elevated [Na+]i in the dendritic tuft is bound to affect the Na+-Ca2+ exchanger , hence to determine the quasi-stable state of [Ca2+]i . We examined this prediction using the model by calculating the stable-state [Ca2+]i for various fixed [Na+]i . values . As shown in Fig 7B ( inset ) , an increase in [Na+]i elevated the stable-state [Ca2+]i non-linearly . This relationship was used to calculate the quasi-stable state of [Ca2+]i during and after the stimulation train , based on the instantaneous values of [Na+]i . As shown in Fig 7B for stimulation frequencies of 15 Hz and 30 Hz , the simulated tuft [Ca2+]i ( solid lines ) quickly dropped to its quasi-stable state level ( dashed lines ) , and then closely followed the slow decrease of the quasi-stable state . The proposed mechanism that maintains [Ca2+]i in a quasi-stable state level is demonstrated in Fig 7C and 7D , where the Ca2+ currents of the exchanger ( dashed green line ) and the Ca2+ pump ( dashed orange line ) are shown along with schematic diagrams depicting each state . Immediately following the stimulus train , both currents are positive ( outward , Fig 7D , middle ) , but then the exchanger current becomes negative ( inward ) while the pump current remains positive . As a result , the net current of the Ca2+ regulatory mechanisms reaches a near-zero value ( Fig 7C , blue line; Fig 7D , right ) . The inward current mediated by the exchanger reflects the condition of high [Na+]i level , that causes the exchanger to operate in a "reverse mode" ( Ca2+ influx ) . In this state [Ca2+]i is determined by the slow change of the quasi-stable state , which is due to the slow return of tuft [Na+]i back to baseline levels ( Fig 7A ) . As described above , the prolonged quasi-stable state of [Ca2+]i is the result of the opposing actions of the pump Ca2+ efflux and Ca2+ influx due to the reverse-mode of the Na+-Ca2+ exchanger ( Fig 7C and 7D ) . Therefore , blockade of the exchanger should result in acceleration of the recovery of [Ca2+]i . We examined this prediction using the model by calculating the decay of the tuft fluorescence under control conditions ( Fig 8A , orange line ) and after blocking the exchanger ( Fig 8A , red line ) . Indeed , a much faster return to baseline levels was obtained in the absence of exchanger activity . Experimentally , such blockade can be realized by substituting Na+ ions in the extracellular solution with Li+ , which cannot be transported by the exchanger [28–30] . This manipulation was simulated by modeling the effect of Li+ on the exchanger and the Na+-K+ pump [31 , 32] . Fig 8B shows that following substitution of Na+ by Li+ in the model , the slow inward current ( orange line ) is replaced by a fast , transient inward current ( red line ) that rapidly declines to baseline . Similar results were obtained experimentally ( Fig 8C and 8D ) by substituting Na+ with Li+ in the bath solution . In the presence of Li+ ( green ) the decay of the fluorescence signal in the tuft ( Fig 8C ) followed a single time constant ( τ = 11 s , dashed line , compare to inset in Fig 8A ) . Thus , in the absence of exchanger activity the elevated quasi-stable state was blocked . In accordance with the fluorescence measurements , the inward current ( Fig 8D ) decayed faster in the presence of Li+ ( compare green to blue lines ) and the initial “bump” ( arrow in Fig 8D ) created by the Na+-K+ pump outward current was absent ( compare to the effect of ouabain application , Fig 2G ) . These experimental observations strongly support the proposed model in which a quasi-stable state of [Ca2+]i is generated by the reversed action of the Na+-Ca2+ exchanger . We examined the response of our detailed model to VNO inputs using a simple feed-forward network simulation ( see Materials and Methods ) . The input stage of the network represents the responses of vomeronasal sensory neurons ( VSNs ) to natural stimuli , which follow a simple ligand-receptor interaction [33 , 34] . A simple model of VSN firing was established by first calculating the predicted time course ( Fig 9A , red line ) of a response to a brief stimulus application ( red bar ) , and then using it to generate a random spike train ( vertical lines ) . For the purpose of the model , we assumed that each tuft is innervated by 13 VSNs ( Fig 9B; within the lower range of reported convergence [35] ) . The unitary synaptic response of an AOB mitral cell to sensory fiber stimulation ( Fig 9C , blue line ) was measured by stimulating the dendritic tuft of a mitral cell ( see Materials and Methods , micrograph in Fig 9C ) . A model of synaptic conductance was fitted to the averaged response ( Fig 9C , orange line ) and assigned to the dendritic tufts of the aforementioned mitral cell model . The simulated response was then tested for two ligands presented simultaneously to two groups of 13 VSNs , converging on two different dendritic tufts of the model mitral cell ( Fig 9B ) . This simulation was run using a range of stimulus durations for both ligands , and a range of concentrations for one of them ( the concentration of the second ligand was kept constant ) . Random white noise was also injected to the model mitral cell . Using these parameters , we encountered two possible outcomes: one was a transient firing response ( Fig 9D ) while in the other firing persisted for ~200 s ( Fig 9E ) . Fig 9F summarizes the average firing durations for different combinations of stimulus duration and concentration . As shown , longer stimulus durations and higher concentrations led to persistent firing responses . The bimodal distribution of the response duration is shown in Fig 9G . As apparent , the response was either transient , following the stimulus application , or persistent peaking at 200 s , as previously demonstrated for AOB mitral cells [3] . Thus , our model cell embedded in a realistic small network simulation reproduces the transition of the responses of AOB mitral cells between transient and persistent modes as a function of stimulation strength and duration , as observed both in vitro and in vivo .
We have previously hypothesized [3] that the accessory olfactory system reports the social context to an animal by inducing specific brain states based on the persistent activity of its mitral cells . These states can then change the processing of sensory information in other brain areas [36–38] . This hypothesis is supported by the current study where we showed , both in vivo and in vitro that AOB mitral cells shift from transient to persistent firing responses to stimuli arriving from the VNO . In agreement with our previous reports [3 , 20] , this transition is relatively sharp and depends on sufficient stimulus strength and duration . This may correspond , for example , to the presence of a rich source of social chemosensory cues , e . g . , a conspecific animal , which would elicit persistent firing in AOB mitral cells , thereby conveying to higher brain centers the presence of a social partner . Persistent activity has been reported in several brain areas , including mitral cells of the main olfactory bulb in vivo [39] , and was suggested to mediate working memory or prolonged brain states [4] . To date , most of the mechanisms purposed for persistent activity include either network or biophysical feedback loops [5 , 10 , 16 , 39 , 40] . We propose a novel mechanism for persistent neuronal activity in AOB mitral cells , in which a train of action potentials that back-propagate to the dendritic tuft [24] elevates the tuft [Na+]i . This increase in the tuft [Na+]i shifts the [Ca2+]i stable state upwards [29] , thus creating an elevated quasi-stable state for [Ca2+]i . The slow decay of [Ca2+]i is dictated by the slow removal of Na+ ions from the tuft ( Fig 7 ) . The novelty of our model is in the absence of biochemical , biophysical , or network feedback mechanisms or hysteresis . Two alternative models were examined in an attempt to reproduce the observed experimental results . One model incorporated two distinct CAN conductances—a fast one and a slow one ( compare with [41] ) , without activity-dependent outward current . Each of these currents produces a different phase of the observed inward current ( Fig 2B and 2C ) . While this model reproduces the results used as objectives in the model training phase ( Figs 5 and 6A–6C ) , it failed to produce the persistent activity ( Fig 6D and 6E ) , emphasizing the importance of this validation . The long time constant kept the slow Ca2+-dependent current away from its stable-state value in the hybrid clamp simulations , but this current would greatly intensify and cause a runaway effect in a current clamp simulation ( S8A Fig ) . In another model , a single Ca2+-dependent non-specific cation conductance was used , along with a Ca2+-dependent K+ conductance ( SK/BK ) that account for the outward current observed immediately after the stimulating train of action potentials . Although this model reproduces the results used as objectives in the model training phase ( Figs 5 and 6A–6C ) , it failed to explain the outward current recorded in the absence of [Ca2+]o ( Fig 2E ) . The ultimate rejection of these models demonstrates the importance of the model validation step . According to our results , [Na+]i plays a key role in both transient and prolonged biophysical processes . First , increase in AIS [Na+]i alters the Na+ Nernst potential , thus lowering the spike amplitude ( Fig 5E–5I and S5 Fig ) . Second , elevation in [Na+]i increases the Na+-K+ pump-mediated outward current that terminates bursts of activity by hyperpolarizing the cell ( Fig 2E ) . Third , increase in [Na+]i decreases Ca2+ efflux by modulating the Na+-Ca2+ exchanger ( Fig 8 ) . The latter endows [Na+]I with the ability to dictate [Ca2+]i and thus the inward ICAN . In thin and slightly active processes like the dendritic tuft , [Na+]i is only moderately increased by neuronal activity and its extrusion is exceptionally slow ( Fig 6F , [27] ) . Thus , [Na+]i can attain a range of quasi-stable states—a property that renders it an ideal candidate to integrate epochs of high neural activity . This is in contrast to [Ca2+]i , which is highly dynamic as a function of neuronal activity on one hand , and rapid extrusion on the other . Notably , changes in [Na+]i are rarely tracked in conductance-based models ( important exceptions in [42–45] ) , although in thin active processes , such as axons and dendrites , they may substantially affect neuronal activity . Indeed , the effects of [Na+]i on a variety of Ca2+-dependent processes were previously demonstrated: reducing Na+ extrusion or inhibiting the Na+-Ca2+ exchanger were shown to extend [Ca2+]i transients , and thus to facilitate Ca2+-dependent mechanisms , such as synaptic plasticity and learning [29 , 46 , 47] . The activity-dependent Na+-K+ pump-mediated outward current is an important factor protecting neurons from a runaway positive feedback loop ( S8B and S8C Fig ) . Without this current , the high [Ca2+]i during and immediately after stimulation would result in a strong inward current ( as demonstrated using ouabain—Fig 2G , red line ) that would in turn evoke high frequency spiking , and thus a further increase in [Ca2+]i . Since the mechanism described here contains mostly elements which are ubiquitous in neurons , we argue that it is relevant ( wholly or partially ) to other brain areas . The essential non-trivial building blocks required to produce persistent activity by this mechanism are: a ) changes in [Na+]i and [Ca2+]i in thin processes ( axon or dendrites ) , attained by ( back ) -propagation of action potentials and/or excitatory synaptic activity; b ) Co-localization of an excitatory Ca2+-dependent conductance at the site of [Na+]i changes; c ) Low density of [Na+]i active extrusion mechanisms at the site of [Na+]i changes . Thus , the proposed mechanism is very likely to explain persistent activity in other brain areas , most likely with some variation of the time scale and of the factors necessary to evoke it . For example , [Na+]i dynamics may take alternative forms in other neuronal types , as a function of the spatial distribution of Na+ channels and Na+-K+ pumps as well as cell morphology . Moreover , the persistent activity may be either superthreshold , i . e . persistent firing , or long term integrative changes in membrane potential and excitability . It may be assumed that very slow changes in resting potential or firing rate , such as those produced by the mechanism presented here , may be under-reported because they are frequently filtered out and require prolonged recording sessions . Furthermore , the importance of the mechanism proposed by us is beyond the scope of persistent activity . We show that the long-term behavior of intracellular Ca2+ depends upon past activity—an idea previously presented in [29] , though in a much shorter time scale . Given that Ca2+ is an important cellular signaling molecule , long-term changes in its concentration may broaden the time scale of processes such as synaptic plasticity [47] , activity-dependent gene expression , and more . Similarly to membrane potential changes , reports about [Ca2+]i changes may also suffer from "time-scale bias , " wherein long-term changes in [Ca2+]i seems to be regularly filtered out or not recorded due to photo damage and dye-bleaching concerns . An even broader consequence is the ability of Na+ to integrate activity ( discussed above ) , which at the very least causes changes to Na+ reversal potential . Thus , persistent activity is only one of several end results of the mechanism we describe . Our results at least demonstrate that changes in [Na+]i should not be regarded as negligible in conceptual and computational models of neuronal activity .
C57BL/6J and BalbC male mice were maintained in the SPF mouse facility of the Hebrew University of Jerusalem under veterinary supervision , according to National Institutes of Health standards , with food and water ad libitum and lights on from 7:00 A . M . to 7:00 P . M . Eight- to twenty-week-old mice ( 25–35 g ) were held in groups of 5–10 mice per cage . All experiments were approved by the Animal Care and Use Committee of the Hebrew University ( permit number: NS-12-13310-4 ) . Mice were anesthetized for in vivo experiments ( ketamine , medatomidine ) . For in vitro experiments , mice were anesthetized ( pentobarbitone ) and killed by cervical dislocation . Secretions for in vivo recordings were collected from C57BL/6J and BalbC females ( housed in the animal facility of the Hebrew University ) . Samples were pooled and immediately frozen in liquid nitrogen and stored at −80°C until use . For urine collection , mice were gently held over a plastic sheet until they urinated . Vaginal secretions were collected by flushing the vagina with 30μl of ringer’s solution repeatedly . 20 μl were stored . For saliva collection , isoproterenol hydrochloride ( 0 . 2 mg/100 g ) and pilocarpine ( 0 . 05 mg/100 g ) were injected i . p . to increase salivation [48] . Following a delay of 5 min , saliva was collected from the mouth using a micropipette . Stimuli were diluted in Ringer’s solution . In vivo multi-unit activity followed by electrical stimuli was recorded in anesthetized mice ( ketamine , 10 mg/kg , medatomidine , Pfizer , 1 mg/kg ) . A recording electrode ( glass micropipette filled with 1 M potassium acetate ) was placed in the AOB external plexiform layer using a micromanipulator ( Luigs and Neumann ) . A stimulating coaxial bipolar electrode was inserted through the medial frontal lobe , to the point where contact was made with the vomeronasal nerve and field potentials appeared in the AOB in response to brief stimuli . A train of brief shocks ( 0 . 1 ms , 1–100 V ) , given at 2 Hz for 2 . 5 s was applied to the vomeronasal nerve at intervals of 60 s via an isolated stimulator . Electrophysiological recordings of AOB neurons followed by natural stimuli were performed as previously described in detail [18] . Briefly , BalbC mice were anesthetized with 100 mg/kg ketamine and 10 mg/kg xylazine . A tracheotomy was made using a polyethylene tube to allow breathing during flushing; a cuff electrode was placed on the sympathetic nerve trunk with the carotid serving as a scaffold . Incisions were closed and the mouse was placed in a custom-built stereotaxic apparatus where anesthesia was maintained throughout the entire experiment with 0 . 5–1% isoflurane in O2 . A craniotomy was opened immediately rostral to the rhinal sinus , the dura was removed around the penetration site , and electrophysiological probes were advanced into the AOB using an electronic micromanipulator ( MP-285; Sutter instruments ) . All recordings were made with 32 channel probes ( NeuroNexus Technologies ) . During each trial , 2 μl of stimulus solution was placed directly in the nostril ( “stimulus application” ) and after 20 s , a square-wave stimulation train ( duration: 1 . 6 s , current: ±120 μA , frequency: 30 Hz ) was applied through the sympathetic nerve cuff electrode to induce VNO pumping and , accordingly , stimulus entry to the VNO lumen ( “sympathetic stimulation” ) . A pump was turned on 40 s after each stimulus presentation , followed ( after 10 s ) by application of Ringer’s solution ( 1–2 ml ) to the nostril that was flushed through the nasopalatine duct to cleanse the nasal cavity . 20 s after Ringer's application sympathetic stimulation was performed to ensure the VNO lumen cleansing ( the second stimulation ) . Using an RZ2 processor , PZ2 preamplifier , and two RA16CH head-stage amplifiers ( Tucker-Davis Technologies ) , neuronal activity was sampled at 25 kHz and band-pass filtered at 0 . 3–5 kHz . Custom MATLAB ( Mathworks ) programs were used to extract spike waveforms . Spikes were sorted automatically according to their projections on two principle components using KlustaKwik [49] and then manually verified and adjusted using the Klusters program [50] . Mice were anesthetized ( pentobarbitone; 60 mg/kg ) and killed by cervical dislocation . Olfactory bulbs were dissected into a physiological solution containing the following ( mM ) : 125 NaCl , 25 NaHCO3 , 5 glucose , 3 KCl , 2 CaCl2 , 1 . 3 NaH2PO4 , and 1 MgCl2 , oxygenated by bubbling through a 95% O2 and 5% CO2 mixture , pH 7 . 4 , 36°C . Parasagittal olfactory bulb slices , 300–400 μm thick , were prepared and equilibrated for 0 . 5–3h in the same solution at physiological temperature [51] . For electrophysiological recordings , slices were submerged in oxygenated physiological solution ( identical to above ) at room temperature in a recording chamber and perfused at a constant rate of 5–7 ml/min . To test the effect of substituting Na+ by Li+ , equimolar amount of LiCl was used instead of NaCl . To test the effect of Ca2+ removal , equimolar amount of MgCl2 was used instead of CaCl2 . Where indicated , picrotoxin ( 100 μM ) was added to the bath solution to block GABAA receptors , or ouabain ( Tocris Bioscience ) was added to the bath solution in excess ( 10–100 μM ) to block the Na+-K+ pump . For electrophysiological recordings , we used an Olympus BX61WIF microscope equipped with a motorized stage and manipulators ( Luigs and Neumann ) , pulse generator ( Master8 , A . M . P . I . ) , isolated stimulator ( ISOFlex , A . M . P . I . ) , and a MultiClamp 700B amplifier ( Molecular Devices ) . Mitral cells were visualized using infrared differential interference contrast ( DIC ) video microscopy via a 40x or a 60x water-immersion objective . Mitral neurons were identified by the location of the cell body on the ventral side of the external plexiform layer of the AOB . Whole-cell recordings were performed using borosilicate pipettes filled with standard intracellular recording solution containing the following ( mM ) : 130 K-gluconate , 10 Na-gluconate , 10 HEPES , 10 phosphocreatine , 4 MgATP , 0 . 3 NaGTP , and 4 NaCl ( pH = 7 . 25 with KOH , 5–12 MΩ ) . When BAPTA was used , BAPTA—tetrapotassium ( Sigma ) was dissolved in this solution to a final concentration of 5 mM . Seal resistance was at least 2 GΩ and typically 5–10 GΩ . In most experiments , a 4-s-long spike train was evoked by injecting a series of depolarizing pulses ( rate , 1–30 Hz; amplitude , 1–2 nA; pulse duration , 10 ms ) . In the hybrid-clamp procedure , membrane potential was clamped to −80 or −70mV throughout the experiment , excluding 4 s periods during which the amplifier was switched to current-clamp mode to deliver the train of current pulses . It should be noted that the hybrid-clamp methodology has been proven useful for investigating the firing activity of neurons , since by preventing most of the feedback and interference that ongoing firing activity may elicit upon itself , it enables a relatively clean examination of the underlying currents . All amplified signals were digitized at 2–20 kHz using a National Instruments board and homemade software written in LabVIEW ( National Instruments ) . A unitary measurement of EPSC was done by filling a cell with Alexa 488 ( Life Technologies ) and visually positioning a bipolar theta electrode filled with physiological solution and Alexa 488 close to one of the dendritic tufts ( Fig 8C ) . For calcium imaging experiments , Oregon Green BAPTA-1 ( OGB-1 , Life Technologies , 50 μM ) was added to the pipette solution . Fluorescence signals were recorded during the hybrid clamp protocol using a high speed camera ( MiCAM Ultima , Brainvisions ) and converted to ( F−Fmin ) /Fminratio after subtracting the ongoing background signal . In order to perform imaging of the dendritic tuft , a 60x water-immersion objective was used , and the dye was allowed to fill the cell for >20 min before recording was started . Evoked spikes were used to increase fluorescence and facilitate the visual search for a dendritic tuft . For sodium imaging experiments , SBFI salt ( TEFLabs , 2mM ) and Sodium Green salt ( Life Technologies , 500uM ) was added to a modified pipette solution containing ( mM ) : 130 K-gluconate , 10 HEPES , 5 phosphocreatine , 4 MgATP , 0 . 3 NaGTP , 20 KCl , and 0 . 2 EGTA ( pH = 7 . 2 with KOH , 5–12 MΩ ) . Fluorescence signals were recorded from the apical dendrite as in the case of calcium imaging . A standard Fura-2 filter set ( Ex . 380 nm; Em . 510 nm , Chroma Technology ) was used for SBFI imaging . In order to cancel the substantial dye bleaching , trials without stimulus were subtracted from stimulus trials . Unless otherwise noted , recorded current or voltage traces were averaged for each recorded cell , and the presented result is the mean of the cell population . Value error range reported is SEM unless otherwise noted . For stimulus-induced in vivo AOB recordings , units from various sets of experiments were considered . Six hundred and sixty-three single units for which at least one stimulus was presented at least five times were considered . The procedure for identifying persistent responses was as follows: For each single trace ( one unit , one presentation of one stimulus ) , the responses in consecutive 2 s bins were defined as significant if the rate within it was larger than the mean baseline rate by at least five times the SEM of the baseline rate . The bins spanned a period of 160 s , which includes the baseline period , stimulus delivery , and the VNO flushing period , extending 20 s after the sympathetic stimulation during flushing ( second stimulation , see above ) . The response duration was defined as a period beginning and ending with a significant bin , and in which at least 85% of the bins were significant . If this response lasted more than 10 s following the second sympathetic stimulation , it was designated as a persistent response . Finally , units for which at least half of the responses were persistent for a given stimulus were defined as persistent . The seven persistent firing units came from three sets of experiments differing in stimulus sets used . For three units the stimuli were undiluted saliva , urine , and vaginal secretions . In three other units the stimuli were saliva , 100F diluted urine , and vaginal secretions . For the remaining units , the stimuli were urine at 1F , 10F , and 30F dilutions . The abstract dynamical model was constructed in MATLAB/SIMULINK ( Mathworks ) . See S1 Text for details and equations . We constructed the conductance-based model using the NEURON simulation environment with Python [52 , 53] . The model was based on experimental measurements and morphological reconstruction of an AOB mitral cell [54] , and included influx , diffusion , and extrusion of Na+ and Ca2+ . It assumed a presence of active Na+ channels in the apical dendrites and tufts [24] , as well as non-uniform channel properties across different compartments ( Fig 4E , [55] ) . Evolutionary multi objective optimization algorithm [25 , 26] was used to find the model parameters , based on recorded electrophysiological and imaging data . Some membranal mechanisms were based upon published models hosted by ModelDB [56–60] . See S1 Text for additional information . The model code is available online at: https://senselab . med . yale . edu/ModelDB/ShowModel . cshtml ? model=185332 In order to test the response of the model mitral cell to natural stimuli , a simple two-layer network model was constructed . The firing response of the VNO sensory neurons was modeled using a simple ligand-receptor interaction [33] that triggers a semi-random spike train ( Fig 8A ) . Thirteen of such sensory neurons converged on each of the mitral cell's dendritic tufts ( Fig 8B ) [35] , where the synaptic current was modeled using a double exponential fit to the unitary response measured experimentally using a theta electrode ( a bipolar micropipette pulled from a tubing with a θ-like cross-section ) positioned next to a dye-filled mitral cell's dendritic tuft ( Fig 8C ) .
|
The accessory olfactory system is essential for chemical communication in animals during social interactions . During this process , the principle cells of the accessory olfactory bulb ( AOB ) may respond to transient stimulation with prolonged activity , sometimes lasting for minutes—a property known as persistent activity . This property , which has been observed in other brain areas , is usually attributed to positive feedback mechanisms either at the cellular or the network level . Here , we show how persistent activity can emerge without feedback , relying on slow changes in internal ionic concentrations , which keep a record of past neuronal activity for long periods of time . We used a combined computational and experimental approach to show that the complex interaction between various ions , their extrusion mechanisms , and the membrane potential leads to stimulus-dependent persistent activity in the AOB . The same mechanism may apply to other neuronal types in various brain regions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Prolonged Intracellular Na+ Dynamics Govern Electrical Activity in Accessory Olfactory Bulb Mitral Cells
|
The interactions between membrane receptors and extracellular ligands control cell-cell and cell-substrate adhesion , and environmental responsiveness by representing the initial steps of cell signaling pathways . These interactions can be spatial-temporally regulated when different extracellular ligands are tethered . The detailed mechanisms of this spatial-temporal regulation , including the competition between distinct ligands with overlapping binding sites and the conformational flexibility in multi-specific ligand assemblies have not been quantitatively evaluated . We present a new coarse-grained model to realistically simulate the binding process between multi-specific ligands and membrane receptors on cell surfaces . The model simplifies each receptor and each binding site in a multi-specific ligand as a rigid body . Different numbers or types of ligands are spatially organized together in the simulation . These designs were used to test the relation between the overall binding of a multi-specific ligand and the affinity of its cognate binding site . When a variety of ligands are exposed to cells expressing different densities of surface receptors , we demonstrated that ligands with reduced affinities have higher specificity to distinguish cells based on the relative concentrations of their receptors . Finally , modification of intramolecular flexibility was shown to play a role in optimizing the binding between receptors and ligands . In summary , our studies bring new insights to the general principles of ligand-receptor interactions . Future applications of our method will pave the way for new strategies to generate next-generation biologics .
Integral membrane proteins are the sensors of extracellular signals , including cell-cell and cell-substrate interactions , as well as environmental queues . Their interactions with extracellular ligands initiate most of the intracellular signaling pathways [1 , 2] , while the dysregulation of these receptor-initiated signaling pathways leads to various diseases , such as cancers [3] , and greater than 60% of current drugs are designed to target specific cell surface receptors [4 , 5] . In many cases , the extracellular ligands are spatially organized into multivalent/multicomponent assemblies . These assemblies , called multi-specific ligands , contain multiple receptor binding sites and are able to target different cell surface receptors simultaneously . For instance , multiple low affinity interactions involving influenza virus hemagglutinin trimers are required for effective recognition of cell surface glycoproteins on bronchial epithelial cells [6 , 7] . Another example is the presence of multiple receptor-binding sites in all classes of antibodies ( e . g . , bivalent , tetravalent and decavalent in IgG , IgA and IgM isotypes , respectively ) . The overall apparent binding affinity is enhanced due to the synergy between the multiple binding interactions within an immune complex [8] , which is commonly referred to as 'avidity' [9] . Although the general properties and biochemical consequences of binding avidity are well appreciated [10] , the detailed mechanisms and underlying energetic contributions remain unclear . The importance of several regulatory factors such as the competition between different binding sites and the conformational flexibility in a complex has not been quantitatively evaluated . Moreover , one of the most promising strategies in drug design is the development of synthetic chimeric ligands [11] , in which multiple natural ligands are artificially fused to target their cognate receptors on the surfaces of specific cell types . These multi-specific targeting reagents can improve the efficiency and selectivity of drug-based therapies; therefore , enhanced understanding of the basic principles underlying the interactions between multi-specific ligands and their receptors is critical for continued development of new therapeutic strategies . Computational approaches allow for a wide range of variables to be systematically examined and a variety of different methods have been recently developed to study the interactions between ligands and cell surface receptors . The chemical kinetics of receptor-ligand binding was first described by simple mathematical models [12 , 13] , and have been improved by consideration of the spatial confinement of membrane receptors [14 , 15] . Reaction rates between receptors and ligands were modulated to explore the impact on binding of the reduction in the dimensionality of receptors confined to a two-dimensional bilayer . However , information such as spatial heterogeneity and molecular details were not be captured . In contrast , atom-based molecular dynamic simulations [16] were used to provide full structural descriptions of both ligands and receptors [17–20] . The primary limitation of these atomistic simulations is the large computational overhead , which prohibits these approaches from being applied to multivalent molecular complexes and slower biological processes ( i . e . , microsecond time scales or longer ) [21] . Other hybrid models have been introduced to bridge the gap between mathematical modeling and atomic simulations [22–25] , which depend on coarse-grained representations of molecules [26–30] , or reduced degrees of freedom in their movements , as captured by lattice-based simplifications [31–33] . For instance , Miguez and colleagues applied Langevin dynamics for ligand-receptor interaction [34] , in which ligands and receptors were represented as simplified spherical particles . However , the theoretically “scaled units” used for the simulation parameters in this study are difficult to directly correlated with biological binding properties in a quantitative manner . Moreover , for all the methods described above , the principles of binding avidity between cell surface receptors and multivalent ligands has not been systematically evaluated . Here we present a computational model to investigate the general mechanism of interactions between membrane receptors and their ligands . The spatial organization of multimeric or multi-domain receptors/ligands is explicitly incorporated into the model . Molecules possessing multiple binding sites are referred as multi-specific receptors/ligands in the following text . In particular , each binding site in a multi-specific receptor/ligand is represented in our coarse grain model as a rigid body , with the binding site explicitly defined on its surface . The overall modeling system contains a large number of individual receptors and ligands , and their diffusion and binding kinetics are simulated by a kinetic Monte-Carlo algorithm . All parameters in the simulation , such as diffusion constants and binding rates , are constrained within biologically relevant ranges . By varying the number of binding sites and the affinity of each binding site , our simulations demonstrate that the overall binding is cooperatively strengthened when multiple binding sites are spatially tethered . Interestingly , this positive coupling effect is reduced in the regime of strong individual binding affinities . Furthermore , by varying the concentrations of receptors on cell surfaces , we illustrate that the cell specificity of ligand binding is highly sensitive to the binding affinity . Finally , by altering the conformational fluctuations within a multi-specific receptor/ligand , we show that molecular flexibility plays an important role in modulating the binding between receptors and ligands . Taken together , our computational model provides insights into both basic mechanisms of ligand-receptor interactions and design principles for new drug candidates . These considerations are especially relevant given the extensive commercial interest in development multi-specific biologics for the treatment of a wide range of clinical indications .
We recently developed a rigid-body ( RB ) based model to simulate molecular binding in cellular environments [35] . This model has now been enhanced to study the binding interactions between cell surface receptors and soluble ligands . Specifically , the plasma membrane is represented by the bottom surface of a three-dimensional simulation box , the receptor is represented by a rigid body ( i . e . , cylinder ) on the plasma membrane ( Fig 1a ) and the space above the plasma membrane represents the extracellular region . In the three-dimensional extracellular region , each ligand monomer is simplified as a spherical rigid body with a given radius . To delineate the binding interface , a functional site is defined on the surface of each ligand , as well as the top of each receptor ( Fig 1a ) . Binding between two molecules is triggered by two criteria: 1 ) the distance between functional sites of two molecules is below a predefined distance cutoff; and 2 ) the relative orientations of the two engaging molecules fall within specific ranges . In contrast to ligands that can randomly diffuse in bulk solvent with three translational and three rotational degrees of freedom , the diffusion of receptors on the plasma membrane surface are confined . This confinement allows each receptor to rotate only about the axis normal to the plasma membrane , and restricts diffusion to two-dimensional translational movements in the plane of plasma membrane . To test the relation between the binding avidity of a multi-specific ligand and the affinity of its individual binding sites to receptors , three scenarios were implemented using the above rigid-body model . In the first scenario , receptors A ( red ) and receptors C ( yellow ) are placed on cell surface , while ligands B ( green ) and ligands D ( blue ) are separately placed in the 3D extracellular region as monomers ( Fig 1b ) . In the second scenario , a ligand B is tethered together with a ligand D in the extracellular region . It is referred as a multi-specific ligand BD in the following text . The multi-specific ligand BD is represented by two tethered rigid bodies with a binding site of B and a binding site of D on each of their surfaces ( Fig 1c ) . Finally , in the third scenario , a higher-order assembly is represented , which contains two ligands B and two ligands D . This assembly is referred to as the multi-specific ligand B2D2 in the following text . The multi-specific ligand B2D2 is represented by four tethered rigid bodies with two binding sites for B and two binding sites for D on each of their surfaces ( Fig 1d ) . Each multi-specific ligand in the second and third scenarios is simulated as a soluble entity in the extracellular region . Additionally , to capture the contributions of conformational flexibility , binding sites in a multi-specific ligand are allowed to undergo small translational and rotational fluctuations around their mean positions and orientations . Given the concentration of each molecular species and the type of simulation scenario , the dynamics of the modeling system is simulated by a kinetic Monte-Carlo algorithm , starting from an initial random configuration . In each simulation time step , molecules are first selected at random to model stochastic diffusion; diffusion of membrane-bound receptors are confined to the plasma membrane , while extracellular ligands are free to diffusion throughout the volume of the simulation box . The acceptance ratio of diffusion movements for each molecule is determined by its diffusion coefficient , which is different for soluble ligands and membrane confined receptors . A 2D periodic boundary condition is applied for membrane receptors . In the extracellular region , periodic boundary conditions are imposed along X and Y directions , while in the Z direction , free ligands are not allowed to move below the plasma membrane at the bottom of the simulation volume . Any ligand moving beyond the top of the simulation box is reflected back . Binding is triggered if both distance and orientation criteria between any receptor and ligand are satisfied . The probability to trigger the association is determined by the association rate kon . In contrast , the dissociation between a ligand-receptor pair is described by a probability that is calculated by association rate and binding affinity: Poff=koffΔt=C0kone−ΔG0Δt , in which koff is the dissociation rate , Δt is the simulation time step , C0 is the standard unit of concentration and ΔG0 is the binding affinity . After a ligand binds to a receptor , the ligand-receptor pair moves as a single unit on plasma membrane . If the ligand contains multiple binding sites , the entire assembly binds together and diffuses with the receptor , such that the remainders of the vacant binding sites in the assembly are accessible for binding by other plasma membrane restricted receptors . The above diffusion-reaction process is iterated until the system reaches equilibrium in both Cartesian and compositional spaces . The basic simulation parameters , including time step and binding criteria , were adopted from our previous study [35] . Other crucial parameters were chosen from ranges typical for proteins . Each subunit or domain in a multi-specific ligand is represented by a spherical rigid body with radius of 5 nm . For a receptor , the radius of the cylinder is also 5nm , while the height is 10nm . The translation diffusion constant of a soluble ligand monomer is taken as 100μm2/s and the rotational coefficient as 5° per ns [36] , while the translation diffusion constant of a multi-specific ligand is 50μm2/s and the rotational coefficient is 1° per ns . The diffusion of membrane receptors restricted to the plasma surface is much slower , with a translational constant of 10 μm2/s and rotational coefficient of 1° per ns . The on-rate for protein association was calibrated to 108M-1s-1 , a relatively high value , to accelerate the simulation . This value is in the typical range of diffusion-limited rate constants , in which association is guided by complementary electrostatic surfaces at binding interfaces [37] . Finally , a wide range of binding affinities , from 5RT to 13RT , was tested , corresponding to dissociation constants between millimolar ( mM ) and micromolar ( μM ) . Binding of many membrane proteins such as the T-cell receptor ( TCR ) and T cell co-modulatory molecules are within this range [38] . It is worth mentioning that , although our simulations did not correspond to any specific biological systems due to the lack of sufficient experimental data , it is possible that we can detect some parameters and integrate them into our simulations in the future . For instance , the diffusion constants of membrane receptors on cell surfaces control the kinetics of ligand binding . They can be measured by Total Internal Reflection Fluorescence ( TIRF ) microscopy by tracking the trajectory of each receptor [39] . Moreover , binding is also affected by the concentrations of ligands and receptors which can be approximately determined by experiments such as flow cytometry [40] .
We first investigated how the spatial organization of a multi-specific ligand affects binding between its individual binding sites and their receptors when their affinities are in different ranges . For the spatial organization of a multi-specific ligand , three different simulation scenarios , described in the methods , are used . In all cases , the binding affinities between receptors and ligand binding sites were varied . In order to exclude other factors that can influence binding , such as receptor concentrations in the plasma membrane ( i . e . , cell surface density ) , the same size of simulation box and the same number of ligand binding sites were assigned in all three scenarios . Consequently , 100 receptors A and 100 receptors C were placed on a 100nm×100nm cell surface ( plasma membrane ) . In the first scenario , 100 monomer ligands B and 100 monomer ligands D were placed in a 100nm×100nm×50nm cubic box above the cell surface . In the second scenario , 100 tethered ligands BD were placed in the box . In the third scenario , 50 assemblies of B2D2 were placed in the box . Therefore , the total number of binding sites , and B and D ligand modules are the same in all there scenarios . The binding affinity between receptor C and ligand D was fixed at -9kT in all three scenarios , while different affinities between receptor A and ligand B were examined . Fig 2a and 2b show the simulation results of the first scenario . In Fig 2a , more interactions between receptor A and ligand B were observed when their binding affinities were stronger . In contrast , the number of interactions between receptor C and ligand D were very close in all simulations , consistent with the invariant binding constant . The results of the second and third scenarios are plotted in Fig 2c to 2f . Similar to Fig 2a , 2c and 2e show more interactions between A and B are formed as the binding affinity increases . However , the numbers of interactions in the second and third scenarios are much higher than the monomer scenario due to the increase of binding avidity . Furthermore , distinct from Fig 2b , 2d and 2f shows that although the affinities between C and D in all simulations are the same , they form very different numbers of interactions . These results indicate that the interaction between receptor C and ligand D can be affected by the interaction between receptor A and ligand B , when ligands B and D are tethered . Overall , our simulations indicated that avidity can enhance binding/occupancy and cause coupling effects between different binding sites . To systematically test the effect of avidity and coupling between different binding sites , we simultaneously changed both binding affinities between receptors A and ligands B ( AB ) , and between receptors C and ligands D ( CD ) . The overall results are illustrated in Fig 3a to 3f as two-dimensional contour plots for all three scenarios . The AB and CD binding affinities are indexed along x axis and y axis , respectively . As shown in Fig 3a , when B and D are unlinked , the numbers of AB interactions do not change with CD binding affinity . Similarly , in Fig 3b , the numbers of CD interaction do not change with AB affinity . Therefore , as expected , the binding of ligands B and D with their respective receptors are independent in the first scenario . In contrast , the diagonal distributions of contours in the second scenario ( Fig 3c and 3d ) suggest that the AB interaction and the CD interaction are correlated with each other . Secondly , comparing with Fig 3a and 3b , the overall contours are shifted to red with the only exception in the high affinity regions . These results demonstrate that if two types of binding sites are tethered in a multi-specific ligand , the binding to their corresponding receptors will be mutually affected ( i . e . , coupled ) . The overall binding will be positively enhanced when their individual affinities are not too strong . Moreover , comparing Fig 3e and 3f with Fig 3c and 3d , when ligands B and D are spatially organized as B2D2 in the third scenario , the regions containing largest number of receptor-ligand interactions ( the red regions ) in their simulated contours are further enlarged . Therefore , the binding between receptors and multi-specific ligands is further strengthened when the avidity of the ligands is increased from BD to B2D2 . It is notable that if both AB and CD affinities are strong ( the upper right corners in Fig 3 ) , the binding of a multi-specific ligand BD OR B2D2 with its receptors A and C will be weakened relative to its binding as a monomeric B or D . Possible mechanisms underlying this behavior are considered in the discussions . Finally , the overall binding , the number of both AB and CD interactions , are plotted in S1 Fig as two-dimensional contour plots for all three scenarios under all combinations of AB and CD affinities . The figure shows that there are optimal combinations of AB and CD affinities in the second and third scenarios . The optimal combinations of affinity maximize the total interactions , while these combinations only exist for multi-specific ligands in which binding sites are spatially coupled . The concentrations of receptors and ligands were fixed in the last section , with the surface density of receptor A equal to that of receptor C . In practice , however , the cell surface expression levels of various proteins vary dramatically . Similarly , the expression levels of a given protein can vary considerably in different cell types . These variations have great functional significance . For example , mutations leading to the overexpression of epidermal growth factor receptor ( EGFR ) are present in a number of cancer cells and are thought to contribute to the malignant phenotype [41] . Therefore , in this section we examined the consequence of altering the relative concentrations of the two receptors on the plasma membrane . We hypothesize that different concentrations of one receptor type may affect the binding of the other receptor type when their ligands spatially coexist in a single tethered assembly . Specifically , we examined a situation in which the total number of receptor A ( 100 ) was fixed in simulations , while the total number of receptor C was varied from 0 to 100 . Ligand B and D were tethered as describing in the second scenario . The affinities of both AB and CD interactions were fixed at -7kT and -9kT , respectively . The simulation results , presented in Fig 4a , show that higher surface densities of receptors C lead to more interactions between receptor A and its ligand , although the binding rate and affinity were the same as those used in the original simulations . When no receptor C is present , the number of AB interactions is equivalent to those formed in the first scenario , in which ligand B and D are separated as monomers . In contrast , the presence of more receptors C increases the AB interactions . We speculate that higher surface density of receptors C provides more surface-bound ligand-receptor complexes due to the interaction between receptors C and ligands D . Because ligand B and D are tethered together , the vacant binding sites of ligands B in these surface-bound complexes provide higher local concentration and better orientation to receptors A . These results demonstrate that expression levels of membrane receptors play an important role in regulating the interactions with their multivalent ligands . In addition to the above simulations , we further changed the affinity between receptor A and ligand B , while maintaining the affinity between receptor C and ligand D . The same simulations were carried out in which ligand B and D are tethered together under different surface densities of receptors C . In short , the concentration of receptor A was fixed , but its affinity with ligand B changed . In contrast , the concentration of receptor C changed , while its affinity with ligand D was fixed . Fig 4b shows how AB interactions change along with receptor C concentrations under different affinities between receptor A and ligand B . The “X index” of the figure is the number of receptors C on cell surfaces . The relative increment of AB interactions , as receptor C changes from 0 to a given concentration , is recorded in the Y axis . The relative increment of AB interactions is calculated as ( NABC−NAB0 ) /NAB0 , in which NABC is the number of AB interactions under a given concentration of receptor C , while NAB0 is the number of AB interactions without receptor C on cell surfaces . It offers a quantitative way to measure the relative increase of AB interactions towards cells that express higher levels of receptor C than normal . Therefore , the relative increment of AB interactions defines the specificity that ligand B recognizes the cells overexpressing receptor C . As a result , in addition to the positive correlation between receptor C concentration and increment of AB interactions , which has already been illustrated in Fig 4a and 4b further shows that the lower binding affinity between receptor A and ligand B enhances the relative increment of AB interactions , given the higher surface concentrations of receptor C . The figure thus indicates that , although relatively small numbers of interactions are formed between A and B when their binding affinity is low , these interaction are more sensitive to the change on concentration of receptor C . In another word , when a variety of ligands are exposed to cells with overexpressing surface receptors , our simulations suggest that the ligands with reduced affinity have higher specificity to distinguish these cells relative to the ligands with higher affinity . This is consistent with a previous study using Langevin dynamic simulation [34] . Of particular relevance is a recent experimental report using a chimera containing epidermal growth factor ( EGF ) as a cell targeting element and interferon-α-2a ( IFNα-2a ) as an activity element to initiate signal transduction [42] . This study demonstrated that mutations in the chimera that reduced the affinity between IFNα-2a and IFNα receptor 2 ( IFNAR2 ) can bind to cells expressing EGFRs , while the same mutants of IFNα-2a monomers cannot . Moreover , the chimera afforded higher selectivity to cells expressing larger number of EGFRs relative to cells expressing fewer EGFRs . This EGFR-dependent effect is more evident when the affinity between IFNα-2a and IFNAR2 in the chimera was reduced . These experimental observations are quantitatively captured by our computational simulations . Consequently , the negative correlation between binding affinity and cell specificity suggested by our studies brings new insights to the rational design of macromolecular compounds as ligands to stimulate important cellular functions . The overall binding properties of a tethered multi-specific ligand can be affected by variables/degrees of freedom other than stoichiometry and affinities . For instance , the precise spatial arrangement and overall architecture of tethered ligand assembly can have significant impact on the overall binding behavior . These topological constraints are naturally embodied in our rigid body modeling approach , and in principle , all possible combinations of spatial arrangement can be enumerated with a given number of binding sites and ligand types . To simplify the analysis , only representative models were considered . Specifically , four different topologies were examined for the multi-specific ligand assembly B2D2 , as shown in the bottom row of Fig 5 . In the first two models , binding sites of all four ligands are oriented in the same direction ( downwards ) , but the relative packing arrangement between ligand B and D is different . In the remaining two models , two groups of binding sites are organized in an anti-parallel fashion ( two upwards and two downwards ) . In the third model , the same types of ligands are in different orientations , while in the fourth model , the same types of ligands are in the same orientations . The binding of all four types of complexes were simulated . The average numbers of interactions between ligands and receptors are plotted as striped bars in Fig 5 for each topology , while the deviations from the average number of interactions are plotted as black bars . The first two models in the figure show similar averages and deviations . In contrast , the last two models show much lower number of interactions . When all binding sites are in the same direction , they can simultaneously engage multiple receptors . Notably , the fourth model has a higher deviation than the third model , although the average numbers of interactions are very similar , suggesting that the anisotropic arrangement of binding sites leads to higher fluctuations in binding . The asymmetry in ligand complexes cause they can only bind to one type of receptors at the same time . This releases the coupling effect between two types of receptors , which further results in the instability and higher fluctuations in binding . Considering that the total binding sites of a multi-specific ligand are the same for all four models , the differences of binding among different model reflected from our simulation results therefore indicate that , in addition to the number of binding sites , the spatial organization of ligands also plays an important role to regulate binding between receptors and ligands . Another important feature is the internal flexibility of a tethered ligand assembly , with flexibility defined as the small range of conformational fluctuations around a given topological arrangement . The flexibility of a tethered ligand assembly is incorporated in our simulation as spatial variations of each binding site relative to its equilibrium position . Specifically , within each simulation time step , an additional operation was added to generate a small random perturbation along three translational and three rotational degrees of freedom for each binding sites in a ligand assembly . Fig 6a gives the comparison between a simulation in which flexibility was incorporated ( red ) and a simulation without flexibility ( black ) . The third scenario of ligand model B2D2 ( Fig 1d ) was used for both simulations and identical values were assigned for all other parameters . The figure shows that flexibility not only leads to more interactions on average , but also causes larger fluctuations in the number of interactions during the simulation . We also changed the maximal ranges of translational and rotational perturbations in each simulation step to adjust the flexibility of the entire ligand assembly . The maximal range within which each ligand binding site in a tethered assembly can be randomly rotated was set from 0 to 30 degrees with an interval of 10 degrees . The maximal range of translational perturbation was set from 0 to 6nm , with an interval of 2nm . Simulations were generated for all combinations and the interactions between ligands and receptors were calculated . The overall results are presented in Fig 6b as a three-dimensional histogram . The figure suggests that binding of a multi-specific ligand assembly is promoted by the appropriate selection of its intramolecular flexibility . If the molecule is overly flexible; however , binding can be negatively affected . Overall , these studies illustrate that topology and flexibility of a multi-specific ligand can be fine-tuned to optimize its binding with cell surface receptors .
Binding of multivalent molecules is a ubiquitous phenomenon in living cells . For instance , intracellular signaling platforms such as apoptosome contain multiple subunits to amplify downstream signal transduction [43] . The cascade of these signaling pathways is initiated by the activation of various cell surface receptors through binding with their extracellular ligands . Similarly , the engagement of cell surface receptors and ligands can be spatially and temporally regulated when extracellular ligands are organized into multivalent assemblies , called multi-specific ligands . To probe the functional role of this multi-specificity in ligand-receptor interactions , a rigid-body based computational model has been developed . The model attempts to realistically simulate the process of binding between receptors and ligands to the greatest extent . To achieve this goal , our previously reported diffusion-reaction algorithm has been enhanced . The new method confines the diffusion of membrane receptors to a two-dimensional surface , while ligands are free to diffuse above the cell surface in three dimensions . The multi-specificity of ligands was implemented by incorporating spatial tethering of different binding sites , which takes both homogeneous and heterogeneous oligomerization into account . Although the model is coarse grained , basic structural details for each receptor and each binding site in a ligand can be captured , such as rotational diffusion and geometric constrains during binding . Finally , the proper selection of model parameters such as molecular size , diffusion coefficient and binding affinities , maximize the biological utility of our simulation results . One of our major observations is the coupling effect between avidity of multiple binding sites and affinity of individual binding sites . When the individual binding affinities are weak , ligands dissociate from receptors relatively soon after they associate . The life-time of a ligand-receptor interaction is much shorter than the average time of ligand diffusion before the ligand can encounter with its binding partner . In another word , a ligand is very likely to diffuse away from surface before it can rebind to the receptor that it originally binds to . In this case , the tethering of different binding sites causes little effect . Therefore , no coupling was observed between binding sites in a multi-specific ligand ( the lower left corners in Fig 3 ) . When the binding affinities increase to the intermediate range , on the other hand , the interaction between one binding site in a multi-specific ligand starts to affect the binding of other sites . More specifically , the life-time of this intermediate-strength interaction is comparable to the average time of diffusion a ligand takes before it can encounter with its binding partner . We speculate that this further causes the following effects . Firstly , binding between any binding sites in a multi-specific ligand with their receptors simultaneously brings other binding sites in the ligand close to cell surface . In another word , the local concentration of different binding sites is increased due to the spatial tethering . Therefore , if the ligand dissociates from its original receptor , it will bind to other receptors with higher probability . Similar phenomena have been observed in the multivalent lectin-glycoconjugate interactions [44] . Moreover , binding between any binding sites in a multi-specific ligand with their receptors causes the entire tethered assembly to diffuse together with the receptors on cell surface , which provide better orientation of other binding sites in the ligand to their receptors . Additionally , a multi-specific ligand will leave the plasma membrane only if all its binding sites dissociate from their receptors , which effectively decrease the overall dissociation rate . Consequently , we observed that the interaction between one binding sites in a multi-specific ligand strengthens the binding of other sites . This effect is more evident when the avidity in a multi-specific ligand is increased . However , when the binding affinities further increase to the very strong range , interestingly , we found the negative coupling between different binding sites in a multi-specific ligand ( the upper right corners in Fig 3 ) . This may be the consequence of the following reason . The life-time of a strong ligand-receptor interaction is much longer than the average time of ligand diffusion before the ligand can encounter with its binding partner . Moreover , the two-dimensional diffusions of receptors on plasma membrane are much slower than the three-dimensional diffusions of proteins in solvent environments . As a result , the binding of different sites in a tethered ligand to their cell surface receptors becomes competitive . In another word , if one site of a ligand binds to its target receptor , it will take very long time for other unbound sites in the same ligand to find their target receptors , as the entire ligand-receptor complex diffuses on cell surfaces . Meanwhile , the long dissociation time of the ligand-receptor complex , due to the strong affinity prevents other sites from diffusing back into the three-dimensional extracellular space and binding to their corresponding receptors . It needs to be noted that this kinetic trapping effect does not change the overall thermodynamics of the system . Therefore , when simulations reach infinite time , we should observe that most ligand sites can ultimately bind to their receptors due to the strong affinities . However , the negative coupling due to the kinetic issue has more functional relevance in the context of understanding the role of spatial organization in multi-specific ligands , because these biological processes occur within the physiologically meaningful time scale . It is reasonable to assume that both increase of encounter probability and decrease of overall dissociation of a multivalent complex are proportional to its internal structural flexibility , which has been validated by the further simulations . Our computational studies therefore provide quantitative insight into the general principles governing the binding between multivalent ligands and surface-bound receptors . In the future , additional features will be integrated into the model for the application to specific biological systems . For instance , more specific information about structural fluctuations between different binding sites of a ligand and the binding constants of wild-type or mutated ligand-receptor interactions can be achieved by higher-resolution simulation methods such as Brownian dynamic simulation [45–54] . These data can be fed into the current rigid-body based model by the further development of a multi-scale framework . Finally , it is worth mentioning that in some cases , binding of one ligand-receptor pair might change the affinity of other ligand-receptor pairs due to the conformational changes of these molecules upon binding . This effect is called allosteric regulation [55] . However , since molecules were simplified by rigid-bodies , the conformational changes within each ligand and receptor cannot be reflected by our model . Therefore , the impacts of allosteric regulation on ligand-receptor interactions were not taken into account here . The principles revealed in this study are purely based on the spatial organization of multi-specific ligands . Future applications of our model include the design of multi-specific ligands to recognize specific cell types based on the differentiated expression levels of their surface receptors . There exist large ranges of expression level for membrane receptors in different types of cells . For instance , expression of immune receptors on the surfaces of different T cells are highly variable , such that a wide spectrum of antigens can be targeted [56] . In cancer biology , specific mutations lead to the overexpression of certain receptors , such as cell adhesion molecules on membrane [57] , which is a hallmark to distinguish tumor cells from normal cells [58] . Therefore , understanding the quantitative relation between ligand binding specificity and receptor expression level is important to maximize drug efficacy and minimize off-target drug toxicity . If a ligand is monomeric , its binding probability depends only on its concentration and the expression level of its target receptor . Interestingly , by linking the ligand into a dimeric complex in which the second ligand subunit binds to a receptor with stable expression on cell surface , we show that the binding specificity of the first ligand not only depends on the expression level of its target receptor , but is also modulated by the binding affinity of the second ligand . These results provide insights to the practical strategies of next-generation drug design . By generating multi-specific ligands with design principles based on binding affinity , topology of binding sites and expression levels of their cognate receptors , we will be able to control the selectivity of these ligands for specific cell types . Conjugating these ligands with traditional cancer drugs may enable delivery to the target tissue with a much higher selectivity and reduced off-target effects [59] . Similarly , the incorporation of T cell receptor-specific recognition modules into tethered ligand assemblies may allow for the selective induction or suppression of disease-relevant T cells [60] . The selectivity associated with such reagents may reduce the extensive side effects associated with nearly all biologics-based immunotherapies , which elicit global immune modulation of the entire T cell repertoire [61] . The practical development of such ligand complexes could pave the way for a new generation of engineered immunotherapies .
|
In order to adapt to surrounding environments , multiple signaling pathways have been evolved in cells . The first step of these pathways is to detect external stimuli , which is conducted by the dynamic interactions between cell surface receptors and extracellular ligands . As a result , recognition of extracellular ligands by cell surface receptors is an indispensable component of many physiological or pathological activities . In both natural selection and drug design , the presence of multiple binding sites in extracellular ligand complexes ( so-called multi-specific ligands ) is a common strategy to target different receptors on surface of the same cell . Such spatial organization of ligand binding sites can elaborately modulate the downstream signaling pathways . However , our understanding to the interactions between multi-specific ligands and membrane receptors is largely limited by the fact that these interactions are difficult to quantify and they have only been successfully measured in a very small number of cases in vivo . Using a simple computational model , we can realistically simulate the binding process between specially designed multi-specific ligands and membrane receptors on cell surfaces . This study therefore provides a useful pathway to unravel basic mechanisms of ligand-receptor interactions and design principles for new drug candidates .
|
[
"Abstract",
"Introduction",
"Model",
"and",
"method",
"Results",
"Discussion"
] |
[
"cell",
"binding",
"biomacromolecule-ligand",
"interactions",
"cell",
"physiology",
"chemical",
"characterization",
"simulation",
"and",
"modeling",
"membrane",
"proteins",
"membrane",
"receptor",
"signaling",
"receptor-ligand",
"binding",
"assay",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"binding",
"analysis",
"biophysics",
"molecular",
"biology",
"cell",
"membranes",
"physics",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"cell",
"signaling",
"biophysical",
"simulations"
] |
2017
|
General principles of binding between cell surface receptors and multi-specific ligands: A computational study
|
The species-specific phenolic glycolipid 1 ( PGL-1 ) is suspected to play a critical role in the pathogenesis of leprosy , a chronic disease of the skin and peripheral nerves caused by Mycobacterium leprae . Based on studies using the purified compound , PGL-1 was proposed to mediate the tropism of M . leprae for the nervous system and to modulate host immune responses . However , deciphering the biological function of this glycolipid has been hampered by the inability to grow M . leprae in vitro and to genetically engineer this bacterium . Here , we identified the M . leprae genes required for the biosynthesis of the species-specific saccharidic domain of PGL-1 and reprogrammed seven enzymatic steps in M . bovis BCG to make it synthesize and display PGL-1 in the context of an M . leprae-like cell envelope . This recombinant strain provides us with a unique tool to address the key questions of the contribution of PGL-1 in the infection process and to study the underlying molecular mechanisms . We found that PGL-1 production endowed recombinant BCG with an increased capacity to exploit complement receptor 3 ( CR3 ) for efficient invasion of human macrophages and evasion of inflammatory responses . PGL-1 production also promoted bacterial uptake by human dendritic cells and dampened their infection-induced maturation . Our results therefore suggest that M . leprae produces PGL-1 for immune-silent invasion of host phagocytic cells .
Leprosy is a chronic human disease of the skin and peripheral nerves caused by the intracellular pathogen Mycobacterium leprae . Although control programs led by governmental and charitable agencies have reduced the number of patients from ∼10–15 million to less than 1 million over the last 10 years [1] , the level of new cases persists at ∼250 , 000 per year in 2008 [2] . Therefore , in order to eradicate this disease , it is essential to assist multi-drug therapy programs with additional control strategies . Lepromatous leprosy is the most severe manifestation of the disease and is characterized by poor cellular responses and uncontrolled proliferation of the bacilli throughout the skin . Lesions contain macrophages filled with bacteria , but few T lymphocytes and no organized granulomas [3] , suggesting that M . leprae evades host immune recognition . Despite the early discovery of M . leprae in 1873 , both the biology of this bacterium and the molecular basis of its pathogenicity remain obscure . Functional studies have been hampered by the incapacity to cultivate the leprosy bacillus in vitro and by its extremely slow growth in animal models ( doubling time of ∼14 days ) . Among the molecules suspected to be critical for the pathogenesis of leprosy is the phenolic glycolipid 1 ( PGL-1 ) , a compound produced in large quantities by M . leprae in vivo [4] . PGL-1 consists of a lipid core formed by a long-chain β-diol , which occurs naturally as a diester of polymethyl-branched fatty acids . This core is ω-terminated by an aromatic nucleus that is glycosylated by a trisaccharide , which is highly specific of M . leprae . In contrast , the lipid core , called phenolphthiocerol dimycocerosates , is conserved in other mycobacterial species like M . tuberculosis and M . bovis , where it is linked to different species-specific saccharidic groups [5] . PGL-1 has attracted a lot of interest because it might represent a key virulence factor of M . leprae . Indeed , this compound is located at the outermost surface of M . leprae and therefore is ideally positioned to interact with host cell components . The trisaccharidic portion of PGL-1 was proposed to promote invasion of Schwann cells via binding to the G domain of the α2 chain of laminin-2 in the basal lamina , and may thus be responsible for the unique capacity of M . leprae to invade peripheral nerves [6] , [7] . However , the critical importance of this interaction has been challenged by observations that mycobacteria unable to produce PGL-1 exhibited similar binding capacities to laminin-2 and Schwann cells [3] , [8] . Therefore , the question of whether PGL-1 is the only determinant of M . leprae conferring tropism for peripheral nerves is still open . Supporting its putative involvement in the pathogenesis of the leprosy bacillus , Neill & Klebanoff have proposed that PGL-1 may be involved in the protection against oxygen radicals , as coating Staphylococcus aureus with purified PGL-1 or deacylated-PGL-1 increased its capacity to survive within human monocyte-derived macrophages and to resist in vitro to reactive oxygen species [9] . Consistent with these results , microbial glycolipids , including PGL-1 , were found to be highly effective in scavenging oxygen radicals [10] . Whether endogenously expressed PGL-1 protects mycobacteria from the bactericidal mechanisms of host cells nevertheless remains to be established . Regarding the modulation of the host immune response , another major aspect of leprosy pathogenesis , several lines of evidence suggest that PGL-1 plays a critical role . First , PGL-1 purified from M . leprae was found to bind the complement component C3 , thereby potentially promoting M . leprae uptake by phagocytes through complement receptors without triggering a strong oxidative burst [11] . Second , exogenously added PGL-1 modulated the cytokine response of human monocytes [12] . Third , M . leprae induced a poor activation and maturation of dendritic cells and dampened the T-cell responses induced by infected dendritic cells [13] , [14] . This inhibition was partially relieved by treatment of M . leprae-infected cells with anti-PGL-1 antibodies [13] . Together , these studies suggested that PGL-1 is a major virulence factor of M . leprae . However , the cellular and molecular mechanisms by which PGL-1 participates in the cross-talk between the pathogen and the host cells remain unclear . Clearly , tools to address these questions were missing . To our best knowledge , the lipid constituents of the M . leprae cell envelope are structurally almost identical to those of M . bovis BCG , except PGL . Importantly , in contrast to M . leprae , M . bovis BCG can be cultivated in vitro and molecular tools are available to modify its genome . Therefore , M . bovis BCG reprogrammed to synthesize PGL-1 constitutes an ideal surrogate organism to investigate the physiological role of this molecule in M . leprae pathogenicity . Here , we have identified the M . leprae genes required for the biosynthesis of the trisaccharidic domain of PGL-1 and we have genetically engineered M . bovis BCG to make it synthesize and export PGL-1 . Using this recombinant strain , we studied the impact of PGL-1 on the initial encounter of mycobacteria with human phagocytes . We found that PGL-1 deviates the route of mycobacterial entry into human macrophages and dendritic cells to suppress the initiation of innate immune responses .
The structure of the main PGL produced by M . bovis BCG consists of phenolphthiocerol dimycocerosates glycosylated at the ω-terminus by a 2-O-methylrhamnose ( Figure 1A ) . The lipid core is structurally identical to that of the PGL-1 from M . leprae , but in PGL-1 the saccharidic domain is 3 , 6-di-O-Me-Glcp ( β1->4 ) 2 , 3 di-O-Me-Rhap ( α1->2 ) 3-O-Me-Rhap ( α1- linked to phenol ring ) ( Figure 1A ) [15] . Therefore , to reprogram the PGL biosynthesis pathways of M . bovis BCG to produce PGL-1 , we needed ( i ) to prevent the methylation at position 2 of the first rhamnosyl residue , ( ii ) to provide M . bovis BCG with the enzymes required for methylation at position 3 of the first rhamnose , and for synthesis and transfer of the terminal disaccharide on position 2 . The Rv2959c methyltransferase responsible for methylation of position 2 of the first rhamnosyl residue in the PGL of M . tuberculosis has been identified [16] . By analogy with M . tuberculosis , inactivation of this gene in M . bovis BCG was expected to result in the production of unmethylated PGL , the starting point of our reprogramming process . The next step was to identify the M . leprae genes required for the transfer of the terminal disaccharide and methylation at the defined positions of the carbohydrate extension . We reasoned that six enzymes would be required: two glycosyltransferases and four methyltransferases , assuming that the same enzyme methylates position 3 on the both rhamnosyl units . Having shown previously that genes involved in the biosynthesis and translocation of lipids in mycobacteria are usually clustered in the genome [17] , we performed bioinformatic analyses of the M . leprae genome using the following criteria: genes encoding proteins with similarities to known glycosyl- or methyltransferases , clustering of these genes within the M . leprae genome , and proximity to orthologs of known PGL biosynthetic genes . Using this strategy , we identified 6 candidates for the methylation and transfer of the two terminal residues and for the methylation of the first rhamnosyl residue: ML0128 and ML2348 encoding proteins with similarities to glycosyltransferases , and ML0126 , ML0127 , ML2346c and ML2347 encoding proteins with similarities to methyltransferases ( Figures 1B and 1C ) [18] , [19] . The six candidate genes were clustered on two genome regions containing orthologs of genes involved in the formation of M . tuberculosis PGL ( PGL-tb ) and the related phthiocerol dimycocerosates ( Figure 1C ) . In M . tuberculosis [20] , these genes map to a single locus that appears to be divided in M . leprae . Sequence similarities between the proteins encoded by the candidate genes and other enzymes allowed us to assign them a putative function ( Figure 1B ) . To reprogram the PGL biosynthesis pathway in M . bovis BCG , we first disrupted the Rv2959c ortholog by allelic exchange [21] . One clone exhibiting the expected PCR profile for a BCG ΔRv2959c::km mutant was retained for further studies ( Figure S1 ) . The kanamycin cassette used in this construct , flanked by two res sites from transposon γδ , was removed after transient expression of the transposon γδ resolvase from plasmid pWM19 [22] to generate the unmarked BCG ΔRv2959c ( Figure S1 ) . The lipids produced by this mutant strain were analyzed by thin layer chromatography ( TLC ) ( Figure 2A ) . As expected , the spot corresponding to PGL-bovis was no longer detectable and a new , more polar , glycolipid ( product 1 ) was observed . Matrix-assisted laser desorption-ionisation time-of-flight ( MALDI-TOF ) mass spectrometry analyses of purified product 1 gave a series of pseudomolecular ions ( M+Na ) + with a major peak at 1516 amu , i . e . 14 mass units lower than those of the usual PGL from wild-type ( WT ) M . bovis BCG [23] . Therefore , we concluded that this compound corresponded to the expected unmethylated rhamnosyl-phenolphthiocerol dimycocerosates . Next , a DNA fragment encompassing the ML0126 , ML0127 and ML0128 genes was inserted into the mycobacterial vector pMIP12H [24] to yield plasmid pBNF03 ( Figure S1 ) . In parallel , a second DNA fragment carrying ML2346c , ML2347 and ML2348 genes was inserted into the integrative vector pMV361 [25] to give pWM76 ( Figure S1 ) . These two constructs were transferred independently or simultaneously into the BCG ΔRv2959c mutant . Lipids were extracted from BCG ΔRv2959c:pBNF03 , BCG ΔRv2959c::pWM76 and BCG ΔRv2959c:pBNF03::pWM76 and analyzed by thin-layer chromatography ( TLC ) ( Figure 2A ) . In the case of BCG ΔRv2959c::pWM76 , no new glycolipid was detected . In sharp contrast , a new glycolipid , product 2 , exhibiting higher mobility was detected in extracts from M . bovis BCG ΔRv2959c:pBNF03 . MALDI-TOF mass spectrometry analysis of product 2 gave a series of pseudomolecular ions ( M+Na ) + centered at m/z 1704 consistent with the addition of a deoxyhexosyl residue and three O-Methyl groups to product 1 produced by BCG ΔRv2959c . These results strongly supported the hypothesis that ML0126 , ML0127 and ML0128 genes are involved in the transfer of the second rhamnosyl unit and the methylation of the first two sugar residues of PGL-1 in M . leprae . When the two plasmids pBNF03 and pWM76 were transferred into BCG ΔRv2959c , at least three glycolipids were detected ( Figure 2A ) . The two quantitatively minor compounds exhibited the same mobility than the PGL-1 intermediates observed in BCG ΔRv2959c and BCG ΔRv2959c: pBNF03 . When analyzed by MALDI-TOF mass spectrometry , the most abundant compound , product 3 , showed a series of pseudomolecular ions ( M+Na ) + with a major peak at m/z 1894 in agreement with the addition of a di-O-Me-hexosyl unit ( 190 uma ) to the PGL-1 intermediate , product 2 . These results suggested that the six proteins encoded by genes ML0126 , ML0127 , ML0128 , ML2346c , ML2347 and ML2348 are sufficient to produce the specific saccharidic domain of PGL-1 . The six genes were grouped in a single integrative plasmid , named pWM122 ( Figure S1 ) . This plasmid was transferred into BCG ΔRv2959 to yield r-BCG PGL-1 . PGL-1 production by the recombinant strain was confirmed by TLC analysis , MALDI-TOF mass spectrometry ( Figure 2B ) and NMR spectroscopy analysis ( Figure 2C ) . When analyzed by mass spectrometry , product 3 purified from r-BCG PGL-1 showed a series of pseudomolecular ions ( M+Na ) + with a major peak at m/z 1894 in agreement with the expected structure ( Figure 2B ) . The characterization of the saccharidic part was achieved by NMR spectroscopy analysis , using PGL-1 from M . leprae as reference . The two 1H-NMR spectra were super imposable ( Figure 2C ) . All the signals unambiguously reflecting the presence of phenolphthiocerol dimycocerosates were seen in the spectrum of product 3: proton resonances of p-substituted phenolic group ( signals g , h at 6 . 95 and 7 . 14 ppm ) , of methine of the esterified β-glycol ( a , 4 . 85 ppm ) , of methyl substituents of polymethyl-branched fatty acids ( e , 0 . 8–1 ppm; f , 1 . 15 ppm ) , of methoxyl and methine groups on the phthiocerol ( b , 3 . 33 ppm and c , 2 . 85 ppm ) . The presence of the three de-shielded anomeric protons confirmed the presence of a trisaccharidyl part in product 3 . The signals i , i′ and i″ at 5 . 43 ppm , 5 . 12 ppm and 4 . 42 ppm were assigned respectively , to the resonances of anomeric protons of 3-O-Me rhamnosyl , 2 , 3-di-O-Me rhamnosyl and 3 , 6-di-O-Me glucosyl residues [15] . In addition , five singlets were observed in the region of sugar-linked methoxyl ( OMe ) proton resonances at 3 . 35–3 . 7 ppm whose chemical shift values were identical to those found for PGL-1 . All these results identified product 3 as PGL-1 and demonstrated the role of the six transferred genes from M . leprae in the formation of the saccharidic domain of PGL-1 ( Figure 2D ) . We first compared the amounts of PGL produced by WT BCG and r-BCG PGL-1 in liquid culture . Each strain was cultured to exponential phase in liquid medium and PGL were labeled for 24h with [1-14C] propionate , a precursor known to be incorporated in methyl-branched fatty acids containing lipids , such as PGL . Analysis of the labeled lipids by TLC showed that both strains produced comparable amounts of PGL , with PGL-1 accounting for approximately 20% of the total PGL in r-BCG PGL-1 after 24h . As M . leprae cannot be cultivated in vitro , we compared the amounts of PGL-1 produced by r-BCG PGL-1 and M . leprae by analyzing on TLC similar quantities of total lipids extracted from in vitro grown r-BCG PGL-1 and WT M . leprae obtained from infected armadillos . The amount of PGL-1 found in r-BCG PGL-1 was approximately 2-fold lower than that found in M . leprae . As observed in Figure 2A and in the labeling experiments ( data not shown ) , several biosynthetic intermediates were found in lipid extracts of r-BCG PGL-1 . Interestingly , some of these intermediates were also found in M . leprae extracts but in lower quantities [26] . One possible explanation for the occurrence of significant amounts of biosynthetic intermediates in the recombinant BCG strain may reside in the fact that M . leprae genes were not optimally expressed in M . bovis BCG , possibly due to their lower GC content . Another explanation might be the different growth conditions used ( in vivo in infected armadillos for M . leprae and in vitro for r-BCG PGL-1 and WT BCG ) . Indeed , we observed that the use of Sauton medium instead of 7H9 to grow r-BCG PGL-1 , led to production of higher proportion of PGL-1 ( data not shown ) . Having modified seven enzymatic steps in BCG , we next evaluated whether this metabolic reprogramming interfered with some basic microbiological properties of BCG . No difference in colony morphology or colony size could be detected in r-BCG PGL-1 following growth on Petri plates , compared to the WT control ( data not shown ) . Moreover , the growth curves of both strains in liquid medium were super-imposable during the 3-weeks observation period ( Figure 3A ) . Since PGL-1 was proposed to confer protection against reactive oxygen species , we also compared the viability of WT BCG and r-BCG PGL-1 exposed for 24 h to increasing concentrations of hydrogen peroxide ( H2O2 ) or sodium nitrite ( NaNO2 ) ( at pH 5 . 5 ) ( Figure 3 ) . Although H2O2 and NaNO2 efficiently reduced bacterial viability at concentrations higher than 6 mM and 2 . 5 mM respectively , PGL-1 production did not modify the cell resistance to reactive oxygen or nitrogen intermediates ( Figure 3B–C ) . Together , these results suggested that basic microbiological properties of BCG such as colony morphology , growth rates or stress resistance were not affected by the metabolic reprogramming . We then used r-BCG PGL-1 to investigate the role of PGL-1 in host cell infection . For this purpose , fluorescent forms of the WT and recombinant BCG strains were constructed by transferring a replicative plasmid carrying the gfp gene under the control of a mycobacterial promoter . We first compared r-BCG PGL-1 and the parental BCG strain for their capacity to invade human monocyte-derived macrophages ( hMDM ) , or human dendritic cells ( hDC ) , as these cell populations play major roles in the initiation and regulation of inflammatory responses . Strikingly , the number of hMDM infected by r-BCG PGL-1 was increased by 30±6% compared to that infected by parental BCG after 2 hours of interaction under non-opsonic conditions . This difference was observed for all the multiplicity of infection ( MOI 10 to 1 ) tested ( Figure 4A ) . Moreover , the number of intracellular r-BCG PGL-1 was increased by 70% compared to WT BCG ( 3 . 2+/−0 . 22 versus 1 . 85+/−0 . 5 bacilli/cell ) at the analyzed MOI 10∶1 ( Figure 4B ) . Opsonisation of the bacilli by pre-incubation with human serum markedly augmented the phagocytosis of both strains by hMDM to reach 205±9% and 218±8% for WT BCG and r-BCG PGL-1 respectively at MOI 10 ( when normalized to 100% for BCG under non-opsonic condition ) . However , it abolished the difference previously observed between WT BCG and r-BCG PGL-1 . Similar observations were made with hDC , e . g . enhanced uptake of r-BCG PGL-1 compared to WT BCG under non-opsonic conditions ( Figure S2 ) . As for hMDM infection , addition of human serum abolished the difference between WT BCG and r-BCG PGL-1 ( data not shown ) . To determine if this effect was directly related to the presence of PGL-1 at the surface of r-BCG PGL-1 , purified PGL-1 or PGL-bovis were adsorbed onto WT BCG and the invasion efficiency of coated and uncoated strains were compared ( Figure 4C ) . Adsorption of purified PGL-1 onto WT BCG increased its capacity to invade hMDM in a dose-dependent manner when compared to the uncoated strain ( Figure 4C ) . In contrast , the coating of WT BCG with PGL-bovis had no significant effect on bacterial internalization ( Figure 4C ) . Together , these results clearly established that , in the absence of opsonin , surface-exposed PGL-1 significantly enhances the bacterial infectivity . Having shown that PGL-1 promotes host cell invasion , we then examined whether WT BCG and r-BCG PGL-1 differed in their capacity to multiply within hMDM . The intracellular loads of WT BCG and r-BCG PGL-1 were evaluated over a 8-day period by counting the intracellular colony forming units ( cfus ) at various time-points post-infection . As depicted in Figure 4D , the number of cfus was higher for r-BCG PGL-1 at every time point due to the enhanced invasion efficiency . In addition , the intracellular growth of r-BCG PGL-1 was superior to that of WT BCG with a two-fold and four-fold higher cfu count at 4 days and 8 days post-infection , respectively . To determine if the growth advantage of r-BCG PGL-1 was associated with altered phagosomal maturation toward fusion with lysosomes , bacilli-containing phagosomes of hMDM infected with either WT BCG and r-BCG PGL-1 were compared for their acquisition of maturation markers . No difference in phagosome staining with lysotracker , v-ATPase , or CD63 could be detected between WT BCG and r-BCG PGL-1 at 24h and 96h , indicating that the maturation of phagosomes containing either WT BCG or r-BCG PGL-1 was not dramatically changed ( Figure 4E and data not shown ) . However , at 2h post-infection , the number of CD63-positive phagosomes was significantly higher in cells infected with WT BCG , compared to cells infected with r-BCG PGL-1 ( Figure 4E ) . This difference was not retained at later time points , suggesting that , although the phagosome maturation was not affected by the occurrence of PGL-1 at the surface of mycobacteria , the initial bacilli-containing vacuole was not exactly the same for WT BCG and r-BCG PGL-1 . Our results established that r-BCG PGL-1 infected hMDM more efficiently than WT BCG via a route leading to poor early acquisition of CD63 . Since complement receptor 3 ( CR3 ) and mannose receptor ( MR ) mediate the non-opsonic internalization of several mycobacterial species , such as M . kansasii [27] , M . tuberculosis [28] or M . leprae [29] , we assessed the possible involvement of these receptors in the uptake of r-BCG PGL-1 . The effect of a pre-treatment with blocking antibodies raised against human CR3 or MR on the differential uptake of WT BCG and r-BCG PGL-1 by hMDM was evaluated . Anti-CR3 blocking antibodies slightly , but not significantly , modulated the phagocytosis of WT BCG ( 25%±17% inhibition with anti-CR3 ) ( Figure 5A ) . Phagocytosis of WT BCG was not affected by the anti-MR antibody ( Figure 5A ) . In contrast , a marked inhibition of r-BCG PGL-1 uptake by hMDM ( 54±11% inhibition , p<0 . 01 ) was observed following pre-incubation with an anti-CR3 antibody ( Figure 5A ) . CR3 blockade restored r-BCG PGL-1 phagocytosis rates similar to those observed with WT BCG ( Figure 5A ) . This effect was specific of the anti-CR3 antibody since it was not observed in the presence of the anti-MR or isotype control antibodies . In the presence of fresh human serum , the uptake of r-BCG PGL-1 was similar to that of WT BCG and superior to that of non-opsonized bacteria ( Figure 5B ) . Pre-treatment with an anti-CR3 antibody reduced the uptake of both r-BCG PGL-1 and WT BCG to a similar extent ( Figure 5B ) . Together , these results strongly suggested that PGL-1 expression confers on BCG the capacity to exploit the CR3 pathway for hMDM invasion in non-opsonic conditions . To investigate further this hypothesis , we evaluated the differential capacity of WT BCG or r-BCG PGL-1 to infect recombinant CHO cells expressing human CR3 ( CHO-Mac1 ) [30] . Following overnight incubation with mycobacteria at MOI ( 100∶1 ) , only 5 to 7% of control CHO cells had ingested at least one bacterium under non-opsonic conditions and up to 10% in the presence of fresh serum . No difference between the WT BCG and r-BCG PGL-1 was observed . Expression of human CR3 by CHO cells resulted in enhanced mycobacterial uptake , with up to 34±7% of CHO-Mac1 cells infected with WT BCG . These results showed that WT BCG may use to some extent the CR3 pathway to invade phagocytes . However , as indicated by the poor inhibition of WT BCG uptake by hMDM treated with anti-CR3 antibody , BCG preferentially employs other routes for macrophage invasion . Importantly , uptake of r-BCG PGL-1 by CR3-expressing CHO was much more important than that of WT BCG , irrespectively of the MOI ( Figure 5C ) . In accordance with our previous findings , opsonic conditions completely abolished the difference between WT BCG and r-BCG PGL-1 ( data not shown ) . Collectively , these results demonstrated that PGL-1 improves mycobacterial entry into hMDM via the CR3 pathway , a route poorly accessible for WT BCG in the absence of opsonins . We next examined whether PGL-1 production influenced the innate responses of human phagocytes to mycobacterial infection . This was first investigated by monitoring the effect of PGL-1 on the activity of the transcription factor NF-κB , which controls the expression of multiple inflammatory genes in hMDM and hDC . We used a THP-1 cell line transfected with a reporter system under the control of a promoter inducible by NF-κB . Infection of THP-1 cells with WT BCG induced strong expression of the reporter gene , indicative of potent activation of the NF-κB pathway . After 16 h of incubation in the detection medium , the mean OD630nm values obtained for the WT BCG were 23% , 60% , 70% higher than for r-BCG PGL-1 at MOI 10∶1 , 1∶1 and 1∶10 , respectively ( Figure 6A ) . Therefore , the NF-κB response triggered by r-BCG PGL-1 was significantly lower ( p<0 . 01 ) than that of WT BCG , whatever the MOI considered . Accordingly , hMDM infected with r-BCG PGL-1 produced lower amounts of the inflammatory cytokine TNF-α than BCG-infected controls after 2 hours of infection , even though bacteria expressing PGL-1 were more efficiently internalized than the WT controls ( Figure 6B ) . Other cytokines , IL-12 ( p40 and p70 ) and IL-10 , were also assayed . Both WT BCG and r-BCG PGL-1 induced the production of poor levels of these cytokines both at 2h and 24h post-infection , and no difference was observed between WT-BCG and r-BCG PGL-1 infected hMDM ( Figure S3 ) . To evaluate if the route of r-BCG PGL-1 entry into hMDM could explain the defective TNF-α production by infected hMDM , we examined the level of production of this cytokine when CR3-mediated phagocytosis was blocked . Incubation of hMDM with an anti-CR3 antibody , or an irrelevant isotype matched antibody did not trigger any significant TNF-α secretion . As expected , the presence of the control antibody did not affect the difference observed between WT BCG and r-BCG PGL-1: infection of hMDM with r-BCG PGL-1 resulted in defective TNF-α production , compared to hMDM infected with WT BCG ( Figure 6C ) . In contrast , hMDM infected with BCG or r-BCG PGL-1 in the presence a blocking anti-CR3 antibody produced equivalent levels of TNF-α , demonstrating the critical role of CR3 in the down modulation of the inflammatory response induced by r-BCG PGL-1 ( Figure 6C ) . In parallel , we evaluated by flow cytometry the effects of PGL-1 on the phenotypic maturation of hDC following infection with WT BCG or r-BCG PGL-1 . Here , only cells harboring fluorescent bacilli were considered , and propidium iodide ( PI ) + hDC were excluded from the analysis . Notably , a reproducible inhibition of maturation was observed in hDC infected with r-BCG PGL-1 compared to BCG-infected hDC , as witnessed by the reduced surface expression of MHC class II , CD80 , CD83 and CD40 ( Figure 6D ) . From these results , we conclude that bacterial production of PGL-1 suppresses the initiation of innate immune responses by infected phagocytes . In the case of hMDM , the immunomodulatory effects of PGL-1 are due to the preferential use of the CR3 pathway for bacterial phagocytosis .
In this study , we successfully modified the PGL biosynthetic pathway in BCG to generate a recombinant strain expressing PGL-1 , thereby circumventing the difficulties in growing and genetically manipulating M . leprae . Like the native molecule in the leprosy bacillus , BCG-expressed PGL-1 was located in the outermost layer of the envelope ( data not shown ) . Since both species have otherwise very similar envelopes , our r-BCG PGL-1 strain represented an ideal surrogate of M . leprae for studying PGL-1 interactions with host cells in a relevant biochemical and structural context . We found that PGL-1 promoted bacterial entry in phagocytes via CR3 , a property not shared by the phenolic glycolipids of other mycobacterial species such as M . bovis . Importantly , deviation of the phagocytosis pathway resulted in reduced innate immune responses and was associated with improved intracellular multiplication . Our findings thus strongly suggest that M . leprae has evolved PGL-1 production as a strategy to escape innate immunity and establish long-term residence in the host . The biosynthesis pathway of PGL involves more than 20 enzymatic steps . The steps required for the formation of the lipid core are common to all mycobacterial species producing PGL , and orthologs of the required genes could be identified by genome comparisons . In contrast , the saccharide appendage of PGL is species-specific . In the present study , we report the identification of the genes of M . leprae that are necessary and sufficient for its synthesis . Our bioinformatic analyses and the finding that the transfer of three M . leprae genes ( ML0126 , ML0127 and ML0128 ) leads to the production of a PGL-1 intermediate harboring 2 , 3 di-O-Me-Rhap ( α1->2 ) 3-O-Me-Rhap domain , led us to propose that: i ) ML0128 is the rhamnosyl transferase involved in the attachment of the second rhamnosyl residue on position 2 of the first unit , ii ) ML0127 is the methyltransferase involved in the methylation of position 2 of the second rhamnosyl residue , iii ) and ML0126 is the enzyme responsible for methylation at position 3 of the first and second sugar residues . With regard to the transfer and modification of the terminal glucosyl unit , we concluded that ML2348 is the glucosyltransferase and ML23246c and ML2347 are the methyltransferases required for the modification of the 3 and 6 positions . We investigated the role of PGL-1 in the early steps of mycobacterial interaction with host immune cells and found that PGL-1 augments the capacity of recombinant BCG to invade phagocytes , improves the multiplication of mycobacteria in infected hMDM , and impairs the infection-induced inflammatory responses . Uptake of BCG by macrophages occurs via various receptors including CR3 [31] , [32] . Nevertheless , CR3 is poorly used by human macrophages to internalize BCG and needs to be activated , notably through cooperation with other cell surface receptors , such as CD14/TLR2 , for efficient phagocytosis [32] . Optimal use of this pathway thus requires the presence of serum , or the addition of a lipopolysaccharide binding protein [32] . In agreement with these previous results , we found that uptake of BCG by hMDM in the absence of serum was poorly inhibited when the CR3 pathway was blocked . Increased uptake of r-BCG PGL-1 by hMDM was only observed in non-opsonic conditions . In addition , strong inhibition of r-BCG PGL-1 entry in hMDM was observed following CR3 blockade . These results are consistent with previous studies showing that M . leprae preferentially invades human monocytes through the CR3 receptor in non-opsonic conditions [29] . Our findings suggest that , in the absence of opsonins , PGL-1 interacts either with a co-receptor of the CR3 mediated phagocytosis pathway or more likely with CR3 itself . This interaction might occur through the lectin site of the CR3 alpha chain which was shown to bind various sugar moieties [33] . The terminal disaccharide of PGL-1 , which is missing in PGL-bovis , may therefore be crucial for the interaction with CR3 . Engagement of CR3 has been reported to be associated either with pro- or anti-inflammatory responses , depending on the ligand and costimuli [34]–[36] . For instance , the fungus pathogen , Blastomyces dermatitidis uses the CR3 phagocytosis pathway for TNF-α suppression and immune evasion [37] . Here we demonstrate that PGL-1 production confers similar properties to mycobacteria , as preferential phagocytosis of r-BCG PGL-1 via CR3 induced lower inflammatory responses than those observed with BCG . With regard to DC , M . leprae has been reported to inhibit their infection-induced cell maturation and subsequent release of proinflammatory cytokines by comparison to BCG , or M . tuberculosis [14] . M . leprae-infected DC showed defective expression of major histocompatibility complex II expression and CD83 costimulatory molecule , resulting in poor induction of CD4+ and CD8+ T cells responses [13] . Our observation that PGL-1 , when expressed by BCG , suppresses the maturation of hDC strongly suggests that PGL-1 is responsible for the impaired maturation of M . leprae-infected hDC . On the basis of our results using human macrophages and dendritic cells , we propose that by promoting phagocytosis of M . leprae bacilli via CR3 , PGL-1 expression may contribute to the defective cellular responses of multibacillary lepromatous leprosy patients . In conclusion , we developed in this study an innovative approach to understand the role of PGL-1 in the leprosy pathogenesis . This approach might be extended to the study of PGL and lipids produced by other mycobacterial species . With regard to PGL , our knowledge of the biosynthesis pathway of PGL-1 and PGL-tb will largely facilitate the construction of BCG expressing PGL of other human pathogens . For instance , the saccharidic domain found in M . marinum and M . ulcerans , i . e . 3-O-Me-Rhap ( α1- linked to phenol ring ) [38] , [39] , corresponds to the first residue of the carbohydrate domain of PGL-1 . Therefore , the microbial tools are now available to compare the biological properties of the various PGL in the context of comparable and relevant mycobacterial cell envelopes . This information is crucial to understand the specificities of the various mycobacterial diseases , such as the different organ tropism or subversion of host immunity .
M . bovis BCG Pasteur 1173P2 was cultured in Middlebrook 7H9 broth ( Invitrogen , Cergy-Pontoise , France ) containing ADC ( 0 . 2% dextrose , 0 . 5% bovine serum albumin fraction V , 0 . 0003% beef catalase ) and 0 . 05% Tween 80 and on solid Middlebrook 7H11 broth containing ADC and 0 . 005% oleic acid ( OADC ) ( Becton Dickinson , Sparks , USA ) . When required , kanamycin ( Km ) and hygromycin ( Hyg ) were added to the medium at the final concentration of 40µg/ml and 50µg/ml respectively . A 4 . 4kb DraI-NsiI fragment was recovered from cosmid B971 that contains a large portion of the M . leprae genome [40] and inserted within plasmid pMIP12H [24] to yield plasmid pBNF03 . This plasmid carried open reading frames ML0126 to ML0128 . Plasmid pWM76 was generated by insertion of a 5 . 4 kb Bst1107-XbaI fragment from cosmid L518 ( containing genes ML2346 to ML2348 ) between the AatII ( previously blunt-ended ) -NheI restriction sites of vector pMV361 [25] . Plasmid pWM122 was constructed by insertion of two PCR fragments between the NdeI and NheI sites of plasmid pMV361e , a derivative of pMV361 containing the mycobacterial promoter pBlaF* [25] [41] . The first PCR fragment , containing genes ML0126 , ML0127 and ML0128 , was obtained with primers 0126 ( 5′-ATACATATGAGAGCAGCCGAAGCTTC-3′ ) and 0128 ( 5′-ATAACTAGTGACACTCAATCCGGTCACC-3′ ) , using plasmid pBNF03 as template DNA . The second PCR fragment , containing genes ML2346 , ML2347 and ML2348 , was amplified using primers 2346 ( 5′-TATAAGCTTCAATCCAGCCGGGCGTGT-3′ ) and 2348 ( 5′-ATATCTAGACGTGTAGTGTCCACCGTT-3′ ) . The mutant M . bovis BCG ΔRv2959c was constructed using the strategy described by Bardarov et al . [42] . Briefly , a PmeI fragment , containing the Rv2959c gene disrupted by a kanamycin cassette flanked by two res sites from transposon γδ , was obtained from plasmid pPET14 [16] and inserted between the XbaI-SpeI sites ( made blunt ) of cosmid pYUB854 [42] . The resulting cosmid was cut with PacI and ligated with the mycobacteriophage phAE87 to form the recombinant mycobacteriophage phWM06 . Phage particules were then used to infect M . bovis BCG and allelic exchange mutants were selected on 7H11 agar plates supplemented with Km and OADC . Mutant clones were screened as previously described [16] and one clone was selected for further study . The unmarked mutant was generated following transient expression of transposon γδ resolvase from plasmid pWM19 [16] . One clone , PMM130 , with an amplification pattern consistent with the excision of the kanamycin cassette was retained for further analysis ( Figure S1 ) . The various plasmids were transferred in M . bovis BCG or PMM130 by electrotransformation and transformants were selected on 7H11 agar plates supplemented with OADC and Hyg . The various M . bovis BCG recombinant strains were rendered fluorescent by the transfer of plasmid pWM124 , a derivative of the mycobacterial plasmid pMIP12H allowing expression of gfp gene from pblaF* promoter . PGL produced by the various M . bovis BCG recombinant strains were extracted and analyzed as previously described [24] . For quantification of the PGL production in WT BCG and r-BCG PGL-1 , each strain was cultured in 7H9 supplemented with ADC and 0 . 05% Tween 80 to exponential growth phase and labeled with 0 . 625 µCi . ml−1 [1-14C] propionate ( specific activity of 54 Ci . mol−1 ) for 24h . Lipids were extracted and analyzed as previously described [43] . To compare the amount of PGL-1 produced by r-BCG PGL-1 and M . leprae , 200 and 400 µg of total lipids extracted from r-BCG PGL-1 grown 20 days in 7H9 supplemented with ADC or M . leprae recovered from infected armadillos ( kind gift from Dr P . J . Brennan and Dr J . S . Spencer from Colorado State University , Fort Collins , CO , USA ) were spotted onto a silica gel 60 thin-layer chromatography ( TLC ) plate ( 20×20 cm , Merck ) . The TLC plate was run in CHCl3/CH3OH ( 95∶5 , v/v ) and PGL were visualized by spraying the plates with a 0 . 2% anthrone solution in concentrated H2SO4 , followed by heating . Lipids were quantified with a CAMAG TLC scanner using the Win CATS v1 . 4 . 3 software . Exponentially growing bacteria were diluted 1∶100 in fresh liquid medium containing various concentrations of H2O2 ( 0 , 3 , 6 or 12 mM ) or NaNO2 at pH 5 . 5 ( 0 , 2 . 5 or 5 mM for NaNO2 ) to generate NO and NO2− . After 24 h of incubation at 37°C with the chemicals , serial dilutions were plated on 7H11 solid medium . Cfus were counted after three weeks of incubation at 37°C . The experiments were performed three times independently . Human blood samples , purchased from the Etablissement Français du Sang of Toulouse ( France ) , were collected from fully anonymous non-tuberculous control donors . Peripheral blood mononuclear leukocytes and hMDM were obtained as previously described [44] . Briefly , peripheral blood monocytes were cultured for 7 days on sterile glass coverslips in 24-well tissue culture plates ( 5×105 cells/well ) containing RPMI 1640 ( Gibco , Cergy Pontoise , France ) supplemented with 2 mM glutamine ( Gibco ) and 7% heat inactivated human AB serum . The culture medium was renewed on the third day . The hMDM were washed twice with fresh RPMI medium before use . For hDC , peripheral blood mononuclear cells were isolated from whole blood by sedimentation over a Ficoll-Hypaque gradient ( GE Healthcare ) and monocytes purified by negative selection ( Miltenyi Biotec ) . Immature DCs ( iDCs ) were prepared from this CD14+ fraction by culture in RPMI 1640 supplemented with 1% human serum ( DC medium ) , in the presence of 1 , 000 U/ml GM-CSF ( and 500–1 , 000 U/ml IL-4 ( Peprotech ) for 6 days . DC maturation was monitored by flow cytometry using APC-conjugated mouse anti-human CD83 ( HB15e ) or CD40 ( 5C3 ) , PE-conjugated mouse anti-human CD80 ( L307 . 4 ) or HLA-DR ( G46-6 ) , all from BD Biosciences . For infection studies , DCs were then plated in 96 well plates at a density of 100 , 000 cells per 200 µl in DC medium . CR3-transfected CHO-Mac1 cells are CHO cells stably expressing human CR3 . A subclone of CHO-Mac1 cells expressing high levels of CD11b/CD18 was used in our experiments . Cells were cultured in α-MEM supplemented with 10% heat-inactivated fetal bovine serum , L-glutamine and for CR3-transfected CHO cells 0 . 1 µM methotrexate ( Sigma , St Louis ) . Prior to infection with mycobacteria , CR3 expression was verified with a PE conjugated anti-CR3 mouse monoclonal antibody ( clone 2LPM ) ( Dako , Trappes , France ) and analyzed by flow cytometry on a FACScalibur ( Becton Dickinson ) and cells were seeded ( 4×105 per well ) over 12 mm diameter glass coverslips in 24-wells plates and grown overnight at 37°C . Immediately before infection , mycobacteria grown to exponential phase were pelleted at 5000 g for 10min , washed twice in PBS , resuspended in serum free interaction medium ( for experiments performed under non-opsonic conditions ) or pre-incubated in fresh human AB serum for 30 minutes at 37°C ( for experiments under opsonic conditions ) . Mycobacteria clumps were dispersed by drawing up and expelling the bacterial suspension 20 times through a 25G needle attached to a 1-ml syringe . After a clarification step , achieved by low speed centrifugation ( 200 g ) , efficiency and reproducibility of dispersal were checked for each strain by microscopic observation ( magnification ×400 ) . The resulting suspension was diluted 1∶10 in the interaction medium ( supplemented with 2% of fresh human AB serum for experiments under opsonic conditions ) and optical density was read at 600 nm versus relevant blanks . For all infection experiments with the WT and recombinant M . bovis BCG strains , we established that 0 . 1 OD unit corresponded to 1×107 bacilli ( this was checked by colony forming unit determination and confirmed by direct numeration of bacteria under microscopic observation in a Thoma chamber . Bacteria were further diluted in serum free interaction medium at the desired multiplicity of infection ( MOI ) , as stated in figure legends . For each strain and each individual experiment , CFU numbers were determined by plating serial dilutions of bacterial suspension on 7H11 agar supplemented with 10% OADC . When specified , bacilli were coated with purified PGL-1 or PGL-bovis by suspending 5×107 bacteria in 100 µl of 0 . 05% PGL in petroleum ether as described previously [44] . Control was prepared by treating bacilli with solvent alone . The solvent was evaporated off and the bacteria were resuspended in PBS . Phagocytosis was assessed as previously described [44] . The role of MR and CR3 in phagocytosis by hMDM was evaluated by incubating cells with anti-CR3 ( 2LPM ) or anti-MR monoclonal antibodies at 10 µg/mL before infection . An irrelevant isotype-matched antibody was used as control . Maturation of phagosomes in hMDM was evaluated as previously described [44] . Recombinant WT BCG or r-BCG PGL-1 expressing the gfp were used for these experiments . Briefly , after infection , hMDM were washed and incubated with fresh medium . LysoTracker Red labelling was performed by washing hMDM at different time points after infection and incubating them with the acidotropic dye ( 1∶2000 ) in RPMI 1640 for 1 h . Rinsed cells were fixed with 3 . 7% paraformaldehyde for 1 h . For v-ATPase or CD63 , mouse monoclonal Ab against human CD63 and rabbit polyclonal anti-serum against v-ATPase proton pump were obtained from Caltag Laboratories ( Burlingame , USA ) and from Synaptic Systems ( Göttingen , Germany ) , respectively . Macrophages were fixed as described above , permeabilized by incubation with 0 . 3% Triton X-100 for 10 minutes at room temperature ( RT ) , blocked by incubation with 0 . 3% BSA and incubated with antiserum against v-ATPase ( 1/100 ) or mouse anti-CD63 Ab ( 1∶100 ) for 1 h at RT , and revealed with Rhodamine-Red conjugated goat anti-rabbit or anti-mouse Ab . Colocalization of WT BCG or r-BCG PGL-1 with the various maturation markers was quantified with a Leica DM-RB fluorescence microscope . Colocalization was determined as the fraction of phagosomes with GFP fluorescence associated with LysoTracker , v-ATPase or CD63 markers . For each marker , 100 phagosomes from at least 10 different fields in duplicate in two independent experiments for each time points were counted . The NF-κB activity resulting from cell stimulation with mycobacteria was studied using the THP-1 Blue-CD14 cell line ( Invivogen , Toulouse , France ) . This cell line is a derivative of THP-1 ( human monocyte/macrophage cell line ) that over-expresses CD14 and is stably transfected with a reporter plasmid expressing a secreted embryonic alkaline phosphatase ( SEAP ) gene under the control of a promoter inducible by NF-κB and AP-1 . Cells were cultured according to the manufacturer's instructions . Bacteria were deposited in 96-wells plates at the indicated concentrations in a volume of 20 µl and cells were added in 180 µl at 105 cells per well in the HEK-blue detection medium ( Invivogen ) that contains a substrate for the SEAP and fetal calf serum . Alkaline phosphatase activity , corresponding to NF-κB activation , was measured after 16h by reading OD at 630 nm . Positive controls lipomanan and lipopolysaccharide induced consistent and relevant NF-κB activity . Unstimulated THP-1 cells had a marginal NF-κB activity representing 2% of the stimulation induced by WT-BCG . TNF-α secretion was assessed in supernatants with a quantitative ELISA test supplied by R&D ( Abingdon , UK ) , according to the recommendations of the manufacturer . Data are presented as the mean ± standard error of the mean ( SEM ) or standard deviation ( SD ) of the indicated number of independent experiments ( n ) performed in duplicates ( phagocytosis assays ) or triplicates ( NF-κB activity detection or TNF-α secretion quantifications ) . The significance of differences was determined with the non-parametric test of Wilcoxon for paired samples to take into account the inter-donor variability in our analysis .
|
Mycobacterium leprae , the causative agent of leprosy , is a chronic human disease responsible for irreversible peripheral nerve damage and deformities . Lepromatous leprosy , the most severe form of the disease , is accompanied by T-cell unresponsiveness , suggesting that M . leprae has evolved strategies to modulate host immune responses . However , the molecular mechanisms of M . leprae infection remain poorly understood , mainly because this bacterium has been to date impossible to grow in vitro . The present study reports an innovative approach to study the contribution of a phenolic glycolipid ( PGL-1 ) specific of M . leprae in the cross-talk of the pathogen with host cells . We reprogrammed a biosynthetic pathway in a surrogate host , M . bovis BCG , to make it synthesize and display PGL-1 in the context of a mycobacterial envelope . Using this novel microbial tool , we found that PGL-1 production enhances the cellular invasiveness of BCG and promotes the entry via complement receptor 3-mediated phagocytosis . Bacterial uptake via this route was associated with reduced inflammatory responses in infected human macrophages . In addition , we showed that PGL-1 production inhibited the infection-induced maturation of human dendritic cells . Our findings thus provide new insights into the contribution and molecular mechanisms of action of PGL-1 in leprosy pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/innate",
"immunity",
"genetics",
"and",
"genomics/functional",
"genomics"
] |
2010
|
Mycobacterium leprae Phenolglycolipid-1 Expressed by Engineered M. bovis BCG Modulates Early Interaction with Human Phagocytes
|
In all models , but especially in those used to predict uncertain processes ( e . g . , climate change and nonnative species establishment ) , it is important to identify and remove any sources of bias that may confound results . This is critical in models designed to help support decisionmaking . The geometry used to represent virtual landscapes in spatially explicit models is a potential source of bias . The majority of spatial models use regular square geometry , although regular hexagonal landscapes have also been used . However , there are other ways in which space can be represented in spatially explicit models . For the first time , we explicitly compare the range of alternative geometries available to the modeller , and present a mechanism by which uncertainty in the representation of landscapes can be incorporated . We test how geometry can affect cell-to-cell movement across homogeneous virtual landscapes and compare regular geometries with a suite of irregular mosaics . We show that regular geometries have the potential to systematically bias the direction and distance of movement , whereas even individual instances of landscapes with irregular geometry do not . We also examine how geometry can affect the gross representation of real-world landscapes , and again show that individual instances of regular geometries will always create qualitative and quantitative errors . These can be reduced by the use of multiple randomized instances , though this still creates scale-dependent biases . In contrast , virtual landscapes formed using irregular geometries can represent complex real-world landscapes without error . We found that the potential for bias caused by regular geometries can be effectively eliminated by subdividing virtual landscapes using irregular geometry . The use of irregular geometry appears to offer spatial modellers other potential advantages , which are as yet underdeveloped . We recommend their use in all spatially explicit models , but especially for predictive models that are used in decisionmaking .
The focus of this study is spatially explicit predictive models designed to support decisionmaking ( e . g . , population establishment and spread , climate change , and flood risk ) , which should have reliable , probabilistic , and mappable results . In cases in which there are few relevant validation data ( e . g . , nonnative species and climate change ) , the model cannot be calibrated statistically , and it is therefore important that biases and uncertainties are dealt with explicitly so that confidence can be placed in the results . Uncertainty may surround all components of a model ( e . g . , input data and processes ) , but bias by definition usually results from the way that processes are implemented in the model . In this study , we explored how spatial structure can be a source of bias , and present an approach that allows uncertain landscape data to be incorporated into model output with minimal bias . There are many different landscape models in the literature ( see [1] for a recent and comprehensive list ) , all of which allow a process ( population ) model to interrogate explicit locations or regions of space , and choosing the most appropriate landscape model for the study in hand is important [1] . We focus on the use of cells in a mosaic-based model [2] to represent processes in space , which requires the subdivision of space into a tessellation of discrete , internally homogeneous patches within which a process occurs . Although this is an elegant , abstract concept , the use of cells to represent uncertain spatial processes is often desirable in real-world applications . First , some information is better represented by an areal unit than by a point location ( e . g . , water ) , whereas other information is considered to conceptually occupy an area of real space defined by its boundary ( e . g . , an animal social group ) . Second , the limited understanding of many of these systems requires us to model at the scale for which most is known ( e . g . , the behaviour of individuals within a social group of animals is often not well-understood , whereas the size , productivity , or spatial description of the whole population may be simpler to study and is well-described ) . Third , the raw data used to describe the landscape ( e . g . , satellite and aerial photography ) are subject to errors and uncertainties . By modelling processes at scales significantly larger than that of the underlying data , these problems become statistically tractable ( e . g . , Land Cover 2000 [3] ) . Although in many such models , the attribute values of cells are directly calculable from habitat or geographical data ( e . g . , vegetation type ) , here we use conceptually abstract and attribute-free cells in order to consider only geometry ( specifically , shape ) and neighbourhood ( number and arrangement of adjacent , interacting patches ) in a homogeneous landscape . In this study , we have used population modelling concepts to demonstrate the potential for bias in cell-to-cell movement of information ( e . g . , individuals ) resulting from the geometry of a mosaic virtual landscape . Population models predominately use raster virtual landscapes , and the description of home ranges or social groups with single squares ( e . g . , [4–6] ) or a square arrangement ( e . g . , [7] ) is not unusual . Cell-to-cell movement is implemented using either von Neumann ( e . g . , [8] ) or Moore ( e . g . , 6 , 9–11] ) neighbourhoods ( four or eight neighbours , respectively ) , often with some directional component [12–14] . Some studies note that the geometry of the virtual landscape has the potential to affect simulation results [15 , 16] , and the interaction strengths of orthogonal and diagonal neighbours in rasters are sometimes weighted using an appropriate algorithm [16 , 17] . Landscape permeability is sometimes defined using raster cells ( e . g . , [18] ) , although some authors have suggested using multiscaled rasters to represent patchy landscapes ( e . g . , [19] ) . Other studies use hexagonal geometry for both spatial analysis [20 , 21] and modelling [15 , 22–25] because the strengths of all neighbourhood interactions are equal . Irregular ( variable shape and size ) geometry has been used extensively in population modelling , but usually to parameterize or display discrete , spatially disparate habitat patches with explicit connectivity based on the distance between patches [26–28] or vector-based movement rules [29–31] , and studies such as that by Ovaskainen [32] exemplify this approach . A few models use tessellated irregular shapes across a whole landscape , and implement cell-to-cell movement as part of the simulation [33 , 34] , whereas Dunn and Majer [35] suggest that Voronoi ( Dirichlet ) cells are a convenient way to represent multiply scaled data , but they do not go into detail about dispersal mechanisms . However , no attempt has been made to specifically test how geometries other than rasters may affect movement in a mosaic landscape , and an explicit consideration of geometry does not appear to be an integral part of most population modelling studies . The representation of real-world landscapes is complex , with the description of features represented as discrete objects subject to both qualitative and quantitative variability , uncertainty , or both [36] . Uncertainty within virtual landscapes is already considered in some disciplines ( e . g . , [37 , 38] ) . We suggest that all spatially explicit population models should consider how uncertainty in landscape representation may affect model output , just as sensitivity analyses on process model parameters have become standard practice . Clearly this is entirely dependent on the nature of the study undertaken ( data , scale , structure , and discipline ) , so we cannot begin to describe how individual studies in diverse disciplines should address this issue . However , it seems inevitable that population modellers will adopt a probabilistic approach to spatial studies ( which can be easily implemented through the use of alternative landscapes in successive runs of the model ) , and we provide a mechanism by which minimally biased landscapes can be created . We created landscape mosaics with raster , hexagonal , and irregular geometries with which to model and compare the cell-to-cell exchange of information . We are not aware of any other study that directly compares the potential for systematic bias in the movement of information across the spectrum of possible geometries of mosaic virtual landscapes . We highlight how the geometry of cells in a raster virtual landscape affects both qualitative and quantitative aspects of spatial representation of irregular shapes , but leave the attributional representation of features ( e . g . , heterogeneous habitat [39 , 40] ) and subsequent impacts on movement or process [41 , 42] to another study . We believe that these concepts are generally applicable across a broad range of spatially explicit modelling disciplines .
Cells in the raster and hexagonal virtual landscapes had a fixed number of neighbours ( Table 1 ) . Cells in the Dirichlet landscape had a mean of exactly six neighbours , though there was variation about this value within individual landscape instances . The CGD virtual landscapes all resembled pixelated versions of the Dirichlet landscape . However , both the visual and mathematical approximation improved as the resolution of the underlying raster was increased , as demonstrated by both the mean and standard deviation of the number of neighbours ( Table 1 ) . Cells in the aggregate map had approximately six neighbours , and were a range of shapes because the sequential building rules meant that growing cells were often geometrically constrained by neighbours . Of the geometries tested in this study , the mean number of neighbours of a cell was six , or its approximation , with the exception of the rasters . There was variation in the distribution of cell sizes within the irregular virtual landscapes ( Table 1 ) . We measured and compared all possible unique cell-to-cell step lengths ( measured between centre-of-mass centroids ) in five landscapes: the three regular landscapes , and single instances of the Dirichlet and the CGD4 landscapes ( Figure 3 ) . In the von Neumann and hexagonal landscapes , only one step length was ever possible , with lengths 1 km and 1 . 074 km , respectively . In the Moore landscape , two steps were equally probable , with lengths 1 km and 1 . 41 km producing a mean step of 1 . 21 km per landscape . Step lengths in the Dirichlet landscape were gamma distributed ( Figure 3 ) with a mean of 1 . 095 km , which is close to that found in the hexagonal landscape; the step lengths of each cell in the CDG4 landscape were similarly distributed with a mean of 1 . 18 km , though the distribution was less smooth as a result of the finite distribution of cell shapes and hence step lengths ( Figure 3 ) . We measured the land area from a number of raster depictions of a fine-scale vector description of a real-world object ( United Kingdom ( UK ) coastline ) . Rasters were created at a range of scales with a variable origin ( shifted successively by 10% of the resolution west and south ) . A further ten rasters , at a resolution of 10 km , were created with a fixed origin but with the orientation of the grid rotated successively by 9° . The qualitative form of a raster representation of the UK differed with a change in origin or orientation of the grid ( Figure 8 ) . Small objects such as islands appeared or disappeared , became connected or disconnected from the mainland or each other , or changed their shape radically . This would have clear effects on any model involving terrestrial movement . Although we have only shown this at one scale , these undesirable effects are fractal , and would be present as a possible bias at all scales , and would worsen at larger resolutions [44] . The mean area reported by the raster representations of the UK decreased with increasing scale ( Figure 9 ) , though as a fractal property , would show bias at all scales . The best mean estimate ( 99 . 9% of the vector original ) was derived from the finest scale raster ( 1 km ) , but mean estimate of national area fell to as low as 98 . 5% at a 100 km resolution . In addition , the variance around these mean figures was considerable and also increased with scale . The worst performing rasters ( two out of ten instances at a 100 km resolution ) showed an area of only 89 . 6% , an alarming loss of 10 . 4% of the British land surface . The estimates of national area produced by iteration across multiple rotations of a raster grid at 10 km resolution had a mean of 100%; individual instances ranged from 99 . 3% to 100 . 6% of the vector original , suggesting that at this scale , the orientation of the raster had only a small quantitative effect on area . All forms of raster representation have biases , variant with scale . In contrast , an irregular geometry ( specifically a vector representation ) can be subdivided into as many randomized vector cells as necessary . Any estimate of gross area , length , or geographic property is perfect ( zero bias ) . When multiple instances of virtual landscapes are required , we recommend the use of irregular geometry to avoid introducing bias to representation of its extent .
Simple random walks in virtual landscapes with regular geometries can produce enormous qualitative biases in the direction and extent of movement and hence bias the distribution of populations in space . For all our investigations ( accessibility , random movement , and directed random movement ) , the regular geometries performed poorly at some or all scales for measures of both distance and direction . The nature and strength of the bias was , in part , a function of the length of the movement and the resolution of the landscape , with the potential for different biases to worsen after both short-distance and long-distance movement . In an ideal homogeneous virtual landscape , the distance travelled by an individual ( from its origin ) after random movement should be independent of the direction travelled at each step . Regular grids all restrict the direction of movement to the same few angles at every step , so the final positions of individuals cannot be independent of the grid structure . Even if the scope of the movement neighbourhood is extended to include more distant cells ( e . g . , to include the 16 cells adjacent to the eight immediate neighbours in the Moore neighbourhood ) , so that single steps may include jumps over the immediate neighbours , a regular geometry restricts the available directions to some degree . In comparison , the direction of neighbouring cells in a single , irregular virtual landscape is not set by the geometry , and when enough multiple irregular virtual landscapes are considered together , available directions assume a uniform circular distribution . We demonstrated that , in the same way that regular landscapes restrict the available directions for movement , they severely restrict cell-to-cell step lengths , and in part , this may explain a component of the bias in movement . The hexagonal landscape is the optimum arrangement of circles packed in space using a single iteration . Dirichlet landscapes emulate circular cells over many iterations ( Figure 10 ) , and the mean step length closely approximates that of the hexagonal landscape , even in a single iteration . Although the step lengths in the CGD landscapes followed the same general distribution as the vector Dirichlet landscape , individual step lengths were more or less frequent than expected . We suggest that although the results of random walks appeared similar in all the irregular landscapes , a smooth distribution of step lengths is less likely to be a source of bias , and therefore , at fine scales in particular , we recommend the vector Dirichlet landscape over the CGD irregular landscapes . We propose that the average number of neighbours per cell is a useful metric for quantifying the potential for landscape geometry to introduce bias in movement . A mean close to six neighbours appears to be the ideal; all the irregular landscapes have this property . Although the hexagonal landscape has six neighbours per cell , the variance is zero , and therefore directional movement must be restricted; we therefore extend the metric to include a nonzero variance . The square geometries fail in both respects . Because we have identified the potential for bias in movement in regular geometries , we believe that studies need to show ( not assume ) that movement in their own spatial models produces no bias . Bias due to geometry may not be apparent in models with complex rules for cell to adjacent cell movement , e . g . , dependence on heterogeneous landscape quality ( e . g . , habitat preference [6] and permeability [47] ) . Where sources of geometrical bias have been identified , individual studies have attempted to compensate with a variety of approaches [16 , 17] . Not only is this compensation dependent on the spatial and temporal scale of the model for which it is designed , it adds more potential for bias to the model ( albeit pulling in the opposite direction ) and may adversely interact with other components of the model . There is no way to be certain that biases masked at one scale will not produce artefacts when a predictive model is extrapolated in time or space . In contrast , there is no potential for bias in both the direction and distance of movement of individuals across virtual landscapes with irregular geometry . Some of the deficiencies of cell-to-cell movement across regular landscape geometries identified here might be overcome by iteration of rasters , specifically by randomizing origin and orientation . However , this process can change the quality of the representation of the whole landscape , and it is this that we concentrate on here . The dependence on scale in the adequate representation of complex shapes with rasters has been well-discussed elsewhere [44 , 48–52] . Our representation of the UK coastline , and the measurement of its area , although not novel , allowed us to focus specifically on the qualitative and quantitative effects of using multiple regular geometries to represent a complete extent . We used the UK landmass as an example of a real-world object that has an irregular extent whose boundaries could not be defensibly redefined as a regular shape for a national-scale model . The representation of real-world features with regular geometries must always be an approximation [50] whose adequacy can only be measured by model output . We have shown that at any finite scale , a feature represented by a single virtual landscape with regular geometry will always show qualitative and quantitative errors . Some of these errors can be extreme , and the modeller choosing a single instance of a regular geometry with which to represent the landscape has no way of knowing how adequate it is without testing several representations . Biases in the mean output could be reduced through iteration of regular geometries ( with random origin and orientation ) and a careful choice of scale , but the interpretation of results would only be acceptable where the bias was quantified . In contrast , any landscape subject to a spatial modelling study can be split up into irregularly shaped cells with no detrimental qualitative or quantitative effect on the representation of the whole . If accuracy in feature representation is important , the superior alternative to the raster is the irregular and vector-based mosaic . The problems of feature representation by regular geometries are compounded by the connection between scale and structure; the form and significance of any feature alters as soon as the scale is changed . This is especially problematic where interacting processes occur at different scales in the same landscape . Because scale has such a strong effect on the properties of model output , it is a pity that studies suggesting quantitative methods for determining the most appropriate scale have not been pursued ( but see [53] for a generic approach , [54 , 55] for more specific applications ) . In the absence of quantitative rules , modellers have to rely on common sense or experience to choose scale , and therefore should clearly demonstrate that their choice is appropriate by showing that the bias produced by the model at that scale is acceptable . The choice of resolution in many raster-based models to date is often either derived from technical data ( e . g . , satellite imagery ) ( e . g . , [39 , 56] ) or chosen to be the nearest integer measurement of an apparently relevant process ( usually biological; e . g . , [4 , 6 , 57] ) . We have shown that the potential for bias is always present in regular virtual landscapes even at high resolutions , and the impact of that bias is a function of scale . If only one raster landscape is used in a model , its origin and orientation appear arbitrary yet are usually unchallenged ( this is apparent from the lack of ability to rotate rasters away from a north–south orientation in some GIS packages ) . If the resolution of the raster is high enough , the representation of features will be little changed by origin and orientation , but model processes may be affected . However , if the data or the process suggests modelling at a low resolution , using a raster must sorely compromise the representation of the landscape . Using irregular mosaic landscapes solves two problems . First , the interaction of bias with scale is removed from the process model ( c . f . , directional bias in movement in this study ) . Second , the quality of the spatial representation of available data no longer depends solely on resolution; landscapes formed from vector-based , irregular cells can remain faithful to the available data at any scale and in any single instance . The modeller is thus free to set a scale appropriate to other model processes . Lindenmayer , Fischer , and Hobbs [1] emphasize that the ability to choose one of a number of landscape models is important in fauna research . We suggest that , in the mosaic-based landscape paradigm , the automatic use of multiple landscape models should be widespread . Real landscapes comprise irregular and complex shapes [58] that can only be well-described using irregular cells . There will always be quantitative uncertainty in the location and boundary shape of cells and qualitative errors in their internal description even if irregular virtual landscapes are created deterministically from underlying habitat patch data rather than randomly ( e . g . , Dirichlet polygons ) . The only way to explore and incorporate the uncertainty associated with landscape representation is to model with multiple , alternative virtual landscapes and present results as probabilistic maps ( e . g . , [59] ) . This applies equally to regular and irregular mosaic landscapes . The computational demands of running irregular models such as the ones in this study are not necessarily more than those of a raster model . Because the movement rules are so simple , all that is required to implement movement is a list of cell IDs and associated neighbours . All the investigations in this study were run in Python , only using the GIS for creating and displaying landscapes . We admit that preparation of an irregular landscape set requires some extra work; however , if it is accepted that multiple virtual landscapes are necessary ( e . g . , different random centroids for Dirichlet tessellations , and different origins and orientations for rasters ) , the effort in preparing irregular and regular landscapes becomes almost indistinguishable . The irregular landscapes also appear to yield greater returns , since they are scale-independent and bias-free , and can represent features within the landscape as well as the available data allow . It is worth pointing out that the ideal vector implementation of an irregular mosaic landscape can be approximated very easily by a raster aggregate; the use of models such as the CGD ( Figure 1E–1G ) brings the benefits of unbiased movement , although the best representation of edges and features is only achieved by using a very high resolution raster . We have shown that all single instances of irregular mosaics have similar structure ( number of neighbours ) and statistical properties ( e . g . , step length distribution ) , which results in scale-free and similarly unbiased movement of information ( populations ) . However , the structure and statistical properties of regular mosaics differ from each other and from that of the irregular mosaics , resulting in biased movement that cannot be easily compared . We suggest that two spatial models using irregular virtual landscapes of any scale may be more easily compared than those using regular geometry , which must have both the same scale and the same structure , and therefore the same bias . We believe that the use of irregular geometry to form virtual landscapes may bring many additional benefits . We illustrate these with examples from population modelling , in which information has an integer form ( e . g . , individuals ) , but the principles should be applicable in many disciplines , including those concerned with the movement of infinitely divisible information ( e . g . , water ) . The area and shape of cells in an irregular geometry can vary across the extent of the virtual landscape so that regions requiring a detailed spatial description ( complex habitat patches , linear features such as rivers , etc . ) can be represented either with a greater density of irregular cells or exclusively with a single cell . This property of irregular landscapes has also been identified by Dunn and Majer [35] and is analogous to the raster approach of Tischendorf [19] . In turn , this permits an improvement in the description of cell attributes and reduces their uncertainty . This is especially important where cell attributes have the ability to affect the spatial output of the model ( e . g . , landscape connectivity , patch permeability , and population persistence [60] ) . We have validated how the aggregation of real-world features into irregular cells can provide a sufficiently irregular virtual landscape to avoid bias ( i . e . , aggregate map , Figure 1H ) . Another useful benefit of irregular geometry is illustrated by considering a fixed point in space . A single irregular cell containing this point is obviously not circular , but cells taken from sufficient iterations of the virtual landscape will approximate a circular kernel around the point ( Figure 10 ) . This concept is useful both in describing individual behaviour ( i . e . , zones of perception ) or group dynamics ( i . e . , social interaction and density dependence ) . Most dispersal kernels in continuous space implicitly define movement to nearest neighbours as the most frequent , with vector movement resulting in individuals moving preferentially to sites that are close [61] . By using irregular cells , such kernels can be tuned with biological and geographical realism ( e . g . , interaction groups are bounded by major roads or coastline , perception zones do not include impenetrable habitat , and movement cannot cross rivers; see Figure 10 ) . Finally , we observe that the geometry and size of real-world processes and objects ( such as home ranges , social group territories , habitat patches , and habitat quality ) are irregular and variable ( [58]; specific examples include the spatial arrangement of subpopulations of rabbits [62] , badgers [63] , and coyotes [64] ) . There is a significant body of literature in identifying habitat patches in real-world landscapes ( e . g . , [65–67] ) . Their subsequent representation with single cells of regular geometries is inappropriate . In addition , if there are few data on how cells are formed in the real landscape , the most defensible way of expressing them in a model is through multiple , randomly generated ( irregular ) mosaic landscapes . This study was prompted by a desire to construct a universal framework within which uncertainties in landscape description could be included explicitly in model function and results , and in which movement could be modelled as simply as possible in an unbiased , generic , and flexible manner . A single virtual landscape formed with regular geometry has a huge potential for bias . In contrast , this study has shown that even a single virtual landscape formed with irregular geometry has no potential to bias the direction or distance of movement of information ( e . g . , individuals ) , and although the defensible use of a random property ( in this case , geometry ) requires multiple instances , the variation between irregular landscapes is small . Any representation using a regular geometry is at best a good approximation . Even when multiple instances of regular geometries with random origins and rotations are measured , the mean output still has the potential for bias , and the variation between instances is large . The representation of an extent by an irregular geometry shows no error . We recommend the use of irregular geometry and multiple random instances in creating any virtual landscape , which eliminates bias in the movement of information and the representation of real-world extents . Both are specifically recommended for models that are designed to help make decisions , so that the probabilistic output encompasses the uncertainty in both population processes and spatial representation . As this is the first study recommending the use of irregular geometries , and we have not covered issues of internal representation ( e . g . , small features and heterogeneous landscape quality ) , it is difficult to state unequivocally that they are completely superior to regular geometries , but the results presented here suggest that they should be the first choice for modelling virtual landscapes .
Our virtual landscapes were created to have a mean cell area of 1 km2 across the study area , which was a 25 , 361 km2 area of southeast England . The two rasters used an arbitrarily chosen origin of ( 0 , 0 ) on the British National Grid ( BNG ) ; the placement of the hexagonal landscape was entirely arbitrary . The Dirichlet landscape was formed by a vector tessellation using the ArcInfo Thiessen on 25 , 361 random points drawn from a uniform distribution within the study area . The coarse-grain landscapes were created from three raster landscapes ( origin at 0 , 0 BNG ) , with 500-m , 333-m , and 250-m resolutions . Within each raster , all squares were associated with , and then aggregated to , the nearest of 25 , 361 randomly chosen squares ( see Figure 11 and pseudocode at the end of this section ) . The aggregate map was created from Land Cover 2000 [3] , a fine-scale vector habitat coverage . A random habitat patch was selected and neighbours absorbed in turn until a total area of 1 km2 ± 5% was achieved . The neighbour with the next-closest centroid was always chosen with the aim of maximizing circularity; other joining rules are possible , e . g . , habitat similarity , but the geometric optimization was chosen for its simplicity . The neighbourhood of any focal cell in the hexagonal and irregular landscapes was defined as all cells in the landscape with a boundary ( or point on the boundary ) shared with the focal cell . The interior of a virtual landscape was defined as all cells that were more than 1 km from its boundary . The number of neighbours ( Ne ) and area ( Ae ) of interior cells were measured across all virtual landscapes and all instances on irregular landscapes . The minimum , mean , maximum , and standard deviation of Ne and Ae were calculated for each virtual landscape . Probes were developed to quantify the effects of landscape geometry on cell-to-cell movements , and are based on the following generalized approach . In a discrete landscape , individuals move from one cell e into any neighbouring cell with probability 1/Ne , where Ne is the number of neighbours of e . We compared the performance of the probes against the simplest vector random walk . This unbiased benchmark moved an individual a fixed distance ( 1 km ) from its starting position ( x , y ) at a randomly chosen angle in ( 0 , 2π ) . Movement probes always consisted of 10 , 000 independent individuals . Irregular virtual landscapes used 1 , 000 individuals across ten randomized instances , with figures and statistics presented as the sum of the instances ( see Figure 10 ) . For the accessibility probe , we calculated the minimum number of steps required for an individual in any cell in the landscape to access the origin ( cell containing BNG 448500 , 104500; southwest corner ) in all virtual landscapes . We mapped the limit of the region accessible from the origin with 100 steps and measured the mean of the minimum number of steps required to reach a range of distances from the origin at 10° angles . The random movement probe started in a cell containing the origin ( BNG 524500 , 179500; centre of virtual landscape ) . Vector random walks started at the origin . We used four sets of parameters ( probability of movement , p , number of time steps , t ) to simulate a variety of forced and unforced , short and long walks: ( p = 1 , t = 5 ) , ( p = 1 , t = 100 ) , ( p = 0 . 5 , t = 10 ) , and ( p = 0 . 5 , t = 200 ) . Direct comparison of population displacements can only be made between probes where the product of p and t is equal , thus unforced walks last twice as long as equivalent forced walks . The population distribution also was calculated for directed random walks , in which neighbouring cells were divided into “backward” ( toward the origin ) and “forward” ( around or away from the origin ) neighbours . Fully directed ( no backward movement ) and semidirected ( 10% possibility of movement backward ) random walks were simulated for ( p = 1 , t = 50 ) and ( p = 0 . 5 , t = 100 ) . Directed movement was implemented in the vector random walk by disallowing any choice of angle that would result in a new location closer to the origin . Notice that in a totally random walk across a landscape with mean of six neighbours , backward movement would be achieved on average in two out of six cell-to-cell movements ( with two radial movements and two forward movements also possible ) , or with 33% chance . Therefore , a 10% chance of backward movement in the directed random walk reduces backward movement by one-third and not by one-half ( if movement was only forward or backward ) . For statistical tests of similarity , population density ( individuals per square kilometre ) was measured in the cell containing locations 0 , 1 , 2 , 3 , . . . km from the origin , at angles 0° , 60° , and 90° from north . The number of individuals within a circle with area 1 km2 centred at these locations after the equivalent vector movement was used as a benchmark . Pearson's product moment correlation coefficient was calculated for the paired datasets for each cross-section . For short random walks , locations from 0 , . . , 8 km were used; for long walks , we used 0 , . . , 36 km . Using a vector polygon of the British coastline ( of area 245 , 660 km2 ) , we created ten raster representations of the UK using a resolution of 1 , 10 , 50 , and 100 km . At each scale , the origin of the raster ( lower left corner ) was initially set at BNG 0 , 0 with nine further representations created by moving the origin 10% of the resolution , both west and south . Pseudocode for creating a CGD landscape with mean cell size of 1 km2 is shown below . See Figure 11 for an illustration of the process . Let D be an X by Y grid with cells dx , y: x , y are the coordinates of the centroid of the raster cell dx , y . belongs_to = null dx , y . id = ( X − 1 ) x + ( Y − 1 ) y dx , y . dist = infinity CG4: Initial raster ( D ) resolution 500 × 500 m ( i . e . , 4 cells = 1 km2 ) CG9: D resolution 333 . 3 × 333 . 3 m CG16: D resolution 250 × 250 m # Choose the centre cells for the aggregation process For i = 1 . . 25361: x = random_integer in range ( 1 , X ) inc . end points y = random_integer in range ( 1 , Y ) inc . end points dx , y . belongs_to = dx , y . id dx , y . dist = 0 chosen_list . append ( dx , y ) # Find which centre cell is closest to every other cell in D For all cells c in chosen_list: For all cells d in D but not in chosen_list: dist = sqrt ( ( cx - dx ) 2 + ( cy - dy ) 2 ) if dist < dx , y . dist: dx , y . belongs_to = c . id if dist = = dx , y . dist: if random_in_[0 , 1] < 0 . 5: dx , y . belongs_to = c . id # Dissolve the grid D according to the . belongs_to attrib ute
|
Many different areas of science try to simulate and predict ( model ) how processes act across virtual landscapes . Sometimes these models are abstract , but often they are based on real-world landscapes and are used to make real-world planning or management decisions . We considered two separate issues: how movement occurs across landscapes and how uncertainty in spatial data can be represented in the model . Most studies represent the landscape using regular geometries ( e . g . , squares and hexagons ) , but we generated landscapes of irregular shapes . We tested and compared how the shapes that make up a landscape affected cell-to-cell movement across it . All of the virtual landscapes formed with regular geometries had the potential to bias the direction and distance of movement . Those formed with irregular geometry did not . We have also shown that describing whole real-world landscapes with regular geometries will lead to errors and bias , whereas virtual landscapes formed with irregular geometries are free from both . We recommend the use of multiple versions of virtual landscapes formed using irregular geometries for all spatially explicit models as a way of minimizing this source of bias and error; this is especially relevant in predictive models ( e . g . , climate change ) that are difficult to test and are designed to help make decisions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"none",
"ecology",
"science",
"policy"
] |
2007
|
Landscape as a Model: The Importance of Geometry
|
Genetic causes for autosomal recessive forms of dilated cardiomyopathy ( DCM ) are only rarely identified , although they are thought to contribute considerably to sudden cardiac death and heart failure , especially in young children . Here , we describe 11 young patients ( 5–13 years ) with a predominant presentation of dilated cardiomyopathy ( DCM ) . Metabolic investigations showed deficient protein N-glycosylation , leading to a diagnosis of Congenital Disorders of Glycosylation ( CDG ) . Homozygosity mapping in the consanguineous families showed a locus with two known genes in the N-glycosylation pathway . In all individuals , pathogenic mutations were identified in DOLK , encoding the dolichol kinase responsible for formation of dolichol-phosphate . Enzyme analysis in patients' fibroblasts confirmed a dolichol kinase deficiency in all families . In comparison with the generally multisystem presentation in CDG , the nonsyndromic DCM in several individuals was remarkable . Investigation of other dolichol-phosphate dependent glycosylation pathways in biopsied heart tissue indicated reduced O-mannosylation of alpha-dystroglycan with concomitant functional loss of its laminin-binding capacity , which has been linked to DCM . We thus identified a combined deficiency of protein N-glycosylation and alpha-dystroglycan O-mannosylation in patients with nonsyndromic DCM due to autosomal recessive DOLK mutations .
Dilated cardiomyopathy ( DCM ) is a life-threatening disease characterized by left ventricular enlargement and systolic dysfunction , which can lead to congestive heart failure and is a common cause of patients requiring heart transplantation . In view of the progressive disease course and the acuteness of presenting symptoms , early recognition and diagnosis of the underlying etiology is essential . Genetic causes for DCM are estimated to explain 20–48% of all idiopathic patients [1]–[3] . Until now , 33 nonsyndromic DCM genes have been identified , two on the X chromosome and 31 on the autosomes , of which only one shows recessive inheritance [4] . We expect more recessive genes , since recessive forms have been shown to explain up to 16% of familial DCM [5] . Especially in young children ( <10 years old ) these are expected to contribute considerably to disease [6] . Protein N-glycosylation is a very common co-translational modification of many proteins , following a sequential and highly ordered pathway in the cytoplasm , endoplasmic reticulum ( ER ) and Golgi apparatus . Genetic defects in this pathway generally lead to a multisystem disease . These inborn errors of metabolism form the group of Congenital Disorders of Glycosylation ( CDG ) for which currently more than 40 different genetic defects are known [7] . Defects in the ER during the assembly of the lipid-linked oligosaccharide [8] , and glycan transfer to nascent protein chains affect all N-linked proteins and typically lead to a multisystem presentation in CDG-I patients . Clinically such patients are characterized by psychomotor and intellectual disability , muscle hypotonia , seizures , ophthalmologic anomalies , failure to thrive , endocrine and coagulation abnormalities and variable dysmorphic features . Dilated cardiomyopathy in CDG is very rare and has only been described in one of the two reported families with dolichol kinase deficiency ( DOLK-CDG , MIM 610768 ) as part of a multisystem presentation with profound muscular hypotonia , ichthyosiform skin , nystagmus , epilepsy and pulmonary infections , leading to death within the first months of life [9] , and in patients with liver involvement [10] or cognitive delay [11] . On the other hand , cardiomyopathy of the hypertrophic type is common in CDG type I [12]–[14]; it is one of the lethal comorbidity factors in CDG-Ia ( PMM2-CDG , MIM 212065 ) patients in infancy . In this paper , we present eleven young patients ( age 5–13 years ) with CDG and recessive mutations in DOLK with a predominantly nonsyndromic presentation of DCM . In addition , we show that the main presenting symptom of DOLK-CDG is caused by deficient O-mannosylation of sarcolemmal alpha-dystroglycan .
Dilated cardiomyopathy was diagnosed in several children ( see pedigree; Figure 1 ) , without significant muscular weakness or creatine kinase ( CK ) elevation . Central nervous system involvement , such as cerebellar ataxia , epilepsy or intellectual disability was not present in the patients , except for transient muscular hypotonia and mild developmental delay with a minor increase of CK in family IV . Decreased coagulation parameters were observed in all individuals . Patient I/2 , the second male child of healthy , consanguineous parents of Druze origin was referred to the pediatric metabolic unit for evaluation of mild failure to thrive and persistent elevated transaminases during infancy . Impaired glycosylation ( CDG-I ) was diagnosed at the age of 10 months [15] . At 6 years of age a mild asymptomatic dilatation of the left ventricle was shown on echocardiogram . He developed acute heart failure at age 11 . Patient I/3 , the younger brother of patient I/2 , is clinically asymptomatic . At age 4 years , following the diagnosis of his brother , mildly elevated transaminases were noticed . He underwent echocardiography , which revealed mild dilated cardiomyopathy . Patient II/2 , the second male child of healthy , consanguineous parents of Druze origin , was clinically healthy until the age of 9 years , when he was admitted with acute congestive heart failure and dilated cardiomyopathy of unknown etiology . He died suddenly following heart arrhythmia . Patient II/5 , sister of patient II/2 , was diagnosed with a dilated cardiomyopathy at age 7 . Biopsied ventricles of the explanted heart revealed myocyte hypertrophy and interstitial fibrosis ( Figure 2 ) , more pronounced in the left ventricle than in the right ventricle . Patient II/6 , a younger sister of patient II/5 , had a history of mild hypotonia , failure to thrive , short stature and ichthyosiform dermatitis . She was diagnosed with mild dilated cardiomyopathy at the age of 6 years . Patient III/2 was the second male child of healthy , consanguineous parents of Beduin origin . At age 9 years , he presented with progressive weakness over the last month . These symptoms led to the diagnosis of “viral myocarditis” resulting in an end-stage dilated cardiomyopathy and death after unsuccessful reanimation . Patient III/1 , a 13 years old sister of III/2 , was found to have asymptomatic dilated cardiomyopathy following the diagnosis of her brother . Her younger brothers , patient III/3 of 11 years old , and III/4 of 9 years old showed asymptomatic minimal evidence of cardiomyopathy detected by repeated echocardiograms . On supportive treatment no further deterioration of the cardiac function was observed during the 3 years of follow up . Ichthyosiform dermatitis was noticed in siblings 2 , 3 and 4 . An 11-year-old female of Indian origin with a background of learning difficulties , mild hypotonia and ichthyosis , presented with cardiac failure secondary to severe dilated cardiomyopathy . Prior to the diagnosis of CDG , her condition deteriorated; she required mechanical support and was listed for cardiac transplant . She died of thrombotic and septic complication whilst having Berlin heart as bridging procedure for transplant . Her younger brother was diagnosed with the same defect . He has mild developmental delay but cardiac function is normal . Transferrin isoelectric focusing for analysis of N-glycosylation abnormalities was performed during metabolic screening . Convincingly abnormal profiles were found for all affected patients , showing an increase of asialo- and disialotransferrin and low or decreased tetrasialotransferrin ( Figure 3 ) . These results indicated a diagnosis of CDG type I with a genetic defect in the cytoplasm or endoplasmic reticulum . The most common subtype PMM2-CDG ( CDG-Ia ) was excluded by analysis of phosphomannomutase activity in patient fibroblasts . In view of the specific clinical symptoms and consanguinity in families I and II , we chose a direct homozygosity mapping approach instead of lipid-linked oligosaccharide analysis . Homozygosity mapping was performed in two Israeli families ( I and II , Figure 1 ) using the Affymetrix GeneChip Mapping 10 K 2 . 0 array ( family I ) and the Affymetrix GeneChip Human Mapping 250 k NspI Array ( family II ) . The largest overlapping homozygous region in families I and II was found on chromosome 9 . In family I , the 19 . 1 Mb region at 9q33 . 1–9q34 . 3 was delimited by SNP_A-1518745 and 9pter . By using the two siblings of family II , the overlapping region could be confined to 5 . 0 Mb at 9q33 . 3 and 9q34 . 11 , delimited by SNP_A-4223282 and SNP_A-2111464 . Short tandem repeat ( STR ) marker analysis confirmed homozygosity of this region and showed that the haplotypes of the two Israeli families were identical ( Figure 1 , I and II ) . The three affected siblings of family III with a similar phenotype were homozygous for the same region , delimited by D9S1872 and 9qter . The overlapping homozygous region of the three families contained 117 genes . Comparison of this region with a list of candidate genes for CDG-I glycosylation defects highlighted two candidate genes known to be involved in protein N-glycosylation , DOLPP1 and DOLK . Analysis of the protein coding sequence of DOLK in family I showed a homozygous missense mutation ( c . 1222C>G; p . His408Asp; Figure 1A ) . The same mutation was identified in family II . The finding in two seemingly unrelated kindreds , who reside in two different villages in Northern Israel , and the presence of an identical 5 Mb interval haplotype including the same mutation , suggest a founder event among these Druze kindreds . In family III , a homozygous c . 912G>T transition was identified resulting in a p . Trp304Cys amino acid change . Both His408 and Trp304 are fully conserved down to zebrafish ( Figure S1 ) and both SIFT [16] and PolyPhen [17] programs predict these changes to be damaging for protein function . On basis of a similar clinical presentation , DOLK was sequenced in DNA of family IV . A third homozygous mutation ( c . 3G>A , Figure 1D ) was identified that removes the initiator methionine residue ( p . Met1Ile ) , which is conserved from human to zebrafish . All three mutations were not present in >1000 healthy Caucasian controls as shown by high resolution melting analysis , by exome sequencing , and by using data from the 1000 genomes project ( www . 1000genomes . com ) ( Text S1 ) . Analysis of the protein coding sequence of DOLPP1 in families I and II did not show any sequence variations . Activity of dolichol kinase was assessed in patient fibroblast homogenates using dolichol-19 as acceptor and γ32P- cytidine 5′-triphosphate ( CTP ) as phosphate donor . Analysis of 32P incorporation into dolichol-P clearly showed strongly reduced enzyme activity for five patients of all four families investigated ( Figure 3B ) . Fibroblasts from CDG-I patients with a different genetic defect showed dolichol kinase activity comparable to controls . SEC59 is the yeast ortholog of DOLK [18] . A sec59 yeast mutant that displays temperature sensitive lethality as well as an underglycosylation of glycoproteins at the restrictive temperature was used to further confirm the non-functionality of the mutations in our patients . In addition , we have compared the novel mutations with the previously reported mutations in the two DOLK-CDG patients . All strains showed comparable growth at the permissive temperatures of 25 or 32°C , whereas at the restrictive temperature of 37°C only wild-type DOLK supported growth ( Figure 4A ) . N-glycosylation of the same mutant alleles was assessed by western blotting of the vacuolar glycoprotein carboxypeptidase Y ( CPY ) , containing four N-glycan chains ( Figure 4B ) . At the restrictive temperature CPY is underglycosylated in sec59 cells , visualized by the appearance of glycoforms lacking one to four N-glycan chains . Consistent with the cell growth results , wild-type DOLK was able to restore the glycosylation of CPY at 37°C , as evidenced by a shift to the more mature forms of CPY and a decrease of underglycosylated isoforms . All mutants failed to restore glycosylation to the same extent as wild-type DOLK . The p . Tyr441Ser and p . Cys99Ser mutants showed no or marginal improvement , respectively , as compared to sec59 cells . The three new mutants , however , improved the glycosylation to a higher extent as the glycosylation patterns showed a more prominent band of CPY with two N-glycans as compared to CPY with only one N-glycan . To explain the tissue-restricted clinical phenotype in our patient group , we performed expression analysis of DOLK in fetal and adult tissue and biochemical analysis of the dolichol-phosphate dependent N-glycosylation and O-mannosylation . Highest expression levels of DOLK mRNA were found in fetal and adult brain , followed by skeletal muscle and heart in fetal tissue and heart in adult ( Figure 5 ) . Dolichol-P is required for N-glycosylation in the ER . In addition , dolichol-P is converted to dolichol-P-mannose , the monosaccharide donor for N-glycosylation inside the ER lumen and for O-mannosylation of alpha-dystroglycan . O-mannosylation was assessed by direct immunofluorescence staining of a frozen heart biopsy with the IIH6 antibody directed against the O-mannosyl glycans of alpha-dystroglycan . Reduced and/or fragmented staining was observed , more pronounced in the right ventricle . The intensity of the sarcolemmal proteins beta-dystroglycan and beta-sarcoglycan was normal , while the intensity of intracellular desmin was somewhat increased ( Figure 2 ) . Western blotting was performed on heart muscle homogenates . IIH6 staining of WGA-enriched fractions was reduced ( Figure 6A ) , which was confirmed in the laminin-overlay ( LO ) assay showing a reduction of the laminin-binding capacity of alpha-dystroglycan . To correct for muscle specific staining , western blotting was performed on non-enriched heart homogenates ( Figure 6A ) using anti-desmin and anti-β-sarcoglycan primary antibodies . Equal signals were observed for control and patient materials . However , the laminin-overlay assay clearly showed a reduction in signal intensity , similar to the results in WGA-enriched fractions . Additional control studies were performed in heart tissues of patients with idiopathic cardiomyopathy ( Figure 6A , PC ) , with similar results as for the healthy controls ( HC ) . N-glycosylation was analyzed by western blotting of the lysosomal glycoprotein CD63 ( LAMP3 ) . In fibroblasts of a DPM1-CDG patient , a clear shift was seen to a lower glycosylated CD63 isoform , indicating aberrant N-glycosylation of CD63 as compared to control fibroblasts . Glycosylation of CD63 in dolichol kinase deficient fibroblasts was comparable to controls ( Figure 6B ) . Analysis of homogenized heart tissue showed a shift in CD63 isoforms in dolichol kinase deficient heart material as compared to control heart , indicating reduced N-glycosylation .
In a cohort of 11 patients , presenting primarily with nonsyndromic dilated cardiomyopathy at the age of 5–13 years , we identified three separate mutations in DOLK as the underlying cause of disease . Some of the patients showed mild additional clinical symptoms , such as ichthyosis , failure to thrive and mild neurological involvement . In contrast , the two families with DOLK mutations originally described by Kranz et al [9] showed a severe congenital multisystem phenotype including a variable presentation of cardiac failure , severe muscular hypotonia , and ichthyosis , with epilepsy due to hypsarrhythmia , microcephaly and visual impairment , leading to death within 6 months after birth . Dolichol kinase is an endoplasmic reticulum resident protein with a cytidine-5′-triphosphate ( CTP ) binding pocket in the C-terminal domain that is exposed to the cytoplasmic face [20] . The exact catalytic mechanism , the hydrophobic binding sites for dolichol , and a possible role in dolichol-P recycling have not been clarified as yet . Our and the previously identified mutations occur in or near transmembrane domains , not associated with a specific function ( Figure S1 ) . Functional investigation of these mutant alleles in the temperature sensitive yeast strain sec59 , deficient in dolichol kinase activity , showed sustained reduced growth at 37°C . Moreover , a less severe underglycosylation of CPY was found in the three new mutants as compared to the two mutations from the previous report , which is in agreement with the milder clinical phenotype in our families . DOLK mutations result in abnormal N-glycosylation as determined by analysis of serum transferrin glycosylation in our patients . Remarkably , only minor classical symptoms of CDG-I could be identified in the patients described here , such as increased liver transaminases in some and a slight decrease in coagulation parameters in all patients . No signs of cerebellar hypoplasia were observed , as commonly seen in the most frequent CDG subtype PMM2-CDG . On the other hand , dilated cardiomyopathy is uncommon in CDG patients with an N-glycosylation defect . A single case out of more than 40 known ALG6-CDG ( MIM 603147 ) patients was reported with a multisystem presentation including DCM [21] . In DPM3-CDG ( MIM 612937 ) , DCM was reported as minor symptom compared to the muscular dystrophy [22] . As deduced from deficient IIH6 staining in skeletal muscle , both clinical symptoms were linked to deficient O-mannosylation of alpha-dystroglycan . In the disorders of dystroglycan O-mannosylation , a subgroup of the congenital muscular dystrophies , DCM is commonly observed in combination with limb-girdle muscular dystrophy at the milder end of the spectrum . Patients with dilated cardiomyopathy and no or minimal muscle involvement were reported with mutations in fukutin ( FKTN , [23] ) and fukutin-related protein ( FKRP , [24] ) , showing reduced laminin binding capacity of alpha-dystroglycan in heart muscle biopsies . The involvement of dystroglycan O-mannosylation in the phenotype of our cohort of dolichol kinase deficient patients was shown by reduced IIH6 staining in frozen heart biopsy material . Western blot analysis showed a reduction in the laminin binding capacity of alpha-dystroglycan , thereby confirming a loss of alpha-dystroglycan function . Recently , the loss of functional alpha-dystroglycan as extracellular receptor in cardiac myocytes was shown to be the cause of dilated cardiomyopathy in mutant mice [25] . Dystroglycan was postulated as an important extracellular matrix receptor to limit the damage of cardiomyocyte membranes after exercise-induced stress to individual cells . O-Mannosylation of alpha-dystroglycan ( Figure 7 ) involves the protein O-mannosyltransferases POMT1 and POMT2 and the GlcNAc transferase POMGnT1 . In addition fukutin , fukutin-related protein and LARGE have been shown to be involved in O-mannosylation , where LARGE is involved in a phosphorylation process of the O-mannosyl glycan [26] . Defects in these six genes have been described as cause for the dystroglycanopathies [27] , explaining disease in only about 50% of the patients [28] . Defects in the biosynthetic genes of the sugar donor dolichol-P-mannose required for the O-mannosylation process , like DPM3 [22] and likely DPM1 [29] , [30] , result in abnormal dystroglycan O-mannosylation . Here , we show that mutations in dolichol kinase also lead to reduced dystroglycan O-mannosylation , likely via reduced availability of dolichol-P-mannose . This is supported by previous studies in yeast cells [31]: amphomycin , which binds to and inhibits the use of dolichol-phosphate , was shown to reduce the production of dolichol-P-mannose with a subsequent reduction of protein O-mannosylation . Interestingly , N-glycosylation of the O-mannosylating enzymes POMT1 and POMT2 was shown to be required for their activity [32] . This implies that in dolichol kinase deficiency , O-mannosylation could be reduced via two independent mechanisms , i . e . via reduced availability of dolichol-P-mannose and via reduced activity of O-mannosylation enzymes due to deficient N-glycosylation . Possibly , this leads to increased susceptibility of the O-mannosylation pathway in defects of dolichol-P or dolichol-P-mannose synthesis . In contrast , the clinical phenotype of CDG-If ( MPDU1-CDG , MIM 609180 ) does not include muscular dystrophy or dilated cardiomyopathy in spite of the reduced availability of dolichol-P-mannose in the ER lumen in this disease [33] , [34] . Also , the recently described polyprenol reductase SRD5A3-CDG ( MIM 612379 ) does not show signs of a congenital muscular dystrophy [35] , [36] . Apparently , additional factors play a role in determining the clinical outcome in deficiencies of dolichol-P-mannose synthesis or utilization . For SRD5A3 , a by-pass synthesis route for dolichol was postulated [35] , while both dolichol and dolichol-phosphate could have a function on their own in organelle membrane fluidity [37] . Clearly , many factors in dolichol and dolichol-phosphate homeostasis remain to be discovered [38] , which could differentially affect the clinical outcome in dolichol cycle defects . In conclusion , we have shown that dolichol kinase deficiency results in abnormal N-glycosylation and reduced O-mannosylation of alpha-dystroglycan , leading to a clinical phenotype of dilated cardiomyopathy . This new entity of cardiomyopathy warrants screening for glycosylation defects in any patient with idiopathic DCM . Dolichol kinase deficiency may initially present with mild or asymptomatic DCM which may deteriorate , underlining the necessity to follow these young patients closely .
Three families residing in the Galilee regions of Northern Israel and one Indian family were clinically and genetically investigated . Over the past five years , 11 children have been diagnosed as suffering from an autosomal recessive dilated cardiomyopathy associated with CDG type I transferrin isoelectric focusing profiles in serum ( see pedigrees in Figure 1 ) . Nine patients and 11 of their close relatives were included in the study . The study protocol was approved by the Institutional Ethics Review Committee and by the National Committee for Genetic Studies of the Israeli Ministry of Health . Informed consent was obtained from all participants and their legal guardians . Transferrin isoelectric focusing was carried out as described before [39] . The clinical symptoms did not show any indication for the presence of fructosemia or galactosemia as possible secondary cause for CDG type I transferrin isoelectric focusing profiles . A protein polymorphism was excluded by neuraminidase digestion of the samples and by the normal profiles of both parents . Phosphomannomutase activity was measured in patient fibroblasts according to [40] . For analysis of dolichol kinase activity , fibroblast homogenates were incubated with [γ-32P]cytidine 5′-triphosphate and dolichol-19 and the formation of 32P-dolichol was measured according to [9] and described in detail in Text S1 . Genomic DNA was extracted from peripheral blood lymphocytes using standard salting out procedures [41] . Genotyping was performed using the Affymetrix NspI 250 K SNP array . All SNP array experiments were performed and analyzed according to manufacturer's protocols ( Affymetrix , Santa Clara , CA , USA ) . Homozygosity mapping was performed using PLINK v1 . 06 [42] , using a homozygous window of 50 SNPs tolerating two heterozygous SNPs and ten missing SNPs per window . Primer sequences for amplification of the only exon of DOLK ( GenBank ID NM_014908 . 3 ) are shown in Table S1 . PCR products were sequenced using the ABI PRISM BigDye Terminator Cycle Sequencing V2 . 0 Ready Reaction Kit and analyzed with the ABI PRISM 3730 DNA analyzer ( Applied Biosystems , Foster City , USA ) . Immunohistochemistry was performed by incubation of heart tissue sections with monoclonal antibodies against alpha-dystroglycan , beta-dystroglycan , beta-sarcoglycan or desmin ( Text S1 ) . WGA-enriched and non-enriched heart homogenates were used for western blotting of CD63 , beta-dystroglycan , desmin , beta-sarcoglycan and alpha-dystroglycan and for the laminin overlay assay as described ( [43] and Text S1 ) . For expression of DOLK , the following strain was used: MATa sec59 ura3-52 . Cells were grown in selective YNB ( yeast nitrogen base ) medium ( 0 . 67% YNB , 0 . 5% casamino acids and 2% glucose ) or in YPD medium ( 1% yeast extract , 2% bacto-peptone and 2%glucose ) . For growth on plates 2% agar was added . To construct the yeast expression plasmids , the DOLK open reading frame ( encoded by a single exon ) was PCR amplified ( Phusion High-Fidelity DNA Polymerase , New England Biolabs ) from the chromosomal DNA of patients I-3 , III-1 , and IV-2 and a healthy control with primers engineered with HindIII and BamHI restriction sites at the 5′and 3′ ends , respectively ( Table S1 ) . For patient IV-2 , where the mutation is located within the primer region , the mutation was introduced in the primer . All four products were subcloned into the pCR4-TOPO vector ( Invitrogen , Breda , The Netherlands ) . The two mutations described previously by Kranz et al . [9] were introduced in the WT-DOLK containing plasmid by site-directed mutagenesis . Subsequently , the WT and mutated forms of DOLK were digested with HindIII/BamH1 and ligated into the HindIII/BamHI digested vector pVT100-ZZ , thereby placing DOLK under the control of the constitutive ADH1 ( alcohol dehydrogenase 1 ) promoter . The correct sequences were verified by sequencing the entire coding region of the constructs . Transformation into yeast cells was carried out using standard techniques [19] .
|
Idiopathic dilated cardiomyopathy ( DCM ) is estimated to be of genetic origin in 20%–48% of the patients . Almost all currently known genetic defects show dominant inheritance , although especially in younger children recessive causes have been proposed to contribute considerably to DCM . Knowledge of the genetic causes and pathophysiological mechanisms is essential for prognosis and treatment . Here , we studied several individual young patients ( 5–13 years old ) with idiopathic and sometimes asymptomatic dilated cardiomyopathy . The key to identification of the gene was the finding of abnormal protein N-glycosylation . Via homozygosity mapping and functional knowledge of the N-glycosylation pathway , the causative gene could be identified as dolichol kinase ( DOLK ) . Since DCM is very rare in N-glycosylation disorders ( Congenital Disorders of Glycosylation , CDG ) and most patients with CDG present with a multisystem involvement , we studied the underlying pathophysiological cause of this life-threatening disease . Biochemical experiments in affected heart tissue showed deficient O-mannosylation of alpha-dystroglycan , which could be correlated with the dilated cardiomyopathy . Our results thus highlight nonsyndromic DCM as a novel presentation of DOLK-CDG , via deficient O-mannosylation of alpha-dystroglycan .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"autosomal",
"recessive",
"diagnostic",
"medicine",
"pediatric",
"cardiology",
"pediatrics",
"and",
"child",
"health",
"genetics",
"and",
"genomics",
"metabolic",
"disorders",
"pediatrics",
"clinical",
"genetics"
] |
2011
|
Autosomal Recessive Dilated Cardiomyopathy due to DOLK Mutations Results from Abnormal Dystroglycan O-Mannosylation
|
Mechanisms for highly efficient chromosome-associated equal segregation , and for maintenance of steady state copy number , are at the heart of the evolutionary success of the 2-micron plasmid as a stable multi-copy extra-chromosomal selfish DNA element present in the yeast nucleus . The Flp site-specific recombination system housed by the plasmid , which is central to plasmid copy number maintenance , is regulated at multiple levels . Transcription of the FLP gene is fine-tuned by the repressor function of the plasmid-coded partitioning proteins Rep1 and Rep2 and their antagonist Raf1 , which is also plasmid-coded . In addition , the Flp protein is regulated by the host’s post-translational modification machinery . Utilizing a Flp-SUMO fusion protein , which functionally mimics naturally sumoylated Flp , we demonstrate that the modification signals ubiquitination of Flp , followed by its proteasome-mediated degradation . Furthermore , reduced binding affinity and cooperativity of the modified Flp decrease its association with the plasmid FRT ( Flp recombination target ) sites , and/or increase its dissociation from them . The resulting attenuation of strand cleavage and recombination events safeguards against runaway increase in plasmid copy number , which is deleterious to the host—and indirectly—to the plasmid . These results have broader relevance to potential mechanisms by which selfish genomes minimize fitness conflicts with host genomes by holding in check the extra genetic load they pose .
The yeast 2-micron plasmid , nearly ubiquitous among Saccharomyces yeast strains , is a highly optimized extrachromosomal selfish DNA element [1–4] . The plasmid resides in the nucleus , offers no apparent fitness advantage to its host , and does not impose any significant disadvantage at its normal copy number of 40–60 molecules per haploid chromosome set . The compact plasmid genome ( ~6 . 3 kbp ) is organized into two functional modules , one devoted to stable propagation ( the plasmid partitioning system ) and the other to copy number maintenance ( the plasmid amplification system ) . The partitioning system [5–7] , comprised of the plasmid-coded Rep1 and Rep2 proteins together with a cis-acting locus STB , promotes nearly equal segregation of plasmid molecules duplicated by the host replication machinery into mother and daughter cells . Current evidence is consistent with a ‘hitchhiking model’ in which the plasmid utilizes chromosomes as a vehicle for segregation by physically associating with them [8–11] . In this respect , the plasmid resembles the episomes of mammalian papilloma and gammaherpes viruses that also resort to chromosome-tethering for stable maintenance during prolonged periods of latent infection [12–20] . It is possible that selfish genomes inhabiting evolutionarily distant hosts have independently converged on the common strategy of chromosome-coupled segregation as a means for self-preservation . The plasmid amplification system , consisting of the plasmid-coded Flp site-specific recombinase and its target FRT sites arranged in head-to-head orientation within the plasmid genome , counteracts any reduction in copy number resulting from rare missegregation events [21 , 22] . Amplification is thought to be triggered by a Flp-mediated recombination event coordinated with bi-directional plasmid replication—DNA inversion within a plasmid monomer or resolution within a plasmid dimer—that reconfigures the mode of replication ( Fig 1A and 1B ) [21 , 23] . A second recombination event can restore normal fork movement , and terminate amplification . The amplified plasmid concatemer may be resolved into monomers by Flp or by the host’s homologous recombination machinery . Positive and negative transcriptional regulation of FLP by plasmid-coded proteins—the putative Rep1-Rep2 repressor and its antagonist Raf1—ensures a prompt amplification response when needed without causing a runaway increase in plasmid copy number [24–27] . Thus , self-imposed moderation of selfishness is an integral element in the survival strategy of the yeast plasmid [2 , 28 , 29] . Interestingly , Raf1 appears to play a dual role in plasmid physiology , contributing to both plasmid stability and copy number control . In addition to blocking the assembly of the Rep1-Rep2 repressor complex , Raf1 is involved in promoting the organization of the Rep1-Rep2-STB partitioning complex [26 , 30] . The post-translational protein modification machinery of the host also contributes to the regulation of 2-micron plasmid stability and copy number [31–33] . Impaired sumoylation of Rep1 and Rep2 interferes with their STB-association , and adversely affects plasmid segregation [32] . Deficient SUMO conjugation to Flp raises its steady-state levels , leading to hyper-amplification of the plasmid . The resulting increase in plasmid load causes cell cycle delays and reduced replicative life-span [31 , 34 , 35] . The 2-micron plasmid exemplifies the collaborative roles of self-regulation and host-mediated regulation in the coexistence of a selfish DNA element and its host genome with minimal mutual conflicts between them . High plasmid copy number and attendant cell death phenotypes are produced by a variety of mutations in protein components associated with SUMO conjugation and deconjugation steps , and with ubiquitin-dependent degradation of sumoylated proteins . These mutations map to E3 ligases ( siz1Δ , siz2Δ ) , the SUMO maturase/deconjugase ( ulp1 or nib1 ) , a SUMO-targeted ubiquitin ligase ( slx5Δ , slx8Δ ) and certain NPC ( nuclear pore complex ) proteins required for normal cellular localization of Ulp1 [31 , 33 , 34 , 36–38] . These mutants exhibit a marked differential killing effect on yeast strains harboring the 2-micron plasmid [Cir+] versus those lacking the plasmid [Cir0] . The misregulated amplification of plasmid DNA likely stems from enhanced single strand nicks at the plasmid FRT sites due to elevated Flp levels ( Fig 1C ) [31 , 33] . Conversion of the nick into a double strand break by encountering an advancing replication fork can trigger strand invasion by the broken end into an intact circular plasmid , to be followed by break-induced replication ( BIR ) ( Fig 1C ) . In principle , BIR in the circular template may persist through multiple rounds , producing large plasmid concatemers . BIR-mediated aberrant amplification is supported by the significant reduction in a high molecular weight DNA form of the plasmid in the absence of Pol32 or of Rad proteins required for known BIR pathways [33] . The reaction is formally analogous to the alternative ( telomerase-independent ) pathway for lengthening of telomeres via telomere mini-circles as templates , which occurs in yeast , many transformed cell lines and in certain human cancers [39 , 40] . We wished to address whether , in addition to lowering Flp levels , the SUMO modification of Flp may also modulate its DNA recognition and/or catalytic properties . To circumvent the technical challenges posed by the low level of the in vivo modification , we utilized a Flp-SUMO fusion protein in which SUMO residues 1–96 are joined in frame to the carboxyl-terminus of Flp . By demonstrating the nearly identical behavior Flp-SUMO and physiologically sumoylated Flp in a variety of in vivo experimental contexts , we validated the utility of the fusion protein in directly probing the effects of SUMO modification on the physicochemical interactions of Flp with the FRT site . In vitro assays using purified Flp and Flp-SUMO revealed that Flp-SUMO binds FRT less efficiently than Flp and with weaker cooperativity , and is preferentially excluded from FRT in the presence of Flp . Consequently , the fusion protein is less active in FRT x FRT recombination than Flp , and the lower activity is reflected in both the strand cleavage and strand joining steps of recombination . The in vitro results are corroborated by an in vivo assay for DNA damage induced by Flp and Flp-SUMO at FRT sites , consistent with the lower occupancy of these sites ( or accelerated exit from them ) by the fusion protein . The present results , in conjunction with previously published reports [24 , 25 , 27 , 31 , 33] , suggest a tripartite mechanism for the copy number control of the 2-micron plasmid involving gene expression , protein turnover and protein activity . The first is imposed by the plasmid itself , while the other two are instituted by the host . Collectively , they provide a paradigm for the bilateral interactions through which selfish DNA elements and their host organisms strike a fine balance between the fitness advantage gained by such an element from high copy number and the fitness cost incurred by the host—and thus indirectly by the element—from the extra genetic load .
The list of yeast strains and plasmids utilized in this study is given in S1 and S2 Tables , respectively . The specific figures and table depicting the experiments in which they were employed are also indicated . The presence or absence of the native 2-micron plasmid in a given strain is denoted as [Cir+] or [Cir0] , respectively . This designation does not include ARS-based or 2-micron-derived plasmid constructs . The genotype of a strain containing such engineered plasmids , but not the native plasmid , is still referred to as [Cir0] with the resident plasmid spelled out . The yeast plasmids ( S1 Fig ) used for genetic assays were constructed by a strategy analogous to that described previously [41] . The rationale is to generate two requisite linear DNA fragments in vitro by PCR amplification , and allow them to self-assemble the desired circular plasmid in vivo by homologous recombination/repair . Recombination is facilitated by overlapping sequences that these fragments carry at their ends . A suitable marker ( ADE2 ) contained in one of the fragments permits the selection of plasmid-containing cells . We used a constant DNA fragment corresponding to the A-form of the plasmid [42] that included , in sequential order , the 2-micron plasmid RAF1 gene , ADE2 inserted into the plasmid HpaI site , STB , ORI , a copy of the inverted repeat , and the REP2 gene . The other variable fragment included the REP1 gene , the second copy of the inverted repeat , and the FLP gene with the incorporated modifications . Sequences adjoining REP1 and RAF1 provided homology at one end . Homology at the other end came from sequences adjacent to FLP and REP2 . An equimolar mixture of the constant fragment with one of the variable fragments was used to transform an ade2 [Cir0] yeast strain to adenine prototrophy . DNA samples isolated from a subset of the transformants were analyzed by PCR to identify those that contained the correct plasmid . Critical regions of the plasmid , including the modified FLP locus and the recombination junction regions , were further verified by DNA sequencing . Once a parental strain harboring the correct plasmid was established , subsequent transfer of the plasmid to other recipient strains was performed by transformation using isolated total DNA . Integration of exogenous DNA cassettes into a specific chromosome locale was accomplished by one of three methods based on homology-dependent double strand break repair: ( 1 ) using a linearized integrative plasmid cut within the region of homology , ( 2 ) using PCR-amplified DNA fragments with flanking homology , or ( 3 ) using the CRISPR ( Cas9-sgRNA ) technology . The first two methods required selection of a marker included in the incoming/editing DNA; no selection was required for the third method . All constructs were authenticated by DNA sequencing . Native Flp , Flp-HA-His8 and Flp-SUMO-HA-His8 , as well as mutant derivatives of the tagged proteins , were overexpressed in E . coli cells using the pBAD system ( Invitrogen ) . Purification of untagged Flp was carried out using previously described procedures [43–45] . Purification of the tagged proteins included an additional first step of nickel chromatography , followed by dialysis to remove the imidazole present in the elution buffer . The final preparations were ≥ 85% pure , as judged by SDS-PAGE and densitometric scanning of the Coomassie Blue stained bands . Protein concentrations in the final preparations were determined using the Bradford assay . Overnight cultures were prepared from purified single transformant colonies containing individual 2-micron circle-derived plasmids by growing them selectively ( in medium lacking adenine ) at 30°C . These cultures were diluted in YEPD medium ( n = 0; 104 cells/ml ) and grown for 10 generations ( n = 10 ) at 30°C . Aliquots were plated out from n = 0 and n = 10 cultures on YEPD medium . Nibbled and mini-colonies were counted after incubating the plates for 5 days at 26°C . A founder cell that had lost the plasmid , and the plasmid-borne ADE2 marker , gave rise to a fully red ( non-sectored ) smooth colony . Such colonies were excluded from the total population in calculating the fraction of nibbled colonies . Plasmid loss rate per generation ‘I’ ( for instability ) was estimated from plates incubated at 30°C based on fully red and total colony counts . I = ( 1/10 ) x [ln ( f0/f10 ) ] [46] , where f0 and f10 are the fractions of plasmid containing cells ( yielding colonies other than the fully red ones ) at n = 0 and n = 10 , respectively . The sample size for the individual estimates of plasmid loss rate and the fraction of nibbled or mini-colonies was a minimum of 800 colonies . The assays were performed as at least three repetitions . The PCR protocols followed those described by Chen et al . [31] , and utilized the same plasmid and reference chromosomal amplicons as well as the primer pairs described by them . The amplification reactions were carried out with ABI PRISM 7900HT SDS using the SYBR Green Master Mix ( Applied Biosystems ) . The number of cycles required to reach the CT number ( preset threshold ) for each DNA sample was calculated from six separate experiments . The relative change in the copy number of a plasmid between two strains , normalized to the chromosomal reference sequence , was calculated by the 2-ΔΔCT analysis [47] . The expression cassette for Flp-SUMO controlled by the GAL1 promoter was inserted at the TRP1 locus ( thus disrupting it ) on chromosome IV in [Cir0] wild type , siz1Δ siz2Δ , slx5Δ and slx8Δ strains . Aliquots of raffinose-grown overnight cultures were inoculated into raffinose medium , grown to mid-log phase at 30°C , and induction was performed by transferring them to 2% galactose with continued incubation at 30°C . The control ( uninduced ) cells were transferred from raffinose to glucose medium and incubated at 30°C . At 2hr , cells were spun down , washed , and suspended in TE buffer before adding cycloheximide ( 100 μg/ml ) to arrest protein synthesis . Cells removed at intervals over a 60 min time course were treated with lysis buffer ( 50 mM HEPES , pH 7 . 0; 75 mM KCl . , 1 mM MgCl2 , 1 mM EGTA , 0 . 5% Triton X-100 , I mM DTT , 1 mM PMSF and one protease inhibitor tablet from Roche/50 ml ) . Cell extracts prepared by bead beating ( 5 min; 4°C ) were fractionated by 12% SDS-PAGE , and analyzed by quantitative western blotting . Flp-SUMO bands were detected by anti-HA antibody ( BioLegend ) at 1:1000 dilution , and normalized against actin bands visualized using anti-β-actin antibody ( Gene Tex ) at 1:1000 dilution . Proteasome function was inhibited with MG-132 according to published procedures [48] . The following modifications were made to the standard protocols for measuring protein turnover . Overnight raffinose cultures were grown in synthetic medium without ammonium sulfate , and supplemented with 0 . 1% proline as well as other appropriate amino acids . In addition , the re-inoculation medium for obtaining mid-log phase cells for galactose induction included 0 . 003% SDS . The induction period was 2 hr , with 75 μm MG-132 ( Biomol , Plymouth Meeting , PA ) being added at 90 min . Control cells received an equivalent volume of DMSO , the solvent for MG-132 . The rest of the procedure—cycloheximide treatment , preparation and fractionation of cell extracts , and western blotting—was performed as described under the previous section on protein stability assays . The experimental strains were derived from [Cir0] wild type and siz1Δ siz2Δ strains expressing the GAL1 promoter driven Flp-SUMO ( see the section above on protein stability assays ) or from an analogous set of strains in which Flp-SUMO was replaced by Flp . The plasmid pADE2-Flp ( S1 Fig ) was introduced into these strains , and maintained by adenine selection . A CEN-TRP1-plasmid expressing Rad52-YFP from the native RAD52 promoter was also maintained in them by selection . The conditions for Flp or Flp SUMO induction were the same as those described for protein stability estimates in the absence of MG-132 . Cells induced for 2 hr in galactose and the corresponding uninduced control cells ( 2 hr in glucose ) were collected , washed and fixed in formaldehyde for scoring fluorescent foci . Each set of assays was repeated three times . The binding assays were performed in 30 μl individual mixtures incubated on ice for 20 min using the buffer conditions described by Prasad et al . [49] . The substrate DNA fragment ( 0 . 05 pmol per binding reaction ) was 262 bp long , and contained one FRT site . Aliquots were fractionated by electrophoresis in 5% polyacrylamide gels ( 29:1 crosslinking ) at 4°C in 1x TBE duffer . The bound complexes and the unbound substrate were visualized by autoradiography or phosphor imaging . The conditions for in vitro recombination were similar to those described previously [50 , 51] . Each 30 μl reaction mixture contained 0 . 2 pmol plasmid substrate ( with two FRT sites oriented head-to-tail ) and 1 pmol of purified Flp or Flp-SUMO . At the end of the 30°C incubation period ( from 0 . 5 to 30 min ) , the reactions were stopped by treatment with 0 . 2% SDS ( final concentration ) followed by proteinase K treatment ( 50 μg/reaction sample ) . DNA purified by chloroform-phenol extraction and ethanol precipitation was digested with NdeI and EcoRV . The digestion products were separated by 1% agarose gel electrophoresis , and DNA bands were visualized by ethidium bromide staining . Strand cleavage and strand joining reactions were carried out in the recombination buffer with 0 . 05 pmol of the respective 32P-labeled half-site substrates per reaction and Flp or Flp-SUMO ranging from 0 . 2 pmol to 2 pmol . At the end of 30 min incubation at 30°C , reactions were stopped by adding 0 . 2% SDS , and processed without proteinase K treatment . The cleavage and joining reactions were analyzed by electrophoresis in 12% SDS-polyacrylamide ( 29:1 crosslinking ) and 12% polyacrylamide-urea ( 19:1 crosslinking ) gels , followed by phosphorimaging or autoradiography . First , tubes were set up in pairs on ice with one tube within a pair containing the 32P-labeled half-site plus the R191A mutant , and the other containing the same labeled half-site plus the Y343F mutant . The amounts of half-site and protein in each tube were 0 . 05 pmol and 0 . 5 pmol , respectively , in 15 μl of 1 . 5x recombination buffer . Following 10 min on ice to allow full occupancy of the half-site by protein , 7 . 5 μl each of the binding mixture were withdrawn from each set of paired tubes , and transferred simultaneously to fresh tubes ( maintained at 30°C ) containing 2 . 5 pmol of an unlabeled DNA fragment with one FRT site in 15 μl 0 . 5x recombination buffer . The contents were gently mixed in each tube and incubated for 30 min . Except for the difference in the substrate half-sites , the strand cleavage and joining reactions were similar in other respects . The reactions were analyzed by SDS-polyacrylamide gel ( 12% ) electrophoresis ( for cleavage ) and by polyacrylamide-urea gel ( 12% ) electrophoresis ( for joining ) . Radioactively labeled DNA bands , captured on a phosphor storage screen ( Bio-Rad ) , were scanned using a Typhoon Trio Phosphorimager ( GE-Healthcare ) . Unlabeled DNA bands were visualized in agarose gels by ethidium bromide staining . Protein bands were detected in western blot analyses using PierceTM ECL protocol ( ThermoFisher Scientific ) . Image analysis and quantitation of band intensities were performed using the software Quantity One ( Bio-Rad; version 4 . 5 . 1 ) . For recombination , strand cleavage and strand joining assays , the extent of reaction was estimated as the ratio of the intensity of product band ( s ) to the sum of the intensities of substrate and product bands . For DNA binding , the ratios of the bound C-I and C-II complexes to the sum of C-I , C-II and unbound DNA were determined . Protein bands were quantitated against actin as the internal control . Multiple exposures were used to compensate for large intensity differences between individual bands . Appropriate correction factors were applied , based on the linear ranges of intensity variation .
Impairment in the regulation of Flp-mediated amplification leads to high 2-micron plasmid copy number [31 , 35 , 52] , which induces characteristic nibbling at colony edges . This phenotype , which is more conspicuous at 20°C than at 30°C , is due to differences in plasmid copy number in individual cell lineages , resulting in variable growth inhibition and cell mortality among them . Loss of plasmid restores normal growth and smooth edges . Over time , plasmid-free [Cir0] cells tend to rise in the population . Thus , colony morphology and plasmid loss rates are reliable reporters of the mean plasmid load carried by cells , and indirectly of the Flp level/activity in them . The steady-state level of sumoylated Flp in a wild type strain is ~10% of total Flp [31] . The predominant site of SUMO conjugation , mediated by Siz1 and Siz2 , is Lys-375 [31] located < 50 amino acids upstream of the carboxyl-terminus ( Ile-423 ) . Replacement of Lys-375 by arginine partially recapitulates the effects of siz1Δ siz2Δ in a wild type background , yielding ~4-fold increase in Flp , ~2-fold higher plasmid copy number , and consistently more abnormal colonies on plates incubated at 20°C [31] . Given the relative proximity of Lys-375 and Ile-423 , we suspected that Flp containing the SUMO moiety ( amino acids 1–96 ) as a carboxyl-terminal extension is likely to functionally mimic Flp ( K375-SUMO ) . If so , Flp-SUMO may justifiably be utilized as a surrogate for Flp ( K375-SUMO ) in addressing potential differences between native and modified Flp in their relative stability in vivo as well as their DNA recognition and catalytic properties in vivo and/or in vitro . It is nearly impossible to study exclusively the naturally sumoylated Flp in a cell , as it would be diluted out by the excess unmodified version . In addition , the extent of the modification ( ~10% ) makes it technically quite challenging to obtain sufficient quantities of Flp ( K375-SUMO ) for in vitro analyses . In order to test whether Flp-SUMO can redress the effects of siz1Δ siz2Δ on colony morphology , we transformed [Cir0] strains with 2-micron plasmid derivatives engineered to express Flp or Flp-SUMO under the control of the native FLP-promoter ( S1 Fig ) . Except for manipulations of the FLP locus and an insertion of the ADE2 marker , the reporter plasmids retained the overall organization of the native 2-micron plasmid genome . Note that the expressed Flp , Flp-SUMO and their variants contained the HA-His8 epitope tag at their carboxyl-termini . For simplicity , these proteins as well as the plasmids expressing them are referred to without mentioning the tag . The large fraction of nibbled colonies ( > 75% at 26°C ) in the siz1Δ siz2Δ strain containing pADE2-Flp was substantially reduced ( ~1% ) when the strain harbored pADE2-Flp-SUMO ( Fig 2A and 2B; S3 Table ) . The enlarged images of colonies shown above S3 Table highlight the difference between smooth and nibbled edges . The frequency of mini-colonies in the population , signifying highly retarded cell growth or extensive cell death , also showed a corresponding reduction ( from ~18% to ~3% ) ( S3 Table ) . The nibbled- and mini-colony phenotypes were consistent with a ~5-fold increase in the mean copy number of pADE2-Flp in siz1Δ siz2Δ compared to the wild type ( S2 Fig ) . For comparison , the increase in the native 2-micron plasmid copy number in the mutant strain was ~10-fold ( S2 Fig ) . As Flp-SUMO is recombination-competent ( S3 Fig ) , the lack of nibbling in siz1Δ siz2Δ harboring pADE2-Flp-SUMO was not due to Flp-SUMO being inactive in generating FRT-nicks , which are intermediates in the recombination reaction ( and potential initiators of BIR ) . The catalytic variant Flp ( H305L ) expressed by pADE2-Flp ( H305L ) ( S1 Fig ) is strongly defective in strand joining in vitro and in vivo in yeast [53–55] , and is expected to cause an accumulation of the FRT-nicked intermediate . The variant is inactive in recombination . The presence of pADE2-Flp ( H305L ) , contrary to expectation , produced few nibbled ( 1–2% ) or mini-colonies ( ~3% ) either in the wild type or in the siz1Δ siz2Δ strain at 26°C ( Fig 2C; S3 Table ) . Presumably , sumoylation of Flp ( H305L ) at Lys-375 in the wild type strain was sufficient to suppress excessive FRT-nicking . The lack of nibbling even in the siz1Δ siz2Δ strain was likely due to high pADE2-Flp ( H305L ) missegregation , signified by the relative abundance of red ( ade2 ) and red-sectored colonies ( Fig 2C ) ( see also plasmid loss rates in Fig 2D ) . As a result , mother cultures ( n = 0 in Fig 2 ) would be enriched in cells with low copy numbers of pADE2-Flp-SUMO as well as plasmid-free cells capable of resuming growth when provided with adenine . Such cells would be further enriched during non-selective growth ( from n = 0 to n = 10 ) due to their fitness advantage . In fact , overexpression of Flp ( H305L ) in [Cir+] cells is a convenient method for rapidly curing them of the endogenous 2-micron plasmid [56] . In sum , the aberrant cell growth typical of under-sumoylation of Flp is alleviated by the expression of Flp-SUMO from the native FLP-promoter . The lack of anticipated nibbling in the siz1Δ siz2Δ strain from Flp ( H305L ) expression is the result of the high rate of plasmid loss induced by this Flp variant . As equal segregation of 2-micron plasmid molecules occurs in physical association with chromosomes [8–11] , the high molecular weight hyper-amplified plasmid concatemers in a siz1Δ siz2Δ strain are likely to interfere with this process . Furthermore , the deficiency in sumoylation of Rep1 and Rep2 partitioning proteins might have an additional effect on segregation [32] . Plasmid-free cells , having higher fitness , will tend to outgrow their plasmid-containing counterparts . ‘Apparent’ plasmid stability during non-selective growth provides a reasonable test of the potential salutary effect of Flp-SUMO under conditions that proscribe normal sumoylation of Flp . The pADE2-Flp plasmid showed a significantly higher loss rate in the siz1Δ siz2Δ strain compared to the wild type ( Fig 2D; left pair of histograms ) . By contrast , there was a modest improvement in the stability of pADE2-Flp-SUMO in the mutant compared to the wild type ( Fig 2D; middle pair of histograms ) . The lower basal stability of pADE2-Flp-SUMO than pADE2-Flp in the wild type strain ( Fig 2D; green histograms of the left and middle pairs ) might result from the larger size of pADE2-Flp-SUMO , the particular modification of the FLP locus , or from potential additional sumoylation at Lys-375 ( which would be ameliorated by siz1Δ siz2Δ ) . Consistent with the expected increase in steady state DNA damage at FRT and the attendant plasmid hyper-amplification , pADE2-Flp ( H305L ) was more unstable than pADE2-Flp in the wild type strain ( Fig 2D; green histograms of the left and right pairs ) . The instability of pADE2-Flp ( H305L ) was worsened by siz1Δ siz2Δ ( Fig 2D; right pair of histograms ) , suggesting that Flp ( H305L ) , analogous to Flp , is also downregulated via sumoylation . The comparable loss rates of pADE2-Flp ( H305L ) and pADE2-Flp in the wild type and the siz1Δ siz2Δ strains , respectively , ( Fig 2D; orange histogram of the left pair and green histogram of the right pair ) might appear to suggest that normal sumoylation of Flp ( H305L ) and strongly reduced sumoylation of Flp are more or less equivalent with respect to the amount of strand nicks that the two proteins produce at FRT . However , unlike Flp , Flp ( H305L ) cannot resolve an amplified plasmid concatemer by recombination , nor can it counter plasmid missegregation by recombination-mediated amplification ( Fig 1A and 1B ) . These factors may aggravate the instability of pADE2-Flp ( H305L ) in the siz1Δ siz2Δ strain . The plasmid stability results demonstrate that the increased formation and expansion of plasmid-free cells triggered by FRT-nicks in a siz1Δ siz2Δ host is strongly suppressed when this strain expresses Flp-SUMO instead of Flp or Flp ( H305L ) . The BIR pathway promotes the repair of one-ended double strand breaks—such as those resulting from the arrival of a replication fork at a Flp-nicked FRT site [31 , 33 , 39 , 57 , 58] . Consistent with the mechanism diagrammed in Fig 1C , the formation of amplified high molecular weight plasmid DNA is dependent on strand cleavage by Flp , and requires Pol32 as well as Rad proteins involved in BIR [33] . The initial D-loop intermediate of BIR formed by strand invasion of homologous DNA may be processed into a replication fork . Alternatively , it may mature into a Holliday junction by convergence with a replication fork from the opposite direction ( Fig 3A; left ) . The coalescence of two D-loops expanding in opposite directions would generate a double Holliday junction ( Fig 3A; right ) . The organization of the two FRT sites and the location of the bi-directional replication origin within the 2-micron plasmid provide opportunities for a Flp-mediated BIR D-loop to meet a replication fork , or a second such D-loop , approaching it head-on ( Fig 3A ) . As the Flp-induced single strand nicks may occur at one or both of the plasmid FRT sites , and on either DNA strand within a site , head-to-tail configuration of two D-loops or a D-loop and a replication fork is also possible . Crystal structures and in vitro experiments rule out double strand breaks at FRT by Flp [50 , 59–61] , minimizing the probability of two-ended double strand break repair in FRT DNA damage . The resolution of specialized DNA structures such as Holliday junctions and D-loops in yeast require Yen1 and/or Mus81-Mms4 activities [53] . Induction of Flp ( H305L ) in a strain containing an FRT site inserted between two strong replication origins in a chromosome results in poor viability in the yen1Δ mus81Δ background [53] . We utilized isogenic [Cir0] and [Cir+] strains harboring an identical chromosomal insertion of FRT to test whether the presence of additional copies of plasmid-borne FRT sites would aggravate the Flp ( H305L ) —yen1Δ or the Flp ( H305L ) -mus81Δ effect , and whether cell survival can be improved by replacing Flp ( H305L ) by Flp ( H305L ) -SUMO . In the [Cir0] strain , mus81Δ caused a decrease in colony forming units upon Flp ( H305L ) induction , with yen1Δ and yen1Δ mus81Δ displaying a stronger effect ( Fig 3B ) . The loss of viable colonies from each single mutation as well as the double mutation was magnified in the [Cir+] strain ( Fig 3C ) . Expression of Flp ( H305L ) -SUMO in place of Flp ( H305L ) restored cell survival in the single mutants to nearly the same level as in the wild type ( Fig 3D ) . The palliative response to Flp ( H305L ) -SUMO , though not as strong , was evident in the double mutant as well ( Fig 3D ) . In the absence of Mus81 or Yen1 or both , the branched intermediates of Flp ( H305L ) -induced BIR ( three or four-way DNA junctions ) appear to accumulate , to the detriment of the cell . The apparent reduction of these intermediates in the presence of Flp ( H305L ) -SUMO is consistent with an abatement in the formation of unsealed strand nicks at FRT that precedes the BIR events . Furthermore , these results corroborate the previous inference that the DNA damage induced by Flp ( H305L ) at plasmid FRT sites may be masked by accelerated plasmid loss from cells . The Slx5-Slx8 STUbL ( sumo targeted ubiquitin ligase ) regulates a wide range of cellular functions in yeast that include gene expression , quality control of nuclear proteins , DNA damage repair , and chromosome stability [62–66] . The coupling to ubiquitin-proteasome systems via Slx5-Slx8-mediated recognition of conjugated SUMO , or native surface features that mimic SUMO , may bring about not only the degradation of particular target proteins but also the functional re-localization of multi-subunit protein machines . Examples include proteolysis of the Mot1 transcription factor [67] or the Matα2 repressor [68] , and the relocation of double strand DNA breaks in G1 cells to repair centers stationed at the nuclear periphery [69] . Ubiquitin-dependent proteolysis of sumoylated Flp appears to be one important mechanism for the post-translational regulation of Flp . The levels of Flp , including its SUMO-conjugated form , are increased in slx5Δ and slx8Δ strains [33] . We therefore tested whether the beneficial effects of Flp-SUMO observed in the siz1Δ siz2Δ strain would be reversed in an slx5Δ or slx8Δ mutant . The slx5Δ and slx8Δ strains containing pADE2-Flp or pADE2-Flp-SUMO ( Fig 4A and 4B; S3 Table ) formed nibbled colonies in contrast to the wild type strain containing either plasmid and the siz1Δ siz2Δ strain containing pADE2-Flp-SUMO ( Fig 2A and 2B; S3 Table ) . The fraction of mini-colonies caused by pADE2-Flp was also elevated in the slx8Δ mutant compared to the wild type ( S3 Table ) . Mini-colonies induced by pADE2-Flp-SUMO in the slx5Δ and slx8Δ strains were only slightly more than those in the wild type or the siz1Δ siz2Δ mutant ( S3 Table ) . The loss of pADE2-Flp-SUMO was more rapid in the slx5Δ and slx8Δ strains than in the wild type ( Fig 4C ) or the siz1Δ siz2Δ ( Fig 2D ) strains . The slx5Δ or slx8Δ mutation caused instability of pADE2-Flp as well ( Fig 4C ) , the plasmid loss rates being comparable to that in the siz1Δ siz2Δ strain ( Fig 2D ) . A key point underscored by these findings is that both slx5Δ and slx8Δ , but not siz1Δ siz2Δ , display the phenotypes of aberrant pADE2-Flp-SUMO amplification ( Figs 2D and 4C; S3 Table ) . Furthermore , slx8Δ phenocopies siz1Δ siz2Δ closely when they contain pADE2-Flp ( Figs 2D and 4C; S3 Table ) . The trend is similar for slx5Δ albeit less striking than slx8Δ . Some of the quantitative differences among individual mutants may be reconciled by the involvement of the ubiquitin and SUMO pathways in the pleiotropic regulation of a variety of cellular processes . The phenotypic responses of the mutants to pADE2-Flp or pADE2-Flp-SUMO suggest that normal regulation of Flp requires sumoylated Flp to be ubiquitinated and channeled to the proteasome for degradation . Furthermore , SUMO conjugated physiologically by Siz1-Siz2 ( principally to Lys-375 ) within Flp and SUMO attached artificially to the Flp carboxyl-terminus are functionally interchangeable in recognition by the Slx5-Slx8 STUbL . As a result , Flp becomes deleterious in siz1Δ siz2Δ , slx5Δ and slx8Δ; Flp-SUMO is harmful only in the latter two . Corroborating these interpretations , the half-life of Flp-SUMO was higher in slx5Δ compared to that in the wild type or the siz1Δ siz2Δ mutant , while Flp-SUMO degradation was almost completely blocked by slx8Δ ( Fig 5A and 5B ) . The turnover of Flp-SUMO in the wild type and siz1Δ siz2Δ was suppressed by the proteasome inhibitor MG-132 ( Fig 5C and 5D ) . In principle , sumoylation may not only control the steady state levels of Flp in vivo but may also modulate its recognition of the FRT site and/or its active site functions . DNA damage and resultant BIR in the 2-micron plasmid may be curtailed by preferential exclusion of sumoylated Flp from FRT sites , reduced strand cleavage and/or enhanced strand joining by the modified protein , or by combinations of these attributes . In order to probe the effects of sumoylation on Flp activities , we utilized in vitro assays using purified Flp and Flp-SUMO . Based on their very similar in vivo responses to multiple mutations in the SUMO/ubiquitin pathways , Flp-SUMO is presumed to be an authentic substitute for Flp ( K375-SUMO ) in vitro as well . This premise is also supported by structural considerations . In the Flp recombination/synaptic complexes [59 , 70] , the distances from Lys-375 or the carboxyl-terminus ( Ile-423 ) to key active site residues are nearly the same , the maximum difference being ~4 Å ( S4 Table ) . As such , potential structure-function perturbations from the SUMO moiety are not expected to be different between Flp sumoylated at Lys-375 and Flp-SUMO . In a time-course deletion reaction between two head-to-tail FRT sites , Flp-SUMO was approximately 60% as active as Flp ( Fig 6A and 6B ) . To be consistent with in vivo experiments , the in vitro analyses were performed using proteins containing the HA-His8 tag at the carboxyl-terminus . Native Flp and the HA-HIS8-tagged variant were nearly identical in recombination activities ( S4 Fig ) . The contra-effect of the SUMO moiety on recombination may occur during Flp-FRT association and assembly of the recombination complex , or within a pre-assembled complex . Allosteric interactions and collaborative assembly of the strand cleavage active site between neighboring FRT-bound Flp monomers are key features of the recombination reaction , which is carried out within a synaptic structure containing four Flp monomers bound to two partner FRT sites [50 , 59] . Thus , sumoylation of just one Flp monomer in vivo , out of two bound to an FRT site or four bound to a pair of synapsed FRT sites , may protect against DNA damage at FRT by attenuating Flp activity . The role of monomer-monomer interactions in Flp catalysis is dealt with in more detail in the context of the strand cleavage and strand joining activities of Flp ( see below ) . The weaker recombinase activity of Flp-SUMO than Flp could , at last in part , be due to its impaired recognition of the FRT site . Flp binds as a monomer to each of the two 13 bp head-to-head binding elements flanking the 8 bp strand exchange region of a minimal FRT site , and cooperative interactions between the bound monomers stabilize the FRT-Flp dimer complex . We tested Flp and Flp-SUMO for differences in the formation of singly or doubly bound complexes with a DNA fragment containing one copy of the FRT site . These two complexes were resolved by their electrophoretic mobility in native polyacrylamide gels . At identical molar ratios of protein per binding element , the combined amount of monomeric ( C-I ) and dimeric ( C-II ) complexes formed were lower for Flp-SUMO than Flp ( Fig 7A and 7B ) . It took approximately twice as much Flp-SUMO as Flp to convert the same amount of FRT-DNA into the bound form . Furthermore , there was a striking difference in binding cooperativity between Flp-SUMO and Flp . At 1:1 molar ratio of protein to binding element ( lane 2 in Fig 7A ) , C-II formation by Flp was favored over C-I by a factor of ~3 . By contrast , Flp-SUMO formed ~2-fold more C-I than C-II under the same condition ( lane 2 , Fig 7B ) . The disparity in FRT binding and cooperativity between Flp and Flp-SUMO was also highlighted by binding reactions containing a mixture of the two proteins ( Fig 7C ) . Even at 1:3 molar ratio , Flp surpassed Flp-SUMO in FRT occupancy , as indicated by the strong reduction of the C-II complex formed by Flp-SUMO and the appearance of the Flp C-I and C-II complexes ( lane 3 , Fig 7C ) . At this ratio , the C-I complexes formed by the two proteins were roughly equal . The plot of C-I ratios underestimates the advantage of Flp over Flp-SUMO in FRT binding as it does not capture the much stronger cooperativity of Flp in C-I → C-II conversion ( Fig 7A and 7B ) . The band corresponding to the heterodimeric C-II complex , expected to migrate just above the Flp C-II complex , was not well resolved from it . At equimolar or higher Flp to Flp-SUMO ratios ( lanes 4–6 , Fig 7C ) , the C-I and C-II complexes were predominantly the Flp-bound forms . Extrapolation of the in vitro DNA binding results to the in vivo situation suggests that sumoylation of Flp would diminish its binding to the 2-micron plasmid FRT sites . In addition , or conversely , sumoylation of FRT-bound Flp might accelerate the dissociation of the modified protein from DNA . Either or both of these mechanisms would provide a safeguard against potentially detrimental strand cleavage events at FRT . The strongly conserved catalytic hexad cluster of the tyrosine site-specific recombinase family ( to which Flp belongs ) , including the invariant tyrosine nucleophile , is represented in Flp by Arg-191 , Lys-223 , His-305 , Arg-308 , Trp-330 and Tyr-343 . In the shared active site for strand cleavage , Tyr-343 from one Flp monomer is donated to the pro-active site of the second neighboring Flp monomer , which provides the other five catalytic residues [50 , 59] . Strand joining in the cleaved intermediate , harboring a 3’-O-phosphotyrosyl bond , is promoted by one Flp monomer utilizing the 5’-hydroxyl group from DNA as the nucleophile [50 , 71] . In order to appraise whether sumoylation affects the chemical competence of the Flp active site , we assayed Flp and Flp-SUMO in strand cleavage and strand joining reactions in vitro . Reactions were performed using half-site substrates containing a single Flp binding element and one scissile phosphate [71–74] . Interactions between two Flp monomers , each bound to a half-site , permits the assembly of the shared active site required for strand cleavage by the Tyr-343 nucleophile [50] . The half-sites are suitably configured , as described below , to avoid interference from the joining reaction during cleavage assays and vice versa . In the strand cleavage substrate , the scissile phosphate is followed by a truncated 3 nt segment from the strand exchange region ( Fig 8A ) . The full 8 nt complement of the exchange region in the opposite strand ends in 5’-phosphate , which prevents strand joining by a hydroxyl group at this position . Furthermore , diffusion of the unstably hydrogen bonded trinucleotide product of cleavage away from the reaction center minimizes cleavage reversal . In the strand joining substrate , the scissile phosphate is covalently linked to a tyrosyl moiety ( Fig 8B ) to mimic the phosphotyrosyl bridge between Flp and DNA formed during strand cleavage . The hairpin product of the joining reaction is refractory to cleavage , as it cannot support the Flp-Flp interactions required for active site assembly in trans . Flp-SUMO was less active than Flp in strand cleavage as well as strand joining . The half-maximal reaction required a ~3-fold higher amount of Flp-SUMO for both steps ( Fig 8A and 8B ) . Flp-SUMO did catch up with Flp in its Vmax for joining , and required a ~3-fold higher protein amount . However , at a ~5-fold higher amount , Flp-SUMO was close to ( but still below ) saturation cleavage by Flp . Thus , the SUMO attachment reduces the catalytic activity of Flp , the adverse effect on strand cleavage being stronger than that on strand joining . Collectively , the binding and activity assays ( Figs 6–8 ) suggest that the differences in the catalytic efficiencies of Flp and Flp-SUMO can be accounted for primarily by reduced FRT binding affinity and cooperativity imposed by the modification , perhaps with some impairment of active site function in addition . The latter possibility was tested more directly by strand cleavage complementation and strand joining assays using cleavage-incompetent Flp mutants ( described below ) . The two steps of single strand exchange during Flp recombination involves the assembly of the shared cleavage pocket ( and trans-donation of Tyr-343 ) within individual FRT sites or between synapsed partner FRT sites [50 , 59] ( Fig 9A ) . The shared active site makes it possible to catalytically complement a Flp monomer lacking Tyr-343 with one lacking a pentad residue within its pro-active site [50 , 71] ( Fig 9B ) . For example , Flp ( Y343F ) bound to a half-FRT site can activate its scissile phosphate , which may then be cleaved by Tyr-343 from Flp ( R191A ) bound to a second half-FRT site . The individual mutants themselves are inactive in cleavage . We took advantage of catalytically complementing Flp and Flp-SUMO partners to ask whether Flp-SUMO is affected in the phosphate activation step or in the tyrosine donation step of strand cleavage . The reactions were done by pre-binding the 5’-end labelled half-site separately with each one of a pair of complementing mutants , and mixing them in the presence of an excess unlabeled FRT to soak up unbound proteins ( Fig 9B ) . With Flp ( R191A ) as the tyrosine donor , the cleavage output was higher ( ~1 . 6-fold ) with Flp ( Y343F ) than with Flp ( Y343F ) -SUMO as its partner ( lanes 1 and 2 , Fig 9B ) . A similar difference in cleavage ( ~1 . 5-fold ) was noted between Flp ( Y343F ) and Flp ( Y343F ) -SUMO when the tyrosine donor was Flp ( R191A ) -SUMO ( lanes 3 and 4 , Fig 9B ) . Flp ( R191A ) -SUMO was fully competent at tyrosine donation , yielding better cleavage than Flp ( R191A ) when partnered with Flp ( Y343F ) ( ~1 . 5-fold higher; lanes 1 and 3 , Fig 9B ) or with Flp ( Y343F ) -SUMO ( ~1 . 6-fold higher; lanes 2 and 4 , Fig 9B ) . The relative cleavage efficiencies are shown as histogram plots with a value of 1 . 0 assigned for the Flp ( R191A ) -Flp ( Y343F ) pair . Thus , SUMO attachment to Flp diminishes its ability to activate an adjacent scissile phosphate , and consequently causes a decrease in the probability of its cleavage . However , Flp-SUMO is more active in Tyr-343 donation for cleavage of an activated scissile phosphate . The two opposing effects would more or less cancel out . Taken together , FRT binding , FRT cleavage and cleavage complementation data suggest that weakening the cleavage potential per se of Flp by sumoylation is unlikely to play a significant role in protecting FRT against strand nicks in vivo . More prominent is the reduced FRT binding affinity and cooperativity resulting from the modification . Note that these effects would have been minimized in the complementation assays , as they utilized pre-bound half-sites . The strand joining reaction , executed by a single Flp monomer , does not require the active site Tyr-343 [72 , 75] . Flp and Flp ( Y343F ) are equally efficient in this reaction . We tested strand joining in half-site substrates pre-bound by Flp ( Y343F ) or Flp ( Y343F ) -SUMO , thereby avoiding the obfuscating effects on DNA binding . A half-site bound by the Y343F mutant was mixed with one bound by the R191A mutant ( Fig 9C ) , exactly as in cleavage complementation ( Fig 9B ) . As the mutant pair may engage in the interactions responsible for the assembly of the shared active site , the native conditions under which joining occurs are recapitulated . Furthermore , this experimental design makes it possible to test whether the joining activity of a given Y343F mutant protein is differentially affected by whether the partner R191A mutant contains SUMO or not . Flp ( Y343F ) and Flp ( Y343F ) -SUMO gave equal amounts of the hairpin product when the reaction also contained the Flp ( R191A ) -bound half-site ( lanes 1 and 2 , Fig 9C ) or the half-site bound by Flp ( R191A ) -SUMO ( lanes 3 and 4 , Fig 9C ) . The histogram plots show the relative joining efficiencies for each protein pair normalized against a value of 1 . 0 for Flp ( Y343F ) in the presence of Flp ( R191A ) . Comparable joining activities of native Flp and Flp-SUMO would suggest that enhanced strand joining ( or cleavage reversal ) by SUMO-modified Flp is probably not a contributing factor in lowering the steady state levels of FRT nicks in vivo . The 2-micron plasmid molecules are organized in vivo into nucleosome-beaded mini-chromatin circles [76–78] . There is concern that the in vitro behavior of Flp-SUMO towards FRT on naked DNA may not be strictly relevant in vivo . DNase I sensitivity assays [79] suggest that FRT sites in the 2-micron plasmid are relatively nucleosome-free . In order to verify that the in vitro differences between Flp and Flp-SUMO are valid in vivo , we estimated the amount of strand nicks at FRT elicited by a relatively brief but sustained burst of induction of each protein in a cell biological assay . This assay , though indirect , avoids potential problems in quantitating FRT nicks by alkaline gel electrophoresis posed by variabilities in the extraction of DNA covalently linked to Flp . The experimental [Cir0] strains , expressing RAD52-YFP , contained the pADE2-Flp plasmid , which has two FRT sites per molecule ( S1 Fig ) and a copy number similar to that of the 2-micron plasmid ( S2 Fig ) . Flp or Flp-SUMO was expressed in this strain from the same chromosome locale ( TRP1; Chromosome IV ) under GAL promoter control . The small background level of Flp expressed by its native promoter from pADE2-Flp does not interfere with the assay , as it would be swamped out by the galactose-induced levels of Flp or Flp-SUMO . Strand nicks formed at FRT were visualized indirectly as Rad52-YFP foci associated with the double strand breaks that such nicks give rise to upon DNA replication [80] . In the wild type strain , the percentage of cells containing Rad52-YFP foci were low ( ~6% or lower; Fig 10A and 10B ) with or without induction of Flp or Flp-SUMO . The foci in uninduced ( glucose- grown ) cells represent background DNA damage plus any damage at plasmid FRT sites due to Flp expressed from pADE2-Flp . The percentage of foci-containing cells was higher in the glucose-grown siz1Δ siz2Δ strain , ( ~12 to ~18% ) ( Fig 10A and 10B ) . Presumably , this increase signifies general DNA repair deficiency caused by the mutations combined with increased FRT strand nicks due to deficient sumoylation of the plasmid-expressed Flp . Induction of Flp by galactose raised the fraction of foci-containing cells from ~18% to ~29% ( Fig 10A ) , signifying a further increase in the frequency of strand nicks at FRT . By contrast , there was no such increase upon similar induction of Flp-SUMO ( Fig 10B ) . Our findings are consistent with a previous demonstration of increased Rad52 foci formation due to siz1Δ siz2Δ in a [Cir+] strain compared to the [Cir0] control [33] . In a strain lacking galactose inducible Flp or Flp-SUMO , but containing the pADE2-Flp plasmid , the fractions of Rad52 foci containing cells were not significantly different in glucose- versus galactose-grown siz1Δ siz2Δ ( Fig 10C ) . This was also the case for the wild type , as expected . The striking difference between Flp and Flp-SUMO in foci formation in siz1Δ siz2Δ is consistent with the decreased occupancy of FRT sites by Flp-SUMO , as suggested by the in vitro results . More rapid turnover of Flp-SUMO in siz1Δ siz2Δ compared to Flp cannot explain the difference , as these foci were scored immediately following an optimal 2 hr galactose induction of both proteins . In fact , the induced level of Flp-SUMO at this time point was higher than that of Flp in both the wild type and mutant strains ( Fig 10D ) .
An important advantage of Flp-SUMO is that it permits direct comparison with unmodified Flp in the DNA binding and cleavage properties of the two purified proteins . We have not utilized in vitro SUMO conjugation [82 , 83] to Flp because of uncertainties in the extent of modification and the potential for conjugation at semi-consensus or non-consensus lysine residues . Structural considerations suggest that the folded SUMO domain linked to Lys-375 or to Ile-423 would be spaced approximately equally from the body of Flp ( S4 Table ) . Furthermore , Flp-SUMO mirrors Flp ( Lys-375-SUMO ) in vivo in that both require Slx5-Slx8 for their normal regulation , while Siz1-Siz2 can be bypassed in the case of Flp-SUMO but not Flp . Equally compelling is the concordance between the reduced in vitro FRT binding/cleavage by Flp-SUMO ( Figs 7 and 8 ) and the inferred extents of in vivo FRT DNA damage effected by unmodified Flp versus natively modified Flp or Flp-SUMO ( Flp >> Flp-SUMO in siz1Δ siz2Δ and Flp ( K375-SUMO ) = Flp-SUMO in wild type ) ( Fig 10 ) . Extrapolating the interpretations from Flp-SUMO to Flp ( K375-SUMO ) is therefore justified on multiple grounds . The adverse physiological effects of gross undersumoylation of Flp ( siz1Δ siz2Δ ) —retarded growth , premature cell death and increased plasmid loss—are consistent with BIR-induced hyper-amplification of FRT-containing plasmids . These phenotypes are strongly ameliorated by switching from Flp to Flp-SUMO expression . According to prior genetic and biochemical work , BIR is triggered by Flp-mediated strand nicks at FRT in conjunction with plasmid replication [31 , 33] . Mutations in BIR pathways suppress unregulated plasmid amplification [33] . High rates of plasmid loss may occasionally mask the typical BIR phenotypes—as was observed with the hyper-cleaving variant Flp ( H305L ) —because of the growth advantage of plasmid-cured cells . However , induction of Flp ( H305L ) leads to cell death when resolution of the branched DNA intermediates of BIR is impaired by yen1Δ , mus81Δ or yen1Δ mus81Δ . Cell killing by Flp ( H305L ) , which correlates with FRT copy number , is strongly curbed when these mutants express Flp ( H305L ) -SUMO instead . The salvaging effects of SUMO fusion to Flp are dependent on the Slx5-Slx8 STUbL , suggesting that SUMO modification of Flp is followed by ubiquitination and proteasome-mediated degradation ( Fig 11B ) . The half-lives of Flp-SUMO in the wild type and mutant strains , and in the presence of the proteasome inhibitor MG-132 , are consistent with this scenario . Natively sumoylated Flp has also been shown to be regulated by Slx5-Slx8 [33] . The cumulative results suggest that the mechanisms responsible for SUMO recognition in Flp ( K375-SUMO ) and downstream processing are shared by Flp-SUMO as well . There are precedents for SUMO and ubiquitin fusion proteins satisfying the functional roles of their naturally modified counterparts . Examples in yeast include the recombination protein Rad52 as well as transcriptional regulatory proteins [84–87] . The properties of Flp-SUMO revealed in our studies vis a vis the siz1Δ siz2Δ and slx5Δ or slx8Δ host strains are in general conformity with the behavior of other biologically active SUMO fusion proteins . While sumoylation may signal protein degradation in certain instances , it may act as a safeguard against degradation in others . The opposing functions may be reconciled if the SUMO-acceptor lysine is also the target for ubiquitination by an STUbL [88] . In one case , the addition of ubiquitin may occur in concert with the removal of SUMO by Ulp1 . In the other , the pre-existing SUMO may sterically block the action of a ubiquitin ligase . The apparent deregulation of Flp by interfering with either SUMO conjugation ( siz1Δ siz2Δ ) [31 , 33] or deconjugation ( ulp1 ) [34] would fit into the first model . It is not known whether Lys-375 of Flp is the site for both sumoylation and ubiquitination under native conditions . If it is , the functional similarity between Flp and Flp-SUMO suggests that SUMO can activate Slx5-Slx8 in cis or in trans during ubiquitination . In spite of the large difference in stoichiometry between SUMO conjugation at Lys-375 ( ~10% ) [31] and SUMO fusion at Ile-423 ( 100% ) , the modifications are nearly indistinguishable in their biological roles . Dynamic modification of Lys-375 by deconjugation and reutilization of the SUMO moiety , targeted modification of FRT-bound Flp or selective exclusion of modified Flp from FRT may overcome the limitations of substoichiometric modification . Deregulation of Flp by ulp1 [34] would be consistent with the dynamic nature of SUMO conjugated to Lys-375 . These general principles—the catalytic nature of the modification , its compartmentalization , and/or its dominance in dimeric or oligomeric protein assemblies—may explain similar biological effects produced by substoichiometric modification of native proteins and stoichiometric modification of the corresponding engineered fusion proteins . As revealed by in vitro analyses , Flp-SUMO has weaker affinity for FRT than Flp , and is less cooperative in FRT-binding . Furthermore , the underrepresentation of FRT-bound Flp-SUMO in binding reactions with mixtures of Flp and Flp-SUMO suggests negative cooperativity between the two . As strand cleavage requires the collaboration of two Flp monomers [50 , 59] , the in vivo implications of negative cooperativity are significant in the context of a single FRT site or a pair of synapsed FRT sites . The reduced association or enhanced dissociation of a sumoylated Flp monomer would protect FRT from strand cleavage , and the potential initiation of BIR , even if it were stably bound by an unmodified Flp monomer ( Fig 11C ) . Similar dissociation within four recombinase monomers bound to a pair of synapsed FRT sites would cause these sites to disengage from each other . Normal plasmid amplification by Flp-mediated reconfiguration of replication forks can thus be regulated as well ( Fig 11D ) . There are several examples for the modulation of the DNA binding affinity of proteins by SUMO or ubiquitin conjugation , in particular proteins associated with DNA damage repair [65 , 89] . The modification promotes protein dissociation from DNA in most instances [90–95] , although the reverse trend has also been observed [96–98] . The properties of Flp-SUMO follow the general theme of fine-tuning DNA-protein interactions through post-translational modification . However , this is the first time that this phenomenon has been demonstrated in the regulation of a site-specific DNA recombinase . As an extrachromosomal element , the 2-micron plasmid derives its evolutionary fitness from its high transmission fidelity during cell division , and its ability to restore copy number in case of a glitch during a segregation event [2] . The steady state plasmid copy number of 40–60 molecules in a haploid nucleus is an optimized maximum value . Higher copy numbers reduce the fitness of the host , and indirectly harm the plasmid . Central to copy number maintenance is the regulation of Flp through modulation of gene expression by plasmid-coded proteins ( Fig 11A ) [24 , 25 , 27] and through post-translational modification by the host’s SUMO and ubiquitin conjugation machineries ( Fig 11B–11D ) [31 , 33] . The post-translational control is two-pronged . As suggested by previous work [31 , 33] , and confirmed by the present study utilizing Flp-SUMO , proteasome-mediated turnover of Flp , sequentially modified by SUMO and ubiquitin conjugation , lessens the probability of unsealed strand nicks at FRT and unwarranted increase in plasmid copy number via BIR ( Fig 11B ) . At another level , as suggested by the in vitro and in vivo properties of Flp-SUMO , the lower propensity of SUMO-conjugated Flp to stay associated with FRT protects it from excessive strand nicks ( Fig 11C ) . By controlling the level and activity of Flp ( Fig 11A–11D ) , SUMO conjugation would also prevent hyper-amplification of the plasmid by FRT x FRT recombination coupled to plasmid replication ( Fig 11D ) [21–23] . Thus , safeguards against plasmid overpopulation through moderation of selfishness exercised by the plasmid itself and through preventive measures implemented by the host guarantee the mutual compatibility between a dependent genome and its guardian genome over evolutionary time .
|
Plasmids of budding yeasts , exemplified by the 2-micron plasmid of Saccharomyces cerevisiae , and mammalian papilloma and gammaherpes viruses typify eukaryotic extra-chromosomal selfish DNA elements . The plasmid and the viral episomes , despite the long evolutionary divergence of their hosts , share striking similarities in lifestyles . These include the ability to segregate to daughter cells by hitchhiking on chromosomes and to switch from cell cycle regulated replication to iterative replication for copy number maintenance . While selfish elements , including those integrated into chromosomes , rely on their hosts’ genetic potential for long-term survival , their genetic load is carefully regulated to minimize fitness conflicts with the hosts . Our study focuses on the Flp site-specific recombinase , which is central to the copy number control of the 2-micron plasmid and whose steady state levels are regulated through transcriptional control by plasmid coded proteins and through post-translational modification by the host’s sumoylation machinery . We demonstrate that sumoylation , in addition , attenuates the catalytic activity of Flp by diminishing its DNA binding affinity and inter-monomer cooperativity , providing another layer of protection against runaway increase in plasmid copy number . Population control by self-imposed and host-mediated mechanisms is likely a general strategy among selfish elements to ensure nearly conflict-free coexistence with host genomes .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"recombination",
"reactions",
"monomers",
"plasmids",
"dna-binding",
"proteins",
"plasmid",
"construction",
"sumoylation",
"dna",
"replication",
"genetic",
"elements",
"forms",
"of",
"dna",
"dna",
"construction",
"dna",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"polymer",
"chemistry",
"proteins",
"chemistry",
"recombinant",
"proteins",
"molecular",
"biology",
"biochemistry",
"post-translational",
"modification",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"chemical",
"reactions",
"physical",
"sciences",
"genomics",
"mobile",
"genetic",
"elements"
] |
2019
|
A Flp-SUMO hybrid recombinase reveals multi-layered copy number control of a selfish DNA element through post-translational modification
|
Imprinted genes undergo epigenetic modifications during gametogenesis , which lead to transcriptional silencing of either the maternally or the paternally derived allele in the subsequent generation . Previous work has suggested an association between imprinting and the products of retrotransposition , but the nature of this link is not well defined . In the mouse , three imprinted genes have been described that originated by retrotransposition and overlap CpG islands which undergo methylation during oogenesis . Nap1l5 , U2af1-rs1 , and Inpp5f_v2 are likely to encode proteins and share two additional genetic properties: they are located within introns of host transcripts and are derived from parental genes on the X chromosome . Using these sequence features alone , we identified Mcts2 , a novel candidate imprinted retrogene on mouse Chromosome 2 . Mcts2 has been validated as imprinted by demonstrating that it is paternally expressed and undergoes promoter methylation during oogenesis . The orthologous human retrogenes NAP1L5 , INPP5F_V2 , and MCTS2 are also shown to be paternally expressed , thus delineating novel imprinted loci on human Chromosomes 4 , 10 , and 20 . The striking correlation between imprinting and X chromosome provenance suggests that retrotransposed elements with homology to the X chromosome can be selectively targeted for methylation during mammalian oogenesis .
Mammals inherit one haploid genome complement from each parent , and in most cases both alleles are expressed and functionally equivalent . Imprinted alleles are an exception to this rule , as their expression in offspring is dependent on the gender of the transmitting parent . These parent-of-origin effects arise due to differential epigenetic reprogramming events occurring in the male and female germ-line . Methylation at CpG dinucleotides is one modification known to play a key role , and germ-line differentially methylated regions ( gDMRs ) have been found in proximity to most known imprinted genes . In addition to performing an essential role in genomic imprinting [1] , DNA methylation also serves to suppress the activity of retrotransposon promoters [2 , 3] . This connection led to the proposal that the two processes may be mechanistically linked [4–7] , which is further supported by the identification of imprinted genes with retrotransposon-like properties [8] . Following the wealth of sequence data that has been made available in recent years , the conceptual distinction between genes and transposons has become increasingly vague . For example , autonomously replicating L1 retroelements can be diverted to act on host cell mRNAs [9] , suggesting that almost any cellular mRNA has the capacity to act as a retrotransposon . A recent survey identified 3 , 590 of these intronless gene duplicates in the human genome , of which 1 , 080 showed evidence of transcription [10] . More than 100 have maintained the capacity to encode proteins , indicating that retrotransposition is a major source of protein-coding novelty in mammals [10] . We adopt the term “retrogene” hereafter to refer to these putatively functional elements [10–12] , as distinct from the genetically disabled “retropseudogenes . ” Due to the mechanistic link discussed above , it is not surprising that a small number of retrogenes have been shown to undergo imprinting [13 , 14] . One such gene , murine U2af1-rs1 , is a retrotransposed copy of the X-linked U2af1-rs2 gene , which lies within an intron of Murr1 on Chromosome 11 [13] . The orthologous human locus lacks the retroposed sequence and a differentially methylated CpG island [15] , indicating that the gene duplication occurred after the divergence of rodents and primates ( ∼65 million years ago ) . The human MURR1 gene shows no evidence of imprinted expression or allele-specific methylation , indicating that imprinting at this locus arose at about the same time point in rodent evolution as the retroposon insertion [15] . To investigate the link between retrotransposition and genomic imprinting further , we performed a systematic screen of known imprinted genes in the mouse to identify candidate retrogenes . Eleven genes were identified , three of which have CpG islands overlapping the retrotransposed exons that undergo differential germ-line methylation . The other eight are likely to be controlled by differentially methylated elements that are not within the duplicated sequences . The three retrogenes share three sequence characteristics , namely , they are located within an intron of another gene , they are derived from an ancestral gene on the X chromosome , and they are associated with an overlapping CpG island . These characteristics alone were used to identify a novel imprinted locus consisting of Mcts2 and H13 , a pair of reciprocally expressed novel imprinted genes on mouse Chromosome 2 . Finally , we show that imprinting is conserved in humans for the three retrogenes that predate the divergence of rodents and primates .
The Inpp5f_v2 and Nap1l5 promoters are known to be methylated on the maternally derived allele in somatic tissues [20 , 21] , but no gDMR had previously been identified at either of these imprinted loci . The methylation status of the CpG islands overlapping the two retrogene promoters was assessed by sequencing bisulphite-modified DNA from ovulated oocytes and mature sperm . Both regions are heavily methylated in female , but not male gametes ( Figure 1 ) . The U2af1-rs1 promoter had previously been shown to undergo methylation specifically during oogenesis ( Figure 1 ) [15] . The finding that the U2af1-rs1 , Nap1l5 , and Inpp5f_v2 retrogenes all overlap gDMRs suggests that the inserted sequences are specifically targeted for methylation in the maternal germ-line . To examine the retrotransposition events that generated these three genes in more detail , BLASTP searches were performed using the retrogene ORFs to identify all family members in mouse and human . Both Inpp5f_v2 and U2af1-rs1 belong to gene families consisting of only two closely related members , whereas the Nap1l family consists of five paralogues . The multi-exonic Tmem114A gene on the X chromosome is the only paralogue of the murine Inpp5f_v2 ORF ( also known as Tmem114B ) . The observation that the ORF of Inpp5f_v2 is contained entirely within the first exon indicates a retrotransposition event originating from the Tmem114A gene on the X chromosome . Comparative sequence analysis using the genomic sequence of the Inpp5f gene in multiple species revealed the retrogene to be present in all eutherian mammals examined ( Figure 2A ) . Absence of the retroposed sequence at the Inpp5f locus in the opossum genome demonstrates that this gene duplication event occurred after the marsupial divergence . The X-linked , multi-exonic U2af1-rs2 gene is the closest paralogue of the imprinted and monoexonic murine U2af1-rs1 [13] . Applying the same logic as described for Inpp5f_v2 , U2af1-rs1 is the product of an X-to-autosome retrotransposition event [13] . A multi-species sequence comparison using the Murr1 genomic sequence revealed that this event occurred in a common ancestor of mouse and rat , after the divergence of rodents and primates ( Figure 2B ) . As previously reported [13 , 15] , no orthologue of the murine U2af1-rs1 sequence is present at the MURR1 locus on human Chromosome 2 . The Nap1l gene family consists of five members , two of which are multi-exonic and possess orthologues in all vertebrates examined ( Nap1l1 and Nap1l4 ) . Of the three monoexonic family members , the imprinted Nap1l5 gene lies within an intron of Herc3 on mouse Chromosome 6 , whereas the Nap1l2 and Nap1l3 genes are situated on the X chromosome . The presence of three monoexonic paralogues makes their precise relationship complicated to determine , and so a maximum likelihood tree was generated using the region of the Nap1 domain common to all five family members ( Figure 2C ) . As the Nap1l5 ORF is truncated and lacks regions of homology shared by all other family members ( Figure S1 ) , this gene cannot have acted as the source of Nap1l2 or Nap1l3 . Given this information , the imprinted paralogue is more likely to have originated from one of the two X-linked genes than from the autosomal Nap1l1 or Nap1l4 ( supported by 93/100 bootstrap re-sampling trials; Figure 2C ) , implicating Nap1l2 or Nap1l3 as the likely source . At the Nap1l5 locus , homology with other family members is limited to the transcribed sequence , and the flanking regions contain short target site duplications that are indicative of L1-mediated retrotransposition [23] . Based on these observations , the most likely origin of the Nap1l5 gene is an X-to-autosome retrotransposition event , although the exact relationship between family members is less clear than for Inpp5f_v2 and U2af1-rs1 . Comparative sequence analysis using the Herc3 genomic sequence reveals that this retrogene originated in a common ancestor of all eutherian mammals examined , but is absent in marsupials and nonmammalian vertebrate species ( Figure 2D ) . The promoter regions of the three retrogenes are associated with CpG islands in all species in which they are present . In contrast , CpG islands are absent in the orthologous intronic regions of genomes lacking the three retrogenes . The regions of CpG-rich sequence that undergo differential methylation in the germ-line therefore arose either during or shortly after the retrogene integration events . While it is possible to correlate the timing of the retroposon integrations with the origin of the corresponding CpG islands , the mechanism by which the CpG-rich sequences arose is unclear . All three imprinted retrogenes that undergo differential methylation in the germ-line are situated within introns of multi-exonic genes and are likely to be derived from ancestral genes on the X chromosome . The X chromosome has generated a disproportionately large number of functional retrogenes over the course of mammalian evolution [24] . To contextualize our data , we collated a larger sample of mouse retrogenes that were assumed not to be imprinted . A detailed survey recently revealed 3 , 590 retrocopied gene duplicates in the human genome , 104 of which showed evidence of expression and originated in a common ancestor of rodents and primates . The 104 mouse retrocopies were manually annotated to identify those that had maintained an intact ORF and showed EST evidence of expression in the mouse genome ( build v35 , Text S1 ) . A total of 74 mouse retrocopies fulfilled both of these criteria and are likely to represent bona fide mouse retrogenes ( Dataset S2 ) . Only one of the known imprinted retrogenes listed in Table 1 also features in this dataset ( Mkrn3 ) , suggesting that this sample does not contain a large proportion of the total number of retrogenes present in the mouse genome . Nonetheless , after excluding Mkrn3 , the remaining 73 were deemed an adequate sample with which to compare the three gDMR-associated retrogenes . Approximately one in four ( 18/73 ) originated from the X chromosome , whereas approximately one in seven ( 10/73 ) were embedded within introns of RefSeq-annotated host genes . Although a formal statistical analysis is not possible with an n of 3 , these data indicate that the properties of X-chromosome derivation and intronic location may be overrepresented among imprinted retrogenes overlapping gDMRs relative to their presumably nonimprinted counterparts . Based on the data obtained from known imprinted loci , we hypothesized that X-derived retrogenes are more likely to be imprinted and associated with gDMRs than those derived from autosomes . In order to test this hypothesis , we selected all murine retrogenes from the sample of 73 ( Dataset S2 ) that were situated within introns of known genes [25] and associated with CpG islands , regardless of their chromosomal origin . Only three retrogenes fulfilled both of these criteria , two of which were derived from parental genes on autosomes and one that was derived from the X chromosome ( Table 3 ) . Single nucleotide polymorphisms ( SNPs ) were identified between C57BL/6J ( B6 ) and Mus mus castaneus ( cast ) , and allele-specific RT-PCR sequencing assays were performed on cDNAs from reciprocal B6 × cast F1 hybrids . Primers were designed to specifically amplify the retrogene while avoiding amplification of other paralogous sequences , and specificity was confirmed by the alignment of sequence reads to the appropriate region of the mouse genome using the BLAT alignment tool [26] . The X-derived Mcts2 was found to be expressed exclusively from the paternally derived allele in newborn brain , and a strong paternal allele bias was also seen in embryonic day ( E ) 13 . 5 embryo ( Figure 4C ) . Expression of the two autosomally derived retrogenes , Dnajb3 and Oxct2a , was not detectable by RT-PCR ( 35 cycles ) in E13 . 5 embryo or placenta or neonatal brain ( unpublished data ) . Although it was not possible to determine the imprinting status of these genes in somatic tissues , EST evidence suggested that they were both expressed exclusively in testes . The maternally and paternally derived alleles were expressed at approximately equal levels ( Figure 3A ) , demonstrating that these two autosomally derived retrogenes do not undergo imprinting at their primary site of expression . We examined the imprinted expression of Mcts2 , U2af1-rs1 , and Inpp5f_v2 in testes . All are expressed from both parental alleles in this tissue ( Figure 3A and 3B ) , reflecting their unmethylated state in the male germ-line ( Figures 1 and 3D ) . Although Nap1l5 is expressed in testes , no SNP was identified within the transcribed region of this gene , and so imprinted expression could not be assessed . The X-derived retrogenes U2af1-rs1 , Nap1l5 , and Inpp5f_v2 are all associated with gDMRs at CpG islands adjoining their promoters , which are in close proximity to the ORF-containing regions showing paralogy with the ancestral gene copy . To determine whether this was also the case at the Mcts2 locus , the methylation status of the CpG island overlapping this promoter was examined by sequencing bisulphite-modified DNA from oocytes and sperm . Consistent with the results obtained for other intronic and X-derived retrogenes ( Figure 1 ) [15] , this region was predominantly methylated in oocytes but unmethylated in sperm ( Figure 3D ) . Differential methylation of this region was also seen in E13 . 5 embryo ( Figure 3D ) . The Mcts gene family consists of two members in both mouse and human . The multi-exonic nature of the X-linked Mcts1 confirms that the monoexonic Mcts2 is an X-to-autosome retrogene , which lies within an intron of H13 . Comparative sequence analysis was performed using the genomic sequence of H13 in multiple species ( Figure 3C ) . Although the retrogene is present in primates and rodents , it is absent in the genome of both dog and cow . Mcts2 therefore originated in the supraprimate clade ( synonymous with Euarchontoglires , including rodents and primates ) , after the laurasiatherian divergence ( including canines and ruminants; Figure 4A ) . Imprinted genes often occur in clusters , and individual gDMR sequences can control the imprinting of multiple neighbouring transcripts [27] . This raised the possibility that the gDMR at the Mcts2 promoter could also control the imprinting of the more ancient H13 gene within which it lies . Primers were designed to amplify exons 3 to 13 , spanning the intron of H13 within which the Mcts2 gDMR is situated . Expression is exclusively from the maternally derived allele in newborn brain ( Figure 4B ) , in contrast to the paternally expressed retrogene ( Figure 3A ) . Although the maternally derived allele of H13 is preferentially expressed in E13 . 5 embryo and placenta , the paternally derived allele is also active in these tissues ( Figure 4B ) . The retrotransposition events that generated the murine Nap1l5 , Inpp5f_v2 , and Mcts2 genes occurred prior to the divergence of rodents and primates ( Figure 4A ) , and the human orthologues are situated on Chromosomes 4 ( NAP1L5 ) , 10 ( INPP5F_V2 ) , and 20 ( MCTS2 ) , respectively . The imprinting status of these three genes had not been previously assessed . To address this , allele-specific assays were performed in fetal spinal cord cDNA with matched maternal DNA ( Figure 5 ) . SNPs were identified in fetal genomic DNA for each gene and the maternal genotype was determined . Where the mother and fetus were both heterozygous ( “noninformative” families ) , the parental origin of the single expressing allele of an imprinted gene could not be determined . One informative family was obtained for each gene , and in every case expression was exclusively from the paternally derived allele in the fetus ( Figure 5A–5C ) . Monoallelic expression was confirmed in two additional noninformative families . For every gene , monoallelic expression was observed in all tissues in which expression was detected , which included fetal brain , heart , and tongue ( unpublished data ) .
Retrogenes that share the properties of X-derivation , intronic location , and association with a CpG island are rare in the mouse genome ( one out of 74 , Dataset S2 ) ; although there are several reasons to believe that additional examples could exist . Firstly , the dataset of retrocopied sequences published by Vinckenbosch et al . focused on the human genome [10]; therefore , only mouse retrogenes that originated in a common ancestor of rodents and primates were examined in this report . Genes acquired more recently in the rodent lineage ( e . g . , U2af1-rs1 ) would not have been detected , and so additional candidates might be revealed by an analysis focused on the mouse genome . Because of the stringent criteria that were necessarily applied , this study would also have omitted potential retrogenes that showed the greatest degree of similarity to monoexonic paralogues ( e . g . , Nap1l5 ) . Regardless of the total number of imprinted retrogenes that are present in the mammalian genome , the properties shared by each of the four examples identified in this report are likely to yield clues to the nature of the imprinting mechanism . All four gDMR-associated retrogenes are situated within introns of actively transcribed host genes . The fact that none are situated in intergenic regions suggests that transcription through the gDMR may be a necessary mechanistic component . Several other maternally methylated gDMRs are situated within introns ( Kcnq1ot1 , Air , Nnat , Nespas , Gnas exon1A , Grb10 ) , indicating that this feature is common among elements that undergo methylation during oogenesis . Further work is required to determine the mechanistic significance of this property , but we speculate that transcription through the CpG island in germ cells may play a role . The observation that all four gDMR-associated retrogenes have paralogues situated on the X chromosome suggests that this feature may also have mechanistic significance . Male and female germ cells differ in their sex chromosome constitution , and meiotic sex chromosome inactivation results in the transcriptional shutdown of X-linked genes during spermatogenesis . In contrast , X chromosomes are transcriptionally active during female meiotic prophase I [41] , when maternal imprint marks are established [42] . It has been proposed by others that homology-dependent interactions between sex chromosomes and autosomes might underlie the sexually dimorphic patterns of DNA methylation that are established at imprinted loci during gametogenesis [43] . The idea that imprint establishment may involve interactions between homologous sequences is supported by the finding that mice carrying multiple copies of a U2af1-rs1 transgene undergo aberrant methylation of the endogenous locus during spermatogenesis [44] . Homology-dependent transcriptional silencing of dispersed repeats has been reported in plants , funghi , diptera , and mammals [45–48] , and dispersed Alu repeats in the primate genome undergo CpG methylation during female gametogenesis [49] . The Alu consensus sequence is <300 bp , suggesting that only relatively short regions of homology are required to induce these effects . The mechanistic similarities between retrotransposon silencing and genomic imprinting have been discussed for over a decade [4 , 5 , 43] , and the discovery of four gDMRs associated with retrotransposed genes lends strong support to this proposed link . The arguments above relate to the mechanisms by which imprinting is established at a locus , but do not extend to the processes by which natural selection may favor the spread of imprinted alleles within a population . In one model , it has been predicted that selection could favor the imprinting of genes that act in a sexually antagonistic manner , including those with roles in reproductive tissues such as the testes [50] . Several X-to-autosome retrogenes have acquired specific roles in the male germ-line [11 , 12] , where they are thought to act as substitutes for their X-linked paralogues that are silenced by sex chromosome inactivation [51] . The expression pattern of U2af1-rs1 , Nap1l5 , Inpp5f_v2 , and Mcts2 appears to fit with this model , raising the possibility that imprinting could serve as a mechanism by which genes that have acquired specialized functions during spermatogenesis are silenced during female meiosis .
Further details of the two screens by which Datasets S1 and S2 were generated are located in Text S1 . Protein sequences from the Nap1l family were aligned using CLUSTALW ( http://www . ebi . ac . uk/clustalw ) , and the largest region showing clear homology between all aligned sequences ( residues 66–137 of human NAP1L1 , Figure S1 ) was used to generate a maximum likelihood tree using ProML within PHYLIP [52] . The tree topology generated by ProML was then tested for support from the alignment using bootstrap resampling analysis in GeneBee ( http://www . genebee . msu . ru/services/phtree_reduced . html ) . mVISTA plots ( http://genome . lbl . gov/vista/index . shtml ) were generated using the following genome builds: mouse ( Mus musculus ) , build 35; human ( Homo sapiens ) , build 35; rat ( Rattus norvegicus ) , Atlas v3 . 1; cow ( Bos taurus ) , Btau_2 . 0; dog ( Canis familiaris ) , v2 . 0; opossum ( Monodelphis domestica ) , MonDom 2 . 0; and chicken ( Gallus gallus ) , WASHUC1 . The following genomic regions were compared: Inpp5f: mouse , Chr7:124707188–124793035; human , Chr10:121475663–121578648; cow , Chr26:25323898–25419575; dog , Chr28:32879108–32946035; opossum , scaffold_12290:987328–1016804; chicken , Chr6:29342310–29378953 . Murr1: mouse , Chr11:22851733–22934290; rat , Chr14:103590704–103686780; human , Chr2:62044453–62274855; cow , Chr11:40264882–40438893; dog , Chr10:65037489–65209929; opossum , scaffold_13632:170442–420565 . Herc3: mouse , Chr6:58800005–58872197; human , Chr4:89870824–89986864; cow , Chr6:19342489–19477683; dog , Chr32:14837425–14937677; opossum , scaffold_15026:3665556–3783621; chicken , Chr4:35520006–35557686 . H13: mouse , Chr2:152410356–152447681; Rat: Chr3:142926299–142963222; human , Chr20:29565902–29621029; cow , Chr13:43262441–43302878; dog , Chr24:23996878–24039707; opossum , scaffold_13306:1536833–1561646; chicken , Chr20:9571122–9582505 . Mouse: oocytes were derived from superovulated adult C57BL/6J females and sperm were dissected from the testes of adult males . Whole testes were used for the RT-PCR experiments , containing both somatic and germ-cell lineages . RNA was prepared by caesium chloride centrifugation , and oligo-dT primed cDNA was generated using the superscript first-strand kit ( Invitrogen , http://www . invitrogen . com ) . Human: samples were collected under the guidelines of the Hammersmith and Queen Charlotte's and Chelsea Hospitals Trust Research Ethics Committee ( Registration Number: 2005/6028 ) . Informed consent was collected from all subjects . Oocytes were treated using a method adapted from Olek et al . [53] . Briefly , 50 oocytes were mixed with 10 μl molten LMP agarose and the mixture was solidified on ice and overlaid with cold mineral oil . After a 14-h incubation in lysis buffer ( 10 mM Tris-HCl [pH7 . 6] , 10 mM EDTA , 1% SDS , 50 μg/ml proteinase K ) , agarose beads were washed for 3 × 15 min in TE before denaturing the DNA strands with 0 . 3 M NaOH for 2 × 15 min then 0 . 1 M NaOH for 1 × 10 min . NaOH solution was removed and replaced with 3 . 25 M Sodium MetaBisulphite ( Sigma , http://www . sigmaaldrich . com ) and 0 . 93 mM hydroquinone solution , which was overlaid with mineral oil prior to incubation at 55 °C for 5 h . Agarose beads were washed for 5 × 5 min in TE prior to incubation in 500 μl 0 . 2 M NaOH for 15 min at 37 °C then water for 2 × 10 min . The water was removed and the beads melted at 80 °C for 5 min and then aliquoted and used directly for PCR analysis . DNA from sperm and E13 . 5 embryos was treated essentially as above without encapsulation in agarose . Between two and five parallel amplifications were performed for each product . All primers and cycling conditions that were used to amplify cDNA , genomic DNA , and bisulphite-modified DNA are detailed in Protocol S1 . RT-PCR was performed for 30–35 cycles and −RT controls were run in parallel to control for genomic DNA contamination . Bisulphite PCR products were gel-purified using the QiaEXII ( Qiagen , http://www1 . qiagen . com ) kit before cloning into the TOPO TA ( Invitrogen ) vector . Individual clones were sequenced using Big Dye v3 . 1 ( ABI , http://www . abionline . com ) sequencing technology . Between two and five independent amplifications were performed for each type of template , and strands from the same amplification that could not be distinguished on the basis of either epigenotype or unconverted non-CpG cytosines were excluded . All strands showed >95% conversion of non-CpG cytosines .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for the genes discussed in this paper are H13 ( NM_010376 ) , INPP5F_V2 ( AK091448 ) , MCTS2 ( BC053868 ) , Mcts1 ( also known as Mct-1; NM_026902 ) , Mcts2 ( NM_025543 ) , NAP1L5 ( NM_153757 ) , Nap1l2 ( NM_008671 ) , Nap1l3 ( NM_138742 ) , Nap1l5 ( NM_021432 ) , Tmem114A ( BC028317 ) , Inpp5f_v2 ( also known as Tmem114B; DQ648020 ) , U2af1-rs1 ( NM_011663 ) , and U2af1-rs2 ( NM_178754 ) .
|
The conventional view is that DNA carries all of our heritable information and our genes control development into adulthood . The discovery of epigenetics , a term coined to describe effects that are not coded for by DNA sequence , but can nonetheless affect our development and well-being , has added another layer of complexity to our understanding of genetics . One class of genes under epigenetic control are imprinted genes . Mammals inherit two copies of every gene , one from mother and one from father , and in most cases , both are active . However , for a small number of imprinted genes in mammals , only one is active , either the maternal or the paternal copy . Epigenetics amounts to a control system for switching genes on and off appropriately . We focus on a group of little-studied imprinted genes that share features that give clues to their evolutionary origins . These so-called “retrogenes” are protein-coding sequences of DNA that have undergone duplication and jumped into novel locations in the genome . Because of this , it is possible to determine where , and roughly when , many of the imprinted retrogenes originated . This provides an opportunity to study the molecular events that have generated imprinted genes during mammalian evolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"cell",
"biology",
"evolutionary",
"biology",
"homo",
"(human)",
"genetics",
"and",
"genomics",
"mus",
"(mouse)"
] |
2007
|
A Screen for Retrotransposed Imprinted Genes Reveals an Association between X Chromosome Homology and Maternal Germ-Line Methylation
|
Genome-wide association ( GWA ) is gaining popularity as a means to study the architecture of complex quantitative traits , partially due to the improvement of high-throughput low-cost genotyping and phenotyping technologies . Glucosinolate ( GSL ) secondary metabolites within Arabidopsis spp . can serve as a model system to understand the genomic architecture of adaptive quantitative traits . GSL are key anti-herbivory defenses that impart adaptive advantages within field trials . While little is known about how variation in the external or internal environment of an organism may influence the efficiency of GWA , GSL variation is known to be highly dependent upon the external stresses and developmental processes of the plant lending it to be an excellent model for studying conditional GWA . To understand how development and environment can influence GWA , we conducted a study using 96 Arabidopsis thaliana accessions , >40 GSL phenotypes across three conditions ( one developmental comparison and one environmental comparison ) and ∼230 , 000 SNPs . Developmental stage had dramatic effects on the outcome of GWA , with each stage identifying different loci associated with GSL traits . Further , while the molecular bases of numerous quantitative trait loci ( QTL ) controlling GSL traits have been identified , there is currently no estimate of how many additional genes may control natural variation in these traits . We developed a novel co-expression network approach to prioritize the thousands of GWA candidates and successfully validated a large number of these genes as influencing GSL accumulation within A . thaliana using single gene isogenic lines . Together , these results suggest that complex traits imparting environmentally contingent adaptive advantages are likely influenced by up to thousands of loci that are sensitive to fluctuations in the environment or developmental state of the organism . Additionally , while GWA is highly conditional upon genetics , the use of additional genomic information can rapidly identify causal loci en masse .
Biologists across fields possess a common need to identify the genetic variation causing natural phenotypic variation . Genome-wide association ( GWA ) studies are a promising route to associate phenotypes with genotypes , at a genome-wide level , using “unrelated” individuals [1] . In contrast to the traditional use of structured mapping populations derived from two parent genomes , GWA studies allow a wide sampling of the genotypes present within a species , potentially identifying a greater proportion of the variable loci contributing to polygenic traits . However , the uneven distribution of this increased genotypic diversity across populations ( population structure ) , as well as the sheer number of statistical tests performed in a genome-wide scan , can cause detection of a high rate of “false-positive” genotype-phenotype associations that may make it difficult to distinguish loci that truly affect the tested phenotype [1]–[5] . Epistasis and natural selection can also lead to a high false-negative rate , wherein loci with experimentally validated effects on the focal trait are not detected by GWA tests [4]–[5] . Repeated detection of a genotype-phenotype association across populations or experiments has been proposed to increase support for the biological reality of that association , and has even been proposed as a requirement for validation of trait-phenotype associations [2] . However , replication across populations or experiments is not solely dependent upon genotypes , but also differences in environment and development that significantly influence quantitative traits [5]–[8] . Thus , validation of a significant association through replication , while at face value providing a stringent criterion for significance , may bias studies against detection of causal associations that show significant Genotype×Environment interactions [9] . In this study we employed replicated genotypes to test the conditionality of GWA results upon the environment or development stage within which the phenotype was measured . Integrating GWA mapping results with additional forms of genome-scale data , such as transcript profiling or proteomics datasets , has also been proposed to strengthen support for detected gene-trait associations and reduce the incidence of false-positive associations [10] . To date , network approaches have largely focused upon comparing GWA results with natural variation in gene expression across genotypes in transcriptomic datasets ( i . e . , expression quantitative trait loci ( eQTLs ) ) [11]–[13] . This requires that candidate genes show natural variation in transcript accumulation , which is not always the functional level at which biologically relevant variation occurs [14] . Another network approach maps GWA results onto previously generated interaction networks within a single genotype , such as a protein-protein interaction network , enhancing support for associations that cluster within the network [15] . This network filtering approach has yet to be tested with GWA data where the environment or tissue is varied . To evaluate the influence of environmental or developmentally conditional genetics on GWA mapping and the utility of network filtering in identifying candidate causal genes , we focused on defense metabolism within the plant Arabidopsis thaliana . A . thaliana has become a key model for advancing genetic technologies and analytical approaches for studying complex quantitative genetics in wild species [16] . These advances include experiments testing the ability of genome resequencing and transcript profiling to elucidate the genetics of complex expression traits [17]–[19] and querying the complexity of genetic epistasis in laboratory and natural populations [20]–[26] . Additionally , A . thaliana has long provided a model system for applying concepts surrounding GWA mapping [3]–[5] , [27]–[30] . As a model set of phenotypes , we used the products of two related A . thaliana secondary metabolite pathways , responsible for aliphatic and indolic glucosinolate ( GSL ) biosynthesis . These pathways have become useful models for quantitative genetics and ecology ( Figure 1 ) [31] . Aliphatic , or methionine-derived , GSL are critical determinants of fitness for A . thaliana and related cruciferous species via their ability to defend against insect herbivory and non-host pathogens [32]–[35] . Indolic GSL , derived from tryptophan , play important roles in resistance to pathogens and aphids [36]–[40] . A . thaliana accessions display significant natural genetic variation controlling the production of type and amount of both classes of GSL , with direct impacts on plant fitness in the field [33] , [41]–[47] . Additionally , GSL display conditional genetic variation dependent upon both the environment and developmental stage of measurement [48]–[51] . GSL thus provide an excellent model to explore the impact of conditional genetics upon GWA analysis . While the evolutionary and ecological importance of GSL is firmly established , the nearly complete description of GSL biosynthetic pathways provides an additional practical advantage to studying these compounds [52]–[54] . A large number of QTL and genes controlling GSL natural variation have been cloned from A . thaliana using a variety of network biology approaches similar to network filtering in GWA studies ( Figure 1 ) [55]–[59] . These provide a set of positive control genes of known natural variability and importance to GSL phenotypes , enabling empirical assessment of the level of false-positive and false-negative associations . Within this study , we measure GSL phenotypes in two developmental stages and stress conditions/treatments using a collection of wild A . thaliana accessions to test the relative influence of these components upon GWA . In agreement with previous analyses from structured mapping populations , we found that differences in development have more impact on conditioning genetic variation in A . thaliana GSL accumulation . This is further supported by our observation that GWA-identified candidate genes show a non-random distribution across the three datasets with the GWA candidates from the two developmental stages analyzed overlapping less than expected . The large list of candidate genes identified via GWA was refined with a network co-expression approach , identifying a number of potential networks . A subset of loci from these networks was validated for effects on GSL phenotypes . Even for adaptive traits like GSL accumulation , these analyses suggest the influence of numerous small effect loci affecting the phenotype at levels that are potentially exposed to natural selection .
We measured GSL from leaves of 96 A . thaliana accessions at 35 d post-germination [27]–[28] using either untreated leaves or leaves treated with AgNO3 ( silver ) to mimic pathogen attack . In addition , we measured seedling glucosinolates from the same accessions to provide a tissue comparison as well as a treatment comparison . Seedlings were measured at 2 d post-germination at a stage where the GSL are largely representative of the GSL present within the mature seed [48] , [60] . GSL from both foliar and seedling tissue grown under these conditions have been measured in multiple independent QTL experiments that used recombinant inbred line ( RIL ) populations generated from subsets of these 96 accessions , thus providing independent corroboration of observed GSL phenotypes [41] , [51] , [61] . For the untreated leaves , this analysis detected 18 aliphatic GSL compounds and four indolic GSL compounds . These combined with an additional 21 synthetic variables that describe discrete components of the biochemical pathway to total 43 GSLtraits for analysis [4] , [61]–[62] . For the AgNO3-treated samples , we detected only 16 aliphatic GSL and four indolic GSL , but also were able to measure camalexin , which is related to indolic GSL ( Table S3 ) , which in combination with derived measures provided us with 42 AgNO3 treated GSL traits [61] . For the seedling GSL samples , we detected 19 aliphatic GSLs , two indolic , and three seedling specific phenylalanine GSLs ( Table S4 ) , which in combination with derived descriptive variables gave us a total of 46 total GSL traits [61] . Population stratification has previously been noted in this set of A . thaliana accessions , where eight subpopulations were proposed to describe the accessions' genetic differences [27]–[28] . Less explored is the joint effect of population structure and environmental factors , both external ( exogenous treatment ) and internal ( tissue comparison ) on GSL . We used our three glucosinolate datasets to test for potential confounding effects of environmental variation , population structure , and their various interaction terms upon the GSL phenotypes ( Figure 2 ) . On average , 36% ( silver versus control ) and 23% ( seedling versus control ) of phenotypic variance in GSL traits was solely attributable to accession . An additional 7% ( silver versus control ) and 14% ( seedling versus control ) of phenotypic variance was attributable to an interaction between accession and treatment or tissue . This suggests that , on average and given the statistical power of the experiments , 30%–50% of the detectable genetically controlled variance is stable across conditions , while at least 20% of the variance is conditional on treatment and/or tissue . In contrast , population structure by itself accounted for 10%–15% of total variance in GSL ( Figure 2 ) . Interestingly , significantly less variance ( <5% ) could be attributed to interaction of treatment or tissue with population structure . This suggests that for GSL , large-effect polymorphisms that may be linked with population structure are stable across treatment and tissue while the polymorphisms with conditional effects are less related to the species demographic structure ( Figure 2 ) . This is consistent with QTL studies using RIL that find greater repeatability of large-effect QTL across populations and conditions than of treatment-dependent loci [41] , [51] , [61] , [63] . This is further supported by the fact that we utilized replication of defined genotypes across all conditions and tissues and as such have better power to detect these effects than in systems where it is not possible to replicate genotypes . As such , controlling for population structure will reduce the number of false-positives detected but lead to an elevated false-negative rate , given this significant association between the measured phenotypes and population structure . Interestingly , developmental effects ( average of 15% ) accounted for 3 times more of the variation in GSL than environmental effects ( average 5% ) . In particular , only three GSL ( two indolic GSL , I3M and 4MOI3M , and total indolic GSL ) were affected more strongly by AgNO3-treatment than by accession ( Table S1 and Figure S1 ) , whereas 11 GSL traits were found to be influenced more by tissue type than accession ( Table S2 ) . This agrees with these indolic GSL being regulated by defense response [36] , [64] . Similarly , twice as much GSL variation could be attributed to the interaction between accession and tissue type compared to the interaction between accession and AgNO3 treatment . Thus , it appears that intraspecific genetic variation has greater impact on GSL in relation to development than in response to simulated pathogen attack . Using 229 , 940 SNP available for this collection of 96 accessions , we conducted GWA-mapping for GLS traits in both the Seedling and Silver datasets using a maximum likelihood approach that accounts for genetic similarity ( EMMA ) [65] . This identified a large number of significant SNPs and genes for both datasets ( Table 1 ) . We tested the previously published criteria used to assess significance of candidate genes to ensure that different treatments or tissues did not bias the results produced under these criteria [4] . These criteria required ≥1 SNP , ≥2 SNPs , or ≥20% of SNPs within a gene to show significant association with a specific GSL trait . This test was independently repeated for all GSL traits in both datasets ( Tables S5 and S6 ) . As previously found using the control leaf GSL data , the more stringent ≥2 SNPs/gene criterion greatly decreased the overall number of significant genes identified while not overtly influencing the false-negative rate when using a set of GSL genes known to be naturally variable and causal within the 96 accessions ( Tables 2 and 3 ) . Interestingly , including multiple treatments and tissues did not allow us to decrease the high empirical false-negative rate ( ∼75% ) in identifying validated causal candidate genes ( Table 3 ) [4] , [31] . Using the ≥2 SNPs/gene criterion identified 898 genes for GSL accumulation in silver-treated leaves and 909 genes for the seedling GSL data . As previously found , the majority of these candidate genes were specific to a subset of GSL phenotypes and no gene was linked to all GSL traits within any dataset ( Figure S2 ) [4] . We estimated the variance explained by the candidate GWA genes identified in this study using a mixed polygenic model of inheritance for each phenotype within each dataset using the GenABEL package in R [66]–[67] . This showed that , on average , the candidate genes explained 37% of the phenotypic variation with a range of 1% to 99% ( Table S10 ) . Interestingly , if the phenotypes are separated into their rough biosynthetic classes of indolic , long-chain , or short-chain aliphatic [68] , there is evidence for different levels of explained phenotypic variation where indolic has the highest percent variance at 45% while short-chain has the lowest at 25% ( p = 0 . 001 ) . This is not explainable by differential heritability as the short-chain aliphatic GSLs have the highest heritability in numerous studies including this one ( Tables S1 and S2 ) [4] , [41] , [61] . This is instead likely due to the fact that short-chain aliphatic GLS show higher levels of multi-locus epistasis that complicates the ability to estimate the explained variance within GWA studies [31] , [41] , [61] . Previous work with untreated GSL leaf samples showed that candidate genes clustered in hotspots , with the two predominant hotspots surrounding the previously cloned AOP and MAM loci [4] , where multiple polymorphisms surrounding the region of these two causal genes significantly associate with multiple GLS phenotypes . We plotted GWA-identified candidate genes for GSL accumulation from the silver and seedling datasets to see if treatment or tissue altered this pattern ( Figure 3 ) . Both datasets showed statistically significant ( p<0 . 05; Figure 3 ) hotspots of candidate genes that clustered predominantly around the AOP and MAM loci with some minor treatment- or tissue-specific hotspots containing fewer genes . This phenomenon is observed across multiple GLS traits ( Figure 3 ) . The AOP and MAM hotspots are known to be generated by local blocks of linkage disequilibrium ( LD ) wherein a large set of non-causal genes are physically linked with the causal AOP2/3 and MAM1/3 genes [4] . Interestingly , while the silver and control leaf GWA datasets showed similar levels of clustering around the AOP and MAM loci , the hotspot at the MAM locus was much more pronounced than the AOP locus in the seedling GWA dataset ( Figure 3 ) , suggesting more seedling GLS traits are associated with the MAM locus . This agrees with QTL-mapping results in structured RIL populations of A . thaliana that have shown that the MAM/Elong locus has stronger effects upon seedling GSL phenotypes in comparison to leaves , whereas the effect of the AOP locus is stronger in leaves than seedlings [41] , [62]–[63] . In addition , the relationship of GSL phenotypes across accessions is highly similar in the two leaf datasets , while the phenotypic relationships across accessions are shifted when comparing the seedling to the leaf ( Figure 4 ) . Together , this suggests greater similarity in the genetic variation affecting GSL phenotypic variation between the two leaf datasets than between leaf and seedling datasets , suggesting that GSL variation is impacted more by development than simulated pathogen attack . This is further supported by the analysis of variance ( Figure 2 ) . To further test if measuring the same phenotypes in different tissues or treatments will identify similar GWA mapping candidates , we investigated the overlap of GWA candidate genes identified across the three datasets . For this analysis we excluded genes within the known AOP and MAM LD blocks as previous research has shown that all of these genes except the AOP and MAM genes are likely false-positives and would bias our overlap analysis [4] , [69]–[71] . The remaining GWA mapping candidate genes showed more overlap between the two leaf datasets than between leaf and seedling datasets ( Figure 5 ) . Interestingly , the overlap between GWA-identified candidate gene sets from seedling and leaf data was smaller than would be expected by chance ( χ2 p<0 . 001 for all three sectors ) ( Figure 5 ) . This suggests that outside of the AOP and MAM loci , distinct sets of genetic variants may contribute to the observed phenotypic diversity in GSL across these tissues , which agrees with QTL-mapping studies identifying distinct GSL QTL for seedling and leaf [41] , [62]–[63] . As such , focusing simply on GWA mapping candidates independently identified in multiple treatments or tissues to call true significant associations will overlook genes whose genotype-to-phenotype association is conditional upon differences in the experiments . Similarly , the amount of phenotypic variance explained by the candidates differed between the datasets , with control and treated having the highest average explained variance , 39% and 41% , respectively . In contrast , the seedling dataset had the lowest explained variance at 32% , similarly suggesting that altering the conditions of the experiments will change commonly reported summary variables such as explained variance . GWA studies generally produce large lists of candidate genes , presumed to contain a significant fraction of false-positive associations . One proposed strategy refines these results by searching for enrichment of candidate genes within pre-defined proteomic or transcriptomic networks [15] . To test the applicability of this approach to our GWA study , we overlaid our list of 2 , 436 candidate genes ( excluding genes showing proximal LD to the causal AOP2/3 and MAM1/2/3 genes [4] ) that associated with at least one GSL phenotype in at least one of the three datasets ( Figure 5 ) onto a previously published co-expression network [72] . If the network filtering approach is valid and there are true causal genes within the candidate gene lists , then the candidate genes should show tighter network linkages to previously validated causal genes than the average gene . Measuring the distances between all candidate genes to all known GSL causal genes within the co-expression network showed that , for all datasets , the GWA candidate genes were on average closer to known causal genes than non-candidates ( Figure S4 ) . Interestingly , the GWA mapping candidate genes actually showed closer linkages to the cysteine , homocysteine , and glutathione biosynthetic pathways than to the core GSL biosynthetic pathways , suggesting that natural variation in these pathways may impact A . thaliana secondary metabolism ( Figure S4 and Dataset S1 ) . The network proximity of GWA mapping candidates to known causal genes supports the utility of the network filtering approach in identifying true causal genes among the long list of GWA mapping candidate genes . To determine if this network filtering approach finds whole co-expression networks or isolated genes , we extended the co-expression network to include known and predicted GSL causal genes ( Table S7 ) . The largest network obtained from this analysis centered on the core-biosynthetic genes for the aliphatic and tryptophan derived GSL as well as sulfur metabolism genes ( Figures 6 and S3 ) . Interestingly , this large network linked to a defense signaling network represented by CAD1 , PEN2 , and EDS1 ( Figure 6 ) [73] . The defense signaling pathway associated with PEN2 and , more recently , CAD2 and EDS1 had previously been linked to altered GSL accumulation via both signaling and biosynthetic roles [36] , [39] , [74]–[75] . However , the current network analysis has identified new candidate participants in this network altering GSL accumulation . To test these predicted linkages , we obtained a mutant line possessing a T-DNA insertional disruption of the previously undescribed locus At4g38550 , which is linked to both CAD1 and PEN2 ( Figure 6 , Table S9 ) . This mutant had elevated levels of all aliphatic GSL within the rosette leaves as well as 4-methoxyindol-3-ylmethyl GSL , shown to mediate non-host resistance ( Table S9 ) [36] , [39] . These results suggest a role for At4g38550 in either defense responses or GSL accumulation . Network analysis also identified several previously described ( RML1 ) and novel candidate ( ATSFGH , At1g06640 , and At1g04770 ) genes that were associated with the core-biosynthetic part of the network . RML1 ( synonymous with PAD2 , CAD2 ) , a biosynthetic enzyme for glutathione , has previously been shown to control GSL accumulation either via a signaling role or actual biosynthesis of glutathione [74]–[75] . To test if ATSFGH ( S-formylglutathione hydrolase , At2g41530 ) , At1g06640 ( unknown 2-oxoacid dependent dioxygenase – 2-ODD ) , or At1g04770 ( tetratricopeptide containing protein ) may play a role in GSL accumulation , we obtained insertional mutants . This showed that the disruption of At1g06640 led to significantly increased accumulation of the short-chain methylsulfinyl GSL but not the corresponding methylthio or long-chain GSL ( Table S9 ) . In contrast , the AtSFGH mutant had elevated levels of all short-chain GSL along with a decreased accumulation of the long-chain 8-MTO GSL ( Table S9 ) . The At1g04770 mutant showed no altered GSL levels other than a significantly decreased accumulation of 8-MTO GSL ( Table S9 ) . This suggests that these genes alter GSL accumulation , although the specific molecular mechanism remains to be identified . Interestingly , network membership is not sufficient to predict a GSL impact , as T-DNA disruption of homoserine kinase ( At2g17265 ) , a gene co-expressed with the GSL core but not a candidate from the GWA analysis , had no detectable impact upon GSL accumulation ( Table S9 ) . Thus , the network filtering approach identified genes closely linked to the GSL biosynthetic network that can control GSL accumulation and are GWA-identified candidate genes . The above analysis shows that GWA candidate genes which co-express with known GSL genes are likely to influence GSL accumulation . However , networks might influence GSL accumulation independent of co-expression with known GSL genes . To test this , we investigated several co-expression networks that involved solely GWA-identified candidate genes and genes not previously implicated in influencing GSL accumulation ( Figure 7 ) . Three of these networks included genes that affect natural variation in non-GSL phenotypes within A . thaliana , namely PHOTOTROPIN 2 ( PHOT2 ) , Erecta ( ER ) [76] , and ELF3/GI ( Figure 7 ) [77] , [78] . The fourth network did not involve any genes previously linked to natural variation ( Figure 7 ) . We obtained A . thaliana seed stocks with mutations in a subset of genes for each of these three networks to test whether loss of function at these loci affects GSL accumulation . The largest network containing no previously known GSL-related genes that we examined is a blue light/giberellin signaling pathway represented by PHOT2 ( Figure 7A ) . This pathway had not been previously ascribed any role in GSL accumulation in A . thaliana . We tested this GWA-identified association by measuring GSL in the single and double PHOT1/PHOT2 mutants [79] . PHOT1 was included as it has been shown to function either redundantly or epistatically with PHOT2 [79] . The single phot1 or phot2 mutation had no significant effect upon GSL accumulation ( Table S9 ) . The double phot1/phot2 knockout plants showed a significant increase in the production of detected methylthio GSL as well as a decrease in the accumulation of 3-carbon GSL compared to control plants . Thus , it appears that GSL are influenced by the PHOT1/PHOT2 signaling pathway , possibly in response to blue light signaling ( Table S9 ) . This agrees with previous reports from Raphanus sativa that blue light controls GSL [80] , [81] . The second non-GSL network we examined contains the ER gene ( Figure 7B ) . The ER ( Erecta ) network and specifically the ER locus had previously been queried for the ability to alter GSL accumulation using two Arabidopsis RIL populations ( Ler×Col-0 and Ler×Cvi ) that segregate for a loss-of-function allele at the ER locus [41] , [51] , [63] , [82]–[86] . In these analyses , the ER locus was linked to seed/seedling GSL accumulation in only one of the two populations and not linked to mature leaf GSL accumulation [41] , [86] . Analysis of the ER mutant within the Col-0 genotype showed that the Erecta gene does influence GSL content within leaves as suggested by the GWA results ( Table S9 , Figure 7A ) . Plants with loss of function at Erecta showed increased levels of methylthio GSL , long-chain GSL , and 4-substituted indole GSL ( Table S9 ) . Interestingly , the ER network contains a number of chromatin remodeling genes . We obtained A . thaliana lines with loss-of-function mutations in three of these genes ( Table S9 ) to test if the extended network also alters GSL accumulation . Mutation of two of the three genes ( At5g18620 – CHR17 and At4g02060 – PRL ) was associated with increased levels of short-chain aliphatic GSL and a corresponding decrease in long-chain aliphatic GSL ( Table S9 ) . This shows that the Erecta network has the capacity to influence GSL accumulation . Two smaller networks containing the ELF3 and GI genes were of interest as these two genes are associated with natural variation in the A . thaliana circadian clock ( Figure 7C ) [77] , [87] , [88] . GSL analysis showed that both the elf3 and gi mutants had lower levels of aliphatic GSL than controls ( Table S9 ) . Comparing multiple gi mutants from both the Col-0 and Ler genetic backgrounds showed that only gi mutants in the Col-0 background altered GSL accumulation ( Table S9 ) . This suggests that gi's link to glucosinolates is epistatic to other naturally variable loci within the genome , as previously noted for natural GI alleles in relation to other phenotypes ( Table S9 ) [78] . An analysis of the elf4 mutant which has morphological similarities to elf3-1 but was not a GWA-identified candidate showed that this mutation did not alter GSL accumulation . Thus , elf3/gi affects GSL via a more direct mechanism than altering plant morphology . Given two genes in the circadian clock network directly affects GSL accumulation and given the expression of these two genes are correlated with other genes in the network , it is fair to hypothesize that circadian clock plays a role in GSL accumulation . While the GSL phenotypes of the above laboratory-generated mutants suggest that variation in circadian clock plays a role in GSL accumulation , they do not prove that the natural alleles at these genes affect GSL accumulation . To validate this , we leveraged germplasm developed in the course of previous research showing that natural variation at the ELF3 locus controls numerous phenotypes , including circadian clock periodicity and flowering time [77] . We utilized quantitative complementation lines to test if natural variation at ELF3 also generates differences in GSL content [77] . This showed that the ELF3 allele from the Bay-0 accession was associated with a higher level of short chain aliphatic GSL accumulation in comparison to plants containing the Sha allele ( Table S9 ) . In contrast , both Bay-0 and Sha allele-bearing plants had elevated levels of 8-MTO GSL in comparison to Col-0 ( Tables S8 and S9 ) . Thus , ELF3 is a polymorphic locus that contains multiple distinct alleles that influence GSL content within the plant and the ELF3/GI network causes natural variation in GSL content . The final network examined here , represented by CLPX ( CLP protease ) , is likely involved in chlorophyll catabolism and possibly also chloroplast senescence [89] . This network is uncharacterized and has not previously been associated with GSL accumulation or natural variation in any phenotype , but participation in chloroplast degradation is suggested by transcriptional correlation of CLPX with several catabolism genes . Analysis of mutants deficient in function for two of these genes showed that they all possessed increased aliphatic GSL in comparison to wild-type controls . These results suggest that natural variation in this putative network could influence GSL content in A . thaliana . The majority ( 12 of 13 ) of genes in this network show significant variation in transcript abundance across A . thaliana accessions , a significantly greater proportion than expected by chance ( X2 p<0 . 001 ) [90]–[92] , further suggesting that this network may contribute to GSL variation across the accessions . Finally , we tested a single two gene network found in the co-expression data wherein both genes had been annotated but not previously linked to GSL content . This network involved AtPTR3 ( a putative peptide transporter , At5g46050 ) and DPL1 ( a dihydrosphingosine lyase , At1g27980 ) . T-DNA mutants in both genes appeared to be lethal as we could not identify homozygous progeny . However , comparison of the heterozygous progeny to wildtype homozygotes showed that mutants in both genes led to elevated levels of aliphatic GSL ( Table S9 ) . Thus , there are likely more networks that are causal for GSL variation within this dataset that remain to be tested . While GSL are considered “secondary” metabolites , these compounds are affected by many aspects of plant metabolism , thus GSL phenotyping is sensitive to any genetic perturbation that affects plant physiology . As such , we identified six genes that were expressed in mature leaves but did not show any significant association of DNA sequence polymorphism with GSL phenotypes and were additionally not identified within any of the above co-expression networks . Insertional mutants disrupted at these loci were designated as random mutant controls ( Table S9 ) . Analyzing GSL within these six lines showed that on average 13%±4% of the GSL were affected in the random control mutant set even after correction for multiple testing . While this suggests that GSL may be generally sensitive to mutations affecting genes expressed within the leaf , this incidence of significant GSL effects is much lower than observed for the T-DNA mutants selected to test GWA mapping-identified pathways ( CLPX - 78%±11% , PTR3 – 61%±6% , Erecta – 45%±10% , GSL – 46%±11% , ELF3/GI – 53%±17% ) . In all cases the mutants deficient in GWA pathway-identified gene function showed significantly greater numbers of altered GSL phenotypes than the negative control T-DNA mutant set ( X2 , p<0 . 001 ) , suggesting that combining GWA-identified candidate genes with co-expression networks successfully identifies genes with the capacity to cause natural variation in GSL content . Identifying the specific mechanisms involved will require significant future research .
A limiting factor for the utility of GWA studies has been the preponderance of false-positive and false-negative associations which makes the accurate prediction of biologically valid genotype-phenotype associations very difficult . In this report , we describe the implementation and validation of a candidate gene co-expression filter that has given us a high success rate in candidate gene validation ( >75% ) . The co-expression dataset is derived from transcript accumulation within a single A . thaliana accession ( Col-0 ) across a wide range of developmental and environmental states [72] . This dataset has previously been used to show that genes showing co-expression often modulate the same phenotype , and may thus also function within the same pathway [57]–[59] , [95]–[99] . This co-expression dataset provides a functional grouping of A . thaliana genes based upon non-genetic variation . This provides an orthogonal grouping to that provided by the GWA mapping which associates genes to phenotypes via natural genetic variation . This approach is similar to other filtering approaches that utilize complementary datasets to rank candidate genes [11] , [100]–[102] . However , most of these other approaches utilize two databases , e . g . GWA and eQTL ( expression quantitative trait loci ) , that are both based upon natural genetic variation and thus do not provide independent filters [11] , [100]–[101] . In contrast to these other network approaches , our methodology does not rely upon a statistical rank or enrichment procedure which can be dominated by individual genes with high significance possibly due to GWA mapping artifacts [102] . Instead , our approach focuses upon relative network size to direct the researcher to the most interesting candidate networks . This approach is less susceptible to statistical artifacts and allows the user to input bait genes suggested by a priori knowledge [95] , [103]–[104] . This approach should be useful in any system possessing genomic networks that are orthogonal to the GWA-identified candidate gene lists . The use of multiple tissues and treatment conditions , as well as a large set of different but related GSL phenotypes , led to the identification of several thousand candidate genes . Even after decreasing this number by using the network expression filter approach , several hundred candidate genes of interest remained . Analysis of a set of these genes via plants bearing single gene mutations showed that disruption of many of these genes can alter the amount or pattern of GSL accumulation ( Table S9 and Figures 6 and 7 ) . Given the observation that the background genotype can influence the capacity to identify a mutational effect ( see gi mutants in Ler v Col-0 , Table S9 ) , our estimate of tested genes influencing GSL accumulation is conservative . Given this , it is likely that a very large number of small to moderate effect loci influence GSL accumulation within A . thaliana , echoing recent findings regarding the genetics of human height , and maize flowering time [105]–[106] . This suggests that the whole genome may have a pattern similar to that found in an analysis of a single Arabidopsis locus that identified several QTL for growth within a small section of the genome [70] . As such , it might be common for quantitative traits to be influenced by thousands of causal loci [107] . The potential existence of thousands of polymorphic genes influencing a phenotype raises a common concern that these effects actually represent indirect pleiotropy , where moderate to small effects of a locus upon a phenotype are not biologically significant and do not reflect direct molecular control of the trait . However , numerous studies on GSL variation within wild populations have shown that changes in GSL accumulation similar to those identified here have selective consequences in field studies [33]–[35] , [43]–[45] , [108] . As such , even if polymorphisms in these identified genes have indirect pleiotropic effects upon GSL accumulation , these changes have a strong potential to influence A . thaliana in natural settings . Thus , it may be more useful to consider , instead of indirect versus direct effects of a locus , a continuous distribution that describes the number of molecular steps required to link a particular gene to the most proximal controller of the phenotype—in this case , an enzyme in the biosynthetic pathway . This raises the distinct problem of adaptive constraint wherein natural variation at a locus is limited by its indirect consequences upon other phenotypes . For instance , a phototropin allele with a beneficial effect on seedling phototropic behavior may be limited in its selective advantage due to a deleterious effect on GSL accumulation [109]–[110] . While this possibility remains to be tested in natural populations , it invites the question of why these phenotypic linkages occur . Is there a benefit to the influence of these loci on GSL accumulation , or has insufficient time passed since the de novo evolution of GSL biosynthesis to generate the genetic modularity to bypass historical linkages between development and metabolism [111] ? A more mundane but significant experimental challenge of generating a list of thousands of candidate genes potentially causing natural variation in a phenotype is validation . Even after our expression network filtering , we were left with hundreds of likely candidates that would take decades to rigorously validate . Given that it is likely that at least several hundred genes lead to natural variation in GSL accumulation [105]–[106] , how do we validate the effects of natural alleles at these loci , and is it worth the effort ? If it is not worth the effort for GSL accumulation , what deciding factors should determine when a single phenotype should be completely dissected ( to the level of knowing all genes containing a causal link to natural variation within a phenotype ) ? Given the importance of quantitative variation in numerous agronomic and medically important phenotypes , this discussion needs to begin , because untested presumptions about the number of causal genes for a phenotype greatly influences current GWA research and associated strategies for avoiding false-positive and false-negative results [2] , [65] , [112] . We identified significant differences in GSL accumulation between two different developmental stages and this led to the identification of GWA candidate genes . While previous work on structured mapping populations , such as RILs , has shown that each tissue may be viewed as a distinct genetic module for both development and biochemistry [41] , [49]–[50] , [113]–[114] , this is one of the first reports about tissue differences in an unstructured population . This tissue specificity indicates that it is not possible to simply require a candidate gene to replicate across tissues to validate its GWA signature . Instead , each tissue has to be looked at as a potentially independent modular system [115] . Such modularity could be mediated by members of a gene family each acting in a limited set of tissues , either as a result of sub- or neo-functionalization [116]–[119] . Both sub- and neo-functionalization have played an important role in the evolution of GSL and other plant secondary metabolites [55] , [69] , [92] , [96] . The impact of development on GWA remains to be tested across a broader range of tissues and developmental stages . In this report , we show that GWA-mapping , like QTL-mapping using structured populations , is sensitive to interaction of genetic variation with the environment and the developmental stage of phenotype measurement . This has not often been considered as a critical factor influencing GWA studies , given the difficulty of obtaining replicated analyses within organisms such as humans . Future work incorporating systematic analysis of how GWA studies are influenced by developmental or environmental gradients will be critical to understanding how the genomic architecture of a species controls its phenotypes . We have developed and validated a new approach to identifying GWA candidate genes and shown that the use of orthogonal genomic network datasets can lead to a very high success rate in the biological validation of candidate genes . This new approach , in combination with the observation of conditional GWA results , suggests that large numbers of genes can have a causal connection to variation within GSL and other phenotypes .
A previously described collection of 96 natural A . thaliana accessions was used to measure GSL accumulation for GWA mapping with existing SNP data from these same lines [3] , [27]–[28] , [120] . Seeds were imbibed and cold stratified at 4°C for 3 d to break dormancy . Seeds were planted in a randomized block design , with multiple seeds of each accession occupying an individual cell within 36-cell flats ( approximately 100 cm3 soil volume per cell ) . Four plantings of the 96 accessions provided four independent replicates for each accession . At 1 wk of age , seedlings were thinned to leave one plant per cell and glucosinolates were extracted from 10 of the removed seedlings . For all experiments , plants were maintained under short day conditions in controlled environment growth chambers . At 35 d post-germination , two fully expanded mature leaves were harvested , digitally photographed , and one was directly analyzed for GSL content as described below [18] , [121] . The other leaf was treated with 5 mM AgNO3 for 48 h prior to harvest for GSL analysis . AgNO3 induces plant responses to pathogens by interfering with ethylene hormone-signaling and inducing reactive oxygen species . We utilized AgNO3 as a treatment to estimate the effect of variation in plant defense response upon GWA mapping [122]–[124] . In total , these datasets contain four measurements per accession per tissue and treatment for a total of 301 assays of seedling GSL ( Seedling Dataset ) , 374 assays of control leaf GSL ( Ctl Dataset ) , and 375 assays of GSL following AgNO3 treatment of leaves ( Silver Dataset ) . The data for the control dataset is reported elsewhere as the “2008 dataset” [4] . GSL content of excised leaves and seedlings was measured using a previously described high-throughput analytical system [62] , [69] . Briefly , for excised leaves , one leaf was removed from each plant , photographed , and placed in a 96-well microtiter plate with 500 µL of 90% methanol and one 3 . 8 mm stainless steel ball-bearing . Seedlings were removed from pots with forceps , gently cleaned with distilled water to remove soil , and similarly placed into 90% methanol in microtiter plates . Tissues were homogenized for 2 min in a paint shaker , centrifuged , and the supernatants transferred to a 96-well filter plate with 50 µL of DEAE sephadex . The sephadex-bound GSL were eluted by overnight , room temperature incubation with sulfatase . Individual desulfo-GSL within each sample was separated and detected by HPLC-DAD , identified , and quantified by comparison to purified standards [125] . Tissue area for each leaf was digitally measured using Image J with scale objects included in each digital image [126] . The GSL traits are reported per cm2 of leaf area for the mature leave data or per seedling for the seedling data . There was no significant variation detected for leaf density within these accessions ( unpublished data ) . In addition to the content of individual GSL , we developed a series of summation and ratio traits based on prior knowledge of the GSL pathways [127] . These ratios and summation traits allow us to isolate the effects of variation at individual steps of GSL biosynthesis from variation affecting the rest of the biosynthetic pathway [127] . To estimate broad-sense heritability due to accession and population structure for the different metabolites , we evaluated the data using a model where the metabolite traits are ysar = μ+Ss+A ( S ) sa+Tt+R ( T ) tr+Tt:Ss+Tt:A ( S ) sa+εsart where s = 1 , … , 8; r = 1 , …4; t = 1 , 2; and a = 1 , … , 95 . The main effects are denoted as S , A , T , and R and represent structure , accession , treatment ( or tissue ) , and replicate block , respectively . Here , the variable T may refer to ( 1 ) treatment corresponding to the two factors with or without AgNO3 treatment or ( 2 ) tissue corresponding to the two factors' mature leaves or seedlings . Population structure is represented as s = 1 , … , 8 , corresponding to eight distinct groups into which these 96 accessions have previously been assigned [27]–[28] . The error , εsart , is assumed to be normally distributed with mean 0 and variance σε2 . Broad-sense heritability was estimated as the percent of total variance attributable to accession nested within structure and that for structure was estimated as the percent of total variance attributable to structure . The data were analyzed independently for the two treatments or conditions: control versus AgNO3 and control versus seedling ( Figure 2; Tables S1 and S2 ) . To conduct single-locus GWA mapping accounting for population structure , we adopted a previously published method , the efficient mixed-model association ( EMMA ) algorithm [65] . EMMA is a statistical mixed model [65] where each SNP is modeled as a fixed effect and population structure , represented as a genetic similarity matrix , is modeled as a random effect . Variance components for this mixed model were estimated directly using maximum likelihood as implemented in the R/EMMA package [65] . Within this model , the independent measures of each metabolite within each accession , obtained from the analysis of variance model ysar = μ+Aa+Rr+εsar , were directly incorporated as genetic averages for the accessions ( Tables S3 and S4 ) . Because GWA was performed independently for each of the three datasets and because EMMA accounts for population structure , the variables Ss , Tt , and Rr were excluded in this model . The average GSL accumulation per accession for the control dataset is reported elsewhere as the “2008 experiment” [4] . The full results are available at http://www . plantsciences . ucdavis . edu/kliebenstein/supplementaldataset1 . zip . We utilized a previously reported criterion for calling significant gene-trait associations in these three datasets [4] . p value distributions of the GWA analysis were not uniform . Accepting an inherently elevated false-positive rate , we identified SNP within the bottom 0 . 1 percentile of each p value distribution , corresponding to each trait , as significant for EMMA . Given previous observations that multiple SNPs per gene are typically associated with a trait for true-positives [30] , we developed a criterion for calling a significant association between a trait and a gene [4] , [30]: requiring at least two significant SNPs within ±1 kb of a gene's coding region to call a gene significant . This approach optimized the ratio of empirical false-positive to false-negative associations . This criterion was independently applied to the GWA results from all tissues and conditions ( Tables S5 and S6 ) . We estimated the variance explained by the candidate GWA mapping genes identified in this study using the GenABEL package in R [66]–[67] . This was done using a mixed polygenic model of inheritance for each phenotype within each dataset . Only SNPs within 1 kb of significant genes were utilized . Co-expression data were obtained from ATTED II [72] , [128] . We extracted correlation values for transcript levels of genes showing significant association in at least one of the three datasets ( Tables S5 and S6 ) [4] as well as a list of genes with predicted or known roles in GSL metabolism or regulation ( Table S7 ) . This latter set of genes was included to act as “bait genes” that might catalyze network formation around a known causal gene [59] , [95] , [98] . GWA candidates located within previously identified regions surrounding the AOP and MAM loci were then excluded to reduce detection of false associations due to linkage with the causal AOP2/3 and MAM1/2/3 genes [4] . Co-expression networks were constructed between these genes using a Mutual Rank threshold of up to 15 [129] . Co-expression networks were visualized using Pajek [130] . To test if GWA-identified candidate genes showed tighter linkage to known GSL networks than expected by chance , the shortest paths between each candidate or randomly selected control gene and all verified GSL genes within the full co-expression network were compared using the R/igraph package [67] , [131]–[133] . This analysis was performed independently for candidate genes found in the control , silver , or seedling datasets as well as for all GSL genes and a subset of randomly selected genes that were not significantly associated with GSL phenotypes within the GWA mapping ( Figure S4 ) . This analysis generated a distribution of path distances linking the set of GWA mapping candidate genes to the known GSL genes . We also repeated the analysis by dividing the GSL genes into each of the specific biosynthetic pathways to test if any specific pathways showed reduced path distances to GWA mapping candidates ( Tables S7 and S8 ) [50] , [92] , [99] , [134]–[135] . We conducted two statistical tests to compare the null distribution ( distances from non-significant genes to known GSL genes ) with the GWA mapping candidate distribution ( distances from GWA candidate genes to known GSL genes ) . The Wilcoxon Rank Sum Test tests the probability of a location shift between the distribution of the shortest paths of all GWA mapping candidate genes ( from one of the three datasets ) to all known GSL genes and the distribution of the shortest paths of all non-significantly associated genes to the all known GSL genes . The Ansari-Bradley Test examines the probability that the two aforementioned distributions are differently dispersed . Both statistic tests were conducted using the full GSL network list as well as each individual biosynthetic pathway ( Tables S7 and S8 ) . We focused our validation efforts on a set of GWA-identified candidate gene co-expression networks that exhibited different numbers of genes that are a member of the network ( levels of membership ) . Criteria for selection of candidate genes from these networks for testing were connectedness ( the gene had to show correlated expression levels ( MR rank of <16 ) with multiple candidate genes within the network ) and availability of viable mutants . These mutants were either a pre-existing characterized mutant line or a homozygous T-DNA mutation within an early exon of the candidate gene available from the Arabidopsis Biological Resource Center ( ABRC ) [136] . For each network tested , we attempted to test at least four separate genes within the network for altered GSL accumulation . We obtained putative homozygous T-DNA mutants for 18 candidate genes and validated their homozygosity using a PCR assay . Primers for the assay were designed using the SALK SIGnAL iSect primer design tool ( http://signal . salk . edu/tdnaprimers . 2 . html ) . Of the 18 T-DNA mutants surveyed , homozygous mutants could not be obtained for 11 mutants , likely from lethality . In these cases , heterozygote lines were allowed to self-pollinate , and homozygous seed stocks were obtained by single seed decent following PCR-based genotyping of the progeny . In the absence of a homozygous line , we tested GSL content within the adult rosette leaves within PCR-confirmed heterozygous individuals . We also obtained mutants deficient in function at the following loci: phototropin1/phototropin2 ( phot1/phot2 ) ( 4 lines ) , Gigantea ( gi ) ( 8 alleles ) , Erecta ( er ) in Col-0 , and early flowering 3-1 ( elf3-1 ) [79] , [137]–[140] . Plants were grown under 10 h of light for 5 wk using a randomized complete block design over two experiments with at least four biological replicates per experiment . Leaf area and GSL content of the first true leaf was obtained as described above . A Dunnett's t-test was conducted to test the statistical significance of differences in GSL content between the mutant and wild-type while correcting for multiple comparisons using the R/multcomp package ( Table S9 ) [141] . GSL were measured in at least two biological replicates per genotype , averaging 17 total individual measurements per genotype across the two replicates ( min = 8 , max = 48 ) ( Table S9 ) . Only wild-type controls grown concurrently with the mutants were used for the statistical comparison . We utilized previously generated quantitative complementation lines to validate that natural variation in the ELF3 locus did alter GSL accumulation [77] . elf3:Bay-0 and elf3:Sha transgenic T1 seeds were planted on soil including elf3 . 1 mutants and wild-type Col-0 as a control [77] . The extreme hypocotyl length and cotyledon color phenotypes of the elf3 . 1 mutants were assessed to distinguish transformed from untransformed plants [137] . Transformed plants were grown for 25 d in a 10 h photoperiod . At 25 d , leaf tissue was harvested from each plant and individually extracted and assayed via HPLC for glucosinolate composition and concentration as previously described [41] , [69] . The experiment was replicated 5 times for a total of 41 elf3:Bay-0 and 44 elf3:Sha independent T1 plants . GSL differences between the two ELF3 alleles were tested as described above .
|
Understanding how genetic variation can control phenotypic variation is a fundamental goal of modern biology . A major push has been made using genome-wide association mapping in all organisms to attempt and rapidly identify the genes contributing to phenotypes such as disease and nutritional disorders . But a number of fundamental questions have not been answered about the use of genome-wide association: for example , how does the internal or external environment influence the genes found ? Furthermore , the simple question of how many genes may influence a trait is unknown . Finally , a number of studies have identified significant false-positive and -negative issues within genome-wide association studies that are not solvable by direct statistical approaches . We have used genome-wide association mapping in the plant Arabidopsis thaliana to begin exploring these questions . We show that both external and internal environments significantly alter the identified genes , such that using different tissues can lead to the identification of nearly completely different gene sets . Given the large number of potential false-positives , we developed an orthogonal approach to filtering the possible genes , by identifying co-functioning networks using the nominal candidate gene list derived from genome-wide association studies . This allowed us to rapidly identify and validate a large number of novel and unexpected genes that affect Arabidopsis thaliana defense metabolism within phenotypic ranges that have been shown to be selectable within the field . These genes and the associated networks suggest that Arabidopsis thaliana defense metabolism is more readily similar to the infinite gene hypothesis , according to which there is a vast number of causative genes controlling natural variation in this phenotype . It remains to be seen how frequently this is true for other organisms and other phenotypes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"functional",
"genomics",
"plant",
"biology",
"population",
"genetics",
"metabolic",
"networks",
"plant",
"science",
"genome",
"complexity",
"genetic",
"polymorphism",
"plant",
"genetics",
"biology",
"systems",
"biology",
"plant",
"biochemistry",
"genetics",
"genomics",
"evolutionary",
"biology",
"gene",
"networks",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2011
|
Combining Genome-Wide Association Mapping and Transcriptional Networks to Identify Novel Genes Controlling Glucosinolates in Arabidopsis thaliana
|
Neurocysticercosis is a leading cause of preventable epilepsy in the developing world . Sustainable community-based interventions are urgently needed to control transmission of the causative parasite , Taenia solium . We examined the geospatial relationship between live pigs with visible cysticercotic cysts on their tongues and humans with adult intestinal tapeworm infection ( taeniasis ) in a rural village in northern Peru . The objective was to determine whether tongue-positive pigs could indicate high-risk geographic foci for taeniasis to guide targeted screening efforts . This approach could offer significant benefit compared to mass intervention . We recorded geographic coordinates of all village houses , collected stool samples from all consenting villagers , and collected blood and examined tongues of all village pigs . Stool samples were processed by enzyme-linked immunosorbent assay ( ELISA ) for presence of Taenia sp . coproantigens indicative of active taeniasis; serum was processed by enzyme-linked immunoelectrotransfer blot for antibodies against T . solium cysticercosis ( EITB LLGP ) and T . solium taeniasis ( EITB rES33 ) . Of 548 pigs , 256 ( 46 . 7% ) were positive for antibodies against cysticercosis on EITB LLGP . Of 402 fecal samples , 6 ( 1 . 5% ) were positive for the presence of Taenia sp . coproantigens . The proportion of coproantigen-positive individuals differed significantly between residents living within 100-meters of a tongue-positive pig ( 4/79 , 5 . 1% ) and residents living >100 meters from a tongue-positive pig ( 2/323 , 0 . 6% ) ( p = 0 . 02 ) . The prevalence of taeniasis was >8 times higher among residents living within 100 meters of a tongue-positive pig compared to residents living outside this range ( adjusted PR 8 . 1 , 95% CI 1 . 4–47 . 0 ) . Tongue-positive pigs in endemic communities can indicate geospatial foci in which the risk for taeniasis is increased . Targeted screening or presumptive treatment for taeniasis within these high-risk foci may be an effective and practical control intervention for rural endemic areas .
Taenia solium , otherwise known as the pork tapeworm , is a common helminthic infection of the human central nervous system ( CNS ) and a leading cause of acquired epilepsy in low and middle income countries . Neurocysticercosis ( NCC ) is a disease which occurs when T . solium larval cysts infect the CNS causing a broad range of neurologic manifestations , including seizures , headache , intracranial hypertension , hydrocephalus , encephalitis , stroke , cognitive impairment , and psychiatric disturbances [1] , [2] . In endemic areas , T . solium infection is a major cause of epilepsy with 30% of seizure disorder attributable to NCC [3]–[5] . In Latin America alone an estimated 400 , 000–1 . 35 million people have seizure disorders secondary to NCC [6] , [7] . Effective resource-appropriate control and elimination strategies are urgently needed to prevent additional disease . Humans are the definitive host of the adult intestinal tapeworm , a condition known as taeniasis . People with taeniasis shed tapeworm eggs in their feces which contaminate the environment , particularly in rural regions where open defecation is common . When pigs are allowed to roam and consume human feces they are at risk of contamination with T . solium eggs and infection with intermediate stage larval cysts in their tissues ( cysticercosis ) . The parasite lifecycle completes when humans consume these larval cysts in undercooked pork , which in turn develop into mature adult tapeworms in the intestine capable of shedding infective eggs . Humans acquire cysticercosis including NCC through incidental ingestion of T . solium eggs in fecal contamination . Treatment of taeniasis is a key component of control and elimination strategies as adult intestinal tapeworms are the immediate common source of cysticercosis in both human and pigs . However , direct identification of taeniasis is complicated by low prevalence in endemic communities and by asymptomatic clinical course of infection [8] . Mass treatment with either niclosamide or praziquantel has been applied in several settings with modest effect [9]–[12] . These drugs are available in single-dose oral regimens and are reported to be 90–95% efficacious for eliminating taeniasis [13] . However , control gains are temporary without repeated interventions [12] . An alternative strategy is to focus resources in specific sub-populations in which the prevalence of taeniasis is increased [14] . Targeting high-risk foci can have substantial benefits in terms of the number of treatments administered , the frequency of adverse events related to treatment and the overall prevalence of infection in the community [15] . However , practical methods to identify high-risk foci of taeniasis are needed in order to apply this approach . It is biologically plausible that pigs infected with a heavy-burden of T . solium cysts could serve as indicators for high-risk geographic foci of taeniasis within endemic villages . These heavy-burden pigs presumably have increased or repeated exposure to feces contaminated with T . solium eggs , which suggests geographic proximity to a case of taeniasis . Exposure was also presumably relatively recent as most pigs raised for consumption have a short lifespan . Pigs with heavy-cyst burden can be identified by examination of the tongue or by visual inspection of the meat at the time of slaughter . Villagers in many parts of Latin America are already familiar with the tongue-exam , as this method of inspection is commonly practiced at the time a pig is sold . From a control perspective , this method of identifying high-risk foci of taeniasis has operational advantages in that surveillance can therefore be community-based . Screening or presumptive treatment for taeniasis can then be targeted to within geographic rings centered on the house where the index pig was raised . The objective of this study was to examine the geospatial relationship between live pigs with visible cysts in their tongues and human taeniasis in an endemic community .
The hypothesis being tested is that the prevalence of taeniasis is higher among households in the immediate vicinity of a tongue-positive pig compared to households that are distant from the tongue-positive pig . The study was conducted in the rural village of Rica Playa , in the Department of Tumbes , Peru . The northern coastal region of Peru is known to be endemic for T . solium with high rates of transmission of cysticercosis to both humans and pigs and substantial neurologic disease attributed to NCC [5] , [16] . Agriculture is the main economic activity in the region and villagers frequently raise pigs both for consumption and sale . Pigs are typically unrestrained and are allowed to roam freely to forage for food . This practice reduces the owner's overall cash investment in feed but potentially exposes their pigs to T . solium eggs in fecal contamination as latrines are limited and open defecation is common . All village residents 2 years of age and older were eligible to participate in this study . Children younger than 2 years old were excluded as taeniasis is very uncommon in this age group . We conducted an initial census by visiting each household in the community and recording the age and sex of all resident household members , the number of pigs raised in the household , and the source of water and type of sanitary facilities available . We considered any individual who slept more than 2 nights per week in the village to be a resident . Latitude and longitude coordinates of each house were recorded using hand-held global positioning system ( GPS ) receivers ( GeoExplorer II; Trimble , Sunnyvale , CA ) with post-processed differential correction for sub-meter accuracy . We then distributed a 500-ml plastic container with lid to all consenting residents ≥2 years old for collection of a whole stool sample . We also collected a 5-ml peripheral blood sample via venipuncture in standard serology vacuum tubes . Blood samples were maintained in coolers with ice-packs while in the field . All blood and stool samples were transported daily to the laboratory facility in Tumbes for processing . Field teams captured all household pigs and a veterinarian inspected the tongue for nodules characteristic of T . solium cysticercosis . To examine the tongue , the animal was manually restrained and a hard wooden pallet was used to keep the mouth open . A veterinarian then gently retracted the tongue with a cloth visually inspecting and palpating the entire length of the underside of the tongue . The pig was considered positive for cysticercosis if cyst-like nodules were either seen or felt [17] . We collected a 5-ml blood sample from the vena cava of all pigs and transported these in the same manner as the human samples . All samples were first processed in the laboratory facilities of the Global Health Center in Tumbes , Peru . Whole stool samples were examined macroscopically for the presence of Taenia sp . scoleces or proglottids . A 10cc fecal sample was then placed in 40 cc of 5% formol-Phosphate Buffered Saline , pH 7 . 2 ( PBS ) in a sealed propylene tube at room temperature . Blood samples were centrifuged and aliquoted in 1 . 5 ml microtubules at −20°C . These serum and fecal samples were then shipped by air to the CNS Parasitic Diseases Research Unit , Universidad Peruana Cayetano Heredia ( Lima , Peru ) for further analysis . Fecal samples were concentrated by sedimentation then examined by light microscopy for the presence of Taenia sp . eggs or proglottids . Fecal samples were also processed with an enzyme-linked immunosorbent assay ( ELISA ) to detect Taenia sp . coproantigens as previously described [18] . However , we used a more conservative cutoff to increase the overall specificity for detection of Taenia sp . and to decrease the likelihood of detection of T . saginata taeniasis , a co-endemic cestode species . For each sample a percent positivity ( PP ) value was calculated as the optical density ( OD ) value of the sample relative to the OD of the positive control . A PP≥14% was considered positive . Human and pig sera samples were analyzed by enzyme-linked immunoelectrotransfer blot for presence of antibodies against T . solium cysts ( EITB LLGP ) as previously described [19] . The EITB LLGP assay uses a semi-purified fraction of homogenized T . solium cysts containing 7 T . solium glycoprotein antigens named after the Kda molecular weights of the corresponding reactive bands ( GP50 , GP42 , GP24 , GP21 , GP18 , GP14 , GP13 ) . Reaction to any of these 7 glycoprotein antigens is considered positive . Reaction to 4 or more bands is associated with heavy infection and viable cysts in pigs [20] . This assay is considered to be 100% specific to the T . solium metacestode stage with no cross-reactions with other co-endemic Taenia sp . Human sera were also analyzed by EITB for presence of antibodies against recombinant antigens specific to T . solium adult stage infection ( EITB rES33 ) . The EITB rES33 is based on baculovirus expression-purified recombinant antigen rES33 [21] . This study was reviewed and approved by the Institutional Review Board and the Institutional Ethics Committee for the Use of Animals at the Universidad Peruana Cayetano Heredia , Lima , Peru . All participants provided written informed consent , with parental or guardian consent required for the participation of minors . Treatment of animals adhered to the Council for International Organizations of Medical Sciences ( CIOMS ) International Guiding Principles for Biomedical Research Involving Animals . We entered individual household coordinates into ArcMAP10 GIS software ( ESRI; Redlands , CA ) to generate a geo-referenced map of the community which included results of tongue-examination and laboratory exams . We used these coordinates to calculate the geodesic distance in meters from each household to the nearest house where a tongue-positive pig was raised . All data were analyzed in STATA SE12 ( StataCorp; College Station , TX ) . Fisher's exact test was used to compare distributions of proportions or to examine association between pairs of categoric measures . A score test was used to evaluate linear trend in log odds across categories . We constructed univariate logistic regression models with random-effects to estimate the odds of a positive test result while accounting for clustering at the household level . All tests were 2-sided , and significance was set at 0 . 05 . We then used binomial family generalized estimating equations ( GEE ) with a log link and robust sandwich-type standard errors to estimate the population-averaged proportions of positive test results among household residents 1 ) living 100 meters or less from a house where a tongue-positive pig was found ( includes index household ) and 2 ) living more than 100 meters from a house where a tongue-positive pig was found . The 100-meter distance was chosen as a familiar measurement which could be readily applied in a community-based control intervention . We used households as the clustering variable and applied Quasilikelihood Information Criteria ( QIC ) in a STATA module to determine which variables to include [22] , [23] . Only those variables which decreased the QIC value compared to that of a full model were included in the final GEE algorithm .
There were a total of 454 residents living in the study area at the time of the census including 240 ( 52 . 9% ) males and 214 females ( 47 . 1% ) . The median age was 28 years ( interquartile range [IQR] 14–47 ) with an overall range of 0–95 years . Residents were distributed among 101 different households ( Table 1 ) . Blood samples were obtained from 385 residents aged 2 years or older for a total blood sampling coverage of 84 . 8% . All 385 individuals who provided blood also provided a stool sample , and an additional 17 ( 3 . 7% ) provided stool but no blood . A total of 402 residents aged 2 years or older provided a stool sample for 88 . 5% coverage of the total population . There were 52 ( 11 . 5% ) unsampled individuals who provided neither blood nor stool , including 12 children <2 years old . People who did not provide samples were more likely to be males and also to raise pigs ( data not shown ) . There were 47 ( 10 . 4% ) residents in 10 households who provided stool or blood samples but did not allow their pigs to be tested . We captured 548 pigs in the village , of which 256 ( 46 . 7% ) were positive for antibodies against one or more bands on EITB LLGP for cysticercosis ( Table 2 ) . The positive band distribution on EITB LLGP was as follows: 109 ( 42 . 6% ) with a single band , 139 ( 54 . 3% ) with 2–3 bands and 8 ( 3 . 1% ) with 4–7 bands . In all 109 single-band positive samples the positive band corresponded to the 50 Kd glycoprotein ( gp50 ) . There were 11 pigs ( 2 . 0% ) that were positive for cysticercosis on tongue exam . Of these 11 tongue-positive pigs , 10 ( 90 . 9% ) were seropositive on EITB LLGP . Of the 402 fecal samples , 6 ( 1 . 5% ) were positive for the presence of Taenia sp . coproantigens . Taenia sp . eggs were seen by light microscopy in only 1 ( 16 . 7% ) of these 6 samples . No other samples had Taenia sp . eggs present . There was no statistical difference between coproantigen-positive residents and coproantigen-negative residents with respect to age ( Figure 1 ) , sex or most demographic characteristics with the exception of household distance to a tongue-positive pig ( Table 3 ) . Of the 6 total coproantigen-positive residents , none lived within the same household as a tongue-positive pig . However , 4/6 ( 66% ) lived within 100 meters of a house where a tongue-positive pig was raised ( Figure 2 ) . The odds of being coproantigen-positive were over 9-fold greater for people who lived within 100 meters of a tongue-positive pig ( OR 9 . 4; 95% CI 1 . 2–71 . 8 ) . Of the 385 serum samples , 19 ( 4 . 9% ) were positive for antibodies against T . solium taeniasis on EITB rES33; 17/19 ( 89 . 5% ) were coproantigen-negative suggesting a history of ingestion of cyst-contaminated pork but absence of current taeniasis infection . Of the 366 samples which were negative on EITB rES33 , 4 ( 1 . 1% ) were coproantigen-positive . Inter-test agreement was poor ( kappa = 0 . 14 ) . As with the coproantigen results , there was no statistically significant difference in the distribution of rES33-positive residents across categories of age ( Figure 1 ) , sex or most demographic characteristics ( Table 2 ) other than distance to a tongue-positive pig . Of the 19 total rES33 seropositive residents , 8 ( 42% ) lived within 100 meters of a house where a tongue-positive pig was raised . The odds of being rES33 antibody-positive were more than 3-fold greater for people who lived within 100 meters of a house where a tongue-positive pig was found ( OR 3 . 4; 95% CI 1 . 1–10 . 9 ) . Of the 385 human sera samples , 142 ( 36 . 9% ) were positive for antibodies against one or more bands on EITB LLGP for cysticercosis . The positive band distribution on EITB LLGP was as follows: 32 ( 22 . 5% ) with a single band , 106 ( 74 . 6% ) with 2–3 bands and 4 ( 2 . 8% ) with 4–7 bands . In all 32 single-band positive samples the positive band corresponded to the 50 Kd glycoprotein ( gp50 ) . There was a significant trend for increasing seroprevalence over increasing categories of age ( Figure 1 ) ( p<0 . 01 , χ2test for trend ) . Other significant differences in seropositivity with respect to demographic characteristics are shown in Table 2 . Of the 19 rES33 positive individuals , 16 ( 84 . 2% ) were also positive on EITB LLGP . The odds of positive EITB LLGP were 10 times greater for rES33-positive than rES33-negative individuals ( OR 10 . 2 , 95% CI 2 . 9–35 . 5 ) . Of the 6 coproantigen-positive individuals , 4 ( 66 . 7 ) were also positive on EITB LLGP . However , there was no significant difference in seropositivity on EITB LLGP with respect to coproantigen results ( p = 0 . 2 ) . The association of seropositivity with distance to a tongue-positive pig was present albeit weaker than with the results for taeniasis . The odds of being EITB LLGP antibody-positive for cysticercosis were 2-fold greater for people who lived within 100 meters of a house where a tongue-positive pig was found ( OR 2 . 2; 95% CI 1 . 2–4 . 1 ) . Of the 4 participants with reactivity to >4 positive bands on EITB LLGP , 3 ( 75% ) lived within 100 meters of a tongue-positive pig . Figure 2 illustrates the geographic locations of tongue-positive pigs with their corresponding 100-meter rings , pigs with 4+ bands of EITB LLGP with their corresponding 100-meter rings , and the locations of all coproantigen-positive individuals in the community . The proportion of coproantigen-positive individuals differed significantly between residents living within 100-meters of a tongue-positive pig ( 4/79 , 5 . 1% ) and residents living >100 meters from a tongue-positive pig ( 2/323 , 0 . 6% ) ( p = 0 . 02 ) . There was a significant trend of decreasing odds of positivity with increasing distance from the tongue-positive pig ( p = 0 . 04 trend ) . A similar pattern was also present for rES33 . The proportion of rES33-positive individuals differed significantly between residents living within 100-meters of a tongue-positive pig ( 8/77 , 10 . 4% ) and residents living >100 meters from a tongue-positive pig ( 11/308 , 3 . 6% ) ( p = 0 . 03 homogeneity; p = 0 . 03 decreasing trend in odds ) . The crude and adjusted prevalence ratios for these distance categories are shown in Table 4 . Constructing 100-meter rings around pigs with 4+ bands instead of around tongue-positive pigs captures an additional coproantigen-positive individual ( 5/6; 83% ) . However , there was no statistically significant difference between the proportion of coproantigen-positive individuals living within 100-meters of a pig with 4+ bands ( 3/288 , 1 . 0% ) and residents living >100 meters from a pig with 4+ bands ( 3/114 , 2 . 6% ) ( p = 0 . 4 ) . Nor was the proportion of rES33-positive individuals living within 100-meters of a pig with 4+ bands ( 11/274 , 4 . 0% ) significantly different from the proportion of rES33-positive individuals living >100 meters from a pig with 4+ bands ( 8/111 , 7 . 2% ) ( p = 0 . 2 ) . The crude and adjusted prevalence ratios for these distance categories are shown in Table 5 .
Our study was conducted in a small rural village in northern Peru , a region in which T . solium is known to be highly endemic . Our results may not be generalizable to other endemic regions in which the underlying composition of risk factors may not be the same . We chose to analyze a 100-meter radius for targeted screening for taeniasis as this is a practical and easily-replicated distance for implementation purposes . However , housing density , agricultural practices , sanitation , topography and climatic factors may all influence geospatial associations between infected pigs and humans with taeniasis . The factors which promote T . solium endemic stability are not fully understood , and it is possible that the foci-centered intervention we propose may not be effective enough to counteract these forces . For example , decreasing pig herd immunity due to reduced exposure to T . solium eggs could potentially promote a higher rate of successful infection and ongoing transmission . Finally , this is a small study and analysis is therefore limited to a low number of tongue-positive pigs and taeniasis cases . Our results and conclusions should be validated in a larger endemic population and in areas with higher prevalence of taeniasis .
|
Taenia solium , aka the pork tapeworm , is an important cause of epilepsy in developing nations . People with intestinal tapeworms , a condition known as taeniasis , pass infectious eggs in their feces which contaminate the environment . These eggs can cause serious disease in both humans and pigs if they are ingested . Treating taeniasis is one way to potentially control transmission of the parasite in affected communities . However , this is difficult because people with taeniasis usually have no symptoms and therefore don't know they are infected . As a result , control programs may resort to offering treatment to entire communities in order to reach a few tapeworm carriers . Focusing detection and treatment on high-risk subgroups is another approach which might reduce unnecessary treatments . In this study , we found that people with taeniasis are more likely to be found living nearby pigs with visible signs of infection , specifically tapeworm cysts on their tongues . This suggests that routine tongue examination by pig owners and buyers could identify neighborhoods where detection and treatment of taeniasis may be more efficient .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"public",
"health",
"and",
"epidemiology",
"neurocysticercosis",
"infectious",
"disease",
"epidemiology",
"parasitic",
"diseases",
"global",
"health",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"veterinary",
"science",
"infectious",
"diseases",
"veterinary",
"diseases",
"veterinary",
"epidemiology",
"zoonotic",
"diseases",
"epidemiology",
"taeniasis",
"cysticercosis"
] |
2012
|
Geographic Correlation between Tapeworm Carriers and Heavily Infected Cysticercotic Pigs
|
Kaposi's sarcoma-associated herpesvirus ( KSHV ) is causally linked to several human cancers , including Kaposi's sarcoma , primary effusion lymphoma and multicentric Castleman's disease , malignancies commonly found in HIV-infected patients . While KSHV encodes diverse functional products , its mechanism of oncogenesis remains unknown . In this study , we determined the roles KSHV microRNAs ( miRs ) in cellular transformation and tumorigenesis using a recently developed KSHV-induced cellular transformation system of primary rat mesenchymal precursor cells . A mutant with a cluster of 10 precursor miRs ( pre-miRs ) deleted failed to transform primary cells , and instead , caused cell cycle arrest and apoptosis . Remarkably , the oncogenicity of the mutant virus was fully restored by genetic complementation with the miR cluster or several individual pre-miRs , which rescued cell cycle progression and inhibited apoptosis in part by redundantly targeting IκBα and the NF-κB pathway . Genomic analysis identified common targets of KSHV miRs in diverse pathways with several cancer-related pathways preferentially targeted . These works define for the first time an essential viral determinant for KSHV-induced oncogenesis and identify NF-κB as a critical pathway targeted by the viral miRs . Our results illustrate a common theme of shared functions with hierarchical order among the KSHV miRs .
Infection by Kaposi's sarcoma-associated herpesvirus ( KSHV ) is associated with Kaposi's sarcoma ( KS ) , the most common cancer in HIV-infected patients [1] . KSHV is also linked to the development of several other lymphoproliferative malignancies including primary effusion lymphoma ( PEL ) and a subset of multicentric Castleman's disease ( MCD ) [1] . KSHV encodes over 90 genes and more than two dozen microRNAs ( miRs ) derived from 12 precursor miRs ( pre-miRs ) [1] , [2] . While diverse functions have been identified for these viral products , viral and cellular determinants required for KSHV-induced oncogenesis remain unknown primarily because of the lack of a trackable system for KSHV cellular transformation [1] . The recent development of a model of KSHV efficient infection and transformation of primary rat mesenchymal precursor cells ( MM ) provides for the first time a reliable system for identifying the viral and cellular determinants essential for KSHV-induced oncogenesis [3] . In this model , KSHV-induced tumors manifest the typical virological and pathological features of human KS tumors . While KS has all the typical cancer hallmarks , unlike other cancers that depend on genome instability and mutation to enable the cancer features , no uniform genetic alteration has been identified in KS tumors so far [4] , [5] . In fact , recent studies have shown that KSHV-induced cellular transformation and tumorigenesis depend on the viral genome [3] , [6] . This unique feature indicates that the induction of KS tumors or at least early stage of KS tumors depends on the KSHV genome and the expression of KSHV genes . Thus , identification of KSHV genes required for cellular transformation and tumorigenesis can provide direct insights into the mechanism of KSHV-induced oncogenesis . Similar to other herpesviruses , the life cycle of KSHV consists of latency and lytic replication phases [7] . Following acute infection , KSHV establishes latency in the immunocompetent hosts . Upon stimulation by specific signals , latent KSHV can be reactivated into lytic replication . During lytic replication , KSHV expresses almost all lytic proteins and produces infectious virions , which often results in cell death . In contrast , KSHV only expresses a limited number of viral proteins during latency . Thus , KSHV latent infection is an effective strategy for evading host immune detection [7] . In KS lesions , most of the tumor cells are latently infected by KSHV indicating that viral latency and latent products are likely essential for the development of KS tumors [7] , [8] . MicroRNAs ( miRs ) are a class of ∼22 nt long non-coding small RNAs involved in diverse cellular functions and in all phases of cancer development [9] . MiRs primarily regulate gene expression at post-transcriptional level mainly through binding to the 3′ untranslated region ( 3′UTR ) of the target mRNAs [9] . The identification of miRs encoded by KSHV implicates that this mode of gene regulation also exists for this herpesvirus [2] . KSHV miRs are highly expressed during latency and in KS tumors [10]–[15] , implicating their essential functions in the viral life cycle and in the development of KS tumors . Indeed , several KSHV miRs regulate viral latency by directly targeting viral genes or indirectly targeting cellular pathways [16]–[21] . KSHV miRs also regulate diverse cellular pathways [17] , [22]–[36] , which might contribute to the development of KSHV-related malignancies . In this study , by using the newly developed cellular transformation system combined with a reverse genetics approach [3] , [37] , we have demonstrated that viral miRs are essential for KSHV-induced cellular transformation and tumorigenesis . Our results show that KSHV miRs redundantly target the NF-κB pathway to regulate cell cycle progression and apoptosis . By using a genomic approach , we have found that KSHV miRs redundantly regulate diverse cellular pathways , however , with several cancer-related pathways preferentially targeted , highlighting the intricacies of KSHV-cell interactions .
To determine the roles of KSHV miRs in cellular transformation , we infected MM cells with a mutant virus containing a deletion of a cluster of 10 pre-miRs including pre-miR-K1–9 and -K11 ( Mut ) together with its corresponding revertant ( Rev ) virus and the wild-type ( WT ) virus ( Figure 1A ) . There is no obvious morphological difference between cells infected by the WT and Mut viruses ( Figure 1B ) . Expression analysis of viral mRNAs and proteins showed that , similar to cells infected by WT and Rev viruses , most Mut cells were in the latent state ( Figure S1 and S2 ) , which were consistent with previous reports using 293 cells [17] , [18] . In culture , Mut cells neither formed any foci nor grew any overlapping cultures while WT and Rev cells did ( Figure 1C ) . While WT and Rev cells formed large colonies in semisolid softagar , Mut cells only formed small colonies of 3–5 cells ( Figure 1D ) . These results indicated that , similar to uninfected cells ( Mock ) , Mut cells were anchorage-dependent and contact-inhibited , and thus were not transformed . Accordingly , no tumor was induced by Mut cells in the nude mice while WT and Rev cells efficiently induced tumors with 80% incidence rates ( Figure 1E ) . Therefore , the Mut virus was neither transforming nor tumorigenic . We performed genetic complementation by stably expressing the miR cluster in Mut cells ( MutCl ) . The expression levels of miRs in MutCl cells were within 1- to 3-fold of those of WT cells ( Figure 2 ) . The miR cluster ( MutCl ) but not the vector control ( MutVt ) restored anchorage independence of Mut cells ( Figure 3A ) , confirming its essential role in KSHV-induced cellular transformation . To determine whether the Mut virus could immortalize MM cells , we serially passaged the Mut cells . Mock cells underwent crisis at around passage 27–30 as previously reported [3] . Similar to WT and Rev cells , Mut cells were continuously passaged for far beyond the crisis point , indicating that the Mut virus had immortalized the primary cells ( Figure S3 ) . Therefore , the miR cluster was not required for KSHV immortalization of MM cells . To identify specific miRs that mediate KSHV cellular transformation , we performed genetic complementation by stably expressing individual pre-miRs in Mut cells ( MutKi ) . The expression levels of miRs in the respective MutKi cells were similar to those of MutCl cells ( Figure 2 ) . As expected , all MutKi cells were immortalized ( Figure S3 ) . MutK1 , MutK4 , and MutK11 cells formed large colonies in softagar and efficiently induced tumors in nude mice ( Figure 3 and Figure S4 ) . In fact , MutK11 cells induced faster tumor formation and progression than WT cells ( Figure 3B–3C and Figure S4 ) , which might reflect the 3-fold higher miR-K11 expression level in MutK11 cells than in WT cells ( Figure 2 ) , and hence a dose-dependent effect . These results are not surprising because miR-K11 is a functional ortholog of miR-155 , a human oncogenic miR [26] , [34] . Tumors induced by MutK1 and MutK4 cells had tumor formation and progression rates similar to those of WT cells ( Figure 3B–3C and Figure S4 ) . Tumors induced by MutK7 cells had a slower tumor formation rate than that of WT cells but the two cell types induced tumors with similar progression rates . MutK2 , MutK3 , and MutK5 cells had partial cellular transformation phenotype , forming smaller colonies in softagar and inducing slower rates of tumor formation and progression ( Figure 3 and Figure S4 ) . MutK10 cells had no visible colony in softagar but induced tumors with slower tumor formation and progression rates than those of WT cells ( Figure 3 and Figure S4 ) . While miR-K10 was not deleted in Mut virus , it was expressed 4 . 5-fold higher in MutK10 than WT cells , suggesting a dose-dependent effect , and that miR-K10 expression from Mut virus alone was insufficient to sustain cellular transformation . Together , these results indicated that multiple miRs mediated KSHV cellular transformation and tumorigenesis . WT virus increases cellular proliferation by promoting cell cycle progression [3] . While Mut cells retained growth advantage over Mock cells , their growth rates were significantly lower than those of WT and Rev cells , particularly after day 3 post-seeding when cultures reached confluency ( Figure 4A ) , which were consistent with their anchorage-dependence . Cell cycle analysis at day 5 post-seeding showed that Mut cells had significantly more G1-phase cells and less S-phase cells than WT and Rev cells had ( Figure 4B ) . In fact , there were more G1-phase cells and less S-phase cells in Mut cells than those of Mock cells . Furthermore , we observed significantly more floating cells in the Mut cultures than the WT and Rev cultures , particularly after they reached confluency ( Figure 4C ) . Analysis of adherent cells showed that Mut cultures had more apoptotic cells than WT and Rev cultures had ( Figure 4D ) . Complementation of Mut cells with the miR cluster rescued the growth rates , reduced the number of apoptotic cells , and increased the number of S-phase cells in cell cycle to levels similar to those of WT cells ( Figure 4E–4G ) . These results indicated that the Mut virus induced cell cycle arrest and apoptosis , which was rescued by the miR cluster . While the Mut cells had higher KSHV lytic activity , over 90% of them were latently infected by the Mut virus ( Figure S1 and S2 ) . Thus , the increased viral lytic activity in a small number of cells was unlikely the cause of the 40% floating cells observed in the Mut cell cultures . Furthermore , treatment with Ganciclovir , an inhibitor of herpesvirus DNA polymerase , did not reduce the number of floating cells in Mut and WT cell cultures ( Figure S5 ) . We therefore concluded that the miR cluster directly regulated cell cycle progression and apoptosis in the KSHV-transformed cells . Because multiple miRs could independently rescue cellular transformation of the Mut virus , it suggested that they might have redundant functions . Indeed , compared to WT cells , the cell growth rates were fully rescued in MutK1 , MutK4 and MutK11 cells , and partially in MutK2 , MutK3 and MutK10 cells ( Figure 4E ) . All MutKi cells had lower apoptosis rates than Mut cells ( Figure 4F ) . In particular , MutK1 , MutK2 , MutK3 , MutK4 and MutK11 cells had apoptosis rates as low as that of WT cells ( Figure 4F ) . Similarly , cell cycle profiles of MutK1 , MutK4 , MutK10 and MutK11 cells were fully rescued to that of WT cells ( Figure 4G ) . These results indicated that several miRs regulated either one or both cell cycle and apoptosis pathways . In agreement with their effects on cellular transformation and tumor induction , pre-miR-K1 , -K4 and -K11 strongly regulated cell cycle and apoptosis . To reveal the cellular pathways targeted by miRs that might be essential for KSHV cellular transformation , we compared gene expression profiles of WT and Mut cells . Consistent with the enhanced growth and survival phenotypes ( Figure 4A–4D ) , Gene Set Enrichment Analysis ( GSEA ) confirmed that oxidative phosphorylation pathway and several other metabolic and energy consumption-related pathways , as well as a number of cell cycle- and apoptosis-related pathways were enriched in WT cells ( Table 1 ) . Unsupervised clustering with other MutKi cells showed similar profiles of MutCl and WT cells with both falling in the same subgroup , indicating that MutCl cells copied the gene expression profile of WT cells , thus validating the genetic rescue experimental approach ( Figure 5A ) . In contrast , the profiles of MutKi cells fell into distinct subgroups implicating their functional divergences . Nevertheless , similar to WT and MutCl cells , GSEA showed that all MutKi cells were enriched for the oxidative phosphorylation pathway with some also enriched for cell cycle- and apoptosis-related pathways ( Figure 5B and Table S1 ) , which were consistent with the ability of the pre-miRs to rescue the cell cycle and apoptosis phenotypes of Mut cells ( Figure 4F–4G ) . To identify the functional genes sets that might be correlated with the tumor phenotype , we divided the MutKi cells into three classes . Class 1 had high tumorigenicity , which included MutK1 , MutK4 and MutK11 cells; Class 2 had medium tumorigenicity , which included MutK2 , MutK3 , MutK5 , MutK7 and MutK10 cells; and Class 3 had low or no tumorigenicity , which included MutK6 , MutK8 , MutK9 and MutK12 cells ( Figure 3 ) . We then performed Analysis of Variance ( ANOVA ) on all genes to predict the subset of signature genes whose expression levels showed significant differences among these three classes . A total of 153 signature genes with significantly differential expression levels across the three classes ( P-value<0 . 05 ) were obtained ( Table S2 ) . Pathway enrichment of the signature genes by Ingenuity Pathway Analysis ( IPA ) revealed that the top enriched network functions were cellular development , cellular growth and proliferation , and reproductive system development and function ( Table 2 ) . To understand how the signature genes were involved in regulating the enriched pathways , we calculated the average expression levels of all signature genes within each class and mapped them to the top enriched networks ( Figure S6 ) . As expected , differential expression of the signature genes among the three classes was evident . We further isolated the 38 signature genes whose expression levels exhibited positive correlation with tumorigenicity and 33 signature genes whose expression levels exhibited negative correlation with tumorigenicity ( Figure 5C ) . Significantly , a number of genes with positive correlation with tumorigenicity such as Akap13 , CSF1 , Grip1 , JunB , Nexn , Nob1 , Nrf2f2 , Pcsk2 , Pde1a , Pomgnt1 , Radil , Usp5 and Wnt2 had previously been shown to have oncogenic , growth-promoting or pro-angiogenic activities [38]–[54] while several genes with negative correlation with tumorigenicity such as Calr , Dpp8 , Fbln1 , Hsd3b1 , Mxd4 , Pten and Mrpl41 had previously been shown to have tumor suppressive , growth inhibitory , proapoptotic or immune regulatory activities [55]–[63] . The deregulation of these genes by KSHV miRs was likely to contribute to KSHV-induced tumorigenesis . We further determined the linear combinatory effect of individual miRs to the overall expression pattern of MutCl cells by Lasso fitting [64] . We performed principal component analysis ( PCA ) , which projected the expression of all analyzed genes to 8 most significant principle components ( Figure 5D ) . Lasso was then applied to regress the principle components among samples to infer the combinatory impact from miRs and identified pre-miR-K1 as the largest contributor followed by pre-miR-K3 ( Figure 5D and 5E ) . MiR-K1 targets IκBα , an inhibitor of the pro-survival NF-κB pathway , and cyclin-dependent protein kinase inhibitor p21/WAF1 , a cell cycle regulator [17] , [65] . Because of the robust phenotypes of MutK1 cells ( Figure 3 and 4 ) , we examined the consequence of targeting IκBα . Indeed , the expression level of IκBα protein was lower in cells expressing miR-K1 , including WT , Rev , MutK1 and MutCl cells , than cells without miR-K1 , including Mut cells , and cells complemented with vector control ( MutVt ) ( Figure 6A and Figure S7 ) . Examination of the 3′UTR of rat IκBα indeed identified a conserved miR-K1 targeting site with a single nucleotide difference ( U to C ) from the human site ( Fig . 6B ) . Our previous study has shown that this site is a functional targeting site of miR-K1 in human cells [17] . In a reporter assay , miR-K1 suppressed the rat IκBα 3UTR reporter activity by 50% ( Fig . 6C ) . Mutation of the putative targeting site abolished the repressive effect of miR-K1 . In WT cells , a miR-K1 suppressor increased the IκBα 3UTR reporter activity by 1 . 6-fold but had no effect on the mutant 3′UTR reporter activity ( Fig . 6D ) . Together , these results indicated that , similar to human IκBα , rat IκBα was also a target of KSHV miR-K1 . To investigate if miR-K1 regulated cell cycle and apoptosis by targeting IκBα , we performed siRNA knock down of IκBα in the Mut cells ( Figure S8A and S8B ) . SiRNA knock down of IκBα in Mut and MutVt cells was sufficient to rescue cell cycle profiles to those of WT cells , reducing G1-phase cells from 82% and 78% to 63% and 65% , and increasing S-phase cells from 4% and 6% to 27% and 20% , respectively ( Figure 6E , and Figure S8A and S8B ) . Similarly , knock down of IκBα was sufficient to reduce apoptotic cells from 20 . 5% and 22% to 6% and 7 . 5% in Mut and MutVt cells , respectively ( Figure 6F ) . Significantly , expression of IκBα with a construct lacking its native 3′UTR , thus escaping miR-K1 targeting , in WT cells was sufficient to alter cell cycle profiles to those resembling Mut cells ( Figure 6G and 6H ) , and increase apoptotic cells from 3% to 10 . 5% ( Figure 6I ) . Together , these results indicated that IκBα targeting by miR-K1 was necessary and sufficient for cell cycle progression and inhibition of apoptosis in KSHV-transformed cells . Similar to IκBα , the expression level of p21 was lower in cells expressing miR-K1 than cells without miR-K1 ( Figure S9A and S9B ) . However , siRNA knock down of p21 in Mut and MutVt cells had no effect on cell cycle ( Figure S8C , S8D and S9C ) , indicating that p21 targeting was not required for miR-K1 regulation of cell cycle . Interestingly , G1-phase cells was increased from 59% to 65% , and S-phase cells was reduced from 26% to 19% following knock down of p21 in MutK1 cells . This was accompanied with an increase in apoptotic cells from 4% to 23% ( Figure S9D ) . Knock down of p21 increased apoptotic cells from 21% to 38% in Mut cells , and from 25% to 43% in MutVt cells . These results indicated that miR-K1 targeting of p21 was not required for miR-K1 inhibition of apoptosis . On the contrary , persistent low level of p21 was likely essential for maintaining the homeostasis and survival of KSHV-transformed cells . The above results indicated that IκBα and its related pathways could be redundantly regulated by KSHV miRs . Bioinformatics and 3′UTR screenings have failed to identify IκBα as the direct target of other KSHV miRs [17] . Nevertheless , IκBα is regulated by as many as 60 cellular pathways ( Table S3 ) . Indeed , IκBα levels were reduced by more than 40% in MutK2 , MutK4 , MutK5 , MutK6 , MutK7 , MutK9 and MutK11 cells besides MutK1 cells ( Figure 7A ) . However , MutK3 , MutK8 , MutK10 and MutK12 cells had minimal changes in IκBα level , suggesting regulation of downstream pathways by these miRs . Because NF-κB is the common effector pathway of IκBα inhibition , we examined its activation by KSHV miRs . All MutKi cells except MutK7 and MutK10 had 1 . 8- to 3-fold higher NF-κB activities than Mut cells had ( Figure 7B ) . These miRs might synergistically or additively contribute to the 5-fold constitutive NF-κB activation in WT cells . Indeed , compared to Mock cells , many components and downstream targets of the NF-κB pathway were upregulated ( Figure S10 ) . In addition to IκBα , several other identified cellular targets of KSHV miRs also regulate cell growth and survival ( Table 3 ) . MiR-K5 , 9 , 10a and 10b target Bcl2-associated factor BCLAF1 [36]; miR-K1 , 3 and 4-3p target caspase 3 [35]; and miR-K10a targets tumor necrosis factor-like weak inducer of apoptosis receptor ( TWEAKR ) [23] . Furthermore , miR-K10a and its variants also target TGF-β type II receptor [28] while miR-K11 is an ortholog of cellular miR-155 [26] , [34] , which is implicated in cancer [66] . It has been shown that miR-K11 targets BACH1 and SMAD5 [26] , [29] , [31] , [34] . Thus , these miRs could directly regulate cell cycle and apoptosis by targeting genes that are downstream of the NF-κB or other pathways . Previous studies have shown that overexpression of KSHV vFLIP activates the NF-κB pathway [67] , [68] . To assess the relative contribution of KSHV miRs and vFLIP to the activated NF-κB activity in the KSHV-transformed cells , we examined the NF-κB reporter activity in cells infected with a KSHV mutant with vFLIP deleted ( ΔvFLIP ) [69] . Similar to the Mut cells , deletion of vFLIP abolished the activation of the NF-κB pathway ( Figure 7C ) . In fact , both Mut and ΔvFLIP cells had lower NF-κB activity than the Mock cells . These results indicated that , in the context of KSHV infection , both miRs and vFLIP were required for the activation of the NF-κB pathway . Because of the observed activation of the NF-κB pathway in WT cells , we explored if targeting of this pathway was sufficient to inhibit cell growth and survival of WT cells . We performed siRNA knock down of RelA , a key component of the NF-κB complexes ( Figure 8A ) . Knock down of RelA significantly induced cell cycle arrest in WT cells ( Figure 8B ) . The two RelA siRNAs had minimal effect on the cell cycle profiles of Mock cells . However , in WT cells , they increased the number of G1-phase cells from 51% to 65% and 72% , respectively ( P<0 . 001 and P<0 . 001 ) , and S-phase cells from 28% to 23% and 16% , respectively ( P<0 . 05 and P<0 . 01 ) . Nevertheless , knock down of RelA efficiently induced cell apoptosis in both WT and Mock cells , indicating that RelA and NF-κB activity were required for the survival of both types of cells ( Figure 8C ) . We further explored the use of specific NF-κB inhibitor Bay-11 . Unlike the siRNA approach , the effect of Bay-11 can be more easily titrated . Significantly , inhibition of the NF-κB pathway in WT cells with low doses of Bay-11 was sufficient to induce cell growth arrest and apoptosis , and change cell cycle profile by increasing G1-phase cells and reducing S-phase cells ( Figure 8D–8G ) . Under the same condition , Mock cells had no increase in apoptotic cells and only marginal change in cell cycle ( Figure 8D–8G ) . However , at higher doses ( >3 µM ) , Bay-11 was toxic to both Mock and WT cells . Thus , while the growth and survival of both Mock and WT cells required the NF-κB pathway , WT cells were more susceptible to the NF-κB inhibitor . These results further confirmed that multiple KSHV miRs might regulate cell cycle and apoptosis by activating the NF-κB pathway . Our results and those from previous studies indicate that KSHV miRs have redundant functions and regulate several common cellular pathways . To examine the extent of these shared functions , we identified other potential targets of KSHV miRs by performing target prediction with SVMicrO [70] , and integrating the identified targets with gene expression profiles using the Borda merging method to improve target prediction precision . Surprisingly , almost all the validated targets of KSHV miRs were identified to have high SVMicro scores by this approach ( Table S4 ) , confirming the effectiveness of this approach . Among those validated target genes , besides the identified miRs , a number of KSHV miRs were newly predicted to target these genes , confirming the theme of redundant functions among KSHV miRs . In fact , results of these systemic analyses showed that KSHV miRs redundantly target a large number of cellular genes in diverse pathways , further revealing the common functions of these miRs ( Table S4 ) . Significantly , a number of cellular pathways including cell cycle , TGF-β signaling , WNT signaling , Vitamin D receptor signaling , TNF/stress-related signaling , mTOR signaling and MAPK signaling were highly enriched ( Figure 9A ) , indicating their top hierarchical positions among the pathways that were regulated by KSHV miRs . Many of these pathways have been implicated in the development of cancer and regulation of metabolic pathways [4] . We mapped the predicted targets of KSHV miRs of the top three pathways ( Figure 9B ) . It was evident that over half of the genes ( 71 of 135 ) were the targets of more than one KSHV miRs .
KSHV encodes diverse genes and miRs with cellular regulatory functions [1] , [2] . When tested by overexpression out of the context of KSHV infection , several KSHV genes manifest cellular transforming potentials [71]–[78] . Nevertheless , the roles of these viral genes in KSHV-induced tumorigenesis remain unclear because of the lack of a KSHV cellular transformation system . The recent development of a model of efficient KSHV infection and transformation of primary MM cells should facilitate the delineation of viral and cellular determinants required for KSHV-induced oncogenesis in the context viral infection [3] . Using this system combined with a reverse genetics approach , we have identified for the first time a viral determinant , the miR cluster , required for KSHV-induced cellular transformation and tumorigenesis . While KSHV lytic replication in a small number of infected cells might promote KS tumor progression through an autocrine and paracrine mechanism as a result of de novo infection and expression of viral lytic gene products , most tumor cells in KS lesions are latently infected by KSHV [7] . Furthermore , results from the new cell model reveal that KSHV-induced cellular transformation depends on the viral genome [3] . Most KSHV-transformed cells as well as tumors derived from this model are latently infected by KSHV . These clinical and laboratory observations implicate that malignant proliferation of KSHV latent cells are the essential driving force behind the full growth of KS tumors . As a result , KSHV latent products are likely to have critical roles in the development of KSHV-induced tumors . KSHV miRs are highly expressed during latency and in KS tumors [10]–[15] . The identification of KSHV miRs as the essential determinant for KSHV-induced tumorigenesis substantiates the critical role of latent infection in KSHV-induced oncogenesis . While several other viral latent genes including LANA , vFLIP and vCyclin possess oncogenic properties [74] , [75] , [77] , [78] , the requirement for KSHV miR cluster implicates that the combined effects of these viral genes are not sufficient to cause cellular transformation . Paradoxically , our results have shown that the miR cluster is not required for KSHV-induced cellular immortalization ( Figure S3 ) indicating the involvement of other viral genes in KSHV-induced oncogenesis in addition to miRs . Indeed , the Mut virus induces cell cycle arrest and apoptosis in MM cells ( Figure 4B–4D ) . These phenotypes are consistent with the outcomes of oncogenic insults , likely exerted by KSHV oncogenes , in primary cells . The ability of KSHV miRs to rescue cell cycle arrest and inhibit apoptosis ( Figure 4 ) indicates that they primarily function in protective roles to rescue the KSHV-infected cells from oncogenic insults . Thus , a fine balance between uncontrolled cell growth elicited by oncogenic signals and cell homeostasis exerted by pro-survival signals as a result of the intricate interactions of KSHV miRs with other viral oncogenes are likely essential for successful KSHV-induced cellular transformation . Our results show that multiple individual KSHV miRs are capable of effectively rescuing the oncogenicity of the Mut virus ( Figure 3 ) . These observations point to the redundant functions of the miRs . Indeed , most KSHV miRs rescue cell cycle progression and inhibit apoptosis in the Mut cells ( Figure 4E–4G ) . Significantly , most KSHV miRs in addition to miR-K1 exert these protective functions at least in part by targeting IκBα and the NF-κB pathway , which contribute to KSHV-induced cellular transformation ( Figure 10 ) . While it would be interesting in examining the role of targeting IκBα in KSHV-induced cellular transformation of human cells , unfortunately , there is currently no valid human cell model available for such studies . As a result of targeting IκBα and the NF-κB pathway , knock down of RelA is sufficient to cause cell cycle arrest in KSHV-transformed cells but has less effect in the uninfected cells ( Figure 8B ) . While knock down of RelA or treatment of the cells with high doses of NF-κB inhibitor kill both uninfected and KSHV-transformed cells , lower doses of the NF-κB inhibitor differentially block cell growth by inducing apoptosis and inhibiting cell cycle progression of KSHV-transformed cells ( Figure 8 ) . Thus , KSHV-transformed cells are addicted to this essential pro-survival pathway , which is constitutively and redundantly activated by KSHV miRs ( Figure 7B ) . Previous studies have shown that the NF-κB pathway is essential for the survival of PEL cells [79] . However , in this model , activation of the NF-κB pathway is primarily exerted by KSHV vFLIP protein [68] , [80] , [81] . In contrast , our results indicate that both KSHV vFLIP and miRs are required for the activation of the NF-κB pathway ( Figure 7C ) . Furthermore , our results have shown that activation of the NF-κB pathway by KSHV miRs is essential for cellular transformation in the KS model ( Figure 10 ) . It can be speculated that vFLIP might also be required for KSHV-induced cellular transformation . It would be important to determine how vFLIP and miRs might concertedly regulate the NF-κB pathway and contribute to KSHV-induced oncogenesis in both PEL and KS models . While our results have shown the important roles of KSHV miRs in cellular transformation , expression of these miRs individually or as a cluster alone out of the context of KSHV infection is not sufficient to induce cellular transformation ( data not shown ) . These outcomes are expected as it is well-known that disruption of multiple checkpoints/tumor suppressor pathways is required for cellular transformation [82] . Similarly , while activation of the NF-κB pathway and inhibition of IκBα are required for KSHV-induced cellular transformation , they are unlikely to be sufficient to cause cellular transformation in primary cells out of the context of KSHV infection . Nevertheless , our observations implicate the essential role of activating the NF-κB pathway and inhibiting IκBα in the development of other cancers . In fact , the role of activated NF-κB pathway in cancer development has been well established [83] . It is also worth noted that knock out of IκBα has been shown to induce tumors in mice while overexpression of IκBα inhibits tumor formation [84] , [85] . Our results suggest that it might be attractive to develop specific inhibitors or suppressors of KSHV miRs for targeting their essential functions in KSHV-induced oncogenesis . However , their redundant functions could make it challenging for therapeutic application . On the other hand , the NF-κB pathway might be a more feasible therapeutic target as it is identified as a common essential target of KSHV miRs . It would be interesting to test the preclinical application of targeting the NF-κB pathway in the KS animal model . The redundant functions of KSHV miRs in activating the NF-κB pathway and regulating cell growth and survival implicate a common theme of shared functions among these miRs . Previous studies have also identified a number of common targets of KSHV miRs . For examples , miR-K1 , 3 and 4-3p target caspase 3 while miR-K5 , 9 and 10a/b target BCLAF1 [35] , [36] . Our initial systemic genomic approach has led to the identification of potential targets in diverse cellular pathways ( Table S4 ) . These results are consistent with the diverse expression patterns of different MutKi cells revealed in the gene expression clustering analysis ( Figure 5A ) . Nevertheless , results of GSEA show that all MutKi cells are enriched in oxidative phosphorylation and cell cycle pathways ( Figure 5B ) , reflecting their enhanced proliferative rates promoted by the miRs . Indeed , among the diverse pathways targeted by KSHV miRs , a number of cancer-related pathways are highly enriched ( Figure 9A ) . Among the top enriched pathways , most of the genes regulated by the miRs are targeted by multiple KSHV miRs ( Figure 9B ) . These results indicate that despite the seemingly complexities , a hierarchal order of functions of KSHV miRs exists with a number of essential cellular pathways positioning at the top ranks . Confirmation of the essential roles of these pathways in KSHV cellular transformation and tumorigenesis should shed further light on the mechanism of KSHV-induced oncogenesis .
KSHV recombinant viruses were previously described [17] , [37] , [69] . Assays for cell growth and proliferation , and methods for foci formation , growth in softagar and tumor growth were previously described [3] , [17] , [28] . Constructs of KSHV pre-miRs were obtained by cloning fragments of the pri-miRs into retroviral vector pSUPER . retro . puro as previously described [86] . The rat IκBα 3′UTR WT luciferase reporter plasmid was obtained by inserting the full-length 3′UTR of IκBα ( Genbank accession no . NM_001105720 . 2 ) into the Kpn I and Xho I sites downstream of the luciferase coding sequence in the pGL3 cm vector following PCR amplification . PCR primers used were 5′AGTGGTACCCCAAAGGAACGTGGACTTGT ( forward ) and 5′AGTCTCGAGCCAAAATAATTACCAACAAAATACACC ( reverse ) with the restriction enzyme sites underlined . Mutagenesis was carried out using the WT reporter as a template to generate the mutant reporter IκBα 3′UTR mutant containing a mutation in the putative site by PCR amplification . The modified primers were 5′AGTGGTACCCCAAAGGAACGTGGACTTGT ( forward ) and 5′AGTCTCGAGCCAAAATAATTACCAACAAAATACACCATATACAACATAATGTACAAAGT ( reverse ) with the restriction enzyme sites underlined . The rat IκBα expression construct lacking its native 3′UTR was obtained by PCR amplification of the IκBα cDNA using primers 5′GCGACCGCCACGACGGCGAC ( forward ) and 5′GTGGAGGCCGCTGTGCGGGTC ( reverse ) . The fragment was cloned into the mammalian expression vector pCMV4 to derive plasmid pCMV4-IκBα ( Addgene , Cambridge , MA ) . SiRNAs to IκBα , p21 , RelA and scrambled controls were obtained from Invitrogen ( Carlsbad , CA ) . Lock-nucleic acid ( LNA ) -based miR-K1 suppresser and the scrambled control were previously described [17] . Cell cycle and apoptosis were analyzed as previously described [3] , [28] . Cell cycle was analyzed by propidium iodide ( PI ) staining . Apoptotic cells were detected by PI staining and with a FITC Annexin V Apoptosis Detection kit ( BD Biosciences , San Jose , CA ) . RT-qPCR for KSHV genes ( vFLIP , LANA , ORF50 , ORF57 and ORF25 ) , KSHV miRs and their primers were previously described [17] . Reporter assays were performed as previously described [17] , [28] , [69] . To determine the NF-κB activity , a NF-κB luciferase reporter construct or a mutant construct and a β-galactosidase expression plasmid pSV-β-gal ( Promega , Madison , WI ) were cotransfected into cells cultured in 24-well plates using the F2 transfection reagent ( Targeting Systems , El Cajon , CA ) . Reporter assays for the 3′UTR reporters were carried out by cotransfection of the luciferase reporter plasmid with pRL-TK ( Promega , Madison , WI ) and a miR mimic ( Sigma , St . Louis , MO ) . The pRL-TK vector providing the constitutive expression of Renilla luciferase was used as an internal control . Other reporter assays were performed with the indicated expression plasmids . Transfection was performed in duplicate or triplicate , and all experiments were independently repeated at least three times . At the indicated time , cells were then lysed , and the luciferase and β-gal activities were measured using luciferase and β-galactosidase kits ( Promega ) . Luciferase activity was normalized to β-galactosidase activity . For the 3′UTR reporter assays , the Dual-Luciferase Reporter Assay System was used as instructed by the manufacturer ( Promega ) . Cells were fixed for 30 min with 2% paraformaldehyde ( Sigma-Aldrich ) or 10 min with methanol , permeabilized with 1% saponin ( Sigma-Aldrich ) for 60 min and blocked with DMEM with 10% FBS for 1 h . The slide was then stained for 1 h with a primary antibody followed for 1 h with an Alex Fluor 568 secondary antibody ( Invitrogen ) and stained with 4′ , 6′-diamidino-2-phenylindole ( DAPI ) ( Sigma-Aldrich ) . A rat monoclonal antibody to LANA ( Abcam , Cambridge , MA ) , a mouse monoclonal antibody to ORF65 [87] , a mouse monoclonal antibody to p21 ( Santa Cruz , Santa Cruz , CA ) , and a rabbit polyclonal antibody to IκBα ( Abcam ) were used for the experiments . Western-blotting was performed as previously described [88] . Protein lysates were resolved in SDS-PAGE and transferred to Hybond-C extra membranes ( GE Healthcare Bio-Sciences , Pittsburgh , PA ) . Following incubation with primary and secondary antibodies , the membranes were developed in chemiluminescence substrate ( Pierce Chemical , Dallas , TX ) . Images were captured using a BioSpectrum 810 Advanced Imaging System ( UVP , Upland , CA ) . Cells were grown to 70–80% confluency . Total RNAs were isolated with Trizol reagent ( Invitrogen ) . The RNA samples were labeled with Biotin using an Illumina TotalPrep RNA Amplification Kit ( Illumina , San Diego , CA ) . Biotin-labeled cRNA samples were hybridized with Rat-Ref-12-V1 Beadchips using the standard protocol recommended by the manufacturer ( Illumina ) . The Beadchips were scanned using a HiScanSQ scanner . The data were analyzed and quantile-normalized using the GenomeStudio software ( Illumina ) . All the array data were submitted to GEO ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE50381 ) . Vector effects were observed between MM cells ( Mock ) and MM cells expressing vector control ( MockVt ) , and between Mut cells and MutVt ( Figure S11A and S11B ) . To remove the vector effect , logarithmic gene expression fold changes in cells with vs without vector were calculated and denoted as MockVt/Mock and MutVt/Mut , respectively . Under the null hypothesis that fold changes of genes ( MockVt/Mock and MutVt/Mut ) were independent of vector effect and distributed jointly according to a zero mean bivariate Gaussian distribution ( Figure S11C ) , 2 , 064 genes were identified ( P<0 . 1 ) to depend on the vector , and thus removed ( Figure S11D and S11E ) . Genes show no differential expression in MutKi cells vs MutVt cells were removed . A gene is considered not differentially expressed if its expression fold change ( MutKi/MutVt ) can be explained by the vector effect in all MutKi cells , i . e . MutKi/MutVt<σ2m for i = 1 , … , or 12 , where σ2m is the variance of vector effect estimated in the step of removal of vector effect . A total of 4 , 236 genes were identified to have no differential expression in all 12 MutKi cells , and thus were removed . The subsequent analysis was carried out on the remaining 16 , 501 genes . We divided the MutKi cells into three classes based on the results of tumor formation and progression ( Figure 3 ) . Class 1 had high tumorigenicity , which included MutK1 , MutK4 and MutK11 cells; Class 2 had medium tumorigenicity , which included MutK2 , MutK3 , MutK5 , MutK7 and MutK10 cells; and Class 3 had low or no tumorigenicity , which included MutK6 , MutK8 , MutK9 and MutK12 cells . We then performed Analysis of Variance ( ANOVA ) on all genes to predict the subset of signature genes whose expressions showed significant differences among these three classes . ANOVA is a statistical test for testing if mean expression levels of a gene in multiple classes of samples are equal . It resembles a generalization of t-test for differential expression under multiple conditions . Genes with P-values<0 . 05 were considered to have significantly differential expression across the three classes ( Table S2 ) . Pathway enrichment of the signature genes by Ingenuity Pathway Analysis ( IPA ) was performed to reveal the top enriched networks ( Table 2 ) . To visualize the expression of signature genes in the networks , we calculated the average expression within each class for all signature genes and then mapped the average expression to the top enriched networks ( Figure S6 ) . To obtain cellular genes that were correlated with tumorigenicity , we isolated the signature genes whose expression levels exhibit strong positive and negative correlation with tumorigenicity ( Figure 5C ) . The linear combinatory effect of individual pre-miRs to the overall expression pattern of MutCl cells was determined by the Lasso fitting . Principle component analysis ( PCA ) was first applied to the expression data of all MutKi cells to reduce the high dimension of 16 , 501 genes in individual MutKi cells down to 8 principle components ( PCs ) , which were sufficient to explain >95% of the variances in the gene expression ( Figure 5D ) . The projection matrix obtained from the PCA was subsequently used to project the expression of MutCl cells , which were also reduced to 8 dimensions . The Lasso was then applied to the 8-dimensional projected expression data to infer the combinatory effect of the expression of individual MutKi cells to that of MutCl cells . Since the Lasso is designed to promote sparse models , it sets the effect ( coefficients ) to “0” if a pre-miR is predicted to be insignificant ( Figure 5E ) . Genome-wide targets of miRs in rat were predicted using SVMicrO [70] . For each miR , target genes were ranked with the decreasing order of SVMicrO score , i . e . , the top ranked genes were more likely to be targets . At the same time , genes were also ranked according to their expression fold changes in the corresponding MutKi cells with more down-regulated genes ranking higher in the list . The ranked genes in the target list and down-regulated gene list were combined using the Borda merging method into a single gene list , where a higher ranked gene was likely to have a higher SVMicrO score and a larger fold change of down-regulated expression , and thus was likely a target of the miR . A total of 457 human pathways were downloaded from NCI Pathway Interaction Database ( NCI-PID ) ( http://pid . nci . nih . gov/ ) . A total of 356 corresponding rat pathways were obtained by mapping human genes in the pathways to their homologues of rat genes . Pathways with less than 5 genes were excluded . Two different GSEA implementations were carried out . To identify differential expressed pathways in each of MutKi cells , GESA was performed on the gene expression fold changes ( Figure 5A and Table S5 ) . To predict pathways that are directly targeted by miRs , GSEA was applied to the ranked list obtained by combining SVMicrO scores and the expression fold changes ( Figure 9A and Table S4 ) . Data are shown as mean ± SD ( standard deviations ) where appropriate . The 1-tailed Student's test was used to compare data between the experimental groups . Statistical significance was assumed at P values less than 0 . 05 , 0 . 01 or 0 . 001 , which is represented by “*” , “**” or “***” respectively . 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 animal protocol was approved by the Institutional Animal Care and Use Committee of the University of Texas Health Science Center at San Antonio ( Animal Welfare Assurance no . A3345-01 ) . All surgery was performed under sodium pentobarbital anesthesia , and all efforts were made to minimize suffering . A web site to facilitate the search of cellular gene targets of KSHV miRs , together with SVMicrO scores , expression levels , and enriched pathways has been established at: http://compgenomics . utsa . edu/kshv/ .
|
Kaposi's sarcoma-associated herpesvirus ( KSHV ) is the causal agent of several human cancers . KSHV encodes over two dozen genes that regulate diverse cellular pathways . However , the molecular mechanism of KSHV-induced oncogenesis remains unknown . In this study , we determined the roles of KSHV microRNAs ( miRs ) in KSHV-induced oncogenesis using a recently developed KSHV cellular transformation system of primary rat mesenchymal precursor cells . A KSHV mutant with a cluster of 10 precursor miRs ( pre-miRs ) deleted failed to transform primary cells , and instead , caused cell cycle arrest and apoptosis . Expression of the miR cluster or several pre-miRs was sufficient to restore the oncogenicity of the mutant virus . KSHV miRs regulated cell cycle progression and inhibited apoptosis in part by redundantly targeting IκBα and the NF-κB pathway . By integrating gene expression profiling and target prediction , we identified common targets of KSHV miRs in diverse pathways . Importantly , several cancer-related pathways were preferentially targeted by KSHV miRs . These works have demonstrated for the first time the important roles of KSHV miRs in oncogenesis and identified NF-κB as a critical pathway targeted by the miRs . Our results reveal that shared function is a common theme of KSHV miRs , which manifest functional hierarchical order .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways
|
Prions are proteinaceous infectious agents responsible for fatal neurodegenerative diseases in animals and humans . They are essentially composed of PrPSc , an aggregated , misfolded conformer of the ubiquitously expressed host-encoded prion protein ( PrPC ) . Stable variations in PrPSc conformation are assumed to encode the phenotypically tangible prion strains diversity . However the direct contribution of PrPSc quaternary structure to the strain biological information remains mostly unknown . Applying a sedimentation velocity fractionation technique to a panel of ovine prion strains , classified as fast and slow according to their incubation time in ovine PrP transgenic mice , has previously led to the observation that the relationship between prion infectivity and PrPSc quaternary structure was not univocal . For the fast strains specifically , infectivity sedimented slowly and segregated from the bulk of proteinase-K resistant PrPSc . To carefully separate the respective contributions of size and density to this hydrodynamic behavior , we performed sedimentation at the equilibrium and varied the solubilization conditions . The density profile of prion infectivity and proteinase-K resistant PrPSc tended to overlap whatever the strain , fast or slow , leaving only size as the main responsible factor for the specific velocity properties of the fast strain most infectious component . We further show that this velocity-isolable population of discrete assemblies perfectly resists limited proteolysis and that its templating activity , as assessed by protein misfolding cyclic amplification outcompetes by several orders of magnitude that of the bulk of larger size PrPSc aggregates . Together , the tight correlation between small size , conversion efficiency and duration of disease establishes PrPSc quaternary structure as a determining factor of prion replication dynamics . For certain strains , a subset of PrP assemblies appears to be the best template for prion replication . This has important implications for fundamental studies on prions .
Prion disease pathogenesis stems from the post-translational conversion of the monomeric , alpha helix-rich host-encoded prion protein ( PrPC ) into misfolded , β sheet-enriched PrPSc aggregates [1] . The process is believed to be initiated by PrPSc seeds [2] , [3] acquired through infection or arising from spontaneous conversion of wild-type or mutant PrPC into PrPSc [4] . The PrPSc seeds would template the remodeling of host PrPC to the PrPSc form [5] . This self-sustained polymerization process , -in which polymer fragmentation is thought to play a key role [2] , [6] , [7]- , leads to deposition of injurious plaques into the brain . PrPSc-templated conversion of PrPC or bacterially-derived PrP has been established in cell-free conditions using protein misfolding cyclic amplification ( PMCA ) assays ( for reviews [8] , [9] ) , further strengthening the conformational changes of the prion protein as the main molecular determinant of prion replication and infectivity . Prion diseases can occur in many mammalian species . Among them are human with Creutzfeldt-Jakob disease , sheep and goat with scrapie , cattle with bovine spongiform encephalopathy ( BSE ) and cervids with chronic wasting disease [10] . A variety of prion variants or strains exist within a given host species . They cause diseases with specific phenotypic traits , including time course to disease and neuropathological features . Differences in PrPSc biochemical ( e . g . resistance to proteases ) and biophysical properties [11] , [12] , [13] , [14] , [15] indicate that strain-specific biological properties reflect differences in the PrPSc “conformation” associated to each strain [16] , [17] , [18] . PrPSc has not been amenable to high-resolution structural studies [3] , due notably to its insolubility in non-denaturing detergents . Thus the conformational underpinnings of the prion strain phenomenon and notably the contribution of PrPSc quaternary structure remain largely elusive . Conceivably these differences must be sufficiently local to allow faithful prion transmission at least within and between individuals of the same species . Non-PrP components might be part of prion infectious particle or act as a scaffold during the conversion and/or aggregation process and thus might also contribute to prion strain biological phenotype ( reviews: [3] , [19] ) . To gain some structural information on the physical relationship between prion infectivity and PrPSc aggregation state , and how it varies among strains , we previously applied a sedimentation velocity ( SV ) -based fractionation technique to solubilized brain homogenates from ovine PrP tg338 transgenic mice infected with distinct scrapie and BSE cloned prion strains [20] . Based on the incubation time to disease in tg338 animals , these strains were classified as fast and slow . These experiments led to the observation that the relationship between prion infectivity and PrPSc aggregation state was not univocal . Regardless of the strain , the bulk of proteinase-K ( PK ) resistant PrPSc was found to sediment in the middle part of the gradient . While for the slow strains , the distribution of infectivity tended to correlate with that of PK-resistant PrPSc , for the fast strains specifically , infectivity peaked markedly in the upper top gradient fractions , which were much less populous in PK-resistant PrPSc aggregates . Although SV is known to separate protein aggregates according essentially to their size , density can often influence their sedimentation properties , thus questioning which parameters would account for the hydrodynamic properties of the fast strain most infectious component . Here , fractioning the same ovine strains by sedimentation equilibrium ( SE ) demonstrates that the density properties of prion infectivity and PK-resistant PrPSc tend to overlap regardless of the strain , fast or slow , and the solubilization conditions . This indicates that a reduced PrPSc aggregation size and not a low density essentially account for the SV properties of the most infectious assemblies from the fast strains . We further show that these SV-isolable , small sized infectious assemblies perfectly resist limited protease-induced proteolysis and that their templating activity by PMCA outcompetes that of the bulk of larger size aggregates by several orders of magnitude .
Animal experiments were carried out in strict accordance with EU directive 2010/63 and were approved by the authors' institution local ethics committee ( Comethea; permit number 12/034 ) . The cloned ovine prion strains used in this study have been previously described [20] . They have been obtained through serial transmission and subsequent biological cloning by limiting dilutions of classical and atypical field scrapie and experimental sheep BSE sources to tg338 transgenic mice expressing the VRQ allele of ovine PrP . Pooled or individual tg338 mouse brain homogenates ( 20% wt/vol . in 5% glucose ) were used in centrifugation analyses , as indicated . The entire , standard procedure was performed at 4°C unless specified otherwise . Mouse brain homogenates were solubilized by adding an equal volume of solubilization buffer ( 50 mM HEPES pH 7 . 4 , 300 mM NaCl , 10 mM EDTA , 2 mM DTT , 4% ( wt/vol . ) dodecyl-β-D-maltoside ( Sigma ) ) and incubated for 45 min on ice . Sarkosyl ( N-lauryl sarcosine; Fluka ) was added to a final concentration of 2% ( wt/vol . ) and the incubation continued for a further 30 min on ice . For SV , a volume of 150 µl was loaded on a 4 . 8 ml continuous 10–25% iodixanol gradient ( Optiprep , Axys-shield ) , with a final concentration of 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM EDTA , 1 mM DTT , 0 . 5% Sarkosyl . For SE , a volume of 220 µl was mixed to reach 40% iodixanol , 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM EDTA , 1 mM DTT , 0 . 5% Sarkosyl final concentration and loaded within a 4 . 8 ml of 10–60% discontinuous iodixanol gradient with a final concentration of 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM EDTA , 1 mM DTT , 0 . 5% Sarkosyl . The gradients were centrifuged at 285 000 g for 45 min ( SV ) or at 115 000 g for 17 hours ( SE ) in a swinging-bucket SW-55 rotor using an Optima LE-80K ultracentrifuge ( Beckman Coulter ) . We found that 5 hours was the minimum time to run proteins at the equilibrium in the optiprep medium . Gradients were then manually segregated into 30 equal fractions of 165 µl from the bottom using a peristaltic pump . Fractions were aliquoted for immunoblot , bioassay or scrapie cell assay analyses . Gradient linearity was verified by refractometry . To avoid any cross-contamination , each piece of equipment was thoroughly decontaminated with 5 M NaOH followed by several rinses in deionised water after each gradient collection . To ascertain the efficiency of the decontamination procedure , solubilized , uninfected brain homogenates were fractionated at the equilibrium . Some resulting fractions were inoculated to tg338 mice ( see below ) . Those were euthanized healthy at 500 days post-inoculation . Their brain and spleen were negative for PrPSc content . Digitonin ( 0 . 1% final concentration; Sigma ) or saponin ( 0 . 5% final; Sigma ) or methyl-β cyclodextrin ( 10 mM final; Sigma ) were added before or after solubilization with dodecyl-β-D-maltoside and Sarkosyl . The incubation was performed for further 30 min on ice . Brain homogenates from tg338 mice infected with LA21K fast prions ( 20% wt/vol . in 5% glucose ) were adjusted to a final concentration of 25 , 50 and 100 µg/ml proteinase K and incubated under constant agitation at 37°C for 1 hour . The digestion was blocked with phenylmethylsulfonyl fluoride ( 10 mM final concentration; Roche ) . Undigested samples treated in the same conditions were used as controls . The samples were solubilized and fractionated by SV as described above . The fractions were then inoculated to tg338 reporter mice to estimate their infectivity ( see below ) . Aliquots of the collected fractions were treated or not with a final concentration of 50 µg/ml PK ( 1 hour , 37°C ) . Samples were then mixed in Laemmli buffer and denatured at 100°C for 5 min . The samples ( 15 µl ) were run on 12% Bis-Tris Criterion gels ( Bio-Rad , Marne la Vallée , France ) and electrotransferred onto nitrocellulose membranes . In some instances , denatured samples ( 100 µl ) were spotted onto nitrocellulose membranes using a dot-blot apparatus ( Schleicher & Schuell BioScience ( Whatman ) ) . Nitrocellulose membranes were probed for PrP with 0 . 1 µg/ml biotinylated anti-PrP monoclonal antibody Sha31 as previously described [20] . Thy . 1 , flotillin and caveolin proteins were probed with anti-CD90 . 1 ( Southern Biotec ) , anti-flotillin-1 ( Abcam ) and anti-caveolin-1 ( Abcam ) antibodies , respectively . Immunoreactivity was visualized by chemiluminescence ( GE Healthcare ) . The amount of PrP present in each fraction was determined by the GeneTools software after acquisition of chemiluminescent signals with a GeneGnome digital imager ( Syngene , Frederick , Maryland , United States ) . The PrP sedimentation profiles obtained by immunoblot were normalized to units and decomposed using multiple Gaussians fits procedures with a maximum entropy minimization approach . Fractions ( unless specified otherwise ) were diluted extemporarily in 5% glucose ( 1∶5 ) in a class II microbiological cabinet according to a strict protocol to avoid any cross-contamination . Individually identified 6- to 10-week old tg338 recipient mice ( n≥5 mice per fraction ) were inoculated intracerebrally with 20 µl of the solution , using a 26-gauge disposable syringe needle inserted into the right parietal lobe . Mice showing prion-specific neurological signs were monitored daily and euthanized at terminal stage of disease . To confirm prion disease , brains were removed and analyzed for PK-resistant PrPSc content using the Bio-Rad TsSeE detection kit [21] before immunoblotting , as above . The survival time was defined as the number of days from inoculation to euthanasia . To estimate what the difference in mean survival times means in terms of infectivity , strain-specific curves correlating the relative infectious dose to survival times were used , as previously described [20] . The Rov-cell assay technique will be published elsewhere . Gradient fractions aliquots ( 20–30 µl ) were methanol precipitated as done previously [20] , before resuspension in Rov cells culture medium . We verified that methanol precipitation did not affect the overall infectious titer of the samples to titrate . Rov cell [22] monolayers established in a 96 well plate were exposed to the fractions for one week . After several washes with sterile PBS , the cells were further cultivated for two weeks before fixation and PrPSc detection by immunofluorescence using the ICSM33 anti-PrP antibody ( D-Gen Ltd , [23] ) . Immunofluorescent PrPSc signals were acquired with an inverted fluorescence microscope ( Zeiss Axiovision ) . The signal was quantified per cell per well , as previously described [20] . Serial tenfold dilutions of infected brain homogenates were prepared in the same conditions and run in parallel experiments to establish a tissue culture infectious dose curve that directly relates to the PrPSc content . The modified PMCA procedure will be published elsewhere . It has been adapted from previously described protocols [24] , [25] . The PMCA substrate was composed of 10% ( wt/vol . ) tg338 brain homogenate in PMCA buffer ( Tris-HC 50 mM pH 7 . 4 , 1% Triton X-100 , 150 mM NaCl ) . Serial ten-fold dilutions of fractions either as pool or individuals were mixed with substrate lysate in 0 . 2 ml thin-wall PCR tubes containing beads . Tubes were placed in the Misonix S3000 or Q700 sonicator horns ( Misonix , Farmingdale USA; Delta Labo , France ) for a round of 96 cycles . Each cycle consisted of a 30 s sonication step at ∼200–250 W followed by a 29 . 5 min incubation at 37°C . Negative controls were run in parallel . They were composed of unseeded substrate or seeded with uninfected fractions . Aliquots of the amplified samples were digested with PK ( 100 µg/ml final concentration ) for 1 h at 37°C before denaturation in Laemmli sample buffer and dot- or western-blot analysis as described above .
PrPSc and infectivity from fast prion strains exhibited dissimilar hydrodynamic properties by SV , the most infectious assemblies sedimenting slowly [20] . While the detergent used to solubilize brain homogenates disrupted the membrane integrity and released PrPC in the soluble phase [20] , -suggesting efficient solubilization conditions- , a tight and specific association of fast prion strains infectivity with lipids , which would also float in the gradient upon ultracentrifugation , could not be totally excluded . To address this possibility , we examined the distribution of LA21K fast infectivity in more stringent solubilization conditions , with the detergents dodecyl maltoside and sarkosyl used sequentially at 37°C instead of 4°C [26] , before standard SV fractionation in an iodixanol ( Optiprep ) gradient [20] . For each fraction , PK-resistant PrPSc was detected by immunoblot and infectivity was measured with a Rov cell-based assay , as previously described [20] . As a result , solubilization at 37°C did not significantly modify the distribution of infectivity in the gradient: the most infectious fractions were found in the top of the gradient , fractions 1 and 2 being 100–1000 fold more infectious than the middle fractions 12–16 containing the bulk of PK-resistant PrPSc ( Figures 1 A–B ) . To gain resolution in the SV profile , the ultracentrifugation time was doubled . As shown in Figure 1 C , the infectivity peak shifted from fraction 1–2 to fractions 2–4 while PK-resistant PrPSc was found to sediment toward the heaviest fractions of the gradient [12]–[26] . However the shift of infectivity downward was considered as too slight to firmly exclude an intrinsically low density . We therefore decided to study the density of PrPSc and infectivity of the fast strains by sedimentation at the equilibrium . This was compared to that of the slow strains , for which infectivity and PK-resistant PrPSc SV profiles overlapped [20] . Sedimentation equilibrium ( SE ) allows macromolecules reaching a position in the centrifuge tube at which their own density equals that of the gradient density , independent of time . To achieve this , the sample is mixed with the gradient material ( encompassing a wider range of densities than for SV ) and the sample is run for a long period of time ( reviewed in [27] ) . To separate PrP assemblies by density , solubilized brain homogenates were centrifuged isopynically in 10–60% discontinuous iodixanol gradient for 17 hours at 115 000 g . The gradient was then fractionated in 30 fractions of equivalent volume and PrP distribution was assessed by immunoblotting . Three or more independent fractionation experiments with different pooled or individual brains were performed for each strain to assess the reproducibility of the partition and to enable quantitative analysis of the data . In uninfected ( Figure 2A , D ) as in infected brain ( Figure 2B , E ) homogenates , PrPC was found in fractions 14–26 and peaked in fraction 18–20 , i . e . at a density of ∼1 . 23–1 . 28 g/ml ( Figure 2A ) . Other GPI and/or lipid rafts-associated proteins such as Thy1 and flotillin were found in the PrPC-enriched fractions or in the vicinity ( Figure 2 A , C ) , further supporting the view that the conditions employed here led to efficient solubilization of proteins present in detergent resistant microdomains . The combined curves resulting from the replicate analysis of PrP content indicated that PK-resistant PrPSc aggregates from five ovine strains , - two fast strains , 127S ( Figure 2B ) and LA21K fast ( Figure 3A ) and 3 slow strains , LA19K , sheep BSE and Nor98 ( Figure 3 B–D ) -distributed in two major populations peaking in fractions 8–10 and 12–14 , i . e . at respective density of ∼1 . 115 and ∼1 . 145 g/ml , nearby that of caveolin , another lipid rafts resident , but oligomeric protein ( Figure 2C ) . Only the proportion of PK-resistant PrPSc per peak varied to a significant degree among the strains . The distribution of infectivity was assessed by a tg338 mouse incubation time bioassay , using one fractionation performed with pooled brains . It was repeated partially with one strain ( Nor98 ) to confirm the reproducibility of the method . In striking contrast with SV [20] , the distribution of infectivity at the equilibrium broadly overlapped that of PK-resistant PrPSc , whether the strain was fast or slow . Thus , for all the strains , fractions 8 to 14 were the most infectious , based on the mean survival times of the mice that succumbed to disease ( Table 1 ) . The mean survival times of mice inoculated with the fractions at the two PrPSc density peaks rarely differed to a significant level ( Figure S1 ) . Standard infectious dose/survival time curves established individually for each strain tested here [20] indicated that the fractions of higher density were at least 100–1000 less-fold infectious than the most infectious fractions ( Figure 3 ) . There was some strain-dependent variation in the distribution of infectivity in the top fractions of very low density ( Figure 3 ) . While for LA21K fast , LA19K and sheep BSE the differences in survival times between the upper top fractions 1–4 and the most infectious fractions 8–14 were statistically significant , those did not always reach significant values for Nor98 ( Figure S1 ) . For the LA21K fast strain , this provided a 100 to 1000-fold difference in infectious titer between the top and most infectious fractions ( Figure 3A ) . For this strain , the cumulated infectivity of the most infectious fractions by SE approached that previously found in the top fractions by SV [20] . This further supported the view that the most infectious population isolated by SV was indeed present in the middle of the SE gradients . The SE distribution profile of LA21K fast infectivity was similar when the mouse incubation time bioassay was substituted with the Rov cell assay ( n = 3 independent experiments , compare Figure 3A and Figure 4A ) . Thus differences in survival times were correlated with differences in infectivity content and not different pathogenic effects . The infectivity distribution profile associated with the other fast strain , 127S was closely related to that of LA21K fast ( Figure 4B ) , as measured by the scrapie cell assay ( n = 3 independent fractionation studies; Figure 4B ) or partly by the incubation time bioassay ( Table 1 ) . For both LA 21K fast and 127S , the relative infectious levels at the two PrPSc density peaks rarely differed one from the other significantly , as estimated by the Rov cell assay ( Figure S2 ) . Collectively , these data showed a good correlation between the density profile of infectivity and that of PK-resistant PrPSc aggregates , regardless the “speediness” of the prion strain . To further ascertain that the relative overlap , at the equilibrium , in the distribution of PrPSc and infectivity of the fast strains truly reflects a physical association with respect to density , we studied the impact on their sedimentation profile of alterations in the solubilization procedure . We added saponin or digitonin ( two closely related detergents ) or the drug methyl-β cyclodextrin before or after the solubilization with dodecyl maltoside and sarkosyl . These agents are known to specifically deplete or sequester membrane lipids such as cholesterol [28] , [29] , [30] . The solubilization was performed at either 4°C or 37°C to increase the treatment stringency . This was tested on the 127S fast strain . None of the molecules tested modified PrPC sedimentation profile ( data not shown ) . Only digitonin modified the distribution profile of PK-resistant PrPSc at the equilibrium . The peak of lower density in fraction 8–10 was blurred leading to a Gaussian-like distribution of the protein centered in fraction 13 ( Figure 4C ) . This digitonin effect was observed at 4°C and 37°C , independently of the order in which the detergent was used ( data not shown ) . Adding digitonin to the solubilization procedure led to the evolution of 127S infectivity density profile towards a single peak consistently associated with PK-resistant PrPSc ( n = 3 experiments , Figure 4C ) . Such effect was not observed with saponin and methyl-β cyclodextrin ( data not shown ) . Together these data further reinforces the view that the density of PrPSc and infectivity of the fast prions strains are physically associated . To conclude with SE experiments , all the data gained using this technique concur to the view that small size and not low density is mostly responsible for the distinctive hydrodynamic properties of the fast strain most infectious component by SV and its partitioning from the bulk of PrPSc . Having undoubtedly identified that PrPSc aggregates from the fast strains segregated in two populations of differing size and infectivity level by SV , we next examined their respective resistance to PK treatment . This was motivated by the low content of PK-resistant PrPSc of the most infectious population ( <10%; Figure 1 and [20] ) and the reported existence of small sized PK-sensitive aggregates [31] , [32] . LA21K fast brain homogenates were treated with concentrations of PK ( 0–100 µg/ml ) for 1 hour at 37°C prior to SV fractionation . These concentrations were chosen to completely digest PrPC while preserving PK-resistant PrPSc ( [21] and unpublished observations ) . The most infectious fractions ( 1+2 ) and the fractions in which PK-resistant PrPSc levels were peaking ( 12+13 ) were then pooled , respectively , and inoculated to reporter tg338 mice to assess their relative infectivity levels by incubation time bioassay . This was done in two independent experiments summarized in Table 2 . In both experiments , the mean survival time of mice inoculated with the top fractions was marginally prolonged upon the different PK treatments . It would correspond to a reduction <0 . 5 Log10 of the infectious titer . In contrast , the mean survival time of mice inoculated with the middle fractions was increased by 7 to 18 days upon PK treatment , i . e . a potential reduction of infectivity of >1 Log10 . Together these data did not reveal an unusual susceptibility to PK of the LA21K fast , small size most infectious assemblies . The effect of PK treatment appeared even more significant on the larger size PrPSc assemblies . SV fractionation and the PMCA technique were used to compare the templating efficiency of LA21K fast PrPSc assemblies with differing size and infectivity levels . Serial ten-fold dilutions of the upper most infectious fractions [1]–[3] , intermediate PK-resistant PrPSc enriched fractions [12]–[14] and heavy [20]–[22] , [28]–[30] fractions were mixed with uninfected tg338 brain lysate and run for one PMCA round of 48 hours . Four independent experiments were performed using four independent fractionations . In each experiment , fractions were amplified in triplicates . The PMCA products were then treated with PK and analyzed by dot-blot based immunoblotting ( Figure 5 ) . A positive PrPres signal was observed after PMCA amplification of the upper fractions 1–3 diluted up to 106–107-fold . In sharp contrast , no PrPres signal was detected when the other pools of fractions were diluted more than 104-fold before the PMCA reaction . Assuming a straight correlation between PMCA activity of the fractions and PrP assemblies' content , the specific templating activity per unit PrPres would be 1000 to 10 000-fold higher for the discrete population of ‘small’ PrPSc oligomers than for the bulk of higher size PrPSc assemblies .
Our initial SV studies revealed striking divergence in the hydrodynamic properties of the most infectious assemblies between distinct ovine prion strains from the same host species . For fast strains specifically , the most infectious assemblies sedimented slightly and were associated with low levels of PK-resistant material ( [20] and this study ) . To carefully separate the respective contributions of size and density to these hydrodynamic characteristics , we varied the solubilization conditions and performed sedimentation at the equilibrium . Incidentally this is the first study that compared the density of prion particles associated with phenotypically distinct strains propagated on the same genetic background . All these experiments concurred with the view that a reduced aggregation size but not a low density accounts for the low SV properties of the fast strain most infectious component . We also provided evidence that these SV-isolated , small sized infectious species resist limited PK-proteolysis and have high templating efficiency as suggested by PMCA assay . Together , the straight relationship between small sized PrP assemblies , conversion efficacy and short incubation time observed for the fast strains establishes PrPSc quaternary structure as a determining factor of prion ( strain specific ) replication dynamics . Running the ovine prion strains at the equilibrium revealed that PrPSc sedimented in two major density peaks , their respective proportions varying among fast and slow strains . The density values of the 2 PrPSc peaks were markedly reduced compared to that of PrPC , suggesting volumetric differences between these two isoforms . Biophysical , structural and molecular dynamics studies have revealed that the transition from the α-helical to the β-sheet enriched conformation had profound effects on recombinant PrP hydration and packing [33] , [34] , [35] , these two properties directly affecting the volume of a protein . Caveolin-1 , a major , -supposedly oligomeric [36] , [37] , [38]- component of ubiquitous plasma membrane invaginations termed caveolae [39] segregated , at the equilibrium , from monomeric lipid raft resident proteins such as Thy1 and flotillin , further supporting the overlooked notion that oligomerization could markedly alter protein density . The existence of two PrPSc density peaks is intriguing and will obviously deserve further investigations . First , this may reflect PrPSc molecular mass variations within the brain , which can affect density [40] . Endogenously , PrPSc is differentially trimmed by certain nerve cell subpopulations [41] , [42] , [43] . The resulting amino-terminal deletion may additionally affect PrP hydration and cavity distribution [44] . Besides , PrPSc aggregation state polymorphism may contribute to differential hydration , as observed with β2-microglubulin fibrils [45] . There is no real consensus over the volumetric properties of amyloid fibrils . They can be associated to compaction or less packed structures [46] , [47] . PrPSc binding to ligands , some being known to target the N-terminal part of PrP ( for review [48] ) could also affect PrP density [33] . The strain-dependent proportions of PrPSc at the peaks of density would be consistent with these hypotheses: prion strains target specific brain area and can exhibit differential PrPSc processing [42] , [43] , different aggregation states [20] and binding to specific ligands might be strain-dependent [49] . Given all the possible reasons for heterogeneous PrPSc density , the alteration in the PrPSc density profile of fast 127S ( Figure 4C ) and slow LA19K strains ( Figure S3 ) upon addition of digitonin to the solubilization procedure remains difficult to explain . Its specificity of action as compared to other cholesterol-depleting agents , its absence of effect on the SV properties of PrPC and PrPSc ( Figure S4 ) together with a yield of protein solubilization equal or inferior to that achieved with dodecyl maltosite [20] , [50] , [51] are strong arguments against an increase in the solubilization yield . Thus differences of densities are more likely to reflect differences in the properties of the bound-detergent species . At the equilibrium , PrPSc and infectivity sedimented relatively congruently , whatever the prion strain studied , yet infectivity was not distributed in two clearly distinct peaks of densities like PrPSc . There are differences in the infectivity density values previously published [52] , [53] , [54] and ours , which are likely explained by the use of different starting material , distinct gradient medium and the degree of solubilization achieved . Our density values found for caveolin , -a protein recovered in fractions nearby PrPSc and infectivity- , are consistent with those published [55] . Importantly , the density distribution of PrPSc and infectivity from the 127S fast strain were jointly altered by digitonin . This result strengthened the truly physical association between PrPSc and infectivity with respect to the density of the fast prion strain assemblies . Collectively , these data indicate that a small size and not a low density accounts for the hydrodynamic behavior of the fast strains most infectious component by SV . Keeping in mind all the uncertainties in determining the molecular mass by SV , we estimated previously that these assemblies might correspond to a pentamer of PrP , if constituted of PrP only [20] . However this value might be underestimated as we showed here that PrP density/volumetry has been dramatically altered by its refolding into PrPSc . There is clear evidence that a variable , strain-dependent proportion of PrPSc can be fairly sensitive to PK treatment [56] , [57] , [58] , [59] . Such PrPSc species have been proposed to be formed of low molecular weight aggregates [31] , [32] . PK-sensitive PrPSc has been shown to support a substantial fraction of infectivity [59] , [60] , -although this might be strain dependent [57] , [61]- , and to have a substantial in vitro converting activity [31] , [62] . The PrPSc content associated with fast strains such as 127S or LA21K fast resists fairly harsh PK treatment conditions , notably compared to Nor98/atypical scrapie ( [21] and unpublished data ) . Subjecting LA21K fast crude brain homogenate to a PK treatment destroying 99% of PK-sensitive PrPSc infectivity [59] prior to SV fractionation negligibly affected the infectivity associated to the small sized assemblies , as measured reproducibly by the incubation time bioassay . These results are consistent with the inability to detect thermolysin-resistant PrPSc [20] , that might be indicative of the presence of PK-sensitive molecules [57] , [63] . Counter-intuitively , the infectivity of LA21K fast higher size PrPSc assemblies appeared more sensitive to the PK treatment than that of the smaller ones , suggesting possible differences in the tertiary structure between the 2 populations of assemblies . These data reinforce the view [20] that PK sensitivity does not inversely mirror the size of PrPSc assemblies , at least for certain prion strains . Here we observed a strict quantitative correlation between the fast prion strains aggregates templating activity , as measured by the conversion of ovine PrPC by PMCA , and their infectivity as measured by mouse incubation time bioassay or replicating activity in cell culture . The templating activity of the smallest size PrPSc aggregates particles was 2–3 logs over that of the bulk of higher sized PrPSc aggregates . Whether this is due to their size , -the smaller , the swifter to polymerize [64]- , or to their specific infectivity remains clearly an open , overlooked question [62] we are currently addressing . Given the superior templating activity of the smallest size PrPSc aggregates , further studies are ongoing to examine whether the SV profile of PMCA-generated PrPSc would be enriched in such assemblies and thus would differ from that of the original brain material . This would be consistent with recent observations suggesting a preferential selection of certain PrPSc conformers during PMCA reactions [65] . The longest PrPSc polymers ( assuming they are linear ) could conceivably [66] , [67] , [68] generate numerous converting pieces as active as the small size oligomers , provided they can be fragmented by the sonication and the beads used in PMCA [69] . They also exhibit low conformational stability values ( Table S1 ) , as assayed by denaturation assay [70] , a characteristic believed to increase the rate of polymer fragmentation [71] , [72] . As the main aggregate type in the fast strains , they were expected to exhibit the best converting activity . Having actually found the opposite situation raises the intriguing possibility that the most infectious and the most aggregated PrPSc populations identified by SV might not derive from the same polymerization pathway , as observed with recombinant PrP oligomers [73] and other protein oligomers [74] , [75] or , alternatively , that an increase in the polymer size led to an irreversible loss of converting activity . It also suggests that the proposed pivotal role of fibril breakage [6] , [7] , [72] in hastening fibril growth is a specific property of certain macromolecular assemblies , at least for prion . The low PMCA activity of the largest PrPSc assemblies further add to the discrepant impact of the overall stability and/or length of PrPSc aggregates on its conversion potency [25] , [42] , [62] , [76] . A clear and confounding limitation in such studies is that the properties of the biochemically dominant PrPSc component are taken as the properties of the whole PrPSc species while it is obvious here that the specific infectivity and templating activity of PrPSc assemblies can be heterogeneous . Another layer of intricacy would be provided by the strain to strain variations . Cumulatively ( this study and [20] ) , the specific infectivity and converting activity ( the levels of infectivity and of PMCA activity divided by the PrP content ) of the fast prions PrPSc aggregates appears essentially supported by a minor fraction ( <10% ) of PK-resistant oligomers of ≤5 PrP molecules , - a size consistent with that deduced from prion radiation inactivation studies [77] , [78] - , whereas the bulk of PrPres ( >90% ) , constituted essentially of 12–30 molecules of PrP [20] , showed over 1000-fold lowered activities . A considerable proportion of PrPres generated during the course of the disease might thus have a negligible contribution to prion replication dynamics . The reported converting activities of small-sized , PK-sensitive particles [31] , [62] or small size PrPres aggregates fractionated by other methods [79] , [80] appeared comparatively low . Although the latter studies were based on fast hamster strains , we found that their most infectious particles were also associated with small sized particles , as in the fast ovine strains [20] . It is worth mentioning their infectious starting material was composed of artificially aggregated PrPres particles that were sedimented before subsequent disaggregation and fractionation [79] , [80] . Such a procedure may have destroyed or permanently altered discrete subpopulations of infectious particles [60] , [81] . Together , our findings suggest that prion infectious particle size is strain-encoded and participates in the strain biological phenotype , in particular the incubation period of disease . For the fast strains , our findings support discrete oligomers as the most effective template in the proteopathic cascade leading to animal death . Their strong converting properties could provide a quick regeneration of templates to sustain prion replication . Their small size could also favor dissemination and initiation of conversion at distance . Whether the oligomeric forms identified in our study demonstrate a more acute neurotoxicity than the larger size aggregates remains to be determined and is currently assessed using prion permissive primary cultures of neurons [82] . As the most potent inducers of the pathogenesis , these oligomers could be in fine the most neurotoxic , incidentally concurring with the view that the oligomers generated during neurodegenerative diseases linked to protein misfolding and aggregation are generally more potent than larger multimers in impairing neuronal metabolism and viability ( for reviews [2] , [83] , [84] ) .
|
Prions are infectious agents causing irremediably fatal neurodegenerative diseases in human and in farmed or wild animals . They are thought to be formed from abnormally folded assemblies ( PrPSc ) of the host-encoded prion protein ( PrPC ) . Different PrPSc conformational variants associated with distinct biological phenotypes , or ‘strains , ’ can propagate in the same host . To gain some structural information on the physical relationship between packing order ( i . e . quaternary structure ) and the strain-specific biological information , we previously subjected PrPSc assemblies from prion strains classified as fast or slow ( according to their survival time in susceptible laboratory animals ) to sedimentation velocity ultracentrifugation experiments . For the fast strains specifically , the most infectious assemblies sedimented slowly and partitioned from the bulk of PrPSc macromolecular complexes . By changing the solubilization and sedimentation conditions , we established here that a small PrPSc aggregation size and not a low density accounts for these hydrodynamic properties . We further showed that these small assemblies resist proteolytic digestion and outcompete by several orders of magnitude the larger-size assemblies in cell-free prion conversion assays . Thus PrPSc quaternary structure appears to be a determining factor of prion replication dynamics . For certain strains , a discrete subset of PrPSc assemblies appears to be the best template for prion replication .
|
[
"Abstract",
"Introduction",
"Material",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Quaternary Structure of Pathological Prion Protein as a Determining Factor of Strain-Specific Prion Replication Dynamics
|
Alternative mRNA splicing adds a layer of regulation to the expression of thousands of genes in Drosophila melanogaster . Not all alternative splicing results in functional protein; it can also yield mRNA isoforms with premature stop codons that are degraded by the nonsense-mediated mRNA decay ( NMD ) pathway . This coupling of alternative splicing and NMD provides a mechanism for gene regulation that is highly conserved in mammals . NMD is also active in Drosophila , but its effect on the repertoire of alternative splice forms has been unknown , as has the mechanism by which it recognizes targets . Here , we have employed a custom splicing-sensitive microarray to globally measure the effect of alternative mRNA processing and NMD on Drosophila gene expression . We have developed a new algorithm to infer the expression change of each mRNA isoform of a gene based on the microarray measurements . This method is of general utility for interpreting splicing-sensitive microarrays and high-throughput sequence data . Using this approach , we have identified a high-confidence set of 45 genes where NMD has a differential effect on distinct alternative isoforms , including numerous RNA–binding and ribosomal proteins . Coupled alternative splicing and NMD decrease expression of these genes , which may in turn have a downstream effect on expression of other genes . The NMD–affected genes are enriched for roles in translation and mitosis , perhaps underlying the previously observed role of NMD factors in cell cycle progression . Our results have general implications for understanding the NMD mechanism in fly . Most notably , we found that the NMD–target mRNAs had significantly longer 3′ untranslated regions ( UTRs ) than the nontarget isoforms of the same genes , supporting a role for 3′ UTR length in the recognition of NMD targets in fly .
Nonsense-mediated mRNA decay ( NMD ) is an RNA surveillance system that down-regulates mRNAs containing early stop codons in all eukaryotes examined [1] . NMD functions to clear the cell of transcripts containing potentially harmful nonsense mutations [2] . In addition to this role in surveillance of mutations , NMD affects the expression of numerous non-mutant endogenous targets [3]–[6] . These natural targets include many mRNAs that are the products of alternative splicing; one study reported that 45% of alternatively spliced human genes have at least one isoform that may be degraded by NMD [7] . In some of these cases , alternative splicing and NMD act together to regulate gene expression , providing an additional layer of post-transcriptional regulation . By altering the abundance and activity of splicing factors , the cell can differentially splice a pre-mRNA into a productive mRNA that encodes a protein or into an unproductive mRNA with an early stop codon that makes the mRNA a target for NMD . Unproductive splicing is used in the regulation and autoregulation of numerous genes [8] including mammalian splicing factors , spliceosome components [9]–[14] and the spermidine/spermine N1-acetyltransferase ( SSAT ) gene [15] . Alternative splicing is prevalent in the fruit fly Drosophila . At least 46% of detected genes show differential expression of alternative regions during development [16] . In flies , alternative splicing plays an important role in many processes including sex determination , neuronal wiring , and eye development [17]–[19] . Although NMD is active in Drosophila , our understanding of its impact on the fly transcriptome is limited . A study of the effect of NMD on gene expression in Drosophila showed that levels of 14% of detected genes increased at least 1 . 5-fold after a key NMD factor , UPF1 , was depleted [20] . This analysis used gene expression microarrays that assess total mRNA from a gene , and thus it could not measure the levels of distinct alternative splice forms . Natural NMD targets produced by alternative splicing in Drosophila have not been assayed previously . The NMD machinery of Drosophila , as in all eukaryotes studied , requires the core set of UPF proteins , UPF1 , UPF2 , and UPF3 [21] , [22] . As in mammals , it also involves SMG1 , SMG5 , and SMG6 ( but , unlike mammals , not SMG7 ) , which are involved in the phosphorylation and dephosphorylation of UPF1 [22] . Although the core NMD machinery is essentially the same in human and Drosophila , the mechanism by which premature termination codons are recognized is different in the two organisms . In both cases , the nonsense codon seems to be recognized as premature based on its position relative to proteins associated with the transcript , downstream of the stop codon . In human , the primary downstream markers are exon junction complexes deposited during splicing [23] , [24] . Exon junction complexes are not required for NMD in Drosophila [22] . A recent study indicates that , instead , some early stop codons are recognized based on their distance from the poly-A tail , mediated by the binding of cytoplasmic poly-A binding protein ( PABPC1 ) [25] . This study provided valuable data about the NMD mechanism based on manipulation of a single reporter construct . Studies of a wider range of NMD targets are necessary before a general rule can be inferred . Splicing-sensitive microarrays have been used successfully to assay alternative splicing on a global scale ( reviewed in [26] ) . This method has been applied in fly to assess global splicing changes when splicing factors are inhibited or overexpressed and to measure sexually dimorphic splicing [27]–[29] . Microarrays have also been used to measure the effect of NMD on the levels of alternatively spliced mRNAs in human , mouse , worm , and yeast [4] , [12] , [30] , [31] . However , most techniques used to analyze these microarrays only measure the change in probes specific to individual alternative splice junctions or alternative exons . One method , successfully used to assay alternative splicing in human , measures changes in exon inclusion events [32] , but has yet to be extended to more general splicing events . None of these methods provide isoform-level fold-changes , limiting their ability to find NMD targets . In this work , we have developed a new algorithm that makes it possible to obtain isoform-level measurements for all categories of alternative splicing and alternative processing events . We use a generative non-linear regression model to deconvolve individual probe measurements into estimates of overall isoform-level fold-changes and relative proportions of isoforms . Our goals in this project were two-fold: first , to determine the effect of NMD on alternatively spliced mRNAs in the Drosophila transcriptome , and second , to identify features of these transcripts that might cause them to be targets of NMD . To assess the effect of NMD , we have inhibited NMD in Drosophila cells and measured changes in expression on a custom splicing-sensitive microarray . After measuring junction and exon splicing changes and then estimating isoform-level fold-changes , we identified NMD targets using a hierarchy of stringent criteria that eliminate many secondary effects and potential artifacts , at the cost of substantially reduced sensitivity to legitimate NMD targets . Using this conservative approach , we have found a high-confidence set of 45 genes where NMD decreases the level of one isoform without impacting the levels of other isoforms . We found that the reading frames of NMD–target mRNAs were often misannotated in sequence databases . After identifying the correct reading frames , we found that the NMD–target mRNAs differed significantly from the nontarget isoforms , with shorter coding regions and longer 3′ untranslated regions ( UTRs ) . Our results show that alternative splicing and NMD affect a diverse set of genes in fly including genes involved in translation and mitosis , suggesting that regulation of unproductive splicing might play important roles in Drosophila .
We previously developed a splicing-sensitive microarray to detect alternative splicing , alternative transcription start sites , and alternative polyadenylation in Drosophila [27] . The array contains 43 , 337 exon and junction probes , targeting 7 , 768 transcripts of 2 , 793 alternatively processed genes in FlyBase 4 . In order to identify cellular mRNAs naturally targeted by the NMD machinery , RNA was obtained from a previous experiment in which levels of the key NMD effectors UPF1 and UPF2 were reduced in S2 cells by dsRNAi , with three independent knockdowns of each effector [20] . Following the functional knockdown of the NMD machinery , confirmed by the stabilization of an NMD reporter , RNA was extracted and the microarray was used to probe the changes in alternative splicing patterns relative to the patterns in control cells treated with an unrelated dsRNA . When compared with the control samples , the upf1 knockdown samples showed substantial probe-level changes , as well as substantial down-regulation of probes targeting upf1 ( Figures S1 , S2 , S3 , S4 ) . The upf2 knockdown showed smaller probe-level changes , and we observed that the probes to the upf2 gene itself showed only a small decrease in the upf2 knockdown compared to control , with the exception of one highly up-regulated probe targeting the same area as the dsRNA . This indicates that the upf2 knockdown was less effective . We have therefore excluded the upf2 results from our primary analysis; further data are available in the Supplementary Results in Text S1 . Splicing-sensitive arrays that contain splice junction probes can easily measure the change in the use of a given splice junction . However , to study the effect of NMD on mRNA stability , we must know the fold-change of the entire set of isoforms , which may include multiple alternatively spliced junctions . This is not trivial because many of the probes on the array target multiple transcripts . We have developed a new algorithm , based on a generative non-linear regression model with least squares estimation , to deconvolve the measurements of multiple probes targeting different combinations of isoforms into an overall fold-change measurement for each isoform . In addition to isoform fold-changes , the algorithm yields estimates of the relative proportions of the different isoforms . Deconvolution requires probes targeting different combinations of isoforms . For a gene with only two isoforms , we require probes targeting the two individual isoforms as well as probes targeting both isoforms; having only probes targeting the individual isoforms would preclude the estimation of relative abundance . As an example of a situation where deconvolution is impossible , alternative polyadenylation can produce two isoforms that differ only in the length of the last exon , and there is no possible probe that uniquely targets the shorter isoform . For genes with more than two isoforms the details are more subtle , but in general a gene with isoforms requires probes targeting at least different combinations . This requirement makes it difficult to deconvolve genes with many isoforms , and , in some cases , it is provably impossible to obtain isoform-level fold-changes . Also , the algorithm and the array design assume that gene structures are known . Unknown alternative splice forms may lead to misinterpretation of the observed probe fold-changes . Examples of gene structures , probe locations , probe and isoform fold-changes , and relative proportions can be found in Figure 1 . The mathematical formulation of the generative model is presented in the Materials and Methods section . The algorithm should be of general use in integrating data from splicing-sensitive microarrays to infer isoform-level changes . The principles behind the algorithm can also be applied to other methods such as high-throughput mRNA sequencing for studying alternative splicing . Using the algorithm , we were able to deconvolve the isoform-level fold-changes in the upf1-knockdown experiment for 1 , 410 of 1 , 576 genes with two isoforms and for 668 of 1 , 124 genes with three or more isoforms ( involving as many as 11 isoforms ) . 574 of the genes were not deconvolved because they did not satisfy the requirement of having different probe combinations . The generative model imposes certain restrictions on the fold-change of a probe targeting multiple isoforms; 38 genes grossly violating these restrictions were flagged as inconsistent and no predictions were made for these genes . Following deconvolution , we used statistical tests to classify the isoform-level changes . In microarray measurements , it is difficult to distinguish mRNAs whose levels do not change between conditions from mRNAs that are not present in either condition . For our study , we are most interested in cases where one isoform is differentially affected by NMD inhibition . An overall change in gene expression , with no change in splicing , can appear to be differential abundance of isoforms if one isoform is never present and is incorrectly called “unchanged . ” To eliminate these false positives we devised a heuristic method to call isoforms “possibly absent , ” at the expense of incorrectly eliminating some unchanged isoforms . The heuristic method is based on the reasoning that an mRNA should have positive evidence for its presence; in the absence of positive evidence we would rather conservatively conclude the transcript is absent than that it is present and unchanged . The “possibly absent” isoforms were excluded from later analyses , greatly improving the reliability of identified NMD–affected genes . Using a cutoff of 0 . 001 , we found that 1 , 553 genes out of the 2 , 078 deconvolved genes show no change in expression upon inhibition of NMD . The remaining 525 genes have a total of 1 , 384 isoforms , of which 285 were “up-regulated , ” 287 “slightly down-regulated , ” 41 “very down-regulated , ” 58 “unchanged , ” and 713 were classified as “possibly absent . ” In order to identify genes with isoforms targeted by NMD , we considered the joint behavior of all isoforms of the gene . To generate a high-confidence set of affected genes , we focused on high specificity with a consequent reduction in sensitivity . Therefore , our results do not provide an estimate of the prevalence of unproductive splicing , as many true NMD targets will be excluded by our criteria . To avoid making predictions based on secondary effects of the knockdown , we used the following reasoning: Using this classification scheme , a two-isoform gene is called an NMD target if the more abundant isoform is unchanged and the less abundant isoform is up-regulated upon NMD inhibition . As a result , this scheme primarily identifies NMD–affected genes that do not show gene-level differential expression , excluding most genes with a change in transcription level . For genes with more than two isoforms , we required that at least one isoform be up-regulated , at least one isoform be unchanged or only slightly down-regulated , and the rest of the isoforms be up-regulated , unchanged , slightly down-regulated , or possibly absent . The full characterization of a gene as affected by NMD involves a number of sequential statistical tests . Correcting for multiple testing in a situation with nested tests is an open problem in statistics . We approach this problem by generating two sets of genes affected by NMD: one highest-confidence set where all significance levels were fixed at 0 . 001 ( stringent ) and one set where all levels were fixed at 0 . 05 ( less stringent ) . Our analysis of the upf1 knockdown revealed 45 genes putatively affected by NMD with the stringent threshold ( Table 1 ) and 189 genes putatively affected by NMD with the less stringent threshold ( Tables S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 ) . We will focus on the stringent set throughout our analysis . We performed a Gene Ontology ( GO ) term enrichment analysis with the program AmiGO on the set of 45 affected genes to assess the effect of alternative splicing and NMD on cellular processes ( Table 2 , Tables S3 , S4 ) [33] . The most significantly enriched biological process term , when comparing the NMD–target genes to all genes represented on our array , was “translation” ( , with no multiple testing correction ) , and parents of this term were also enriched . The NMD–target genes in this category encode five ribosomal proteins and two other RNA-binding proteins with roles in translation . The NMD–target genes also include another five genes encoding RNA-binding or splicing-related proteins , but related GO terms were not significantly enriched ( ) . Terms related to the mitotic spindle were also enriched ( e . g . , for “mitotic spindle elongation” ) . Interestingly , ribosomal protein genes were also largely responsible for this enrichment; many ribosomal proteins were previously identified in a genome-wide screen for mitotic spindle defects [34] . It was previously observed that knockdown of upf1 or upf2 caused cell cycle arrest in the G2/M phase [20] . To further investigate the connection between mitosis and NMD , we compared our set of NMD–affected genes to sets of genes associated with mitosis . Amongst our NMD targets , there was a significant enrichment ( ) of a set of 402 genes with known mitotic defect phenotypes ( 119 of which were alternatively spliced and thus measured on our array ) [34] , [35] . The overlap comprised six genes , including the five genes with mitotic spindle GO annotations found in our AmiGO analysis . However , there was no enrichment of a set of 1000 genes that are co-expressed with known mitotic genes and likely to be differentially expressed in mitosis [35] . We believe it is unlikely that the mRNAs identified in our analysis as NMD targets are , instead , predominantly secondary effects of mitotic arrest , although we do not rule out the possibility that a subset of putative NMD targets actually represent such secondary effects . Indeed , the AmiGO results suggest that unproductive splicing of the six ribosomal and RNA-binding proteins may play a more direct role in cell cycle progression . We experimentally tested the NMD status of isoforms of 10 genes chosen for having a large fold-change in at least one junction probe after upf1 inhibition . Four of these genes had been called NMD–affected based on the microarray deconvolution , four genes had been called unaffected , and two genes had complex splicing patterns that had prevented their deconvolution . We used RT-PCR to measure the effect of upf1 and upf2 knockdowns on the 10 genes ( Figure 2 and Figures S7 , S8 ) . We saw that the ratio of NMD–target∶nontarget mRNA increased upon upf1 and upf2 knockdown for all four genes called NMD–affected , confirming the array analysis . For three of the four genes called unaffected , we also confirmed the array analysis . One gene , CG8046 , was called unaffected based on the array data , but RT-PCR showed that it is probably an NMD target because the ratio of isoform B∶A increases substantially upon NMD inhibition . Finally , two genes could not be deconvolved in the array analysis but have large individual probe fold-changes . Both genes , RpS9 and RpL3 , are shown by RT-PCR to have an NMD–target isoform ( Figure S7 ) . In all , we found that the array analysis properly classified all isoforms of 7 out of 8 genes it was able to deconvolve . The analysis had no false positives , but as expected our analysis sometimes missed true NMD targets . Our results complement those of a previous study that identified NMD targets in Drosophila using a microarray approach that did not distinguish between alternative splice forms . Rehwinkel et al . used a gene expression microarray to measure the effect of inhibiting each of six NMD effectors [20] . They found that 525 mRNAs , or 14 . 3% of genes detected on the array , were up-regulated at least 1 . 5-fold after depleting UPF1 . They focused on a core group of 184 genes that were up-regulated in at least 10 of their 12 knockdowns . For each gene on our array , we compared the fold-change from the Rehwinkel et al . upf1 knockdown with the gene-level fold-change from our analysis , obtained by averaging constitutive probes ( Figures S5 , S6 ) . The two experiments have a correlation of 0 . 6 . As described above , our classification scheme focuses on genes that generally do not show differential expression at the gene level . For that reason , we would not expect a strong concordance between the NMD–affected genes identified in the two studies . Also , we only assayed genes annotated with multiple isoforms , which are only a small subset of the genes present on the Rehwinkel et al . platform . Indeed , there is almost no overlap between the two sets of inferred NMD targets; the only genes that were found to be affected by NMD in both studies are CG13900 , CG10948 , and glorund , all three involved in RNA processing . Rehwinkel et al . validated the direct effect of NMD on nine genes , three of which were present on our array . One of these , CG13900 , is an NMD target in our set . The other two genes are not classified as NMD targets in our results because they showed a change in overall expression rather than a differential effect on different isoforms . Rehwinkel's validation also demonstrated that two genes in their core set of NMD–affected genes , pgi and CG30035 , do not appear to be direct NMD targets . Both genes were present on our array and both were correctly called nontargets . Although the exact mechanism of premature stop codon recognition is unknown in Drosophila , it is generally assumed that NMD–target mRNAs have early stop codons relative to nontarget mRNAs . In light of this , it was startling that 35 of 45 genes in the set of NMD affected genes were annotated in FlyBase with the same stop codon in the NMD–target and nontarget isoforms . We determined that the annotated FlyBase coding sequence ( CDS ) was often unlikely to be the biologically accurate CDS . The FlyBase annotation protocol automatically chooses the longest open reading frame ( ORF ) of each transcript as the CDS , unless other evidence is available [36] . For the thousands of alternatively spliced genes , this annotation strategy may introduce substantial misinformation into gene and protein databases . To understand the effect of NMD on a transcript , we identified the reading frame most likely to be recognized by the ribosome . In general , a eukaryotic ribosome initiates translation at the 5′-most AUG of an mRNA [37] . However , the ribosome may skip one or more AUG codons before initiating translation , or it may first translate a short upstream ORF ( uORF ) [38] . No single strategy for annotating reading frames will correctly represent the biology in all cases . We were guided by the principles that a gene should have at least one transcript that encodes a full-length , functional protein , and that the start codon of that transcript is likely to be recognized in the other , alternative transcripts as well . We employed two distinct methods to choose the correct CDS . One method makes use of the upf1 knockdown data to help identify the transcript most likely to encode a full-length , functional protein . We assumed that in most cases this transcript would not be a target of NMD . Therefore , we chose the longest ORF found in any NMD nontarget isoforms of a given gene as the canonical CDS . We then assumed that the start of this canonical CDS is recognized in vivo , regardless of whether it begins at the first AUG codon in the transcript . We inferred the CDS of each isoform by choosing the ORF beginning at this canonical start codon ( Figure 3 ) . In some isoforms , alternative processing has introduced isoform-specific sequence upstream of the canonical start codon , e . g . , due to an upstream promoter or alternative splicing in the first intron ( Figure 3B ) . In these cases , we considered the possibility that the alternative sequence contains a new , upstream AUG that is recognized by the ribosome , perhaps as the start of a short uORF with an early stop codon . The second method to annotate CDSs is blind to NMD status . The longest ORF present in any transcript , NMD–target or nontarget , was chosen as the canonical CDS , and its start codon was used to annotate the CDS in all transcripts . This second method has the benefit of being unbiased , but because it ignores some data , it is likely to be less accurate . The results from this second , unbiased method were used in our statistical analysis of gene features correlated with NMD status . Full details of our reannotation methods are found in the Supplementary Methods in Text S1 . Basing the CDS annotation on the NMD status of each transcript in the set of 45 upf1-affected genes , the first annotation algorithm found 27 genes with a noticeably early stop codon in the NMD–target isoform relative to the nontarget isoform , out of 41 genes ( four genes were removed from the analysis because of inconsistencies between FlyBase 4 and more recent transcript data ) . Without using NMD status as input , the second annotation algorithm found early stop codons in 23 out of 41 genes . The NMD–affected isoforms without early stop codons may represent unknown aspects of the NMD mechanism or , more likely , secondary effects of the knockdowns . We also re-annotated the CDS of the upf1-affected genes identified with the less stringent cutoff . Early stop codons are found in a lower percent of the NMD targets in this set compared to the strict set: 92/181 using NMD status , and 65/181 without using NMD status . This suggests that the less stringent may include more genes that are not directly affected by NMD . If the NMD–target mRNAs do not encode functional proteins , we would not expect their CDSs to be optimized for translation efficiency or under selective pressure to maintain amino acid sequence . A comparison to overall Drosophila codon usage showed that the nontarget mRNAs were significantly skewed towards preferred codons and the NMD–target mRNAs showed less preference for preferred codons . This indicates that the unproductive reading frames are less optimized for translation efficiency . We also estimated the ratio of non-synonymous to synonymous substitutions ( dN/dS ) in dual-coding regions in which the reading frame of the NMD–target isoform is shifted relative to the nontarget isoform , comparing D . melanogaster to D . ananassae using PAL2NAL [39] ( Supplementary Results in Text S1 ) . In 3 of 4 dual-coding regions , from glo , robo , and CG4452 , the NMD–target reading frame had a very high dN/dS , indicating that it was probably not under coding sequence constraints , and the nontarget reading frame had a low dN/dS as expected . In only one gene , CG9413 , dN/dS was lower in the NMD–target reading frame than in the nontarget reading frame , indicating that this sequence might be under protein-coding constraints in both frames . Overall , these results suggest that our CDS annotation was generally accurate , and support the notion that our NMD–target mRNAs do not yield protein . We sought to find features of mRNAs that were correlated with NMD target status . These features could reveal aspects of the NMD mechanism for recognizing premature stop codons . We considered the lengths of the 5′ UTR , 3′ UTR , and CDS; the number of introns in the UTRs , the CDS , and the transcript as a whole; the number and size of potential uORFs; and sequence features such as A-rich regions . These features were chosen based on existing hypotheses about NMD . The presence of introns in the 3′ UTR triggers NMD in human , while the length of the 3′ UTR has been implicated in NMD in Drosophila [25] . Small upstream ORFs might trigger NMD of some transcripts [40] , and A-rich elements in mammalian 5′ UTRs also destabilize some mRNAs via the binding of PABPC1 [41] . Although experiments have shown that NMD of a reporter construct in Drosophila does not depend on components of the exon junction complex [22] , we also tested the possibility of a rule akin to the human 50-nucleotide rule . We computed the distance between the stop codon and the position of the last exon junction in the transcript . The NMD targets and NMD nontargets were first compared using an unpaired analysis , where we compare the marginal feature distributions for each of the two sets of isoforms ( Figure 4A ) . Such a comparison yielded little difference between the two groups , mostly due to high heterogeneity between genes relative to differences between distinct isoforms of the same gene . We therefore proceeded with a more powerful paired analysis in which we compared each feature of the NMD–target isoform with the corresponding feature of the NMD nontarget isoform for the same gene . In case a gene has two or more isoforms that are labeled target or nontarget , the feature values for the isoforms of the given gene were averaged to yield a single number per gene per category . For each comparison we considered both one- and two-sided tests with the alternative hypothesis that the NMD–target isoforms have , for instance , longer 3′ UTRs or more introns in the 3′ UTR region . We found six features to be correlated with NMD status in the upf1-affected genes ( Figure 4 and Figures S9 , S10 , S11 , S12 , S13 , S14 , S15 , S16 , S17 , S18 , S19 , S20 , S21 ) . Relative to the nontarget isoforms , the NMD–target isoforms have shorter CDSs , fewer introns in the CDS , longer 3′ UTRs , more introns in the 3′ UTR , longer ORFs in the 3′ UTR , and a greater distance between the stop codon and the last intron . All of these features were significant at a 5% level with between 0 . 0008 and 0 . 003 ( one-sided tests; for two-sided tests were twice as large ) for the stringent set of upf1-affected genes . All six features had somewhat less significant using the less stringent set of targets ( between 0 . 042 and 0 . 09 for one-sided tests ) . In our set of NMD–affected genes we find that there are essentially two subgroups . One subgroup of genes shows the differences described above , with longer 3′ UTRs in NMD–target mRNAs . In the other group , the NMD target and nontarget isoforms of a given gene share the same 3′ UTR structure – implying that no feature in the 3′ part of the gene can be responsible for NMD recognition . Some of these genes might have been classified incorrectly and may instead reflect secondary effects of NMD inhibition . We also used MEME to search for overrepresented sequence motifs within the 3′ UTRs of NMD–target mRNAs [42] , analogous to the downstream element implicated in NMD in yeast [43] . The only motifs found to be enriched within the UTRs of NMD–target mRNAs were repetitive sequences ( Figure S22A ) . When we limited the search to the UTRs of NMD–target mRNAs with early stop codons , we found two additional non-repetitive motifs ( Figure S22A ) , but both are similar to known splicing enhancers [44] . No significant motifs were found in the UTRs of the nontarget mRNAs . The features correlated with NMD status are obviously not independent , and our data cannot resolve which , if any , of these are detected directly by the NMD mechanism . Alternative splicing has only a small effect on the length of the mRNAs produced from most of the NMD–affected genes; its principal effect is to change the position of the stop codon , simultaneously shortening the CDS and lengthening the 3′ UTR . The change in 3′ UTR length may also account for the significance of the other features that distinguish NMD–target from nontarget isoforms . Our observation of longer 3′ UTRs agrees with previous work indicating that the NMD mechanism in Drosophila is affected by the length of the 3′ UTR . Behm-Ansmant et al . determined that nonsense codons in an adh reporter construct are recognized as premature based on the distance between the stop codon and PABPC1 bound to the poly-A tail of the transcript [25] . Stop codons 379 nucleotides or fewer upstream of the poly-A tail did not elicit NMD , but stop codons 397 nt or more upstream of the poly-A tail caused degradation . Our larger set of natural NMD targets allows us to compare this length to the UTR lengths of the transcripts identified by our array to see if that result is more generally applicable . We found that on average the NMD–target isoforms have longer 3′ UTRs than the nontarget isoforms , but 397 nt is not a discriminant . Almost all ( 33/41 ) of the NMD–target isoforms have UTRs longer than 397 nt , but over a third ( 17/44 ) of the nontarget isoforms also have UTRs longer than 397 nt . It may be more appropriate to include only genes that are more likely to be direct NMD targets . The 397 nt cutoff does describe all but one of the 27 NMD–target isoforms with an early stop codon relative to the non-target isoform of the same gene . However , 9 of 28 nontarget isoforms also have a 3′ UTR longer than 397 nt . From our data , the best descriptor seems to be a length cutoff of 742 nt , which correctly classifies 26/27 NMD–target mRNAs and 22/28 nontarget mRNAs . It is clear that the length of the 3′ UTR is a key determinant of NMD , but neither our statistical correlation nor the published experimental study provide a general rule for predicting NMD status .
We have found that alternative splicing in Drosophila can produce mRNAs that are targets of NMD . Using strict criteria , we find 45 genes with both an isoform that is stabilized after NMD inhibition and an isoform that is not affected by NMD inhibition . Our set includes examples of many different modes of alternative processing , including cassette exon skipping or inclusion , alternative 5′ or 3′ splice sites , intron retention , and alternative splicing combined with alternative transcription start sites or polyadenylation . Note that our conservative criteria are not intended to provide a full measure of the true prevalence of unproductive splicing in fly . Most of the NMD–target isoforms have early stop codons relative to the unaffected isoform of the same gene , indicating that our results include many direct targets of NMD . However , a third of the apparent NMD–target isoforms in the stringent set do not have early stop codons . While some may be false positives , others are likely to represent secondary effects of NMD inhibition , genes with unannotated alternative splicing events , or unknown aspects of the NMD pathway . The NMD machinery may recognize and degrade some mRNAs whose stop codons do not appear premature , as occurs in the mammalian UPF1-dependent process known as Staufen mediated decay [45] , [46] . Many NMD–affected genes without early stop codons may not be direct targets of NMD and may instead demonstrate the downstream effects of unproductive splicing . Secondary splicing effects are particularly likely in cases when splicing factors are direct targets of NMD . In C . elegans , the altered expression of splicing factors after NMD inhibition may affect the splicing of numerous genes [30] . Our set of NMD–affected genes includes at least seven genes encoding characterized RNA-binding or splicing-related proteins . One of these splicing factors , Squid , is known to affect the splicing of at least 255 other genes [29] . Among its targets are five genes identified as NMD–affected in this study , and one of these genes has no early stop codon . There may be additional splicing factors affected by NMD which our methods overlooked . Alternative isoforms of the SR splicing factor B52 were dramatically stabilized upon NMD inhibition , but this gene was not classified as an NMD target because we could not determine if the nontarget isoforms were present . Ten NMD–affected genes in our results are known targets of B52 , three of which have no early stop codon [19] , [27] . Our set of targets also includes at least two transcription factors , Dll and FTZ-F1 , and altered transcription may thus be a secondary effect of inhibiting NMD . Further , upf1 and upf2 knockdowns cause cell cycle arrest in mitosis [20] , which may cause secondary splicing effects and confound our interpretation of NMD targets . However , the six known mitosis-related genes amongst our targets almost all have early stop codons and thus appear likely to be direct NMD targets . This leads to the intriguing possibility that the mitotic arrest phenotype is due to the misregulation of specific unproductive splicing events after NMD inhibition . NMD was initially identified for its role in clearing the cell of erroneous and potentially harmful mRNAs . However , unproductive splicing can also be used to regulate gene expression . In mammalian systems , alternative splicing and NMD are combined to regulate the expression of numerous genes . RNA-binding proteins and ribosomal proteins , in particular , seem to employ unproductive splicing to autoregulate their expression , perhaps to maintain homeostasis ( [11] , [12] , [47] , [48]; reviewed in [49] ) . We have shown that this theme is continued in Drosophila . Many translation and splicing-related proteins are found in our set of fly NMD targets , and further investigation may elucidate important roles of unproductive splicing in the regulation of Drosophila processes .
RNA interference was performed against upf1 and upf2 and RNA was obtained from cultured Drosophila Schneider cells as described in [20] . As a reference , RNA was obtained from mock-treated cells as in [20] . Samples from three independent knockdowns of upf1 and upf2 were amplified , labeled , and hybridized onto a custom two-color microarray as described in [27] . Reverse transcription and amplification were performed as described in [27] . For each experiment , 1 µg of RNA was reverse transcribed using SuperScript II ( Invitrogen ) following the manufacturer's protocol . One-fiftieth of the RT reaction was used in a PCR reaction with Taq polymerase ( NEB ) following the manufacturer's protocol . PCR primers were chosen to flank the alternatively spliced region and the primer sequences are available upon request . The microarray was manufactured by Agilent using the 44 k platform with a custom array design . The array was designed using the methods described in [27] , but updated to use data from FlyBase 4 . 0 . The updated array design had two improvements: the exonic probes were chosen to be isothermal with the average of the junction probes , and a 20-nt dT stilt was added to decrease the chance of steric hindrance between the labeled probes and the glass surface . The 43 , 337 probes on the array ( excluding control probes ) measure the following target sequences: 10 , 694 alternative exons or splice junctions , 25 , 213 constitutive exons or splice junctions , 2 , 798 alternative polyadenylation events , and 4 , 632 alternative transcription start events . In addition , there are 10 negative controls and 23 positive controls . In total , the array interrogates 7 , 768 transcripts of 2 , 793 genes . The image analysis was performed by Agilent Feature Extraction version 7 . 5 . 1 . The scanned images were preprocessed using the limma package [50] from Bioconductor release 2 . 1 [51] . The background correction was done using the normexp method [52] , with an offset of 10 , and was followed by loess normalization between the red and the green channel within each array . Raw and preprocessed data have been submitted to GEO with accession number GSE13532 . As a motivating example we start by considering the behavior of a probe targeting two different isoforms of the same gene ( for example , an exon probe for a constitutively expressed exon ) . Let and be the absolute amounts of mRNA of isoforms 1 and 2 in the control sample and let and be the absolute amounts of mRNA of isoforms 1 and 2 in the treatment sample . The treatment-control fold-change for the probe is then ( 1 ) with , , . We recognize and as the fold-changes associated with isoforms 1 and 2 and as the relative proportion of isoform 1 in the control sample . These relative expression parameters are estimable from a microarray experiment , as opposed to the absolute mRNA amounts . This approach can be immediately generalized to a probe targeting out of isoforms of a given gene . In this case , the treatment-control fold-change associated with such a probe becomes ( 2 ) with being the fold-change associated with the isoform and being the relative proportion of isoform out of all isoforms . Because noise in microarray experiments appears to be additive on the log scale , we propose the following model ( 3 ) with being the observed fold-change for probe and sample , ( 4 ) being the fold-change parameter defined above , and being a noise term . is a function that for every probe yields which isoforms the probe targets . We propose to estimate the parameters and using non-linear least squares , i . e . , by solving the following minimization problem ( 5 ) Based on a heuristic argument , we expect the presence of the logarithm to turn this into a non-convex optimization problem . A variant of this minimization problem , where the constraint is replaced by , is solved using an adaptive barrier method proposed by [53] and implemented in the R function constrOptim , using a collection of suitably chosen starting points intermixed with random points . Hypotheses related to the differential expression parameters , such as ( is isoform not differentially expressed ) or ( are isoforms 1 and 2 similarly expressed ) , are tested using F-statistics ( for details see a reference on non-linear regression such as [54] ) . For each gene , isoform-level measures were deconvolved using the approach described above , and each isoform classified according to the process depicted in Figure 5 . For every isoform in the gene , the following hypotheses were tested: ( is isoform not differentially expressed ) , , ( is isoform possibly absent ) . Any given hypothesis was considered rejected if the nominal was lower than 0 . 001 ( “stringent” set ) or 0 . 05 ( “less stringent” set ) and accepted otherwise . An isoform was characterized as “up-regulated” if was rejected and , “slightly down-regulated” if was rejected and , “very down-regulated” if was rejected and , “unchanged and present” if was accepted and a nested test of against was rejected , and finally as “possibly absent” if was accepted ( was not tested for this classification ) . With this characterization , it is possible for an isoform to be labeled as “unchanged and present” as well as “possibly absent . ” In that case , “possibly absent” takes precedence . Based on this characterization , a gene was labeled as “NMD affected” if at least one isoform was “up-regulated , ” at least one isoform was “slightly down-regulated” or “unchanged and present , ” and the remaining isoforms were either “slightly down-regulated , ” “unchanged and present” or “possibly absent . ” The isoforms for such a gene were labeled as either “NMD target , ” “NMD nontarget , ” or “possibly absent . ” These labels were used as input in the feature correlation . We used AmiGO [33] to compare the GO terms of all genes on our array vs both the strict and less-strict sets of NMD–affected genes , with a p-value cutoff of 0 . 01 . Annotations were obtained from FlyBase via AmiGO . For every gene that had an isoform affected by NMD , we labeled the isoforms affected by NMD as NMD targets , the isoforms present but not affected by NMD as NMD nontargets , and we discarded the isoforms that were not present . For each feature we performed a paired as well as an unpaired analysis . The unpaired analysis compares the distribution of a feature for the NMD–target isoforms to the corresponding distribution for the NMD nontarget isoforms . The paired analysis computes , for each gene , the difference between the feature for the NMD–target isoform and the feature for the NMD nontarget isoform . The distribution of these differences are then compared to zero . In case there were two or more isoforms in a group , the values of the feature were averaged . As expected , we found the paired comparison to be more powerful . Specifically , for every feature , we performed a Wilcoxon rank sum test with exact permutation . The test was either paired or unpaired depending on the analysis being done . The permutation were computed using the package coin , see [55] . We also visually inspected the distributions using boxplots and scatterplots; see Figures S9 , S10 , S11 , S12 , S13 , S14 , S15 , S16 , S17 , S18 , S19 , S20 , S21 and Tables S1 , S2 .
|
A gene can be processed into multiple mRNAs through alternative splicing . Alternative splicing increases the number of proteins encoded by the genome , but not all alternative mRNAs produce protein . Instead , some are degraded by nonsense-mediated mRNA decay ( NMD ) , a surveillance system that was originally identified as a means of clearing the cell of mRNAs with nonsense , or stop codon , mutations . Alternative splicing that introduces early stop codons will lead to NMD , offering a way for the cell to down-regulate gene expression after a gene has been transcribed . In this paper , we have developed a new analysis method to study the combined effect of alternative splicing and degradation in the fruit fly Drosophila melanogaster using microarrays . We have found a stringently defined set of 45 genes that can be spliced either into an mRNA that encodes a protein or into an mRNA that is degraded by NMD , down-regulating the overall gene expression . The affected genes include a number that are central to the cell's regulatory processes , including translation , RNA splicing , and cell cycle progression . Our results also help shed light on how NMD determines whether a stop codon is premature , and thus whether to target an mRNA for degradation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/post-translational",
"regulation",
"of",
"gene",
"expression",
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/alternative",
"splicing",
"molecular",
"biology/mrna",
"stability",
"molecular",
"biology/rna",
"splicing",
"genetics",
"and",
"genomics/bioinformatics"
] |
2009
|
Genome-Wide Identification of Alternative Splice Forms Down-Regulated by Nonsense-Mediated mRNA Decay in Drosophila
|
Buruli ulcer [BU] is a chronic and debilitating neglected tropical skin disease caused by Mycobacterium ulcerans . The treatment of moderate to severe BU affects the well-being of entire households and places a strain on both gender relations within households and social relations with kin asked for various types of support . In this paper , we employ the conceptual lenses provided by the Household Production of Health approach to understanding the impact of illness on the household as a unit of analysis , gender studies , and social support related research to better understand BU health care decision making and the psychosocial experience of BU hospitalization . An ethnography attentive to circumstance and the nested contexts within which stakeholders respond to BU was conducted employing semi-structured interviews , illness narratives , and case studies . An iterative process of data collection with preliminary analyses and reflection shaped subsequent interviews . Interviews were conducted with 45 women in households having a member afflicted with BU in two communes of Benin with high prevalence rates for BU . The first commune [ZE] has a well-established decentralized BU treatment program and a well-functioning referral network linked to the Allada reference hospital specializing in the care of BU and other chronic ulcers . The second commune [Ouinhi] is one of the last regions of the country to introduce a decentralized BU treatment program . A maximum variation purposeful sample was selected to identify information-rich health care decision cases for in-depth study . Study results demonstrated that although men are the primary decision makers for healthcare decisions outside the home , women are largely responsible for arranging care for the afflicted in hospital in addition to managing their own households . A woman’s agency and ability to influence the decision-making process is largely based on whatever social support and substitute labor she can mobilize from her own network of kin relations . When support wanes , women are placed in a vulnerable position and often end up destitute . Decentralized BU treatment is preferred because it enables a woman to remain in her own household as a patient or caretaker of an ill family member while engaging in child care and petty revenue earing activities . Remaining in the hospital ( a liminal space ) as either patient or caretaker also renders a woman vulnerable to rumor and innuendo about sexual liaisons and constitutes a form of social risk . Social risk in some cases eclipses the physical risk of the disease in what we would describe as a hierarchy of risks . This study illustrates the importance of decentralized treatment programs for NTDs such as BU . Such programs enable patients to remain in their homes while being treated , and do not displace women responsible for the welfare of the entire household . When women are displaced the well-being of the entire household is placed in jeopardy .
Much has been written about the health care seeking process in low and middle income countries ( LMICs ) and the predisposing , enabling , and service-related factors that contribute to health care decision making for different types of health conditions and diseases[1 , 2] . Studies have also addressed how cultural perceptions and past interactions with practitioners affect present and future health care actions in a pluralistic health care arena . What has been underrated is how households cope with the direct , indirect , and opportunity costs of health care and the impact of illness on not just the afflicted , but other household members and the members of one’s broader social network[3–6] . A more complete understanding of health care decision making demands greater attention to the household production of health , gender relations , the mobilization of therapy management groups , and the ripple effect of illness on social support networks . Adopting a household production of health ( HHPH ) approach to decision making[7 , 8] situates health care within the full range of activities undertaken to achieve well-being for the household as a unit of analysis . Well-being extends beyond physical health to considerations of social relations , moral identity , and psychological health . Notably , this approach considers a household’s selective investment of time and limited resources , the trade-offs it makes when addressing pressing needs and real-world contingencies , and the opportunity costs of different courses of action . Anthropologists have drawn an important distinction between the household as a structural and a functional unit[9] . An HHPH approach favors a functional , task oriented definition of the household that privileges the processual study of how health is produced , promoted , maintained , and protected by household members defined less by cohabitation ( structural criteria ) and more by routine participation ( functional criteria ) in health/well-being related activities . Households can include kin [and fictive kin] who are working or living elsewhere , but contribute to the household in some way , especially at times of urgent need , and who derive part of their identity by an affiliation to the household . Social scientists studying the household as a unit of analysis are well aware that relations within households are both competitive and cooperative at different times , that the social status of members is not equal , that status changes over time according to varying criteria ( e . g . , age , work , financial contribution , marital status ) , and that intrahousehold negotiation between men and women over the use of resources takes place in subtle ways [10 , 11] . Times of sickness in households with scarce resources are often occasions when tensions run high , especially when health care decisions implicitly or explicitly ( dis ) favor particular household members or courses of action . Decisions often take place in the context of ambiguity , do not reflect consensus , and are contingent . Gender has been recognized as an important factor in studies of the HHPH , health care decision-making , and the allocation of scarce resources in times of sickness [12–14] . However , good case studies that illustrate different ways in which gender roles and relations within a household are affected by illness in LMICs , especially longstanding and chronic illness , are rare . Needed is research that examines differing demands treatment places on men and women during different points in an illness treatment trajectory . Given that women typically attend to the ill , special attention needs to be focused on economic , social , and affective challenges to women tasked with being caretakers for both children and the ill , and the ramifications of health care decisions . A third dimension of health care decision-making addressed in the literature is social support . Of particular importance is the mobilization of therapy management groups ( TMGs ) from within one’s larger support networks . TMGs are the constellation of individuals who take charge of various aspects of therapy management with or on behalf of the afflicted[15 , 16] . They are composed of all members of one’s social support network having an impact on any aspect of health care decision-making , care seeking , and support . Members may include kin , friends , community health workers , health staff , and traditional healers . In short , the TMG is composed of everyone who weighs in or contributes to health care in some way . TMG address many “works of illness” from decision making and economic assistance to substitute labor and psychosocial support [17] . To date , few studies have addressed gender and temporal dimensions of TMG mobilization . Men and women have different social support networks and resources to draw upon . We know far too little about who each turns to , for what , and with what expectations . We also know little about how the composition of TMGs change over time and the degree to which levels of support are responsive to competing demands on member’s time , resources , and other social obligations . Missing in the therapy management literature is adequate consideration of the temporal dimension of TMGs mobilized to respond to longstanding and chronic disease . Also missing are studies that address reasons for TMG failure and patient abandonment . In this paper , we employ the conceptual lenses provided by HHPH , gender studies , and social support related research to better understand health care decision making for Buruli ulcer ( BU ) , a neglected tropical skin disease endemic in West Africa . Buruli ulcer is a chronic , debilitating disease caused by Mycobacterium ulcerans[18] . It usually manifests through non-ulcerated lesions such as nodules , plaques , or edema that may evolve into massive skin ulcerations , joint and bone deterioration if left untreated[19] . Most cases of BU are found in West Africa and Benin is one of the endemic countries[20] . Fifty percent of those afflicted with BU are adults and 50% children . Most of those afflicted experience lesions on their limbs , although lesions may appear any place on the body[21] . The disease is non-contagious , the route of BU transmission unknown , and its incubation period poorly understood[22 , 23] . The poorly understood transmission of the disease and the fact that scattered households , not clusters of households , are typically affected has reinforced local speculation about BU related wounds being possible signs of supernatural contact or witchcraft . Up until 13 years ago , the management of BU required surgical removal of all sites of infection . In 2004 , antibiotic treatment was found effective at early stages of the disease ( category I: lesions < 5cm in diameter; and category II: lesions between 5 and 15cm in diameter ) [19] . At present , the management of BU has three main components . Antibiotic treatment is based on daily oral rifampicin ( 10 mg/kg ) and streptomycin ( 15 mg/Kg ) injection for 56 days , which allows lesions whose diameter is less than 10 cm to heal without surgery[24 , 25] . Effective outpatient antibiotic treatment at early stages reduces wound dressings and avoids skin grafts , which are needed for large ulcerations . More advanced cases often require long-term hospital treatment of indeterminate duration and physical therapy to prevent disability , amputation , and functional limitations after care[26] . Treatment for BU is provided free in most West African countries either at hospitals ( centralized in-patient care ) or at local health stations ( decentralized outpatient treatment care ) . Studies of BU in West Africa have found that biomedical treatment for BU is often delayed for reasons linked to perceptions of causality , fear of surgery and amputation , and the logistics and costs of seeking “free care , ”[27] . With respect to cost , several studies [4 , 28–30] , have drawn attention to indirect and opportunity costs of “free medical care” to households . BU provides an excellent opportunity to address limitations in the health care seeking literature highlighted above . More specifically , it provides an opportunity to more closely examine both how households and social networks are affected by hospital based medical treatment of indefinite duration , and risks to female patients and patient caretakers .
The study took place in Benin West Africa . Benin is bordered by Togo to the west , Nigeria to the east , and Burkina Faso and Niger to the north . The country is highly dependent on subsistence farming , regional trade , cotton as a cash crop , and remittances from seasonal migrant work largely to Nigeria . Over twenty different sociocultural groups inhabit Benin , the vast majority of which are patrilineal , meaning that children are part of their father’s lineage . Women typically maintain close ties with their own female kin . While both men and women contribute to household economics , women are largely responsible for providing resources for routine household needs . Women generally do so through the cultivation and sale of agricultural products as well as petty trade . Microfinance schemes for women are available in some , but not all regions of Benin . Benin is one of the most endemic countries for BU in West Africa [20] . Benin is divided into four regions and twelve departments subdivided into 77 communes . The National Control Program for BU in Benin supports four reference centers ( CDTUB ) located in Allada ( Atlantic region ) , Lalo ( Couffo region ) , Pobè ( Ouémé region ) , and Zagnanando ( Zou region ) . Each referral center supports a number of peripheral health centers that provide decentralized case management [31] . The mission of peripheral centers , which are state run health stations , is to provide accessible care for simple cases of BU ( category 1 and 2 ) . Reference centers , like the Catholic mission hospital of Zagnanado , are in charge severe cases . Field sites chosen for this study were located in two regions with high BU prevalence rates [31]: the Atlantic region ( Zè commune ) and Zou region ( Ouinhi commune ) . Decentralized management of BU patients is well established in the Atlantic , Ouémé and Couffo regions of Benin . In these regions , most cases of mild to moderate BU ( Category I and II ) are treated at health stations staffed by nurses . More serious ( category II and III ) cases are referred to reference hospitals . Decentralized treatment of BU has only recently been introduced in the Zou region[31] . Up until 2016 when a pilot decentralization project was initiated in Ouinhi commune , BU patients in the region were served almost entirely by a Catholic mission hospital renowned for surgery-based treatment for all cases ( category I , II , III ) of BU [31] . Zou region has only begun the process of adopting decentralized BU treatment . In Ouinhi commune , only one of four health stations are presently treating BU cases . As noted in an earlier publication [31] this health station become very popular and is receiving patients who had previously refused to be treated in hospital . A circumstantial ethnography[32] was conducted employing semi-structured interviews , illness narratives , and case studies . A circumstantial ethnography focuses on how nested sets of actors influenced by differing life circumstances respond to a focal phenomenon , in this case the treatment of BU . The ethnography was attentive to the experiences of patients and caretakers as well as responses of therapy management group members responding to requests for support . The study design allows for an iterative process of data collection with preliminary analyses and reflection shaping subsequent interviews . Case studies were collected using a “life history” approach , which focuses on the interviewee , and their storytelling to understand how perspectives and discourses are constructed [33] . In the present study , the focus was on the experiences of women deliberating and reflecting on BU treatment decisions , institutional care , household survival issues , and social support relationships . A narrative approach was chosen in which the focus is on people’s evaluations of their own life experiences [34] . A maximum variation purposeful sample[35] was selected to identify information-rich experiences for in-depth study . Interviews were conducted with 45 women who were either afflicted with BU themselves , caretakers for a family member with the disease , or the decision maker for whom in a household should accompany a patient to the hospital . One man who had uncharacteristically taken on the task of managing his son’s BU treatment was also interviewed . The sample included women whose husbands resided for most of the year in their homes and women whose husbands were migrant workers , married and widowed women , and women faced with managing moderately severe and more advanced cases of BU in the hospital ( centralized treatment ) and by daily visits to a health station ( decentralized treatment ) . Informants were identified with the help of community health volunteers and clinic staff in community , clinic , and hospital settings in both Ze and Ouinhi communes . Hospital patients included both residents from the region in which the hospital was located , and patients and caretakers traveling to the hospital from outside the region . Once community health workers and health care providers identified people afflicted with BU , they were contacted and asked to participate in qualitative interviews about their illness experience . The principles of thematic narrative analysis were followed [36] . After re-reading interview transcripts , the findings of interviews and narratives were discussed by team members , and coded for both focal and emergent themes . Focal themes included predisposing , enabling , and service related factors influencing BU treatment decisions , gender relations , social support , choice of patient-caretakers , and patient abandonment . Emergent themes include rumor and social risk , quality of childcare , and impact of treatment on children’s schooling . After an extensive consideration of the data obtained along , short vignettes and interview extracts in line with the study’s objectives were chosen as exemplars for use in this publication . Vignettes chosen illustrate the backstage of treatment decision-making and care management along with the complexities and contradictions revealed by a study of real-life circumstances[37] . Themes introduced in the results section provide answers to core research questions posed as a heuristic [38] . Ethical approval was obtained from Benin’s National Ethical Committee of Health Research before the start of the research ( IRB00006860 N° 148 /MS/DC/SGM/DFRS/CNPERS/SA ) . Informed consent procedures already in place at Allada hospital were strictly adhered to over the course of the project . All patients and staff interviewed were assured that interviews would be kept confidential . The use of oral consent was approved by the ethical review board because many study participants were illiterate . When a participant was under 18 years of age , both the child/adolescent and his/her caretaker were informed about the nature and aim of study before being asked to give oral consent .
In Sub-Saharan Africa , gender roles and social norms of seniority and power strongly influence how health care decisions are made [39–43] . In our research sites , most ethnic groups are patrilineal . Health care decisions that entail treatment outside of the home are made by husbands or senior members of their kin network . This is true even if a husband is employed as a migrant worker and absent from home much of the year . In our sample , 21 women acted as heads of their household during all or much of the year . Only two women reported making a BU related health care decision on their own . Women followed the health care advice of a husband or his kin regardless of whether they offered any financial support for BU treatment . Our informants noted that if a woman did not seek approval from her husband or senior members of his family , she left herself open to social censure . In some cases , however , a husband and his kin abandoned a sick child , an issue we will address shortly . It has been widely reported in studies of health care seeking in West Africa that enabling factors are as important as predisposing factors ( such as perceived cause ) in determining when and what kind of health care is sought [6 , 27] . Our study corroborated this finding . The enabling factors most commonly referenced in interviews about hospital-based BU care were the indirect costs of “free treatment” such as transportation costs , food and incidental costs ( soap , mobile phone credit , etc . ) , and the opportunity cost of lost labor . Decision makers ( husbands , elder kin ) took stock of available sources of substitute labor within the household as well as a wife’s social capital , her ability to mobilize support from her own kinship network . A mother’s absence from home on a daily basis to obtain outpatient treatment for BU or her need to remain at a hospital as either a patient or caretaker was deemed feasible only when essential household duties were taken on by somebody else . Women’s labor demands varied by season and household composition and encompassed agricultural labor , cooking , securing water and firewood , and childcare . Daughters were generally turned to first to take on a mother’s responsibilities in the household or to serve as a caretaker for a hospitalized family member . When a mother did not feel it was safe to leave small children at home to be cared for by an older child , or her labor in the fields was required for household survival , a daughter was commonly sent to care for a sibling in the hospital . This often interfered with her own schooling or apprenticeship activities . If , on the other hand , a mother was the patient , a daughter was sometimes asked to take charge of household duties in her absence . The following cases illustrate the complexity of patient caretaker deliberations as an important factor in health care decisions , and the role children play as patient caretakers given household production of health concerns . Madeleine ( daughter , patient ) , aged 11 , was admitted to Allada hospital for treatment of BU after initially receiving decentralized treatment at a health station near her village . Her mother suffers from poor health , making it difficult for her to manage the household and tend to the fields . As a result , her husband took on a co-wife , who has three children of her own . Madeleine’s mother was afraid to accompany Madeleine to hospital and leave her other four children at home under her co-wife’s charge as she suspected they might be mistreated . Madeleine’s mother received assistance from her own mother and two sisters when she took Madeleine for decentralized care at a local health station a few kilometers away . However , when the child’s wounds did not heal , they were reluctant to offer long-term support for Madeleine if she was hospitalized . Madeleine’s father decided that the best option was to send Madeleine to the hospital along with her 8-year-old sister , Reine . Reine was taken out of school to care for Madeleine . Madeleine’s two older brothers were not asked to be a caretaker as this was seen as women’s work . Both Madeleine and Reine wished to continue their education , but their mother recognized that this was unlikely if long term BU treatment was required . In effect , Reine’s future was sacrificed to attend to her sister . Clemency ( mother of three , patient ) needed to be hospitalized for an advanced case of BU , but she had no adult family member able to provide support . Her own mother was deceased and her two sisters were working in Nigeria . It was decided that Clemency’s teenage daughter would remain at home to tend to the household and that her younger , five-year old daughter would serve as her caretaker in the hospital . Her husband agreed to supply necessary resources during treatment . Clemency entered the hospital with her five-year old daughter and her 18-month-old son . Clemency required several surgeries and was confined to bed and a wheel chair . Her five-year-old daughter performed all tasks necessary for their survival in the hospital including going to the market , cooking , washing clothes , taking care of her baby brother , and making sure her mother took her medicine on time . Clemency’s daughter was helped by other caretakers and nurses in the hospital who spoke of her with great admiration . One often saw Clemency’s daughter going about her business with her younger brother on her back . Her mother described her daughter as a gift from God . However , she worried about her future , especially her schooling . She noted: “I do not know when I will finish with this treatment , no one tells me . If I can finish in a few months then my daughter will be able to go to school and can catch up . But , if I have to remain in the hospital longer , what will happen to her ? While she is very intelligent , it will be hard for her to succeed in school . ” As in the case of Reine , the future of a young patient caretaker was placed in jeopardy as an opportunity cost of treating a sibling afflicted with BU in hospital . Two other household production of health issues emerged in BU illness narratives that are rarely discussed in the health care seeking literature . The first is a mother’s concern about the quality of childcare in her absence . This psychosocial concern sometimes eclipsed concerns about a child’s physical condition . The following case illustrates the importance of the quality of childcare in health care decision-making . The case involves a decision to decline free hospital treatment for a child afflicted with BU . Prisca ( mother , caretaker ) is the sole resource-provider for her household . Her husband works , but most of the money he earns is spent on sodabi palm wine . One of Prisca’s children , an 11-year-old daughter , suffers from advanced ( category II ) BU , which requires hospitalization and possibly surgery . At first , Prisca administered home treatment to her daughter . When her lesions grew in size , Prisca asked permission from her husband to seek outpatient treatment for her daughter from the district health center 4 KM away . This proved challenging as Prisca still had to find the means to support the household on a daily basis through petty trade . After two months of treatment at the health center , her daughter’s condition was still serious and health staff referred her to Allada hospital , where she could receive free treatment . At first Prisca refused to take her daughter to the Allada hospital even though she was concerned about the size of her lesions . Health staff and a doctor from Allada visited Prisca and attempted to change her mind , but she did not agree , stating she had no one to look after her other young children . She did not feel secure leaving her children in the hands of her husband . She stated , “Seeking care at the district health center is possible because it does not prevent me from going about my business and ensuring the well-being of everyone . Leaving the house for who knows how long , that is simply not possible . ” A few days later , a social worker from the hospital returned to Prisca’s house and offered to look after her daughter while in the hospital if no family member could accompany her . Prisca spoke to her husband , who agreed to allow their daughter to go to the hospital as long as significant cost was not involved . After two days of treatment at the hospital , however , Prisca returned and took her daughter home . When interviewed as to why she did so , the mother stated that her heart would not allow her to leave her daughter in the hands of an unknown woman . She went on to note: “I prefer that my daughter continue with the bandaging at the district health center even if this is not the best treatment . Some infirmity may result , but it is better than the total destruction of all members of my house . ” She then when on to state: “When it comes to sickness only a mother can comfort and care for a child properly . In the hands of someone my daughter does not know , she is likely to suffer . How can I have a quiet heart at home worrying about her ? ” A second notable concern that we identified as having a big effect on health care seeking and patient caretaker decision making was social risk ( risk to reputation and to present and future social relationships ) [44 , 45] . When a woman leaves the confines of her village either to visit a health post some distance away or to reside in a hospital , she risks becoming the subject of rumors about sexual indiscretion . Such rumors question a woman’s moral identity and a husband’s masculinity and cause strife between husbands and wives . We found this to be a common reason a mother took a child with her when visiting a health post or when residing in a hospital . However , we found that even when a woman brought young children with her to hospital , she was still subject to rumor . Fear of rumor was a constant worry for some women , adding to the stress of social isolation and trying to survive with minimal resources . The following case illustrates how an apparently stable marriage was destroyed by rumor and innuendo: Ruth ( mother , caretaker ) , was given permission by her husband to care for their five-year-old daughter while she was being treated for BU in hospital . Ruth also brought her infant son to the hospital , as she was still breastfeeding . Ruth received regular visits from her husband , who was very attentive to her needs and those of their children . However , during one visit to the hospital he became quite agitated . Late in the evening , he awoke to see someone enter the ward , approach the bed of a young female patient , hold her hand and kiss her before departing . This event shocked her husband and he began to suspect his own wife’s fidelity . He began to see the hospital as a site of moral dissolution where patients and caretakers engaged in extramarital behavior . Without evidence of any wrongdoing on the part of his wife , he took the extraordinary measure of abandoning his wife and small children . When interviewed , he remained resolute , exclaiming , “These doctors , they may bring healing , but they destroy homes ! ” Fear of rumor influenced who was chosen to be a patient caretaker in hospital . Daughters who had not yet reached puberty were preferred . The hospital was seen as a liminal space and time in the hospital to be quite boring . Several informants noted that “people” suspect that any young woman with limited resources will engage in sexual relations if outside the watchful eye of community members . We recorded cases where a daughter as young as 15 was sent to the hospital as a caretaker only to be returned home when rumors about sexual relations emerged . The following is an example: Florent ( son , patient ) aged 18 , was admitted to the Allada center for BU treatment . Florent is the third of seven children . His father lives and works as a brick maker in Nigeria with one of Florent’s brothers . Florent’s older sister , an apprentice seamstress , was asked to leave her apprenticeship to be his caretaker at hospital . Florent’s father suggested this course of action given that there were young children at home that needed their mother’s care . During Florent’s hospitalization , his mother heard a rumor that Florent’s sister charged with his care was becoming romantically involved with men at the hospital . Fearing that her daughter’s reputation might be spoiled or that she might become pregnant , Florent’s mother sent her daughter back to her apprenticeship and replaced her as Florent’s care provider . This necessitated bringing four of her children with her: her two-year-old daughter and three children who had been attending elementary school . Taking care of young children in the hospital wards is not easy for Florent’s mother . Her husband supports her , but sends money irregularly and what is sent is not enough to meet their needs . Because she can no longer work in the fields or engage in petty commerce in her village , Florent’s mother tries to make money any way she can while in the hospital by washing clothes , cleaning , and running errands for staff and other patients . As has been noted elsewhere in sub-Saharan Africa[46] , although women do not have the same kind of authority as men , it would be misleading to present them as having no impact on health care decision making . Most women we interviewed asserted that although men have the final say in decisions about health care , women’s input and counsel influence decisions . Women typically accepted their husband’s initial health care decision , even if they did not agree with it . However , they often encouraged husbands to reconsider decisions based on shifts in disease trajectory as well as the availability of different types of material and social support . And in a few cases , they took matters into their own hands when they felt they were being abandoned by a husband and his kin . Also , as in other parts of Africa [6 , 47] , we found that women’s agency in health care decision-making was largely based on two things: the resources she has at hand , and her ability to mobilize resources from kin in the form of material goods , labor , and childcare . The best way a woman could influence BU-related health care decisions was by working out how a treatment option she favored could take place with only minimal disturbance to essential household production activities . Having a daughter , as noted in the cases of Madeleine and Clémency , was an asset . If one’s own daughters were old enough to serve as the caretaker of a sick family member , or to remain home and assume household responsibilities , a mother had some flexibility . However , when a woman did not have a daughter to assist her , she was compelled to approach kin and ask them for support and to play a more active role in therapy management . Based on our data , most of a woman’s requests for assistance were to her own mother and sisters , followed by friends and neighbors . Asking members of her husband’s family for assistance was only a last resort . The following case illustrates kin coming to the aid of a sick relative wanting to be treated in hospital for BU and in need of a caretaker . In this instance , a niece was removed from vocational training to care for her aunt and as a result experienced biographical disruption , an interruption and destabilization of the life trajectory of the caretaker [48] . Gisèle ( caretaker ) , aged 23 , has been the caretaker for her aunt in Allada hospital for the last 19 months . Her aunt’s wounds form BU are quite serious and her treatment is likely to go on for some time . Prior to coming to the hospital , Gisèle was an apprentice seamstress attending a vocational training course . She planned to open up her own small tailoring shop soon after graduation . Gisèle was asked by her mother to take leave from her tailoring course to care for her aunt while in hospital . Her aunt had assisted their family in the past and she had no daughters of her own to ask for help . When interviewed , the first thing Gisèle said was that she had never imagined how much her life would change when assuming a caretaker role in the hospital . She did not resent taking care of her aunt , but was sad about her fate stating that her “heart was in a vice . ” She noted “I agreed to stay with my aunt because she is like a mother to me . She has always helped my family . But , by being here I have lost many things . I have no income-generating activities here . I have lost both financially and professionally . I was at the end of my apprenticeship and I was working to raise money necessary to obtain my diploma . My classmates have already graduated and they are now employed , but I am here . I worry about losing my tailoring skills , and I worry how I will raise money for my graduation . I try to find small jobs in the hospital , but whatever money I make is spent on food . My boyfriend has also become distant . He came here once and saw a male nurse teasing me , and he now suspects that I found a ‘doctor . ’ I call him and he does not pick up the phone . ” As noted in the case of Gisèle , BU hospitalization does not just affect members of one’s immediate household; it also affects one’s broader social support network . In short , asking for and receiving support from kin in times of illness creates a ripple effect . For women living on the margin and having multiple work responsibilities of their own , assisting kin ( and fictive kin ) out of friendship or obligation is an effective means of reaffirming and strengthening reciprocal exchange relations . Volunteering to take children afflicted with BU to health stations for outpatient care , watching children when a mother is away from home , and lending money or supplying food were all found to be means of solidifying social bonds between women . This form of “bonding social capital” [49–51] provides women with a safety net associated with norms of social reciprocity and cooperation for mutual benefit . On the other hand , we found that when requests for time or resources exceeded the capacity of kin to provide , social bonds were weakened . The same was true when a mother felt the amount of resources or care provided to her children by kin was inadequate . Requests for long term support often caused conflict within the households of kin . In some cases , there just were not enough material resources to share , and in other cases the opportunity costs of attending to somebody else’s children reduced the time a woman had available to generate revenue needed to support her own household . In short , social capital was a contingent and conditional resource dependent on the presence of resources [52] . Some women interviewed belonged to microfinance schemes and they had to repay loans in order to maintain the integrity of the group . The ripple effect of BU affected the entire group when members were unable to live up to their financial obligations due to the indirect and opportunity costs of BU treatment . The following case illustrates how BU affected one woman’s livelihood and microfinance group membership . Her predicament affected not just her present , but her chances of recuperating economically in the future . Juliette [mother , caretaker] is a food vendor and a palm oil processor who is a member of a local micro-finance group . She contributes to the group monthly to pay off loans she has taken for her business . When her daughter , aged eight , was diagnosed with BU , Juliette took her to the hospital for treatment and resided with her for the next year . Remaining in the hospital disrupted her ability to pay back loans and this affected the entire microfinance group . Even though group members understood that she was caring for a sick child , they pressured her to find money . Her inability to repay loans compromised both the financial standing of the group and her future ability to borrow money . She noted: “I have so much worry and stress now . What should I do to pay off my debts ? The whole village knows that I owe money . I tried to arrange my business affairs from here . I entrusted my aunt with the sale of my goods in order to allow me to pay my debt each month . However , she mismanaged my business . What can I do now ? … ( she cries ) . I feel my reputation is now destroyed and I am resented . Women in the microfinance group will not welcome me back into the group . Without a loan , how can I reestablish my business ? ” As noted by Ribera et al . in Cameroon [53] , abandonment is an extreme household coping strategy initiated during catastrophic or protracted illness to avoid plunging a household into a “spiral of impoverishment . ” Abandonment was a major concern voiced by our informants . Those residing in hospital as well as those contemplating going to hospital worried they might be abandoned if they remained in hospital beyond the length of time their household could provide for their basic needs . Wives under treatment worried that a husband might find it necessary to take a co-wife to maintain the house in her absence , and husbands afflicted with BU worried that wives might find other men to take care of them in their absence . We documented cases of both scenarios . Hospital administrators in Allada were especially concerned with child abandonment and noted cases where caretakers suddenly just disappeared . They pointed to several abandoned children now residing on the hospital grounds post treatment because they had no place to go . The presence of these children at the hospital was a constant reminder to others of what can happen when household resources are stretched too thin . When interviewed , women who were abandoned displayed considerable psychological distress related to failed expectations of support in keeping with cultural values based on reciprocity . A common narrative emphasized how much a woman had sacrificed in the past to support other family members in times of need . The following are two examples: Honon ( mother , caretaker ) is a widow with six children , the youngest of whom are twins . After the death of her husband , his family encouraged her to remarry one of his younger brothers , a proposal that Honon refused for undisclosed reasons . As a result , her husband’s family abandoned her and offered no support for her children . Honon was forced to return to her own family . She and three of her children went to live with her paternal uncle . The other three children were entrusted to other family members in a foster care arrangement ( vidomègon ) common in West Africa wherein children receive care in return for labor . One of her young twins developed BU . Honon’s mother , sisters , and uncle encouraged her to try various types of home remedies for the child . When the child’s wounds became more serious and required hospital treatment , her family members were unwilling to offer support either for Honon to care for the child while in hospital or to care for her other two children in her absence . Her kin felt that the burden of either action would place the household in jeopardy . Honon was pressured to return the ill child to her deceased husband’s household . This suggestion was quite unsettling to Honon for two reasons . First , her deceased husband’s family had taken no responsibility for his children up to this point in time . She felt that if the child was received , they would be neglected . Second , she strongly suspected that someone in her deceased husband’s family had sent bad luck to the child resulting in wounds that would not heal . Honon stated that she felt abandoned by both her own family and the household of her husband . Against the advice of her own family , she opted to go to Allada hospital and care for her sick daughter . She brought two of her other children along with her as there was no one willing to care for them at home . Honon received basic food rations from the hospital and otherwise survived by taking on small jobs when she could find them . She was very bitter about her abandonment . In her own words “Before leaving for Allada my mother promised to come visit me during our stay . It has been 18 months and neither she nor any of my sisters or brothers have visited or contacted me . When my sister was sick and hospitalized , I was the one who had been at her bedside . I was there for so many others in my family in their time of need . For me , no one is offering assistance or showing love . It is as if I am without parents . If it was not for the generosity of the hospital to whom I owe everything , I would be destitute . ” Conforte ( female adult patient ) , aged 38 , is Togolese and traveled to nearby Benin in search of treatment for an advance case of BU . Her older sister resides in Benin and informed her about Allada hospital . Conforte traveled to Allada and her sister provided her support during the first months of her treatment . However , as the months passed her sister’s resources dwindled and she began to tire of her sister’s illness . Then one day , Conforte noted with bitterness , her big sister stopped coming to visit and would not return her calls . Conforte never heard from her sister again and survived on charity offered to her by her church and hospital staff . Conforte noted , “When I came here to seek treatment , I gave my big sister all my savings and belongings to hold for me while in the hospital . However , she abandoned me in the most difficult moments of my life . She is the one who asked me to come to Benin . I helped her so much when she faced illness in the past , she felt obliged to support me . It is true that she really helped me during the first months of my hospitalization , but over time , she regretted encouraging me to come here for treatment . She abandoned me . It is nasty , no ? When our parents died , I was the one who helped all members of my family until I became ill . Now , where is their support for me in return ? Today , strangers help me . In the past , I have helped others outside my family as a good Christian . Perhaps it was the help I gave to others , that led others to help me now . ” Widows are in a structurally vulnerable position in Beninese society whether or not they agree to a levirate marriage to a brother of a deceased husband who has other wives . In many cases , when a woman with young children is widowed or divorced , she raises her children in the house of her own kin until they are old enough to be sent to the household of their deceased husband . However , as we noted in the case of Honon , if a child becomes ill and is a burden to the household , a widow may be pressured to send the sick child to her deceased husband’s household to bear the costs and responsibility of treatment . If her deceased husband’s family does not offer support , and she opts to bring a sick child to the hospital she may be encouraged to place her other children in foster care as she will no longer be able to provide for them . The following case illustrates the predicament in which many widows find themselves . Aline ( mother , caretaker ) is a former BU patient herself . When her husband died seven years ago due to an accident , she found herself having to care for their six children on her own . One of her daughters developed BU . When it became clear that she would have to be hospitalized , her deceased husband’s family remained silent about what should be done , and did not offer financial support for treatment . Aline’s mother’s household is very poor and was unable to offer her support . In order to admit her daughter to the hospital , Aline was compelled to place four of her children in foster care in the households of distant kin . This was an act of desperation . Aline was upset by the decision , but felt she had no other option . Aline brought her ill daughter and a young son to Allada hospital . Because of their dire financial situation , the hospital offered Aline’s daughter basic daily food rations . To otherwise survive , Aline’s mother ( like Honon ) is constantly looking for work to support herself . Every day , she sells dumplings to schoolchildren at a nearby school , and gets a small payment in return from the dumpling-makers . The hospital is not happy having caretakers like Aline leave the hospital grounds to engage in petty business , but this is the only way she is able to survive . Our research revealed a strong preference for decentralized BU treatment . Six reasons were identified from interviews that asked women about the advantages of decentralized care . First and foremost , decentralized care does not disrupt a household’s daily routine by removing a mother from her household . Decentralized treatment , for all but very severe cases of BU , still enables a woman to do chores and watch her children as well as engage in entrepreneurial activities essential for household survival . Second , decentralized treatment avoids the many indirect and opportunity costs associated with hospitalization . Third , it allows children to stay in school while being treated , and it reduces the need to remove children from school and apprenticeships to serve as patient caretakers . Fourth , a mother does not have to worry about the quality of sibling care and foster care in her absence . Fifth , she also does not have to worry about pernicious rumors undermining her own or a daughter’s reputation . Sixth , remaining at home during treatment is less stressful . A woman worries less about abandonment . Fathers who take responsibility for the treatment of a child with BU also favored decentralized care . Although not the focus of our research , we encountered one father who , having refused to send his son to the hospital for BU treatment , agreed to take him for decentralized care . The case illustrates both why he favored decentralized care and why he accepted responsibility for taking his child to a health station . He acted in accord with kinship norms and obligations , and cared for a child from a co-wife he was not currently living with instead of asking a wife home maintaining his home to attend to the health needs of a child by a different marriage . Djalil ( father caretaker—outpatient ) lives with his first wife and two children in a village in Ouinhi commune . His first wife was infertile and his two sons are children by a second wife , a Nigerian shopkeeper whom he cohabits with while working in Nigeria for some months each year . When his son aged 8 was diagnosed with BU by heath staff attached to the mission hospital he refused to send the boy to the hospital to be treated . He was afraid the boy would have to undergo surgery and remain in an unfamiliar environment for a long time . He was not comfortable asking his first wife to remain with the boy in the hospital . She was not his mother and was involved in petty trade activities that both helped support his household and allowed her to offer some level of support to her mother . It was not possible for him to remain at the hospital , as he too had to work to sustain his household . Djalil also did not want the boy to lose a year or more of school . The boy continued to go to school because his wounds were not painful and his ulcer was not noticeable if hidden under clothing . Djalil initially planned to send the boy to his grandmother’s house in a nearby village during school holidays to be treated by a traditional healer . As a result of a mass BU outreach education program , Djalil learned about the availability of decentralized treatment at a health station a few kilometers from his house . He consulted the nurse at the health station , who assured him that he could treat his son’s wounds with medicine and bandaging if he adhered to a treatment that required daily visits to the health station for some months . Djalil agreed and engaged himself in agricultural activities at home instead of returning to Nigeria to work . He strictly adhered to the decentralized care offered by the nurse for eight months until his son had fully recovered . In his words , “I was greatly relieved to receive treatment here from the major ( nurse ) as this type of treatment is not available in the place of his mother in Nigeria . I brought my son to the health station every day for five months . Then , for two months , we visited the station every three days . Finally , in the eight month , I brought him every four days . My son remained at home , surrounded by family , and was able to complete his school year as well . ”
A qualitative study utilizing a purposeful sample is designed to identify the range of factors effecting phenomena: in this case decision making about BU treatment and the experience of patients and caretakers in hospital . The study was not designed to measure which factors are most responsible for treatment delay , drop out , or no show after BU identification . This will require a quantitative study , which measures the order of magnitude of factors identified in this study . In this paper , we have used an ethnographic study of BU to make a case for focusing on the household as a unit of analysis when studying NTD related health care decision-making and its sequela in LMICs . Toward this end , we have found three conceptual lenses to be particularly useful: HHPH , gender relations , and TMG mobilization . Use of these lenses broadens our understanding of factors influencing treatment choice and patient caretaker selection when hospitalization is required . They also provide us with a nuanced account of the ripple effects of longstanding illnesses like BU beyond the household , and an appreciation that social support from kin is contingent and conditional . In the areas of Benin studied , like many other regions of West Africa , men are the primary decision makers for healthcare decisions outside the home . A woman’s agency and ability to influence the decision-making process is largely based on whatever social support and substitute labor she can mobilize from her own network of kin relations . The brief BU case vignettes presented in this paper speak to the importance of bonding social capital for women in times of illness , describe ways in which this capital is accrued through reciprocal assistance , and draw attention to strains on social relationships when the duration of support needed is longer than expected and/or indeterminate . The three lenses further help us identify groups at risk for treatment delay , non-adherence , and patient abandonment at hospital due to structural vulnerability . In conclusion , we argue that public health programs for diseases requiring long-term treatment , like BU , need to take into consideration household survival and gender relations and not just the medical needs of the afflicted . We concur with Grietens et al . [29] who argue that public health programs need to recognize the folly of designing programs that save the patient at the cost of compromising the integrity of the household and the health and well-being of other household members , especially children .
|
In this gender-focused study of the neglected tropical disease Buruli ulcer ( BU ) in Benin , West Africa , we document how seeking care for BU is influenced by broad-based concerns about the household production of health and the availability of resources women can mobilize from their social support networks . Women and girls shoulder a disproportionate share of the burdens incurred by BU treatment and prefer decentralized treatment from local health stations to free hospital care . Long term and often-indeterminate residence in hospital threatens the integrity of households and results in marital stress , economic vulnerability , school and vocational training dropout , and loss of essential income-generating activities . The case study of BU clearly demonstrates the necessity of recognizing the household , and not just the patient , as a unit of analysis in public health and the need to consider the ripple effect of serious illness beyond the household to one’s social network . We draw attention to the fact that while men are the decision makers about health care in patrilineal Beninese society , a women’s agency in influencing decision making is tied to her accumulation of social capital , capital that is taxed by long term medical treatment weakening her safety net in the future .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2019
|
The gendered impact of Buruli ulcer on the household production of health and social support networks: Why decentralization favors women
|
Long after a new language has been learned and forgotten , relearning a few words seems to trigger the recall of other words . This “free-lunch learning” ( FLL ) effect has been demonstrated both in humans and in neural network models . Specifically , previous work proved that linear networks that learn a set of associations , then partially forget them all , and finally relearn some of the associations , show improved performance on the remaining ( i . e . , nonrelearned ) associations . Here , we prove that relearning forgotten associations decreases performance on nonrelearned associations; an effect we call negative free-lunch learning . The difference between free-lunch learning and the negative free-lunch learning presented here is due to the particular method used to induce forgetting . Specifically , if forgetting is induced by isotropic drifting of weight vectors ( i . e . , by adding isotropic noise ) , then free-lunch learning is observed . However , as proved here , if forgetting is induced by weight values that simply decay or fall towards zero , then negative free-lunch learning is observed . From a biological perspective , and assuming that nervous systems are analogous to the networks used here , this suggests that evolution may have selected physiological mechanisms that involve forgetting using a form of synaptic drift rather than synaptic decay , because synaptic drift , but not synaptic decay , yields free-lunch learning .
Each association consists of an input vector x and a corresponding target value d . For a network with weight vector w , the response to an input vector x is y = w·x . We define the performance error for input vectors x1 , … , xk and desired outputs d1 , … , dk to be ( 1 ) where yi = w·xi is the output response to the input vector xi . By putting X = ( x1 , … , xk ) T , d = ( d1 , … , dk ) T andwe can write Equation 1 succinctly as ( 2 ) The two subsets A1 and A2 consist of n1 and n2 associations , respectively . Let w0 be the network weight vector after A1 and A2 are learned . When A1 and A2 are forgotten , the network weight vector changes to w1 , say , and the performance error on A1 becomes Epre = E ( X;w1 , d ) . Finally , relearning A2 yields a new weight vector , w2 , say , and the performance error on A1 is Epost = E ( X;w2 , d ) . Free-lunch learning has occurred if performance error on A1 is less after relearning A2 than it was before relearning A2 ( i . e . , if Epost<Epre ) . Given weight vectors w1 and w2 , a matrix X of input vectors , and a vector d of desired outputs , define ( 3 ) which we shall also refer to simply as δ . In previous work [12] , we assumed that the “forgetting vector” v ( defined as v = w1−w0 ) has an isotropic distribution . Here we shall assume instead that the post-forgetting weight vector w1 is given by ( 4 ) for some ( possibly random ) scalar r , so that ( 5 ) and therefore ( 6 ) The interpretation of Equation 6 is that forgetting consists of making the optimal weight vector w0 “fall” towards the origin by a falling factor 1−r .
Our two main theorems are summarised here , and proofs are provided in the Methods section . These theorems apply to a network with n weights which learns n1+n2 associations A = A1∪A2 , and then after partial forgetting , relearns the n2 associations in A2 . We prove that if n1+n2≤n ( so that , in general , the associations A1 and A2 are consistent ) and the joint distribution of ( X1 , d1 ) is isotropic ( where X1 and d1 are the matrix of inputs and the vector of desired outputs for subset A1 of associations ) then the expected value of δ is negative ( recall that δ is defined in Equation 3 ) . We then prove that the probability P ( δ<0 ) that δ is negative approaches unity as n1 approaches ∞ . For every non-zero value of r , the expected value of δ given r is negative . More precisely , ( 7 ) with equality only in trivial cases , and where the constant of proportionality is guaranteed to be positive . Thus , the expected amount of FLL is negative ( or zero ) . From a physiological perspective , the case r<1 is obviously of interest because it represents synaptic weight decay . However , from a mathematical perspective , Theorem 1 applies to every value of r , and so it also holds for r>1 . In other words , any movement of the weight vector w along the the line connecting w0 to the origin yields an expectation of negative FLL , in accordance with Theorem 1 . Under mild conditions on the distributions of the input/output pairs ( X1 , d1 ) and ( X2 , d2 ) , ( 8 ) where x and are any columns of and , respectively , and Theorem 2 implies that , if ( i ) the number ( n1 ) of associations in A1 is a fixed non-zero proportion ( n1/n ) of the number n of connection weights , ( ii ) E[∥d1∥2]E[∥d2∥−2] is bounded as n → ∞ , and ( iii ) γ ( n ) → 0 as n → ∞ then P ( δ>0 ) → 0 as n → ∞ , i . e . , the amount of FLL is negative , with a probability which tends to 1 as n → ∞ . For example , if we assume that ( i ) each input vector x = ( x1 , … , xn ) is chosen from an isotropic Gaussian distribution and ( ii ) the variance of xi is then γ ( n ) = 2/n , , and E[∥d1∥2]E[∥d2∥−2] = n1/ ( n2−1 ) . This ensures that P ( δ>0 ) → 0 as n → ∞ . Simulation was carried out on a network with n input units and one output unit . The set A of associations consisted of k input vectors ( x1 , … , xk ) and k corresponding desired scalar output values ( d1 , … , dk ) . Each input vector comprised n elements x = ( x1 , … , xn ) . The values of xi and di were chosen from a Gaussian distribution with unit variance ( i . e . , ) . A network's output yi is a weighted sum of input values , where xij is the jth component of the ith input vector xi , and each weight wj is the connection between the jth input unit and the output unit . Given that the network error for a given set of k associations is , the derivative of E with respect to w yields the delta learning rule , where η is the learning rate , which is adjusted according to the number of weights . However , in order to save time , we used an equivalent learning method . Learning of the k = n associations in A = A1∪A2 was performed by solving a set of n simultaneous equations using a standard method , after which the weight vector w0 was obtained; this provided perfect performance on all n associations . Partial forgetting was induced by making weights “fall” towards the origin w1 = rw0 , after which performance error was Epre . Relearning the n2 = n/2 associations in A2 was implemented with k = n2 as above , after which performance error was Epost . In each simulation , each value in each input vector xi , and each target value di was chosen from the same isotropic gaussian distribution with unit variance . There were 100 input units , and one output unit . The subsets A1 and A2 each consisted of 50 associations . The value of δ = Epre−Epost was obtained in each of 100 simulations , using a different random seed for each simulation . In Figure 2 , the mean of 100 values of δ is shown for various values of the falling factor 1−r . We present a brief account of the geometry which underpins the results reported here , for a network with two input units and one output unit , as shown in Figure 3A . This network learns two associations A1 = ( X1 , d1 ) and A2 = ( X2 , d2 ) . Figure 3B provides a geometric example of how relearning A2 increases the error on A1 . After learning A1 and A2 , w = w0 . The effects of forgetting and relearning can be seen by ignoring the ± superscripts and subscripts for now . After partial forgetting , w = w1 , and performance error Epre = p2 . Relearning A2 yields w2 , the orthogonal projection of w1 on to L2 , and performance error is Epost = q2 . FLL occurs if δ = Epre−Epost>0 , or equivalently if p2−q2>0 ( see [12] , Appendices A–C for proofs ) . Forgetting here consists of reducing w0 by a factor r<1 , so that w1 = rw0 . The plus and minus signs in Figure 3B refer to two versions and of association A1 , in which X1 is the same and the target d1 has the same magnitude , but opposite signs: and . We now find the expected change in error induced by relearning a given association A2 . After learning followed by forgetting , the change in error on after relearning A2 is . After learning followed by forgetting , the change in error on after relearning A2 is . Using similar triangles in Figure 3B , ( 9 ) ( 10 ) Therefore , the total change in error on and induced by relearning A2 ( on different occasions ) is ( 11 ) ( 12 ) ( 13 ) Irrespective of the precise value of the target output value d1 in A1 , if the distribution of d1 is isotropic then +d1 is as probable as −d1 . If the total change in error for two instances ( and ) of A1 is −2 ( 1−r ) 2e2 then the expected change ( conditional on e ) is E[δ|e] = − ( 1−r ) 2e2 . Therefore , if forgetting is induced by falling weight values , then the expected change in error E[δ]<0 .
We have proved and demonstrated that , in one of the simplest forms of neural network model , relearning part of a previously learned set of associations reduces performance on the remaining non-relearned associations . This result is in stark contrast to our previous results , which proved that relearning induced partial recovery of non-relearned items [12] . The only difference between these two studies is the way in which forgetting was induced . An obvious physiological concomitant of Hebbian learning is long-term potentiation ( LTP ) , which seems to underpin learned behaviors [14] . LTP can last for hours , days or even months , and usually follows an exponential decay [3] . However , some forms of LTP do not seem to decay [15] , and have been shown to be stable for up to one year [16] . Such stability is remarkable , but from a statistical point of view , would almost certainly be accompanied by random fluctuations which would have a cumulative effect over time; and indeed , fluctuations are apparent in the stable LTP reported in [16] . Crucially , it is not known if the forgetting of learned behaviors is caused by decaying efficacy at many synapses , or by the cumulative effect of random fluctuations in stable LTP-induced synaptic efficacies . Here , decaying efficacy is analogous to weight values that fall toward zero in network models , whereas the cumulative effect of random fluctuations is analogous to the addition of random noise , or drifting , of weight values in network models . Given a choice between forgetting via synaptic weights that fall towards zero and weights that drift isotropically , has evolution chosen drifting or falling ? If all other things were equal then forgetting via synaptic drift would seem to be the obvious choice . This is because drifting ensures that relearning a subset of associations improves performance on other associations , whereas falling decreases performance . However , other things are rarely equal . The expected magnitude of weights increases with drifting but decreases with falling . ( Consider a hypersphere centered on the origin , with radius ∥w0∥ . Simple geometry shows that more than half of all directions emanating from w0 yield a new weight vector w1 which lies outside the hypersphere , and therefore E[∥w1∥]>E[∥w0∥] ( assuming , for example , that all vectors w1−w0 have the same length ) . ) This decrease in weight magnitudes effectively reduces neuronal firing rates , which reduces metabolic costs relative to costs incurred by synaptic drift . Synaptic drift therefore confers mnemonic benefits , but these benefits come at a metabolic price . Thus the increased fitness gained from the mnemonic benefits of synaptic drift must be offset against their metabolic costs . In essence , even free-lunch learning comes at a price .
Given a c×n matrix X and a c -dimensional vector d , let LX , d be the affine subspaceof . If X and d are consistent ( i . e . , there is a w such that Xw = d ) thenGiven weight vectors w1 and w2 , a matrix X of input vectors , and a vector d of desired outputs , definewhere Epre = E ( X;w1 , d ) and Epost = E ( X;w2 , d ) . Let be any element of LX , d . Then ( 14 ) If Xi has rank ni then transposing the QR decomposition of ( or , equivalently , using Gram–Schmidt orthonormalisation of the rows of Xi ) givesfor unique ni×ni and ni×n matrices Ti and Zi with Ti lower triangular with positive diagonal elements , and . Simple calculation shows that , for any weight vector w , and are orthogonal . Since , it follows that the matrix represents the operator that projects orthogonally onto the image of . Because ( 15 ) the image of is contained in that of . As both these images have dimension ni , they must be equal , and so represents the operator which projects orthogonally onto the image of . Now suppose that X and d are consistent , where Then , after the network has learned A1 and A2 , the weight vector w0 satisfies ( 16 ) ( If , as below , n1+n2≤n , X2 and d2 are consistent , and ( X1 , d1 ) has a continuous distribution then Equation 16 holds with probability 1 . ) We now assume that forgetting is induced by weight values “falling” towards the origin at zero , i . e . , forgetting consists of shrinking the weight vector w0 by a ( possibly random ) factor r towards the “dead state” 0 . Thus the post-forgetting weight vector w1 is given by ( 17 ) and so the “forgetting vector” v = w1−w0 is ( 18 ) The form of forgetting given by Equation 17 is very different from that investigated in [12] , where v has an isotropic distribution and is independent of ( X1 , d1 ) and ( X2 , d2 ) . Let w2 be the orthogonal projection of w1 onto L2 . Then Manipulation gives ( 19 ) and so ( 20 ) Then Equations 14 , 16 , and 18–20 yield ( 21 ) In this section we assume that the distribution of ( X1 , d1 ) is isotropic , i . e . , that ( UX1V , Ud1 ) has the same distribution as ( X1 , d1 ) for all orthogonal n1×n1 matrices U and all orthogonal n×n matrices V . Then taking the conditional expectation of Equation 21 for given X2 , d2 , and r gives the following theorem . If then ( 22 ) where x is any column of . If 1 . -3 . of Theorem 1 hold then ( 23 ) with equality if and only if either r = 1 or d2 = 0 . Corollary 1 says that ( apart from trivial exceptions ) the expected amount of FLL is negative . To obtain Theorem 2 , it is useful to have some moments of isotropic distributions . Let x be isotropically distributed on . Then Equations 9 . 6 . 1 and 9 . 6 . 2 of Mardia and Jupp ( 2000 ) , together with some algebraic manipulation , yield ( 24 ) ( 25 ) as in Equations A . 14 and A . 15 of [12] . The other tool used in proving Theorem 2 is the formula ( 26 ) for any random variables X , Y , Z for which these quantities exist . Equation 26 is an application to the conditional distribution of Y|Z of the standard conditional variance formula that is given in Equation 2b . 3 . 6 on page 97 of [17] . Taking the expectation and variance of Equation 21 as only d1 varies and using Equation 24 gives ( 27 ) ( 28 ) Taking the expectation of Equation 28 as only X1 varies and using Equation 24 gives ( 29 ) We now suppose that ( 30 ) Then taking the variance of Equation 27 as only X1 varies and using Equation 25 gives ( 31 ) Adding Equations 29 and 30 and using Equation 26 yields ( 32 ) To obtain an upper bound on the conditional probability of FLL ( i . e . , on P ( δ≥0|X2 , d2 , r ) ) , we use Chebyshev's inequality , which states that , for any random variable Y and any positive value of t Applying Chebyshev's inequality to the conditional distribution of δ ( w1 , w2 , X1 , d1 ) given ( X2 , d2 , r ) , taking t = E[δ ( w1 , w2;X1 , d1 ) |X2 , d2 , r] , and noting that ( by Equation 23 ) t≤0 , we obtain ( 33 ) Substituting Equations 22 and 32 into Equation 33 gives ( 34 ) where For any positive-definite symmetric matrix A and vector x , diagonalization of A , together with the fact that x+1/x≥2 for positive x , yields ( 35 ) Combining Equations 34 and 35 with the fact that gives ( 36 ) Taking the expectation of Equation 36 over X2 yields ( 37 ) where x and are any columns of and , respectively . Taking the expectation of Equation 37 over d2 and r yields the following theorem . If ( a ) conditions 1 . -4 . of Theorem 1 hold , ( b ) the columns of are distributed independently , ( c ) X2 , d2 , and r are independent , ( d ) the distribution of ( X2 , d2 ) is isotropic , and ( e ) E[∥d2∥−2] is finite then ( 38 ) where x and are any columns of and , respectively , and If the conditions of Theorem 2 hold andwhere x and are any columns of and , respectively , then Thusprovided that n1/n and n2/n are bounded away from zero .
|
If you learn a skill , then partially forget it , does relearning part of that skill induce recovery of other parts of the skill ? More generally , if you learn a set of associations , then partially forget them , does relearning a subset induce recovery of the remaining associations ? In previous work , in which participants learned the layout of a scrambled computer keyboard , the answer to this question appeared to be “yes . ” More recently , we modeled this “free-lunch learning” effect using artificial neural networks , in which the synaptic strength between each pair of model neurons is a connection weight . We proved that if forgetting is induced by allowing each weight value to drift randomly , then free-lunch learning is almost inevitable . However , if , after learning a set of associations , forgetting is induced by allowing each connection weight to decay or fall toward zero , then relearning a subset of associations decreases performance on the remaining associations . This suggests that evolution may have selected physiological mechanisms that involve forgetting using a form of synaptic drift rather than synaptic decay , because synaptic drift yields free-lunch learning , whereas decay does not .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/evolutionary",
"modeling",
"computational",
"biology/computational",
"neuroscience"
] |
2008
|
Falling towards Forgetfulness: Synaptic Decay Prevents Spontaneous Recovery of Memory
|
The genus Paracoccidioides comprises human thermal dimorphic fungi , which cause paracoccidioidomycosis ( PCM ) , an important mycosis in Latin America . Adaptation to environmental conditions is key to fungal survival during human host infection . The adaptability of carbon metabolism is a vital fitness attribute during pathogenesis . The fungal pathogen Paracoccidioides spp . is exposed to numerous adverse conditions , such as nutrient deprivation , in the human host . In this study , a comprehensive response of Paracoccidioides , Pb01 , under carbon starvation was investigated using high-resolution transcriptomic ( RNAseq ) and proteomic ( NanoUPLC-MSE ) approaches . A total of 1 , 063 transcripts and 421 proteins were differentially regulated , providing a global view of metabolic reprogramming during carbon starvation . The main changes were those related to cells shifting to gluconeogenesis and ethanol production , supported by the degradation of amino acids and fatty acids and by the modulation of the glyoxylate and tricarboxylic cycles . This proposed carbon flow hypothesis was supported by gene and protein expression profiles assessed using qRT-PCR and western blot analysis , respectively , as well as using enzymatic , cell dry weight and fungus-macrophage interaction assays . The carbon source provides a survival advantage to Paracoccidioides inside macrophages . For a complete understanding of the physiological processes in an organism , the integration of approaches addressing different levels of regulation is important . To the best of our knowledge , this report presents the first description of the responses of Paracoccidioides spp . to host-like conditions using large-scale expression approaches . The alternative metabolic pathways that could be adopted by the organism during carbon starvation can be important for a better understanding of the fungal adaptation to the host , because systems for detecting and responding to carbon sources play a major role in adaptation and persistence in the host niche .
Metabolic adaptability and flexibility are important attributes for pathogens to successfully colonize , infect , and cause disease in a wide range of hosts . Therefore , they must be able to assimilate various carbon sources . Carbohydrates are the primary and preferred source of metabolic carbon for most organisms and are used for generating energy and producing biomolecules [1] . Studies have highlighted the importance of carbon metabolism in fungi [2] , [3] . Pathogens such as Candida albicans display sufficient metabolic flexibility to assimilate the available nutrients in diverse niches such as the skin , mucous membranes , blood , and biofilms [4] , [5] . The mucosal surface of the lung may provide a more nutrient-limited condition because it is not in direct contact with nutrients from food intake [6] . Additionally , in the lungs , macrophages rapidly phagocytize inhaled microorganisms supported by neutrophils and dendritic cells [7] . Macrophages are considered a glucose- and amino acid-poor environment [8] , [9] and may form extremely nutrient-limited conditions causing severe starvation [10] . In C . albicans and Cryptococcus neoformans , alternative carbon metabolism was detected after internalization by macrophages playing a role in fungal survival in these host cells [9] , [11] , [12] . In contrast , in the case of systemic infections , pathogens can reach different internal organs such as the liver , which is the main storage compartment of glucose in the form of glycogen . The bloodstream , for example , is the major carrier of nutrients , glucose , proteins , amino acids , and vitamins in larger quantities [10] . In this way , metabolic and stress adaptation represent vital fitness attributes that have evolved alongside virulence attributes in fungi [8]–[10] , [13] . Alternative carbon metabolism was also described to be important to protozoa and bacteria [14] , [15] . Entamoeba histolytica uses an alternative source of energy when the microorganism is exposed to glucose starvation . In the specific pyruvate-to-ethanol pathway in E . histolytica , acetyl-CoA is converted to acetaldehyde , which is then reduced to ethanol [16] . Reduced levels of the long-chain fatty-acid-CoA ligase protein during glucose starvation conditions in E . histolytica may explain a mechanism by which acetyl-CoA is shuttled from the fatty acid metabolism into this pyruvate-to-ethanol pathway . In addition , the glucose starvation modulates the protozoa virulence , based on proteome analysis [15] . The transcriptome and large-scale proteome dynamics were also analyzed in Bacillus subtilis from glucose-starved cells . A direct consequence of glucose depletion on proteins was the switch from glycolytic to gluconeogenic metabolism and elevated abundance of proteins of the tricarboxylic cycle used for energy generation . Genes that are involved in exponential growth , amino-acid biosynthesis , purine/pyrimidine synthesis and the translational machinery were down-regulated in the bacteria cells under glucose starvation [14] . The species of the Paracoccidioides genus represent the causative agents of paracoccidioidomycosis ( PCM ) , one of the most frequent systemic mycoses in Latin America [17] . The genus comprises four phylogenetic lineages ( S1 , PS2 , PS3 , and Pb01-like ) . The phylogenetic analysis of many Paracoccidioides isolates has resulted in the differentiation of the genus into two species: P . brasiliensis , which represents a complex of three phylogenetic groups , and P . lutzii , which represents the Pb01-like isolates [18]–[21] . Paracoccidioides spp . grows as a yeast form in the host tissue and in vitro at 36°C , while it grows as mycelium under saprobiotic condition and in culture at room temperature ( 18–23°C ) . As the dimorphism is dependent on temperature , when the mycelia/conidia are inhaled into the host lungs , the transition of the mycelia to the pathogenic yeast phase occurs [22] . One of the first lines of defense faced by Paracoccidioides spp . during host invasion is the lung resident macrophages . Despite being phagocytosed , the fungus conidia differentiate into the parasitic yeast form that subverts the normally harsh intraphagosomal environment and survives and replicates into murine and human macrophages [23] . It has been proposed for PCM and other systemic mycoses that the fungal intracellular parasitism is a major event for disease establishment and progression in susceptible hosts . The survival inside the macrophage may allow fungal latency and/or dissemination from the lungs to several organs such as observed in C . neoformans [24] , [25] . In this sense , Paracoccidioides spp . has evolved defense mechanisms to survive under nutritionally poor environments . It has been suggested that alternative carbon metabolism plays a role in the survival and virulence of Paracoccidioides spp . within the host [26] , [27] , as occurs in C . albicans and C . neoformans [9] , [12] . Transcriptional analysis of Paracoccidioides spp . upon internalization by macrophages , as determined by a DNA microarray , consisting of 1 , 152 cDNA clones , showed that the fungus responds to the glucose-depleted environment found in the macrophage phagosome , by the expression of 119 classified genes , differentially transcribed . Genes involved in methionine biosynthesis ( cystathionine β-lyase ) , oxidative stress response ( superoxide dismutase and heat shock protein 60 ) , and cytochrome electron transport system ( cytochrome oxidase c ) were induced by the fungus . Moreover , Paracoccidioides spp . reduced the expression of genes that are involved in the glycolysis pathway such as the key regulatory phosphofructokinase ( pfkA ) and genes related to cell wall polysaccharides such as β-glucan synthase ( fks ) [27] . In addition , studies of the transcriptome profiling from yeast cells of Paracoccidioides spp . derived from mouse liver revealed that the fungus most likely uses multiple carbon sources during liver infection . Genes encoding enzymes , regulators , and transporters in carbohydrate and lipid metabolism were significantly overexpressed . Ethanol production was also detected , indicating that it may be particularly important during infection [28] . Here , we described the response of Paracoccidioides facing carbon starvation using a high-throughput RNA Illumina sequencing ( RNAseq ) and quantitative proteome NanoUPLC-MSE , a two-dimensional liquid chromatography-tandem mass spectrometry approach . RNAseq is a developed approach to transcriptome profiling that uses deep-sequencing technologies and has already been applied to organisms such as Saccharomyces cerevisiae , Arabidopsis thaliana , mouse , and human cells [29]–[33] . With regard to proteomic analysis , our group has developed detailed proteome maps of the process of the fungus dimorphism , the response to iron and zinc deprivation , the fungus exoproteome , and the response to oxidative stress as well as comparative proteome maps of members of Paracoccidioides phylogenetic species [34]–[39] . In this study , a comprehensive response of Paracoccidioides , isolate Pb01 , under carbon starvation was performed by transcriptional and proteomic approaches . To the best of our knowledge , this is the first description of high-resolution transcriptomics and proteomics applied to study the response of Paracoccidioides spp . to carbon starvation . We believe that the obtained data can be relevant in the understanding of the fungal establishment in the host .
Paracoccidioides , Pb01 ( ATCC MYA-826 ) , was used in the experiments . The yeast phase was cultivated for 7 days , at 36°C in BHI semisolid medium added to 4% ( w/v ) glucose . When required , the cells were grown for 72 h at 36°C in liquid BHI , washed with PBS 1× , and incubated at 36°C in a McVeigh/Morton ( MMcM ) medium with the following composition per 100 mL: KH2PO4 0 . 15 g; MgSO4 . 7H20 0 . 05 g; CaCl2 . 2H20 0 . 015 g; ( NH4 ) 2SO4 0 . 2 g; vitamin 1 mL and trace element supplements 0 . 1 mL ) . The stock vitamin solution contained , also per 100 mL: thiamine hydrochloride , 6 . 0 mg; niacin , 6 . 0 mg; calcium pantothenate , 6 . 0 mg; inositol , 1 . 0 mg; biotin , 0 . 1 mg; riboflavin , 1 . 0 mg; folic acid , 10 mg; choline chloride , 10 mg; and pyridoxine hydrochloride , 10 mg . The trace element solution contained , per 100 mL: H3BO3 , 5 . 7 mg; CuSO4 . 5H20 , 15 . 7 mg; Fe ( NH4 ) 2 ( SO4 ) 2 . 6H2O , 140 . 4 mg; MnSO4 . 14H2O , 8 . 1 mg; ( NH4 ) 6Mo7O24 . 4H2O , 3 . 6 mg; ZnSO4 . 7H2O , 79 . 2 mg , as described previously [40] , except for removal of the amino acids . All components except the vitamin supplement were mixed , and the pH was adjusted to 7 . 0 with 1N NaOH . The vitamin solution was filter sterilized and added after the remainder of the medium had been autoclaved at 121°C for 15 min and cooled . Paracoccidioides yeast cells were subjected to carbon starvation as following . The Pb01 yeast cells were grown for 72 h at 36°C in liquid BHI added to 4% ( w/v ) glucose . The cells were harvest and washed three times with PBS 1× . A total of 106 cells/mL were inoculated in modified MMcM medium [40] with 4% ( glucose , carbon source ) or 0% of glucose ( carbon starvation ) . The cells were incubated at 36°C . Following Paracoccidioides incubation in carbon starving condition , cells were centrifuged at 1 , 500× g , frozen in liquid nitrogen , and disrupted by maceration as described in [38] . Briefly , cells were treated with TRIzol reagent ( Invitrogen , Carlsbad , CA , USA ) . The manufacturer's protocol was followed to extract total RNA . The RNA was reversibly transcribed using the high capacity RNA-to-cDNA kit ( Applied Biosystems , Foster City , CA , USA ) . We confirmed the specificity of each primer pairs for the target cDNA by the visualization of a single PCR product following agarose gel electrophoresis and melting curve analysis . The cDNA was quantified by qRT-PCR using a SYBR green PCR master mix ( Applied Biosystems Step One Plus PCR System ) . qRT-PCR analysis was performed in biological triplicate for each cDNA sample as previously described [38] . The data were normalized using the constitutive gene encoding the 60S ribosomal L34 as the endogenous control . In order to analyze the reliability of the normalizer used in our qRT-PCRs we used data obtained from three different housekeeping genes and the software NormFinder ( Aarhus University , Aarhus , Denmark ) . The software identify the most suitable reference genes as previously described in [41] . We used the actin ( PAAG_00564 ) , tubulin alpha-1 chain ( PAAG_01647 ) and 60S ribosomal protein L34 ( PAAG_00746 ) genes and the results show that the L34 is the best gene to be used as normalizer in our qRT-PCRs . It was demonstrated by the lower stability value by comparing with actin and tubulin genes after two different runs ( Table S1 ) . The 60S ribosomal L34 gene was amplified in each set of qRT-PCR experiments and was presented as relative expression in comparison to the experimental control cells value set at 1 . Data were expressed as the mean ± standard deviation of the biological triplicates of independent experiments . Standard curves were generated by diluting the cDNA solution 1∶5 . Relative expression levels of genes of interest were calculated using the standard curve method for relative quantification [42] . Statistical comparisons were performed using the student's t test and p-values≤0 . 05 were considered statistically significant . The specific primers , both sense and antisense , are described in Table S2 . Proteins were fractionated by 12% SDS-polyacrylamide gel electrophoresis , and stained with Coomassie Blue R or transferred to Hybond ECL membrane ( GE Healthcare ) . Membranes were blocked for 1 h at room temperature in a solution containing 10% ( w/v ) skim milk powder and 0 . 1% Tween 20 in Tris-buffered saline ( TBS-T ) . The primary polyclonal antibody anti-isocitrate lyase [43] was diluted in blocking solution and incubated with the membrane for 1 h at room temperature . Membranes were washed in Tris-buffered saline and then incubated with alkaline phosphatase conjugated secondary antibodies for another hour at room temperature . Labeled bands were revealed with 5-bromo-4-chloro-3-indolylphosphate/nitroblue tetrazolium and negative controls were obtained with rabbit preimmune . Images from western blots were acquired with ImageMaster 2D Platinum 6 . 0 ( Geneva Bioinformatics , GeneBio ) . Raw Tiff images were analyzed by densitometric analysis of immunoblotting bands using the software AphaEaseFC ( Genetic technologies Inc . ) . Pixel intensity for the analyzed bands was generated and expressed as Integrated Density Values ( IDV ) . Following Paracoccidioides growth in the presence or not of carbon for 6 h , cells were treated with TRIzol reagent ( Invitrogen , Carlsbad , CA , USA ) to obtain RNA molecules , from biological replicates . The cDNAs libraries were prepared from poly ( A ) -fragment selected mRNA and processed on the Illumina HiSeq2000 Sequencing System ( http://www . illumina . com/ ) . As a result , approximately 40 million of reads of 100 bp paired-end sequencing were obtained for each sample . The sequencing reads were mapped to reference the Paracoccidioides genome ( Pb01 ) , ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/MultiHome . html ) , using the Bowtie 2 tool [44] . Mapped reads data were analyzed by the DEGseq package [45] . Briefly , each read was allowed to alignment in just one site of the genome and the reads were counted . The default parameters were used to perform the alignment . The number of mismatches allowed in seed alignment ( -N ) is 0 , and the length of each seed ( -L ) is 20 . The fold change selection method was used for differentially expressed genes selection using a Fisher exact test , and a p-value of 0 . 001 was considered to select the genes . From the selected genes , the 2-fold change cut-off was considered . Genes with log2 ( fold change ) higher than 1 or less than −1 were selected and classified as up- and down-regulated genes , respectively . Gene's identifications and annotations were determined from the Paracoccidioides genome database ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/MultiHome . html ) . The biological processes were obtained using the Pedant on MIPS ( http://pedant . helmholtz-muenchen . de/pedant3htmlview/pedant3view ? Method=analysis&Db=p3_r48325_Par_brasi_Pb01 ) which provides a tool to browse and search the Functional Categories ( FunCat ) of proteins . All scripts can be obtained on request . Following Paracoccidioides cell incubation in carbon-starved media for up to 48 h , the cells were centrifuged at 1 , 500× g , resuspended in a 50 mM ammonium bicarbonate pH 8 . 5 solution and disrupted using glass beads and bead beater apparatus ( BioSpec , Oklahoma , USA ) in 5 cycles of 30 sec , while on ice . The cell lysate was centrifuged at 10 , 000× g for 15 min at 4°C and the supernatant was quantified using the Bradford reagent ( Sigma-Aldrich ) [46] . The samples were analyzed using nanoscale liquid chromatography coupled with tandem mass spectrometry . Sample aliquots ( 50 µg ) were prepared for NanoUPLC-MSE as previously described [47] , [48] . Briefly , 50 mM ammonium bicarbonate was added and was followed by addition of 25 µL of RapiGEST ( 0 . 2% v/v ) ( Waters Corp , Milford , MA ) . The solution was vortexed and then incubated at 80°C for 15 min; 2 . 5 µL of a 100 mM DTT solution was then added and incubated for 30 min at 60°C . The sample was cooled at room temperature and 2 . 5 µL of 300 mM iodoacetamide was added followed by sample incubation in a dark room for 30 min . A 10 µL aliquot of trypsin ( Promega , Madison , WI , USA ) prepared with 50 mM ammonium bicarbonate to 50 ng/uL , was added . The sample was vortexed slightly and digested at 37°C overnight . Following the digestion , 10 µL of 5% ( v/v ) trifluoroacetic acid was added to hydrolyze the RapiGEST , followed by incubation at 37°C for 90 min . The sample was centrifuged at 18 , 000× g at 6°C for 30 min , and the supernatant was transferred to a Waters Total Recovery vial ( Waters Corp ) . A solution of one pmol . ul−1 MassPREP Digestion Standard [rabbit phosphorilase B ( PHB ) ] ( Waters Corp ) was used to prepare the final concentration of 150 fmol . ul−1 of the PHB . The buffer solution of 20 mM ammonium formate ( AF ) was used to increase the pH . The digested peptides were separated further via NanoUPLC-MSE and analyzed using a nanoACQUITY system ( Waters Corporation , Manchester , UK ) . Mass spectrometry data obtained from NanoUPLC-MSE were processed and searched using ProteinLynx Global Server ( PLGS ) version 3 . 0 ( Waters Corp ) as previously described [49] . Protein identifications and quantitative data packaging were performed using dedicated algorithms [50] , [51] and a search against the Paracoccidioides database ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/MultiHome . html ) . The ion detection , clustering , and log-scale parametric normalizations were performed in PLGS with an ExpressionE license installed ( Waters , Manchester , UK ) . The intensity measurements were typically adjusted for these components , i . e . , the deisotoped and charge state reduced EMRTs that were replicated throughout the entire experiment for the analysis at the EMRT cluster level . STY phosphorylations were set as variable modification . Components were typically clustered with a 10 ppm mass precision and a 0 . 25 min time tolerance against the database-generated theoretical peptide ion masses with a minimum of one matched peptide . The alignment of elevated-energy ions with low-energy precursor peptide ions was performed with an approximate precision of 0 . 05 min . One missed cleavage site was allowed . The precursor and fragmention tolerances were determined automatically . The protein identification criteria also included the detection of at least three fragment ions per peptide , 7 fragments per protein and the determination of at least one peptide per protein . The maximum protein mass was set to 600 kDa and trypsin was chosen as the primary digest reagent . The identification of the protein was allowed with a maximum 4% false positive discovery rate in at least two out of three technical replicate injections . Using protein identification replication as a filter , the false positive rate was minimized because false positive protein identifications , i . e . , chemical noise , have a random nature and do not tend to replicate across injections . For the analysis of the protein identification and quantification level , the observed intensity measurements were normalized to the intensity measurement of the identified peptides of the digested internal standard . Protein and peptides tables generated by PLGS were merged and the dynamic range of the experiments , peptides detection type , and mass accuracy were determined for each condition as described in [47] by setting the minimum repeat rate for each protein in all replicates to 2 . Normalization was performed with a protein that showed no significant difference in abundance in all injections [52] to accurately compare the expression protein level to carbon and carbon-starved samples . Paracoccidioides yeast cells were grown for 72 h at 36°C in liquid BHI , washed with PBS 1× , and filtered using a nylon mesh filter to yield small and non-aggregated cells . A total of 5×107 cells/50 mL were inoculated in modified MMcM medium [40] with carbon source ( 4% glucose ) or under carbon starvation ( absence of glucose ) and were incubated at 36°C . In each time-point , 10 mL of culture were centrifuged at 1 , 500× g and the supernatants were carefully removed . The cells were ressuspended in PBS 1× up to 500 µl and subjected to 95°C heating for 1 h . The cells were centrifuged , frozen in liquid nitrogen and lyophilized for 24 h . Dry weight was determined . Data are expressed as the mean ± standard deviation of the triplicates of independent experiments . Statistical comparisons were performed using the Student's t test and p-values≤0 . 05 were considered statistically significant . The viability was determined by membrane integrity analysis using propidium iodide as dead cells marker as previously described [34] , [35] . Briefly , cell suspension ( 106 yeast cells/mL ) were centrifuged and the supernatant was discarded . The cells were stained by addition of the propidium iodide solution ( 1 µg/mL ) for 20 min in the dark at room temperature . After dye incubation , stained cell suspension was immediately analyzed in a C6 Accuri flow cytometer ( Accuri Cytometers , Ann Arbor , MI , USA ) . A minimal of 10 , 000 events per sample was acquired with the FL3-H channel . Data was collected and analyzed using FCS Express 4 Plus Research Edition software ( Denovo Software , Los Angeles , CA , USA ) . The concentration of ethanol was quantified by enzymatic detection kit according to the manufacturer's instruction ( UV-test for ethanol , RBiopharm , Darmstadt , Germany ) . Ethanol is oxidized to acetaldehyde by the enzyme alcohol dehydrogenase , in the presence of nicotinamide-adenine dinucleotide ( NAD ) . Acetaldehyde is quantitatively oxidized to acetic acid in the presence of aldehyde dehydrogenase , releasing NADH , which is determined by means of its absorbance at 340 nm . Paracoccidioides Pb01 yeast cells were subjected or not to carbon starvation , and 106 cells were used to assay . Briefly , cells were counted , centrifuged , and lysed using glass beads and bead beater apparatus ( BioSpec , Oklahoma , USA ) in 5 cycles of 30 sec , keeping the samples on ice . The cell lysate was centrifuged at 10 , 000× g for 15 min at 4°C and the supernatant was used for enzymatic assay according to the manufacturer's instructions . The concentrations of ethanol were obtained in triplicate . Following Paracoccidioides growth under carbon source ( 4% glucose ) or carbon starvation ( absence of glucose ) the cells were centrifuged at 1 , 500× g , resuspended in a solution containing 20 mM Tris-HCl , pH 8 . 8 , 2 mM CaCl2 [53] and disrupted using glass beads and bead beater apparatus ( BioSpec , Oklahoma , USA ) in 5 cycles of 30 sec , while on ice . The cell lysate was centrifuged at 10 , 000× g for 15 min at 4°C and the supernatant was quantified using the Bradford reagent ( Sigma-Aldrich ) [46] . Formamidase activity was determined by measuring the amount of ammonia formation as previously described [38] , [54] . One µg of Paracoccidioides total protein extract prepared as described above was added to 200 µl of a 100 mM formamide substrate solution in 100 mM phosphate buffer containing 10 mM of EDTA , pH 7 . 4 . Samples were incubated at 37°C for 30 min . A total of 400 µl of phenol-nitroprusside and the same volume of alkaline hypochlorite ( Sigma Aldrich , Co . ) were added on the tube . The samples were then incubated for 6 min at 50°C and the absorbance was read at 625 nm . The amount of ammonia released for each sample was determined by comparing to a standard curve . One unit ( U ) of formamidase specific activity was defined as the amount of enzyme required to hydrolyze 1 µmol of formamide ( corresponding to the formation of 1 µmol of ammonia ) per min per mg of total protein . Isocitrate lyase activity was determined by measuring the formation of glyoxylate as its phenylhydrazone derivative [55] . Glyoxylate-phenylhydrazone formation was determined by measuring the absorbance at 324 nm , using an extinction coeficient of 16 . 8 mM−1 cm−1 , in a reaction mixture containing 2 mM threo-D , L-isocitrate ( Sigma Aldrich , Co . ) , 2 mM MgCl2 , 10 mM phenylhydrazine HCl ( Sigma Aldrich , Co . ) , 2 mM dithiothreitol and 50 mM potassium phosphate at pH 7 . 0 . Specific activity was determined as the amount of enzyme required to form 1 µmol of glyoxylate-phenylhydrazone per min per mg of total protein . For both assays , the statistical comparisons were performed using the Student's t test and p-values≤0 . 05 were considered statistically significant . To evaluate whether carbon starvation influenced the internalization and survival of Paracoccidioides , the survival rate ( viable fungi after co-cultivation ) was determined by quantifying the number of colony-forming units ( CFUs ) recovered from macrophage infection . Macrophages , cell line J774 A . 1 ( Rio de Janeiro Cell Bank – BCRJ/UFRJ , accession number: 0121 ) , maintained in RPMI medium ( RPMI 1640 , Vitrocell , Brazil ) , with 10% FBS ( fetal bovine serum , [v/v] ) were used in assays . A total of 106 macrophages were seeded into each well of a 24-well tissue culture plate and 100 U . mL−1 of IFN-gamma ( murine IFN-γ , PeproTech , Rocky Hill , New Jersey , USA ) was used for 24 h at 36°C with 5% CO2 for activation of macrophages [56] . Prior to co-cultivation , Paracoccidioides yeast cells were grown in BHI liquid medium ( 4% [w/v] glucose , 3 . 7% [w/v] brain heart infusion , pH 7 . 2 ) for 72 h and subjected to both conditions , a carbon source ( 4% [w/v] of glucose ) or carbon starvation ( no glucose ) at 36°C , in McVeigh/Morton medium ( MMcM ) for 48 h . The same ratio of 1∶2 . 5 macrophage∶yeast was used for infection of both , carbon and carbon-starved yeast cells . The cells were co-cultivated for 24 h with 5% CO2 at 36°C to allow fungal internalization . Prior to macrophages lysis , dilutions of the supernatant ( culture from co-cultivation ) removed by aspiration , were plated in BHI medium supplemented with 5% FBS ( fetal bovine serum , [v/v] ) incubated in 5% CO2 at 36°C for 10 days . The number of viable cells was determined based on the number of CFUs . Infected macrophages were lysed with distilled water and dilutions of the lysates were plated in BHI medium supplemented with 5% FBS ( fetal bovine serum , [v/v] ) . Colony-forming units ( CFU ) were determined after growth at 36°C , in 5% CO2 , for 10 days . For both experiments , the CFUs data were expressed as the mean value ± the standard deviation from triplicates and the statistical analyses were performed using Student's t test . To analyze the expression of genes from Paracoccidioides yeast cells infecting macrophages , the same cell line described above was used . Paracoccidioides yeast cells were cultivated in BHI liquid medium ( 4% [w/v] glucose , 3 . 7% [w/v] brain hearth infusion , pH 7 . 2 ) for 72 h and incubated with macrophages for 24 h , followed by washing with distilled water to promote macrophages lysis . RNAs and cDNAs were obtained as previously described . Specific oligonucleotides were used to amplify the fructose-1 , 6-biphosphatase , isocitrate lyase and 3-ketoacyl-CoA thiolase genes from Pb01 yeast cells . The negative amplification was obtained when cDNAs were used only from macrophages . To determine the number of adhered/internalized fungi cells by macrophages , the same macrophage cell line described above was used . A total of 106 macrophages were plated on glass coverslips per 12-well tissue culture plate and 100 U . mL−1 of IFN-gamma ( murine IFN-γ , PeproTech , Rocky Hill , New Jersey , USA ) was used for 24 h at 36°C with 5% CO2 for its activation , as described above [56] . Prior to co-cultivation , Paracoccidioides yeast cells were grown in BHI liquid medium ( 4% [w/v] glucose , 3 . 7% [w/v] brain heart infusion , pH 7 . 2 ) for 72 h , and subsequently transferred to McVeigh/Morton medium ( MMcM ) , containing or not a carbon source , for 48 h , at 36°C . The same ratio of 1∶2 . 5 macrophage∶yeast was used for infection in both conditions . The cells were co-cultivated for 6 and 24 h with 5% CO2 at 36°C , to allow fungal adhesion/internalization . The supernatants were then aspirated , the monolayer was gently washed twice with PBS 1× to remove any non-adhered/internalized yeast cells , and the samples were processed for light microscopy . The glass coverslips were fixed with methanol and stained with Giemsa ( Sigma ) . The cells were observed using the Axio Scope A1 microscope and digital images were acquired using the software AxioVision ( Carl Zeiss AG , Germany ) . A total of 300 macrophages were counted to determine the average number of adhered/internalized fungal cells , as described before [37] , [57] . For both experiments , the number of adhered/internalized fungal cells was shown in percentage of the total as the mean value ± the standard deviation from triplicates . The statistical analyses were performed using Student's t test .
We first sought to set up a time-point to analyze the fungus response to carbon starvation at transcriptional and proteomic levels . Hence , we analyzed gene and protein expression of genes known to be regulated under carbon-limited microenvironments in other fungi [9] , [12] , by using qRT-PCR and western blot assays , in Pb01 ( Fig . 1A ) . Changes in the expression of genes encoding fructose-1 , 6-biphosphatase , isocitrate lyase , and 3-ketoacyl-CoA thiolase , which are representatives of gluconeogenesis , the glyoxylate cycle , and β-oxidation , respectively , were analyzed in the Paracoccidioides , Pb01 , yeast cells subjected to carbon starvation . As depicted in Fig . 1A , carbon starvation promoted the increase of gene expression at 6 h and at 12 h for the treatments . At protein level , the differential expression of isocitrate lyase suggested that Paracoccidioides , Pb01 , up-regulated the glyoxylate cycle after 48 h of carbon starvation ( Figs . 1B and S1 ) . In this way , the 6 h and 48 h treatments were considered in further transcriptional and proteomic analysis , using a high-throughput RNA Illumina sequencing ( RNAseq ) and NanoUPLC-MSE , respectively . The transcriptome analysis was performed using next generation sequencing and the Paracoccidioides , isolate Pb01 , genome database ( http://www . broadinstitute . org/annotation/genome/paracoccidioides_brasiliensis/MultiHome . html ) was used as a reference genome for mapping the reads which were analyzed by DEGseq package [45] . For the global analysis , plotting graphs were performed ( Fig . S2 ) . The number of the reads counted for each transcript in carbon and carbon-starved conditions was represented by scattered dots ( Fig . S2 ) . The transcripts are represented by dots , which could present a different number of reads in each condition ( Fig . S2A ) . We applied a statistical test to identify differentially expressed transcripts , represented by red dots ( Fig . S2B ) . A significant number of genes were regulated during carbon starvation . Although 1 . 5-fold change can be statistically significant [58] , a cut-off of 2-fold change was applied to determine the up- and down-regulated transcripts ( Tables S3 and S4 , respectively ) totaling 1 , 063 differentially expressed transcripts in Pb01 yeast cells under carbon starvation . A biological process classification was performed to gain a general understanding of the functional categories affected by carbon starvation . A total of 64 . 6% ( 687 transcripts ) were represented by miscellaneous and unclassified categories , and the other 35 . 4% ( 376 transcripts ) were represented by classified biological categories . The functional classifications and the percentage of up- and down-regulated transcripts in each classified category are shown in Fig . S3 . The transcriptome analysis showed that transcripts associated with metabolism were the most represented during 6 h of carbon starvation in Pb01 ( Fig . S3A ) . From these , approximately 27% were represented by up-regulated and 17% by down-regulated transcripts ( Fig . S3B ) . Subcategories of metabolism related to amino acid , nitrogen/sulfur , C-compound/carbohydrate , lipid/fatty acid , purines , secondary , and phosphate metabolisms were regulated under carbon starvation stress , and all of them showed a higher number of up- than down-regulated transcripts ( Tables S3 and S4 ) . Other categories were also regulated in Pb01 under carbon starvation . The categories associated with protein fate , cell cycle/DNA processing , transcription , and cellular transport were largely represented in the transcriptome ( Fig . S3A ) . The number of transcripts with increased or reduced expression was also investigated for these categories and the results show that , in contrast to metabolism , the number of down-regulated transcripts was generally higher than that of up-regulated transcripts for each category ( Fig . S3B ) . Down-regulated transcripts associated with cell cycle , transcription , cell growth morphogenesis , and signal transduction could reflect the reduced growth of this fungus subjected to carbon starvation , as demonstrated in Fig . 2 . Although the reduced growth of Paracoccidioides during carbon starvation , the cells viability , assayed using propidium iodide , was not significantly different from those cultivated with a carbon source ( Fig . S4 ) . In the same way , the cells are metabolically active as demonstrated by the high activity of the enzyme formamidase in Paracoccidioides grown under glucose deprivation ( Fig . S5 ) . The cellular transport process was also representative in our transcriptome analysis . The abundance of specific transporters was elevated such as those of copper , hexoses , and monosaccharides ( Table S3 ) indicating that carbohydrate , amino acid and metal-uptake processes are required for Pb01 cells to survival under carbon starvation . Additionally , the abundance of transcripts related to cellular response against ROS ( reactive oxygen species ) such as superoxide dismutases , catalase and cytochrome c peroxidase were also elevated ( Table S3 ) indicating that Paracoccidioides possibly has evolved the ability to respond to oxidative stress also under carbon starvation . The proteomic approach was performed using NanoUPLC-MSE as previously described [47] , [48] . This method has been shown to improve protein and proteome coverage compared to the conventional LC-MS/MS approach [48] . The resulting NanoUPLC-MSE protein and peptide data generated by PLGS process are shown in Fig . S6 , S7 and S8 . First , the false positive rates of proteins from carbon and carbon starvation data were 0 . 34 and 0 . 27% , respectively . The experiments resulted in 3 , 327 and 3 , 842 identified peptides , where 45 and 57% of these were obtained from peptide match type data in the first pass , and 19 and 14% from the second pass [50] to carbon and carbon-starving conditions , respectively ( Fig . S6 ) . A total of 17 and 14% of total peptides were identified by a missed trypsin cleavage in carbon and carbon-starving conditions , respectively , whereas an in-source fragmentation rate of the same 4% was obtained for both ( Fig . S6 ) . Fig . S7 shows the peptide parts per million error ( ppm ) indicating that the majority , 94 . 8 and 95 . 7% , from identified peptides were detected with an error of less than 15 ppm for carbon and carbon starvation conditions , respectively . Fig . S8 depicts the results obtained from dynamic range detection indicating that a 3-log range concentration and a good detection distribution of high and low molecular weights were obtained for the both conditions . A total of 421 differentially expressed proteins were identified in our proteomic analysis . As previously described [59] , a 1 . 5-fold change was used as a threshold to determine the up- and down-regulated proteins ( Tables S5 and S6 , respectively ) . Approximately 20% of them ( 86 proteins ) were represented by miscellaneous and unclassified categories , and the remaining 80% ( 335 proteins ) were represented by classified biological categories . The biological processes and the percentage of up- and down- regulated proteins in each classified category are shown in Fig . S9 . The proteome analysis showed that proteins associated with metabolism were also the most represented during 48 h of carbon starvation in Pb01 ( Fig . S9A ) . The metabolism was represented by amino acid , nitrogen/sulfur , C-compound/carbohydrate , lipid/fatty acid , purines , secondary , and phosphate metabolisms . All of these subcategories showed more up- than down-regulated proteins ( Tables S5 and S6 ) . Interestingly , the nitrogen/sulfur metabolism was detected as up-regulated only at protein level ( Table S5 ) . Other categories presented a high number of regulated proteins such as translation , protein fate , energy and cell defense . On the other hand , processes involved with transcription , cellular transport , cell growth/morphogenesis , and signal transduction presented a lower number of regulated proteins in which the majority was down-regulated ( Fig . S9A and B ) . Thus , a large part of the proteomic response to carbon starvation in Pb01 is involved in an increase of proteins associated with metabolism and reduction of those involved with core cellular processes , in agreement with transcriptome analysis . The responses of the Paracoccidioides , Pb01 , to carbon starvation , as revealed by transcriptome and proteomic analysis , are summarized in Fig . 3 , which depicts the metabolic and energy adaptation of the fungus to this stress . Pathways associated with ethanol , acetyl-CoA , oxaloacetate , and consequently glucose production were induced . Moreover , amino acid degradation supply precursors such as pyruvate , oxaloacetate , succinate and also acetyl-CoA for glucose production pathways ( Fig . 3 ) . Specific enzymes related to ethanol production were up-regulated in the absence of carbon sources . The ethanol molecule is derived from pyruvate that , in turn , is not involved directly in oxaloacetate production because the pyruvate carboxylase ( PYC ) enzyme is down-regulated ( Fig . 3 ) . Ethanol measurement was performed , and the results showed that after up to 48 h under carbon starvation , a significantly higher level of ethanol was produced compared with glucose-rich cells ( Fig . 4 ) . Regarding the acetyl-CoA molecule , several enzymes associated with its production from pyruvate , via the acetaldehyde precursor , and β-oxidation were also up-regulated . Once produced , acetyl-CoA may be used by the glyoxylate shunt to generate glyoxylate and succinate molecules . This is reinforced by fact that the TCA cycle enzyme isocitrate dehydrogenase is repressed , so the acetyl-CoA pool should be consumed by the glyoxylate cycle . Additionally , succinate can be converted in oxaloacetate by enzymes from the tricarboxylic acid cycle ( Fig . 3 ) . The activity of isocitrate lyase , a representative enzyme of the glyoxylate cycle , was also determined confirming our proteomic data and reinforcing our suggested carbon flow . The analysis revealed that a significant higher specific isocitrate lyase activity was obtained after Paracoccidioides yeast cells were subjected to carbon starvation for 48 h ( Fig . 5 ) . The oxaloacetate molecule is a key intermediate of gluconeogenesis . Once produced , gluconeogenic enzymes convert it into glucose . We detected up-regulated specific enzymes that support this suggestion in Pb01 under carbon starvation , such as phosphoenolpyruvate carboxykinase ( PEPCK ) , fructose-1 , 6-biphosphatase ( FBPase ) , and phosphoglucomutase ( PGM ) ( Fig . 3 ) . In order to perform additional analysis , we applied a highly stringent criterion ( ≥5×-fold ) to analyze the most induced or repressed proteins in yeast cells upon carbon starvation ( Tables 1 and 2 ) . Proteins which were detected only in carbon or carbon starved conditions were considered as down and up-regulated proteins at a high level ( Table 1 and 2 ) , respectively [52] . Even at this high cut-off , up-regulated proteins involved in amino acids degradation , in β-oxidation , in ethanol production , among others are present using this high stringent criterion ( Table 1 ) , in agreement with the metabolic overview presented in Fig . 3 . In the same way , down regulated proteins such as pyruvate dehydrogenase and enzymes involved in fatty acids biosynthesis were detected using this highly stringent fold change criteria ( Table 2 ) . Proteins related to cell defense such as thioredoxin reductase , superoxide dismutase and cytochrome c oxidase were also detected among the up-regulated proteins . To compare similar aspects between transcriptome and proteome data , we sought the same transcripts and proteins detected by both analyses . The transcripts and proteins identities ( ID ) , from the Paracoccidioides , Pb01 , database , were shown including their levels of abundance ( Tables S7 , S8 , S9 and S10 ) . Fifty seven identities ( IDs ) were matched of which 32 and 17 of them presented the same abundance profile , up- or down-regulated in both data , respectively ( Tables S7 and S8 ) . In this way , approximately 86% of the matches showed the same pattern of transcript and protein levels . On the other hand , the minority of IDs showed discrepancy in their abundance . Several of the transcripts in these groups were decreased in abundance , while the protein levels were increased and vice – versa ( Tables S9 and S10 ) . A comparative analysis including all transcripts and proteins for metabolism and energy categories from RNAseq and NanoUPLC-MSE analysis was also performed ( Fig . 6 ) . Metabolism , which was the most regulated category in our data and energy are considered essential categories for understanding the carbon flow used by Pb01during carbon starvation . The results show that the amino acid , carbohydrate/C-compound , and lipid metabolism were similarly regulated in both approaches , showing the consistency with the suggested carbon flow in Paracoccidioides , Pb01 , under carbon starvation ( Figs . 3 and 6A ) . Amino acids and lipids are supposed to be intensively degraded ( Tables S3 and S5 ) suggesting the production of precursors during carbon starvation , which include acetyl-CoA , pyruvate , oxaloacetate and succinate . Furthermore , the percentage of transcripts and proteins related to energy categories such as glycolysis/gluconeogenesis , electron transport/membrane associated energy conservation , and TCA cycle are also similar , in accordance with suggested responses of Pb01 to carbon starvation ( Fig . 6B ) . Thus , the induction of gluconeogenesis , β-oxidation , part of TCA , and glyoxylate cycles was required to compensate for the absence of glucose and depicts the rearrangement of pathways when a carbon source condition is changed . We investigated the response to macrophages in Paracoccidioides , Pb01 , under carbon starvation . We analyzed whether the fungus differentially expresses genes involved in gluconeogenesis , glyoxylate cycle , and β-oxidation pathways after internalization by the J744 A . 1 macrophages . The relative expression analysis of transcripts encoding fructose-1 , 6-biphosphatase , isocitrate lyase and 3-ketoacyl CoA thiolase was performed using qRT-PCR . The Fig . 7A demonstrates that genes encoding isocitrate lyase and 3-ketoacyl CoA thiolase were induced ( p≤0 . 05 ) , suggesting a response of Paracoccidioides to carbon starvation in phagosomes . Furthermore , whether yeast cells under carbon starvation were more susceptible to macrophage killing than cells growing in plentiful glucose was analyzed . Firstly , plating of recovered yeast cells by aspiration of culture supernatant ( non-internalized yeast cells ) and from lysis of macrophages ( internalized yeast cells ) was performed ( Fig . 7B ) . The result showed that the number of yeast cells recovered from culture supernatant was not significantly different between carbon and carbon starved yeast cells ( Fig . 7B , on the left ) . On the contrary , the Paracoccidioides , Pb01 , yeast cells pre-exposed to carbon starvation were recovered in a lower number than those grown under carbon source ( Fig . 7B , on the right ) . In addition , to verify if the yeast cells were , in fact , more susceptible to macrophage killing we evaluated the average number of adhered/internalized Paracoccidioides , Pb01 , using the light microscopy after 6 and 24 h of infection ( Fig . S10 and S11 ) . After 24 h of infection , it was observed , in carbon-starved condition , a significant decrease in the number of yeast cells adhered/internalized by macrophages ( Fig . S10 ) . Overall , the data suggest that yeast cells pre-exposed to carbon starvation were more susceptible to macrophage killing , reinforcing our suggestion that the carbon starvation can affect the survival of the Pb01 yeast cells inside of the macrophages .
The major focus of this work is directed towards a global view on the responses of Paracoccidioides , Pb01 , to carbon starvation using both , high-throughput transcriptome and proteomic analysis . Pb01 yeast cells were able to adapt to carbon starving conditions . The data presented in this study reflected how carbon-starved cells modulate the metabolism by induction or repression of cellular activities . We show that the fungus regulates pathways that lead to glucose production to compensate the effect of stress . The fungus regulates transcripts and proteins that are mainly associated with gluconeogenesis and ethanol production via precursors from β-oxidation , glyoxylate and tricarboxylic acid cycles . Our study presents a detailed response of Paracoccidioides spp . facing carbon starvation and contributes to investigations of the importance of alternative carbon adaptation during fungus pathogenesis . Changes in the kinetics of expression of representatives of gluconeogenesis , the glyoxylate cycle , and β-oxidation as well as the differential expression of isocitrate lyase at the protein level could establish a better time-point for our transcriptional ( 6 h ) and proteomic ( 48 h ) analysis , using RNAseq and NanoUPLC-MSE , respectively ( Fig . 1 ) . The transcriptome and proteomic analysis demonstrated that general metabolism and energy were the most represented regulated categories . Transcripts/proteins classified in energy and cell rescue , defense , and virulence categories were also induced in both approaches although less representative than metabolism ( Fig . S3 and S9 ) . Categories involved in the cell cycle , transcription , cellular transport , growth/morphogenesis , and signal transduction were predominantly down-regulated at transcription and protein levels . Although Pb01yeast cells displayed no significant difference in cell viability until 72 h under carbon starvation ( Fig . S4 ) , the yeast biomass was significantly reduced in this condition ( Fig . 2 ) . In addition , the abundance of specific transporters was elevated such as those related to copper , hexose , and monosaccharide uptake , suggesting a nutrient limitation and a hostile environment as detected in other fungi [60] . Paracoccidioides , Pb01 , presented a complex mechanism to respond to nutrient deprivation . The suggestion is that the fungus uses a carbon flow ( Fig . 3 ) through classical biochemical pathways such as glyoxylate cycle , β-oxidation , and gluconeogenesis which is in accordance with previous data on other fungi facing nutrient deprivation [8] , [9] , [12] . Fungi such as C . albicans and C . neoformans appear to experience a nutrient limited and stressful environment in the context of interaction with host cells . The elevated expression of the glyoxylate pathway and gluconeogenesis genes during Cryptococcus interactions with host tissue and phagocytic cells is similar to the regulation observed in C . albicans . These pathogenic fungi present a niche dependent metabolism , with activation of an alternative carbon source consuming process and the up-regulation of transcripts for enzymes of the glyoxylate cycle , β-oxidation , and gluconeogenesis [9] , [12] which was also detected in Paracoccidioides , Pb01 . Our data suggest that Pb01 yeast cells facing carbon starvation use the oxaloacetate molecule as a key intermediate of gluconeogenesis . It is possibly supported by β-oxidation and , in part , by glyoxylate and TCA cycles activation . The glyoxylate cycle can allow the fungus to assimilate two-carbon compounds , a relevant aspect to the viability and growth inside macrophages [61] , [62] . Studies involving the isocitrate lyase gene , representative of glyoxylate cycle , displayed that this gene is important as a marker for gluconeogenic carbon source utilization and starvation rather than a marker for lipid metabolism [63] , [64] . Despite induction of isocitrate lyase and genes required for fatty acid utilization especially after phagocytosis by macrophages [9] , this induction may derive from a general stress response due to nutrient or glucose limitation rather than a specific induction from fatty acid utilization [64] . In fact , null mutants to isocitrate lyase in C . albicans , A . fumigatus and C . neoformans and for β-oxidation genes in C . albicans revealed no virulence defects , showing that fatty acids do not provide an essential nutrient source during infection [2] , [64]–[67] . In addition , isocitrate lyase activity increased when C . albicans was subjected to carbon starvation or other carbon sources such as acetate , glutamate and peptone as solely carbon source [63] reinforcing its importance as a marker for gluconeogenic carbon source utilization and starvation . Moreover , the isocitrate dehydrogenase enzyme was detected as down-regulated in our proteomic data ( Table S6 ) . The activities of isocitrate dehydrogenase and isocitrate lyase enzymes , regulate the flow of isocitrate into either the tricarboxylic acid cycle or the glyoxylate cycle [68] , [69] . Here , while the isocitrate dehydrogenase is down , the isocitrate lyase is up-regulated , in accordance with suggested carbon flow in Pb01 through glyoxylate shunt . As the same importance , the increased expression of protein phosphoenolpyruvate carboxykinase confirms that gluconeogenesis process is ongoing ( Table S5 ) . Then , the global characterization of the responses of Pb01 to carbon starvation becomes relevant in this context especially by flow of carbon used by the fungus during this stress . The same transcripts and proteins detected by both analyses were identified ( Tables S7 , S8 , S9 and S10 ) . Approximately 86% of the matches ( a total of 57 ) showed the same pattern of transcript and protein levels . In this way , we believe that the transcriptional and proteome time-points were enough to characterize the global responses of the Paracoccidioides to carbon starvation conditions . Similarly to previous study in A . fumigatus , few times , transcripts and proteins do not follow the same trend of expression that could be explained , for example , by mRNA stabilization process or by active post-transcriptional and translational regulatory mechanisms [70] . In our data , in many metabolic aspects , transcripts and protein levels were correlated . The identities to amino acid degradation , β-oxidation and ethanol synthesis were increased in expression while processes involving the pyruvate molecule were down-regulated ( Table S7 and S8 ) corroborating our hypothesis of a well established response to carbon starved environments ( Fig . 3 ) . The pyruvate-acetyl-CoA conversion , for example , is diminished by repression of pyruvate dehydrogenase enzyme . In addition , pyruvate carboxylase is repressed and it is possibly not converted in oxaloacetate . These observations strongly suggest that the available pyruvate would end up in ethanol via acetaldehyde ( Fig . 3 ) . In addition , comparison of regulated molecules in transcriptome and proteome data with focus in metabolism and energy categories can support the use of alternative carbon sources by Pb01 under carbon starvation ( Fig . 6 ) . Metabolism of amino acids , lipids , and carbohydrate/C-compound was the most regulated in both used approaches ( Fig . 6A ) . The amino acids and lipids likely are been used as precursors to important molecules involved in alternative carbon metabolism in Pb01 as depicted in Fig . 3 . In fact , Paracoccidioides spp . can use a relatively wide range of amino acids and peptides rather than carbohydrates [21] . In addition to the structural importance of lipids , these molecules provide an energy-rich nutrient source . β-oxidation is a common pathway for the utilization of fatty acids [71] in which of the 3-ketoacyl-CoA thiolases enzymes are important [64] . Recent studies have highlighted the relevance of the β-oxidation in response to nutrient or glucose limitation rather than a specific induction from fatty acid utilization . In fact , fatty acids do not provide an essential nutrient source during infection in C . albicans but is important for coupling the glyoxylate cycle and fatty acid β-oxidation during host-pathogen interactions , regulating responses related to carbon starvation [63] , [64] . Here , the induction of β-oxidation pathway in Pb01 likely reflects the requirement of this metabolic pathway for carbon starving adaptation , which is consistent with previous data in C . albicans subjected to a poor-nutrition environment [9] . Regarding energy producing pathways , the gluconeogenesis , tricarboxylic acid and glyoxylate cycles are well represented , which reinforces our model of adaptation to carbon starvation conditions ( Fig . 6B ) . Moreover , the electron transport and ethanol subcategories were also shown . Ethanol metabolism was previously described in Pb01 showing evidence for a more anaerobic metabolism of this fungus compared with other isolates of Paracoccidioides [35] , [36] , [72] , and this metabolite has also been described as relevant to pathogenic fungi such as A . fumigatus [70] , [73] , [74] . The general response to oxidative stress mediated by enzymes provides multiple resistance strategies to Paracoccidioides yeast cells [34] . There was a high increase in production of antioxidant proteins such as thioredoxin reductase , superoxide dismutase and cytochrome c peroxidase that are possibly involved in defense against reactive oxygen species ( ROS ) during carbon starvation ( Tables S5 and 1 ) . In low amounts , ROS are generated continuously as side products of aerobic respiration in the mitochondria and , although potentially cytotoxic , function as signal molecules in cellular processes [75] , [76] . In this way , the production of ROS during carbon starvation could be related to the increase in electron transport activity in respiratory chain ( Table S5 and 1 ) as well as to the production of endogenous free radicals in β-oxidation . The response of Pb01 to macrophage infection shows that the fungus most likely faces carbon starvation in macrophages , because a significant higher expression of genes encoding isocitrate lyase and 3-ketoacyl-CoA thiolase was detected . It is important to note the similarities in the transcriptional profile in inducing alternate carbon metabolism between C . albicans phagocytosed cells and those submitted to carbon starvation ( 39 ) . In fact , in terms of usable nutrients , the phagosome has been reported to not have a rich environment evidenced by the unsubstantial quantities of glucose , other sugars and amino acids [9] , [11] , [12] , [27] , [77] . Here , we showed that Pb01 yeast cells were more susceptible to macrophage killing when were previously starved of carbon ( Fig . 7 , S10 and S11 ) . This conclusion is based on the fact that the multiplication of Paracoccidioides inside activated macrophages is inhibited [23] . In this sense , we suggested that Paracoccidioides , Pb01 , yeast cells pre-exposed to carbon starvation , were more susceptible to macrophage killing , reinforcing that carbon deprivation affects the survival of the Pb01 yeast cells inside of the macrophages . Taken together , our data suggest that Pb01 changes its metabolism under carbon starvation reprogramming several biological processes to facilitate its maintenance under this condition . These programs are mainly related to gluconeogenesis , β-oxidation and the glyoxylate cycle to compensate for the starved carbon environment . Considering a new perspective , the transcriptome and proteome data could reinforce the responses of this fungus , which is able to survive in the hostile environment during macrophage infection . This study may elucidate potential molecules involved in host-fungus interactions , an important factor related to pathogenic organisms .
|
The species of the Paracoccidioides genus , a neglected human pathogen , represent the causative agents of paracoccidioidomycosis ( PCM ) , one of the most frequent systemic mycoses in Latin America . Despite being phagocytosed , the fungus conidia differentiate into the parasitic yeast form that subverts the normally harsh intraphagosomal environment and survives and replicates into murine and human macrophages . It has been suggested that alternative carbon metabolism plays a role in the survival and virulence of Paracoccidioides spp . within host cells . We used large-scale transcriptome and proteome approaches to better characterize the responses of Paracoccidioides , Pb01 , yeast parasitic cells , to carbon starvation . We aimed to identify important molecules used by the fungus to adapt to these hostile conditions . The shift to a starvation mode , including gluconeogenesis and ethanol increases , activation of fatty acids , and amino acid degradation are the strategies used by the pathogen to persist under this stress . Our study provides a detailed map of Paracoccidioides spp . responses under carbon starvation conditions and contributes to further investigations of the importance of alternative carbon adaptation during fungus pathogenesis .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biochemistry",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"proteomics",
"microbiology",
"molecular",
"cell",
"biology"
] |
2014
|
Transcriptional and Proteomic Responses to Carbon Starvation in Paracoccidioides
|
Recent heritability analyses have indicated that genome-wide association studies ( GWAS ) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score ( PRS ) , a simple modelling technique that can be implemented using summary-level data from the discovery samples . We herein propose modifications to improve the performance of PRS . We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms ( SNPs ) in PRS . Further , as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations , we propose variable thresholds for SNPs selection . We applied our methods to GWAS summary-level data of 14 complex diseases . Across all diseases , a simple winner’s curse correction uniformly led to enhancement of performance of the models , whereas incorporation of functional SNPs was beneficial only for selected diseases . Compared to the standard PRS algorithm , the proposed methods in combination led to notable gain in efficiency ( 25–50% increase in the prediction R2 ) for 5 of 14 diseases . As an example , for GWAS of type 2 diabetes , winner’s curse correction improved prediction R2 from 2 . 29% based on the standard PRS to 3 . 10% ( P = 0 . 0017 ) and incorporating functional annotation data further improved R2 to 3 . 53% ( P = 2×10−5 ) . Our simulation studies illustrate why differential treatment of certain categories of functional SNPs , even when shown to be highly enriched for GWAS-heritability , does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure .
Large genome-wide association studies ( GWAS ) have accelerated the discovery of dozens or even hundreds of common single nucleotide polymorphisms ( SNPs ) associated with individual complex traits and diseases , such as height [1 , 2] , body mass index [3] and common cancers ( e . g . , breast [4] and prostate [5] cancers ) . Although individual SNPs typically have small effects , cumulative results have provided insight about underlying biologic pathways and for some common diseases like breast cancer have yielded levels of risk-stratification that could be useful as part of prevention efforts [6] . Analyses of GWAS heritability using algorithms such as GCTA [7 , 8] have shown that common SNPs have the potential to explain substantially larger fraction of the variation of many traits . The future yield of GWAS studies , for both discovery and prediction , depends heavily on the underlying effect-size distribution ( ESD ) of susceptibility SNPs [9 , 10] . A number of alternative types of analyses of ESD now point towards a polygenic architecture for most complex traits , in which thousands or even tens of thousands of common SNPs , each with small estimated effect sizes together can explain a substantial fraction of heritability [11 , 12] . Mathematical analyses of power indicates that because of the polygenic nature of complex traits , future studies will need large sample sizes , often by an order of magnitude higher than even some of the largest studies to date , for improving accuracy of genetic risk-prediction [10 , 11] . Nevertheless , for current datasets , there remains an opportunity to develop more efficient algorithms for improving the models [13] . Available algorithms for polygenic risk score ( PRS ) prediction models have varying degrees of complexity . The simplest of these methods , widely implemented in large GWAS , selects SNPs based on a threshold for the significance of the marginal association test-statistics and then the cumulative weighting of these SNPs by their estimated marginal strength of association is applied [14] . The threshold for SNP selection can be optimized to improve the predictive performance in an independent validation dataset . For a number of traits with large GWAS sample sizes , it has been shown that an optimally selected threshold can improve risk prediction compared to that based on the genome-wide significance threshold used for discovery [15] . A number of newer methods involving the joint analysis of all SNPs using sophisticated mixed-effect modeling techniques have recently been developed and may lead to further increases in model performance [16–18] . In this report , we propose simple modifications to the widely used PRS modeling techniques using only GWAS summary-level data . Drawing from the lasso [19] algorithm , we propose a simple threshold dependent winner’s curse adjustment for marginal association coefficients that can be used to weight the SNPs in PRS . Second , to exploit external functional knowledge that might identify subsets of SNPs highly enriched for association signals , we consider using multiple thresholds for SNPs selection based on group membership and identify an optimal set of thresholds through an independent validation dataset . We demonstrated the value of our new method using summary-level results from large GWAS across a spectrum of traits , some with available independent validation datasets to assess the performance of these methods . Available resources , such as annotation databases , expression and methylation quantitative trait locus ( QTL ) analyses were employed to identify groups of SNPs that are likely to be enriched with the trait of interest . We evaluated the utility of this information for risk-prediction for respective outcomes . We also report on the performance of new algorithm using simulation studies that incorporate realistic genetic architecture , linkage disequilibrium pattern and enrichment factor for underlying functional SNPs .
Let Zm , Pm , β^m , and σ^m ( m = 1 , … , T ) denote the Z-statistics , the two-sided P-values , the estimated association coefficients and their standard deviations available as part of summary-level results for T SNPs from a GWAS . We assume that each genotypic value is normalized to have mean zero and unit variance and that β^m is rescaled to correspond to the normalized genotypic values . We assume that M SNPs are selected after LD-clumping , a SNP pruning procedure guided by the association P-values [20] . Let gim be the genotype of SNP m for subject i . The simplest and most popular form of the PRS has the form PRSi ( α ) =∑m=1Mβ^mI ( Pm<α ) gim , ( 1 ) where the threshold α for the P-values can be chosen to optimize the predictive performance of PRS in an independent validation dataset . Here , I ( ⋅ ) is an indicator function . Because PRSi ( α ) uses a single threshold to select SNPs , we refer this as one-dimensional PRS or 1D PRS . In what follows , we extend PRSi ( α ) by incorporating annotation data and correcting for the upward bias in β^m caused by winner’s curse . We performed simulations to evaluate the performance of six PRS prediction methods: 1D and 2D PRS without and with winner’s curse correction ( MLE and lasso-type correction ) . To make simulations realistic in terms of the distribution of minor allele frequencies ( MAF ) and LD , we simulated quantitative traits with specific genetic architecture by conditioning on the genotypes of a lung cancer GWAS [27] , which had 11 , 924 samples of European ancestry and 485 , 315 autosomal SNPs after quality control . We randomly selected 10 , 000 samples as a discovery set and 1 , 924 as a validation set . The causal SNP set consisted of 5 , 000 SNPs in linkage equilibrium . In the first set of simulations , the HP SNPs were randomly selected from LD-pruned SNPs across the genome . In the second set of simulations , we simulated HP SNPs located in conserved regions ( CR ) [28] , which were recently reported to be highly enriched for association signal of 17 complex traits based on a heritability partitioning analysis [23] . The simulation results are summarized in Fig 2 . First , winner’s curse corrections slightly improved prediction in most if not all simulations and in particular improved more for the 1D PRS than the 2D PRS . We also observed that the two winner’s curse correction methods performed similarly . Second , if HP SNPs were chosen randomly in the LD-pruned SNP set and were strongly enriched for causal SNPs , 2D PRS substantially improved the prediction over 1D PRS . As expected , the improvement increased quickly with the enrichment fold change Δ . Consistent with theoretical analysis assuming independent SNPs ( Fig 1B ) , the optimal P-value threshold for HP SNPs was more liberal than that for LP SNPs ( S1 Table ) . However , when we used CR-SNPs as the HP SNPs , the improvement of 2D PRS was less compared to the simulations with randomly selected HP SNPs , even with the same enrichment fold change . To investigate whether the difference was caused by different local LD structure , for each SNP , we counted the number of SNPs located less than 1Mb from the given SNP and had r2 ≥ 0 . 8 with the SNP in The 1000 Genomes Project [29] . For 9 , 940 CR-SNPs used for our simulations , the average number of LD SNPs is 22 . 4 ( median = 12 ) while the average number is 6 . 4 ( median = 2 ) for non-CR SNPs . See also the histograms in S1 Fig . Thus , CR-SNPs are enriched in regions with strong LD and may suggest a possible explanation why CR-SNPs ( and other functional categories with similar LD structure ) may not lead to improvement in risk prediction as much as would be expected based on enriched heritability . We applied the six PRS methods to 14 traits with either individual level GWAS data or summary level data ( Tables 1 and 2 ) . We defined the HP SNP set S1 using expression QTL SNPs ( eSNPs ) in blood , tissue specific eSNPs and methylation QTL SNPs ( meSNPs ) , SNPs related with cis-regulatory elements ( referred to as CRE-SNPs ) , SNPs related with genomic regions conserved across mammals ( referred to as CR-SNPs ) and SNPs identified by pleiotropic analyses ( referred to as PT-SNPs ) . Details about annotation data are provided in Materials and Methods . The annotation data used for each trait is summarized in S2 Table . For those with individual level data but without independent validation samples , we used cross-validation to estimate performance .
Our study demonstrates that the predictive performance of GWAS PRS models can be improved based on a combination of a simple adjustment to the threshold levels of SNP selection and weights of selected SNPs . The degree of gain , however , is not uniform and depends on multiple factors , including the genetic architecture of the trait , sample size of the discovery sample set , degree of enrichment of association in selected set of “high-prior” SNPs and the linkage disequilibrium patterns of these SNPs with the rest of the genome . The simple winner’s curse correction of SNP weights using the lasso-type method leads to an improvement in performance of PRS uniformly across all studied diseases . For some diseases , such as type-2 diabetes ( Fig 3 and S3 Table ) or Crohn’s disease ( Fig 4 and S6 Table ) , this correction alone led to notable improvement in the performance of PRS . The optimal weighting of SNPs would depend on the true effect size distribution of the underlying susceptibility SNPs . Lasso-type weights can be expected to be optimal under a double exponential distribution [19 , 32] , and it is possible that the weighting could be improved further under alternative models of effect-size distribution . It is , however , encouraging that irrespective of what might be the true effect-size distribution , which is likely to vary across the diseases of study; our simple lasso-type correction improves over the standard PRS without adding any additional computational complexity . The effect of using various threshold levels for different functional categories of SNPs on the performance of the model varied by disease as well as the functional annotation of external data sets employed in our analytical approach . After adjustment with lasso-type weights , the use of two-dimensional threshold based on prioritized SNPs led to notably higher values of R2 for lung cancer in Caucasians , bladder cancer , type-2 diabetes and pancreatic cancer . Consistent with theoretical expectations , for each of the traits , the optimal thresholds selected were more liberal for the associated category of high-prior SNPs than those for complementary set . Our simulation study illustrated how the improvement in performance of the PRS model due to differential treatment of certain categories of SNPs is modest even when these SNPs have been categorized to be highly enriched for heritability [22] . For example , recent heritability partitioning analysis has identified SNPs in conserved DNA regions , representing 2 . 6% of the genome , to be highly enriched for GWAS heritability for many diseases ( explaining 35% heritability on average ) . Our theoretical calculations suggest that if only independent SNPs are analyzed , use of a subset of SNPs similarly enriched for heritability is expected to yield much higher improvement in the performance of the model ( Fig 1 ) . Our simulation studies showed that a similarly large gain is expected even in the presence of naturally occurring LD pattern if these SNPs are selected randomly from the genome . However , when we simulated high-prior SNPs based on the exact location of conserved regions , the improvement was modest , within the range of observed data . The CR-SNPs represent a highly unusual linkage disequilibrium pattern in that they are in high degree of LD with an unusually large number of neighboring SNPs ( S1 Fig ) . In the future , more detailed and accurate assessment of the functional annotation of SNPs should improve performance of PRS models . Our method requires only simple modifications to the standard PRS algorithm and can thereby be used to rapidly evaluate the effectiveness of many alternative strategies . In the current study , we used physical location information pertaining to histone marks to define high-priority SNP . However , a SNP located in histone marks does not necessarily cause the variation in histone binding . Thus , a more reasonable approach is to identify genetic variants associated with histone variation across subjects in order to define high-priority SNP sets . These types of histone QTLs have recently been reported in small-scale studies based on HapMap samples [33 , 34] . We expect that histone QTL SNPs identified in future large-scale tissue specific studies might be more informative for risk prediction . We have investigated the performance of the various algorithms using criteria that reflect how much of the variability of the observed outcomes can be explained by the PRS in the validation dataset . For clinical applications of risk-models , however , it is important to evaluate whether models are well calibrated that is to what extent they can produce unbiased estimates of risk for individuals with different SNP profiles . Earlier studies have noted that the standard PRS can be mis-calibrated and additional calibration steps may be needed when applying PRS in a clinical setting . In this regard , we find that a winner’s curse correction can alleviate calibration bias of the standard PRS , but substantial residual bias remains in some situations ( S11 Table ) . The regression relationship between overall PRS and disease status can be estimated based on a relatively small validation sample and can also be used to re-scale PRS for producing calibrated risk estimates . We used several different metrics for evaluating the potential impact of an improved PRS for risk-stratification . The percentage gain in prediction R2 due to improved PRS is substantial for several diseases . For these diseases , the impact of an improved PRS on overall discriminatory performance of the models is noticeable but small ( increase in AUC value between 1–2% ) . However , even a modest increment in AUC value can lead to identification of substantially higher fraction of individuals who are at the tails of risk distribution and hence likely to consider clinical decisions ( S12 Table ) . A limitation of our method is that we use stringent LD-pruning for creating sets of independent SNPs . However , this may result in loss of predictive power of models as SNPs in moderate or low LD may still harbor independent association signals . The LD-pred [31] method has been proposed to better account for correlated SNPs in building PRS using GWAS summary-level data and has been shown to lead to improved performance over standard PRS for some diseases such as schizophrenia . The LD-pred method also uses a specific form of prior distribution for obtaining “shrunken” estimates of the regression coefficients for the SNPs in the model . Although we did not make direct comparisons , it appears that the LD-pred method gains over standard PRS by improving the accounting for correlation between risk SNPs . In contrast , in our algorithm , which used stringent LD pruning , the gain in performance over the standard PRS mainly came from the lasso-type winner’s curse correction and the use of variable thresholds to account for HP and LP SNPs . Thus it is possible that in the future the complementary strengths of the algorithms can be combined to develop more powerful PRS . In conclusion , we have proposed a set of simple methods for constructing PRS for genetic risk prediction using GWAS summary-level data . The proposed methods are computationally not onerous and yet show a noteworthy gain in performance . A major strength of our study is that we evaluated the proposed methods across a large number of scenarios reflecting a spectrum of underlying genetic architectures for different complex diseases , sample size of the study and available functional annotation . These studies and additional simulations provide comprehensive insights to promises and limitations of genetic risk prediction models in the near future .
The performance of PRS is typically improved if genetic markers are pruned for LD . LD-pruning procedures that ignore GWAS P-values frequently prune out the most significant SNPs and may reduce performance . Instead , we use the LD-clumping procedure implemented in PLINK [20] that chooses the most significant SNP from a set of SNPs in LD guided by GWAS P-values . After LD-clumping , no SNPs with physical distance less than 500kb have LD r2 ≥ 0 . 1 . Suppose S1 is a given HP set defined based on external annotation data ( see section Annotation datasets ) . Any SNP in high LD with a SNP in S1 is also considered to be an HP SNP . Thus , we expanded S1 by including all SNPs that were in high LD ( r2 ≥ 0 . 8 ) with any SNP in the original S1 . We simulated quantitative traits with specific genetic architecture by conditioning on the genotypes of a lung cancer GWAS [27] , including 11 , 924 samples of European ancestry and 485 , 315 autosomal SNPs after quality control . The simulation scheme is summarized in the following steps: Recently , Finucane et al . [23] reported the heritability explained by common SNPs in multiple functional categories for 17 traits . Interestingly , they found that common SNPs located in regions that are conserved in mammals [28] accounted for about 2 . 6% of total common SNPs but explained approximately 35% of total heritability in average across these traits , suggesting a 13 . 5-fold enrichment . Thus , we were motivated to investigate whether SNPs related with the conserved regions ( CR ) may be useful for 2D PRS methods . We downloaded the CR annotations ( http://compbio . mit . edu/human-constraint/data/gff/ ) , identified common SNPs located in any CR and also identified their LD SNPs with r2 ≥ 0 . 8 . These SNPs are referred to as CR-SNPs , which were used as HP S1 in simulations . We found 9 , 940 CR-SNPs overlapping with the 53 , 163 LD-pruned SNPs . To investigate whether specific genomic locations of CR-SNPs influence the performance of 2D-PRS , we also performed simulations using a set S1 of random SNPs that has the same size and associated heritability as the CR-SNPs . The Wellcome Trust Case Control Consortium [30] ( WTCCC ) data consisted of two control data sets ( 1958 Cohort samples and NBS control samples ) and seven diseases: bipolar disorder ( BD ) , coronary artery disease ( CAD ) , Crohn’s disease ( CD ) , hypertension ( HT ) , rheumatoid arthritis ( RA ) , Type 1 diabetes ( T1D ) and Type 2 diabetes ( T2D ) . Since we analyzed T2D using a much larger discovery sample , we did not analyze the T2D data in WTCCC . Because cases and controls were genotyped in different batches , differential errors between cases and controls might cause a serious overestimate of the risk prediction . Thus , we performed very rigorous quality control ( QC ) by removing duplicate samples , first or second degree relatives , samples with missing rate greater than 5% and non-European samples identified from EigenStrat [35] analysis . For each disease , we excluded SNPs with MAF<5% , missing rate >2% , missing rate difference >1% between cases and controls or PHWE<10−4 in the control samples . For each PRS method and each disease , we estimated the prediction R2 by five-fold cross-validation . We analyzed three cancer GWAS with individual level genotype data: the bladder cancer [36 , 37] GWAS of European ancestry including 5 , 937 cases and 10 , 862 controls , the pancreatic cancer GWAS [38] of European ancestry ( after excluding samples with Asian or African ancestry ) including 5 , 066 cases and 8 , 807 controls , and the Asian non-smoking female lung cancer GWAS [39] with 5 , 510 cases and 4 , 544 controls . After QC , the bladder cancer GWAS had 463 , 559 autosomal SNPs and the Asian lung cancer GWAS had 329 , 703 autosomal SNPs . The pancreatic cancer GWAS included samples from three studies that used different genotyping platforms . For convenience , we analyzed 267 , 935 autosomal SNPs that overlapped in all three platforms . The prediction performance was evaluated using ten-fold cross-validation . For T2D , we downloaded the summary statistics of the DIAGRAM ( DIAbetes Genetics Replication And Meta-analysis ) consortium [40] with 12 , 171 cases and 56 , 862 controls for 2 . 5 million SNPs imputed to the Hapmap2 reference panel . We also downloaded the GERA ( Genetic Epidemiology Research on Adult Health and Aging ) GWAS data of European ancestry with 7 , 131 T2D patients and 49 , 747 samples without T2D ( but may have other medical conditions , e . g . , 27 . 4% with cancers , 25 . 4% with asthma , 25 . 4% with allergic rhinitis and 12 . 4% with depression ) . We randomly selected 5 , 631 T2D patients and 48 , 247 non-T2D subjects from GERA as discovery set , performed association analysis adjusting for top 10 PCA scores and meta-analyzed with the summary statistics from DIAGRAM for 353 , 196 autosomal SNPs overlapping between the two studies . The resulting summary statistics were used to build PRS risk models , which were validated in the remaining 1500 T2D patients and 1500 non-T2D subjects in GERA . The PGC2 ( Psychiatric Genetics Consortium ) schizophrenia GWAS meta-analysis consisted of 34 , 241 cases and 45 , 604 controls [41] ( http://www . med . unc . edu/pgc/downloads ) . Summary statistics were obtained by meta-analyzing all PGC2 schizophrenia GWAS except the MGS [42] ( Molecular Genetics of Schizophrenia ) subjects of European ancestry . The summary statistics were used to build PRS models , which were validated in MGS samples with 2 , 681 cases and 2653 controls . The TRICL ( Transdisciplinary Research in Cancer of the Lung ) GWAS consortium consisted of 12 , 537 lung cancer cases and 17 , 285 controls [43 , 44] . We performed meta-analysis using TRICL samples excluding the samples from the PLCO [27] ( Prostate , Lung , Colon , and Ovary Cohort Study ) study . The summary statistics based on 11 , 300 cases and 15 , 952 controls were used to build risk models , which were validated in the PLCO lung GWAS samples with 1 , 237 cases and 1 , 333 controls . For colorectal cancer , we performed meta-analysis for the GECCO ( Genetics and Epidemiology of Colorectal Cancer Consortium ) [45] GWAS data after excluding the PLCO GWAS data . The PLCO samples were genotyped using two different genotyping platforms with different marker densities: one had approximately 500K SNPs and the other had only 250K SNPs . Thus , we first imputed the genotypes to the Hapmap2 reference panel using IMPUTE2 [46] and selected SNPs with imputation r2 ≥ 0 . 9 for risk prediction . The discovery sample consisted of 9 , 719 cases and 10 , 937 controls from 19 studies . The PLCO validation sample had 1 , 000 cases and 2 , 302 controls . The summary statistics for prostate cancer were obtained from the PRACTICAL ( PRostate cancer AssoCiation group To Investigate Cancer Associated aLterations ) consortium and The GAME-ON/ELLIPSE ( Elucidating Loci Involved in Prostate Cancer Susceptibility ) Consortium with samples from populations of European , African , Japanese and Latino ancestry [5] . The discovery samples consisted of 38 , 703 cases and 40 , 796 controls after excluding the NCI Pegsus GWAS samples with 4 , 600 cases and 2 , 941 controls , which were used for validation . We analyzed 536 , 057 autosomal SNPs after QC that overlapped between the validation and the discovery sample summary statistics . For many traits , GWAS risk SNPs have been reported to show enrichment for eQTLs , methylation QTLs ( meQTLs ) and cis-regulatory elements ( CREs ) . In addition , recent studies have reported extensive genetic pleiotropy across diseases and traits , e . g . psychiatric diseases [47 , 48] , schizophrenia and cardiovascular-disease risk factors , including blood pressure , triglycerides , low- and high-density lipoprotein , body mass index ( BMI ) and waist-to-hip ratio ( WHR ) [49] . This information may potentially improve risk prediction if the SNPs identified from the secondary trait are highly enriched in the GWAS of the primary trait . Thus , we defined the HP SNP set S1 using eQTL SNPs ( referred to as eSNPs ) in blood , tissue specific eSNPs and meQTL SNPs ( referred to as meSNPs ) , SNPs related with CREs ( referred to as CRE-SNPs ) , SNPs related with genomic regions conserved across mammals ( referred to as CR-SNPs ) and SNPs identified by pleiotropic analyses ( referred to as PT-SNPs ) . Here , LD was calculated based on the genotype data of relevant ancestry in The 1000 Genomes Project [29] . Note that the availability of functional annotation data depends on tissue types . However , for all diseases studied in the paper , we have used blood eSNPs and CR-SNPs because blood eSNPs are enriched for GWAS of all these traits and CR-SNPs were highly enriched in many traits by a heritability partitioning analysis [23] . For WTCCC and three cancer GWAS data sets with individual genotype data , we used K-fold cross-validation to estimate prediction R2 . Here , K = 5 for WTCCC data and K = 10 for cancer GWAS data . We were interested in testing whether the prediction of a new PRS method was significantly better than that of the standard 1D PRS defined in Eq ( 1 ) . For the ith cross-validation , we denote Ri , 02 as the maximum prediction for the standard 1D PRS optimized across P-value thresholds , Ri , 12 as the maximum prediction for a new PRS method optimized across all P-value thresholds for 1D PRS and all pairs of P-value thresholds for 2D PRS . We defined δi= Ri , 12 −Ri , 02 and estimated its variance as σ^2=Σi=1K ( δi−δ¯ ) 2/ ( K−1 ) with δ¯=Σi=1Kδi/K . We calculated the statistic T=δ¯/σ^2/K and evaluated its significance using the t-distribution . For the five diseases with independent validation samples , we used bootstrap to estimate the variance of the R2 estimates to test significance [29] . Suppose that for a given trait of interest Y , there are two predefined SNP sets: the high priority ( HP ) SNP set S1 and the low priority ( LP ) SNP set S2 . SNPs have been pruned and are in linkage equilibrium . We assume that S1 has M1 independent susceptibility SNPs and M3 null SNPs while S2 has M2 susceptibility SNPs and M4 independent null SNPs . Following Chatterjee et al . [11] , we assume that the true relationship between outcome Y and independent susceptibility SNPs is modeled as follows: Y=∑i=1M1β1ig1i+ ∑j=1M2β2jg2j+∑k=1M30⋅g3k+∑l=1M40⋅g4l+ϵ , where all Y and the genotypic values g’s are standardized so that E ( Y ) = 0 , Var ( Y ) = 1 , E ( g ) = 0 and Var ( g ) = 1 , and the error term ϵ ~ N ( 0 , σ2 ) and is independent of the genotypic values . From a discovery GWAS data set of size N , we have regression coefficient β^i- and two-sided p-value Pi for each SNP . We build an additive prediction model by including SNPs in S1 with P-value ≤ α1 and SNPs in S2 with P-value ≤ α2: Y^ ( α1 , α2 ) =∑i=1M1β^1iγ1i ( α1 ) g1i+ ∑j=1M2β^2jγ2j ( α2 ) g2j+∑k=1M3β^3kγ3k ( α1 ) g3k+∑l=1M4β^4lγ4l ( α2 ) g4l , where γ ( α ) = I ( P ≤ α ) with I ( ⋅ ) being an indicator function . The predictive correlation coefficient ( PCC ) for the predictive model can be expressed as PCC ( α1 , α2 ) =cor ( Y , Y^ ( α1 , α2 ) ) =∑i=1M1β1iβ^1iγ1i ( α1 ) + ∑j=1M2β2jβ^2jγ2j ( α2 ) ∑i=1M1β^1i2γ1i ( α1 ) + ∑j=1M2β^2j2γ2j ( α2 ) +∑k=1M3β^3k2γ3k ( α1 ) +∑l=1M4β^4l2γ4l ( α2 ) . Following Chatterjee et al . ( 2014 ) , one can verify that PCC follows a normal distribution by the central limit theorem and the strong law of large numbers . Therefore , the expected value of PCC can be approximated as E ( PCC ( α1 , α2 ) ) =∑i=1M1β1ieN , α1 ( β1i ) pow ( N , β1i , α1 ) + ∑j=1M2β2jeN , α2 ( β2j ) pow ( N , β2j , α2 ) ∑i=1M1νN , α1 ( β1i ) pow ( N , β1i , α1 ) +∑j=1M2νN , α2 ( β2j ) pow ( N , β2j , α2 ) +M3α1νN , α1 ( 0 ) +M4α2νN , α2 ( 0 ) , ≈M1∫βeN , α1 ( β ) pow ( N , β , α1 ) f1 ( β ) dβ+ M2∫βeN , α2 ( β ) pow ( N , β , α2 ) f2 ( β ) dβM1∫βνN , α1 ( β ) pow ( N , β , α1 ) f1 ( β ) dβ+M2∫βνN , α2 ( β ) pow ( N , β , α2 ) f2 ( β ) dβ+M3α1νN , α1 ( 0 ) +M4α2νN , α2 ( 0 ) where eN , α ( β ) =E ( β^|γ ( α ) =1 ) , νN , α ( β ) =E ( β^2|γ ( α ) =1 ) , pow ( N , β , α ) is power to detect a SNP with effect size β at a significance level α in a GWAS with size N , and f1 ( ⋅ ) and f2 ( ⋅ ) are effect-size distributions for HP and LP susceptibility SNPs , respectively . In our numerical calculations , we assumed that the effect sizes of the susceptibility SNPs in the HP and LP sets followed the same distribution β~πN ( 0 , σ12 ) + ( 1−π ) N ( 0 , σ22 ) , consistent with simulations . We performed grid search to identify the p-value thresholds ( α1 , α2 ) that maximizes E ( PCC ( α1 , α2 ) ) . For binary disease outcomes , AUC can be expressed as a function of PCC [11] .
|
Large GWAS have identified tens or even hundreds of common SNPs significantly associated with individual complex diseases; however , these SNPs typically explain a small proportion of phenotypic variance . Recently , heritability analyses based on GWAS data suggest that common SNPs have the potential to explain substantially larger fraction of phenotypic variance and to improve the genetic risk prediction . Because of the polygenic nature , improving genetic risk prediction for complex diseases typically requires substantially increasing the sample size in the discovery set . Thus , it is crucial to develop more efficient algorithms using existing GWAS summary data . In this article , we extend the polygenic risk score ( PRS ) method by adjusting the marginal effect size of SNPs for winner’s curse and by incorporating external functional annotation data . Theoretical analysis and simulation studies show that the performance improvement depends on the genetic architecture of the trait , sample size of the discovery sample set and the degree of enrichment of association for SNPs annotated as “high-prior” and the linkage disequilibrium patterns of these SNPs . We applied our method to the summary data of 14 GWAS . Our method achieved 25–50% gain in efficiency ( measured in the prediction R2 ) for 5 of 14 diseases compared to the standard PRS .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"urology",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"sociology",
"cancers",
"and",
"neoplasms",
"dna-binding",
"proteins",
"genitourinary",
"tract",
"tumors",
"social",
"sciences",
"oncology",
"bladder",
"cancer",
"mathematics",
"forecasting",
"statistics",
"(mathematics)",
"genome",
"analysis",
"research",
"and",
"analysis",
"methods",
"proteins",
"lung",
"and",
"intrathoracic",
"tumors",
"mathematical",
"and",
"statistical",
"techniques",
"statistical",
"methods",
"consortia",
"histones",
"hematology",
"biochemistry",
"blood",
"anatomy",
"physiology",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"genetics",
"of",
"disease",
"computational",
"biology",
"human",
"genetics"
] |
2016
|
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data
|
Oral transmission of Chagas disease has been documented in Latin American countries . Nevertheless , significant studies on the pathophysiology of this form of infection are largely lacking . The few studies investigating oral route infection disregard that inoculation in the oral cavity ( Oral infection , OI ) or by gavage ( Gastrointestinal infection , GI ) represent different infection routes , yet both show clear-cut parasitemia and heart parasitism during the acute infection . Herein , BALB/c mice were subjected to acute OI or GI infection using 5x104 culture-derived Trypanosoma cruzi trypomastigotes . OI mice displayed higher parasitemia and mortality rates than their GI counterparts . Heart histopathology showed larger areas of infiltration in the GI mice , whereas liver lesions were more severe in the OI animals , accompanied by higher Alanine Transaminase and Aspartate Transaminase serum contents . A differential cytokine pattern was also observed because OI mice presented higher pro-inflammatory cytokine ( IFN-γ , TNF ) serum levels than GI animals . Real-time PCR confirmed a higher TNF , IFN-γ , as well as IL-10 expression in the cardiac tissue from the OI group compared with GI . Conversely , TGF-β and IL-17 serum levels were greater in the GI animals . Immunolabeling revealed macrophages as the main tissue source of TNF in infected mice . The high mortality rate observed in the OI mice paralleled the TNF serum rise , with its inhibition by an anti-TNF treatment . Moreover , differences in susceptibility between GI versus OI mice were more clearly related to the host response than to the effect of gastric pH on parasites , since infection in magnesium hydroxide-treated mice showed similar results . Overall , the present study provides conclusive evidence that the initial site of parasite entrance critically affects host immune response and disease outcome . In light of the occurrence of oral Chagas disease outbreaks , our results raise important implications in terms of the current view of the natural disease course and host-parasite relationship .
Chagas disease ( American trypanosomiasis ) , caused by the protozoan Trypanosoma cruzi , affects 6–7 million people worldwide , with an annual incidence of 28 thousand cases in the Americas [WHO , 2015] . Chagas disease is endemic in 21 countries in Latin America and was previously confined to this region . However , it has spread to other continents due to the migration of infected people [1] . Transmission to humans occurs through excreta deposition after biting of contaminated insect vectors belonging to the Reduviidae family , blood transfusion , organ transplantation , laboratory accident as well as congenitally and orally [2 , 3] . The first case of T . cruzi oral transmission in Brazil was reported in 1965 in Teotonia , Rio Grande do Sul [4] . Since then , outbreaks of orally transmitted Chagas disease have occurred in several Brazilian states , such as Amazonas , Amapá , Bahia , Ceará , Pará , Paraíba , Rio Grande do Sul and Santa Catarina . Although underestimated , oral transmission of Chagas disease was responsible for more than 739 cases in the Pará State in legal Amazônia , Brazil ( 1986–2012 ) ; 369 cases in Venezuela ( 2007–2009 ) ; 45 cases in Colombia ( 2008–2010 ) ; 14 cases in Bolivia ( 2010 ) , and orally transmitted Chagas disease was reported in Argentina and Ecuador [5 , 6 , 7 , 8 , 9 , 10 , 11] . All of these outbreaks were associated with food/beverages consumption like: wild infected animal meat , vegetables , sugar cane extract , açaí pulp , goiaba juice , bacaba , babaçu and vino de palma [5 , 12 , 13 , 14] . Interestingly , oral transmission of Chagas disease is currently the most important transmission pathway in the Brazilian Amazon after the pan-American Health Organization declared the interruption of vectorial transmission in this area [6] . In past years , the proportion of orally infected patients has increased . From 1968 to 2000 , 50% of acute cases in the Amazon region were attributed to oral transmission , [9] and between 2000–2010 , the rate reached 70% [7] . Furthermore , wild strains of the parasite in oral transmission lead to cardiac involvement in patients in the Amazon region [14 , 15 , 16 , 17] . Mortality rates in these orally infected patients are higher ( 8–35% ) when compared with the classical vectorial transmission through triatomine excreta deposition after biting ( <5–10% ) [18] . Despite this , there are controversies about mortality rates , because oral transmission gained more attention after outbreaks over the years . Previous studies suggested that metacyclic trypomastigotes are more infective by oral than cutaneous challenge , emphasizing the higher severity of oral infection [19] . The common immunological knowledge of experimental T . cruzi infection comes from studies with intraperitoneal/ subcutaneous infected mice [20 , 21] . Although relevant , there are few reports regarding T . cruzi oral transmission in the literature . Some authors have demonstrated parasite-mucosa interaction and few aspects of immune response and disease outcome after intragastric , pharyngeal or oral cavity parasite challenge . These models of oral T . cruzi infections result in parasitemia and heart parasitism , which indicates systemic infection [22 , 23 , 24 , 25 , 26] . In addition , T . cruzi glycoproteins ( e . g . , gp82 ) seem to bind gastric mucin , promoting invasion and replication in epithelial cells from the gastric mucosa [27] . This initial invasion is related to the establishment of a progressive gastritis and allowing further systemic dissemination of the parasite . Nonetheless , the short replication period at this mucosal site induces specific immunity , as protection was observed after a secondary mucosal challenge , involving the production of IgA and IgG antibodies [23] . Interestingly , humoral and cellular responses are also protective after parasite inoculation in the conjunctival mucosa , a natural portal of entry for T . cruzi that leads to nasal infection with subsequent systemic spreading [28] . In orally infected mice , inflammatory infiltrates are observed in several tissues , such as the pancreas , spleen , liver , bone marrow , heart , duodenum , adrenal glands , brain and skeletal muscle [23] . Moreover , it was suggested that intraepithelial and lamina propria lymphocytes are involved in IFN-γ , but not IL-4 production , in orally infected hosts [23] . Interestingly , this infection route does not affect CD8+ T cell response [26] . Following disease outbreaks caused by food contamination with T . cruzi , a clear increase in the severity of clinical manifestations was observed in these infected patients compared with other types of transmission routes [9 , 18] . These observations raise important questions concerning the particular features of T . cruzi entry via the mucosa , including the possible modulation of local immune mechanisms and the impact on regional and systemic immunity [20 , 21] . Herein , we demonstrate that the site of parasite entrance , through the oral cavity ( as observed in natural infection- OI ) or directly into the stomach ( GI ) , differentially affects host immune response and mortality . In this study , we demonstrate that a highly severe acute disease follows in mice subjected to OI , compared with GI . They presented elevated parasitemia , high TNF serum levels , hepatitis and mild carditis , as well as a high mortality rate , which were partly reverted by anti-TNF therapy . This pioneer study approaches two distinct routes of oral infection that not only provides new clues for understanding Chagas pathology but also stimulates background for the elucidation of disease features in orally exposed populations .
BALB/c mice were infected with the highly virulent T . cruzi Tulahuén strain ( DTU- TcVI ) . In order to assess whether the route of infection interferes in the course of infection , infectivity , mortality and parasitemia were analyzed in intragastrically ( GI ) , oral cavity/orally ( OI ) or intraperitoneally ( IP ) infected mice ( Fig 1A and 1B and S1 Fig ) . IP infection , with 5x104 trypomastigotes promoted elevated infectivity , parasitemia and mortality ( Figs 1 and S1 ) . Regarding the mucosal pathway of infection , OI mice were more susceptible to T . cruzi infection than GI mice , with higher parasitemia , mortality ( Fig 1A and 1B ) and infectivity ( 97 . 5% and 49 . 3% , respectively ) ( S1 Fig ) . Differences in the infectivity rate may be associated with the low stomach pH , affecting parasite burden or its molecules . In our model of infection , mice were kept without water and food for 4 hours , and at that moment , the gastric pH was 3 and the oral cavity pH was 5 . Treatment with the antacid Magnesium Hydroxide ( Mg ( OH ) 2 Phillips—19 . 4 mg/Kg ) immediately neutralized the stomach pH to 7 and maintained the gastric pH at 5 for 30 minutes . In our study , differences in parasitemia observed between GI and OI could not be attributed to the acidic gastric pH in the mucosa because the Mg ( OH ) 2 suspension addition at the time of inoculation ( pH = 7 ) in both experimental groups did not interfere with blood-parasite burden ( Fig 1C ) . Antacid treatment five minutes before infection showed similar results . Taken together , our data clearly demonstrate that T . cruzi trypomastigote exposure in the oral cavity leads to a highly severe acute disease in mice . Moreover , although GI and OI are considered similar mucosal infection routes , their pattern of host response is not the same . The myocardium is one of the most affected tissues during T . cruzi infection in patients [18] . As we observed that different inoculation routes could distinctly affect acute phase severity , a histopathological analysis of heart sections was performed in 3 , 9 , 15 , 21 and 25 dpi ( days post-infection ) . At initial stages of infection ( 3–9 dpi ) , scarce infiltration is observed in the pericardium of both GI and OI groups ( S3 Table ) . Nevertheless , inflammatory infiltration was significantly higher in the GI-infected mice than in OI after 15 dpi , affecting both the pericardium and the myocardium ( Fig 2A and 2B and S3 Fig ) . Mild collagen deposition was observed in both groups when compared with uninfected mice ( S3 Fig ) . In conformity with previous studies in these experimental models , IP-infected mice showed extensive inflammatory infiltration in the heart throughout the course of the acute phase [29] . As observed in Fig 2 , both groups showed a similar profile of infiltrating cells ( CD4 and CD8 cells , F4/80+ macrophages and Ly6G+ neutrophils ) . Orally administered drugs/antigens are usually absorbed by the gastro-intestinal tract and transported to the lymphatic or hepatic portal system [30] . Moreover , the liver is known to be a target tissue for the parasite and plays a role in clearance of blood trypomastigotes [31] . As such , the liver may be involved with acute phase development in an orally infected host . To test this hypothesis , a comparative analysis of hepatic sections between GI and OI infected mice was necessary . As judged by liver histopathological analysis in 3 , 9 , 15–17 , 25 dpi , OI promoted severe hepatitis . During the initial stages of infection ( 3–9 dpi ) , hepatic infiltrates showed mild intensity mainly around the small and medium size vessels and it was higher in OI than GI . As the infection develops ( 15–17 dpi ) , infiltration notably increased also affecting the parenchyma in both OI and GI mice ( Fig 3A and S4 Table ) . Amastigote nests were rarely detected in the liver . Moreover , it was evident that medium vessels with blood stasis and suggestive formation of thrombotic masses occurred mainly in the OI-infected mice ( S4 Table ) . Picrossirius Red staining revealed progressive deposits of collagen in blood vessel walls , mainly in OI infected mice ( S4 Table ) . Immunofluorescence analysis from two different lobes showed that the inflammation was mainly composed by F4/80+ macrophages . However , CD4+ cells , CD8+ cells and Ly6G+ neutrophils were also observed ( Fig 3B ) . Furthermore , the OI group presented hepatic damage given the increased ALT and AST serum levels ( 17 dpi ) . Apoptotic ( TUNEL+ ) cells were also detected in the inflammatory infiltrate and at the parenchyma at 16 dpi ( Figs 3C and S4 ) . In immune response to infection , Th1 , Th2 , Th17 and regulatory cytokines play an important role in the control of parasite and disease development [32] . To investigate the impact of the route of infection on systemic cytokine levels , a thorough multiplex analysis was performed . As demonstrated in Fig 4 , OI mice showed higher type 1 cytokines levels , i . e . , IFN-γ ( 3 dpi ) and TNF ( 12 , 17 dpi ) but also IL-10 ( 17dpi ) , than GI mice . Conversely , IL-17 ( 3 dpi ) and the regulatory cytokine TGF-β ( 12 dpi ) was increased in GI mice ( Fig 4 ) . Elevated levels of pro-inflammatory cytokines are also associated with cardiac tissue damage [32] . In order to analyze cytokine presence in the cardiac tissue of infected mice , real time PCR was performed for IFN-γ , TNF , IL-10 and TGF-β cytokines . Interestingly , IFN-γ , TNF , and IL-10 gene expression was increased in the OI group ( Fig 4 ) . Moreover , TNF increase was evident in OI mice , but not GI mice ( Fig 4B ) . Immunofluorescent staining from heart and liver samples of 16 dpi mice showed the presence of TNF in these tissues . TNF labeling was evident in inflammatory cells , mainly in macrophages ( Figs 5 and S2 ) . OI hepatic sections presented a higher number of macrophages than GI ( mean ± SEM , standard error of the mean: 1200 ±18 . 68 versus 963 ± 43 . 15 , respectively ) analyzed in 14 fields from two different liver lobes . To evaluate the impact of elevated TNF serum levels in host resistance , OI mice were treated with the anti-TNF , etanercept ( Enbrel ) . Enbrel treatment in OI mice started at 14 dpi . As demonstrated in Fig 6 , our protocol of TNF blockade did not affect blood trypomastigotes number , but treated mice presented a longer survival than non-treated ones ( Fig 6 ) .
Currently , oral transmission of Chagas disease is the most important route of transmission in Brazil ( 70–80% of cases ) [7] . Venezuela , Colombia , Bolivia , Argentina and Ecuador have also reported to have acute cases of Chagas disease associated with food/beverage consumption , but a significant study in the region is lacking [5] . These orally infected patients progress with a highly symptomatic disease ( fever , facial edema , exanthema , hemorrhage , meningoencephalitis , abdominal pain , others ) , beyond the classical cardiac involvement . Additionally , increased mortality rate is marked in the first 2 weeks ( 8–35% ) , surpassing the calculated mortality produced by the disease resulting from the biting of infected insect vectors ( 5–10% ) [18] . It has been well accepted that the route of parasite entry into the host is a key factor in Chagas pathogenesis [21] . Previous reports demonstrated that systemic versus mucosal infection promotes a distinct disease pattern . It has been shown that CFI mice infected with the Peruvian strain ( TcII ) of T . cruzi through systemic routes IP , intravenous , or subcutaneous ( s . c ) have higher infection rates ( 67–100% ) and mortality than mucosal routes [OI , GI , intrarectal , genitalia , or conjunctival] ( 17–67% ) [33] . Moreover , Caradonna and Pereiraperrin [34] infected BALB/c and C57BL/6 mice with the Tulahuén strain ( TcVI ) of T . cruzi via s . c . and intranasal routes ( i . n . ) , and found higher mortality in the s . c . group . Furthermore , mice infected via the i . n . route developed a higher brain parasitism and lower parasitemia than animals infected via the s . c . route , suggesting a preferential homing of the parasite to the brain after intranasal administration [34] . Interestingly , when mice are infected with the same strain simulating natural infection , by oral ( oropharynx ) or cutaneous ( over a puncture wound ) challenge , insect-derived trypomastigotes are more infective through oral inoculation [19] . Regardless of DTU ( TcI or TcII strains ) , infection through gavage ( intragastrically ) presents less infectivity , parasitemia and mortality than intraperitoneal injection [35] , as we have also observed with the Tulahuén strain ( TcVI ) . Here , we demonstrated that OI infected mice induced a higher infective rate when compared with GI infected mice ( S1 Fig ) . As well as the inoculation route , certain factors , such as the inoculum size , DTU and T . cruzi developmental stage , may be involved in the disease outcome . Previous studies demonstrated that the Y strain ( TcII ) with 5x104 blood trypomastigotes infection by gavage ( GI ) showed higher parasitemia than the Colombian ( TcI ) strain [35] . In addition , IP infection with blood trypomastigotes shows higher infectivity than insect-derived trypomastigotes [36] . Gp82 and gp30 are involved in gastric invasion and can be differently expressed among distinct strains and developmental stages . Culture-derived metacyclic trypomastigotes from the Tulahuén strain ( TcVI ) was already described as expressing gp82 and CL strain , from the same DTU , presented high expression of gp82 and gp30 , similarly to human isolates . These strains induce high parasitemia [25 , 27 , 37 , 38] . Another glycoprotein , gp90 ( impairs cell invasion ) , is less expressed in CL ( TcVI ) parasites , when compared with the SC strain ( isolated from a patient who ingested contaminated sugar cane ) . However , the SC strain is highly infective because its gp90 is susceptible to gastric juice [27] . Cortez and colleagues identified the mucin-ligand sequence present in gp82 from metacyclic Y strain ( TcII ) and its counterparts Tc85-11 , involved in cell invasion by tissue culture-derived trypomastigotes ( TCT ) [39] . In our study , both TCT and insect derived trypomastigotes were able to promote mice infection trough the GI route . Moreover , it has been shown that both insect-derived metacyclic trypomastigotes ( triatomine insects that were crushed along with fruits ) and blood/ cell-derived trypomastigotes ( consumption of infected wild T . cruzi reservoir hosts- marsupials , bats and others ) are associated with human outbreaks of oral Chagas disease [5 , 40 , 41] . The primary site of parasite entry in oral infection is still unknown . Previous data proved that in orally infected mice , parasites are not detected within the oropharynx and esophagus , instead , amastigote nests are present in the stomach [23] . In line with these findings , another group suggested that parasite glycoproteins , such as gp82 and gp30 , are involved in gastric invasion following intragastric/intrapharyngeal inoculation [27 , 37 , 42 , 43] . Altogether , these reports name intragastric , intrapharyngeal and oral cavity parasite delivery as “oral” infection . Here , we demonstrate that BALB/c mice infected through the oral cavity ( OI ) experienced a higher infective rate when compared with GI infected mice . To our knowledge , this constitutes the first report addressing the potential differences in disease outcome according to OI or GI route . After reaching systemic circulation , T . cruzi can multiply inside several cell types , such as macrophages , fibroblasts , skeletal and cardiac muscle , neurons and epithelial cells . Notably , the parasite presents tropism for cardiac tissue , where it forms amastigote nests and triggers immune cell recruitment [32] . Here , we demonstrated that in spite of the lower parasitemia and mortality , GI- mice developed a more severe myocarditis than OI mice , suggesting that cardiac involvement might not be related to the elevated mortality of the OI group . Tulahuén ( TcVI ) is not described as myotropic strain , but , this reticulotropic strain still affects the heart of infected mice . Moreover , in our study , this strain induced differences in inflammatory infiltration in the heart and damages in the liver between OI and GI groups . In addition , the intragastric infection with Colombiana ( TcI ) myotropic strain induces inflammation and amastigote nests formation in the heart [24] . It has been shown that T . cruzi is able to infect the reticuloendothelial system , including bone marrow , spleen and liver . Moreover , IP infection leads to apoptosis of hepatic cells and liver inflammation due to TNF production . In this regard , it was previously shown that the Tulahuén strain of T . cruzi induces TNF- production and death of hepatocytes by apoptosis , involving tBid and Bax proteins and influencing organ function [44] . OI and GI infection also promote apoptosis in the liver and in the heart of acute infected mice ( S4 Fig ) . Interestingly , hepatic damage in OI is severe , as judged by histopathology and elevated ALT and AST serum levels . It has been shown that hepatocytes are not commonly infected in vivo , but amastigote nests may be observed in sinusoidal and Kupffer cells [45] . Here , we have hardly detected amastigote in hepatic tissue , most likely in Kupffer cells . This scarcity of T . cruzi amastigotes in hepatic cells is assumed to occur because of efficient control of the parasite clearance within this organ [46] . Additionally , the liver is described as the first line of protection against pathogens and in tolerogenic responses to antigens coming from the gut through the portal system [46 , 47 , 48 , 49] . Our results demonstrated that macrophages in the liver are TNF+ cells ( Fig 5 ) . Activated macrophages producing TNF in the liver may be involved with T . cruzi killing in the tissue . Nevertheless , considering the role of TNF in cell death , apoptotic bodies were also evident in TNF-rich regions ( S4 Fig ) . Paralleling parasitological and histological differences , GI- and OI- mice presented elevated IFN-γ levels in the serum , whereas higher levels of TNF were only observed in OI mice . Cellular adaptive immune response during infection is essential for parasitism control [50] . Cytokines play important roles in regulating T . cruzi replication and the immune response of infected animals . Th1 cytokines , such as IFN-γ and TNF , are involved in parasite control and host resistance whereas Th2 cytokines , such as IL-4 and IL-5 , are associated with host susceptibility [32] . In the initial stage of infection , parasite DNA and surface glycoconjugates are able to trigger innate immune response through TLR-2 , -4 and -9 in macrophages and dendritic cells , enhancing their endocytic capacity and killing by oxidative burst . Pro-inflammatory cytokines , such as IFN-γ , favors inflammatory cell activation that migrate to control parasite burden [50] . Elevated TNF levels , as observed in OI , may be associated with cardiac , spleen , hepatic damage , and toxic shock in mice , as reported in other studies [51 , 52] . Reinforcing this view , OI mice showed a peak in TNF serum levels at 17 dpi , the time when they started to die . GI mortality also started at this time point , but the rate was lower than in OI mice . In the same sense , TNF detection by RT-PCR was higher in cardiac cells from OI than GI mice . Extending these findings , we have also shown that both GI and OI mice presented a high serum concentration of IL-10 and IL-4 ( 17 dpi ) . Meanwhile , the OI group had lesser amounts of the TGF-β regulatory cytokine . Both cytokines were proven to inhibit macrophage microbicidal function and protect the host from tissue damage [52 , 53] . It has been shown that IL-17 producing cells contribute to the formation of the gastrointestinal barrier [54] . Our results demonstrated that , as expected , parasite inoculation into mucosal routes ( GI and OI ) triggered IL-17-producing cell activation , given its high serum levels . Studies addressing different routes of infection are relevant as they seem to lead different immune responses and disease outcome . Infection with bacteria , such as Listeria monocytogenes , Streptococcus pyogenes and Francisella tularensis through mucosal sites ( intranasal or oral ) promotes Th17 response with the systemic route ( intravenous/s . c . ) triggering a Th1 response . Antigens delivered into mucosal tissues stimulate these IL-17 producing cells [55] , which was also observed in T . cruzi infection . In OI animals , elevated circulating levels of TNF after 17 dpi were strongly associated with hepatic damage and death . Similar results were observed in mice deficient of IL-10 , which display enhanced hepatic cell destruction and toxic shock by increased TNF . To comprobate that TNF is involved in the death of OI mice , we blocked this molecule . Because TNF is critical to control parasitemia [32 , 48] , etanercept administration started at 14 days post-infection , at the time that parasitemia could also be controlled by humoral response . Etanercept treatment statistically delayed mortality without altering the levels of parasitemia , revealing the critical role of TNF in the course of OI infection , ( Fig 6 ) . Strikingly , similar results were also observed by Rodriguez-Angulo [56] . In fact , there is a clear association between peaked 17 dpi TNF circulating levels and liver alterations in terms of inflammatory infiltrates , which may impact organ functionality . For several years , the IP route of T . cruzi infection has been chosen as the main pathway of parasite challenge in experimentally infected hosts in attempting to reproduce the vectorial route . We and others have already demonstrated that the route of parasite challenge is a major issue in terms of infection outcome and hence , worth considering in the human counterpart , mainly because oral transmission is becoming more epidemiologically relevant . Here , we clearly demonstrate that the host response differs when parasites are delivered into the mouth or by gavage . If compared with patients , oral outbreaks are related to contaminated food ingestion [5] and interestingly , the appearance of facial edema is frequent in these patients [57] . Parasite/antigens can be captured in the oral mucosa by tolerogenic dendritic cells that produce IL-10 and IL-12 ( regulatory and inflammatory profile ) , or in the gastrointestinal tract from where they are drained to the liver by the portal system [49 , 58] . Even considering oral infection , it should not be assumed that the infectious processes are the same when parasites are delivered into the oral cavity or by gavage ( intrapharyngeal /gastrointestinal ) . On a theoretical basis , our studies also raise a series of questions worth exploring . For instance , the immunopathological and parasitological consequences of oral and systemic infection in the same host at different times are of interest . Whether there is a favorable influence of oral or systemic infection depending on which comes first is also interesting . From a translational standpoint , the finding that the route of parasite contact is involved in a differential pathophysiology and disease morbidity provides information that sounds suitable for clinical management and disease control strategies .
Male BALB/c mice were obtained from the Oswaldo Cruz Foundation animal facilities ( Brazil ) . Mice ( 6–8 weeks old ) were infected by gavage as a gastrointestinal attempt ( GI ) or in the oral cavity ( OI ) with 5x104 T . cruzi tissue culture-derived trypomastigotes forms ( Tulahuén strain , TcVI [59] ) . Parasites were obtained from infected cultures of a highly susceptible lineage of monkey kidney epithelial cell line ( Vero cells ) [60] . Oral Chagas disease outbreaks are related to the consumption of contaminated food with infected triatomine excreta ( metacyclic trypomastigotes ) or consumption of wild T . cruzi reservoir hosts ( blood and cell-derived trypomastigotes ) . The purpose of this study was to analyze the immune response of infected mice . As components from excreta could interfere in this response , some experiments were performed with trypomastigotes mixed with or without non-infected Triatoma infestans excreta . In these infections , uninfected mice were stimulated with saline . For both GI and OI , mice were maintained starving 4 hours before and at least 15 minutes after inoculation ( 100 μL of parasites suspension ) . GI was performed using a gavage canule and OI , by pipeting the volume into the mouth . As GI infection is the less effective route of infection , 5x104 , 105 or 106 inocula size were tested by gavage . In this study , the lower ( 5x104 ) inoculum capable of infecting mice GI was chosen . For comparative purposes , in some experiments , intraperitoneally infected mice were also analyzed . In other series of experimental rounds , mice were also infected concomitantly with an inoculation of antacid ( 19 . 4 mg/Kg of Magnesium Hydroxide [Mg ( OH ) 2] suspension , Phillips- Brazil ) , and trypomastigotes . This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation and the Federal Law 11 . 794 ( 10/2008 ) . The Institutional Ethics Committee for Animal Research of the Oswaldo Cruz Foundation ( CEUA-FIOCRUZ , License: LW-23/12 ) approved all of the procedures used in this study . Parasitemia was detected at different days post-infection ( dpi ) by counting trypomastigotes in 5 μL of tail blood , and parasite number was calculated using the Brener method . Mortality and survival were followed until 60 days post-infection . Parasitemia and survival rates were evaluated with all inoculated animals . Due to differences in infectivity from OI and GI mice , in the other figures throughout this paper were included infected mice with patent parasitemia . Hearts and livers from infected or uninfected mice were fixed in buffered 5% formalin . Heart samples were sliced longitudinally in two parts , and the liver , in several fragments from different lobes . Paraffin-embedded 5 μm sections were mounted on glass slides and stained with the Hematoxylin-Eosin and Picrossirius Red technique to evaluate infiltrating cells and collagen fibers . Photos were taken using the Leica DM 2500 microscope and then analyzed using Image J software . In the heart , the percentage area with inflammatory infiltrates or amastigote nests was calculated by analyzing 50 fields/section/mouse . In addition to the area of inflammation , the degree of pericarditis and myocarditis was classified according to the extension of infiltrating area: + , very mild ( similar with uninfected ) ; ++ , mild ( small areas of infiltrates ) ; +++ , moderate ( moderate areas of infiltrates ) ; ++++ , severe ( extensive areas of infiltrates ) ; +++++ , very severe ( very extensive areas of infiltrates ) . In hepatic tissue , the percentage area with inflammatory infiltrates was calculated by analyzing 25 fields/mouse ( two different sections from each mouse ) . The inflammatory infiltrates were scored as:- , without infiltrates ( without alterations ) ; + , mild lesions areas ( small focal infiltrates mainly around the vessels , parenchyma not infiltrated ) ; ++ , moderate areas of infiltrates ( infiltrates with intermediate size , around the vessels , but few diffuse and microgranulomas also within the parenchyma and thrombus formation in some vessels ) ; +++ , severe areas of infiltrates ( areas of infiltrates with microgranulomatous structure , diffuse infiltrates within the parenchyma and more vessels with thrombus formation ) , ++++ very severe ( extensive areas of infiltrates with microgranulomatous structure and diffuse infiltrates within the parenchyma ) . Mice were bled by cardiac puncture at 3 , 9 , 12 , 15 , 17 , 21 , 25 and 27 days post-infection . Serum was stored frozen at -70°C until used . Serum levels of IL-4 , IL-10 , IL-17 , IFN-γ , TNF and TGF-β were measured in a Multiplex analysis , Milliplex MAP—mouse cytokine / chemokine magnetic bead panel kit and TGFβ1 single plex kit ( Merk Millipore , USA ) . The assay was performed by the Gênese Institute of Clinical Analyses , São Paulo/SP , Brazil . Cytometric beads assay ( CBA ) , using the Mouse Th1/Th2/Th17 Cytokine kit ( BD Biosciences , USA ) was also performed for serum cytokine analysis according to the manufacturer’s instructions . Samples were immediately acquired using FACSCanto II ( Becton and Dickinson , USA ) equipped with FACSDiva software ( Becton and Dickinson , USA ) . Data were analyzed using FCAP Array software ( Becton and Dickinson , USA ) . For real-time quantitative RT-PCR ( RT-qPCR ) , the total RNA from one-half of the heart ( longitudinal section; average weight: 87 . 9 mg ) samples was extracted using Trizol Reagent ( Ambion , Life Technologies ) associated with the RNeasy Mini kit assay ( Qiagen ) , from the phenol-chloroform aqueous phase , following the manufacturer’s instructions . Reverse transcriptase reactions were performed on 3 . 5 μg RNA using Super Script II kit ( Invitrogen , USA ) according to the manufacturer’s instructions . Real-time RT-PCR assays were performed on StepOnePlus ( Applied Biosystems , USA ) using Power SYBR Green Master Mix ( Applied Biosystems ) , and the primers for cytokines , IFN-γ , TNF , TGF-β , IL-10 and IL-17 , purchased from IDT ( Integrated DNA Technologies ) ( S2 Table ) . Hypoxantine-guanine phosphoribosyltranseferase ( HPRT ) and β-actin genes were used as endogenous housekeeping controls ( S2 Table ) . cDNA were diluted 1:10 and reactions were performed in duplicate using 2 μL per reaction , in a total volume of 20 μL . After amplification , dissociation curves were performed , revealing only one melting peak for each amplified fragments . Relative quantifications of target gene levels were performed using ΔΔCt method [61] . RT-qPCR data were normalized to the housekeeping genes , and fold changes were determined compared with uninfected control samples using the Expression Suite software ( Life Technologies , USA ) . Statistical analysis was performed from ΔCt values . Blood samples obtained from mice of all groups were allowed to coagulate , and the serum was then isolated . Serum ALT ( alanine transaminase ) and AST ( aspartate transaminase ) activities were measured with the Reflotron ( Roche , Germany ) apparatus according to the manufacturer's instructions . Hearts and livers from infected or uninfected mice were included in tissue tek ( OCT , Sakura , USA ) . Heart samples were sliced longitudinally in two parts , and the liver , in several fragments . To evaluate TNF-producing cells , double immunofluorescences were performed . Cryosections with 3 μm were fixed in acetone for 5 minutes at 4°C . After two washes in cold PBS , a blocking solution of 10% normal serum goat and 1% BSA was applied to the sections for 1 hour at room temperature . Samples were incubated overnight at 4°C with primary antibodies and washed three times in PBS and subjected to the appropriate secondary antibodies for 45 minutes at room temperature , Alexa-488 goat anti-rat for anti-CD4 , -CD8 , -F4/80 and -Ly6G , and Alexa-546 goat anti-rabbit for anti-TNF . The characteristics of the antibodies used in immunostaining are listed in S1 Table . After three washes in PBS , the slides were mounted in ProLong Gold Antifade Mountant with DAPI ( Molecular Probes , USA ) . To evaluate apoptosis , the ApopTag In Situ Apoptosis Detection Kit ( Merk Millipore , USA ) was used following the manufacturer instructions . Counterstaining/mounting was performed using ProLong Gold Antifade Mountant with DAPI ( Molecular Probes , USA ) . All images were visualized using the Zeiss microscope ( Germany ) and digitalized using AxioCam HRm and AxioVision Rel 4 . 8 software . The subsets ( CD4+ , CD8+ , F4/80+ and Ly6G+ ) present in the infiltration were counted in 10 and 14 fields/photos for the heart and liver , respectively , and TNF+ cells were quantified . Two sections from the heart and two from different liver lobes from each mouse were analyzed . The percentage of CD4 , CD8 , macrophages and neutrophils was calculated over the sum of all subsets from different sections . Orally infected BALB/c mice were treated intraperitoneally with a quimeric anti-TNF protein ( Etanercept Enbrel , Wyeth Pharmaceuticals , 0 . 83 mg/Kg ) . The treatment began at the 14th day post-infection with weekly subsequent doses . Parasitemia and mortality were analyzed throughout the course of infection . Kruskal-Wallis ( Dunn’s post-test ) or Mann-Whitney tests were used for the statistical analyses . Survival was analyzed by Log-rank ( Mantel-Cox Test ) and Gehan-Breslow-Wilcoxon test . P values < 0 . 05 were considered statistically significant . Tests were performed using GraphPad Prism 5 . Genes: IFN-γ ( NM_008337 . 3 ) , TGF-β ( gb_M13177 . 1 ) , TNF ( NM_013693 . 3 ) , IL-17A ( NM_010552 . 3 ) , IL-10 ( NM_010548 . 2 ) , HPRT ( gb_J00423 . 1 ) , β-actin ( NM_007393 . 3 ) . Proteins: IFN-γ ( gb_EDL24379 . 1 ) , TGF-β1 ( NP_035707 . 1 ) , TNF ( gb_AAC82484 . 1 ) , IL-17A ( NP_034682 . 1 ) , IL-10 ( NP_034678 . 1 ) , gp82 ( gb_ABR19835 . 1 ) , gp30 ( gb_AEF13371 . 1 ) , gp90 ( gb_AAM47176 . 1|AF426132_1 ) , mucin ( gb_AAA39755 . 1 ) , IL-4 ( NP_067258 . 1 ) , TLR-2 ( gb_AAD49335 . 1|AF165189_1 ) , TLR-4 ( NP_067272 . 1 ) , TLR-9 ( NP_112455 . 2 ) .
|
Chagas disease caused by the protozoan Trypanosoma cruzi is endemic in Latin America and a neglected tropical disease , which affects 6–7 million people worldwide . Currently , oral transmission is the most frequent pathway of infection in Brazil but also occurs in other endemic countries . This important infection route is underestimated and understudied . Here , we demonstrate that the site of parasite entrance , in the oral cavity ( OI ) , as observed in natural infection , or directly to the gastrointestinal tract ( GI ) , differentially affects the host-immune response and mortality . OI promotes a severe acute disease , elevated parasitemia and TNF mediated mortality . OI showed intense hepatitis and mild heart damage . Interestingly , GI mice presented mild disease , along with less circulating TNF and higher TGF-β and IL-17 serum contents . GI animals showed mild liver damage and intense heart inflammation . Our study is a pioneer work that analyzes the features of two distinct routes of oral infection . In addition , it provides new clues for Chagas pathology and stimulates background for the elucidation of disease features in orally exposed populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Trypanosoma cruzi Infection through the Oral Route Promotes a Severe Infection in Mice: New Disease Form from an Old Infection?
|
The influenza A viruses genome comprises eight single-stranded RNA segments of negative polarity . Each one is included in a ribonucleoprotein particle ( vRNP ) containing the polymerase complex and a number of nucleoprotein ( NP ) monomers . Viral RNA replication proceeds by formation of a complementary RNP of positive polarity ( cRNP ) that serves as intermediate to generate many progeny vRNPs . Transcription initiation takes place by a cap-snatching mechanism whereby the polymerase steals a cellular capped oligonucleotide and uses it as primer to copy the vRNP template . Transcription termination occurs prematurely at the polyadenylation signal , which the polymerase copies repeatedly to generate a 3′-terminal polyA . Here we studied the mechanisms of the viral RNA replication and transcription . We used efficient systems for recombinant RNP transcription/replication in vivo and well-defined polymerase mutants deficient in either RNA replication or transcription to address the roles of the polymerase complex present in the template RNP and newly synthesised polymerase complexes during replication and transcription . The results of trans-complementation experiments showed that soluble polymerase complexes can synthesise progeny RNA in trans and become incorporated into progeny vRNPs , but only transcription in cis could be detected . These results are compatible with a new model for virus RNA replication , whereby a template RNP would be replicated in trans by a soluble polymerase complex and a polymerase complex distinct from the replicative enzyme would direct the encapsidation of progeny vRNA . In contrast , transcription of the vRNP would occur in cis and the resident polymerase complex would be responsible for mRNA synthesis and polyadenylation .
The influenza A viruses are the causative agents of yearly epidemics of respiratory disease and occasionally more severe pandemics [1] . The latter are the consequence of transfers from the avian virus reservoir to humans by either genetic reassortment or direct adaptation [2] . Thus , current occasional infections of humans with highly pathogenic H5N1 avian strains have raised fears about a possible new pandemic of great severity . The influenza A viruses belong to the family Orthomyxoviridae and posses a single-stranded , negative-polarity RNA genome made up by 8 RNA segments , that form ribonucleoprotein ( RNP ) complexes by association to the polymerase and the nucleoprotein ( NP ) . Such RNPs are independent molecular machines responsible for transcription and replication of each virus gene and contain an RNA-dependent RNA polymerase composed by the PB1 , PB2 and PA subunits [3] . The polymerase complex recognises the RNA promoter comprising both 5′-terminal and 3′-terminal sequences of each segment , by preferentially binding the 5′-terminal end [4]–[6] , and in this way stabilises a supercoiled conformation of the RNPs [7] . Upon infection of susceptible cells , the parental RNPs are first transcribed in the nucleus ( primary transcription ) . Transcription initiation takes place by a cap-snatching process whereby the viral polymerase recognises the cap structure of cellular pre-mRNAs in the nucleus , cleaves these some 15 nt downstream the cap and utilises such capped-oligonucleotides as primers to copy the virus template RNA [8] . Transcription finalises by reiterative copy of the virus polyadenylation signal , an oligo-U sequence located close to the 5′-end of the template [9] , [10] . Synthesis of new virus proteins is required to proceed to RNP replication [11] , that takes place first by the generation of complementary RNPs ( cRNPs ) . These RNPs are structurally analogous to those present in the virions ( vRNPs ) but contain complete positive-polarity copies of the virus RNA segments , that are neither capped nor polyadenylated . The structural differences between the vRNP transcription and replication products ( mRNAs and cRNPs ) led to the proposal of a transcription-to-replication switch by which the parental RNPs would change from capped-RNA-dependent to de novo initiation , from polyadenylation to full copy of the template , and in addition would induce encapsidation of the RNA product into new RNPs ( reviewed in [12] . Such notion has been challenged recently by a new model proposing that parental vRNPs can directly synthesise cRNA but require newly synthesised polymerase and NP to stabilise the product in the form of cRNPs [13] . The cRNPs accumulate to low levels but serve as efficient templates for the synthesis of large quantities of progeny vRNPs that can be transcribed ( secondary transcription ) and eventually be incorporated into progeny virions [3] . Much information has been obtained during recent years on structural aspects of the RNPs [14] , [15] ( R . Coloma , unpublished results ) and their components , like the NP [16] , [17] , the polymerase complex [18] , [19] and specific domains of the polymerase subunits [20]–[24] . Likewise , a number of host cell factors have been identified that may play important roles in the transcription and replication processes [25]–[34] . However , much remains to be learned about the detailed mechanisms for RNP transcription and replication . For instance , it is not clear whether the polymerase complex present in the template RNP is able to synthesise the progeny vRNA or whether the replicative complex directs the encapsidation of progeny RNA it into a new vRNP . Likewise , it has been assumed that the polymerase complex present in the vRNP accounts for viral mRNA synthesis , but it is not clear whether other vRNPs or other soluble polymerase complexes perform this step in trans . In this report we used efficient in vivo recombinant replication and transcription systems and defined polymerase mutants specifically affected in either transcription or replication to answer these questions . Our results are consistent with a new model whereby polymerase complexes not associated to the template cRNP are responsible for the replicative synthesis of vRNPs in trans and polymerase complexes distinct from the replicative one specify the encapsidation of viral RNAs . On the contrary , no vRNP transcription could be detected by other RNP or a soluble polymerase complex in trans , suggesting that it takes place by the activity of the RNP-associated polymerase complex .
To gather information on the mechanisms of influenza virus transcription and replication we have adopted a genetic trans-complementation approach . This is based on the ability to reconstitute in vivo an efficient transcription-replication system that mimic these steps of the infection cycle and is more amenable to experimental manipulations [15] , [35] . Furthermore , the vRNP products can be efficiently purified , their structural and biological properties can be easily analysed [14] , [18] , [20] and they can in turn be used as templates for further rounds of in vivo replication . Essential for these approaches is the availability of well-defined mutants to be used as genetic markers . We have earlier described point mutants in the PB2 subunit of the viral polymerase that are defective in viral RNA replication but fully efficient in virus transcription [36] . Likewise , we have recently reported polymerase PB2 mutants that are affected in the cap-binding activity and hence are defective in cap-snatching , but retain their capacity to replicate virus RNPs [20] . Using the approaches indicated above we first addressed the question whether the replication deficiency of point mutants within the N-terminus of PB2 [36] could be rescued in trans by co-expression of PB2 point mutants defective in cap-binding [20] . Cultures of HEK293T cells were co-transfected with plasmids encoding PB1 , PA , NP and a deleted NS virus replicon ( clone 23 , 248 nt in length; [14] , [15] ) . In addition , either PB2wt or PB2 mutants R142A or F130A ( replication-defective ) or mutant E361A ( transcription-defective ) were co-expressed . Alternatively , pair wise combinations of these PB2 mutants were co-expressed ( R142A+E361A and F130A+E361A ) . Among the PB2 proteins expressed , either wt or the replication-defective mutants R142A or F130A were His-tagged at the C-terminus , a modification that does not alter their biological activity and allows the efficient purification of the in vivo RNP replication progeny [18] . The expression levels of all PB2 mutants were shown to be similar to that of PB2wt ( Fig . S1 ) and the untagged PB2wt was used as a control for purification ( see diagram of the experimental setting in Fig . 1A ) . After incubation , the cell extracts were used for Ni2+-NTA-agarose purification as described in Materials and Methods and the accumulation of progeny RNPs was determined by means of Western-blot assays using anti-NP sera . The purification of the complete RNPs was verified by Western-blot with antibodies specific for PB2 and PA ( Fig . 1B ) . This strategy allows measuring the replication capacity of the RNPs formed in vivo , as omitting any RNP element or using a defective point mutant leads to undetectable RNP accumulation [15] , [36] , [37] . Amplification of virus RNPs was expected for wt and mutant polymerase containing transcription-defective PB2 ( E361A ) , but not for those containing replication-defective PB2 ( R142A and F130A ) . However , since no tag is present in the former mutant , only RNPs derived from cultures containing PB2His were expected in the Ni2+-NTA-agarose purified material . This was indeed the case , as shown in Fig . 1B . If the transcription-defective polymerase were able to rescue in trans the defect in replication of polymerase mutants R142A or F130A , one would expect the accumulation and purification of RNPs containing these mutant PB2 . The results obtained by the co-expression of pairs of replication- and transcription-defective polymerases indicate that such prediction is hold ( Fig . 1B ) . The transcription-defective mutant could rescue both R142A and F130A alleles and similar rescue was obtained when other transcription-defective mutants , like H357A , K370A , F404A [20] were used ( Fig . S2 ) . The progeny RNPs contained the replication-defective PB2 allele , since ( i ) they could be purified by Ni2+-NTA-agarose chromatography and ( ii ) the mobility of the PB2 subunit in the Western-blot assay corresponded to the His-tagged subunit and not to the untagged one . It is important to mention that only His-tagged PB2 protein was detected in the purified RNPs and not the untagged counterpart , indicating that no transcription-defective polymerase was co-purified ( Fig . 1B ) . Furthermore , the phenotype of the rescued RNPs was tested by determination of their in vitro transcription activity ( Fig . 2 ) . Since the transcription-defective mutants had alterations in their cap-binding pocket , they show low in vitro transcription activity when a mRNA is used as a cap-donor , whereas cap-independent transcription is observed with a general primer as the dinucleotide ApG [20] . The transcription activity profile of rescued RNPs using ApG or β-globin mRNA as primers was identical to that of wt RNPs , as expected , and not to that of mutant E361A , that is unable to use β-globin as primer [20] ( Fig . 2 and Fig . S3 ) . In the experimental approach used , the reconstitution of a RNP from the viral proteins and genomic RNA has to take place first and its subsequent amplification would account for the bulk of RNPs that can be purified from the transfected cells . Since a RNP template with the replication-defective polymerase does not replicate [36] , only transcription-deficient polymerase could perform RNP replication . The results obtained ( Figs . 1 , 2 ) demonstrate that a polymerase complex distinct from that responsible for RNP replication ( replication-defective versus transcription-defective ) is incorporated into the progeny vRNP and suggest that a replication-defective polymerase can direct the encapsidation of the progeny vRNA , i . e . can bind the 5′-terminus of newly synthesised vRNA and direct the incorporation of NP monomers into the progeny vRNP . It could be argued that the incorporation of the replication-defective polymerase to the progeny RNP might occur by exchange with replication-competent during purification in vitro . Two lines of evidence argue against such possibility: ( i ) Our transcription experiments verify that the polymerase present in an RNP complex is stably bound ( see below ) and ( ii ) The data reported by Wreede et al . [38] suggest that the binding of a polymerase complex to the 5′-terminal sequence of viral RNA can not competed by a pre-expressed polymerase . In fact , the average rescue efficiency obtained ( 55+/−18% ) ( Figs . 1 , 2 ) was very high , and is in line with the possibility that both types of soluble polymerase complexes , transcription- and replication-deficient , are incorporated in the progeny viral RNA , around half of which would not be detected because are not His-tagged . The rescue of viral RNPs containing the mutant R142A polymerase complex , as described above , enabled us to purify these RNPs and use them as templates for a second in vivo reconstitution experiment in which instead of a template RNA we introduced the rescued and purified R142A mutant RNPs in the system . This strategy ensured that only replication-defective RNPs are used as templates for in vivo replication and allowed us to ask whether the resident polymerase complex or a distinct , soluble polymerase is responsible for replication of RNPs in vivo . The concentration and biological activity of these purified RNPs was first controlled by Western-blot and in vitro transcription . The results are presented in Fig . 3B and show that higher yields were obtained for RNPs containing the E361A mutation in PB2 than those containing the R142A mutation . This was expected , as the latter could only be amplified by trans-complementation ( see Fig . 1 above ) . The transcription phenotype of these purified RNPs was in agreement with the mutations present in PB2 ( Fig . 3B , right panel ) . Therefore , cultures of HEK293T cells were co-transfected with purified RNPs containing either the R142A mutation or the E361A mutation in PB2 , plasmids encoding PB1 , PA , NP and a plasmid encoding either PB2-His R142A ( replication-defective ) or PB2-His E361A ( transcription-defective ) ( see Fig . 3A for a diagram of the experimental setting ) . As controls , the RNPs were co-transfected with empty pCMV vector or the expression plasmids were transfected in the absence of template RNPs . The intracellular accumulation of progeny RNPs was determined by Ni2+-NTA-agarose purification , Western-blot and in vitro transcription as indicated above and the results are presented in Fig . 3C and Fig . 4 . The cultures co-transfected with RNPs E361A and plasmids including PB2 E361A ( Fig . 3C; RNP361-Pol361 ) served as positive control and , indeed gave rise to the accumulation of RNPs to levels similar to the standard , wt system ( see Fig . 1B , HisPB2 ) . No background was observed when template RNPs were transfected ( Fig . 3C; RNP142/CMV , RNP361/CMV ) . A fraction of the NP expressed was retained in the Ni2+-NTA-agarose resin ( Fig . 3C; Pol142 , Pol361 ) and defined the background level of the purification protocol ( but see Fig . 4 below ) . The co-transfection of RNPs containing mutation PB2 R142A and the same mutant plasmids yielded no increase above background in the level of purified RNPs ( Fig . 3C; RNP142/Pol142 ) but the mixed transfection of RNPs with the mutation PB2 R142A and the expression plasmids with mutation PB2 E361A led to a high level of replication ( around 80% of control values ) ( Fig . 3C; RNP142/Pol361 ) . To verify these results and to determine the polarity of the progeny RNA , similar experiments were carried out and the RNA present in the purified his-RNPs was analysed by hybridisation with positive- and negative-polarity RNA probes comprising the NS sequence . The results reinforced the data obtained by Western-blot and indicated that most of the progeny RNPs are vRNPs ( Fig . 4 ) , as previously reported [15] . The accumulation and phenotype of the progeny RNPs was also verified by in vitro transcription using either ApG or β-globin as primers ( Fig . 5 ) . The accumulations observed paralleled those shown in Fig . 3 but the background levels from samples Pol142 , Pol361 and RNP142-Pol142 were negligible . Much higher activity levels were obtained with ApG primer , indicating that the progeny RNPs contained PB2 with mutation E361A . The results presented in Figs . 3 and 4 indicate that a polymerase complex distinct from that present in the template RNP can perform the replicative synthesis of viral RNA . The high level of replication detected by trans-complementation suggests that virus RNA replication mostly occurs in trans . It could be argued that the mutation R142A in PB2 might destabilise the polymerase-promoter complex , allowing the efficient replacement by a polymerase complex containing the E361A mutation . However , RNPs containing the R142A mutation are as efficient in transcription as wt RNPs , suggesting that they are not affected in promoter recognition . It is well established that vRNPs can transcribe mRNAs in the absence of any newly synthesised viral proteins ( primary transcription ) [39] , [40] and highly purified recombinant RNPs can transcribe in vitro [18] ( R . Coloma , unpublished results ) . However , it is not clear whether transcription takes place intramolecularly , i . e . in cis , or a RNP can transcribe another RNP . To test this possibility we reconstituted in vivo two genetically distinct RNPs , one containing a cat virus replicon ( with the cat negative-polarity ORF flanked by the UTRs of the NS segment of influenza virus ) , the other one being the NS deletion mutant clone 23 [14] , [15] . Both RNPs contained a His-tagged PB2 subunit to allow purification by affinity chromatography as indicated above but two PB2 alleles were used , either wt or mutant E361A , which is defective in the recognition of the cap structure [20] . Purified RNPs were used either separately or in combination for in vitro transcription with ApG or β-globin as primers and the transcription products were analysed by denaturing polyacrylamide gel electrophoresis and autoradiography . The results are presented in Fig . 6 . As expected , the purified wt RNPs were active , both when ApG or β-globin were used as primers ( Fig . 6A ) . The RNPs containing the mutation PB2 E361A could transcribe mRNA with ApG as primer , but did so less efficiently when using β-globin mRNA as primer donor ( Fig . 6A ) . These results allowed us to test whether a purified , wt clone 23 RNP could rescue the transcription activity of mutant E361A cat RNP in trans , since the mRNA products would be distinguishable by size ( 720 nt versus 248 nt ) . The wild-type cat RNPs could transcribe efficiently , both when incubated on their own and when mixed with clone 23 RNPs ( Fig . 6B , middle panel ) . The cat RNPs containing PB2 E361A only produced background transcription levels and no increase in the amount of cat mRNA was observed when wt clone 23 RNP was co-transcribed ( Fig . 6B , right panel ) . Quantisation of the relevant bands indicated that the increase in cat transcript in the co-transcription of clone 23 RNP+E361 cat RNP versus the transcription of E361 cat RNP was less than 3% of the cat transcript value obtained by co-transcription of clone 23 RNP+wt cat RNP . These results suggest that , at least in vitro , no transcription in trans among different RNPs takes place . However , the possibility still persists that a soluble polymerase complex is able to transcribe a vRNP template in trans . To analyse this alternative we generated in vivo recombinant RNPs containing the negative polarity cat virus replicon , purified them by affinity chromatography as indicated above and used them to transfect HEK293T cultures . Alternatively , the cultures were co transfected with the purified cat-containing RNPs and plasmids expressing the polymerase subunits ( see Fig . 7A for a diagram of the experiment ) . As no plasmid expressing NP was used , no in vivo replication of the RNPs can take place [41] , [42] . Two RNP versions were used , either wt or transcription-defective ( containing PB2 E361A mutant ) . Three alternative alleles were used to express in vivo PB2 , generating wt polymerase , transcription-defective E361A or replication-defective R142A polymerase complexes , and various RNP-polymerase combinations were used in co-transfection experiments . In this way the experiment would mimic the situation of primary transcription ( transfection of purified RNPs ) or secondary transcription ( co-transfection of RNPs with plasmids expressing the polymerase complex ) . At 24 hours post-transfection total cell extracts were prepared and the CAT protein accumulation was determined by ELISA . To ensure that the purified RNPs used for transfection were biologically active , two assays were carried out . First , their transcription activity was determined in vitro . As shown in Fig . 7B , there was a good correlation between the concentration of the RNPs , as determined by Western-blot with anti-NP and anti-PA antibodies , and the their capacity to synthesise RNA in vitro . Furthermore , the relative activity when using ApG or β-globin mRNA as primers verified that the purified mutant RNPs contained the E361A mutation ( Fig . 7B , 361 ) . In addition , the biological activity of the purified 361 RNPs was verified in vivo , by their co-transfection with plasmids expressing the polymerase subunits and NP . The results are presented in Fig . S4 and indicate that they can serve as templates for replication and transcription in vivo . Expression of the polymerase subunits did not yield any detectable CAT protein , as expected ( Fig . 7C , Pol wt ) , but the transfection of wt purified RNPs lead to clearly measurable CAT accumulation ( Fig . 7C , RNP wt ) and co-expression of wt RNPs with wt polymerase did not lead to any increase of CAT accumulation ( Fig . 7C , Pol wt-RNP wt ) . As control transfections with CAT-containing cellular extracts indicated that the carry-over of the protein was in the range of 10−3 to 10−4 ( data not shown ) , the CAT protein generated by transfection of wt purified RNPs should represent primary transcription . In agreement with their transcription-defective phenotype , transfection of purified mutant 361 RNPs produced much less CAT accumulation ( Fig . 7C , RNP 361 ) . No significant increase in the level of CAT protein was observed by co-transfection of the RNPs containing the E361A mutation with polymerase-expressing plasmids , neither wt nor mutant polymerase and no correlation was observed between the accumulation of CAT and the phenotype of the polymerase co-expressed ( Fig . 7C; compare Pol wt vs Pol 142 vs Pol 361+RNP 361 ) . These results indicated that , under the experimental conditions used , no trans-activation of transcription occurs in vivo .
The processes of virus RNA replication and transcription usually require the action of one to several virus-specific proteins , notably the RNA-dependent RNA polymerase ( RdRp ) , and various host cell factors ( for a review see [43] . To unravel the complex procedures involved , genetic experimental approaches have been particularly useful . For example , genetic data have strongly supported the requirement of RdRp oligomerisation for RNA replication in several virus groups , like poliovirus [44] , [45] , HCV [46] , [47] and Sendai virus [48] , [49] . Early studies on the dominance of RNA-synthesis negative ts mutants of VSV suggested that the oligomerisation of virus factors involved in RNA replication is an essential step in the process [50] , a conclusion that could be also verified in the poliovirus system [51] . More generally , the multimeric nature of complex viral systems , as the virus particles , has profound consequences in the apparent phenotype observed [52] , [53] . In the case of influenza , early data on the intragenic complementation of mutants affecting the PB1 and PA proteins suggested the potential role of virus polymerase interactions in the infectious cycle [54]–[56] and the recent biochemical evidence for virus polymerase oligomerisation supported such contention [57] . Here we have taken advantage of the availability of well-established recombinant systems for RNP replication and transcription and well-characterised polymerase mutants to address specific questions on the mechanisms of these processes . Due to the segmented nature of the influenza virus genome it is essential to use mutant polymerases having phenotypically distinct mutations in the same subunit , thus avoiding the problems of reassortment . Hence , we have used point mutants of polymerase PB2 subunit that abolish RNA replication but transcribe normally ( R142A or F130A ) [36] and/or mutants that are defective in cap-recognition and transcribe poorly , but replicate virus RNA normally ( E361A among others ) [20] . With these experimental tools we have asked whether the polymerase complex present in an RNP actually perform the replicative or transcriptional synthesis of RNA and whether the polymerase complex present in the progeny RNP is identical to that performing replicative synthesis of RNA . Our results will be discussed on the basis of the model presented in Fig . 8 , in which only the replication step cRNP-to-vRNP is presented . The results shown in Figs . 1 and 2 indicated that two such phenotypically distinct mutant polymerases can complement to perform viral RNP replication in vivo and demonstrated that a replication-defective polymerase can be incorporated into progeny RNPs . These results are consistent with the model presented in Fig . 8A , step 4 , that suggest that a polymerase complex distinct from that performing replicative synthesis is involved in the recognition of the 5′-end of the progeny vRNA . This model is also consistent with the results published earlier indicating that a pre-expressed polymerase can protect newly synthesised cRNA [13] , [38] . The identity of the replicative polymerase complex could be tested by directly transfecting mutant RNPs as templates for the replication reaction and asking whether co-expressed replication-defective or transcription-defective polymerase complexes could carry out the replication process in trans . The results shown in Figs . 3 and 4 demonstrated that a polymerase complex genetically distinguishable from that present in the parental RNP was able to perform replication and became incorporated into the progeny RNPs . These results are compatible with the model presented in Fig . 8A , steps 2–4 , whereby a soluble polymerase complex would interact with that resident in the parental RNP and gain access to the 3′-terminal sequence in the promoter . Such polymerase-polymerase interaction is supported by the genetic data presented here , by the intragenic complementation reported earlier [54] , [55] and by the oligomerisation of influenza polymerase in vivo [57] . Although not shown in Fig . 8A , we can not exclude that a host factor ( s ) participate in the polymerase-polymerase interaction and in fact several nuclear factors have been described previously that could play such a role [25]–[30] , [32] . The trans-replication model depicted in Fig . 8A , steps 2–4 relates to the cRNP-to-vRNP phase in replication . However , earlier data published on the protection of newly synthesised cRNA by pre-expressed polymerase would suggest that the vRNP-to-cRNA phase can occur in cis , since a pre-expressed , catalytically inactive polymerase allowed the accumulation of cRNA in cicloheximide-treated , virus-infected cells [13] . According to the model proposed here , the soluble polymerase complex would act as replicative enzyme by de novo initiation and elongation through the NP-RNA template ( Fig . 8 , steps 3–4 ) . We propose that the 3′-end of the parental RNA is used repetitively for further initiation rounds , thereby leading to several progeny vRNPs generated from a single cRNP template . For simplicity , the model presented in Fig . 8A does not show the interaction of the new incoming replicative complex with the parental polymerase bound to the 5′-end of the template , but such interaction might be required . In view of our previous evidence on polymerase-polymerase interaction [57] , an appealing possibility is that the replicative polymerase complexes would oligomerise to form a fixed replication platform along which the NP-RNA template would move 3′-to-5′ to generate many progeny vRNPs . Such strategy has a precedent in other positive-strand RNA viruses [44]–[47] and would be consistent with the localised synthesis of influenza virus RNA in the nucleus [58] , [59] . A critical point in the generation of progeny vRNP is the recognition and packaging of its newly synthesised 5′-end . Our results are compatible with the proposal that a polymerase complex distinct from the replicative enzyme can protect the newly synthesised vRNA ( Fig . 8A , step 4 ) and this event probably represents the sequence-specific step in the encapsidation of RNA into progeny RNP . The subsequent incorporation of successive NP monomers would be directed by polymerase-NP interactions [60] , that have been shown as essential for RNP replication [61] , [62] , as well as by the NP-NP oligomerisation [17] ( R . Coloma , unpublished results ) . Another critical point in the replication process is the displacement of the parental polymerase complex bound to the 5′-end of the template , a step necessary to avoid polyadenylation ( see below ) and to allow the complete copy of the RNA . Our results do not permit us to distinguish whether the elimination of such interaction is transient or permanent , but an attractive possibility would be that the reiterative copy of the NP-RNA template on a fixed platform of replicative polymerases would force the displacement of the parental polymerase bound to the 5′-terminal sequence . Such displacement could be transient , leading to the replacement of the parental polymerase by each successive replicative polymerase , or permanent , leading to a linearised NP-RNA complex ( Fig . 8A , step n ) . In contrast to the positive complementation obtained for the replication process , no trans-complementation could be detected in the transcription assays using either in vitro ( Fig . 6 ) or in vivo experiments ( Fig . 7 ) . In vitro transcription of a recombinant RNP containing a cap-binding defective polymerase could not be rescued by a wt RNP holding a template of different length ( Fig . 6 ) . Similar negative results were obtained in vivo , by transfection of a cap-binding defective RNP and co-expression of wt or replication-defective but transcriptionally functional polymerase ( Fig . 7 ) . These results are not compatible with the possibility of transcription among viral RNPs in trans and do not support the possibility of a soluble polymerase transcribing an independent RNP . Furthermore , these results indicate a high stability of the polymerase binding to the RNP structure during the transcription process , as no polymerase exchange could be functionally detected . In view of the lack of detectable transcription in trans , we propose the model presented in Fig . 8B for the generation of viral mRNAs . The resident polymerase complex would be transcriptionally activated by recognition of the cap-containing cellular mRNA and proceed to cap-snatching and elongation of the virus transcript ( Fig . 8B , step 2 ) , but still keeping hold of the 5′-terminal sequence of the promoter [63] . Such process would lead to a running knot structure with a diminishing loop length ( Fig . 8B , steps 2–4 ) until the transcribing polymerase reaches the oligo-U polyadenylation signal [10] . Due to steric hindrance , the polymerase would stutter around the oligo-U sequence and generate a 3′-terminal polyA ( Fig . 8B , step 4 ) . For simplicity , the model presented in Fig . 8B shows the transcribing RNP in a linearised form , but the polymerase complex should recognise the 3′-terminal side of the promoter at some time later in the process , in order to recycle and allow further rounds of transcription . This model for transcription of RNPs in cis is compatible with the fact that parental RNPs perform primary transcription as a first step in the infection and with the possibility to rescue virus by transfection of purified virion and/or recombinant RNPs [64] , [65] . It also would fit with the correlation of vRNA and mRNA levels of the various RNA segments along the infection cycle [66] , [67] . In summary , using genetically distinct RNA polymerase complexes , we have presented direct evidence for trans-complementation during the influenza virus RNA replication process . These results are compatible with a new model for viral RNA replication whereby a template RNP would be replicated in trans by a soluble polymerase complex and the progeny RNP encapsidation would be specified in trans by a polymerase complex distinct from the replicative enzyme . In contrast , no transcription in trans could be detected in vitro or in vivo and hence we propose a model for cis-transcription of the RNPs whereby the resident polymerase complex would be responsible for mRNA synthesis and polyadenylation .
The HEK293T cell line [68] was used throughout . The origins of plasmids pCMVPB1 , pCMVPB2 , pCMVPB2His , pCMVPA and pCMVNP , as well as pHHclone 23 , have been described [20] , [64] . Plasmid pHHCAT was kindly provided by A . Rodriguez . The antibodies specific for PB2 and PA have been described [69] , [70] . Antibodies specific for NP were prepared by immunisation with purified His-NP protein . Mutant PB2 plasmids including mutations in the N-terminal region [36] or the cap-binding site [20] have been reported earlier . The mutations F130A , R142A , E361A , H357A , K370A and F404A were transferred to pCMVPB2 by swapping the appropriate restriction fragments . The genotype of the mutant plasmids was verified by sequencing . Recombinant RNPs containing either the ΔNS clone 23 ( 248 nt ) or the NSCAT ( 720 nt ) genomic RNAs were generated and amplified in vivo by transfection of plasmids pCMVPB1 , pCMVPB2His , pCMVPA , pCMVNP and either pHHclone23 or pHHNSCAT into HEK293T cells , using the calcium phosphate protocol [71] . For RNP purification , cell extracts were prepared at 24 hours post-transfection and incubated overnight at 4°C with 30 µl of Ni2+-NTA-agarose resin in a buffer containing 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol-1 u/µl RNAsin-EDTA-free protease inhibitors cocktail , pH 8 . The resin was washed with 100 volumes of 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol , pH 8 and eluted with 50 mM Tris-HCl-100 mM KCl-0 . 5% Igepal-175 mM imidazol , pH 8 . Under these conditions , binding of the progeny RNPs to the resin was quantitative , as using three-fold excess of Ni2+-NTA-agarose did not result in any increase in the yield of purified RNPs ( see Fig . S5 ) . Western-blotting was performed as described [30] . The replication of RNPs in vivo was determined as described [20] . In brief , cultures of HEK293T cells were transfected with plasmids pCMVPB1 , pCMVPB2His ( or mutants thereof ) or pCMVPB2 ( or mutants thereof ) , pCMVPA , pCMVNP and pHHclone 23 . In some experiments pHHclone 23 plasmid was omitted and purified clone 23 RNPs were transfected instead , 24 hours after plasmid transfection . Total cell extracts were prepared at 24 hours post-transfection and used for purification by affinity chromatography on Ni2+-NTA-agarose as indicated above and the accumulation of progeny RNPs was determined by Western-blot with anti-NP-specific antibodies . The transcription of RNPs in vivo was assayed by transfection of purified NSCAT RNPs into HEK293T cells . The cultures were first transfected with plasmids pCMVPB1 , pCMVPB2 ( or mutants thereof ) and pCMVPA [20] and 24 hours later were further transfected with purified His-tagged NSCAT RNPs under the same conditions . At 24 hours post RNP-transfection , total cell extracts were prepared and the CAT protein concentration was determined by ELISA ( GE Healthcare ) . To determine the transcription activity of purified RNPs , samples were incubated in a buffer containing 50 mM Tris-HCl-5 mM MgCl2-100 mM KCl-1 mM DTT-10 µg/ml actinomycin D-1 u/µl RNAsin-1 mM ATP-1 mM CTP-1 mM UTP-10 µM α-P32-GTP ( 20 µCi/µmol ) and either 100 µM ApG or 10 µg/ml β-globin mRNA , for 60 min at 30°C . The RNA synthesised was TCA precipitated , filtered through a nylon filter in a dot-blot apparatus and quantified in a phosphorimager . To analyse the transcription products , similar reactions were carried out but the specific activity of the labelled GTP was increased to 200 µCi/µmol . The synthesised RNA was isolated by treatment with proteinase K ( 50 µg/ml ) for 30 min at 37°C in TNE-1% SdS and phenol extraction . The RNA was ethanol precipitated , resuspended in formamide loading buffer and analysed by electrophoresis in a 4% polyacrylamide-urea denaturing gel . To analyse the progeny RNA , purified RNPs were incubated with proteinase K ( 200 µg/ml ) in a buffer containing 100 mM NaCl-5 mM EDTA-0 . 5% SDS-50 mM Tris . HCl , pH 7 . 5 for 60 min at 37°C , phenol extracted with ethanol precipitated . Samples of the purified RNAs were denatured by boiling for 3 min in 7 . 5% formaldehyde-10SSC and were fixed onto nylon filters . Replicate filters were hybridised at 37°C with full-length NS riboprobes of either positive- or negative-polarity in a buffer containing 6SSC-40% formamide-0 . 5% SDS-5xDenhart's mixture-100 µg/ml single-stranded DNA . After washing at 60°C with 0 . 1SSC-0 . 1%SDS , hybridisation signals were quantitated in a phosphorimager .
|
The influenza A viruses produce annual epidemics and occasional pandemics of respiratory disease . There is great concern about a potential new pandemic being caused by presently circulating avian influenza viruses , and hence increasing interest in understanding how the virus replicates its genome . This comprises eight molecules of RNA , each one bound to a polymerase complex and encapsidated by multiple copies of the nucleoprotein , in the form of ribonucleoprotein complexes ( RNPs ) . These structures are responsible for virus RNA replication and transcription but the detailed mechanisms of these processes are not fully understood . We report here the results of genetic complementation experiments using proficient in vitro and in vivo recombinant systems for transcription and replication , and polymerase point mutants that are either transcription-defective or replication-defective . These results are compatible with a new model for virus replication whereby a polymerase distinct from that present in the parental RNP is responsible for RNA replication in trans and the progeny RNP is associated to a polymerase distinct from that performing replication . In contrast , transcription is carried out in cis by the polymerase resident in the RNP .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/rna-protein",
"interactions",
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"infectious",
"diseases/respiratory",
"infections"
] |
2009
|
Genetic trans-Complementation Establishes a New Model for Influenza Virus RNA Transcription and Replication
|
Albinism is a genetic defect characterized by a loss of pigmentation . The neurosensory retina , which is not pigmented , exhibits pathologic changes secondary to the loss of pigmentation in the retina pigment epithelium ( RPE ) . How the loss of pigmentation in the RPE causes developmental defects in the adjacent neurosensory retina has not been determined , but offers a unique opportunity to investigate the interactions between these two important tissues . One of the genes that causes albinism encodes for an orphan GPCR ( OA1 ) expressed only in pigmented cells , including the RPE . We investigated the function and signaling of OA1 in RPE and transfected cell lines . Our results indicate that OA1 is a selective L-DOPA receptor , with no measurable second messenger activity from two closely related compounds , tyrosine and dopamine . Radiolabeled ligand binding confirmed that OA1 exhibited a single , saturable binding site for L-DOPA . Dopamine competed with L-DOPA for the single OA1 binding site , suggesting it could function as an OA1 antagonist . OA1 response to L-DOPA was defined by several common measures of G-protein coupled receptor ( GPCR ) activation , including influx of intracellular calcium and recruitment of β-arrestin . Further , inhibition of tyrosinase , the enzyme that makes L-DOPA , resulted in decreased PEDF secretion by RPE . Further , stimulation of OA1 in RPE with L-DOPA resulted in increased PEDF secretion . Taken together , our results illustrate an autocrine loop between OA1 and tyrosinase linked through L-DOPA , and this loop includes the secretion of at least one very potent retinal neurotrophic factor . OA1 is a selective L-DOPA receptor whose downstream effects govern spatial patterning of the developing retina . Our results suggest that the retinal consequences of albinism caused by changes in melanin synthetic machinery may be treated by L-DOPA supplementation .
Albinism is a group of inherited genetic diseases in which there is a variable loss of pigmentation in the eye , hair , or skin . When the eye is affected , there are significant alterations in neurosensory retina development that lead to low vision [1–8] . There are two broad classes of albinism , ocular-cutaneous albinism ( OCA ) and ocular albinism ( OA ) . OCA occurs when all pigmented tissues exhibit hypopigmentation and involves genetic mutations that result in defects in the melanin synthetic machinery [3 , 7–9] . OA occurs when cutaneous tissues pigment normally , but the ocular tissues are hypopigmented [10 , 11] . Since the same proteins produce pigment in all tissues , OA most likely results from lack of expression of the melanogenic enzymes in ocular tissue rather than an inability to synthesize melanin , because the other tissues pigment normally . OA can be linked to at least one gene , Oa1 , which is found on the X chromosome . Oa1 encodes a 404–amino acid protein likely to be an orphan G-protein coupled receptor ( GPCR ) , OA1 ( GenBank GPR143 ) [12 , 13] based upon sequence analysis [14] . Schiaffino et al . have demonstrated that OA1 associates with several Gα subunits as well as Gβ , adding further evidence that OA1 is a GPCR [14 , 15] . Indeed , Innamorati et . al . used a combinatorial expression strategy to illustrate GPCR-like activity from OA1 , as well as β-arrestin association , even in the absence of a ligand [16] . This work suggested that OA1 could signal through a Gαq subunit through phospholipase C and inositol triphosphate second messengers . In a yeast-based expression system , Staleva and Orlow have demonstrated GPCR signaling from OA1 that appeared to be activated by a component in the melanosomal compartment [17] . Despite the significant amount of circumstantial evidence that OA1 is a GPCR , confirmation is lacking because no ligand has been identified . Other data have called into question the idea that OA1 is a GPCR . For example , the localization of OA1 as a fully intracellular protein is not typical of GPCRs and suggests that it would be a unique member of the family [14] . OA1 is primarily localized to the endolysomal compartment [14 , 15 , 18–21] and melanosomes [11 , 14 , 22] rather than the cell surface . In this study , we investigated the function of OA1 as a potential GPCR . Our hypothesis was that the endosomal localization of OA1 in cultured cells was due to internalization of OA1 in response to an agent in the culture medium . Further , we sought a ligand for OA1 based on the observation that all forms OCA and OA appear to have the same retinal phenotype , indicating that tyrosinase activity and OA1 signaling are coupled upstream of retinal development . Thus , we tested whether tyrosinase activity produces the ligand for OA1 . A by-product of melanin synthesis is L-DOPA , which is released to the retina during melanin synthesis in the RPE at a critical time in retinal development [23 , 24] . Our data suggest that OA1 is a highly selective L-DOPA receptor , and that L-DOPA causes OA1 signaling with the downstream effect of neurotrophic factor secretion by RPE . Thus , we present the first evidence of a ligand for OA1 , and provide a mechanism through which either tyrosinase or OA1 deficiency results in changes to retinal development .
OA1 has previously been localized in pigment granules in situ [22]; however , using transfected cells of various types , OA1 also has been localized to both the plasma membrane [16 , 17] and the endosomal fraction of cultured cells [14 , 16–18 , 20 , 21] . We began our investigation by determining where OA1 resides in the human tissue , using cell surface biotinylation/western blot strategies . In the human eye , OA1 was present on the apical cell surface of the retinal pigment epithelial cells ( RPE ) in situ ( Figure 1A ) . Quantification of cell surface , biotinylated OA1 in five human eyes indicated that at least 3 . 5 ± 0 . 7% of the total OA1 resided on the apical cell surface of RPE in situ . Access to the biotinylation reagent using eye cup preparations is restricted to the apical surface , so the polarity of OA1 in the epithelium cannot be determined . Further , the total cell surface OA1 is likely underestimated because of the lack of access to the basal cell surface . Blots were also probed with antibodies against actin as a control to verify that cytoplasmic proteins were not biotinylated , and in each experiment , actin was only found in the unbound fraction . Others have reported that recombinant OA1 and OA1-GFP is almost exclusively localized to the endosomal compartment in cultured cells [14 , 15 , 17 , 18 , 20–22] . However , when overexpressed [16] , or when endocytosis is inhibited [17] , OA1 accumulates at the cell surface . Our observation that OA1 protein is present on the apical surface of RPE in situ led us to explore the issue further . Endosomal localization of GPCRs occurs normally after exposure to a ligand . Therefore , we investigated whether a ligand for the receptor was present in the standard incubation medium that could drive internalization of OA1 . Since the standard culture medium contains 500 μM tyrosine , and tyrosine is the starting material for pigment synthesis , we evaluated the effect of tyrosine on receptor distribution . To test whether tyrosine affected OA1 distribution in cultured cells , we formulated DMEM without tyrosine , and used dialyzed fetal bovine serum . In the presence of tyrosine-free medium , OA1 was detected on the plasma membrane of cultured RPE cells both in the absence ( unpublished data ) , and in medium containing low concentrations of tyrosine ( 1 μM , Figure 1B ) . Averaged over five experiments , 4 . 5 ± 1% of total OA1 protein was observed on the surface of cultured RPE maintained in 1 μM tyrosine , similar to what was observed for RPE in situ . In all experiments , actin was observed in the unbound protein fraction , demonstrating the absence of any cytoplasmic protein in the cell surface assay . Similarly , OA1-GFP expressed in COS cells illustrated a cell surface expression that was tyrosine sensitive ( Figure 1C ) . Quantification of six such experiments indicated significant variability in the amount of OA1 found at the cell surface using transient transfections . The range of OA1 in the bound fraction of transfected cells maintained in 1 μM tyrosine ranged between 5%–40% , unlike the results with the endogenous OA1 protein that were reproducibly approximately 5% . Not only was the distribution of OA1 in transfected cells sensitive to tyrosine levels in the medium , but total OA1-GFP expression was increased 5-fold in cells maintained in 1 μM tyrosine . To verify that this difference related to OA1 expression rather than cell number , we evaluated actin expression from the paired samples . The data ( Figure 1D ) presented as optical density units indicate no difference in actin . We also compared the amount of cell surface OA1 between the normal and low-tyrosine groups . Importantly , in the five RPE experiments and six OA1-GFP in COS experiments , we were unable to reproducibly detect OA1 in the plasma membrane fraction of cells in standard medium , similar to that found by others . The distribution of OA1 in RPE cells also was evaluated by confocal microscopy . OA1 has previously been characterized as an endosomal protein in cultured RPE cells as shown in Figure 1E . In contrast , the distribution of OA1 in low-tyrosine medium was diffuse on the plasma membrane of cultured RPE cells , with little endosomal accumulation ( Figure 1F ) , an observation consistent with the results obtained using biochemical methods . Tyrosinase function in melanogenesis begins with its activity on tyrosine to create L-DOPA , followed by a second reaction to create dopaquinone that leads to pigment formation [25] . Of the intermediates between tyrosine and melanin , L-DOPA has the greatest half-life , and L-DOPA is released into the subretinal space apical to the RPE when melanin synthesis occurs [23 , 24] . L-DOPA is also the precursor to dopamine , a neurotransmitter produced by dopaneurgic neurons from tyrosine . The release of calcium from intracellular stores is a common downstream effect of GPCR activation by a ligand . Since the expression of OA1 on the cell surface appears to be sensitive to tyrosine , we examined whether tyrosine , or its metabolites L-DOPA and dopamine , could stimulate influx of Ca2+ into the cytoplasm in an OA1-dependent manner . CHO cells were transfected with an OA1 expression vector then maintained in DMEM containing 1 μM tyrosine for 48 h , followed by tyrosine-free DMEM for 24 h to facilitate cell surface expression of OA1 . Intracellular Ca2+ was evaluated using Fura-2 , and [Ca2+]i was determined by ratiometric imaging [26] . In the absence of any ligand , [Ca2+]i was not significantly different between transfected and untransfected cells ( Figure 2 ) . Tyrosine and several tyrosine metabolites were tested at 1 μM for an effect on [Ca2+]i . As a positive control , we ended each experiment by treatment with 20 mM KCl to depolarize the cell and increase [Ca2+]i via activation of voltage-gated channels . This maneuver served to verify the Fura-2 loading and responsiveness of the cells being tested ( Figure 2 ) . Only L-DOPA elicited a significant increase in [Ca2+]i ( Figure 2A ) . Tyrosine and dopamine had no positive effect on intracellular at [Ca2+]i concentrations up to 1 mM ( unpublished data ) . The slight negative effect of 1 μM dopamine was not statistically significant , but it was reproducible among the 11 experiments with dopamine ( Figure 2B ) . Overexpression of GPCRs in nonnative cell lines can lead to false signal transduction coupling . To verify that OA1 signaling in response to L-DOPA was indeed a natural response , we expressed OA1 in RPE cells ( Figure 2C ) . Results using transfected RPE cells were similar to those achieved with transfected CHO cells . RPE cells transfected to express OA1 responded to 1 . 0 μM L-DOPA with an increase in [Ca2+]i . We next sought to determine whether RPE cells expressing the endogenous OA1 receptor , at endogenous levels exhibited L-DOPA responsiveness . Like all of the transfected cell experiments , RPE expressing OA1 demonstrated an increase in [Ca2+]i after treatment with 1 . 0 μM L-DOPA ( Figure 2C ) . To further characterize OA1 signaling activity , we used pertussis toxin to distinguish between Gq coupled [Ca2+]i signaling and Gi linked signaling ( Figure 2C ) . In all cells studied , pertussis toxin lowered the basal level of [Ca2+]i , indicating its activity on inhibition of the background signaling through Gi subunit activity . Pertussis toxin was used in experiments conducted in cells transfected to express OA1 including both CHO and RPE , as well as RPE expressing the endogenous OA1 protein at natural levels . In all transfected cells tested , the measured [Ca2+]i response to L-DOPA was greater than in the absence of the toxin ( Figure 2 ) , owing largely to the lower initial [Ca2+]i . Thus , the signaling through OA1 in response to L-DOPA that results in increase [Ca2+]i is not pertussis toxin sensitive and likely Gq subunit mediated . We also measured the second messenger cAMP in CHO cells transfected to express OA1 ( Figure 2D ) . Using inactive cells or a submaximal forskolin treatment , the experiments were set up to measure either an increase or decrease in cAMP in response to L-DOPA . In six such experiments , no change in cAMP was observed suggesting neither Gs nor Gi subunits are involved in OA1 signaling . We utilized standard methods of radiolabeled ligand binding to characterize the interaction between OA1 and L-DOPA ( Figure 3A ) . CHO cells were transfected to express OA1 , then binding of L-DOPA was quantified in a concentration-dependent manner , and the results were further characterized by Scatchard plot analysis ( Figure 3A , inset ) . Results illustrate saturable binding of L-DOPA to OA1 expressing cells with a Kd of 9 . 35 × 10−6 M . No specific binding was observed in untransfected CHO cells , indicating that the cells do not have an endogenous L-DOPA receptor ( unpublished data ) . All binding parameters , total , specific , and nonspecific , are shown as supplemental data ( Figure S1A ) . Tyrosine exhibited the potential to interact with OA1 , but neither tyrosine nor dopamine stimulated OA1 signaling ( see Figure 2 ) . We next used competitive ligand binding to determine whether either tyrosine or dopamine competed with L-DOPA for OA1 binding . At high concentrations ( 1 mM ) , both tyrosine and dopamine competed with L-DOPA for OA1 binding ( Figure 3B ) . To further characterize this , we examined the kinetics of the competition between L-DOPA and either dopamine ( Figure 3C ) or tyrosine ( Figure S1B ) . Dopamine exhibited competitive binding to a single site with L-DOPA with a Ki of 2 . 33 × 10−6 ± 0 . 2 × 10−6 M . Similar experiments with tyrosine demonstrated inhibition of L-DOPA binding only at high concentrations ( Figure S1B ) . Saturation kinetics were not possible with tyrosine because of its low affinity and insolubility at the high concentrations . Given the relatively low affinity of OA1 for L-DOPA , we sought to determine whether its signaling activity was dose dependent in the range of this binding affinity . We tested the concentrations in which binding data suggested the steepest rise in association between L-DOPA and OA1 , 1 . 0–10 μM , and results illustrate a concentration-dependent GPCR response as measured by [Ca2+]i ( Figure 3C ) . Thus , the activation kinetics of L-DOPA and OA1 matched the concentration range observed in radiolabeled ligand binding experiments . In response to ligand binding , GPCRs recruit β-arrestin to the plasma membrane , which is followed by internalization of the ligand–receptor complex [27–33] . We next sought to test the effect of L-DOPA on β-arrestin localization ( Figure 4 ) . Cells were transfected to express OA1 and then cultured in 1 μM tyrosine DMEM for 48 h prior to analysis to allow cell surface expression of the protein . Cells were then treated with 1 μM L-DOPA , followed by rapid fixation on ice in cold methanol . Initially , under resting conditions in the absence of an agonist , OA1-GFP was found at the cell surface and β-arrestin was diffuse in the cytoplasm ( Figure 4A–4C ) , with no colocalization between the proteins . After stimulation with L-DOPA , OA1 and β-arrestin were colocalized at the plasma membrane ( Figure 4D–4F ) . Untransfected cells showed no response to L-DOPA treatment ( Figure 4G and 4H ) , illustrating that the L-DOPA effect on β-arrestin distribution was OA1 dependent , similar to results obtained for [Ca2+]i . Mutations in OA1 cause defects in the development of the neurosensory retina . In previous work , we have shown that pigmented RPE secrete significantly more PEDF than nonpigmented RPE [34] , and PEDF is a neurotrophic factor with the potential of altering neurosensory retina development [35–41] . Mutations in OA1 cause a loss of pigmentation in the RPE , suggesting that OA1 activity governs RPE pigmentation . Thus , we sought to determine whether L-DOPA stimulation of pigmented RPE cells caused increased secretion of PEDF ( Figure 5 ) . This assay is made somewhat more difficult because pigmenting RPE cells produce L-DOPA , which is the agonist for OA1 , and OA1 is not readily detectable in nonpigmented cultures of RPE . Thus , we used pigmented RPE to determine whether L-DOPA stimulation increases PEDF expression/secretion . RPE cells were placed in tyrosine-free medium for 24 h and then treated with 1 μM L-DOPA for 1 h . After treatment , the cells were returned to standard medium without exogenous L-DOPA for 3 d . Control cells were not treated with L-DOPA , but the medium was changed at the same time the experimental cells were returned to normal medium . Conditioned medium was collected after 3 d , and PEDF was measured . Results illustrate a significant increase in the secretion of PEDF in pigmented cells treated with L-DOPA when compared to paired , control monolayers of pigmented RPE ( Figure 5A ) . Importantly , this significant increase occurred in cells that were pigmenting and therefore expressed OA1 and had a basal level of PEDF expression . To determine whether pigmented RPE cells secrete PEDF through an autocrine loop involving tyrosinase activity and OA1 signaling , we used a specific tyrosinase inhibitor phenylthiourea ( PTU ) to inhibit pigmentation and L-DOPA production ( Figure 5B ) . In these experiments , pigmented RPE cells were either maintained in DMEM , or DMEM containing 200 μM PTU , for 3 d , then PEDF secretion was measured . Pigmented RPE secreted substantial PEDF , but PTU caused a significant decrease in PEDF secretion , indicating that tyrosinase activity is necessary for the high level of PEDF secretion observed in pigmented RPE cells . To verify that it was the lack of L-DOPA in the PTU-treated cells that caused the decreased PEDF secretion , we used three different cultures of pigmented RPE and exposed them to PTU for 48 h , then treated them with 1 . 0 μM L-DOPA in the continued presence of PTU and measured PEDF after 72 h ( Figure 5C ) . The data are presented as percent of control for this experiment because the cultures used varied in both pigmentation and PEDF expression before the experiment began . PTU-treated RPE responded to the added L-DOPA by increasing PEDF secretion , indicating that the effect of PTU on PEDF secretion is caused be the lack of L-DOPA production when tyrosinase is inhibited .
There is a complex intertissue relationship between the RPE and the neurosensory retina . One aspect of this relationship is centered on RPE pigmentation , and defects in melanin synthesis that result in significant neurosensory retina alterations [8 , 23 , 42] . Our data suggest that OA1 and tyrosinase participate in an autocrine loop through L-DOPA that regulates the secretion of at least one potent neurotrophic factor , PEDF . We also suggest that the pathologic changes in retinal development that occur in albinism may result from changes in the activity of the OA1 signaling pathway . Reduced OA1 signaling activity can be caused either directly through OA1 mutations or indirectly through changes in L-DOPA production by tyrosinase activity . Thus , we hypothesize that the similar retinal phenotypes that accompany the diverse forms of albinism can be reconciled to a single common pathway , OA1 signaling . In our study , we observed OA1 on the apical surface of human RPE in situ . Previous reports have suggested that OA1 in mice is localized to the melanosome [22] , and in cultured cells to the endosomal compartment [15–18 , 20–22 , 43] . Our results from in situ RPE preparations indicate that OA1 is distributed to the apical surface of the RPE . The limited quantities of OA1 on the surface of the RPE ( ∼3 . 5% of total OA1 ) may account for the lack of observation of the protein in previous studies in which immunogold electron microscopy was used . Like many cell surface GPCRs , OA1 is not an abundant protein . The endosomal localization of OA1 reported in previous studies using cultured cells was reproduced in this study for both the endogenous protein and the transgenic protein . When it was tested in normal culture medium , we found little detectable OA1 protein on the cell surface , in agreement with all previous work . However , reduction of tyrosine in the medium caused a modest increase in cell surface receptor accumulation of both the endogenous and recombinant OA1 proteins . This suggests that the distribution of OA1 to the cell surface in cultured cells is sensitive to tyrosine . A previous study has demonstrated OA1 could be localized to the cell surface when endocytosis is inhibited [17] , and we observed OA1 on the apical surface of human RPE in situ . We suggest OA1 is a cell surface GPCR , but is a target for endocytosis that may be stimulated by tyrosine or tyrosine metabolites . In this regard , our results differ from past reports of OA1 localization that have classified OA1 as a unique type of intracellular GPCR . Most GPCRs are cell surface proteins that are internalized by a variety of signals , and our data suggest OA1 is similar to most other GPCRs . OA1 signaling activity was stimulated by L-DOPA , but not by either its precursor , tyrosine , or its neuronal metabolite dopamine . This result suggests an exquisitely sensitive receptor activity able to distinguish between closely related molecules; after all , L-DOPA and tyrosine differ by a sole hydroxyl group . OA1 is sensitive to tyrosine , as tyrosine causes an intracellular localization of OA1 in cultured cells . However , we noted no signaling response to tyrosine , and competition binding studies suggest that tyrosine has a low affinity for OA1 . Our data suggest that the continuous exposure of cells to high concentrations of tyrosine present in normal medium is sufficient to result in internalization of OA1 , but it is unlikely to result in measurable OA1 activation . We found strong evidence of a single-site competitive interaction between L-DOPA and dopamine . The Ki observed for dopamine was similar to the Kd observed for L-DOPA , suggesting that the affinity for the two tyrosine metabolites is similar . Our results illustrated a slight , but reproducible , decrease in OA1 signaling from dopamine , suggesting that dopamine may be an effective antagonist or inverse agonist for OA1 . As an orphan GPCR , its signaling pathway has not previously been identified . In this study , we illustrate that OA1 signaling in response to L-DOPA causes an increase in [Ca2+]i . Our data illustrate that the increased [Ca2+]i observed in response to L-DOPA was insensitive to pertussis toxin , and we measured no effects on cAMP , indicating that OA1 is likely signaling through a Gq subunit . Previous work has suggested that OA1 can associate with multiple subunits in transfected cells , including members of the Go , Gi , and Gq subunit families . Innamorati et al . has shown that spontaneous activity of overexpressed OA1 is likely signaled through a Gq subunit [16] . Our data indicate that ligand-dependent signaling from endogenous OA1 in RPE most likely occurs through a Gq-mediated pathway , and we observed no promiscuous coupling activities when comparing OA1 overexpression in CHO and RPE to natural OA1 expressed in RPE . Interestingly , two overactive mutant forms of Gq subunits cause hyperpigmentation in skin and hair [44] , but whether they have an effect in RPE is unknown . RPE and cutaneous melanocytes use the same enzymes to produce pigmentation but differ in their control of melanogenesis . A recent report suggests that OA1 may signal through Gαi3 , because the retinal phenotype of OA1−/− and Gαi3−/− are similar [45] . That study provided no data regarding interaction or signaling between Gαi3 and OA1 , and our results do not support OA1 signaling through Gαi3 . However , both OA1 and Gαi3 could have activity in convergent pathways that govern some part of the complex system of retinal development . The response of OA1 to L-DOPA was measured in three ways , increased [Ca2+]i , recruitment of β-arrestin to plasma membrane OA1 , and the increased secretion of PEDF . In addition , we have inhibited the activity of tyrosinase in pigmented RPE , which inhibits L-DOPA production and observed a decreased secretion of PEDF . Taken together , these studies present a strong argument for a productive ligand:receptor relationship between L-DOPA and OA1 . Further , our data suggest selectivity among tyrosine and its metabolites , with only L-DOPA being a productive ligand for OA1 . We have determined the binding kinetics between OA1 and L-DOPA , and observed a typical one-site receptor:ligand relationship between the two . The binding affinity between OA1 and L-DOPA , with a Kd in the micromolar range , is not uncommon for an endogenous ligand:receptor relationship . Future identification of a specific , high-affinity antagonist for OA1 will aid in further biochemical characterization of the interaction between OA1 and L-DOPA , and be useful in determining whether dopamine is an inverse agonist . We have illustrated the selective activation of OA1 , an orphan GPCR , by L-DOPA , an intermediate product of melanin synthesis . We have also illustrated that OA1 activity stimulates PEDF secretion by RPE , a molecule that has the potential to support normal retinal development [40 , 41] . In humans , this suggests that pharmacologic intervention through OA1 activation could be useful for albinism caused by defects in the melanogenic machinery ( OCA 1–4 ) . Unfortunately , our data also suggest that OA1 is necessary for such pharmacologic intervention , and mutations in Oa1 are the most common cause of albinism .
RPE were isolated as described [46] and maintained in Dulbecco's modified essential medium ( DMEM ) supplemented with 5% fetal bovine serum ( FBS ) . For experiments in which tyrosine concentrations were lowered , we used custom manufactured DMEM produced without tyrosine by JRH Biosciences . Dialyzed FBS was purchased from Invitrogen . COS-7 and CHO cells were obtained from ATCC and cultured in DMEM supplemented with 5% FBS . For analysis of OA1 distribution , cells were cultured in tyrosine-free DMEM supplemented with 1 μM tyrosine , 5% dialyzed FBS for 2–4 d , then tyrosine-free medium as described for the experiment . Human RPE in situ . Human eyecups were produced by dissection approximately 2 mm anterior to the equator and removal of the anterior segment . The vitreous and retina were removed without impairing the underlying RPE monolayer , and the retina was cut at the optic nerve head . The resulting eyecups with RPE exposed were rinsed three times with reaction buffer ( 100 mM NaCl , 50 mM NaHCO3 [pH 8 . 0] ) and then filled with Sulfo-NHS-LC-Biotin ( 1 mg/ml ) two times for 30 min . The reaction was stopped with TG buffer ( 25 mM Tris , 192 mM glycine [pH 8 . 3] ) and then the cells were harvested in lysis buffer ( 2 mM EDTA , 1% Triton X , and 1% Tween 20 in Tris Base Saline Buffer ) containing Halt Protease Inhibitor Cocktail . Intact cells and pigment granules were removed by centrifugation at 14 , 000 rpm for 20 min . Biotinylated proteins were captured overnight with immobilized streptavidin beads and then mixed with 4× reducing buffer ( 250 mM Tris [pH 6 . 8] , 8% SDS , 40% glycerol , 20% beta-mercaptoethanol , 0 . 08% bromophenol blue ) . The OA1 protein was separated on a 10% SDS-PAGE gel and identified by a using a polyclonal rabbit OA1 antibody for western blot analysis . Paired western blots were probed with a monoclonal antibody directed against actin . Cultured cells . RPE and transfected cells were maintained in DMEM containing tyrosine concentrations described for the experiment . Cultures were rinsed three times in reaction buffer , then biotinylated as described above for the in situ preparation . A cDNA library was constructed from pooled tissue from six human donor eyes . Total RNA was harvested using Trizol reagent , then cDNA was synthesized using Poly-T primers for the first-strand synthesis , and random hexamers for the second strand . Following cDNA synthesis , RNA was removed using RNase A . The coding sequence for OA1 was obtained by PCR using terminal primers that added restriction sites to the 5′ and 3′ ends and removed the native stop codon . The PCR product was ligated in frame with GFP in the pEGFP N-1 vector ( Clontech ) . The sequence was verified by automated sequencing in both directions over the entire sequence . Cells on slides were fixed with 3% paraformaldehyde at room temperature , rinsed with 0 . 1% Triton X-100 in 10% milk in TBST , and then blocked with 10% milk in TBST . β-arrestin was visualized using a polyclonal antibody directed against β-arrestin , and incubated overnight at 4 °C . Cover slips were mounted using 50% glycerol , and immunostaining was analyzed by optical sectioning using a Nikon Eclipse E800 laser scanning confocal microscope powered by Compix Confocal Imaging Systems software ( Simple PCI Version 4 . 0 . 6 . 1605 ) . Three-dimensional analysis of OA1-GFP and β-arrestin distribution was performed in ImageJ 1 . 32 . OA1-GFP expressing CHO cells plated on glass cover slips were rinsed in Ca2+-containing HEPES-buffered Hanks Balanced Salt Solution ( HBSS ) ( pH 7 . 45 ) and then incubated with 2 . 5 μM Fura-2 ( solubilized in anhydrous dimethylsulfoxide and 0 . 002% pluronic acid ) for 20 min at 37 °C , 5% CO2 . The Fura-2–loaded cells were rinsed with HBSS for 15 min at 37 °C , 5% CO2 to allow for full cleavage of the dye to its active form . Each cover slip was incubated in 1 ml of HBSS in a chamber held at 37 °C on the stage of an inverted Olympus IX70 microscope equipped with a 40 × 1 . 35 NA UV-fluor objective . Using a filter wheel , excitation light from a 200 W Xe bulb was passed alternately through 340- and 380-nm filters . A 10-nm bandpass filter , centered at 510 nm , selected for the emitted fluorescence that was passed to a CCD camera ( Photometrics CH-250 ) . For each experiment , image pairs were taken every minute for the first 3 min , which established a stable baseline . L-DOPA ( 1 μM final concentration ) was then added , and image sets were taken every 30 s for the next 3 min . Finally , KCl ( 20 mM final concentration ) was added 1 min before completion of each experiment as a positive control to establish that the cells were loaded with Fura-2 . The same was repeated independently for tyrosine and dopamine ( both at 1 μM final concentration ) . Using a Silicon Graphics Personal IRIS computer , the 340/380-nm ratio was computed for each pixel within a cell , and then analyzed using Microsoft Excel version 4 . 0 ( Microsoft ) . Once the 340/380-nm ratio was determined , each ratio was normalized to 1 ( ratio at time zero divided by itself ) , and then the free ion concentration was calculated using the following equation: in which R , Rmin , and Rmax are the measured , minimum , and maximum ratios , respectively . Rmax represents the ratio of fluorescence intensity of ion-sensitive wavelengths under fully deprotonated conditions , whereas Rmin is the ratio for the dye when it is fully protonated . In the case of Fura-2 , R increases with increasing Ca2+; hence Rmin represents Fura-2 in the absence of Ca2+ ( Ca2+ < 1 nM ) , whereas Rmax represents the Ca2+-Fura-2 chelate as previously described [26] . Rmin , Rmax , and Kd were determined in independent experiments in Fura-2–loaded cells , and subsequently utilized for calculation of free Ca2+ for the experimental procedures . CHO cells that were transfected to express OA1-GFP were plated into 24-well plates . Cells were chilled to −2 °C , then rinsed in cold binding buffer , 25 mM Tris , 150 mM NaCl , 5 mM EDTA , 5 μM digitonin ( pH 7 . 45 ) . Cells were incubated for 2 h in binding buffer containing [3H]-l-DOPA ( Moravek Biochemicals ) at concentrations between 10−4 M to 10−9 M . The temperature was not allowed to exceed −2 °C at any step of the assay . Controls included assays conducted on nontransfected CHO , and specific binding was determined by competition with excess unlabelled L-DOPA at 10−3 M . Bound L-DOPA was quantified by scintillation spectroscopy . Cells were pretreated with forskolin ( 15 min ) and then challenged with L-DOPA using an assay setup as previously described [47] . After 1 min of ligand exposure , cells were scraped into ice-cold buffer , boiled , and then centrifuged . Equivalent volumes , 50 μl , of supernate and 3H-cAMP ( New England Nuclear ) were then combined with 100 μl of cold PKA . After 2 h , the solution was passed over activated charcoal , and supernates were counted in a scintillation counter . Results were compared to those achieved using a standard curve , instead of cytosol , produced using 50 μl of cAMP 0 . 25–32 . 0 pmol/50 μl .
|
Albinism is the loss of pigmentation caused by mutations in one of several different genes that alter pigment synthesis by different mechanisms . In the eye , albinism impairs sensory retina development and causes significant vision problems . Regardless of the genetic mutation that causes albinism , the associated vision problems are the same . Interestingly , none of the pigmentation genes are expressed by the sensory retinal cells affected by albinism but by neighboring , retinal pigment epithelial cells ( RPE ) . Furthermore , loss of pigmentation in RPE somehow leads to imprecise retinal development . To investigate this cellular relationship , we studied OA1 , which is encoded by a gene in which mutations cause ocular albinism . OA1 is unique among proteins involved with albinism because OA1 is a potential receptor that could participate in signal transduction rather than being a direct member of the pigment synthesis machinery . We show that the ligand for OA1 is L-DOPA , thus removing OA1 from orphan G-protein coupled receptor ( GPCR ) status . L-DOPA is a by-product of pigment synthesis , indicating that pigment synthesis and OA1 signaling are intertwined . OA1 signaling is highly selective for L-DOPA , and we show that two closely related molecules , dopamine and tyrosine , bind to OA1 but fail to stimulate signaling . We also show that OA1 signaling controls secretion of a potent neuron survival factor . Taken together , our data suggest that all forms of albinism produce the same retinal defects because of a final common pathway through OA1 signaling with downstream effects on RPE neurotrophic factor secretion .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"developmental",
"biology",
"cell",
"biology",
"ophthalmology"
] |
2008
|
L-DOPA Is an Endogenous Ligand for OA1
|
Cryptococcus neoformans is a facultative intracellular pathogen and its interaction with macrophages is a key event determining the outcome of infection . Urease is a major virulence factor in C . neoformans but its role during macrophage interaction has not been characterized . Consequently , we analyzed the effect of urease on fungal-macrophage interaction using wild-type , urease-deficient and urease-complemented strains of C . neoformans . The frequency of non-lytic exocytosis events was reduced in the absence of urease . Urease-positive C . neoformans manifested reduced and delayed intracellular replication with fewer macrophages displaying phagolysosomal membrane permeabilization . The production of urease was associated with increased phagolysosomal pH , which in turn reduced growth of urease-positive C . neoformans inside macrophages . Interestingly , the ure1 mutant strain grew slower in fungal growth medium which was buffered to neutral pH ( pH 7 . 4 ) . Mice inoculated with macrophages carrying urease-deficient C . neoformans had lower fungal burden in the brain than mice infected with macrophages carrying wild-type strain . In contrast , the absence of urease did not affect survival of yeast when interacting with amoebae . Because of the inability of the urease deletion mutant to grow on urea as a sole nitrogen source , we hypothesize urease plays a nutritional role involved in nitrogen acquisition in the environment . Taken together , our data demonstrate that urease affects fitness within the mammalian phagosome , promoting non-lytic exocytosis while delaying intracellular replication and thus reducing phagolysosomal membrane damage , events that could facilitate cryptococcal dissemination when transported inside macrophages . This system provides an example where an enzyme involved in nutrient acquisition modulates virulence during mammalian infection .
C . neoformans , a major life-threatening fungal pathogen predominantly infects severely immunocompromised patients and causes over 180 , 000 deaths per year worldwide [1] . C . neoformans is ubiquitous , although is most frequently found in soils contaminated with bird excreta or from trees [2–11] . Current treatments for Cryptococcosis often fail , are inadequate and/or unavailable for these infections , especially in developing countries . Therefore , it is important to study the fundamental pathogenic processes of C . neoformans to discover new treatments against this pathogen . Human infection with C . neoformans follows inhalation of spore or yeast cells . In healthy individuals , pulmonary infections with C . neoformans are normally controlled and macrophages play a central role [12 , 13] . Soon after phagocytosis , the Cryptococcus-containing phagosome undergoes maturation , acidification and lysosome fusion [14–17] . However , C . neoformans is a facultative intracellular pathogen that it is able to survive and persist in mature phagolysosome , and can become latent and localized within the giant cells or macrophages in granulomas [15 , 18–22] . Depletion of macrophages is associated with improved survival of infected mice , supporting the notion that yeast cell cells are maintained within macrophages and as such , this host cell can constitute a niche for dissemination and persistence [23] . In the rat , latent infection resides in macrophages [18] . Infection can reactivate in conditions of weakened immunity , with intracellular replication and dissemination [15 , 17 , 20 , 22 , 24] . Consequently , the ability of C . neoformans to survive and replicate intracellularly contributes to different stages of cryptococcal pathogenesis [25–27] . It has been proposed that this intracellular pathogenic strategy emerged from interactions with amoebae in the environment [28–30] . A recent study reports that C . neoformans spends a relatively short time ( ~80 min ) inside Dictyostelium discoideum and is expulsed before yeast replication occurs [30] . C . neoformans expresses virulence factors that promote its pathogenicity , including formation and enlargement of a polysaccharide capsule , melanin production , extracellular secretion of various enzymes including phospholipase , urease , etc . The role of capsule and melanin in macrophage-pathogen interaction are well understood . The capsule interferes with phagocytosis , by potentially masking macrophage receptor binding sites and polysaccharide shed by the yeast is immunosuppressive [31–34] . Moreover , both capsule and melanin protect C . neoformans from intracellular killing by providing protection against reactive oxygen species ( ROS ) as well as antimicrobial peptides [35 , 36] . However , mechanism by which urease contributes to intracellular pathogenesis is unknown . Urease functions as a general virulence factor for many bacterial pathogens , such as Helicobacter pylori [37] , and fungal pathogens Cryptococcus spp . and Coccidioides posadasii [38–40] . Urease catalyzes the hydrolysis of urea into carbon dioxide and ammonia [41 , 42] . Ammonia generated from ureolytic activity can serve as a nitrogen source . Since urea is evenly distributed throughout the human body it is conceivable it is used as a nutrient by mammalian pathogens [43] . Beyond its nutritional role , ureolytic activity enhances the invasion of C . neoformans to the central nervous system by promoting the yeast sequestration within the microcapillary beds of blood-brain barrier . The underlying mechanism is not known , but was hypothesized that ammonia generated by urease activity was toxic to microvascular endothelial cells [44–46] . Urease-mediated ammonia can also neutralize any acidic microenvironment and thus help pathogens to survive harsh pH of the phagolysosome . The neutralizing effect of H . pylori’s urease is well established , enabling that bacterium to colonize gastric mucosa [47 , 48] . In addition to its role in gastric colonization , H . pylori urease regulates the host-macrophage interaction by retarding the opsonization of H . pylori [49] . The enzyme can also modulate phagosomal pH and disrupt phagosome maturation to enhance the intracellular survival of H . pylori in macrophages [50] . Furthermore , it induces the expression of inducible NO-synthesizing enzyme ( iNOS ) , a M1 macrophage polarization marker , in mouse macrophages [51] . In contrast , in both C . neoformans and C . posadasii , urease-producing strains promote the polarization of immune responses to a nonprotective Type 2 ( T2 ) rather than a fungicidal Type 1 ( T1 ) immune response [38 , 52] . Hence , bacterial and fungal urease may have different effects on macrophage activation , and the role of cryptococcal urease during macrophage-pathogen interaction , which may affect the appropriate immune response , is unexplored . In this paper , we evaluated the role of urease on intracellular pathogenesis of C . neoformans in both amoebae and macrophages . We studied the effect of urease on the macrophage response to C . neoformans , as measured by host cell lysis and non-lytic exocytosis , cryptococcal replication inside macrophages and phagolysosomal pH . The results indicate that C . neoformans urease affects the non-lytic exocytosis and intracellular replication of the yeast by modulating phagolysosomal pH thus illustrating a new mechanism of by which this enzyme contributes to virulence .
Many virulence factors used by C . neoformans for survival in mammalian host such as capsule , melanin and phospholipase B1 are also important for the survival of C . neoformans in its natural environment , where it is subject to predation by amoebae [28 , 53] . The capacity of C . neoformans for mammalian virulence was proposed to result from the fortuitous selection of traits that allow survival in animal hosts by environmental predators [30 , 54] . To explore whether urease plays a role in the virulence towards amoebae we examined the viability of A . castellanii during the co-incubation with C . neoformans . Consistent to previous studies [28 , 55] , the percentage of dead A . castellanii cells in the presence of C . neoformans ( 25–26% ) was significantly higher than in PBS alone ( 14 . 7% ) after 48 h co-incubation ( Fig 1A ) . However , the percentage of A . castellanii cells killed by cryptococcal urease-positive and negative strains was similar ( Fig 1A ) . We also examined the survival of C . neoformans with or without urease during co-incubation with A . castellanii . In addition , the buffer solution was supplemented with 7 . 5 mM urea to test whether the process of ureolysis could improve the survival of C . neoformans during the interaction with A . castellanii . However , no significant difference in survival between strains with urease and without urease was observed in all the tested conditions ( Fig 1B ) . These results suggest that urease , which is a virulence factor for mammalian hosts , is not necessary for virulence in amoebae . Macrophages play a central role in host response to cryptococcal infection and harbor the organism as an intracellular pathogen during latent infection . To investigate the effect of urease in intracellular pathogenesis we studied the response of macrophage when they were infected with either C . neoformans ure1 deletion strain , its parental H99 or the URE1 complemented strains . Studies have shown that H . pylori urease can affect phagocytosis [49] , modulate the recruitment of lysosomal marker LAMP-1 to phagosome and thus prevent phagosomal maturation [50] , and stimulates the expression of iNOS to induce nitric oxide generation production in mouse macrophages [51] . Therefore , we first measured the efficiency of phagocytosis of antibody-opsonized cells for the three strains by calculating the phagocytic index after incubation for 2 h ( S1A Fig ) . We then tested if cryptococcal urease can affect the recruitment of LAMP-1 to phagosomes by examining percentage of LAMP-1 positive phagosomes ( S1B Fig ) . To study if cryptococcal urease affects host iNOS expression , we infected macrophages with urease-positive and negative strains and measured the concentration of nitrite , a stable oxidation product of nitric oxide , in the culture supernatant using Griess assay ( S1C Fig ) . Our data shows that cryptococcal urease does not affect the efficiency of antibody-mediated phagocytosis , phagosomal maturation , and nitric oxide production of macrophages during the infection . We also determined the ability of wild-type , urease deletion and complemented strains to survive intracellularly by enumerating the colony forming units ( CFU ) . Our result show that all strains had similar intracellular survival in macrophages after 2 h phagocytosis ( S1D Fig ) . We studied host cell outcomes for infected macrophages with regards to non-lytic exocytosis . Three subcategories of non-lytic exocytosis , as defined on a previous study [56] were used: complete non-lytic exocytosis ( type I ) , partial non-lytic exocytosis ( type II ) and cell-cell transfer ( type III ) ( Fig 2A ) . Macrophages infected with ure1Δ mutant underwent fewer non-lytic exocytosis than those infected with urease producing C . neoformans , in particular partial non-lytic exocytosis ( type II ) and cell-cell transfer ( type III ) ( Fig 2B ) . This result implies that the presence of urease has an effect on non-lytic exocytosis . We hypothesized that if ureolytic activity of urease influenced non-lytic exocytosis , that the addition of urea to the media would also affect the frequency of these events . Consequently , we adjusted the concentration of urea in cell media to 9 mM , which is the level found in plasma from mouse [57] , and studied the interaction of C . neoformans and macrophages . Total non-lytic exocytosis events remained higher with urease-positive strains and increased by approximately 23% with increasing concentration of urea ( Fig 3A ) . However , the increase in exocytosis was also noted when macrophages were infected with urease deleted strain ( Fig 3A ) . Hence , urea appeared to affect the frequency of non-lytic exocytosis independently of any effect related to the urea hydrolysis by urease , precluding definitive conclusions . To investigate whether urease enzymatic activity affected non-lytic exocytosis , we infected BMDM with H99 in the presence of 5 mM of the urease inhibitor acetohydroxamic acid ( AHA ) , a concentration that inhibits 50% of yeast urease activity while presenting minimal toxicity to murine macrophages ( S2 Fig ) , and measured the frequency of non-lytic exocytosis . Addition of AHA decreased non-lytic exocytosis to a level comparable to that observed with the urease deletion mutant ( Fig 3A and 3B ) . This result is consistent with and supports the notion that urease mediated urea hydrolysis modulates the frequency of non-lytic exocytosis . We observed no significant difference in the frequency of host cell lysis in the presence versus absence of urease when there is no urea supplementation ( Fig 3C ) . However , after the concentration of urea in the culture medium was adjusted to 9 mM cell lysis events of macrophages infected with urease-positive strain decreased by 38% , whereas the event of cell lysis with urease-negative strain increased by 35% ( Fig 3C ) . That in turn led to the significant difference of cell lysis between macrophages containing urease-positive strain and urease-negative strain ( Fig 3C ) . We compared the intracellular replication of cryptococcal strains with or without urease . For each strain , we analyzed more than 800 internalized cryptococcal cells in five 24-hour time-lapse movies for their ability to replicate intracellularly . In wildtype C . neoformans , 39 . 6% of cells underwent replication inside macrophages while 63 . 5% occurred for ure1Δ cells , i . e . , ure1Δ cells replicated nearly twice as more than wildtype ( Fig 4A ) . Adding 9 mM urea to the medium resulted in larger difference in intracellular replication between wild-type and urease deletion mutant , with decreased number of replication on H99 and increased number of replication on ure1Δ ( Fig 4A ) . We also investigated whether the presence of urease affected the onset of intracellular replication , and we measured the time of first budding after the cells were phagocytized by macrophages . Urease-positive strains had more cells that started replication later than urease-negative cells although both of the peaks are at 4–6 h after phagocytosis ( Fig 4B ) . Therefore , even among those cells that replicated inside macrophages , urease-positive strains manifested slightly delayed replication compared to ure1Δ strain . The delay became more pronounced when urea ( 9 mM ) was supplemented to the medium , resulting in a peak shift of urease-positive cells from 4–6 h to 8–10 h , implying that cells took longer to begin to replicate in this condition ( Fig 4B ) . Collectively , these data demonstrate that urease ureolytic activity is strongly linked to the delayed onset of cell replication . However , once the cells started to replicate , the doubling time of all strains was very similar ( Fig 4C ) , although ure1Δ strain had approximately 30 min longer doubling time in a standard laboratory condition ( Sabouraud broth with shaking at 30 °C ) when comparing to H99 and the complemented strain ( Fig 4D ) . A prior study had shown that prolonged cell cycle progression resulted in cells with larger capsule , which was associated with protection during phagocytosis and enhance intracellular survival [58] . Since urease-positive strains manifested delayed intracellular replication , we investigated whether urease-positive strains had larger capsules after phagocytosis . Consequently , we measured the capsule size of H99 , urease deletion and complemented strains harvested from macrophages after 16 h infection . There was no significant difference in capsule size between urease-positive and negative strains inside macrophages ( S3 Fig ) . The intracellular replication of C . neoformans is tightly correlated to lysosomal damage , such that macrophages with higher numbers of cryptococcal cells manifest greater lysosomal membrane permeabilization [59] . Consequently , we investigated whether lysosomal membrane permeabilization was associated with the urease activity for C . neoformans strains . We stained macrophages with Lysotracker Deep Red , which localizes to and labels acidic organelle such that loss of fluorescence signal indicates lysosomal damage . The number of cells manifesting loss of Lysotracker fluorescence was then quantified by flow cytometry after 24 h of infection . C . neoformans infected macrophages developed loss of Lysotracker signal ( Q1 population in Fig 5A and 5B ) . As negative control , macrophages infected with heat killed H99 manifested no loss of Lysotracker fluorescence [60] . The percentage of Lysotracker-loss macrophages infected with H99 , ure1 deletion and complemented strains was highest for ure1Δ-infected macrophages ( Fig 5C ) . This result suggests that host cells which are infected with ure1 deletion strain undergo significantly more lysosomal membrane damage . We further investigated if different degrees of lysosomal damage were associated with the presence or absence of urease resulted in different degree of apoptosis given that lysosomal damage can release cathepsins into the cytosol and trigger programmed cell death [61] . Therefore , we stained macrophages with SYTOX as an indicator of death cells and F2N12S ( dye that indicates membrane potential ) to distinguish apoptotic from healthy cells ( Fig 5A ) . There was no significant difference in the percentage of live cells in the sets infected with H99 , ure1 deletion and complemented strains respectively ( Fig 5D and 5E ) . However , there was a slight difference of the proportion of apoptotic and death cells between urease-positive strains and urease deletion mutant ( Fig 5D and 5E ) . These results suggest that the presence or absence of urease translates in different intracellular growth rates with the absence of urease being associated with greater phagolysosomal membrane permeabilization , and a shift on how macrophage death occurs . C . neoformans urease breaks down urea into ammonia and carbon dioxide , and subsequently ammonia reacts with water to produce hydroxyl ions that increase pH . A previous study shows that non-lytic exocytosis is influenced by phagolysosomal pH [62] . In addition , C . neoformans does not grow well in alkaline pH [14] . We hypothesized that the presence of urease would increase phagolysosomal pH , and the alterations in pH would then increase the frequency of non-lytic exocytosis events and affect cryptococcal growth [62 , 63] . To test this hypothesis , we measured and compared the phagolysosomal pH with the urease-positive or negative strains . Unlike prior studies , we devised a method to measure pH in specific C . neoformans-containing phagolysosomes by conjugating a pH sensitive probe to 18B7 antibody , which binds to cryptococcal capsule [59] . To validate our methodology , we measured phagolysosomal pH associated with the ingestion of Oregon green conjugated 18B7 labeled polystyrene beads , which have been widely used to study phagocytosis in macrophages . Absolute pH was calculated using a pH standard curve obtained from the measurements of pH of phagosomes containing beads ( S4A Fig and Materials and methods ) . The data showed that the acidification started rapidly and by 2 h phagolysosomes had reached the lowest pH ( mean = pH 4 . 5 ) , which remained constant until 4 h ( Fig 6A and S4B Fig ) . Previous study has shown that the bead-containing phagolysosomes reach pH = 5 in 15 min and up to 30 min [64] , which is consistent with our result showing average pH of 4 . 9 in bead-containing phagolysosomes in the first hour after ingestion . We proceeded to measure the pH of phagolysosome containing wild-type , ure1 deletion and complement strains . Phagolysosomes containing wild-type cells had a pH ranging from 4 . 6 to 5 . 1 , which was consistently higher than those containing urease deletion mutant cells ( pH ranging from 4 . 2 to 4 . 7 ) through all the infection periods measured ( Fig 6B , S4C and S4D Fig ) . Our result is consistent with a prior study showing that the pH of phagolysosomes containing live cryptococcal cells is 4 . 7 after 3 h infection using a different methodology [14] . The phagolysosomes containing the cells from the urease complement strain had a similar pH ( 4 . 7–5 . 1 ) as those containing wild-type cells and constantly had higher pH than cells deficient in urease at the first two hours of phagocytosis , but the results were inconsistent with those containing wild-type for longer time intervals ( Fig 6B and S4C Fig ) . This finding could result from differences in urease level expression in the complemented strain relative to wild-type expression and/or other uncharacterized factors affecting the reconstituted strain . We also observed considerable pH variation among individual phagolysosomes , which could reflect many factors including differences in the timing of phagocytosis , heterogeneous microenvironments or the cellular position of the phagolysosomes ( peripheral vs juxtanuclear ) [65] or plain stochastic variation . Overall , phagolysosomes containing both wild-type and urease complemented strains had higher pH than the bead- and ure1Δ strain-containing phagosomes in the first two hours after phagocytosis , consistent with a mechanism whereby urease increases phagolysosomal pH through hydrolysis of urea , which is present in the system from the metabolism of macrophages and from fetal calf serum in the macrophage media . We also determined the pH of phagolysosome containing heat-inactivated H99 ( 50 °C for 30 min ) . Although we expected that it would behave comparably pH of phagolysosomes containing polystyrene beads , the pH was significantly higher ( pH 5 . 1 ) from 2–4 h ( Fig 6C and S4E Fig ) . Surprisingly , we found that urease activity was not abolished by heat-inactivating to 50 °C for 30 min , since the plating of heat-inactivated cells on Christensen urea agar turned pink ( Fig 6D ) . Of note , the color effect in urea agar was faster with heat-inactivated cells than with alive cells , suggesting that the heating may have liberated the enzyme . Given this result we repeated the experiment with H99 cells killed by heating to 50 °C for a longer period of time ( 4 h ) which was effective in inactivating the urease activity ( Fig 6C ) . The phagolysosomal pH indeed became lower ( pH 4 . 7 ) after macrophages ingested H99 heat killed at 50 °C for 4 h when compared to 30 min ( Fig 6C and S4E Fig ) . We note that phagolysosomal pH of heat-killed H99 at 50 °C for 4 h was not as low as the pH of phagolysosome containing beads or the urease deletion mutant . A similar phagosomal pH value was observed after ingestion heat-killed urease deficient cells ( 50 °C for 4 h ) ( Fig 6C and S4E Fig ) , suggesting that part of the increase in pH is independent of the presence of urease . We hypothesize these components could derive from leakage of intracellular contents including proteins with functional groups such as amino acids and carboxylic groups that can absorb hydronium ions , and buffer the acid flux such that it does not reach the low pH observed with polystyrene beads . However , the data with urease deficient mutant and urease inactivation by heat killing demonstrates that cryptococcal urease contributes to neutralize and therefore increase the phagosomal pH after ingestion by murine macrophages . To confirm whether the increase of pH was a result of urease activity , we supplemented the media with urea at different concentrations ( 9 mM or 50 mM ) . To establish that urea can freely pass across cell membrane , we measured urea in macrophages using a colorimetric assay . Incubation of macrophages with urea raised their urea content to physiological urea concentration of 9 mM ( S5A Fig ) . Urea supplementation was associated with a more alkaline phagolysosomal pH in macrophages containing wild-type and urease complemented strains as well as heat-inactivated H99 ( 50 °C for 30 min ) , but not urease deletion strain , bead or heat killed H99 ( 50 °C for 4 h ) ( Fig 5E and S5B Fig ) . These results suggest that the increased phagolysosomal pH is associated with urease degradation of available urea . To investigate how pH could affect the intracellular growth results we studied the growth of C . neoformans as a function of the pH . We used the growth curves to determine three characteristic growth values i . e . growth rate represented by the maximum slope , length of lag phase , and the maximum cell growth and compared them among strains ( Fig 7A and 7B ) . Our experiment displayed an inverse relationship between the growth rate of all tested strains and increasing pH . The growth rate decreased approximately 3-fold from pH 4 . 2 to 5 . 6 . Hence , C . neoformans grew best in the most acidic milieu , a finding that combined with phagolysosomal pH measured for urease sufficient and deficient strains suggests that the effect of urease on cryptococcal intracellular replication is due to its effect in neutralizing pH since pH affects yeast growth rate . The strain ure1 Δ had shorter lag phase than H99 and URE1-complemented strains at all pH tested ( Fig 7C ) . One possible explanation is that there is a metabolic cost of producing the enzyme , especially when adjusting to the nutrient-limited medium we used in this particular experiment . However , once the urease-positive strains adapt to the environment , the rate of growth can return to maximum . Interestingly , the maximum cell growth of ure1Δ was similar to that of H99 and URE1-complemented strains in acidic pH , but gradually decreased closer to neutral pH ( Fig 7D ) . Therefore , we questioned if the mammalian physiological pH affected the growth of ure1Δ , so the growth curves of H99 , ure1Δ and URE1-complemented strains were determined in minimal medium buffered at pH 7 . 4 . The result showed that ure1Δ had a severe growth defect at pH 7 . 4 ( Fig 7E ) while all strains grow equally in unbuffered minimal medium ( Fig 8A ) , suggesting that urease is required for neutral and alkaline tolerance . To study further the association of phagolysosomal pH and the intracellular replication of C . neoformans , we added the weak base ammonium chloride to the medium , which is known to neutralize phagosomal acidity and inhibit cryptococcal intracellular growth [14 , 62 , 66] , and measured intracellular replication of urease deletion mutant in a 24 h time-lapse movie . The addition of ammonium chloride retarded intracellular replication of C . neoformans independent of the presence of urease ( Fig 7F ) , providing strong support for the notion that the higher phagosomal pH was the cause of the retarded cryptococcal intracellular replication . Urease is not only able to elevate the pH of microenvironment , but it also serves as a nitrogen source for pathogenic microbes such as Actinomyces naeslundii and Bacillus cereus during infection by hydrolyzing urea to ammonia [67–69] . A prior study showed that urease activity is required for cryptococcal growth in agar medium with urea as the only nitrogen source [45] . However , the role of cryptococcal urease in nutrition and metabolism has not been fully explored . Consequently , we tested whether C . neoformans could use urea as both a nitrogen and carbon source and whether this was urease-dependent . The growth of the three strains were very similar in minimal medium containing glycine as sole nitrogen source and glucose as a carbon source ( Fig 8A ) . When urea was the sole nitrogen source , there was no growth of urease deletion mutant up to 72 h ( Fig 8D ) , consistent to prior results . The addition of ammonium salt partially complemented the growth defect of urease deletion mutation , suggesting that the growth defect was the result of an inability to produce ammonia ( Fig 8E ) . On the other hand , although urea could serve as a nitrogen source , cryptococcal strains were not able to grow when urea was the sole carbon source ( Fig 8F ) . Taken together , our results show that C . neoformans has an ability to utilize urease to hydrolyze urea into ammonia and use it as nitrogen source , but C . neoformans cannot utilize urea as carbon source for growth . C . neoformans brain invasion can occur by carriage in macrophages in a Trojan Horse-like mechanism or through transcytosis of endothelial cells [19 , 23 , 26 , 70–77] . Previously , it was reported that urease-negative strains cannot reach the brain from the lungs [44 , 46] . It is still not clear if this defect was due to ineffective dissemination from the lung or whether this reflected the fact that urease-negative strains are less effective in crossing the BBB [44 , 46] . Furthermore , it was not clear if Trojan-horse transport inside macrophages was affected by the presence and absence of urease . Since urease retards the intracellular replication , it could promote the coexistence and persistence of C . neoformans within macrophages and thus increase the chance for dissemination by a Trojan Horse-like mechanism . Alternatively , since C . neoformans urease also induces non-lytic exocytosis , this could facilitate escape of C . neoformans from macrophages in the lung or the bloodstream and enhance brain invasion of free yeasts by transcytosis , which is facilitated by urease . Therefore , we hypothesized that macrophages infected with urease-positive strain would be more efficient at the brain invasion than ure1 infected macrophages . To test this , we injected mice with H99- and ure1Δ-infected macrophages and quantified brain and lung fungal burden by CFU at 72 h post-infection . We found lower CFU in the brain of mice infected with BMDM containing ure1 mutant relative to H99-containing BMDM ( Fig 9 ) . In contrast , lung CFU were comparable for mice given macrophages containing either strain , suggesting that brain invasion by Trojan-Horse mechanisms depends on active urease .
Urease is an important virulence factors of C . neoformans [40] . However , most studies have focused on urease role in brain invasion and its effects on the host immune response . Cryptococcal urease facilitates transmission of C . neoformans across the blood-brain barrier [44–46] and polarizes the immune system to a Th2 response , which translates into greater fungal burden in lung in mouse model [52] . Yet the effect of urease in macrophage interaction has not been explored despite the fact that the outcome of the interaction of C . neoformans with macrophages is a key determinant of the outcome of infection [13 , 78 , 79] . In this study , we analyzed the role of urease in C . neoformans-macrophage interactions . Our results provide new insights on how this enzyme can affect the pathogenesis of Cryptococcus spp since we show that urease influences the intracellular growth of C . neoformans , affects non-lytic exocytosis from macrophages , is critical for growth at mammalian physiological pH and confers upon the yeast the potential for using urea as a nitrogen source in nutrition . Urease can break down urea to produce ammonia , which in turn raises pH . In our study , the measurements of phagolysosomal pH show that cryptococcal urease contributes to buffering acidic pH in the phagolysosome , which is almost certainly a consequence of the hydrolysis of urea . Urea is present in the fetal bovine serum in the culture medium and easily crosses cell membranes . Urea is also a product of macrophages arginase catalysis , which provides C . neoformans an additional intracellular source of substrate for urease . Moreover , human body fluids normally contain between 2 . 5 to 7 . 1 mM urea that is evenly distributed in all body compartments [80–82] . These concentrations make it feasible for C . neoformans to utilize urea in the host and alter its microenvironmental pH such as phagolysosome . Phagolysosomal alkalization has also been observed with other urease-positive microbes . Mycobacterial urease contributes to the alkalization of the phagolysosomal pH in resting macrophages , but its effect is not sufficient to neutralize the pH in the more acidic phagolysosome of activated macrophages [83] . In contrast , the presence of cryptococcal urease was sufficient to raise pH by an average of 0 . 4 pH units . Helicobacter pylori urease is also known to significantly elevate phagolysosomal pH as well as retard recruitment of Lamp-1 , a marker for late endosome and lysosome [50] . However , unlike H . pylori cryptococcal urease did not affect the LAMP-1 acquisition . Phagosome acidification is usually considered an important component of the antimicrobial machinery [84] , but C . neoformans grows best in acidic environments while other pathogens’ growth is inhibited by phagolysosome acidification [63 , 85–87] . Our analysis shows that even inside the mammalian macrophage C . neoformans grows at a higher rate in pH 5–6 . Our results , together with studies of other groups , showed that neutralizing phagolysosomes by treating the macrophages with ammonium chloride inhibited intracellular growth of C . neoformans [14 , 63] . Therefore , phagolysosomal acidification does not appear to be an important element of in the control of C . neoformans by macrophages . However , we show that urease is required for the optimal growth of C . neoformans at physiological pH . Therefore , we describe a previously unknown role of urease in neutral/alkaline pH tolerance . The majority of intracellular cryptococcal cells in the urease-positive population did not undergo replication immediately upon entry into macrophages and ure1 deletion strain initiated replication earlier than wild-type counterparts . This effect was pronounced when the culture medium was supplemented with urea . We attribute this effect to an increase in the phagolysosomal pH from 4 . 4 to 4 . 8 , a pH that reduced the maximum growth rate of C . neoformans . Together , urease retards growth in macrophages in vitro , and at first glance this is the opposite of what would be expected from a virulence factor . However , this effect has to be considered in the context of the larger picture of cryptococcal pathogenesis . Urease-positive strains still possessed the ability of resisting killing by macrophages and delayed replication could promote a quiescent state intracellularly , which may be associated with persistence of infection [88] . A previous study reported a strong correlation between intracellular replication of C . neoformans and lysosomal damage due to the increased number of yeasts in macrophages [59] . Indeed , macrophages containing urease-positive cells manifested less phagolysosomal permeabilization than those containing urease-negative cells . Loss of phagolysosomal membrane integrity could be expected to benefit to C . neoformans by allowing the fungus access to host cytosolic nutrients . However , many bacterial pathogens thrive in vacuoles or phagosome rather than nutrient rich cytoplasm and have developed strategies to maintain phagosomal membrane integrity to avoid immune surveillance pathway and eventually prevent inflammasome-mediated lytic cell death called pyroptosis [89] . A temporary maintenance of membrane integrity could contribute to persistent infections by prolonging the interaction of macrophages and cryptococci . Consistent with this , our results show that when the medium is supplemented with urea , urease-positive cryptococcal cells manifest pronounced growth retardation and cause fewer events of host cell lysis . This correlation suggests that the growth retardation associated with urease mediated alkalization results in fewer yeasts in macrophages , which in turn protects C . neoformans from humoral immune responses and facilitates persistent infection and dissemination by reducing the likelihood of lytic exocytosis . The cellular and molecular mechanism of the non-lytic exocytosis is poorly understood . It is a highly choreographed process where both host and pathogen factors are involved [17 , 24] . Previous studies have shown that cryptococcal capsule and phospholipase B1 contribute to non-lytic exocytosis [17 , 90] . Host cell membrane protein annexin A2 and signaling kinase ERK5 have also been identified to regulate non-lytic exocytosis [27 , 91] . Repeated cycles of actin polymerization that form around cryptococci-containing phagosome could potentially inhibit non-lytic exocytosis [92] . Moreover , phagolysosome neutralization by the addition of weak base ammonium chloride and chloroquine increased the frequency of non-lytic exocytosis events [62] . Here we identify urease as new fungal factor that modulates non-lytic exocytosis . Moreover , chemical inhibition of urease enzymatic activity decreased the frequency of non-lytic exocytosis , suggesting that the effect was related to urea hydrolysis . The most likely mechanism is that the pH alteration caused by ureolytic reaction contributes to increasing the frequency of non-lytic exocytosis , which is consistent with our observation that urease activity raised phagolysosomal pH . However , the mechanism on how phagolysosomal pH influences the non-lytic exocytosis remains to be elucidated . Given that phagolysosomal alkalization can increase non-lytic exocytosis [62] , we evaluated whether higher concentrations of urea would increase the frequency of this phenomenon . While increase in exogenous urea increased phagolysosomal pH in those containing urease-positive cells , we also observed increased non-lytic exocytosis in macrophages containing urease-negative cryptococcal cells . Therefore , the effect was not entirely dependent on urease-mediated alkalization , but could also be affected by urea , which can promote the fusion of vesicles with bilayer lipid membrane and thus induce exocytosis [93] . Hence , one possible mechanism for the increased non-lytic exocytosis observed for urease-negative cells is that urea encourages phagolysosome-cell membrane fusion to disgorge yeast cells . The phagolysosomal pH was measured by conjugating a pH sensitive probe to 18B7 antibody , which binds to cryptococcal capsule , a method that can adapted to any system using antibody-mediated phagocytosis . It is noteworthy that we observed considerable pH variation among individual phagolysosomes . This heterogeneity has been observed in many other studies , and could be caused by many factors [65 , 94–96] , such as differences in the timing of phagocytosis . Despite using centrifugation to enhance the yeast cells contact with macrophages , yeast cells might attach to macrophages but not be engulfed synchronously . A recent study also report that the position of lysosomes determines their pH , such that peripheral lysosomes are less acidic than juxtanuclear ones [65] . Phagolysosomal pH variation could also be attributed to the heterogeneity in protein composition of V-ATPases and NADPH oxidase among individual phagolysosomes [95] . Differences in the metabolic state or age of the infecting cells could also contribute to phagolysosomal pH heterogeneity . In this regard , variability in the infecting Salmonella population resulted in heterogeneous macrophage response [97] . Finally , it is possible that the heterogeneity in phagolysosomal pH reflects the outcome of the individual battles between C . neoformans and macrophages that are fought phagolysosome-to-phagolysosome , such that in some phagolysosomes the microbe gains ascendancy while in others it is suppressed . C . neoformans was able to utilize urea as nitrogen source for growth . This metabolic process was entirely urease dependent since the urease deficient strain was unable to grow in medium where urea was the sole nitrogen source . Supplementation with ammonium salt partially rescued the growth of urease-negative strain , suggesting that ammonia generated from ureolytic activity , not urea itself , is the actual nitrogen source for the growth of C . neoformans . We also showed that urea could not serve as the sole source of carbon for C . neoformans . Therefore , we propose that C . neoformans urease activity may also provide an important nutritional function for in vivo under nitrogen-limited conditions since urea diffuses easily in tissues and macrophages can generate urea . Of note , two conundrums arise from our studies . Firstly , the partial rescue suggests that cryptococcal urease is further involved in the metabolism of ammonia . Secondly , it remains to be investigated why urease is required for growth at physiological pH . In contrast to other virulence factors such as the capsule , melanin , and phospholipase , a deficiency in urease did not increase the vulnerability of C . neoformans for amoebae . Hence , the main role of urease in C . neoformans in its natural environment appears to be nutritional in nature and this enzyme provides an example on how a protein involved in nutrition acquisition can serve a fortuitous role during pathogenesis as a modulator of virulence . Taken together , we propose the following model for the role of urease in intracellular pathogenesis . Urease secreted by C . neoformans into phagolysosome hydrolyses urea , releases ammonia and increases the pH of this compartment by approximately half of a pH unit , which is sufficiently to retard the replication of C . neoformans inside macrophages . The growth retardation in turn leads to fewer macrophages with phagolysosomal membrane permeabilization and fewer host cell lysis . In parallel , C . neoformans urease induces non-lytic exocytosis events of macrophages . Crossing of the blood brain barrier can be done by yeast cells in a transcytosis event or inside macrophages in a Trojan horse-like mechanism [19 , 23 , 26 , 70–77] . For yeast cells crossing the blood brain barrier alone urease has been shown to promote brain invasion [44 , 46] . In addition , urease contributes to optimal C . neoformans growth at physiological pH , which may facilitate the its extracellular growth and dissemination to tissues . Urease can also play a role in cryptococcal nitrogen metabolism by providing a source of nitrogen , and that could support the long-term survival of C . neoformans in nitrogen-limited conditions such as macrophages . Our observation shows that the presence of urease promotes non-lytic exocytosis , delays intracellular replication , allows for use of an abundant nitrogen source and facilitates growth at mammalian physiological pH . Therefore , urease can affect all of the types of blood-brain barrier crossing mechanisms by providing more extracellular yeasts and increased pH fitness for transcytosis , as well as increasing residence time in macrophages , with the latter promoting macrophage-associated crossings . This supposition was supported by the observation of higher fungal dissemination to the brain in mice injected with macrophage containing urease producing C . neoformans . Furthermore , it is consistent with the model proposed by others that elevating the frequency of non-lytic exocytosis by altering host cell signaling reduces dissemination , presumably by limiting the opportunity for Trojan horse transport [27] . Overall , we propose that urease helps C . neoformans to both persist in and exit from macrophages , events that could facilitate the dissemination of the pathogen to brain through macrophage-dependent transport mechanisms . We anticipate that the findings here for C . neoformans may also be relevant to other urease-positive fungal pathogens such as Aspergillus fumigatus and Histoplasma capsulatum , two pathogens that modulate phagolysosomal pH and for which acidification is critical for control of infection [87 , 98 , 99] . Analysis of urease effects on macrophages is likely to be a fertile area of investigation for the many fungal pathogens that express this enzyme .
All animal procedures were performed with prior approval from Johns Hopkins University ( JHU ) Animal Care and Use Committee ( IACUC ) , under approved protocol numbers M015H134 . Mice were handled and euthanized with CO2 in an appropriate chamber followed by thoracotomy as a secondary means of death in accordance with guidelines on Euthanasia of the American Veterinary Medical Association . JHU is accredited by AAALAC International , in compliance with Animal Welfare Act regulations and Public Health Service ( PHS ) Policy , and has a PHS Approved Animal Welfare Assurance with the NIH Office of Laboratory Animal Welfare . JHU Animal Welfare Assurance Number is D16-00173 ( A3272-01 ) . JHU utilizes the United States Government laws and policies for the utilization and care of vertebrate animals used in testing , research and training guidelines for appropriate animal use in a research and teaching setting . The C . neoformans strains were used in this study are C . neoformans var . grubii serotype A strain H99 , ure1Δ ( derived from H99 and lacking urease ) and ure1Δ::URE1 ( complemented urease mutant ) . All the strains were kindly provided by Dr . John Perfect ( Duke University , USA ) and have been described previously ( Cox et al . 2000 ) . The urease production phenotype of these strains was validated by using Christensen’s urea agar ( 2% urea , 1 . 5% agar , 0 . 2% KH2PO4 , 0 . 1% peptone , 0 . 1% dextrose , 0 . 5% NaCl , 0 . 0012% phenol red ) . Cryptococcal cells were cultivated in Sabouraud dextrose broth with shaking ( 120 rpm ) at 30 °C for overnight ( 16 h ) . Heat inactivated or killed C . neoformans was prepared for various experiments by incubating the cells at 50 °C for 30 min or 4 h . To study the effect of pH on cryptococcal growth , the yeast cells were grown in minimal medium ( 15 mM dextrose , 10 mM MgSO4 , 29 . 4 mM KH2PO4 , 13 mM glycine , 3 μM thiamine-HCl ) buffered with 100 mM citrate buffer ( Sodium citrate and citric acid ) at various pH ranging from 4 . 2 to 5 . 4 at 0 . 2-pH unit increments . The pH of the medium was measured using an Accumet Basic AB15 pH meter ( Thermo fisher Scientific , Waltham , MA ) . The pH was further verified by use of MColorpHast pH-indicator strips ( EMD Millipore , Jaffrey , NH ) before and after the growth assay to ensure the pH keep constant throughout the assay . To study the utilization of urea in C . neoformans , cells were grown in minimal medium at pH 5 . 5 with substitution of 7 . 5 mM urea or ammonium sulfate for glycine as sole nitrogen source and substitution of 7 . 5 mM urea for dextrose as sole carbon source . Cryptococcal strains were also grown in Sabourand broth to determine their growth and doubling time . Growth studies were done using a Bioscreen C plate reader ( Growth Curves USA ) starting the cultures with 105 yeast cells per well in honeycomb plate in different conditions mentioned above at 30 °C and measuring cell density for 72 h . Bone-marrow derived macrophages ( BMDM ) were isolated from the marrow of hind leg bones of 5- to 8-wk-old C57BL-6 female mice ( Jackson Laboratories , Bar Harbor , ME . For the differentiation , cells were seeded in 100 mm TC-treated cell culture dishes ( Corning , Corning , NY ) in Dulbecco’s Modified Eagle medium ( DMEM; Corning ) with 20% L-929 cell-conditioned medium , 10% FBS ( Atlanta Biologicals , Flowery Branch , GA ) , 2mM Glutamax ( Gibco , Gaithersburg MD ) , 1% nonessential amino acid ( Cellgro , Manassas , VA ) , 1% HEPES buffer ( Corning ) , 1% penicillin-streptomycin ( Corning ) and 0 . 1% 2-mercaptoethanol ( Gibco ) for 6–7 days at 37 °C with 9 . 5% CO2 . Fresh media in 3 ml were supplemented on day 3 and the medium were replaced on day 6 . Differentiated BMDM were used for experiments within 5 days after completed differentiation . Urea in 9 mM or 50 mM were supplemented in the medium during infection of macrophages with C . neoformans in some of the experiments . The amount of urea inside macrophages were measured using urea colorimetric assay ( Sigma-Aldrich , St . Louis , MO ) according to the manufacturer’s instruction . J774 . 16 cells , which were obtained from the American Type Culture Collection ( ATCC ) , is a murine ( BALB c , haplotype H-2d ) macrophage-like cell line derived from a reticulum sarcoma . J774 . 16 cells were maintained in DMEM with 10% NCTC109 medium ( Gibco ) , 10% FBS , 1% nonessential amino acid , 1% penicillin-streptomycin at 37 °C with 9 . 5% CO2 . Acanthamoeba castellanii strain 30234 was obtained from the American Type Culture Collection ( ATCC ) was maintained in peptone-yeast extract-glucose ( PYG ) broth ( ATCC medium 712 ) at 25 °C according to instructions from ATCC . C . neoformans strains were grown overnight in Sabouraud broth , and diluted into 5 × 107 cells in 2 ml of rapid urea broth ( RUH ) developed by Roberts [100] and adapted by Kwon-Chung [101] . Different concentrations ( 1 . 25–40 mM ) of urease inhibitor acetohydroxamic acid ( AHA ) were added . Cells were incubated at 37 °C for 7 and 24 h . In parallel , H99 and ure1Δ strains were grown without AHA as positive and negative controls . After incubation , cells were collected by centrifugation and 200 μl of supernatant were transferred to 96-well plate . The absorbance of the supernatant was measured at 570 nm using EMax Plus microplate reader ( Molecular Devices ) . The assay was performed in duplicate for each time interval . BMDM were seeded ( 5 × 104 cells/well ) on poly-D-lysine coated coverslip bottom MatTek petri dishes with 14mm microwell ( MatTek Brand Corporation ) in medium containing 0 . 5 μg/ml lipopolysaccharide ( LPS; Sigma-Aldrich ) , 100 U/ml gamma interferon ( IFN-γ; Roche ) . Cells were then incubated at 37 °C with 9 . 5% CO2 overnight . On the following day , macrophages were infected with cryptococcal cells ( 1 . 5 × 105 cells/well ) in the presence of 10 μg/ml monoclonal antibody ( Mab ) 18B7 . After 2 h incubation to allow phagocytosis , culture was washed five times with fresh medium to remove extracellular cryptococcal cells . Images were taken every 4 min for 24 h using a Zeiss Axiovert 200M inverted microscope with a 10x phase objective in an enclosed chamber under conditions of 9 . 5% CO2 and 37 °C . For some of the experiments , 9 mM urea was added into the BMDM culture for overnight incubation , or both BMDM and cryptococcal cells were pretreated with 20 mM ammonium chloride or 5 mM acetohydroxamic acid ( AHA ) for 30 min before phagocytosis . The chemicals were also present during both the 2 h incubation to permit phagocytosis and the 24 h incubation during time-lapse imaging . Phagolysosomal pH was measured using ratiometric fluorescence imaging involving the use of pH-sensitive probe Oregon green 488 . Oregon green 488 was first conjugated to monoclonal antibody 18B7 using Oregon Green 488 Protein Labeling Kit ( Molecular Probes , Eugene , OR ) . The Oregon Green 488 dye has a succinimidyl ester moiety that reacts with primary amines of proteins to form stable dye-protein conjugates . The labeling procedure is according to the manufacture’s instruction . BMDM were plated ( 1 . 25 × 105 cells/well ) on 24-well plate with 12 mm circular coverslip . Cells were cultured with completed BMEM medium containing 0 . 5 μg/ml LPS and 100 U/ml IFN-γ; as well as supplemented with or without urea at 9 mM or 50 mM , and then incubated at 37 °C with 9 . 5% CO2 overnight . Prior to infection , macrophages were placed at 4 °C for 15 min . In the meanwhile , live , heat inactivated , heat killed cryptococcal strains or anti-mouse IgG coated polystyrene bead ( 3 . 75 × 106 cells or beads/ml ) were incubated with 10 μg/ml Oregon green conjugated 18B7 Ab for 15 min . Macrophages were then infected with Oregon green conjugated 18B7-opsonized samples in 3 . 75 × 105 cells or beads per well . Cells were centrifuged immediately at 1200 rpm for 1 min and culture were incubated at 37 °C for 10 min to allow phagocytosis . Extracellular cryptococcal cells or beads were removed by washing three times with fresh medium . Samples on coverslip were collected at 1 , 2 , 3 , 4 h after phagocytosis by washing twice with pre-warmed HBSS and placing upside down on MatTek petri dish ( MatTek , Ashland , MA ) with HBSS in the microwell . Images were taken by using Olympus AX70 microscopy ( Olympus , Center Valley , PA ) with objective 40x at dual excitation 440 nm and 488 nm , and emission 520 nm . Images were analyzed using MetaFluor Fluorescence Ratio Imaging Software ( Molecular Devices , Downingtown , PA ) . Relative phagolysosomal pH was determined based on the ratio of 488 nm/440 nm . The relative pH was converted to absolute pH by obtaining the standard curve in which the images are taken as above but intracellular pH of macrophages was equilibrated by adding 10 μM nigericin in pH buffer ( 140 mM KCl , 1 mM MgCl2 , 1 mM CaCl2 , 5 mM glucose , and appropriate buffer ≤ pH 5 . 0: acetate-acetic acid; pH 5 . 5–6 . 5: MES; ≥pH 7 . 0: HEPES . Desired pH values were adjusted H using either 1M KOH or 1M HCl ) . Buffers were used at pH 3–7 . 5 using 0 . 5-pH unit increments . J774 . 16 cells were plated ( 2 × 106 cells/well ) on 6-well plate with completed DMEM containing 0 . 5 μg/ml LPS , 100 U/ml IFN-γ , with or without urea at 9 mM , and incubated at 37 °C with 9 . 5% CO2 overnight . On the following day , cryptococcal cells were stained with 0 . 0015% Uvitex 2B ( Polysciences ) for 1 min and wash once with medium . Macrophages were infected with Uvitex 2B-stained cryptococcal cells ( 1 × 106 cells/well ) in the presence of 10 μg/ml 18B7 for 24 h . After 24 h infection , Lysotracker deep red ( Thermo Fisher Scientific ) at 1 nM is added to the culture and incubated for 1 h . Cells were harvested from plates and washed once with HBSS . Anti-mouse CD11b-PE ( 1:1000 ) ( M1/70 , eBioscience , San Diego , CA ) was added and incubated for 5 min and washed once with HBSS . SYTOX and F2N12S ( Thermo Fisher scientific ) were added 5 min before flow cytometry analysis . Single color and fluorescence minus one ( FMO ) controls were used for fluorescence spectral compensation and gating . Flow cytometry analysis were performed by LSRII ( BD Biosciences , San Jose , CA ) . Data were analyzed using FlowJo software ( Ashlan , OR ) . BMDM cells ( 5 × 104 cells/well ) were seeded in 96-well plates with BMDM containing 0 . 5 μg/ml LPS and 100 U/ml IFN-γ for overnight . To initiate the phagocytosis , C . neoformans with 1 . 5 × 104 cells in the presence of 10 μg/ml 18B7 mAb were added in each well of BMDM culture . The culture plates were centrifuged at 1200 rpm for 1 min to settle yeast cells on the monolayer of macrophage culture . After 2 h infection , phagocytized cryptococcal cells were released by lysing the macrophages with sterilized water . The lysates were serially diluted , plated onto Sabouraud agar and incubated at 30 °C for 48 h for colony form unit ( CFU ) determination . This experiment was performed in triplicates for each strain . The survival of C . neoformans in amoebae culture was performed as described previously [55] . Briefly , A . castellanii were washed twice with DPBS ( Corning ) and diluted in DPBS to appropriate density . A . casterllanii cells ( 1 × 104 cells/well ) were added to 96-well plates and allowed to adhere for 1 h at 25 °C . C . neoformans cells were washed twice with DPBS and diluted in DPBS to appropriate density . Fungal cells ( 1 × 104 ) were added to wells containing amoebae or control wells containing DPBS alone , and the plates were incubated at 25 °C . At 0 , 24 , and 48 h , the amoebae were lysed by pulling the culture through a 27-gauge syringe needles five to seven times . The lysates were serially diluted , plated onto Sabouraud agar and incubated at 30 °C for 48 h for colony form unit ( CFU ) determination . Two different conditions were tested with DPBS supplemented with or without 7 . 5 mM urea . Two biological independent experiments were performed for each strain and condition . Viability of A . castellanii was also determined under the same conditions and time intervals by adding 1:80 dilution of Trypan Blue stain . The percentage of dead amoebae was determined by counting the number of Trypan Blue stained cells per total cell number counted . Minimal of 100 cells were counted . Control wells contain A . castellanii without C . neoformans . Two biological independent experiments were performed for each strain and condition . After 16 h infection , macrophage-internalized cryptocooccal strains were released by lysing the host cell with sterile water . Cryptococcal cells were washed twice with PBS . The capsule was visualized by India ink negative staining by mixing cell samples with equal volume of India ink on glass slides and spreading the smear evenly with coverslips . The images with a minimum 100 randomly chosen cells was taken by using Olympus AX70 microscopy with 100x oil objective using the QCapture Suite V2 . 46 software ( QImaging , Surrey , Canada ) . The areas of cell body and whole cell ( cell body plus capsule ) were measured using image J software . The capsule area was calculated by subtracting the area of whole cell from that of cell body . Three biological independent experiments were performed for each strain . BMDM ( 1 . 5 × 105 cells ) were seeded on 12 mm circular coverslip in 24-well plate with completed BMDM containing 0 . 5 μg/ml LPS and 100 U/ml IFN-γ for overnight . C . neoformans with 1 . 5 × 105 cells in the presence of 10 μg/ml Alexa Fluor 568 conjugated 18B7 mAb were then added into BMDM culture . The culture plates were centrifuged at 1200 rpm for 1 min to settle yeast cells on the monolayer of macrophage culture . After 10 min , 30 min , 1 h and 2 h infection , cells were fixed with 4% paraformaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) , followed by permeabilization with 0 . 3% Triton X-100 in PBS for 5 min and incubated with blocking solution containing 3% bovine serum albumin and 1:250 dilution of purified rat anti-mouse CD16/CD32 ( Mouse BD FC Block; BD Pharmingen , San Diego , CA ) for 45 min . Cells were next incubated with 1:50 dilution of Alex Fluor 488 conjugated anti-mouse Lamp-1 ( rat IgG2a monoclonal antibody 1D4B; Santa Cruz Biotechnology Inc . , Santa Cruz , CA ) at 4 °C overnight and then washed three times with 1×PBS for 5 min each time . Coverslips were mounted using ProLong Gold Antifade Mountant ( Thermo Fisher Scientific ) and cured for 24 h at room temperature . The images with a minimum 100 randomly chosen cells was acquired by Zeiss Axiovert 200M inverted microscope with a 40x objective . Z-stacks were taken at 1 μm intervals through entire macrophage . Phagosome-lysosome fusion was considered to take place when there is co-localization of cryptococcal capsule ( Alexa Fluor 568 ) and Lamp-1 ( Alexa Fluor 488 ) . Two biological independent experiments were performed for each strain . BMDM cells ( 1 × 105 cells/well ) were activated by LPS ( 0 . 5 μg/ml ) and IFN-γ ( 100 U/ml ) for overnight in 96-well plates . To initiate infection , C . neoformans ( 1 × 105 cells ) with 10 μg/ml 18B7 mAb were added and settled down on macrophage monolayer culture using centrifugation at 1200 rpm for 1 min . After 24 h infection , culture supernatant in 100 μl was collected and equal volume of Griess reagent ( 1: 1 ratio of 0 . 1% naphtylethylenediamine dihydrochloride and 1% sulfanilamide in 5% H3PO4 ) was added . The mixture was incubated in the dark for 10 min at room temperature . The absorbance of the mixture was measured at 562 using EMax Plus microplate reader ( Molecular Devices ) . Nitrite concentration was determined from a standard curve constructed with 0 μM–50 μM sodium nitrite . Two biological independent experiments were performed for each strain and condition . Animal studies were performed using 6 to 8-week-old female C57BL/6 mice . Cryptococcal strains were grown for 2 days at 37 °C with shaking at 180 rpm in Sabouraud broth . Cells were washed with PBS and resuspended to 1 × 107 cells/ml in BMDM medium . Cryptococcal cells in 1 ml was added to BMDM in triplicates ( 1 × 107 cells per replicate ) together with opsonizing 18B7 ( final concentration of 10 μg/ml ) . After 1 h incubation to allow phagocytosis , extracellular cryptococcal cells were washed with HBSS and infected BMDM were detached with CellStripper ( Corning ) , collected by centrifugation . The infected BMDM were then resuspended in USP grade sterile saline solution ( BD , Franklin Lakes , NJ ) . Each mouse was injected i . v . with 200 μl of cell suspension . Mice were anesthetized with 2% isoflurane anesthesia followed by retroorbital injection of BMDM suspension , according to standard procedures [102] . Infected BMDM were lysed with sterilized water and cell lysis were plated in YPD plates to confirm inoculum CFU . After 72 h post-injection , mice were euthanized , lung and brain were isolated and homogenized by passing through a 100 μm filter . Homogenates were plated onto YPD agar for CFU enumeration . Three mice per strain per experiment were studied and two independent biological experiments were performed . Pairwise comparisons depicted in Fig 6 are Mann-Whitney U test with both wild-type and ure1 complement strains comparing to ure1 deletion mutant . One-way ANOVA , followed by Tukey’s multiple-comparison test was used to evaluate the statistical parameters of characteristic growth values . For categorical data , Fisher’s exact test was used when sample sizes were less than 1000 or chi-square test was used when sample sizes were larger than 1000 . All other continuous data were analyzed by Student’s t test .
|
Cryptococcus neoformans is a relatively frequent cause of life-threatening infection in severely immunocompromised patients , especially those with AIDS . Persistence of infection involves residence within macrophages , where C . neoformans can survive and replicate while residing in the phagolysosome . New treatments may be developed from a better understanding of how this pathogen resists clearance from and adapts for persistence in host phagocytic cells . In this study , we demonstrate a novel role for urease , a major virulence factor of C . neoformans , in its interaction with macrophages . This enzyme is able to break down urea into ammonia , which is a base , thus raising the surrounding pH . In the context of a mammalian infection , we show that cryptococcal urease increases the phagolysosomal pH which delays yeast replication , therefore causing less damage to macrophages and prolongs intracellular residence . Moreover , urease promotes C . neoformans exit from macrophages without killing the host cells . Overall , our data implies that urease also contributes to virulence by allowing the pathogen to persist and disseminate in macrophages .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"cryptococcus",
"neoformans",
"medicine",
"and",
"health",
"sciences",
"urea",
"cryptococcus",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"chemical",
"compounds",
"ureases",
"yeast",
"infections",
"enzymes",
"pathogens",
"immunology",
"cell",
"processes",
"enzymology",
"microbiology",
"organic",
"compounds",
"fungi",
"fungal",
"diseases",
"fungal",
"pathogens",
"infectious",
"diseases",
"mycology",
"white",
"blood",
"cells",
"animal",
"cells",
"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"chemistry",
"phagocytosis",
"exocytosis",
"biochemistry",
"eukaryota",
"cell",
"biology",
"organic",
"chemistry",
"secretory",
"pathway",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"macrophages",
"organisms"
] |
2018
|
Cryptococcus neoformans urease affects the outcome of intracellular pathogenesis by modulating phagolysosomal pH
|
Human rabies is a significant public health concern in mainland China . However , the neglect of rabies expansion and scarce analyses of the dynamics have made the spatiotemporal spread pattern of human rabies and its determinants being poorly understood . We collected geographic locations and timeline of reported human rabies cases , rabies sequences and socioeconomic variables for the years 2004-2013 , and integrated multidisciplinary approaches , including epidemiological characterization , hotspots identification , risk factors analysis and phylogeographic inference , to explore the spread pattern of human rabies in mainland China during the last decade . The results show that human rabies distribution and hotspots were expanding from southeastern regions to north or west regions , which could be associated with the evolution of the virus , especially the clade I-G . A Panel Poisson Regression analysis reveals that human rabies incidences had significant correlation with the education level , GDP per capita , temperature at one-month lag and canine rabies outbreak at two-month lag . The reduction in the overall human rabies incidence was accompanied by a westward and northward expansion of the circulating region in mainland China . Higher risk of human rabies was associated with lower level of education and economic status . New clades of rabies , especial Clade I-G , played an important role in recent spread . Our findings provide valuable information for rabies control and prevention in the future .
Rabies is a viral zoonotic infection of the central nervous system caused by a lyssavirus , and its mortality rate is nearly 100% without proper post-exposure prophylaxis ( PEP ) . As one of the most feared diseases throughout human history , rabies is widely distributed throughout the world with high mortality , leading to 55 , 000 human deaths each year [1] . China has the second highest rate of human rabies in Asia , where domestic dogs are the main source of infection and are the primary vector for human rabies . Towards the end of the last century , China encountered the third wave of human rabies since 1949 [2 , 3] , and the reemerging disease was among the top three causes of human death due to infectious diseases in the country [4] . The rapid increase of domestic dog population and inadequate PEP for humans bitten by dogs were thought to be the important factors driving the high incidence of human rabies in mainland China [5–8] . However , data about the burden of canine rabies in China is limited given the lack of detailed data on the number of domestic dogs and comprehensive rabies surveillance among dogs in the country [9 , 10] . Although previous studies had revealed the number of human rabies cases slightly decreased since 2008 , the rabies seemed to be gradually expanding to the low-incidence or non-epidemic areas due to human-related activities ( i . e . human migration , pets keeping ) [11 , 12] , which would hinder the goal to eliminate rabies by year 2020 [13] . In order to control the burden of rabies expansion , a comprehensive understanding about the spatiotemporal feature and evolution dynamic of rabies is of great importance . However , the previous studies were limited , giving the hotspots and risk factors for the occurrence of human rabies over the years and the spread dynamic of rabies remain unclear . In this study , we conducted multidisciplinary analyses to characterize the spatiotemporal movement of human rabies cases , to describe the spread pattern and rabies evolution , to identify the risk factors for the occurrence of human rabies cases , which could provide evidence-based guidance for policy-makers and service providers to control and prevent the disease .
In China , human rabies is a class B notifiable infectious disease , and information regarding each laboratory-confirmed case must be reported to the Chinese CDC ( CCDC ) through the National Notifiable Disease Surveillance System ( NNDSS ) [14] . Data on human rabies cases , including age , gender , occupation and month of onset , from January 2004 to December 2013 in mainland China was obtained from the NNDSS . Demographic data , gross domestic product ( GDP ) per capita and education level specific to each county were obtained from the China Bureau of Statistics from the sixth national census in 2010 . Average monthly temperature covering 700 surveillance stations in mainland China from 2004 to 2013 were collected from the China Meteorological Data Sharing Service System ( http://cdc . cma . gov . cn ) . Monthly outbreaks of canine rabies at the province level were obtained from official veterinary bulletin from the ministry of agricultural of People’s Republic of China . For the phylodynamic analysis , the full sequences of the N gene of rabies with background information including isolation year , host , and province were retrieved from GenBank and literatures [6 , 15–19] , accessed on April 15 , 2014 . Then we formed a data set including 219 N gene sequences from 19 provinces ranging from 1986 to 2012 . According to the results of phylogenetic trees , we chose two main lineages named Clade I and Clade II for the discrete phylogeographic analysis . For all available sequence of the two main lineages , we excluded high homologous sequences with the same background information . Then we formed two datasets for the 141 Clade I and 62 Clade II sequences . The accession numbers and strains’ information used in this study are shown in S1 Table . The bar chart of monthly incidence was produced to check seasonality , and annual incidence curves were plotted to examine the overall temporal trend . Average annual incidences over the whole study period were compared across gender and age groups , and the proportions of human cases by occupation were calculated . To assess the spatiotemporal distribution of human rabies , map series were created to show the spatial distribution of annual incidence of each county . In addition , to better present the epidemic dynamic of the disease , the number of cases of each province was mapped from 2004 to 2013 . Hotspots are important characteristics that can be used to target interventions at most- needed places . The spatial movement of hotspots over time is useful not only in describing the disease spread dynamic but also in assessing the effectiveness of disease control and prevention programs . We evaluated the presence of space-time hotspots using Kulldorff’s spatiotemporal scan statistic implemented in SaTScan software ( version 9 . 0 ) [20] . In order to find as many as possible spatially refined areas with reasonable LLR values , a discrete Poisson model was fitted to identify space-time hotspots , and 90% of the study period and the areas with 10% of the total population size in mainland China were set as the upper search bounds , respectively . Hotspots were detected using the log likelihood ratio ( LLR ) test statistic whose significance was evaluated with 999 Monte Carlo samples . Spatiotemporal hotspots were then mapped at the county level , together with a map of diffused counties by year , to show the geographic movement of human rabies after 2004 . To explore potential factors related to spatiotemporal heterogeneity of human rabies , a panel Poisson regression was fitted using STATA software ( Version 10 . 0 , StataCorp LP , Texas , USA ) for the 2004–2013 period . The monthly number of human rabies for each county was set as the outcome variable , and population number was included as the offset variable . Potential risk factors at the county scale , such as temperature , average education level and GDP per capita , were included as covariates in the analysis . Because the incubation of rabies was average 1 to 3 month and the temperature was of lag effect , we explored the time lags of 0 to 4 months for the number of canine rabies outbreaks and 0 to 2 months for temperature . Univariate analysis was performed to examine the effects of individual variables . Multivariate analysis was performed using the variables with a P-value < 0 . 05 in the univariate analysis after colinearity among these variables was examined . The percentage change ( PC ) in incidence in response to the change of a variable by a given amount ( 5°C for temperature , 1000 yuan for GDP per capita , and one year for education level ) , was used to determine the impact of each variable on disease incidence . The formula for calculating PC is 100* ( exp ( coefficient ) -1 ) . The 95% confidence interval ( CI ) and corresponding P-value were estimated . As the data of canine rabies outbreaks were collected only at the province level , we fitted a multivariate model to examine the association between monthly human rabies incidences and monthly numbers of canine rabies outbreaks and monthly temperature at provincial level . Spatiotemporal movement of rabies is often coupled with genetic evolution [19 , 21] . To explore the evolutionary history of rabies and its association with the spatiotemporal spread , we collected available sequences of the N gene of rabies and applied a relaxed-clock Bayesian Markov chain Monte Carlo method [22] . Multiple sequence alignment was performed using Muscle [23] with the default setting . The best-fit nucleotide substitution model for each alignment was carried out by using Akaike information criterion ( AIC ) implemented in JModeltest2 . 0 . 2 [24] . We applied a relaxed-clock Bayesian Markov chain Monte Carlo method to explore the genetic diversity of rabies in the BEAST package v1 . 8 . 0 [22] . In order to elucidate phylogeographic spread of Clades I and II in time and space , a Bayesian stochastic search variable selection ( BSSVS ) approach was used to identify significant transition rates between locations [21] . The spread events between two provinces with a Bayes factor of greater than 3 were examined . For these analyses , we used an uncorrelated lognormal distribution relaxed molecular clock model [25] along with the GMRF Bayesian Skyride model [26] as a coalescent prior . Two independent runs were undertaken for each analysis . The numbers of MCMC iterations and tree-sampling frequencies were shown in S2 Table . Posterior distributions were inspected to ensure adequate mixing in Tracer v1 . 5 ( http://tree . bio . ed . ac . uk/software/tracer ) . We used Tree Annotator program in the BEAST package to generate a maximum clade credibility ( MCC ) tree with a burn-in of 10% of the sampled trees . The MCC trees were visualized using FigTree v1 . 4 . 0 ( http://tree . bio . ed . ac . uk ) . To explore the spread events , we used SPREADv1 . 0 . 6 [27] to calculate the Bayes factor . The genetic diversity distribution and possible spread events of lineage Clades I and II were mapped at the provincial scale .
From 2004 to 2013 , there were 22 , 684 cases reported in 30 provinces , across 1821 of 2922 counties in mainland China . The monthly incidence showed a significant seasonal pattern peaking in the Summer and Autumn , especially in the months from August to October each year ( Fig . 1 ) . The average seasonal incidence were 5 . 22 and 5 . 28 ( 1/1 , 000 , 000 ) in Summer and Autumn compared 3 . 62 and 3 . 32 ( 1/1 , 000 , 000 ) in Spring and Winter . The annual incidence curve in Fig . 1 showed that the human rabies rapidly increased since 2004 , reached its peak in 2006 and 2007 ( 3267 and 3288 cases ) , and plunged in 2008 and kept declining slowly afterwards . Males had a significantly higher incidence than females in all age groups ( P < 0 . 001 ) , and the total risk ratio was 2 . 18 , and the 50+ , 0- and 40- age groups had the highest incidence in both males and females ( Fig . 2 ) . In addition , 70 . 38% of all cases were peasant and herdsman , and followed by student ( 14 . 09% ) and pre-school children ( 8 . 61% ) . No significantly temporal or spatial heterogeneity was found for the distribution of cases by age or occupation . During 2004–2013 , 30 of the 31 provinces in China ( except Tibet ) reported human rabies cases , and the high-incidence provinces were mostly in the southern , the eastern and part of the central China . The high-incidence provinces were mainly in the south , such as Guizhou , Guangxi , Guangdong and Hunan provinces ( Fig . 3 ) . Overall , a decreasing annual incidence was found in the high-incidence provinces , while the low-incidence provinces had an increasing incidence ( i . e . Shaanxi , Shanxi , and Yunnan ) over the 10 years . This phenomenon is more clearly shown by the mapped number of cases across years at the provincial level and provinces with an increasing pattern over the years were shaded by orange dots ( Fig . 4A ) . As shown in Fig . 4B , the endemic areas were expanding from areas with green color grads to red color grads over the 10 years , and the hotspots were moving towards the west and the north over the years . The primary hotspot of human rabies ( HS-I ) was located in southern China and included 281 counties , covering most of Guangxi , Hunan , and Guizhou provinces and spanning from January of 2004 to November of 2012 . The relative risk ( RR ) of reporting human rabies , as compared to the reference regions , was 6 . 65 for the primary hotspot ( Table 1 ) . The hotspots were mainly distributed in the southern , southeastern and central regions before 2006 ( HS-I , HS-II , HS-IV , HS-VII , HS-XV , HS-VI HS-XX and HS-XXII ) , and shifted towards the north ( HS-XI ) and the west ( HS-III ) during 2006–2008 , and further towards the northwest after 2010 ( HS-V , HS-VI , HS-X , HS-XIV , HS-XVII and HS-XXI ) . Many latest hotspots identified during 2012 to 2013 , most of which contained only one county with a high RR ( e . g . HS-V , HS-VI ) , were located in the north and the west , and had very few human cases before 2009 . The univariate analyses revealed that the spatial-temporal distribution of rabies incidence was associated with monthly temperature at time-lags from 1 to 2 months , GDP per capita , and average education years ( Table 2 ) . The multivariate analysis showed that the disease incidence was positive correlation with temperature at a one-month lag ( PC = 19 . 1%; 95% CI = 18 . 0% , 20 . 3% ) , and negative correlation with GDP per capita ( PC = -5 . 7% , 95% CI = -10 . 2% , -1 . 0% ) and average education years ( PC = -13 . 7%; 95% CI = -17 . 6% , -9 . 7% ) . The risk ratios were similar to those obtained from the univariate analyses , except that the effect size of GDP per capita is smaller . In addition , we found that monthly incidence of human rabies was highly correlated with monthly number of canine rabies outbreaks at a 2-month lag ( PC = 11 . 6%; 95% CI = 10 . 6% , 12 . 6% ) and temperature at a one-month lag ( PC = 3 . 0%; 95% CI = 2 . 7% , 3 . 2% ) , and each additional canine rabies outbreak will increase the risk of human rabies by about 11 . 6% . There were a total of five lineages with high posterior node probabilities ( >0 . 5 ) , while only two main lineages , Clade I and Clade II , contributed to rabies epidemic in mainland China from 2004 to 2013 ( Fig . 5 ) . In addition , seven sub-clades with high posterior value were identified in Clade I , and six in Clade II . Interestingly , the sequences from 2009 to 2012 were mostly clustered in Clade I ( colored red in Fig . 5 ) , indicating the dominant role of Clade I in recent rabies epidemics . The genetic diversities of Clade I and Clade II were mapped at the provincial level in Fig . 4C and 4D . The southern and southwestern provinces , in particular Hunan and Yunnan , had more genetic diversities than the northern and eastern provinces , such as Shanxi , Hebei , Beijing , Shanghai , Zhejiang and Fujian , which were mostly associated with Clade I-G . The abundant genetic diversity of rabies in Guangxi and Hunan province ( Fig . 4C and 4D ) suggested they might be the center for rabies circulation and evolution . Fig . 5 and 4C imply possible diffusion of Clade I-G from eastern region ( e . g . , Shanghai , Zhejiang ) to southwestern ( Sichuan ) and northern ( Shaanxi ) regions and of Clade I-B from Henan to Anhui and Yunnan in recent years . It remains to be verified whether these two clades have gained enhanced fitness or transmissibility . To further explore the diffusion pattern implied by genetic linkage while accounting for phylogenetic uncertainty , we summarized rates yielding a Bayes factor >3 in Figs . 4C and 4D and S3 Table , which reveal more spread events of Clade I than that of Clade II . Interestingly , in Clade I , we found some rabies spread events that could be related to the spread of human rabies from the south towards the north and west . The migrations of Clade I-G between Shaanxi and Zhejiang and between Shaanxi and Yunnan were strongly supported with high Bayes factors 33 . 97 and 12 . 36 . Shanxi and Shaanxi , which have latest hotspots of human rabies , were involved in two and three migration pairs with Bayes factor >3 for Clade I-G ( Fig . 4B ) . Additionally , spread events could have also occurred between southwestern provinces ( Sichuan , Yunnan ) and central , eastern and southern provinces ( Henan , Anhui , Zhejiang and Guizhou ) .
The reduction of human case reports has been observed in mainland China but was accompanied with geographic expansion towards the north and the west . Our multidisciplinary study identified this new challenge with both epidemiological and phylogenetic evidence , and provided new insights on risk factors and control strategies for the disease spread .
|
Although the number of human rabies cases has slightly decreased since 2008 in mainland China , the rabies seemed to be gradually expanding to the low-incidence or non-epidemic areas . The neglect of rabies expansion and scarce analyses of the dynamics have made the spatiotemporal spread pattern of human rabies and its determinants poorly understood . Here , we integrate multidisciplinary approaches to explore and describe the spread pattern and evolution dynamic of human rabies in mainland China during the last decade . The results indicated that the reduction in the overall human rabies incidence was accompanied by a westward and northward expansion of the circulating region , which could be associated with the evolution of the virus , especially the clade I-G . And the education level , GDP per capita , temperature at one-month lag and canine rabies outbreak at two-month lag were firstly found to be significant correlation human rabies incidences according to the Panel Poisson Regression analysis . Our findings give a relatively complete picture about the human rabies spatiotemporal dynamics and spread pattern , thus provide new insights on risk factors and control strategies for the disease spread .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
The Spatiotemporal Expansion of Human Rabies and Its Probable Explanation in Mainland China, 2004-2013
|
Group VI Ca2+-independent phospholipase A2 ( iPLA2 ) is a water-soluble enzyme that is active when associated with phospholipid membranes . Despite its clear pharmaceutical relevance , no X-ray or NMR structural information is currently available for the iPLA2 or its membrane complex . In this paper , we combine homology modeling with coarse-grained ( CG ) and all-atom ( AA ) molecular dynamics ( MD ) simulations to build structural models of iPLA2 in association with a phospholipid bilayer . CG-MD simulations of the membrane insertion process were employed to provide a starting point for an atomistic description . Six AA-MD simulations were then conducted for 60 ns , starting from different initial CG structures , to refine the membrane complex . The resulting structures are shown to be consistent with each other and with deuterium exchange mass spectrometry ( DXMS ) experiments , suggesting that our approach is suitable for the modeling of iPLA2 at the membrane surface . The models show that an anchoring region ( residues 710–724 ) forms an amphipathic helix that is stabilized by the membrane . In future studies , the proposed iPLA2 models should provide a structural basis for understanding the mechanisms of lipid extraction and drug-inhibition . In addition , the dual-resolution approach discussed here should provide the means for the future exploration of the impact of lipid diversity and sequence mutations on the activity of iPLA2 and related enzymes .
Many membrane proteins remain unexplored at the molecular-level despite their clear pharmaceutical relevance [1] , [2] . It is therefore crucial to develop computational methods for the structure prediction of membrane proteins . Homology modeling is a common technique to build an initial model when an appropriate template can be identified . Subsequently , all-atom ( AA ) molecular dynamics ( MD ) simulations have been used in the refinement of homology models with some success [3] , [4] . However , for protein-membrane systems the construction of structural models is complicated by the need to equilibrate all the possible orientations of the protein in the membrane . Because the current time-scale accessed by AA-MD ( hundreds of nanoseconds ) is typically too short to simulate the complete insertion process directly , an effective approach to study membrane proteins is to start with a low-resolution model and subsequently go to higher resolution . Coarse-grained ( CG ) models for proteins [5] such as the MARTINI force field [6] , [7] have been used to extend the time-scale of MD simulations by ∼3–4 orders of magnitude , allowing the direct simulation of membrane insertion processes . The force field performs roughly a 4 to 1 mapping between atoms and particles , which has been shown to be sufficiently accurate to study membrane insertion processes [8] , [9] , including for surface enzymes [10] , [11] . However , like other resolution exchange methods [12] , [13] , this approach remains relatively new and untested and structural models should be validated experimentally whenever possible . Phospholipase A2 ( PLA2 ) [1] is one of the largest protein superfamilies identified to date , with 16 groups and many subgroups resulting in more than 35 forms , and represents a promising target for computer-aided drug design ( CADD ) [14] . All PLA2s stabilize at the membrane surface where they can catalyze the hydrolysis of phospholipids to yield fatty acids , involved in signaling , inflammation and in membrane maintenance [15] . The four predominant well-studied types of PLA2s found in human tissues are the cytosolic ( also known as cPLA2 ) , the secreted ( sPLA2 ) , the calcium-independent ( iPLA2 ) , and the lipoprotein-associated ( Lp-PLA2 ) enzymes . The structures of PLA2s–bilayer complexes have been previously approached with deuterium exchange mass spectrometry ( DXMS ) [16] . These experiments provide information about the solvent accessible surface of the proteins by measuring the rate and number of backbone amide N-H groups that can exchange hydrogen with deuterium when in D2O . In this technique , the protein is first enzymatically digested into fragments of several residues in length and mass spectrometry is used to weight the fragments . The experiment is then repeated after the protein is inserted into a membrane to quantify the difference in the number of hydrogen atoms exchanged . These studies have helped define the location of the binding interface with the phospholipid membrane , and have shown that sPLA2 , cPLA2 , iPLA2 and Lp-PLA2 not only have different structures , but also very different membrane association mechanisms . For example , for sPLA2 , a positively charged protein surface facilitates interactions with the anionic headgroups of the lipid surface [17] . For cPLA2 , an additional domain ( C2 ) directs the binding [18] . In the case of Lp-PLA2 , two membrane association helices assist the membrane binding [19] . Finally , for iPLA2 , an anchor region is directly inserted into the phospholipid membrane [20] . These studies also indicated that the catalytic residues stabilize at a location that is remote from the sn-2 position of phospholipids localized in bilayer membranes . This implies that the phospholipids must be extracted from the membrane to be enzymatically hydrolyzed [20] . In this paper , we focus on the modeling of the iPLA2 enzyme at the membrane surface . The iPLA2 is of particular interest for structure-based drug design , as it is believed to be implicated in a large number of diseases , including Alzheimer disease [21] , hypertensive heart failure [22] , neurological disorders [23] , multiple sclerosis [24] , and cancer [25] . There is currently no X-ray or NMR information about the iPLA2 structure , or its membrane-associated complex . The chemical properties of the active site of iPLA2 are very similar to other PLA2s ( in particular to cPLA2 ) , which can cause inhibitors to display a lack of selectivity [26] , leading to unwanted side-effects and toxicity . Thus , a current challenge for inhibitor design targeting iPLA2 lies in optimizing the potency and selectivity of promising new compounds , which can be aided by modeling based on DXMS and molecular dynamics [27] , [28] . To assist the development of new therapeutic approaches , it is also crucial to understand the detailed interactions of PLA2 enzymes with phospholipid bilayers . Previously [20] , a structural model for the iPLA2–membrane complex was built by homology modeling in combination with DXMS to guide the position of the enzyme model at the membrane surface ( Figure 1 ) . However , several approximations limit the accuracy of this approach for drug design applications . First , the DXMS data is subject to interpretation . For instance , the H/D exchange signal is typically averaged over many possible orientations and conformations of the protein , and it is measured only for the amide N-H bond with a resolution of several residues . Second , the use of a rigid protein model does not allow the protein to relax upon binding to the phospholipid bilayer . To overcome these limitations , in this paper we conduct CG-MD simulations to provide equilibrated models of the iPLA2–membrane complex . The structures were further refined with AA-MD simulations , and can be used to better understand the mechanisms of the iPLA2 membrane insertion and activation .
Different subtypes and splice variants of iPLA2 exist in humans [1]; therefore , we have chosen to focus on the catalytic domain which is conserved throughout . Before conversion into a CG representation , an AA structure was first built by homology with the crystal structure of patatin . Patatin has ∼40% sequence similarity with iPLA2 and was solved at a resolution of 2 . 2 Å ( Protein Data Bank code 1OXW ) [29] . Prime [30] was used to build the 332 residue homology model . The ionizable side chains of the enzyme were chosen in their default charge states for pH 7 and histidine residues were kept uncharged . Because there is currently no NMR or X-ray information about the iPLA2 structure , the stability of the homology model was previously tested with MD simulations [28] . The ranking and scoring of docked compounds , as well as deuterium exchange experiments , in the presence , and in the absence of an inhibitor , also suggested that the active-site residues are well described in our model [28] . The most populated structural cluster in the AA simulations was used as the starting structure for generating the CG structural model . The same AA structure was also used for fitting atomic coordinates into equilibrated CG coordinates of the membrane complex , as described in the next subsection . All CG-MD simulations were conducted with GROMACS 4 . 5 . 4 [31] . The MARTINI 2 . 1 force field was used for the protein [7] , together with non-polarizable CG water particles [6] . To maintain the protein secondary and tertiary structure an elastic network was applied composed of harmonic restraints ( with a force constant of 10 kJ mol−1 Å−2 ) between all backbone particles within 7 Å of each other [32] . Palmitoyl oleoyl-phosphatidylcholine ( POPC ) molecules were used for the lipid membrane simulations , because the iPLA2 is known to be active on this type of membrane [33] . The final simulation system was neutral and contained the protein , 390 randomly positioned POPC molecules , and 11871 CG water particles . The chosen CG water model does not bear charges , and it is blind to electrostatic fields and polarization effects . To compensate for the neglect of explicit polarization , screening of electrostatic interactions is done implicitly , assuming a uniform relative dielectric constant of 15 that is smoothly shifted to zero between 0 and 12 Å [6] . The initial box dimensions were 140×140×140 Å3 . Prior to the production runs , energy minimization was carried out for 5000 steps using a steepest-descent algorithm . The integration timestep in CG-MD simulations was 25 fs . Temperature was kept constant at 323 K using the velocity rescaling thermostat of Bussi et al [34] . A Berendsen barostat [35] was used to apply anisotropic pressure coupling , using a coupling constant of 10 . 0 ps , a compressibility value of 3×10−5 bar−1 , and a reference pressure of 1 bar . Van der Waals interactions were also smoothly shifted to zero between 9 and 12 Å [6] . For simplicity , the time-scale of CG simulations is reported here without a scaling factor; it is however possible to use a scaling factor of ∼4 to account for the speed-up in the diffusive dynamics of the CG water model with respect to real water [7] . During the CG-MD simulations , the bilayer was found to self-assemble in the presence of the protein within ∼50 ns , leading to a local energy minimum . A total simulation time of ∼30 µs was accumulated to obtain ample statistics about the enzyme–membrane association process . The AA models were built in VMD [36] by aligning our best equilibrated AA structure onto the CG structures for the membrane complex , using a least squares fitting of alpha carbons on CG particles . Following this , an equilibrated POPC membrane patch of area 103×103 Å2 was aligned on the CG membrane phosphate groups . Lipids within 0 . 6 Å of the protein were removed , and the system was solvated with TIP3P water . 42 Na+ and Cl− ions were added to create a solution with a physiological ion concentration of ∼0 . 1 mol/L . The AA simulations were conducted in NAMD 2 . 9 [37] with the CHARMM36 force field [38] , [39] , using a time step of 2 fs in combination with the SHAKE algorithm [40] . Long-range electrostatic interactions were calculated using the particle mesh Ewald method , and van der Waals interactions utilized a cutoff of 10 Å . The temperature was regulated with a Langevin thermostat [41] , using a damping coefficient of 5 ps−1 . Energy-minimization was carried out for 10 , 000 steps , followed by an equilibration simulation in the NPT ensemble of 10 ns . Positional restraints ( force constant 10 kJ mol−1 Å−2 ) were applied to the protein during equilibration , while the system was slowly heated-up , from 0 to 310 K , by 1 K every 4 ps . Water was prevented from entering the empty space between the protein and the membrane using a repulsive potential implemented in NAMD as an external Tcl script . At the end of the equilibration phase , the protein structure was released . Six AA-MD simulations were initiated from CG structures generated by extensive CG-MD sampling and separated by at least 1 µs . The six simulations were conducted in the NPT ensemble with isotropic pressure scaling , and lasted for 60 ns . The coordinates of an equilibrated AA system are included as supporting information ( Text S1 ) . To analyze the structure of the iPLA2–membrane complex , both the insertion depth and the insertion angle were monitored . We define the insertion angle as the angle between the long helix ( residues 724 to 750 ) and its projection on the membrane surface . The depth of penetration is measured as the distances between the center-of-mass ( c . o . m . ) of the alpha carbons of anchor residues , and the c . o . m . of the bilayer along the bilayer normal ( z axis ) . Finally , two additional AA-MD simulations in solution were conducted for 100 ns , in order to compare the properties of the iPLA2 catalytic domain in solution and in the membrane .
Two different approaches were employed to study the insertion of iPLA2 into a phospholipid bilayer with CG-MD ( Figure 2 ) . In the first approach ( Figure 2A ) , ten CG-MD simulations of ∼500 ns were conducted starting from a random configuration of the lipids around the protein . The membrane was found to self-assemble within ∼50 ns into a fully formed phospholipid bilayer . In seven out of these ten simulations , the bilayer formed around the protein and the enzyme–membrane complex equilibrated with the enzyme adopting an interfacial location . In the other three simulations , the bilayer formed and the enzyme remained in the aqueous environment for the entire simulation . In the second approach ( Figure 2B ) , the membrane was already formed and the protein was in solution , and three long CG-MD simulations were conducted . The time before the onset of anchoring was much longer in this case: ∼2 . 5 µs , 3 . 2 µs , and 4 . 1 µs . Inspection of the trajectories reveals that multiple collisions between the enzyme and the membrane occurred before the formation of the enzyme–membrane complex . More specifically , insertion occurred only when the enzyme collided with its anchoring region ( residues 710 to 724 ) pointing toward the membrane , and therefore , ∼90% of the collisions between the protein and the membrane were unproductive . After adopting an interfacial location , iPLA2 remained at the interface for the remainder of each simulation , consistent with a stable configuration . In Figure 2C , an alignment of the resulting iPLA2–membrane structures with both approaches is shown . A limitation of the first approach is that the membrane can form too rapidly around the protein , and kinetically trap a less stable conformation that corresponds only to a local minimum . This occurred in two of the seven simulations , leading to a conformation with a lower insertion angle of ∼10 deg . The second CG-MD approach did not appear to suffer from this limitation , but it required hundred times longer simulations to observe successful insertion events . An important methodological question concerns the ability of multi-scale simulations to generate a unique atomistic structure for the iPLA2–membrane complex that corresponds to the global energy minimum . To bring back an all-atoms level of details , six AA-MD simulations were seeded from distinct CG structures of the catalytic domain at the membrane surface . AA-MD simulations were conducted for 60 ns to allow the relaxation of the protein structure that was previously restricted by the use of an elastic network . Detailed interactions such as hydrogen bonding are not represented in the chosen CG model , but were probed with AA-MD . We found that in all six AA-MD simulations , the equilibration of side-chains lead to additional hydrogen bonds with the phospholipids headgroups that equilibrated within ∼20 ns ( Figure 3C ) . Importantly , no significant drift occurred in AA trajectories initiated from the CG structures , and for all six simulations the protein structures could be aligned with a root-mean-square deviation ( RMSD ) for backbone atoms below 4 Å . In addition , the residues in contact with the lipid headgroups/tails , and the insertion angle ( 67±8 deg ) , were in agreement between all simulations within the naturally occurring fluctuations ( Figure 3C , D ) . The stability of the AA models was further examined by MD relaxation from high-energy structures . Four new models were generated in two tense orientations on the membrane: two models were positioned perpendicularly 3 Å , and 6 Å , deeper into the membrane than the equilibrium model; the other two were tilted by +15 degree ( deg ) , and −15 deg angles . After equilibration of the membrane for 10 ns , the protein was allowed to relax and the changes in the orientation and depth of the protein were monitored for over 40 ns . These simulations were conducted at 340 K to accelerate the protein relaxation . In all four simulations , the average displacement with respect to the initial structure was >6 Å and indicated that the protein has not yet reached a stable position after 40 ns . The simulations also showed a significant displacement of the enzyme out of the lipid bilayer when the insertion was too deep . Similarly , the tilted enzyme models rotate toward the original binding model as expected . Thus , in both relaxation experiments , the enzyme transits from a tense mode to a relaxed mode and slowly converges toward our best guess for the protein position , suggesting that it corresponds to a favorable conformation at the membrane surface . Therefore , we conclude that our multi-scale simulation approach is robust for determining the lowest energy structure for the iPLA2–membrane complex . Figure 3A shows a representative snapshot of an equilibrated AA-MD simulation , and highlights the membrane surface , the anchoring region , and the contours of a protein cavity at the membrane surface . In all six equilibrated structures , the same surface of the protein was found to bind to the membrane . In particular , three regions are in direct contact with the membrane ( regions 552–555 , 643–646 , and 710–724 ) . The main contact region ( 710–724 , shown in Figure 3B ) folds into an amphipathic alpha helical structure that is inserted into the membrane . One side of the helix consists of a cluster of hydrophobic residues ( Pro711 , Pro714 , Trp715 , Leu717 , Val721 , and Phe722 ) that have their side-chains pointing toward the lipid tails and act as hydrophobic anchors . The other side of the helix consists of basic ( Arg710 , and Lys719 ) and polar residues ( Ser712 , Asn713 , Glu716 , Thr720 , and Gly723 ) that mainly interact with the lipid headgroups . In addition , a second region ( 643–646 ) contains Try643 and Arg645 that can also interact with the membrane surface . Tyrosine and arginine residues are known to bind near the lipid tail/headgroups interface , as they can create both favorable hydrogen bonds and hydrophobic interactions . Finally , a third contact region ( 552–555 ) helps to stabilize the angle between the protein and the membrane . It is formed by Arg553 that can interact with the lipid headgroups , Pro554 that can interact with the lipid tails , as well as Ser552 and Tyr555 that can form hydrogen bonds with the headgroups . In the CG-MD simulations , the insertion angle was about ∼45 deg when Arg553 was unable to form a hydrogen bond with the lipid headgroups , but it equilibrated to >65 deg when this interaction was formed . The depth of penetration of iPLA2 into the lipid bilayer was assessed by measuring the distances between the center-of-mass ( c . o . m . ) of the alpha carbons relative to the c . o . m . of the bilayer , along the bilayer normal ( z axis ) . In both CG-MD and AA-MD simulations , a similar distribution of residues was observed with respect to the components of the bilayer system ( Figure 4A ) . Among the different surface residues , the positions of basic ( Arg710 , and Lys719 ) and polar residues ( Ser712 , Asn713 , Glu716 , Thr720 , and Gly723 ) were found to coincide roughly with the phosphate peak , at ∼18 . 9 ( ±1 . 6 ) Å from the bilayers center ( Table 1 ) . The cluster of hydrophobic residues ( Pro711 , Pro714 , Trp715 , Leu717 , Val721 , and Phe722 ) was found to be inserted ∼3 Å deeper into the membrane in a region occupied by the lipid tails . The perturbation induced by the anchor residues on the vertical packing of the lipids was found to be small . In all six simulations , the location of the catalytic site residue Ser519 was at least 10 Å away from the lipid headgroups , and at an average distance of ∼36 Å from the membrane center . This strongly suggests that the phospholipid substrate molecule must be extracted from the membrane at the beginning of the catalytic reaction . DXMS experiments were carried out on the Group VIA-2 iPLA2 enzyme , which is composed of seven consecutive N-terminal ankyrin repeats , a linker region , and a C-terminal phospholipase catalytic domain . Deuterium exchange on iPLA2 was carried out in the presence of the phospholipid substrate , palmitoyl-arachidonyl-phosphatidylcholine ( PAPC ) , and a methyl arachidonyl fluorophosphonate ( MAFP ) inhibitor to prevent the digestion of the membrane . It identified four regions with significant changes in deuterium exchange upon membrane binding , all located in the catalytic domain ( Figure 4B ) . The region 708–730 showed the largest deuteration levels ( >90% ) in solution , showing that it is solvent-exposed in the absence of phospholipid vesicles . However , in the presence of phospholipid vesicles , the same region did not become highly deuterated , suggesting that it is involved in the membrane anchoring and therefore no longer solvent accessible . When the incubation time in D2O was 10 s , mass spectrometry shows a difference of 13 . 2 in the average number of deuterium exchanges for this region . Similarly , in the case of a 5 min incubation time , a ∼70% decrease in deuteration levels was measured in the presence of the membrane . The computational models are consistent with this result , as they show that hydrophobic residues Val708 , Phe709 , Trp715 , Leu717 , Val721 , Phe722 , and Leu727 , are no longer solvent-accessible in the protein-membrane complex . Interestingly , the negatively charged region 773–778 and the regions 631–655 and 658–664 also showed a decrease of deuteration that is however less pronounced than for the anchor residues ( <40% decrease in deuteration levels after 5 min of incubation ) . In the computational models , these residues belong to a hydrophobic cavity at the membrane surface that leads to the active-site serine . A likely explanation for the weak observed DMXS effect is that a single phospholipid can transiently occupy the cavity , and prevent the solvent from accessing these residues . We are currently exploring this hypothesis in more details and will publish these results separately . In order to detect properties of the enzyme that change upon binding to the membrane , we compared two simulations of iPLA2 , in solution , and in a membrane . In both these simulations , the RMSD for the protein backbone atoms was found to stabilize below 3 Å , which is indicative of a stable protein model ( Figure 5A ) . Moreover , both simulations showed that the anchor region of iPLA2 ( 710–724 ) is very dynamic in nature ( Figure 5B ) . However , in solution the amphipathic helix was observed to unfold , indicating that it is stabilized by the interaction with the membrane [42] . In addition , residues of the anchor region were found to move in solution by as much as ∼7 Å , and in some structures to block the entrance of the active-site cavity ( Figure 5C ) . In particular , the bulky Tyr643 residue was found to act as a gatekeeper that can prevent the entrance of incoming ligands . To show this , the free energy profile was calculated for a methane probe entering the active-site with the implicit ligand sampling ( ILS ) method [43] . The ILS method allows the rapid post-processing an entire MD trajectory to collect qualitative information about the interaction free energy of a small probe . The calculation showed that a favorable free energy pathway exists connecting the membrane to the catalytic Ser519 , which is accessible only in the membrane-activated ( open ) state of the protein ( Figure 5D ) . This uncovers a new mechanism by which the flexible anchor region of iPLA2 may provide a form of active-site regulation similar to the lid structure in cPLA2 .
The present study on iPLA2 complements two previous CG simulation studies [10] , [44] that also demonstrated the utility of a multi-resolution simulation approach for predicting the surface location of PLA2 enzymes . These two previously published CG studies focused on the smaller sPLA2 ( ∼14–19 kDa ) , which has a known structure , and bears no sequence similarity with the much larger iPLA2 ( ∼84–90 kDa ) . In one study [10] , it was found that the sPLA2 protein equilibrated further away from the membrane than suggested by experiments . However , the authors concluded that these differences were most likely due to difficulties in interpreting tryptophan fluorescence experiments , as no drift was observed in their AA-MD simulations originated from the CG models . In support for this explanation , no drift was observed here in the AA-MD simulations originated from the CG models , and the agreement between computer simulations and DXMS experiments was excellent for the prediction of membrane-bound residues . Our results show that the catalytic domain of iPLA2 adopts a well-defined orientation at the membrane surface that is aimed to facilitate the vertical extraction of phospholipids from the membrane . Both the insertion depth and the orientation of iPLA2 on the lipid surface are therefore finely tuned to facilitate rapid turnover by coupling hydrolysis and product release with the binding of the next substrate [19] , [20] . In addition , the enzyme was observed to diffuse laterally at the membrane surface without any disruption of the membrane complex , with the lipids dynamically repacking around the enzyme . This lateral motion is in agreement with the so-called scooting mechanism , which has been proposed to be important to facilitate the detection of lipid protrusion at the membrane surface [45] . The scooting mechanism contrasts with the hopping mechanism , in which the enzyme dissociates and re-associates with the membrane to explore the lipid surface . The hopping mechanism is believed to be favored by sPLA2 in the presence of zwitterionic membranes [44] . The Group VIA-2 splice variant of the iPLA2 contains in addition to the catalytic domain , seven ankyrin repeats , and a linker region ( ∼700 residues ) . The ankyrin repeats are believed to directly or indirectly assist membrane association because the catalytic domain by itself does not have activity [46] . Our efforts to simulate the full GVIA-2 iPLA2 structure did not uncover an alternative mechanism for the membrane insertion in the presence of ankyrin repeats . In particular , two CG-MD simulations of the full GVIA-2 iPLA2 structure were conducted for 500 ns starting from a random configuration of the lipids . After insertion into the membrane , no significant differences were observed in the catalytic domain region , suggesting that the catalytic domain alone is able to successfully complete the insertion process . However , it cannot be excluded that the additional structural elements increase the probability of membrane insertion , either by providing a second interaction point with the membrane , or by stabilizing the correct orientation of the catalytic domain . Moreover , the rotation of the catalytic domain around an axis perpendicular to the membrane plane was hindered in the full protein structure , which may be indicative of a more stable protein–membrane complex . Group VIA iPLA2 has also been shown to be active as an oligomer through radiation inactivation studies [47]; thus , the ankyrin repeats may be crucial for stabilizing the complex by taking part in the assembly of an oligomeric structure . For a successful hydrolysis of phospholipids , one substrate molecule must be extracted from the lipid aggregate into the active-site of iPLA2 . Because natural fluctuations in the fluid membrane mostly cause lateral motions in the lipid molecules , an exquisite mechanism must exist to allow the lipid molecule to escape the lipid surface . The structure of the iPLA2–membrane complex shows the existence of a hydrophobic cavity near the membrane surface that is likely to assist the lipid extraction by competing with hydrophobic interactions between the substrate and the lipid aggregates . The deuterium exchange experiments also support a scenario in which the cavity is often transiently occupied by a lipid substrate extracted from the membrane . However , the lipid extraction could not be directly observed in our simulations , presumably because the lipid-binding cavity remained closed in the CG simulations due to the use of an elastic network model to stabilize the protein structure . In the AA simulations the time-scale ( nanoseconds ) was too short to observe the opening of the cavity and the extraction of a lipid from the membrane . Ongoing work in our labs will address this problem by conducting longer time-scale ( microsecond ) AA simulations , as well as steered MD simulations [48] , and provide a more complete picture of the lipid extraction . The high degree of conformational flexibility of iPLA2 during simulations leads us to believe that the flexible loops that form the entrance of the cavity regulate the active-site accessibility . For instance , the hydrophobic residues in the anchor region ( 710 to 724 ) fold into an amphipathic helix in the membrane , but adopt an extended conformation when they are in solution , leading to the partial closure of the active-site cavity . We [27] have recently hypothesized that each type of PLA2 contains a distinct “membrane interaction site ( s ) ” that should be considered as a typical allosteric site . When the PLA2 is associated with a ligand ( in this case , the membrane ) , the enzyme exists in a different conformational state than in solution ( R to T transition ) in accord with the basic ideas of allostery [49] as recently reviewed by Changeux [50] . Although further work will be needed to confidently map the conformational landscape of iPLA2 , the results reported herein are consistent with the novel notion of considering the membrane as a ligand , which causes a conformational change in certain water-soluble proteins ( such as various PLA2s ) when they productively associate with a membrane [27] . In addition , the present study identified an amphipathic helix as the anchor region of iPLA2 . Amphipathic helices have been recently proposed as interesting motifs that help recognize hydrophobic defects in the membrane , such as those created when bending the bilayer [42] , [51] , [52] . It is possible that the helix in iPLA2 confers the enzyme with the ability to detect membrane defects , which is believed to be a key property of PLA2s [45] . In future studies , a deeper understanding should be gained of the roles of different protein conformations accessed during the catalytic cycle of iPLA2 . This will be crucial for inhibitor design , as interrupting a single catalytic step could be sufficient to inhibit the entire reaction . MD simulations could be utilized to explore the role of the enzyme flexibility during the different phases of the catalytic process , including the extraction , binding , and hydrolysis of the substrate and the release of the products in the bilayer . These models will open the door to virtual screening techniques aimed at finding new inhibitors of iPLA2 with improved potency and selectivity . Finally , we feel that the multi-scale approach discussed here should prove helpful in designing initial computational models of various PLA2 enzymes at the membrane surface , and will lead to further studies on the impact of lipid diversity and sequence mutations on the activity of iPLA2 and related enzymes .
|
The Ca2+-independent phospholipase A2 ( iPLA2 ) enzyme is a potential target for the development of medicinal agents against heart and neurological diseases , multiple sclerosis , arthritis , and cancer . However , no structural information is currently available for the iPLA2 . The binding of the enzyme to human membranes is driven by favorable electrostatic and non-polar interactions , but the detailed influence of these factors is not well understood . In this paper , we have combined coarse-grained and all-atom simulations of a homology model of the iPLA2 . The coarse-grained description allows highly efficient simulations of the protein insertion into a lipid bilayer , while the all-atom simulations are used to refine the structures of the protein–membrane complexes . Finally , the resulting structures are validated experimentally with deuterium exchange experiments . In future works , this approach could be used to build models of other PLA2s . The iPLA2 models presented here open the door to the computational design of new inhibitors with improved potency and selectivity .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"physics",
"biochemistry",
"enzyme",
"structure",
"computational",
"chemistry",
"molecular",
"dynamics",
"biophysic",
"al",
"simulations",
"enzyme",
"regulation",
"enzymes",
"macromolecular",
"assemblies",
"chemistry",
"theoretical",
"chemistry",
"biology",
"computational",
"biology",
"biophysics",
"simulations",
"biophysics"
] |
2013
|
Insertion of the Ca2+-Independent Phospholipase A2 into a Phospholipid Bilayer via Coarse-Grained and Atomistic Molecular Dynamics Simulations
|
Horses belong to the order Perissodactyla and bear the majority of their weight on their third toe; therefore , tremendous force is applied to each hoof . An inherited disease characterized by a phenotype restricted to the dorsal hoof wall was identified in the Connemara pony . Hoof wall separation disease ( HWSD ) manifests clinically as separation of the dorsal hoof wall along the weight-bearing surface of the hoof during the first year of life . Parents of affected ponies appeared clinically normal , suggesting an autosomal recessive mode of inheritance . A case-control allelic genome wide association analysis was performed ( ncases = 15 , ncontrols = 24 ) . Population stratification ( λ = 1 . 48 ) was successfully improved by removing outliers ( ncontrols = 7 ) identified on a multidimensional scaling plot . A genome-wide significant association was detected on chromosome 8 ( praw = 1 . 37x10-10 , pgenome = 1 . 92x10-5 ) . A homozygous region identified in affected ponies spanned from 79 , 936 , 024-81 , 676 , 900 bp and contained a family of 13 annotated SERPINB genes . Whole genome next-generation sequencing at 6x coverage of two cases and two controls revealed 9 , 758 SNVs and 1 , 230 indels within the ~1 . 7-Mb haplotype , of which 17 and 5 , respectively , segregated with the disease and were located within or adjacent to genes . Additional genotyping of these 22 putative functional variants in 369 Connemara ponies ( ncases = 23 , ncontrols = 346 ) and 169 horses of other breeds revealed segregation of three putative variants adjacent or within four SERPIN genes . Two of the variants were non-coding and one was an insertion within SERPINB11 that introduced a frameshift resulting in a premature stop codon . Evaluation of mRNA levels at the proximal hoof capsule ( ncases = 4 , ncontrols = 4 ) revealed that SERPINB11 expression was significantly reduced in affected ponies ( p<0 . 001 ) . Carrier frequency was estimated at 14 . 8% . This study describes the first genetic variant associated with a hoof wall specific phenotype and suggests a role of SERPINB11 in maintaining hoof wall structure .
Many of the signaling molecules involved in patterning ectodermal derivatives , such as teeth and hair , are also involved in organizing mammalian distal limb appendages , including nails , claws and hooves [1] . Perissodactyla ( odd-toed ungulates ) are a diverse group of mammals that include the horse , zebra and donkey . These animals bear their weight almost entirely on the third toe . In the modern horse , the non-weight bearing digits have atrophied while the third digit has enlarged . The nail has also evolved to become fully interdigitated with the underlying soft tissue and to form a full weight bearing structure , the hoof . As horses are prey animals , the development of hooves illustrates a major evolutionary innovation based on the need for rapid acceleration and sustained speed . Since only the hooves touch the ground , the remaining portions of the foot have become parts of the limb , substantially increasing the length of stride . Additionally , raising the heel and digits off the ground increased the number of joints that move the limbs forward and thereby increased the rate of stride . Although these modifications substantially increase the potential speed and acceleration of these animals , extensive structural integrity of the hoof is required to support all of the body weight . On average , adult horses and ponies weigh 1000 and 880 lbs , respectively . At an evenly balanced stance when motionless , this places an average of 220–250 lbs of force on each limb . When in motion , ground reaction forces increase at various phases of the gait , resulting in additional force applied to each hoof [2] . Ectodermal dysplasias ( EDs ) are a heterogenous group of congenital disorders characterized by alterations in two or more ectodermal structures ( hair , teeth , nails or sweat glands ) [3] . Genetic conditions that exclusively involve the nails are rarer and have been classified as nonsyndromic congenital nail disorders ( NDNC ) . Currently , there are ten characterized NDNCs in people , of which four have associated genetic alterations . Leukonychia ( NDNC3 ) , characterized by white discoloration of the nails , is caused by an alteration in PLCD1 [4]; Anonychia/hyponychia ( NDNC4 , absence of hypoplasia of nails ) due to an alteration in RSPO4 [5]; toenail dystrophy ( NDNC8 ) , caused by an alteration in COL7A1 [6] and onychodystrophy ( NDNC10 ) , associated with an alteration in FZD6 [7] . There are currently six NDNCs for which a genetic alteration has not been identified , including isolated congenital onychodysplasia ( NDNC7 ) , a disease characterized by longitudinal streaks , thinning and splitting at the distal nail edge of all finger and toenails [8] . This study describes an inherited disorder , termed Hoof Wall Separation Disease ( HWSD ) , characterized by separation and breaking of the dorsal hoof wall in the Connemara pony . Without the integrity of the hoof wall , ponies cannot support their weight effectively . The associated chronic inflammation often leads to laminitis , a debilitating condition characterized by separation of the third phalanx from the epidermal laminae that connect the bone to the dorsal hoof wall . Chronic laminitic episodes in horses are very painful and often warrant euthanasia . There are no known alterations that affect the hoof wall in horses and a comparative approach was not feasible due to the unique nature of the hoof . Our goal was to identify the molecular etiology of the disease in order to reduce its prevalence through genetic testing and to provide insight into this unique ectodermal structure . Genome-wide association analysis , coupled with whole genome next-generation sequencing , identified a frameshift variant in SERPINB11 associated with this novel , hoof specific phenotype in Connemara ponies . SERPINB11 remains an uncharacterized protein in humans [9 , 10] and further investigation of the potential role of SERPINB11 in NDNCs may be warranted .
Two clinically-affected Connemara females were examined at 5-months and 1-year of age . The onset of hoof pathology in these two index cases had become evident at 3 and 5-months of age , respectively . With the exception of the hooves , physical examinations revealed no other abnormalities; haircoat , underlying skin , mucous membranes and mucocutaneous junctions appearing normal . Abnormal sweating was not reported in either case . In both cases , all four hooves displayed a dorsal hoof wall separation at the sole with a normal coronary band appearance ( Fig 1A ) . Proliferative horn was evident on the solar aspect of all four hooves ( Fig 1B ) . The 5-month old pony , which had markedly proliferative solar horn , was lame on both front feet at the walk while the yearling , which had undergone a recent hoof trimming , appeared sound at the walk . Distal extremity radiographs of both front feet and the dental arcade revealed no abnormalities . Hooves from three additional HWSD-affected female ponies ( aged 1 , 4 and 5 years ) underwent complete gross examination . Age of onset in these three cases was less than 6-months of age . In the three cases , all four hooves showed variable degrees of splitting within the dorsal hoof wall , most prominent along the distal margin and variably spreading more proximally . The horn of the hoof wall was brittle and easily broken while the horn of the sole appeared stronger . The coronary band appeared normal . All four feet were bisected sagittally . Coffin bone rotation , an indication of laminitis where the toe of the distal phalanx ( i . e . coffin bone ) has dropped due to loss of lamellae support , was evident in 2/3 ponies ( aged 4 and 5 years ) . The white line was intact and there was no hyperaemia or scarring in the corium or lamina . In the one HWSD pony ( 1-year of age ) with no evidence of coffin bone rotation , the distance of the white line to the horn capsule measured 1 cm and was consistent proximal to distal . This horn to white line distance is within the normal radiographic reference range reported for adult ponies [11] . The dorsal hoof wall separation was outside of the white line ( Fig 1C ) . Histologic examination of coronary band ( Fig 1D ) , periople and proximal lamina , skeletal muscle and liver performed in one of three ponies ( 1-year of age ) revealed no pathologic changes . A case-control allelic GWA was performed on the 51 , 453 SNPs that passed quality control . Initial genomic inflation ( λ = 1 . 48 ) was reduced ( λ = 1 . 09 ) by eliminating seven unaffected outliers identified by multi-dimensional scaling ( Fig 2A and 2B ) . Within this new sample set , there was a positive association between HWSD and a 1 . 7 Mb region on equine chromosome 8 ( Fig 2C and 2D ( S3 Table ) ) ; top SNVs at chr8: 80 , 772 , 490 and chr8:80 , 648 , 576 praw = 1 . 37x10 = 10 , pcorrected = 1 . 92x10-5 ) . Ponies affected with HWSD had a distinct homozygous haplotype within the identified region; the same haplotype was not observed in control animals in the homozygous state . SNP genotypes of the associated region are provided in S4 Table . The homozygous region identified in affected ponies ( 79 , 936 , 024–81 , 676 , 900 bp ) , contains a family of SERPINB genes . Based on the Equus caballus 2 . 0 genome assembly [12] , horses have three additional copies of SERPINB3/B4 within this interval as compared to humans ( Fig 3A ) . The rest of the genes , orientations and order are conserved with respect to human . Due to limited information regarding comparative diseases related to the SERPIN gene family , whole-genome next generation sequencing was performed instead of selectively sequencing predicted candidate genes . Whole-genome sequencing was performed on two Connemara HWSD cases and two unaffected Connemara ponies that were homozygous reference for the associated haplotype . A published Quarter horse whole-genome sequence was used as an additional control [13] . Sequencing revealed a total of 9 , 758 single nucleotide variants ( SNVs ) within the interval , of which 363 segregated with HWSD in the two cases and three controls ( S5 Table ) . Of these 363 SNVs , 16 were located within annotated genes or were fewer than 700 base pairs from the ATG . Although the promoters for the genes are not identified in horses , we selected 700 base pairs in order to cover key regulatory regions close to the start of translation . One additional SNV was chosen based on its location between SerpinB2 and SerpinB10 . These gene-associated SNVs were selected for follow-up genotyping within a larger sample set . In addition , sequencing identified 1 , 230 small insertions and deletions ( indels ) , of which 28 segregated with disease ( S6 Table ) ; 11 were within or close ( <700 bp ) to genes . Of these 11 indels , 6 were intronic , 1 was coding , and 4 were up/downstream . The coding and up/downstream indels were tagged for genotyping in a larger sample set . Of the 22 variants ( 17 SNVs and 5 indels ) genotyped on the custom Sequenom panel , 21 passed quality control . Three variants were unique to the 23 affected Connemara ponies and heterozygous in 27 obligate carriers ( Table 1 ) . Although one indel failed Sequenom genotyping , we retained it in our analysis ( Table 1 ) . None of these variants were identified in the 169 non-Connemara equines also genotyped on the Sequenom array , however 82 unaffected Connemara ponies were heterozygous for the three variants and 244 were wild-type . On chromosome 8 , base pair 80 , 259 , 666 is located downstream of SERPINB2 and upstream of SERPINB10; 80 , 319 , 671 and the position of the four-base-pair deletion that failed quality control ( 80 , 319 , 673 ) are both within the rolling circle ( RC ) repeat element Helitron3Na_Mam located upstream of SERPINB8; 80 , 111 , 598 is in the fifth exon of SERPINB11 ( Fig 3 ) . The insertion within SERPINB11 introduces a frameshift that first alters the two amino acids following residue 168 , and then introduces a premature STOP codon . 55% of the protein is predicted to be truncated , including a large portion of the serpin scaffold and the entire reactive site loop [9] . As potential regulatory variants were not identified in our analysis and in order to prioritize the four remaining candidate variants , expression analysis of SERPINB2 , SERPINB8 , SERPINB10 and SERPINB11 was performed . These were the four genes closest to segregating SNVs and indels ( Fig 3 ) . Evaluation of mRNA levels in coronary band samples from four HWSD-affected ponies and four unaffected controls revealed that coronary band SERPINB11 expression was significantly reduced in affected ponies . Relative Expression Software Tool ( REST ) analysis indicated down-regulation ( 0 . 064; S . E . range 0 . 010–0 . 274 ) in the affected group by a mean factor of 16 and a probability that the difference between sample and control groups was due only to chance [P ( H1 ) ] of <0 . 001 ( Fig 4A ) . By contrast , REST showed no difference in expression of SERPINB2 , SERPINB8 , or SERPINB10 between the affected and unaffected sample groups ( Fig 4B ) . SERPINB11 was also found to be a very abundant transcript in the coronary band of an unaffected horse , relative to its levels in other tissues . RT-PCR showed gene expression in lung , stomach , skin , coronary band , and brain . Levels were subjectively highest in the stomach and coronary band . By contrast , SERPINB2 appeared most highly expressed in stomach and skin; SERPINB8 was widely expressed and most abundant in stomach and skin; SERPINB10 was minimally expressed in coronary band and most prominent in stomach and skin ( Fig 4C ) . The SERPINB11 frameshift variant was found to be homozygous in a total of 31 affected ponies indicating complete penetrance . The severity of phenotype ranged from mild ( cleft between dorsal hoof wall and white line apparent on solar aspect of hoof but the pony was able to be maintained with frequent hoof trimming and shoeing ) to severe ( Fig 1 ) . Within the entire 423-Connemara-pony data set , allele frequency was 18 . 7% and a total of 96 ponies were heterozygous for the SERPINB11 insertion . The heterozygous animals are all phenotypically unaffected by HWSD . Within a 324-pony subset of individuals unrelated to the affected animals , carrier frequency for the variant is 14 . 8% .
Based on our clinical and histologic assessment , we have defined the phenotype of HWSD in the Connemara pony to include an early age of onset ( within the first 6-months of life ) and characteristic splitting and separation of the dorsal hoof wall ( Fig 1A ) . Lesions are specific to the dorsal hoof wall and do not appear to involve any other ectodermal structures . Laminitis may be a sequelae to HWSD . Splitting of the dorsal hoof wall may be observed in inflammatory or infection conditions of the equine hoof , including white line disease or as a complication of laminitis or solar abscessation . For this study , we characterized HWSD-affected ponies based upon the following criteria: ( 1 ) Connemara pony breed ( 2 ) age of onset within the first six months of life and ( 3 ) characteristic clinical signs , supported by digital photographs of all four feet . Other diseases , including white line disease and abscessation , are associated with infection; the sole or white line is visibly discolored and unhealthy on examination . With HWSD , the sole and white line appear healthy , with the exception of proliferative sole in the animal’s effort to support the limb on a structure other than the dorsal hoof wall . The Connemara pony originated in the County Galway of western Ireland and the breed standard is characterized by hard strong hooves [14] . These ponies are often unshod when used for performance activities ( jumping , dressage , driving ) , evidence of a strong and healthy hoof within the breed . Based on the breed standard , this makes the phenotype of HWSD all the more striking in affected individuals . The age for the control ponies within this study was set at >2 years . We have not observed any reports of an older age of onset of HWSD; most ponies are affected within the first 6 months of life . In the 23 ponies positive for the identified SERPINB11 variant , all demonstrated an abnormal dorsal hoof wall . However , variable expressivity was apparent in HWSD-affected cases . Mild HWSD cases demonstrated evidence of the dorsal hoof wall separation on the solar aspect of the hoof without severe splitting evident on a lateral view . In more severely affected cases ( Fig 1 ) , the dorsal hoof wall separation was readily apparent . We did not identify any potential genetic modifiers that could account for the variability in phenotype . Owners of HWSD-affected ponies often notice that the splitting of the distal hoof wall worsens when the environmental conditions change from dry to wet or vice versa . Management typically consists of frequent trimming of hooves and , in some cases , glue-on shoes as the dorsal hoof wall of most HWSD-affected ponies will split if nails are used . The dorsal hoof wall is composed of keratins , which provide strength , hardness and insolubility due to disulfide bonds between and within the long chain fibrous molecules [15] . There are dozens of different keratin molecules , with molecular weights in the range of 40–70 kDa and varying degrees of hardness and sulfur concentration [16] . Terminally differentiated keratinocytes , originating from the coronary band , are arranged in specialized tubular and intertubular configurations in four distinct zones [17] . The gradient in tubule density mirrors the gradient in water content across the hoof wall , with the innermost layers of the hoof having the highest relative water content , which confers high crack resistance [18] . In healthy horses , by the time the shock of the impact with the ground reaches the first phalanx , about 90% of the energy has been dissipated , mainly at the innermost hoof wall layer ( i . e . the lamellar interface ) [19] . Distal hoof wall splitting does not , in it of itself , result in lameness . Rather , repeated stress on the innermost layer of the lamellar interface results in separation of the interdigitating lamellae from the distal phalanx ( i . e . laminitis ) . As laminitis progresses , radiographs of the distal phalanx may reveal the separation of the dorsal hoof wall from the distal phalanx . Although radiographs were unremarkable in the examined 5-month old filly , the lameness was most likely due to lamellar inflammation that had not yet progressed to full separation . The original population of ponies used in this study was stratified , which was markedly improved by removing seven control ponies visualized as outliers on the multidimensional scaling plot ( Fig 2A ) . Alternatively , a mixed model approach could have been utilized to correct for the level of stratification; however , removal of the seven control ponies did not affect power to detect a significant association on ECA8 . The associated region on ECA8 encompassed ~1 . 7 Mb , which contained 13 annotated SERPBINB genes . None of these genes have been previously associated with any of the human EDs nor was there any supporting evidence documenting expression of these genes in any ectodermal structures . Targeted re-sequencing of the ~1 . 7 Mb candidate region was considered; however , it has become more cost effective to perform whole-genome sequencing , when an autosomal recessive mode of inheritance is suspected . With Mendelian disorders , putative functional variants may more readily be uncovered on smaller sample sets using whole genome sequencing whereas if a more complex mode of inheritance was likely , capture re-sequencing could allow for many individuals to be sequenced for the particular region of interest at a relatively comparable cost [20] . Sequencing revealed 363 SNVs and 28 indels that segregated with HWSD , with 17 and 11 , respectively , located within or adjacent to annotated genes . After genotyping a larger population of HWSD-affected and unaffected equids , four segregating variants remained ( Table 1 ) , including a 4-bp deletion that had failed Sequenom genotyping . Of these four variants , only one was coding . The insertion within SERPINB11 introduced a frameshift , leading to a premature stop codon . Based upon the severity of the HWSD phenotype , priority was placed on coding variants as we presumed the variant would alter the amino acid structure of the protein involved . In addition , q-RT-PCR data demonstrated decreased expression of SERPINB11 in coronary band tissue , where keratinocytes of dorsal hoof wall originate . The decision to focus on the one coding variant for HWSD was validated in this study; however , we acknowledge that non-coding variants have been increasingly associated with disease [21] . The three other HWSD-segregating variants may be a part of the haplotype on which the frameshift variant originated . Across species , serpins represent that largest and most functionally diverse family of serine protease inhibitors . Some serpins exhibit alternative functions , such as hormone transport and blood pressure regulation [22] . Serpins have been classified into clades according to their sequence similarity . Clades are classified as A-P , with clades A-I representing human serpins [22] . Serpins have a highly conserved secondary structure , with three β-sheets ( A , B and C ) , nine α-helices and a reactive center loop ( RCL ) , which serve as bait for the target proteases . The tertiary structure allows for a conformational change critical to protease inhibitor activity [22] . Serpins exist as monomeric proteins in their native state , which is defined by an exposed RCL that allows it to interact with the protease . Serpins can transition back and forth between latent and active forms [10] . The SERPINB clade is considered the ovalbumin or ov-serpin clade , based on their high sequence similarity to chicken ovalbumin , and exist intracellularly [23] . In humans , clusters of genes on HSA6 and 18 have evolved from a common ancestor by one or two interchromosomal duplications with several intrachromosomal duplications [24] . A similar clustering is evident in the horse , with the clusters of SERPINB genes located on ECA8 and 20 [12] . Of the clade B serpins , only human SERPINB11 and mouse Serpinb11 have yet to be characterized [9 , 10] . Current evidence suggests that , in mice , Serpinb11 can function as a trypsin inhibitor yet SERPINB11 has lost inhibitory activity in humans and may have evolved a non-inhibitory function [9] . The inhibitor function of SERPINB11 has not been assessed in the horse . In the chicken , there is no orthologue for human SERPINB11 , suggesting that these genes were either lost in the chicken or arose after the merging of avian and mammalian lineages [25] . Based on these limited studies of SERPINB11 , it may be that there exist different roles , including varying abilities to function as protease inhibitors , of SERPINB11 between species . Most research efforts into the characterization of major structural proteins of the equine hoof wall are targeted at the lamellae , as this is the site of biomechanical failure in equine laminitis . One study focused on characterizing proteins of the equine hoof wall that included the laminar epithelium ( i . e . stratum internum ) , outer highly cornified hoof wall ( stratrum medium ) and coronary band epithelium [26] . From this investigation , it was evident that the keratin types within the coronary band epithelium were highly similar to those found in the stratum medium . Additionally , the high-sulfur and high-tyrosine protein components were rich in cysteine only in the stratum medium . Experimental studies from the same laboratory have determined that 35S-cysteine is preferentially taken up into the terminally differentiating hoof wall layers [26] . These cysteine-rich proteins are thought to contribute to stabilization of the interfibrillar matrix of the stratum medium through disulfide bonding . If SERPBINB11 retains cysteine peptidase inhibitory activity in the horse , as it does in the mouse [9] , it may function to inhibit proteolytic cleavage of cysteine residues in the terminally differentiating hoof wall layer . A loss of SERPINB11 function could therefore result in a loss of peptidase inhibition and structural failure of the cysteine-rich hoof wall upon impact . Alternatively , SERPINB11 may play a specific role in relative keratinocyte proportions in the equine hoof wall , thereby weakening the overall structure and allowing it to fragment at the most distal end . Although keratinocytes may appear histologically intact at the level of the coronary band , the most distal aspect ( i . e . most mature portion of the cell that is undergoing the majority of concussive impact ) may be structurally abnormal . The distal dorsal hoof wall is difficult to evaluate histologically unless tissue is embedded in acrylic , which was not performed in this study . Further histologic investigation of the entire population of mature keratinocytes within the dorsal hoof wall of HWSD-affected ponies is warranted . A bacterial artificial chromosome transgene expressing Cre under the control of Serpinb7 regulatory elements was recently developed [27] . Although the original aim of the study was to evaluation the expression in kidney mesangial cells , the authors discovered that the Serpinb7-Cre transgene mediated loxP-recombination in all epidermal layers of the skin , hair follicle cells and the epithelium of the mouse forestomach and esophagus . The transgene colocalized with Keratin10 and Keratin14 in the suprabasal and basal layer of the epidermis , respectively [27] . Similar to these expression patterns , the expression of SERPINB11 in our study was highest in the coronary band and stomach , with expression also detected in skin . In humans , SERBPINB11 is located on chromosome 18q21 . 33 . To date , there have been no reports of naturally occurring alterations of clade B serpins leading to a disease phenotype in humans . The only disease association of SERPINB11 is with endometroid ovary carcinoma [28] . Of the six characterized NDNCs that do not have an associated genetic alteration to date , none map to this region , including isolated congenital onychodysplasia ( NDNC7 ) , which phenotypically resembles HWSD with thinning and splitting at the distal nail edge of all finger and toenails [8] . Of interest , SERPINB11 was identified as a potential candidate gene for adaptive evolution in Yoruba [29] . Another mechanistic alternative is that SERPINB11 functions as member of a protein chaperoning complex , similar to the role of SERPINF1 and SERPINH1 in association with procollagen . A loss-of-function alteration in SERPINF1 has been demonstrated to cause osteogenesis imperfecta type VI in humans [30] while variants in SERPINH1 have been associated with ostogenesis imperfecta in both humans [31] and dogs [32] . In a similar manner , SERPINB11 may have a role as a chaperone protein for those proteins involved in hoof wall structure . In the horse , we identified one full-length transcript variant from hoof capsule . In humans , there have been seven major SERPINB11 transcripts identified; three correspond to a protein product with various splice variants and one contains an insertion , leading to a frameshift and premature stop codon at position 90 that results in a nonfunctional variant ( NP_001278207 ) [9] . There are no deleterious effects of this truncated transcript reported in humans . However , based on the results from this study , further investigation into the potential role of the truncated transcript in nail health may be warranted . Tissue expression of SERPINB11 in humans demonstrates expression in the tonsil , lung , placenta and prostate while Serpinb11 demonstrates expanded expression in mice with transcripts identified in eye , lung , lymphocytes , thymus , stomach , uterus , heart , brain , liver , skeletal muscle and whole embryonic tissue at day 7 , 15 and 17 [9] . Tissue expression of SERPINB11 in the horse was similar to mouse and included lung , stomach and brain ( Fig 4A ) . However , expression of SERPINB11 in the horse was strongest in the coronary band , or most proximal part of the hoof capsule . The coronary band is the tissue from which the dorsal hoof wall arises and is analogous to a human cuticle . To the authors’ knowledge , expression of SERPINB11 in skin and nail tissue has not been examined in humans and mice . The results of this study demonstrate a strong association of the SERPINB11 c . 504_505insC variant with the HWSD phenotype . Additionally , HWSD is the first disease to be described that results in a hoof-specific phenotype , with no other ectodermal structures affected . Further studies are necessary to determine the mechanism by which SERPINB11 maintains structural integrity of the hoof wall of healthy ungulates and if SERPINB11 plays a similar role in nail and claw health of non-ungulate species .
Blood samples from index cases were collected at the University of California , Davis School of Veterinary Medicine William R . Pritchard Veterinary Medical Teaching Hospital . Additional samples were drawn by private veterinarians and mailed by individual Connemara owners . All animal samples were obtained following protocol number 17491 approved by the University of California Davis Institutional Animal care and Use Committee . Two affected Connemara ponies were evaluated at the University of California , Davis ( UCD ) Veterinary Medical Teaching Hospital in 2011 by a board-certified equine internist ( CF ) . Following humane euthanasia , hooves from three additional affected ponies were evaluated by a board-certified pathologist ( VKA ) . Distal extremity radiographs were available from the five index cases . From these index cases and the histologic assessment of affected hooves , the phenotype of HWSD was established . For additional cases , inclusion criteria as an HWSD case consisted of ( 1 ) Connemara pony breed ( 2 ) age of onset within the first six months of life and ( 3 ) clinical signs consistent with a receding dorsal hoof wall and secondary solar proliferation , supported by digital photographs of all four feet . Inclusion criteria for unaffected animals in the genome wide association study consisted of ( 1 ) Connemara pony breed ( 2 ) >2 years of age ( 3 ) no apparent hoof pathology , supported by digital photographs of all four feet when available . Distal extremity radiographs of affected horses were evaluated , when available . Control animals used for genotyping of putative functional variants were reported by their owners to be >2 years of age and had no apparent hoof pathology . DNA was collected and purified from all horses ( Gentra Puregene blood kit , Quiagen , Valencia , CA ) . Hooves from four affected Connemara ponies [3 female ( aged 2 . 5 , 3 , and 4 . 5 years ) and 1 male ( 6 months ) ] , including three of the index cases , were available for RNA purification . Four unaffected [2 female ( 1 . 5 years ) , 2 male ( 1 and 5 years ) ] horses were euthanized for reasons unrelated to this study and hooves collected as controls . All tissue samples were flash frozen in liquid nitrogen and stored at -80°C until RNA isolation . RNA was isolated ( RNeasy Fibrous Tissue Mini Kit , Quiagen , Valencia , CA ) and cDNA synthesized ( QuantiTect Reverse Transcription Kit , QIAGEN , Valencia , CA ) . Negative reverse transcriptase controls were made simultaneously and the final products were assessed with the housekeeping gene , ACTB , as previously described [33] . 15 affected ( 4 male , 11 female ) and 24 unaffected ( 7 male , 17 female ) Connemara ponies were genotyped on the Illumina SNP70 Genotyping Beadchip ( Illumina , San Diego , CA ) . Quality control was implemented using PLINK ( Purcell et al 2007 ) and Single Nucleotide Polymorphisms ( SNPs; based on EquCab2 . 0 ) were excluded with a minor allele frequency ( MAF ) <5% or genotyping call rate <90% . A case-control standard allelic genome-wide association ( GWA ) was performed using PLINK . Population stratification was determined using genomic inflation values in PLINK and visualized using multi-dimensional scaling ( MDS ) and quantile-quantile ( Q-Q ) plots . After removing seven control animals based upon the MDS plot , a repeat case-control standard allelic GWA analysis was performed with the remaining 15 affected and 17 unaffected ( 4 male , 13 female ) animals . To correct for multiple testing , 52 , 000 permutations were performed . Manhattan plots and QQ plots were generated using the ggplot2 package [34] implemented in R v . 3 . 1 . 1 [35] . Sequence data was generated using an Illumina HiSeq . Library preparation and sequencing was performed by the UC Davis Genome Center . Average library insert size was 300 bases . Four samples ( 2 affected [1 female , 1 male] and 2 unaffected [1 female , 1 male] ) were bar-coded and pooled and 2 lanes of sequence of 100bp paired-end reads were obtained . An average of 79 . 2M reads were obtained per sample . Average read length after trimming was 98 . 25bp , resulting in 5 . 4-6X coverage for each of the four horses . Sequence data was processed on a 2U custom built rack server with 256GB DDR3 memory , 2 x Xeon ( R ) E5-2690 Eight-Core Processor ( 32 virtual cores ) , 22TB HD and Ubuntu 12 . 04 . 2 operating system . Read quality was assessed using qrqc ( version 1 . 9 . 1 ) [36] , while Scythe ( version 0 . 990 ) [37] and Sickle ( version 1 . 20 ) [38] were used for Illumina adapter & quality trimming . The Burrows-Wheeler Aligner ( BWA version 0 . 6 . 2-r126 ) [39] was used to align reads to the horse genome ( UCSC assembly ID: equCab2 ) . Sequenced reads from an American Quarter Horse [13] were downloaded and used as an additional control . To call variants , the Genome Analysis Toolkit ( GATK version 2 . 5-2-gf57256b ) [40] best practices for variant calling was followed , including duplicate removal , indel realignment , base quality score recalibration , and SNP and INDEL discovery and genotyping across all samples using GATK recommended hard filtering parameters [41 , 42] . Variant effect prediction was assessed using SnpEff [21] ( version 3 . 5f ) [43] . Of the segregating variants identified , UCSC was used to identify the syntenic region on hg19 [12] and and the ECR browser ( http://ecrbrowser . dcode . org/ ) used to identify potential highly conserved non-coding regions . FASTq files have been uploaded for the sequence data to the NCBI-SRA ( submission # SRP052751 ) . A custom Sequenom SNP panel ( GeneSeek , Lincoln , NE ) was designed to genotype the 17 SNPs and 5 indels that segregated with the affected phenotype on an additional 369 Connemara ponies , 18 non-Connemara ponies , 50 Arabians , 51 Quarter Horses , and 50 Thoroughbreds . An additional 54 Connemara ponies were genotyped only for SERPINB11 , using the forward primer CAAGGGGATGAGGGAGTTCT and reverse primer CCTCACTTAGCCGAAAAGGA; the 296bp product was sequenced and assessed for the presence of a 1bp insertion . RT-PCR was performed to evaluate relative expression of SERPINB2 , SERPINB8 , SERPINB10 , SERPINB11 in the spleen , heart , lung , kidney , stomach , liver , skin , coronary band , brain , and spinal cord of one control horse . The predicted cDNA sequences for each SERPIN gene were pulled from the equCab2 assembly , as viewed on the UCSC Genome Browser [12] and PRIMER3 software [44] was used to design primers ( S1 Table ) . Beta-Actin cDNA was also amplified to ensure equal loading of template , with previously published primers [45] . PCR was performed with the following cycling conditions: 12 minute melt at 95°C; 40 cycles of 30 seconds at 94°C , 30 seconds at 58°C , and 3 minutes at 72°C; final 20-minute extension at 72°C . PCR products were Sanger sequenced to ensure that the correct product was amplified . The SERPINB11 primer set was also used to sequence multiple affected ponies to ensure that the frameshift variant was detectable in transcribed cDNA . Primers for the four SERPIN genes ( B2 , B8 , B10 , B11 ) surrounding disease-associated variants were designed using the PRIMER3 software loaded with server settings for qPCR ( Untergasser et al . , 2012 ) ( S2 Table ) . cDNA sequences were pulled from the equCab2 assembly [12] . Primers were selected only if pairs spanned at least one intron , did not bind to regions with an identified SNP , produced a single product with In-Silico PCR , and showed a single BLAT search result . Primers were used to PCR amplify from control coronary band cDNA , and products were sequenced to confirm specificity . Three reference genes were identified as potential internal controls: beta-actin ( ACTB ) , beta-2-microglobulin ( B2M ) , and ubiquitin B ( UBB ) [33] . All primers were synthesized by Eurofins MWG Operon , Huntsville , AL , USA ( http://www . operon . com ) . Housekeeping gene stability was determined by calculating Ct mean , standard deviation , and coefficient of variation [46] . B2M demonstrated the most stable expression profile across our eight coronary band cDNA samples . qRT-PCR Reactions were performed in a 10uL reaction volume using the QIAGEN Rotor-Gene SYBR Green PCR Kit . Primers are listed in S2 Table . Each tube contained 20ng cDNA , and 0 . 5μM final primer concentration for each forward and reverse primer . PCR was performed on a Rotor-Gene Q 72-well thermocycler ( QIAGEN , Valencia , CA ) as follows: 5 minutes at 95°C; 40 cycles of 20 seconds at 95°C and 40 seconds at 60°C; melt curve ramping from 50°C to 99°C , rising by 1°C at each step . Each reaction was run in triplicate . Each run included a no-template control , negative-RT control , and standard concentration ( 40ng , 20ng , 10ng , 5ng , 2 . 5ng ) cDNA for each SERPIN gene under investigation and B2M . Quantitative RT-PCR results were analyzed by group-wise comparison between quantification cycle values obtained from coronary band samples donated by four affected and four unaffected animals . The Relative Expression Software Tool ( REST ) platform was used to evaluate expression levels [47] . Experiments showing group-based differences were repeated and analyzed in triplicate . Pedigree information was collected for all genotyped ponies , using Certificates of Registry issued by regional Connemara societies and the Pedigree Online All Breed Database ( www . allbreedpedigree . com ) . To assess carrier frequency in the general Connemara population , a sample set that excluded affected individuals and close relatives ( siblings , half-siblings , sires , dams , grand-sires , and grand-dams ) was created . Carrier frequency was calculated by dividing the total number of unrelated animals heterozygous for the frameshift variant by the total number of animals within the unrelated sample set .
|
Inherited diseases affecting only the nails in humans are rare; however , humans do not support themselves entirely on one appendage . Horses bear their entire weight on their third toe , resulting in a large amount of force on each hoof . An inherited disease characterized by a phenotype restricted to separation and breaking of the dorsal hoof wall was identified in a specific breed of pony , the Connemara . This disease has been termed hoof wall separation disease ( HWSD ) . Parents of affected ponies appeared clinically normal , suggesting an autosomal recessive mode of inheritance . A genome-wide association analysis identified a region associated with HWSD which was further assessed through whole genome next-generation sequencing and genotyping of potential variants . Here , we present the discovery of a frameshift variant , leading to a premature stop codon in SERPINB11 of HWSD-affected ponies . Significantly decreased expression of the SERPINB11 transcript was identified in the hoof capsule of HWSD-affected ponies . This study describes the first genetic variant associated with a hoof wall specific phenotype and suggests a role of SERPINB11 in maintaining hoof wall structure .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
SERPINB11 Frameshift Variant Associated with Novel Hoof Specific Phenotype in Connemara Ponies
|
Yersinia pestis is the causative agent of human plague and is endemic in various African , Asian and American countries . In Madagascar , the disease represents a significant public health problem with hundreds of human cases a year . Unfortunately , poor infrastructure makes outbreak investigations challenging . DNA was extracted directly from 93 clinical samples from patients with a clinical diagnosis of plague in Madagascar in 2007 . The extracted DNAs were then genotyped using three molecular genotyping methods , including , single nucleotide polymorphism ( SNP ) typing , multi-locus variable-number tandem repeat analysis ( MLVA ) , and Clustered Regularly Interspaced Short Palindromic Repeats ( CRISPR ) analysis . These methods provided increasing resolution , respectively . The results of these analyses revealed that , in 2007 , ten molecular groups , two newly described here and eight previously identified , were responsible for causing human plague in geographically distinct areas of Madagascar . Plague in Madagascar is caused by numerous distinct types of Y . pestis . Genotyping method choice should be based upon the discriminatory power needed , expense , and available data for any desired comparisons . We conclude that genotyping should be a standard tool used in epidemiological investigations of plague outbreaks .
Yersinia pestis , the causative agent of plague , is one of the most deadly zoonotic pathogens on record , with hundreds of millions of human deaths attributed to it over the course of three historical pandemics [1] . Human cases typically present in one of three forms , including bubonic , septicemic , and the contagious pneumonic form [1] , and are notifiable to the World Health Organization [2 , 3] . Since its registration as a notifiable disease in 1954 , plague has been reported in various Asian , American and African countries [2 , 3] . Plague persists in these countries in various known and other cryptic rodent reservoir species in multiple established foci [1 , 4 , 5] . Understanding the epidemiology of this pathogen in these natural reservoirs and in human outbreaks requires the development and implementation of effective molecular genotyping tools that can successfully identify and characterize Y . pestis , preferably from a wide variety of sample types . Plague was first introduced to Madagascar in 1898 during the third pandemic . The disease then spread to the capital city of Antananarivo in 1921 and became established in the surrounding highlands while disappearing from the coastal areas [6 , 7] . Plague currently persists in two large foci above 800 m in elevation in the central and northern highlands . It remains a significant public health concern , with hundreds of human cases reported annually [7] . Malagasy plague cases are categorized as confirmed ( isolation of Y . pestis ) , presumptive ( positive by microscopy but no strain isolation ) , or suspected ( negative test results or no tests performed , but clinical symptoms ) . Due to logistical difficulties , the frequency of biological case confirmation ( confirmed and presumptive cases ) is very low in Madagascar ( 21 . 4% of suspected cases from 1957–2001 ) [8] , although this can be increased using F1 antigen detection [9] . Historically , genotyping of Y . pestis in Madagascar has been limited to confirmed cases [10–12] . However , recently developed molecular assays provide the opportunity to directly investigate clinical samples without the need for strain isolation . In the present study , we investigated clinical samples by extracting DNA and then using three different genotyping methods . Each method could successfully be applied despite the background of human DNA and revealed important genotype information useful for understanding the molecular epidemiology of Y . pestis in Madagascar .
Samples were de-linked from the originating patients and analyzed anonymously . All adult subjects provided informed consent , and a parent or guardian of any child participant provided informed consent on their behalf . The consent was approved by signature . Data collection and investigation on human samples were finally approved by the Ethical Committee of the Ministry of Health of Madagascar . In 2007 , 99 human clinical samples were collected from 21 districts in Madagascar ( S1 Table ) . They originated from suspected and confirmed bubonic and pneumonic human plague cases . Tested clinical material included bubo aspirates or sputum collected by the Malagasy Central Laboratory for plague and the Institut Pasteur de Madagascar ( provided by Lila Rahalison ) . All 99 cases from which the clinical specimens were collected were F1 antigen positive and 93 were culture positive ( S1 Table ) . DNA was extracted from inactivated clinical samples using the QIAamp DNA Mini Kit ( Qiagen , Hilden , Germany ) . DNAs were screened , as previously described , across assorted SNPs—Mad-08 through Mad-49 from reference [12] and Mad-57 through Mad-78 from reference [11]—in a hierarchical fashion . Specifically , SNP Mad-43 was screened first to determine whether a sample belonged in Group I or II , two previously described major groups in Madagascar [12 , 13] . Then , additional Group I or II SNPs were screened to determine what SNP-determined group ( i . e . , node ) each sample belonged in . Sequencing was performed to determine SNP states for some samples that yielded ambiguous results using the melt mismatch amplification mutation assays ( Melt-MAMA ) ( contact corresponding author for specific methods ) . Further genotyping was attempted on all 99 samples using a 43-locus MLVA system [14] and by sequencing three CRISPR loci [15–18] , as previously described . Clinical samples that were successfully genotyped using MLVA were analyzed in conjunction with data from 262 previously published samples in a neighbor-joining analysis to determine MLVA subclades [12] . Phylogenies were then constructed using the SNP , MLVA , and CRISPR data for the successfully genotyped clinical samples , with separate phylogenies generated for Groups I and II ( as determined by SNP data ) for the MLVA and CRISPR based phylogenies ( Fig 1 and S1 Table ) . The MLVA based phylogenies were generated in MEGA6 [19] as neighbor-joining dendrograms using mean character based distance matrices and include bootstrap values ≥50 generated in PAUP 4 . 0b10 ( D . Swofford , Sinauer Associates , Inc . , Sunderland , MA ) based upon 1 , 000 simulations . The geographic distributions of the identified MLVA subclades were mapped using ArcGIS 10 . 2 . 1 for Desktop ( ESRI , Redlands , CA ) ( Fig 2 ) .
SNP , MLVA , and CRISPR typing all provided robust genotyping overall , despite being used on DNA extracted from clinical samples that included serous , bloody , mucous , or putrified materials . Only four samples amplified very poorly in the SNP and MLVA assays and so were not included in the phylogenetic or geographic analyses . These failures appeared unrelated to culturing success ( S1 Table ) . Two other samples displayed mixed genotypes when analyzed using MLVA and so were also excluded , leaving 93 samples in the phylogenetic and geographic analyses ( S1 Table ) . The MLVA , SNP , and CRISPR phylogenies demonstrated remarkable congruence . Ten MLVA subclades were identified , eight of which corresponded to previously described subclades [12] and two of which were new ( Fig 1 and S1 Table ) . The ten identified subclades were found in geographically distinct areas ( Fig 2 ) and most corresponded to a SNP phylogeny node/lineage and/or to a CRISPR group ( Fig 1 and S1 Table ) . Although all three methods were able to identify major groups , they differed in their discriminatory power and general applicability to clinical samples . MLVA provided the greatest discriminatory power with 81 unique MLVA genotypes among the 93 samples . However , this method was expensive to run ( ten multiplexed PCR reactions and six capillary electrophoresis runs per sample [14] ) and was not successful for 4 of the 99 samples ( S1 Table ) . In contrast , there were 22 CRISPR and 13 SNP genotypes among the 93 samples ( Fig 1 ) . CRISPR required only three sequencing runs and was successful for all 99 samples , identifying 17 new and , to our knowledge , Madagascar-specific CRISPR spacers . These new spacers were named in accordance with previously published Y . pestis CRISPR spacers [15 , 16 , 18] ( S2 Table ) , with new consecutive numbers assigned to new spacers within each of the three loci . This is in contrast to the CRISPR naming strategy recently published for Y . pseudotuberculosis , in which CRISPR spacers were assigned consecutive numbers without regard to the specific locus [20] . The Melt-MAMA assays predominantly used to genotype the SNPs were very economical but demonstrated a higher failure/ambiguous call rate than either MLVA or CRISPR on these clinical samples ( S1 Table ) .
Multiple genotypes continue to cause human plague in Madagascar . Within this single plague season , a total of ten MLVA subclades were identified to cause disease , eight of which were previously described ( Fig 1 ) . These subclades showed geographic distributions consistent with earlier observations ( Fig 2 ) [12] . The geographic distributions of the SNP genotypes in lineages q and s ( i . e . , MLVA subclades I . B and I . A , respectively ) were similarly compatible with previous reports [11 , 12] . Some expansions in the known geographic distributions of several subclades were observed in this study , the most significant of which involved the observation of several subclade I . B samples in three additional northeastern central highlands districts ( Fig 2 ) . Whether these are true expansions or if this is due to a lack of samples from these geographic areas in the previous study is unknown . Of seven other previously described MLVA subclades not seen here , two were speculated to be currently extinct ( I . I and I . K ) and three were restricted to geographic areas not sampled in this study ( I . C and I . G in the northern highlands and II . C in the Betafo district ) [12] . The failure to observe the remaining two previously described subclades ( I . F and II . D ) , despite sampling within their known geographic distributions , could be due to chance or may indicate the extinction of these two subclades as of 2007 . In addition to the above , two new MLVA subclades were also identified ( Fig 1 ) . Subclade II . A . 2 appears to be a previously unrecognized subdivision within the previously identified subclade II . A based upon a neighbor-joining analysis of the 93 samples from this study and 262 previously published samples [Fig 2 , S1 Table in 12] . Subclade II . A was not statistically supported in the previous analysis and so may not be a robust group [12] . Subclade I . L appears entirely new and was geographically restricted to district Ambalavao in the southern central highlands ( Fig 2 ) . This subclade may be newly emerged or may not have been previously observed due to the very low sampling in this district in the previous study [12] . Using CRISPR , a total of 18 new and , apparently , Madagascar-specific spacers have been identified , 17 here and one previously [17] . Of these , 13 belong to the A locus , two to the B locus , and three to the C locus ( S2 Table ) . Compared to a total of 140 Y . pestis specific spacers published worldwide [15 , 16 , 18] , this is a fairly high number for a geographic area the size of Madagascar , although it is consistent with the high plague activity in this endemic country and the similarly large numbers of SNP and MLVA genotypes that have been reported from Madagascar [11–13] . Of the CRISPR genotypes observed in Madagascar , the a1-a2-a3-a4-a5-a6-a7-a8 b1-b2-b3-b4-b5 c1-c2-c3 CRISPR genotype may represent the root CRISPR genotype in Madagascar , as it was shared by samples in Groups I ( CRISPR genotype I . 1 , n = 14 ) and II ( CRISPR genotype II . 1 , n = 1 ) ( Fig 1 and S1 Table ) . This CRISPR genotype was also observed in the majority of the genotyped samples in a recent study of a 2011 pneumonic plague outbreak that occurred outside the recognized northern highlands plague focus in northern Madagascar . These samples all belonged to SNP node k [17] , placing them in CRISPR genotype I . 1 described here . Two other genotyped samples from that study , one from a rat trapped in one of the outbreak areas , commune Ambarakaraka , and one from a reference sample from the northern Bealanana district , possessed another CRISPR genotype seen here , genotype I . 15 [17] ( Fig 1 and S1 Table ) . This is interesting , given the association of this CRISPR genotype with MLVA subclade I . A ( Fig 1 ) , as this is the only indication of this MLVA subclade in northern Madagascar . This hypothesis could be confirmed by genotyping these samples with either SNP Mad-57 , marking the s lineage [11] , or with MLVA , but is strongly suggested by the congruence observed here between MLVA and CRISPR ( Fig 1 ) . The spread of MLVA subclade I . A to northern Madagascar would not be unexpected , given the previous success of this genotype in spreading throughout the central highlands and to the port city of Mahajanga [11 , 12] . Previously published SNP and MLVA data for samples from northern Madagascar are limited [11 , 12] and a more extensive analysis using a larger set of samples from this region could determine the timing and extent of the spread of this MLVA subclade to northern Madagascar . In this clinical sample analysis , SNPs provided less discriminatory power than MLVA or CRISPR and the Melt-MAMA SNP genotyping assays demonstrated a higher failure/ambiguous call rate . However , additional whole genome sequencing and SNP discovery , particularly of strains from the d or k nodes , will likely lead to additional SNP genotypes and could potentially allow identification of all currently described MLVA subclades in Madagascar . Also , SNP analysis remains the only reliable method for accurately differentiating between Groups I and II `12] ( Fig 1 ) . In addition , the failure/ambiguous call rate could likely be improved using a different SNP genotyping method and , given the hierarchical nature of the SNP analysis , could still be relatively economical even if a more expensive method were used .
The MLVA , SNP , and CRISPR results reported here indicate that direct DNA extraction and genotyping of clinical samples is possible using these methods and may be used for epidemiological investigations , sidestepping the need for obtaining bacterial cultures . Method choice should be based upon the discriminatory power needed , expense , and available data for any desired comparisons . Multiple genotypes continue to be responsible for causing human plague in Madagascar and continue to be observed in geographically distinct areas .
|
Yersinia pestis is a highly pathogenic bacterium and the causative agent of human plague . It has caused three recognized pandemics and is a current human health problem in many countries of Africa , Asia and the Americas , including Madagascar . The pathogen cannot be eradicated from natural plague foci as it persists in various known and cryptic rodent reservoir species . Genotyping is a critical tool in understanding the molecular epidemiology and possible kinetics of plague . In the present study , we succeeded in extracting DNA and genotyping directly from human clinical samples from Madagascar . We applied three different methods , including single nucleotide polymorphism ( SNP ) typing , multi-locus variable-number tandem repeat ( VNTR ) analysis ( MLVA ) , and Clustered Regularly Interspaced Short Palindromic Repeats ( CRISPR ) analysis . Relative to their discriminatory power , all three methods provided important genotype information useful for understanding the molecular epidemiology of the disease , revealing that multiple , distinct genotypes caused human plague in Madagascar within one year , 2007 .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusions"
] |
[] |
2015
|
Diverse Genotypes of Yersinia pestis Caused Plague in Madagascar in 2007
|
We describe a new syndrome of young onset diabetes , short stature and microcephaly with intellectual disability in a large consanguineous family with three affected children . Linkage analysis and whole exome sequencing were used to identify the causal nonsense mutation , which changed an arginine codon into a stop at position 127 of the tRNA methyltransferase homolog gene TRMT10A ( also called RG9MTD2 ) . TRMT10A mRNA and protein were absent in lymphoblasts from the affected siblings . TRMT10A is ubiquitously expressed but enriched in brain and pancreatic islets , consistent with the tissues affected in this syndrome . In situ hybridization studies showed that TRMT10A is expressed in human embryonic and fetal brain . TRMT10A is the mammalian ortholog of S . cerevisiae TRM10 , previously shown to catalyze the methylation of guanine 9 ( m1G9 ) in several tRNAs . Consistent with this putative function , in silico topology prediction indicated that TRMT10A has predominant nuclear localization , which we experimentally confirmed by immunofluorescence and confocal microscopy . TRMT10A localizes to the nucleolus of β- and non-β-cells , where tRNA modifications occur . TRMT10A silencing induces rat and human β-cell apoptosis . Taken together , we propose that TRMT10A deficiency negatively affects β-cell mass and the pool of neurons in the developing brain . This is the first study describing the impact of TRMT10A deficiency in mammals , highlighting a role in the pathogenesis of microcephaly and early onset diabetes . In light of the recent report that the type 2 diabetes candidate gene CDKAL1 is a tRNA methylthiotransferase , the findings in this family suggest broader relevance of tRNA methyltransferases in the pathogenesis of type 2 diabetes .
Type 2 diabetes ( T2D ) is a heterogeneous polygenic disease with dramatically increasing worldwide incidence as a consequence of the obesity epidemic [1] . Environmental factors ( energy dense diets rich in saturated fat and sedentary lifestyle [2] , [3] ) and genetic predisposition contribute to its pathogenesis . T2D develops when β-cells fail to compensate for peripheral insulin resistance by increasing insulin secretion [4] , [5] as a consequence of β-cell dysfunction and reduced β-cell mass . Genome-wide association studies have identified a number of loci where genetic polymorphisms associate with T2D [6] . Inherited mutations in genes at some of these loci have been shown to cause monogenic forms of diabetes , indicating that genetic variants of different severity can generate a spectrum of monogenic and polygenic forms of diabetes [7] . An example of a T2D risk gene is CDK5 regulatory associated protein 1-like 1 ( CDKAL1 ) . Polymorphisms in this gene have been associated with T2D across ethnic populations [8] . CDKAL1 encodes a transfer RNA ( tRNA ) methylthiotransferase that catalyzes the methylthiolation of tRNALys ( UUU ) [9] . Cdkal1-deficient β-cells have impaired glucose-induced insulin secretion , and Cdkal1 knockout mice develop glucose intolerance due to aberrant insulin synthesis [9] . tRNAs undergo modifications of their bases or sugar moieties that are crucial for proper cellular function . Mammalian cells have an average of 13–14 modifications per tRNA [10]–[12] , methylation being the most common one [12] . Chemical modifications of nucleotides surrounding anticodons of tRNAs are important to preserve translational efficiency and fidelity [13] , modifications in the main body of the tRNA affect its folding and stability , and other modifications at various positions influence tRNA identity [14] , [15] . Here we identified a nonsense mutation in TRMT10A ( also called RG9MTD2 ) in a new syndrome of young onset diabetes and microcephaly . The TRMT10A yeast ortholog YOL093w codes for the protein TRM10 that has tRNA methyltransferase activity . TRM10 specifically methylates tRNA-Arg , -Asn , -Gln , -Thr , -Trp , -Met and -Lys at position 9 ( m1G9 ) , using S-adenosylmethionine ( SAM ) as methyl donor [16] . TRM10 was shown to be the major if not the only m1G9 methyltransferase in yeast , but its knockout did not alter cell survival or growth [16] . Mutational analysis in yeast revealed potential interactions between TRM10 , TRM8/TRM82 , and TRM1 [17] . These latter proteins have tRNA methyltransferase activity towards m7G46 and m22G26 , respectively [12] . The concomitant deletion of TRM10 with TRM8 , TRM82 or TRM1 induced growth arrest in S . cerevisiae exposed to high temperature , suggesting enhanced tRNA instability [17] . Here we describe the affected siblings and the identification of the TRMT10A mutation . We followed this up with studies of TRMT10A expression in tissues and subcellular localization , and interrogated the functional consequences of TRMT10A deficiency .
The proband was born to consanguineous parents of Moroccan origin , her paternal and maternal grandmothers being sisters ( Figure 1 ) . Head circumference , weight and length at birth are unknown . At age 26 years she had short stature ( 143 cm ) , microcephaly ( adult head circumference 49 cm , -5SD ) and intellectual disability , with a history of petit mal epilepsy in adolescence . Magnetic resonance imaging of the head showed a small brain with no malformation or other abnormality ( Figure 1 ) . She had developed diabetes at the age of 22 years . At diagnosis her body mass index ( BMI ) was 26 . 9 kg/m2; plasma glucose was 176 mg/dl and HbA1c 11 . 3% . Other features were a short neck , wide nose , low hairline , buffalo hump , retraction of the right 5th toe , scoliosis , and joint laxity . She also had osteoporosis , with dual-energy X-ray absorptiometry T-scores of -2 . 7 and -3 . 5 at the lumbar spine and femoral neck , respectively . A skeletal survey revealed no epiphyseal dysplasia or other bone abnormality ( e . g . normal X-ray of the hands , Figure 1 ) . Her sister had short stature ( 154 cm ) , microcephaly ( adult head circumference 51 cm , -3SD ) and intellectual disability ( IQ 69 ) . She developed diabetes at the age of 19 years , presenting with a fasting glucose of 365 mg/dl and HbA1c 13 . 2% . Her BMI was 21 . 7 kg/m2 . A younger brother had short stature ( 141 cm at age 14 years and final height of 157 cm at 21 years ) , microcephaly ( head circumference 51 cm , -3SD ) and mental retardation ( IQ 52 ) . His head circumference at birth was reportedly normal ( 36 cm ) . He was diagnosed with diabetes at 14 years of age , with a plasma glucose of 251 mg/dl and HbA1c 11 . 1% . His BMI was 20 . 6 kg/m2 . None of the patients had ketoacidosis and all three were treated with insulin at diagnosis . They were negative for anti-insulin , anti-GAD65 , anti-IA2 and islet cell autoantibodies and had a HLA genotype that did not confer risk for type 1 diabetes . Endogenous insulin secretion persisted , shown by C-peptide measurements for up to 20 years of follow-up . The insulin requirements were moderate with an average insulin dose of 0 . 4–1 . 2 U/kg/day; glycemic control ranged from good to insufficient ( HbA1c 6 . 5–8 . 5% ) . After 18 years of diabetes , the proband's ophthalmologic examination revealed bilateral diabetic retinopathy and cortical cataract . The parents and non-affected siblings had normal size ( parents 166 and 157 cm , siblings 160 , 175 , 183 and 159 cm ) and head circumference ( both parents 58 cm , P97 ) . The parents developed diabetes at age 58 years ( BMI 30 . 9 and 31 . 6 kg/m2 , plasma glucose 124 and 169 mg/dl and HbA1c 8 . 3 and 7 . 6% in the mother and father , respectively ) and were treated with metformin and a sulphonylurea . One grandfather and two aunts had adult onset diabetes ( Figure 1 ) . One sister had gestational diabetes at the age of 22 years; her fasting plasma glucose was normal ( 90 mg/dl ) at age 30 ( Figure 1 ) . The GeneChip SNP array analysis identified only one large ( >3 cM ) homozygous genomic region that was common to the three affected siblings . It was located on chromosome 4q22-23 and spanned 12 . 4 Mb between heterozygous SNPs rs4128340 and rs10516462 . In this segment , we genotyped 15 microsatellite markers , which confirmed homozygosity and biparental inheritance of a haplotype shared by both parents ( Figure S1 ) . The multipoint LOD score was 3 . 0 . Microsatellite analysis in the unaffected sister with a history of gestational diabetes ( Figure 1 ) showed inheritance of the non-mutated maternal haplotype and of the mutated paternal haplotype . In an additional unaffected brother with normal fasting plasma glucose ( 84 mg/dl ) and HbA1c ( 5 . 1% ) at age 23 years , we observed a critical meiotic recombination event , resulting in homozygosity for all disease-associated markers except those distal to microsatellite D4S1628 . This recombinant chromosome reduced the critical linkage region to a 3 . 1 Mb segment at 4q23 . We initially sequenced the following genes located in the 3 . 1 Mb segment and considered as candidates: H2AFZ ( H2A histone family , member Z ) , LAMTOR3 ( late endosomal/lysosomal adaptor , MAPK and MTOR activator 3 ) , DDIT4L ( DNA-damage-inducible transcript 4-like ) , RAP1GDS1 ( RAP1 , GTP-GDP dissociation stimulator 1 ) and METAP1 ( methionyl aminopeptidase 1 ) , but no mutation was identified . Exonic sequences-enriched DNA ( whole exome ) sequencing was performed in one proband and results were analyzed for variants that were not found in: dbSNP135 database , the Thousand Genomes database , the Exome Variant Server , or in-house exome sequencing on 51 individuals . There was only a single candidate mutation in the 3 . 1 Mb critical linkage segment , a homozygous G to A transition in exon 4 of gene TRMT10A ( tRNA methyltransferase 10 homolog A ( S . cerevisiae ) at position 379 of the coding DNA sequence , predicted to replace an Arginine residue with a premature termination codon at position 127 of the polypeptide ( c . 379 G>A; p . Arg127Stop ) . Sanger sequencing confirmed the mutation ( Figure 2A ) , which was homozygous in the three affected patients and heterozygous in both parents as well as in the unaffected brother with the critical recombination event . A comparison across species shows that Arg127 and the surrounding region are highly conserved ( Figure S2 ) . Outside the linkage region , exome analysis in the proband identified biallelic , potentially damaging mutations in the six following genes: BCLAF1; CES1; EVC2; PTPN22; ST13; ZNF626 . As none were concordant in the three affected siblings , we rejected them as candidate mutations . We sequenced the 8 exons and flanking intronic sequences in 20 patients with a similar phenotype of young onset diabetes associated to intellectual disability , microcephaly , epilepsy , developmental delay and/or short stature , five of whom were born to consanguineous parents , but failed to identify another patient with biallelic disease-causing mutations . We furthermore sequenced TRMT10A in 26 patients with non-autoimmune diabetes with onset before 25 years and a positive family history of diabetes , in whom no mutation was identified in known MODY-associated genes , but did not identify any mutation in TRMT10A . To examine the outcome of the TRMT10A nonsense mutation on TRMT10A protein and mRNA expression , we performed Western blot and real-time PCR on lymphoblasts from two patients , a heterozygous carrier of the mutation , and three healthy controls . TRMT10A protein was absent in lymphoblasts from patients homozygous for the Arg127Stop mutation ( Figure 2B ) . TRMT10A mRNA expression was much reduced in patients , and intermediate in the carrier ( Figure 2C ) . This finding is consistent with nonsense-mediated mRNA decay induced by the premature translation-termination codon ( PTC ) and/or by PTC-induced transcriptional silencing of the affected gene , a mechanism known to prevent the synthesis of potentially deleterious truncated proteins [18] , [19] . We next evaluated TRMT10A transcript and protein expression in rat tissues . TRMT10A was ubiquitously expressed with similar mRNA abundance in liver , kidney , spleen , lung , fat , and brain . Heart and muscle showed lesser TRMT10A mRNA expression , while pancreatic islets were enriched in TRMT10A transcripts ( Figure 3A ) . TRMT10A protein was ubiquitously present and 2- to 3-fold more abundant in brain and pancreatic islets compared to other tissues ( Figure 3B–C ) . In situ hybridization studies were performed in human embryonic brain samples at 8 , 11 , 17 and 19 gestational weeks ( GW ) . TRMT10A was expressed throughout the whole thickness of the dorsal telencephalon ( presumptive cerebral cortex ) at 8 and 11 GW , with higher expression in the ventricular zone and marginal zone ( Figure 4 ) . The ventricular zone contains most neural progenitors at early stages of corticogenesis , while the marginal zone is the region where the first post-mitotic neurons migrate . At later stages TRMT10A expression was not detected in the dorsal telencephalon but was found in the cerebellar cortex and cerebellar nuclei ( Figure S3 and data not shown ) . To examine TRMT10A subcellular localization we first performed in silico TRMT10A topology prediction using PSORII and WoLF PSORT [20] . These softwares detected monopartite and bipartite nuclear localization signals in the first 89 amino acids of the protein . This was confirmed with cNLS Mapper [21] , [22] suggesting predominant nuclear localization . To experimentally demonstrate the TRMT10A subcellular localization we took two approaches: 1 ) Expression of a fluorescent recombinant fusion protein , human TRMT10A ( hTRMT10A ) -humanized Renilla green fluorescent protein ( hrGFP ) ; 2 ) Detection of endogenous TRMT10A by immunofluorescence . Confocal analysis of clonal rat INS-1E β-cells expressing the TRMT10A-hrGFP fusion protein showed nuclear fluorescence with intense signal accumulation in nuclear regions of low Hoechst 33342 staining ( Figure 5A ) . Cells expressing hrGFP alone showed homogeneous cytosolic and nuclear fluorescence . The identity of the recombinant fusion protein expressed in these cells was confirmed by Western blot ( Figure 5B ) using an antibody raised against purified recombinant hTRMT10A . Similar results were obtained in dispersed rat and human islet cells expressing the recombinant fusion protein ( Figure S4 ) . To identify the nuclear compartment enriched in TRMT10A , we performed immunofluorescence in rat and human islet cells using antibodies against hTRMT10A and fibrillarin , a nucleolar marker [23] . Immunostaining of endogenous TRMT10A ( Figure 6 , red ) mimicked the fluorescence profile of recombinant TRMT10A-hrGFP . Fibrillarin immunolabeling showed a similar punctuate nuclear pattern ( Figure 6 , green ) . TRMT10A and fibrillarin images were superimposable ( Figure 6 , merge ) indicating that TRMT10A expression is enriched in the nucleolus . RNA interference technology was used to knock down TRMT10A in β-cells . TRMT10A mRNA and protein expression was reduced by 50% in INS-1E cells ( Figure S5 ) . TRMT10A silencing did not modify glucose-induced insulin secretion and insulin content ( Figure S6 ) , but enhanced total protein biosynthesis by 25% in clonal rat β-cells ( Figure 7 ) . We next examined whether TRMT10A silencing affects β-cell survival . TRMT10A knockdown induced apoptosis in clonal and primary rat β-cells and dispersed human islets ( Figure 8 ) . TRMT10A deficiency further sensitized rat β-cells to oleate- , palmitate- and ER stress-induced apoptosis ( Figure 8A–D ) . These results were confirmed by Western blot for cleaved caspase-3 , showing increased caspase-3 activation basally and after palmitate and cyclopiazonic acid exposure ( Figure 8E ) . High glucose-induced β-cell apoptosis was also increased by TRMT10A silencing ( Figure 8A ) . We observed that TRMT10A expression in β-cells is modulated by ER stress . Exposure of rat or human β-cells to the saturated FFA palmitate , previously shown to induce ER stress [3] , [24] , [25] , or to chemical ER stressors enhanced TRMT10A expression ( Figure S7 ) to an extent that was correlated with the intensity of ER stress ( measured by the expression of ER stress markers , Figure S8 ) . TRMT10A expression was induced in a PERK- but not IRE1-dependent manner ( Figure S9 ) . TRMT10A silencing did not induce expression of the ER stress markers BiP , XBP-1s , ATF3 and CHOP ( data not shown ) .
In a large consanguineous family of Moroccan origin , we identified a new syndrome of severe insulinopenic young onset diabetes and microcephaly with intellectual disability . We used linkage analysis and whole exome sequencing to identify the causal mutation . We found only one region of homozygosity by descent shared by the three affected patients , and only one potentially damaging rare genetic variant in this region , located in the TRMT10A gene , changing an arginine codon at position 127 of the protein into a stop codon ( Arg127Stop ) . In the rest of the patients' exome , we found no potentially damaging , rare biallelic variants shared by the three patients that might have qualified for a causal mutation . Among the family members , four were heterozygous carriers of a mutant allele . Of these , the parents developed diabetes in their fifties , one sister had gestational diabetes , and one brother had normal plasma glucose levels at the age of 23 ( Figure 1 ) . Other family members were not available for testing . It is possible that TRMT10A haploinsufficiency increases the risk for adult onset diabetes . TRMT10A contains 8 exons , the first exon being non-protein coding . The mutated codon 127 is in exon 4 . The protein environment of Arg127 is extremely conserved across species . Little is known about the role of TRMT10A in mammals . A single study suggested altered TRMT10A mRNA expression in colorectal cancer [26] . Blast analysis indicated that TRMT10A is the mammalian ortholog of S . cerevisiae TRM10 , previously shown to be involved in guanine 9 tRNA methylation m1G9 [16] . TRMT10A has seven transcripts in the Vega database . Two of them are non-protein coding due to a retained intron , three contain 8 exons coding for identical proteins of 339 amino acids , and differ only in their untranslated regions . InterProScan analysis indicates that these three proteins have a tRNA ( guanine 9-N1 ) methyltransferase domain as well as tRNA ( guanine-N1 ) methyltransferase domain , both of them present in TRM10 . The last two TRMT10A transcripts contain only 6 exons and code for shorter proteins of 200 and 206 amino acids . These two variants are truncated at the C-terminus and only have the tRNA ( guanine-N1 ) methyltransferase domain . In rat only one isoform of TRMT10A containing both domains is found . Based on these analyses , we suggest that TRMT10A functions as a tRNA-modifying enzyme , but this remains to be experimentally confirmed . The Arg127Stop mutation is predicted to block the expression of the five coding human TRMT10A isoforms . The nonsense mutation abolished TRMT10A protein expression , and also significantly reduced its mRNA expression ( Figure 2 ) , probably by nonsense-mediated decay and/or transcriptional silencing [18] , [19] . We show that TRMT10A is ubiquitously expressed but enriched in brain and pancreatic islets ( Figure 3 ) , consistent with the tissues affected in this new syndrome of diabetes and microcephaly . In silico topology prediction indicates that the five human TRMT10A isoforms , as well as the rat enzyme , have predominant nuclear localization . This was confirmed by immunofluorescence and confocal microscopy , with TRMT10A mainly localizing in the nucleolus of β- and non-β-cells ( Figure 5–6 and S4 ) . tRNA transcription and early processing occurs in several subcellular compartments including the nucleus , cytoplasm and cytoplasmic surface of the mitochondria [14] . tRNA genes are recruited to the nucleolus for transcription [27] , 5′ leader sequence removal and 3′ end modification , removal of the 3′ trailer and addition of the CCA , which is required for efficient tRNA nuclear export [28] . Mature tRNAs are exported to the cytosol for aminoacylation and function in translation . This transport is not unidirectional; cytosolic tRNAs can follow a retrograde transport to the nucleus ( e . g . during nutrient deprivation ) , to be re-exported to the cytosol following nutrient availability [14] . Some tRNA modifications occur on initial tRNA transcripts , while others are introduced in end-matured tRNAs [29] . Since tRNA transcription and maturation occurs in the nucleus it is expected that the enzymes catalyzing these modifications have a nuclear localization . Studies in yeast confirmed that a subset of tRNA methyltransferases is located in the nucleus [28] , [30] , [31] , with distinct subnuclear distribution , i . e . nucleolus , nucleoplasm , or inner nuclear membrane; the reason for these different localizations is not known [14] , [31] . The predominant nucleolar localization of TRMT10A is consistent with its proposed tRNA modifying activity . Alterations in tRNA modification are expected to affect protein translation . We showed that TRMT10A knockdown in rat β-cells enhances total protein biosynthesis ( Figure 7 ) . TRMT10A silencing does not impair glucose-induced insulin secretion or content in β-cells ( Figure S6 ) , suggesting that TRMT10A deficiency has no major impact on β-cell function . TRMT10A knockdown sensitizes β-cells to apoptosis in control condition and after exposure to FFAs , high glucose or synthetic ER stressors ( Figure 8 ) , conditions related to T2D . It has been proposed that mammalian cytosolic and mitochondrial tRNAs prevent apoptosis by blocking the binding of cytochrome c to Apaf-1 , thus preventing the formation of the apoptosome [32] , [33] . It is not known whether tRNA modifications affect this tRNA-cytochrome c interaction . Primary microcephaly refers to a congenitally small but otherwise normally structured brain , with a head circumference later in life that remains 3 SD below the mean for age and gender . Primary microcephaly is a very rare disorder affecting approximately 1/100 , 000 live births , mainly inherited as an autosomal recessive trait , and is associated with a high rate of parental consanguinity [34] . Microcephaly and young onset diabetes co-segregate in the present family , as both features were present in the three affected siblings and absent in the six unaffected siblings , defining a new syndrome . Our linkage analysis identified a single region where all affected siblings were homozygous over a significant length of genomic DNA . It is hence likely that the whole phenotype results from pleiotropic effects of a single mutated gene . Microcephaly in our patients was associated with intellectual disability and no other neurological feature , except for a history of petit mal seizures in the proband . This clinical presentation fits with the phenotype of primary microcephaly [35] . Primary microcephaly is vastly heterogeneous , and many genes that cause primary microcephaly play a role in mitotic spindle organization and/or DNA repair , presumably affecting the proliferation of neural progenitors and the generation of an adequate pool of neurons in the developing brain [36] . The expression pattern of TRMT10A in the ventricular zone of the developing cortex is consistent with its influence on neural progenitor properties , including control of survival that is known to affect brain size . In addition it may act in subsets of differentiated neurons , as suggested by its expression in cortical marginal zone and cerebellum . Early onset diabetes has been associated with microcephaly in other genetic disorders . Homozygous mutations in the IER3IP1 gene encoding the immediate and early response 3 interacting protein 1 result in infantile diabetes and congenital microcephaly with simplified gyration , hypotonia , intractable seizures , and early death [37] , [38] . Cases of microcephaly with severe neurological expression were also described in Wolcott-Rallison syndrome , which includes permanent neonatal diabetes , multiple epiphyseal dysplasia , osteoporosis and liver dysfunction . This syndrome is due to biallelic mutations in EIF2AK3 encoding translation initiation factor 2-α kinase-3 [39] . EIF2AK3 is activated upon the accumulation of unfolded proteins in the ER and inhibits protein translation initiation [40] . Other human diseases are caused by mutations in genes encoding tRNAs and tRNA modifying enzymes . Pontocerebellar hypoplasia , characterized by hypoplasia and atrophy of ventral pons , cerebellum and the cerebral cortex , is caused by mutations in genes encoding tRNA splicing endonuclease subunits ( TSEN ) or mitochondrial arginyl-tRNA synthetase ( RARS2 ) [41] . Mutations in mitochondrial tRNA genes and in aminoacyl-tRNA synthetases cause myopathies and neurodegenerative diseases , sometimes in association with diabetes . Recently , a syndrome of mental retardation , microcephaly and short stature was described , caused by mutations in NSUN2 , encoding a methyltransferase that catalyzes the intron-dependent formation of 5-methylcytosine at C34 of tRNA-leu ( CAA ) [42] , [43] . NSUN2 is the ortholog of yeast TRM4 . Wild-type NSUN2 localized to the nucleolus , whereas mutant NSUN2 accumulated in the nucleoplasm and cytoplasm [42]; other NSUN2 mutations resulted in nonsense-mediated mRNA decay [43] . Inactivation of the X-linked gene FTSJ1 , another RNA methyltransferase and ortholog of yeast TRM7 , gives rise to non-syndromic intellectual disability [44] . In addition to causing microcephaly and short stature , the TRMT10A mutation causes a severe form of diabetes , which was not reported for these other RNA methyltransferase mutations . This may be related to cell-specific requirements of RNA modifications . It is of particular interest that CDKAL1 polymorphisms predispose to insulin secretion defects and T2D [8] . CDKAL1 was recently shown to methylthiolate tRNALys ( UUU ) [45] . The β-cell-specific Cdkal1 knockout mouse develops impaired glucose tolerance , due to misreading of Lys codons in proinsulin , defective insulin biosynthesis and increased susceptibility to ER stress and high fat diet [9] . In conclusion , we describe a nonsense mutation in the TRMT10A gene in a new syndrome of young onset diabetes and microcephaly . Based on its cellular localization and by homology with its yeast counterpart , we propose that TRMT10A has methyltransferase activity . We show that TRMT10A is expressed in human fetal brain; TRMT10A silencing does not impair β-cell function but induces apoptosis , suggesting that TRMT10A deficiency may negatively affect β-cell mass and the pool of neurons in the developing brain . Our findings may have broader relevance for the understanding of the pathogenesis of T2D and microcephaly .
The ethics committee of the Erasmus Hospital , Université Libre de Bruxelles approved of the study . The three patients , their parents , and two unaffected siblings gave informed consent . Human fetal brain was collected and used according to the guidelines of the local ethics committees on research involving human subjects ( Erasmus Hospital , Université Libre de Bruxelles and Belgian National Fund for Scientific Research ) . Adult male Wistar rats were housed and used following the rules of the Belgian Regulations for Animal Care , with approval of the ethics committee of the Université Libre de Bruxelles . A peripheral blood sample was obtained for genetic analysis from the three patients , their parents , and two unaffected siblings . Leukocyte DNA was extracted using proteinase K digestion followed by phenol-chloroform extraction and ethanol precipitation [46] and samples were stored at 4°C in T10E1 buffer . We used Affymetrix 11K-GeneChip microarrays representing 10 , 000 autosomal single nucleotide polymorphisms ( Affymetrix , High Wycombe , United Kingdom ) to genotype the three patients' DNA ( 500 ng each ) on an Affymetrix platform following the instructions of the manufacturer . Regions of homozygosity were delineated using the ExcludeAR algorithm [47] . In chromosomal regions with apparent homozygosity by descent , microsatellites were genotyped in individual subjects . Marker order was obtained from the University of California at Santa Cruz ( UCSC ) physical map ( http://genome . ucsc . edu/cgi-bin/hgGateway ) . A multipoint LOD score was computed using the MAPMAKER/HOMOZ software [48] assuming a gene frequency of 0 . 005 and marker allele frequencies as observed in a series of control subjects , with a minimal minor allele frequency of 0 . 10 . Genomic DNA from the proband ( Figure 1 , arrow ) was sonicated and enriched for exonic sequences by hybridization on an Agilent SureSelect All Exon v1 capture kit . Exon-enriched DNA was paired-end sequenced over 90 bp by an Illumina HiSeq2000 sequencer ( Beijing Genomics Institute ) . An average of 55 . 6 million paired-end reads were filtered to eliminate reads with more than 6 undetermined nucleotides or 40 identical bases in tandem . The filtered reads were then aligned to the human genome GRCh36 assembly using the SOAPaligner 2 . 20 software [49] and the genotypes were called using the SOAPsnp program [50] . Resulting single nucleotide variants ( SNVs ) were filtered according to the following rules: base quality larger than 20 , read depth equal to or larger than 4 , and a distance between two variants larger than 4 . Insertions and deletions were identified separately , through alignment to GRCh36 using the Burrows-Wheeler alignment tool [51] , and detection using the Genome Analysis Toolkit [52] . SNVs and indels were annotated using the Ensembl V54 database . We considered SNVs and indels that were not found in the dbSNP135 database , nor in the Thousand Genome ( www . 1000genomes . org ) database , nor in the Exome Variant Server ( http://evs . gs . washington . edu/EVS/ ) , and that were not found in our other in-house exome sequencing results . PCR primers for all exons and flanking intronic sequences were designed using the Exonprimer software ( http://ihg . helmholtz-muenchen . de/ihg/ExonPrimer . html ) . All exons and flanking intronic regions of the candidate genes were sequenced by the Sanger method using the Big Dye Terminator cycle sequencing kit v2 ( Applied Biosystems , Foster City , California , USA ) , and analyzed on a 3130 Genetic Analyser sequencing machine ( Applied Biosystems ) . Sequences were analyzed in silico for mutations using the SeqScape software V . 2 . 0 . ( Applied Biosystems ) . In situ hybridization was done on human fetal brain ( GW 8 , 11 , 17 , 19 ) as previously described [53] . Riboprobe template was generated by PCR using TRMT10A specific pairs of primers: F: CCAAGCTAATACGACTCACTATAGGGAGATGTGAACCAATATCTAAACGACAAA – R: GGATCCATTAACCCTCACTAAAGGGAGAGATTTTCCTTATCCTGCTTTTCTTC . Clonal rat INS-1E cells ( a kind gift from Dr C Wollheim , Centre Médical Universitaire , Geneva , Switzerland ) were cultured in RPMI medium as previously described [54] , [55] . Tissues were obtained from adult male Wistar rats ( Charles River Laboratories ) . Rat islets were isolated by collagenase digestion followed by hand picking under a stereomicroscope . Islets were dispersed and β-cells purified by autofluorescence-activated cell sorting ( FACS , FACSAria , BD Bioscience ) and cultured as described [56] , [57] . Human islets from non-diabetic organ donors ( n = 13 , age 68±4 years , BMI 27±1 kg/m2 ) were isolated by collagenase digestion and density gradient purification [58] . The islets were cultured , dispersed and transfected as previously described [59] . The mean percentage of β-cells of the human islet preparations was 50±5% , as determined by insulin immunofluorescence [25] , [60] . Human lymphoblasts from three control individuals , two patients and one heterozygous carrier of the mutation were cultured in RPMI 1640 medium supplemented with 20% FBS , 100 mU/ml penicillin and 100 mU/ml streptomycin . hTRMT10A was amplified by PCR from lymphoblast cDNA using oligonucleotides spanning the start and stop codons of the TRMT10A open reading frame ( ORF ) , using primers F CGGAATTCATGTCATCTGAAATGTTGCC and R CGCTCGAGGTGTGGCAGAGAGTTCACTG . The restriction sites EcoRI and XhoI ( underlined ) were added to facilitate the directional cloning into the expression vector pGEX-6P-1 ( GE Healthcare ) . This vector allows the expression of recombinant proteins fused to glutathione-s-transferase ( GST ) at its N-terminus . E . coli BL21 cells were transformed with the pGEX-6P-1-TRMT10A plasmid by electroporation . Positive clones were selected by colony PCR and sequenced . For recombinant protein expression , a single colony was grown overnight at 37°C in LB medium containing 100 µg/ml ampicillin . Cells were then diluted 1∶50 in the same medium and grown at 37°C until an optical density of 0 . 6 at 600 nm was reached . Isopropyl-β-D-thiogalactoside ( 0 . 25 mM ) was then added and cells were grown at 28°C for 3 h to induce recombinant protein expression . Cells were harvested by centrifugation at 3000×g for 10 min , lysed by sonication in 20 mM Tris buffer pH 8 containing 0 . 5% Triton ×100 , 10 mM dithiothreitol , 0 . 1 mM PMSF and protease inhibitor cocktail ( Roche ) , and centrifuged for 15 min at 15 , 000×g at 4°C . The supernatant was applied to 1 ml glutathione spin columns ( Pierce ) and washed with ice-cold lysis buffer . The recombinant hTRMT10A was separated from the GST moiety by in column site-specific proteolysis using PreScission protease ( GE Healthcare ) following the manufacturer's instructions . The purified recombinant hTRMT10A was used for rabbit polyclonal antibody production ( Eurogentec ) . hTRMT10A was amplified by PCR from HeLa cDNA using the oligonucleotides F 5′-AAAAAACCCGGGAATGTCATCTGAAATGTTG-3′ ( start codon is indicated in bold ) , and R 5′-AAAAAAGGATCCTGAGTGTGGCAGAGAGTT-3′ in which the restriction sites SmaI and BamHI ( underlined ) were added to facilitate the directional cloning into the mammalian expression vector Vitality phrGFP-1 ( Stratagene ) . The stop codon of the TRMT10A ORF was removed to allow the production of the recombinant TRMT10A fused to the N-terminus of hrGFP . The PCR product was purified using the Wizard SV Gel and PCR clean-up system ( Promega ) , sequentially digested with SmaI and BamHI ( New England Biolabs ) , and cloned into the Vitality phrGFP-1 vector digested with the same restriction enzymes . The plasmid was introduced into electrocompetent One Shot E . coli ( Invitrogen ) by electroporation , and positive clones were identified by colony PCR . A single colony containing hrGFP ( empty vector ) or TRMT10A-hrGFP plasmid was grown overnight at 37°C in LB medium with 100 µg/ml ampicillin . Plasmids were purified with PureYield Midiprep ( Promega ) and quantified by NanoDrop ( Thermo Scientific ) . Expression of recombinant TRMT10A-hrGFP in rat β-cells was confirmed by Western blot . Cells were transfected overnight with 30 nM of a control siRNA ( Qiagen ) , or two single siRNAs targeting rat or human TRMT10A using Lipofectamine RNAiMAX ( Invitrogen ) . siRNA-lipid complexes were formed in Opti-mem ( Invitrogen ) for 20 min as previously described [61] . hrGFP and TRMT10A-hrGFP plasmids were introduced by lipofection in INS-1E cells or dispersed rat and human islet cells using Lipofectamine 2000 ( Invitrogen ) . siRNA sequences , plasmid and Lipofectamine concentrations are described in Tables S1 and S2 . TRMT10A subcellular localization was examined by expressing recombinant hTRMT10A fused to GFP , or by immunolabeling endogenous TRMT10A in INS-1E cells and dispersed rat and human islet cells . Cells plated on poly-lysine coated cover slips were transfected or not with hrGFP and TRMT10A-hrGFP plasmids , fixed with 4% formaldehyde [62] , permeabilized with methanol , blocked with goat serum and incubated or not for 1 h with rabbit anti-hTRMT10A ( 1∶200 , Eurogentec ) , mouse anti-human/rat fibrillarin ( 1∶200 , EnCor Biotechnology ) and mouse anti human/rat insulin ( Sigma ) . Alexa Fluor 546 goat anti-mouse IgG ( H+L ) , Alexa Fluor 488 goat anti-mouse IgG ( H+L ) and Alexa Fluor 546 goat anti-rabbit IgG ( H+L ) ( 1∶500 , Molecular Probes , Invitrogen ) were used as secondary antibodies . Nuclei were stained with Hoechst 33342 . Slides were analyzed by inverted fluorescence microscopy ( Zeiss Axiovert 200 , Oberkochen , Germany ) . Confocal analysis was performed on a LSM510 NLO multiphoton confocal microscope fitted on an Axiovert M200 ( Zeiss ) [63] . Poly ( A ) + mRNA was isolated from INS-1E cells , dispersed human islets , or primary rat β-cells using the Dynabeads mRNA DIRECT kit ( Invitrogen ) . For total RNA purification , rat tissues and pancreatic islets were resuspended in RNeasy Minikit lysis buffer ( Qiagen ) , homogenized using a T10 basic ULTRA-TURRAX disperser ( IKA ) or lysed by sonication in a Bioruptor NGS ( Diagenode ) , respectively . Total RNA was purified with the RNeasy Minikit and quantified by NanoDrop . mRNA and total RNA were reverse transcribed as previously described [25] , [56] . Real-time PCR was performed using Rotor-Gene SyBR Green on a Rotor-Gene Q cycler ( Qiagen ) , or FastStart SYBR Green on the LightCycler ( Roche Diagnostics ) [62] , [64] . Standards were prepared using suitable primers in a conventional PCR . Gene expression was calculated as copies/µl using the standard curve approach [65] . Expression values were corrected for the expression of the reference genes GAPDH , OAZ1 and/or β-actin , which were not modified by the experimental conditions . The primers are provided in Table S3 . Rat tissues and pancreatic islets were resuspended in ice-cold PBS containing protease inhibitor cocktail and homogenized as described above . Total protein was measured in the lysates using the Protein Assay Dye Reagent ( BIO-RAD ) . INS-1E cells and human lymphoblasts were lysed with Laemmli buffer [59] . Cell lysates were resolved in 10 or 14% SDS-PAGE gels and transferred to nitrocellulose membranes . Immunoblotting was performed using antibodies against hTRMT10A , human cleaved caspase-3 ( Cell Signaling ) , human α-tubulin ( Sigma-Aldrich ) or human β-actin ( Cell Signaling ) . Protein detection was done using horseradish peroxidase-conjugated secondary antibodies and SuperSignal West Femto chemiluminescence revealing reagent ( Thermo Scientific ) . Immunoreactive bands were detected with a ChemiDoc XRS+ system and with Image Lab software ( BIO-RAD ) . Protein levels were corrected for α-tubulin and/or β-actin . Insulin secretion was performed as previously described [61] . Briefly , 72 h after transfection , INS-1E cells were cultured for 1 h in RPMI without glucose , washed with modified Krebs-Ringer bicarbonate HEPES solution , and insulin secretion was induced by 30 min incubation with KRBH containing 1 . 67 or 16 . 7 mM glucose , alone or in combination with 10 µM forskolin . Insulin was measured by ELISA ( Mercodia ) in cell-free supernatants and acid-ethanol extracted cell lysates [61] , [66] , [67] . Total protein was measured in cell lysates as described above . FFA treatment was performed in RPMI 1640 containing 0 . 75% FFA-free BSA ( Roche ) . Oleate and palmitate ( sodium salt , Sigma ) were dissolved in 90% ethanol and diluted 1∶100 to a final concentration of 0 . 5 mM [25] , [68] . The chemical ER stressors cyclopiazonic acid and thapsigargin ( two SERCA pump blockers ) , tunicamycin ( an inhibitor of N-glycosylation ) and brefeldin-A ( an inhibitor of ER-to-Golgi vesicle transport ) were used at 25 µM , 1 µM , 5 µg/ml and 0 . 1 µg/ml , respectively . The IRE1 inhibitor 4μ8C was used at 25 µM [69] For all treatments the control condition contained the same dilution of vehicle . Apoptotic cell death was detected and counted by fluorescence microscopy after Hoechst 33342 ( 5 µg/ml; Sigma-Aldrich ) and propidium iodide ( 5 µg/ml ) staining as described [25] , [60] , [62] , [70] . Apoptosis was also examined by Western blotting for cleaved caspase-3 . 72 h after transfection INS-1E cells were cultured for 2 h in Krebs-Ringer buffer containing 11 mM glucose , 1% BSA and 10 µCi/ml L- ( 3 , 4 , 5 3H ) -leucine ( Perkin Elmer ) . Cells were then washed with Krebs-Ringer solution containing 10 mM non-radioactive leucine . Cells were collected in ice-cold water and lysed by sonication . Total protein was precipitated with 10% trichloro-acetic acid . The content of 3H-labeled proteins was determined in a liquid scintillation analyzer ( Packard ) [70] . Protein biosynthesis was expressed per total protein content to correct for differences in cell number in the experimental conditions . Data are presented as means ± SE . Non-normally distributed variables were log-transformed before statistical testing . Comparisons between groups were made by ANOVA followed by two-sided Student's paired t test with Bonferroni correction for multiple comparisons . A p value<0 . 05 was considered statistically significant .
|
The inherited predisposition to type 2 diabetes is attributed to common variants in over 60 loci . Among these risk variants is CDKAL1 , which has recently been shown to be a tRNA modifying enzyme ( methylthiotransferase ) . Genetic variants of different severity can generate a spectrum of monogenic and polygenic forms of diabetes . Here we describe a new syndrome of young onset diabetes , short stature and microcephaly ( small brain size ) with intellectual disability in a large consanguineous family . By linkage analysis and whole exome sequencing we identified a nonsense mutation in TRMT10A , a gene that has hitherto not been studied in mammals . The yeast homolog TRM10 has been shown to be a tRNA modifying enzyme with methyltransferase activity . We demonstrate that TRMT10A mRNA and protein are absent in cells from the affected siblings . TRMT10A localizes to the nucleolus , where tRNA modifications occur . TRMT10A silencing induces cell death in insulin-producing pancreatic β-cells , suggesting that TRMT10A deficiency may reduce β-cell mass and the pool of neurons in the brain . This is the first study describing the impact of TRMT10A deficiency in man . Our findings may have broader relevance for the understanding of the pathogenesis of type 2 diabetes and microcephaly .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
tRNA Methyltransferase Homolog Gene TRMT10A Mutation in Young Onset Diabetes and Primary Microcephaly in Humans
|
Helminth infections are prevalent in rural areas of developing countries and have in some studies been negatively associated with allergic disorders and atopy . In this context little is known of the molecular mechanisms of modulation involved . We have characterized the innate immune responses , at the molecular level , in children according to their helminth infection status and their atopic reactivity to allergens . The mRNA expression of several genes of the innate immune system that have been associated with microbial exposure and allergy was examined in 120 school children in a rural area in Ghana . Helminth infections were common and atopy rare in the study area . The analysis of gene expression in ex vivo whole blood samples reflected the levels of corresponding proteins . Using this approach in a population of school children in whom the presence of Schistosoma haematobium infection was associated with protection from atopic reactivity , we found that the level of TLR2 and SOCS-3 , genes associated with atopy in the children , were significantly downregulated by presence of S . haematobium infection . S . haematobium infections modulate the expression of genes of the innate immune system ( TLR2 and SOCS-3 ) ; these are genes that are associated with increased allergic inflammatory processes , providing a molecular link between the negative association of this infection and atopy in rural children in Ghana .
In the last few decades , allergic diseases have become a major health burden in the western world . Although these disorders clearly have a genetic component , their rapid change in prevalence points to environmental conditions that have changed during this time frame . In the same time frame , there has been a decrease in exposure to microbial products as a result of changing lifestyle with , among others , improved sanitation and access to clean water . Interestingly , in the developing world , the prevalence of allergies is relatively low , particularly in rural areas , where exposure to infectious agents is high . There is increasing evidence that exposure to pathogen-derived compounds influences the maturation of the immune system and therefore the balance reached between pro- and anti-inflammatory responses , such that Th2 responses are kept under control when allergens are encountered . In rural areas in the developing world , chronic helminth infections are highly prevalent . These infections not only result in skewing of the immune responses towards Th2 , but also induce the higher production of anti-inflammatory molecules such as IL-10 to prevent the elimination of helminths , which at the same time protect the host against the pathological consequences of excessive inflammation [1] . Such an anti-inflammatory environment induced by chronic helminth infections might modulate immune responses to other antigens . For example , chronic infection with schistosomes or Onchocerca was shown to modulate the immune response to tetanus toxoid following vaccination [2] , [3] . Epidemiological studies have revealed both positive and negative associations between helminth infections and allergies ( reviewed in [4] ) . It is thought that severe , chronic infections are often associated with suppression of allergic reactivity . For example chronic infections with intestinal helminth , such as with hookworm , have been shown to suppress allergic diseases [5] , [6] . These observations have been confirmed for schistosomiasis , demonstrating lower skin reactivity to allergen in infected individuals [7] , [8] . Additionally , removal of helminths by long-term anti-helminth treatment in Venezuelan or Gabonese children resulted in increased atopic reactivity to house dust mite [9] , [10] , even though a shorter anti-helminth treatment did not show an effect on atopy in one study [11] . In a population of rural Ghanaian school children a negative association was found between infection with Schistosoma haematobium and skin reactivity to mite allergen ( Obeng et al , submitted for publication ) . Within this study we aimed to identify the molecular mechanisms by which schistosome infections may modify immune responses and modulate inflammatory reactions such as atopy . To address this we selected two groups of genes that have been described previously to play a role in allergic diseases . Toll-like receptors ( TLRs ) have been shown in several studies outside Africa to change in expression levels following exposure to microorganisms [12]–[15] . In a European study these molecules were linked to allergy: children of farmers in Alpine regions , exposed to high microbial burden and with a low prevalence of atopy , had altered levels of TLR2 [16] , suggesting that exposure to microorganisms might modulate the innate immune system and thereby suppress the development of allergic disorders . The molecules suppressor of cytokine signalling ( SOCS ) -1 and SOCS-3 have recently been described and reported to be involved in TLR signalling and inflammatory diseases [17]–[22] . Elegant studies by Kubo and co workers in animal models have shown SOCS-3 to be involved in regulation of immune responses in allergic disease [23] , [24] . Given that helminth products have been shown to modulate cells of the innate immune system and to interfere with pathways that are activated via TLR stimulation [15] , [25] , we asked whether in an area in Africa where helminth infections are highly prevalent and allergic disorders are low , we can find molecular pathways that may explain the relationship . To this end , gene expression not only of TLRs but also of molecules such as SOCS-1 and SOCS-3 , involved in downstream signalling , were studied in whole blood samples of rural Ghanaian school children . The results of this study showed that high expression of TLR2 and SOCS-3 was associated with allergic skin reactivity , whereas helminth infection was associated with lower expression levels of TLR2 and SOCS-3 , providing a potential regulatory link between helminth infection and allergies at the molecular level .
The study population consisted of schoolchildren between 5 and 14 years of age . Children whose parents consented by signing or thumb printing an informed consent form were registered to participate in a large study on allergy and parasitic infections ( B . B . Obeng et al , manuscript submitted ) . The Institutional Review Board of the Noguchi Memorial Institute for Medical Research , Accra , Ghana approved the study . Skin reactivity to mite was negatively associated with S . haematobium infection ( OR 0 . 5 , 95% CI 0 . 2–1 . 0 , p = 0 . 05 ) , particularly in areas where prevalence of schistosomiasis is high ( OR 0 . 3 , 5% CI 0 . 1–0 . 9 , p = 0 . 04 ) . Blood samples from children from two rural schools with high prevalence of S . haematobium infection were used for RNA isolation to study gene expression . In these schools , the reactivity to house dust mite was low ( 9% ) compared to school children from Accra ( 15% ) , free of any helminth infections , and with a relatively high socioeconomic status . The study subjects were fist selected randomly , one out of three children from whom blood samples were available were selected ( 107 children ) . In order to increase power , all skin prick test ( SPT ) positive children from these schools were added to our randomly selected subjects , along with randomly selected SPT negative children ( 16 children in total ) , resulting in a group of 123 children ( Table 1 ) . The participants were given specimen bottles and were asked to collect a fresh stool and urine sample for the detection of helminth infections . Stool examination was performed by the Kato-Katz method for the detection of hookworm and trichuris , and the total number of eggs was calculated per gram of faeces . Urine samples were used for the detection of S . haematobium by passing 10 ml of urine through a filter with 10-micron pore size . A subject was considered positive for helminth infection if eggs of any of the helminth species were detected . Blood samples were collected from all participants for the detection of the malaria infection by Giemsa-stained thick smear ( GTS ) examination . The immediate hypersensitivity skin prick test with inhalant allergen extracts was performed by using the standard prick method on the volar surface of the right forearm with the standardized extracts of 6 allergens; Dermatophagoides pteronyssinus ( Der P ) , Dermatophagoides farinae ( Der F ) , cat , dog , peanut and grass mix ( HAL Allergen Laboratories , The Netherlands ) . Histamine dihydrochloride ( 1/1000 ) and glycerinated saline solution were used as positive and negative controls , respectively . The wheal diameter was measured after 15 minutes and the result considered positive when the wheal size was at least 3 mm in diameter , in the absence of significant reactivity of the diluent negative control . None of the children were taking anti-allergic medicine that might interfere with SPT or had ever been treated with specific immunotherapy . Since most of the positive reactions seen were against mite allergen , we have focused on children having a positive skin test for mite allergen . Serum levels of total IgE were measured by the enzyme linked immunosorbent assay ( ELISA ) as described before [26] . Results were expressed as international units per ml ( IU/ml ) . Serum levels of house dust mite ( HDM ) antibodies were determined by radio allergosorbent test ( RAST ) as described previously [27] ( CLB , Amsterdam , The Netherlands ) . Results were expressed as international units per ml ( IU/ml ) . One IU is 2 . 4 ng IgE . Subjects were considered sensitised when concentrations of specific IgE of more than 0 . 7 IU/ml were measured . Immediately after venapuncture into heparinised tubes , 0 . 8 ml of whole blood was added to 3 . 6 ml of Nuclisens lysis buffer ( Biomérieux , Boxtel , The Netherlands ) to stabilise the RNA . Samples were stored for a maximum of two weeks at 4°C , after which they were put at −80°C for long-term storage . The Nuclisens Isolation kit ( Biomérieux ) was used for the isolation of total nucleic acid ( approximately 1 ml of blood mixed with lysis buffer per isolation ) according to the manufacturer's instructions . Genomic DNA was removed by treating the samples with RNAse-free DNAse ( Invitrogen , Breda , The Netherlands ) for 30 minutes at 37°C , followed by the Nuclisens isolation procedure to isolate the purified RNA . RNA was isolated from the same samples before and after one year of storage at −80°C , and mRNA levels of several genes of interest were compared . There was no difference in gene expression indicating that mRNA was stable in this buffer for at least one year at −80°C , and that the procedure was consistent . From a subset of the donors , PBMC were isolated and monocytes and T cells were separated by subsequent labelling and magnetic cell separation of cells with CD3 and CD14 Microbeads ( Miltenyi Biotech , Germany ) . Fractionated monocytes and T cells were mixed with Nuclisens lysis buffer . Fluorescence activated cell sorting ( FACS ) of the isolated cells indicated that these fractions were at least 90% pure . The unlabeled cell fraction depleted for monocytes and T cells was also collected and mixed with lysisbuffer ( remaining cell fraction ) . Samples were stored and RNA was isolated as described for whole blood samples . The percentages of monocytes and T cells in all donors were determined by flow cytometry of the PBMC prior to the isolation procedure . Reverse transcription of RNA was performed using moloney murine leukaemia virus reverse transcriptase ( M-MLV RT ) ( Invitrogen ) . Samples without RT were regularly taken along to control for genomic DNA contamination . Gene expression was assessed with real-time quantitative PCR ( Prism 7700 , Applied Biosytems ) . PCR reactions were performed in duplicate in accordance with the TaqmanTM assay instructions using Taqman probes and qPCR Core kit reagents ( both Eurogentec , Seraing , Belgium ) . Gene expression was normalized to the housekeeping gene 18S rRNA and calculations were performed as described [28] . Analysis of the expression of 8 different housekeeping genes in a subset of the samples indicated that 18S rRNA was a stable housekeeping gene in our samples . Sequences of primers and probes have been obtained from Dr . Roger Lauener ( TLR2 , TLR4 , 18S rRNA , [16] ) and from Dr . Masato Kubo ( SOCS-1 , SOCS-3 , [23] ) . IgE mRNA levels were determined by a primer and probe set specific for the CH1 region of IgE ( forward primer 5′-CAA TGCCACCTCCGTGACTC-3′ , reverse primer 5′-CGTCGCAGGACGACTGTAAG-3′ and probe 5′-ATCGTCCACAGACTGGGTCGACAACAAA-3′ ) . For each gene , after normalisation for the housekeeping gene , the donor with the lowest expression was set to 1 . For the expression levels of TLR2 and SOCS-3 in isolated cell subsets , expression of SOCS-3 or TLR2 in T cells was set to 1 for each donor . Whole blood from 5 donors was diluted 1∶1 with RMPI 1640 medium ( Gibco ) and stimulated for 16 hours and 24 hours in 96-well round bottom plates with medium , 10 µg/ml SEA ( schistosomal egg antigens ) , 100 ng/ml LPS ( Sigma ) , 100 µg/ml poly I:C or 5 µg/ml TNF-α ( Sanquin , The Netherlands ) . After 16 hours the blood was mixed with ABI Lysis buffer ( Applied Biosystems ) and RNA was extracted using the ABI6100 according to their protocol ( Applied Biosystems ) . cDNA synthesis and quantitative PCR for TLR2 was performed as described above . 24 hours after stimulation , cells were mixed with FACS lysing solution ( BD Biosciences ) to lyse the erythrocytes , washed with PBS and cells stained with anti-TLR2 PE ( clone T2 . 5 , eBioscience ) . Flow cytometric analyses were performed using a Becton Dickinson FACSCalibur flow cytometer ( BD Biosciences ) and analysed using FlowJo analysis software ( Tree Star Inc . ) . Association of total and mite-specific IgE with skin reactivity to mite was analysed by logistic regression of log-transformed values . The association of gene expression with skin reactivity or helminth infection was analysed by logistic regression using a value of each gene to separate high and low gene expression , since the association between gene expression and allergen reactivity might not be a linear one . This value was based on the geomean of the relative expression data ( low expression: below geomean; high expression: above geomean ) . The regression analysis was performed adjusting for age , sex and helminth infection ( Table 2 ) or for age , sex and skin reactivity to mite ( Table 3 ) . The comparison of the non-adjusted means of gene expression between skin prick positive and negative children and between helminth-infected and non-infected children was determined by the non-parametric Mann-Whitney test . Correlation between mRNA and surface TLR2 levels and between mRNA and serum IgE levels was compared using the non-parametric Spearman's correlation test .
The study population here originates from two schools selected from a large study on allergy and parasitic infections . The schools were in a rural area highly endemic for helminth infections ( see Material and Methods section ) . Fifty-four percent of the children were infected with at least one helminth species ( Table 1 ) . As indicated in Table 1 , the most prevalent helminth species was Schistosoma haematobium , followed by hookworm , Ascaris lumbricoides and Trichuris trichiura . In the population where mRNA expression was analyzed ( see Materials and Methods ) , 14 out of 74 schistosome negative children had a positive skin reaction to mite ( 19% ) , whereas 5 out of 46 schistosome-infected children were SPT positive for mite ( 11% ) resulting in a significant negative association between infection with S . haematobium and atopy ( OR 0 . 26 [0 . 07–1 . 00] , p = 0 . 05 , adjusted for age , sex , school and levels of mite IgE ) . Additionally , the odds ratio of the association between the log of mite IgE and atopy is clearly lower in children infected with helminths ( OR 5 . 8 [1 . 0–33 . 2]; p = 0 . 05 ) compared to the odds ratio in non-infected children ( OR 18 . 7 [2 . 4–145 . 8]; p = 0 . 005 ) . The level of mite-specific IgE was a strong determinant for the risk of positive skin reactivity to mite ( Table 2 ) . In contrast , the level of total IgE was not significantly associated with atopy . However , total IgE was associated with atopy in the children that were not infected with helminths ( OR = 5 . 6 , CI 95%: 1 . 0–30 . 9; p<0 . 05 ) . In order to evaluate whether the in vivo status of the immune system could be evaluated by analysing the mRNA expression in whole blood , RNA was isolated from peripheral blood samples collected in our study population . In agreement with IgE serum levels , the levels of IgE mRNA were significantly higher in helminth-infected children compared to non-infected children ( Figure 1A ) , and a high correlation was seen between the mRNA expression and serum IgE levels ( r = 0 . 58 , p<0 . 001; Figure 1B ) . In addition the mRNA levels of TLR2 were compared to surface TLR2 protein expression using flow cytometry in whole blood samples . There was a strong correlation between TLR2 mRNA expression and TLR2 surface expression ( r = 0 . 58; p<0 . 001 ) , validating the use of mRNA levels measured in whole blood samples as a reflection of the measured immunological events in vivo . In Ghanaian school children living in an area highly endemic for parasitic infections , there was a significantly higher expression of TLR2 in subjects with positive skin reactivity to house dust mite ( Figure 2A ) ; high expression of TLR2 doubled the risk of atopy ( OR 2 . 6 , Table 2 ) , whereas there was no such association for TLR4 and skin reactivity ( OR 0 . 9; Table 2 and Figure 2B ) . Children with high expression of SOCS-1 or SOCS-3 had a significantly increased risk for skin reactivity ( OR 5 . 8 and 4 . 4 , respectively , Table 2 ) . Both SOCS-1 and SOCS-3 expression were significantly elevated in those who were skin prick positive to house dust mite compared to non-atopic children ( Figures 2C and 2D ) . To determine the source of TLR2 and SOCS-3 expression in whole blood samples , monocytes and T cells were isolated from five donors . Although the mRNA expression of TLR2 was high in monocytes ( 63 to 275-fold higher than in T cells , Figure 3A ) , correction for the percentages of monocytes and T cells in the peripheral blood mononuclear cells , indicated that monocytes , T cells and other cells contributed similarly to the TLR2 expression measured in whole blood ( Figure 3C ) . In contrast , the mRNA expression of SOCS-3 could clearly be attributed to the T cell fraction with little contribution from monocytes or other cells ( Figure 3B–C ) . TLR expression can be altered following exposure to ligands expressed by microorganisms and parasites . Helminth parasites carry signature molecules that can interact with TLRs and therefore could affect their expression . The expression of both TLR2 and TLR4 genes was lower in children with a helminth infection; the effect being more prominent for TLR2 ( Figures 4A and 4B ) . Indeed , infection with helminths predicted low expression of TLR2 and , to a lesser extent , of TLR4 ( Table 3 ) . Two major helminth species prevalent in the study area were Schistosoma haematobium and hookworm . The mRNA expression of TLR2 in helminth-infected children was significantly lower only in children infected with S . haematobium , which was associated with low expression of TLR2 ( Table 3 ) , whereas no such association was found for TLR4 . Malaria infection was associated with higher levels of TLR2 expression ( not shown ) , and adjustment for malaria infection did not change the association between helminth infection and TLR2 expression . Thus , induction of lower expression of TLR2 by infection with schistosomes seems to be specific . The analysis of the expression levels of SOCS-1 and SOCS-3 revealed that children infected with helminths had significantly lower expression levels of SOCS-3 , but not of SOCS-1 ( Figures 4C and 4D ) . Helminth positivity in a child was associated with low gene expression of SOCS-3 , but not of SOCS-1 ( Table 3 ) ) . As for TLR2 , low expression of SOCS-3 was associated with S . haematobium rather than hookworm infection .
Using gene expression profiles in whole blood from children living in a rural area in Ghana , we found that high expression of TLR2 , SOCS-1 and SOCS-3 mRNA was associated with positive skin reactivity to house dust mite . Presence of Schistosoma haematobium infection , reported to decrease the risk of atopy [7] and observed in the current study , affected the expression levels of TLR2 and SOCS-3 , which were significantly lower in infected children . There are few studies that have looked at the association of TLR expression and allergy , and those that have , are all in European populations [29] , [30] . Of these , only one study has investigated the levels of TLR expression in an age group comparable to our study . European children living on farms and reported to have a lower risk of developing atopic disorders were shown to have higher expression levels of TLR2 and CD14 , compared to non-farmer children [16] . As farmer children would be expected to be exposed to a high burden of environmental microorganisms , the results are in contrast to the lower expression of TLR2 in our helminth infected subjects compared to uninfected Ghanaian school children . Children living in a rural area in Ghana are expected to have exposures that are higher in intensity , and different in nature in terms of the sort of microorganisms and parasites , compared to European farmer children . Moreover , the European study did not examine the TLR2 levels in atopic and non-atopic individuals as we do here , showing that atopy was associated with high expression of TLR2 . Our data support a role for suppression of atopy by current infection with a systemic helminth , Schistosoma haematobium . The finding that S . haematobium infected children show a lower expression of TLR2 gene , is supported by the results that baseline expression levels of TLR2 protein were also shown to be lower in individuals infected with another systemic helminth infection , the filarial nematode , Wuchereria bancrofti [31] , [32] . Importantly , lower expression of TLR correlated with a lower expression of co-stimulatory molecules such as CD80 and CD86 and lower production of the inflammatory cytokines IL-6 and TNF-α [32] . Our results also indicated an association of skin reactivity to house dust mite with higher gene expression for both SOCS-1 and SOCS-3 . SOCS genes are involved in the pathogenesis of several inflammatory diseases . They are induced upon cytokine signalling or by stimulation of TLR and limit the production of inflammatory cytokines [33] . In murine models , transgenic over expression of SOCS-3 has been shown to mediate and maintain allergic responses [23] . Furthermore , T cell expression of SOCS-3 , but not of SOCS-1 , was associated with atopic disease in humans and increased with disease severity [23] . These results suggest that SOCS-3 is involved in Th2 skewed responses . However , although helminth infections are clearly associated with Th2 responses , we have found that SOCS-3 expression is decreased in helminth-infected children . Cell subset analysis indicated that T cells were the main source of SOCS-3 mRNA leading us to conclude that in helminth infected children , with strong Th2 responses , SOCS-3 expression is low in T cells . So although both allergic disorders and helminth infections are characterized by Th2 responses , SOCS-3 is associated with allergic disorders , but not with helminth infection . This would suggest that in allergic subjects , with high expression of SOCS-3 , Th2 responses are associated with inflammation; whereas in allergic subjects with a helminth infection and consequent low expression of SOCS-3 , Th2 cells are not associated with inflammation . Interestingly , a recent report using T cell-specific SOCS-3 conditional knockout mice indicated that in the absence of SOCS-3 expression , the levels of typical Th2 cytokines in peripheral CD4+ T cells were either unaffected ( IL-5 ) or only slightly lower ( IL-4 ) , whereas following T cell stimulation , the production of the anti-inflammatory cytokines , IL-10 and TGF-β1 , were significantly higher . The abolition of SOCS-3 expression in T cells ameliorated ovalbumin-induced airway hyperresponsiveness in vivo [34] . Furthermore , CD4+CD25+foxp3 positive regulatory T cells were shown to have low SOCS-3 expression as compared to Th2 cells , indicating that low SOCS-3 expression in T cells is associated with suppressive function [35] . These data raise the possibility that a decrease in SOCS-3 T cell expression by helminth infection might shift the balance towards a modified Th2 response [36] with a more anti-inflammatory function , and thereby might suppress allergic inflammation [1] . As malaria infection was prevalent in our study area , we looked at this infection and found that it had no effect on atopy ( multivariate analysis , data not shown ) and interestingly found that malaria infection was associated with a higher expression of TLR2 ( data not shown ) , indicating that different pathogens might induce different regulation of TLR expression . Indeed , other protozoa such as Entamoeba histolytica and Trypanosoma cruzi inhibit immune responses by down-regulating TLR2 expression [37] or signalling via TLR2 [38] , [39] . There are numerous studies supporting either up- or downregulation of TLR2 and TLR4 , depending on the stimulus and cell type studied [40] . Both an increase and a decrease in TLR2 expression might reflect repeated TLR stimulation , the direction as well as the downstream signaling pathways being dependent on the type of pathogen . Alternatively , the cytokine environment might influence the expression of TLR . Th1 cytokines such as IFN-γ seems to increase expression levels of TLR [41] , whereas Th2 cytokines as IL-4 and IL-13 , abundantly present in helminth infected individuals , downregulate TLR expression and function [42] , [43] . Thus , the exposure of European farmers to Th1 inducing agents might be reflected in higher TLR2 expression , whereas in our subjects the exposure to Th2 inducing agents might lead to low TLR2 expression . The relationship between TLR2 and SOCS-3 expression might not be a direct one . In rural Ghana , helminth infection is associated with low TLR2 as well as low SOCS-3 expression , whereas the expression of TLR2 is high in a European rural area . If TLR2 expression is merely the result of exposure to pathogens , and low SOCS-3 expression is associated with protection from allergy , it would be of interest to know whether SOCS-3 is also lower in the European farmers' environment despite a higher TLR2 expression . In summary , ex vivo whole blood analysis of mRNA profiles in children infected with helminths compared to non-infected children living in a rural area in Ghana have shown that chronic helminth infections are associated with a lower expression of TLR2 and SOCS-3 . The difference in the expression of SOCS-3 in helminth infected and uninfected children might result from the interaction of helminth derived molecules with the immune system leading to modulation of downstream signalling and induction of “modified” Th2 cells . Larger epidemiological studies will be needed to be able to test this hypothesis directly and to confirm that helminths modify the development of allergy by modulating the expression levels of TLR2 and SOCS-3 .
|
Inflammatory diseases such as atopic disorders are a major health problem in the Western world , but their prevalence is also increasing in developing countries , especially in urban centres . There is increasing evidence that exposure to a rural environment with high burden of compounds derived from parasites and microorganisms is associated with protection from atopic disorders . Since urbanisation is progressing at a rapid pace , particularly in less-developed nations , there is a need to understand the molecular processes that control the progress towards the development of allergic diseases in developing countries . In this study we have examined a population of school children living in a rural area of Ghana , where helminth ( worm ) infections are prevalent and associated with protection from skin reactivity to house dust mite . Blood samples were collected from these children and analysed for the expression levels of several genes involved in the development of a pro allergic immune system . The results point at a potential molecular link that might explain the negative association between schistosome infections and allergies .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunology/immunomodulation",
"immunology/leukocyte",
"signaling",
"and",
"gene",
"expression",
"immunology/allergy",
"and",
"hypersensitivity",
"infectious",
"diseases/helminth",
"infections"
] |
2008
|
Lower Expression of TLR2 and SOCS-3 Is Associated with Schistosoma haematobium Infection and with Lower Risk for Allergic Reactivity in Children Living in a Rural Area in Ghana
|
Despite the success of genome-wide association studies in medical genetics , the underlying genetics of many complex diseases remains enigmatic . One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses . Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges . There is , therefore , a need for computationally efficient methods that build on models appropriately capturing interaction . We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested , in order of their complexity , against a saturated alternative hypothesis representing interaction . This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test . We devise two different methods , one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests , and a second that does this adaptively . We show that our methodology in general has an improved statistical power in comparison to seven other methods , and , using the idea of closed testing , that it controls the family-wise error rate . We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions . While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases , we explore strategies for a priori selecting subsets of variant pairs to test . Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data , which may involve genes with previously unknown association to the disease . Moreover , it enables construction of interaction networks that provide a systems biology view of complex diseases , serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences .
Cardiovascular disease , cancers , diabetes and chronic obstructive pulmonary disease , accounting for almost 60% of the causes of death 2013 , globally [1] , are all examples of complex diseases . A complex disease is characterized by an intricate system of interactions between genetic , epigenetic , other intrinsic factors , and environmental factors , that constitutes its pathophysiology . The genetic architecture of many common complex diseases is poorly understood . For example , the 46 robustly associated variants that have been found for coronary artery disease ( CAD ) only explain 10 . 6% of the estimated heritability; this was shown in a recent meta-analysis of almost 200 , 000 individuals [2] . The same pattern of unexplained , or missing , heritability is found in most common complex diseases [3] . Assuming that the estimated heritability is correct , the possible explanations for the high ratio of missing heritability include 1 ) a large number of causal genetic variants , each with a small effect , 2 ) sequence variation that is commonly excluded from analysis , e . g . copy number variation or rare variants , 3 ) other commonly unmeasured heritable components , e . g . heritable epigenetic modifications , and 4 ) interaction effects between common variants . Moreover , any combination of these explanations is plausible . In this paper we focus on the inference of interaction in genetic association studies; this is sometimes called epistasis , epistatic interaction or genetic interaction; here we will refer to it as genetic interaction or simply interaction . Genetic interactions are characterized by two or more variants producing an unexpected phenotype that is not easily explained by the marginal effects of the individual variants . Extensive studies in model organisms have shown that genetic interactions are common phenomena [4] . The field was pioneered by Bateson [5] , who studied genetic interactions in plants and chicken . More recently , synthetic lethal interactions ( in which the simultaneous occurrence of two mutations , by themselves without effect , lead to cell death ) have been studied extensively in yeast and Caenorhabditis [6–8] and interactions between quantitative trait loci have been studied in mouse , Drosophila and Caenorhabditis [9–11] . Since interactions are widespread in other organisms , it seems unlikely that such effects would not exist in humans . Furthermore , genes are linked in metabolic , regulatory and signaling pathways and it is likely that this will be reflected as interactions between variants , as has been shown for transcriptional regulation in Drosophila [12] . Therefore , studies of genetic interactions have a strong potential to provide important insights about disease biology—specifically , interactions reflect dependencies in pathophysiology and may allow predictions of effects ( and side effects ) that are relevant for disease prognosis and treatment . Several approaches have been developed to study genetic interactions ( see [13–15] for three excellent methodology reviews ) . In medical genetics , the prevalent tool for modeling single variant association in unrelated individuals has been generalized linear models ( GLM ) . The advantages of GLMs are flexibility in modeling the phenotype , easy interpretation and straightforward adjustment for confounders . Although the GLM framework can model both discrete and continuous outcomes; we will , in this work , concentrate on the case-control outcome . Studies of interactions are , however , not without issues . Firstly , the identification of interactions depends on the scale relating the genotypes to phenotypes . Secondly , because the GLMs are fitted by iterative procedures , the computational burden is high . Thirdly , straightforward multiple testing correction leads to low statistical power . We now elaborate briefly on these three issues . The dependency of GLMs on a scale sometimes causes confusion [16 , 17] . The scale is determined by a link function that maps the phenotype to the linear predictors . For example , for two predictors a and b , the phenotype y can be determined by an additive ( y = a + b ) or by a multiplicative ( y = ea+b ) model . A commonly used link function in case/control studies is the logit , which is used in logistic regression . This displays a combination of mathematically favorable properties: it models the case/control selection bias , the parameters have minimal sufficient statistics , and it is the maximum entropy null model [18] . However , the choice of scale is to a large extent a modeling issue and should not be based on mathematical convenience alone . For example , when , for a set of variants , the presence of a risk allele in any single variant is sufficient to cause the disease , the log-complement link function yields an appropriate model [17 , 19] . Ultimately , the best choice of scale depends on the unknown biological model that has generated the data . The choice of scale is very problematic because , even if the true model underlying the data displays interaction , it is often possible to select a scale that diminishes the interaction effect [20] . Conversely , if the true model does not display interaction , then there is another scale that , in the asymptotic case , will display interaction [17] . In response to this , Knol and VanderWeele [21] suggest that the p-value of an interaction should be reported on a set of reasonable scales to show whether the interaction seems invariant of scale . We follow this suggestion and , furthermore , extend it by constructing a test for interaction that is invariant over a set of link functions . A different approach builds on the rationale that if , for interacting variants , certain combinations of alleles affect disease risk , then this would be reflected in differential enrichment for these allele combinations between cases and controls , and therefore a difference in their linkage coefficients ( LDcases and LDcontrols ) . The LD-contrast test [22] compares the normalized difference of LDcases and LDcontrols as a χ2-distributed statistic for interaction . A recently been proposed version of the LD-contrast test [23] uses genotypes recoded to a pair of binary variables ( according to model of inheritance ) . A third approach , the multi-factor dimensionality reduction ( MDR ) [24] , uses dimensionality reduction techniques to recode the 3 × 3 penetrance matrix into a binary variable that optimally classifies cases and controls . This is then evaluated by cross-validation and a permutation procedure is used to estimate significance . Several variants of MDR have been developed [25 , 26] . One common approach to improve the computational complexity , has been to introduce the naive assumption that it is impossible for two variants to be simultaneously associated with the phenotype unless they interact . Examples include the method of [27] , using a log-linear GLM , and many of the variations of the MDR method , including the original one [24 , 25] . Under this naive assumption it is , in the GLM setting , sufficient to compare three models: two single variant association models and the saturated model , which will represent interaction . The parameters of these models can be efficiently estimated since they all have closed form solutions . Unfortunately , this simplification allows interactions to be incorrectly inferred between two variants that both are associated with a main effect , but there is no interaction ( we will refer to this as double main association ) . As a consequence , genuine genetic interactions may be obscured by these double main associations [28] . In this work , we will focus on inference of genuine interactions . Finally , the reduction in statistical power implied by correction for multiple tests constitutes a major limitation for performing interaction studies on a larger scale . For an investigation of interaction between all pairs of a set of n = 500 , 000 variants , a Bonferroni correction for n ( n − 1 ) /2 tests gives a significance threshold of 4 . 0 ⋅ 10−13 , which is considerably lower than the corresponding significance level 1 . 0 ⋅ 10−7 for a standard single variant analysis . The burden of multiple tests grows exponentially with the number variants involved in the tested interactions , and , henceforth , we will limit ourselves to the case of pair-wise interactions . Various screening strategies have been applied in attempts to improve power . These may use prior information that identify a smaller set of candidate variant pairs ( we will investigate two such approaches in our analysis of biological data ) or they may be based on the data at hand . An example of the latter include the screening test of Marchini [29] , which removes variant pairs lacking a marginal effect for one or both of the participating variants . Millstein et al . [30] , using a reasoning similar to the LD-contrast test above , suggested a LDcases screening test for significant linkage enrichment among cases . However , observing that this induced a bias in the subsequent main test , they also proposed a LDcohort screening test . The latter test relies on the linkage enrichment in cases also showing as a linkage enrichment of the pooled cases and controls , but formally does not use any prior information about disease state . Various combinations of screening and main tests have been proposed: marginal screen with logistic GLM main test [29] , LDcohort screen with logistic GLM main test [30 , 31] , and LDcases screen with LD-contrast main test [23] . In this work we introduce a stage-wise multiple testing methodology that exhaustively tests all variant pairs . In this methodology , a sequence of hypotheses is considered in order of increasing complexity . Only variant pairs that cannot be explained better by a simpler hypothesis compared to the most complex hypothesis ( representing interaction ) are tested at subsequent stages . This is conceptually different from the screening approach by Marchini [29] , which instead requires that a variant pair fits an intermediate screening hypothesis ( of single marginal association ) better than the simplest hypothesis ( of no association ) for it to be tested at the subsequent stage . Because the hypotheses considered are closed under intersection , we show , in two situations , that the family-wise error rate is controlled . Furthermore , since the models under the simpler hypotheses can be estimated efficiently , our methodology allows the use of full GLMs . The multiple testing correction is alleviated and results in a substantial increase of power compared to the Bonferroni correction . We also construct a scale-invariant test for interaction using several link functions . Furthermore , we assess a set of statistical methods for inferring genetic interactions on synthetic data and show that our methodology improves on these . Lastly , we discover three distinct interactions that are associated with CAD , of which one includes a novel locus .
In this section , we describe our multiple testing methodology , which is aimed at large-scale pairwise interaction testing . We show that it gains additional power by separating a complex hypothesis into stages of simpler null models . We have derived two methods that rely on different assumptions , having different effects on the bounds of the family-wise error rate ( FWER ) . We start by briefly reviewing general linear models ( GLM ) , which we use to express our model of interaction , as well as the simple null models . Frequently , when GLMs are applied in pairwise interaction testing , FWER is bounded using Bonferroni . In this section , we will give an account of three investigations of statistical power that all indicate the utility of our stage-wise methodology . The generation of simulated data used in these investigations are described in Material and methods section Generation of synthetic data for estimation of statistical power . The intuitive idea behind the stage-wise methodology is that we aim to ( 1 ) reduce the number of tests in later stages compared to earlier , while ( 2 ) asserting that actual interactions advance to later stages . We show in the Results section Analysis of biological data , below , that the number of tests in the last stage is in fact substantially reduced , suggesting that aim ( 1 ) is unlikely to be a problem . Here , we have investigated aim ( 2 by comparing the power of the tests in the first and last stage . That is , for data generated from HA , we compare the power of the likelihood ratio test of H1 against HA to that of the test of H4 against HA . Indeed , the results in Fig 2 ( using data generated from a double dominant interaction model ) suggests that the test in the first stage , at least under these conditions , have substantially greater power than that in the last stage . However , the test in the first stage can obviously not be used as a test for interaction by itself , since it measures any kind of association to the phenotype , including , for example , pairs for which only one of the variants is associated . We further investigated the distribution of statistical power of seven methods using simulated data generated from the spectrum of all possible interaction models , following the ideas of [35] ( see Material and methods section Generation of synthetic data for estimation of statistical power for details ) . The first of these methods is our static method , and the remaining methods include four methods based on a logit-link GLM with different screening strategies , Logistic ( without screening ) , Marginal+logistic [29] , CSS+logistic [30] and R2+logistic [31] ) and two methods based on the LD-contrast test with different screening strategies , LD-contrast ( without screening ) , and Sixpac [23] ( a LDcases+LD-contrast method ) , for details , see Material and methods section Comparison of statistical methods . It should be noted that none of the latter six methods are scale-invariant—one may expect that this property would enhance their power . For simplicity of simulations , we only evaluated the static method here; however , since the adaptive method is more powerful than the static , this can also be viewed as a conservative estimate of the power of the adaptive method . As can be seen in Fig 3 , the static method consistently has greater power than the other approaches . The marginal+logistic method performs best of the remaining methods , while the the LD-contrast method have the worst performance . In S1–S4 Figs , we also report the result of a more computationally intensive power comparison , including the above methods , as well as our adaptive stage-wise scale-invariance method and the Model-based MDR ( MB-MDR ) method [26] ( see S2 Text for details ) . These results corroborate those above , that is , for most models our stage-wise methods performs better than the other methods ( see further discussion in S2 Text ) . Intuitively , when more variants are associated with the phenotype in our stage-wise methodology , the multiple testing correction in the intermediate stages becomes larger , and therefore statistical power is reduced . For this reason , we investigated how the statistical power depends on the number of associated variants using data simulated from the double-dominant interaction model ( see Material and methods section Generation of synthetic data for estimation of statistical power ) . As shown in Fig 4 , the power decreases as the number of associated variants increases . Because of the additional penalty of the weight , the static method can have lower power than directly testing interaction using a Bonferroni correction , precisely when M ( M − 1 ) > w4 N ( N − 1 ) ( where N is the total number of variants and M is the number of associated variants ) . It can be noted that for our biological data , M ( M − 1 ) = 306 ≪ w4 N ( N − 1 ) = 346 , 035 , 421 . 8 ( based on the N = 33 , 963 tested variants and the M = 18 robustly associated CAD variants present on the IBC-chip , cf . S1 Table ) . Both the static and adaptive stage-wise methods are based on the likelihood ratio test , which is asymptotically correct . As we show in S1 Text , the adaptive method controls the FWER asymptotically . For the static method on the other hand , we can even show that the FWER is controlled for any data size . Consequently , it is interesting to investigate the behavior of both methods on finite data and compare it with that of the same seven methods as in the power comparison ( Logistic , Marginal+logistic , CSS+logistic , R2+logistic , LD-contrast , Sixpac , and MB-MDR ) . We considered two cases , one close to the assumptions of our methods , and one designed to be challenging . We investigated these two cases using simulated data ( see further Material and methods section Generation of synthetic data for estimation of statistical power ) . In the first case , we used each of our null models , H1: no association , H2/H3: single main association , and five models for double main association ( H4 ) with the identity , log , log-complement , odds and a logit link functions , respectively , to generate the phenotype based on a single pair of variants . The first seven rows in Table 2 show that both the static and adaptive methods accurately control the FWER under these circumstances ( i . e . , FWER is below or close to the expected value of 0 . 05 ) . All seven other methods control the FWER for the no association and single main models . However , for the double main models , they control FWER only on the multiplicative scale ( i . e . , with the log , logit and log complement link functions ) . For the remaining models ( double main:identity and double main:odds ) these methods fail to control FWER , with the exception of the R2+logistic that controls FWER for the double main:identity model . In the second case , where we attempted to construct instances that challenge the additional asymptotic assumptions made in the adaptive method . The phenotype was here determined by an multivariate additive GLM with logit link function on a set of L ∈ {10 , 20 , 30} markers . The parameter distributions were chosen with the intention to let only a small and difficult subset of the variant pairs to reach the stage they belong to . The last three rows in Table 2 show that the static method controls the FWER , but suggest that for the adaptive method , FWER is inflated by approximately a factor 3 compared to the desired rate . The remaining methods controls FWER in this setting , possibly an effect of the data being generated on a multiplicative scale . We applied our stage-wise methodology on genome-wide CAD case-control data from the PROCARDIS study , using our five link functions . To enhance such a large-scale analysis , we explore three strategies for selecting subsets of variant pairs to test . The first strategy represents a genome-wide approach , while the latter two strategies were designed to a priori enrich for pairs likely to exhibit interaction . For the same reason , our main focus will be on the more powerful adaptive method , combined with validation of any significant discoveries in a separate cohort . In the first strategy all 229 , 050 , 992 pairs , for which the product of the minor allele frequencies > 0 . 04 , were selected . The stage-wise methodology subsequently reduced the number of pairs to 15269 , 7712 and 93 . This analysis resulted in seven variant pairs that were significant for at least one link function , see Table 3 . We used genomic proximity to coarsely estimate genes corresponding to these variant pairs . One variant pair , indicating an interaction between IL1R1 and CDNK2B-AS1 , was significant on the additive odds scale , only . The p-values for the other scales were quite far from significance , indicating that this association is not scale-invariant . In other words , this interaction should be interpreted with care , as we cannot exclude the possibility that this is the effect of double main association , e . g . , on the logit scale , without interaction . The remaining six variant pairs indicated an interaction between the genes MIA3 and CDNK2B-AS1 . None of these passed the scale-invariance test ( i . e . , was significant for all link functions ) . However , for most of these variant pairs , the p-values for all scales are of the same magnitude and reasonably close to the significance level of 0 . 05 ( see , e . g . , the rs4846770–rs518394 variant pair ) , perhaps suggesting that this could be an effect of insufficient power rather than scale dependency . In the second strategy 314 , 445 pairs were selected based on loci previously associated with CAD . This is based on the common hypothesis that some robust CAD associations may be the marginal effects of interacting variant pairs . Candidate pairs were formed by taking each of the previously associated CAD variants , see S1 Table , and combining it with each other variant . Interestingly , similar to the results from the all-vs-all strategy above , one variant pair indicating an interaction between MIA3 and CDNK2B-AS1 was significant for several link functions , but again , just , failed the scale-invariance test , see Table 3 . Somewhat unexpectedly , this variant pair did not coincide with any of those in the all-vs-all analysis . However , it turns out that , while variants for both MIA3 and CDNK2B-AS1 have previously been robustly associated to CAD ( see S1 Table ) , these variants did not include any member of the top-scoring variant pairs in the all-vs-all analysis . This enrichment strategy might therefore have been suboptimal . In the third strategy , we used prior information from HumanNet [36] , a probabilistic functional gene network that links genes for which significant evidence of interaction have been provided in one or more omics experiments; this resulted in 2 , 319 , 906 variant pairs . We found two variant pairs that were significant for all five link functions , thereby passing the scale-invariance test , see Table 3 . For each of these two pairs , genomic proximity suggests that an interaction between PSRC1 and CXCL6 is associated to CAD , and , thus , may play a role in its pathophysiology . The exact mechanism of the interaction is , however , unknown , and the evidence for it in HumanNet was merely reported as co-expression between human genes . The maximal effect size for each discovered interaction range from 0 . 4718 to 1 . 379 ( S2 Table ) ; as a reference , the effect sizes for robustly CAD-associated single variants are commonly around 0 . 285 [37] . While , after adjustment for age , sex , smoking , and population stratification , most effect sizes were reduced , this was not the case for the CXCL6-PSRC1-related interactions ( see S3 Table ) . The penetrance pattern of one of the CXCL6-PSRC1 variant pairs , rs4694178 and rs602633 , is shown in S5 Fig . Of note , it shows a marked directional change in risk for individuals carrying the major rs4694178 homozygote and the minor rs602633 homozygote . We then investigated the reproducibility of the CXCL6-PSRC1-related interaction on a non-overlapping sub-cohort of PROCARDIS . This sub-cohort consists of 1797 cases and 2677 controls , which were genotyped on the Illumina Human1M Quad chip . The exact variants of the significant pair were not genotyped , and was therefore imputed ( and hard-called ) using the 1000 Genomes phase 3 reference panel . We tested interaction directly using a GLM combined with our link functions . This resulted in the p-values 0 . 174 , 0 . 241 , 0 . 103 , 0 . 056 , 0 . 156 , for the identity , log , log-complement , odds and logit link functions respectively . We note that , while the p-value for the odds scale is close to significance , the replication clearly did not pass the scale-invariance test . Despite this , the penetrance patterns for different allele combinations were very consistent between discovery and replication analyses , compare S5 and S6 Figs . Of note , is that the minor allele frequency for rs602633 is relatively different between the two cohorts , see S4 Table . We , furthermore , expanded the search to the ten closest variants on both sides of both significant variants . The best variant pair , rs11730560 and rs1277930 , reached nominal significance , and the p-values were 0 . 023 , 0 . 0311 , 0 . 014 , 0 . 0084 , 0 . 0209 , again for the identity , log , log-complement , odds and logit link functions respectively . It did , however , not pass multiple testing accounting for all the 380 tested variant pairs . We also performed analyses using the static method assuming 100 marginally associated variants with the same search strategies , but no variant pair was significant on any scale for any of the strategies . This may be a consequence of the expected lower power of the static method .
We have introduced a new stage-wise methodology that is statistically and computationally efficient for large-scale inference of genetic interactions . We have derived two separate methods: The first is the static method that uses a priori estimated multiple testing correction factors; here we have used the number of published robustly associated CAD SNPs to obtain such an estimate . The second adaptive method does not rely on the assumption of known correction factors , but uses the number of associated variant pairs at each stage to compute the multiple testing factors . To the best of our knowledge , this is the first method that uses the idea of a closed set of hypotheses to perform an exhaustive pairwise scan of interactions . We have shown that this stage-wise method performs better on a large number of interaction models compared to other statistical methods . The basic idea is that instead of directly testing all possible variant pairs for interaction , we use a sequence of more general association tests as a filter to reduce the number of pairs until only potential interactions remains . This shifts much of the multiple testing burden from the final interaction test to the preceding general tests . Because the tests leading up to the interaction test in general are more powerful ( i . e . , interactions will not be discarded ) , this results in higher overall power . Our simulation results show that our new methods in general have higher statistical power than other common interaction inference methods . For certain specific models and low MAFs , the Sixpac method [23] perform relatively well , but its performance over the spectrum of all possible interaction models is low . The simulations suggest that , in ideal cases , it may be possible to infer interactions using our stage-wise methodology even when correcting for 1012 pairs , since each stage greatly reduces the number of tested interactions . However , we conjecture that , in practice , it will be important to take advantage of prior information in order to reduce the number of tested interactions; for example , we used information from the HumanNet database to select candidate interactions . Moreover , the methodology presented in this paper can also be combined with screening procedures such as LDcohort [30 , 31] or the efficient probable approximate complete search algorithm of [23] . This may give even further gains in power and computational speed . Deciding which scale to work on ( i . e . , which link function to use , see Table 1 ) can be troublesome and many researchers advocate a favorite scale for statistical or biological reasons . Testing on a single scale will improve the statistical power for interactions that fit that scale compared to testing multiple scales . However , if pairs of variants are additive on another scale , this approach will lead to an increased number of false positives , in the sense that there exists simpler models that explain the data . In our framework we offer a compromise: we display all pairs that are significant on at least one scale , but also provide a test that require significance on multiple scales . In this way , a researcher can interpret the significance of an individual scale in the context of the other scales . From our analyses of biological data , no particular scale appear to consistently be the critical one for the scale-invariance test . We note that the scale-invariance test provides an advantage in terms of FWER control . While most other methods failed to control FWER for data generated with a link function that was sufficiently dissimilar from that underlying the method , the scale-invariance test allowed our methods to control FWER for data generated with any tested link function . Although the static method could be derived using closed testing , the derivation of the adaptive method relied on additional assumptions that may be difficult to satisfy in practice . We observed that this could cause inflation of the FWER under a specifically designed additive model with multiple weakly associated variants . We note that , while analytically straight-forward to work with , the FWER is known to be a conservative control of the experimental error at the expense of power [38] . One future direction could therefore be to investigate other error control measures , for example the false discovery rate ( FDR ) [38] . Moreover , there are several cases where the advantages , in terms of computational efficiency and statistical power , of the adaptive method may compensate for a relatively modest inflation in the FWER . Specifically , as validation is conventionally required in genetics studies , the adaptive method can be used as a powerful tool in the discovery phase of large-scale studies . Our biological analysis identifies the well known CDKN2B-AS1 locus , or ANRIL , which encodes an anti-sense RNA [39] . The region contains several variants that are robustly associated with CAD but the pathophysiology of ANRIL is unknown . Interestingly , we detect an interaction between CDKN2B-AS1 and MIA3 , another established CAD locus [2] , potentially indicating a new lead on CAD pathophysiology . Variants in the CELSR2-PSRC1-SORT1 gene cluster have previously been shown to be associated to CAD and lipid traits [2] , although the exact causal relation of the genes is unclear . Our results suggest that HumanNet’s co-expression-based connection between CXCL6 and PSRC1 in fact mirrors a genetic interaction in CAD , supporting a role of PSRC1 in CAD ( in line with recent results [40] ) . Moreover , inflammation has long been seen as an important component of the pathology of atherosclerosis , but few inflammation genes have been implicated by genome-wide association studies [41] , and only in meta-analyses . It is therefore interesting that in the two sets of variant pairs unbiased with respect to CAD , we find interactions involving genes clearly implicated in regulation of inflammation , i . e . , the interleukin- and chemokine-related genes IL1R1 and CXCL6 ( IL8 ) . Of course , follow-up functional investigations are required to fully understand the potential pathophysiological consequences of these interactions . Complex diseases are multi-gene and multi-factorial diseases characterized by complex interactions between genetic , regulatory , metabolic and environmental factors . The majority of complex disease genome-wide association studies have employed traditional association analyses of single genetic markers , which only have been able to explain a small fraction of the disease heritability . A perhaps more conclusive approach would be to reconstruct the complex dependencies between factors as an interaction network reflecting the disease pathophysiology . This approach , however , has so far been hampered by the lack of efficient methods for inference of interactions associated to disease . The static and adaptive methods are two effective ways to discover genetic interactions , and the flexibility of GLMs allows them to be applied to a wide range of different phenotypes . Genetic interactions , and in particular the construction of interaction networks explaining the pathophysiology of the disease , have a potential for clinical relevance , both in terms of prognosis , treatment and drug development . The ideas of stage-wise testing is furthermore applicable outside medical genetics , whenever a large number of complex hypotheses are tested .
The PROCARDIS study was carried out in accordance with the Helsinki Declaration and approved by the Institutional Review Board ( IRB ) at each one of the 4 recruiting centers: the Regional Ethics Review Board at Karolinska Institutet , Stockholm in Sweden ( approval number 98-482 and 03-491 ) , the IRB at the University of Munster , Munster , in Germany , the IRB at the Mario Negri Institute , Milano in Italy and the IRB at the University of Oxford , Oxford , United Kingdom . All study participants provided their written informed consent to participate in the study , a procedure approved by each one of the local ethical committees . A subset of the PROCARDIS cohort has previously [42] been genotyped with the Illumina IBC chip , a iSelect Custom Genotyping BeadChip [43] . This chip contains 48 , 742 variants in approximately 2 , 100 candidate genes that are believed to be involved in vascular disease processes . The subset of PROCARDIS used in this study are 3 , 162 cases and 3 , 353 controls of which 3 , 865 are males and 2 , 650 are females . The disease phenotype is CAD ( including myocardial infarction ) . Multidimensional factor analysis indicated no significant population structure . The following quality control was performed . We removed variants with a minor allele frequency < 0 . 05 , with significant deviation from Hardy-Weinberg equilibrium p < 10−6 , and removed the variants from the X-chromosome to avoid confounding with gender , leaving us with 33 , 963 variants . We performed simulations of all possible weight combinations with a precision of 0 . 1 , the results can be seen in S5 Table . The choice seems to have little impact , and the best weight combination was 0 . 1 , 0 . 3 , 0 . 3 and 0 . 3 , which is the one we used on biological data . We used two different simulation strategies for the power estimation . The first of these was used to compare the stage-wise scale-invariance method to other methods , see further next section . Models were constructed by enumerating all possible penetrance matrices displaying interaction for a single variant pair [35] , as follows: The models were initially restricted to complete penetrance , that is , the penetrance is either 0 or 1 , which allowed us to enumerate all 29 = 512 penetrance matrices . Only models considered to interact were included , here a model was defined as an interaction if the penetrance matrix could not be decomposed according to Risch’s [19] definition of genetic heterogeneity . That is , formally , let P be 3 × 3 binary penetrance matrix . Then P is not an interaction if and only if there exists two 3 × 3 binary matrices , R with identical rows , and C with identical columns , and P cannot be written as the logical OR between R and C . The genetic heterogeneity definition was chosen because it excludes most marginal effect-only models , thereby reducing noise in the power estimation , and because it can easily be evaluated for complete penetrance matrices . The penetrance matrix was then reduced to continuous values by changing the 0’s to a specified base risk of β0 and the 1’s to β0 + β1 . To enhance comparison of models , we used heritability , H2 , as a summary measure of all genetic effects in a model , where heritability was defined as H 2 = ∑ i , j ( Pr ( Y = 1 ) - Pr ( Y = 1 ∣ X 1 = i , X 2 = j ) ) 2 Pr ( X 1 = i , X 2 = j ) Pr ( Y = 1 ) ( 1 - Pr ( Y = 1 ) ) . For each model , the parameter β1 was adjusted to obtain heritabilities of 0 . 005 , 0 . 010 and 0 . 015 . Using this enumeration we obtain a set of models , each defined by a matrix of penetrances for each genotype combination Pr ( Y = 1∣X1 = i , X2 = j ) ( cf . S7 , S8 and S9 Figs and S2 Text ) . The genotypes for cases and controls were then generated using Bayes’ theorem Pr ( X 1 = i , X 2 = j ∣ Y = 1 ) = Pr ( Y = 1 ∣ X 1 = i , X 2 = j ) Pr ( X 1 = i ) Pr ( X 2 = j ) ∑ k , l Pr ( Y = 1 ∣ X 1 = k , X 2 = l ) Pr ( X 1 = k ) Pr ( X 2 = l ) to get the multinomial distribution over genotypes . We generated 1 , 000 data sets from each of these models . We assumed a balanced design ( i . e . , same number of cases and controls ) , the sample size for each group was varied over 2 , 000 , 3 , 000 , and 4 , 000 , the heritability was varied over 0 . 015 , 0 . 020 and 0 . 025 , and the minor allele frequency was fixed to 0 . 3 . Each data set comprised a single interacting variant pair , and to model multiple testing , we assumed that there were 106 variants and 1012 variant pairs tested . For each model and each parameter combination , the power of a method to detect interaction was estimated over the 1 , 000 replicates . The method’s power over the spectrum of tested interaction models were then summarized in an exceedence plot . We performed two additional power analyses using a second simulation strategy , where we used data simulated from a specific interaction model , the double dominant model ( described in S2 Text . 1 ) , in which α = β1 = β2 = γ1 = γ2 = 0 and δ11 = δ12 = δ21 = δ22 = x , and a logit link function was used . The value x was then varied to get the heritability 0 . 01 , 0 . 02 and 0 . 03 . This analysis used a fixed sample size of 3 , 000 cases and 3 , 000 controls . The minor allele frequency was set to 0 . 3 at both loci . In the first of these two power analyses , we investigated how the relative power in detecting an associated variant pair generated under an interaction model varies over the different individual stages in the stage-wise approach , specifically we compared the power in the first and the last stages ( i . e . , using the null models H1 and H4 ) . The parameters of the double dominant model can be seen in S6 Table . In the second power analysis , where we studied the power of the static method to detect an interacting pair as a function of the estimated number of marginally associated variants , we set the total number of variants tested , N = 1 , 000 , 000 , and the number of marginally associated variants was varied , M ∈ {10 , 20 , 100} . For the static method the corrections for each stage , in order , then was set to N ( N − 1 ) /2 , M ⋅ N , M ⋅ N and M ⋅ ( M − 1 ) /2 . The parameters of the double dominant model can be seen in S7 Table . The FWER estimation was based on simulated data . We generated data from ten different null models representing two different cases: The first case corresponds to the null models in our stage-wise methodology: no association , single main association and five null models with double main effects corresponding to the link functions in Table 1; the second case represents a more challenging scenario and comprise three null models with multiple main effects . For each null model we generated 200 data set replicates that contained 500 − L unassociated and L associated variants , where L depends on the null model , L = 0 for the no association , L = 1 for the single association , L = 2 for the double main models , and L = 10 , 20 , 30 for the multivariate model . For each data set there was therefore L associated variants according to the null model . For each replicate this resulted in 124 , 750 pairs . The minor allele frequency was sampled uniformly between 0 . 2 and 0 . 4 . We sampled individuals until we obtained 4000 cases and 4000 controls . The parameters used in each null model can be seen in S8 Table . For the null models with multiple main effects , we used an additive logistic regression model to generate the phenotype . Let L ∈ {10 , 20 , 30} be the number of variants to include from the chromosome , then the model was defined log ( p 1 - p ) = β 0 + ∑ i = 1 L β i x i where xi ∈ {0 , 1 , 2} and βi ∼ N ( 0 . 15 , 0 . 01 ) . The intercept was set to −9 . 0 . We evaluated the power of our static stage-wise scale-invariant method in comparison to six other statistical methods . In our main , large-scale analysis using data generated from an enumeration of all possible interaction models ( see Material and methods section Generation of synthetic data for estimation of statistical power ) , we restricted ourselves to statistical methods that could efficiently compute a p-value with enough precision to test how they performed in realistic scenarios: Four methods based on a direct interaction test ( i . e . , in our framework description above , testing hypothesis H4 against HA ) with a logistic link function GLM , but employing different screening strategies: Logistic—no screening . Marginal+logistic—the marginal screening method described by [29] , which uses a GLM that tests the marginal effect of each variant at an optimistic significance level 0 . 1 for screening . The screening approaches used in CSS+logistic [30] and R2+logistic [31] are both LDcohort-based , but differ in the definition of the χ2-based statistic , and the choice of significance threshold used for the screening: χ2 ≥ 3 ( corresponding to p ≤ 0 . 39 ) and p ≤ 10−4 , respectively . Thirdly , we test two methods based on the LD-contrast test with different filtering strategies: LD-contrast—no screening . Sixpac—the method of [23] , which recodes variant genotypes into two binary variables ( according to dominant and recessive coding ) and then combines LDcases screening with a LD-contrast main test . The significance level was set to 0 . 05 . We assumed that there were 1012 variant pairs present on the chip and that there existed one interacting pair . For the methods without screening ( Logistic and LD-contrast ) , as well as for the Sixpac method , we corrected for 1012 pairs . For the remaining screening methods , we corrected for the expected number of null variant pairs passing the screening , by taking the product of the p-value threshold and the total number of pairs ( i . e . , Marginal+logistic: ( 0 . 1⋅1062 ) ≈5⋅109 , CSS+logistic: 0 . 39 ⋅ 1012 = 3 . 9 ⋅ 1011 , and R2+logistic: 10−4 ⋅ 1012 = 108 ) . A pair was declared significant if it passed the significance level of both the screening and the main test . For all these methods we used the Holm-Bonferroni correction for multiple testing , which is more powerful than the classic Bonferroni correction . For the Static stage-wise method we corrected for 1012 pairs , 100 ⋅ 106 pairs , 100 ⋅ 106 pairs and 4950 pairs in each of the four stages respectively , to simulate the situation with 100 associated variants . We also performed a second , smaller-scaled , but computationally more demanding , power comparison using data generated from specific interaction models and null models ( described in detail in S2 Text ) . In addition to the seven methods enumerated above , this comparison also included our adaptive stage-wise , scale-invariant method and the Model-Based MDR ( MB-MDR ) method [26] , which is a parametric extension of the MDR method that addresses some shortcomings of the original MDR method , in particular adjustment for main effects ( these methods require the generation of data sets complete with both null and interaction pairs and could not be evaluated in the main power comparison above ) . Lastly , we also used the same nine methods in a FWER comparison using simulated data generated as described in Material and methods section Generation of synthetic data for FWER estimation . A C++ implementation of all methods and source code for all experiments is available at: https://github . com/mfranberg/besiq .
|
Many of our common diseases are driven by complex interactions between multiple genetic factors . Disease-specific , genome-wide association studies have been the prominent tool for cataloging such factors , by studying the genetic variation of a gene in a population and its association with the disease . However , these studies often fail to capture interactions between genes despite their importance . Interactions are notoriously difficult to investigate , because testing the large number of possible interactions using contemporary statistical methods requires very large sample sizes and computational resources . We have taken a step forward by developing a new statistical methodology that significantly reduces these requirements , making the study of interactions more feasible . We show that our methodology makes it possible to study interactions on a large scale without compromising the statistical accuracy . We further demonstrate the utility of our methodology on data relating to coronary artery disease and discover three distinct interactions that might provides new clues to the pathophysiology . The study of genetic interactions have the potential to link disease genes together into disease networks that provide a more detailed description of the interaction between genes and how it drives the disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests
|
Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions . To meet the accompanying change in metabolic demands , the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state , which is characterized by reaction fluxes and metabolite concentrations . These state transitions are the effect of simultaneously manipulating fluxes through several reactions . While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control , its applications are restricted by the limited availability of kinetic information . Here , we introduce structural metabolic control as a framework to examine individual reactions' potential to control metabolic functions , such as biomass production , based on structural modeling . The capability to carry out a metabolic function is determined using flux balance analysis ( FBA ) . We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality ( FC ) . This framework is based on the Shapley value from cooperative game theory and FBA , and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions . A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control . We propose a Monte Carlo algorithm to estimate FCs for large networks , based on the enumeration of elementary flux modes . We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions .
Organisms perpetually face changes in environmental conditions . Bacteria may be confronted with variations in oxygen [1] or carbon sources [1] , [2] , while plants may be exposed to changes in light quality [3] and intensity [4] as well as in availability of carbon [5] and nitrogen [6] . Animals , on the other hand , may have to cope with shifts in temperature [7] . To ensure survival , growth , and reproduction , organisms adapt to these perturbations . The adaptation is likely to be reflected in physiological changes across some or all levels of biological organization , from single cells to tissues , organs , and the organism itself . The molecular mechanisms of adaptation involve concerted action through gene regulatory and signaling interactions which ultimately induce the modification of the metabolic state to meet the change in metabolic demands [8]–[10] . Such transitions in metabolic state are not only the response to shifts in environmental conditions , but also occur upon changing demands during development , e . g . , in the switch from sink to source leaf [11] or during the cell cycle [12] , as well as during metabolic cycles [13] . The metabolic state is determined by the concentration of metabolites and fluxes of biochemical reactions interconverting the metabolites . Changes in metabolite concentrations are governed by alterations of reactions' fluxes which depend on the concentration of reactants themselves . Moreover , fluxes are often under tight condition-specific regulation by means of varying enzyme concentrations/activities via transcriptional and ( post ) translational modifications [14] , [15] as well as through metabolic and allosteric regulation via various effectors ( i . e . , activators and inhibitors ) [16] . However , reaction fluxes are not equally regulated . For instance , regulation targeted at specific enzymes has been observed in facultative anaerobic bacteria upon change from oxic to anoxic environment [17] . The adaptation processes resulting in a preferable metabolic state are crucial to guarantee functioning and , hence , survival . Therefore , the ability of metabolism to adapt to changing conditions , via regulation of its state , can be regarded as a potential for controlling this complex dynamical system . A dynamical system is called controllable if it can be driven from an initial state to a desirable state by the manipulation of a suitable set of variables [18] . Since regulation of the transition between metabolic states is targeted at enzymes and corresponding reactions , reaction fluxes can be considered as the metabolic control variables . Biochemical reactions do not act in isolation and , hence , the process of metabolic adaptation is a result of complex interactions among the system components ( i . e . , metabolites and enzymes ) . Therefore , assessing the extent to which reaction fluxes have to be manipulated to control the metabolic state is a nontrivial task , as metabolic control is a systemic phenomenon [19] . A recent study has shown that , in general , multiple metabolites have to be manipulated to control metabolic networks [20] . While it remains unclear to what extent this holds if control is exerted on reaction fluxes obeying stoichiometric and physiochemical constraints , it indicates that metabolic control is the result of cooperative action . Furthermore , metabolic control can be assumed to depend on the preferred metabolic state an organism aims to attain , which may exhibit altered concentrations [21] or modified fluxes [22] . Mathematical models , capturing the interactions of the most relevant components , provide tractable means for understanding metabolic control [23] , [24] . A frequently utilized framework is metabolic control analysis ( MCA ) [25]–[27] which enables the examination on the basis of kinetic modeling . It has generated diverse insights in metabolic control and served as the basis of rational strain design in metabolic engineering [28]–[31] . However , the limited availability and accuracy of condition-specific kinetic parameters render the applicability of kinetic models to metabolic processes of small scale [32] . On the other hand , structural modeling , although neglecting the details of the kinetics , has proven to be successful in describing and predicting phenotypes across different organisms , from bacteria to plant and human [33]–[40] . Nevertheless , a computational framework for quantifying and investigating metabolic control based on structural models is currently lacking . Here , we introduce structural metabolic control as a framework to examine the effect of the manipulation of a metabolic network ( via enabling and disabling the utilization of reactions ) on the operation of selected metabolic functions . In structural modeling , metabolic functions are equivalent to combinations of fluxes and can correspond to the objective function of flux balance analysis ( FBA ) . The synthesizing capacity of a metabolic function can then be determined by optimizing the objective upon the given constraints [33] , [41] . We examine structural metabolic control by employing functional centrality ( FC ) [42] which quantifies a reaction's contribution to the synthesizing capacity of a metabolic function via a modified version of the Shapley value from cooperative game theory [43] , [44] . This framework integrates the potential interactions between reactions by considering the multiplicity of subnetworks capable of performing the metabolic function . We demonstrate that FC is suitable for elucidating metabolic control and for identifying reactions as potential sites of control . Moreover , this framework allows investigations of the dependency of metabolic control on the environmental conditions , providing insights in the environment-specificity of the distribution of control among reactions . From a computational point of view , we propose an approximation algorithm based on Monte Carlo sampling which extends the applicability of FC to metabolic networks of large size . The algorithm is based on the calculation of elementary flux modes ( EFMs ) , inheriting its computational complexity . In addition , we provide a comparative analysis of FCs and other structural approaches related to metabolic control , namely , control-effective fluxes ( CEFs ) [45] , reaction participation in EFMs [46] and flux couplings [47] . To this end , we use a model of central carbon metabolism of Escherichia coli ( E . coli ) [22] in combination with a Monte Carlo multiple knockout study . Furthermore , we examine structural metabolic control with respect to lactate and ATP production in E . coli for different respiratory states by FC , and discuss and biologically interpret the variation due to metabolic functions and environmental conditions . Our results indicate that FC can further expand the current understanding of metabolic control .
Mathematical modeling of metabolic processes can be divided essentially into two distinct conceptual approaches: kinetic modeling and structural modeling [48]–[50] . Kinetic modeling provides the means to describe and predict both steady-state and transient behavior of metabolite concentrations and reaction fluxes prevalently via ordinary differential equations . However , kinetic models are based on largely unavailable or unreliable information on kinetic rate laws , on the nature of regulation processes and on values for the kinetic parameters [32] , [51] . Already the modeling of small and well-investigated pathways , such as the Calvin-Benson cycle , has shown to be challenging [51] . Structural modeling circumvents the problem of uncertainties with regard to kinetic information by relying only on structural information . It utilizes reaction stoichiometry , reaction directionalities obtained from basic thermodynamics , and flux boundaries [49] , [50] , [52] , which could often be confined through integration of condition-specific high-throughput data [53] . The resulting metabolic network then is prevalently examined either by determining particular steady-state flux distributions guided by optimization principles [33] , [41] or by characterizing all steady-state fluxes via the set of all minimal nondecomposable functional ( i . e . , flux carrying ) pathways [45] , [46] . It is apparent that the price for this simplification is the restriction to steady-state behavior and the potential occurrence of physiologically unrealistic flux distributions . We examine structural metabolic control on the example of central carbon metabolism of E . coli . For this purpose , we utilize a stoichiometric model introduced by Schütz et al . [22] ( see Methods section and Figure 1 ) . We analyze the potential of the presented structural measures , i . e . , coupling degree , reaction participation in EFMs , CEFs and FC , to identify potential sites of control . To this end , we conduct a comparative in silico knockout study for all described measures , demonstrating the superior capability of FC to identify potential sites of control . Structural metabolic control ought to depend on metabolic function . We determine FCs for two metabolic functions , lactate and ATP production , under aerobic conditions and give a detailed biological interpretation of the results . Environmental conditions should also be reflected in the structural metabolic control capabilities . We examine the changes in FCs upon change in environmental conditions for lactate and ATP production and give detailed biological interpretation . Furthermore , we utilize CEFs and FCs to predict changes of transcript levels upon switch of substrate , and analyze the association of the two measures with number of transcription factors . Stelling et al . [45] have shown for E . coli that changes in CEFs with respect to growth on different substrates are correlated to changes in messenger RNA levels which predominantly account for physiological changes on longer timescales . To compare the capability to predict changes in transcript levels , we repeat the respective analysis [45] and calculate CEFs and FCs for the substrates acetate and glucose and relate the differences to transcript data [80] . CEFs are calculated for biomass and ATP production and averaged with weighting by maximal yield as described in the Methods section . Accordingly , we calculate FCs for biomass and ATP production and average the normalized FCs . Graphical analysis exhibits good agreement for the ratio of transcript levels with the ratio of CEFs ( Figure 5 ) and also reasonably good agreement with the ratio of FCs ( Figure 6 ) . We find for CEFs as well as for FCs three reactions exceeding two-fold deviation of the regression line: pfk , ack and pta . The reactions ack and pta account for acetate production and should not have large relevance for ATP and biomass production under aerobic conditions for either utilization of acetate or of glucose . In line with this , transcript levels differ only slightly for the two substrates . In contrast , CEFs and FCs change largely between the conditions . This shift to larger values for growth on glucose is reasonable: while utilization of these reactions has no advantage for growth on acetate ( because acetate is imported and therefore does not have to be produced ) , acetate production can make sense for growth on glucose , e . g . , in the course of fermentation to adapt to fluctuations of the availability of exogenous electron acceptors . CEFs and FCs suggest an upregulation of pfk , which is part of glycolysis , upon switch from acetate to glucose . The reason that transcript levels of pfk change only slightly might be a strategy of anticipation , enabling quick response if glucose , the preferred carbon source , is available again . The discrepancies between predictions based on structural metabolic control capabilities and experimental data may indicate that realization of control through manipulation of potential control sites is not necessarily unique . Kendall rank correlation of the average of transcript ratios with CEFs ( , ) is larger than with FCs ( , ) . The reason may be that CEFs , in contrast to FCs , incorporate an efficiency criteria aiming at the minimization of total flux . Minimal metabolic investment might be another important factor in the realization of metabolic control , which has also been proposed as a design principle of metabolism elsewhere [81] , [82] . Changes of CEFs between conditions show larger association with transcript ratios compared to FCs . However , it is unclear how the shift in transcript expressions affect metabolic flux , which finally is the target of FCs . We further tested the hypothesis of an association between structural metabolic control capabilities and the number of transcription factors affecting individual enzyme catalyzed reactions . CEFs as well as FCs are dependent on the environment and on the metabolic function . As the number of transcription factors is a static feature , we expect an association only in a metabolic function that is vital to the survival of E . coli . The composition of biomass may change with conditions , e . g . , with growth rate [83] . Therefore , we choose ATP production , whose control is vital and which does not exhibit a problem of likely condition-dependent composition . We examine consumption of glucose under conditions of aerobic respiration , which is the environment with largest scope in controlling ATP production . The number of transcription factors associated to individual enzymes where derived from RegulonDB [84] . We obtain a Kendall rank correlation coefficient of in the case of CEFs and in the case of FCs . Both measures yield a comparable significant association between number of transcription factors affecting reactions and their ranking in the respective measure of structural metabolic control . We conclude that both measures capture to some degree the feature of regulatory effectors . However , the association is relatively small . This is in line with our expectation as metabolic control is a dynamic feature depending on environment and physiological state in contrast to the number of transcription factors .
In this study , we have presented an algorithm to estimate functional centralities ( FCs ) for large metabolic networks . Moreover , we have demonstrated the potential of FCs to determine structural metabolic control capabilities of individual reactions . FC accomplishes this by explicitly integrating the potential interactions of reactions with the remaining system , covering interactions with individual reactions , with pathways and with subnetworks . The study exemplifies that important properties of metabolic control can be accessed from structural information of a metabolic network . The results of FBA can be improved by integration of additional information about physiological flux boundaries obtained from high-throughput measurements [90] . This may , in turn , also improve the predictions obtained by FC . Moreover , while FC cannot directly integrate kinetic parameters , it is possible to modify the framework to incorporate extensions of FBA accounting for , e . g . , molecular crowding [91] or membrane economics [92] . Currently , the most established measure to capture control in metabolic networks are flux control coefficients ( FCCs ) of MCA . Besides the discussed drawbacks of this approach , we want to highlight that FC captures the potential effect of the manipulation of multiple reactions , while FCCs only describe the effect of individual manipulations . FC enables the design of knockout and overexpression strategies , taking into account the complexity of metabolic control . For instance , FC can be utilized to suggest targets of metabolic engineering to decrease the production of an unwanted byproduct which limits the yield of a desired product . To this end , the two production processes are defined as metabolic functions and FCs are calculated individually . Those reactions which are functionally central for the production of the unwanted byproduct but not for the production of the desired product are suggested as knockout candidates . A similar strategy could be applied in the identification of drug targets . We have shown that FCs are shaped by environmental conditions and the physiology of metabolic functions . It would be of further interest to examine if an association between FCs and synthesis costs of the corresponding enzymes [73] exists , as it is reported for utilization of EFMs and their resource requirements under certain conditions [93] . Such analysis could also provide insights into how a manipulation pattern of potential sites of control , which is not necessarily unique , might be chosen . We have formulated the calculation of estimated FCs such that the calculation can be performed for only a subset of reactions . While an application would have been out of scope of this study , it provides the means to examine control exerted by a subset of reactions on an enclosing system . In that case , elementary flux pattern calculation [94] could serve to determine the elemental coalitions , which would even enable the examination of control for the case that the enclosing system is of genome scale . This may enable , e . g . , the calculation of structural metabolic control capabilities of the reactions of the central carbon metabolism on biomass production of the genome-scale network . FCs do quantify the relevance of reactions to a metabolic function considering all possible interactions , but they do not quantify the interactions themselves . Grabisch and Roubens have defined a measure extending the classical Shapley value to capture interactions among any set of elements [95] . This approach has been utilized to determine interactions between any two elements to examine perturbation experiments in the setting of neuroscience [96] and gene regulation [97] . While extension of FC in accordance to this measure is appealing , it is unclear if the approach can be modified to apply to the restriction to functional subnetworks only . Furthermore , it is ambiguous if the accompanying increase in computational demands can be met . Linking metabolism and regulatory events is inherently difficult and by far from being completely characterized . Regulatory mechanisms act on multiple levels , such as transcriptional regulation , post-translational modifications and metabolite-protein interactions; all of which ultimately exerting control on metabolic flux [98] , [99] . Functional centralities have shown to be a prospective approach for deeper understanding of metabolic control . To analyze the association between regulatory events and potential sites of control , sophisticated and targeted experiments are needed .
Flux balance analysis ( FBA ) is a structural modeling framework developed to characterize the synthesizing capabilities of metabolic networks at steady state [67] . A metabolic network consists of metabolites ( ) and reactions . The change of the concentration of a metabolite can be described as , where is the stoichiometric coefficient associated with the flux through reaction and is the net transport flux of metabolite . The mass conservation relation under steady-state conditions , i . e . , , results in the following expression: ( 3 ) where is the stoichiometric matrix ( with rows and columns ) , is the vector of metabolic fluxes of the reactions and is the vector representing consumption/production fluxes of the metabolites . The consumption/production fluxes are set to zero for internal metabolites . In contrast , external metabolites constitute an interface to the environment and do not have to obey the steady-state condition . A metabolic flux crossing the system boundary is normally realized by a transporter reaction which converts an internal metabolite into an external metabolite . Constraints on the fluxes of the transporter reactions importing or exporting metabolites across the system boundary are utilized to establish environmental conditions , e . g . , determining the availability of nutrients . As the system of equations described in ( 3 ) is usually under-determined ( ) , there exist multiple solutions corresponding to feasible flux distributions , each representing a particular metabolic state ( with respect to fluxes ) satisfying the stoichiometric constraints . The question usually addressed by FBA is that of determining which of the feasible metabolic states is manifested in the studied metabolic network . FBA relies on the assumption that the metabolic system exhibits a state that is optimal with respect to some objective . Usually , the objective is expressed as a linear combination of fluxes in , which leads to a linear programming problem: ( 4 ) with representing the phenotypic property to be optimized , and being a vector of coefficients quantifying the contribution of each flux to this objective . The bounds and , represent constraints on the fluxes , i . e . , the minimum and maximum values for the fluxes and , thus , determine reaction reversibility . A common choice for the objective function is the maximization of biomass production , which allows a wide range of predictions consistent with experimental observations for simple model organisms [35]–[37] . This function could be employed for environments with nutrient excess . Another possible choice is minimization of production which can be used to determine the conditions for energy efficiency and minimization of nutrient uptake , and is usually applied in modeling the case of nutrient scarcity ( an overview of objective functions can be found in [22] ) . We identify a metabolic function with the objective function and calculate the synthesizing capacity of the metabolic function by maximizing the objective . It should be noted that this does not necessarily coincide with the objective responsible for a specific phenotype . Functional centrality ( FC ) aims at assigning individual reactions of a metabolic network their contribution to a metabolic function [42] . Technically , it combines a solution concept from cooperative game theory with FBA . A cooperative game is defined by a set of players and a characteristic function with which assigns every subset of the player set the worth it generates by cooperation . To find a fair and unique distribution of the cooperatively gained worth amongst the set of players , the solution concept of the Shapley value [43] has been introduced . The Shapley value is axiomatically founded and associates with every game with transferable utility , i . e . , without restrictions on the division of , a unique and fair payoff vector . Thereby , the property of fairness of this solution is implied by the required axioms . The Shapley value of a single player is given by the weighted sum of the player's contribution to all subsets of players . We identify with the set ( or a subset ) of reactions forming the metabolic network and with the optimal value of the objective function determined by FBA for a subnetwork formed by the members of ( if not all reactions of the network are considered , the subnetwork is formed by the members of and the not considered reactions; in that case the objective function has to be formulated such that is valid ) . An extensive derivation is to be found in [42] . The classical Shapley value incorporates all subsets of . With respect to metabolic networks , some of these subsets correspond to nonfunctional subnetworks which are incapable of carrying nonzero flux with respect to the objective function . There are two possibilities to address the issue: ( i ) assigning zero worth to the nonfunctional subnetworks , and ( ii ) excluding the nonfunctional subnetworks from the calculation of the Shapley value . FCs make use of a modified version of the Shapley value [44] to exclude the nonfunctional subnetworks and account only for the functional ones . It has been shown that exclusion of nonfunctional subnetworks is superior for determining reactions' contribution to metabolic function [42] . To introduce the modified version of the Shapley value , as proposed by Aguilera et al . [44] , let be a directed graph . The set of nodes encompasses all subsets which correspond to functional subnetworks plus the empty set . The set of arcs consists of all with ⊆ for which it holds that there exists no with ⊆⊆ . The graph is induced by inclusion on and is equivalent to a Hasse diagram [100] . In , every path from the empty set to the set , encompassing all ( considered ) reactions , represents one possibility to add players successively in such a way that the corresponding subnetworks belong to the family of functional subnetworks . These paths are called maximal chains . The graph is called regular , if all maximal chains of have equal length , otherwise it is called irregular . The calculation of the modified Shapley value is as follows: Let with and be a maximal chain for inclusion in , implying ⊆⊆⊆ . Then , the contribution of player in maximal chain is given by ( 5 ) with being the length of the maximal chain . Let the set of all maximal chains be denoted by . If satisfies ( 6 ) then the weighted sum of contributions over all maximal chains ( 7 ) defines the Shapley value of player for arbitrary families of subsets . By providing an appropriate definition for the weighting factor , the value becomes uniquely determined [44] . In the calculation of FCs , we weight the maximal chains equally: ( 8 ) This facilitates the error calculation of estimated functional centralities and results in only subtle differences to weighting by chain length [42] ( data not shown ) . The axioms defining the modified Shapley value with respect to FC are given in the Supplementary Text S1 . The exact calculation of FCs is limited to small metabolic networks . The accessible network size , in terms of number of reactions , depends on computer hardware and network structure . The reason is the combinatorial explosion of the number of functional subnetworks and the number of corresponding maximal chains . Castro et al . describe an approach to estimate classical Shapley values , considering all subsets of , of large systems utilizing Monte Carlo sampling of maximal chains [101] . We modify this approach to estimate FCs which , in contrast , are based on a restricted set of subsets . We first present the Monte Carlo algorithm , followed by explaining the sampling procedure . At the end of the section , we derive an approximation of the error of the calculated FCs by utilizing resampling , once the calculation has finished . The error of the estimated FCs depends on the utilized sample size . In [101] , the authors give a description on determining a sample size that guarantees a specific upper boundary of the error . Instead , we describe how resampling can be utilized to obtain a good approximation of the real error , once the calculation has finished . ( We observe that the real error is by far smaller than its upper bound , data not shown . ) Since it is impractical to define a stopping criteria , the error has to be obtained a posteriori . Utilizing a sample of sufficient size guarantees that the error of the estimated Shapley value of reaction is smaller than with a probability larger than : from the central limit theorem follows , where is the population variance of the contributions of reaction and the normal distribution with expectation and variance . Hence , if for the sample size ( 12 ) holds , then ( 13 ) With being the value , such that , . For a given sample size the error then is ( 14 ) In this study , we have chosen . This guarantees that the error is accurate with probability larger 97 . 5% . We utilize a sample size of 200 , 000 , which has shown a good balance between accuracy and computational demand in this study . It should be noted , that the error calculation is irrespective of the number of maximal chains due to the implications of the central limit theorem . An approximation of the population variance is given in the next section . Special attention has to be paid to reactions which obtain zero marginal contribution in all samples , which could falsely lead to the conclusion of zero error . In that case , we assign the error that would be obtained for a single nonzero contribution with the value of the largest contribution found in the sampling process . Control-effective fluxes ( CEFs ) are originally defined by efficiencies of the individual EFMs with respect to a substrate and the production of biomass ( ) and ATP ( EFMs are normalized by substrate uptake in advance ) [45]: ( 16 ) ( 17 ) whereby denotes the flux through reaction in the EFM . The CEFs are then given by ( 18 ) with being the maximum yield of biomass or ATP production , respectively , for substrate . In the case examining a specific metabolic function as it is the case in the Monte Carlo multiple knockout study , the calculation of CEFs is restricted to this metabolic function . In the multiple knockout study , we examine biomass production as metabolic function and glucose ( GLC ) as substrate . In that case , we have to divert the term accounting for ATP production . Then CEFs reduce to: ( 19 ) We utilize a model of central carbon metabolism of E . coli published by Schütz et al . [22] . The model comprises 74 unique reactions ( given in Table S11 ) , whereof 14 are transporters , and 61 metabolites ( given in Table S12 ) , whereof 47 metabolites are internal and , therefore , their concentrations have to obey the steady-state assumption . The metabolic network is able to import acetate , ethanol , glucose , nitrate and oxygen and to export acetate , ATP , biomass , carbon dioxide , ethanol , formate , lactate , nitrite , pyruvate and sucrose . Import of ethanol is disabled in the examined settings . Acetate import is disabled for growth on glucose and vice versa . Since ADP is an external metabolite in this model , the export of ATP does account for ATP hydrolysis rather than ATP de novo synthesis . The biomass export reaction comprises all metabolites from central carbon metabolism in appropiate ratios to support growth . The sum of glucose import by the reactions mglABC and ptsGHI has been constrained from above arbitrarily by one . Isozymes in the model were deleted since they add multiple layers of combinatorial complexity and , moreover , would bias FCs . An upper bound of one has also been utilized for acetate import in the calculation of FCs with acetate as a substrate . We examine three environmental conditions ( following the study of Schütz et al . ) : ( i ) aerobic respiration , with no further restrictions ( which implies nitrate being available as alternative terminal electron acceptor ) , ( ii ) nitrate respiration , anaerobic growth in the presence of nitrate ( no oxygen import ) , ( iii ) fermentation , anaerobic growth without nitrate as electron acceptor ( no oxygen and no nitrogen import ) . The utilized objective functions are the fluxes through the reactions: ( i ) maint ( ATP production ) , ( ii ) biomass ( biomass production ) , ( iii ) lac ( lactate production ) .
|
Insight into the functioning of metabolic control to meet changing demands is a first step in rational engineering of biological systems towards a desired behavior . Metabolic control analysis provides the means to examine the impact of change of reaction fluxes on a specific target flux based on kinetic modeling , but suffers from limitations of the kinetic approach . Here , we introduce and analyze structural metabolic control as a framework to overcome these limitations . We utilize functional centrality , a framework based on the Shapley value from cooperative game theory and flux balance analysis , to determine the contribution of individual reactions to the functions accomplished by a metabolic network . These contributions , in turn , depend on the control exerted on the remaining network . Functional centrality provides the mathematical means to gain further understanding of metabolic control . The potential applications range from facilitating strategies of rational strain design to drug target identification .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"complex",
"systems",
"systems",
"biology",
"game",
"theory",
"mathematics",
"theoretical",
"biology",
"applied",
"mathematics",
"biology",
"computational",
"biology"
] |
2013
|
Structural Control of Metabolic Flux
|
We describe the development and evaluation of a novel method for targeted amplification and Next Generation Sequencing ( NGS ) -based identification of viral hemorrhagic fever ( VHF ) agents and assess the feasibility of this approach in diagnostics . An ultrahigh-multiplex panel was designed with primers to amplify all known variants of VHF-associated viruses and relevant controls . The performance of the panel was evaluated via serially quantified nucleic acids from Yellow fever virus , Rift Valley fever virus , Crimean-Congo hemorrhagic fever ( CCHF ) virus , Ebola virus , Junin virus and Chikungunya virus in a semiconductor-based sequencing platform . A comparison of direct NGS and targeted amplification-NGS was performed . The panel was further tested via a real-time nanopore sequencing-based platform , using clinical specimens from CCHF patients . The multiplex primer panel comprises two pools of 285 and 256 primer pairs for the identification of 46 virus species causing hemorrhagic fevers , encompassing 6 , 130 genetic variants of the strains involved . In silico validation revealed that the panel detected over 97% of all known genetic variants of the targeted virus species . High levels of specificity and sensitivity were observed for the tested virus strains . Targeted amplification ensured viral read detection in specimens with the lowest virus concentration ( 1–10 genome equivalents ) and enabled significant increases in specific reads over background for all viruses investigated . In clinical specimens , the panel enabled detection of the causative agent and its characterization within 10 minutes of sequencing , with sample-to-result time of less than 3 . 5 hours . Virus enrichment via targeted amplification followed by NGS is an applicable strategy for the diagnosis of VHFs which can be adapted for high-throughput or nanopore sequencing platforms and employed for surveillance or outbreak monitoring .
Outbreaks of viral hemorrhagic fever ( VHF ) occur in many parts of the world [1 , 2] . VHFs are caused by various single-stranded RNA viruses , the majority of which are classified in Arenaviridae , Filoviridae , and Flaviviridae families and Bunyavirales order [3] . Human infections show high morbidity and mortality rates , can spread easily , and require rapid responses based on comprehensive pathogen identification [1 , 3 , 4] . However , routine diagnostic approaches are challenged when fast and simultaneous screening for different viral pathogens in higher numbers of individuals is necessary [5] . Even PCR as a widely used diagnostic method , usually providing specific virus identification , requires intense hands-on time for parallel screening of larger quantity of specimens and provides limited genetic information about the target virus . Multiplexing of different specific PCR assays aims at dealing with these drawbacks; however , until recently , it was limited to a few primer pairs in one reaction due to a lack of amplicon identification approaches for more than five targets [6 , 7] . Next Generation Sequencing ( NGS ) has provided novel options for the identification of viruses , including simultaneous and unbiased screening for different pathogens and multiplexing of various samples in a single sequencing run [8] . Furthermore , the development of real-time sequencing platforms has enabled processing and analysis of individual specimens within reasonable timeframes [9] . However , virus identification with NGS is also accompanied by major drawbacks , such as diminished sensitivity when viral genome numbers in the sample are insufficient and masked by unbiased sequencing of all nucleic acids present in the specimen , including the host genome [10 , 11] . Attempts to increase the sensitivity of NGS-based diagnostics have focused on enrichment of virus material and libraries before sequencing , including amplicon sequencing , PCR-generated baits , and solution-based capture techniques [12–14] . The strategy of ultrahigh-multiplex PCR with subsequent NGS has previously been employed for human single nucleotide polymorphism typing , genetic variations in human cardiomyopathies , and bacterial biothreat agents [15–17] . In this study , we describe the development and initial evaluation of a novel method for targeted amplification and NGS-identification of viral febrile disease and hemorrhagic fever agents and assess the feasibility of this approach in diagnostics .
The human specimens , used for the evaluation of the developed panel were obtained from adults after written informed consent and in full compliance of the local ethics board approval ( Ankara Research and Training Hospital , 13 . 07 . 11/0426 ) . Viruses reported to cause VHF as well as related strains , associated with febrile disease accompanied by arthritis , respiratory symptoms , or meningoencephalitis , were included in the design to enable differential diagnosis ( Table 1 ) . For each virus strain , all genetic variants with complete or near-complete genomes deposited in GenBank ( https://www . ncbi . nlm . nih . gov/genbank/ ) were assembled into groups of >90% nucleotide sequence identity via the Geneious software ( version 9 . 1 . 3 ) [18] . The consensus sequence of each group was included in the design . The primer sequences were deduced using the Ion AmpliSeq Designer online tool ( https://ampliseq . com/browse . action ) which provides a custom multiplex primer pool design for NGS ( Thermo Fisher Scientific , Waltham , MA ) . For initial evaluation of the approach and as internal controls , human-pathogenic viruses belonging in identical and/or distinct families/genera but not associated with hemorrhagic fever or febrile disease were included in the design ( Table 1 ) . The designed primers were tested in silico for specific binding to the target virus strains , including all known genotypes and genetic variants . The primer sets were aligned to their specific target reference sequences and relative primer orientation , amplicon size and overlap , and total mismatches for each primer were evaluated using the Geneious software [18] . Pairs targeting a specific virus with less than two mismatches in sense and antisense primers were defined as a hit and employed for sensitivity calculations . Unspecific binding of each primer to non-viral targets was investigated via the BLASTn algorithm , implemented within the National Center for Biotechnology Information website ( https://blast . ncbi . nlm . nih . gov/Blast . cgi ) [19] . The sensitivity and specificity of the primer panel for each virus were determined via standard methods as described previously [20] . The performance of the novel panel for the detection of major VHF agents was evaluated via selected virus strains . For this purpose , nucleic acids from Yellow fever virus ( YFV ) strain 17D , Rift Valley fever virus ( RVFV ) strain MP-12 , Crimean-Congo hemorrhagic fever virus ( CCHFV ) strain UCCR4401 , Zaire Ebola virus ( EBOV ) strain Makona-G367 , Chikungunya virus ( CHIKV ) strain LR2006-OPY1 and Junin mammarenavirus ( JUNV ) strain P3766 were extracted with the QIAamp Viral RNA Mini Kit ( Qiagen , Hilden , Germany ) with subsequent cDNA synthesis according to the SuperScript IV Reverse Transcriptase protocol ( Thermo Fisher Scientific ) . Genome concentration of all strains was determined by specific quantitative real-time PCRs using plasmid-derived virus standards , as described previously ( protocols are available upon request ) . Genome equivalents ( ge ) of 100–103 for each virus were prepared and mixed with 10 ng of human genetic material recovered from HeLa cells . In order to compare the efficiency of amplification with the novel panel versus direct NGS , all virus cDNAs were further subjected to second strand cDNA synthesis using the NEBNext RNA Second Strand Synthesis Module ( New England BioLabs GmbH , Frankfurt , Germany ) according to the manufacturer’s instructions . Reagent-only mixes and HeLa cell extracts were employed as negative controls in the experiments . The performance of the panel was further tested on clinical specimens from individuals with a clinical and laboratory diagnosis of VHF [21] . For this purpose , previously stored sera with quantifiable CCHFV RNA and lacking IgM or IgG antibodies were employed and processed via High Pure Viral Nucleic Acid Kit ( Roche , Mannheim , Germany ) and the SuperScript IV Reverse Transcriptase ( Thermo Fisher Scientific ) protocols , as suggested by the manufacturer . Two human sera , without detectable nucleic acids of the targeted viral strains were tested in parallel as negative controls . The specimens were amplified using the custom primer panels designed for HFVs with the following PCR conditions for each pool: 2 μl of viral cDNA mixed with human genetic material , 5 μl of primer pool , 0 . 5 mM dNTP ( Invitrogen , Karlsruhe , Germany ) , 5 μl of 10 x Platinum Taq buffer , 4 mM MgCl2 , and 10 U Platinum Taq polymerase ( Invitrogen ) with added water to a final volume of 25 μl . Cycling conditions were 94°C for 7 minutes , 45 amplification cycles at 94°C for 20 seconds , 60°C for 1 minute , and 72°C for 20 seconds , and a final extension step for 6 minutes ( at 72°C ) . Thermal cycling was performed in an Eppendorf Mastercycler Pro ( Eppendorf Vertrieb Deutschland , Wesseling-Berzdorf , Germany ) with a total runtime of 90 minutes . The amplicons obtained from the virus strains were subjected to the Ion Torrent Personal Genome Machine ( PGM ) System for NGS analysis ( Thermo Fisher Scientific Inc . ) . Initially , the specimens were purified with an equal volume of Agencourt AMPure XP Reagent ( Beckman Coulter , Krefeld , Germany ) . PGM libraries were prepared according to the Ion Xpress Plus gDNA Fragment Library Kit , using the “Amplicon Libraries without Fragmentation” protocol ( Thermo Fisher Scientific ) . For direct NGS , specimens were fragmented with the Ion Shear Plus Reagents Kit ( Thermo Fisher Scientific ) with a reaction time of 8 minutes . Subsequently , libraries were prepared using the Ion Xpress Plus gDNA Fragment Library Preparation kit and associated protocol ( Thermo Fisher Scientific ) . All libraries were quality checked using the Agilent Bioanalyzer ( Agilent Technologies , Frankfurt , Germany ) , quantitated with the Ion Library Quantitation Kit ( Thermo Fisher Scientific ) , and pooled equimolarly . Enriched , template-positive Ion PGM Hi-Q Ion Sphere Particles were prepared using the Ion PGM Hi-Q Template protocol with the Ion PGM Hi-Q OT2 400 Kit ( Thermo Fisher Scientific ) . Sequencing was performed with the Ion PGM Hi-Q Sequencing protocol , using a 318 chip . Amplicons obtained from CCHFV-infected individuals and controls were processed for nanopore sequencing via MinION ( Oxford Nanopore Technologies , Oxford , United Kingdom ) . The libraries were prepared using the ligation sequencing kit 1D , SQK-LSK108 , R9 . 4 ( Oxford Nanopore Technologies ) . Subsequently , the libraries were loaded on Oxford Nanopore MinION SpotON Flow Cells Mk I , R9 . 4 ( Oxford Nanopore Technologies ) using the library loading beads and run until initial viral reads were detected . The sequences generated by PGM sequencing were trimmed to remove adaptors from each end using Trimmomatic [22] , and reads shorter than 50 base pairs were discarded . All remaining reads were mapped against the viral reference database prepared during the design process via Geneious 9 . 1 . 3 software [18] . During and after MinION sequencing , all basecalled reads in fast5 format were extracted in fasta format using Poretools software [23] . The BLASTn algorithm was employed for sequence similarity searches in the public databases when required .
The AmpliSeq design for the custom multiplex primer panel resulted in two pools of 285 and 256 primer pairs for the identification of 46 virus species causing hemorrhagic fevers , encompassing 6 , 130 genetic variants of the strains involved . All amplicons were designed to be within a range of 125–375 base pairs . Melting temperature values of the primers ranged from 55 . 3°C to 65 . 0°C . No amplicons <1 , 000 base pairs with primer pairs in relative orientation and distance to each other could be identified , leading to an overall specificity of 100% for all virus species . The primer sequences in the panels are provided in S1 Table . The overall sensitivity of the panel reached 97 . 9% , with the primer pairs targeting 6 , 007 out of 6 , 130 genetic variants ( 1 mismatch in one or both of each primers of a primer pair accepted , as described above ) ( Fig 1 ) . Impaired sensitivity was noted for Hantaan virus ( 0 . 05 ) . Evaluation of all Hantaan virus variants in GenBank revealed that newly added virus sequences were divergent by up to 17% from sequences included in the panel design , leading to diminished primer binding . These sequences could be fully covered by two sets of additional primers . Amplification of viral targets with the multiplex PCR panel prior to NGS resulted in a significant increase of viral read numbers compared to direct NGS ( Figs 2 and 3 , S2 Table ) . In specimens with 103 ge of the target strain , the ratio of viral reads to unspecific background increased from 1×10−3 to 0 . 25 ( CCHFV ) , 3×10−5 to 0 . 34 ( RVFV ) , 1×10−4 to 0 . 27 ( EBOV ) , and 2×10−5 to 0 . 64 ( CHIKV ) with fold-changes of 247 , 10 , 297 , 1 , 633 , and 25 , 398 , respectively . In direct NGS , no viral reads could be detected for CCHFV and CHIKV genomic concentrations lower than 103 , and this approach failed to identify YFV and JUNV regardless of the initial virus count . In targeted NGS , the limit of detection was noted as 100 ge for YFV , CCHFV , RVFV , EBOV , and CHIKV and 101 ge for JUNV . For the viruses detectable via direct NGS , amplification provided significant increases in specific viral reads over total reads ratios , from 10−4 to 0 . 19 ( CCHFV , 1 , 900-fold change ) , 2×10−5 to 0 . 19 ( RVFV , 9 , 500-fold change ) , and 3×10−4 to 0 . 56 ( EBOV , 1 , 866-fold change ) . The average duration of the workflow of direct and targeted NGS via PGM was 19 and 20 . 5 hours , respectively . In all patient sera evaluated via nanopore sequencing following amplification , the causative agent could be detected after 1 to 9 minutes of the NGS run ( Table 2 ) . The characterized sequences were 89–99% identical to the CCHFV strain Kelkit L segment ( GenBank accession: GQ337055 ) known to be in circulation in Turkey [24 , 25] . No targeted viral sequence could be observed in human sera used as negative controls during 1 hour of sequencing . The preparation , amplification , and sequencing steps of the clinical specimens could be completed with a total sample-to-result time of less than 3 . 5 hours .
In this study , we report the development and evaluation of an ultrahigh-multiplex PCR for the enrichment of viral targets before NGS , which aims to provide a robust molecular diagnosis in VHFs . The panel was observed to be highly specific and sensitive and to have the capacity to detect over 97% of all known genetic variants of the targeted 46 viral species in silico . The sensitivity of the primer panel was impaired by virus sequences not included in the original design , as noted for Hantaan virus in this study . As 36 out of a total of 59 isolates have been published after panel design was completed , these genetic variants of Hantaan virus could not be detected with a comparable sensitivity or not at all with the current panel . This indicates that the panel has to be adapted to newly-available sequences in public databases . We have evaluated how the panel could be updated to accommodate these recently-added sequences and observed that two additional primer pairs could sufficiently cover all divergent entries . Although the approach for the panel design as well as the actual design with the AmpliSeq pipeline was successful for all genetic variants included , the amplification of viral sequences significantly diverging from the panel could not be guaranteed , which may also apply for novel viruses . Unlike other pathogenic microorganisms , viruses can be highly variable in their genome . Only rarely do they share genes among all viruses or virus species that could be targeted as a virus-generic marker by amplification . Our strategy for primer design and the AmpliSeq pipeline do not permit the generation of degenerated primers or the targeting of very specific consensus sequences . However , the design of the primer panel is relatively flexible , and additional primer pairs can be appended in response to recently published virus genomes . Moreover , an updated panel will also encompass non-viral pathogens relevant for differential diagnosis , and syndrome-specific panels targeting only VHF agents or virally induced febrile diseases such as West Nile fever and Chikungunya can be developed . We have further tested the panel using quantitated nucleic acids of six well-characterized viruses responsible for VHF or severe febrile disease , with a background of human genetic material to simulate specimens likely to be submitted for diagnosis , using the semiconductor PGM sequencing platform . The impact of amplification was evaluated with a comparison of direct and amplicon-based NGS runs . Overall , targeted amplification prior to NGS ensured viral read detection in specimens with the lowest virus concentration ( 1 ge ) in five of the six viruses evaluated and 10 ge in the remaining strain , which is within the range of the established real-time PCR assays . Furthermore , this approach enabled significant increases in specific viral reads over background in all of the viruses , with varying fold changes in different strains and concentrations ( Figs 2 and 3 ) . The increased sensitivity and specificity provided with the targeted amplification suggest that it can be directly employed for the investigation of suspected VHF cases where viremia is usually short and the time point of maximum virus load is often missed [1 , 5] . Finally , we evaluated the VHF panel by using serum specimens obtained during the acute phase of CCHFV-induced disease and employed an alternate NGS platform based on nanopore sequencing . This approach enabled virus detection and characterization within 10 minutes of the NGS run and can be completed in less than 3 . 5 hours in total ( Table 2 ) . The impact of the nanopore sequencing has been revealed previously , during the EBOV outbreak in West Africa where the system provided an efficient method for real-time genomic surveillance of the causative agent in a resource-limited setting [26] . Field-forward protocols based on nanopore sequencing have also been developed recently for pathogen screening in arthropods [27] . Specimen processing time is likely to be further reduced via the recently developed rapid library preparation options . While the duration of the workflow is longer , the PGM and similar platforms are well-suited for the parallel investigation of higher specimen numbers . Although we have demonstrated in this study that targeted amplification and NGS-based characterization of VHF and febrile disease agents is an applicable strategy for diagnosis and surveillance , there are also limitations of this approach . In addition to the requirement of primer sequence updates , the majority of the workflow requires non-standard equipment and well-trained personnel , usually out of reach for the majority of laboratories in underprivileged geographical regions mainly affected by these diseases . However , NGS technologies are becoming widely available with reduced total costs and can be swiftly transported and set up in temporary facilities in field conditions [26 , 27] . During outbreak investigations , where it is impractical and expensive to test for several individual agents via specific PCRs , this approach can easily provide information on the causative agent , facilitating timely implementation of containment and control measures . Additional validation of the approach will be provided with the evaluation of well-characterized clinical specimen panels and direct comparisons with established diagnostic assays . In conclusion , virus enrichment via targeted amplification followed by NGS is an applicable method for the diagnosis of VHFs which can be adapted for high-throughput or nanopore sequencing platforms and employed for surveillance or outbreak monitoring .
|
Viral hemorrhagic fever is a severe and potentially lethal disease , characterized by fever , malaise , vomiting , mucosal and gastrointestinal bleeding , and hypotension , in which multiple organ systems are affected . Due to modern transportation and global trade , outbreaks of viral hemorrhagic fevers have the potential to spread rapidly and affect a significant number of susceptible individuals . Thus , urgent and robust diagnostics with an identification of the causative virus is crucial . However , this is challenged by the number and diversity of the viruses associated with hemorrhagic fever . Several viruses classified in Arenaviridae , Filoviridae , and Flaviviridae families and Bunyavirales order may cause symptoms of febrile disease with hemorrhagic symptoms . We have developed and evaluated a novel method that can potentially identify all viruses and their genomic variants known to cause hemorrhagic fever in humans . The method relies on selected amplification of the target viral nucleic acids and subsequent high throughput sequencing technology for strain identification . Computer-based evaluations have revealed very high sensitivity and specificity , provided that the primer design is kept updated . Laboratory tests using several standard hemorrhagic virus strains and patient specimens have demonstrated excellent suitability of the assay in various sequencing platforms , which can achieve a definitive diagnosis in less than 3 . 5 hours .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"rift",
"valley",
"fever",
"virus",
"pathology",
"and",
"laboratory",
"medicine",
"togaviruses",
"pathogens",
"tropical",
"diseases",
"microbiology",
"alphaviruses",
"viruses",
"next-generation",
"sequencing",
"chikungunya",
"virus",
"rna",
"viruses",
"genome",
"analysis",
"neglected",
"tropical",
"diseases",
"molecular",
"biology",
"techniques",
"microbial",
"genetics",
"bunyaviruses",
"microbial",
"genomics",
"research",
"and",
"analysis",
"methods",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"viral",
"genomics",
"genomics",
"crimean-congo",
"hemorrhagic",
"fever",
"virus",
"medical",
"microbiology",
"microbial",
"pathogens",
"molecular",
"biology",
"virology",
"viral",
"pathogens",
"transcriptome",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"computational",
"biology",
"dna",
"sequencing",
"hemorrhagic",
"fever",
"viruses",
"organisms"
] |
2017
|
Development and preliminary evaluation of a multiplexed amplification and next generation sequencing method for viral hemorrhagic fever diagnostics
|
HIV-1 particles assemble and bud from the plasma membrane of infected T lymphocytes . Infected macrophages , in contrast , accumulate particles within an apparent intracellular compartment known as the virus-containing compartment or VCC . Many aspects of the formation and function of the VCC remain unclear . Here we demonstrate that VCC formation does not actually require infection of the macrophage , but can be reproduced through the exogenous addition of non-infectious virus-like particles or infectious virions to macrophage cultures . Particles were captured by Siglec-1 , a prominent cell surface lectin that attaches to gangliosides on the lipid envelope of the virus . VCCs formed within infected macrophages were readily targeted by the addition of ganglioside-containing virus-like particles to the extracellular media . Depletion of Siglec-1 from the macrophage or depletion of gangliosides from viral particles prevented particle uptake into the VCC and resulted in substantial reductions of VCC volume . Furthermore , Siglec-1-mediated virion capture and subsequent VCC formation was required for efficient trans-infection of autologous T cells . Our results help to define the nature of this intracellular compartment , arguing that it is a compartment formed by particle uptake from the periphery , and that this compartment can readily transmit virus to target T lymphocytes . Inhibiting or eliminating the VCC may be an important component of strategies to reduce HIV transmission and to eradicate HIV reservoirs .
Macrophages are readily infected by HIV and make important contributions to AIDS pathogenesis . Currently there is growing interest in this cell type as a potential reservoir for persistent infection and as an important target in efforts to cure individuals of HIV [1–3] . Macrophages are present throughout every organ of the human body , and tissue-resident macrophages may be extremely long-lived , having been derived from progenitor cells during embryogenesis rather than being replaced at short intervals from circulating monocytes [4] . Efforts to understand in detail the interactions between HIV and macrophages are therefore of considerable significance . One of the most enigmatic features of the HIV-infected macrophage has been the presence of the VCC , variously characterized as a source of virus for trans-infection , an immune-protected reservoir , a site of virus assembly , or a site of virus storage following assembly on the plasma membrane . The VCC demonstrates features of the late endosome or MVB compartment , including enrichment of CD9 , CD53 , CD81 , CD82 and MHC class II [5–7] . Unlike late endosomes , however , the compartment is non-acidic and often demonstrates tubular connections that can lead to the plasma membrane [5 , 8–11] . The presence of plasma membrane connections to this compartment has led some investigators to refer to the VCC as the intracellular plasma membrane-connected compartment or IPMC [12] . The accessibility of antibodies to this compartment is limited [13 , 14] but protection from antibodies may be incomplete [5] . Tetherin plays a role in the formation of the VCC , and tetherin limits HIV transmission from infected macrophages to T cells [15 , 16] . We proposed previously that retention of HIV-1 virions by tetherin on the plasma membrane of macrophages contributed to the formation of the VCC , allowing internalization of virions into this compartment [15] . Recently the Gummuluru and Martinez-Picado groups reported an important mechanism utilized by dendritic cells ( DCs ) to capture , internalize and retain exogenous virus [17–20] . These investigators demonstrated that HIV-1 capture by DCs is dependent on the incorporation of the α-2 , 3-siaylated gangliosides on the viral membrane . Both GM1 and GM3 contain α-2 , 3 linkages and were shown to be capable of mediating capture by DCs , while GM3 was more efficient in mediating particle capture at limiting ganglioside concentrations [19 , 21] . Virions were captured through an interaction of gangliosides with sialic acid-binding immunoglobulin-like lectin ( Siglec-1 , also known as CD169 ) , an interferon-inducible member of the I-type lectin receptor family that is present on the plasma membrane of myeloid cells . Depletion of gangliosides from viral membranes or depletion of Siglec-1 in DCs potently inhibited HIV-1 capture and internalization , and also inhibited trans-infection of T cells by mature DCs . The accumulation of particles within intracellular compartments in DCs shares many characteristics with the VCCs of HIV-infected monocyte-derived macrophages ( MDMs ) . In DCs these compartments appear to sequester virus away from the external environment , potentially protecting them from neutralization or other immune defenses . Notably , MDMs have also been shown to express Siglec-1 , and capture of virions by MDMs through interaction with sialic acid on gp120 has been proposed as an important mechanism for macrophage infection [22] . Recently it was shown that Siglec-1 on macrophages lining lymphoid sinuses captures murine leukemia virus ( MLV ) [23] and HIV [24 , 25] . In the case of MLV , Siglec-1-mediated capture by macrophages is followed by migration to lymphoid follicles and trans-infection of B cells . Therefore there is increasing evidence that Siglec-1 plays an important role in retroviral particle capture and subsequent transmission events in vivo . Here we examined Siglec-1-mediated virion capture in HIV-infected macrophages , and asked if the Siglec-1-ganglioside interaction plays a role not only in capture of virions but also in the formation of the VCC itself . Exogenous addition of virus-like particles ( VLPs ) led to their rapid internalization into the VCC in a Siglec-1- and ganglioside-dependent manner , and was not dependent on the presence of the viral envelope glycoprotein . Siglec-1 was highly concentrated , along with tetherin , within VCCs of infected MDMs . Remarkably , VLPs added exogenously became intermingled in the same VCC compartment with viral particles that had originated from the infected macrophage . Furthermore , depletion of Siglec-1 in HIV-1 infected MDMs resulted in a drastic reduction in overall VCC volume and reduced transmission of virus to autologous T lymphocytes . These data demonstrate a prominent role for Siglec-1 in the internalization of HIV-1 to the VCC in infected MDMs , supporting a model in which particle retention on the plasma membrane by tetherin is followed by Siglec-1-driven internalization of particles into the VCC . Furthermore , our results demonstrate that Siglec-1-mediated particle capture and uptake of exogenous HIV-1 particles by uninfected macrophages creates a VCC that is phenotypically identical to that formed in infected macrophages .
Siglec-1 on the surface of DCs is capable of capturing HIV-1 in a glycosphingolipid-dependent manner [19 , 20 , 26] . We hypothesized that Siglec-1 may also be responsible for virion capture and subsequent concentration within VCCs of HIV-1 infected monocyte-derived macrophages ( MDMs ) . To address this , we first examined Siglec-1 cell surface levels in MDMs . Siglec-1 was expressed constitutively in human MDMs , and its surface expression was increased upon stimulation with IFN-alpha ( Fig 1A , upper plot ) . The amount of cell surface tetherin was examined in parallel , and was increased approximately 2-fold by IFN stimulation ( Fig 1A , lower plot ) . Total Siglec-1 expression and upregulation by IFN was also apparent by Western blotting , with a 1 . 9-fold increase in Siglec-1 expression following treatment with 500 U/ml and a 2 . 7-fold increase with 1000 U/ml of IFN ( Fig 1B ) . Next we asked whether IFN stimulation would enhance the capture and uptake of HIV-1 Gag-EGFP VLPs lacking Env . MDMs were incubated for 1 hour with 100 ng HIV-1 VLP-associated p24/105 cells , vigorously washed , and the amount of p24 internalization measured . Remarkably , IFN stimulation resulted in a 4 . 3-fold increase in MDM-associated HIV-1 p24 ( Fig 1C ) . This result confirmed that in MDMs , an IFN-stimulated factor or factors was enhancing the capture of exogenously-added HIV VLPs for MDMs , as had been previously shown for Siglec-1 in DCs . In order to confirm that HIV-1 capture by MDMs requires particle-associated gangliosides as shown for DCs [19 , 26] , we treated HIV-1 Gag-EGFP VLPs with an α2–3 NeuNAc-specific neuraminidase ( NA ) . We confirmed neuraminidase removal of NeuNAc residues from NA-treated VLPs by staining treated and untreated VLPs with Alexa Fluor 647-conjugated wheat germ agglutinin ( WGA ) ( S1 Fig ) . Neuraminidase treatment of VLPs resulted in a 3 . 4-fold reduction in NeuNAc detection as compared with untreated VLPs ( S1 Fig ) . As an additional means of depleting gangliosides on the virion envelope , VLP producer cells were grown in the presence of 10 μM D-threo-1-phenyl-2-decanoylamino-3-morpholino-1-propanol ( PDMP ) , a glucosylceramide synthase competitive inhibitor , 2 days prior and during HIV-1 VLP production . We then evaluated the effect of either neuraminidase treatment or PDMP-mediated depletion of gangliosides on particle uptake by MDMs . Inhibition of glucosylceramide synthesis by PDMP resulted in a 4 . 6-fold decrease in VLP capture by MDMs , while NA-treatment resulted in a 3 . 9-fold reduction ( Fig 1D ) . These results establish that GM3 ( or potentially GM1 ) on the VLP surface is critical for the capture and internalization of VLPs by Siglec-1 on macrophages in a manner entirely consistent with published findings for DCs [20 , 26] . In order to further understand the differences in particle capture , we examined MDMs 6 hours following exposure to wildtype VLPs or neuraminidase-treated VLPs by fluorescence microscopy . Only a few scattered neuraminidase-treated VLPs were detected on the cell surface of MDMs ( Fig 1E ) . Notably , Siglec-1 displayed a diffuse punctate appearance identical to that of untreated MDMs when exposed to ganglioside-depleted VLPs ( Fig 1E ) . In striking contrast , wildtype VLPs were taken deep into the MDM , and Siglec-1 was found to strongly colocalize with VLPs in this internal compartment ( Fig 1F ) . The position of the VLPs suggested similarities to the VCC of infected macrophages , and the redistribution of Siglec-1 to this compartment suggested to us an active role in VLP internalization . We next performed time course experiments to examine the capture and internalization of VLPs by Siglec-1 . HIV-1 VLPs captured by MDMs were internalized and concentrated centrally through a series of sequential steps , including initial attachment ( 10 minutes , Fig 2A ) , internalization into small colocalizing puncta ( 30 minutes , Fig 2B ) , organization into a ring-like structure surrounding what is assumed to be the ER/TGN ( 2 hours , Fig 2C ) , and finally concentration into a central perinuclear location ( 6 hours , Fig 2D ) . Siglec-1 colocalization with captured HIV-1 Gag-EGFP VLPs was readily apparent throughout each stage of capture , internalization , and concentration into a perinuclear compartment ( Fig 2A–2D ) . In order to examine the capture and internalization of Gag-EGFP VLPs by individual MDMs over time , we performed live cell confocal microscopy . The rapid centripetal movement of exogenous VLPs into the VCC of MDMs is dynamically illustrated in S1 and S2 movies . Together these data indicate that Siglec-1 and VLPs move together from the plasma membrane to the VCC , and show that the internalization of VLPs occurs over a period of minutes to a few hours , resulting in the formation of a concentrated central compartment where both VLPs and Siglec-1 are concentrated . In order to further demonstrate the functional significance of Siglec-1 in HIV-1 capture and internalization , MDMs were transfected with Siglec-1-specific or control siRNAs . Siglec-1 expression was analyzed by Western blotting of harvested cell lysates over an 11 day time-course following siRNA transfection . Siglec-1 levels were reduced by more than 75% by day 5 post-transfection and by 91% at day 11 ( Fig 3A ) . Siglec-1 expression in MDMs was largely unaffected by transfection with control siRNA ( Fig 3A , control panel ) . In some experiments , MDMs were also treated with tetherin siRNA as described previously [15] ( S2 Fig ) . We then performed VLP capture experiments in control and Siglec-1 siRNA-treated MDMs . Knockdown of Siglec-1 reduced VLP capture efficiency to 38% of control , a 2 . 6-fold reduction ( Fig 3B ) . To further demonstrate sialyllactose-dependent Siglec-1 capture of HIV-1 VLPs in MDMs , competitive inhibition experiments were performed . MDMs were treated with either lactose as a control or with the GM3 polar head group mimetic 3’-sialyllactose at concentrations ranging from 1 to 50 μM for 30 minutes prior to the addition of HIV-1 VLPs . VLPs were then incubated in MDM culture for an additional 2 hours at 37°C . Addition of lactose to the culture medium had no effect on MDM VLP capture , whereas 3’sialyllactose inhibited VLP capture by 60% , a 2 . 4-fold reduction ( Fig 3C ) . Next , we performed imaging of MDMs exposed to HIV Gag-EGFP VLPs to determine the effect of Siglec-1 knockdown on particle uptake . Control ( scrambled ) siRNA treatment did not inhibit VLP uptake and colocalization with Siglec-1 ( S3A Fig ) , while only few scattered VLPs were visible following depletion of Siglec-1 ( S3B Fig ) . The volume of the VCC , measured as EGFP voxels , was dramatically reduced following Siglec-1 depletion ( S3C–S3E Fig , Siglec-1 siRNA ) . In control siRNA-treated MDMs , the volume of the VCC increased over time ( measured at 30 minutes , 2 hours , and 6 hours , S3C–S3E Fig ) . Together , these data indicate that like DCs , MDMs capture and internalize HIV-1 VLPs predominantly in a Siglec-1 dependent manner . Because VLP capture created a compartment in uninfected MDMs that resembled the VCC , we next asked if Siglec-1 is concentrated within the VCC of HIV-1-infected MDMs ( in the absence of any non-infectious VLP addition ) . MDMs were infected at an MOI of 0 . 5 with either the macrophage-tropic BaL strain of HIV , or with VSV-G-pseudotyped NL4 . 3 or NLUdel , and cultures maintained for 10 days prior to imaging . As expected , CD9 co-localized extensively with p24 in large , multi-vesicular compartments when MDMs were infected with the macrophage-tropic BaL strain of HIV . Downregulation of tetherin by BaL Vpu was apparent , as tetherin signal was localized to a compartment consistent in terms of location with the trans-Golgi network with relatively low-level presence in the VCC ( Fig 4A ) . Remarkably , Siglec-1 was found to be highly concentrated within the VCC ( Fig 4B ) . Colocalization of Siglec-1 and p24 was consistently observed in BaL-infected MDMs from multiple donors . We expanded this analysis to include VSV-G-pseudotyped HIV-1 molecular clones NL4 . 3 and its vpu-deficient partner , NLUdel , in order to allow comparison with prior work and to define colocalization with tetherin . Siglec-1 colocalized significantly with HIV virions in NL4 . 3-infected MDMs ( Fig 4C ) . Tetherin was more prominent within the VCC in NL4 . 3-infected MDMs as compared with BaL ( Fig 4B and 4C ) . Within NLUdel-infected MDMs , a striking colocalization between Siglec-1 , p24 and tetherin was observed ( Fig 4D ) . Measures of colocalization applied to multiple images confirmed the results represented in Fig 4 . For BaL-infected MDMs , p24 colocalization with Siglec-1 was high at 86 . 0 ± 6 . 9% , while tetherin colocalization was only 16 . 5 ± 13 . 7% ( see Experimental Procedures for colocalization methods ) . For NL4 . 3 and NLUdel-infected MDMs , p24 colocalization with Siglec-1 was also high at 93 . 9 ± 8 . 2 and 86 . 1 ± 12 . 7% , respectively . In contrast to results observed with BaL infected MDMs , p24/tetherin colocalization was markedly higher for NL4 . 3 and NLUdel infected MDMs at 64 . 6 ± 20 . 9 and 78 . 7 ± 20 . 9% , respectively . We attribute the differences observed in p24/tetherin colocalization observed from BaL and NL4 . 3 infected MDMs to allelic differences in the efficiency of counteraction of tetherin by BaL vs . NL4 . 3 vpu genes , as NL4 . 3 vpu has been shown to be relatively weak compared to many naturally-occurring vpu genes [27] . These data overall demonstrate that Siglec-1 is highly concentrated in the VCC of infected macrophages , consistent with what had been observed upon addition of non-infectious VLPs , implying a role for Siglec-1 in virion capture and VCC formation during infection of macrophages . Based on the strong colocalization data demonstrating concentration in the VCC of infected MDMs , we examined the effect of Siglec-1 depletion on the formation of the VCC . We first employed NLUdel , as the effect of tetherin on VCC size is enhanced in the absence of Vpu . MDMs were infected overnight with VSV-G-pseudotyped NLUdel at a TCID50 of 2 . 0/cell . On the following day , MDMs were treated with 60 nM of either control , Siglec-1 or tetherin-specific siRNAs , and samples fixed on day 10 post-infection . VCC volume was quantified by measuring the volume of intracellular HIV-1 p24 immunostained areas . Control siRNA-treated MDMs were indistinguishable from untreated MDMs , containing large , intracellular accumulations of p24 that colocalized strongly with both Siglec-1 and tetherin ( Fig 5A , top row of images ) . Siglec-1 siRNA-treatment resulted in a substantial reduction in VCC volume within HIV-1 infected MDMs , measured as 6 . 1 ± 4 . 3% of control ( Fig 5A , middle row and quantified in 5B and 5C ) . Previous work in our lab by Chu and coworkers identified tetherin’s role in enhancing VCC formation in HIV-1 infected MDMs [15] . Therefore , we also quantified VCC volumes from tetherin siRNA-treated MDMs . Tetherin siRNA-treated MDMs infected with NLUdel also exhibited a large decrease in VCC volumes , although somewhat less than that observed with Siglec-1 siRNA-treated MDMs ( 6 . 3 v 16 . 4-fold reduction , respectively ) ( Fig 5A , Tetherin knockdown row and 5B and 5C ) . Furthermore , the remaining p24 signal in tetherin siRNA-treated , HIV-1-infected MDMs colocalized significantly with Siglec-1 ( Fig 5A , tetherin knockdown ) . The average VCC volume quantified from 30 control siRNA-treated HIV-1 infected MDMs on day 10 post-infection was 1185 μm3 , whereas the average VCC volume of Siglec-1-depleted MDMs was radically reduced to 72 . 3 μm3 ( Fig 5B and 5C ) . Tetherin knockdown in NLUdel-infected MDMs also resulted in a significant VCC reduction , averaging 190 . 1 μm3 . The VCC volume distribution of control siRNA-treated MDMs was large , ranging from 286 . 4 to 3419 μm3 . VCC volume ranges for Siglec-1 and tetherin siRNA-treated MDMs were greatly reduced , ranging from 248 . 1 to 2 . 2 and 486 . 1 to 19 . 9 μm3 , respectively ( Fig 5C ) . These data demonstrate that reduction in Siglec-1 is dramatically associated with a reduction in the formation of the VCC in HIV-infected MDMs , similar to but to an even higher level than depletion of tetherin . It is logical to expect that if less virus is internalized by Siglec-1 and tetherin , there would be greater amounts of virus released into the cellular supernatant . Indeed this was the case . We measured p24 within NLUdel-infected MDMs and in the supernatant over time . Depletion of either Siglec-1 or tetherin resulted in a significantly higher percentage of released/accumulating virions in the cell supernatant over time ( Fig 5D ) . Results here are shown as % release , calculated as total p24 in supernatants/p24 in supernatants + cells . We repeated these siRNA knockdown experiments using the primary HIV-1 isolate BaL . BaL-infected MDMs treated with control siRNA contained large , concentrated areas of HIV-1 p24 immunostaining on day 10 post-infection consistent with VCC morphology . Siglec-1 colocalization with HIV-1 p24 signal was nearly complete ( S4 Fig , top row of images ) . Within BaL-infected MDMs , tetherin signal was largely found in locations consistent with the TGN rather than the VCC , consistent with the presence of active Vpu expression and tetherin downregulation from the plasma membrane and from virion assembly sites . Depletion of Siglec-1 in HIV-1 infected MDMs via siRNA-treatment resulted in a substantial reduction in VCC volume ( S4 Fig , middle row ) . Alterations to VCC morphology were noted to include loss of concentration and smaller , individual p24-positive compartments . Tetherin siRNA depletion also resulted in smaller whole cell VCC volumes on average , though the change was less dramatic than that seen with NLUdel ( S4 Fig , lower row ) . Interestingly , and not unexpectedly , remaining p24 signal within VCCs of tetherin siRNA-treated , BaL-infected MDMs significantly colocalized with Siglec-1 . Taken together , these data demonstrate a critical role for Siglec-1 in the formation of the VCC of infected MDMs . The VCC has been defined as a compartment arising only in HIV-infected macrophages , rather than as a compartment that could be formed upon addition of viruses exogenously . We next asked if exogenously added HIV-1 Gag-EGFP VLPs captured by BaL-infected MDMs were destined to be transported into the same compartments occupied by particles arising within the infected macrophage ( the VCC ) . In order to perform this experiment , we designed a method to distinguish exogenous VLPs from endogenous , infectious virions by fluorescence microscopy . The murine anti-p24 mAb , KC57-RD1 ( Beckman Coulter ) , fails to recognize immature HIV-1 Gag-GFP VLPs under our immunostaining protocols , while mature HIV-1 virions are efficiently detected by this reagent ( S5 Fig ) . Remarkably , HIV-1 Gag VLPs were substantially concentrated together with endogenous virions in VCCs ( Fig 6A and 6B ) . Both exogenous VLPs and endogenous BaL virions colocalized significantly with Siglec-1 in VCCs . The extent of colocalization between exogenous VLPs and BaL p24 was on average 73 ± 12 . 9% . Remarkably , 75 ± 15 . 7% of exogenous VLPs and 70 ± 10 . 6% of endogenous BaL p24 colocalized with Siglec-1 within BaL-infected MDMs . To further prove that this compartment is identical to the VCC previously described , the compartment was shown to concentrate CD9 together with both infectious virions and VLPs ( Fig 6C and 6D ) . Interestingly , while near-complete colocalization was demonstrated in the VCC of the majority of cells examined , the internalized VLPs sometimes seemed to surround existing VCC material ( as in Fig 6D ) , suggesting that the VLPs were being added to the outer layer of the pre-existing compartment prior to any further mixing . These data indicate that MDMs capture exogenous HIV-1 and internalize these particles into the VCC . In other words , the compartment where virions from the infected macrophage reside , the VCC , is identical to the compartment to which exogenously-added VLPs are delivered to through the Siglec-1-ganglioside interaction . In order to further confirm the delivery of VLPs into the VCC , we added Gag VLPs to infected MDMs as before , followed by fixation and preparation for transmission electron microscopy . HIV-1 Gag-EGFP VLPs added exogenously to mature uninfected MDMs were efficiently internalized into compartments morphologically resembling VCCs ( Fig 7A and 7B ) . These particles can be clearly distinguished from mature virions by their immature and sometimes irregular Gag core . In contrast , the majority of particles in the VCC of control infected MDMs demonstrated dense conical cores indicative of mature virions as expected ( Fig 7C and 7D ) . To assess whether exogenous HIV-1 VLPs are delivered to the VCC alongside endogenous HIV-1 in infected MDMs , HIV-1 VLPs were added to both NLUdel ( Fig 7E and 7F ) and BaL-infected ( Fig 7G–7L ) MDMs . In order to further accentuate the difference between the VLPs and the native mature particles , VLPs employed in the experiments shown in Fig 7G–7L were produced at a ratio of wild-type to Gag-GFP of 1:1 ( rather than 3:1 ) , producing VLPs with a very irregular core morphology . In both scenarios , exogenous VLPs were delivered into compartments containing mature HIV-1 virions ( Fig 7E–7L ) . The compartments bearing mixed virions and VLPs were often deep in the cell as shown in Fig 7I , and displayed complex shapes as well as areas with a tubular appearance ( Fig 7I and 7J ) . These experiments confirmed to us that the addition of VLPs to infected MDM cultures led to internalization of the VLPs into the pre-existing VCCs of infected MDMs . Our results above showed that HIV-1-infected macrophages take up and deliver non-infectious VLPs into a VCC containing infectious virions . We next asked if infectious HIV-1 particles will be taken up in a similar manner . We hypothesized that the low amount of CD4 on the macrophage cell surface may not allow receptor binding and fusion of all virions , and that Siglec-1-mediated capture may allow uptake of infectious virions from the surrounding media just as had been seen with VLPs . Macrophages were exposed to higher levels of viral particles than used in typical infection experiments in order to visualize uptake and VCC formation exactly as we had done for VLP uptake ( i . e . 100 ng p24 of NLΔEnv virus pseudotyped with BaL Env /1 x 105 cells ) . As a control , we added BMS-626529 , a small molecule that binds gp120 and prevents conformational changes in Env that are required for attachment and entry [28 , 29] , to some wells . 10μM BMS-626529 was able to completely block infection of TZM-bl cells by NLΔEnv/BaL virus ( S6A Fig ) . Remarkably , infectious virions were taken into an intracellular compartment with characteristics of the VCC as shown by CD9 staining ( Fig 8A ) . Uptake into this compartment occurred in both the absence ( Fig 8A ) or presence ( Fig 8B ) of blockade of gp120-CD4 interactions . Staining for Siglec-1 revealed striking colocalization with p24 in this compartment ( Fig 8C and 8D ) . NLΔEnv/BaL p24 colocalized with Siglec-1 with a colocalization coefficient ( M1 , green/red pixels ) of 64% ± 3 . 1% in the absence of inhibitor vs . 69% ± 3 . 4% in the presence of inhibitor ( S6B Fig ) . The extent of Siglec-1/p24 ( M2 , red/green pixel ) colocalization was slightly lower overall but similar between treatment groups ( S6C Fig ) . We conclude that the presence of a fusion-competent envelope on exogenous virions does not prevent uptake of virions into the VCC , likely due to inefficient fusion in this cell type that exhibits low levels of surface CD4 . The volume of the VCCs formed in presence of BMS-626529 was 38 . 9 ± 3 . 1 μm3 , as compared with 65 . 2 ± 7 . 3 μm3 in absence of inhibitor ( S6D Fig ) . Electron microscopic analysis of macrophages fixed 24 hours following addition of NLΔEnv/BaL virus revealed intact , mature virions within convoluted intracellular membranous compartments consistent with a classical VCC ( S7A and S7B Fig ) . We could not discern a morphologic difference in this compartment conferred by the presence of the attachment/fusion inhibitor ( S7C and S7D Fig ) . The significance of Siglec-mediated virion uptake into the VCC was next investigated . Macrophages were infected with HIV-1BAL , followed by siRNA-mediated depletion of either Siglec-1 or tetherin on the following day . We added indinavir as a control at early timepoints ( day 3 ) as a means of preventing production and accumulation of infectious virus in the VCC . After 7 days of infection , autologous CD4+ T cells were added ( 3 cells/MDM ) , and transmission allowed to proceed for an additional 12 hours . Indinavir was then added 2 hours prior to macrophage-T cell co-culture to cells that had not received indinavir at day 3 , in order to prevent transmission occurring through new virion formation during the period of cell-cell contact . T cells were then separated from macrophages and stained for CD3 and p24 and counted by flow cytometry . A schematic of this experiment is presented in Fig 9A . Addition of early ( day 3 ) indinavir prevented transmission events in each group ( Fig 9B , d3 lanes ) . Remarkably , Siglec-1 knockdown significantly reduced transmission ( Fig 9B , compare ( - ) indinavir bar with and without Siglec-1 depletion , and compare d7 indinavir with and without Siglec-1 depletion ) . Tetherin knockdown in this experiment did not have any significant effect on transmission ( due to the potent vpu allele of BaL as will be discussed below ) . In each group , late indinavir addition reduced transmission , indicating that some new virus formation during the contact period contributed to transmission events . However , the majority of the virus transmitted in these experiments came from pre-formed virions within the macrophage . We conclude that Siglec-mediated internalization of virions into the VCC plays a significant role in VCC formation , and that virions retained in the VCC can subsequently contribute to transmission to T cells .
HIV-1- infected macrophages demonstrate prominent intracellular compartments filled with virions ( VCCs ) . These compartments have been postulated to be HIV-1 assembly sites and feature tubular connections with the plasma membrane . However , VCCs also share many features of the viral storage compartment in uninfected monocytoid DCs ( mDCs ) . It has become increasingly clear in recent years that particle capture by mDCs is mediated by the cell surface lectin Siglec-1/CD169 [19 , 26] . Siglec-1 interacts with gangliosides on the virion lipid envelope , to mediate particle capture and internalization in mDCs [19 , 20 , 26] . The major ganglioside involved in HIV-1 particle capture events is GM3 , although others such as GM1 may also play a role [19 , 21] . Siglec-1-mediated particle capture is also a prominent feature of macrophages , where it similarly facilitates particle capture and trans-infection of T lymphocytes in the case of HIV-1 or B lymphocytes in the case of MLV [23–25] . Here we confirm that Siglec-1-mediated particle capture leads to internalization of exogenous virus-like particles or infectious virions into human macrophages , and show that the internalized particles and Siglec-1 colocalize with known markers of the VCC . Depletion of Siglec-1 led to markedly diminished formation of VCCs within infected macrophages , suggesting that the majority of virions within the VCC of infected macrophages are formed peripherally and then are internalized together with Siglec-1 to this compartment . Siglec-1 moved together with VLPs toward the perinuclear region of macrophages , sometimes accompanied by the formation of narrow Siglec-1 and VLP+ tubules , eventually becoming highly concentrated deep in the cell . The compartment to which VLPs were delivered was proven to be identical to the VCC , as added VLPs colocalized with mature virions in the infected cells and with typical VCC markers . This argues for a common internalization pathway that brings particles formed in an infected macrophage into the VCC and is accessible to exogenous particles . We suggest that this common pathway is formed through a macropinocytosis-like process , and that the common element involved in determining the location of both the endogenous particles from the infected cell and the exogenous VLPs is Siglec-1 . Macropinocytosis of HIV-1 virions into macrophages has been previously described [30 , 31] . This same process is likely to occur when particles bud from infected macrophages , as Siglec-1 is found highly concentrated in VCCs without adding any VLPs exogenously . Our data support a model in which Siglec-1 attaches to gangliosides , most prominently GM3 [19] , on the virion envelope during the budding process on the plasma membrane , followed by internalization of the virion-Siglec-1 complex along a tubular assembly and into the VCC . Thus we propose that the connections observed previously leading from the VCC to the plasma membrane [5 , 8 , 9] are likely the conduits of virion internalization , rather than exit , for HIV-1 . The site of assembly in macrophages has been debated . Immature and budding particles can sometimes be seen within VCCs in electron micrographs , and this provides visual evidence that budding can occur into the VCC [6 , 7 , 10] . Our data do not contradict these observations . However , the finding that exogenous VLPs concentrate Siglec-1 and move rapidly from the plasma membrane to the VCC suggests that the plasma membrane is likely to be the major site of assembly in macrophages . Because the internalization of virions occurs within minutes , any static imaging analysis of infected macrophages in culture will visualize a predominance of virion particles in the VCC , rather than on the plasma membrane . Quantification of the amount of assembly and budding from the macrophage plasma membrane versus potential assembly on intracellular membranes of the VCC will require future dynamic imaging studies in which internalization of captured virions is included in the analysis . The significance of Siglec-1-mediated capture of virions by macrophages and of VCC formation itself is most likely in providing a storage or transport compartment for infectious virions that mediate trans-infection of CD4+ T lymphocytes . Data presented here indicate that formation of the VCC can be mediated by infection of the macrophage , or alternatively by uptake of exogenous virions into an identical compartment in uninfected macrophages . The fact that either route can lead to VCC formation and mediate infection of T cells raises interesting questions relevant to HIV transmission and pathogenesis in humans . The majority of transmitted HIV-1 isolates are not truly macrophage-tropic , as defined by the ability to infect macrophage-like cells bearing low amounts of surface CD4 . However , such isolates arise later in infection in a number of tissues such as the central nervous system [32] . We postulate that early in infection , Siglec-1-mediated capture of virions that are inefficient in infecting macrophages can lead to VCC formation and contribute to trans-infection of T cells , while macrophage populations that become infected as macrophage-tropic viruses evolve within an individual form VCCs bearing their “own” viruses . In both scenarios , the captured virions may be at least transiently protected from immune surveillance and from neutralization [13 , 14] . Our results also raise the interesting possibility that some VCCs may bear a mixture of particles , some arising from the infected macrophage and some captured from surrounding cells and tissues , having originated from other infected cells . What is the role of tetherin in this process ? Tetherin has been noted to inhibit transmission from myeloid cells to T cells in some studies [15 , 16 , 33] while having more variable effects in others [34] . We propose a model in which tetherin restricts release of virus at the plasma membrane , and then Siglec-1 interaction with GM3 on the virion membrane in cis leads to internalization of the retained virions ( Fig 10 ) . Tetherin and Siglec-1 subsequently both are internalized together with the retained virions to the VCC , where they are readily seen to colocalize . This model provides an explanation for the finding that knockdown of either Siglec-1 or tetherin leads to diminished volumes of the VCC . We found that Siglec-1 knockdown was somewhat more potent in reducing VCC volume than tetherin knockdown , suggesting a very prominent role in this process . The relative role of tetherin may be affected by the activation state of the macrophage , as well as by the activity of the particular Vpu protein expressed by the infecting virus . Neil and colleagues have shown that there is a viral allele-specific variation in the ability of Vpu to downregulate tetherin [27] , and we found that Vpu from NL4 . 3 was only partially effective at tetherin downregulation in MDMs [15] . Thus when a vpu allele is potent , the role of tetherin in VCC formation and its negative effect in transmission can be largely negated . This explains differences observed in viral transmission following tetherin depletion in the present study ( no apparent effect upon transmission of virus expressing a potent vpu allele ) in contrast with our prior findings using NL4 . 3 or NLUdel ( enhanced transmission of infection upon tetherin depletion ) [15] . Siglec-1-mediated virion capture during particle budding , on the other hand , is not altered by Vpu , and therefore Siglec-1-mediate effects on VCC formation and transmission of virus is preserved regardless of the presence or absence of a potent vpu allele . Siglec-1 may be a more important contributor to VCC formation than tetherin in the setting of primary HIV-1 isolates , as most of these isolates will encode a vpu allele that is more active than that of NL4 . 3 [27] . Another difference is that Siglec-1 is able to mediate capture of exogenous virions or of endogenous virions and subsequently generate VCCs , whereas tetherin can only contribute to the retention and capture of virions arising from the infected cell membrane . We note that although both tetherin and Siglec-1 contribute to VCC formation , they play opposing roles in cell-cell transmission . Tetherin plays a negative role in macrophage-to-T cell transmission when it is not downregulated by Vpu or depleted by siRNA [15 , 16] . Siglec-1 , on the other hand , captures virions and leads to their internalization into the VCC in a manner that allows and perhaps facilitates subsequent transmission events . It will be interesting to dissect more completely how Siglec-1-mediated capture allows transmission to occur at the virologic synapse , while tetherin does not . If the VCC serves as a reservoir in long-lived tissue-resident macrophages , then strategies designed to eradicate HIV will need to target this compartment . Because the Siglec-1-GM3 interaction is common to both the intracellular compartment in DCs and in macrophages , a common strategy could potentially target HIV captured by both cell types . Delivery of an inhibitory agent to the VCC of macrophages or to the DC could serve the dual function of eradicating a potential reservoir and preventing trans-infection of T cells . Pursuit of strategies targeting this common pathway in HIV-infected individuals are warranted .
Human blood for the preparation of monocyte-derived macrophages and other experiments in this work was obtained from volunteer donors and was de-identified prior to handling by the investigators . Informed consent was obtained from participants . Blood was collected under a protocol approved by the Emory Institutional Review Board . Human peripheral blood mononuclear cells ( PBMCs ) were isolated from fresh heparinized blood by Ficoll-Hypaque gradient centrifugation . PBMCs from buffy coats were pooled and extensively washed to remove platelets . Monocytes were enriched by magnetic-labeling using Monocyte Isolation Kit II ( Miltenyi Biotec Inc ) according to manufacturer’s protocol . Enriched monocytes were adhered to poly-D-lysine coated plates ( Corning ) and 35 mm MatTek dishes ( MatTek Corporation ) . Monocytes were maintained in RPMI-1640 supplemented with 10% FBS , 100 ug/ml streptomycin , 100 U/ml penicillin , 2 mM glutamine and 5 ng/ml GM-CSF or 20 ng/ml M-CSF ( R&D Systems ) . Monocytes cultures were maintained in cytokine supplemented media for 7 days to facilitate maturation into monocyte-derived macrophages ( MDMs ) . Media was replaced every 2–3 days . Macrophage purity was assessed by CD14 staining on day 8 . P24 content of HIV-1 Gag-EGFP virus-like particles ( VLPs ) from stocks and MDM cell lysates were measured using a p24 antigen capture ELISA . Accurate p24 measurement of immature HIV-1 VLPs requires raising SDS level of lysis solution to 0 . 5% and heating for 10 min at 60°C . Murine anti-p24 capture antibody 183-H12-5C ( CA183 ) was obtained from Bruce Chesebro and Kathy Wehrly through the NIH AIDS Research and Reference Reagent Program . CA183 was coated onto 96-well plates at a dilution of 1:2000 in PBS and incubated overnight at 37°C . Plates were blocked for 1 hour at 37°C with 5% fetal calf serum in PBS . The detection of bound p24 was determined using HIV-Ig , obtained from NABI through the NIH AIDS Research and Reference Reagent Program , at a dilution of 1:20 , 000 for 1 hour at 37°C . Colorimetric analysis was performed using the Immunopure TMB Substrate Kit ( Pierce , Rockford , IL ) and absorbance was read at 450 nm . Recombinant p24 was used for the standard curve and sensitive to less than 20 pg of p24 . Siglec-1 ( HSS110029 ) , tetherin ( HSS101115 ) and negative control Med GC Stealth siRNAs were obtained from Life Technologies ( Grand Island , NY ) . For knockdown experiments , MDMs were transfected with 60 nM Stealth siRNAs using Lipofectamine RNAiMax ( Life Technologies ) according to manufacturer’s protocols . On the following day , transfection complex containing media was removed and cells washed once with complete media . Samples were collected at various time points post-transfection and either stored at -80°C until analysis or further processed for immunofluorescence microscopy as described . Both M-CSF and GM-CSF matured MDMs were assayed for Siglec-1 and tetherin cell surface concentrations in the presence or absence of 1000 U/ml Universal Type I IFN Alpha ( PBL Assay Science ) . Mature MDMs were stimulated overnight with IFN , cells washed with PBS and detached using Versene ( Life Technologies ) with gentle scraping . Surface CD14 ( BD Pharmingen , Cat . No . 555399 ) , sheep anti-Siglec-1 ( R&D Systems , Cat . No . AF5197 ) and tetherin staining procedures were performed as previously described [35] . FACS Canto II flow cytometer ( BD Biosciences ) and FlowJo software ( Treestar Inc ) were used for analyses . pNL4-3 proviral plasmid was obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH; from Malcolm Martin . pNLUdel proviral plasmid [36] was obtained from Klaus Strebel , NIAID , NIH . pHCMV-G [37] for VSV-G expression was obtained from Jane Burns at UC San Diego . Vesicular stomatitis virus g glycoprotein ( VSV-G ) -pseudotyped or wild type HIV-1 NL4 . 3 and NLUdel stocks were created by transfection of 293T cells ( CRL 3216 from American Type Culture Collection , ATCC ) using jetPRIME ( Polyplus ) transfection reagent according to manufacturer’s instructions . Virus was harvested from transfected cell supernatants at 36 hours post-transfection , clarified , filtered through a 0 . 45-μm filter and stored at -80°C . Primary HIV-1 isolate BaL stocks were prepared as follows: Human peripheral blood mononuclear cells ( PBMCs ) were isolated from fresh heparinized blood by standard Ficoll-Hypaque gradient centrifugation methods . PBMCs were resuspended in RPMI 1640 supplemented with 20% heat-inactivated fetal bovine serum and 50 μg/ml gentamicin ( RPMI 1640-GM ) . Primary HIV-1 isolates were propagated in PBMCs stimulated with 5 μg/ml phytohemagglutinin ( PHA ) and 5% interleukin 2 ( IL-2 ) . The IL-2/PHS-stimulated cells were infected using a high-titer seed stock of virus minimally passaged in PBMCs , starting from a viral stock obtained through the NIH AIDS Reagent Program ( from Dr . Suzanne Gartner , Dr . Mikulas Popovic and Dr . Robert Gallo ) . One ml of virus was transferred to the flask containing freshly stimulated PBMCs and incubated overnight at 37°C in 5% CO2 . The cells were washed extensively and resuspended in 30 ml of RPMI-GM with IL-2 . Typically , the virus was harvested two times; the first harvest was on day 4 post-infection , with subsequent harvest on day 7 . The virus-containing supernatants were collected , clarified by centrifugation , and filtered through a 0 . 45-μm filter . The virus was then aliquoted into 1-ml sterile screw-cap cryovials and stored at -80°C . Infectivity of viral stocks were assayed for infectivity using TZM-bl indicator cells ( obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH; from Dr . John C . Kappes , Dr . Xiaoyun Wu and Tranzyme Inc . ) . TZM-bl were incubated for 48 hours , and 100 μl of supernatant was removed from each well prior to the addition of 100 μl of Bright Glo substrate ( Promega , Madison , WI ) . Measurement of infectivity involved transfer of 150 μl of cell/substrate mixture to black 96-well solid plates and measurement of luminescence using a Packard TopCount luminometer . MDMs were infected with VSV-G-pseudotyped HIV-1 at a TCID50 of 1–2/cell and primary HIV-1 isolate BaL at 0 . 5 TCID50/cell . The exception to this is the experiment in which a higher MOI of input virus was utilized to assess for uptake of infectious virions by Siglec-1 . In this experiment , MDMs were infected/exposed to 100ng/1 x 105 cells of NLΔEnv virus pseudotyped with BaL Env in the presence or absence of 10 μM BMS-626529 ( Aurum Pharmatech , Catalogue number W-5929 ) . NLΔEnv/BaL stocks were prepared by transfection of 293T cells as outlined above using pNLEnv-1 proviral plasmid [38] from Klaus Strebel and BAL . 26 Env pseudotyping construct [39] from David Montefiori , Duke University . 5 . 0 x 105 MDMs were seeded on Collagen-I coated 35 mm MatTek dishes ( MatTek ) and allowed to mature for a minimum of 7 days as described . At the appropriate time , MDMs were fixed with 4% paraformaldehyde ( PFA ) in sodium phosphate buffer ( PBS ) for 10 min , permeabilized with 0 . 2% Triton X-100 , and blocked with Dako blocking buffer ( Dako ) supplemented with 6 μg/ml human IgG . Cells were incubated with combinations of rabbit anti-tetherin antisera [35] , murine anti-siglec-1 ( AbD Serotec , clone 7–239 ) , murine anti-p24 ( Beckman Coulter , KC57-FITC or RD1 ) , or murine anti-CD9 ( BD Pharmingen , Cat . No . 555370 ) in DAKO Background-Reducing Antibody Diluent , washed thoroughly , and incubated with the appropriate secondary antibodies . Immunostaining requiring a murine primary and the directly conjugated murine anti-p24 KC57 FITC were performed as follows . Primary murine anti-CD9 or anti-Siglec-1 labeling was performed as previously described . MDMs were then blocked with 6 μg/ml murine IgG in DAKO Background-Reducing Antibody Diluent for 1 hour , washed and then immunostained with anti-p24 KC57-FITC in DAKO Background-Reducing Antibody Diluent supplemented with 6 μg/ml murine IgG . In order to visualize the nucleus , cells were subsequently stained with DAPI ( 4′ , 6′-diamidino-2-phenylindole ) at 300 nM in PBS for 15 minutes at room temperature , washed several times with PBS , and imaged . Immunofluorescence images were acquired using a DeltaVision RT deconvolution microscope ( Applied Precision/GE Life Sciences ) , and data analyses were performed with Volocity 6 . 3 software ( Perkin-Elmer ) . Immunofluorescence colocalization was calculated using stringent image thresholding and confirmed by visual object identification methods [40] . For most colocalization analyses reported here , five representative images were quantified . For the volume measurements of the VCC , 30 images were quantified for each experimental arm . 3D time-lapse live cell imaging was carried out with a Zeiss LSM780 confocal microscope using a C-Apo 40x/1 . 2NA water-immersion objective . A suitable field of view was selected , and full cell volume was imaged by acquiring 8–12 Z-stacks spaced by 1 μm every 2 . 5 minutes , using a minimal power of 405 and 488 nm lasers for Hoechst-33342 , Gag-EGFP VLPs respectively . The DefiniteFocus module ( Carl Zeiss ) was utilized to correct for axial drift . Imaging was done at 37°C using the Zeiss environmental chamber maintained at 5% CO2 . A single Z-plane of the cells showing VCC formation was converted into each of the movies shown . MDMs were cultured on poly-D-lysine ( PDL ) coated ACLAR embedded film ( Ted Pella; Redding , CA ) as described . MDMs were harvested 10 days post-infection unless indicated otherwise , fixed in 2 . 5% PFA and 2 . 5% glutaraldehyde for 2 hours followed by embedding in Epon . Serial 100 nm sections were stained with heavy metals and images were obtained using a Hitachi H-7500 transmission electron microscope at 120 kV . HIV-1 Gag-EGFP VLPs were generated by transient transfection of HEK 293T cells with pVRC-3900 and pVRC/GAGOPT-GFP at a ratio of 3:1 respectively . For experiments where morphologically aberrant VLPs were desired a ratio of 1:1 was used . The HIV-1 Pr55Gag construct , pVRC-3900 , is an expression plasmid encoding a codon-optimized HIV-1 Pr55Gag polyprotein and was kindly provided by Gary Nabel ( VRC , NIH ) [41] . A c-terminal EGFP fusion Pr55Gag construct , pVRC/GAGOPT-GFP , was generated by PCR amplification of the Pr55Gag region from pVRC-3900 and subsequent subcloning into the HindIII/BamHI sites of pEGFP-N3 . VLPs were harvested 48 hours post-transfection , supernatants clarified , filtered through a 0 . 45 μm filter and concentrated through a 20% sucrose cushion . VLP pellets were resuspended in ice cold PBS and stored at -80°C . Additionally , HIV-1 Gag-EGFP VLP stocks were produced in HEK 293T cells pre-treated for 2 days and throughout VLP production with 10 μM PDMP ( Calbiochem ) . PDMP ( 1-phenyl-2-decanoylamino-3-morpholino-1-propanol ) inhibits the activity of glucosylceramide synthase ( UDP-glucose:ceramide glucosyltransferase ) which initiates the biosynthesis of animal gangliosides ( GSLs ) . HIV-1 Gag-EGFP VLP capture and internalization assays were performed on GM-CSF matured MDMs from between day 7 to 10 post-plating in 35 mm Collagen-I coated MatTek dishes . MDMs were incubated with 100 ng of HIV-1 Gag-EGFP VLPs/1 . 0x105 cells in plain RPMI media in a 37°C/5% CO2 for times indicated . MDMs were washed extensively with PBS prior to either cell lysis for p24 quantification by ELISA or fixation and immunofluorescent staining . Sialic acid removal from HIV-1 Gag-EGFP VLP membrane associated glycosphingolipids was performed by incubating sucrose-purified VLPs with 1 . 0 U/μl of a neuraminidase ( NA ) ( NEB; P0728S ) . This enzyme is a specific exoglycosidase that hydrolyzes α2–3 N-acetyl-neuraminic acid residues , and exhibits a 260-fold preference for α2–3 sialyl linkages versus α2–6 sialyl linkages while exhibiting only trace hydrolysis of α2–8 sialyl linkages . VLPs were treated with neuraminidase in PBS for 6 hours at 37°C . In order to assess efficiency of sialic acid removal , 100 ng of both mock and NA-treated VLPs were incubated on poly-D-lysine coated 35 mm MatTek dishes for 1 hour at room temperature . The solution was then aspirated and washed several times prior to fixation with 4% paraformaldehyde . VLPs were then stained with wheat germ agglutinin conjugated with Alexa Fluor 647 ( Life Technologies , W32466 ) . VLPs from analyzed preparations were also used for MDM uptake experiments . MDMs were GM-CSF matured for seven days on Poly-D-Lysine coated 12-well plates ( Corning ) prior to overnight infection at 3 TCID50/cell with a biological stock of HIV-1 BaL . Following day knockdown of Siglec-1 and tetherin were performed as previously described . Co-cultures in some instances were treated with 1 μM Indinavir Sulfate ( reagent obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH ) at either 3 days post-infection or 2 hours prior to CD4+ T cell addition on day 7 . On day 7 post-infection , autologous CD4+ T cells were added at 3 cells/MDM in complete medium . Co-cultures were incubated for 12 h at 37°C and 5% CO2 . CD4+ T cells were isolated from MDMs using Versene solution ( Thermo Fisher Scientific ) , washed in serum-free RPMI-1640 and cultured for 24 h in complete medium supplemented with 5 ng/ml rhIL-2 ( R&D Systems ) and 1 μM indinavir sulfate . CD4+ T cells were then fixed and permeabilized using Fixation/Permeabilization Solution Kit ( BD Biosciences , San Jose , CA ) prior to staining with anti-human CD3-APC ( Cat . No . 555342 , BD Biosciences ) and anti-HIV-1 p24 KC57-FITC ( Cat . No . 6604665; Beckman Coulter ) . Cells were analyzed using a FACSCanto II flow cytometer ( BD Biosciences ) . All graphical data are presented as means +/- SD . Statistical significance between groups was determined by unpaired t test using GraphPad Prism 4 . 02 . Significant P values <0 . 05 are noted within figures .
|
T lymphocytes and macrophages are the two major cell types involved in HIV replication and transmission events . When a T cell is infected , virus particles assemble and bud from the plasma membrane of the cell . In contrast , infected macrophages develop an intracellular collection of viruses termed the virus-containing compartment or VCC . Many aspects of the formation and function of the VCC remain unclear . Here we show that VCC formation does not actually require infection of the macrophage , but can be reproduced through the addition of virus-like particles or infectious virions to macrophages . HIV-1 particles were captured by the cell surface carbohydrate-binding protein Siglec-1 , followed by co-migration of Siglec-1 and captured viral particles to the VCC . Depletion of Siglec-1 from the macrophage prevented VCC formation , and inhibited the ability of infected macrophages to transmit HIV to T cells . Our results help to define the origin of this intracellular compartment , arguing that it is a compartment formed by particle uptake from the periphery . Inhibiting or eliminating the VCC may be an important component of strategies to reduce HIV transmission and to eradicate HIV reservoirs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"pathogens",
"immunology",
"microbiology",
"viral",
"structure",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"non-coding",
"rna",
"rna",
"viruses",
"cellular",
"structures",
"and",
"organelles",
"small",
"interfering",
"rnas",
"white",
"blood",
"cells",
"animal",
"cells",
"medical",
"microbiology",
"hiv",
"gene",
"expression",
"microbial",
"pathogens",
"t",
"cells",
"hiv-1",
"virions",
"cell",
"membranes",
"intracellular",
"membranes",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"virology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"macrophages",
"lentivirus",
"organisms"
] |
2017
|
Siglec-1 initiates formation of the virus-containing compartment and enhances macrophage-to-T cell transmission of HIV-1
|
Meiotic recombination is initiated by large numbers of developmentally programmed DNA double-strand breaks ( DSBs ) , ranging from dozens to hundreds per cell depending on the organism . DSBs formed in single-copy sequences provoke recombination between allelic positions on homologous chromosomes , but DSBs can also form in and near repetitive elements such as retrotransposons . When they do , they create a risk for deleterious genome rearrangements in the germ line via recombination between non-allelic repeats . A prior study in budding yeast demonstrated that insertion of a Ty retrotransposon into a DSB hotspot can suppress meiotic break formation , but properties of Ty elements in their most common physiological contexts have not been addressed . Here we compile a comprehensive , high resolution map of all Ty elements in the rapidly and efficiently sporulating S . cerevisiae strain SK1 and examine DSB formation in and near these endogenous retrotransposable elements . SK1 has 30 Tys , all but one distinct from the 50 Tys in S288C , the source strain for the yeast reference genome . From whole-genome DSB maps and direct molecular assays , we find that DSB levels and chromatin structure within and near Tys vary widely between different elements and that local DSB suppression is not a universal feature of Ty presence . Surprisingly , deletion of two Ty elements weakened adjacent DSB hotspots , revealing that at least some Ty insertions promote rather than suppress nearby DSB formation . Given high strain-to-strain variability in Ty location and the high aggregate burden of Ty-proximal DSBs , we propose that meiotic recombination is an important component of host-Ty interactions and that Tys play critical roles in genome instability and evolution in both inbred and outcrossed sexual cycles .
Meiosis is the specialized cell division that halves the genome complement to produce gametes for sexual reproduction . During meiosis , homologous recombination is induced by programmed DNA double-strand breaks ( DSBs ) made by the topoisomerase-like Spo11 protein in a reaction in which Spo11 attaches covalently to 5′ strand termini of the DSB [1] . Endonuclease cleavage releases Spo11 from the DSB ends in a covalent complex with a short oligonucleotide [2] . Subsequent resection of DSB 5′ strand ends generates 3′ single-stranded tails , which are substrates for proteins that search for a homologous DNA duplex and effect the templated repair of the break [3] . DSBs in single-copy sequences usually induce recombination between allelic segments on homologous chromosomes , which promotes pairing and accurate segregation of homologs and increases genetic diversity in gametes . However , eukaryotic genomes are replete with repetitive elements that share high sequence identity . A DSB formed in a repeat can induce recombination between non-allelic DNA segments , which can in turn result in chromosome rearrangements such as duplications , deletions , inversions or translocations [4]–[6] . In humans , such non-allelic homologous recombination ( NAHR , also referred to as ectopic recombination ) in the germ line contributes to non-pathogenic structural variation [7] and is linked to numerous genomic disorders [5] . NAHR is thus a driving force in genome evolution and a source of genome instability . Meiotic DSBs are distributed non-randomly across genomes [8] , [9] , so the propensity toward NAHR depends strongly on how likely it is that Spo11 cuts in and around repetitive elements [6] . A major class of repetitive element in S . cerevisiae comprises the Ty elements , ∼6-kb retrotransposons related to mammalian retroviruses [10] . Each contains an internal region encoding Gag- and Pol-like proteins required for retrotransposition , flanked by ∼330-bp long terminal repeats ( LTRs ) . The S288C strain ( source of the yeast reference genome ) contains 50 Ty elements in five distinct families: 31 Ty1 , 13 Ty2 , 2 Ty3 , 3 Ty4 and 1 Ty5 [11] . S288C also contains a much larger number of solo LTRs or LTR fragments , which likely arise from homologous recombination between the LTRs of full-length Tys [11] . The predominant families , Ty1 and Ty2 , exhibit high sequence identity: >90% in pairwise comparisons within families and >70% between Ty1 and Ty2 [11] , [12] . Because of their sequence similarity and dispersed distribution , Ty elements are potent sources of gross chromosomal rearrangements . Numerous studies have documented Ty-mediated NAHR induced by DSBs or replication errors in vegetatively growing cells [e . g . ] , [ 12]–[15] . Comparatively little is known about Ty recombination provoked by Spo11-generated DSBs in meiosis . A URA3-marked Ty2 element inserted in the HIS4 promoter caused a >13-fold reduction in DSBs at this site , which is normally a strong DSB hotspot [16] . An open ( nucleosome-depleted ) chromatin structure is an important determinant of Spo11 hotspots [17]–[19] . The HIS4 promoter , like most yeast promoters , displays hypersensitivity to DNase I digestion of chromatin , but the inserted Ty ( which is itself resistant to nuclease digestion ) , converted the local chromatin structure to a nuclease-resistant state [16] . Thus , a Ty can suppress DSB formation nearby , possibly via spreading of a closed chromatin structure into the surrounding region [16] . However , although this element mimicked a spontaneous Ty insertion [20] , it is in an unusual position since Tys most often integrate near tRNA genes and only rarely into RNA pol II promoters [11] , where most DSB hotspots occur in yeast [19] . By direct restriction mapping and Southern blotting on chromosome III in the rapidly and efficiently sporulating S . cerevisiae SK1 strain , two novel Tys were identified [21] . DSBs were not detected within or adjacent to these Tys , but it remained unknown whether DSBs are infrequent in or near other natural Tys . Also , Ty elements can differ widely from one another in many of their behaviors . For example , the few Tys examined to date undergo NAHR at dissimilar frequencies , ranging from ∼10−5 to ∼10−2 per meiosis [4] , [22] , [23] , and expression of Tys and Ty-adjacent genes varies substantially between individual elements [10] , [24] . Thus , it is presently unknown whether local DSB suppression can be extrapolated to be a general feature of natural Ty elements . Deep sequencing of the Spo11 oligos that are byproducts of DSB formation provided a high resolution DSB map and suggested that DSBs are moderately suppressed within Tys on average [19] . However , average behavior does not reveal the extent of variation between different sites . Here , we examine DSB formation in and around endogenous Ty elements in SK1 and explore how the presence of natural Ty elements affects local DSB frequency .
Full-length Ty1 and Ty2 elements were previously mapped in SK1 by microarray hybridization of genomic DNA containing Ty sequences [25] . Twenty-five Ty-containing regions were identified , but spatial resolution ( ranging 1–21 kb ) was not high enough for us to assess nearby DSBs . The Saccharomyces Genome Resequencing Project ( SGRP ) generated an SK1 genome assembly from shotgun sequencing combined with phylogenetic comparisons [26] . The number of Ty-containing reads led to an estimate that retrotransposons are ∼2% of the SK1 genome vs . >3% for S288C , implying SK1 has ∼30 Tys . However , the SGRP assembly and its subsequent refinement [27] did not compile all Ty elements , identify Ty families , or reveal precise Ty positions , because repetitive elements pose a computational challenge in genome assembly [26] , [28] , [29] . Moreover , the SK1 strain sequenced by SGRP is a homothallic , prototrophic strain ( HO , LYS2 , URA3 , LEU2 ) related to an ancestor of the strains most widely used in meiosis research [30] . To overcome these limitations , we took a multipronged approach to precisely map all full-length Ty elements in the Kleckner laboratory-derived SK1 lineage . We identified a total of 30 Tys and fine-mapped their positions , in most cases to single-nucleotide resolution ( Figure 1 and Table 1 ) . First , we asked whether SK1 has Tys that are present in S288C . The SGRP data consist of paired-end sequence reads with an average insert of 4–5 kb . These reads can reveal structural differences between a reference genome and the DNA source if the distance between mapped pairs is substantially larger or shorter than the average , or if orphans are present , where one read maps but its mate fails to map or maps to a different genomic region and/or multiple locations ( Figure 2A ) . From inspection of SGRP read maps , we found only one S288C element that was also present in SK1: YCLWTy5-1 at the left end of Chr III ( Figure 1 , Figure 2B and Table 1 ) . This is the only full-length Ty5 family member in either strain , although there are Ty5 solo LTRs and LTR fragments in both ( data not shown ) . Ty5-family insertions are found preferentially near telomeres and silent mating type loci [reviewed in 11] . YCLWTy5-1 contains mutations rendering it nonfunctional for transposition [31] , so this is an ancient Ty present in the last common ancestor of these strains . The remaining 49 S288C Tys are not present in SK1 ( Figures S1A , S1B and data not shown ) . This manual inspection also identified eight S288C Ty sites for which SK1 has one or more Ty elements nearby , subsequently confirmed by PCR ( 11 Tys total; Figure S1B , Table 1 and data not shown ) . These novel Tys are at different positions in SK1 than in S288C and often of a different family or in opposite orientation , thus are independent integration events . Second , we evaluated Ty1 and Ty2 sites mapped by Gabriel et al . in the Kleckner lineage [25] . Using SGRP sequence patterns plus PCR and sequencing of genomic DNA , we validated and fine-mapped 22 SK1-specific elements ( Figure S1C and data not shown ) , but 3 sites showed no evidence of a Ty in SGRP data . Two of these reflect differences between SGRP and Kleckner SK1 strains: a spontaneous ura3 mutation selected during derivation of the Kleckner strains and caused by Ty integration [30] , [32] ( Figure 2C ) ; and a Ty1 on Chr XIII ( Figure 2D and data not shown ) . The latter is likely a de novo , unselected integration that passed through the bottlenecks of strain derivation , demonstrating the potential for occult differences between otherwise isogenic strains . The third discrepancy is a single Ty incorrectly assigned to two separate sites on Chr IV . S288C contains a tandem duplication of similar genes encoding a hexose transporter ( HXT6 and HXT7 ) [33] , but SK1 has only one HXT copy in this region and lacks the intervening sequence ( Figure 2E and data not shown ) . This structural difference caused the microarray hybridization data to artifactually give two peaks from a single Ty when projected onto S288C sequence space . Third , we used an unbiased approach to ensure that all Ty elements were identified , using SGRP data and a paired-end genomic sequence library from NKY291 , a Kleckner-lineage haploid . We retrieved sequence pairs in which one mate matched non-LTR parts of Tys , then mapped the non-Ty mate on the S288C genome ( 277 SGRP reads and 4 , 963 NYK291 reads , <1% of the total from each ) . Tys appear as clusters of reads pointing from both directions at the insertion site ( Figures 3A and 3B ) . We identified all of the Tys described above , and also found an additional element on Chr II , present in both libraries and confirmed by PCR ( data not shown ) . On average , 8 . 6 SGRP reads tagged each SK1-specific Ty or cluster of Tys , and the read counts matched a Poisson distribution ( Figure 3C ) . Thus , we estimate the probability to be <0 . 0002 that a Ty was missed because of chance failure to recover supporting reads . The NKY291 library provided even more reads identifying each Ty ( mean 158 . 2 , range 30–304 ) , so it is highly likely we identified all of the Tys in SK1 . SK1 Tys showed both conserved and non-conserved features with their S288C counterparts . Ty1 and Ty2 are the predominant families , as in S288C , and only one each of Ty3 and Ty5 are present ( Figure 3D ) . Of the Ty1 or Ty2 elements that could be typed by established criteria [11] or mapped by a prior study [25] , 21 are Ty1 and 5 are Ty2 ( Figure S1D and Table 1; two could not be typed with available data ) . Since we did not determine the entire sequence of the Ty elements , it is unknown which are capable of autonomous transposition . No Ty4 element was found and none of the SGRP or NKY291 reads matched Ty4 internal sequences , but Ty4-derived solo LTRs are present ( data not shown ) . Thus the Ty4 family is extinct in SK1 . Most LTR-retrotransposons generate sequence duplication at the integration site [34] . Among SK1 Ty elements whose insertion sites were precisely mapped , >95% showed perfect target sequence duplication with a good match to the consensus for elements in S288C ( Table 1 and Figure S1E ) . Ty integration can be potentially deleterious , by inactivating or altering expression of neighboring genes [35] , [36] . However , obviously deleterious insertions are relatively rare in S288C [11] . Selection may account for some of this pattern , but target site bias is also a major factor: ∼90% of Ty1–Ty4 insertion sites in S288C ( including solo LTRs ) are near RNA pol III-transcribed genes such as tRNAs [11] , mediated by interaction of Ty integrase with factors required for RNA pol III transcription [35] . Similarly , most SK1 Ty1 , Ty2 , and Ty3 elements ( 26 of 29 ) are near tRNA genes ( Table 1 ) , and one of the exceptions ( at ura3 ) was selected because it conferred a desirable phenotype . We previously showed that Spo11 oligo counts covary linearly with DSB levels , so the frequency of mapped Spo11 oligos is a proxy for DSB frequency [19] . To assess global trends for DSB formation near Ty elements , we compiled densities of Spo11 oligos within 0 . 5 , 1 , and 2 kb windows on both sides of each SK1 Ty ( Figure 4A and Table S1 ) . These densities varied widely between different Ty insertion sites , covering 80 to 500-fold ranges , depending on window size . Many Ty-flanking regions differed substantially from genome average , both hotter and colder . There was no obvious distinction between Ty families , in that the five elements unambiguously identified as Ty2 showed 33-fold variation in local Spo11 oligo density , and overlapped extensively with densities for Ty1 elements ( p = 0 . 25 , Wilcoxon rank sum test ) ( Table S1 ) . The mean Spo11 oligo density near Ty elements was higher than genome average , irrespective of window size ( Figure 4A ) . However , since Tys are not randomly positioned , genome average may not be the most informative comparison . Although SK1 does not have full-length Ty elements where most of the Tys in S288C are found , integration bias with respect to tRNA genes was similar in the two strains . We reasoned that S288C integration sites can be viewed as potential integration sites in SK1 , i . e . , that S288C sites provide a good negative control for correlations between DSBs and Ty presence . In three window sizes analyzed , Spo11 oligo densities around these control sites varied as widely as for bona fide Ty integration sites ( Figure 4A ) . However , while the density ranges overlapped , the values were consistently higher around SK1 Ty elements than around control sites , with mean Spo11 oligo densities 2 . 3–2 . 7-fold higher around the SK1 Tys ( Figure 4A ) . We conclude that natural Ty insertion sites display a great degree of individual variability with respect to local Spo11 activity , comparable to the variability that would be seen for similar genomic locations without a Ty present . Moreover , these data do not provide evidence that Ty presence invariably causes DSB suppression nearby , and instead raise the possibility that Tys may tend to increase the local likelihood of DSB formation . DSBs are preferentially formed at RNA pol II promoters [8] , [37] . Intergenic regions between divergent transcription units , i . e . , containing two promoters , tend to be somewhat hotter on average than intergenic regions between tandemly oriented genes , i . e . , with just one promoter , while intergenic regions between convergent transcription units tend to be much colder than either [19] . All SK1 Ty elements , except the one in ura3 , are in intergenic regions . When Ty elements were divided according to type of intergenic region , the local Spo11 oligo densities mirrored the trends seen for all intergenic regions genome-wide: Tys in divergent regions tended to have more Spo11 oligos mapped nearby than Tys in tandem regions , and both tended to be hotter than Tys in convergent regions ( p = 0 . 0337 , one-way ANOVA; Figure 4B ) . These findings imply that Ty elements do not necessarily override the intrinsic DSB-forming potential of the intergenic regions where they reside . Spo11 oligo patterns were confirmed by direct detection of DSBs near a subset of Ty elements . Since meiotic DSBs are transient in wild type , DSBs were detected in repair-deficient mutants . Sae2 is required for removal of Spo11 from DSB ends , so sae2 mutants accumulate unresected DSBs that can be precisely mapped [2] , [38]–[40] . However , these DSBs can differ quantitatively from wild type in a region-specific manner , for unknown reasons [41] . Dmc1 is a meiosis-specific strand exchange protein; dmc1 mutants can remove Spo11 and generate ssDNA tails , but are unable to carry out further recombination steps and thus accumulate hyper-resected DSBs that migrate faster on agarose gels [42] . Wild-type DSB distributions appear to be more faithfully represented in dmc1 mutants [41] , [43] . Genomic DNA was purified from meiotic cultures of these mutants , restriction digested , and DSBs were detected by Southern blotting and indirect end-labeling ( Figures 4C–4F ) . We chose four sites for physical analysis , reflecting a range of local Spo11 oligo distributions . As detailed below , all four showed good agreement between DSBs and Spo11 oligo maps , both quantitatively and spatially ( Figures 4C–4G ) . TyPEX25-CAR1 had the highest Spo11 oligo density nearby because of a strong hotspot immediately adjacent to its 5′ LTR ( Figure 4C and Table S1 ) . This hotspot was among the hottest 0 . 5% of all hotspots compiled previously [19] . A much weaker hotspot was also present adjacent to the 3′ LTR . TyEST3-FAA3 also had a strong hotspot near its 5′ LTR ( Figure 4D ) . This hotspot was again within the hottest 0 . 5% , but was relatively wide . A weaker hotspot was present on the 3′ side of this Ty , close to a tRNA gene and the EST3 promoter ( discussed further below ) . Both TyPEX25-CAR1 and TyEST3-FAA3 are in intergenic regions containing a tRNA gene between divergently transcribed genes ( Figures 4C and 4D ) . In both cases , the region next to the 5′ LTR carries the strong hotspot even though the region next to the 3′ LTR also contains a promoter . These two loci demonstrate that presence of a Ty can be compatible with very high DSB activity nearby . TyCGR1-SCW11 showed weak DSB levels adjacent to the 5′ LTR ( Figure 4E ) as well as within the Ty , discussed below . This Ty is in an intergenic region containing a tRNA gene between convergent genes . A modest DSB and Spo11 oligo hotspot was also observed ∼2 kb away in the SCW11 promoter ( Figure 4E ) . TyURA3 also had a weak DSB hotspot nearby ( Figure 4F ) . This hotspot was in the ura3 promoter , coinciding with the 5′ LTR side of the Ty . Trace numbers of Spo11 oligos mapped in the ura3 coding sequence adjacent to the 3′ LTR , but the corresponding DSB signal was too weak to be detected ( Figure 4F and data not shown ) . TyCGR1-SCW11 and TyURA3 exemplify a situation in which presence of a Ty correlates with low DSB levels nearby , but do not speak to whether the Ty causes the low DSB activity . Spo11 oligo mapping showed that meiotic DSBs occur within Ty elements [19] , but individual Tys could not be evaluated . Physical analysis revealed a modest DSB hotspot inside TyCGR1-SCW11 ( Figure 4E ) . DSBs overlapped the 5′ LTR and a region ∼1 . 8 kb from the 5′ end of the Ty , inside the Gag coding sequence . DSB signal was not detected near the 3′ end when the Southern blot was reprobed from the opposite side of the restriction fragment ( data not shown ) , thus DSBs are more frequent near the 5′ end for this Ty . The Ty element that disrupts ura3 also showed evidence of DSBs near its 5′ end , but at a level too low to be quantified ( Figure 4F , inset ) . We did not observe discrete DSB signals inside either TyPEX25-CAR1 or TyEST3-FAA3 ( Figures 4C and 4D ) , so these Tys lack hotspots above the limit of detection by Southern blotting ( ∼0 . 01% of DNA ) . Infrequent , relatively disperse DSBs would not be detected in this analysis . These results show that Tys differ significantly from one another in terms of number and location of internal DSBs . Interestingly , break levels in the flanking regions do not necessarily correlate with levels inside the Ty . Open chromatin structure provides a window of opportunity for Spo11-dependent DSB formation [37] . To investigate the relationship between DSBs and chromatin structure at Ty elements , intact nuclei were prepared from meiotic cultures of wild-type cells and partially digested with micrococcal nuclease ( MNase ) . DNA was extracted and digested with appropriate restriction enzymes , and MNase cleavage sites were identified by Southern blotting and indirect end-labeling ( Figure 5 ) . MNase digestion of purified genomic DNA was examined in parallel . Nucleosomal DNA is relatively resistant to MNase cleavage ( Figure 5A ) . For example , the SCW11 promoter showed a broad band of preferred MNase digestion indicative of a nucleosome-depleted region ( NDR ) typical of many yeast promoters , flanked by ladders of bands from cleavage in the linkers between positioned nucleosomes upstream and downstream of the promoter ( Figure 5B , lanes 2–3 ) . As expected , the DSB hotspot in the SCW11 promoter corresponded to the MNase-hypersensitive NDR ( Figure 5B , lanes 2–3 vs . lane 5 ) . TyCGR1-SCW11 showed dispersed MNase cleavage inside , with two prominent MNase-hypersensitive zones toward its 5′ end , one of which corresponded to the DSB hotspot within this Ty ( Figure 5B , lanes 2–3 vs . 5 ) . Within each hypersensitive zone a weak banding pattern could be seen , suggesting a modest tendency for nucleosomes to occupy certain preferred positions in subpopulations of cells . Within the Ty element , 28 . 3% of DNA was cleaved ( 4 . 7% per kb ) , compared with 30 . 7% of DNA cleaved between the 5′ LTR and the end of CWH41 ( 11 . 8% per kb ) . Thus , this Ty overall is only about two-fold more resistant to MNase than the intergenic and genic regions flanking it . In contrast , TyPEX25-CAR1 appeared less sensitive to MNase compared to flanking genic regions . Whereas 17 . 3% of DNA was cleaved in the intergenic region between the 3′ LTR and the start of PEX25 ( 43% per kb ) , 33 . 3% of DNA was cleaved within TyPEX25-CAR1 ( 5 . 6% per kb ) . TyPEX25-CAR1 did not show prominent hypersensitivity toward its 5′ end ( Figure 5C , lanes 2–3 ) . Instead , it showed a broad region of modest hypersensitivity at its 3′ end , suggestive of an array of weakly positioned nucleosomes extending into the flanking intergenic region . These results show that chromatin structure can vary between individual Ty elements . Importantly , MNase-hypersensitive sites indicative of NDRs were present at both the strong DSB hotspot in the CAR1 promoter and the weaker hotspot in the PEX25 promoter flanking TyPEX25-CAR1 ( Figure 5C , lanes 2–3 vs . 5 ) . Thus , presence of a Ty close by need not result in elimination of the open chromatin structure typical of promoters and DSB hotspots . To test whether natural Ty elements directly affect adjacent DSB formation , we individually deleted two Tys and compared DSB patterns with and without these elements present . As a control , we quantified DSBs in the same cultures at the YCR048W hotspot on Chr III; DSBs at this hotspot were similar between the parental and Ty deletion strains ( Figure 6E ) . Remarkably , a strain lacking TyEST3-FAA3 experienced ∼2–3 fold fewer DSBs in the FAA3 promoter region than the parental strain carrying this Ty ( hotspot i in Figures 6A and B ) . Results were similar irrespective of which side of the genomic restriction fragment was probed . Although DSB levels were different , their distribution within the hotspot was unchanged ( Figure 6B ) . The other hotspots in the probed region were affected little if at all in the strain lacking the Ty ( hotspots ii , iii , and iv in Figures 6A and 6B ) . In a strain lacking TyCGR1-SCW11 , the weak DSB signal near the 5′ end of the Ty element became undetectable ( hotspot v , Figure 6C ) , and the hotspot in the SCW11 promoter showed 2 . 3-fold lower DSBs than the parental strain ( hotspot vi , Figure 6C ) . The weak hotspots on the other side of the Ty insertion site were essentially unchanged ( hotspots vii–ix , Figure 6D ) . As expected , the DSB signal inside the retrotransposon was not observed in the Ty-deletion strain ( hotspot x , Figure 6C ) , but no new DSB signal arose in its place as would have been expected if presence of the Ty were suppressing an otherwise active DSB site . These findings do not support the hypothesis that Ty elements invariably suppress meiotic DSB formation in their vicinity . Instead , we conclude that at least some Ty insertions cause an increase in DSBs nearby .
Prior analyses of nucleotide variation demonstrated that SK1 is genetically distant from S288C [26] , [44] . Accordingly , we find that the catalogs of full-length Ty elements are completely different in these strains , except for an ancient and immobile copy of Ty5 . Full-length Tys are prone to loss by LTR-LTR recombination [45] . S288C does not have full-length Tys or solo LTRs where Ty elements reside in SK1 ( data not shown ) , suggesting that transposition of the SK1 Tys occurred after SK1 and S288C diverged from their last common ancestor . While SK1 does not have full-length Tys at the same sites as in S288C , we did not comprehensively map solo LTRs , so it is possible that some S288C Ty elements predate divergence of the strains and were lost in SK1 by LTR-LTR recombination . It will be interesting to identify if any solo LTRs are shared between SK1 and S288C . Such LTRs would be “fossils” of ancestral transposition events , and comparison of their features with those of younger LTRs or Tys may illuminate how host-Ty element relationships have evolved . In principle , the deep sequencing approach we used for Ty mapping should be broadly applicable to repetitive elements of any type in any organism . Indeed , while this work was in progress , others independently used a similar method to identify new transposon insertions in Drosophila [46] . This approach , combined with growing libraries of whole-genome , paired-end sequencing data from widely divergent S . cerevisiae strains , will facilitate assembly of complete genome sequences and also permit genealogical analysis of Ty insertion site diversity . Chromosomal rearrangements can arise in vegetatively growing cells as a consequence of NAHR between Ty elements [12]–[15] , [47]–[49] . Ty location and orientation dictate the degree of susceptibility to rearrangement , the structures of rearranged chromosomes , and whether the outcome is deleterious , neutral , or advantageous . For example , in S288C , deletion of HTA1-HTB1 ( one of two gene pairs encoding histones H2A and H2B ) causes pleiotropic defects that both promote and select for amplification of the separate HTA2-HTB2 locus [47] . Amplification occurs via NAHR between two flanking Ty elements in direct repeat orientation near the centromere of Chr II ( see Figure 1 ) . These Ty elements are not present in the W303 strain , so facile amplification of HTA2-HTB2 is not possible and deletion of HTA1-HTB1 is lethal in this strain [47] . SK1 also lacks similarly positioned Tys ( Figure 1 ) , so we anticipate that deletion of HTA1-HTB1 would be lethal in this strain too . Moreover , closely juxtaposed Ty elements in inverted orientation can create fragile sites predisposed to chromosome rearrangement [14] , [50] . SK1 has no instances of closely spaced , inverted , full-length Ty pairs , but the Ty fragment TyEXG2-YDR262W-2 is juxtaposed in inverted orientation to full-length TyEXG2-YDR262W-1 on Chr IV , and TyNCE103-YNL035C-2 is inserted in inverted orientation into TyNCE103-YNL035C-1 on Chr XIV . These are thus candidates for fragile sites in this strain . More generally , these scenarios ( histone gene amplification and Ty-associated fragile sites ) illustrate the importance of Ty maps in different strains because the particular details of Ty element distribution are critical for understanding the influence of these retrotransposons on genome instability and evolution of genome structure . Ty-mediated NAHR also occurs during meiosis [4] , [22] , [23] . We show here that four individual Ty elements experience different frequencies of DSBs inside . To our knowledge , this is the first direct detection of meiotic DSBs in Ty elements , confirming the inference from Spo11 oligo mapping that significant numbers of DSBs occur within Tys [19] . We detected a total internal DSB frequency of at least 0 . 1–0 . 3% of DNA in TyCGR1-SCW11 . Assuming at most one DSB per four chromatids in a given cell , this frequency predicts that 0 . 4–1 . 2% of meiotic cells experience a DSB within this Ty element alone . This number is small on a per-cell basis , but becomes substantial when considered from the perspective of a population of cells or over many generations . Furthermore , we previously showed that ∼0 . 28% of Spo11 oligos map to Ty-derived sequences , indicating that one in every 2–3 meiotic cells experiences a DSB in a Ty or solo LTR , assuming an average of ∼160 DSBs per cell [19] . Excluding LTRs , ∼0 . 1% of Spo11 oligos map to Ty-internal sequences , which predicts a DSB frequency of 1 . 1–2 . 5% of DNA summed over all Ty elements , based on linear regression of Spo11 oligo counts vs . DSB levels ( see Materials and Methods ) . This estimate is higher than the total DSB frequency observed in the four Tys assayed here , so it is likely that other Ty elements experience a significant number of DSBs as well . Based on copy number compiled here , we estimate that Tys account for ∼1 . 5% of genomic DNA , not including rDNA or the contribution of solo LTRs . In turn , this suggests that DSBs within Tys are ∼15-fold suppressed relative to genome average since only ∼0 . 1% of total Spo11 oligos came from Ty-internal sequences . However , genome average includes many strong DSB sites , such as promoters , that are structurally and functionally dissimilar from the inside of a Ty , which is principally coding sequence . Genome wide , coding sequences account for only ∼11 . 5% of Spo11 oligos but occupy ∼69 . 4% of the genome . Thus , on average , Tys are only ∼2–3-fold colder than the typical open reading frame . Our findings have implications for understanding behavior of outcrossed yeast strains: as a consequence of different Ty distributions , any DSB within a Ty would lack a recombination partner at the allelic position , so such DSBs are most likely repaired from the sister chromatid , by NAHR , or by single-strand annealing between 5′ and 3′ LTRs ( which deletes the Ty-internal sequence leaving behind a solo LTR ) . It will be interesting to determine whether large-scale differences in Ty distributions contribute to reduced ability of hybrids to produce viable spores [51] , [52] , in turn contributing to reproductive barriers between strains . Our findings also have implications for inbred strains , including diploids produced by homothallic strains: DSBs within Tys have potential to provoke NAHR even if there is a Ty present at the allelic position on the homologous chromosome . Such NAHR may contribute to sequence homogenization and co-evolution of Ty elements . Moreover , our results provide a framework for studying mechanisms that act after DSB formation to minimize the risk of deleterious chromosome rearrangements [6] . Chromatin structure may play an important role in DSB formation within Ty elements , as suggested by the observation of MNase hypersensitivity at the 5′ end of TyCGR1-SCW11 where DSBs are formed . Our findings show that different Tys can have different chromatin architecture . In a similar vein , relative transcription levels of Ty1 elements in vegetatively growing S288C were found to differ by ∼50 fold [24] . Thus , Ty elements can differ greatly from one another , precluding generalization of a one-size-fits-all pattern from any given element . Although DSBs wholly within Tys have greater potential to instigate NAHR , breaks in unique sequences near Ty elements may also be at risk because DSB resection generates recombinogenic ssDNA for significant distances ( up to a kb or more ) from the Spo11 cleavage site [53]–[55] . We find here that DSB levels vary substantially in regions flanking different Ty elements and that presence of a Ty does not invariably cause suppression of adjacent DSB activity . These findings are counter to predictions from prior analysis of a Ty in the HIS4 promoter [16] , further highlighting the individual variability of Ty elements . We propose that differences between the studies reflect aspects of host-transposon interactions that evolved to minimize deleterious effects of retrotransposition . The Ty at HIS4 mimicked a spontaneous Ty integration that disrupted HIS4 expression ( Ty917 ) [20] , [22] . It was inserted ∼70 bp upstream of HIS4 , moving the TATA box and upstream activator sequence ∼6 kb away from their normal position and eliminating the DNase I hypersensitivity of the HIS4 promoter [16] . The altered chromatin structure was interpreted as spreading of closed chromatin from the Ty into surrounding regions [16] , but an alternative interpretation is that Ty917 is simply an insertional mutation that compromises the cis-acting elements defining the HIS4 promoter NDR , thereby disrupting both promoter activity and Spo11 access . In this view , the effect of Ty917 on DSB formation is context dependent and intimately tied to its deleterious effect on a host gene . In contrast to Ty917 , most naturally occurring Ty elements are found near tRNA or other RNA pol III-transcribed genes , likely targeted there via interactions of integration complexes with RNA pol III transcription machinery [11] , [56] , [57] . Ty elements inserted near ( and especially upstream of ) tRNA genes will tend to be distant from regulatory regions of other adjacent genes because the mean distance between tRNA genes and their upstream neighbors ( excluding Ty and LTR sequences ) is ∼500 bp larger than the distance from tRNAs to downstream genes or the average size of intergenic regions genome-wide [58] . Thus , while Ty integration site preference may have evolved to prevent deleterious mutations [11] , [36] , [59] , it has the additional consequence that Ty elements tend to avoid the very RNA pol II promoters where most meiotic DSBs are formed , and tend not to impinge on promoter properties that favor Spo11 activity , such as transcription factor binding and nucleosome depletion . Our direct analysis of chromatin structure and DSB formation around TyPEX25-CAR1 supports this view . The correlation between DSB levels and the class of Ty-bearing intergenic region ( Figure 4B ) also supports this idea by implying that DSB frequency is substantially influenced by the local DSB-forming potential of the neighborhoods where Ty elements reside . We were surprised to find that deletion of two Ty elements in different genomic contexts caused decreased DSB formation nearby . Thus , at least some Tys stimulate adjacent DSB formation , and our genome-wide analysis suggested this may be a fairly general property . The mechanism behind this effect is as yet unclear . Both Ty deletions showed an apparent polarity in that the regions where DSB levels were most affected were adjacent to the 5′ LTRs . Although sample size is too small to know if this is a general pattern , it may indicate that adjacent DSB formation is modulated by properties of Ty 5′ LTRs , which in some cases carry promoter activity and contain binding sites of transcriptional activators [e . g . , 24] . Alternatively , it may be that DSB stimulation is not a unique property of the Ty itself , but instead is simply a consequence of a structural change in the chromosome . Indeed , there are numerous examples where heterologous DNA insertions generate new DSB hotspots [reviewed in 8] , although such insertions rarely , if ever , cause enhanced activity of natural , promoter-associated hotspots nearby . Regardless of the mechanism , this finding has implications for inheritance of Tys across sexual cycles . The chromosome that experiences a DSB is the recipient of genetic information from its homologous partner , in part because of net degradation of the broken chromosome by DSB resection and resynthesis using the intact partner as the template [60] . As a consequence of this gene conversion bias , an allele with a higher propensity toward DSB formation will tend to be under-transmitted during meiosis . Thus , elevated DSB frequency near Tys might tend to favor elimination of Ty copies by meiotic recombination in diploids heterozygous for the Ty insertion . In principle , this tendency could affect new Ty insertions in a diploid or inbred population , as well as older Ty insertions in outcrosses between diverged strains . Our findings thus raise new questions about retrotransposon-host relationships and the roles of the intersection between Ty elements , meiotic recombination initiation , and NAHR .
Yeast strains are listed in Table S2 . Ty elements were deleted by two-step gene replacement , resulting in precise replacement of each Ty element with a diagnostic restriction site ( see legend to Tables S2 and S3 ) . Other alleles were introduced by genetic crosses or by one-step gene replacements using standard methods . All gene replacements were confirmed by Southern blotting . A whole-genome mate pair library was prepared according to manufacturer's recommendations ( Roche ) from genomic DNA purified from a vegetative culture of NKY291 , and sequenced on the Roche 454 platform . The NKY291 library had an average sequence length of 173 bp and an average insert size of 2 . 8 kb ( 16-fold coverage in 653 , 261 sequence pairs ) . Sequence data are available at http://cbio . mskcc . org/public/SocciN/SK1_MvO/Data/GCL0188__454__PE_3k/ To evaluate presence of Tys from S288C or previously identified in SK1 [25] , SK1-derived sequence reads from the SGRP were viewed in the genome browser provided by the Sanger Institute ( http://www . sanger . ac . uk/research/projects/genomeinformatics/sgrp . html ) . When read alignment patterns were indicative of Ty presence , partial DNA sequences of the Tys were deduced from contigs assembled from these reads and used to determine Ty orientation and family , by comparison to exemplars of Ty families from S288C . Ty insertion sites were mapped by identifying SGRP reads overlapping boundaries between Tys and flanking genomic sequence . If no overlapping reads were present , PCR products spanning the Ty-element-containing region were partially sequenced to determine the precise insertion sites . For systematic Ty mapping , the SK1 mate pair libraries from SGRP and from our sequencing of NKY291 were mapped against a compilation of non-LTR portions of S288C Ty elements . Mapping was performed using LastZ on the Galaxy server ( http://main . g2 . bx . psu . edu/ ) . Mate pairs of reads that aligned with Ty-internal sequence were then mapped onto the S288C genome using LastZ , and reads that mapped to multiple positions were discarded . Candidate Ty insertion sites identified from the remaining reads were validated by manual inspection of sequence alignments and/or PCR of genomic DNA . In addition to the full-length Tys and large Ty fragments listed in Table 1 , this analysis identified three small ( ∼120–180 bp ) non-LTR Ty fragments at ∼805 kb on Chr IV , ∼78 kb on Chr VIII , and ∼338 kb on Chr XVI ( data not shown ) . How these insertions arose is uncertain , but because they are so short , they were not considered as Tys in this study . Synchronous meiotic cultures were prepared essentially as described [61] . Cells were harvested from a single culture of SKY4121 , two independent cultures of SKY4151 and of SKY4153 , and single cultures of SKY4188 , SKY4189 , SKY4191 and SKY4192 at 6 hr in meiosis , and genomic DNA was isolated in low melting temperature agarose plugs , digested with appropriate restriction enzymes , electrophoresed on agarose gels , and analyzed by Southern blotting and indirect end-labeling , as described previously [19] , [61] . Restriction enzymes and probes are as follows and primers used to prepare probes are in Table S3: TyPEX25-CAR1 , BamHI , PEX25 probe; TyEST3-FAA3 , Bsu36I , DOT5 or EPS1 probe; TyCGR1-SCW11 , BamHI , RPS24A or CWH41 probe; TyURA3 , BamHI , GEA2 probe; YCR048W hotspot , BglII , RCS6 probe . Hybridization signal was detected and quantified with Fuji phosphor screens and ImageGauge software . DSB frequency was determined as the percent of radioactivity in DSB fragments relative to total radioactivity in the lane . Signals from the spo11-Y135F strain were used to subtract background . The large difference in size of the parental-length restriction fragments between Ty-containing and Ty-deleted loci ( experiments in Figures 6A–6D ) could be expected to cause differences in Southern blot transfer efficiencies , which could lead to incorrect estimates of relative DSB levels . To account for this , we applied the following strategy . First , Ty+ and TyΔ samples were run on the same gel , transferred together , and hybridized together to the appropriate probe for the Ty locus . The membranes were then stripped and re-hybridized to probes from different loci to serve as loading controls: YCR057C probe for the blots shown in Figures 6A and 6B and YKL182W probe for the blots in Figures 6C and 6D ( Table S3 ) . We used the loading controls to correct DSB estimates by assuming that there was “missing signal” from the Ty+ lanes because of less efficient transfer of Ty-containing DNA fragments . From this analysis , we estimated that the parental bands in the Ty-containing strains were transferred at ≥75% the efficiency seen with the Ty-deletion strains ( data not shown ) . Meiotic culture of wild-type diploid , SKY41 , was prepared as described above . Intact meiotic nuclei were prepared 4 hrs after induction of sporulation by spheroplasting , hypotonic lysis , and centrifugation on sucrose step gradients , as described previously [62] . Nuclei were quantified by fluorometry with Hoechst 33258 dye . A volume of nuclear suspension containing 4 µg DNA was diluted with an equal volume of ice-cold 10 mM Tris-HCl , pH 8 . 0 , 5 mM MgCl2 and 1 mM Pefabloc . Nuclei were collected by centrifugation and resuspended in 90 µl of 10 mM Tris-HCl , pH 8 . 0 , 2 . 5 mM CaCl2 , 3 . 5 mM MgCl2 on ice . Ten µl of appropriate concentration of MNase ( Worthington ) was added , digestion was performed for 5 min at 37°C , then terminated by addition of 0 . 4 ml of 62 . 5 mM EDTA , 125 mM Tris-HCl , pH 8 . 0 , 0 . 625% SDS and 5 µl of 20 mg/ml proteinase K . Samples were incubated at 58°C for 2 hrs to overnight . DNA was extracted twice with phenol∶chloroform∶isoamyl alcohol ( 25∶24∶1 ) and once with chloroform , then precipitated with isopropanol with 10 µg of glycogen and dissolved in 10–20 µl of dH2O . As a control , genomic DNA was purified from vegetatively growing cells and treated with MNase , followed by purification as above . DNA from MNase-treated nuclei or naked DNA was digested with BamHI , electrophoresed on agarose gels , and analyzed by Southern blotting and indirect end-labeling using the CWH41 probe ( TyCGR1-SCW11 ) or PEX25 probe ( TyPEX25-CAR1 ) ( Table S3 ) . For the analysis in Figure 4A , groups of closely neighboring Tys in SK1 ( between EXG2 and YDR262W on Chr IV , and between NCE103 and YNL035C on Chr XIV ) were treated as single Tys . Furthermore , the Ty5 at the left end of Chr III was excluded , as DSBs are known to be suppressed in subtelomeric regions [19] , [41] , [43] . Therefore , Spo11 oligo counts were determined in 27 Ty-bearing regions in SK1 ( Table S1 ) . As controls , we used the coordinates of S288C Ty elements . We excluded S288C Ty positions within 2 kb of SK1 Ty elements , closely neighboring Tys were considered as a single element , and YCLWTy5-1 was excluded as above . Spo11 oligo densities adjacent to 37 control sites were determined . To estimate the genome-wide percentage of DNA broken in Tys , we summed Spo11 oligos that mapped to non-LTR Ty sequences . Excluding LTRs means that we are underestimating DSBs associated with full-length Tys , but this is necessary because we cannot distinguish Spo11 oligos from LTRs flanking Ty elements from those originating within solo LTRs or LTR fragments . Using our previously defined regression relationship [19] , we converted Spo11 oligo counts to DSB frequency , yielding estimates in dmc1 and sae2 background of 2 . 0% and 0 . 85% , respectively . Since the prior study used the spo11-HA strain , which forms DSBs at a reduced frequency of ∼80% of a SPO11+ strain [63] , we therefore estimate the Ty DSB frequency to be ∼2 . 5% in dmc1 and 1 . 1% in sae2 in the SPO11+ background .
|
Meiosis is the cell division that generates gametes for sexual reproduction . During meiosis , homologous recombination occurs frequently , initiated by DNA double-strand breaks ( DSBs ) made by Spo11 . Meiotic recombination usually occurs between sequences at allelic positions on homologous chromosomes , but a DSB within a repetitive element ( e . g . , a retrotransposon ) can provoke recombination between non-allelic sequences instead . This can create genomic havoc in the form of gross chromosomal rearrangements , which underlie many recurrent human mutations . It has been thought that cells minimize this risk by disfavoring DSB formation in repetitive elements , partly based on studies showing that presence of a Ty element ( a yeast retrotransposon ) can suppress nearby DSB activity . Whether this is a general feature of Tys has not been evaluated , however . Here , we generated a comprehensive map of Tys in the rapidly sporulating SK1 strain and examined DSB formation in and around all of these endogenous Ty elements . Remarkably , most natural Ty elements do not appear to suppress DSB formation nearby , and at least some of them increase local DSBs . These findings have implications for understanding the relationship between host and transposon , and for understanding the impact of retrotransposons on genome stability and evolution during sexual reproduction .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Meiotic Recombination Initiation in and around Retrotransposable Elements in Saccharomyces cerevisiae
|
Tumorigenesis requires the re-organization of metabolism to support malignant proliferation . We examine how the altered metabolism of cancer cells is reflected in the rewiring of co-expression patterns among metabolic genes . Focusing on breast and clear-cell kidney tumors , we report the existence of key metabolic genes which act as hubs of differential co-expression , showing significantly different co-regulation patterns between normal and tumor states . We compare our findings to those from classical differential expression analysis , and counterintuitively observe that the extent of a gene's differential co-expression only weakly correlates with its differential expression , suggesting that the two measures probe different features of metabolism . Focusing on this discrepancy , we use changes in co-expression patterns to highlight the apparent loss of regulation by the transcription factor HNF4A in clear cell renal cell carcinoma , despite no differential expression of HNF4A . Finally , we aggregate the results of differential co-expression analysis into a Pan-Cancer analysis across seven distinct cancer types to identify pairs of metabolic genes which may be recurrently dysregulated . Among our results is a cluster of four genes , all components of the mitochondrial electron transport chain , which show significant loss of co-expression in tumor tissue , pointing to potential mitochondrial dysfunction in these tumor types .
All cellular events , from the transduction of signals to the translation of nucleic acids , rely on the interaction of molecular entities . Indeed , one may argue that the fundamental unit of a biological network is not its constituent components ( e . g . proteins or genes ) , but rather the edges representing the interactions between them . Then , it follows that the manifestation of disease , of a deranged phenotype of this network , should be evident by observing changes in the wiring and activity of these edges . Here , we study the interactions between pairs of genes encoding metabolic enzymes , and how these interactions change in the course of transformation of normal cells to malignant tumor . This notion of studying “interactions” is particularly important for understanding the network of coupled enzymatic reactions which constitute metabolism . It is well-known that tumors , which are under strong selection for proliferative capacity , must re-organize their metabolism in order to deliver the precursors and energy needed to grow as quickly as possible . Otto Warburg published a series of key findings highlighting a fundamental dysregulation in glycolytic metabolism in cancer , whereby cancer cells metabolized high levels of glucose to lactate [1] . Some of the earliest chemotherapies ( e . g . methotrexate ) targeted a metabolic phenotype which distinguished tumor from normal tissue . In recent years , an invigorated field has identified a number of distinct “metabolic lesions” in various tumors , including , for example , the preferential expression of PKM2 [2] and the presence of an oncometabolite , 2-hydroxyglutarate , in cells with activating IDH1 and IDH2 mutations [3] . Our use of the term “interaction” above is loose: for the purposes of our study , which focuses on the analysis of gene expression data , we say that two metabolic genes putatively interact if we observe they are co-expressed . This co-expression may occur by chance , or as a result of co-regulation by a set of common factors . Furthermore , while strong co-expression is more likely to occur between proteins which physically interact with each other , the highly connected structure of the metabolic network suggests that even genes residing in opposing corners of metabolism may be coupled to each other . Regardless of the source of co-expression , our goal is to identify regions of the metabolic network whose co-expression patterns appear fundamentally different between normal and cancerous tissue samples . Put another way , we intentionally search for cases where two genes are co-expressed in one manner in normal tissue , and then co-expressed in an entirely different manner in the tumor tissue . Our approach follows other studies employing techniques to detect so-called “differential co-expression” of genes [4–11] . Differential expression analysis is the standard method for comparing the expression patterns of genes across conditions . Aside from its ubiquitous use in research , several large-scale surveys of differential expression focusing exclusively on metabolic genes in cancer have been completed [12 , 13] . In contrast , while a handful of publications have examined differential co-expression in various cancer settings ( for example , [9 , 14–17] ) , differential co-expression analysis remains largely absent in most studies of gene expression and ( to our knowledge ) , no survey of differential co-expression among metabolic genes in cancer ahs been undertaken . This is , at least in part , due to the requirement for large sample sizes in order to detect statistically significant differential co-expression patterns . Here , we embark on such a large-scale analysis of RNA-Seq data from 3000 samples of primary tumor and adjacent normal samples from seven distinct tissues , and focus our attention squarely on the expression patterns of 1789 metabolic genes . Among our main findings is the ( previously known , see [18] , but potentially under-appreciated ) observation that genes with strong differential co-expression patterns are not necessarily differential expressed . A relatively large fraction of the genes we identify in our study show no substantial difference in their absolute expression between tumor and normal tissue , but nevertheless exhibit recurrent differential co-expression . The results to be presented will encompass a variety of analyses , studying differential co-expression patterns first across two cancer types for which we have the most data available ( breast and clear cell renal cell carcinomas , ( KIRC ) ) , and then expanding to include five other cancer types ( lung , thyroid , prostate , liver , and head and neck ) , as described in Table 1 . In the course of doing so , we propose two simple , but novel , analyses which integrate pathway information to assess the functional role of differentially co-expressed gene pairs . We examine the association between differential co-expression and differential expression , and identify genes which are strongly enriched for one measure but not the other . By leveraging our findings against regulatory ( i . e . transcription factor binding ) data , we identify transcription factors whose targets are highly enriched for differential co-expression . Among our findings is a previously unreported loss of co-expression between HNF4A , a transcription factor , and its regulatory targets in KIRC . Finally , we leverage the scale of our study to complete a “Pan-Cancer” analysis of differential co-expression , searching for those pairs of metabolic genes which are recurrently differentially co-expressed across multiple cancer types . Our results highlight a small group of four mitochondrial electron transport chain ( ETC ) genes which are recurrently differentially co-expressed , hinting at a fundamental alteration in the function of the ETC in tumors .
All TCGA expression data were accessed using the Broad Institute Firehose . RSEM-normalized expression was used for the co-expression calculations . Entrez IDs of metabolic genes were extracted from the Recon2 genome scale metabolic network reconstruction [19] , and used to extract the corresponding metabolic gene expression data from the TCGA datasets . We begin by describing the methodology , broadly illustrated in Fig 1 , to detect changes in co-expression patterns between normal and tumor samples . After obtaining RNA-Seq data , we calculate the Spearman correlation ( a non-parametric measure of the correlation of two random variables employing ranks ) of each pair of genes i , j , and record the p-value pij associated with this correlation . These calculations are performed separately for tumor and normal samples . To account for multiple hypothesis testing , we apply the conservative Bonferonni correction [20] , yielding corresponding adjusted p-values p ^ . The results of these correlation calculations are stored in two matrices , CT and CN ( corresponding to tumor and normal samples , respectively ) , with entries C i j = { r i j , if p ^ i j < τ 0 , otherwise ( 1 ) Here , τ is a significance threshold for our Bonferroni-corrected p-values . Throughout the manuscript unless otherwise stated , we employ a threshold τ = 1×10−2 . Our goal is to identify significant differences between the strength of co-expression ( as quantified by the correlation coefficients ) in tumor and normal samples . Such a comparison of sample correlation coefficients must be done with care . In fact , the difference between two correlation coefficients is not sufficient information to determine how often such a difference would appear by chance . We offer an example to illustrate this phenomenon . Very small correlation coefficients ( say , r1 = 0 . 1 , r2 = −0 . 1 ) may appear in random , uncorrelated data simply by chance . In this case , the difference between the two correlation coefficients ( r1−r2 = 0 . 2 ) should be categorized as statistically insignificant because it is quite likely to happen by chance . On the other hand , the same difference for two very large correlation coefficients ( say , r1 = 0 . 99 , r2 = 0 . 79 ) appears less likely to happen by chance; instead , this difference is more likely to arise via the corruption of a nearly perfect correlation by a confounding factor or noise . The basis of this intuition is that very large correlation coefficients are observed quite rarely by chance . More importantly , the variance of the correlation coefficient estimated from the data ( referred to as the sample correlation coefficient , r ) depends on the value of the true correlation coefficient underlying the data ( referred to as the population correlation coefficient , ρ ) . In particular , the variance of sample correlation coefficient is approximately [21 , 22] Var ( r ) ∝ ( 1 − ρ 2 ) 2 Thus , as the population correlation coefficient tends to ±1 , the variance of the sample correlation coefficient asymptotically approaches zero . This dependence of the variance of r on ρ itself makes it very difficult to carry out hypothesis tests comparing two sample correlation coefficients . A standard method for testing for a difference between correlation coefficients is to employ a transformation to stabilize the variances , making them independent of ρ . Here , we use the Fisher r to z transformation: z = 1 2 log ( 1 + r 1 − r ) . ( 2 ) The change of variables in Eq ( 2 ) is well-known , and has been used in prior work on differential co-expression [7] . When applied to data drawn from a bivariate normal distribution , this transformation yields a quantity which is approximately normally distributed with variance σ 2 = 1 N − 3 independent of the population mean , with N equal to the size of the population . By applying this transformation to our measured correlation coefficients in normal tissue and tumor samples , we are able to apply a Z-test to determine if the correlation coefficients r i j T and r i j N are significantly different . In particular , the quantity ( 3 ) , which measures the difference between the two transformed correlation coefficients , is approximately normally distributed with mean zero and variance one: Δ z i j = z T − z N 1 N T − 3 + 1 N N − 3 , ( 3 ) where NT is the number of tumor samples , NN is the number of normal samples , zT is the Fisher-transformed tumor sample correlation coefficient , and zN is the Fisher-transformed normal sample correlation coefficient . Python code for the differential co-expression test is included in S1 Code . Thus , we can associate p-values p i , j z with the Z-test in ( 3 ) for each pair of genes i , j . After again correcting pz for multiple hypothesis testing using the Bonferonni correction , we stored the results of our calculations in a matrix D with entries D i j = { Δ r i j , if p ^ i j z < τ and ( p ^ i j T < τ or p ^ i j N < τ ) 0 , otherwise ( 4 ) where p ^ i j z is the Bonferonni adjusted value of p i j z . The entries of the matrix D correspond to the change in gene co-expression between tumor and normal samples , and will be our main object of study . We emphasize one final , but important , feature of Eq 4: an entry of D is nonzero if and only if that gene pair shows both ( 1 ) a significant change in co-expression between tumor and normal samples , and ( 2 ) the genes were co-expressed at a statistically significant level in tumor or normal samples ( or both ) . This ensures that those gene pairs which we call differentially co-expressed are also co-expressed at a statistically significant level in at least one group of samples . We assigned each gene in our study to one or more pathways using the subsystem assignments in the Recon2 human metabolic reconstruction [19] . Then , for each TCGA study , we calculated a score for each pathway i , Ei , using: E i = ∑ j ∈ P i S j 0 , ( 5 ) where Pi is the set of all genes in pathway i . Thus , Ei counts the total number of dysregulations for all genes in pathway i . We then divided each Ei by the number of genes in pathway i to obtain a normalized pathway score E ^ i . Thus , E ^ i quantifies the differential co-expression of all genes in a pathway , averaged over the number of genes in that pathway . We excluded from our analysis pathways composed of fewer than five genes . We obtained data on transcription factor targets from the Broad Institute’s MSigDB website [23] . Assuming that a particular regulatory factor has m targets , we calculate the total number of differential co-expression edges in the sub-network composed of only these m gene targets . In this subnetwork , there are t = ( m 2 ) = m × ( m − 1 ) 2 total possible edges . If we see e edges in the true subnetwork , we can calculate the probability that these edges would appear by chance . Given that the probability of a random“differential co-expression edge” in the network is p ( e . g . for a Bonferonni-corrected p-value threshold of 1 × 10−2 for detecting differential co-expression , p ≈ 8 × 10−3 ) , the probability of seeing at least e edges by chance is P = ∑ i = e t ( t i ) i p ( t − i ) 1 − p ( 6 ) A Bonferonni correction is then applied to the vector of p-values for all transcription factor motifs .
With our analytical framework established , our first aim was to assess how pervasive differential co-expression was among metabolic genes in cancer samples . We used the Recon2 human metabolic network reconstruction [19] to identify metabolic genes , and extracted expression corresponding to these genes from the TCGA datasets . We applied the differential co-expression analysis described above to two TCGA studies ( breast , BRCA; and clear cell renal cell carcinoma , KIRC ) with large numbers of both tumor and normal RNA-Seq samples ( 106 and 71 normal samples , 914 and 480 primary tumor samples , respectively ) . Using the list of metabolic genes from Recon2 , we were able to extract data for 1 , 789 unique metabolic genes . We used a strict Bonferonni corrected p-value threshold of 1 × 10−2 to identify pairs of genes which we called differentially co-expressed . Across the total number of pairs of metabolic genes in our dataset ( approximately ( 2 × 103 ) 2/2 = 2 × 106 distinct pairs ) , we calculated ( for each of the two studies ) that approximately 2 . 5 percent of gene pairs were differentially co-expressed . The top differentially co-expressed gene pairs are reported in Tables 2 and 3 . To independently test the extent of differential co-expression in our data , we followed the protocol presented in [17] and completed a permutation test to assess how frequently we would expect the observed changes in correlation coefficients by chance ( S1 Fig ) . In this analysis , we shuffled the labels ( e . g . tumor or normal ) of all samples , and calculated the difference in correlation coefficients and transformed correlation coefficients in the new , permuted data . This process was repeated 10000 times , and the results aggregated to form a distribution . Inspection of the results confirmed that for a large number of gene pairs , the differences in correlation coefficients were larger in the real data than in the permuted data ( S1 Fig ) . Although it was computationally intractable to complete enough permutations of the data to generate robust p-values ( because of the large correction for multiple hypothesis testing ) , we nevertheless found that 12% of gene pairs showed a higher difference in both ( 1 ) tumor and normal correlation coefficients and ( 2 ) transformed correlation coefficients than in any of the 10000 permuted data sets . These findings supported our observation of extensive differential co-expression in metabolic genes . Naturally , we were interested in identifying those genes which were enriched for membership in differentially co-expressed gene pairs . To find these genes , we calculated two “scores” for each gene: S i 0 , the number of differentially co-expressed gene pairs which gene i participates in S i = − ∑ j : p ^ i j z < τ ln ( p ^ i j z ) , a weighted sum of the number of differentially co-expressed pairs gene i participates in The score S , based on Fisher’s method for combining p-values from independent statistical tests [24] , accounted for both the frequency of a gene’s membership in differentially co-expressed pairs , as well as the confidence with which we could claim the gene pair was differentially co-expressed ( i . e . by the magnitude of p ^ i − z ) . It is important to note that each test of differential co-expression in our dataset is not independent , so we cannot use S as a formal test statistic . However , its use as a measure of the recurrence and magnitude of a gene’s overall differential co-expression is nevertheless useful . In breast cancer , the top-ranked gene was ACAT1 ( Acetyl-CoA acetyltransferase , not be confused with the enzyme acyl-Coenzyme A: cholesterol acyltransferase 1 , which is encoded by the gene SOAT1 ) . The enzyme translated from ACAT1 catalyzes the formation of acetoacetyl-CoA , which along with acetyl-CoA is the precursor to 3-hydroxy-3-methylglutaryl-CoA . These two metabolites lie at the beginning of the mevalonate pathway , which generates precusors for cholesterol and steroid biosynthesis . Intriguingly , Freed-Pastor and colleagues [25] recently reported that upregulation of the mevalonate pathway is sufficient and necessary for mutant p53 to have phenotypic effects on cell architecture in mammary tissue . Overexpression of various genes in the mevalonate pathway has also been shown to associate with poor prognosis in breast cancer [26] . Interestingly , ACAT1 is differentially coexpressed with 11 genes for which it is a catalytic partner: ACAA2 , DLD , MLYCD , HADHB , HADH , OXCT1 , PCCA , PDHA1 , PDHB , and ACSS1 . A plot of the differences in the correlation of these genes with ACAT1 is in S6 Fig . In many cases , the co-expression patterns show remarkably tight correlations in normal tissue , and these correlations are partially or completely eroded in the tumor samples . Functionally , many of these genes are part of the terminal reactions in glycolysis , lipid biosynthesis and fatty acid oxidation . This loss of co-expression suggests that the flux generated by these pathways is no longer coupled to the flux through ACAT1 in tumor cells . For KIRC , the highest-scoring differentially co-expressed gene was PSAT1 ( phosphoserine aminotransferase 1 ) , a key enzyme in the serine biosynthesis pathway which has already been associated with breast and colorectal cancers before [27 , 28] , but has not yet been associated with kidney cancer . PSAT1 was differentially co-expressed with 492 other metabolic genes in the dataset , with the strongest signals coming from genes like GATM ( glycine aminotransferase ) , GBA3 ( a beta-glucosidase ) , and SLC10A2 ( a bile transporter ) ( S4 Fig ) . Because nearly all of the strongest signals came from loss of positive correlation in normal samples , we further identified those genes with which PSAT1 was more strongly co-expressed in tumor samples than in normal samples ( S5 Fig ) . These genes included several galactosidases ( GLA , GLB1 ) , glycogen phosphorylase ( PYGB ) , and SLC35A2 , which transfers nucleotide sugars into the Golgi body for the purposes of glcosylation . Neither the substrates ( 3-phosphonoxypyruvate , glutamate ) nor the products ( phosphoserine , 2-oxoglutarate ) of PSAT1 participate in the glycogenolysis pathway , suggesting that the positive correlation between PSAT1 and glycogen breakdown in tumors may be the result of indirect couplings . In particular , it is possible that the overexpression of glycogen phosphorylase may liberate carbon units to be shunted from glycolysis into the serine biosynthesis pathway through PSAT1 , as well as into the Golgi body for glycosylation in tumor cells . Following our analysis of PSAT1 , we reasoned that a particularly interesting set of genes were those showing a higher degree of co-expression ( as quantified by the magnitude of the Spearman correlation coefficient ) in tumor samples relative to normal samples . For both BRCA and KIRC , we isolated pairs of genes exhibiting this property , and scored each metabolic gene based on how many such interactions it participated in . Interestingly , in both studies the highest-scoring gene was associated with the metabolism of lipids . In KIRC , the highest scoring gene was mevalonate kinase , MVK , a key gene in the cholesterol pathway described above for BRCA . In breast tissue , the highest scoring gene was LIPG , an endothelial lipase which catalyzes the hydrolysis of lipids . The products of this hydrolysis can then be used for the production of signaling lipids as well as cell membrane components . We decided to investigate more comprehensively whether differential co-expression patterns were similar between BRCA and KIRC . To probe whether common , “global” patterns of differential co-expression existed between the two studies , we completed a principal components analysis ( PCA , Fig 1A ) . We assembled a concatenated differential co-expression matrix: D C = [ D BRCA D KIRC ] ( 7 ) with dimension 2m × m , where m is the number of genes under study . For a given index i < m , row i corresponded to the differential co-expression pattern of that gene in BRCA , while row m+i corresponded to the differential co-expression pattern in KIRC . Thus , each column of DC corresponded to a metabolic gene , and stored the differential co-expression of that gene with all other metabolic genes in both breast and kidney studies . Our expectation was that PCA would identify patterns of differential co-expression which breast and kidney cancers might share in common . Instead , we found that genes in the two studies displayed completely distinct patterns of differential co-expression ( Fig 2A ) . While a large portion of the variance in the data was captured by the first two principal components ( 33 and 19 percent of the total variance in the data , respectively ) , most genes from breast cancer had nearly no loading on component 2 , while most genes from kidney cancer had nearly no loading on component 1 . The result was the cross pattern evident in Fig 2A . Despite the results above , we still found a small positive correlation ( Spearman ρ 0 . 28 , p-value < 1e−30 , Fig 2C ) between differential co-expression in the two cancer types , suggesting that many genes showed high ( or low ) levels of differential co-expression in both studies . In general , most genes participated in relatively few differential co-expression interactions , while a small subset of “hub” genes participated in hundreds ( Fig 2C , histograms ) . A particularly interesting example was ASS1 , an enzyme involved in arginine synthesis and the synthesis of nitric oxide and polyamines . It is known that several tumor types exhibit an arginine auxotrophy phenotype , and are unable to proliferate in the absence of arginine [29] . Intriguingly , Qiu and colleagues recently reported the killing of triple-negative breast cancer cell lines under arginine deprivation , identifying it as a lucrative therapeutic target [30] . It is not clear from our analysis whether differential co-expression of ASS1 is associated with such a vulnerability , but its recurrent differential co-expression in both studies suggests that its activity may play an important role in malignancy . The results of the PCA analysis above reflected the large number of cases of high differential co-expression in one tumor type , but none in the other . We explicitly identified such cases by calculating the mean and standard deviation of S0 ( the number of differentially co-expressed gene pairs a gene participates in ) for each study . We then searched for genes with S0 greater than two standard deviations above the mean S0 in one study , but with S0 = 0 in the other study ( Fig 2C , blue and green points ) . KIRC-specific differentially co-expressed genes were highly enriched for SLC and ABC transporters . A particularly interesting kidney-specific gene was DPEP1 ( a dipeptidase ) in light of the recently observation of elevated dipeptide levels in a subset of clear cell renal carcinoma tumors ( manuscript in preparation ) . In contrast , BRCA-specific genes included CDO1 ( cysteine dioxygenase Type 1 , whose inactivation was recently reported to contribute to survival and drug resistance in breast cancer [31] ) and a number of genes involved in glycerolipid/lipid biosynthesis and associated with malignancy in breast cancer ( GPAM [32] and MGLL [33] ) . We also made special note of those pairs of differentially co-expressed genes which took part in a known , previously reported biological interaction . To do so , we extracted from the Pathway Commons database [34] a list of pairs of genes known to interact in either of two ways: 1 ) through the formation of a complex with each other ( “In-Complex-With” interactions ) , and 2 ) through the production of a metabolite by the enzyme encoded by one gene in the pair , and subsequent use of that metabolite as a substrate for the enzyme encoded by the other gene in the pair ( “Catalysis-Precedes” interactions ) [35] . We then identified which pairs of differentially co-expressed genes participated in either of these kinds of interactions . These results were summarized in two additional gene-level statistics , S i C o m p and S i C a t , indicating the number of differentially co-expressed catalysis-precedes and in-complex-with interactions , respectively , a gene i participates in . We compared the incidence of differential co-expression among pairs of genes participating in the binary interactions described above , to genes not participating in such interactions . To do so , we compared the distribution of transformed correlation coefficients ( defined in 3 ) for the two groups of genes . We found a striking drop in co-expression among metabolic genes participating in a common molecular complex , an effect that was evident in both BRCA and KIRC ( t-test , p-value < 1 × 10−200 BRCA , < 1 × 10−75 KIRC , S2 Fig , S3 Fig ) . A much weaker , but statistically significant , effect was also observed for catalytically adjacent genes ( t-test , p-value < 1 × 10−18 BRCA , . 0002 KIRC ) . Together , the results suggest a disruption of metabolic complexes in these two cancers . A more detailed future investigation is required to determine if this phenomenon is limited to metabolism , or is evident across all molecular complexes . Finally , we analyzed the pattern of differential co-expression across metabolic pathways , as annotated in the Recon2 metabolic network [19] ( see Methods ) . The results of our analysis are highlighted in Fig 2B , where we compared the score of each pathway in BRCA and KIRC , respectively . In breast cancer , among the most enriched pathways is peroxisomal transport genes , including the peroxisomal transporters ABCD1 , ABCD2 , and ABCD3 , which transport fatty acids and acyl-CoAs and have been shown to be markers of tumor progression and response to therapy [36] . Notably , genes in the vitamin C pathway were enriched for differential co-expression in both cancers , possibly as an indirect consequence of high oxidative stress within the tumors . A common first step in the analysis of gene expression data across samples is the identification of differentially expressed transcripts . The underlying rationale behind differential expression analysis of metabolic genes is intuitive: higher expression of genes in one condition over another suggests a difference in metabolic flux through those sets of genes . In this study , we are more concerned with the coupling of genes together: since metabolic genes are components of a network , different co-expression patterns may lead to differences in metabolic flux . Naturally , one may ask whether the two measures are in agreement; in other words , do genes which are up- or down-regulated in tumor ( compared to normal tissue ) also exhibit large differences in co-expression patterns in tumor ( compared to normal ) samples ? To explicitly test the connection between differential co-expression and differential expression , we compared the two measures for metabolic genes in BRCA and KIRC ( Fig 3 ) . We assessed differential expression using the limma voom package [37] . We found that the magnitude of differential expression ( as quantified by the log2 ratio of tumor to normal expression ) was weakly associated with the frequency of differential co-expression of a gene ( BRCA , Spearman ρ 0 . 21 , p-value 3 × 10−17; KIRC , Spearman ρ 0 . 11 , p-value 4 × 10−6 ) . In spite of this weak association , many of the most differentially expressed genes were members of very few dysregulated gene pairs , and conversely many genes which exhibited no substantial change in expression levels nevertheless were found to be frequent members of dysregulated gene pairs ( S7 Fig ) . The most intriguing observation we made was that a number of genes showed no measurable change in absolute expression levels , but nevertheless were among the most differentially co-expressed genes in the entire dataset ( green dots , Fig 3 ) . To find exceptional cases like these , we identified genes with S0greater than 2 standard deviations above the mean S0 for the study , but with an absolute log2 ratio of less than 0 . 2 . For breast cancer , these genes included PLOD2 ( procollagen lysyl hydroxylase 2 [38] , recently reported to be essential for hypoxia-induced breast cancer metastasis ) , and LDHA , a key enzyme in the terminal end of glycolysis . In KIRC , several of the genes we identified ( RENBP , GNE , and CTSA ) were members of the glycoprotein sialyation pathway , which has also been associated with metastasis [39] . The presence of genes with exceptionally high differential co-expression and eseentially no differential expression ( and the converse ) deserves further discussion . It is possible that , depending on how the activity of a metabolic pathway is modulated , either differential expression or differential co-expression may be a more suitable technique for identifying such modulation . In one case , a gene may change in synchrony with its regulatory partners; that is , regardless of whether the gene is over- or under-expressed relative to normal tissue , it exhibits precisely the same co-expression patterns . Such an effect may be observed , for example , following the over-expression of a transcription factor common to all the genes in a co-expressed cluster . As we suggested earlier , synchronous regulation of a metabolic pathway may serve as a mechanism for increasing flux through the pathway , and would be detected through standard differential expression analysis . In contrast , a gene’s expression may correlate with different sets of genes in different conditions . In our case , the control over expression wielded by one transcription factor in normal tissue TFN would be ceded to a different transcription factor in tumor tissue TFT . The consequence is that the gene of interest is co-expressed with a completely distinct set of genes under the control of TFT . The differential co-expression of such a gene provides indirect evidence that the source or destination of metabolic flux through the enzyme encoded by this gene may be changing from normal to tumor tisues . As alluded to above , the expression of genes is fundamentally orchestrated by regulatory factors such as transcription factors and microRNAs . Thus , the differential co-expression patterns we observe are likely due , at least in part , to differential regulatory activity by these molecules . Inspired by prior work linking transcription factors with observations of differential co-expression [18 , 40] , we examined our differential co-expression networks for an enrichment of targets associated with particular transcription factor motifs annotated in MSigDB [23] . To detect such enrichment , we isolated metabolic genes which were reported targets of a particular transcription factor . Then , we applied a binomial test ( see Methods ) to quantitatively assess whether the number of differential co-expression edges existing between only these target genes was higher than would be expected by chance . We used only highly significant differentially co-expressed edges , with a p-value threshold of 1 × 10−10 . Among the 556 transcription factor motifs we examined , only a handful were enriched in either kidney or breast cancer . In breast cancer , 21 transcription factors were identified as enriched in differentially co-expressed gene targets . The most enriched transcription factors ( reported in Table 4 ) included SP1 , NFAT , and ERR1 . Several of these transcription factors have already been reported to play important roles in breast cancer throughout the literature . SP1 is known to be involved in cell proliferation , apoptosis , and cell differentiation and transformation , and has been reported as a prognostic marker for breast cancer [41 , 42] . Both NFAT and SP1 have been shown to induce invasion of breast tissue via the transcriptional modulation of downstream genes [42 , 43] . Perhaps most interesting is the identification of ERR1 ( estrogen-related-receptor 1 , also known as as ERR-α ) , an orphan receptor known to interact with PGC1-α to regulate a number of metabolism-related genes . ERR-α is regulated by ErbB2/Her2 signaling [44] , and is associated with poor outcomes in breast cancer patients [45] . For kidney cancer , the pattern was far more unanimous: several of the most enriched transcription factor target sites were targets of HNF4A ( Table 5 ) . Out of the 15 transcription factors identified as enriched for differentially co-expressed gene targets , 5 were associated with HNF4 . HNF4A is known to control cell proliferation in kidney cancer cell lines , and regulates a number of well-known cancer-associated genes to do so ( e . g . CDKN1A and TGFA ) [46–48] . Interestingly , HNF4A ( one of the two isomers of HNF4 , which was most enriched for differential co-expression targets ) shows no clear differential expression pattern between KIRC tumor samples and adjacent normal tissue samples ( Fig 4A ) , but does seem to exhibit more variation in tumor samples than in normal samples . On the other hand , the co-expression of HNF4A and its metabolic gene targets is markedly different in normal and tumor samples ( Fig 4B ) . A number of these genes ( including PIK3R3 , a member of the PI3K pathway , and PKLR , an isoform of pyruvate kinase ) showed exceptionally strong co-expression with HNF4A in normal samples , only to have this co-expression abrogated in tumor samples ( S8 Fig ) . Similarly , many of the strong co-expression patterns existent between the targets of HNF4A and HNF4A itself in normal samples wre also abrogated in tumor samples ( Fig 4B ) . Together , these findings suggest that the regulatory program associated with HNF4A in normal tissue is disrupted in tumor tissue , a hypothesis in line with previous findings implicating its dysregulation with increased cell proliferation [46] . Given its high score in our enrichment analysis , we tested whether the expression of HNF4A was associated with patient survival in the TCGA data . After stratifying patients into groups with high and low expression ( relative to the mean expression of HNF4A in the tumor samples ) , we found that low HNF4A expression is associated with shorter survival in KIRC patients ( Fig 4D , log-rank p-value 0 . 007 ) . Taken together , our observations above suggest that HNF4A’s control over the expression of its targets changes in at least a subset of clear cell kidney tumors when compared to normal kidney tissue . It is possible that this loss of control occurs via under-expression of HNF4A itself . It is also possible that ( as we proposed in the prior section ) other transcription factors exert a more dominant control over HNF4A’s targets . In either case , this leads to the loss of co-expression among HNF4A’s targets , and between HNF4A itself and its targets . This final section of our work strikes out into more difficult territory: we ask whether some patterns of differential co-expression may exist throughout different cancer types , regardless of their tissue of origin . While we have found a number of apparently dysregulated metabolic genes specific ( and in some cases , common ) to breast and clear cell renal cell carcinoma tumors , we have made little effort to search for common patterns across many different types of tumors . Such a search is necessarily complicated by the fact that our analytical method requires large numbers of normal and tumor samples for sufficient statistical power . The TCGA features few studies with large numbers of normal RNA-Seq samples . In order to balance the need for statistical power with our desire to detect so-called “PanCancer” patterns of differential co-expression , we included five more studies ( lung adenocarcinoma , LUAD; hepatocellular carcinoma , LIHC; prostate adenocarcinoma , PRAD; head and neck squamous cell cancer , HNSC; and thyroid cancer , THCA ) with at least 30 normal RNA-Seq samples , in our analysis . To increase the confidence of our predictions , we used a stricter p-value threshold of τ = 1 × 10−4 to call statistically significant differential co-expression . The results of the PanCan analysis are shown in Fig 5 . We retained only those genes which were members of a gene pair which was differentially co-expressed in at least three of the seven studies . Out of the 1789 metabolic genes under study , only 50 genes satisfied this criteria . Interestingly , many of these genes encode key enzymes in central metabolism ( for example , PC , pyruvate carboxylase; LDHD , D-lactate dehydrogenase; IDH1 , isocitrate dehydrogenase 1; ALDOA , aldolase A ) , pointing to apparently recurrent dysregulations of core pathways . Among the many individual results of our PanCan analysis , perhaps the most interesting was the recurrent dysregulation of four genes in the mitochondrial electron transport chain ( ETC ) : two genes associated with mitochondrial ATP synthase complex V ( ATP5F1 and ATP5L ) , COX7B ( part of the complex IV cytochrome c oxidase ) , and NDUFV2 ( complex I ) . A number of other mitochondrial ETC genes are also differentially co-expressed ( but to a lesser extent ) , including UQCR10 , UQCRC2 , UQCRC1 , ATP5A1 , and NDUFS3 . Given how critical these protein complexes are to energy production and proliferation , we examined in detail the co-expression patterns of ATP5F1 and ATP5L . We found an exceptionally strong correlation in the expression of both genes in normal tissue . Across all seven studies , the expression of both genes was almost precisely equal ( Fig 5B , blue dots ) . However , in tumor samples , the strength of the co-expression ( as measured by the correlation coefficient ) was substantially weaker . Notably , ATP5F1 and ATP5L were not differentially expressed; instead , their co-expression simply appeared “noisier” in tumor samples . To quantify whether this “noisier” co-expression may be occuring by chance , we fit each co-expression pattern in Fig 5B to a line , and then calculated the variance of the residuals of the fit . We used Levene’s test to test whether the variance of the residuals associated with tumor samples was larger than the variance of the residuals associated with normal samples . In all four tumor types , we confirmed that the tumor samples showed higher variance ( p-value 7 × 10−17 , 3 × 10−3 , 6 × 10−9 , 6 × 10−6 , 3 × 10−11 , 2 × 10−3 , 5 × 10−3 for BRCA , HNSC , KIRC , LIHC , LUAD , PRAD , and THAC samples , respectively ) . The functional consequences of these increasingly “noisy” co-expression patterns in ATP5F1 and ATP5L are unclear . It is known that stoichiometric imbalances of proteins ( for example as a result of changes in gene dosage ) in complex with each other can manifest phenotypically [49] . Given the recurrence of differential co-expression of three different gene pairs containing ATP5F1 and a second mitochondrial matrix member ( ATP5L , COX7B , and NDUFV2 ) , it is tempting to speculate that differential co-expression of ATP5F1 may lead to an altered mitochondrial phenotype . In particular , an imbalance in the levels of ATP5F1 and ATP5L may cause defects in the ability of mitochondria to efficiently conduct oxidative phosphorylation via the electron transport chain . Further experiments are required to evaluate this hypothesis .
In this work , we have searched for signals of differential co-expression in tumors . Among our findings , the most relevant is simply the prevalence of differential co-expression throughout metabolism . Gene expression studies are frequently the “first-step” analytical method of choice for understanding the consequences of a perturbation on an organism , or for the comparison of two distinct subsets of samples . While standard methods for differential expression analysis offer useful insights into the differential regulation of genes , our findings here ( and the prior findings of others studying differential co-expression ) suggest that a great deal of information remains to be culled from the study of “second-order” co-expression patterns between pairs of genes . We have shown that these two measures ( differential expression and differential co-expression ) are not interchangeable , and in many cases point to distinct regions of the metabolic network that may be dysregulated . Of course , it is important to remember that while the statistical power of both approaches relies on large sample sizes , differential co-expression is significantly more sensitive to sample size upon multiple hypothesis correction because of the large number of independent statistical tests ( equal to the square of the number of genes ) under evaluation . It will be interesting in the future to compare the results of our work to other methods for calculating differential interactions ( e . g . partial correlations ) . The orthogonality of differential expression and differential co-expression described above suggests that , to detect changes in the activity of a pathway , one must separately investigate the unilateral increase/decrease of enzyme levels , as well changes in their coordinated co-expression . In the first case , the expression of a large set of genes ( for example , those in a long , linear metabolic pathway ) may be synchronously upregulated . This coordinated up-regulation of transcription may , for example , enable the pathway to carry substantially more metabolic flux . In the second , perhaps more subtle case , the characteristic pattern of flux through a pathway may be re-wired ( as illustrated in Fig 6 ) . In Fig 6 , the mechanism for this re-wiring is transcriptional , but in principle this type of coupling may arise through a variety of distinct mechanisms ( such , as , for example , post-translational modification ) . In both cases , changes in intra- or extra-cellular conditions across a set of samples induces variation in the expression of genes . However , the manifestation of these changes may be hidden from either differential expression or differential co-expression analysis . Thus , we argue that both differential expression and differential co-expression analysis should play central , complementary roles in the analysis of gene expression data [11] . Our findings here are a small , first step in applying such a second-order analysis to cancer data , and in particular to the study of cancer metabolism . We have made a number of assumptions in order to make progress in the analysis , and these assumptions should be re-visited in future work . In particular , we have repeatedly assumed that the expression of a gene roughly correlates with the abundance of its translated protein product , and that this abundance correlates with enzyme activity . An entire field of theoretical study ( metabolic control analysis , [50] ) and a number of experimental studies ( e . g . [51] ) have shown that metabolite abundances are equally , if not more , important for the control of fluxes . We note , given an adequately large number of samples , an analogous “differential correlation analysis” is possible for metabolomics data . It would be especially interesting to compare the results from such an analysis with the analogous results using expression data . One major concern with our results are the confounding effects of ( 1 ) contamination by stromal and immune cells , and ( 2 ) existence of heterogeneous tumor subtypes in the data . Tumor samples are often contaminated with mixtures of normal adjacent tissue and immune cells . Deconvolving the contribution of non-cancerous cells from the total signal obtained from a tumor sample remains a major computational challenge , and it is unclear how the contribution of this non-cancerous signal affects our differential co-expression results . A separate but related concern is the existence of distinct molecular subtypes in a set of samples ( e . g . ER+ , ER− breast cancer samples ) . We have not made any efforts to tease apart the confounding effects of these distinct subtypes in our work . Interestingly , it possible that a significant portion of the differential co-expression signal we identify derives directly form these subtypes; in other words , the primary differences between subtypes may lie among the differentially co-expressed genes . Evaluating such a hypothesis will require substantially larger sample sizes . Nevertheless , we feel that a more careful analysis of such patterns after subtype separation and stromal deconvolution is a lucrative route for future studies . Finally , we would like to comment on the complementarity of differential expression and co-expression which we have proposed . In the course of responding to environmental stresses and stresses , it is inevitable that some genes will be both differentially expressed as well as differentially co-expressed . We are not arguing that one measure is superior to the other; rather , each offers a different glimpse onto the response of a highly-connected network to a perturbation . Neither the over-expression of a single gene , nor an increase in the co-expression of a pair of genes , signals a change in a pathway’s activity . However , by monitoring both measures , one univariate and the other multivariate , one may obtain a more complete picture of the complex system under examination .
|
The metabolism of malignant tumors is deranged . The transition from healthy to cancerous state involves , among other factors , the transcriptional coordination of genes spread throughout the cell’s metabolic pathways . An examination of this multivariate regulatory effort can offer insights which may remain hidden from analyses focusing on a single gene in isolation . Such an analysis is particularly relevant for metabolic networks , whose constituent enzymes are fundamentally linked through their common utilization of a limited pool of substrates . Here , we examine the extent to which altered metabolism is reflected in the co-expression patterns of genes , shedding light on the differential regulation of metabolic genes within tumors . We study patterns of differential co-expression across metabolic pathways in both breast and kidney tumors , and integrate regulatory information to study the drivers of these changes . Among the results of our analysis is the apparent dsyregulation of genes controlled by HNF4A in clear-cell kidney tumors . Finally , by combining the results of our analyses across seven different tissues , we identify the recurrent decoupling of a set of mitochondrial genes , pointing to possible mitochondrial dysfunction in these cancers .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Extensive Decoupling of Metabolic Genes in Cancer
|
Multiple GWAS studies have reported strong association of cardiac QT-interval to a region on HSA17 . Interestingly , a rat locus homologous to this region is also linked to QT-intervals . The high resolution positional mapping study located the rat QT-interval locus to a <42 . 5kb region on RNO10 . This region contained no variants in protein-coding sequences , but a prominent contiguous 19bp indel polymorphism was noted within a novel predicted long non-coding RNA ( lncRNA ) , which we named as Rffl-lnc1 . To assess the candidacy of this novel lncRNA on QT-interval , targeted CRISPR/Cas9 based genome-engineering approaches were applied on the rat strains used to map this locus . Targeted disruption of the rat Rffl-lnc1 locus caused aberrant , short QT-intervals and elevated blood pressure . Further , to specifically examine the significance of the 19bp polymorphism within the Rffl-lnc1 locus , a CRISPR/Cas9 based targeted knock-in rescue model was constructed by inserting the 19bp into the strain which contained the deletion polymorphism . The knock-in alleles successfully rescued the aberrant QT-interval and blood pressure phenotypes . Further studies revealed that the 19bp polymorphism was necessary and sufficient to recapitulate the phenotypic effect of the previously mapped <42 . 5kb rat locus . To our knowledge , this study is the first demonstration of a combination of both CRISPR/Cas9 based targeted disruption as well as CRISPR/Cas9 based targeted knock-in rescue approaches applied for a mammalian positional cloning study , which defines the quantitative trait nucleotides ( QTNs ) within a rat long non-coding RNA as being important for the pleiotropic regulation of both cardiac QT-intervals and blood pressure .
It is estimated that hypertension affects nearly 75 million Americans ( about 1 in every 3 U . S . adults ) [1] . Essential hypertension is the most common type of hypertension and remains a major risk factor for cardiovascular diseases , such as cardiomyopathy [2] , coronary artery diseases [3] and peripheral vascular diseases [4] . However , essential hypertension is of unknown origin and is characterized as a multifactorial disease involving genetic and environmental factors [5] . Familial and twin studies show that 30%-50% of the phenotypic variation of blood pressure ( BP ) is attributable to genetic heritability [6] . This implies that the contributions of genetic determinants to the development of hypertension are significant and elements on our genome may predispose some people to develop hypertension [7] . Over the past 50 years , several animal models of essential hypertension , predominantly in the rat , have been developed as valuable tools to study the genetic factors associated with hypertension [8] . One such tool generated from our laboratory is the inbred Dahl salt-sensitive ( S ) rat . The S rat develops hypertension even on a low-salt diet but develops more severe hypertension when fed with a high-salt diet . Using this rat strain , we and others have applied classic genetic approaches of linkage followed by substitution mapping to locate regions of its genome as quantitative trait loci ( QTLs ) that are inherited causes of hypertension [9–19] . As relevant to the current study , we have previously mapped one such BP QTL on rat chromosome 10 by linkage followed by the construction and characterization of a custom series of congenic strains which contained introgressing genomic segments of normotensive Lewis rat ( LEW ) on the genetic background of the S rat . The mapped locus was within a <42 . 5kb region and reported as a quantitative trait locus ( QTL ) for BP as well as cardiac QT-interval [20–25] ( Fig 1A ) . LEW alleles within the <42 . 5kb region significantly shortened QT-interval and increased blood pressure of the hypertensive S rat [20] . Interestingly , a large meta-analysis of three genome-wide association studies ( GWAS ) using 13 , 685 individuals reported that the region homologous to the rat 42 . 5kb region in humans , which lies on human chromosome 17 , has multiple minor alleles that are reportedly associated with shorter QT-intervals [26] ( Fig 1B ) . Of notable interest , nearly 30% of individuals in the GWAS study also had hypertension [26] . A second GWAS further confirmed the association of this locus to QT-interval [27] . Collectively , these observations suggest that the critical region in focus for the current report is of significance in the cardiovascular health of two mammalian species , the rat and human . The <42 . 5kb critical region in rats contains a single protein coding gene , Rffl , which is without any exonic variants . The region also contains a novel long non-coding RNA ( lncRNA ) , named Rffl-lnc1 , located within Rffl 5’-UTR intronic region . Rffl-lnc1 harbors a 19bp indel polymorphism between the S ( +19bp ) and the S . LEW congenic strain ( -19bp ) , which were the two strains used to map this locus ( Fig 1C ) . Based on this observation , we hypothesized that Rffl-lnc1 is a genetic determinant of QT-interval and blood pressure . To test this hypothesis , using the CRISPR/Cas9 technology , a panel of Rffl-lnc1 disruption models was developed on the genomic background of the Dahl S rat . These models harbored varied disruptions around the critical 19bp region . The disruption of Rffl-lnc1 significantly shortened QT-interval and increased blood pressure of the S rat , suggesting an important role of Rffl-lnc1 in regulating cardiovascular function . To further evaluate the specific effect of the 19bp indel polymorphism within the Rffl-lnc1 locus , a 19bp knock-in rescue model was developed on the genomic background of the S . LEW congenic strain using the CRISPR/Cas9 technology . The 19bp insertion successfully corrected the aberrant short QT-interval phenotype and lowered blood pressure of the S . LEW congenic strain , demonstrating that the 19bp indel polymorphism within Rffl-lnc1 is an inherited genetic variation responsible for regulating cardiovascular disease in the rat . Further , our study has demonstrated that among all the variants located within the <42 . 5kb QTL region , the 19bp polymorphism was sufficient to regulate both QT-intervals and blood pressure . Overall , this study is the first to precisely define the quantitative trait nucleotides within a long non-coding RNA as a genetic determinant of cardiovascular function and is also the first to apply both gene-disruption and knock-in strategies using the CRISPR/Cas9 based genome editing approaches for delineating a complex cardiovascular trait locus in a mammalian model .
To disrupt Rffl-lnc1 on the genomic background of the Dahl S rat , a custom gRNA , rRffl . g4 , was designed to target the 19bp containing genomic segment . In vitro validation of rRffl . g4 using mismatch detection assay confirmed its target efficiency ( S1 Fig ) . Microinjection of gRNA and Cas9 mRNA into single cell embryos of the S rat followed by implantation into 6 pseudo-pregnant females resulted in a total of 67 pups . Genotyping and sequencing data showed that 21 out of these 67 pups were mutants within the Rffl-lnc1 locus with disruptions both within and outside of the 19bp critical region . We used 4 founders with different deletions occurring within the 19bp locus for subsequent phenotypic studies ( Fig 2A ) . All 4 Rffl-lnc1 disruption models demonstrated elevated systolic , diastolic and mean arterial pressures compared to wild-type hypertensive S rats ( Fig 2B–2M ) . Interestingly , these disruption models exhibited different levels of BP increasing effects ( Fig 2B–2M ) . The heart/body weight ratios were also higher in Rffl-lnc1 disruption models ( S2 Fig ) , suggesting BP associated cardiac hypertrophy and potential dysfunction . To further assess cardiac function , we focused on QT-interval because shorter QT-intervals were reported to be associated with alleles within the homologous segment in humans as well as observed in our previous high resolution positional mapping study in rats [20 , 26] . As shown in Fig 3A and 3B , the QT-intervals of the gene-edited Rffl-lnc1 model were significantly shorter than that of the S rat . Collectively , these results demonstrate that Rffl-lnc1 is a potential genetic determinant of blood pressure and QT-interval . Since the annotation for the novel Rffl-lnc1 was limited to a few base-pairs , we performed RACE experiments to ascertain its full sequence . 5’RACE amplifications using the primer P1 ( Fig 4A ) and Universal Primer A Mix ( UPM ) for the initial PCRs followed by nested PCR amplification using the primer P2 ( Fig 4A ) and Universal Primer Short ( UPS ) resulted in four 5’RACE products , labeled as a , b , c and d , in Fig 4B . Unlike 5’RACE , Fig 4C shows the unique 3’RACE product in lane 5 , which was obtained using the primer P3 ( Fig 4A ) and UPM for the initial PCR followed by nested PCR amplification using P4 ( Fig 4A ) and UPS . Further characterization of these PCR products by sequencing confirmed the existence of four different isoforms of Rffl-lnc1 , each with a different 5’ end . Each isoform contained a single-exon of more than 3000bp ( Fig 4D ) . The secondary structures of these isoforms of Rffl-lnc1 were predicted using RNAfold Webserver [28] ( Fig 5 ) . The 4 isoforms of Rffl-lnc1 showed different secondary structures in the wild-type S rat ( Fig 5A–5D ) . Interestingly , it was observed that sequence deletions around the critical 19bp region of Rffl-lnc1 caused a range of perturbations of the secondary structures , such as double helices , internal loops and stem loops , of all the four isoforms in each of the gene-disruption models ( Fig 5E–5Q ) . The most deleterious perturbation was that in Rffl-lnc1 disruption model 4 , wherein , due to the large deletion of the 5’ end of Rffl-lnc1 , the secondary structural integrity was lost in all the isoforms except one ( Fig 5Q ) . Secondary structures of Rffl-lnc1 in all the disruption models appeared to correlate well with physiological impact . For instance , the secondary structure of Rffl-lnc1 transcript 1 was drastically altered in Rffl-lnc1 disruption model 2 compared to other models and the S rat ( Fig 5A , 5E , 5I , 5M and 5Q ) , which correlated with a dramatic BP increasing effect observed in Rffl-lnc1 disruption model 2 compared to other models ( Fig 2 ) . The correlation indicates a potential link between lncRNA structure and its physiological impact . The above evidence obtained with gene-disruption models , albeit strong , does not directly test causality for the 19bp as the naturally occurring quantitative trait nucleotides within Rffl-lnc1 affecting cardiovascular function . To directly evaluate the contribution of the 19bp indel polymorphism on cardiovascular function , we further used the CRISPR/Cas9 system to generate a targeted knock-in rescue model by precisely inserting the 19bp into the Rffl-lnc1 locus of the S . LEW congenic strain . A total of 73 pups were born after the microinjection of rRffl . g4 , Cas9 mRNA and donor oligonucleotide into single cell embryos of the S . LEW congenic strain , followed by implantation into 7 pseudo-pregnant females . Genotyping and sequencing validations identified 3 successful 19bp knock-in founders ( Fig 6A–6C ) . Knock-in rescue rats exhibited significantly lower systolic , diastolic and mean arterial pressures compared to wild-type S . LEW congenic rats ( Fig 6D ) . Echocardiac evaluation demonstrated that knock-in rescue rats tended to have lower relative wall thickness and exhibited significantly improved cardiac function and contractility as evidenced by significantly lower MPI and increased FS/MPI , respectively ( S1 Table ) . Moreover , short QT-intervals were also improved in targeted knock-in rescue rats compared to wild-type congenic rats ( Fig 6E ) . Fig 7 catalogs the structural differences of the four Rffl-lnc1 transcripts between targeted knock-in rescue rats and wild-type S . LEW congenic rats , which reflect the direct rescue status of the transcripts . These results demonstrate that the 19bp indel polymorphism is specifically responsible for functioning as quantitative trait nucleotides within four isoforms of a long non-coding RNA that are involved in cardiovascular regulation . The critical <42 . 5kb QTL region contains 171 variants including the continuous 19bp variation [20] . To evaluate whether the 19bp are sufficient to regulate cardiac function , we further compared the phenotypes between S and targeted knock-in rescue rats . Interestingly , knock-in rescue rats demonstrated no differences in blood pressure and QT-interval compared to S rats ( Fig 8 ) . No significant differences in echocardiographic parameters were seen ( S2 Table ) . These results demonstrate that the other variants within the QTL region are not contributing to the QTL effect and importantly , that the 19bp indel polymorphism within the previously resolved <42 . 5kb QTL region is necessary and sufficient to demonstrate the full effect of the QTL region independent to the other allelic variations within the QTL segment .
This study has contributed to the advancement of QTL mapping in the rat for cardiovascular phenotypes in general by pinpointing the quantitative trait nucleotides underlying a QT-interval and a BP QTL . It is also the first report of a polymorphism detected within a long non-coding RNA as a candidate gene verified within a mammalian QTL . The translational significance of the study is that it provides additional confirmatory evidence for the homologous region in humans detected to be associated with QT-interval . Due to the lack of sequence conservation between rats and humans , the precise polymorphisms within the homologous Rffl-lnc1 locus may or may not exist in humans . The relevance of this positional cloning study is therefore that the results obtained by mapping a locus in the rat provide a functional basis to assess similar effects of variants within the lncRNA as candidates within the homologous region in humans reported by GWAS for QT-interval .
All animal procedures and protocols described in this study were approved by the University of Toledo Institutional Animal Care and Use Committee . Animal experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals . The inbred Dahl salt-sensitive ( SS/Jr or S ) rat strain was from stocks maintained in our animal facility at our institution . Rats were weaned at 28–30 days of age and fed with a low-salt diet ( 0 . 3% NaCl , TD 7034 , Harlan Teklad ) . High-salt diet ( 2% NaCl , TD 94217 , Harlan Teklad ) was used for experiments involving a high-salt regimen . Only male rats were used for the current study , in order to match the blood pressure QTL inference drawn from the previous study [20] conducted using male rats . In each phenotypic study , any different experimental rat groups were concomitantly bred and co-housed to minimize environmental effects . Guide RNAs ( gRNAs ) were designed to target the 19bp locus within Rffl-lnc1 ( Genome Engineering and iPSC Center , Washington University , St . Louis , MO ) . Bioinformatics analysis was performed to detect potential off-target sites of all gRNA candidates on the rat genome . The gRNA , rRffl . g4 ( AAGCCATGGAGTTAGGCCATNGG ) , which had minimum off-target potential based on homology , was further validated in rat C6 cells . The gRNA , rRffl . g4 , was chosen for the transgenesis in generating both disruption and knock-in rescue models . Additionally , a donor oligonucleotide ( CACCACCCCAGCAGCTCCTGTTGAGCACTGCAGCGGCCTCATCCATGTGACAGGCCTGACGCCCTCACGCAGGTCTGGCCTATGGCCTAACTCCATGGCTTTCCAAGTGCTGGAAGTTCCCCAGGCGACATTCAGTGTC ) , which contains the 19bp sequence , was designed for the knock-in rescue model . Oocyte microinjection was conducted at the University of Michigan Transgenic Animal Model Core ( Ann Arbor , MI ) . For the disruption model , a mixture of rRffl . g4 ( 2 . 5 ng/μl ) and Cas9 mRNA ( 5 ng/μl ) was injected into one-cell stage Dahl salt-sensitive ( S ) rat embryos . Microinjected embryos were implanted into 6 pseudo-pregnant Sprague-Dawley female rats and a total of 67 pups were born . For the knock-in rescue model , a mixture of rRffl . g4 ( 2 . 5 ng/μl ) , Cas9 mRNA ( 5 ng/μl ) and the donor oligonucleotide ( 10 ng/μl ) was injected into one-cell stage S . LEW congenic strain embryos . Microinjected embryos were implanted into 7 pseudo-pregnant Sprague-Dawley female rats and a total of 73 pups were born . At 14 days of age , tail tip biopsies were collected from transgenic pups for extracting genomic DNA . Three different primer sets were used for initial genotyping . The forward ( F ) and reverse ( R ) sequences of these three primer sets ( 1 , 2 , 3 ) are: 1-F: AGCAGCTCCTGTTGAGCACT; 1-R: GAACTTCCAGCACTTGGAAAGC; 2-F: ACTGCCCTGAACCAAACCTG; 2-R: ACTTGGAAAGCCATGGAGTTAG; 3-F: ATGCAGACGATTTCTGACAGC; 3-R: ATCCCTGAGGGCTTTTCTACA . Due to large deletions in Rffl-lnc1 disruption model 4 , the forward ( ATGCAGACGATTTCTGACAGC ) and reverse ( GGTCTTCACTCTCCAGAATATG ) primers were used for further genotyping . After breeding all the potential founders to homozygotes , the PCR products of the genotyping from the homozygotes were sent for sequencing validation ( Eurofins MWG Operon , https://www . eurofinsgenomics . com/en/home . aspx ) and sequencing data was analyzed using Sequencher 4 . 10 . 1 . The homozygotes of disruption and knock-in models were used for subsequent phenotypic studies . Blood pressure was recorded and analyzed using radiotelemetry transmitters ( HD-S10 or previously C40 ) , receivers and software from Data Sciences International , as described previously [21] . The specific details on the age of the rat and type of diet used in each study are provided in the legend to each figure . ECG data was collected and analyzed using CTA-F40 transmitters , receivers and software from Data Sciences International . Briefly , the transmitters were surgically implanted into the peritoneal cavity of rats under anesthesia and transmitter electrodes were arranged in Lead II configuration . ECG data was collected at 5-minute intervals and analyzed using Ponemah v . 5 . 2 ( Data Sciences International ) . Bazett’s formula was used as the standard correction method for normalizing QT-intervals specifically for rats . The specific details on the age of the rat and type of diet used in each study are provided in the legend to each figure . Total RNA was extracted from heart tissues of the Dahl S rat using the TRIzol reagent ( Life Technologies ) according to the manufacturer’s instructions . The integrity and concentration of the RNA was assessed by gel electrophoresis and NanoDrop 2000 ( Thermo Scientific ) . 5’RACE and 3’RACE procedures were performed according to the SMARTer RACE 5’/3’ Kit ( Clontech ) protocol . Briefly , about 5μg RNA was used for making 5’RACE and 3’RACE cDNA , respectively . For 5’RACE amplification , P1 ( GATTACGCCAAGCTTACCCCAGCAGCTCCTGTTGAGCACT ) and Universal Primer A Mix ( UPM ) were used for initial PCR amplification according to Program 1 ( touchdown PCR ) in the protocol . Using the diluted ( 50X ) PCR product from the previous step , P2 ( GATTACGCCAAGCTTTGGGCACAATAGCTTGGCTTTTATGGAC ) and Universal Primer Short ( UPS ) were used for the nested PCR according to Program 2 in the protocol to obtain the 5’RACE products for the following characterization . For 3’RACE amplification , P3 ( GATTACGCCAAGCTTAACCATTCAGGAAGCCACAGGCCTTCC ) and UPM were used for initial PCR amplification according to Program 1 ( touchdown PCR ) in the protocol . Using the diluted ( 50X ) PCR product from the previous step , P4 ( GATTACGCCAAGCTTGTCCCGCCTTCCTATTTTCCAGATGAGG ) and UPS were used for the nested PCR according to Program 2 in the protocol to obtain the 3’RACE product for the following characterization . The 5’RACE and 3’RACE products were further characterized following the steps of gel extraction and in-fusion cloning in the protocol . The cloned inserts were PCR amplified and sent for sequencing ( Eurofins MWG Operon , https://www . eurofinsgenomics . com/en/home . aspx ) and sequencing data was analyzed using Sequencher 4 . 10 . 1 . Left ventricular function and geometry of Dahl S rats , S . LEW congenic rats and 19bp targeted knock-in rescue model were evaluated by echocardiography , as described previously [45 , 46] . The specific details on the age of the rat and type of diet used in each study are provided in S1 and S2 Tables . Two-tailed Student’s t-test was used for statistical analyses . Data are presented as mean ± SEM . A p-value of <0 . 05 was considered to be statistically significant .
|
Diseases of the cardiovascular system such as essential hypertension do not have a clear cause , but are known to run in families . The inheritance patterns of essential hypertension and other cardiac diseases suggest that they are not due to a single defective gene but instead are caused by multiple genetic defects that are inherited together in a patient . This complex inheritance makes it difficult to pinpoint the underlying defects . Here , we describe a panel of genetically-engineered rats , using which we have discovered a novel gene , which does not code for any protein , as a gene required for maintenance of normal blood pressure . Structural defects within this non-coding RNA cause hypertension and cardiac short-QT interval . Further , by performing genome surgery to correct the gene defect , we demonstrate the precise error in nucleotides that was inherited and caused hypertension and cardiac short-QT interval syndrome .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"quantitative",
"trait",
"loci",
"diet",
"long",
"non-coding",
"rnas",
"electrocardiography",
"nutrition",
"bioassays",
"and",
"physiological",
"analysis",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"artificial",
"gene",
"amplification",
"and",
"extension",
"rapid",
"amplification",
"of",
"cdna",
"ends",
"hypertension",
"blood",
"pressure",
"electrophysiological",
"techniques",
"molecular",
"biology",
"genetic",
"loci",
"biochemistry",
"rna",
"cardiac",
"electrophysiology",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna",
"vascular",
"medicine",
"polymerase",
"chain",
"reaction"
] |
2017
|
Positional cloning of quantitative trait nucleotides for blood pressure and cardiac QT-interval by targeted CRISPR/Cas9 editing of a novel long non-coding RNA
|
Introduced transinfections of the inherited bacteria Wolbachia can inhibit transmission of viruses by Aedes mosquitoes , and in Ae . aegypti are now being deployed for dengue control in a number of countries . Only three Wolbachia strains from the large number that exist in nature have to date been introduced and characterized in this species . Here novel Ae . aegypti transinfections were generated using the wAlbA and wAu strains . In its native Ae . albopictus , wAlbA is maintained at lower density than the co-infecting wAlbB , but following transfer to Ae . aegypti the relative strain density was reversed , illustrating the strain-specific nature of Wolbachia-host co-adaptation in determining density . The wAu strain also reached high densities in Ae . aegypti , and provided highly efficient transmission blocking of dengue and Zika viruses . Both wAu and wAlbA were less susceptible than wMel to density reduction/incomplete maternal transmission resulting from elevated larval rearing temperatures . Although wAu does not induce cytoplasmic incompatibility ( CI ) , it was stably combined with a CI-inducing strain as a superinfection , and this would facilitate its spread into wild populations . Wolbachia wAu provides a very promising new option for arbovirus control , particularly for deployment in hot tropical climates .
The mosquito Aedes aegypti ( Linneaus ) is the most important vector of human arboviruses . Although native to Africa it now has a broad distribution throughout the tropics and subtropics and is peridomestic , often laying its eggs in man-made water containers , and displaying a strong preference for feeding on humans . Attempts to reduce the global incidence of dengue fever and stem the spread of recent chikungunya , Zika and yellow fever virus outbreaks have focused on Ae . aegypti control [1 , 2] , which has proven challenging . An emerging vector control strategy utilizes mosquitoes artificially transinfected with virus-blocking strains of the alpha-proteobacterium Wolbachia pipientis [3] . Wolbachia are obligate intracellular endosymbionts naturally found infecting a wide range of terrestrial arthropods . The natural abundance of Wolbachia can be partly attributed to its capacity to spread through naïve populations by manipulating host reproduction . Although several forms of reproductive manipulation are found across different arthropod species , the only form observed in mosquitoes is a type of crossing sterility known as cytoplasmic incompatibility ( CI ) . Wolbachia modifies the sperm of infected males [4] , which results in the generation of non-viable progeny when mated to uninfected females . Infected females , in contrast , ‘rescue’ this sperm modification , producing viable progeny and resulting in a relative fitness advantage that can drive and maintain Wolbachia at high population infection frequencies [5] . While Ae . aegypti is not a natural Wolbachia host , stable transinfections with the wAlbB strain from Aedes albopictus and wMelPop/wMel strains from Drosophila melanogaster have been generated in the laboratory using embryonic microinjection , with the resulting lines showing reductions in vectorial capacity for a number of arboviruses and other pathogens [6–11] . Ae . aegypti transinfected with wMel have significantly reduced vector competence for dengue virus [7 , 12] , yellow fever virus [10] , chikungunya [10] and Zika [13] viruses in laboratory challenges . However , mosquito challenges with patient-derived dengue infected blood have indicated that wMel-mediated blocking is incomplete , and modelling predicts that wMel would be insufficient to achieve complete control in some settings [14] . Field trials aimed at spreading Wolbachia in Ae . aegypti for dengue control have to date focused primarily on wMel [15 , 16] . Different strains of Wolbachia reach varying intracellular densities and display divergent tropism within host tissues; the magnitude of the pathogen inhibition effect shows a positive correlation with Wolbachia intracellular density in several species [17–19] . The wMelPop strain reaches very high densities in Ae . aegypti , which probably contributes to an almost complete blocking of dengue virus transmission [6 , 12] . However , wMelPop imposes significant costs on a variety of traits including reduced longevity , fecundity and egg survival in quiescence [20–23] . These negative fitness effects have made the introduction of wMelPop into wild host populations problematic , despite the presence of strong uni-directional CI—recent field trials in Vietnam and Australia failed to achieve population replacement using this strain [24] . Recently several studies have reported the influence of a variety of factors on Wolbachia intracellular density . Larval rearing temperature in particular has a significant impact on the densities of wMel and the over-replicating wMelPop strain in Ae . aegypti [25 , 26]: exposure of larvae to diurnal rearing temperatures cycling between 27–37°C resulted in dramatic reductions in total Wolbachia density , and rates of maternal transmission—ultimately leading to the loss of the wMel and wMelPop infections when the high temperature regimes were maintained for more than one generation [26] . In addition to environmental factors , a genetic basis to density determination has been postulated based on duplications of a set of eight genes in the wMelPop genome , with copy number reported to correlate with wMelPop density in Drosophila melanogaster [27] . However , further studies failed to find a straightforward causal role for copy number in Wolbachia density regulation [28] , but see [29] and [30] . So far , only a few of the vast repertoire of naturally-occurring Wolbachia strains have been introduced into Ae . aegypti . It is important to create and characterize further transinfections in this species since they might provide improved characteristics such as viral blocking under particular environmental conditions , especially in hot climates [3] , and offer insights into the regulation of intracellular density and its role in inducing pathogen inhibition and effects on host fitness . Limitations are imposed by the technical demands of embryo cytoplasmic transfer by microinjection and the need for robust lab colonies of the insects to be used as the source of Wolbachia . While wAlbA and wAlbB are naturally found superinfecting Ae . albopictus , wAlbA is maintained at around 10% of the density of wAlbB [31] and only wAlbB established itself following previous embryo cytoplasm transfers from Ae . albopictus into Ae . aegypti [32] . Strain wAu does not induce CI in its native host Drosophila simulans [33] , but confers a notably high degree of protection from pathogenic viruses of Drosophila [34 , 35] . We therefore aimed to generate and characterize Ae . aegypti lines containing wAlbA and wAu for a variety of traits relevant to transmission-blocking and population-replacement potential , in comparison with the previously reported wAlbB and wMel transinfections .
Using embryonic cytoplasmic transfer and taking advantage of incomplete maternal inheritance we generated Wolbachia transinfected lines carrying strains wAlbA , wAlbB , wMel , and wAu in the same host background of Ae . aegypti . Each of the Wolbachia strains apart from wAu was capable of inducing full unidirectional cytoplasmic incompatibility with wild-type mosquitoes , and therefore showed population replacement potential . wAu produced no detectable CI ( Table 1 ) , consistent with observations in its native host ( Drosophila simulans ) [33] and providing further evidence that it is genetically incapable of generating CI , as opposed to a strain-specific suppression of the phenotype in its native host . Wolbachia-infected lines were crossed to determine the crossing types between strains . For lines that induced unidirectional CI with wild-type mosquitoes , no hatching of the resulting eggs was observed , in other words between-strain crosses resulted in complete bidirectional CI . Rates of Wolbachia maternal inheritance were determined by PCR of progeny from compatible crosses between wild-type males and infected females . All lines showed complete ( 100% ) maternal transmission of all strains in 200 progeny assessed . Since wAu does not induce CI , its maintenance in the wAu line is facilitated by high rates of maternal inheritance , and it is hypothesized to produce positive host fitness effects under some conditions based on increases in its frequency in native D . simulans host populations [36] . To assess its stability in Ae . aegypti populations , 200 individuals from the wAu colony were randomly selected and tested for the presence of Wolbachia at the fourth , seventh , and tenth generations post initial establishment . Colonies of this line had been maintained at relatively high numbers ( >2 , 000 individuals per generation from G4 ) with no direct selection for wAu infection from G1 onwards . All individuals tested positive at each generation , indicating that wAu is maternally transmitted at very high fidelity under these laboratory conditions .
In light of the failure to establish wMelPop in wild populations [24] , and with the finding that wMel densities are unstable under high temperature treatments [25 , 26] , it is important to investigate the properties of additional Wolbachia strains in Ae . aegypti . The novel lines generated here highlight the variability in phenotypic effects caused by different Wolbachia strains in a common host background , and emphasizes the difficulties in making reliable predictions of phenotype based solely on observations of the strain in single host species . The high level of virus inhibition by wAu observed here is consistent with results obtained in Drosophila species . A comprehensive assessment of Wolbachia-mediated antiviral protection , comparing Drosophila C virus ( DCV ) and Flock House virus ( FHV ) inhibition by 19 Wolbachia strains in same background of D . simulans , found that wAu caused the strongest blocking of both viruses , greater than both wMel and a higher density wMel variant , wMelCS [38] . Similar observations of stronger blocking of DCV and FHV by wAu relative to wMelCS have also been made in D . melanogaster [34] . Although wAu produces some costs to host fitness , these were modest compared to the wMelPop strain . In previous experiments carried out by McMeniman and colleagues [23] , the median longevity of wMelPop-infected females was found to be approximately 26 days , compared to 62 days for wild-type controls . The wAu line produced median female longevity of 40 days , compared to 60 days for wild-type controls . It was surprising to find significantly higher densities of wAlbA than wAlbB in Ae . aegypti , given that wAlbA is maintained at much lower densities than wAlbB in its native host Ae . albopictus [31] , and is strongly suggestive of the presence of host factors/interactions determining Wolbachia density in a strain-specific fashion , rather than simple differences in replication rates between Wolbachia strains . The influence of host factors has been previously suggested , when densities of wMelPop were found to vary significantly between the native host D . melanogaster and the closely related Drosophila simulans [39] . Studies comparing wMel with the high-density variant wMelPop have correlated duplications of a region of eight genes with increases in Wolbachia density in the native host Drosophila melanogaster [27 , 29 , 30] . However , this region is completely deleted in other wMelPop sub-strains [40] . Moreover , wAu lacks this locus [34] , but reaches higher densities than wMel and provides greater pathogen protection in D . simulans [35] . The apparent strain-specific nature of Wolbachia density control is encouraging in terms of maximizing the long-term effectiveness of Wolbachia-based strategies for virus control . Even if mean Wolbachia density reduction occurs over time due to selection on the host , and thus amelioration of virus transmission-blocking , other strains could subsequently be introduced to restore high density and thus the effectiveness of disease control . We show that the effects of high temperature on density can vary dramatically between Wolbachia strains , and confirm previous studies showing that wMel is particularly susceptible to maternal leakage over consecutive generations of heating . The upper temperature used here is high but realistic for larvae in tropical regions [41] . Choosing Wolbachia strains that show the greatest density stability of natural environments where releases take place should therefore be a key concern when considering the suitability of strains in a given location . Higher temperature conditions may result in lower Wolbachia densities in the field , which could cause reduced pathogen inhibition . However lower densities also correlate with lower fitness costs; high temperatures may therefore also result in improved fitness characteristics and population spread capacity . Likewise , in hot tropical regions without a marked dry season , reduced embryo hatch after quiescence may have little impact on spread dynamics . The direct comparison of Wolbachia strains presented here also highlights the utility of wAlbB , which combines similar levels of virus blocking to wMel , with greater temperature stability—suggesting it may be more effective at spreading and blocking virus transmission in very hot climates . The demonstration of a stable superinfected line carrying wAu and wAlbB demonstrates one of several possible methods by which wAu could be spread through populations . When used in combination with a ‘driver’ strain , there is always the risk that a decoupling of the strains may occur over time in the field , although the rate at which this would occur is difficult to predict , and may vary between environmental conditions and thus locations . Further experiments can explore different strain combinations with wAu to maximize co-transmission stability under field-approximating conditions , but the driver strain should also reduce or block virus transmission in case wAu is lost , as is the case for wAlbB . The combination of two Wolbachia strains was previously reported in Ae . aegypti , where wAlbB was stably combined with wMel , resulting in a superinfected strain that showed unidirectional CI with wt , wAlbB-only and wMel-only lines [12] . Interestingly the superinfected line showed increased levels of pathogen inhibition compared to the constituent strains . The recent discovery in wMel that at least two of the genes required for CI induction form an operon located in an integrated WO prophage region [42 , 43] , which is notably absent in the wAu genome [44] , opens the intriguing possibility that wAu could be converted into a CI-inducing stain following integration of a suitable WO phage element . Crossing-type conversion has been previously reported in Nasonia wasps , whereby an incompatibility phenotype was transferred between different strains following innoculation with a 0 . 23μm pore-filtred pupal homogenate [45] . Overall fitness benefits are also possible under some field conditions , perhaps including protection from harmful viruses , as hypothesized for wAu in its native host Drosophila simulans—where it is capable of spreading and maintaining high population infection frequencies [36] . Little is known about the frequency of natural entomopathogens to which wAu could provide protection in Ae . aegypti . wAu could also potentially be spread through a mosquito population by applying suitable selection pressures such as bacterial , fungal or viral entomopathogens; Wolbachia wMelPop has for example been shown to provides resistance to several such agents [46] . In addion to the applied potential of wAu , the differences in virus inhbiiton between wAu and wAlbA , despite reaching similar densities in Ae . aegypti , provide excellent in vivo systems for comparative studies to better understand the mechanistic basis of the phenotype .
The Ae . aegypti wild-type line used was colonized from Selangor State , Malaysia in the 1960s . All mosquito colonies were maintained at 27°C and 70% relative humidity with a 12-hour light/dark cycle . Larvae were fed tropical fish pellets ( Tetramin , Tetra , Melle , Germany ) and adults were given access to a sucrose meal ad libitum . Blood meals were provided using a Hemotek artificial blood-feeding system ( Hemotek , UK ) using defribrinated sheep blood ( TCS Biosciences , UK ) . Eggs were collected by providing damp filter-paper ( Grade 1 filter paper , Whatman plc , GE healthcare , UK ) for oviposition . Eggs were desiccated for 5–10 days prior to hatching in water containing 1g/L bovine liver powder ( MP Biomedicals , Santa Ana , California , USA ) . wMel , wAlbA and wAlbB Ae . aegypti lines were generated by transferring cytoplasm from superinfected Ae . albopictus ( origin Indonesia ) embryos carrying wMel , wAlbA and wAlbB to wild-type Ae . aegypti embryos . Microinjections were performed using methods described previously [17] . Female G0 survivors were back-crossed to wild-type males , blood-fed and separated individually for oviposition . G0 females were analysed for Wolbachia infection by strain specific PCR ( see primer table in Supporting Information for sequences ) and eggs from Wolbachia negative G0 females were discarded . Eggs of positive females were hatched and G1’s were assessed for Wolbachia G0-G1 germ-line transmission . Injections from the superinfected Ae . albopictus line initially resulted in the generation of a triple-infected Ae . aegypti line ( wMelwAlbAwAlbB ) , which showed unstable maternal inheritance of Wolbachia strains . Individualizing the progeny of triple infected females resulted in the isolation and establishment of the wAlbA-only , wAlbB-only and wMel-only lines . The wAu line was generated as above , but involved transfer of cytoplasm from wAu infected Drosophila simulans embryos ( origin Australia ) . The wAuwAlbB line was generated as above but involved the transfer of cytoplasm from the wAu-infected Ae . aegypti line into embryos of the wAlbB-infected line . To assess rates of maternal inheritance , females from each Wolbachia transinfected line were crossed to wild-type males in pools of 20 males and 20 females . A blood-meal was provided and females were individualised for oviposition . The resulting eggs were hatched and DNA from a selection of 10 of these ( 200 assessed for each line in total ) was extracted at the pupal stage and a PCR for Wolbachia was performed . Rates of CI induction and rescue both with wild-type mosquitoes and between infected lines were assessed by crossing 20 males and 20 females of each line . A blood-meal was provided and females were individualised for oviposition . Eggs were collected on damp filter paper , which was subsequently desiccated for 5 days at 27°C and 70% relative humidity . Eggs were counted and hatched in water containing 1g/L bovine liver powder . Larvae were counted at the L2-L3 stage to provide hatch rates . Females with no egg hatch were dissected to check spermathecae for successful mating . Unmated females were excluded from hatch rate evaluations . For PCR analysis , genomic DNA was extracted from mosquitoes using the Livak method [47] . For primer sequences see primer table in supporting information . For measurements of Wolbachia density by qPCR , genomic DNA was extracted from mosquitoes using phenol/chloroform . Unless stated otherwise , mosquitoes used in density experiments were adults 5-days post pupal eclosion . gDNA was diluted to 100ng/μl using a NanoDrop spectrophotometer ( Thermo Scientific , Waltham , Massachusetts , USA ) . A BioRad CFX-96 real-time PCR detection system was used ( Bio Rad , Hercules , California , USA ) with 2 x SYBR-Green mastermix ( Biotool , Houston , Texas , USA ) . Total Wolbachia density was analysed by absolute quantification against a dilution curve of a vector containing single copies of the homothorax ( HTH ) gene and Wolbachia surface protein ( wsp ) . To specifically quantify the wAlbA , wAlbB , wAu , and wMel strains , the following primers were used: wAlbA– ( QAdir1 and QArev2 ) ; wAlbB– ( 183F and QBrev2 ) ; wAu– ( wAuF and wAuR ) ; wMel– ( qMel-F and qMel-R ) . All were normalized against HTH copies . The following program was used to run the qPCRs: 95°C for 5mins , 40x cycles of 95°C for 15sec and 60°C for 30sec , followed by a melt-curve analysis . Primer sequences can be found in S1 Table . Ovaries were dissected from 5-day old adult females in a drop of PBS buffer , and were immediately transferred to a tube containing Carnoy’s fixative ( chloroform:ethanol:acetic acid , 6:3:1 ) and fixed at 4°C overnight . Samples were then rinsed in PBS and transferred to a 6% hydrogen peroxide in ethanol solution for 72 hours at 4°C . Samples were then incubated in a hybridization solution containing: 50% formamide , 25% 20xSSC , 0 . 2% ( w/v ) Dextran Sulphate , 2 . 5% Herring Sperm DNA , 1% ( w/v ) tRNA , 0 . 015% ( w/v ) DTT , 1% Denhardt’s solution , and 100ng/ml of each probe . Probe sequences were as follows: wAu ( green ) 5’-ACCTGTGTGAAACCCGGACGAAC- ( Alexa flour 488 ) -3’; wAlbB ( Red ) 5’-TAGGCTTGCGCACCTTGCAGC- ( Cyanine3 ) -3’ . Samples were left to hybridize overnight in a dark-damp box at 37°C . Samples were washed twice in a solution containing: 5% 20xSSC , 0 . 015% ( w/v ) DTT , and twice in a solution of 2 . 5% SSC , 0 . 015% ( w/v ) DTT in dH2O , with each wash performed at 55°C for 20 minutes . Samples were then placed on a slide containing a drop of VECTASHIELD Antifade Mounting Medium with DAPI ( Vector Laboratories , California , USA ) and were visualized immediately using a Zeiss LSM 880 confocal microscope ( Zeiss , Oberkochen , Germany ) . Both the red and green probes were added to the hybridization solution to produce the images of wAlbB , wAu , wAuwAlbB and wild-type ovaries . Twenty 5-day old female mosquitoes of each Wolbachia-infected line and wild-type were injected with the respective virus in the thorax using a pulled glass capillary and a Nanoject II ( Drummond Scientific , Pennsylvania , USA ) hand-held microinjector . Injected mosquitoes were immediately transferred to an incubator set at 27°C and a 12-hour light/dark cycle for recovery . SFV injected females were left for ten days prior to RNA extraction and virus quantification by qRT-PCR . RNA was extracted using TRI Reagent ( Sigma-Aldrich , Missouri , USA ) . cDNA was synthesized using 1μg of total RNA and the All-In-One cDNA Synthesis SuperMix ( Biotool , Houston , Texas , USA ) . qRT-PCRs were performed on a 1 to 20 dilution of the cDNAs . Virus levels were normalized to the RPS17 house-keeping gene . Semliki Forest virus was sub-type C ( catalogue number 1112041v ) obtained from Public Health England culture collections . SFV was propagated on C6/36 cells to a final injection concentration of 1 . 78x1013 FFU/ml . Primers used for viral detection were: SFV4-F and SFV4-R . Five day-old females were fed an infectious blood-meal containing a mixture of 800μl defibrinated sheep blood and 400μl viral suspension supplemented with a phagostimulant ( ATP ) at a final concentration of 5mM . Dengue virus was serotype 2 , New Guinea C strain , obtained from Public Health England culture collections . Zika virus was strain MP1751 , obtained from Public Health England culture collections . The final concentration of dengue virus in the blood meal was 8 . 3x107 FFU/ml . The final concentration of Zika virus in the blood meal was 1 . 6x108 FFU/ml . Engorged females were separated and maintained in a climactic chamber at 27°C and 75% humidity . After 12 days females were salivated by inserting the proboscis into a capillary containing mineral oil and placing a drop of 1% pilocarpine nitrate onto the thorax . Collected saliva was ejected into tubes containing Dulbecco’s Modified Eagle Medium ( DMEM ) medium supplemented with 2% fetal bovine serum ( FBS ) , 10-fold serially diluted , and added to pre-seeded Vero cells for fluorescent focus assay ( FFA ) . Primary antibody for dengue was the MAB8705 Anti-Dengue Virus Complex Antibody clone D3-2H2-9-21 ( Millipore , Massachusetts , USA ) . Primary antibody for Zika was the MAB10216 Anti-Flavivirus Virus Complex Antibody clone D1-4G2-4-15 ( Millipore , Massachusetts , USA ) . Secondary antibody for both viruses was the Goat anti-mouse Alexa Fluor 488 , A-11001 ( Thermo Scientific , Waltham , Massachusetts , USA ) . Plates were imaged using a Typhoon 9400 plate reader ( GE Healthcare , Little Chalfont , UK ) and images were analysed using ImageJ ( NIH , USA ) . Once saliva was collected , mosquito salivary glands were dissected and RNA was extracted using the QIAamp Viral RNA Mini kit ( Qiagen , Hilden , Germany ) according to manufacturers guidelines . Abdomens were removed and placed into tubes containing RNAzol reagent ( Sigma-Aldrich , Missouri , USA ) . RNA was extracted according to manufacturers guidelines . cDNA synthesis was performed using the All-In-One cDNA Synthesis SuperMix ( Biotool , Houston , Texas , USA ) , and qPCRs were run using NS5-F and NS5-R primer set for dengue and the ZIKV 835 and ZIKV 911c primers for Zika virus . For Zika infected mosquitoes , the numbers of samples analysed were 22 , 21 , 16 and 18 for wAlbB , wAu , wMel and wild-type , respectively . For dengue infected mosquitoes the numbers of samples analysed were 19 , 18 , 19 and 17 for wAlbB , wAu , wMel and wild-type , respectively . Levels of target cDNA sequences were normalized against the RpS17 house-keeping gene using the Pfaffl method . Primer sequences can be found in S1 Table . Eggs of the wAlbA , wAlbB , wAu and wMel strains were hatched under either constant 27°C ( control ) or 27–37°C at a 12:12hr cycle ( heat-stressed ) in a Panasonic MLR-352-H Plant Growth Chamber incubator ( Panasonic , Osaka , Japan ) and corresponding light and dark photoperiods ( light during 37°C ) . Immediately upon hatching , larvae were picked and placed into trays containing 1L of water and larval food at a density of 50 larvae per tray ( three replicate trays per strain ) . Larvae were reared until pupation with water temperatures monitored daily using a glass thermometer placed inside a water-filled beaker . Water in the larval trays was replaced every 2 days to reduce bacterial growth . A selection of adults was removed upon emergence and split into two groups with a batch of approximately 15 ( pooled into 5 repeats each containing 3 pooled adults ) analysed by qPCR for Wolbachia density using the WSP and HTH primer sets . The remaining adults were set up in cages maintained at a constant 27°C and allowed to recover for 7 days . These were also split into two groups with a sub-set analysed by qPCR for Wolbachia density , and the remainder ( five females from each line ) mated to wild-type males and blood-fed at day 5-post emergence . Eggs were collected and hatched at constant 27°C and reared to pupation . A selection of 10 pupae from each female were chosen at random and assessed for Wolbachia infection by PCR . A subset of adult females emerging from heat-stressed larvae were maintained under temperature cycling , mated to wild-type males , blood fed at day 5 post emergence and allowed to oviposit . Eggs were hatched and reared to pupation under heat stress at which point a selection of 10 pupae from each female were chosen at random and assessed for Wolbachia infection by PCR . Adult survival was assessed using groups of 50 individuals at a sex ratio of 1:1 , with four replicates for each line . Experiments were performed in 24 . 5x24 . 5x24 . 5cm insect rearing cages inside an incubator set to 27°C and 70% relative humidity with a 12-hour light/dark cycle . Cages were blood-fed once a week from day 5 onwards and damp filter paper was provided for oviposition . A sucrose meal was accessible ad libitum . Cages were checked daily for mortality . Experiments ran for 70 days at which time approximately 10% of the wMel and wild-type females remained alive . Female fecundity was assessed by feeding 5-day old males and females of Wolbachia-infected and wild-type mosquitoes on a hemotek feeder containing defribrinated sheep blood . 20 fully engorged females ( considered fully engorged when a female had a full abdomen and voluntarily dropped off the blood source ) were isolated using an aspirator . Females were placed individually inside up-turned cups on top of a circle of filter paper . Cotton-wool soaked in a 10% sucrose solution was made available through a hole in the cup . 3 days post-feeding the filter paper was wetted and left overnight . The filter paper was replaced the next day and the process was repeated for a second night . Eggs from each filter paper were counted using a clicker-counter and a dissecting microscope . Egg survival in desiccated quiescence was assessed by feeding one week old Wolbachia-infected or wild-type females in cages and collecting eggs 3 and 4 days after feeding by placing three separate damp filter-paper cones in each cage—each cone collected >1 , 000 eggs . Egg papers were stored at 27°C and 70% relative humidity . At 5 , 10 , 20 , 35 and 50-days post oviposition a section of each of the egg papers containing approximately 200–300 eggs was cut from the original paper , the eggs counted using a clicker-counter and dissection microscope , and hatched by placing in water containing 1g/L bovine liver powder . Hatch rates were assessed 10 days later by counting larvae using a Pasteur pipette and a clicker-counter . All statistical analyses were performed in the RStudio interface ( version 0 . 99 . 489 ) ( RStudio Inc . , Boston , Massachusetts , USA ) of the R software ( version 3 . 4 . 0 ) . Graphics were generated using the ‘ggplot2’ package . Normality of data distributions were assessed using a Kolmogorow-Smirnov Test prior to hypothesis test selection . Multiple comparisons were performed using the ‘multcomp’ package and used the Bonferroni multiple comparisons p value correction method . Survival analyses were performed using the ‘Survival’ and ‘SurvMiner’ packages . Survival analyses were performed using a Cox proportional hazard regression model with cage repeats clustered as a random effect .
|
Mosquito-borne viral diseases represent an increasing threat to human and animal health globally . The mosquito species Aedes aegypti , a primary vector of the most significant human arboviral infections including the dengue , Zika and Chikungunya viruses , is highly invasive and is almost ubiquitous in tropical urban areas . Mosquito control remains the main approach for preventing and controlling outbreaks . A novel control strategy that is currently being trialed in several countries utilizes Ae . aegypti mosquitoes artificially infected with a bacterial symbiont known as Wolbachia pipientis . Although many insect species harbor native Wolbachia infections , Ae . aegypti is naturally uninfected . Wolbachia lives within host cells and is passed-on from mother to offspring , and can block virus transmission; once released it can invade and persist in host populations . Here we present the infection and assessment of two novel Wolbachia strains in Ae . aegypti . We show that one of the strains , wAu , provides particularly strong blocking of dengue and Zika virus transmission and offers greater stability at higher temperatures when compared to wMel—currently the most widely used strain for field releases . These results suggest that wAu is promising option for arbovirus control , especially in hot climates .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"invertebrates",
"dengue",
"virus",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"animals",
"wolbachia",
"viruses",
"developmental",
"biology",
"rna",
"viruses",
"insect",
"vectors",
"bacteria",
"digestive",
"system",
"infectious",
"diseases",
"aedes",
"aegypti",
"medical",
"microbiology",
"microbial",
"pathogens",
"life",
"cycles",
"exocrine",
"glands",
"disease",
"vectors",
"insects",
"arthropoda",
"ovaries",
"mosquitoes",
"eukaryota",
"anatomy",
"flaviviruses",
"viral",
"pathogens",
"salivary",
"glands",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"larvae",
"organisms",
"zika",
"virus"
] |
2018
|
The Wolbachia strain wAu provides highly efficient virus transmission blocking in Aedes aegypti
|
Prion proteins can adopt self-propagating alternative conformations that account for the infectious nature of transmissible spongiform encephalopathies ( TSEs ) and the epigenetic inheritance of certain traits in yeast . Recent evidence suggests a similar propagation of misfolded proteins in the spreading of pathology of neurodegenerative diseases including Alzheimer's or Parkinson's disease . Currently there is only a limited number of animal model systems available to study the mechanisms that underlie the cell-to-cell transmission of aggregation-prone proteins . Here , we have established a new metazoan model in Caenorhabditis elegans expressing the prion domain NM of the cytosolic yeast prion protein Sup35 , in which aggregation and toxicity are dependent upon the length of oligopeptide repeats in the glutamine/asparagine ( Q/N ) -rich N-terminus . NM forms multiple classes of highly toxic aggregate species and co-localizes to autophagy-related vesicles that transport the prion domain from the site of expression to adjacent tissues . This is associated with a profound cell autonomous and cell non-autonomous disruption of mitochondrial integrity , embryonic and larval arrest , developmental delay , widespread tissue defects , and loss of organismal proteostasis . Our results reveal that the Sup35 prion domain exhibits prion-like properties when expressed in the multicellular organism C . elegans and adapts to different requirements for propagation that involve the autophagy-lysosome pathway to transmit cytosolic aggregation-prone proteins between tissues .
Transmissible spongiform encephalopathies ( TSEs ) or prion diseases are fatal , age-related , and infectious neurodegenerative disorders that affect humans ( e . g . , Creutzfeldt-Jakob disease ) and animals ( e . g . , scrapie in sheep and bovine spongiform encephalopathy in cattle ) [1] . At the molecular level , prions propagate by recruitment and conversion of the soluble α-helix-rich cellular PrPC into toxic aggregates of the pathological β-sheet-rich PrPSc isoform , via a mechanism described as seeded or nucleated polymerization [2]–[5] . The TSE agent is also infectious at the cellular level , where it transmits from cell-to-cell and infects naïve cells , both from within and outside the central nervous system [6] , [7] . In yeast , prions can function as heritable epigenetic factors [8]–[12] upon forming an alternative self-propagating β-sheet-rich state from a soluble α-helix-rich fold . Yeast and mammalian prion determinants , however , do not share similarities in amino acid sequence , function , or subcellular localization . Yeast prions are naturally propagated within the cytosol from mother to daughter cells during cell division and require the disaggregase activity of the molecular chaperone Hsp104 to generate seeds and ensure dissemination [13] . In contrast , cell-surface localized mammalian prions are transmitted from cell-to-cell in terminally differentiated non-dividing cells . Sup35 , like the majority of yeast prion proteins , contains a glutamine/asparagine ( Q/N ) -rich domain that confers the prion phenotype [14] . Although the mammalian prion protein PrP lacks this domain , other neurodegenerative disease proteins such as FUS ( Fused in Sarcoma ) and TDP-43 ( TAR DNA-binding protein 43 ) have been shown to contain Q/N-rich prion-like domains [15]–[17] . There is increasing evidence that proteins closely associated with the neurodegenerative diseases Alzheimer's , Parkinson's , Huntington's , frontotemporal lobar degeneration ( FTLD ) and amyotrophic lateral sclerosis ( ALS ) , exhibit prion-like properties [18]–[20] . Amyloid fibril assembly in general follows a nucleated polymerization reaction in vitro [9] , and the addition of preformed fibrils or pathological brain extract seeds the polymerization of the corresponding monomeric protein in cell culture models or following injection into healthy mouse brains [24]–[31] . Furthermore , many proteins that form aggregates and fibrils exhibit cell non-autonomous effects and might spread among tissues within an organism [32]–[37] . The cellular processes and mechanisms that underlie cell-to-cell transmission of toxic protein species remain elusive in the current animal models that employ tissue culture cells and mice to investigate prion biology . The nematode Caenorhabditis elegans has been widely used as a model system to investigate the biology of protein misfolding and toxicity [38]–[43] , and has the advantage of transparent tissue types including muscle , intestinal , and neuronal tissue . We aimed to establish a new prion model system in this metazoan to examine the mechanisms of propagation of protein misfolding across tissues in a living organism . Since there are no known prions in C . elegans , and we wanted to avoid potential complications of infectious mammalian prions , we used the well-characterized cytosolic yeast prion protein Sup35 [8] . Here , we show that a cytosolic prion domain , NM , is highly toxic and can spread among tissues within the animal . The cell non-autonomous organismal toxicity of Sup35NM was associated with the accumulation of autophagy-derived vesicles , disruption of mitochondrial integrity , and the dynamic movement of the prion domain protein between tissues via autolysosomal vesicles .
Three versions of Sup35NM , corresponding to the full-length wild-type domain , NM , a deletion of the oligorepeat region ( RΔ2-5 ) , and an expansion of the oligorepeats ( R2E2 ) , were fused to YFP ( yellow fluorescent protein ) ( Figure 1A ) and expressed under the control of the body wall muscle ( BWM ) cell specific ( m ) promoter unc-54p . These NM constructs were selected based on previous observations that deletion of four of the five oligorepeats of the prion domain ( RΔ2-5 ) leads to a strong decrease in prion induction , while expansion of this region ( R2E2 ) significantly increases spontaneous prion formation [44] . In C . elegans lines expressing approximately similar levels of the transgenes ( Figure 1C ) , NMm::YFP aggregates appeared in early embryonic stages of development and persisted through all larval stages into adulthood ( Figure 1B ) . The appearance of aggregates was strictly related to the length of the oligorepeats such that R2E2 formed aggregates more rapidly and to higher levels than NM , while deletion of the oligorepeats in RΔ2-5 resulted in expression of a mostly soluble and diffuse protein ( Figure 1B , 1D ) . The fluorescent foci in NMm::YFP and R2E2m::YFP coincided with higher levels of detergent insoluble protein relative to RΔ2-5m::YFP ( Figure 1D ) . To further characterize the biochemical and biophysical properties of the NM aggregates , we employed the dynamic imaging method of fluorescence recovery after photobleaching ( FRAP ) . The diffuse fluorescence observed in RΔ2-5m::YFP expressing animals was shown to correspond to highly mobile protein species by FRAP analysis , in addition to the infrequent appearance of foci that were too small to be assessed by FRAP ( Figure 2A ) . In contrast , examination of NMm::YFP and R2E2m::YFP foci at high magnification revealed highly diverse shapes and sizes that can be described as long fibril-like species ( ∼10 µm ) , large ( ∼2 µm ) round spherical structures , and small ( ∼0 . 1 µm ) foci ( Figure 2B , 2C ) . These foci did not exhibit any obvious patterns among the BWM cells and were randomly distributed . Moreover , each progeny descending from a single hermaphrodite exhibited a unique pattern of R2E2m::YFP foci ( Figure 2C ) suggesting an influence of the individual cellular environment on aggregate phenotypes . FRAP analysis on animals expressing NM and R2E2 ( Figure 2A ) revealed foci ranging from immobile aggregates that exhibited no FRAP recovery to foci that rapidly recovered fluorescence and thus were comprised of mixed populations of slowly mobile protein species . These two biophysical states of prion domain aggregates were closely aligned with the distinct visual morphologies , in that every fibril-like aggregate tested was comprised of immobile species , and that spherical aggregates detected in both R2E2 and NM animals corresponded to mobile aggregates that showed recovery following photobleaching ( Figure 2A ) . R2E2m::YFP animals exhibited a severe reduction in motility relative to wild-type N2 or RΔ2-5m::YFP animals ( Figure 3A; Video S1 , S2 , S3 ) that was associated with a disruption of muscle ultrastructure revealed by rhodamine-phalloidin staining of myofilaments ( Figure 3B ) . Moreover , nearly all of the R2E2m::YFP and NMm::YFP adults exhibited developmental delay and reduced fecundity , with R2E2m::RFP adults being often sterile ( Ste ) ( Figure 3C , 3D ) . Whereas adult N2 wild-type and RΔ2-5 animals lay approximately 16 eggs within a 2 . 5 hour period , NM and R2E2 animals laid 8 . 5 and 4 eggs , respectively ( Figure 3C ) . Furthermore , only 8% of R2E2m::YFP embryos and 1% of NMm::YFP embryos attained adulthood over a three day period at 20°C , relative to >93% achieving adulthood for wild-type N2 or RΔ2-5m::YFP embryos ( Figure 3C ) . The slightly higher fraction of adult R2E2 animals detected after 72 hours is due to a more severe egg laying defect ( Egl ) of these animals . R2E2 animals often retained their eggs due to dysfunction of the vulva muscle leading to embryos being laid at later time points of development . Consequently , eggs laid by R2E2 animals tended to be older than corresponding NM , RΔ2-5 , and wild-type N2 embryos that are deposited at the same time . NM animals exhibited a more severe embryonic lethal phenotype ( Emb ) than R2E2 , while the latter animals exhibiting increased sterility ( Ste ) and producing fewer total progeny ( Figure 3C ) . Animals that reached the L4 state of development after 72 hours became adult animals on the next day , whereas younger larvae were more likely to arrest in development ( Figure 3D , Video S1 , S2 , S3 , data not shown ) . In summary , while the populations of NM and R2E2 animals differed in their distribution among developmental states after 72 hours , expression of the highly aggregation-prone R2E2 resulted in a more severe toxic phenotype than NM ( Figure 3C , 3D; Video S1 , S2 , S3 ) . Another characteristic of R2E2m::YFP expressing animals was a plethora of morphological defects that included reduced size ( Sma ) , vacuolation ( Vac ) , defective molting ( Mlt ) , clear appearance ( Clr ) , and disrupted gonadal and intestinal morphology ( Figure 3E , and data not shown ) . Such defects affecting other tissues were observed to a lesser extent in NMm::YFP animals , and not detected in RΔ2-5m::YFP lines or in C . elegans lines expressing other aggregation-prone proteins [38] , [39] , [41]–[43] ( data not shown ) . The NM-dependent cellular defects were examined in more detail using transmission electron microscopy ( TEM ) . Compared to wild-type N2 animals , the muscle cells of R2E2 expressing animals exhibited disrupted sarcomeres , fragmented mitochondria containing a drastically reduced number of cristae , and a complex array of double and single membrane bound organelles ( Figure 4A ) . These vesicular structures are a hallmark of autophagy . Surprisingly , the cellular pathology observed in R2E2 expressing animals was not restricted to BWM cells and was also observed in other tissues in which R2E2 was not expressed . For example , intestinal cells that did not express R2E2 exhibited mitochondrial fragmentation with loss of cristae , and reduced levels of yolk and lipid droplets ( Figure 4B ) . These studies show that the number of oligorepeats in the prion domain directs the toxicity that results in multiple organismal phenotypes that extend beyond the primary tissue of NM expression . To examine whether the induction of autophagy is a secondary cellular response due to damage of essential components like mitochondria , or if the prion domain is directly targeted by the autophagy-lysosome pathway ( ALP ) , we employed C . elegans lines expressing markers of specific membraneous organelles . As the available markers for C . elegans are tagged with green fluorescent protein ( GFP ) , we generated a C . elegans line expressing R2E2 tagged with red fluorescent protein ( RFP ) under the control of the myo-3 promoter for BWM cell specific expression ( R2E2m::RFP ) . LGG-1::GFP transgenic animals that express the ortholog of the autophagosome marker LC3 ( in mammals ) or ATG8 ( in yeast ) were used to monitor autophagic vesicles [45] . In R2E2; LGG-1 transgenic lines , we observed co-localization of a subset of R2E2m::RFP foci with autophagosomes ( Figure 5A ) . We also detected co-localization of R2E2m::RFP foci with RAB-7 positive late endosomes and specifically with LMP-1 positive lysosomes ( Figure 5C , 5D ) . The majority of these lysosomes exhibited an unusual tubular shape ( Figure 5D ) . Co-localization was not observed with RAB-5 ( early endosomes ) ( Figure 5B ) , indicating that the R2E2-containing vesicles were derived from the autophagy pathway rather than from endocytosis . Our studies do not distinguish whether the R2E2m::RFP that co-localizes with vesicular structures corresponds to specific classes of aggregate species described before , as these vesicles have been excluded from FRAP analysis due to their small size ( see materials and methods for more details ) . These data , together with the TEM analysis , suggest that the prion domain is a target of quality-control autophagy and is transferred from autophagosomes to RAB-7 positive amphisomes and LMP-1 positive autolysosomes , respectively . Another striking characteristic of the tubular vesicles containing R2E2m::RFP was their dynamic movement within and between muscle cells , monitored by live-cell time-lapse imaging ( Video S4 , S5; Figure 6B ) . In particular , the over-expression of RAB-5 enhanced ( and facilitated by visualizing single muscle cells ) the detection of cell-to-cell transmission of RFP-positive vesicles between BWM cell quadrants ( Video S5 , Figure 6B ) . These observations are consistent with previous findings that RAB-5 over-expression increases autophagy [46] . The intercellular transport of R2E2-containing vesicles was unexpected as C . elegans body wall muscles are composed of individual mononucleated cells that are connected through gap junctions to allow electrical coupling for coordinated body movement [47] ( Figure 6A ) . No dye coupling has been observed between single muscle cells , implying that there is no unregulated transfer of cytosolic proteins under normal physiological conditions [47] . This leads us to propose that R2E2 is actively transported by tubular vesicles from cell-to-cell . As mentioned before , these vesicles are different from the foci described in Figure 2 . Neither the spherical ( mobile in FRAP analysis ) nor the fibril-like ( immobile in FRAP analysis ) aggregates are moving within or between cells . Only the small tubular vesicles are getting transmitted and we do not know the conformational state of R2E2 protein within these vesicles . Nevertheless , misfolding and aggregation is central to the toxicity phenotype , as RΔ2-5m::YFP , which does not form these aggregates , exhibits neither a cell autonomous nor cell non-autonomous toxicity . The moving , tubular-shaped vesicles were only detected in R2E2m::RFP animals , but not observed with the corresponding proteins tagged with YFP . In contrast , the diverse aggregate species and other small vesicular structures ( neither tubular nor moving ) were visible with both YFP and RFP ( compare Figure S1A and Figure 1A , Figure 2B and 2C ) . In transgenic animals expressing only the RFP fluorescent tag in BWM cells ( RFPm ) , no movement of RFP between cells was observed ( Figure 6C , Figure S1B , data not shown ) . This apparent discrepancy with the fluorescent tags can be explained by RFP being more stable in acidic environments whereas YFP is pH sensitive [48] , indicating that these vesicles might exhibit a low lumenal pH that could explain the lack of similar fluorescent structures in R2E2m::YFP expressing animals . This speculation is supported by our results that the moving tubular vesicles co-localize with LMP-1::GFP , but not with LGG-1::GFP ( compare Figure 5A and 5D ) . Indeed , staining of R2E2m::YFP animals with an anti-GFP antibody by indirect immunofluorescence revealed tubular structures in addition to foci visible with YFP fluorescence ( Figure S2 ) . This further supports our conclusion that acidified lysosomal vesicles containing the prion domain are transported from cell-to-cell . Muscle cell-expressed R2E2 was also detected in vesicles of coelomocytes and the intestine ( Figure 6D , Figure S3 ) . Both , the intestine and coelomocytes , have been shown to endocytose molecules from the body cavity ( pseudocoelom ) [49] , [50] , suggesting that the tubular vesicles containing R2E2 could be released from BWM cells into the pseudocoelom and taken up by endocytosis from surrounding coelomocytes or intestinal cells . While the uptake of proteins from the pseudocoelom into coelomocytes and the intestine is not specific [49] , [50] , the amount of R2E2m::RFP that accumulates in these tissues is much more pronounced than for RFPm ( compare Figure 6D and 6E ) . These results suggest that R2E2m::RFP is actively released from muscle cells into the pseudocoelom . To examine the specificity of tissue movement of R2E2 , we expressed RFP-tagged R2E2 in intestinal cells and monitored the dynamics of R2E2i::RFP-containing vesicles ( Figure S4 ) . Movement of R2E2 was observed by real-time imaging within intestinal cells ( Video S6 ) , and from the intestine into adjacent non-expressing muscle cells ( Figure 6F , Figure S5 , Video S7 ) , thus confirming the spreading of the aggregation-prone prion domain across tissues . Taken together , these observations reveal that R2E2m::RFP accumulates in tubular vesicles of autolysosomal origin that spread from expressing cells to non-expressing tissues in C . elegans . Furthermore , R2E2 seems to spread by two different pathways , either by a direct cell-to-cell transport of lysosomes , or through release into and endocytic uptake from the pseudocoelom ( Figure 6B , 6D , 6F , Video S5 and S7 ) . We next examined whether the prion domain induces aggregation of its soluble isoform in C . elegans . Such a self-templating or seeding activity forms the basis of amyloid infectivity [9] , [22] . To address this , we took advantage of the different aggregation properties of the prion domain constructs and used the non-aggregating RΔ2-5m::YFP as a folding sensor . The seeding model posits a direct interaction of newly forming with preexisting aggregates , which in part is sequence-specific [51] . To examine this , we introduced in vitro generated recombinant fibrils by microinjection ( Figure S7 ) , into intestinal cells expressing RΔ2-5 , as muscle cells were too small for microinjection . These studies were based on previous in vitro and cell culture observations that addition of preformed fibrils induces aggregation of the corresponding soluble NM in a sequence-specific manner [9] , [44] , [52] . We monitored the aggregation state of the RΔ2-5 folding sensor expressed under an intestine-specific ( i ) promoter ( vha-6p ) . RΔ2-5 and NM constructs exhibited similar patterns of aggregation in the intestine as in muscle cells ( Figure S6 ) . Analogous to the biophysical properties exhibited in BWM cells , RΔ2-5i::GFP is soluble in intestinal cells ( Figure S6; Figure 7A , 7E ) . Introduction of recombinant Sup35NM fibrils into intestinal cells resulted in the conversion of RΔ2-5 from a soluble to an aggregated state ( Figure 7A , 7B , 7C , 7D ) that did not co-localize with the injected Sup35NM fibrils . To address the sequence specificity of these effects , RΔ2-5 animals were also injected with recombinant fibrils of the asparagine-rich yeast prion protein Ure2p with high alpha-helical content [53] , [54] , or β-sheet rich amyloid Aβ1-42 , respectively ( Figure 7E ) . No aggregation of RΔ2-5 was observed upon injection of either protein . To test whether cross-seeding occurs when both proteins are co-expressed in C . elegans tissues , we crossed RΔ2-5m::YFP with R2E2m::RFP animals . Despite being impaired for spontaneous aggregation , RΔ2-5m::YFP readily formed aggregate species that exhibited slow exchange in FRAP when co-expressed with R2E2m::RFP in BWM cells ( Figure 8A , 8B , 8C ) . RΔ2-5m::YFP aggregates , however , only rarely co-localized with R2E2m::RFP foci ( Figure 8B ) , consistent with observations from the injection experiments ( Figure 7B , 7C ) . The RΔ2-5 sensor was further employed to test whether protein misfolding spreads from R2E2-expressing muscle cells to the intestine . Indeed , aggregation of RΔ2-5i::GFP increased when R2E2m::RFP and RΔ2-5i::GFP were co-expressed ( Figure 8E , 8F ) . The absence of co-localization of RΔ2-5 and R2E2 foci , even when co-expressed ( Figure 8B ) , indicates that aggregation of RΔ2-5 could be due to the global disruption of the folding environment , as seen in tissues co-expressing aggregates of polyglutamine and temperature sensitive mutant proteins [55] , rather than from cross-seeding , which would imply co-aggregation of both proteins into heterologous aggregates [51] . Indeed , expression of R2E2 in muscle cells accelerated the age-dependent aggregation of an intestinal polyglutamine ( polyQ ) folding sensor ( Q44i::YFP ) [56] ( Figure S8 ) . Taken together , these results show that R2E2m::RFP aggregates have multiple effects by seeding homologous soluble proteins in a sequence-specific manner and causing an imbalance of organismal proteostasis .
We have developed a metazoan prion model and examined the properties of a Q/N-rich prion domain in non-dividing terminally differentiated cells using C . elegans . A summary model describing the different aggregate species , vesicular structures and phenotypes observed in the C . elegans prion model , is shown in Figure 9 . As the mechanism of prion propagation differs between unicellular eukaryotes and metazoans , it was unclear whether the prion propensities of Q/N-rich domains are universal and can adapt to different biological systems of cell-to-cell transmission . Spreading of the prion domain from an initial site of expression via autolysosomal vesicles occurs through actively regulated cellular processes , involving a direct transport from cell-to-cell and the release and endocytic uptake of these vesicles from the body cavity . This differs substantially from the propagation of [PSI+] in yeast that involves transfer of cytosolic NM propagons from mother to daughter cells during cell divison , that neither requires uptake into membraneous compartments nor active transport . Rather , the transmission of NM between cells and tissues in C . elegans is reminiscent of mammalian PrPSc propagation between post-mitotic neurons . Exosomes [57] and tunneling nanotubes [58] have been suggested as possible routes for cell-to-cell transmission of PrPSc . Either way , cytosolic content also gets transmitted , suggesting that cytosolic and membrane localized prion-like proteins might share some mechanistic aspects of transmission [59] . Indeed , there is now growing evidence that other disease-associated cytosolic protein aggregates can spread from cell-to-cell [19] , [20] , [26] , [34] , [35] . The spreading of the prion domain described here in C . elegans will allow us to compare the relative potential of tissue transmission with other aggregation-prone amyloidogenic proteins in our model system . It remains to be established if all major disease-associated proteins can spread from cell-to-cell themselves in a similar fashion like NM . Alternatively , prion-like domains might have implications in the spread of pathology throughout the nervous system by allowing a subset of modifiers like FUS and TDP-43 to transmit between cells , which then cause the subsequent aggregation of other disease-linked proteins . Although motility defects are often associated with the expression of protein aggregates in C . elegans muscle cells [38] , [39] , [41]–[43] , the expression of NM was unusually toxic compared to the expression of other disease-associated aggregation-prone proteins [38]–[43] . Aggregation and toxicity of NM were dependent on the oligopeptide repeats . Likewise , in yeast and mammals , the oligorepeats affect spontaneous prion induction and disease prevalence , respectively [44] , [60] , [61] . In yeast and infected tissue culture cells , prions often elicit no toxicity , suggesting that only non-toxic rapidly replicating variants are selected upon infection in these systems [62] . The unc-54 promoter used to express NM becomes active post-mitotically in 81 of 95 body wall muscle cells [63] , [64] . The toxicity in C . elegans could therefore reflect the vulnerability of terminally differentiated non-dividing cells . Autophagy is important for protein quality control and homeostasis in non-dividing neuronal cells [65] , [66] , consequently , autophagic failure has been implicated in prion diseases and other neurodegenerative disorders [67]–[69] . While activation of autophagy is beneficial to promote the clearance of disease-associated proteins [70]–[75] , the chronic induction of autophagy could have deleterious consequences and may be insufficient to suppress toxicity [76]–[78] , in particular if lysosomal function is already compromised during aging or by the chronic expression of mutant proteins [77]–[79] . In line with this , our preliminary results revealed that blocking autophagy by RNAi , to inhibit prion transmission , has only marginal effects to ameliorate NM toxicity in BWM cells ( as measured by motility assays , data not shown ) , indicating that the autophagy-lysosomal pathway has a dual role and also reduces the load of misfolded proteins . Future studies using genome-wide RNAi screens will identify the cellular pathways that improve fitness in these animals . One of the most striking consequences attributed to expression of the prion domain in C . elegans was mitochondrial fragmentation and loss of cristae . An equilibrium of steady fission and fusion events is critical for mitochondrial structure and function , and disruption of this homeostasis has been observed in disease and aging [80] . Intriguingly , a collapse of mitochondrial function was also observed in lysosomal storage disorders associated with impaired lysosomal degradation [81]–[83] , and has been proposed to be a common secondary and final mediator of cell death in several diseases associated with autophagic failure and lysosomal dysfunction [81]–[83] . It remains to be determined whether related mechanisms are associated with the disruption of mitochondrial ultrastructure observed here for the C . elegans prion model . There is accumulating evidence that lysosomes have additional roles to their conventional function as digestive organelles . Lysosomes constitute the exosomes of nonsecretory cells [84] , are exocytosed during plasma membrane repair [85] , and were shown to be transferred via tunneling nanotubes from endothelial progenitor cells to rescue prematurely senescent endothelia [86] . Our results reveal the involvement of lysosomes in the cell-to-cell transmission of cytosolic aggregation-prone proteins . Of note , the exocytosis or transfer of lysosomes may represent a specific cellular response to the prion domain as a cargo , because non mitotic aging tissues fail to secrete indigestible lysosomal content , which leads to the characteristic accumulation of lipofuscin [87] . It is tempting to speculate that proteins with prion domains might trigger a specific cellular response that leads to the release of LMP-1 positive vesicles . Aggregation of NM in C . elegans occurs spontaneously upon its over-expression , in contrast to observations in bacterial and mammalian models [18] , [88] , [89] . In yeast , the induction of [PSI+] is dependent on the co-existence of [PIN+] or other compatible aggregation-prone proteins [90]–[92] . Perhaps similar factors such as endogenous Q/N-rich proteins are expressed in C . elegans that can act as [PIN+] [93] . The injection of preformed fibrils or co-expression of aggregation-prone variants seeds the polymerization of the corresponding monomeric protein [12] , [22] , [24] , [28] , [29] by a reaction known as nucleated or seeded polymerization [9] , [22] . Only Sup35NM fibrils were able to cross-seed RΔ2-5 to form aggregates , whereas injection of fibrillar Abeta1-42 or Ure2p failed to do so , which suggests that seeding of RΔ2-5 is sequence-specific . However , Sup35NM fibrils or R2E2 aggregates did not co-localize with RΔ2-5 foci . The absence of co-aggregation might be due to conformational variations resulting from sequence differences within the NM oligorepeats [94] , [95] . While the different prion domain variants might initially form heterologous seeds below the resolution of our imaging approaches , the preferred coalescence of molecules that have the same conformation might lead to distinct aggregates [51] . Alternatively , the ability to induce polyQ aggregation in a cell non-autonomous manner , suggests that expression of the aggregation-prone prion domain causes a global disruption of cellular proteostasis , and subsequent misfolding of unrelated metastable proteins , perhaps by titrating chaperones and other anti-aggregation factors [55] , [90] . Most likely , misfolding of RΔ2-5 upon co-expression of R2E2 in the same or neighboring tissue results from a combinatory effect of sequence-specific cross-seeding together with an overload of the cellular folding capacity . Under these chronic proteotoxic stress conditions , one misfolded protein can accelerate aggregate formation of another aggregation-prone protein independent of protein sequence homology [41] , [55] . In summary , this study provides new insights on the intrinsic properties of Q/N-rich prion domains in metazoans . Although the yeast prion domain NM is not a disease relevant peptide , this novel genetic C . elegans prion model can elucidate cellular pathways underlying the prion-like propagation of conformational changes in proteins between cells and tissues of multicellular organisms in health and disease .
Sup35NM constructs were amplified from the yeast expression vector p316CUP1-3SGFPSG [44] containing either the full-length NM , NM with a deletion of oligorepeats # 2-5 ( aa 56-93 ) ( RΔ2-5 ) , or NM with a two-times extended oligorepeat # 2 ( QGGYQQYNP ) ( R2E2 ) [44] , by PCR standard methods . Insertion of appropriate restriction sites allowed cloning of the PCR amplicons into pPD30 . 38 [39] . This vector contains the promoter and enhancer elements from the unc-54 gene [96] , as well as EYFP from the vector pEYFP-N1 ( Clontech ) [39] . For constructing myo-3p::sup35 ( r2e2 ) ::rfp , myo-3p::rfp , vha-6p::sup35 ( rΔ2-5 ) ::gfp , vha-6p::sup35 ( nm ) ::gfp , vha-6p::sup35 ( r2e5 ) ::rfp , unc-54p::cfp::rab-5 , and unc-54p::lmp-1::gfp expression vectors , the MultiSite Gateway Three-Fragment Vector Construction Kit ( Invitrogen ) was used . NM constructs were amplified from the pPD30 . 38 vectors using appropriate oligonucleotides for gateway cloning and inserted into the pDONR 221 entry vector by recombination . Likewise , the lmp-1 coding sequence was amplified from a N2 cDNA sample and inserted into the pDONR 221 entry vector . The plasmid pCZGY#3 ( = pDONR 201_rab-5 ) was a kind gift from Dr . Yishi Jin . Entry vectors pDONR P4-P1R containing myo-3 ( approx . 2 . 4 kb upstream of the myo-3 gene ) , vha-6 ( approx . 1 . 2 kb upstream of the vha-6 gene ) , or unc-54 ( approx . 1 kb upstream of the unc-54 gene ) promoter region and pDONR P2R-P3 coding for the C-terminal monomeric RFP or GFP tag , were generated accordingly . The N-terminal CFP was cloned between the unc-54 promoter and rab-5 using appropriate restriction sites . For co-localization , CFP::Rab-5 was false-colored green . All pDONR P2R-P3 entry vectors also contained the unc-54 3′UTR . Promoters , genes of interest and fluorescent tags were finally inserted into the destination vector pDEST R4-R3 in an in vitro recombination reaction . Wild-type ( N2 , Bristol ) and transgenic worms were handled using standard methods [97] . If not otherwise indicated , nematodes were grown on NGM plates seeded with the E . coli strain OP50 at 20°C . The strains NP1129 cdIs131[cc1p::gfp::rab-5+unc-119 ( + ) +myo-2::gfp] , NP871 cdIs66[cc1p::gfp::rab-7+unc-119 ( + ) +myo-2::gfp] , NP744 cdIs39[cc1p::gfp::rme-1 ( 271alpha1 ) +unc-119 ( + ) +myo-2::gfp] , RT258 pwIs50[lmp-1p::lmp-1::gfp+Cb-unc-119 ( + ) ] , and DA2123 adIs2122[lgg-1::GFP + rol-6 ( su1006 ) ] were ordered from the Caenorhabditis Gene Center ( CGC ) . The strain FY777 lin-15 ( n765ts ) ; grEx170[Pmyo-3::gfp::rab-7; lin-15 ( + ) ] was a kind gift of Dr . Bruce Bamber . The following strains were generated for this study using germline transformation by microinjection: AM801 rmIs319[unc-54p::sup35 ( rΔ2-5 ) ::yfp] , AM803 rmIs321[unc-54p::sup35 ( nm ) ::yfp] , AM806 rmIs324[unc-54p::sup35 ( r2e2 ) ::yfp] , AM815 rmIs323[myo-3p::sup35 ( r2e2 ) ::rfp] , AM809 rmEx319[vha-6p::sup35 ( rΔ2-5 ) ::gfp+myo-2p::mcherry] , AM823 rmEx326[vha-6p::sup35 ( rΔ2-5 ) ::gfp] , AMf814 rmIs326[vha-6p::sup35 ( nm ) ::gfp+myo-2p::mcherry] , AM883 rmEx338[myo-3p::rfp::rfp] , AM887 rmEx339[unc-54p::cfp::rab-5] , AM890 rmEx340[unc-54p::lmp-1::gfp] . Transgenic lines carrying extrachromosomal arrays were generated by microinjection of the above-mentioned plasmids into N2 wild-type animals . Integrations were obtained by gamma irradiation of animals carrying the respective extrachromosomal array . Integrated lines were backcrossed at least five times . Importantly , due to the high toxicity of some of the transgenes , the lines had to be backcrossed into N2 wild-type background regularly to avoid the occurrence of mutations that improve the health or suppress the NM aggregation phenotype of the transgenic lines . For the same reason , assays were performed on freshly backcrossed or crossed animals . Nematodes were synchronized by transferring adult animals on a fresh plate and were allowed to lay eggs for 2 . 5 hours before removing . The amount of eggs laid was determined . Embryos were grown at 20°C for 72 hours before assessment of their developmental stage . Data obtained in at least three independent experiments were analyzed ( ≥200 worms total ) . In parallel , 20 L1 larvae were picked on fresh plates and grown for four days at 20°C before pictures and movies were taken . L4 larvae from N2 and transgenic lines were transferred on fresh plates . Movement of crawling animals was recorded 24 hours later ( with young adult worms ) using a Leica M205 FA microscope with a Hamamatsu digital camera C10600-10B ( Orca-R2 , Leica Microsystems , Switzerland ) , and the Hamamatsu Simple PCI Imaging software . Videos were imported into ImageJ and speed ( measured as body length per second ) was analyzed using the wrMTrck plugin for ImageJ . Each sample containing 20–30 worms was recorded three times and the average speed of these movies was considered one biological sample . At least three biological replicates were obtained for each strain tested . For rhodamine-phalloidin staining , transgenic lines were fixed ( 4% formaldehyde solution ) , permeabilized ( 130 mM Tris , pH 6 . 8; 700 mM ß-mercaptoethanol; 1% Triton X-100 ) and stained with rhodamine-phalloidin ( Molecular Probes ) . For indirect antibody staining of R2E2 , R2E2m::YFP animals were washed in M9 , transferred onto Poly-L-Lysine coated microscope slides ( Electron Microscopy Sciences ) , covered with coverslips and frozen on a metal block chilled to about −70°C on dry ice . The coverslips were snapped off and the slides were fixed in −20°C methanol , washed twice ( 1x PBS ) , blocked ( 1x PBS; 4% BSA; 0 . 1% Triton X-100 ) , and incubated with anti-GFP antibody ( ab6556 from Abcan ) in blocking solution at 4°C over night . The next day , slides were washed 4x ( 1x PBS ) , incubated with secondary antibody ( Alexa-456 conjugated goat anti-rabbit IgG , Invitrogen ) for 1 h at room temperature , before being washed again , mounted ( PermaFluor Aqueous Mounting Medium , Thermo Scientific ) and sealed . For light and fluorescence microscopy , animals were mounted on 2% agarose pads and immobilized in 2 mM levamisole . DIC ( Nomarski ) images were obtained using a Zeiss Axiovert 200 microscope with a Hamamatsu Orca 100 cooled CCD camera . Fluorescence microscopy and FRAP analysis were performed on a Zeiss LSM 510 confocal microscope with a 488 nm , 514 nm , or 563 nm line for excitation . FRAP was performed by using the 63 x objective lens at 5 x zoom , with the 514 or 488 nm line for excitation of YFP or GFP , respectively . An area of 0 . 623 µm2 was bleached for 50 iterations at 100% transmission , after which time an image was collected every 123 . 35 ms . The relative fluorescence intensity ( RFI ) was determined by using RFI = ( Tt/Ct ) / ( T0/C0 ) , where T0 denotes the fluorescence intensity of the bleached region and C0 the control unbleached region , prior to bleaching , and Tt and Ct represent the fluorescence intensity at time t after photobleaching for the bleached and control region , respectively . Results show an average of at least 20 independent measurements for each strain . Foci that got rapidly and evenly bleached allover beyond the outline of the bleached region of interest ( ROI ) were excluded from these analysis , as they likely constitute vesicles containing soluble protein . In addition , foci , that had the same or a smaller size than the bleached ROI of 0 . 623 µm2 were not taken into account , as the motility of the protein within the same focus could not be assessed and therefore , vesicles containing soluble protein could not be distinguished from aggregates . FRAP analysis of RΔ2-5 foci in RΔ2-5m::YFP;R2E2m::RFP animals , RΔ2-5i::GFP animals 24 h after injected with rec . fibrils , and RΔ2-5i::GFP;R2E2m::RFP animals were performed on young adult ( day 1 and 2 of adulthood ) worms . Where indicated , a Leica SP5 II LSCM equipped with HyD detectors was used , especially for time-lapse imaging . Nematodes were collected from a densely populated not starved 6 cm or 10 cm plate , washed in M9 buffer , and resuspended in lysis buffer ( 20 mM Tris , pH 7 . 5; 10 mM ß-mercaptoethanol; 0 . 5% Triton X-100; supplemented with complete protease inhibitor ( Roche ) ) before shock freezing in liquid nitrogen . After three freeze-thaw cycles , the worm pellet was grinded with a motorized pestle , and lysed on ice , in the presence of 0 . 025 U/ml benzonase ( Sigma ) . The lysate was centrifuged at 1000 rpm for 1 min in a tabletop centrifuge to pellet the carcasses . Protein concentration was determined using Bradford assay ( Bio-Rad ) . For the solubility assay , 200 µg of total protein was mixed with 2% N-Lauroylsarcosine , before ultracentrifugation at 100 . 000 g for 1 hour at 4°C . Supernatant and pellet fractions were separated and subjected to SDS-PAGE and subsequent immunoblotting . For transgene detection , the mouse anti-GFP monoclonal antibody ( MMS-118R , Covance ) was used , together with ECL plus ( GE Healthcare ) . As a loading control , α-tubulin was detected by anti-α-tubulin antibody ( T-5168 , Sigma ) . Samples were high pressure frozen ( HPF ) using a Leica EM PACT2 , and maintained in LN2 until processed at low temperature in an automated freeze substitution unit ( Leica EM AFS2 ) . The freeze substitution solution ( 2% OsO4 , 0 . 5% uranyl acetat , 3% H20 in Acetone ) was cooled to −90°C before adding the HPF sample . Low temperature processing was performed over 3 days where the temperature was gradually increased to room temperature , followed by a gradual infiltration with EMBed 812 resin and polymerization . 90 nm thin sections were collected on Formvar-coated slot grids and stained with 2% uranyl acetate and Reynold's lead citrate . Samples were imaged at 80 kV in a JEOL 1230 transmission electron microscope . 4 different R2E2 expressing animals and 2 N2 animals were imaged . Animals were synchronized via bleaching as described earlier [39] . Synchronized L1 larvae were transferred on fresh OP50-seeded plates ( = day 1 ) . Animals were observed at 30–40x magnification with a stereomicroscope equipped for epifluorescence . The number of animals containing intestinal polyQ aggregates was determined on day 1 to 5 after synchronization . At least 300 animals ( total ) were assessed in 3 independent experiments . Sup35NM , Aß ( 1–42 ) , and full-length Ure2p expression , purification and assembly were performed as described [98]–[100] . Sup35NM , Aß ( 1–42 ) and Ure2p fibrils were spun at 15 . 000 g for 15 min at 4°C . The fibrils were resuspended in 50 mM HEPES , pH 7 . 5 . Labeling was achieved by addition of 2 molar excess of the aminoreactive fluorescent dye Alexa Fluor 555 carboxylic acid , succinimidyl ester ( Invitrogen ) following the manufacturer's recommendations . Unreacted dye was removed by 3 cycles of sedimentation and resuspension of the fibrils in HEPES buffer . The amount and quality of recombinant fibrils was determined by solubility assay ( 16 . 000 g , 30 min , 4°C ) and TEM . The fibrillar nature of the generated assemblies was assessed using a JEOL 1400 transmission electron microscope following adsorption of the samples onto carbon-coated 200-mesh grids and negative staining with 1% uranyl acetate . The images were recorded with a Gatan Orius CCD camera . Immediately before injection , recombinant fibrils were diluted into 50 mM HEPES buffer , pH 7 . 5 , to a final concentration of 100 µM and sonicated ( 1510 Branson water sonicator ) for 30 min . 50 mM HEPES buffer only was used as a control . Young adult worms were microinjected according to standard methods into the cytosol of intestinal cells . After 24 h nematodes were imaged using a Zeiss LSM 510 confocal microscope . At least 5 animals were injected with each fibril preparation and analyzed per experiment and experiments were repeated 2–3 times . Our microinjection setup does not allow controlling for the injection of the exact same amount of protein fibrils into each animal . Therefore , we assessed different concentrations of the fibril preparations in their ability to induce RΔ2-5 aggregation . The fibril concentration had only an impact on the quantity ( how much protein was seeded ) , but not on the quality ( if there was seeding ) of aggregation induction .
|
Alzheimer's , Parkinson's , Huntington's , frontotemporal lobar degeneration ( FTLD ) , amyotrophic lateral sclerosis ( ALS ) , and prion diseases are all age-related , fatal neurodegenerative disorders . Hallmarks of these diseases include the expression of toxic protein species . The ability to spread and infect naive cells was thought to be limited to prions but has recently been observed for other disease-linked protein aggregates in tissue culture cells and transgenic mice . The underlying cellular pathways of this cell-to-cell transmission , however , remain elusive . We have developed a new prion model in the roundworm Caenorhabditis elegans and show that the appearance of aggregate species is associated with cellular toxicity , not only in the expressing cell but as well as in adjacent tissues . We monitored in real time the spreading of prion domains by autophagy-derived lysosomal vesicles from cell-to-cell . Given that autophagy and lysosomal degradation have a role in several neurodegenerative diseases , this cellular pathway might be the basis of amyloid infectivity in general .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biochemistry",
"infectious",
"diseases",
"model",
"organisms",
"neurological",
"disorders",
"neurology",
"genetics",
"biology",
"molecular",
"cell",
"biology",
"neuroscience",
"genetics",
"and",
"genomics"
] |
2013
|
Spreading of a Prion Domain from Cell-to-Cell by Vesicular Transport in Caenorhabditis elegans
|
Neuritogenesis is a critical early step in the development and maturation of neurons and neuronal circuits . While extracellular directional cues are known to specify the site and orientation of nascent neurite formation in vivo , little is known about the genetic pathways that block inappropriate neurite emergence in order to maintain proper neuronal polarity . Here we report that the Caenorhabditis elegans orthologues of Van Gogh ( vang-1 ) , Prickle ( prkl-1 ) , and Dishevelled ( dsh-1 ) , core components of planar cell polarity ( PCP ) signaling , are required in a subset of peripheral motor neurons to restrict neurite emergence to a specific organ axis . In loss-of-function mutants , neurons display supernumerary neurites that extend inappropriately along the orthogonal anteroposterior ( A/P ) body axis . We show that autonomous and non-autonomous gene activities are required early and persistently to inhibit the formation or consolidation of growth cone protrusions directed away from organ precursor cells . Furthermore , prkl-1 overexpression is sufficient to suppress neurite formation and reorient neuronal polarity in a vang-1– and dsh-1–dependent manner . Our findings suggest a novel role for a PCP–like pathway in maintaining polarized neuronal morphology by inhibiting neuronal responses to extrinsic or intrinsic cues that would otherwise promote extraneous neurite formation .
Post-mitotic neurons undergo a transition from a more or less symmetrical morphology to a highly polarized one with axonal and dendritic projections that are precisely oriented along body or tissue axes [1] , [2] . Neurite emergence is the first overt sign of polarization in newly born neurons followed by differentiation of these neurites into axons and dendrites . While many molecules have been implicated in neuritogenesis and polarity control in cultured neurons , few have been verified by in vivo loss-of-function studies [1] , [2] . Recently , asymmetrically distributed directional cues such as Netrins and Slits have been shown to polarize protrusive activity in neuronal somas to specify the site of nascent neurite emergence in vivo [3] , [4] . However , in addition to mechanisms that promote neurite formation , the establishment and maintenance of a polarized morphology also require mechanisms that suppress non-specific neurite growth at all other times . At present , the inhibitory pathways that act in vivo to prevent extraneous neurite formation are poorly understood . Wnt/Frizzled ( Wnt/Fz ) pathways have been implicated in multiple aspects of post-mitotic neuronal development in animals [5] . Examples in C . elegans alone include cell migration [6] , axon guidance [7] , axo-dendritic polarity [8] , and synapse formation [9] . In general , Wnt/Fz signaling pathways can be subdivided into two types , a canonical pathway that involves activation of the transcriptional regulator β-catenin and non-canonical pathways that are β-catenin independent [10] . The Fz/planar cell polarity ( Fz/PCP ) pathway is a non-canonical Fz pathway that was first identified in Drosophila as a key regulator of cell polarity and cell alignment in the plane of the epithelium in eyes , wings , and abdomen [11]–[13] . Subsequent genetic studies in Drosophila identified a core group of conserved molecules that are either specific to polarity signaling ( Van Gogh/Vang , Prickle , and Flamingo/Fmi/Celsr ) or common to all Fz pathways ( Dishevelled/Dsh/Dvl ) [11]–[13] . In vertebrates , Fz/PCP signaling has been implicated in a diverse set of polarized cellular outcomes such as the ordered alignment of sensory hairs of the inner ear [14] , oriented cell division [15] , and directed convergent extension movements during gastrulation and neurulation [16]–[18] . The recent finding that Frizzled3 , Vangl2/Van Gogh , and Celsr3/Flamingo mutants share similar axon guidance defects in mice also implicate Fz/PCP signaling in growth cone navigation [19] , [20] . To identify determinants of neuronal polarity in C . elegans , we utilized the bipolar VC neurons , a set of six peripheral motor neurons that mediate egg-laying [21] . The VC neurons are an ideal polarity model as they display stereotypical differences in the orientation and extension of process outgrowth relative to the A/P axis and proximity to target structures . VC1–3 and VC6 project processes along the A/P axis , whereas the processes of VC4 and VC5 project along the orthogonal left-right ( L/R ) axis generated by the developing vulva , an intermediate target tissue during organ innervation ( Figure 1A and Figure S1 ) . The lateral placement of VC4 and VC5 processes ensures that connections are made with HSN neuronal and vm2 vulval muscle targets located on the left and right sides of the vulva [22] . The directional cues that orient VC process growth bidirectionally along the A/P axis or toward vulval cells are not known . However , in animals in which the vulva has been physically or genetically ablated , the processes of VC4 and VC5 , like those of VC1–3 and VC6 , extend along the A/P axis [23] , suggesting that bidirectional A/P growth constitutes the default polarity in the absence of vulval-derived cues . In this study , we show that the worm orthologues of the core PCP molecules Van Gogh ( VANG-1 ) , Prickle ( PRKL-1 ) , and Dishevelled ( DSH-1 ) are required to ensure that neurite emergence in VC4 and VC5 occurs exclusively along the vulval organ axis during innervation . Loss of these molecules result in the emergence of ectopic VC4 and VC5 neurites along the A/P axis directed away from vulval cells . We show that mature neurons are more severely affected than developing ones suggesting a primary fault in polarity maintenance . We also show that neurite growth inhibition involves autonomous and non-autonomous components and is required in a persistent manner to maintain neuronal morphology . PRKL-1 plays a key role in this process as prkl-1 overexpression is sufficient to suppress neurite formation and reorient VC polarity along the A/P axis in a vang-1 and dsh-1-dependent manner . These findings demonstrate a novel role for PCP-like signaling in blocking inappropriate neurite formation to maintain the polarity of initial neurite emergence in neurons .
To begin to investigate how the polarity of VC neurite outgrowth is specified we characterized zy2 , a mutation that was identified serendipitously in a genetic screen for VC neurite branching defects ( A . C . and M . Tessier-Lavigne , unpublished results ) . As VC processes , like those of many C . elegans neurons , display a mixed axonal and dendritic identity along their lengths [24] , they will herein be referred to as neurites . In zy2 mutants , VC4 and VC5 , visualized using the Pcat-1::GFP transgene cyIs4 [25] , project inappropriate neurites along the A/P axis ( Figure 1B and 1C ) . These extraneous neurites result in highly penetrant tripolar VC4 and VC5 morphologies in which two neurites project normally along the L/R axis of the vulva and a third extends inappropriately along the A/P axis away from the vulva . These mutants also displayed less penetrant A/P-bipolar VC4 and VC5 morphologies like those of VC1–3 and VC6 ( Figure 1F ) . All other aspects of VC morphology including terminal arborizations [26] are normal in zy2 mutants . High resolution genetic mapping followed by sequencing of candidate loci ( data not shown ) revealed zy2 to be a premature stop within vang-1 ( Figure S2 ) , the sole worm orthologue of Van Gogh/Strabismus , a core component of the Fz/PCP pathway in vertebrates and invertebrates [27] . tm1422 , a vang-1 deletion and putative null allele [27] , displayed similar VC phenotypes . To determine if the identification of vang-1 implicated a conserved planar polarity pathway in regulating VC polarity , we examined VC4 and VC5 morphology in other candidate PCP mutants . In addition to Van Gogh , other core PCP mediators include the cytoplasmic proteins Prickle and Dishevelled ( Dsh ) and the cell-surface proteins Frizzled ( Fz ) and Flamingo [11] , [12] , [13] . The worm genome contains a single Prickle ( prkl-1 ) and Flamingo ( fmi-1 ) and several Fz ( lin-17 , mig-1 , cfz-2 , and mom-5 ) and Dsh ( dsh-1 , dsh-2 , and mig-5 ) orthologues ( WormBase , http://www . wormbase . org ) . As Fz and Dsh molecules are common to both canonical and non-canonical Wnt/Fz signaling pathways and some of these play essential roles during early embryonic and vulval development [28] , we limited our analysis to homozygous viable mutants with grossly normal vulval morphology ( Table S1 ) . Of the core PCP components mentioned above , only Van Gogh and Prickle are specific to Fz/PCP signaling pathways . vang-1 has been implicated in intestinal tube formation [27] and the orientation of vulval precursor cell ( VPC ) polarity [29] . prkl-1 function in C . elegans has not been previously characterized . PRKL-1 exists as two isoforms , a long isoform ( PRKL-1A ) containing an N-terminal PET domain , three LIM domains , and a C-terminal CAAX prenylation motif and a short isoform ( PRKL-1B ) lacking the PET domain . Blast searches reveal several PET and LIM containing proteins , but only PRKL-1A displays a domain organization similar to the Prickle orthologues in Drosophila , fish and mammals ( Figure S3 ) . RNAi-mediated knock-down of prkl-1 produced ectopic A/P-directed VC4 and VC5 neurites similar to those displayed by vang-1 mutants ( data not shown ) . We subsequently confirmed this finding in prkl-1 ( zy11 ) , a premature stop allele , retrieved in a non-complementation screen over a chromosomal deficiency ( nDf41 ) that deletes the prkl-1 locus and prkl-1 ( ok3182 ) , a deletion and candidate null allele generated by the C . elegans Gene Knockout Consortium . VC defects were not detected in mig-1/Fz and cfz-2/Fz single or double mutant combinations and in maternally rescued mom-5/Fz mutants ( Table S1 ) . Loss of cat-1 promoter activity from cyIs4[Pcat-1::GFP] ( Table S1 ) , our only VC4 and VC5 specific reporter gene , and secondary neuronal morphology defects caused by vulval abnormalities in lin-17 mutants and combinations with other Fz mutants [29] , [30] , precluded an unambiguous assessment of Fz function in VC polarity . Overall , vang-1 , prkl-1 , and dsh-1 mutants share strikingly similar VC4 and VC5 polarity defects , although those of prkl-1 were quantitatively more severe ( Figure 1D–1F ) . dsh-1 mutants also displayed a slightly greater proportion of bipolar VC4 and VC5 neurons with A/P-oriented neurites like those of VC1–3 and VC6 ( Figure 1E and 1F ) . Interestingly , VC polarity defects were not detected in fmi-1 ( tm306 ) , a strong loss-of-function ( lf ) Flamingo mutant ( A . Steimel and H . Hutter , personal communication ) ( Figure 1G ) . This contrasted sharply with a role for Flamingo in most , if not all , currently known manifestations of PCP mediated through Fz/PCP signaling [11]–[13] . We therefore further assessed the involvement of FMI-1 in VC polarity by examining worms bearing simultaneous losses in both fmi-1 and vang-1 or prkl-1 . Loss of fmi-1 in a vang-1 ( lf ) or prkl-1 ( lf ) background resulted in a mild but significant suppression of VC polarity defects ( Figure 1G ) . This finding suggests that FMI-1 normally promotes neurite growth in VC4 and VC5 , a role which is revealed only when the normally neurite inhibitory roles of VANG-1 or PRKL-1 are removed . Excluding ectopic neurites , we did not observe additional VC4 and VC5 morphology , branching , or pathfinding defects in vang-1 , prkl-1 , or dsh-1 mutants consistent with a specific dysregulation of neurite formation and not general growth cone motility or axon guidance . A survey of major longitudinal and commissural axon tracts also failed to reveal additional wiring defects in vang-1 and prkl-1 mutants ( data not shown ) . This survey included the PLM mechanosensory neurons which have been shown to require a lin-44/Wnt-lin-17/Fz pathway to specify the A/P orientation of axon and dendrite polarity [8] . In lin-44 or lin-17 mutants , PLM polarity is reversed with axon and dendrites projecting in opposite directions . In addition to normal PLM polarity in vang-1 and prkl-1 mutants , the penetrance of lin-17 polarity defects were also not affected in double mutants with vang-1 ( lf ) or prkl-1 ( lf ) ( data not shown ) . Although it is not known if Fz genes act in VC polarity , this finding suggests that distinct Fz pathways regulate neuronal polarity in different subtypes of neurons in C . elegans . Our findings suggest that a PCP-like pathway that includes VANG-1 and PRKL-1 acts to block the emergence of VC4 and VC5 neurites along the A/P axis . Alternatively , we considered the possibility that the polarity phenotype was a secondary consequence of a cell fate change in VC4 and VC5 or VPCs that resulted in the acquisition of VC1–3 and VC6-like characteristics such as A/P-directed neurite growth . Previous work has shown that VC4 and VC5 extend neurites bidirectionally along the A/P axis in mutants affecting VPC induction , such as those in the RAS-MAP kinase pathway [23] . Furthermore , in addition to roles for Wnt/Fz signaling in VPC fate specification [30] , distinct Wnt pathways involving lin-17/Fz , lin-18/Ryk , and a vang-1 pathway that includes egl-20/Wnt and cam-1/Ror regulate VPC polarity [29] . However , several observations suggest that VC polarity phenotypes do not arise from cell fate or VPC polarity defects . First , vang-1 , prkl-1 , and dsh-1 single mutants display normal vulval morphology suggesting normal vulval cell induction and polarity ( Figure S4 ) . Second , we did not find strong VC polarity defects in lin-18 , cam-1 , or egl-20 VPC polarity mutants ( Table S1 ) . Indeed , while approximately 36% of P7 . p VPCs display reversed polarity in lin-18 ( e620 ) mutants [29] , VC4 and VC5 polarity in these animals is largely unaffected ( Table S1 ) . Finally , promoter activity from the cat-1/vesicular monoamine transporter gene ( Pcat-1::GFP ) , a molecular marker that is specifically expressed in VC4 and VC5 but not VC1–3 and VC6 [31] , is largely unaffected in vang-1 and prkl-1 mutants ( Table S1 ) . This contrasts sharply with a prominent role for a β-catenin-dependent lin-44/Wnt-lin-17/Fz pathway in promoting cat-1 gene expression in VC4 and VC5 ( Table S1 ) . The similar polarity defects displayed by vang-1 , prkl-1 , and dsh-1 mutants suggest that these genes act in a common pathway to block neurite growth . To test this notion , we examined double mutant combinations , reasoning that if two genes act in the same pathway , then the resulting defects should not be more severe than those of the strongest single mutant . The molecular lesions in vang-1 ( tm1422 ) , prkl-1 ( zy11 , ok3182 ) , and dsh-1 ( ok1445 ) are predicted to generate early terminations in gene products and are thus likely to generate strong reduction or loss of gene function ( Figure S2 ) . We found that the penetrance of A/P-oriented VC4 and VC5 neurites in vang-1; prkl-1 double mutants was not more severe than prkl-1 single mutants consistent with vang-1 and prkl-1 acting in the same pathway . Indeed , we found a small but significant ( P>0 . 01 ) suppression of the polarity phenotype in these animals compared to loss of prkl-1 alone ( Figure 1F ) . In contrast , the proportion of bipolar VC4 and VC5 neurons displaying an A/P-bidirectional neurite orientation increased in vang-1; dsh-1 and dsh-1; prkl-1 double mutants compared to single mutants ( Figure 1F ) . However , we found that these double mutants , unlike single mutants or vang-1; prkl-1 double mutants , displayed vulval morphology defects ( Figure S4 ) , suggesting that part or all of this increase may be secondary to underlying defects in VPC specification . Alternatively , in addition to blocking A/P-directed neurite growth , vang-1 and prkl-1 may also act in parallel with a dsh-1-containing pathway to inhibit default A/P polarity and/or promote neurite growth along the L/R vulval axis . Therefore , it is not clear at this time if dsh-1 acts in the same pathway as vang-1 and prkl-1 or in a parallel pathway to block A/P-directed neurite growth in VC4 and VC5 . We next made transgenic animals carrying GFP transcriptional fusions to determine vang-1 , prkl-1 , and dsh-1 expression patterns during the period of neurite extension and pathfinding in L4 . Pvang-1::GFP and Pdsh-1::GFP reporters each contain at least 3 kb of promoter region upstream of the ATG start site fused to GFP . The Pprkl-1::GFP reporter contains a 9 . 2 kb genomic region fused in-frame to GFP at exon 3 ( Figure 2A ) . In L4 larvae , vang-1 and dsh-1 promoter activity was detected in many head and ventral cord neurons , some bilaterally located mid-body neurons including the HSNs , as well as non-neuronal tissue such as somatic gonad cells , uterine cells , vulval muscle and vulval epithelial cells ( Figure 2C and 2D ) . Co-expression with the VC1–6 specific reporter Plin-11::RFP [32] revealed that the subset of ventral cord neurons expressing vang-1 and dsh-1 includes all six VC neurons . Interestingly , in contrast to the more widespread neuronal expression of vang-1 and dsh-1 during L4 , Pprkl-1::GFP was only consistently detected in VC1–6 and the HSN neurons and a small subset of neurons in the head and ventral nerve cord ( Figure 2B ) . In non-neuronal tissue , prkl-1 promoter activity was detected in somatic gonad cells , uterine cells , distal tip cells , and vulval cells ( Figure 2B ) . These findings indicate that vang-1 , prkl-1 , and dsh-1 are expressed in the VC neurons as well as the vulval guidepost cells that act as VC intermediate target cells during pathfinding . We also made GFP-tagged genomic translational fusions but these constructs ( Ex[prkl-1 ( + ) ] , Ex[vang-1 ( + ) ] , and Ex[dsh-1 ( + ) ] ) disrupted embryonic development when over-expressed from transgenic arrays . At lower expression levels , these constructs could restore normal VC polarity in PCP mutants ( Figure 2E ) and , despite generally weaker fluorescence , displayed the same VC and vulval cell expression patterns observed with transcriptional reporters . To determine if vang-1 , prkl-1 , or dsh-1 act cell autonomously to regulate neuronal polarity , we assessed the ability of extrachromosomal arrays bearing genes expressed from either neuronal or epithelial specific promoters to restore normal VC4 and VC5 polarity in mutant animals . Neuronal specific expression was realized using the unc-4 promoter which is active transiently in a subset of ventral cord neurons in the early larva and continuously in VC1–6 beginning in L3 [33] and the cat-1 promoter which is active in VC4 and VC5 beginning in early L4 [31] . Epithelial specific expression was obtained using the col-10 or ajm-1 promoters which are active in all epithelial cells including vulval cells [34] , [35] . In each case , proteins were also N-terminally ( VANG-1 and PRKL-1 ) or C-terminally ( DSH-1 ) fused to GFP to confirm gene expression in vivo . We found that prkl-1 expressed in neurons but not epithelial cells restored normal polarity in mutant animals similar to that achieved using a genomic prkl-1 construct ( Figure 2E ) . In contrast , both neuronal and epithelial specific-expression of vang-1 and dsh-1 rescued polarity phenotypes , although with the exception of Pcol-10::dsh-1 , less strongly than the genomic constructs ( Figure 2E ) . Interestingly , Pcat-1::vang-1 expression did not rescue vang-1 ( lf ) polarity defects . This may be explained by the later onset of Pcat-1 expression in VC4 and VC5 compared to Punc-4 and may imply that VANG-1 is required at an earlier stage than PRKL-1 or DSH-1 to regulate polarity . Combined , these data suggest that prkl-1 acts autonomously and that vang-1 and dsh-1 act both autonomously and non-autonomously to inhibit inappropriate VC4 and VC5 neurite growth . These findings mirror those in other PCP models that involve prominent roles for both cell autonomous and non-cell autonomous gene activity [12] , [36] . In addition to VC1–6 , the unc-4 promoter is also expressed at early stages in the DA and VA motor neurons that mediate backward movement [33] . Interestingly , Punc-4::GFP::prkl-1 expressing lines , but not Punc-4 driven vang-1 and dsh-1 lines , displayed a variable but strong inability to move backwards ( data not shown ) , suggesting a specific ability of ectopic prkl-1 expression to compromise the function of the DA and/or VA neurons . VC neurons are born during L1 with neuritogenesis and neurite outgrowth delayed until the late L3 stage after the onset of vulval development [22] ( Figure 3A and Figure S1 ) . Vulval organogenesis begins with the induction and subsequent divisions of three vulval precursor cells ( P5 . p , P6 . p , P7 . p ) followed by morphogenetic movements to generate the mature vulva [37] . To understand how A/P versus L/R neuronal polarity is established in VC4 and VC5 and to determine when polarity defects first manifest in PCP mutants we monitored VC neuritogenesis in wild-type and mutant animals at specific milestones during early vulval development: the 1-cell P6 . p VPC stage and following two P6 . p divisions , the 2-cell and 4-cell stages [37] ( Figure 3A and 3B ) . An early morphological marker of neurite emergence is the formation of polarized leading edge protrusions [3] . These were visualized using a soluble GFP expressed from the VC1–6 reporter transgene cyIs3[Punc-4::GFP] . Neuritogenesis was followed in the vulval-proximal VC4 and VC5 neurons and VC3 , a representative vulval-distal neuron . At the 1-cell P6 . p stage , approximately 40–60% of VC3 , VC4 and VC5 neurons displayed bipolar morphologies with anterior and posteriorly-directed protrusions . After each subsequent P6 . p division , the proportion of bipolar A/P-elongated morphologies was seen to increase in VC3 neurons . In contrast , during this period , an increasing proportion of VC4 and VC5 neurons polarized growth cone protrusions exclusively toward the vulval axis of symmetry ( Figure 3C ) . By the 4-cell P6 . p stage , we found that L/R-bipolar morphology was established by bifurcation of these protrusions into two neurites that extend or squeeze laterally around the nearest VPC ( Figure 3B and Figure S1 ) . This nascent L/R polarity is maintained during the later morphogenetic VPC migrations and fusions that generate the mature vulva ( Figure S1 ) . These observations suggest that the mature L/R polarity of VC4/VC5 neurite extension is established after an earlier polarity-breaking step in which default bipolar protrusions along the A/P axis are converted into unipolar protrusions directed towards the vulval axis of symmetry and their subsequent consolidation into neurites growing laterally along the vulval epithelium . In vang-1 and prkl-1 mutants , the morphology of VC4 and VC5 neurons resembled wild-type at first , but by the 4-cell P6 . p stage displayed significantly more ( p<0 . 05 ) A/P-elongated bipolar morphologies compared to wild-type ( Figure 3D and 3E ) . Interestingly , in mutant animals , the proportion of VC4 and VC5 neurons that displayed misoriented protrusions at this early stage appeared to be less than the proportion that displayed ectopic neurites in adults . For example , in prkl-1 mutants , approximately 50% of VC4 and VC5 neurons displayed protrusions directed away from P6 . p vulval cells at the 4-cell stage compared to approximately 95% with A/P-directed neurites in adults . A subsequent comparison of polarity phenotypes at early , mid-L4 , and adult revealed a gradual loss in polarized neuronal morphology as manifested by an increase in the proportion of A/P-directed ectopic neurites with developmental time ( Figure 3F ) . These findings suggest that the predominant role of PCP-like signaling in VC4 and VC5 is to maintain neuronal morphology by blocking neurite formation away from vulval guidepost targets during and/or following a bipolar to unipolar reorientation of neuronal polarity along the A/P axis . Because many VC4 and VC5 neurons polarize normally during neuritogenesis in PCP mutants , the initial switch from a default A/P bipolar morphology to one oriented unidirectionally toward the vulva may not involve PCP-like signaling . Loss of vang-1 , prkl-1 , or dsh-1 disrupts polarity maintenance in VC4 and VC5 as shown by a bipolar to multipolar change in neuronal morphology . We therefore asked if overexpression of these components in VC4 and VC5 could suppress neurite formation to instruct a shift in polarized morphology . Alternatively , if vang-1 , prkl-1 , or dsh-1 are simply required to maintain polarity once independently established , overexpression of these genes would not be expected to affect neurite number or growth . To distinguish between these possibilities , we crossed transgenic arrays carrying our Punc-4-driven GFP fusions , shown to support protein expression at levels that restore gene function in mutants ( Figure 2E ) , into wild-type backgrounds . We found that 16–19% of neurons expressing Punc-4::prkl-1 were unipolar compared to 4–8% expressing vang-1 , 1–2% expressing dsh-1 , and 2% expressing a GFP control ( Figure 4A and 4D ) . In contrast , Punc-4-driven vang-1 and dsh-1 but not prkl-1 resulted in tripolar neurons that resembled the loss-of-function ( 20–28% compared to 0% expressing prkl-1 or GFP alone ) ( Figure 4B–4D ) . This finding is consistent with previous reports showing that , in some cases , both loss and hyperactivation of PCP proteins lead to similar polarity phenotypes [38] . Remarkably , excluding the complete loss or addition of a neurite ( defined as a projection of at least 3 VC cell body diameters in length ) , enforced vang-1 , prkl-1 , or dsh-1 expression , like the loss-of-function mutants , did not result in L/R pathfinding defects along the vulval epithelium or disorganized neuronal morphology consistent with a specific disruption in neurite formation and not general growth cone motility or neuronal homeostasis . These findings suggest that PRKL-1 activity but not VANG-1 or DSH-1 is sufficient to suppress neurite formation in VC4 and VC5 . This notion is supported by the ability of a genomic prkl-1 ( + ) transgene , which results in a 30% unipolar phenotype when expressed in a wild-type background , to suppress the tripolar phenotype in vang-1 and dsh-1 mutants ( Figure 4E ) . Together , these findings are consistent with a role for PCP-like signaling , and in particular , PRKL-1 , in maintaining polarized neuronal morphology by actively suppressing inappropriate neurite growth rather than a more permissive ‘locking in’ role once polarity has been established . Asymmetric membrane distributions of core PCP proteins are an important feature of PCP signaling in epithelial cells [36] . Likewise , polarized protein distributions in VC4 and VC5 may provide some insight into how neurite formation along the A/P axis is suppressed . We therefore examined the distribution of VANG-1 and PRKL-1 GFP fusions in VC4 and VC5 during the late L3 4-cell ( P6 . pxx ) stage when polarity defects first manifest . GFP fusions were expressed from genome integrated versions of the extrachromosomal Punc-4 transgenes used for rescue and overexpression studies . The ability of these fusions to restore normal polarity in PCP mutants suggest that they are able to substitute for endogenous protein function and therefore act as reasonable surrogates from which to infer endogenous localization patterns . We found that both GFP::VANG-1 and GFP::PRKL-1 were localized symmetrically in punctate patterns on the plasma membrane of VC4 and VC5 ( Figure 5A and 5B ) . Patterns varied from a uniform distribution along the entire membrane , including lamellipodial and neurite-like protrusions , to individual or patches of puncta distributed , in most neurons ( n>20 neurons examined per fusion ) , without apparent polarization . These localization patterns were substantially unchanged when fusion proteins were expressed in various mutant backgrounds ( n>20 neurons examined per background ) ( Figure 5C and 5D ) . Notably , PRKL-1 localization to the membrane did not appear to be appreciably affected in a vang-1 ( lf ) background . This contrasts with findings in Drosophila cells where Van Gogh has been shown to recruit Prickle to the cell membrane during PCP activation [39] , [40] . However , it is consistent with our observation that prkl-1 overexpression can restore normal VC polarity in vang-1 and dsh-1 mutants suggesting that elevated PRKL-1 activity is sufficient to bypass the requirement for VANG-1 or DSH-1 during polarity signaling . Although we cannot exclude a recruitment role for VANG-1 under physiological conditions , in this situation , PRKL-1 membrane insertion may be mediated through its C-terminal CAAX farnesylation signal or its PET domain , which has recently been shown to be sufficient for membrane localization [41] . These findings suggest that polarized membrane distributions of VANG-1 or PRKL-1 do not predict sites of neurite formation or suppression; however , because asymmetries could be masked by non-physiological expression levels from transgenic arrays , a more definitive understanding requires an examination of endogenous protein distributions . It is important to note however , that polarized protein distributions are not a prominent feature of other PCP-like processes such as muscle fibre elongation and may thus reflect divergent mechanisms used to coordinate polarized activity among many cells in an epithelial sheet or tissue compared to more loosely packed or individual cells [11] , [42] . The distinct phenotypes generated by a prkl-1 loss ( too many neurites ) and gain-of-function ( too few neurites ) and the finding that PRKL-1 is sufficient to suppress neurite formation in VC4 and VC5 suggests a key role for PRKL-1 in organizing VC polarity along the A/P axis . If correct , prkl-1 overexpression in VC1–3 or VC6 should act to suppress neurite growth and thereby reorient polarity along the A/P axis . To test this , we again utilized transgenic arrays carrying Punc-4::prkl-1 to drive gene expression in all VC neurons continuously beginning in L3 prior to neurite extension . These arrays were crossed into wild-type or PCP mutant backgrounds containing cyIs4[Pcat-1::GFP] and zyIs1[Plin11::RFP] to visualize VC4 and VC5 ( GFP ) and VC1–6 ( RFP ) respectively . For this experiment we could only unambiguously assess the polarity of VC6 as VC1–3 neurites display extensive overlap along their lengths and therefore cannot be individually visualized ( Figure 6A ) . We found that prkl-1 overexpression in VC6 led to a dramatic asymmetric neuronal morphology in which the anteriorly-directed neurite was either absent ( 14–16% ) or shortened ( 11% ) compared to the posteriorly-directed neurite ( 0% absent and 1–3% shortened ) ( Figure 6B , 6C , and 6F ) . Strikingly , in a vang-1 ( lf ) background , prkl-1 overexpression resulted in the reverse phenotype in which the posteriorly-directed neurite was either absent ( 2–4% ) or shortened ( 19–21% ) compared to the anteriorly-directed neurite ( 1% absent and 2% shortened ) ( Figure 6D , 6E , and 6F ) . Polarity was unaffected when vang-1 was overexpressed in VC6 ( Figure 6F ) . In a dsh-1 ( lf ) background , prkl-1 overexpression also affected posterior neurites ( 2–3% absent , 17–20% shortened ) more severely than anterior neurites ( 6–8% absent , 1–4% shortened ) ( Figure 6F ) . These findings suggest that PRKL-1 is sufficient to inhibit neurite growth in VC6 and thereby shape neuronal morphology , but in a manner that involves VANG-1 and DSH-1 to orient inhibition along the A/P axis . Moreover , the ability of elevated levels of PRKL-1 to polarize VC6 is suggestive of an instructive role and further supports the notion that polarity maintenance in VC4 and VC5 involves a mechanism that actively blocks neurite emergence . Interestingly , PRKL-1-induced anterior neurite growth defects in VC6 appear to be only partially suppressed in a dsh-1 ( lf ) background . This result may be explained if DSH-1 also acts downstream of PRKL-1 or normally antagonizes PRKL-1 activity . A striking feature of vang-1 , prkl-1 , and dsh-1 mutants is the large proportion of VC4 and VC5 neurons that undergo normal morphological polarization early , but fail to maintain polarity during development and in the adult ( Figure 3F ) . This situation may arise due to a transient requirement for these components before or during neuritogenesis which then indirectly maintains neuronal polarity at later stages of development . Alternatively , these components may be required persistently during development to actively maintain polarized morphology . To distinguish between these possibilities , we utilized a temporally-inducible RNAi approach to inactivate prkl-1 after the establishment of normal VC4 and VC5 polarity . We reasoned that ectopic neurites would only be observed if PRKL-1 was required persistently to maintain polarity . Temporal control of prkl-1 knock-down was achieved using an extrachromosomal transgene bearing sense and anti-sense prkl-1 sequences under the control of the hsp16-2 heat shock-inducible promoter [43] . This approach allows prkl-1 RNAi to be temporally induced in transgenic animals by a heat-shock that triggers the co-expression of prkl-1 sense and antisense transcripts throughout the animal . We found that prkl-1 RNAi induction at the mid-L4 stage , after VC4 and VC5 neurons had undergone normal polarization and begun laterally-directed neurite extension along the vulva , resulted in the emergence of new A/P-directed neurites ( ∼40% ) in adult animals compared to non-transgene heat shocked ( ∼1% ) or transgene-containing non-heat shocked ( ∼1% ) controls ( Figure 7 ) . These findings strongly support the notion that active PCP-like signaling or at least PRKL-1 activity is required persistently during development to maintain neuronal polarity .
Planar polarity refers to the polarization of cells along a specific axis of a two-dimensional plane . This definition is consistent with a process that ensures that the polarity of initial neurite emergence is restricted to a specific tissue axis . VC neurons , which appear to be morphologically similar during early larval stages , undergo a symmetry-breaking event during neuritogenesis which ultimately results in the bidirectional extension of VC1–3 and VC6 neurites along the A/P axis and VC4 and VC5 neurites laterally along the L/R axis of the developing vulva , an intermediate target path during organ innervation . We show that a PCP-like pathway in VC4 and VC5 contributes to the maintenance of this differential polarity by suppressing neurite formation along the A/P axis after normal polarization toward vulval guidepost cells ( Figure 8 ) . Loss of the core PCP genes vang-1/Van Gogh , prkl-1/Prickle , or dsh-1/Dishevelled results in the extension of VC4 and VC5 neurites along both the L/R organ and A/P body axes . During late L3 , prior to neurite extension , we found that VC4 and VC5 undergo a transition from a unipolar or bipolar morphology with anterior and/or posteriorly-directed leading edge protrusions into an exclusively unipolar one directed along the A/P axis towards the vulval center axis . L/R polarity is subsequently established after bifurcation of this polarized leading edge and lateral extension along vulval epithelial cells . For the most part , this earlier morphogenetic remodeling in the A/P plane , presumably due to vulval-derived polarization cues , proceeds normally but fails to be maintained in PCP mutants , giving rise to supernumerary neurites . These changes in leading edge formation during VC neuritogenesis evoke comparisons to PCP-dependent polarized cytoskeletal changes in migrating mesenchyme [17] , [44] , [45] . During this process , PCP disturbance results in lamellipodia that are randomized or not aligned with the normal axis of polarity causing a disruption in intercalation or migratory movements [17] , [44] , [46] . In worms , PCP gene loss does not lead to randomized lamellipodial protrusions but likely renders VC4 and VC5 responsive to default A/P neuritogenic or guidance cues which promote neurite growth along the A/P axis . Although the default polarity cues for VC neurons have yet to be identified , they are inferred from cell ablation studies that show VC4 and VC5 display VC1–3 and VC6-like A/P bidirectional polarity in vulva-less animals [23] . We found that vang-1 and dsh-1 act autonomously and non-autonomously to regulate VC polarity . In contrast , prkl-1 is required exclusively in VC neurons . Several findings position PRKL-1 as a key regulator of polarity signaling in VC4 and VC5 . First , prkl-1 polarity defects ( supernumerary VC4 and VC5 neurites ) are more severe than those of vang-1 or dsh-1 at all stages examined . Second , prkl-1 overexpression but not vang-1 or dsh-1 is sufficient to suppress neurite formation in VC4 and VC5 and restore normal polarity in vang-1 and dsh-1 mutants . Indeed , while vang-1 and dsh-1 display similar loss and gain-of-function phenotypes , loss and gain of prkl-1 result in distinct and opposite effects on VC4 and VC5 polarity ( gain or loss of a neurite respectively ) . Third , prkl-1 but not vang-1 overexpression in the vulval-distal VC6 neuron inhibits neurite growth to generate a bipolar to unipolar morphology change along the A/P axis that resembles the transition in VC4 and VC5 polarity towards VPCs during neuritogenesis . Strikingly , except for the addition or loss of a neurite , neither loss nor gain-of-functions in vang-1 , prkl-1 , or dsh-1 affect other aspects of VC4 and VC5 wiring such as axon guidance along the vulval epithelium or terminal arborization , suggesting highly specific roles in regulating nascent neurite emergence . Superficially , the fact that enforced prkl-1 expression is sufficient to suppress neurite growth in the absence of vang-1 and dsh-1 and genetic interactions that place vang-1 and prkl-1 in the same genetic pathway are consistent with a downstream role for PRKL-1 during at least some aspects of neuronal polarity signaling . However , while PRKL-1 is sufficient to inhibit neurite growth in a vang-1 and dsh-1-independent manner , the orientation of neurite inhibition along the A/P axis in VC6 is influenced by vang-1 and dsh-1 activity , suggesting that genetic interactions among these genes are likely more complex than a simple linear relationship . The finding that PRKL-1-GFP expression in VC neurons displayed a non-polarized cortical distribution in both wild-type and vang-1 ( lf ) and dsh-1 ( lf ) backgrounds argues against a simple explanation where VANG-1 and/or DSH-1 instruct PRKL-1 localization . Furthermore , although we did not investigate it further in this study , the possibility that the mature L/R-polarity of VC4 and VC5 may be specified through the partially redundant activities of a VANG-1-PRKL-1 pathway and a second DSH-1-containing pathway provides an additional level of complexity to polarity signaling in VC neurons . In the Drosophila wing and thorax , the transmembrane proteins Frizzled , Van Gogh , and Flamingo interact directly across cell boundaries to propagate polarity information and thereby align the polarity of neighboring cells [12] , [36] , [47] , [48] . The autonomous and non-autonomous activities of vang-1 and dsh-1 suggest that cell-cell interactions may also be involved in polarizing VC4 and VC5 . vang-1 and dsh-1 expression in vulval cells is consistent with non-autonomous activity residing in the vulval epithelial cells that act as guidepost cells during organ innervation . Of the six VC neurons , only VC4 and VC5 are in continuous contact with their intermediate vulval cell targets throughout the period of neuritogenesis and neurite extension . Therefore , it is reasonable to speculate that these contacts contribute to multiple aspects of VC4 and VC5 identity , including the polarity of neurite emergence . Given this caveat , the finding in VC6 that the orientation of PRKL-1-induced neurite inhibition along the A/P axis is vang-1 and dsh-1-dependent is consistent with the notion that VANG-1 and DSH-1 in VC4 and VC5 may act to align the output of PCP-like polarity signaling ( neurite inhibition ) to a specific directional vector ( away from the vulva ) . Furthermore , since vang-1 , prkl-1 , and dsh-1 are expressed in all VC neurons , vulval cell-VC communication , which may be absent in VC6 , may play an important role in differentially activating and orienting neurite inhibition in VC4 and VC5 . It is not known if any combination of the four worm Fz genes act in VC polarity given the confounding effects of multiple Fz loss on vulval cell specification and polarity . Loss of fmi-1 , the sole worm Flamingo gene , does not affect VC morphology in a wild-type background but suppresses ectopic neurite extension promoted by loss of vang-1 and prkl-1 . This contrasts with the similar phenotypes displayed by Van Gogh , Prickle , and Flamingo mutants on planar polarized epithelia in flies and mammals [11]–[13] , suggesting that core PCP components may be utilized differently to affect polarity control in VC neurons . Interestingly , the finding from cell-specific rescue studies that epithelial derived VANG-1 can repolarize VC4 and VC5 neurons that are presumably vang-1 ( lf ) suggests that a transmembrane protein on VC neurons capable of receiving polarity information remains to be identified . At present , the PCP-like mechanism that maintains neuronal polarity by blocking neurite emergence along one directional vector but not another is not known . An attractive possibility is that PCP-like signaling acts to silence or override autonomous VC responses to default A/P guidance cues in order to limit responses to vulval-derived ones . Such switches in neuronal responses are a prominent feature of growth cone navigation at intermediate target sites where growth cones often encounter multiple guidance cues [49] , [50] . We propose a model in which PCP-like proximal interactions between vulval guidepost cells and VC4 and VC5 activate a PRKL-1-dependent effector pathway in VC4 and VC5 that acts persistently to maintain morphological polarization laterally along the vulval epithelium by actively blocking neurite emergence along the orthogonal A/P axis . This model is supported by PRKL-1 overexpression in VC6 which may mimic PCP-like signaling activation , presumably not normally encountered in a vulval-distal VC neuron , and thereby suppress neurite growth along the A/P axis . Future studies will be required to elucidate the molecular and cellular interactions involving PCP pathway components and how these interactions ultimately lead to cytoskeletal rearrangements that inhibit nascent growth cone formation . RhoA and its associated kinase ROCK are key effectors driving PCP signaling-dependent cytoskeletal changes in migrating mesenchyme [17] , [44] , [45] . Given that Rho and ROCK are also known to inhibit neurite growth and promote growth cone retraction in cultured neurons and in vivo [51] , [52] , it will be interesting to determine if these molecules also regulate neurite growth inhibition in VC neurons . A better understanding of how polarity control in VC neurons is achieved will also require the future elucidation of both the vulval-derived and default A/P neuritogenic or guidance cues that promote VC neurite outgrowth . The recent discovery that extracellular guidance cues such as netrins and slits specify the site and direction of initial neurite emergence in vivo provided key insight into our understanding of neuronal polarity . Our study provides evidence that the maintenance of neuronal polarity in vivo also involves mechanisms that specifically inhibit inappropriate neurite formation along specific directional vectors that are otherwise permissive for neurite growth . These findings suggest that PCP-related signaling in higher organisms may also be involved in blocking extraneous neurite formation in developing or mature neurons that may be exposed to multiple neuritogenic or polarity cues .
Worms were maintained at 20°C unless otherwise specified on E . coli-seeded nematode growth medium plates as described ( 26 ) . The N2 strain was used as wild-type . The following alleles were used: LGX: vang-1 ( zy2 , tm1422 ) , lin-18 ( e620 ) , bar-1 ( ga80 ) . LGI: lin-44 ( n1792 ) , lin-17 ( n677 ) , mig-1 ( n687 ) , mom-5 ( or57 ) . LGII: dsh-1 ( ok1445 ) , mig-5 ( tm2639 ) , cam-1 ( gm122 ) . LGIV: prkl-1 ( zy11 , ok3182 ) , egl-20 ( n585 ) . LGV: fmi-1 ( tm306 ) , cfz-2 ( ok1201 ) . VC4 and VC5 were visualized with cyIs1[Pcat-1::GFP] ( X ) or cyIs4[Pcat-1::GFP] ( V ) . VC1–6 were visualized with cyIs3[Punc-4::GFP] or zyIs1[Plin-11::RFP] . The Gene CATCHR yeast homologous recombination method [53] was used as described to generate GFP translational fusions of VANG-1 , PRKL-1 , and DSH-1 while maintaining exon-intron genomic structure . Briefly , ∼9 . 9 kb of vang-1 ( 5′-gctctgtagagg…aaatcgagagta-3′ ) , ∼15 kb of prkl-1 ( 5′-tgcattcttcgg…ttcgtaatgccc-3′ ) , and ∼15 . 8 kb of dsh-1 ( 5′-gatcaatcgtgg…tttgcttctgcc-3′ ) containing genomic regions were recombined from YAC strains Y7110 , Y40D6 , and Y54E1 ( gifts from A . Coulson ) , respectively . A GFP cassette was then inserted in-frame following the ATG start codons of vang-1 , prkl-1 ( isoform A ) , and dsh-1 ( isoform C ) to generate genomic vang-1 ( + ) , genomic prkl-1 ( + ) , and genomic dsh-1 ( + ) , respectively . These constructs were injected , using standard methods ( 28 ) , at 5 ng µl−1 , 1 ng µl−1 and 10 ng µl−1 respectively along with 40 ng µl−1 of a Podr-1::dsRed co-transformation marker into their respective vang-1 ( tm1422 ) , prkl-1 ( zy11 ) , or dsh-1 ( ok1445 ) mutant backgrounds to generate the extrachromosomal arrays named [vang-1 ( + ) ] , [prkl-1 ( + ) ] , and [dsh-1 ( + ) ] , respectively . 3 kb of vang-1 upstream sequence was PCR-amplified from wild-type genomic DNA using primers 5′-tatgtcgactttctgggttcgtcttgagttac and 5′-ttactgcagttgatacgacatgttccacctg and inserted into pPD95 . 77 ( a gift from A . Fire ) to generate the Pvang-1::GFP . Similarly , 4 . 3 kb upstream of dsh-1 ( isoform C ) was amplified using 5′-cctgtcgacgatcaatcgtggagcacatc and 5′ctggatccgtttggagcatttaatgacg to generate Pdsh-1::GFP . A PCR overlap extension protocol was used to make Pprkl-1::GFP by fusing a 9 . 2 kb prkl-1 genomic region ( 5′-accagaatttcc…gatgttctactt-3′ ) ending in exon 3 in-frame to the GFP-unc-54 3′UTR cassette in pPD95 . 77 . 4 kb of lin-11 promoter was PCR-amplified using primers 5′atactgcagcccgactaaatccgacaattccg and 5′ttaggtacctgagaagggagtaaaaggaggag from genomic DNA , and inserted into pPD95 . 77 . Plin-11::RFP was generated from this clone by swapping the GFP cassette with RFP . Pvang-1::GFP and Pdsh-1::GFP were individually injected at 40 ng µl−1 with 20 ng µl−1 of Plin11::RFP to label VC1–6 and 40 ng µl−1 of Podr-1::dsRed as a co-transformation marker into wild-type worms to generate [Pvang-1::GFP; Plin-11::RFP] and [Pdsh-1::GFP; Plin-11::RFP] transgenes . Pprkl-1::GFP was each injected at 30 ng µl−1 with 40 ng µl−1 of Podr-1::dsRed into wild-type worms . Plin-11::RFP was injected at 20 ng µl−1 with 50 ng µl−1 of pRF4 ( rol-6gf ) and integrated using UV irradiation ( UVP CL-1000 cross-linker , 254 nm , 3 . 25×104 µJ cm−2 ) to generate zyIs1 . vang-1 and dsh-1 cDNAs were PCR-amplified from yk211g1 and yk291a11 ( isoform C ) templates ( gifts from Y . Kohara ) , respectively . A prkl-1 cDNA ( isoform A ) was obtained by RT-PCR using N2 RNA extracts . unc-4 , cat-1 , ajm-1 , and col-10 promoters were PCR-amplified ( primer sequences available upon request ) from previously described plasmids [26] . A vang-1 cDNA , amplified using primers 5′cgcggatccatgtcgtatcaagataacaggaaac and 5′-tgctctagaacaaatcaaactgccgactcattgc , was inserted into pSK ( Stratagene ) . Into this vector , we inserted a SacI-digested vang-1 genomic fragment containing two introns and the 3′UTR PCR-amplified from genomic DNA using primers 5′aacaaccagatggactcactgaccg and 5′aacgagctcccgagactttttgtgtaatccaac . A PCR-amplified GFP cassette from pPD95 . 77 was then inserted in-frame into the BamHI site immediately upstream of the vang-1 ATG start to generate GFP::vang-1 . Cell-specific promoters were inserted immediately upstream of GFP::vang-1 to generate Punc-4::GFP::vang-1 , Pcat-1::GFP::vang-1 , and Pajm-1::GFP::vang-1 , respectively . A prkl-1 cDNA , amplified using primers 5′-catctagaatgagcgaacgaattcgccgtc and 5′caggatcctcaagatactgtacatctggaac , was inserted into XbaI/BamHI of a GFP-less pPD95 . 77 vector . A GFP cassette from pPD95 . 77 was then inserted in-frame into SalI/XbaI upstream of the prkl-1 ATG start to generate GFP::prkl-1 . Cell-specific promoters were inserted upstream of GFP::prkl-1 to generate Punc-4::GFP::prkl-1 , Pcat-1::GFP::prkl-1 , and Pcol-10::GFP::prkl-1 , respectively . dsh-1 cDNA was amplified using primers 5′tatctgcagatggccgagtctccacctcc and 5′-ctacatcccgggacatactcgtatctttgtcc and inserted into PstI/SmaI pPD95 . 77 to generate dsh-1::GFP . Cell-specific promoters were inserted into dsh-1::GFP to generate Punc-4::dsh-1::GFP , Pcat-1::dsh-1::GFP , and Pcol-10::dsh-1::GFP respectively . All constructs were injected at 10–50 ng µl−1 with 40 ng µl−1 of Podr-1::dsRed into cyIs4 wild-type or their respective vang-1 ( tm1422 ) , prkl-1 ( zy11 ) , or dsh-1 ( ok1445 ) mutant backgrounds to generate the following transgenes: [Punc-4::GFP::vang-1] , [Pcat-1::GFP::vang-1] , [Punc-4::GFP::prkl-1] , [Pcat-1::GFP::prkl-1] , [Pcol-10::GFP::prkl-1] , [Punc-4::dsh-1::GFP] , [Pcat-1::dsh-1::GFP] . All experiments were performed using at least two independent transgenic lines per construct . Extrachromosomal [Punc-4::GFP::vang-1] and [Punc-4::GFP::prkl-1] transgenes for localization studies were integrated using UV irradiation as described above . Worms were immobilized with 10 mM levamisole ( Sigma ) and imaged using an AxioplanII/Apotome or LSM510 confocal microscope and Axiovision software . Worms were staged with respect to vulval developmental milestones visualized using differential interference contrast ( DIC ) microscopy . See Figure S1 for images of developmental stages used in this study . Supernumerary VC4 and VC5 neurites , visualized with cyIs4 , were scored if they were at least equal to the length of a VC cell soma in early-L4 and mid-L4 stage animals ( ∼5 µm ) or a wild-type VC4/5 neurite ( ∼15 µm ) in adults . The orientation of VC cell protrusions , visualized with cyIs3 , during P6 . p VPC ( 1-cell ) and P6 . p daughter ( 2-cell ) and granddaughter ( 4-cell ) L3 stages were determined from Apotome/Axiocam images captured sequentially with fluorescence and DIC optics . Protrusions were defined as any spike-like or ruffle-shaped extension from the cell soma . An A/P bipolar orientation was scored if a VC soma displayed at least one anteriorly and one posteriorly-directed protrusion . Unipolar orientations were scored if a VC soma displayed at least one protrusion directed either anteriorly or posteriorly . VC6 overexpression: Neurites were scored as shortened if an anterior-directed neurite failed to extend all the way to the vulva or if the posterior-directed neurite was less than approximately 60% normal length . Normally , the posterior VC6 neurite is approximately twice the length of the anterior neurite ( see Figure 6A ) . In vang-1 and dsh-1 mutants , only VC6 neurons in which VC6 processes ( labeled with RFP ) could be distinguished from any overlapping supernumerary VC5 processes ( labeled with GFP and RFP ) were scored for defects . In wild-type animals , 17% ( n = 225 ) of VC5 cell bodies are displaced one to two cell body lengths posterior to the vulval epithelium ( for example , see VC5 in Figure 1D and 1E ) . Therefore , to further avoid ambiguities caused by overlapping VC5 neurites , only animals in which the VC5 cell body was located immediately adjacent to the vulval epithelium were scored for VC6 defects ( for example , see Figure 1B and 1C ) . Modifications were made to a previously described transgene-driven and heat shock-inducible RNAi protocol [43] , [54] . A 1 . 1 kb fragment from a prkl-1 cDNA ( not including start codon ) was PCR-amplified using primers 5′attggatcctgtgctttggacgagtatgc and 5′tatggatccgctctttgtggtggttttgg and inserted in sense and antisense orientations into BamHI pPD49 . 78 ( a gift from A . Fire ) downstream of the hsp16-2 promoter to generate Phsp16-2::prkl-1 sense and Phsp16-2::prkl-1 antisense . These plasmids were then co-injected at 35 ng µl−1 each with 40 ng µl−1 of Podr-1::dsRed into cyIs4 worms to generate the [Phsp16-2::prkl-1sense; Phsp16-2::prkl-1antisense] transgene . Coexpression of prkl-1 sense and antisense transcripts was achieved using a 2 hour 35°C heat shock on synchronized mid-L4 stage worms with and without the transgenic array . Transgene and non-transgene bearing sib animals were then scored for ectopic VC4 and VC5 neurites in adults after an incubation of 32–34 hours at 20°C . Semi-quantitative single worm RT-PCR was used to verify RNAi-depletion of prkl-1 mRNA . Total RNA from individual synchronized worms ( chosen blindly from heat shocked and control worms ) was isolated using Trizol reagent ( Invitrogen ) following manufacturer's instructions . Single worm RNA was solubilized in DEPC-treated water and a single step of reverse transcription ( using oligo dT primers ) followed by PCR was performed according to manufacturer instructions ( SuperScript III One-Step RT-PCR System , Invitrogen ) . RT-PCR was performed with gene-specific primer pairs to prkl-1 ( 5′cgaattgcagctgatgctcacag and 5′gatgtaggaagctcatgagagtac ) and the internal control myo-3 ( 5′atgtctggaaatccagacgcattc and 5′cgtggctccaacaatagcgaagtag ) . Data was analyzed by measuring the area and density of the electrophoresis bands using Scion software . Changes in prkl-1 mRNA levels before and after RNAi induction were normalized to myo-3 levels and expressed as arbitrary densitometry units . For each primer pair , cycle times and primer concentrations were optimized to ensure linear amplification .
|
Neurons are among the most morphologically complex cells in the body . Early in development , newly born neurons project one or more processes called neurites that will eventually mature into axons and dendrites . While the genetic determinants that promote neurite emergence along specific trajectories are beginning to be elucidated , the cellular and molecular pathways that prevent inappropriate neurite formation to maintain proper neuronal morphology and prevent superfluous connections are largely unknown . Van Gogh and Prickle dependent-PCP signaling is a well-established regulator of cellular polarity especially along the surface of epithelial cells . In this study , we show that a conserved PCP–like pathway consisting of VANG-1/Van Gogh , PRKL-1/Prickle , and DSH-1/Dishevelled is involved in maintaining the polarized morphology of a subset of neurons in the nematode C . elegans . In particular , we show that loss of PRKL-1 results in neurons with too many neurites while PRKL-1 overexpression results in too few neurites . Our findings suggest that mechanisms that specifically block inappropriate neurite formation may be required to ensure proper neuronal connectivity in higher organisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"model",
"organisms",
"genetics",
"biology",
"neuroscience",
"genetics",
"and",
"genomics"
] |
2011
|
VANG-1 and PRKL-1 Cooperate to Negatively Regulate Neurite Formation in Caenorhabditis elegans
|
Genome-wide association studies ( GWAS ) have generated sufficient data to assess the role of selection in shaping allelic diversity of disease-associated SNPs . Negative selection against disease risk variants is expected to reduce their frequencies making them overrepresented in the group of minor ( <50% ) alleles . Indeed , we found that the overall proportion of risk alleles was higher among alleles with frequency <50% ( minor alleles ) compared to that in the group of major alleles . We hypothesized that negative selection may have different effects on environment ( or lifestyle ) -dependent versus environment ( or lifestyle ) -independent diseases . We used an environment/lifestyle index ( ELI ) to assess influence of environmental/lifestyle factors on disease etiology . ELI was defined as the number of publications mentioning “environment” or “lifestyle” AND disease per 1 , 000 disease-mentioning publications . We found that the frequency distributions of the risk alleles for the diseases with strong environmental/lifestyle components follow the distribution expected under a selectively neutral model , while frequency distributions of the risk alleles for the diseases with weak environmental/lifestyle influences is shifted to the lower values indicating effects of negative selection . We hypothesized that previously selectively neutral variants become risk alleles when environment changes . The hypothesis of ancestrally neutral , currently disadvantageous risk-associated alleles predicts that the distribution of risk alleles for the environment/lifestyle dependent diseases will follow a neutral model since natural selection has not had enough time to influence allele frequencies . The results of our analysis suggest that prediction of SNP functionality based on the level of evolutionary conservation may not be useful for SNPs associated with environment/lifestyle dependent diseases .
Both environmental and genetic factors influence risk of common human diseases , however , the relative significance of genetic and environmental factors in disease etiology differs for different diseases . A number of common human diseases including cardiovascular diseases and type 2 diabetes are believed to predominantly result from changes in lifestyle and environment [4 , 5] Having environment or lifestyle as a major risk factor does not rule out an influence of genetic polymorphisms . An assessment of the effects of selection on the risk alleles of the common diseases stratified by the importance of the environmental/lifestyle component has never been conducted before . A number of methods to detect signatures of recent positive selection have been proposed , including Tajima’s D [6 , 7] , selective sweep [8] , tests based on fixation index used as a measure of population differentiation [9] , haplotype analysis [10] , tests based on the ratio of nonsynonymous and synonymous substitutions [11] , and others [12–15] . The aforementioned methods work well when adaptation is driven by a single polymorphic locus ( monogenic model ) ; however , in the situation when adaptation is driven by multiple loci ( polygenic model ) selection may not produce the classical signature of selective sweep [16] , see also [17] and [18] . Fixation of a beneficial mutation is also strongly affected by temporal variation in population size and selection pressure [19] . Some studies suggest that SNPs with the signature of recent positive selection tag regions associated with common human diseases [20] . Raj et al . 2013 [21] found that several loci linked to the risk of inflammatory diseases carry genomic signatures of recent positive selection . It also has been demonstrated that SNPs associated with the risk of type II diabetes carry signature of recent positive selection [22] . Then again , other studies found no evidence of positive selection at loci linked to common human diseases [23 , 24] . Unfortunately , the cited studies do not provide an answer to the important question whether the SNPs with a signature of recent positive selection have higher likelihood to be detected ( and reported ) as disease-associated compared to those without such signature . Wang and Pike [25] suggested that allelic spectra of SNPs associated with common diseases should be similar to the allelic spectra for the entire human genome ( which basically follow neutral model ) . They built their hypothesis based on the fact that the number of disease loci for common disease is usually high and each locus makes only a minor contribution to a disease . They argue that natural selection has been operating weakly and for a short time , suggesting that the majority of SNPs associated with common disease may be near-neutral . We and others hypothesized that disease-associated SNPs experience negative selection [26–30] . Detection of negative selection is more challenging than detection of recent positive selection because it does not reshape genetic variation in selected region . The main indicator of negative selection is deviation of allelic frequencies from the distribution expected under the neutrality model towards lower values [28 , 31] . Lower minor allele frequency expected as a result of negative selection cannot be estimated for individual SNPs but only for SNP classes , e . g . nonsynonymous or disease risk-associated SNPs . Even though it has been shown that disease associated SNPs tend to occur in evolutionary conserved regions [32] the effect of negative selection on disease risk associated SNPs is poorly understood . Genome-wide association studies are widely used to identify SNPs associated with risk of common diseases . Thousands GWASs have been conducted with the results reported in several databases . One of the most comprehensive databases is the catalogue of published GWASs ( CPGWAS ) [33] ( http://www . genome . gov/gwastudies/ ) . More than 7 , 000 SNPs linked to nearly 5 , 000 genes have been reported in CPGWAS making it a valuable resource to study the role of natural selection in the shaping of genetic variation of common human disease . The goal of our study was to evaluate the effect of positive and negative selection on allelic spectra of SNPs associated with the risk of common human diseases and to assess how allelic spectra differ for environment/lifestyle dependent versus environment/lifestyle independent diseases .
Fig 1 shows the proportions of the SNPs with the signature of recent positive selection across commonly used genotyping platforms . The lowest proportion was on Illumina OmniChip 2 . 5M platform—0 . 58% , and the highest proportion was on Illumina Human Hap550 platform—0 . 91% . The average proportion of SNPs with the signature of recent positive selection across all genotyping platforms was essentially the same as the proportion of SNPs with the signature of recent positive selection among GWAS-detected disease associated SNPs reported in CPGWAS—0 . 75%±0 . 09% versus 0 . 76%±0 . 17% ( x2 = 0 . 09 , df = 1 , P = 0 . 95 ) . A comparison of the proportion of SNPs with the signature of recent positive selection among CPGWAS-reported disease-associated SNPs with proportions of the SNPs on individual platforms demonstrated that it was significantly higher for Illumina 1M platform ( P = 0 . 03 ) and lower for OmniChip 2 . 5M ( P = 0 . 02 ) . The differences , however , became statistically non-significant after multiple testing adjustments . Table 1 shows details of the estimation of the proportions of SNPs with the signature of recent positive selection on the 10 most commonly used genotyping platforms . Therefore we found that among GWAS detected SNPs the proportion of SNPs with evidence of recent positive selection is the same as the proportion of the SNPs on genotyping platforms . This result suggests that recent positive selection does not increase ( or decrease ) the chance that a SNP will be reported as disease-risk associated . This also suggests that reported evidence of positive selection on disease risk associated SNPs [21 , 34 , 35] may largely result from simple random overlap between disease associated and positively selected SNPs . Minor Risk Alleles ( MiRA ) were defined as risk-associated alleles with frequency less than 50% . We used MiRA proportion as an estimator of the effect of negative selection on allelic frequencies . If the probability to be risk-associated or protective does not depend on allelic frequency , the expected MiRA proportion will be 0 . 5 . Table 2 shows MiRA proportions for the diseases with at least 20 CPGWAS reported risk associated SNPs . Analyses of Variance ( ANOVA ) show significant variation of the MiRA proportion among common diseases: F = 2 . 3 , df = 24 , P = 0 . 000001 . The MiRA proportions vary from 0 . 45±0 . 1 for Graves' disease to 0 . 96±0 . 04 for chronic kidney disease . There is a considerable heterogeneity between diseases by GWAS sample sizes . A larger sample size translates into a higher statistical power to detect SNPs with a low minor allele frequency ( MAF ) . However it is unlikely that the sample size will influence the probability that a minor allele will be associated with risk rather than protection . Consistent with this expectation we found that larger studies were more likely to detect rare ( MAF≤0 . 05 ) SNPs ( Spearman rank order correlation = 0 . 14 , N = 1 , 657 , P = 0 . 00002 ) . However , no association was found between the sample size and direction of the effect of minor alleles ( MiRA ) ( Spearman rank order correlation = -0 . 04 , N = 1 , 657 , P = 0 . 56 ) . Text mining is a powerful tool to infer the relationships between diverse biological entities [36 , 37] . We used it to assess the role of environment/lifestyle factors in disease etiology . We cannot simply search for publications linking disease to environmental or lifestyle factors because we will find such publications for any human disease . A more objective measure of influences of environment/lifestyle factors on disease etiology is needed . It is reasonable to suggest that the proportion of papers simultaneously referring to a disease and environment/lifestyle factors will be higher for diseases with strong environment/lifestyle influences . Table 3 shows estimated environment/lifestyle indices ( ELIs ) for the diseases that were targeted by at least 3 independent GWASs . The highest ELIs were detected for obesity and type II diabetes , 112 . 8 and 76 . 4 correspondingly , and the lowest for pancreatic cancer and primary biliary cirrhosis , 10 . 9 and 9 . 6 correspondingly . To test stability of ELI-based ranking of human diseases we extended the ELI by including the additional term: “exposure” . S1 Table shows disease ranking based on ELI and extended ELI . In the ranking based on the extended ELI , lung cancer moved from 29th position to 9th following obesity , asthma , atopic dermatitis , type 2 diabetes , major depressive disorder , melanoma , autism , and attention deficit hyperactivity disorder . Overall disease ranking was similar for ELI and extended ELI ( S1 Fig ) . Out of the top 10 ELI-defined environment/lifestyle dependent diseases all except “bipolar disorder” are also among the top 10 environment/lifestyle dependent diseases defined based on the extended ELI . The correlation coefficient between ELI and extended ELI was 0 . 92 , N = 44 , P = 1 . 6x10-17 . Replacing ELI by extended ELI for disease ranking did not change our conclusions . Analysis of the extended ELI demonstrates that ( i ) the ELI-based approach for identification of environment/lifestyle dependent disease is not perfect and can rank some diseases ( such as lung cancer ) lower than we may think is accurate , and ( ii ) overall ELI based classification provides a sufficiently accurate and robust assessment of environmental and lifestyle related effects on disease risk to capture most known influences on disease risks . Analysis of the human disease with extended ELI suggests that the proposed classification works effectively on the large collection of diseases used in this analysis . We found that risk allele frequencies were higher for environment/lifestyle dependent diseases: Spearman rank order correlation coefficient between risk allele frequencies and ELI was 0 . 1 ( P = 0 . 0002 ) . We further subdivided diseases in tertiles based on ELI and estimated MiRA proportions in each tertile ( Fig 2 ) . There was a significant variation among tertiles by the MiRA proportions: ANOVA-test F = 5 . 02 , df = 2 , P = 0 . 007 . The proportion was highest in the first and lowest in the third tertile . The analysis indicates that the risk alleles for environment/lifestyle dependent diseases tend to be more common compared to the risk alleles for environment/lifestyle independent diseases . We additionally performed nonparametric Spearman rank order correlation analysis . Significant positive association between risk allele frequency and ELI was detected ( Spearman R = 0 . 12 , N = 1547 , P = 4 x 10−6 ) . This result supports the conclusion that risk alleles for environment/lifestyle dependent diseases tend to have a higher frequency compared to the risk alleles for environment/lifestyle independent diseases . To get a more detailed picture of the association between risk allele frequency and disease dependences on environmental/lifestyle factors we assessed risk allele frequency distributions for the diseases from the first , second and third ELI tertiles ( Fig 3a ) . The diseases were selected based on the condition that they have at least 50 reported risk associated SNPs to allow a reliable estimation of the frequency distribution . We took 3 individual diseases from the first tertile ( rheumatoid arthritis , systemic lupus erythematosus and pathological myopia ) and compared them to 3 environment/lifestyle dependent diseases—those from the third tertile ( type 2 diabetes , coronary heart disease and obesity ) . The distributions of the risk allele frequencies for environment/lifestyle independent diseases were asymmetrical and shifted to the left , indicating effect of negative selection ( Fig 3b ) . The distributions of the risk allele frequencies for environment/lifestyle dependent diseases were more symmetrical indicating a weak influence of negative selection ( Fig 3c ) . The differences between environment/lifestyle dependent and independent diseases were more evident when we compared the proportions of risk alleles averaged across diseases ( Fig 3d ) . The distribution of the risk alleles for environment/lifestyle independent diseases was shifted towards a predominance of rarer SNPs while the distribution of proportions of the risk alleles for environmental lifestyle dependent diseases was almost perfectly symmetrical and bell-shaped . Common human diseases may create conditions for positive selection for disease protective alleles , while risk associated alleles are expected to be slightly deleterious [26 , 27 , 30] and therefore to be under pressure of negative selection . Based on these considerations we expected that GWAS detected disease-associated SNPs will show signals of positive and/or negative selection . We found , however , that the proportion of GWAS-detected SNPs with the signature of recent positive selection does not differ from the proportion of SNPs with the signature of recent positive selection on genotyping platforms , suggesting that disease associated SNP have the same chances to be positively selected during the process of GWAS analysis as an average SNP in the human genome . On the other hand , our analysis supports the hypothesis that risk-associated alleles frequently undergo negative selection . We found that risk-associated alleles are more common among minor alleles . The overall distribution of the risk alleles is shifted to lower frequencies indicating an effect of negative selection against risk-associated variants . We further hypothesized that the effects of negative selection on allelic spectra may be different for environment/lifestyle dependent versus environment/lifestyle independent diseases . We found environment/lifestyle dependent diseases tend to have a higher frequency of the risk associated variants suggesting a weaker effect of negative selection . It is widely accepted that the majority of genetic variants in human populations are neutral [38 , 39] . It is also known that selective value of the variants depends on the environment [40 , 41] . A neutral variant may become advantageous ( or disadvantageous ) when environment changes . For example , mutations controlling lactose tolerance were initially neutral and became advantageous about 5 , 000–8 , 000 BC , after domestication of cattle [42 , 43] . It is becoming more and more evident that many common human diseases are caused by changes in environment and/or lifestyle [44–48] . Changes in environment or lifestyle may redefine functional significance of existing neutral SNPs . One can expect that the majority of risk associated variants for environment/lifestyle dependent diseases are recently recruited from the pool of selectively neutral variants . Whether those formerly neutral variants will be risk-associated or disease protective depends on how they influence biology . It is unlikely that direction of the effect ( risk-associated or protective ) of a recently neutral variant will depend on its frequency . Let’s assume , for example , that there is a SNP that slightly modulates the expression level of some gene and its effect is selectively neutral . In this case the frequency of the allele associated with a low expression level is not influenced by selection , so this variant can be minor ( <50% ) or major ( >50% ) . Let’s assume that changes in environment or lifestyle made a low expression level of the gene associated with increased risk . In this scenario the distribution of the risk-associated alleles will initially follow the neutral model even though it is not selectively neutral anymore . It will take time ( tens to hundreds generations , depending on the selective pressure ) for the negative selection to reduce the frequency of the risk alleles . Therefore , even though many formally neutral risk variants are ( currently ) deleterious , their allelic spectra will follow the neutral model for some time . Fig 4 depicts the hypothesis of recently neutral , currently deleterious risk-associated variants . The figure shows a hypothetical example with individual selection coefficients ( upper panel ) and frequency distributions of the risk alleles ( lower panel ) . According to the proposed model , changing environment reassigns selective values of existing SNPs which is indicated by different profile of selection coefficients ( upper panel ) before and after the change in the environment took place . Immediately after changes in the environment the frequency distribution of risk associated alleles is symmetrical . Negative selection against risk alleles reduces their frequencies , shifting the distribution to the left and making it asymmetrical . Environmental diseases are defined as diseases whose incidence can be directly related to effects of environmental factors . Disease-causing environmental factors include but are not limited to stress , physical and mental abuse , diet , exposure to toxins , pathogens , radiation , and chemicals . Many common human diseases are considered to be environmental [49–51] . In the context of this study by environment/lifestyle factors we mean recent ( less than several generations away ) changes in lifestyle and environment . Such changes redefine selective profiles on existing SNPs , but because they are recent , there is not sufficient time for selection to change allelic frequencies . Based on the results of our analysis , risk-associated alleles can be roughly divided into two categories: evolutionarily old and evolutionarily young . Old alleles have a long history of being risk associated so natural selection has had enough time to influence their frequencies . Young risk alleles recently came from the pool of selectively neutral variants and because of that history , selection has not had sufficient time to influence their frequencies . One can expect that alleles associated with the risk of environment/lifestyle dependent diseases will most often be young whereas the alleles associated with the risk of environment/lifestyle independent diseases will more often be evolutionarily old . The proportions of young and old alleles for a given disease can be roughly estimated by comparing the frequency distribution of risk variants with the distribution expected under the null , under which the probability to be risk associated is frequency independent . Currently the frequency distribution of risk variants can be reasonably estimated for a limited number of well-studied diseases only , but with the advance of GWASs this information will be available for more and more diseases . The hypothesis of recently neutral , currently disadvantageous risk-associated alleles has several practical implications . First of all , recently neutral , currently deleterious alleles do not carry a signature of positive or negative selection which makes the prediction of their functionality based on the level of evolutionary conservation questionable . Besides , because frequency spectra of the risk-associated variants follow the neutral model , one may predict the number of risk-associated variants in different frequency groups ( under the neutral model we assume that the effect size is independent of allelic frequency ) which can be used to estimate the sample size required for the detection of SNPs from a specified frequency range . The results of our analysis suggest that the nearly-neutral model is applicable to common disease variants resulting from recent changes in environment and/or life style which convert neutral variants into slightly deleterious ( risk associated ) or advantageous ( protective ) .
SNPs associated with the risk of human diseases were retrieved from the Catalogue of Published Genome-Wide Association Studies ( CPGWAS ) ( http://www . genome . gov/26525384/ ) [33] . The CPGWAS was accessed on December 15 , 2014 . SNPs with reported P-values of 5·10−8 or lower were used in the analysis . All tests for detecting the signature of recent positive selection are quantitative and a decision is made based on specified thresholds [12 , 52] . As a result , the lists of the SNPs with the signature of recent positive selection vary depending on the method and thresholds chosen . We used 24 , 060 SNPs with the signature of recent positive selection reported in the database of positive selection in human populations ( dbPSHP ) [53] . Those SNPs were identified by applying a set of stringent filters that are consistent across 6 most commonly used approaches to detect a signature of recent positive selection: Tajima’s D , Integrated Haplotype Score , Extended Haplotype Homozygosity , Cross-Population Composite Likelihood Ratio , Difference of Derived Allele Frequency , and Fixation Index [53] . Both minor alleles ( those with the frequency < 50% ) and major alleles ( those with the frequency >50% ) can be risk associated—risk alleles . The frequency distribution which includes frequencies of both minor and major alleles of the SNPs by definition will be symmetrical , since the absolute majority of the SNPs ( more than 95% ) are biallelic with one minor and one reciprocal major allele . If a minor allele has the same chance to be risk-associated as the reciprocal major allele , the frequency distribution of the risk alleles should be symmetrical . Note that overall distribution of SNP’s allele frequencies is symmetrical and U-shaped [54] . This is because proportion of rare SNPs in the human genome is higher than proportion of common SNPs . Distribution of the GWAS-detected disease-associated SNPs is bell-shaped because common SNPs are overrepresented on genotyping platforms and also because GWASs are underpowered to detect rare disease-associated SNPs . Negative selection against risk-associated variants will increase the proportion of the risk-associated variants among alleles with minor frequency . We used the proportion of the risk alleles with minor allele frequencies—minor risk alleles ( MiRA ) as an estimator of the effect of negative selection . Under the null hypothesis—rare ( minor ) alleles have the same chances to be risk associated as the reciprocal common ( major ) allele—MiRA proportion is expected to be 0 . 5 . The stronger the negative selection against the risk-associated variant , the higher MiRA proportion will be . We also assessed the distributions of the risk-associated alleles by their frequencies . Risk alleles were binned into 5 frequency ( F ) groups: 0<F<0 . 2 , 0 . 2≤F<0 . 4 , 0 . 4≤F<0 . 6 , 0 . 6≤F<0 . 8 , 0 . 8≤F<1 . We chose 5 groups because it is optimal for the available sample sizes . Data on the population frequency of the risk alleles were from original GWASs . We used reported frequencies of the risk-associated alleles in controls . We did not use ancestral/derived allele status in the analysis even though it has been shown to be relevant to selection and risk of common diseases [55 , 56] . The reason for this was that ancestral/derived status is not available for many SNPs , especially those located in intronic or intergenic regions . Using ancestral information in this analysis would have reduced the number of SNPs we could evaluate and introduce a bias because SNPs would have been excluded from analysis based on the level of evolutionary conservation ( ancestral/derived status information is only available for SNPs located in evolutionary conserved regions , which allows sequence alignment from multiple species [57] ) . Genetic share of the disease risk can be assessed by disease heritability . Estimated disease heritability varies from less than 5% for stomach cancer [58] to almost 90% for type 1 diabetes [59] . Unfortunately estimates of the disease heritability are not reliable [60] and can be confounded by shared environment [61 , 62] . We applied text mining to estimate relative influence of environmental and lifestyle factors on disease etiology . Environment/Lifestyle Index ( ELI ) was used as a measure of the influence of environmental and lifestyle factors on disease etiology . To estimate ELI we first searched PubMed for the disease name , e . g . “rheumatoid arthritis” , and identified papers with disease name in the abstract . Next we identified the number of papers mentioning together disease name and “environment” or “lifestyle” . ELI was computed as the number of the papers mentioning disease name AND environment or lifestyle per 1 , 000 papers mentioning disease name . As an example , there are 120 , 346 abstracts mentioning rheumatoid arthritis , 1 , 578 abstracts mentioning “rheumatoid arthritis” and “environment” , and 393 abstracts mentions “rheumatoid arthritis” and “lifestyle” which give ELI for rheumatoid arthritis: ELIRI = ( 1 , 578+393 ) /120 , 346*1000 = 16 . 4 . Analysis of the temporal dynamics of disease prevalence might be useful in identification of diseases influenced by recent changes in environment and lifestyle . Unfortunately information on disease prevalence is not available for many diseases , especially concerning the temporal dynamics in disease prevalence . This was the reason that we used environment/lifestyle index rather than disease prevalence as a measure of disease dependence of environment/lifestyle . The list of 10 most commonly used genotyping platforms was obtained by reviewing platforms listed on CPGWAS database . For each platform we retrieved the list of SNPs using manufacturers’ data and among them identified SNPs with the signature of recent positive selection from dbPSHP database [53] . Statistical analysis was done using STATA software ( version 10 , StataCorp LP , College Station , TX ) . We used x2 test to compare observed to expected proportions . We applied nonparametric statistical tests , e . g . Spearman rank test , for the datasets with significant deviation from normal distribution .
|
We reviewed several thousand genome wide association studies that were conducted to identify genetic variants influencing risk of human diseases . We tested the hypothesis that single nucleotide polymorphisms ( SNPs ) that influence disease risk undergo positive or negative selection more frequently than an average SNP in the human genome . We found no evidence for excess of positive selection on disease-associated SNPs . At the same time we found that alleles associated with a higher disease risk undergo negative selection . We also demonstrated that risk alleles for diseases with strong influence of environment/lifestyle factors ( e . g . Type II diabetes ) show little evidence of negative selection , while risk alleles for diseases with weak influence of environment/lifestyle factors ( e . g . Pathological myopia ) show clear signs of negative selection . The approach used in this study can be used to estimate the number of genetic variants in the human genome influencing risk of human diseases .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases
|
Identifying the genes that influence levels of pro-inflammatory molecules can help to elucidate the mechanisms underlying this process . We first conducted a two-stage genome-wide association scan ( GWAS ) for the key inflammatory biomarkers Interleukin-6 ( IL-6 ) , the general measure of inflammation erythrocyte sedimentation rate ( ESR ) , monocyte chemotactic protein-1 ( MCP-1 ) , and high-sensitivity C-reactive protein ( hsCRP ) in a large cohort of individuals from the founder population of Sardinia . By analysing 731 , 213 autosomal or X chromosome SNPs and an additional ∼1 . 9 million imputed variants in 4 , 694 individuals , we identified several SNPs associated with the selected quantitative trait loci ( QTLs ) and replicated all the top signals in an independent sample of 1 , 392 individuals from the same population . Next , to increase power to detect and resolve associations , we further genotyped the whole cohort ( 6 , 145 individuals ) for 293 , 875 variants included on the ImmunoChip and MetaboChip custom arrays . Overall , our combined approach led to the identification of 9 genome-wide significant novel independent signals—5 of which were identified only with the custom arrays—and provided confirmatory evidence for an additional 7 . Novel signals include: for IL-6 , in the ABO gene ( rs657152 , p = 2 . 13×10−29 ) ; for ESR , at the HBB ( rs4910472 , p = 2 . 31×10−11 ) and UCN119B/SPPL3 ( rs11829037 , p = 8 . 91×10−10 ) loci; for MCP-1 , near its receptor CCR2 ( rs17141006 , p = 7 . 53×10−13 ) and in CADM3 ( rs3026968 , p = 7 . 63×10−13 ) ; for hsCRP , within the CRP gene ( rs3093077 , p = 5 . 73×10−21 ) , near DARC ( rs3845624 , p = 1 . 43×10−10 ) , UNC119B/SPPL3 ( rs11829037 , p = 1 . 50×10−14 ) , and ICOSLG/AIRE ( rs113459440 , p = 1 . 54×10−08 ) loci . Confirmatory evidence was found for IL-6 in the IL-6R gene ( rs4129267 ) ; for ESR at CR1 ( rs12567990 ) and TMEM57 ( rs10903129 ) ; for MCP-1 at DARC ( rs12075 ) ; and for hsCRP at CRP ( rs1205 ) , HNF1A ( rs225918 ) , and APOC-I ( rs4420638 ) . Our results improve the current knowledge of genetic variants underlying inflammation and provide novel clues for the understanding of the molecular mechanisms regulating this complex process .
Inflammation is a critical response to pathogens and injuries . Its control entails a coordinated cascade of biological events regulated by specific cells and molecular signals , in a complex process that is only partially understood . In this context , genetics can provide important clues , given that population studies indicate that about half of the inter-individual variability in biomarkers of inflammation is genetically determined and considering the achievements of GWA scans ( GWAS ) in complex trait analysis during the last few years [1]–[6] . To date , however , the genetic variants involved in the control of inflammation are still largely unidentified . The relevance for clarifying the genetic bases of inflammation and understanding their mechanistic consequences is multi-fold . The immediate importance regards a better understanding of the regulation of the components of inflammation itself . Furthermore , recent population genetic studies have suggested that natural selection has shaped the evolution of innate immunity , with a specific pressure on those inflammatory genes that play a pivotal role in host-pathogen interactions [7] , [8] . In addition , the inflammatory response can also influence in a positive or negative way the risk for several complex non-infectious diseases , as highlighted by recent studies on cardiopathologies and metastatic processes [9] , [10]; knowing the variants involved in the process can thus have implications in different clinical settings . To identify the genetic variants explaining the inter-individual variability in biomarkers of inflammation , we conducted a GWAS for the levels of the key inflammatory biomarkers interleukin-6 ( IL-6 ) , erythrocyte sedimentation rate ( ESR ) , monocyte chemotactic protein-1 ( MCP-1 ) and the C-reactive protein using the high-sensitivity assay ( hsCRP ) in a large cohort of Sardinian individuals from the SardiNIA study [11] . These markers represent different pathways and stages in the inflammatory cascade and their serum levels are used for the diagnosis and management of different inflammatory conditions during both the acute and chronic immune response .
We identified several SNPs above the standard genome-wide significant threshold ( 5×10−08 ) in the SardiNIA discovery cohort ( Table 1 , Table S1 and Figure S1 ) . The region surrounding each of these SNPs was studied in more detail as the respective traits were analyzed ( see below ) . For IL-6 , SNPs with p-values<5×10−8 were all located in the ABO ( a-1-3-N-acetylgalactosaminyltransferase ) locus on chromosome 9q34 . 1-q34 . 2 ( Table S1 ) , encoding the Histo-blood group ABO system transferase , with the strongest signal at rs643434 in intron 1 of the gene ( p = 2 . 69×10−21 , 0 . 69 pg/ml average increase per G allele ) ( Table 1 ) . This SNP is in strong linkage disequilibrium ( LD ) with another associated variant , rs687289 , which tags the O allele of the ABO locus ( r2 = 0 . 931 in HapMap CEU ) . For ESR , we identified several associated SNPs on chromosome 1q32 , all within the CR1 ( complement component ( 3b/4b ) receptor 1 ) gene , a member of the receptors of the complement activation family , recently shown associated with ESR ( Table S1 ) [14] . The strongest signal was observed at rs12034598 in intron 22 of CR1 , with a p-value of 9 . 31×10−11 ( 1 . 024 mm/h average increase per G allele ) ( Table 1 ) . This SNP is in strong LD with other associated variants including rs2274567 ( r2 = 1 ) , a non-synonymous SNP in exon 22 that causes a His1208Arg substitution predicted as potentially damaging by PolyPhen and affecting expression levels of CR1 on the erythrocytes ( Table S1 ) [15] , [16] . In the genomic region covered by CR1 several copy number variations ( CNVs ) have been identified . However , none of the 38 SNPs in this region with p-value<5×10−08 ( Table S1 ) tags the CNVs reported in a previous study [17] , [18] . In addition , CNVs analysis with PennCNV [19] in individuals genotyped with the Affymetrix 6 . 0 microarray did not show presence of CNVs in our samples ( unpublished data ) . Still , we could not exclude the presence of population-specific CNVs or common CNVs not directly interrogated by the Affymetrix probes . We also found a locus suggestively associated with ESR on chromosomes 11p15 in the β-globin locus control region ( LCR ) , which coordinates the expression of the globin genes ( Table S1 ) . The top signal was at marker rs4910742 ( p = 6 . 34×10−08 ) ( Table 1 ) , which is a surrogate for the β039 mutation carried by a large portion ( 11–13% ) of the Sardinians and able to influence the levels of several blood indices , including number of RBCs [20] , a parameter that has an inverse relationship with ESR . Accordingly , when we repeated the association analysis including in the model β-Thalassemia ( β-Thal ) carrier status as a covariate , the association at rs4910742 disappeared ( p = 0 . 54 in SardiNIA ) . The GWAS results for MCP-1 levels revealed strong association signals on chromosome 1q22-q23 ( Table S1 ) . The associated region encompassed ∼500 kb and contained several genes , with the top signal detected in the DARC ( Duffy blood group chemokine receptor ) gene at marker rs12075 ( p = 1 . 68×10−30 , 36 . 78 pg/ml average increase per A allele ) , as also shown in a recent meta-analysis ( Table 1 ) [21] . The association curve encompasses several other genes , including the CADM3 ( cell adhesion molecule 3 ) locus upstream of DARC , as well as in the FCER1A ( Fc fragment of IgE , high affinity I , receptor for alpha polypeptide ) , OR10J1 ( olfactory receptor , family 10 , subfamily J , member 1 ) , and OR10J5 , that have been previously reported to be associated with MCP-1 levels ( Table S1 ) [22] . Interestingly , when we performed a conditional analysis on the top SNP in the DARC gene , SNP rs3026968 in the CADM3 gene still showed a strong association ( p = 4 . 26×10−08 ) , indicating that this marker represents an independent signal ( r2 with rs12075 = 0 . 043 ) . SNPs with borderline association signals with MCP-1 levels were also found on chromosome 6p21 . 3 , near the HLA-DRB9 ( major histocompatibility complex , class II , DR beta 9 ) pseudogene ( rs9405112 , p = 6 . 43×10−08 ) ; on chromosome 20q13 , near the CDH4 ( cadherin 4 ) gene ( rs6513566 , p = 5 . 29×10−08 ) , and on 3p21 at the 5′ of the CCR2 gene ( rs3918357 , p = 8 . 49×10−08 ) , encoding the chemokine ( C-C motif ) receptor 2 , which acts as the MCP-1 receptor . For hsCRP , the strongest association signal was observed in the CRP ( C-reactive protein ) gene on chromosome 1q21-q23 , confirming previous findings [23]–[25] . The top marker ( rs1341665 , p = 2 . 82×10−16 , 0 . 692 mg/L average increase per G allele ) is in strong LD with several variants , including rs1205 , a 3-prime flanking region SNP previously implicated in CRP expression and systemic lupus erythematosus susceptibility ( Table 1 and Table S1 ) [26] . In addition , we detected the presence of a novel independent signal at rs3845624 downstream of the DARC gene ( p = 1 . 43×10−10 , r2 = 0 . 015 with rs1341665 and r2 = 0 . 009 with rs1205 ) . Indeed , when accounting for rs1341665 , several SNPs in the DARC locus , and in particular rs3845624 , still showed evidence for association ( p = 4 . 75×10−07 ) , suggesting a role for this gene in the regulation of CRP levels . To corroborate our initial findings , we examined with TaqMan genotyping technology the 4 top associated SNPs ( p<5×10−08 ) , as well as 3 additional SNPs including the 2 independent signals in the CADM3 and DARC loci ( rs3026968 and rs3845624 ) , and one suggestive SNP with p<10−06 near HLA-DRB9 ( rs9268858 ) , in a group of 1 , 392 Sardinians enrolled in the same SardiNIA study but unrelated to the individuals analyzed in Step 1 GWAS . This independent cohort has been previously described as SardiNIA stage 2 [27] . Table 1 provides a summary of the follow-up results for the SNPs with the strongest association signal at each locus as well as a combined analysis . Follow-up analysis of the top SNP rs643434 in ABO showed replication of this signal in SardiNIA stage 2 ( p = 4 . 07×10−05 , Table 1 ) , supporting a role for this gene in regulating the levels of IL-6 . For ESR , replication was observed for both the top marker in the known CR1 gene ( rs12034598 , p = 2 . 19×10−04 for its genotyped proxy rs650877 with r2 = 1 ) , and the SNP in the β-globin LCR , ( rs4910742 , p = 8 . 62×10−03 for its genotyped proxy rs10500647 with r2 = 0 . 661 ) ( see Table 1 ) . As observed in the Step 1 , when we repeated the association analysis for the latter SNP , including in the model β-Thal carrier status as a covariate , the association disappeared ( p = 0 . 34 ) . The top SNP in the chemokine receptor gene DARC known to be associated with MCP-1 levels was also strongly replicated ( rs12075 , p = 4 . 93 . ×10−25 ) . The relatively lower association signal showed by rs12075 in the SardiNIA discovery cohort ( Step 1 ) compared to the follow-up cohort SardiNIA stage 2 is most likely due to the fact that it was imputed with a modest imputation score in the initial GWAS , whereas it was directly genotyped in the replication cohort . The association signal at rs3026968 in CADM3 was also confirmed in SardiNIA stage 2 ( p = 0 . 0147 , Table 1 ) , whereas the association at rs9405112 in the HLA-DRB9 region was not ( p = 0 . 59 ) . Neither the signal in the CDH4 gene , or that at rs3918357 in the CCR2 gene were followed up; however , the latter supports a previously reported suggestive association in the CCR2/CCR3 cytokine receptor gene cluster ( rs12495098 , r2 = 1 with rs3918357 ) [21] . Finally for hsCRP , the top SNP associated in the known CRP gene was fully confirmed ( p = 1 . 32×10−05 for its perfect proxy rs2808628 , r2 = 1 ) ( Table 1 ) , and replication was observed also for the independent signal at rs3845624 , near DARC ( p = 0 . 027 , Table 1 ) . To refine the contribution of the detected loci and increase the power to detect novel signals , we performed an additional association scan by testing 293 , 875 variants assessed in the whole SardiNIA cohort ( 6 , 145 individuals , including the discovery and follow-up cohorts from Steps 1 and 2 ) by genotyping with the ImmunoChip [12] and the MetaboChip [13] , two Illumina custom arrays designed to follow up regions of prior interest in immune- and metabolic-related traits and diseases , respectively , as detailed in the Methods section . With this approach , we not only validated with an independent genotyping method , and refined all the association results at the previously described loci ( see Figure S1 , Table 1 ) , but also identified novel signals for all traits ( Figure 1 , Table 2 ) . A detailed view of the associated regions is illustrated in Figure 2 and Figure 3 , and results discussed below . The effect of the associated variants on trait variability per genotype is represented in Figure S2 and Figure S3 . For IL-6 levels , besides corroborating the association at the ABO gene ( strongest hit rs657152 , p = 2 . 13×10−29 ) , we also observed a signal at rs4129267 ( p = 2 . 36×10−08 , with an average increase of 0 . 220 pg/ml per T allele ) , in the IL-6R ( IL-6 receptor ) gene ( Table 2 , Figure 2A and 2B , and Figure S2 ) . This SNP is a proxy of the functional SNP rs8192284 ( r2 = 0 . 982 in HapMap CEU ) affecting cleavage of IL-6 soluble receptor ( IL-6 sR ) , which was previously found associated with both IL-6 sR and IL-6 levels by admixture mapping and candidate gene analysis in African and European Americans [28] . SNP rs4129267 was genotyped in the Step 1 GWAS but observed with a lower p-value ( p = 2 . 45×10−04 ) . Conditional analysis did not reveal any independent signals at such loci . The top variants at ABO ( rs657152 ) and IL-6R ( rs4129267 ) explain 2 . 2% of the total phenotype variation . The scan for ESR , in addition to strong confirmatory signals in CR1 ( strongest hit rs12567990 , p = 8 . 26×10−15 ) , detected a novel associated SNP , rs10903129 , in TMEM57 ( Transmembrane protein 57 ) ( p = 3 . 91×10−08 , with an average increase of 0 . 581 mm/h per G allele ) ( Table 2 , Figure 2C and 2D , and Figure S2 ) . SNP rs10903129 was analysed in the Step 1 GWAS , but its p-value did not reach genome-wide significance ( p = 9 . 30×10−05 ) and thus it was not considered for follow-up in Step 2 . Although this gene encodes a largely uncharacterized protein , polymorphisms in the region have been previously reported associated with lipid levels , CHD and more recently with ESR [14] , [29] . We also detected a novel signal on chromosome 12q24 . 31 near the UNC119B ( Unc-119 homolog B ) and SPPL3 ( Signal peptide peptidase-like 3 ) genes at a low frequency SNP , rs11829037 , with a large effect ( p = 8 . 91×10−10 , MAF = 0 . 009 , average increase of 4 . 657 mm/h per the minor T allele ) ( Figure 2E and Table 2 ) . It was genotyped by the MetaboChip , but the association at this locus was supported by SNPs genotyped with both arrays , and by more common SNPs ( MAF up to 0 . 05 for the 18 SNPs with p-value<10−6 ) . It is missing and not well tagged in the HapMap data set , which provides an explanation as to why it was not discovered in the initial scan ( step 1 ) . Finally , we confirmed the association at rs4910742 ( p = 2 . 31×10−11 ) in the HBB locus ( Figure 2F ) . Conditional analysis did not reveal any independent signals at such loci . The top variants at CR1 ( rs12567990 ) , HBB ( rs4910742 ) , TMEM57 ( rs10903129 ) and UNC119B/SPPL3 ( rs11829037 ) explain 2 . 3% of the total trait variation . For MCP-1 , the association with the coding SNP in DARC was corroborated with a striking p-value ( rs12075 , p = 7 . 43×10−102 ) ( Figure 3A and Table 2 ) . In addition , SNP rs17141006 , 10 kb upstream of its receptor CCR2 , correlated with the previous borderline signal ( r2 = 0 . 997 ) , reached genome-wide significance ( rs17141006 , p = 7 . 53×10−13 , average increase per C allele was 42 . 14 pg/ml ) ( Table 2 , Figure 3B and Figure S2 ) . As mentioned earlier , SNPs in the CCR2/CCR3 receptor cluster associated with MCP-1 levels were previously reported by Shnabnel et al . [21] , although these associations did not reach the genome-wide significance threshold . Our study thus refines the association and points to CCR2 as the most likely candidate for a role in the levels of MCP-1 . We also carried out a search for independent SNPs by conditioning on the strongest associated variant , but this analysis did not reveal any evidence . The independent signal in CADM3 or an adequate proxy were not included on the custom arrays , and thus it could not be tested in this data set . However , since the SNP was available in both the SardiNIA discovery cohort ( Step 1 ) and SardiNIA stage 2 data sets , genotypes were accessible for the entire cohort and independency was confirmed . We estimated that all together the top variants at DARC ( rs12075 ) , CCR2 ( rs17141006 ) , and CADM3 ( rs3845624 ) explain 9 . 8% of the phenotypic variation . For hsCRP , strong association signals were detected in the previously described CRP gene ( rs1205 , p = 8 . 20×10−30 ) and in the 3′ of APOC-I ( Apolipoprotein C-I ) gene ( rs4420638 , p = 7 . 12×10−11 ) ( Figure 3C and 3D and Table 2 ) , a well known determinant of serum hsCRP that did not reach statistical significance ( p = 2 . 85×10−5 ) in our initial scan ( Stage 1 ) [25] . A novel and previously unknown signal with a large phenotypic impact was identified at a rare variant , rs113459440 ( p = 1 . 54×10−08 , MAF = 0 . 003 , average increase of 5 . 35 mg/L per T allele ) , near ICOSLG ( Inducible T-cell co-stimulator ligand ) and the AIRE ( Autoimmune regulator ) genes ( Figure 3E and Figure S3 ) . Common variants at the first gene have been associated by GWAS with the risk of Ulcerative colitis , Celiac disease , Chron's disease and ankylosin spondilytis in Europeans [30]–[32] , whereas at the latter gene with Rheumatoid arthritis in Japanese [33] . The AIRE gene is also responsible for Autoimmune polyendocrinopathy syndrome , type I ( APECED ) , an autosomal recessive autoimmune disease relatively common in Sardinia ( OMIM # 607358 ) . Association was supported by other SNPs genotyped with the ImmunoChip ( 7 SNPs with p-value<10−6 , with MAF up to 0 . 007 ) . In addition , we observed that the same low frequency SNP at UNC119B/SPPL3 associated with ESR levels was also associated with hsCRP ( rs11829037 , p = 1 . 50×10−14 , average increase of 3 . 68 mg/L per T allele ) ( Figure 3F , Table 2 and Figure S3 ) . Similarly to ESR , association is likely to be genuine , supported by SNPs genotyped with both arrays and several common SNPs ( MAF up to 0 . 38 for the 22 SNPs with p-value<10−6 ) . Notably , signals at SNPs within SSLP3 were previously detected associated with CRP levels in an isolated founder population from the Pacific Island of Kosrae , although the p-values did not reach the genome-wide threshold [34] . None of those SNPs was correlated to the top SNP associated in our study; however the four SNPs which were genotyped ( rs10437838 , rs6489780 , rs1039302 , rs10431387 ) showed consistent direction of allele effects ( increasing value for the minor allele ) as in Lowe et al . , albeit with weak evidence ( 0 . 04<p<0 . 09 ) . This further indicates that association at this locus cannot be spurious . Conditional analysis revealed the presence of two independent signals . The first was within the CRP gene , at SNP rs3093077 ( p = 9 . 02×10−11 after conditioning for rs1205 , with average increase of 0 . 724 mg/L per G allele ) ( Figure 3C , Table 2 and Figure S3 ) . This marker is independent from the signal observed 461 Kb downstream in the Step 1 GWAS scan , rs3845624 , near the DARC gene ( r2 = 0 . 043 ) . Indeed , when accounting for rs1205 and rs3093077 in the HapMap-based GWAS data set , the association signal at rs3845624 was still significant . The second independent signal was at rs2259816 ( p = 7 . 58×10−10 after conditioning for rs11829037 , with average increase of 0 . 381 mg/L per C allele ) , in an intron of the HNF1A ( Hepatic nuclear factor-1α ) gene about 300 Kb downstream from the UNC119B/SPPL3 locus ( Figure 3F , Table 2 , and Figure S3 ) . This marker is a perfect proxy of rs1169310 , a variant reported by a previous study [24] . The best signal at this locus on our initial GWAS ( Step 1 ) was at a linked SNP , rs7953249 ( r2 = 0 . 5 ) , which did not reach genome-wide significance level ( p = 7×10−06 ) . Overall the top variants at CRP ( rs1205 ) , APOC-I ( rs4420638 ) , ICOSLG/AIRE ( rs113459440 ) , UNC119B/SPPL3 ( rs11829037 ) , and the independent variants at CRP ( rs3093077 ) , DARC ( rs3845624 ) and HNF1A ( rs2259816 ) explain 5 . 6% of the phenotypic variation of this trait .
Our results , besides confirming previous associations , highlight new determinants for variation at the major inflammatory biomarkers IL-6 , ESR , MCP-1 and hsCRP . Specifically , we found a novel highly significant association between IL-6 expression levels and the ABO locus , with our top associated marker tagging the O allele . This association is of special interest , given the numerous biological effects of this cytokine as well as the associations previously reported of the O allele with both inflammatory traits and diseases [35]–[38] . In contrast to individuals with A and B alleles , individuals with the O blood group do not produce either the A or B antigens because of a single-base deletion in the gene sequence , whose product catalyzes the transfer of carbohydrates to the H antigen , forming the antigenic structure of the ABO blood group . Our data show that individuals carrying two copies of the G allele at our top SNP , corresponding to blood type O carriers , display highly increased IL-6 circulating levels compared to non-O carriers ( average increase of 1 . 38 pg/ml for homozygotes of the G allele , compared to opposite homozygotes ) . This is consistent with the observation that blood type O individuals show an enhanced inflammatory response to Helicobacter pylori , with a significantly higher release of IL-6 [39] . The detected association at the ABO locus with the IL-6 phenotype may also provide a mechanistic clue for previous associations of the O blood group with various diseases with an inflammatory component such as cancer and heart disease , although determining the workings of this puzzle will likely also require specific functional studies . Our study also revealed novel associations with ESR levels at the HBB and the UNC119B/SPPL3 loci . Although the impact of the associated variants at HBB in ESR values is somewhat expected in Sardinia because of the high frequency of carriers of β-Thalassemia ( see Results ) , our work indicates a direct link supported by a genetic association . The link of UNC119B/SPPL3 with ESR is currently less clear . Interestingly , we also found that the same UNC119B/SPPL3 variant was associated with hsCRP levels , a finding supported by a recent study showing suggestive evidence at SNPs within the SPPL3 gene with CRP levels variation [34] . As expected by the strong correlation of CRP blood circulating levels and ESR ( i . e . , high blood levels of acute phase proteins increase ESR ) , the same allele of the associated SNP at UNC119B/SPPL3 increases both CRP and ESR , further supporting that the association at this locus is genuine . Our results highlighted a novel association at the MCP-1 receptor CCR2 , with a clear involvement with MCP-1 levels , previously only suggestively associated with this trait [21] . Confirming previous findings of Schabnel and colleagues [21] , we also found robust evidence of association between MCP-1 and SNPs in the DARC gene , an unusual transmembrane chemokine receptor , which binds the two main families of inflammatory chemokines , CXC and CC ( i . e . , MCP-1 ) . The top signal ( rs12075 ) is a non-synonymous SNP located in exon 3 of the gene , that generates a Gly42Asp amino acid change in the DARC protein . The predicted impact of the mutation , as well as the strength of the association signal compared to all nearby variants , suggests that it represents a causal variant , as previously hypothesized [21] . However and intriguingly , our results indicate that the association in the region is complex , with one novel genome-wide significant independent signal at the upstream gene CADM3 . In addition , we also observed that SNPs near the DARC gene are associated with variation in CRP levels . Although the biological implications of these SNPs on DARC function are at present unclear , this is consistent with the observation that MCP-1 production by endothelial cells rises in response to CRP [40] . The DARC associations with CRP and MCP-1 were genetically independent of each other , supporting the notion of a complex correlation between hsCRP and MCP-1 , and suggesting a multi-layered control of expression of the inflammation response in the DARC region . Finally , in spite of the small sample size compared with the large meta-analyses conducted so far [25] , our study identified several new variants associated with hsCRP levels , including an independent signal at CRP , the signal at the previously discussed UNC119B/SPPL3 locus and an unexpected signal at ISOCG/AIRE . Although the SNP associated with hsCRP at the UNC119B/SPPL3 locus is independent and not correlated ( r2<0 . 1 ) with the known signal at HNF1A located about 300 kb downstream [24] , at present we cannot exclude that this SNP may act as an eQTL or more generally in the regulation of HNF1A expression . Interestingly , the majority of the association signals ( and specifically at IL6R , TMEM57 , UNC119B/SPPL3 , CCR2 , APOC-I , HNF1A , the CRP independent signal , and ICOCG/AIRE ) , were observed , at least at the genome-wide level of significance , only after genotyping the MetaboChip and ImmunoChip custom arrays , which were typed in our cohort primarily to assess other phenotypes . All these signals , apart from that at ICOCG/AIRE , had supporting evidence for the involvement in the specific trait variation from previous reports , indicating that the associations are not spurious . The strongest variants were either not genotyped with the commercial arrays used in our initial scan , missing or poorly tagged in the HapMap-based reference panel we used for imputation , or only partially genotyped ( given our genotyping strategy ) , resulting in inadequate power for being detected at the required significance level in the GWAS scan . This suggests that cost effective custom arrays could improve our understanding of the genetics underlying trait variation even for a phenotype , such as inflammation , for which the array was not specifically designed . Understanding the effects of the protein products of all the discovered loci in inflammation is an important goal , which may also likely have clinical implications . For instance , whereas CRP and ESR are the most widely used non-specific diagnostic markers of inflammation , the factors and fine mechanisms regulating their levels and interfering with them are only partially understood . Overall , our results contribute to improve the current knowledge of the regulation of the inflammatory response . While inflammation is canonically thought of as involving leukocyte migration and infiltration , the fact that several of the variants identified are better noted in erythrocyte function may suggest a more active role for the red cell in this process , beyond its obligate role in ESR . Notably , four of the associated loci ( ABO , HBB , DARC and CR1 ) have been implicated in resistance to malaria , a disease endemic in Sardinia until a few decades ago [41]–[45] . This raises the possibility that the genetic selection imposed by malaria may have contributed to shaping levels of inflammation , at least for these specific inflammatory biomarkers , in this population [46] . Still , a link between these specific genes and variants with malaria remains speculative and needs to be further assessed with adequate biological and genetic analyses; for instance , they could be tested and cross-compared with future statistically well powered GWAS on Malaria and other infectious disorders . A related potential detrimental consequence of inflammation is that polymorphisms which have been selected because they promote pro-inflammatory responses may increase the risk for diseases with an inflammatory component [47] , [48] , particularly those that show a high frequency in Sardinia , such as Multiple Sclerosis ( MS ) and Type 1 diabetes ( T1D ) [49] , [50] . However , we could not find any evidence of association of the top SNPs associated with pro-inflammatory markers in a sample-set of 2 , 280 MS cases , 1 , 377 T1D cases and 1 , 922 unrelated controls , all from Sardinia [51] , with a power of 60% and 33% to detect variants with an odds ratio of 1 . 4 and MAF of 0 . 1 at a significance level of 1×10−07 , indicating that larger sample sizes are required to identify association at variants with smaller effects or of lower frequency ( data not shown ) . Similarly , these variants were not found associated to other autoimmune diseases in larger data-sets ( T1Dbase , http://t1dbase . org; and the GWAS Catalog , http://www . genome . gov/gwastudies/ ) . Another possibility is that these pro-inflammatory variants play a positive role in protection against serious diseases . For instance , the ABO O allele is also associated with a reduced risk of myocardial infarction and pancreatic and skin cancer [52]–[54] . Our results suggest that an increase in the circulating levels of IL-6 can indeed contribute to these associations involving the O group . In conclusion , our work highlights important aspects of the complex and multilayered regulation of inflammation and may provide a route to understanding possible attendant effects on a number of serious diseases .
All individuals studied and all analyses on their samples were done according to the Declaration of Helsinki and informed consents were approved by the local ethics committee for the Istituto di Ricerca Genetica e Biomedica-CNR ( IRGB-CNR; Cagliari , Italy ) and by MedStar Research Institute , responsible for intramural research at the National Institutes of Aging , Baltimore , Maryland , United States . We recruited and phenotyped 6 , 148 individuals , males and females , ages 14–102 y , from a cluster of four towns in the Lanusei Valley of Sardinia [11] . During physical examination , a blood sample was collected from each individual and divided into two aliquots . One aliquot was used for DNA extraction and the other to characterize several blood phenotypes , including evaluation of serum levels of hsCRP , IL-6 , MCP-1 , and values of ESR . Descriptive statistics of the study cohort are shown in Table S2 . Serum levels of hsCRP were measured by the high sensitivity Vermont assay ( University of Vermont , Burlington ) , an enzyme-linked immunosorbent assay calibrated with WHO Reference Material [55] . The lower detection limit of this assay is 0 . 007 mg/l , with an inter-assay coefficient of variation of 5 . 14% . Serum levels of IL-6 and MCP-1 were measured by Quantikine High Sensitive Human Immunoassays ( R&D Systems , Inc . ) , according to manufacturer's instructions . This method employs solid-phase ELISA techniques . For IL-6 , the lower detection limit is 0 . 039 pg/ml . The intra-assay coefficient of variations ( CVs ) were 6 . 9% to 7 . 8% over the range 0 . 43–5 . 53 pg/ml . For MCP-1 , the lower detection limit is 5 . 0 pg/ml . The intra-assay coefficient of variations ( CVs ) were 4 . 7% to 7 . 8% over the range 76 . 7–1121 pg/ml . ESR was measured using sedimentation measurement tubes buffered with 3 . 8% sodium citrate ( Venoject-Terumo ) . After mixing of 2 . 4 ml of blood with the additive , tubes were left in a vertical position in the specific support with graduation markings for 30 minutes to allow sedimentation of the erythrocytes by gravity . The erythrocyte sedimentation rate is calculated in Westergreen units ( mm/h ) determining the length at the plasma/erythrocyte cell interface level within the sedimentation tube . Samples affected by multiple sclerosis ( MS ) and type 1 diabetes ( T1D ) used for the side case-control analysis briefly reported in the Discussion were recruited from all the island as previously described ( 51 ) . Only 20 of these samples overlapped with those in the SardiNIA cohort . During the study , we genotyped 4 , 694 individuals selected from the whole sample to represent the largest available families , regardless of their phenotypic values . Specifically , 1 , 412 were genotyped with the 500 K Affymetrix Mapping Array set , 3 , 329 with the 10 K Mapping Array set , with 436 individuals genotyped with both arrays . We also recently typed 1 , 097 individuals with the Affymetrix 6 . 0 chip , of which 1 , 004 and 66 were also typed with the 10 K and 500 K chips respectively . This genotyping strategy allowed us to examine the majority of our cohort in a cost-effective manner since genotypes for the SNPs that passed quality control checks could be propagated through the pedigree using imputation . Measurements of inflammatory biomarkers were available for 4 , 137 , 4 , 292 , 4 , 295 and 3 , 596 individuals for hsCRP , IL-6 , MCP-1 and ESR , respectively , among the 4 , 694 genotyped . A total of 731 , 209 autosomal SNPs passed stringent quality control checks . Quality checks for the 10 K and 500 K chips were described previously [56] . For the Affymetrix 6 . 0 chip , similar criteria were used , as detailed in Table S3 . In addition , we also removed SNPs in common between the other chips that showed an high level of discordance or that generated too many discrepancies when comparing genotypes across 11 duplicates . After performing quality control checks and merging genotypes from the three gene chip platforms , we used the quality controlled 731 , 209 autosomal markers to estimate genotypes for all polymorphic SNPs in the CEU HapMap population ( release 22 ) [57] , in the individuals genotyped with the 500 K Array and the 6 . 0 Affymetrix chip separately using the MaCH software [58] . Taking advantage of the relatedness among individuals in the SardiNIA sample , we carried out a second round of computational analysis to impute genotypes at all SNPs in the individuals who were genotyped only with the Affymetrix Mapping 10 K Array , being mostly offspring and siblings of the individuals genotyped at high density . At this second round of imputation , we focused on the SNPs for which the imputation procedure predicted r2>0 . 30 between true and imputed genotypes and for which the inferred genotype did not generate an excess of Mendelian errors . We then used a modified version of the Lander-Green algorithm , as previously described [3] , [56] to estimate IBD sharing at the location of the SNPs being tested and identify stretches of haplotype shared with close relatives who were genotyped at higher density and probabilistically infer missing genotypes . The within-family imputation procedure and the association test are implemented in Merlin software [59] , [60] . Due to computational constraints , we divided large pedigrees into sub-units with “bit-complexity” of 21 or less ( typically , 25–30 individuals ) before analysis . For association , we evaluated the additive effect of genotyped and imputed SNPs on inflammatory biomarker levels using a family-based association test implemented in Merlin ( –fastassoc option ) [59] , [60] . This test accounts for relatedness under the assumption that the samples analyzed are from an ethnically homogeneous population [59] , [60] , and this is suggested by demographic records indicating that 89% of the participants were born in the same 31 Km2 area and for 95% of the volunteers , both parents and all grandparents were born in Sardinia [11] . At each SNP , levels of each biomarker of inflammation were regressed onto allele counts in a regression model that included gender , age , and age-squared as covariates . We also used a second model , which included body-mass index ( BMI ) and smoking status as additional covariates , as these have been previously implicated as being associated with inflammatory biomarker levels [24] . Here we report the results from the second model , where the inclusion of the additional covariates improved the variance explained by the model ( from 1 . 8% to 3 . 7% for CRP , from 18 . 1% to 19 . 5% for ESR , from 6 . 8% to 7 . 6% for IL-6 , and from 3% to 3 . 3% for MCP-1 ) . Genomic control parameters show negligible inflation ( 1 . 049 , 1 . 039 , 1 . 031 and 1 . 115 , respectively for hsCRP , MCP-1 , IL-6 and ESR ) ; nevertheless the corresponding correction factors were applied to the GWAS results to completely avoid spurious associations . The SardiNIA stage 2 cohort was used to follow-up initial findings [27] . Genotyping of specific SNPs was performed in Sardinian individuals selected for replication efforts using TaqMan single SNP genotyping assays ( Applied Biosystems ) . In particular , we genotyped and analysed 1 , 392 individuals from the SardiNIA stage 2 cohort , who were unrelated ( kinship coefficient = 0 ) to the individuals analysed in the GWAS . We successfully genotyped 6 , 145 samples using the MetaboChip and ImmunoChip arrays ( Illumina ) . The MetaboChip was designed in collaboration with several international consortia [3] , [61] , [62] with the aim to fine map association loci detected through GWAS for a variety of traits . Part of the design included a set of wild-card SNPs chosen by individual research groups; the SardiNIA study promoted several SNPs associated with a wide range of traits , including rs12075 . The ImmunoChip is also a consortium based array , designed to fine map loci associated to 12 immunologically related human diseases , or immune-mediated disease loci , as well as a set of wild-card SNPs . The SardiNIA study had not role in the design of this array , and a full detailed description is provided elsewhere [12] . All samples had a genotyping call rate >98% , and SNP genotypes were carefully assessed though several quality control checks . In particular , we removed markers with call rate <98% , with strong deviation from HWE ( p<10−6 ) , that were monomorphic or leading to an excess of Mendelian errors ( defined as >1% of the families ) . A detailed breakdown of markers excluded by each filter criteria is provided on Table S4 . Since the majority of the variants included on these custom arrays are of low frequency compared to the GWAS data set ( average MAF = 0 . 176 , compared to 0 . 219 observed in GWAS ) , the impact of hidden population structure and imprecise modelling of relatedness due to pedigree splitting ( task we performed on the GWAS data set due to computational constraints ) could be problematic . Analysis was thus carried out using EMMAX , a variant component model that overcomes such issues by using a genomic-based kinship matrix [63] . To calculate the kinship matrix , we used all SNPs that passed quality control checks but excluding those with MAF between 0 and 1% . Association analysis was subsequently performed testing all QCed SNPs ( Table S4 ) , in spite of their minor allele frequency . Observed genomic lambda were 1 . 01 , 0 . 962 , 1 . 00 and 1 . 01 , respectively for IL6 , VES , MCP1 , and hsCRP ( as a note , genomic lambda using Merlin on the same data set were 1 . 41 , 1 , 1 . 26 and 1 . 14 , respectively ) . To declare an association significant , we used a Bonferroni threshold of 0 . 05/293 , 875 = 1 . 7×10−7 . The variance explained by the strongest associated SNPs was calculated , for each trait , as the difference of R2 adjusted observed in the full and the basic models , where the full model contains all the independent SNPs in addition to the covariates . We performed conditional analysis at each locus by adding the top associated SNP to the already included covariates , and testing for association the remaining SNPs at the locus . A marker was declared independent only if the p-value observed in the conditional analysis reached genome-wide significance threshold ( 5×10−08 in the Step1 GWAS , and 1 . 7×10−07 in the custom-array based dataset ) [64] .
|
Inflammation is a protective response of our organism to harmful stimuli—such as germs , damaged cells , or irritants—and to initiate the healing process . It has also been implicated , with both protective and predisposing effects , in a number of different diseases; but many important details of this complex phenomenon are still unknown . Identifying the genes that influence levels of pro-inflammatory molecules can help to elucidate the factors and mechanisms underlying inflammation and their consequence on health . Genome-wide association scans ( GWAS ) have proved successful in revealing robust associations in both common diseases and quantitative traits . Here , we thus performed a multistage GWAS in a large cohort of individuals from Sardinia to examine the role of common genetic variants on the key inflammatory biomarkers Interleukin-6 , erythrocyte sedimentation rate , monocyte chemotactic protein-1 , and high-sensitivity C-reactive protein . Our work identified new genetic determinants associated with the quantitative levels of these inflammatory biomarkers and confirmed known ones . Overall , the data highlight an intricate regulation of this complex biological phenomenon and reveal proteins and mechanisms that can now be followed up with adequate functional studies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"mathematics",
"statistics",
"genetics",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
A Genome-Wide Association Scan on the Levels of Markers of Inflammation in Sardinians Reveals Associations That Underpin Its Complex Regulation
|
Although host genetics influences susceptibility to tuberculosis ( TB ) , few genes determining disease outcome have been identified . We hypothesized that macrophages from individuals with different clinical manifestations of Mycobacterium tuberculosis ( Mtb ) infection would have distinct gene expression profiles and that polymorphisms in these genes may also be associated with susceptibility to TB . We measured gene expression levels of >38 , 500 genes from ex vivo Mtb-stimulated macrophages in 12 subjects with 3 clinical phenotypes: latent , pulmonary , and meningeal TB ( n = 4 per group ) . After identifying differentially expressed genes , we confirmed these results in 34 additional subjects by real-time PCR . We also used a case-control study design to examine whether polymorphisms in differentially regulated genes were associated with susceptibility to these different clinical forms of TB . We compared gene expression profiles in Mtb-stimulated and unstimulated macrophages and identified 1 , 608 and 199 genes that were differentially expressed by >2- and >5-fold , respectively . In an independent sample set of 34 individuals and a subset of highly regulated genes , 90% of the microarray results were confirmed by RT-PCR , including expression levels of CCL1 , which distinguished the 3 clinical groups . Furthermore , 6 single nucleotide polymorphisms ( SNPs ) in CCL1 were found to be associated with TB in a case-control genetic association study with 273 TB cases and 188 controls . To our knowledge , this is the first identification of CCL1 as a gene involved in host susceptibility to TB and the first study to combine microarray and DNA polymorphism studies to identify genes associated with TB susceptibility . These results suggest that genome-wide studies can provide an unbiased method to identify critical macrophage response genes that are associated with different clinical outcomes and that variation in innate immune response genes regulate susceptibility to TB .
TB , a leading cause of death worldwide , is characterized by different clinical forms including latent TB ( LTB ) , localized pulmonary infection , and various forms of extrapulmonary TB including TBM . 90% of people infected with Mtb have latent infection with no symptoms and an immune response that contains the bacilli . In 10% of infected individuals , symptoms develop and most commonly manifest as pulmonary disease , which accounts for 80% of all forms of TB disease [1] . TBM develops in around 1% of all cases of active TB [1] and is the most severe form with mortality rates of 20–25% and high rates of neurological sequelae in many of those who survive [2] , [3] . Although only 10% of individuals who are infected with Mtb develop active disease , it is not known which immune responses are associated with susceptibility or resistance . In addition , it is not known why some individuals have disseminated TB that spreads to the meninges and central nervous system , while most people have localized disease in the lungs . Although environmental exposures , pathogen virulence traits , and host genetics have the potential to influence the different clinical manifestations of TB , it is not currently understood which factors are the most important [4] . Several lines of evidence , including twin and genome-wide linkage studies , suggest that host genetics strongly influences susceptibility to TB [5]–[9] . Candidate gene association studies have implicated common polymorphisms in genes that may influence the development of TB [10] , [11] . Although there is potential for candidate gene study designs to be successful when sample sizes are sufficient and case and control groups are accurately defined , candidate genes are usually selected from lists of genes with known functions . A fundamental problem with this strategy is an inherent selection bias dominated by well-characterized genes . Furthermore , many genes are selected based on phenotypes identified from in vivo murine studies . Although mouse studies have provided powerful methods to dissect TB immunopathogenesis , the murine system models primary , progressive disease , which is only one of several phenotypes observed in humans . There are no well-established murine models of latent infection or the various types of disseminated disease , including TBM . Mtb intrathecal infection of rabbits recapitulates some of the inflammatory pathology but does not provide insight into the steps in immunopathogenesis involved in dissemination and invasion of the central nervous system [12] . To identify genes involved in TBM pathogenesis and to avoid gene selection bias , we chose to directly examine humans with different clinical types of TB with an array-based method to identify candidate genes . Macrophages mediate the host innate immune response to Mtb through pathogen recognition and activation of an inflammatory response . Mtb resides in the macrophage phagolysosome , where it evades the immune response in the majority of infected individuals . Successful containment of Mtb replication results in LTB with no clinical symptoms , which depends on stimulation of innate and adaptive immune responses that lead to macrophage activation , formation of granulomas and elimination of the bacilli . In contrast , failure to contain bacilli replication is associated with active pulmonary disease and/or the development of disseminated disease . We hypothesized that different macrophage responses to Mtb are associated with distinct clinical outcomes that are genetically regulated . Expression microarrays have been previously used to examine gene expression profiles in the immune response to TB [13]–[17] . None of these studies attempted to distinguish different clinical forms of active TB such as pulmonary and meningeal disease . In addition , the sample sizes were generally small and the findings were often not validated in independent sample sets . Finally , these previous approaches were not coupled with human genetic studies to examine the clinical significance associated with variation in the identified genes . In this manuscript , we examined ex vivo Mtb-stimulated monocyte-derived macrophages ( MDMs ) from subjects with pulmonary , meningeal and latent infection . We attempted to find unique gene expression profiles to determine whether clinical phenotypes in TB are associated with distinct early macrophage responses to Mtb stimulation . We then used a case-control genetic association study to examine whether genetic variation of these selected genes was associated with susceptibility to Mtb .
TBM subjects were recruited as part of a larger clinical study at the Hospital for Tropical Diseases , in Ho Chi Minh City ( HCMC ) , Vietnam [18] . All subjects were >14 years of age and HIV-negative . TBM patients were described as having clinical meningitis ( defined as nuchal rigidity and abnormal cerebrospinal fluid parameters ) in addition to having a positive Ziehl-Neelsen stain for acid-fast bacilli and/or Mtb cultured from the cerebrospinal fluid . Subjects were treated for TBM and were clinically well ( recovered for >3 years ) when samples for this study were taken . For PTB subjects , samples were taken from individuals who had been previously treated and had recovered from uncomplicated PTB ( no evidence of miliary or extrapulmonary TB ) . LTB subjects were defined as highly exposed individuals who had no history of active TB disease . LTB subjects were healthy nursing staff members who had worked at Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease , HCMC , Vietnam for more than 20 years . They were tested for Mtb exposure using an ESAT-6 and CFP-10- specific IFN-γ ELISPOT assay using a previously described method [19] . For the initial microarray study , twelve subjects were enrolled with three clinical forms of TB; TBM ( n = 4 ) , PTB ( n = 4 ) and LTB ( n = 4 ) . All of the LTB subjects tested positive in the ESAT-6 and/or CFP-10- specific IFN-γ ELISPOT assay , suggesting previous or current infection with Mtb . An extended sample set containing 34 subjects with TBM ( n = 10 ) , PTB ( n = 12 ) and LTB ( n = 12 ) was used in validation experiments . Of the 12 LTB subjects , 10 were IFN-γ ELISPOT positive according to our defined cut-off [at least 10 spot forming units ( SFU ) more than the negative PBS control and at least twice as many SFU as the negative PBS control] . The 2 IFN-γ ELISPOT indeterminate LTB subjects had borderline responses ( 6 . 7 SFU with a ratio of 2 and 6 SFU with a ratio of 2 . 5 ) which were considerably higher than an unexposed population ( average of −2 . 8 SFU with a ratio of 0 . 8 ) . For the case-control genetic association study the cohort of TBM ( N = 114 ) and PTB ( N = 159 ) patients , and population controls ( cord blood; N = 188 ) has been previously described [20] . All samples came from unrelated individuals who were ethnic Vietnamese Kinh , as assessed by questionnaire . Written informed consent was obtained from each patient . Protocols were approved by human subjects review committees at the Hospital for Tropical Diseases and Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease , Ho Chi Minh City , Vietnam . Ethical approval was also granted by the Oxford Tropical Research Ethics Committee , UK ( OXTREC ) , The University of Washington Human Subjects Committee ( USA ) and the Western Institutional Review Board ( USA ) . Peripheral blood mononuclear cells ( PBMCs ) were separated from heparinized whole blood by Lymphoprep ( Asix-Shield , Norway ) gradient centrifugation according to the manufacturer's protocol . From 20 ml of blood we obtained approximately 1–1 . 5×107 PBMCs . To derive monocytes , PBMCs were plated in Nunclon Suface 6-well plates ( Nunc , Denmark ) containing RPMI-1640 ( Sigma , Germany ) with 10% heat-inactivated fetal calf serum ( FCS; Sigma , Germany ) , 2 mM L-glutamine and 100 units of penicillin for 2 hours at 37°C . Non-adhered cells were removed by washing with phosphate buffered saline ( PBS ) containing 3% FCS and adhered cells were incubated for 5 days at 37°C , 5% CO2 to obtain MDMs . Cells were subsequently stimulated with PBS or 5 µg/ml of an irradiated , soluble , whole cell lysate of Mtb H37Rv [obtained from the Mycobacteria Research laboratories at Colorado State University , USA ( http://www . cvmbs . colostate . edu/microbiology/tb/top . htm ) ] for 4 hours before RNA extraction . Pilot studies indicated that 5 µg/ml was an optimal dose for stimulating TNF-α production . RNA was extracted from macrophages using Trizol according to the manufacturer's protocol ( Invitrogen , USA ) , dissolved in RNase-free water and stored at −70°C until use . Total RNA ( 100 ng ) was reverse transcribed to cDNA , amplified , labeled , and hybridized to the Human Genome U133 Plus 2 . 0 Array ( Affymetrix , USA ) , according to the manufacturer's instructions . This array contains probe sets to measure the expression level of 47 , 000 transcripts , including 38 , 500 well-characterized human genes . Twelve Mtb-stimulated ( TBM n = 4 , PTB n = 4 , and LTB n = 4 ) and 12 PBS-stimulated ( hereafter called unstimulated ) samples were hybridized to the array . The microarray data is publicly available at ArrayExpress , EMBL-EBI ( Submission in progress , awaiting Accession number; http://www . ebi . ac . uk/microarray-as/aer/ ? #ae-main0 ) . After normalization of the expression values , the data from 12 Mtb-stimulated samples were compared with the 12 unstimulated samples . Data were considered significant when ( 1 ) the false discovery rate ( FDR ) from the Significance Analysis of Microarray ( SAM ) analysis for the comparison of stimulated and unstimulated expression values was <0 . 05 , and ( 2 ) the P value of the comparison between stimulated versus unstimulated expression values by Student's t-test was <0 . 05 . In order to focus on highly regulated genes , we also restricted the majority of the analysis to genes with changes in expression levels of at least 2-fold . To compare gene expression levels among the three different clinical types of TB , we first calculated the fold stimulation of each gene for each individual by dividing the Mtb–stimulated value by the unstimulated control values . The averages of the 4 samples in each clinical group were calculated and then compared to the other groups by calculating the ratios of expression levels . The pair-wise comparisons included TBM vs . PTB , TBM vs . LTB , and PTB vs . LTB . SAM [20] was used to derive the FDR for microarray data , which is the proportion of genes likely to have been identified as significant by chance . Student's t-test and analysis of variance ( ANOVA ) were used to compare mean expression levels . To analyze expression patterns in multiple genes simultaneously we used Hierarchical Clustering [21] . Analyses were performed using MultiExperiment Viewer ( MeV version 4 . 0 , USA ) [22] and SPSS ( version 14 . 0 , USA ) . Taqman real time PCR was used to validate microarray gene expression results . cDNA was synthesized from total RNA samples using reverse transcription with Superscript II following the manufacturer's protocol ( Invitrogen , USA ) . A commercial Low Density Array ( LDA ) format with Taqman probes and primers was then used for PCR validation ( Applied Biosystems , USA ) . Expression levels in 88 genes [86 selected genes and 2 controls ( GAPDH; Hs00237184_m1 and Hs00266705_g1 ) ] were examined in each sample according to the manufacturer's instructions . CCL1 gene expressions on human and mice were examined by using Taqman probes and primers ( Applied Biosystems , USA ) . Samples were normalized to GAPDH and analyzed by using either Applied Biosystems SDS 2 . 1 Relative Quantification software or an Excel spreadsheet to perform relative quantification analysis . PBMC were isolated from whole blood and cytokine assays were prepared by plating 105 cell per well with RPMI ( Life Technologies ) in a 96-well dish , stimulating for 24 hours , and then harvesting supernatants . Stimuli included: Ultrapure lipopolysacharide ( LPS ) at 100 ng/ml , from Salmonella minnesota R595 ( List Biological Labs , Inc . ) , Mtb H37Rv whole cell lysate , Mtb H37Rv cell wall fraction and Mtb H37Rv cytosol fraction ( TB Vaccine Testing and Research Materials Program at Colorado State University ) . Chemokine levels were determined with a sandwich ELISA technique ( Duoset , R&D Systems , Minneapolis , MN ) . SNPs in the CCL1 and CCR8 genes were genotyped in patients with TBM ( N = 114 ) , PTB ( N = 159 ) , and in Vietnamese Kinh population controls ( N = 188 ) . This genotyping was performed as part of a larger genome-wide genetic association study of TB using the Affymetrix 250K NspI Chip ( unpublished ) . The whole genome SNP genotyping was performed according to the manufacturer's specifications and the data obtained was analyzed following rigorous quality control . Briefly , data quality control was performed using DM , BRLMM , RELPAIR , and manual viewing of cluster plots prior to statistical analysis . STRUCTURE and Eigentstrat were also used to analyse the population structure of the sample set . Genomic DNA quality was first assessed with 50 control SNPs and only samples with a call rate of greater than 93% were studied further . For each polymorphism in the full dataset , filter criteria were applied that included <5% missing values and HWE P value>10−5 . Power for this study was calculated by using Power Calculator for Genetic Studies , CaTS version 0 . 0 . 2 ( http://www . sph . umich . edu/csg/abecasis/CaTS ) . With a sample size of controls = 188 and PTB = 159 we have 82% power to detect an effect with an odds ratio of 2 for SNPs with an allele frequency of 10% and significance level of 0 . 01 . With a sample size of controls = 188 and TBM = 114 , we have a power of 71% to detect the same effects . Genotyping was also carried out on selected CCL1 SNPs using a larger sample set TBM ( N = 162 ) , PTB ( N = 175 ) , and in Vietnamese Kinh population controls ( N = 380 ) . This was performed by a MassARRAY™ technique ( Sequenom , San Diego , USA ) using a chip-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometer as previously described [18] . All of the CCL1 SNPs genotyped by Sequenom were in Hardy Weinberg Equilibrium ( HWE ) ( P>0 . 05 ) in population controls . Univariate analysis was performed for categorical variables with a Chi-Square test . Two-sided testing was used to evaluate statistical significance .
We hypothesized that macrophages from individuals with different TB clinical phenotypes have distinct gene expression profiles in response to Mtb stimulation . All subjects with pulmonary and meningeal disease had been treated and were free of symptoms at the time of venipuncture . Gene expression of MDMs from subjects with three clinical forms of TB including LTB , PTB , and TBM ( n = 4 in each group ) was examined by microarray . MDMs were stimulated either with a whole cell lysate of Mtb H37Rv or PBS for 4 hours . RNA expression was analyzed using a Human Genome U133 Plus 2 . 0 Array ( Affymetrix , USA ) which contains probe sets for 47 , 000 transcripts including 38 , 500 well-characterized human genes . We compared RNA transcription levels in Mtb-stimulated ( n = 12 ) versus PBS-stimulated ( n = 12 ) MDMs . 1 , 608 genes with a FDR of <5% and a P value of <0 . 05 by Students's t-test were differentially expressed by greater than 2-fold ( Table 1 ) . Of these genes , 1 , 260 were up-regulated and 348 genes were down-regulated . A list of the 1 , 608 genes that were differentially expressed in the two groups ( n = 24 ) with their mean expression intensities , FDR and P values are presented in Table S1 . 74 genes were up-regulated more than 10-fold , whereas only one gene was down-regulated by greater than 10-fold ( Table 1 ) . We used PANTHER ( Protein Analysis Through Evolutionary Relationships; http://www . pantherdb . org/ ) to analyze the molecular functions and biological processes of genes induced and repressed in Mtb-stimulated MDMs . The changes in gene expression induced after stimulation contained 144 ( 8 . 4% ) immunity and defense genes , including cytokines , chemokines , and receptors . Thirty six of these genes ( 25% ) were up-regulated more than 10-fold . In contrast , no immunity and defense genes were repressed more than 10-fold . Other categories included; development ( 6 . 7% ) , protein and nucleic metabolism ( 19 . 2% ) and signal transduction ( 11 . 9% ) . By comparison to the entire human genome , the proportion of immunity and defense genes is 5 . 2% . Percentages of other categories include: development ( 8 . 5% ) , protein and nucleic metabolism ( 25 . 1% ) and signal transduction ( 13 . 4% ) . To examine whether individuals with different clinical forms of TB have distinct gene expression profiles , we calculated the fold stimulation of each gene for each individual ( dividing Mtb stimulated value by the unstimulated value ) and then calculated the ratios of gene expression levels in each pair of TB forms . Six pair-wise comparisons in Table 1 show the change of gene expression between disease types ( in fold change ) . 33 genes were differentially expressed between disease types with a ratio >10 and 228 genes had a ratio from 5 to 10 . In Table 2 , half of the genes with a ratio >10 ( 16/33 ) were immunity genes including chemokines , cytokines and immune receptors . Others such as MMP1 and HAS1 are involved in degrading the extracellular matrix [23] . When all 3 clinical groups were compared , 16 genes had expression values that were significantly different ( CXCL5 , EREG , TNIP3 , INHBA , HAS1 , MGC10744 , CCL1 , KCNJ5 , SERPINB7 , HS3ST2 , APOBEC3A , MYO10 , SLC39A8 , CXCL11 , F3 , and DUSP5 , ANOVA <0 . 05 ) . We then compared expression values of pairs of clinical groups . There were 11 genes highly expressed in TBM in comparison to other forms of TB ( Table 2 ) . 6/11 genes ( IL1B , CXCL5 , EREG , TNIP3 , CCR2 , and INHBA ) were significantly induced in TBM in comparison to PTB ( t test , P<0 . 05 ) , and all are genes related to immune function . 5/11 genes were highly expressed in TBM in comparison to LTB ( IL12B , PTGS2 , MMP1 , IL23A , and CCL20 ) however this did not reach statistical significance due to a consistent outlier in the LTB group ( L2 which does not cluster with the other samples; see below ) . Twelve genes were highly expressed in PTB in comparison to LTB and TBM ( PTB/LTB; MMP1 , IL23A , HAS1 , PTGS2 , MGC10744 , CCL20 , CCL1 , and IL12B , PTB/TBM; HAS1 , KCNJ5 , SERPINB7 , and HS3ST2 ) . 6/12 had significantly different expression levels ( t test , P<0 . 05; Table 2 ) . Nine genes were induced in LTB more than in other TB and 7 of these reached statistical significance ( LTB/TBM; APOBEC3A , LTB/PTB P2RY13 , MYO10 , SLC39A8 , CXCL11 , F3 , APOBEC3A , DUSP5 ) . Together these results suggest that gene expression profiles in Mtb-stimulated macrophages may distinguish between the 3 different clinical forms of TB , LTB , PTB , and TBM . We used real-time PCR using a TaqMan Low Density Array technique to confirm microarray results in 86 genes in an extended sample set which included 12 LTB , 12 PTB , and 10 TBM individuals . Fifty-eight of the 86 genes were selected from the microarray data based on high levels of induction ( >15 fold ) or repression ( >5 fold ) following Mtb stimulation . Forty six genes were selected based on array expression differences among the 3 clinical groups ( >5 fold ) . We first assessed whether the expression patterns of the 58 up and down-regulated genes were replicated in the independent sample set using RT-PCR . In total , 90% ( 52/58 ) of the microarray results were confirmed by RT-PCR when assessing Mtb and PBS-stimulated expression values in the validation sample set ( Table 3 and Table S2 ) . The RT-PCR results showed that 5/58 genes ( IFIT1 , CXCL6 , MERTK , CD36 , and MS4A6A ) were not significantly induced or repressed by Mtb stimulation ( n = 34; P>0 . 05 by t-test ) and the expression pattern of one gene , CCR2 , was reversed ( Table 3 ) . In addition , the majority of the genes in the validation group ( n = 34 ) had a higher induction level in comparison to the microarray group ( n = 12; Table 3 ) . We next compared gene expression levels in the 3 clinical groups in the validation sample set . The RT-PCR results showed that 2/46 genes ( CCL1 and HS3ST3B1 ) were differentially expressed in groups with different TB phenotype ( P<0 . 05 by t-test; Table 4 ) . CCL1 was up-regulated in PTB when compared to LTB in both the RT-PCR LDA validation samples ( P = 0 . 02 by t-test; 1 . 9-fold ) and the initial microarray analysis ( 12 . 8-fold; Table 4 and Table S3 ) . HS3ST3B1 was down regulated in LTB when compared to TBM in the RT-PCR LDA validation samples ( P = 0 . 008 by t-test; ratio = 0 . 4 ) but this pattern of expression was reversed in the initial microarray analysis ( ratio = 12 . 8 ) ( Table 4 ) . Scatter plots of CCL1 and HS3ST3B1 are shown in Figure 1 along with 3 other representative genes . Seven other genes ( INHBA , TSLP , LY6K , IL12B , MMP1 , CCL20 and HAS1 ) had a greater than 2-fold change in expression ratios of the validation samples in each pair-wise comparison , but these differences did not reach statistical significance ( P>0 . 05; Table 4 ) . These results suggest that the different TB clinical phenotypes cannot easily be distinguished by examining expression levels of single genes . We next hypothesized that expression profiles from multiple genes would need to be combined to detect patterns that could distinguish the different clinical disease phenotypes . We selected 1 , 608 highly induced or repressed genes from the microarray data set ( Table S1 ) and used an unsupervised , hierarchical clustering algorithm [21] of 12 individual samples to attempt to distinguish the profiles of the 3 groups ( Figure S1 ) . These results show that , ( 1 ) there was more relatedness between expression levels of samples from the same clinical group , i . e . L1 and L3 are very similar , P1 , P2 and P3 are very similar , and M1 and M4 are very similar , and ( 2 ) one large cluster containing data from all TBM subjects , all PTB subjects and one LTB subject ( L4 ) is very distinct to data from subjects L2 , L1 and L3 . Together , these findings suggest that cluster analysis can partially distinguish different clinical forms of TB . CCL1 was the only gene whose expression was up-regulated in both the microarray and validation data sets when comparing clinical forms of TB ( PTB vs LTB ) . We next examined whether genetic variants of CCL1 were associated with susceptibility to TB in a case-control study with TBM ( N = 114 ) and PTB patients ( N = 159 ) , and population controls ( N = 188 ) by using gene chip mapping assays . Forty nine SNPs were genotyped across a 200 kb region of the chromosome 17 CCL gene family cluster . Eight of the forty nine SNPs were associated with TB . To further locate the region associated with TB , we arbitrarily divided the whole region into four 50 kb sections . The first section containing CCL2 had 1/9 associated SNPs , the second containing CCL7 and CCL11 had 1/9 associated SNPs , the third containing CCL8 and CCL13 had 1/7 associated SNPs and the fourth containing CCL1 had 4/23 associated SNPs ( Figure 2 ) . To investigate this further we genotyped 10 SNPs nearby and in the coding region of CCL1 using Sequenom . Two more SNPs in the CCL1 gene were significantly associated with TB by genotypic comparison ( Table 5 ) . Together these results suggest that polymorphisms near and within the CCL1 genomic region are associated with susceptibility to different TB phenotypes . To further investigate the role of CCL1 in Mtb pathogenesis , we examined regulation of its expression . We found that CCL1 mRNA expression was cell-specific and highly induced in monocytic ( THP-1 , U937 , & PBMCs ) cells stimulated with Mtb lysates or TLR ligands ( LPS , PAM2 , PAM3 ) ( Figure 3A ) . In contrast , no expression was found in epithelial cell lines ( HeLa & A549 , data not shown ) . We also found that CCL1 protein secretion was induced in THP1 cells and PBMCs by Mtb , including whole cell lysates , cell wall and cytosolic fractions [Figure 3B and data not shown; PBS vs TB whole cell lysate ( TBWCL; P = 0 . 01 ) , PBS vs TB cell wall ( TBCW; P = 0 . 006 ) and PBS vs TB cytosol ( P = 0 . 02 ) ] . Finally , we examined CCL1 expression in murine bone-marrow derived macrophages stimulated with PBS , LPS or Mtb from wild-type ( WT ) and Myd88−/− mice . CCL1 expression was highly induced by LPS and Mtb in WT bone marrow macrophages ( BMMs ) . However , CCL1 expression was decreased in MyD88-deficient BMMs stimulated with LPS ( P = 0 . 03 ) or Mtb ( P = 0 . 002 ) ( Figure 3C ) . Together , these results suggested that CCL1 expression is highly enriched in monocytes and induced by Mtb components in a MyD88-dependent manner .
In this study we examined macrophage transcriptional profiles in individuals with different clinical forms of TB . The majority of reported TB microarray studies have examined healthy donors , cell lines or murine cells [13]–[17] . Only one previous study has compared gene expression profiles of individuals with different clinical forms of TB [24] . Mistry et al obtained whole blood from individuals with active , latent , cured ( following 1 disease episode ) and recurrent TB ( following 2–3 episodes ) [24] . Discriminant analysis suggested that 9 genes could distinguish the 4 clinical TB groups [24] . We examined these 9 genes in our data set and found these genes could not differentiate our latent and cured TB groups . These differences may be attributable to the study design , which was substantially different from the current investigation with regard to cell population ( whole blood vs MDMs ) , stimuli ( none vs whole cell Mtb lysate ) , ethnic background ( South African vs Vietnamese ) and comparison of different clinical phenotypes . Despite these methodologic differences , both studies suggest that host gene expression profiles uniquely identify groups of individuals with different types of TB . Our study further illustrates that macrophages , the primary host cell involved in TB pathogenesis , are a key source of the unique transcriptional profile that distinguishes clinical forms of TB . One limitation of our study was the small sample size . Although this is the largest number of individuals ever studied in a TB microarray study , comparable only to the study by Mistry et al [24] , the sample size remains small for this statistically challenging question . To overcome some of the limitations of a small sample size for microarrays ( n = 12 ) , we included an independent set of samples for validation ( n = 34 ) . We also chose to use a whole cell lysate of a standardized Mtb strain rather than live organisms and a relatively short stimulation time ( t = 4 hours ) to minimize variation in our stimulation conditions and to enhance the detection of early innate immune response genes . We examined these cells in an ex vivo environment to avoid variability that is attributable to complex in vivo environments . For example , we studied individuals after they had been treated for TB to avoid detecting gene expression changes that are attributable to stimulation of in vivo inflammatory pathways from active disease . We also chose to study macrophages rather than whole blood in order to concentrate on a single cell population that is most relevant for Tb pathogenesis . A number of studies have shown that the strain of Mtb induces different immune responses [25] , [26] . Although the choice of Mtb strain could stimulate different gene expression profiles , we chose to study the commonly used laboratory strain ( Mtb H37Rv ) . Each of these experimental conditions was selected to maximize the opportunities of detecting differences attributable to genetic variation in the macrophage innate immune response to TB . Comparison of gene expression results with alternative experimental conditions ( such as different cell types , Mtb strains , Mtb growth conditions , and time points ) could further illuminate the role of these genes in Tb pathogenesis . In addition to comparing expression profiles among people with different types of TB , our study contributes further data on the set of genes that are activated in response to Mtb stimulation of macrophages . Our results demonstrated that 1 , 608 genes in macrophages were stimulated ( up or down-regulated ) by Mtb . Furthermore , 90% of a subset of these genes ( n = 58 genes induced >15 fold by Mtb stimulation ) in a second round validation also showed altered expression . Many genes identified in our study have also been detected in previous studies investigating the host response to Mtb infection [13] , [16] , [17] . Ragno et al studied THP-1 cells stimulated with live TB and measured the expression of 375 genes after 6 or 12 hours of stimulation . Our data confirmed 15 genes significantly induced following 6 hr stimulation in their data set ( MIP-1α , MIP-1β , MIP-3α , MPIF-1 , PARC , RANTES , IL-8 , GRO-α , GRP-β , GRO-γ , CCL1 , CCR3 , IL-1β , TNFα , and VEGF ) [17] . Nau et al studied primary human MDMs stimulated with live Mtb [16] . Eleven genes were highly expressed in both data sets ( TNFAIP6 , CXCL3 , CXCL1 , CCL4 , PTGS2 , SERPINB2 , PTX3 , INHBA , TRAF1 , JAG1 , and SOD2 ) and 3 genes were inhibited ( MERTK , GLUL , and DAB2 ) . These gene lists include cytokines , chemokines and immune receptors , which may be involved in inflammatory responses in the early phases of defense against Mtb . All of the up-regulated genes identified by Nau et al were found in our dataset [16] . In contrast , only 50% ( 24/50 ) of highly expressed genes in our dataset were identified by Nau et al , a difference that is likely due to the array sizes that were utilized ( 38 , 000 vs . 980 genes ) . Although these microarray studies have important methodologic differences ( e . g primary cells vs cell lines , healthy subjects vs . TB patients , live versus dead Mtb stimulation , stimulation times , arrays and genes analyzed ) , all of these studies have identified novel genes potentially related to the host macrophage response to Mtb . Our study compares transcriptional profiles of individuals with TBM with individuals with other forms of TB . We identified genes that were distinctly expressed in macrophages from individuals with a history of TBM . After bacilli invade the host lung within the pulmonary alveolar macrophage , they replicate and disseminate to the regional lymph nodes . During this early stage of infection , before the development of adaptive immunity , the bacteria can spread haematogenously to other organs in the body and cause extrapulmonary disease , such as TBM [27] , [28] . This step may be determined by the nature and extent of the innate immune response activated by infected macrophages . We found that several macrophage immune response genes ( IL1B , IL12B , TNF , TNIP3 , CXCL10 , CXCL11 , CCL12 , and CCL1 ) were up-regulated in TBM subjects in comparison to those with PTB and LTB . In addition , some genes , such as MMP1 and HAS1 , were found with differing expression in PTB and TBM patients . These genes are involved in degrading the extracellular matrix and could mediate a role in granuloma formation and bacillus containment , which could influence dissemination and development of TBM [23] . Although the relationship between the inflammatory response and TBM pathogenesis is only partially understood , excessive immune activation may be intimately associated with disease severity and outcome . Case-control genetic association studies of biologically plausible candidate genes have been performed with the hope to identify genes involved in susceptibility to , and clinical outcome of , TB . However it has always been challenging to identify potential candidate genes in an unbiased manner . The expression profiling study we describe here can serve as a hypothesis generating , unbiased methodological approach to identify genes for potential association studies . Despite this advantage , gene regulation is not the only mechanism for genetic resistance or susceptibility and non-synonymous coding region SNPs which alter protein structure and function also play an important role . From the genes that were differentially expressed between TB disease types , as assessed by microarray , we tested 46 genes in a separate , larger sample set by RT-PCR . The expression of only one of these genes , CCL1 , remained significantly different between patients with different clinical TB outcomes . To test our selection approach we performed a case-control genetic association study and found that SNPs near CCL1 were associated with susceptibility to PTB . The fact that SNPs near CCL1 were significantly associated with PTB in our study highlights the feasibility of this unbiased selection approach . Even though the associated SNPs are not within the CCL1 coding region , it is a likely candidate gene due to it's proximity to the cluster of associated SNPs and its functional relevance . CCL1 , like other members of the CC chemokine family , is an inflammatory mediator that stimulates the migration of human monocytes [29] . CCL1 is produced by monocytes ( as well as other cells ) and binds its receptor CCR8 , which is present on lymphocytes and monocytes [30] . Interestingly , CCR8 has enriched expression on Th2 and regulatory T cells and may influence the development of Th2 type T cell responses in vivo [31] , [32] . In addition , CCR8 regulates migration of dendritic cells to lymph nodes [33] . Hoshino et al [34] found that the expression of CCR8 was specifically up-regulated by CCL1 stimulation of peritoneal macrophages , which may lead to cell aggregation at a site of tissue damage . In the lungs , CCL1 expression was up-regulated in Mycobacterium bovis purified protein derivative ( PPD ) induced granulomas [35] . In this study , we found that CCL1 expression was induced by Mtb and TLR ligands in several monocyte/macrophage lineages . Furthermore , we found that its expression was MyD88-dependent when cells were stimulated with LPS or Mtb . Genetic variation leading to the loss or alteration of CCL1 function may influence the ability of T cells , monocytes and dendritic cells to migrate to the site of infection , aggregate into granulomas and develop an effective immune response . This may result in inadequate containment of the bacterium and allow unimpeded bacterial growth leading to pulmonary disease . With currently available tools , clinicians are unable to identify the subset of latently infected patients who will develop active disease . Furthermore , there are no techniques available to prospectively identify individuals at risk for the devastating consequences of TBM versus more treatable forms of TB such as localized pulmonary disease . Further studies in this area could lead to tests that could alter treatment algorithms with more accurate prognostic information . In addition , such studies may lead to novel molecular insight into TB pathogenesis .
|
Although TB is a leading cause of death worldwide , the vast majority of infected individuals are asymptomatic and contains the bacillus in a latent form . Among those with active disease , 80% have localized pulmonary disease and 20% have disseminated forms . TB meningitis ( TBM ) is the most severe form of TB with 20–25% of sufferers dying , and of the survivors , many have disability . We currently do not understand the host factors that regulate this diverse spectrum of clinical outcomes . We hypothesized that variation in innate immune gene function is an important regulator of TB clinical outcomes . We measured the mRNA expression levels of >38 , 500 genes in macrophages taken from people with a history of latent , pulmonary , or meningeal TB and found genes with unique activation patterns among the clinical groups . Furthermore , we studied one of these genes further and found that CCL1 polymorphisms were associated with pulmonary TB ( PTB ) but not other types of TB disease . To our knowledge , this is the first study to combine mRNA expression studies with genetic studies to discover a novel gene that is associated with different clinical outcomes in TB . We speculate that this approach can be used to discover novel strategies for modulating immune function to prevent adverse outcomes in TB .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/infectious",
"diseases",
"of",
"the",
"nervous",
"system",
"respiratory",
"medicine/respiratory",
"infections",
"genetics",
"and",
"genomics/gene",
"expression",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"infectious",
"diseases/bacterial",
"infections",
"immunology/immunity",
"to",
"infections",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2008
|
Identification of Tuberculosis Susceptibility Genes with Human Macrophage Gene Expression Profiles
|
The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation . In this paper , we describe how Bayesian belief propagation in a spatio-temporal hierarchical model , called Hierarchical Temporal Memory ( HTM ) , can lead to a mathematical model for cortical circuits . An HTM node is abstracted using a coincidence detector and a mixture of Markov chains . Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation . Anatomical data provide a contrasting set of organizational constraints . The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others . We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect . We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model .
Understanding the computational and information processing roles of cortical circuitry is one of the outstanding problems in neuroscience . The circuits of the neocortex are bewildering in their complexity and anatomical detail . Although enormous progress has been made in the collection and assimilation of data about the physiological properties and connectivity of cortical neurons , the data are not sufficient to derive a computational theory in a purely bottom-up fashion . The theoretical setting of hierarchical Bayesian inference is gaining acceptance as the framework for understanding cortical computation [1]–[5] . Tai Sing Lee and David Mumford [1] suggested that algorithms for Bayesian belief propagation might model the interactive feed-forward and feedback cortical computations . Concurrently , Karl Friston [5] reviewed the structure of the anatomical organization of the neocortex and suggested its strong correspondence to hierarchical Bayesian generative models . Friston recently expanded on this to suggest an inversion method for hierarchical Bayesian dynamic models and to point out that the brain , in principle , has the infrastructure needed to invert hierarchical dynamic models [6] . However , there still remains a gap between our understanding of learning and inference in hierarchical Bayesian models and our understanding of how it is implemented in cortical circuits . In a recent review , Hegde and Felleman pointed out that the “Bayesian framework is not yet a neural model . [The Bayesian] framework currently helps explain the computations that underlie various brain functions , but not how the brain implements these computations” [2] . This paper is an attempt to fill this gap by deriving a computational model for cortical circuits based on the mathematics of Bayesian belief propagation in the context of a particular Bayesian framework called Hierarchical Temporal Memory ( HTM ) . Belief propagation techniques can be applied to many different types of networks . The networks can vary significantly in their topology , in how they learn ( supervised , unsupervised , or non-learning ) , and in how they incorporate or do not incorporate time . Therefore , to map the mathematics of Bayesian belief propagation onto cortical architecture and microcircuits we must start with a particular Bayesian framework that specifies these variables . The starting point for the work presented in this paper is a model called the Memory-Prediction Framework , first described by one of this paper's authors , Hawkins , in a book titled “On Intelligence” [7] . The Memory-Prediction Framework proposed that the neocortex uses memory of sequences in a hierarchy to model and infer causes in the world . The Memory-Prediction Framework proposed several novel learning mechanisms and included a detailed mapping onto large scale cortical-thalamic architecture as well as onto the microcircuits of cortical columns . However , the Memory-Prediction Framework was not described in Bayesian terms and was presented without the rigor of a mathematical formulation . This paper's other author , George , recognized that the Memory-Prediction framework could be formulated in Bayesian terms and given a proper mathematical foundation [8] , [9] . We call this formulation Hierarchical Temporal Memory ( HTM ) and it is currently being applied to problems of machine learning and inference . The final step in this theory is to map the mathematics of HTM directly to cortical-thalamic anatomy and the microcircuits of cortical columns . That is the goal of this paper . We will work back from the formal expression of HTM and derive cortical microcircuits by matching the computational specifications of the theory with known biological data . The resultant biological circuit supports all the Bayesian computations required for temporal , feed-forward , and feedback inference . The elements of the circuits are also consistent with each other in that they operate under the same set of assumptions and work together in a hierarchy . Several researchers have proposed detailed models for cortical circuits [10]–[12] . Some of these models exhibit interesting pattern recognition properties and some have been used in the explanation of physiological phenomena . However , these models do not incorporate the concepts of Bayesian inference in a hierarchical temporal model . Other researchers [4] , [13] have proposed detailed mechanisms by which Bayesian belief propagation techniques can be implemented in neurons . Their work suggests that , at a neuron level , machinery exists for implementing the types of computations required for belief propagation . However , they did not attempt to map these implementations to detailed cortical anatomy . To our knowledge , the work in this paper is the first attempt to map the theory of Bayesian belief propagation and hierarchical and temporal inference onto cortical circuitry . ( Partial details of this work have been published earlier [9] , [14] . ) Deciphering the functional connectivity of the cortical circuits is a formidable task and is associated with the perils involved in the reverse engineering of a complex system . The circuits derived in this chapter can provide a hypothesis-driven framework for examining the neural connectivity . As with any theory , it is expected that the particular instantiation described here will need to be revised as more data is obtained and more aspects of cortical computations , like attention , timing , and motor action , are incorporated . The circuit derived here could act as a basis for such explorations . In addition to providing a template for understanding cortical circuits [15] , the theory presented here can be useful in the modeling of physiological phenomena . As an example , we simulate the subjective contour effect using feedback from a high-level belief using the derived circuits . Having a complete biological mapping of a computational theory can also help in the design of hypothesis-driven biological experiments . The rest of this paper is organized in such a manner that the computational parts are clearly separated from the biological aspects . The Model section deals exclusively with the computational aspects of HTMs . In this section , we briefly describe the HTM theory and take a detailed look at the inference mechanism in HTM nodes . The Bayesian belief propagation equations for the computations in an HTM node are described . We then describe an abstract circuit implementation of these equations using neuron-like elements . The Results section of the paper , which deals primarily with the biological implementation , maps this abstract neural implementation to the laminar biological cortical circuitry by matching the computational specifications with anatomical data . This section also provides example applications of this circuit in the modeling of physiological phenomena . In the Discussion section we discuss variations , omissions , and extensions of the proposed circuits .
Hierarchical Temporal Memory is a theory of the neocortex that postulates that the neocortex builds a model of the world using a spatio-temporal hierarchy . According to this theory , the operation of the neocortex can be approximated by replicating a basic computational unit – called a node – in a tree structured hierarchy . Each node in the hierarchy uses the same learning and inference algorithm , which entails storing spatial patterns and then sequences of those spatial patterns . The feed-forward output of a node is represented in terms of the sequences that it has stored . The spatial patterns stored in a higher-level node record co-occurrences of sequences from its child nodes . The HTM hierarchy is organized in such a way that higher levels of the hierarchy represent larger amounts of space and longer durations of time . The states at the higher levels of the hierarchy vary at a slower rate compared to the lower levels . It is speculated that this kind of organization leads to efficient learning and generalization because it mirrors the spatio-temporal organization of causes in the world . In our research , HTMs have been used successfully in invariant pattern recognition on gray-scale images , in the identification of speakers in the auditory domain and in learning a model for motion capture data in an unsupervised manner . Other researchers have reported success in using HTMs in content-based image retrieval [16] , object categorization [17] , and power system security analysis [18] . Another set of researchers has explored hardware implementations and parallel architectures for HTM algorithms [19] . HTMs can be specified mathematically using a generative model . A simplified two-level generative model is shown in Figure 1 . Each node in the hierarchy contains a set of coincidence patterns and a set of Markov chains where each Markov chain is defined over a subset of the set coincidence patterns in that node . A coincidence pattern in a node represents a co-activation of the Markov chains of its child nodes . A coincidence pattern that is generated by sampling a Markov chain in a higher level node concurrently activates its constituent Markov chains in the lower level nodes . For a particular coincidence pattern and Markov chain that is ‘active’ at a higher-level node , sequences of coincidence patterns are generated concurrently by sampling from the activated Markov chains of the child nodes . The process of learning an HTM model for spatio-temporal data is the process of learning the coincidence patterns and Markov-chains in each node at every level of the hierarchy . Although algorithms of varying levels of sophistication can be used to learn the states of an HTM node , the basic process can be understood using two operations , ( 1 ) memorization of coincidence patterns , and ( 2 ) learning a mixture of Markov chains over the space of coincidence patterns . In the case of a simplified generative model , an HTM node remembers all the coincidence patterns that are generated by the generative model . In real world cases , where it is not possible to store all coincidences encountered during learning , we have found that storing a fixed number of a random selection of the coincidence patterns is sufficient as long as we allow multiple coincidence patterns to be active at the same time . Motivation for this method came from the field of compressed sensing [20] . The HMAX model of visual cortex [21] and some versions of convolutional neural networks [22] also use this strategy . We have found that reasonable results can be achieved with a wide range of the number of coincidences stored . We have not yet developed a good heuristic for determining an optimal value of this parameter . For simplicity , we will only illustrate the case where a single coincidence pattern is active in a node at a time , but in our real implementations we use sparse distributed activations of the coincidence patterns . Each Markov chain in a node represents a set of coincidence patterns that are likely to occur sequentially in time . This temporal proximity constraint is analogous to the temporal slowness principle used in the learning of of invariant features [23]–[26] . The learning of the mixture of Markov chains is simplified considerably because of the slowness constraint . We have found that a simple way to learn the mixture of Markov chains for real world cases is to learn a large transition matrix that is then partitioned using a graph partitioning algorithm [27] . Details of one method of learning higher order Markov chains is available in [28] . For the rest of this paper , we will focus on the inference mechanism in HTM nodes that have finished their learning process . A node that has finished its learning process has a set of coincidence patterns and a set of Markov chains in it . Figure 2 ( A ) shows a node that has 5 coincidence patterns and 2 Markov chains . The inference mechanism in an HTM network is based on the propagation of new evidence from anywhere in the network to all other parts of the network . The presentation of a new image to the first level of an HTM vision network is an example of new evidence . Propagation of this evidence to other parts of the network results in each node in the network adjusting its belief states given this evidence . For example , a new image can lead to a different belief in the top level of the network regarding the identity of the object in that image . In general , HTM networks infer on time-varying inputs . Inference on a static input is a special case of this computation . Information can also be propagated down in the hierarchy for attention , segmentation , and filling in missing inputs . HTM networks use Bayesian belief propagation for inference . Bayesian belief propagation originally was derived for inference in Bayesian networks [29] . Since an HTM node abstracts space as well as time , new equations must be derived for belief propagation in HTM nodes . These equations are described in the next section . In general , the messages that come into an HTM node from its children represent the degree of certainty over the child Markov chains . The node converts these messages to its own degree of certainty over its coincidence patterns . Based on the history of messages received , it also computes a degree of certainty in each of its Markov chains . This is then passed up to the next higher-level node . What the node receives from its parent is the parent's degree of certainty over this HTM node's Markov chains . The Markov chains are then ‘unwound’ in a step-by-step manner to find the top-down probability distribution over coincidence patterns . From this , the node's degrees of certainty over its child nodes' Markov chains are calculated . These feedback messages are then sent to the child nodes . Table 1 summarizes the computation of belief propagation messages in an HTM node . We will now describe the notation and meaning of these equations using the reference HTM node shown in Figure 2 . Detailed derivations of these equations are given in supporting information Text S1 . A summary of the notation in these equations is given in Table 2 . Each equation is considered in detail in the sections that follow . In these equations , the coincidence patterns are referred to using and the Markov chains are referred to using . The HTM node shown in Figure 2 ( A ) contains 5 coincidence patterns and 2 Markov chains . The transition probability matrix of the Markov chain is denoted by . This term appears in Equations 4 and 7 . Each coincidence pattern in the node represents a co-occurrence of the temporal groups from its children . Coincidence pattern specifications are used in the computations described in equations 2 and 9 . Each node receives feed-forward input messages from its children and sends feed-forward messages to its parent . The feed-forward input messages are denoted by . The feed-forward output message of the node is denoted by . Similarly , the node receives feedback messages from its parent and sends feedback messages to its child nodes . The feedback input message to the node is denoted by . The feedback output messages that the node sends to its child nodes are denoted by . The equations shown in Table 1 describe how the output messages are derived from the input messages . From the viewpoint of the node , the feed-forward messages carry information about the evidence from below . Evidence from below at any time is denoted by . Similarly evidence from the parent is denoted by . Equation 2 describes how the node calculates its likelihood of coincidence patterns , using the messages it gets from the children . The bottom-up likelihood of coincidence pattern at time is represented by . The likelihood of each coincidence pattern is calculated as the product of the message components corresponding to that coincidence pattern . In Equation 3 , the bottom-up likelihood of Markov chain at time is denoted by , where the term represents the sequence of bottom-up evidences from time to time . This reflects that the likelihood of the Markov chains depends on the sequence of inputs received by the node . The variables and defined in Equations 4 and 7 are state variables that are updated in a recursive manner at every time instant . These are dynamic programming [30] , [31] variables , each defined over all pairwise combinations of coincidence patterns and Markov chains . For example , is value of the feed-forward dynamic programming variable at time corresponding to coincidence and Markov chain . In Equations 4 and 7 , the states are updated every time step by passing the state from the previous time step through the Markov transition matrices and by combining them with bottom-up/top-down evidence . An illustrative example showing how the belief propagation equations map onto a toy visual pattern recognition problem is given in supporting information Text S2 . Readers who are not familiar with belief propagation can use this example to develop intuition for the nature of the messages . We examine the equations in Table 1 in more detail in the next section as we consider how to implement them using neuron-like elements . This section describes an implementation of the HTM belief propagation equations using neuron-like elements . The implementation will be described with respect to the reference HTM node in Figure 2 . The neuronal implementation of the equations in Table 1 is described in the following subsections . The subsections follow the order of table row numbers . The purpose of this section is to show how the equations of HTM belief propagation can map onto a hypothetical neuronal system . In the Results section , we map this hypothetical model onto actual cortical anatomy . The equations in Table 1 are self-consistent and sufficient for some learning and inference tasks . However , they do not address several issues required for many real world problems . Specifically , they do not address how feedback from a parent node to a child node can influence the child node's feed-forward output , and they do not address issues of specific timing . The following sections address these issues .
An area of cortex can be thought of as encoding a set of patterns and sequences in relation to the patterns and sequences in regions hierarchically above and below it . The patterns correspond to the coincidence patterns in an HTM node and the sequences correspond to the Markov chains . An HTM Node , as described earlier in this paper , encodes a set of mutually exclusive patterns and Markov chains . A region of cortex that has several patterns simultaneously active will be implemented using several HTM nodes . Figure 8 ( D ) shows the HTM implementation of the logical cortical hierarchy shown in 8 ( C ) . This arrangement corresponds to one of the basic organizing principles of the visual system where neurons in higher-level visual areas receive inputs from many neurons with smaller receptive fields in lower-level visual areas [36] . In addition , due to the temporal nature of HTM , this arrangement corresponds to a temporal hierarchy analogous to the kind reported by Hasson and colleagues [37] . In this highly simplified mapping , the area V1 is implemented using 4 HTM nodes while area V2 is implemented using 2 HTM nodes . Typically , the number of non-exclusive patterns that needs to be maintained decreases as you ascend in the hierarchy . Therefore , higher-level cortical regions can possibly be modeled using a fewer number of HTM nodes . Note that this is a representative diagram . A cortex-equivalent implementation of V1 and V2 could require several thousand HTM nodes for each cortical area and the receptive fields of the nodes would typically be overlapping . The coincidence patterns and Markov chains in an HTM node can be represented using random variables . A cortical column can be thought of as encoding a particular value of the random variable that represents the coincidence patterns in the HTM node . The feed-forward and feedback connections to a set of cortical columns carry the belief propagation messages . Observed information anywhere in the cortex is propagated to other regions through these messages and can alter the probability values associated with the hypotheses maintained by other cortical columns . In HTMs these messages are computed using the mathematics of Bayesian belief propagation as we described earlier . Our proposal for the function , connectivity and physical organization of cortical layers and columns is shown in Figure 9 . This figure corresponds to the laminar and columnar cortical circuit implementation of the belief propagation equations for the reference HTM node in Figure 2 . Figure 9 was created by arranging the neurons of the abstract neuronal implementation of HTM belief propagation into columns and laminae in such a way that the resultant circuit matched most of the prominent features found in mammalian neocortex . In the following sections we de-construct this picture and examine the anatomical and physiological evidences for the specific proposals . This will also illuminate the process that we went through to arrive at the circuit shown in Figure 9 . The circuits in Figure 9 provide an exemplar instantiation of the Bayesian computations in laminar and columnar biological cortical circuits . Several plausible variations and exceptions of this circuit can be found because of the degrees of freedom in the implementation of the belief propagation equations and because of the incompleteness of anatomical data . We will tackle some of these exceptions and variations as they come up in the appropriate context and also in the Discussion section . Although the main purpose of this paper is the exposition of HTM theory and its connection to biology , we believe it is useful to discuss our work applying HTMs to practical problems . In this section , we summarize the results of the work being done at Numenta in applying HTMs to the problem of visual object recognition . A detailed treatment of this topic is beyond the scope of this paper . We started by applying HTMs to a line drawing recognition problem that we call the Pictures problem . The Pictures data set consists of line drawings of 48 categories of objects . These line drawings are shown in Figure S1 . Each pattern is of size 32 pixels by 32 pixels . The goal was to train an HTM network to recognize test patterns with translations , severe distortions , scale and aspect ratio changes , clutter and noise . The Pictures data set has some properties that make it attractive for applying HTMs . Most objects occupy only a fraction of the 32×32 pixel input . This enables the creation of test images with large translations and scale variations while still maintaining the 32×32 pixel input dimensions . The objects are of different sizes . Some objects ( for example , the ‘dog’ ) contain other objects ( the ‘cat’ ) . Most of the objects are constructed from the same set of local features . This means that techniques that use local features alone are not adequate to recognize these objects . The spatial configuration of the local features ( i . e , the shape ) is important . Recognizing test patterns despite translations , distortions and clutter is a challenging task even on this seemingly simple data set . We found that HTM network hierarchies with four levels work best for the Pictures task . Adding more levels did not help in improving the recognition accuracy on our test set . The HTM networks are trained in a level-by-level manner , starting with the coincidence patterns and Markov chains at the first level and then moving up the hierarchy . During training , the network is shown programmatically constructed movies in which the objects undergo translations and scale variations in a smooth manner . The training strategy we outlined in the Model section was used for learning the coincidence patterns and Markov chains . More details about the training methods and the learned coincidence patterns and Markov chains can be found in [8] . A representative set of learned Markov chains is shown in Figure S2 . A challenging test set was created by programmatically distorting the training images and by adding noise . Examples of test images for the ‘table lamp’ category are shown in Figure S3 . The HTM networks reported in our previous work [9] used Markov chains based temporal pooling only at level 1 of the hierarchy and gave 49% recognition accuracy on this test set . We found that incorporating Markov chains based temporal pooling at higher levels increased the recognition accuracy on test sets to 72% . In comparison , a nearest neighbor classifier using exactly the training paradigm used to train the top level of the HTM gives only 35% accuracy . A stand-alone demonstration of this project that lets users interactively draw images to test the network is included with the NuPIC software available for download from Numenta's website ( http://www . numenta . com ) . The network performs impressively in qualitative testing . The Pictures demo , data set , and parameter files are supplied as part of the NuPIC software available from Numenta . We modified the network structure while maintaining the same spatial and temporal learning/inference algorithms to create an HTM network that can recognize grayscale images . In this network , the first level of coincidences were replaced with Gabor filters of different orientations . At all levels , the coincidence patterns were restricted to have spatial receptive fields smaller than that of the Markov chains . With these modifications , we could successfully train several gray scale image recognition networks . On the standard Caltech-101 benchmark [60] , our initial experiments with the network achieved 50% recognition accuracy with 15 training images and 62% recognition accuracy with 30 training images . We used a simple nearest neighbor classifier at the top of the hierarchy . Experiments on the Caltech-101 dataset were performed primarily to make sure that we are within the range of reported accuracies . We share many of the concerns expressed by Pinto et al [61] that the Caltech-101 data set and the associated train/test protocols are not sufficiently informative of the overall recognition capability of a system . For this reason , we did not spend time optimizing the performance of our networks for this data set . Caltech-101 images have low intra-category variation . Most of the images are centered and approximately of the same size . To see whether our system can handle large intra-category variations in gray-scale images , including translations and scale variations , we trained a network with 4 categories of images . These categories had a large amount of intra-category variation . The top of the network was exposed to over 10000 different training images . Figure S4 shows some examples of training images and Figure S5 shows some examples of test images for this network . On a hold out set , this network gave 92% accuracy . We also found that the network performs impressively in qualitative testing . A stand-alone demonstration of this network that lets users test their own images under different transformations is available for download from Numenta's website ( http://www . numenta . com/about-numenta/demoapps . php ) . We are also happy to note that researchers outside Numenta have had success training recognition systems using HTMs . A case study on recognizing architecture drawings , including detailed parameter files for NuPIC software , is available at http://www . numenta . com/links/vision_exp . php We have done a small set of experiments exploring the use of temporal information during inference . These experiments were performed on the Pictures data set . During inference , the network was shown a sequence of images . The first level of the network used the sequential information to compute the likelihood of Markov chains according to the equations we described in the Model section . We measured the recognition accuracy , on a frame-by-frame basis , while playing short ( 4 time frames ) of translating inputs in a noisy background . The temporal boundaries where the input switched from one category to another were not marked or transmitted to the network . The recognition accuracy of the network that used temporal inference was up to 30% higher compared to the recognition accuracy obtained by a sliding window averaging ( window length = 4 ) of frame-by-frame instantaneous recognition . More details on this experiment is available on Numenta's website ( http://www . numenta . com/links/tbi_overview . php ) . This experiment is also available as part of the NuPIC software from Numenta . We have not done any studies incorporating temporal inference for grayscale image recognition or incorporating it at multiple levels of the hierarchy . These topics are currently under investigation and development . We have also done experiments using feedback propagation in HTMs . The goal of these experiments was to verify that top-down propagation in HTMs can be used to locate and segment out objects in cluttered scenes with multiple objects . Figure 11 shows the results of inference and top-down propagation in a network that was trained on eight categories of images . During training , the objects were shown in isolation on a clean background . The test images contained multiple novel objects superposed on busy backgrounds . In most cases , one of the objects in the test image was the top result in the inference . Feedback propagation is initiated from the top of the network after the first flow of feed-forward propagation . After bottom-up propagation , the belief vector at the top of the network is modified such that the winning coincidence has strength one and all other coincidences have strength zero . This message is then propagated down in the network by combining with bottom-up information in the rest of the levels of the hierarchy . The resultant image obtained at the lowest level of the network isolates the contours of the recognized image from the background clutter and from other objects in the scene . These experiments show how top-down propagation in the current model can be used for segmentation , for the assignment of border-ownership , and for the ‘binding’ of features corresponding to a top-level hypothesis [62] . More examples of top-down propagation are available at http://www . numenta . com/links/top_down . php The cortical circuit described in this paper can be used for studying and modeling physiological phenomena . In this section , we report some preliminary positive results that we obtained modeling the subjective contour effect in visual inference [63] using these circuits . The primary goal of this section is to serve as a proof of concept for the possible applications of the circuit model . A detailed investigation of the subjective contours effect is beyond the scope of this paper . The subjective contour effect is a well known cognitive and physiological phenomenon . Figure 12 shows examples of Kanizsa diagrams that produce this effect . When viewing such diagrams , humans perceive edges even in regions where there is no direct visual evidence for edges . Lee and Nguyen [64] found that neurons in area V1 responded to such illusory contours even though their feed-forward receptive fields do not have any evidence supporting the presence of a line . In addition to finding the neurons in V1 that respond to the illusory contours , Lee and Nguyen also studied the temporal dynamics of their responses . The summary of their findings is that the population averaged response to illusory contours emerged 100 milliseconds after stimulus onset in the superficial layers of V1 and at approximately 120 to 190 millisecond in the deep layers . The responses to illusory contours in area V2 occurred earlier , at 70 milliseconds in the superficial layers and at 95 milliseconds in the deep layers . These findings suggest that top-down feedback is used in the generation of illusory contours . In [1] , Lee and Mumford suggested this could be the result of Bayesian computations . Their argument was that the presented stimulus , according to the statistics of the visual world , is adequate to create a high-level hypothesis of the rectangle even though the edges are missing . The activation of this global hypothesis , at areas V2 and above , in turn constrains the activity of lower level neurons through the feedback messages . The HTM theory provides a mechanism for training a visual cortical hierarchy and the HTM circuit model gives a detailed anatomical circuit that can be used to test this hypothesis .
In this paper we have mapped a model of how the neocortex performs inference onto neocortical anatomy . The model , called Hierarchical Temporal Memory ( HTM ) , is a type of Bayesian network which assumes a hierarchy of nodes where each node learns spatial coincidences and then learns a mixture of Markov models over the set of coincidences . The hierarchy of the model corresponds to the hierarchy of cortical regions . The nodes in the model correspond to small regions of cortex . We performed the mapping to biology in two stages . Starting with a mathematical expression of how each node performs inference , we created an abstract neuronal implementation . Next we mapped this abstract implementation onto observed anatomical data of cell types , cell layers , and micro-circuits in the cortex . We also showed results of an experiment where an HTM-based vision system exhibited the effects of illusory contours . There are many unknowns and variations in cortical anatomy , and similarly there are many functions of the neocortex that are not accounted for by the HTM model . However , we believe the theoretical and anatomical constraints are sufficiently strong that the merger of the two is non-trivial and instructive . The ultimate goal of our work is to have a theoretical model of neocortex sufficiently tied to biological data so that the biology can lead to refinements of the theory , and the theory can lead to testable predictions about the biology . The work we have done , including that in this paper , suggests HTM is a good starting point for such a biologically grounded neocortical model .
|
Understanding the computational and information processing roles of cortical circuitry is one of the outstanding problems in neuroscience . In this paper , we work from a theory of neocortex that models it as a spatio-temporal hierarchical system to derive a biological cortical circuit . This is achieved by combining the computational constraints provided by the inference equations for this spatio-temporal hierarchy with anatomical data . The result is a mathematically consistent biological circuit that can be mapped to the cortical laminae and matches many prominent features of the mammalian neocortex . The mathematical model can serve as a starting point for the construction of machines that work like the brain . The resultant biological circuit can be used for modeling physiological phenomena and for deriving testable predictions about the brain .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"neuroscience/natural",
"and",
"synthetic",
"vision",
"neuroscience/theoretical",
"neuroscience"
] |
2009
|
Towards a Mathematical Theory of Cortical Micro-circuits
|
The rhoptry of the malaria parasite Plasmodium falciparum is an unusual secretory organelle that is thought to be related to secretory lysosomes in higher eukaryotes . Rhoptries contain an extensive collection of proteins that participate in host cell invasion and in the formation of the parasitophorous vacuole , but little is known about sorting signals required for rhoptry protein targeting . Using green fluorescent protein chimeras and in vitro pull-down assays , we performed an analysis of the signals required for trafficking of the rhoptry protein RAP1 . We provide evidence that RAP1 is escorted to the rhoptry via an interaction with the glycosylphosphatidyl inositol-anchored rhoptry protein RAMA . Once within the rhoptry , RAP1 contains distinct signals for localisation within a sub-compartment of the organelle and subsequent transfer to the parasitophorous vacuole after invasion . This is the first detailed description of rhoptry trafficking signals in Plasmodium .
Malaria is a disease that causes severe morbidity , mortality and socio-economic hardship in tropical and sub-tropical areas of Africa , South America and Asia . Plasmodium falciparum causes the most serious form of the disease and is responsible for more than 2 million deaths annually [1]–[3] . The development and implementation of novel intervention strategies in the form of drugs , vector control measures and an effective vaccine remains an urgent global health priority [4] . Plasmodium spp . belong to the phylum Apicomplexa – protozoan parasites characterised by a complex lifecycle consisting of invasion followed by rounds of intracellular replication . The invasion is mediated by a set of molecules distributed on the parasite surface and within specialised apical secretory organelles . Regulated secretion from these organelles allows the parasite to adhere to an appropriate target cell , invade and induce the formation of a specialised parasitophorous vacuole ( PV ) in which it subsequently resides ( reviewed in [5] ) . Rhoptries are the largest of the Plasmodium secretory organelles and contain more than 20 proteins , many of which are unusual and have no recognisable orthologues , even in the closely related apicomplexan parasite Toxoplasma gondii ( reviewed in [6] ) . Rhoptries are pear-shaped and membrane bound , and in transmission electron micrographs the bulb and neck appear to form distinct sub-compartments . The neck is electron-lucent while the bulb is electron-dense and contains internal membranes reminiscent of multivesicular endosomes in higher eukaryotes [7]–[9] . Individual proteins are not distributed throughout the rhoptry but are sub-compartmentalised within either the bulb or the neck [10]–[12] . Rhoptry biogenesis occurs by sequential fusion of Golgi-derived vesicles which deliver protein cargo into the rhoptry lumen [9] , [13] . Rhoptry proteins pass through the endoplasmic reticulum ( ER ) and the Golgi [14] , [15] , but specific targeting signals which direct protein sorting into rhoptry destined vesicles remain poorly understood . In mammalian cells , sorting of transmembrane proteins is mediated by cytoplasmic adaptor complexes ( APs ) which recognise specific motifs ( e . g . the YXXΦ motif , where Φ is a hydrophobic amino acid ) within their cytoplasmic tails . APs select cargo for inclusion into a transport vesicle and recruit coat components ( e . g . clathrin ) necessary for vesicular budding and transport [16] , [17] . This mechanism has been shown to operate in Toxoplasma , and may also be conserved in Plasmodium [18] , [19] . However , most Plasmodium rhoptry proteins described to date do not possess a transmembrane region and cytoplasmic tail , implying the existence of additional sorting pathways [6] . One possibility is that sorting within the Golgi occurs via a clustering mechanism whereby proteins en route to a particular destination aggregate into distinct sub-domains [20] . The rhoptry associated membrane antigen ( RAMA ) is a glycosylphosphatidyl inositol ( GPI ) -anchored protein that is expressed early in the asexual red blood cell ( RBC ) cycle . Most rhoptry proteins are expressed at the late trophozoite stage but RAMA is first synthesised during the late ring stage , before the appearance of recognizable rhoptries , and appears to temporarily accumulate within compartments of the secretory pathway [15] . This unusual expression pattern suggests that RAMA may be involved in rhoptry biogenesis and protein targeting . Fluorescence Resonance Energy Transfer ( FRET ) experiments indicate that RAMA interacts with the low molecular weight ( LMW ) rhoptry complex [15] . The LMW complex is a heterodimer composed of rhoptry associated protein 1 ( RAP1 ) , and RAP2 or RAP3 [21] . Rhoptry targeting of the LMW complex occurs via the N-terminus of RAP1 , although the mechanism is not understood [22] . We hypothesised that RAMA acts as an escorter for RAP1 to recruit RAP1 , −2 and −3 into a rhoptry-destined protein complex . Here we have used expression of heterologous reporter constructs and pull-down assays to map the RAP1 targeting signals and define the RAMA-RAP1 interaction . Our results provide evidence of a novel mechanism for trafficking of proteins to this unusual secretory organelle .
In P . falciparum schizont stage parasites , RAP1 is localised in the rhoptry bulb [21] , [23] . Previously , it has been shown that the first 344 amino acids of RAP1 are sufficient for rhoptry targeting [22] . To more precisely define these targeting signals , we used constructs consisting of regions of RAP1 fused to green fluorescent protein ( GFP ) . GFP was chosen as a reporter because it has previously been used in a variety of studies in Plasmodium and does not possess any endogenous targeting signals . When expressed on its own , GFP localises to the parasite cytoplasm . However , addition of sorting signals can result in trafficking of GFP to compartments of the secretory system [24] , [25] . The expression of RAP1-GFP chimeras was driven by an inducible promoter with a pattern of expression similar to merozoite surface protein 2 ( MSP2 ) [26] . A late stage promoter was selected to avoid aberrant targeting as a result of incorrect timing of expression [27] , [28] . To verify that GFP could be trafficked to rhoptries , we initially generated two constructs – GFP fused to amino acids 1-344 of RAP1 ( RAP1-344 ) and GFP fused to the entire RAP1 sequence ( RAP1-FL ) . The constructs were introduced into P . falciparum 3D7 parasites and the trafficking of GFP was followed using fluorescence microscopy ( Figure 1A ) . As expected , both constructs produced a punctate pattern of staining in schizonts , characteristic of localisation within the apical secretory organelles . Surprisingly however , the two constructs demonstrated subtly different localisation patterns . For RAP1-FL , GFP fluorescence co-localised with the rhoptry bulb marker RAMA [15] , and mimicked the localisation of native RAP1 in wild-type parasites ( Figure 1Aii and 1Aiii ) . In contrast , for RAP1-344 , GFP staining only partially overlapped and was anterior to a larger RAMA-positive structure ( Figure 1Ai ) . To ascertain the precise localisation of the RAP1-344GFP chimera , we performed double labelling experiments with PfRON4 ( a rhoptry neck marker ) [29] and apical membrane antigen 1 ( AMA1 , a microneme marker ) [30] using specific antibodies ( Figure 1B ) . Our results strongly suggest that for RAP1-344 , GFP is not localised in micronemes ( Figure 1Bi ) but is localised in the rhoptry neck ( Figure 1Bii ) . To explore whether rhoptry neck localisation of truncated RAP1 was an artefact of our heterologous expression system , we reanalysed the original rap1 truncation mutant ( D10ΔRAP1 ) [22] . This mutant , generated by single cross-over homologous recombination in the parasite line D10 , has a truncated rap1 gene expressing amino acids 1-344 of RAP1 under the control of its native promoter . Interestingly , the same pattern was observed for D10ΔRAP1 as for RAP1-344 , with GFP co-localising with PfRON4 ( Figure 1Biii ) . To further confirm this finding , we localised RAP1 in D10ΔRAP1 and its parent line using immunoelectron microscopy ( Figure 1C ) . In D10 , native RAP1 is localised in the rhoptry bulb , whereas in D10ΔRAP1 the truncated RAP1 protein is localised in the rhoptry neck , adjacent to PfRON4 . Taken together , this data strongly suggests that RAP1 contains a bi-partite rhoptry signal: amino acids 1-344 are sufficient for targeting RAP1 to the rhoptry and amino acids 344-782 are necessary to avoid re-localisation of the protein from the bulb of the rhoptries to the neck . Having confirmed the ability of RAP1-344 to target GFP to the rhoptries , we set out to define the minimal region sufficient for rhoptry targeting . To this end , we generated a series of N-terminal RAP1 truncation-GFP fusions ( Figure 2A and S1 ) . RAP1-244 , RAP1-144 , RAP1-65 and RAP1-55 were all able to direct trafficking of GFP to the rhoptries . In contrast , for the RAP1-35 construct , GFP fluorescence produced a ‘cluster of grapes’ pattern . Co-localisation with serine repeat antigen 5 ( SERA5 ) ( Figure 2Aiii ) , confirmed that RAP1-35-GFP was targeted to the parasitophorous vacuole ( PV ) , the default destination for the secretory pathway [24] , [25] . RAP1 possesses a typical N-terminal signal sequence that is presumably cleaved upon entry into the ER [31] . SignalP analysis of the RAP1 sequence predicts that this cleavage occurs between amino acids 21 and 22 . Replacement of the RAP1 signal sequence with a signal sequence from the acyl carrier protein ( ACP – normally targeted to the apicoplast ) [24] had no effect on rhoptry localisation ( Figure 2B ) . Our data strongly suggests that the signal sequence of RAP1 directs the protein into the secretory pathway . Information contained in amino acids 22-55 ( hereafter referred to as the RAP1 rhoptry signal ) is then sufficient to divert the protein to the rhoptries . In T . gondii , proteins that are targeted to the rhoptries can contain multiple signals that are independently sufficient but not necessary for correct localisation [32] , [33] . To determine whether this is the case for RAP1 , we generated a construct that contains the ACP signal peptide fused to amino acids 56-782 of RAP1 ( i . e . the entire RAP1 sequence lacking the signal peptide and the rhoptry signal ) fused to GFP ( Figure 2C ) . Although this construct was partially targeted to discrete foci that co-localised with RAMA , the bulk of the fluorescence was distributed in the PV . This data suggests that amino acids 22-55 of RAP1 are necessary for optimal targeting to the rhoptries . Having defined the RAP1 rhoptry signal , we were interested in the mechanism by which this region mediates targeting . Since RAMA is refractory to genetic deletion [34] , we were unable to study the trafficking of RAP1 in RAMA deletion mutants . Furthermore , repeated attempts to overexpress full length RAMA , or RAMA lacking various domains ( e . g . R1 , R2 or R3 repeats ) failed ( results not shown ) , presumably due to toxic effects of overexpression of this protein . Instead , we decided to map the RAMA-RAP1 interaction in vitro . We reasoned that if RAMA acts as an escorter for the LMW complex , it should interact with the RAP1 rhoptry signal which is responsible for correct targeting of the complex . To test this hypothesis , we initially made a recombinant His6-tagged RAP1 protein representing amino acids 22-152 ( RAP1 ( 22-152 ) ) , which contains within it the RAP1 rhoptry signal , and used it in a pull-down assay ( Figure 3 ) . Our results indicate that RAP1 ( 22-152 ) but not MSP4 ( an irrelevant His6-tagged protein ) bound RAMA in a schizont stage parasite extract ( Figure 3A ) . To confirm these findings and more precisely map the RAP1 binding site within RAMA , we made RAMA-GST fusion proteins representing amino acids 482-758 ( RAMAD ) , 759-840 ( RAMAE ) , 759-798 ( RAMAE1 ) and 799-840 ( RAMAE2 ) . We used these proteins together with RAP1 ( 22-152 ) in pull-down assays ( Figure 3B ) . RAMAE and RAMAE1 both bound to RAP1 ( 22-152 ) , whilst GST alone did not bind . Truncation of the C-terminus of RAP1 ( 22-152 ) did not affect RAMAE binding , whereas deletion of the RAP1 rhoptry signal from RAP1 ( 22-152 ) ( construct RAP1 ( 57-152 ) ) abolished RAMAE binding ( Figure 3C ) . Taken together these results demonstrate that the RAP1 rhoptry signal , involved in the targeting of the LMW complex to the rhoptries , acts as the binding site for the C-terminus of RAMA . We had mapped the RAP1 rhoptry signal and the RAMA binding site to the N-terminus of RAP1 . Due to low expression levels of RAP1-GFP chimeras we could not directly confirm RAMA binding by immunoprecipitation . Instead , we made a series of RAP1 ( 22-152 ) mutant proteins and tested them in pull-down assays against RAMAE ( Figure 4A ) . The same amino acids were also mutated in the RAP1-55 targeting construct so that their affect on RAP1 targeting in vivo could be examined ( Figure 4B and S2 ) We focussed on residues 30–55 as these contain at least part of the information required for correct trafficking of RAP1 . Amino acid alignment of RAP1 orthologues from different Plasmodium spp . failed to identify any potential conserved motifs within the RAP1 rhoptry signal ( data not shown ) . Mutation of negatively charged residues ( aspartate 39 , 43 and 44 ) to either non-polar ( alanine ) or positively charged ( arginine ) residues failed to disrupt either rhoptry targeting or RAMA binding ( Figure 4ii and 4iii ) . By contrast , mutation of aromatic residues ( at positions 40 , 42 , 45 , 47 and 48 ) to glycine abolished the RAMA-RAP1 interaction and resulted in mistargeting of GFP to the PV ( Figure 4iv ) . To analyse the individual importance of each of the aromatic residues we made mutants where only some of the aromatic residues were changed to glycines . Mutation of residues 40 , 42 , and 45 was insufficient to alter either RAMA binding or in vivo targeting ( Figure 4Av and 4Bv ) . Simultaneous mutation of residues 40 , 42 , 45 and 47 or 47 and 48 abolished RAMA binding ( Figure 4Avi and 4Avii ) . The same mutations in the RAP1-55 targeting constructs resulted in significant mistargeting of GFP to the PV , although some chimeric GFP could be observed in rhoptries ( Figure 4Bvi and 4Bvii ) . This is likely a reflection of the sensitivity of the in vitro assay . In vivo , the reduced affinity of the interaction results in partial mistargeting , whereas in vitro the interaction falls below detectable levels . These results indicate that although residues 47 ( tyrosine ) and 48 ( tryptophan ) play a significant role in RAP1 targeting , it is the overall nature of the RAP1 rhoptry signal that is important . The interaction between RAMA and RAP1 in vivo was initially demonstrated by FRET , a technique that measures photon transfer between two fluorophores that are in close proximity [15] . In our attempts to affinity purify the RAMA-RAP complex from schizont stage parasites , we found that only a small amount of RAMA co-precipitated with RAP1 , and vice versa ( results not shown ) . This data is consistent with previous studies [21] , [35]–[37] , and suggests that the RAMA-RAP interaction is transient . Both RAMA and RAP1 are synthesised as pre-proteins that are proteolytically processed within nascent rhoptries , presumably by a rhoptry-resident protease [15] , [38] . We hypothesised that this processing may serve to dissociate the transient RAMA-RAP complex . The N-terminal pro-peptide of RAMA is unusually large and comprises more than 50% of the entire protein [15] . The N-terminus of the mature RAMA protein ( RAMA p60 ) has recently been mapped using N-terminal sequencing ( cleavage occurs between residues 477L and 478Q ) . Analysis of RAMA orthologues from different Plasmodium spp . indicates that the protease responsible for this cleavage recognises the sequence ( D/E ) SFL ( Q/E ) [39] . We examined the primary structure of RAMA and found that this sequence and/or closely related sequences are repeated eight times within the pro-peptide region but are not present within RAMA p60 ( Figure 5 ) . A putative cleavage site was also identified at amino acids 67-71 ( ESFLE ) of RAP1 . Cleavage of the RAP1 pro-peptide has been mapped upstream of A124 and involves the removal of approximately 40 amino acids ( in addition to the signal peptide ) [38] . We attempted N-terminal sequencing of immunoaffinity-purified RAP1 , but did not obtain any data presumably due to N-terminal blockage of the protein . We also performed a trypsin digestion and liquid chromatography-mass spectrometry ( LC-MS ) analysis . In two independent analyses , we obtained >60% coverage of RAP1 downstream of the putative cleavage site , but did not detect any peptides upstream of the cleavage site . The most N-terminal peptide detected corresponded to amino acids 74-91 of RAP1 ( results not shown ) . The peptide corresponding to amino acids 71-73 ( which would be present if cleavage occurs between 70L and 71E ) is too small to be detected . This data , in combination with previously published data , strongly suggests that both RAMA and RAP1 are processed by the same rhoptry-resident protease . Many , though not all , rhoptry proteins are secreted during merozoite invasion and are transferred to the PV of nascent ring stage parasites where they presumably play a role in the establishment of the PV membrane [40] . Earlier studies using D10ΔRAP1 , have demonstrated that full length RAP1 is transferred to the PV during invasion , whereas truncated RAP1 is not [22] . Given our finding that the C-terminus of RAP1 contains a rhoptry bulb retention motif ( see above ) , it is possible that RAP1 secretion is dependent on correct sub-organellar localisation . To more precisely map the signals within RAP1 required for rhoptry bulb retention and PV transfer , we generated a further series of RAP1 truncation-GFP fusions that included regions of the C-terminus of the protein . These parasites were examined by fluorescence microscopy both at schizont stage ( to establish rhoptry bulb or rhoptry neck localisation ) and at ring stage ( to ascertain transfer to the PV ) . As expected , RAP1-344GFP , which is localised in the rhoptry neck ( Figure 1Bii ) , was not transferred to the PV during invasion ( Figure 6i ) . In contrast , for the full-length RAP1 , RAP1-644 and RAP1-544 constructs , chimeric GFP was localised in the rhoptry bulb at schizont stage ( Figure 1Aii and S3 ) , and could be observed as a rim of fluorescence around newly formed ring-stage parasites indicating transfer to the PV ( Figure 6iii and S3 ) . The RAP1-444GFP chimera appeared to be only partially localised in the rhoptry bulb ( Figure S3 ) , but was nonetheless transferred to the PV during invasion ( Figure 6ii ) . These results indicate that amino acids 344-444 of RAP1 are required for transfer of the protein to the PV . Our attempts to confirm sub-organellar localisation of the RAP1-GFP chimeras using immunoelectron microscopy were unsuccessful due to the relatively low level of expression of episomal constructs . However , our confocal microscopy results provide preliminary evidence that amino acids 344-544 of RAP1 are required for correct sub-organellar localisation of the protein within the rhoptry .
Apical organelles of apicomplexan parasites play a key role in invasion of target cells and the subversion of host cell function . Rhoptries of P . falciparum merozoites contain a complex proteome including components that have been identified as potential vaccine candidates ( reviewed in [6] ) . However , little is known about mechanisms of rhoptry biogenesis and discharge . In the present study , we examined the trafficking of the rhoptry protein RAP1 . RAP1 , together with RAP2 or RAP3 , form the heterodimeric LMW complex which is localised in the rhoptry bulb of schizonts [21] , [23] . During invasion , the LMW complex is secreted from the rhoptries and transferred to the PV of the nascent ring-stage parasite [35] . Truncation of the C-terminus of RAP1 results in disruption of its interaction with RAP2/RAP3 and causes RAP2 ( and probably RAP3 ) to be retained in the ER [22] . In contrast , truncated RAP1 is still targeted to rhoptries , but is not transferred to the PV during invasion [22] . Our results confirm and expand on these earlier observations . Using expression of GFP chimeras we were able to show that information present between amino acids 23 and 55 of RAP1 is necessary and sufficient for optimal targeting to the rhoptries . We compared the RAP1 rhoptry signal to protein regions that have been implicated in rhoptry targeting in Toxoplasma , as well as other Plasmodium rhoptry proteins but were unable to identify a conserved motif . This suggests that the RAP1 rhoptry signal is specific for the LMW complex and it may be that unlike proteins targeted to the apicoplast [41] or exported into the host RBC [42] , [43] , many proteins destined for the rhoptries do not possess a common targeting signal . We have provided evidence that RAMA , a protein synthesised in the late ring stage and GPI-anchored in the Golgi lumen , acts as an escorter for the LMW complex via a direct association with the N-terminus of RAP1 . Bulky aromatic amino acid clusters are known to be important for protein-protein interactions . In the case of the RAMA-RAP1 interaction , it appears that the overall organisation of aromatic residues within the RAP1 rhoptry signal is important for correct binding . However , in the absence of structural information , we cannot determine whether any or all of these residues directly contact RAMA , or whether disruption of RAMA binding and mistargeting in our mutants occurs as a result of conformational perturbation caused by glycine substitution . RAP1 appears to possess a distinct signal for localisation within the rhoptry bulb and subsequent transfer to the PV during invasion . These findings are consistent with an earlier study in Toxoplasma which demonstrated that the pro-domain of the rhoptry protein ROP1 directs trafficking of a reporter to the rhoptry neck , whereas full-length ROP1 is preferentially enriched in the bulb [44] . The mechanism by which proteins can be partitioned within a single membrane bound organelle is not understood . Our data argues for the presence of a bulb-retention motif within the C-terminus of RAP1 which may allow interaction with other rhoptry bulb proteins ( e . g . RAP2 and −3 ) . It is worth noting that RAP1 is a major constituent of detergent-resistant microdomains ( DRMs ) in schizont stage parasites [37] . RAP1 has no obvious lipid anchor and is likely recruited into DRMs via association with some other protein . Whether or not localisation of RAP1 in the rhoptry bulb is necessary for transfer of the protein to the PV per se , is not clear . One possibility is that rhoptry neck proteins are secreted before rhoptry bulb proteins and are deposited onto the surface of the target RBC . In contrast , rhoptry bulb proteins are trapped within the PV because their secretion occurs after the formation of the tight junction between the parasite membrane and the RBC membrane . The alternative explanation is that amino acids 344-444 of RAP1 contain a specific protein-protein interaction motif ( e . g . necessary for interaction with RAP2 and −3 ) which is required for transfer to the PV . Detailed mapping of other sub-organellar localisation signals and PV transfer signals will help to differentiate between these two alternatives . Based on the data presented above , we propose a model whereby RAMA binds RAP1 in the Golgi lumen and recruits RAP1 , −2 and −3 into a complex ( Figure 7 ) . GPI-anchored proteins have a tendency to cluster in lipid rafts , and thus the complex is presumably anchored within a lipid raft at the Golgi exit face [45] , [46] . Other proteins ( e . g . the RhopH proteins which also interact with RAMA ) may be recruited into the raft as well , thus generating rhoptry destined aggregates [15] . DRM clustering associated with protein oligomerisation has been shown to be essential for polarised trafficking of GPI-anchored proteins to the apical membrane in epithelial cells ( reviewed in [47] , [48] ) . Several of the known P . falciparum rhoptry proteins , including RAMA and RAP1 , are associated with DRMs and it is tempting to speculate that this mechanism is involved in differential sorting of proteins within the Golgi . Interestingly , none of the known micronemal proteins have been found associated with DRMs , whereas several merozoite surface proteins do associate with DRMs [45] . This suggests the presence of distinct regions of membrane at the Golgi exit face which are defined by their protein and/or lipid composition that bud off as individual vesicles . Each vesicle then presumably interacts with specific components of the cellular trafficking machinery , possibly via a transmembrane escorter . In Toxoplasma , the cytoplasmic adaptor complex AP-1 has been implicated in rhoptry protein trafficking . A study by Hoppe and colleagues demonstrated that AP-1 binds in vitro to a region of the Toxoplasma rhoptry protein ROP2 that is sufficient to mediate rhoptry targeting in vivo [18] . The biological relevance of this finding has recently come into question as ROP2 appears to lack a transmembrane domain that is necessary in order for the ROP2 targeting region to be exposed at the Golgi exit face and available for binding to AP-1 [49] . Nonetheless , components of vesicular trafficking machinery , including AP-1 , have been identified in the P . falciparum genome but their precise roles remain to be determined ( reviewed in [40] ) . Upon arrival at the rhoptry , the RAMA-LMW complex is dissociated by proteolytic cleavage [15] , [38] . The presence of putative cleavage sites in the N-terminus of RAP1 and RAMA suggests that a single rhoptry-resident protease is responsible for their processing [39] . Cleavage of the N-terminus of RAP1 releases the LMW complex from RAMA . This may allow the LMW complex to interact with other proteins in the rhoptry bulb , potentially via the bulb-retention domain of RAP1 identified in this study [37] . In turn , degradation of the N-terminus of RAMA may release it from the hypothetical transmembrane escorter . Proteins destined for the apicoplast or mitochondrion each possess an appropriate signal that allows their post-translational translocation into a pre-formed organelle [41] , [50]–[53] . In contrast , many proteins destined for the apical secretory organelles appear to be targeted by a clustering mechanism . In Toxoplasma , the soluble microneme proteins MIC1 , MIC3 and MIC4 are targeted via an interaction with transmembrane escorter proteins [54] , [55] . In Plasmodium , microneme proteins of the EBL family are targeted courtesy of a conserved luminal domain presumably via interaction with a transmembrane escorter [56] , [57] . In the current study , we present evidence that proteins can be similarly targeted to rhoptries via the formation of transient complexes that are packaged into transport vesicles and dissociated by proteolytic processing upon arrival at their destination . Given that most Plasmodium rhoptry proteins are not type 1 membrane proteins and therefore lack a cytoplasmic tail , it is likely that targeting to rhoptries via this mechanism is the rule rather than the exception .
P . falciparum asexual stage parasites were maintained in human erythrocytes ( blood group O+ ) at a hematocrit of 4% with 10% Albumax ( Invitrogen ) [58] . P . falciparum 3D7 parasites were originally obtained from David Walliker at Edinburgh University . Cultures were synchronised as previously described [59] . All oligonucleotide primers used in this study are listed in Table S1 . GFP fusion proteins for localization studies were encoded in transfection constructs under the regulation of the tetracycline-inducible expression system [26] . Regions of RAP1 were PCR amplified from P . falciparum 3D7 genomic DNA . For mutagenesis experiments , mutations were introduced into primers during synthesis . PCR products were digested with PstI and MluI and cloned in frame upstream of GFP . RAMAE1 and RAMAE2 recombinant fragments were PCR amplified from P . falciparum cDNA and cloned as previously described [15] into the GST-fusion vector pGEX-4T-1 ( GE Healthcare ) . RAP1 recombinant proteins were PCR amplified from P . falciparum 3D7 genomic DNA . PCR products were digested with NcoI and XhoI and cloned into the His6-fusion vector pET28b in frame upstream of the His6 tag . Constructs were sequenced and confirmed to be free of unintended mutations . His6-tagged RAP1 recombinant proteins were expressed in E . coli BL21 ( DE3 ) ( Novagen ) and purified using TALON Metal Affinity Resin ( Clontech ) in accordance with manufacturer's instructions . RAMA-GST fusion proteins were expressed in E . coli BL21 ( DE3 ) and purified using glutathione resin ( Sigma ) as previously described [15] . Protein expression was analysed using SDS-PAGE and immunoblotting with anti-His6 or anti-GST antibodies . Protein concentration was determined using the Bradford Assay ( Bio-Rad ) . Purified recombinant proteins were buffer exchanged into pull-down buffer ( 50 mM Na2HPO4 , 75 mM NaCl , 0 . 1% TrionX-100 , 5 mM imidazole , pH 7 . 4 ) . P . falciparum 3D7 parasites were extracted from parasitised RBCs by lysis with 0 . 15% ( w/v ) saponin in phosphate buffered saline and solubilised in RIPA buffer ( 50 mM TrisCl ( pH 8 . 8 ) , 150 mM NaCl , 1% NP40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS ) containing Complete Mini protease inhibitor cocktail ( Roche ) . After lysis on ice for 5 min the insoluble material was spun down and the supernatant collected . The supernatant was diluted 1 part in 10 in pull down buffer prior to use . For the pull-down assay , 100 µg of the various His6-tagged RAP1 recombinant proteins were used as bait . These proteins were immobilised on TALON Metal Affinity Resin ( Clontech ) and incubated with either GST-fusion proteins or parasite lysate O/N at 4°C . The resin was washed with pull-down buffer and specifically bound proteins were eluted using imidazole ( 20 mM Na2HPO4 , 0 . 5 M NaCl , 400 mM imidazole , pH 7 . 4 ) . Eluted proteins were analysed on Coomassie stained SDS-PAGE gels and by immunoblotting with anti-RAMA [15] or anti-GST antibodies . P . falciparum 3D7 parasites were transfected as described previously [52] with 100 µg of purified plasmid DNA ( Qiagen ) . Positive selection for transfectants was achieved using 10 nM WR99210 and 0 . 5 µg/ml Anhydrotetracycline to prevent transgene expression . Anhydrotetracycline was removed from parasite cultures 72 h prior to live imaging ( in the presence of 10 nM WR99210 ) to allow expression of the GFP fusion . Prior to microscopy , parasites were incubated in culture medium containing 100 ng/ml 4′ , 6-diamidino-2-phenylindole ( DAPI; Roche Molecular Biochemicals ) . Fluorescence images of schizont stage parasites were captured using a Carl Zeiss Axioskop microscope with a PCO Sensicam and Axiovision 2 software . For immunofluorescence assays , schizont stage parasites were fixed using 4% paraformaldehyde ( ProSciTech ) and 0 . 0075% glutaraldehyde ( ProSciTech ) as previously described [41] . After blocking in 3% bovine serum albumin ( Sigma ) the cells were incubated for 1 hour with rabbit anti-RAMA [15] , mouse anti-AMA1 [60] , mouse anti-RAP1 or rabbit anti-PfRON4 ( Richard and Cowman , manuscript in preparation ) antibodies . Bound antibodies were then visualised with Alexa Fluor-594 anti-rabbit IgG or anti-mouse IgG ( Molecular Probes ) diluted 1∶1000 . Parasites were mounted in Vectashield ( Vecta Laboratories ) containing DAPI . Parasites for electron microscopy immunolabeling were fixed and prepared as described previously ( Healer et al . , 2002 ) . The primary antibodies used were mouse monoclonal anti-PfRAP-1 ( 1/500 ) , rabbit anti-PfRON-4 ( 1/100 ) . Samples were washed , then incubated with secondary antibodies conjugated to either 10 nm or 15 nm colloidal gold ( BB International ) . Samples were then post-stained with 2% aqueous uranyl-acetate then 5% triple lead and observed at 120 kV on a Philips CM120 BioTWIN Transmission Electron Microscope .
|
The malaria parasite Plasmodium falciparum is a eukaryotic organism with multiple membrane bound organelles with discrete functions . The rhoptry is an unusual secretory organelle that participates in host cell invasion and the formation of a specialised vacuole that the parasite occupies during the intracellular part of its lifecycle . Rhoptries contain an extensive collection of proteins , but little is known about how these proteins are trafficked to their destination . Understanding determinants of rhoptry protein trafficking will help us to identify novel rhoptry proteins , and may provide targets for therapeutic intervention . In the current study , we focussed on the trafficking of the rhoptry protein RAP1 . By making parasites that express regions of RAP1 fused to Green Fluorescent Protein ( GFP ) , we were able to map in detail the domains of RAP1 that are necessary for correct trafficking . We also provide evidence that RAP1 is targeted to rhoptries via its interaction with another rhoptry protein , RAMA . This is the first detailed description of rhoptry trafficking signals in Plasmodium .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/membranes",
"and",
"sorting",
"infectious",
"diseases/protozoal",
"infections"
] |
2009
|
Identification of Rhoptry Trafficking Determinants and Evidence for a Novel Sorting Mechanism in the Malaria Parasite Plasmodium falciparum
|
To investigate the DDT and deltamethrin susceptibility of Phlebotomus argentipes , the vector of Leishmania donovani , responsible for visceral leishmaniasis ( VL ) , in two countries ( India and Nepal ) with different histories of insecticide exposure . Standard WHO testing procedures were applied using 4% DDT and 0 . 05% deltamethrin impregnated papers . The effect of the physiological status ( fed and unfed ) of females on the outcome of the bioassays was assessed and the optimal time of exposure for deltamethrin was evaluated on a colony population . Field populations from both countries were tested . Fed and unfed females responded in a similar way . For exposure time on field samples 60 min was adopted for both DDT and deltamethrin . In Bihar , knockdown and mortality with DDT was respectively 20 and 43% . In Nepal almost all sand flies were killed , except at the border with Bihar ( mortality 62% ) . With 0 . 05% deltamethrin , between 96 and 100% of the sand flies were killed in both regions . Based on literature and present data 4% DDT and 0 . 05% deltamethrin seem to be acceptable discriminating concentrations to separate resistant from susceptible populations . Resistance to DDT was confirmed in Bihar and in a border village of Nepal , but the sand flies were still susceptible in villages more inside Nepal where only synthetic pyrethroids are used for indoor spraying . The low effectiveness of indoor spraying with DDT in Bihar to control VL can be partially explained by this resistance hence other classes of insecticides should be tested . In both countries P . argentipes sand flies were susceptible to deltamethrin .
Approximately 200 million people are at risk of visceral leishmaniasis ( VL ) – also known as kala-azar – in Bangladesh , India and Nepal [1] . In South East Asia , VL is caused by Leishmania donovani Laveran & Mesnil ( Kinetoplastida: Trypanosomatidae ) which is transmitted by Phlebotomus argentipes Annandale & Brunneti ( Diptera: Psychodidae ) , the only incriminated vector in the region [2] . VL is fatal if untreated and current control measures rely on diagnosis and treatment of cases and Indoor Residual Spraying ( IRS ) to reduce or interrupt transmission in the affected communities . In India , two annual rounds of DDT spraying at 1 mg/m2 have been conducted in VL endemic districts since more than two decades [3] . In Nepal , the use of DDT to control VL was stopped in 1995 and IRS has been based since on synthetic pyrethroids ( i . e alphacypermethrin or lambdacyhalothrin ) targeting communities reporting at least one VL case in the previous year [4] . In Bangladesh vector-control activities are practically inexistent [5] . The use of Long Lasting Insecticidal Nets ( LN ) , deltamethrin , alphacypermethrin or permethrin based [6] , have been postulated as an alternative or complimentary approach as the current vector control strategies are failing to control VL in the region [7] , [8] . Among other reasons , P . argentipes resistance to the insecticides used in the national programs may explain the lack of effect observed , particularly in India and Nepal . In a recent review , Ostyn et al . [8] reviewed the published reports on P . argentipes susceptibility to different insecticides in the Indian subcontinent since 1978 . The results of this review show that DDT resistance has been reported in India since early 1990's but the results were variable and patchy . P . argentipes were consistently susceptible to DDT in Nepal and Bangladesh but the number of reports from those two countries was limited . Studies in the region showed susceptibility to deltamethrin , except for a report from Pondicherry , India [9] . However the methodologies used in those surveys were not standardized ( i . e . insecticide concentration and time of exposure varied ) and none of the studies applied the same protocol in different regions simultaneously . In this paper we present the results of two studies on P . argentipes susceptibility to insecticides . First , a laboratory test to asses the influence of the physiological status of the sand fly on insecticide efficacy and to standardize the time of exposure to deltamethrin for field assays . Secondly , a field study was carried out to assess P . argentipes resistance to DDT and deltamethrin in VL endemic villages in India and Nepal .
The protocol study was approved by the ethical review boards from the London School of Hygiene and Tropical Medicine , University of Antwerp , Rajendra Memorial Research Institute and B . P . Koirala Institute of Health Sciences . Written informed consent was obtained from the head of the household where the sand flies were collected .
No significant differences were observed between unfed ( E ) and fed ( F ) sand flies for knockdown and mortality ( Fig . 2 ) for DDT ( Chi Square KD: p = 0 . 46; mortality: p = 0 . 99 ) and for deltamethrin ( Chi Square: KD: p = 0 . 17; mortality: p = 0 . 12 ) . For DDT only 38% mortality ( N = 248; 11 replicates ) was observed indicating DDT resistance in this colony population . For deltamethrin knockdown increased with time of exposure and mortality after 60 min exposure reached 99% ( N = 193; 9 replicates ) . The exposure time of 60 min with a concentration of 0 . 05% was further adopted for testing the field populations . For Bihar all replicates performed on specimens coming from the eight study villages were put together as no difference occurred in knockdown and mortality among the study sites . For DDT , knockdown was of 20% and only 43% died after 24 h ( 1 h exposure , N = 211; 16 replicates ) suggesting DDT resistance . Deltamethrin 0 . 05% induced a knockdown of 86% and a mortality of 100% ( N = 162; 8 replicates ) ( Fig . 3 ) . In Nepal , the results were presented by village as there were differences between study sites . DDT resistance was only observed in one of the villages ( i . e . Amahibelha ) ( KD 51% , mortality 62% , N = 113; 6 replicates ) while full susceptibility was observed in the other three sites . For deltamethrin , knockdown fluctuated between 85 and 93% and mortality between 96–99% ( Fig . 3 ) . Mean mortalities and knockdown rates were very similar to the rates calculated on total specimens tested . Results of the bioassays are provided in detail as Dataset S1 .
No discriminating concentrations or time to kill all susceptible specimens have been established for sand flies as is the case for malaria vectors [10] . Based on literature data [8] , [15] 4% DDT and 1 h exposure seems to be an acceptable discriminating concentration . The sand fly colony of Patna can then be considered as resistant to DDT , as well as the wild population in the study area of Bihar . This DDT resistance in the colony of P . argentipes in RMRI is not surprising as it is regularly mixed with wild specimens and cannot be considered a reference strain . Previous data ( 1998–1999 ) in the area [16] showed a patchy distribution of DDT resistance ( mortality between 100 and 71% ) . The observed mortality of around 40% in present study could suggest an increasing trend in DDT resistance in Bihar . However , dose or time response assays are needed to compare the levels of resistance between the different populations [17] . In Nepal , DDT resistance was only observed in the study site of Amahibelha ( mortality 62% ) , a location close to the border with Bihar ( Fig . 1 ) , while P . argentipes was susceptible in the other 3 more inland located study sites . In Nepal the use of DDT for IRS was stopped in early 1990's and from 1995 the IRS policy was mainly based on the use of pyrethroids ( mainly alphacypermethrin ) but only in villages with VL cases [4] . This underlines once more that DDT resistance in P . argentipes has been mainly attributed to indoor spraying with this insecticide and its frequency of application [8] , [18] , but the use of sublethal doses as consequence of poor management and supervision of the IRS control programs may also enhance the selective pressure . As no fully susceptible reference strain of P . argentipes was available , it was not possible to estimate a discriminating concentration with deltamethrin . Deltamethrin 0 . 05% is the discriminating concentration established for anopheline vectors , but it is not obvious to extrapolate this to sand flies or P . argentipes . In Brazil , bioassays with 0 . 05% deltamethrin were used and a clear difference between the insecticide susceptibility of two sand fly populations was observed [17] . In that study the sand fly population without previous specific insecticide exposure , a Lethal Time 50% ( LT50 ) of 25 min was obtained and all sand flies died after one 1 h . In the population exposed to sand fly control measures using pyrethroids , LT50 was significantly higher ( 40 min ) and the mortality was only 62% after 1 h [17] . Bioassays performed on the colony population of RMRI indicate a LT50 lower ( <15 min ) than the one observed in the most susceptible population in Brazil . One hour exposure induced a knockdown of around 70% and a mortality of 99% and these exposure conditions were further maintained for testing field populations . Similar results were obtained for the field populations ( KD: 81–92%; mortality 95–100% ) suggesting , and contrasting with the Brazilian study , a relatively good susceptibility to deltamethrin of the wild P . argentipes populations of Nepal and Bihar . Moreover , in P . argentipes , there is no indication of DDT-deltamethrin cross resistance , commonly found in anophelines where target Kdr resistance is present [19] . So far only metabolic mechanisms have been reported in sand flies [15] , [17] and acetylcholinesterase and esterase-based insecticide resistance mechanisms have been observed in P . argentipes of Sri Lanka which probably arose from IRS with Malathion of the Anti-Malaria Campaign [20] . The limited but significant reduction ( 25% ) of P . argentipes densities induced by mass use of deltamethrin-based long lasting insecticidal nets ( LNs ) observed in a trial conducted recently in the same areas in India and Nepal [21] cannot therefore be explained by a low susceptibility to deltamethrin but resides in the behavior of vector . Indeed P . argentipes , although known as being endophilic , are mainly opportunistic blood feeders and feed in a significant proportion on bovines[22] . Hence , this will reduce the mass effect of LNs on P . argentipes populations . The current failure to control the transmission of L . donovani in the region relying on IRS with DDT can be partially explained by the resistance to this compound and other insecticides should be evaluated to replace it . However , the first requirement for a successful control program remains the quality of IRS implementation .
|
Visceral leishmaniasis ( VL ) , also know as kala azar , is one of the major public health concerns India , Nepal and Bangladesh . In the Indian subcontinent , VL is caused by Leishmania donovani which is transmitted by Phlebotomus argentipes . To date , Indoor Residual Spraying ( IRS ) campaigns have been unable to control the disease . Vector resistance to the insecticides used has been postulated as one of the possible reasons explaining this failure . A number of studies in the region have shown a variable degree of resistance to DDT in areas where this insecticide has been widely used for IRS ( mainly India ) . However there is no coordinated and standardized program to monitor resistance to insecticides in the region . In this study we tested P . argentipes susceptibility to DDT and deltamethrin in VL endemic villages in India and Nepal . The results confirmed the DDT resistance in India and in a border village of Nepal . P . argentipes from both countries were in general susceptible to deltamethrin , an insecticide used in some long lasting insecticidal nets .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"public",
"health",
"and",
"epidemiology/infectious",
"diseases"
] |
2010
|
Insecticide Susceptibility of Phlebotomus argentipes in Visceral Leishmaniasis Endemic Districts in India and Nepal
|
What is the underlying mechanism behind the fat-tailed statistics observed for species abundance distributions ? The two main hypotheses in the field are the adaptive ( niche ) theories , where species abundance reflects its fitness , and the neutral theory that assumes demographic stochasticity as the main factor determining community structure . Both explanations suggest quite similar species-abundance distributions , but very different histories: niche scenarios assume that a species population in the past was similar to the observed one , while neutral scenarios are characterized by strongly fluctuating populations . Since the genetic variations within a population depend on its abundance in the past , we present here a way to discriminate between the theories using the genetic diversity of noncoding DNA . A statistical test , based on the Fu-Li method , has been developed and enables such a differentiation . We have analyzed the results gathered from individual-based simulation of both types of histories and obtained clear distinction between the Fu-Li statistics of the neutral scenario and that of the niche scenario . Our results suggest that data for 10–50 species , with approximately 30 sequenced individuals for each species , may allow one to distinguish between these two theories .
One of the most interesting peculiarities of mother nature is the large variance in abundance of otherwise similar species . In the tropical rainforest , for example , there are differences of 4–5 orders of magnitude in the observed abundance of tropical trees [1]–[5] . Moreover , the abundance distribution admits a fat tail , which may be described by power-law or log-normal statistics . This observation is somewhat puzzling , as on the basis of evolutionary mechanisms and the competitive exclusion principle one expects the survival of only a few , most fit , species . The simplest explanations for this phenomenon are based on “niche theory” [5]–[7] . This theory suggests that the abundance differences reflect fitness , or competitive ability variations . Strong species defeat the weak , and thus their population is large; weaker species survive due to geographical variations ( regions where their fitness is better ) , symbiosis with strong species , or spatio-temporal fluctuations of the environmental conditions . Mathematically speaking , the system may be described by a series of coupled differential equations of , say , the Lotka-Volterra type , where each of the species undergoes logistic growth , but the growth rate and the carrying capacity are determined by the abundance of other species . The actual abundance distribution reflects a stable fixed point of this set of equations . In a fixed environment , thus , the abundance ratios among species are fixed up to demographic stochasticity; if the deterministic equations predict population size for certain species , one should expect temporal fluctuations proportional to . The observed frequency at present reflects the intrinsic fitness of that species , and thus one conjectures a similar community structure in past generations . Another theory that gained much popularity in the last decade is the neutral theory of species abundance . It assumes [1] , [2] that the fitness differences between species are negligible , and that the system is controlled solely by demographic stochasticity . The underlying dynamic that controls the abundance of different trees in the tropical forest is similar to the dynamic that governs surname frequency . The fact that there are many “Smith”s but only a few “Maruvka”s does not reflect ( we hope ) the undesirable features of the infrequent surname , but rather the stochastic inheritance , appearance ( mutation ) and “death” of surnames along genealogic lineages [8] . Within the framework of the neutral model , demographic stochasticity may be described as a multiplicative random walk along the abundance line . Multiplicative random processes are known for many years as the underlying mechanism behind fat-tailed statistics , e . g . , firm size distributions [9] , [10] . In fact , niche models for species abundance , like MacArthur's broken stick [6] or May's independent factor explanation [7] , are also based on some sort of multiplicative process . The difference we intend to extract here is that in the neutral scenario this random process characterizes the actual time evolution of species abundance , while the adaptive theories assume such a process in the fitness/resource space . Thus , if niche-based theories are correct , the real-time stochastic birth-death process is biased towards the observed ( present ) frequency . If the neutral theory is right , the random walk is almost unbiased ( a tiny bias towards extinction is related to the mutation rate ) , and the species frequency undergoes huge fluctuations . An illustration of the temporal abundance fluctuations for the two scenarios is given in Figure 1 . Confronting the different models on the basis of current community structure data poses a very difficult statistical problem [5] , [11]–[13] . Even in the presence of a reliable datasource , distinguishing between the various fat-tailed distributions ( e . g . , zero-sum multinomial , multivariate Poisson lognormal , broken stick distribution , etc . ) is a demanding task . The noisy measurement of relative abundance in ecosystems renders this analysis even harder to accomplish . On the other hand , it would be very easy to recognize the underlying mechanism if the history of the frequency variations was given , as seen in Figure 1 . Unfortunately , the Neanderthal men were too busy to conduct large-scale surveys of species abundance . In order to gather the relevant information one must seek out traces of the past in the present , i . e . , the genetic polymorphism of the community . In this work we present an experimental method that extracts these differences and allows one to distinguish between the two scenarios . It requires the collection of a large amount of genetic data from the current population , in particular noncoding DNA from either haploid ( mtDNA , Y-chromosome or cpDNA ) or diploid sequences . Intuitively , the genetic diversity of these sequences should reflect the history of the species abundance; one expects different results for a more or less fixed population ( as suggested by the niche theory ) , than for a strongly fluctuating population with bottlenecks and high prevalence times ( as suggested by the neutral theory ) . Here we quantify this concept , explain how to distinguish between the two scenarios , and demonstrate our results in a numerical experiment using “DNA sequences” obtained from simulated data with different histories . Our technique is limited by two time scales . The sequence mutation time sets its resolution , as no reliable conclusion may be drawn on the basis of only a few mutations . The abundance history may be recovered for timescales that are much larger than the typical time needed for a single mutation to appear in the whole sequence . The time to the most recent common ancestor sets , of course , the maximal timescale . For an almost fixed population ( niche scenario ) of size , the most recent common ancestor of any typical collection of sampled individuals appears about generations before present . This implies that our method , which uses the “structure” of the phylogenetic tree , enables differentiation between scenarios if the abundance differences were substantial in the last generations . Accordingly , our techniques are not limited to small , local ecosystems but are applicable to the metacommunity as well , since the “time horizon” to the past is proportional to the abundance . A similar idea , utilizing the differences in assumed history to distinguish between the two hypotheses , was suggested by Ricklefs [14] ( see also similar approach used in [15] ) . Relying on data from passerine birds , Ricklefs compared the species' lifetime ( i . e . , the time elapsed since the species first appeared ) and its contemporary abundance . Under the assumption of neutrality , the average species' lifetime is almost linearly proportional to the current abundance ( technically , this is the first passage time [16] of a multiplicative random walk started at ) . According to Ricklefs , [14] the species' actual lifetime ( obtained from genetic divergence data ) is much shorter than expected by Hubbell's neutral theory . His method , however , requires prior knowledge of the current population size and mutation rate , two parameters that may be difficult to obtain . Here we suggest a method that only requires knowledge of the genetic variability . Before we discuss the polymorphism analysis itself , let us add an important comment . Restricting our considerations to “pure” adaptive/neutral histories , like those demonstrated in Figure 1 , is clearly a simplification . In reality , one should expect , for example , larger fluctuations for an ecosystem that obeys the rules of the niche theory , due to the effects of environmental stochasticity . We do assume , however , that these fluctuations either conserve the species abundance ratio ( i . e . , are not species specific ) or are relatively weak . If environmental fluctuations cause rapid shifts in the relative species frequency , the conceptual meaning of the “niche theory” becomes unclear and the difference between the two scenarios is not so interesting . Throughout this work we therefore assume that the effect of environmental stochasticity is weak and yields only minor corrections to the niche/neutral predictions . In the final section we return to this issue , and discuss in detail the various types of environmental stochasticity , together with their identification using genetic polymorphism data .
We tried a number of methods in order to distinguish between the genetic polymorphism of the two scenarios , and found that the most efficient one is Fu & Li F-statistic [17] . Originally , this method was developed in order to measure the similarity of a given phylogenetic tree to the one expected from the Kingman Coalescent Model [18] , [19] . It was used by Sjödin [20] to measure when fluctuations in the population size cancel the similarity with the Kingman Coalescent . Here we used this method in order to distinguish between the two scenarios of fluctuating populations . The Fu-Li F-statistic compares the sum of the lengths of the external branches to the average internal branch length . Under the correct scaling , these lengths should be the same , if the assumptions of the Kingman Coalescent Model ( fixed population size , small sample size , and neutrality of mutations ) are fulfilled . Therefore , in the Wright-Fisher process , for example , the value of the F-Statistic is zero . In a growing population , this value is negative , and for a shrinking population it is positive . Basically , the Fu-Li F-statistic compares the features of the recent past , which affect the external branch length , to the features of the far past , affecting the internal branches . Thus , it emerges as a suitable technique for distinguishing between the two scenarios . In the niche scenario , the population in the past is similar to the population in the present , so the statistic should be approximately zero . For a neutral scenario , the population in the present differs from the past population; in most cases , the population in the present is larger than the population in the past ( this is an interesting feature of a multiplicative random walk with an absorbing state , see [21] ) . Therefore , one expects that the statistic for that scenario will admit a broad distribution with a negative average . The F-statistic is defined by: ( 1 ) where is the sample size , is the average number of pairwise nucleotide differences ( the average being over all possible pairs in the sample ) , S is the number of segregating sites , is the number of singletons ( mutations that appear in only one individual in the sample ) , and and are constants given the sample size . We also worked out the Fu and Li D-statistic for the same datasets . The results were similar to those of the F-statistic but the resolution obtained from the F-statistic was better and is therefore preferable . We performed many numerical experiments simulating the niche scenario and the neutral scenario , and calculated the F-Statistic for each realization . We then produced the probability distribution of the F-Statistic for the two types of histories . As can be seen in Figure 2 the F-statistic differs in the two scenarios; both the width of the distribution and its average are not the same , as expected . An important feature of these statistics is that they do not depend on the species' abundance , only on their history . Given real DNA sequences from several species , this difference in the distributions can be used to determine whether the species followed the Niche history or the Neutral history , and end the ongoing debate between these two hypotheses . Since we do not currently have enough DNA sequences from many different species , we did not try to check the common method of test , that given a few data points can distinguish between two similar distributions , rather we give here only a rough estimation for the number of species needed to distinguish between the two hypotheses . For species , the standard error of the F-statistics is , and in order to discriminate between the two scenarios this quantity should satisfy: ( 2 ) where are the averages of the F-statistic of the neutral and niche scenarios respectively . For a sample size of individuals per species , as in Figure 2 , the required number of species necessary to decide between the two theories is 10–50 . While our analysis until this point assumed one independent community , i . e . meta-community [2] , our approach can also be applicable to local communities . For local communities ( like those described by an island-mainland model ) , in cases of weak migration , the abundance fluctuations for neutral population are still much larger than those expected from the niche theory and one can still distinguish between the two scenarios . The migration is “weak” when the relaxation time towards the metacommunity's relative abundance is large relative to the time scale associated with the demographic stochasticity - this is the limit considered by [22] . Moreover , if there is a possibility to recover the migration rates from the abundance data ( e . g . , in the case of a few local communities coupled to the same metacommunity , such that the parameter used in [1] , [2] is the same for all islands ) , one can calculate the Fu-Li statistic for different local communities with different migration rates . This statistic should approach the neutral case as the migration rate gets smaller . In this section we present a short survey of other methods we examined to distinguish between niche and neutral histories . At the end of the day we concluded that the Fu-Li method is superior if no information is given beyond the genetic data . Yet , in the presence of other pieces of information , one of the following techniques may be preferable to the Fu-Li method . As we have mentioned above , the “pure” niche/neutral scenarios considered here are an idealization that may be true in some cases ( e . g . small communities ) , but in other cases one should expect large fluctuations in the abundance of a species due to environmental stochasticity . Indeed , for certain ecosystems ( like phytoplankton ) some degree of environmental fluctuations has been suggested in order to explain , in the framework of niche theory , how the system overcomes the competitive exclusion principle [23] . It is thus important to consider the conceptual and the practical implications of this stochasticity in both frameworks . We stress that the following discussion is relevant not only to the polymorphism-based technique presented here , but also to the niche-neutral debate in general , including the analysis of the observed species abundance ratios . One fundamental observation is that all theories become practically neutral in the limit of large , fast and independent environmental fluctuations . If the fitness of a species varies tremendously in time , and is uncorrelated with the ( also fluctuating ) fitness of other species , and if the correlation time of these fluctuations approaches zero , the fitness differences are averaged out and the adaptive dynamics is equivalent to the neutral one . Niche theories are meaningful only if ( at least ) one of the three conditions mentioned above is not satisfied: the environmentally-induced fitness fluctuations should be either weak , slow , or correlated . If the effect of environmental stochasticity is weak , the corrections to the predictions of the “pure” theory are relatively small . In that case one may use our Fu-Li technique , expecting only small deviations from the predictions for the idealized case . In other occasions , however , one can find evidence for large perturbations ( e . g . , climate changes ) that affect the ecosystem . Here the timescale is important: one may try to relate observed quantities ( abundance ratios , genetic polymorphism ) to the predictions of an adaptive theory only if the characteristic time needed for the ecosystem to reach demographic equilibrium is much shorter than the typical period between environmental shifts . The Fu-Li statistic in that case will fit our predictions if the genetic time horizon , , is smaller than the characteristic period between environmental transitions . Even if these conditions are not satisfied and the Fu-Li statistic ( as well as the species abundance ratio ) fails to follow the pure scenario predictions one may still uses other polymorphism based methods . The basic challenge , now , is to discriminate between the neutral scenario suggested by [24] , with a neutral drift superimposed on the overall carrying capacity fluctuations , and between two competing niche scenarios . One can imagine an adaptive ecosystem that is subject to uniform ( correlated ) pressure , such that the abundance of all species shrinks or grows in the same proportions ( the relative abundance is conserved and is independent of the total population size ) . On the other hand , the pressure may be uncorrelated ( niche-selective ) , not affecting all species in the same manner , in which case the abundance ratio is time-dependent [23] , [25] . As suggested above , polymorphism data may be used in order to calculate , the number of lineages as a function of time , and from this quantity can be calculated [note that the rate of disappearance of lineages in the Wright-Fisher coalescence model is proportional to the abundance ] . This technique may be used in order to extract past abundance ratios for different species . With the abundance history at hand one can distinguish between the three different possibilities . In the case of niche scenarios with uniform pressure , one expects the abundance-ratio to be fixed in time . The neutral scenario suggests that the abundance ratio is not varying but for different species is correlated , i . e . , a global catastrophe resulted in a decrease of all the species and vice versa . An uncorrelated historic abundance ( i . e . , both abundance of any species and the abundance ratio fluctuate in time ) corresponds to niche-selective pressure . Thus , even in the case of large environmental stochasticity , more sophisticated genomic techniques can be used to differentiate between the two histories . Most of the empirical tests suggested for the niche-neutral debate rely on snapshots , such as via comparison of the predicted and the observed species abundance ratio . Some authors did consider historic abundance data [25]–[27] , but the populations they dealt with are relatively small . Moreover , these authors tested only the neutral hypothesis against the data; in order to have a well defined niche theory , one must clarify the relative weight given to the stochasticity in comparison with the deterministic part of the dynamics . In this work , we suggest the use of current genetic polymorphism as an indicator for past abundance fluctuations . We believe that due to the fast-paced development of sequencing techniques , this data will be available for analysis in the near future . By sequencing more and more individuals from different species , one may use our technique to improve the quality of the results in any ecosystem and for a large time horizon . As explained in the last section , the results may be used as a test for both niche and neutral scenarios , and may allow one to establish a “mixed” theory , comparing the importance of stochasticity vs . deterministic dynamics .
We have gathered our data from a simulation of the Wright-Fisher model with discrete generations . We initiate the system with individuals , each carrying a “genome” of 1000–10000 sites . At each generation , any of the individuals produces offspring , where is a random number generated from a Poissonian distribution with an average of 2 . Each of the offspring carries the exact DNA sequence of its ancestor with probability , and mutates at a single , randomly chosen site with probability . From all of the offspring in a generation , only are selected at random to survive , where is the ( time dependent ) carrying capacity . For niche histories , fluctuate around with , as expected for a population with a well-defined carrying capacity subject to demographic stochasticity . For neutral histories , the population size follows a Markovian process: given , the carrying capacity of the last generation , is chosen at random from a Poissonian distribution with average . In order to compare the predictions of the two theories for a species given the current abundance , we have created first the sequence starting from and go backwards in time up to . We then simulate the genealogic process from past to present , and obtain the Fu-Li statistics using the algorithm presented in [28] . For a different simulation technique has been used: the genealogic tree has been generated from the sampled population at present using the “ball in a box” procedure [29] , an implementation of the Wright-Fisher process . The number of “boxes” changes from generation to generation according to the above mentioned procedure for the corresponding scenario . As seen in Figure 2 below , the Fu-Li statistic obtained using the two procedures are essentially the same .
|
One purchases 100 wineglasses and 100 pairs of pants . After one year , 10 glasses and 10 pants survive . What can be said about the relative quality of the survivors ? Well , clothes “die” as a result of accumulated wear; the surviving items are of better quality . The breaking of a wineglass is an external , random event: here the survivors are not the best , but the luckiest . To tell apart the superior from the fortunate , one should examine the development over time: the number of surviving items decays exponentially with time for the glasses and follows a sigmoid curve for the pants . An ongoing argument among macroecologists deals with similar issues . Adaptive theories suggest that the frequent species are the fittest , while the neutral theory explains the observed frequencies as a result of demographic stochasticity , assuming all species to have the same fitness . The histories suggested by the two scenarios are clearly different , but how can one probe the prehistoric abundance of species ? In fact , past abundance is reflected in current genetic variance within a population . Here , we present a new technique , based on the Fu-Li F-statistic , which allows one to distinguish between niche and neutral scenarios and to resolve this important debate .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"ecology/theoretical",
"ecology",
"ecology/population",
"ecology",
"ecology/evolutionary",
"ecology",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2009
|
Polymorphism Data Can Reveal the Origin of Species Abundance Statistics
|
During sensory deprivation , the barrel cortex undergoes expansion of a functional column representing spared inputs ( spared column ) , into the neighboring deprived columns ( representing deprived inputs ) which are in turn shrunk . As a result , the neurons in a deprived column simultaneously increase and decrease their responses to spared and deprived inputs , respectively . Previous studies revealed that dendritic spines are remodeled during this barrel map plasticity . Because cofilin1 , a predominant regulator of actin filament turnover , governs both the expansion and shrinkage of the dendritic spine structure in vitro , it hypothetically regulates both responses in barrel map plasticity . However , this hypothesis remains untested . Using lentiviral vectors , we knocked down cofilin1 locally within layer 2/3 neurons in a deprived column . Cofilin1-knocked-down neurons were optogenetically labeled using channelrhodopsin-2 , and electrophysiological recordings were targeted to these knocked-down neurons . We showed that cofilin1 knockdown impaired response increases to spared inputs but preserved response decreases to deprived inputs , indicating that cofilin1 dependency is dissociated in these two types of barrel map plasticity . To explore the structural basis of this dissociation , we then analyzed spine densities on deprived column dendritic branches , which were supposed to receive dense horizontal transcolumnar projections from the spared column . We found that spine number increased in a cofilin1-dependent manner selectively in the distal part of the supragranular layer , where most of the transcolumnar projections existed . Our findings suggest that cofilin1-mediated actin dynamics regulate functional map plasticity in an input-specific manner through the dendritic spine remodeling that occurs in the horizontal transcolumnar circuits . These new mechanistic insights into transcolumnar plasticity in adult rats may have a general significance for understanding reorganization of neocortical circuits that have more sophisticated columnar organization than the rodent neocortex , such as the primate neocortex .
Experience-dependent plasticity ( EDP ) in adult neuronal circuits is considered to form the basis of learning and memory [1–3] . EDP is also related to the recovery of cortical responses after the disruption of peripheral inputs [4–6] . The rodent barrel cortex provides a key model system for studying adult EDP in which response field patterns ( or functional maps ) can change in a sensory experience-dependent fashion [7–9] . In this regard , the response field corresponding to a spared whisker ( spared cortical barrel column ) expands during sensory deprivation into neighboring cortical columns that correspond to deprived whiskers ( deprived columns ) [10] . The deprived inputs associated with these columns in turn shrink [11] . The primary locus of these plastic changes is layer 2/3 ( L2/3 ) in adult barrel cortex [12 , 13] . An L2/3 neuron increases its responses to spared inputs and simultaneously decreases its responses to deprived inputs [14] . These plastic changes have been suggested to be mediated by different neuronal circuits . Horizontal transcolumnar projections from neighboring column neurons ( spared column → deprived column L2/3 ) [15 , 16] are important for map expansion because electrolytic lesioning of this pathway prevents plasticity [17] and synaptic transmission in this pathway is potentiated after sensory deprivation [18] . In contrast , ascending intracolumnar projections from layer 4 ( deprived column L4 → deprived column L2/3 ) are depressed during shrinkage of deprived whisker representation [19] . Morphological studies indicate that sensory deprivation promotes the turnover of dendritic spines in the rodent barrel cortex [20] . Within dendritic spines , actin filaments are highly concentrated [21 , 22] and provide the structural foundation for synaptic plasticity [23–25] . Actin depolymerizing factor ( ADF ) /cofilin regulate dendritic spine structure through their actin filament-severing and monomer-binding activities [26] . ADF/cofilin are thus predominant regulators of dendritic spine structure and synaptic plasticity [27–32] . Indeed , ADF/cofilin govern both the expansion and shrinkage of the spine structure of the hippocampal neuronal dendrite in vitro [28 , 33] , and postnatal knockout impairs both stimulus-induced long-term potentiation ( LTP ) and depression ( LTD ) in hippocampus [31] . However , it remains untested whether ADF/cofilin regulate both directions of response changes during adult EDP . In the present study , therefore , we investigated causal impact of perturbation of ADF/cofilin function on two different components of barrel map plasticity: spared input expansion and deprived input shrinkage . For this purpose , we knocked down cofilin1 ( CFL1 ) within excitatory neuron in an L2/3- and deprived ( D2 ) column-restricted manner ( Fig . 1A ) . This strategy enabled us to examine the impact of CFL1 knockdown ( KD ) only in the direct postsynaptic neurons ( in this case , the L2/3 excitatory neurons of the deprived column ) involved in the horizontal transcolumnar and ascending intracolumnar connections . We found that the response field expansion of spared whisker input was impaired by CFL1 KD in the deprived column , while the response field shrinkage of deprived whisker input was preserved . We then explored the mechanistic insights for this dissociation in the effects of CFL1 KD , and found that spine densities increased in a CFL1-dependent manner at the dendritic branch segments around spines connecting with transcolumnar projections , selectively in a part of the supragranular layer where dense transcolumnar projections were observed . These results provide the first direct evidence that a CFL1-mediated change in synaptic connectivity underlies the EDP in a circuit-specific manner .
To manipulate CFL1 gene expression , we used a microRNA ( miRNA ) -based gene KD system in which the polymerase II promoter was available for driving miRNA expression . Because several weeks are typically required for the induction of EDP [10 , 14] , we employed a lentiviral vector for the stable in vivo expression of miRNA for CFL1 KD ( Fig . 1B ) . The vectors co-expressed channelrhodopsin-2 ( ChR2 ) -enhanced yellow fluorescent protein ( eYFP ) , with ChR2 as a light-activatable tag for extracellular single-unit recordings [34–38] and eYFP as a fluorescent marker of CFL1 KD neurons , under the control of the excitatory neuron specific Ca2+/calmodulin-dependent protein kinase II alpha ( CaMKIIα ) promoter [39] . Targeted injection of the lentiviral vector into the right D2 barrel column of rats was achieved by functionally identifying the column center via intrinsic signal optical imaging ( Fig . 1C-1F ) [40] . Single column-restricted expression of eYFP was confirmed in tangential sections that were processed via cytochrome oxidase staining ( Fig . 1G and 1H ) . By targeting the vector injection to a shallow depth within the cerebral cortex ( ~300 μm from the pial surface ) , viral infection was restricted to L2/3 ( Fig . 1I ) , and strong eYFP expression was confined to L2/3 ( Fig . 1J ) . Owing to the existence of axonal projections from L2/3 to L5 [16] , weak fluorescence derived from eYFP-positive axons originating from L2/3 neurons was observed in L5 ( Fig . 1J ) . Immunostaining of a neuronal marker , microtubule-associated protein 2 ( MAP2 ) , confirmed that most of the eYFP+ neurons resided within L2/3 , whereas eYFP+ neurons were rarely found in L5 ( 90 . 1% in L2/3 versus 0 . 2% in L5 ) ( Fig . 1J and 1K ) . These results demonstrate that viral expression was mostly restricted to the L2/3 neurons in the D2 barrel column . In the present study , the CFL1 KD experiments made use of a negative control miRNA ( miR-Neg ) and two miRNAs ( miR-CFL1_1 and miR-CFL1_2 ) with different target sequences within the CFL1 gene . The KD efficiencies of miR-CFL1_1 and miR-CFL1_2 were first assessed in vitro . Both miRNAs showed high KD efficiency at the mRNA level in rat CFL1-overexpressing human embryonic kidney ( HEK ) 293T cells ( miR-CFL1_1 , 95 . 0 ± 0 . 9%; miR-CFL1_2 , 89 . 3 ± 0 . 3%; n = 3: miR-CFL1_1 , p = 2 . 7 × 10-8; miR-CFL1_2 , p = 2 . 8 × 10-8 versus miR-Neg , Dunnett’s multiple comparison test ) ( Fig . 2A ) . Similar results were observed at the protein level in rat pheochromocytoma-12 ( PC-12 ) cells ( miR-CFL1_1 , 96 . 8 ± 1 . 5%; miR-CFL1_2 , 83 . 8 ± 5 . 7%; n = 3: miR-CFL1_1 , p = 0 . 0036; miR-CFL1_2 , p = 0 . 0073 versus miR-Neg ) ( Figs . 2B and S1A ) . We next confirmed CFL1 KD in vivo . CFL1 protein expression is observed in neuronal somata , dendritic spines , and astrocytes in the normal cortex [21] . Immunostaining of CFL1 in a miR-CFL1_1- or miR-CFL1_2-expressing rat showed that CFL1 expression decreased locally in an L2/3 subregion corresponding to the region where eYFP expression was observed in the adjacent section ( Figs . 2C , 2D , and S1B ) . This finding is in clear contrast with the miR-Neg-expressing control section , where no decrease in CFL1 protein expression was observed ( Fig . 2E and 2F ) . Double staining of CFL1 and NeuN in a miR-CFL1_1-expressing rat revealed that CFL1 immunoreactivity decreased in neurons within eYFP+ region compared to eYFP− region ( Fig . 2G and 2H ) . Indeed , percentage of CFL1-positive cells in NeuN-positive cells significantly decreased in eYFP+ region ( eYFP− region , 81 . 6 ± 1 . 2%; eYFP+ region , 17 . 8 ± 4 . 4%; n = 3; p = 0 . 0003 , t-test with Bonferroni’s correction ) ( Fig . 2I ) . This percentage did not decrease in miR-Neg-expressing rats ( eYFP− region , 75 . 2 ± 3 . 8%; eYFP+ region , 71 . 7 ± 3 . 0%; p = 0 . 51 , t-test ) ( Fig . 2I ) . These results demonstrate that expression of miR-CFL1 knocked down CFL1 in neurons and this effect was not due to overexpression of ChR2-eYFP or miRNA . To ensure the specificity of miR-CFL1 , we next examined mRNA expression levels of three genes related to CFL1 ( ADF , Twinfilin 1 and 2 ) [41] in PC-12 cells . Expression of miR-CFL1 through the infection of Lenti-CMV-ChR2-eYFP-miR-CFL1_1 or-CFL1_2 did not affect the mRNA levels of these genes ( ADF; miR-CFL1_1 , p = 0 . 11; miR-CFL1_2 , p = 0 . 50: Twinfilin1; miR-CFL1_1 , p = 0 . 66; miR-CFL1_2 , p = 0 . 99: Twinfilin2; miR-CFL1_1 , p = 0 . 86; miR-CFL1_2 , p = 0 . 69: versus miR-Neg , Dunnett’s multiple comparison test ) , although CFL1 expression significantly decreased ( miR-CFL1_1 , p = 2 . 6 × 10-7; miR-CFL1_2 , p = 5 . 2 × 10-7 versus miR-Neg ) ( Fig . 2J ) . As for ADF , which is closely related to CFL1 in terms of structure and function [26] , we examined its expression at the protein level both in PC-12 cells and rats expressing miR-CFL1 . ADF protein expression was not significantly affected by CFL1 KD ( miR-CFL1_1 , p = 0 . 094; miR-CFL1_2 , p = 0 . 078: versus miR-Neg , Dunnett’s multiple comparison test ) , although there was a slight increase ( Figs . 2K , 2L , and S1C ) . This tendency of increase is consistent with the previous observations in CFL1 knockout mice [31] . These data clearly demonstrate the specificity of genetic manipulation mediated by miR-CFL1 . In the present study , extracellular single-unit recordings were only taken from regular-spiking neurons ( S2A–S2C Fig . ) [42] . For efficient recording from CFL1 KD neurons that existed only within a small cortical region ( approximately , a sphere with a radius of 200–250 μm ) ( Fig . 1H and 1I ) , we searched CFL1 KD neurons that co-expressed ChR2 ( Fig . 1B ) with illuminating blue laser ( peak wavelength: 473 nm ) . Furthermore , to exclude neurons that weakly expressed or did not express ChR2 , we used only the data of light-responsive L2/3 neurons that showed high response reliability [37] to repetitive light ( 20 Hz ) stimulation ( S2D–S2K Fig . ) ( for details , see Materials and Methods ) . To examine the effects of CFL1 KD on EDP , lentiviral vectors were injected at 2 weeks before the onset of sensory deprivation ( Fig . 3A ) . Sensory deprivation was induced by using the single whisker experience protocol [7 , 10] , in which all whiskers but the D1 whisker were trimmed on the left side of the face . We first confirmed that the L2/3 neurons in the D2 column of the right hemisphere showed increased responses to spared D1 whisker stimulation after sensory deprivation in wild-type ( WT ) rats ( WT non-deprived versus WT deprived , p = 2 . 7 × 10-6 , Tukey-Kramer’s multiple comparison test ) ( Fig . 3B-3E ) . This observation indicates that the cortical representation of spared whisker inputs expanded into surrounding deprived columns ( Fig . 1A ) . In contrast , the neuronal response increase was almost completely absent in the putative ChR2+ neurons from which recordings were taken in the miR-CFL1_1-expressing deprived rats ( WT deprived versus miR-CFL1_1 deprived , p = 3 . 3 × 10-7 ) ( Fig . 3C-3E ) . On the other hand , expression of miR-CFL1_1 in non-deprived rats did not affect neuronal responses to D1 stimulation ( WT non-deprived versus miR-CFL1_1 non-deprived , p = 0 . 91 ) ( Fig . 3C-3E ) , indicating that CFL1 KD in and of itself does not decrease responses to the D1 whisker . We performed three lines of control experiments . The first control experiment demonstrated that the enhanced spared whisker response was not altered in neurons in which miR-Neg and ChR2-eYFP were co-expressed ( WT deprived versus miR-Neg deprived , p = 0 . 98 ) ( S3A Fig . ) . This finding suggests that the overexpression of miRNA and ChR2-eYFP itself does not affect the expansion of spared input representation . The second control experiment demonstrated that CFL1 KD with miR-CFL1_2 also impaired the increase in the spared whisker response as well as miR-CFL1_1 ( WT deprived versus miR-CFL1_2 deprived , p = 4 . 1 × 10-6 ) ( S3A Fig . ) . This finding suggests that the observed effects were not due to “off-target” actions of miR-CFL1 [43] . Finally , the effect of CFL1 KD on experience-dependent response increase was weaker in neurons determined as putative ChR2− than those determined as putative ChR2+ both in miR-CFL1_1 and miR-CFL1_2 ( F1 , 90 = 9 . 01 , p = 0 . 0035 , main effect of factor 1; factor 1 , neuron type; factor 2 , miR type; two-way ANOVA: miR-CFL1_1 , p = 0 . 017; miR-CFL1_2 , p = 0 . 15; ChR2+ versus ChR2− , t-test with Bonferroni’s correction ) ( S2L Fig . ) , validating further that experience-dependent potentiation to D1 deflections was impaired in miR-CFL1-expressing D2 neurons . To exclude the possibility that biased recording locations within the D2 column affected our results , we performed another analysis . Recording locations in the D2 column were reconstructed based on lesion marks ( Fig . 3F and 3G ) . Responses recorded from CFL1 KD neurons of deprived rats were lower than that recorded from WT deprived rats , regardless of the distance from the D1 column ( Fig . 3H ) and the cortical depth ( Fig . 3I ) ( comparison of distance distribution , WT deprived versus miR-CFL1_1 deprived , p = 0 . 37; comparison of depth distribution , WT deprived versus miR-CFL1_deprived , p = 0 . 19 , Mann-Whitney’s U-test ) . Accordingly , the observed effects of CFL1 KD in this study were not due to recording location bias . To further confirm the specificity of the effects of miR-CFL1 , we next examined whether the impaired experience-dependent increase in neuronal responses ( Fig . 3C-3E ) recovers by expression of a mutant CFL1 resistant to miRNA . We first designed three resistant CFL1s ( resCFL1s ) that had seven or eight point mutations , which did not change the amino acid sequences , within the miR-CFL1_1 target sequence ( 21 bp ) ( Fig . 4A ) . Impaired CFL1 expression by miR-CFL1_1 was indeed rescued by resCFL1 expression in vitro , and the efficiency of expression recovery was highest in resCFL1_1 ( resCFL1_1 , 36 . 3 ± 3 . 4%; resCFL1_2 , 32 . 8 ± 1 . 7%; resCFL1_3 , 28 . 4 ± 2 . 8%; n = 4: resCFL1_1 , p = 5 . 8 × 10-6; resCFL1_2 , p = 2 . 2 × 10-5; resCFL1_3 , p = 1 . 3 × 10-4 versus miR-CFL1_1 group , Tukey-Kramer’s multiple comparison test ) ( Fig . 4B ) . Therefore , resCFL1_1 ( henceforth “resCFL1” ) was selected for in vivo experiments . We also confirmed that the expression level of resCFL1 was not affected by miR-CFL1_1 in vitro ( p = 0 . 30 , t-test ) ( Fig . 4C ) . We injected the lentivirus which co-expressed resCFL1 and mCherry into the same cortical region ( D2 barrel column ) where the lentivirus expressing ChR2-eYFP/miR-CFL1_1 was also injected ( Fig . 4D and 4E ) . After inducing EDP by whisker deprivation , the responses of putative ChR2+ neurons were selectively recorded . This procedure assured miR-CFL1 was expressed in the recorded neurons . Co-expression of ChR2-eYFP and mCherry in the infected cortical area was confirmed by histological analysis ( Fig . 4F and 4G ) . We showed that responses to D1 whisker deflections were significantly larger in neurons expressing both miR-CFL1_1 and resCFL1 than in neurons expressing only miR-CFL1 ( miR-CFL1+resCFL1 deprived versus miR-CFL1 deprived , p = 0 . 0069 , Tukey-Kramer’s multiple comparison test ) ( Fig . 4H-4J ) . These data clearly demonstrate that the effects of miR-CFL1 expression on experience-dependent potentiation were not due to non-specific effects of miR-CFL1 on genes other than CFL1 , but due to an impairment of CFL1 function . The same set of cells from which we recorded responses to D1 stimulation was also tested for D2 stimulation ( S1 Data ) . We first confirmed that the L2/3 neurons in the D2 column showed decreased responses to deprived D2 whisker stimulation after sensory deprivation in WT rats ( WT non-deprived versus WT deprived , p = 0 . 0009 , Tukey-Kramer multiple comparison test ) ( Fig . 5A-5C ) . This process is indicative of the shrinkage of deprived whisker input representation ( Fig . 1A ) . We then examined the effects of CFL1 KD on this process , and found that experience-dependent response decrease to the deprived D2 whisker was preserved in either miR-CFL1_1-expressing neurons ( WT deprived versus miR-CFL1_1 deprived , p = 0 . 89 ) ( Fig . 5B and 5C ) or miR-CFL1_2-expressing neurons ( WT deprived versus miR-CFL1_2 deprived , p = 0 . 89 ) ( S3B Fig . ) . Expression of miR-CFL1_1 in non-deprived rats did not affect L2/3 neuronal responses to D2 stimulation ( WT non-deprived versus miR-CFL1_1 non-deprived , p = 0 . 81 ) ( S3B Fig . ) , suggesting that CFL1 KD itself does not affect L2/3 neuronal responses to whisker stimulation . Taken together , these results suggest that CFL1-mediated actin dynamics is necessary for the experience-dependent expansion of spared input representation in L2/3 , but not for the experience-dependent shrinkage of deprived input representation . To simultaneously visualize the transcolumnar ( D1 → D2 ) projections and dendritic spines of D2 neurons , we next constructed a new set of lentiviral vectors expressing either tdTomato or enhanced green fluorescent protein ( eGFP ) /miRNA ( Fig . 6A and 6B ) . Injection of these vector solutions was targeted to the D1 column ( tdTomato ) or the D2 column ( eGFP/miRNA ) ( Fig . 6C ) . To avoid dense neuronal expression of eGFP , a low-titer solution of the eGFP/miRNA vector ( 3 . 0 × 108 − 1 . 0 × 109 gc·ml−1 ) was employed ( Fig . 6D and 6E ) . Because the eGFP expression level was low under this low-titer condition and eGFP fluorescence was unendurable for repeated confocal imaging , we used sections stained with an antibody to eGFP for morphological experiments ( Fig . 6E ) . The eGFP/miRNA vector effectively knocked down CFL1 even in the low-titer condition in vivo ( S4A and S4B Fig . ) and in the low multiplicity-of-infection condition in vitro ( S4C Fig . ) . Our findings so far indicate that CFL1 dependency dissociates between two types of barrel map plasticity . Together with the fact that CFL1 is a predominant regulator of dendritic spine structure [29 , 32] , it can be hypothesized that CFL1-mediated structural modifications underlie this dissociation; modification of spine structure occurs selectively in cortical regions where horizontal transcolumnar axons emanating from the spared barrel column exist densely . We thus performed the step-by-step test of this hypothesis . We first found that the tdTomato intensity emanating from the D1 axons peaked at 150–200 μm from the cortical surface in the D2 column , and decreased with cortical depth ( Figs . 6F , 6G , and S5A–S5C ) . This was in clear contrast to the vertical profile of L4 input strength [44] , which was fairly weak at shallow depths and stronger at deeper zones , forming a complementary pattern with that of the D1 axonal intensity profile ( S5C Fig . ) . There was a significant difference in tdTomato intensity between the distal ( 0–200 μm from the cortical surface ) and proximal ( 200–500 μm ) portion ( p = 0 . 022 , paired t-test ) ( S5D Fig . ) . These results suggest that a greater number of transcolumnar synaptic connections from the D1 column are generated in the distal portion of the D2 column than in the proximal portion at which the ascending deprived axonal inputs from L4 are thought to predominate . We next tested the effects of sensory deprivation on dendritic spine number in the miR-Neg ( non-deprived and deprived ) groups . Learning/experience-driven dendritic spine formation and synaptic plasticity spatially cluster on dendritic branches in cortical pyramidal neurons [45 , 46] as well as hippocampal neurons [47] , and thus spine densities were measured in dendritic branch segments that are supposed to receive dense transcolumnar inputs . For this purpose , we measured spines around ( <15 μm ) the identified putative transcolumnar connections ( Fig . 6H ) . In non-deprived rats , spine densities were relatively low at the cortical region just below the surface and increased with depth , while densities were nearly constant throughout the supragranular layer in deprived rats ( comparison of slopes of regression lines , miR-Neg non-deprived versus deprived , F1 , 63 = 9 . 33 , p = 0 . 0033 , F-test ) ( S5E Fig . ) . These results demonstrate that sensory deprivation affects dendritic spine numbers in a cortical depth-dependent manner . We thus separately compiled dendritic spine densities measured at distal and proximal portions of the D2 column supragranular layer , and examined the impact of CFL1 KD on these values . In the distal portion , spine densities significantly increased with sensory deprivation in the control miR-Neg-expressing neurons ( miR-Neg non-deprived versus miR-Neg deprived , p = 1 . 1 × 10-4 , Tukey-Kramer’s multiple comparison test ) , but this increase was impaired in the CFL1 KD neurons ( miR-Neg deprived versus miR-CFL1_1 deprived , p = 0 . 0027 ) ( Fig . 6I-6K ) . MiR-CFL1 expression under non-deprived condition did not affect baseline spine densities ( miR-Neg non-deprived versus miR-CFL1_1 non-deprived , p = 0 . 97; miR-CFL1_1 deprived versus miR-CFL1_1 non-deprived , p = 0 . 46 ) , suggesting that the effects of CFL1 KD are specific for the deprivation and that the absence of an increase in spine density in miR-CFL1_1 deprived rats was not due to a general reduction in spine density caused by miR-CFL1_1 expression . In contrast , sensory deprivation did not affect dendritic spine densities in the proximal portion of the supragranular layer ( miR-Neg non-deprived versus miR-Neg deprived , p = 0 . 34; Fig . 6L-6N ) . Furthermore , the same conclusion was reproduced even if dendritic spine densities were measured within 5 μm around transcolumnar connections ( distal; 1 . 06 ± 0 . 07 , 1 . 44 ± 0 . 07 , 1 . 06 ± 0 . 06 , and 1 . 09 ± 0 . 05 spines·μm−1 for miR-Neg non-deprived , miR-Neg deprived , miR-CFL1_1 non-deprived , and miR-CFL1_1 deprived groups , respectively; mean ± standard error of the mean [SEM]; miR-Neg non-deprived , p = 0 . 00056; miR-CFL1_1 non-deprived , p = 0 . 0005; miR-CFL1_1 deprived , p = 0 . 0015; versus miR-Neg deprived , Tukey-Kramer’s multiple comparison test ) ( proximal; 1 . 18 ± 0 . 08 , 1 . 31 ± 0 . 07 , 1 . 24 ± 0 . 06 , and 1 . 21 ± 0 . 07 spines·μm−1 for Neg non-deprived , Neg deprived , CFL1_1 non-deprived , and CFL1_1 deprived ) . These observations suggest that during sensory deprivation , CFL1-mediated actin dynamics causally regulate the spine numbers around dendritic spines receiving horizontal transcolumnar synaptic inputs from the spared D1 column . Moreover , these events take place in the distal portion of the D2 column supragranular layer , where dense horizontal transcolumnar projections reside ( Fig . 7 ) . In addition to the dendritic spine density , we also analyzed the sizes of the D2 spines that made putative synaptic connections with horizontally projecting D1 axons . Sensory deprivation did not influence spine sizes in miR-Neg-expressing neurons in either the distal or the proximal portion ( miR-Neg ND versus miR-Neg D; distal , p = 0 . 99; proximal , p = 0 . 97; Tukey-Kramer’s multiple comparison test ) ( S6A and S6B Fig . ) . In agreement with a previous observation regarding the hippocampal neurons of CFL1 knockout mice [31] , spine sizes increased in CFL1 KD neurons in the distal portion ( F1 , 181 = 10 . 7 , p = 0 . 0013 , main effect of factor 1; factor 1 , miR type; factor 2 , deprivation type; two-way ANOVA ) ( S6A and S6B Fig . ) . However , this increase did not correlate with the neuronal response changes described above ( Fig . 3D and 3E ) , suggesting that the observed spine size changes might not contribute to the functional barrel map plasticity induced by sensory deprivation .
In the present study , CFL1 was locally knocked down in L2/3 excitatory neurons of a deprived column ( D2 column ) using a lentiviral vector-based RNAi approach . In rats injected with miR-CFL1-expressing vectors , the experience-dependent expansion of the spared input representation was prevented in CFL1 KD neurons , whereas the shrinkage of the deprived input representation was preserved . Furthermore , the spine density around the dendritic spines receiving transcolumnar axonal projections from the spared D1 column was increased in L2/3 neurons of the deprived D2 column , and this increase was impaired by CFL1 KD . These results provide , to the best of our knowledge , the first direct evidence that CFL1-mediated actin dynamics are necessary for plasticity in horizontal transcolumnar circuits during adult cortical EDP . In rodents , the ADF/cofilin family consists of three genes , namely , ADF , CFL1 , and cofilin2 . Among their gene products , only ADF and CFL1 proteins are found within the neuronal dendritic spine [21 , 31] . Importantly , CFL1 knockout mice exhibit impaired synaptic plasticity at hippocampal synapses [31] , while ADF knockout mice do not show such deficits [48] . The current investigation therefore focused on CFL1 . In the present study , we validated the specificity of the effects of CFL1 KD based on three different lines of evidence . Firstly , we showed that miR-CFL1 expression did not affect the expression levels of genes related to CFL1 including ADF ( Figs . 2J-2L and S2C ) . Secondly , we showed that the effect of CFL1 KD was consistent between the two miR-CFL1s with different target sequences within the CFL1 gene ( Figs . 3D and S3A ) . Because miRNAs can downregulate genes bearing sequences complementary to their seed sequences ( positions approximately 2–7 of the guide strand ) [43] , evidence of the same outcome with different RNAi sequences reduces concerns about the target specificity of RNAi experiments [43 , 49] . Finally , we showed that impairments of EDP caused by miR-CFL1_1 could be rescued by expression of resCFL1 ( Fig . 4 ) . Therefore , our results suggest that the effects observed in the CFL1 KD rats were not due to off-target actions of the miRNAs . The effects of CFL1 KD on experience-dependent increase of responses to D1 deflections were larger in putative ChR2+ neurons than those in light-responsive putative ChR2—neurons ( S2L Fig . ) . This fact confirmed that experience-dependent potentiation to D1 deflections was impaired in miR-CFL1-expressing D2 neurons . We also showed that responses of light-responsive ChR2—neurons were significantly lower than neurons in WT deprived rats ( p = 0 . 0063 , t-test ) ( S2M Fig . ) . This fact suggests that the potentiation of putative ChR2—neurons was also slightly impaired . There are two possible explanations for this observation ( which are not mutually exclusive ) : ( 1 ) false negative categorization of ChR2+ neuron as ChR2—neurons by our criteria , and ( 2 ) an indirect decrease of responses within the D2 column resulting from a response decrease in ChR2+ neurons surrounding ( and potentially connected with ) light-responsive ChR2—neurons . MiR-CFL1 expression did not affect spine density in the proximal part of the L2/3 where ascending axons from L4 dominate , whereas it impaired experience-dependent increase in densities in the distal part where transcolumnar axons dominate ( Fig . 6I-6N ) . In addition to these facts , it is also important to note that CFL1 KD itself does not affect basal spine density in non-deprived rats expressing miR-CFL1 ( Fig . 6I-6N ) . These data suggest that effects of CFL1 KD were specific for dendritic branches receiving “potentiated” transcolumnar inputs . We found that sensory deprivation increases dendritic spine density selectively in a part of the supragranular layer where dense transcolumnar projections were observed ( Fig . 6I-6K ) . This finding is consistent with those from previous studies showing that sensory deprivation promotes the formation of stable dendritic spines in a deprived column located adjacent to a spared column [20] and also increases the density of the horizontal projections from a spared column to the adjacent deprived columns [50] . By contrast , the absence of dendritic spine structural plasticity in the proximal portion of the D2 column supragranular layer ( Figs . 6L-6N and S6B ) is consistent with the idea that CFL1-mediated actin dynamics do not involve a decrease in deprived input representation because intracolumnar deprived inputs from L4 are thought to prevail in the proximal portion ( S5C Fig . ) [15 , 44] . Taken together , our results suggest that dendritic spines receiving horizontal transcolumnar inputs from the spared column are selectively generated , whereas those receiving ascending intracolumnar inputs from L4 remain constant during sensory deprivation . The previous report demonstrated that ADF could compensate CFL1 function in the presynapse but not in the postsynapse [51] . Therefore , the absence in functional compensation in deprived rats expressing miR-CFL1 ( Fig . 3D and 3E ) may suggest that the experience-dependent potentiation in transcolumnar circuits is , at least in part , postsynaptic origin . Interestingly , CFL1 is under the control of calcineurin , a regulator of LTD , and is necessary for the dendritic spine shrinkage associated with hippocampal LTD [28] . Given that LTD contributes to the response decrease to deprived inputs in the barrel cortex [19] , it is expected that CFL1 also participates in this process . In hippocampal neurons , however , it is also known that spine shrinkage and the decrease in synaptic transmission efficacy during LTD are dissociated processes at the level of molecular pathways [28] . The present data together with these previous observations suggest that the functional depression of the deprived input representation is likewise dissociated from the CFL1-dependent structural changes in the dendritic spines in the rat barrel cortex . However , this view is inconsistent with the observation reported by Rust and colleagues that postnatal CFL1 knockout impairs hippocampal LTD as well as LTP in mice [31] . Although the reason for this inconsistency is unclear , the function of CFL1 in the experience-dependent depression of the deprived input representation in adult cortex may differ from that in stimulus-induced LTD in hippocampal slices . We labeled the D1 and D2 columns simultaneously ( Fig . 6E ) . The tdTomato-positive structure observed within the D2 column mostly consisted of axons ( Fig . 6G ) , which validates that our measurements for tdTomato intensity ( S5A–S5D Fig . ) did not include D1 neuron dendrites travelled from the neighboring column and also that tdTomato signals within the D2 column were not derived from ectopic tdTomato expression outside the D1 column caused by a horizontal spillover of the injection . We accomplished the localized cortical labeling by utilizing the property of lentiviral vectors that have relatively large particle size ( ~100 nm; [52] ) and are thus restricted in its diffusion in vivo compared to other vectors such as adeno-associated viral vectors [53] . This labeling technique allowed the demonstration of CFL1-dependent changes in dendritic spine density that could not have been previously accomplished ( Fig . 6I-6K ) . In this analysis , we measured dendritic spines only around the identified putative transcolumnar connections ( Fig . 6H ) . Because tdTomato-expressing area was almost limited within the L2/3 of the D1 column ( S7 Fig . ) , the putative synaptic connections that we identified should consist of monosynaptic connections between D1 L2/3 neurons and D2 L2/3 neurons . On the other hand , it is possible that polysynaptic connections from D1 L2/3 to D2 L2/3 ( e . g . , D1 L2/3 → D1 L5 → D2 L2/3 [54] ) could also have contributed to the dendritic spines that increased around the detected connection , although they are considered to be relatively weak due to the low efficacy of L2/3 → L5 connections [55] . With regard to changes in numbers of synaptic connections , we found it difficult to measure it because the number of D1 neurons labeled with tdTomato ( and thus the number of D1 axons that could be detected on D2 dendrites ) varied between animals in our small-volume ( 200 nl ) injection method . Indeed , 2-D measurements of the infected areas ( on histological sections ) in all animals used for spine morphological analysis showed that the sizes of tdTomato-expressing areas varied from the entire supragranular layer of D1 column ( ~500 μm × 500 μm ) to a portion of it ( S7 Fig . ) . This variability in labeled D1 neuron numbers was unavoidable to limit tdTomato labeling within D1 column , and it directly influenced the numbers of synaptic connections that could be detected by this labeling method . It is important to note that it did not affect the spine morphological measurements that were confined to D2 spines connected with D1 axons and the spines around them . Therefore , our strategy may be currently optimal for examining structural modifications in transcolumnar circuits while maintaining between-animal variance at a minimum . However , alternative approaches ( e . g . , within-animal chronic monitoring of dendritic spines [56] ) may also reveal similar conclusions and will be the focus of future studies . Spine areas measured in the L2/3 distal portion of miR-CFL1 ND and miR-CFL1 D groups showed similar tendency to increase compared to those measured in miR-Neg groups ( S6A Fig . ) . This fact indicates that , regardless of animal’s sensory experience , CFL1 KD itself slightly expands spine sizes . The slight spine expansion was also observed in the hippocampal neurons of CFL1-knockout mice reared under normal environment [31] . Therefore , it is suggested that blocking or decreasing CFL1 action expands dendritic spine sizes independently of each spine’s involvement in sensory EDP . This finding is consistent with the typical role of cofilin as an actin-depolymerizing factor [26] . On the other hand , CFL1 action also promotes actin polymerization [57] and active turnover of actin filaments [29] . Considering these factors together , KD-mediated decrease in CFL1 activity might make dendritic spines more “fixed” state with decreased turnovers and slightly increased sizes . Because active spine turnover is involved in cortical EDP [20 , 58] , our results might suggest that CFL1 regulates EDP by controlling the turnover rates of dendritic spines/actin filaments . In conclusion , we demonstrate here that CFL1-mediated actin dynamics function in a horizontal connection-specific manner during EDP induced by the single whisker experience protocol . In addition to the rodent vibrissal system , the primate brain is characterized by a highly sophisticated neocortical columnar organization [2] . Cortical circuit reorganization across functional columns through horizontal connections is necessary for functional cortical recovery after peripheral deficits , such as focal retinal lesions [5] . Moreover , plasticity in horizontal transcolumnar or interareal connections is also critical for learning [5]; thus CFL1-mediated circuit reorganization may possibly be a general mechanism for the flexible nature of the human brain . However , this proposal will require further study .
All procedures were performed in accordance with a protocol approved by the University of Tokyo Animal Care Committee ( permit number , MED: P11–050 ) . Surgical procedures for lentiviral injection were performed with isoflurane induction ( 3% ) and under maintenance with isoflurane ( 1% ) or ketamine/xylazine ( 90 mg·kg−1 and 10 mg·kg−1 , respectively ) anesthesia . Surgical procedures for electrophysiology experiments were performed under ethyl carbamate ( 1 . 2 g·kg−1 ) anesthesia . All efforts were made to minimize suffering and the number of animals employed . Fifty-four male Wistar rats ( Nihon SLC ) were used for the study ( ten were used as WT and 44 were used for viral injection ) . Oligonucleotides encoding miRNAs that target the CFL1 gene were designed with BLOCK-iT Pol II miR RNAi expression vector kits and the associated software ( Invitrogen ) . miR-CFL1_1 , miR-CFL1_2 , and a negative control miRNA ( miR-Neg ) , which is predicted to not target any known vertebrate gene , were purchased from Invitrogen . miR-CFL1_1 and miR-CFL1_2 target two different regions within the CFL1 gene ( target sequences: miR-CFL1_1 , 5′-AGGAATCAAGCACGAATTACA-3′; miR-CFL1_2 , 5′-GTTCGCAAGTCTTCAACGCCA-3′ ) . HEK293T cells ( for miRNA screening ) or rat PC-12 cells ( for examining effects on endogenous gene expression ) were used for the in vitro experiments . For miRNA screening ( Fig . 2A ) , plasmid vectors expressing miR-CFL1 ( pcDNA6 . 2-GW/EmGFP-miR vector; Invitrogen ) and the rat CFL1 gene ( pCAG-rCFL1 , the kind gift of H . Kasai , University of Tokyo ) were co-transfected into the HEK293T cells . Three days after transfection , total RNA was prepared and used as a template for real-time reverse-transcriptase PCR with the StepOne Real-Time PCR system ( Applied Biosystems ) . For resCFL1 screening ( Fig . 4B ) , pcDNA6 . 2-GW/EmGFP-miR-CFL1 , pCAG-rCFL1 , and the plasmid vector expressing resCFL1 ( pCMV-resCFL1 ) were co-transfected . For generation of the pCMV-resCFL1 , PCR fragments encoding each of the N-terminal and C-terminal side of CFL1 and an annealed oligonucleotide encoding mutated portion of CFL1 ( resCFL1_1 , 5′-CAAGAAGAAACTGACTGGCATTAAACATGAGCTCCAAGCTAACTGCTACGA-3′; resCFL1_2 , 5′-CAAGAAGAAACTGACGGGTATCAAACATGAGCTCCAAGCTAACTGCTACGA-3′; resCFL1_3 , 5′-CAAGAAGAAACTGACCGGGATAAAACATGAGCTCCAAGCTAACTGCTACGA-3′ ) were simultaneously fused to EcoRI/NotI-digested pIRES2-AcGFP1 ( Clontech ) by using InFusion Cloning kit ( Clontech ) . Primer sequences were as follows: CFL1-N-F , 5′-CTCAAGCTTCGAATTACCGGTATGGCCTCTGGTGTGGCT-3′; CFL1-N-R , 5′-GTCAGTTTCTTCTTGATGGCATCC-3′; CFL1-C-F , 5′-AGCTAACTGCTACGAGGAGGTCAA-3′; CFL1-C-R , 5′-TCTAGAGTCGCGGCCGCTCACAAAGGCTTGCCCTC-3′; To examine the effects of miR-CFL1 expression on endogenous gene expression ( Figs . 2B , 2J , 2K , S1A , and S1C ) , the PC-12 cells ( 1 × 105 ) were transfected with the Lenti-CMV-hChR2-eYFP-miR-CFL1_1 or-miR-CFL1_2 vector ( 2 . 0 × 107 gc ) . Four days later , total RNA was prepared from these cells or the cells were solubilized and the cell extracts were obtained . For protein expression analysis , the extracts were immunoblotted [59] using the following antibody combinations: rabbit antibody to cofilin ( 1:250; Cytoskeleton ) or to destrin ( 1:1 , 000; Sigma-Aldrich ) /horseradish peroxidase-conjugated antibody to rabbit IgG ( 1:2 , 000; Rockland Immunochemicals Inc . ) , and mouse antibody to β-actin ( 1:5 , 000; Sigma-Aldrich ) /horseradish peroxidase-conjugated antibody to mouse IgG ( 1:1 , 000; Vector Laboratories , Inc . ) . The band intensities were quantified by using ImageJ software ( National Institutes of Health ) . The primer sets used for reverse-transcriptase PCR experiments were as follows: CFL1-F , 5′- GCTCTTTTGCCTGAGTGAGG-3′; CFL1-R , 5′-CTTAAGGGGTGCACTCTCG-3′; ADF-F , 5′-GTGCATAGTCGTTGAAGAAGG-3′; ADF-R , 5′-CCTTCGAGCTTGCATAGATC-3′; Twf1-F , 5′-CTGAGTAAGAGACAGCTCAACTATG-3′; Twf1-R , 5′-GCTCTCTTATGCTGCATGTG-3′; Twf2-F , 5′-CTGAAGATGCTGTATGCAGC-3′; Twf2-R , 5′-CTGGTGCTTACTCTCCACAC-3′; GAPDH-F , 5′-TGAACGGGAAGCTCACTGG-3′; GAPDH-R , 5′-TCCACCACCCTGTTGCTGTA-3′ . For generation of the lentiviral transfer vector , pCL20c CaMKIIα-hChR2-eYFP-miR , the PacI/BamHI-digested mouse CaMKIIα promoter ( 1 . 3 kb ) from pLenti-CaMKIIα-hChR2-mCherry-WPRE ( the kind gift of K . Deisseroth ) was inserted into MluI/EcoRI-digested pCL20c MSCV-hChR2-eYFP [37] by blunt-end ligation to replace the MSCV promoter . A PCR fragment encoding miR-CFL1 or miR-Neg was inserted into the ClaI site of pCL20c CaMKIIα-hChR2-EYFP using an InFusion Cloning kit . For generation of pCL20c CaMKIIα-eGFP-miR , a PCR fragment encoding miRNA was inserted into the ClaI site of pCL20c CaMKIIα-eGFP [39] . For generation of pCL20c CaMKIIα-mCherry-P2A-resCFL1 , PCR fragments encoding each of mCherry ( derived from pmCherry-N1 vector , Clontech ) and resCFL1 were simultaneously fused to AgeI/NotI-digested pCL20c CaMKIIα-eGFP by using InFusion kit . The P2A sequence was separately added to each of the primers used to amplify mCherry and resCFL1 ( primer sequences: mCherry-F , 5′-CCCGGGATCCACCGGCGCCACCATGGTGAGCAA-3′; mCherry-P2A-R , 5′-CTGCTTGCTTTAACAGAGAGAAGTTCGTGGCTCCGGAGCCCTTGTACAGCTCGTCCATGCC-3′; P2A-resCFL1-F; 5′-TGTTAAAGCAAGCAGGAGACGTGGAAGAAAACCCCGGTCCCATGGCCTCTGGTGTGGCTGTC-3′; resCFL1-R , 5′-ATTATCGATGCGGCCTCACAAAGGCTTGCCCTC-3′ ) . The lentiviral vectors were produced and titrated by using the DNA titration method , as described previously [35] . All whiskers , except for the D1 whisker , on the left side of the face were trimmed by cutting the whiskers to fur level ( <1 mm ) under brief isoflurane anesthesia ( 3% ) by using an anesthetizer ( MK-AT200D; Muromachi Kikai ) . The ipsilateral whiskers were not trimmed . Rats were 10 weeks old at the onset of whisker trimming . Subsequently , the whiskers were re-trimmed every 2 days . Trimming was continued for more than 3 weeks ( range 23–45 days ) and ceased at 1 week before electrophysiological recordings to stimulate the regrowth of the trimmed whiskers . Each whisker was inserted into a glass capillary ( inner diameter , 0 . 5 mm ) glued to a piezoelectric bending element . A stereoscope was used to insert the whisker to a distance of 10 mm from the whisker pad . For electrophysiological recording , 200 μm ventral-dorsal deflections ( 10 ms at 1 Hz , repeated 50 times ) were applied , resulting in an angular deflection of 1 . 14° . For intrinsic signal optical imaging , 1–2° amplitude ventral-dorsal deflections ( 50 ms at 10 Hz , repeated 50 times ) were applied . Rats were 8 weeks old at the time of lentiviral vector injection . Anesthesia was induced with 3% isoflurane and anesthesia was maintained by either 1% isoflurane , ketamine ( 90 mg∕kg IP ) ∕xylazine ( 10 mg∕kg IP ) . Each rat was positioned in a stereotaxic apparatus ( SR-6R; Narishige ) . The skull over the barrel cortex was carefully thinned to create a cranial window . Functional maps of the barrel cortex were determined by using intrinsic signal optical imaging . The cortical surface was illuminated with a red light ( wavelength , 705 nm ) while stimulating a single whisker . Images were collected with a charge-coupled device ( CCD ) camera ( Tokyo Electric Industry ) and digitized with an IBM/PC-compatible video system equipped with a video frame grabber board ( Matrox Imaging ) . The imaged area was a 4 . 2 × 3 . 1 mm region with a spatial resolution of 320 × 240 pixels . The surface blood vessels were imaged by using a green light ( wavelength , 540 nm ) . The focusing depth was adjusted to 500 μm below the cortical surface . For each recording trial , data were collected for 8 s with a frame length of 0 . 5 s ( 16 frames per trial ) . Reflectance changes in response to whisker stimulation were estimated by subtracting a 3 s averaged frame taken before the onset of whisker stimulation from a 3 s averaged frame taken at the time of whisker stimulation . To prepare the rats for the electrophysiology experiments , the D2 barrel column was first identified , and a glass pipette ( tip diameter , ~40 μm ) ( sharply [>45°] grinded so as to make depth control easier and to mitigate the spillover of cerebrospinal fluid ) attached to a 1 μl Neuros syringe ( 7001 KH; Hamilton Company ) was then vertically inserted into the center of the D2 barrel column to a depth of 300 μm from the cortical surface . Before the pipette insertion , a mannitol solution ( 25% in saline ) was intraperitoneally injected to mitigate the spillover of cerebrospinal fluid . Next , a solution of the lentiviral vector ( Lenti-CaMKIIα-hChR2-eYFP-miR vector containing miR-CFL1_1 , miR-CFL1_2 , or miR-Neg; 200 nl of 1 . 0 × 1010 gc·ml−1 solution; n = 10 , 5 , and 6 rats , respectively ) or a mixed solution ( 1:1 ) of two vectors ( Lenti-CaMKIIα-hChR2-eYFP-miR-CFL1_1 [1 . 0 × 1010 gc·ml−1] and Lenti-CaMKIIα-mCherry-P2A-resCFL1 [1 . 0 × 1010 gc·ml−1] , 200 nl , n = 4 rats ) was injected at a flow rate of 25–50 nl·min−1 with the aid of a micropump ( UltramicroPump III; World Precision Instruments [WPI] ) and a microprocessor-based controller ( Micro4; WPI ) . The needle was left in place for additional 15 min . The scalp incision was carefully sutured , and the rat was returned to a standard cage after recovering from anesthesia . To prepare the rats for the morphology experiments , both D1 and D2 barrel columns were functionally identified prior to lentiviral vector injection . Solutions containing either the Lenti-CMV-tdTomato-WPRE vector ( 200 nl; 1 . 0 × 1010 gc·ml−1 ) or the Lenti-CaMKIIα-eGFP-miR vector ( 200 nl; 3 . 0 × 108 − 1 . 0 × 109 gc·ml−1; miR-CFL1_1 , n = 7 rats; miR-Neg , n = 7 rats ) were then injected into the center of the D1 or D2 barrel column , respectively . A fiber enclosed in the glass coated optrode was coupled to a diode laser ( peak wavelength at 473 nm , Omicron Laserage Laserprodukte GmbH ) . The timing of the stimulation was managed with an electrically controlled mechanical shutter ( UNIBLITZ ) . The light power was controlled with a Neutral Density ( ND ) filter ( Thorlabs ) . For each neuron , 10 × 10 light pulse trains , each with a duration of 5 ms , were delivered at 1 and 20 Hz . The interval between each train was 15 s [37] . The light intensity was adjusted after observing the neuronal responses so as to avoid the skew of light-evoked spike waveforms from spontaneous spike waveforms [36] . More specifically , the maximum light intensity that did not skew waveforms under visual inspection was employed . The light power at fiber input was in the range of 0 . 1 − 5 mW . Each rat was anesthetized with ethyl carbamate ( 1 . 2 g·kg−1 ) . The body temperature was maintained at 37 . 5°C throughout the experiment . A catheter ( Natsume Seisakusho ) was surgically inserted into the left femoral vein [35] . Ringer’s solution and additional doses of anesthesia ( urethane , 0 . 2 − 0 . 4 g·kg−1 ) were then administered through the catheter . The skull over the barrel cortex was exposed and carefully removed . In vivo eYFP fluorescence was identified by using a cooled CCD camera ( VB-7000; Keyence ) attached to a fluorescence stereoscopic microscope ( VB-G05; Keyence ) . The activities of single neurons were extracellularly recorded using a glass-coated tungsten microelectrode ( impedance < 1 MΩ ) in WT rats , or a glass-coated tungsten optrode ( impedance < 1 MΩ ) [37] in ChR2-expressing rats . The electrode was vertically inserted into the cortex via a hydraulic micromanipulator ( MO-10; Narishige ) . Neuronal signals were amplified with an AB651J amplifier ( Nihon Kohden ) , band-pass filtered ( 0 . 4–5 kHz filter; Nihon Kohden ) , digitized at 25 kHz , and stored by using the Recorder Software ( Neural Data Acquisition System ) . Single units were obtained in the off-line analysis with Offline Sorter Software ( Plexon ) [60 , 61] . Briefly , the SD was first calculated to estimate the variance of the baseline noise . Spikes were then extracted using a threshold of >5 × SD from the baseline mean . The information encoded in spike waveforms was compressed using principal component analysis . If waveforms with shapes uncharacteristic of neuronal action potentials were existed , they were excluded before the calculation of principal components . A cluster was selected in 2-D or 3-D feature ( typically using the first three principal components ) space by drawing a contour manually . The presence of a refractory period was confirmed in the autocorrelogram . If the number of spikes with interspike intervals < 2 ms exceeded 1% of the total for a given unit , the unit was discarded or additional feature combinations were examined to subdivide the cluster further until meeting the criteria in the autocorrelogram [60 , 61] . Single-unit data were analyzed with MATLAB software ( MathWorks ) . Recordings were performed from both L2/3 ( depth from the pial surface , 0–500 μm ) and L5 ( 800–1250 μm ) [62] neurons . In some cases , electrolytic lesions ( 1 μA , 5 s , tip negative ) were applied at a depth corresponding to L4 ( 750 μm ) to map recording locations onto the barrel pattern . In parallel with the single-unit recordings , cortical electroencephalograms were also recorded to monitor the cortical state . A stainless steel screw was threaded into the bone above the occipital cortex . For reference , another screw was threaded into the bone above the cerebellum . Signals were amplified with an AB-610J amplifier , band-pass filtered ( 0 . 5–100 Hz ) , and stored . During whisker stimulation trials , anesthesia was maintained to a depth equivalent to stage III slow-wave sleep , as described previously [63] . Rats were perfused with saline , followed by 4% paraformaldehyde in phosphate buffer . The brains were post-fixed in 4% paraformaldehyde for 2–4 h and immersed in a solution of 20% sucrose in PBS . To recover the location of the electrophysiological recordings and virus expression , the cortex was flattened between two glass slides , sectioned at 50 μm , and processed for cytochrome oxidase staining ( 2–3 h at room temperature [RT] in 20 ml phosphate buffer containing 10 mg diaminobenzidine [DAB] , 10 mg cytochrome c , and 0 . 8 g sucrose ) [64] . Blood vessels were used as a reference for projecting the barrel patterns in L4 onto the eYFP-expressing L2/3 sections . Although ChR2-eYFP expression is mostly restricted to the cell membrane [65] , weak fluorescence was also observed at the cytoplasm ( Fig . 1J ) . No eYFP fluorescence was observed at the cell nuclei . To confirm L2/3-restricted expression of eYFP , we thus stained ChR2-eYFP-expressing brain sections with an antibody against MAP2 , which stains neuronal cytoplasm as well as dendrites [66] , rather than with an antibody against neuron-specific nuclear protein ( NeuN ) , which mainly stains nuclei [67] . For the detection of MAP2 or NeuN in selected neuronal populations , coronal sections ( 25 μm thick ) were immunostained with either mouse antibody to MAP2 ( 1:2 , 000; Sigma-Aldrich ) or mouse antibody to NeuN ( 1:1 , 000; Millipore ) . The sections were reacted with an Alexa Fluor 647-conjugated antibody to mouse IgG ( 1:500; Invitrogen ) . Sections were counterstained with the DNA-specific fluorescent dye , Hoechst 33342 ( Invitrogen ) . To visualize expression of CFL1 or ADF , sections were immunostained with a rabbit antibody to cofilin ( 1:250 ) or a rabbit antibody to ADF ( 1:100 ) , followed by either an Alexa Fluor 647-conjugated antibody to rabbit IgG ( 1:500; Invitrogen ) or a horseradish peroxidase-conjugated antibody to rabbit IgG ( Dako Corporation ) and DAB . The stained images were obtained by using a BZ-9000 fluorescence microscope ( Keyence ) and a TCS-SPE confocal microscope ( Leica ) . Confocal images were used for cell counting . For estimating layer distribution of eYFP+ neurons , sections double-stained with MAP2 and Hoechst were used . Layers were manually identified based on differences in cell density and size . Three non-adjacent sections were chosen that encompassed each injection point , and all eYFP+ neurons were counted in each section . For counting the percentages of CFL1+ neurons , CFL1- and NeuN-stained sections were used . Three region-of-interests ( ROIs , 183 . 3 × 183 . 3 μm ) were randomly selected from both eYFP-expressing region and neighboring normal cortical region ( where eYFP was not expressed ) , and all CFL1+ and NeuN+ neurons were counted within each ROI . For all morphological analyses , the parasagittal floating sections ( 50 μm thick ) were prepared from the rat cortex that included both the D1 column and the D2 column . Immunohistochemistry was performed as described previously [25] . Briefly , sections were immunostained with chicken antibody to GFP ( 1:500; Abcam ) and rabbit antibody to DsRed ( 1:500; Clontech ) , followed by Alexa Fluor 488-conjugated anti-chicken ( 1:200; Invitrogen ) and Alexa Fluor 546-conjugated anti-rabbit ( 1:200; Invitrogen ) secondary antibodies . For morphometric analysis of dendritic spine density and area on immunostained section [24] , confocal immunofluorescence images ( voxel size , 0 . 1 × 0 . 1 × 0 . 5 μm3 ) were acquired with a CSU-22 spinning-disc confocal unit ( Yokogawa Electric ) coupled to an Axiovert 200M microscope through a Plan Apochromat 63× objective ( NA 1 . 4; Carl Zeiss ) . The acquired images were then analyzed using MetaMorph software ( Universal Imaging Corporation ) . Given sets of tdTomato+ D1 axons and eGFP+ D2 dendritic spines were defined as synaptically connected if both fluorescent signals were found within the same voxel . The density of the dendritic spines ( including all types of spines , e . g . , thin , mushroom-shaped , and stubby spines ) was measured at dendritic branch segments that met the following criteria: ( 1 ) the segment was nearly parallel to the xy plane ( 1 , 280 × 1 , 010 pixels ) in a stacked image of consecutive focal planes ( typically , 10–50 stacked planes ) taken at 0 . 5 μm intervals in the z direction; ( 2 ) at least one putative synaptic connection with a tdTomato+ axon was identified; ( 3 ) no bifurcations were present within the segment; ( 4 ) the segment demonstrated no crossing with other branches . For identification of putative synaptic connections , we did not consider overlaps between dendritic shaft and axons . Therefore , all putative synapses were identified on the basis of overlaps between dendritic spines and axons . Branch segments in which the total measured length was less than 4 μm were discarded . Spines within 15 μm of the identified putative synaptic connection were counted , along with dendritic length . In some cases ( eight of 51 dendrites in the distal portion and five of 41 dendrites in the proximal portion ) , two or more connections were identified on a single dendritic branch . If distance between a given connection pair was less than 2 × 15 μm ( five of eight in distal and four of five in proximal ) , two connections were regarded as forming a single branch segment . If this was not the case ( three of eight dendrites in distal and one of five in proximal ) , two connections were regarded as forming different branch segments with each other . We also measured spine densities within 5 μm from connections using the same criteria . For measurement of the dendritic spine area on immunostained section , the fluorescence images were first thresholded , where the threshold was more than the mean plus 5 × the standard deviation ( SD ) of the background intensity distribution . We did not normalize intensity levels in each image . Spines that made putative synaptic connections with D1 axons were then identified . Next , a 2-D projection image was reconstructed from the z plane that included the identified spine . Spine area was estimated in the 2-D image by manually enclosing the spine head , followed by measurement of the total pixel number included within the enclosure . All types of spine morphologies were included in the analysis , and all measurements of spine density and spine area were performed by investigators who were blind to the animal’s sensory experience and the identity of the injected virus . For analysis of axonal density on immunostained section , fluorescence images were acquired with a BZ-9000 microscope through a Plan Apochromat 20 × objective ( NA 0 . 75; Nikon ) . The intensity of the tdTomato fluorescence attributable to D1 column axons was measured within a region of interest ( 100 × 50 μm2 ) localized within the D2 column where the eGFP signal was observed . The region of interest was vertically scanned from the surface of the cortex to the superior end of L4 ( depth , 500 μm ) . Measurements were performed in three sections for each rat . The background intensity ( measured at L4 for each section ) was first subtracted from the determined intensity of the tdTomato fluorescence , and then the subtracted intensity was normalized to the value of the baseline-subtracted maximum intensity , and then averaged across the three sections . For measurement of tdTomato-positive area ( S7 Fig . ) , fluorescent images were thresholded at 10× background intensity of each section ( measured within L4 ) and binarized . TdTomato-positive area was estimated by manually enclosing the signal-positive area in the binarized image . Data analysis was performed with MATLAB software . Light-responsive neurons were identified using 1 Hz stimulation data , by comparing firing rates as a function of stimulation latency during the first 100 ms after each light pulse with the firing rates obtained for similar time blocks after shuffling the spike times of each cell within an interval ( −100 , +100 ms ) around stimulation onset [38] . After each shuffling , spikes were counted in 1 ms bins , and the three successive bins that showed the maximum spike numbers during the 100 ms period after stimulation onset were identified . The spike times were shuffled 10 , 000 times for each cell . Three successive bins with a maximum spike numbers were also identified for the real data . Cells were classified as light-responsive if the number of spikes in the three-bin block with the maximal spike numbers in the real data exceeded the 99 . 9th percentile value of the distribution of the maximum spike numbers in the shuffled data . The latency of the response was defined as the mean latency of all spikes contributing to this block . Light-responsive ChR2− neurons were previously reported as showing a lower spike probability for high frequency repetitive light stimulation compared with ChR2+ neurons [34 , 36–38] . Therefore , repetitive light pulses ( 20 Hz ) were applied to each neuron to examine the probability of spike . The first light pulse in each 10-pulse train was excluded from the analysis . The number of spikes evoked by 90 light pulses was estimated as the spike number detected during the 25 ms after light onset subtracted by the spike number detected during the 25 ms before light onset . In our experimental preparation , ChR2+ somata were almost completely absent in L5 , but ChR2+ axons originating from ChR2+ L2/3 neurons were abundant in L5 . Thus , light-responsive L5 neurons were considered to be a pure population of “indirectly” activated neurons . Indeed , neurons that reliably responded to repetitive optical stimulation were observed in L2/3 , while the firing probability was lower in L5 ( S2D–S2G Fig . ) , consistent with previous reports; thus putative ChR2+ neurons were defined as those neurons exhibiting a higher reliability than the neuron that showed the highest reliability in L5 ( S2F and S2G Fig . ) . Recording locations were reconstructed based on the relative location of the lesion marks within the barrel patterns , as visualized by cytochrome oxidase staining in tangential sections . To allocate each recording location within the D2 barrel column , the center of the mass was calculated for D1 and D2 columns . A line passing through the center of both D1 and D2 was drawn , and each recording location was then vertically projected onto the line . Distance of each recording location from the D1 column center was measured and normalized to the distance between D1 and D2 centers . In one out of 32 rats ( miR-CFL1_1 deprived rats , seven putative ChR2+ units from four recording tracks ) , we failed to reconstruct the barrel pattern histologically , and thus estimated column centers and recorded locations based on intrinsic signal optical imaging data . All statistical tests for the in vivo study were performed with MATLAB and freely available R software . Data are given as the means , and error bars denote the standard error of the mean , except when indicated otherwise .
|
Plasticity in the adult neocortex is the basis of our learning and memory . However , its molecular mechanisms are still unclear . In the sensory barrel cortex of rodents , a well-characterized model for neocortical plasticity , neurons directly code for whisker displacement—neurons within a given barrel will fire when the whisker that that barrel represents is moved . Strikingly , the deprivation of all but a single whisker alters the original representations—cortical columns representing the deprived inputs shrink and that representing the spared inputs expands , intruding into the surrounding deprived columns . Because single-neuron-level structural changes are suggested to be involved in this plasticity , here we focused on cofilin1 , a protein that is known to modulate the cytoskeleton and to regulate the structure of dendritic spines . We induced experience-dependent plasticity in the D1 column by sparing only the D1 whisker , and knocked down the expression of cofilin1 in the D2 column . Cofilin1 knockdown differentially affected plasticity , such that experience-dependent increases in spared input representation were impaired , whereas decreases in deprived input representation were intact . We then found that during these plastic changes , the density of dendritic spines increased in a cofilin1-dependent manner around the connections between the D1 and D2 columns . Cofilin1-dependent density increase was observed only in the most superficial part of the cortex but not in deeper parts , consistent with the distribution patterns of axons that transmit spared and deprived information , respectively . These results suggest that cofilin1 regulates neocortical functional plasticity through the remodeling of dendritic spines within circuits that connect columns .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Cofilin1 Controls Transcolumnar Plasticity in Dendritic Spines in Adult Barrel Cortex
|
Ectopic heartbeats can trigger reentrant arrhythmias , leading to ventricular fibrillation and sudden cardiac death . Such events have been attributed to perturbed Ca2+ handling in cardiac myocytes leading to spontaneous Ca2+ release and delayed afterdepolarizations ( DADs ) . However , the ways in which perturbation of specific molecular mechanisms alters the probability of ectopic beats is not understood . We present a multiscale model of cardiac tissue incorporating a biophysically detailed three-dimensional model of the ventricular myocyte . This model reproduces realistic Ca2+ waves and DADs driven by stochastic Ca2+ release channel ( RyR ) gating and is used to study mechanisms of DAD variability . In agreement with previous experimental and modeling studies , key factors influencing the distribution of DAD amplitude and timing include cytosolic and sarcoplasmic reticulum Ca2+ concentrations , inwardly rectifying potassium current ( IK1 ) density , and gap junction conductance . The cardiac tissue model is used to investigate how random RyR gating gives rise to probabilistic triggered activity in a one-dimensional myocyte tissue model . A novel spatial-average filtering method for estimating the probability of extreme ( i . e . rare , high-amplitude ) stochastic events from a limited set of spontaneous Ca2+ release profiles is presented . These events occur when randomly organized clusters of cells exhibit synchronized , high amplitude Ca2+ release flux . It is shown how reduced IK1 density and gap junction coupling , as observed in heart failure , increase the probability of extreme DADs by multiple orders of magnitude . This method enables prediction of arrhythmia likelihood and its modulation by alterations of other cellular mechanisms .
In cardiac myocytes , dyads are sites where the junctional sarcoplasmic reticulum ( JSR ) membrane closely approaches ( ~ 15 nm ) invaginations of the cell membrane known as transverse tubules ( TTs ) . Voltage-sensitive L-type calcium ( Ca2+ ) channels ( LCCs ) are preferentially localized to the TT membrane of the dyad , where they closely appose Ca2+-binding Ca2+-release channels known as ryanodine receptors ( RyRs ) in the dyad JSR membrane . Depolarization of the cell membrane during an action potential ( AP ) increases LCC open probability , generating a flux of Ca2+ ions into the dyad . The resulting local increases of dyad Ca2+ concentration ( [Ca2+]d ) increase RyR open probability , which when open allow flux of Ca2+ ions from the JSR into the dyad . This process , known as Ca2+-induced Ca2+ release ( CICR ) , causes brief , spatially localized release events known as a Ca2+ sparks [1] . Due to their synchronization , these Ca2+ sparks cause a cell-wide rise in cytosolic [Ca2+] ( [Ca2+]i ) , leading to myofilament activation and force generation . This process , known as excitation-contraction coupling ( ECC ) , is central to the function of the myocyte [2] . Ca2+ sparks can also occur randomly at a single release site when the spontaneous opening of a single RyR triggers the CICR process [3] . Under conditions promoting cellular Ca2+ overload , Ca2+ sparks are more likely to trigger RyRs at nearby release sites , thereby generating propagating Ca2+ waves [4] . Spontaneous Ca2+ release events generate an inward current via the Na+/Ca2+ exchanger ( NCX ) , which transports 3 Na+ ions into the cell for every Ca2+ ion extruded , and additionally in canine myocytes via the Ca2+-activated chloride channel [5] . In diastole , this produces a net inward current , resulting in an elevation of cell membrane potential ( V ) known as a delayed afterdepolarization ( DAD ) [6] . DADs of sufficient amplitude can lead to the activation of the fast inward Na+ current ( INa ) and trigger a premature AP . Gap junctions joining adjacent cells then conduct the aberrant AP across the myocardial syncytium . Such ectopic events in the heart can induce reentrant ventricular arrhythmias that lead to sudden cardiac death [7] . Furthermore , the propensity for spontaneous Ca2+ release is increased in heart diseases such as heart failure , hypertrophy , and some forms of long-QT syndrome , which are associated with increased risk for sudden cardiac death . Therefore , understanding Ca2+ dynamics in ventricular myocytes and the Ca2+ handling instability that arises under pathological conditions is fundamental to our understanding of cardiac arrhythmogenesis . Experimental studies have observed triggered activity under conditions evoking spontaneous Ca2+ release in myocardial wedge preparations [8] and whole heart [9 , 10] . These studies show that the likelihood of observing ectopic foci is correlated with the degree of Ca2+ loading . In isolated myocytes , Ca2+ waves are observed when the sarcoplasmic reticulum ( SR ) Ca2+ load achieves a critical level [11] . However , electrotonic coupling in tissue attenuates DAD amplitude by diverting inward current to adjacent myocytes through the gap junctions . Wasserstrom et al . reported that with increasing SR Ca2+ load , spontaneous Ca2+ waves exhibited greater synchrony following cessation of rapid pacing in intact heart [10] . Synchronous DADs result in smaller spatial gradients in membrane potential , less loss of depolarizing current into neighboring cells , and therefore larger DAD amplitude . An elegant theoretical study by Chen et al . analytically investigated the probability of triggered events in a 1D fiber as a first passage time problem using a minimal model of Ca2+ release and membrane currents [12] . They showed that the expected time to a triggered event decreased according to a power law of the number of cells in the fiber , and that this effect depended on the balance of gap junction , NCX , and inwardly-rectifying potassium current ( IK1 ) conductance . However , to be tractable , the model employed simplified models of the membrane currents . Here we present an approach that retains mechanistically realistic characterization of membrane current properties while at the same time is computationally tractable , permitting the evaluation of a sufficiently large number of stochastic simulations to estimate rare event probabilities . In this study , we present a multicellular model of cardiac tissue which incorporates a stochastic biophysically detailed spatial model of the ventricular myocyte as its fundamental building block . The single cell model is used to study the roles of stochastic RyR gating and Ca2+ wave dynamics on the statistical distribution of DADs under pathophysiological conditions . The cellular Ca2+ load and density of IK1 are shown to be two important factors influencing the mean and variance of DAD amplitude and timing . We then develop a one-dimensional ( 1D ) myocyte tissue model comprised of these cells , and present a method for estimating the probability of rare events that are “surrogates” for ectopic beats . These surrogate events are defined as the occurrence of ( rare ) high-amplitude DADs with membrane potential exceeding a threshold value VT . We develop a computationally efficient approach for estimating the probability of these threshold crossings . This method enables us to estimate how changes in model parameters influence the probability of these surrogate events . As an example , the effects of reductions of IK1 density and gap junction coupling on the distribution of DAD amplitude and therefore the probability of threshold crossings are demonstrated , providing quantitative insight into how electrophysiological remodeling affects the probability of potentially arrhythmogenic ectopic beats .
We have developed a three-dimensional ( 3D ) spatial model of a single myocyte based on the ( non-spatial ) Greenstein-Winslow canine ventricular myocyte model [13] . To enable reproduction of realistic Ca2+ waves and DADs , the original model was adapted to include spatial Ca2+ diffusion on a rectangular lattice of 25 , 000 Ca2+ release sites ( Fig 1 ) distributed within the cell . The cell was divided into 25 and 20 lattice points in the two transverse directions and 50 lattice points in the longitudinal direction . Release sites were spaced 1 and 2 μm in the transverse and longitudinal directions , respectively [14] . The time constant for longitudinal Ca2+ diffusion was twice ( i . e . 2x slower ) that for the transverse direction such that the model exhibited symmetric Ca2+ wave propagation [15] . This difference in diffusion rates arises from differences in diffusion along versus across TTs . A sub-membrane ( SM ) release site compartment ( Fig 1A ) was added to describe the volume under the TT membrane where the Ca2+ concentration ( [Ca2+] ) is elevated during Ca2+ sparks and cell-wide Ca2+ release [16] . Detailed imaging studies of Ca2+ release sites have revealed that RyR clusters exhibit edge-to-edge spacing of less than 100 nm [17 , 18] . This suggests that neighboring sites may be functionally coupled through local Ca2+ diffusion . Therefore Ca2+ diffusion between SM compartments was implemented to reflect Ca2+ transport across steep [Ca2+] gradients on the periphery of the release site during release [19] . The SM compartment was modeled as a cylinder encircling the TT membrane with inner radius 100 nm , outer radius 180 nm , and 1 μm axis . It was assumed that 50% of NCX are located in the TT membrane of the SM compartment , and the remaining 50% are in the sarcolemmal membrane of the cytosolic compartment [20 , 21] . Ca2+ in the SM is buffered by calmodulin and sarcolemmal binding sites . Ca2+ transport rates from the SM to the cytosol and between SM compartments were constrained to yield a realistic Ca2+ wave threshold ( ~100–150 μmol/[L cytosol] ) [22] and velocity ( 50–100 μm/s ) [4] in the baseline model . Each spatial site , with location defined by coordinate ( i , j , k ) , is represented by a set of ordinary differential equations describing local Ca2+ transport ( Fig 1A ) . [Ca2+] is assumed to be uniform within the local JSR ( [Ca2+]JSR , i , j , k ) , dyadic subspace ( [Ca2+]d , i , j , k ) , and SM ( [Ca2+]SM , i , j , k ) compartments . Spatial Ca2+ diffusion is modeled as transport between SM compartments of adjacent release sites in the 3D lattice ( Fig 1B ) . Model equations and parameters are given in S1 Equations and S1 Table . Global compartments are used to represent average cytosolic [Ca2+]i and network SR [Ca2+] ( [Ca2+]NSR ) . The use of global compartments to represent these quantities eliminates two Ca2+ diffusion parameters and provides a three-fold reduction in the number of Ca2+ diffusion terms , thus substantially reducing model complexity and computational burden . While these simplifications result in over-estimation of the rise in [Ca2+]i and fall of [Ca2+]NSR at sites far away from an initiating Ca2+ wave , the model produces realistic spatiotemporal dynamics of spontaneous Ca2+ release and DADs . Each release site contains a set of 48 RyRs and 8 LCCs that gate stochastically according to Markov chain models . The LCC model is as described in the Greenstein-Winslow model [13] , with adjustments made to the rate of Ca2+-dependent inactivation ( see S1 Text ) . Briefly , the LCC inactivation rate is a saturating function of [Ca2+]SS , which was necessary to reproduce inactivation kinetics consistent with the original model . Note , however , that as a result AP duration does not substantially decrease at reduced SR Ca2+ loads as exhibited previously [13] . RyR gating is described by a minimal two-state Markov model based on the work of Williams et al . [23] , described in detail in Walker et al . [19] . Briefly , mean open time of each channel is 2 ms , and the opening rate is given by r o =ϕ k + ( [C a 2+ ] SS , i , j , k ) η ( 1 ) where k+ = 1 . 107 × 10−4 ms-1 μM-η is the opening rate constant , η = 2 . 1 is the Ca2+ Hill coefficient , and ϕ is a [Ca]JSR , i , j , k-dependent regulation term given by ϕ=0 . 8025+ [ [C a 2+ ] JSR , i , j , k 1 . 5mM ] 4 ( 2 ) The 3D cell model was incorporated into a tissue-scale model of a 1D fiber of myocytes . Fig 1C depicts the multiple biological scales represented in the model , from single ion channels to the multicellular fiber . The 3D cell model was augmented with a current carried by the gap junctions at either end of each cell ( Igap ) . The current from cell i into an adjacent cell i+1 is given by: I gap , i , i+1 = g gap ( V i − V i+1 ) ( 3 ) where ggap is the gap junction conductance , which was adjusted to yield a conduction velocity of 55 cm/s . The membrane potential in the fiber was solved using the Crank-Nicolson method [24] with 50 μs time steps . Operator splitting was used to explicitly solve each cell model using an embedded adaptive Runge-Kutta method described previously [13] . Sympathetic stimulation of the heart occurs through β-adrenergic receptor activation , which activates intracellular signaling pathways , most notably Protein Kinase A ( PKA ) and Ca2+/calmodulin-activated protein kinase II ( CaMKII ) , that increase contractility [25] . β-adrenergic stimulation is also known to be pro-arrhythmic , and can contribute to spontaneous Ca2+ release [26] in pathological conditions . Cell model parameters were modified to reflect the effects of acute β-adrenergic stimulation . LCC open probability was increased [27] by changing the fraction of gating LCCs from 25% to 60% and setting 3–5% of the channels to gate in a high-activity mode in which the mean open time was increased from 0 . 5 to 5 . 8 ms [28 , 29] . Enhanced activation of inward currents was implemented for IKr using modifications described previously [29] and for IKs by shifting the voltage-dependence of activation by -35 mV and increasing conductance by 40% [30] . SR Ca2+ loading was facilitated by reducing SERCA pump Kd for [Ca2+]i by 50% [31] . RyR opening rate was increased by either 50% to reflect increased SR Ca2+ leak observed in experimental studies [32] or 400% to reproduce pathological behavior after ouabain overdose [33] . Unless otherwise noted , these conditions were applied to all simulations in this study .
In order to reproduce protocols designed to measure ECC properties , membrane potential was stepped to varying test potentials for 200 ms from a holding potential of -80 mV . The dependence of normalized peak RyR and LCC Ca2+ fluxes on the test potential and corresponding ECC gain values are similar to those observed experimentally [34] ( Fig 2A and 2B ) . The peak normalized RyR flux curve is right-shifted with respect to that of the LCC flux curve . The ECC gain is 13 . 8 at 0 mV and decreases monotonically with increasingly depolarized test potentials . The lack of high gain at potentials below -10 mV is a result of consolidating the four distinct dyadic subspace compartments utilized in the previous Greenstein-Winslow canine ventricular myocyte model [13] into one , a simplification of the Ca2+ release site model resulting in improved computational efficiency , but which attenuates the local [Ca2+]d signal caused by the brief high-amplitude unitary LCC currents characteristic in this potential range . Under control conditions , model APs and [Ca2+]i transients are similar to those of normal canine ventricular myocytes [35] , with an AP duration ( APD ) , defined as the time to reach 90% repolarization , of approximately 320 ms ( Fig 2C and 2D ) . The model reaches a stable steady state after ~ 10 seconds when paced at 1 Hz ( S2 Fig ) . The NCX current during an action potential ( S1 Fig ) is in agreement with experimental and theoretical studies [36–38] . Reducing IK1 density by 50% prolongs the action potential to 469 ms due to the reduction in repolarizing current . Note that this is shown for the first paced beat with identical initial conditions as the baseline model , as the model fails to repolarize under these conditions after continued pacing ( see Discussion ) . Simulating the effect of β-adrenergic stimulation increases the amplitude of the AP plateau as well as [Ca2+]i transient amplitude and decay rate in addition to decreasing the APD to ~255 ms [29] . Additionally reducing IK1 density by 50% resulted in a marked increase in APD to ~300 ms at steady state . This prolonged AP increased Ca2+ loading , resulting in marginally higher systolic [Ca2+]i . [Ca2+]NSR was clamped at increasing values to test the relationship between SR Ca2+ load and leak . The model exhibits an exponential leak-load relationship that is similar to experimental estimates [39 , 40] ( Fig 2E ) . Spontaneous Ca2+ waves form at a threshold SR Ca2+ load , at which wave fronts of propagating Ca2+ sparks emanate from random regions of high spark activity . Fig 2F shows a plot of SR Ca2+ leak along a cross-section through the center of the cell during a representative Ca2+ wave under β-adrenergic stimulation , this wave occurred at a lower SR Ca2+ load ( 82 μmol/L cytosol ) compared to under baseline conditions due to greater RyR Ca2+ sensitivity . The wave shape and velocity of 68 μm/s are similar to those observed in experimental studies [4] . Liu et al . demonstrated that ouabain overdose causes accumulation of [Na+]i , leading to Ca2+ overload and DADs [33] . In addition , the authors showed that the production of reactive oxygen species , which are known to oxidize RyRs [41] and CaMKII [42] , both of which enhance RyR activity , contributed to DAD generation . To induce Ca2+ overload , model parameters were modified to simulate β-adrenergic stimulation and to reflect the conditions in Liu et al . [33] by inhibiting the Na+/K+ ATPase by 90% , which leads to accumulation of intracellular Na+ ( >20 mM ) . To demonstrate the emergence of DADs in the model , we simulated a protocol in which the RyR opening rate and [Na+]i were controlled over time . β-adrenergic stimulation was applied and the cell was paced at 1 Hz . First , [Na+]i was fixed to 15 mM and the RyR opening rate was ramped from 1 . 5x to 5x that of baseline over t = 0 to 5 s . Intermittent Ca2+ waves , triggered by overload of JSR Ca2+ , cause spontaneous [Ca2+]i transients , which began to occur ( Fig 3A ) . RyR sensitization initially causes an increase in [Ca2+]i transient peak from 1 . 73 to 1 . 93 μM , followed by a decrease ( Fig 3B ) as the Ca2+ wave threshold decreases ( Fig 3C ) and cellular Ca2+ is extruded by NCX . Many of these events begin just prior to ( <100 ms before ) the next stimulus ( Fig 3A inset ) . Prominent DADs occur at t = 4 and 6 s . This causes lower Ca2+ transient amplitude in the following beats due to the lost Ca2+ stores . The threshold SR load for Ca2+ waves was reduced to 80 μmol/[L cytosol] compared to the baseline model threshold of 140 μmol/[L cytosol] ( see Fig 2E ) . This is due to high [Na+]i and the greater RyR opening rate , which increases Ca2+ spark frequency and results in Ca2+ wave nucleation and propagation . To induce triggered APs , [Na+]i was ramped from 15 to 23 mM over t = 10 to 15 s . This causes Ca2+ waves to form more readily by reducing extrusion of Ca2+ via NCX in the SM compartment , thus elevating [Ca2+]d at sites adjacent to Ca2+ sparks and increasing the probability of Ca2+ spark propagation . Note that elevated diastolic [Ca2+]i has been implicated in DAD formation in experimental studies [8 , 43] . Diastolic [Ca2+]i was at first ~140 nM when the DADs are sub-threshold . Higher [Na+]i decreases the delay until the DADs , resulting in higher diastolic [Ca2+]i . A DAD of sufficient amplitude to activate INa occurs at t = 18 . 7 s and triggers a spontaneous AP . Triggered APs result in greater spontaneous [Ca2+]i transients due to activation of LCCs , and the cells exhibit elevated diastolic [Ca2+]i in the range of ~470–520 nM . This causes a reduction in the SR Ca2+ load threshold for spontaneous release to 63 μmol/[L cytosol] due to the resulting increase in RyR opening rate and Ca2+ spark frequency . Note that under these conditions , triggered APs exhibit pacemaker-like automaticity , occurring every ~570 ms after cessation of pacing ( S3 Fig ) but stop when [Na+]i is lowered below ~20 mM . This behavior is consistent with spontaneous contractions observed in mouse heart in the presence of ouabain [44] . The protocol also produced changes in AP duration ( APD ) ( S3 Fig ) . The first beat has an APD of 235 ms , 27% shorter than under normal conditions due reduced inward NCX current in the presence of elevated [Na+]i , [45] . Upon RyR sensitization , APD decreases to ~215 ms due to decreased inward NCX current accompanying the smaller [Ca2+]i transients . Further increasing [Na+]i to 23 mM reduced APD to 150 ms . A previous study showed that APD increases following spontaneous Ca2+ release due to slowed Ca2+-dependent inactivation of the LCCs [46] . The model does not reproduce this behavior because of the LCC Markov chain has a saturating dependence on [Ca2+]d and is therefore not sensitive to Ca2+ load ( see Discussion ) . Rather , the lower [Ca2+]i transient results in less inward NCX current , thus reducing time to repolarization . These results demonstrate how the model reproduces SR Ca2+ overload as a driver of DADs under pathophysiological conditions . Elevated RyR sensitivity and [Na+]i accumulation led to DADs of sufficient amplitude to trigger action potentials during the diastolic intervals . In addition , these results illustrate the interplay between [Ca2+]i and SR load dynamics during spontaneous Ca2+ release . The relationship between SR Ca2+ load and spontaneous Ca2+ release was investigated . Simulations were run using initial conditions reflecting the cell state just prior to the moment when SR Ca2+ load reaches the Ca2+ wave threshold following an AP . Initial [Ca2+]i was set to 150 nM , similar to the level during the late decay phase of a cytosolic Ca2+ transient . Fig 4A shows DADs occurring at the five different values of initial SR Ca2+ load shown in Fig 4B . At the highest SR Ca2+ load , DAD amplitude is large enough to trigger an AP , as shown in simulation ( v ) in Fig 4A . Elevating SR Ca2+ load reduces the delay until the spontaneous release event , consistent with the observations of Wasserstrom et al . [10] . The increase in DAD amplitude is consistent with a study by Schlotthauer and Bers , who demonstrated increased amplitude of caffeine-induced DADs at higher SR Ca2+ loads [47] . Fig 4C shows volume renderings of [Ca2+]SM at three time points in each simulation . The number of Ca2+ wave nucleation sites ( Nnuc ) was defined as the number of independently formed wave front epicenters and was estimated by inspection of the volume renderings ( S1 Movie ) . Nnuc generally increases with SR Ca2+ load , in agreement with experimental studies in intact heart [9 , 10] . Therefore , the increase in SR Ca2+ load also increases RyR Ca2+ release flux ( JRyR ) by enhancing the synchrony of RyR opening and number of nucleation sites . We hypothesized that stochastic gating of the RyRs drives variability in Ca2+ wave dynamics and thus DAD amplitude and timing . To test this , five independent realizations were generated , each of which had identical initial conditions similar to simulation ( i ) from Fig 4 . The pseudorandom number generator seed was varied among the realizations in order to produce independent patterns of RyR gating . Fig 5A shows the resulting DADs , which exhibit marked variability in timing and amplitude . Time of occurrence of the DAD peak varies from 520 to 1209 ms and peak amplitudes range from 2 . 3 to 6 . 2 mV . These DADs appear qualitatively similar to experimental observations in rat myocytes , with delays ranging by ~1 s and amplitude ~2-fold , although the SR Ca2+ load was not reported [6] . The area under the curve of each DAD , measured relative to the resting potential , varied from 0 . 79 in ( iii ) to 1 . 18 in ( ii ) ( 50% greater ) . This roughly correlated with DAD amplitude , with the exception of the prolonged DAD ( iv ) , which had the second greatest area under the curve despite having the lowest amplitude . Thus substantial DAD variability can be attributed to the stochastic nature of RyR gating . Spontaneous Ca2+ release generates DADs by driving an inward current through NCX [6] . Imaging , electrophysiological , and modeling [38] studies have all suggested that NCX senses a [Ca2+]SM that is greater than [Ca2+]i because it can be localized near the release sites [16 , 20 , 21] . Therefore , the driving force for inward NCX current is likely to be determined , in part , by the aggregate number of concurrent Ca2+ sparks occurring across the cell . This is consistent with a study showing that peak Ca2+ release flux is strongly correlated with the likelihood of ectopic activity [48] . An ensemble of 98 independent simulations were performed to determine how peak [Ca2+]i and JRyR are related to DAD amplitude . Fig 5B and 5C show there is a strong linear correlation between the peak membrane potential during the DAD ( Vmax ) and the maximum of [Ca2+]i during the spontaneous [Ca2+]i transient ( r2 = 0 . 950 ) . There is an even stronger relationship between Vmax and the peak JRyR value ( r2 = 0 . 998 ) . This confirms that the inward NCX current is primarily driven by JRyR via the resulting rise in [Ca2+]SM . Note that JRyR reflects the combined Ca2+ release flux associated with all Ca2+ sparks occurring at any time . It was therefore expected that the variability in DADs was the result of spatio-temporal variations in Ca2+ wave dynamics . Fig 5D and S2 Movie show volume renderings of the Ca2+ waves in each simulation . Waves emanate from nucleation sites of high Ca2+ spark activity . For example , in the simulation ( iv ) of Fig 5D at 700 ms , a large cluster of Ca2+ sparks is visible at the left end of the cell , and by 900 ms a wave front of Ca2+ is seen propagating radially away from this cluster site . The random nature of nucleation site locations results in DAD variability across simulations . Simulations i and ii are both associated with the highest-amplitude DADs as well as the greatest number of nucleation sites , as shown in Fig 5D . These nucleation sites are spaced widely across the cell , resulting in independent propagating wave fronts of high [Ca2+]SM that drive inward INCX . The remaining three simulations have lower amplitudes due to fewer nucleation sites . Note that in simulation iv , two separate Ca2+ waves form over 100 ms apart , resulting in a prolonged low-amplitude DAD with two peaks . These results are consistent with the strong correlation between Vmax and maximum JRyR , which reflects the timing and pattern of Ca2+ wave formation . In this section , the statistical relationships between Ca2+ loading and DAD amplitude and timing in ensemble simulations are investigated . Fig 6A shows variability in sub-threshold DAD properties when initial SR Ca2+ load is varied . The dark lines indicate the median value of membrane voltage over time , and the shaded regions illustrate the range of the second and third quartiles ( 25th to 75th percentile ) . Consistent with findings from the individual cell simulations of Fig 4 , DAD delay decreases and amplitude increases with increasing SR Ca2+ load . The peaks of the upper and lower bounds are -92 . 4 and -90 . 6 , -82 . 7 and -79 . 5 , and -73 . 2 and -68 . 1 mV for initial SR [Ca2+] values of 0 . 32 , 0 . 36 , and 0 . 40 mM , respectively . This suggests that there is an increase in DAD amplitude variability at higher SR Ca2+ loads . Note also that the width of the shaded regions decreases as SR load increased , reflecting increased DAD synchrony . Recall that diastolic [Ca2+]i plays a critical role in determining the SR Ca2+ wave threshold during pacing ( Fig 3 ) . The effect of increasing [Ca2+]i on the DAD distribution with SR Ca2+ load held constant was next tested ( Fig 6B ) . Elevating [Ca2+]i yields identical effects as seen when increasing SR Ca2+ load , with both reducing the delay and increasing the amplitude of DADs . The inward rectifier K+ current , IK1 , is the primary membrane current that stabilizes V at the cell’s resting potential and plays a critical role in protecting the cell from triggered APs . Down-regulation of IK1 is associated with ventricular arrhythmias in diseases such as heart failure [49] , Andersen’s syndrome [50] , and long QT syndrome [51] . Fig 6C shows DAD distributions when IK1 density is reduced by 50% and [Ca2+]i is varied . These changes increase DAD amplitude and variability compared to cells with normal IK1 . Note that in the 350 nM [Ca2+]i case , only one of the 98 realizations produces a sub-threshold DAD , while all others exhibit triggered APs . Fig 6D shows the average and SD of Vmax as a function of total cell Ca2+ , defined as the total of buffered and free Ca2+ in the cytosol and SR . Changes in either initial [Ca2+]i or [Ca2+]SR ( which implicitly include changes in Ca2+-bound buffer concentrations to their equilibrium values ) are reflected in initial total cell Ca2+ . The effect of 50% IK1 density reduction on the [Ca2+]i-dependence of Vmax was also analyzed . These results demonstrate two important conclusions . First , in the baseline model with normal IK1 , the distributions of Vmax as a function of initial total cell Ca2+ are identical in both cases , as shown by overlap of the blue ( initial [Ca2+]i varied ) and red ( initial [Ca2+]SR varied ) traces . This strongly suggests that DADs are driven by both [Ca2+]i and [Ca2+]SR and is consistent with the notion that total cell Ca2+ plays a major role in setting the DAD distribution , as observed in other models [52] . Second , reducing IK1 by 50% causes a marked increase in the average and SD of Vmax . In this case , Vmax was more sensitive to inward INCX during the DAD because IK1 rectification reduces its outward current upon membrane depolarization which increases the contribution of INCX to the net membrane current , and this imbalance between the currents becomes greater with reduction in IK1 density . This phenomenon is illustrated in a phase plane plot of example simulations at baseline and 50% IK1 densities exhibiting both sub- and supra-threshold DADs ( Fig 6E ) . For a given NCX current , V rises substantially higher at 50% IK1 density compared to the baseline model . The rightward shift of the trajectories also illustrates the ~50% reduction in peak NCX current required to trigger an AP ( ~3 . 4 to ~1 . 7 pA/pF ) . Therefore , DAD amplitudes are greater for a given INCX amplitude , causing an increase in the mean . Similarly , amplitude SD is also increased . At the lowest Ca2+ load tested , 50% IK1 reduction caused ~3x greater SD , which is mainly accounted for by the fact that the amplitude is on average ~2 . 6x greater . DAD delay and synchrony is also strongly correlated with total cell Ca2+ , as measured by the distribution of the time until the DAD peak occurred ( Fig 6F ) . Increasing initial total cell Ca2+ reduces DAD delay and increases synchrony , similar to the observations of Wasserstrom et al . [10] . In the baseline model , the standard deviation of the delay was 178 ms at the lowest SR load tested . This variability is similar to that measured by Wasserstrom et al . ( 162 ms ) in whole rat heart paced at 1 Hz and elevated [Ca]o = 7 . 0 mM . DAD delay does not considerably change with 50% IK1 reduction because it is determined primarily by the timing of Ca2+ wave formation , which is not affected by IK1 density . With increasing initial total cell Ca2+ , in the form of increased initial [Ca2+]i or [Ca2+]SR , the higher DAD amplitudes makes it more likely that the threshold membrane potential for triggering an AP ( ~ -55 mV ) will be reached . The probability that a DAD-triggered beat occurred is shown in Fig 6G . Increasing cell Ca2+ results in a steep increase in trigger probability . This switch-like behavior is due to the DAD amplitude reaching threshold AP-triggering potential with high certainty . Reducing IK1 by 50% reduces the critical Ca2+ load at which APs are triggered , consistent with the observed increase in DAD amplitude . The effect is significant: at normal IK1 density zero of 96 cells exhibit triggered APs when initial [Ca2+] is 5 . 8 fmol , but reducing IK1 density by 50% increases the probability to ~ 0 . 5 . Thus IK1 density plays a critical role in modulating the Ca2+ load at which arrhythmia-triggering events occur . In the previous section , it was shown that triggered APs occur with a probability that depends on Ca2+ load and that varying IK1 density shifts the Ca2+ load dependence . Simulations were performed to test whether the model could produce probabilistic triggered activity in a 1D fiber of myocytes during pacing , where electrotonic loading of any cell by adjacent cells becomes important . Fig 7A shows the membrane potential of a fiber paced at 0 . 5 Hz under conditions similar to those in Fig 3 with elevated [Na+]i set to 19 mM , as may be observed in heart failure [53] . Note also that [Na+] sensed by membrane channels during contraction may be higher than [Na+]i in the bulk cytosol due to local buildup of Na+ imported by NCX [54] . To reflect a state of pathological remodeling , IK1 density and gap junction conductance were each reduced by 50% as observed in HF [49 , 55] . To limit boundary effects , [Na+]i was set to 10 mM in the outer twenty-four cells near each end of the cable in order to prevent DAD generation . DADs , appearing as faint bands of depolarization between the paced beats , and reach Vmax values between approximately -70 and -60 mV . This is consistent with experimental observations of synchronized spontaneous Ca2+ release causing DADs in intact heart following rapid pacing [9] . The model exhibited considerable variability in Vmax along the length of the fiber . To investigate the source of this variability , the state of the model at time trestart immediately after the third paced beat was recorded . A set of three independent realizations were run , each starting from this same model initial condition but with simulations performed using different initial pseudorandom number generator seeds . These realizations therefore differ in the particular pattern of LCC and RyR channel gating . Fig 7B shows: ( i ) the DAD from the original simulation in Fig 7A at 4 . 9 s; and ( ii ) - ( iv ) the three additional simulations initialized at trestart . Each simulation exhibits substantial differences in DAD amplitude along the fiber . In panel ( iv ) , a spontaneous traveling action potential wave is observed . The wave originates from a region of locally high DAD amplitude near cell #50 and emanates in either direction along the fiber . These results illustrate that arrhythmic events can occur probabilistically in tissue . By restarting the simulations immediately prior to the DADs , it became evident that the variability in DAD amplitude was due to variations in the stochastic events that occurred in this brief time window . Taken together with the results from Figs 5 and 6 , the variability in DAD amplitude in the fiber can therefore be attributed to the underlying randomness of Ca2+ wave dynamics and thus stochastic RyR gating . Conditions reflecting pathological remodeling influenced the distribution of Vmax in the fiber . Fig 8A shows fiber simulations when all cells are initialized to identical initial conditions with Ca2+ overload and β-adrenergic stimulation . The resulting DADs resemble those from the paced fiber shown in Fig 7 . As in individual cells , Vmax increases with initial [Ca2+]i . At the highest [Ca2+]i , Vmax lies between -74 . 1 mV and -70 . 7 mV , a range of 3 . 4 mV . There is notably less variability than in individual cells at a similar Ca2+ load ( SD of 0 . 7 compared to 4 . 6 mV , see Fig 6D ) . This reduction in variability is due to electrotonic coupling through inter-cellular gap junctional conductance , ggap , which attenuates spatial gradients in V . Fig 8B shows similar simulations where IK1 density was reduced by 50% . This results in greater fluctuations of Vmax over a range of 8 . 0 mV ( SD 1 . 5 mV ) compared to baseline . This is consistent with the increase in Vmax SD from 1 . 7 to 6 . 1 mV when IK1 density was decreased to 50% in isolated cells ( see Fig 6D ) and also reflects a substantial reduction in Vmax variability due to electrotonic coupling in the fiber . Reducing ggap by 50% in addition to the 50% IK1 reduction further amplifies Vmax spatial fluctuations , resulting in a range of 10 . 2 mV ( SD 2 . 2 mV , Fig 8C ) . The increase in variability arises from the reduced electrotonic load experienced by each cell . These results demonstrate how perturbations of IK1 and ggap increase the likelihood of observing large DADs . The DAD results presented thus far indicate that spatial fluctuations in Vmax can result in a triggered beat emanating from a cluster of cells in which Vmax exceeds the threshold voltage ( ~ -55 mV ) . For a fiber of a given length , the probability of this event depends upon both the mean Vmax and the likelihood of a deviation from the mean with sufficient amplitude to reach threshold . Under conditions where the mean Vmax is far below threshold , however , it is unclear whether it would be plausible to observe a sufficiently large fluctuation that causes a triggered beat . We sought to characterize the likelihood of such events by estimating the upper tail of the Vmax distribution . In Fig 5 , we showed that Vmax was strongly correlated with Jmax in DAD simulations of isolated cells . In tissue , however , electrotonic coupling attenuates DAD amplitude by drawing current to neighboring cells via gap junctions . However , if spontaneous Ca2+ release occurs synchronously in a cluster of cells , the potential gradient between cells is smaller , thus reducing the gap junction current and increasing DAD amplitude . Therefore the Vmax of a given myocyte in tissue depends on the relative timing and amplitude of Ca2+ release in nearby cells . We hypothesize that extreme deviations in Vmax are caused by rare spatially clustered synchronized Ca2+ release events . However , estimating the probability of extremely rare events ( e . g . 1 in 106 ) by direct simulation is computationally prohibitive using the full biophysical model since it would require potentially millions of simulations . Therefore , a method was developed for estimating the probability of such events using the output from a limited set of simulations . As shown in the previous sections , stochastic RyR activity gives rise to random Ca2+ wave patterns and thus variable profiles of JRyR . Therefore JRyR is a stochastic process that is dependent on complex microscopic events , which are computationally intensive to sample . The method leverages the fact that JRyR has a relatively simple distribution by resampling JRyR profiles from a set of simulations that is sufficiently large to approximate the distribution of JRyR . Multiple realizations of release events ( i . e . , the JRyR values from each separate simulation of the full cable model ) are generated using a resampling method in which cell positions along the cable are shuffled to produce independent , distinct realizations . The key to this approach is the fact that spontaneous Ca2+ release events are decoupled across the cells in the fiber ( see Discussion ) . In principle , in using this approach one could develop a modified tissue model in which JRyR in each cell is fixed to one of the resampled JRyR profiles rather than being simulated , thus greatly reducing the computational burden . However , this would still require immense computational power to simulate the remaining differential equations when estimating the probability of extreme events . The second part of the method further increases computational efficiency by fitting a linear spatial-averaging model to predict Vmax . from JRyR alone . This permits the rapid estimation of the greatest Vmax in a 1D tissue model in millions of simulations and thus can estimate the probability that the cable will reach a proarrhythmic threshold potential . The following sections describe this method and validate its accuracy for estimating DAD probabilities . We then apply this new technique to show how myocyte properties within a fiber , using IK1 density and ggap as examples , affect the likelihood of rare large amplitude DADs . A filtering method was developed for estimating Vmax from the spatiotemporal profile of JRyR in a single 1D fiber simulation . The left column of Fig 9A shows the simulation from Fig 8A where [Ca2+]i was initialized to 300 nM , and the right column illustrates the steps used in the filtering method . The first step in the method is to apply a uniform averaging filter to JRyR at each point in time to obtain a spatially smoothed profile J RyR f ( x , t ) = 1 W ∑ k=− ( W−1 ) /2 ( W−1 ) /2 J RyR ( x+k , t ) ( 4 ) where x refers to cell index , t refers to time , and W is the width of the filter ( an odd integer ) . The rational for doing this is that membrane potential fluctuations of the cell at position x is influenced not only by the way its own complement of NCX responding to the local JRyR , but is also influenced by membrane potential fluctuations produced in response to JRyR and NCX activity in neighboring cells . The filter width W will therefore depend on the strength of gap junction coupling . For each cell , the maximum JfRyR value over all time was computed as J max f ( x ) = max t { J RyR f ( x , t ) } ( 5 ) The value of W maximizing the correlation coefficient between Vmax and Jfmax was calculated at each different value of gap junction coupling conductance used . Jfmax was then normalized to obtain estimates of Vmax using the formula V max f ( x ) = μ V + σ V σ V ( J max f ( x ) − μ J ) 6 where μV and σV are estimates of the mean and SD respectively of Vmax , and μJ and σJ are estimates of the mean and SD respectively of Jfmax . These estimates are calculated from a single full 1D tissue simulation containing ~ 450 cells . Note how in the example of Fig 9A the estimated voltage profile Vf closely resembles that of V . This approach therefore leverages the strong linear correlation between JRyR and Vmax ( Fig 5B and 5C ) and the statistical independence of Ca2+ release events to dramatically reduce the number of simulations needed to estimate the probability distribution of membrane potential fluctuations . The filtering method was applied to simulations of DADs with baseline conditions , with 50% IK1 , and with both 50% IK1 and 50% ggap ( Fig 9B ) . In the non-baseline conditions , initial [Ca2+]i was adjusted from 300 to 220 nM so that μV would be approximately equal to that of the baseline . The width W of the smoothing filter is dependent on the fiber model parameters and therefore was optimized separately for each case . Table 1 shows the parameter fits for each condition . The resulting fits of Vfmax to Vmax have high correlation coefficient values ( r2 ≥ 0 . 90 ) . All values of μV fall within a narrow range from -77 . 0 mV to -79 . 1 mV , while the values of μJ in the two non-baseline conditions are less than half of the baseline value due to their lower Ca2+ loads . The increase in Vmax variability in the two pathological conditions is reflected in the parameters of the filtering model . It can be shown that the quantity SV = ( σV/σJ ) /W scales with the SD of Vfmax ( see S1 Text ) . Recall that 50% IK1 reduction increased variability of V in the cell model ( Fig 6 ) . For this condition in the fiber , SV is 28% larger compared to baseline , primarily reflecting the higher value of σV . Imposing 50% ggap results in a filter width of only 27 cells compared to 43 and 49 in the other cases due to local decoupling of cells when gap junction conductance is decreased . This resulted in an SV that is 87% larger than baseline and 46% larger than with 50% IK1 alone . These results show that the filtering method accurately estimates fluctuations in Vmax based on the JRyR profile . Moreover , the new empirical relationships derived here between JRyR and Vmax provide an approach to yield new insight and intuition into how changes in cellular properties and environment , such as reductions in outward currents and electrotonic coupling analyzed here , alter and in this case enhance spatial fluctuations in Vmax . The filtering method enables studies of properties of Vmax without the need to simulate the full biophysical model . Studying the statistical properties of Vmax requires the generation of large numbers of realizations . To do this , independent samples of Vmax were generated by shuffling cell positions in the JRyR profile , as illustrated in Fig 10A . Note that boundary effects the outer 24 cells on either end of the fiber are initialized to normal SR Ca2+ loads and are not included in the shuffle . The filter method was then applied to the shuffled JRyR profile to estimate Vmax . An example Vmax profile obtained using this method is shown on the right . Note that the fluctuations about the mean μV are qualitatively similar to those of the original simulation . In Fig 7 , it was shown how a triggered propagating ectopic beat originated from a region of the fiber that experienced extreme DADs . Unfortunately it is impossible to perform the millions of cable simulations using the full biophysical model that are needed to study the probability of generating ectopic beats as model parameters are varied . However , our shuffling and filtering method does enable us to study the probability of a surrogate event of interest for each fiber simulation , with the surrogate event defined as the greatest value of Vmax realized along the fiber , referred to here as Vpeak . The shuffling of cell positions makes two assumptions . The first is that the JRyR profiles of the cells are decoupled stochastic processes . The second assumption is that the fiber contains a sufficient number of cells such that the true distribution of JRyR is well represented by the collection obtained from a single simulation . Previous work has shown that sub-threshold membrane depolarization induces Ca2+ sparks and waves [56] . It is unclear whether local depolarizations in the membrane potential caused by spontaneous release affects release in neighboring cells , which would invalidate the independence assumption . This was tested by computing the peak amplitude and peak time of [Ca2+]i in the fiber simulation represented by the red trace in Fig 8C . The absolute difference in the peak amplitude and peak time were then computed for all adjacent cell pairs and all cell pairs separated by a 50-cell distance along the fiber . Assuming that Ca2+ release in 50th neighbors are decoupled , if release in adjacent cells were coupled then one would expect the distributions of peak amplitude and timing to differ from those of the 50th neighbor pairs . In particular , if local Ca2+ release events were more synchronized , one would expect the difference in timing to be smaller . However , no substantial differences in Ca2+ release peak or timing were observed ( S4 Fig ) , as these distributions also were not significantly different according to non-parametric Kruskal-Wallis tests ( p = 0 . 77 , 0 . 66 ) . Therefore the coupling effect of adjacent cells does not substantially affect spontaneous Ca2+ release , which can thus be treated as a decoupled process in each cell in the fiber . Validation of these assumptions also requires that the distribution of Vpeak generated using this method match that obtained when using the detailed biophysical model . This distribution was estimated from 1 , 000 independent realizations . Simulating the ~500-cell fiber with 25 , 000 release sites in each cell is computationally prohibitive . For the purposes of the validation , the number of release sites was reduced to 2 , 500 and the fiber length to 96 cells . The values of μV and σV were computed using the Vmax values from all cells in all fibers . Fig 10B compares the true distribution of Vpeak computed using the full biophysical model to that obtained from applying only the filtering method to the simulated JRyR profiles ( without shuffling ) . While this distribution exhibits a very small bias of ~ -0 . 05 mV , it is not significantly different from the true distribution on the basis of a Kruskal-Wallis test after correcting for the bias by subtracting their means ( p = 0 . 89 ) . The shuffling method was then validated by resampling from the JRyRs in all 1 , 000 fibers to produce 1 , 000 96-cell fiber realizations and computing Vpeak with the filtering method . While the resulting distribution also exhibits a very small bias of ~ -0 . 08 mV , it is not significantly different from the true distribution after adjusting the means ( p = 0 . 69 ) ( Fig 10B ) . The upper tails of the distributions containing the extreme DADs of interest are also similar ( Fig 10C ) . To validate the second assumption that the sampling population of JRyR is sufficiently large , these tests were repeated using a subset of 5 of the 1 , 000 fibers to compute μV , σV , and the population of resampled JRyR’s ( bias +0 . 05 mV , p = 0 . 68 ) . Therefore , the method is accurate using a total of ~ 500 simulated cells . In summary , the method outlined here is a computationally efficient approach that permits the rapid estimation of the Vpeak distribution using output from a single 500-cell fiber simulation . Fig 10D plots the probability that Vpeak—μV exceeds a given potential estimated from an ensemble of 106 realizations . Reducing IK1 by 50% shifts the distribution tail to greater amplitude DADs compared to the baseline model . The probability of observing a Vpeak 3 mV greater than μV ( -77 . 1 mV ) is 9 . 9 × 10−5 in the baseline model compared to 0 . 096 with 50% reduction in IK1 , a nearly 1000-fold increase . The most extreme event observed with 50% IK1 was 6 . 1 mV greater than μV compared to 3 . 4 mV in baseline , corresponding to DAD amplitudes 41% and 21% above average , respectively . Reducing ggap by 50% further increases the likelihood of large-amplitude DADs , with the probability of a 3 mV DAD reaching 0 . 50 , a 5000-fold increase over baseline , and the most extreme event observed at 7 . 5 mV , corresponding to a DAD amplitude 53% higher than average . These results demonstrate how changes ion channel expression or function can alter the probability of supra-threshold membrane potential fluctuations . In this case , reductions in IK1 and ggap considerably widen the distribution of Vpeak and increase the probability of occurrence of larger Vpeak values by multiple orders of magnitude . To illustrate the nature of these rare events , the realizations exhibiting the greatest Vpeak values were examined in each condition . Fig 11A plots Vmax—μV . Because Vmax at any position is determined by an average of the Ca2+ release profiles over a window of cells , each rare depolarization occurs at a cluster of cells whose width corresponds to the window size . Fig 11B shows the underlying JRyR profiles and the filter window centered on the Vpeak . In each case , the extreme event occurs at a cluster of cells within the window where JRyR tends to be greater and more synchronized than in the rest of the fiber . This result demonstrate that rare ( occurring ~1 in 106 beats in a 500-cell fiber ) proarrhythmic events result from spontaneous Ca2+ release exhibiting high ( 1 ) flux magnitude , ( 2 ) synchronization , and ( 3 ) spatial clustering . None of the 999 , 999 other simulations result in such an extreme event due to a lack of one or more of these properties . Note that because the window size is smaller in the case of 50% ggap , fewer cells need to fulfill these three criteria and thus the probability of extreme events is much higher .
In this study we have presented a biophysically detailed stochastic computational model of the ventricular cardiac myocyte describing spatial Ca2+ diffusion between release sites and incorporated it into a tissue-scale model to study the mechanisms and statistical properties of DADs . Loading of SR Ca2+ is known to cause spontaneous Ca2+ release [6 , 47 , 57] . In this model , Ca2+ waves were generated when passive diffusion of Ca2+ between release sites caused Ca2+ sparks to propagate across the cell . In agreement with experimental studies , RyR sensitivity modulated the threshold SR Ca2+ load at which this instability arises [58] . Rather than explicitly modeling RyR regulation mechanisms such as interaction with calmodulin [59] , phosphorylation by activated CaMKII [60] and PKA [61] , allosteric channel decoupling due to PKA-dependent dissociation of FKBP12 . 6 [62] , and oxidation by reactive oxygen species [33 , 41] , RyR opening rate was scaled such that the model reproduced experimentally observed triggered APs at 1 Hz pacing [33] . [Ca2+]JSR-dependent regulation of the RyRs increases their sensitivity to cytosolic [Ca2+] [63 , 64] , but its role in spontaneous Ca2+ release is controversial . In our model of RyR gating , it was assumed that this mechanism has little effect on RyR open probability when [Ca2+]JSR < 1 mM [65 , 66] and therefore played a negligible role in dynamically regulating the RyRs in this study . In our investigation , we found that pacing the baseline model at 1 Hz with 50% IK1 inhibition ( in the absence of β-adrenergic stimulation ) caused increased APD and cellular Ca2+ loading over time . This resulted in greater inward NCX current during the repolarization phase , thus further increasing APD and Ca2+ loading . This positive-feedback loop led to EADs and ultimately failure to repolarize . It is possible that the saturating behavior of LCC Ca2+-dependent activation in this model prevents sufficient LCC inhibition required for repolarization in the presence of elevated SR Ca2+ . The LCC model also does not incorporate “global” [Ca2+]i sensing [67] , which would inhibit LCC openings in the presence of sustained elevated [Ca2+]i . Future work is needed to understand the contributions of these mechanisms , as they may affect the conditions under which the model exhibits DADs during pacing . The distribution of DADs was controlled by both [Ca2+]i and [Ca2+]SR . This was revealed by the apparent change in the threshold SR Ca2+ load for spontaneous Ca2+ wave formation during pacing ( see Fig 3 ) . Elevated diastolic [Ca2+]i increased RyR opening rate and thus perpetuated Ca2+ wave formation at lower SR Ca2+ loads . It also caused more rapid loading of the SR to induce overload . Cellular Ca2+ loading also increased the amplitude of DADs due to the greater number and concurrence of Ca2+ wave nucleation sites , which is in agreement with experimental studies [9 , 10] . Consistent with our results , Wasserstrom et al . reported that cellular Ca2+ loading reduced DAD delay and increased synchrony associated this with greater likelihood of triggered activity [10] . The formation of Ca2+ sparks and waves may be influenced by additional factors not considered in this study . These include intrinsic properties such as release site heterogeneity , including dyad geometry , RyR cluster morphology [19 , 68] , local SR connectivity [69] , and release site spacing [70 , 71] . Additionally , cell-to-cell heterogeneity would also affect DADs in tissue . Further work is needed to quantify the contributions of these factors to DAD variability . Triggered events were investigated using a fiber model . Experimental study of the nature of DAD-induced arrhythmias is difficult due the limited temporal and spatial resolutions of live multiplex fluorescence imaging of tissue preparations . Xie et al . used a computational tissue model to study how the critical size of an isolated cluster of cells exhibiting identical spontaneous Ca2+ release events required to trigger an AP depended on a variety of tissue geometries and was reduced under pathophysiological conditions [72] . They further showed that DAD amplitude and the critical cell mass depended sensitively on the amplitude of the spontaneous [Ca2+]i transient . Here we have investigated stochastic variability in DAD amplitude in a long fiber of Ca2+-overloaded cells . We showed that large DADs occurred probabilistically due to random patterns of RyR gating and the formation of Ca2+ waves that gave rise to high-amplitude , synchronized Ca2+ release flux in a cluster of cells . Such events trigger arrhythmias by causing ectopic beats , inducing regional conduction block via Na+ channel inactivation [73] , and increasing variability of repolarization [46] . Heterogeneity of cell types and intercellular variability of Ca2+ handling may also play an important role in determining the likelihood and location of triggered foci [10] but exploration of these factors remains beyond the scope of this study . Furthermore , the potential effects of pro-arrhythmic beat-to-beat AP variability [74] or EADs [75] , which could affect Ca2+ loading and DAD timing , were not examined . The role of IK1 , which acts to stabilize the resting membrane potential , affected the DAD distribution in isolated cells . Loss of IK1 function has been associated with arrhythmogenesis in diseases such as heart failure [49] , Andersen’s syndrome [50] , and long QT syndrome [51] . Maruyama et al . showed that the Ca2+-membrane voltage gain , defined as the ratio of DAD amplitude to spontaneous [Ca2+]i transient amplitude , increased following IK1 suppression [76] . Consistent with this finding , the model exhibited a considerable increase in DAD amplitude in both isolated cells and in the fiber when IK1 density was reduced by 50% . Most notably , IK1 suppression increased DAD amplitude variability , dramatically increasing ( by 1000-fold ) the probability of occurrence of potentially arrhythmogenic large-amplitude DADs . Note that the model does not include Ca2+-dependent regulation of IK1 , as this mechanism has been controversial [77–79] . While a recent study by Nagy et al . [80] suggests elevating [Ca2+]i at a fixed concentration upregulates IK1 via an increase in CaMKII activity , there remains insufficient data to constrain a model of this mechanism . Future models could be informed by further studying dynamic changes in CaMKII activity and IK1 in response to elevated [Ca2+]i . Reduction of gap junction conductance , another pathological feature of diseases such as HF [55] , further increased the variability of DAD amplitude in the fiber by reducing the spatial scale of electrotonic coupling . These findings are consistent with a previous modeling study that showed that fewer contiguous cells are required to exhibit DADs to produce a triggered beat under such conditions [72] . The relationship between IK1 and/or ggap to the probability of occurrence of extreme DADs demonstrated here is but one of many possible examples of how alterations of cellular mechanisms or environment can modulate propensity for arrhythmia . For example , HF also causes extensive remodeling of the TTs and dyads , leading to reduced CICR efficiency [81 , 82] . The model can be used to directly probe the effects of corresponding perturbations such as the number of release sites , RyR cluster size , and dyad volume on intracellular Ca2+ cycling , the formation of spontaneous Ca2+ sparks and waves , and rare arrhythmic events . Thus the novel method presented here provides a general framework for prediction of arrhythmia likelihoods in response to specific aspects of disease-related physiological remodeling . A significant contribution of this work is that the emergence of sudden arrhythmias can be causally linked to stochastic molecular events . A computationally efficient method was developed to estimate the probability of extreme DADs . An important assumption of this method is that the spontaneous Ca2+ release events in neighboring cells are decoupled . It is known that membrane depolarization increases the frequency of Ca2+ waves by reducing NCX-mediated Ca2+ efflux and thus promoting Ca2+ waves due to increased intracellular [Ca2+] [56] . Therefore , a release event in one cell that causes a local increase in V may hasten spontaneous Ca2+ release in its neighbors . Validation of our method suggests that this coupling phenomenon has negligible impact on the Vmax distribution because the effect is weak compared to the intrinsic variability of spontaneous Ca2+ release . Note that the model does not account for gap junction mediated intercellular Ca2+ diffusion , resulting in cell-to-cell transfer of Ca2+ waves , though the prevalence of such events remains unclear [9 , 83–87] . There are two important conclusions that emerge from using this method . First , variability of the inward current due to stochastic RyR gating causes random patterns of Ca2+ wave dynamics and results in substantial DAD variability at the tissue scale , particularly in the pathological states tested where IK1 and ggap were reduced . Second , while one can imagine a case where a contiguous cluster of cells exhibit large synchronized spontaneous Ca2+ release , the probability distribution of such events has not been well characterized . In a 496-cell fiber with reduced IK1 and gap junction coupling , the largest DAD amplitude observed in an ensemble of 106 realizations had amplitude ~50% greater than the mean DAD amplitude . For such a fiber paced at 1 Hz and exhibiting a DAD after every beat , one could expect to observe such an event approximately once every 11 days . Thus extreme DADs , while quite rare , are still possible over relevant time frames . Further work is needed to estimate the probability of such events in whole heart , as 3D tissue is ~1–2 orders of magnitude less likely to exhibit triggered beats due to the increased electrotonic coupling [72] but also contain a much greater number of cells than tested here . Nevertheless , the results presented here suggest that variability due to stochastic molecular events play a large role in the initiation of cardiac arrhythmias and sudden cardiac death .
|
Arrhythmias are electrical abnormalities of the heart that can degenerate into fibrillation , thus preventing normal heartbeats and leading to sudden cardiac death . The mechanisms leading to ventricular arrhythmias and the unexpected nature of sudden cardiac death are not fully understood . One hypothesis is that a group of cardiac myocytes , which generate contraction , spontaneously depolarize at precisely the same moment to excite the surrounding tissue . In individual myocytes , such misfires , known as delayed afterdepolarizations , are driven by random ion channel gating and thus stochastic in nature . While incidental afterdepolarizations in a large number of myocytes is highly improbable on any given beat , it may be feasible over a long time frame , thus explaining the unpredictability of arrhythmias . We developed a detailed model spanning the molecular , cellular , and tissue scales that realistically reproduces the mechanisms underlying this hypothesis . An efficient method is presented for estimating the probability of extremely rare delayed afterdepolarizations in tissue from a limited set of simulations . Furthermore , we demonstrate how altered tissue and ion channel properties in heart disease increase the risk of arrhythmia . This approach can be used generally to probe the effects of specific molecular mechanisms on the likelihood of rare delayed afterdepolarizations .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"cell",
"physiology",
"medicine",
"and",
"health",
"sciences",
"action",
"potentials",
"muscle",
"tissue",
"nervous",
"system",
"membrane",
"potential",
"condensed",
"matter",
"physics",
"junctional",
"complexes",
"electrophysiology",
"neuroscience",
"simulation",
"and",
"modeling",
"gap",
"junctions",
"waves",
"research",
"and",
"analysis",
"methods",
"cardiology",
"arrhythmia",
"animal",
"cells",
"biological",
"tissue",
"muscle",
"cells",
"physics",
"wave",
"propagation",
"cell",
"biology",
"anatomy",
"synapses",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"neurophysiology",
"nucleation"
] |
2017
|
Estimating the probabilities of rare arrhythmic events in multiscale computational models of cardiac cells and tissue
|
An understanding of the factors driving the distribution of pathogens is useful in preventing disease . Often we achieve this understanding at a local microhabitat scale; however the larger scale processes are often neglected . This can result in misleading inferences about the distribution of the pathogen , inhibiting our ability to manage the disease . One such disease is Buruli ulcer , an emerging neglected tropical disease afflicting many thousands in Africa , caused by the environmental pathogen Mycobacterium ulcerans . Herein , we aim to describe the larger scale landscape process describing the distribution of M . ulcerans . Following extensive sampling of the community of aquatic macroinvertebrates in Cameroon , we select the 5 dominant insect Orders , and conduct an ecological niche model to describe how the distribution of M . ulcerans positive insects changes according to land cover and topography . We then explore the generalizability of the results by testing them against an independent dataset collected in a second endemic region , French Guiana . We find that the distribution of the bacterium in Cameroon is accurately described by the land cover and topography of the watershed , that there are notable seasonal differences in distribution , and that the Cameroon model does not predict the distribution of M . ulcerans in French Guiana . Future studies of M . ulcerans would benefit from consideration of local structure of the local stream network in future sampling , and further work is needed on the reasons for notable differences in the distribution of this species from one region to another . This work represents a first step in the identification of large-scale environmental drivers of this species , for the purposes of disease risk mapping .
Knowledge of the spatial distribution of an environmentally persistent pathogen is often key in creation of environmental hazard maps for disease control . Yet , despite the importance of this spatial information , only 4% of such pathogens have been mapped [1] . The reason for this gap in our knowledge is practical . It is often difficult to produce large maps of the distribution of these microbial pathogens as they are difficult to detect in nature . A solution to this is to describe the distribution of the pathogens suitable habitat . For example , an environmentally persistent pathogenic bacterium may have a certain pH range within which it can survive , a specific range of microaerobic oxygen concentrations [2] , and survive preferentially on certain algae [3] . In cases where we have a suitable range of pH , a suitable range of oxygen , and suitable algae , we expect to find the bacterium . Herein , this suitable range of microhabitat is termed the ecological niche of the species . Every species in nature , including vectors such as mosquitoes , and pathogens such as Plasmodium protozoans , has a unique ecological niche [4] , [5] . Knowledge of the distribution of suitable habitats would allow us to predict the expected distribution of the pathogen . This approach has been successfully applied to the vectors of diseases such as malaria , plague and dengue [6] , [7] , [8] , but it is rarely applied to environmentally persistent pathogenic microbes . The range of suitable habitat is , practically , much easier to describe for insect vectors than for microbes . For example , the suitable habitat of mosquitos is driven by factors such as rainfall , which is much easier to describe on a large scale . To describe pH in the environment we must visit each site and use a probe at each location . This quickly becomes expensive and time consuming when we consider multiple variables , or if we wish to describe the distribution of a pathogen over large extents . We hypothesised that these microhabitat variables could be indirectly inferred from large scale macroecological patterns . The distribution of swamp and forested environment , the shape and structure of the landscape , should predict the distribution of these microhabitats . For example , while the suitable habitat of a bacterium may be driven by the suitable combination of pH , oxygen , and algae , and other factors , the distribution of these conditions is in turn driven by the landscape . For example , the pH and oxygen content of water in swamps is lower , on average , than of water in savannahs . We can use the landscape , which is more easily described , as a proxy to describe the spatial distribution of this suitable microhabitat . Though this approach is limited in lacking a physiological understanding of direct influences on the pathogen , it has the great benefit of inferring the potential distribution of the pathogen , opening new opportunities to disease control . We undertook ecological niche modelling of Mycobacterium ulcerans , an environmentally acquired pathogenic bacterium , and causative agent of Buruli ulcer . The ecological niche refers to this range of conditions within which a species can survive and maintain a population . We infer that , if a species has a large population , it presumably is able to maintain that population , and is in a suitable environment . By understanding the environmental parameters that describe population size , we can predict the distribution of the pathogen . Maps of the distribution of pathogens are often a key step in control of disease , producing environmental hazard maps . The pathogen of our study , Mycobacterium ulcerans , infects up to 10 , 000 people per year in more than 30 countries around the world [9] , [10] . Infection leads to the Buruli ulcer , an emerging neglected tropical disease [10] which results in a necrotizing infection of the skin and can lead to crippling deformity [9] . The transmission route of M . ulcerans remains unknown , and though several competing hypotheses exist [11] , [12] our work herein does not address transmission , but focuses on the distribution of the pathogen . Identification of the landscape variants that indicate suitable habitat for this particular pathogen has proven remarkably difficult , despite decades of research ( see [13] for a review ) . Previous research on M . ulcerans has found several apparently contradictory facts about the bacterium , making it difficult to establish a generalised picture of its ecology . In 2007 the genome of M . ulcerans was sequenced , and analysis revealed extensive evidence for reductive evolution , with massive gene loss . M . ulcerans evolved from M . marinum , and appears to have undergone a bottleneck event in the process , losing many of the genes M . marinum uses to sustain itself in free living environments , apparently now favouring protected environments with low sunlight [14] . This is suggestive of a highly specialised ecological niche , implying that the bacterium cannot survive in a large range of environmental conditions . Detection of the bacterium in the environment is normally via PCR; M . ulcerans is very slow growing and extremely difficult to culture from the wild [15] , and most attempts at culture result in M . ulcerans being overgrown by other bacteria which are ubiquitous in the environment . However , the implication that the microbe is a specialist has been ( apparently ) contradicted by recent detection of the bacterium in the environment . M . ulcerans DNA has been detected in a bewildering variety of environmental samples , including aquatic insects , biofilms , crustaceans , detritus , fish , frogs , possums and various small mammals , soil , snails , water and worms [3] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] . This large range of suitable conditions is odd , in light of the bacterium's apparent status as a specialist with a small niche . The many different species that M . ulcerans infects in the local community may become infected due to differences in their feeding habits , position in the trophic web , or relative abundance [13] , [30] , [31] . Herein , we use samples of the five dominant Orders of the aquatic insect community , which have been tested for M . ulcerans positivity rates , and correlate changes in M . ulcerans positivity in these 5 Orders to changes in the environmental conditions of land cover and topography . These 5 Orders may not be the primary habitat of M . ulcerans in the wild , as the full biotic extent of M . ulcerans distribution is still unknown , but they are commonly found to be persistently infected and appear to be important hosts [32] . Previous work has found that M . ulcerans abundance does respond to water body type , being more commonly detected in swamps ( still lentic systems ) than rivers ( flowing lotic systems ) in Ghana [33] , [34] . The pathogen is associated with lowland , flat , swampy areas in contact with stagnant water [35] , is known to have complex seasonal dynamics [32] , and appears to be present at low levels throughout the entire local biotic community along the year [29] . The distribution of the disease may also inform us on the distribution of the pathogen; the distribution of Buruli ulcer is known to be more spatially restricted than the distribution of M . ulcerans [36] , and is known to respond to low elevation , forested land cover , and previous rainfall [37] , [38] , which would suggest that perhaps these factors are also important in the distribution of M . ulcerans . Taken together , these facts suggested that changes in the biotic distribution of the pathogen could be mapped using landscape variables . Often , sampling of river systems results in the unexpected presence of M . ulcerans; if factors at the larger watershed scale add substantial information on the distribution of M . ulcerans a description of the upstream region of the river may help to explain this unexpected presence . We describe the condition of the landscape using land cover , such as forest and savannah , and topography , such as elevation and slope . These landscape scale factors are expected to indirectly influence M . ulcerans abundance via their influence on the microhabitat the bacterium inhabits , for example affecting the pH , dissolved oxygen content , and composition of the aquatic insect community , which are known to influence M . ulcerans distribution [12] , [29] . To address our questions we describe landscape variables correlated to the presence of the bacterium in aquatic macroinvertebrates in Cameroon , Central Africa . We then test our model against data collected in French Guiana to explore the generalizability of our findings . This will contribute to an understanding of the spatial distribution of this environmental pathogen , and further our ability to control Buruli ulcer disease .
The Cameroon dataset is a subset of that published in [29] , which comprises 16 sites in Akonolinga , sampled every month for 12 months ( Figure 1 ) . Identical methods were carried out by the same investigators for all sites throughout the study . In brief , at each site , 4 locations were chosen in areas of slow water flow and among the dominant aquatic vegetation and at each location , 5 sweeps with a dip net within a surface of 1 m2 were done to sample the aquatic community . Aquatic organisms were classified down to the Family level whenever possible and stored separately in 70% ethanol . Individuals belonging to the same taxonomic group were pooled together for detection of M . ulcerans DNA by quantitative PCR . Among these , the 5 most abundant Orders ( Diptera , Hemiptera , Coleoptera , Odonata and Ephemeroptera ) were consistently analysed for all sites and months . Pooled individuals were all ground together and homogenized and DNA from tissue homogenates was purified using QIAquick 96 PCR Purification Kit ( QIAGEN ) . Finally , amplification and detection of MU DNA were performed through quantitative PCR by targeting the ketoreductase B domain ( KR ) of the mycolactone polyketide synthase and IS2404 sequence from MU genome . This resulted in 5 analyzed samples ( each Order ) per month , per site , which we use to infer M . ulcerans presence or absence . Summary statistics are described in Table 1 . Sampling effort varied from month to month , as is discussed in [29] , however we have used a subset of that data in order to gain the most consistent representation of the biotic community possible . A data set following the same methodology was independently collected in French Guiana , South America [28] . DNA extraction was carried out with the same two primer pairs and methodology as above . In French Guiana eighteen sites were sampled twice during the wet season , which lasts from December to July . The entire biotic community was sampled , and for consistency the same 5 taxonomic Orders as in Akonolinga ( Table 2 ) were compared . M . ulcerans has previously been found to respond to variables that are influenced by rainfall [35] , [38] . To explore differences in the seasonal distribution of the bacterium , the wet season months and the dry season months were analysed separately . In Cameroon wet season months are April , May , June , August , September and October . The dry season is January , February , March , July , November and December . For each site , the proportion of positive samples at a site in a season was determined by summing the number of positive samples in that season , then dividing by the total number of samples sampled in that season ( which is 5 multiplied by the number of sampled months ) . This resulted in two response variables , Ywet and Ydry , which we use to describe the proportion of M . ulcerans positive samples in the 5 dominant insect Orders in the wet and dry seasons respectively . This resulted in a general , standardised view of the mycobacterium distribution in both the dry and wet seasons . The habitat suitability is determined by the proportion of samples of the biotic community that are M . ulcerans positive . Land cover in Akonolinga was described using several multispectral satellite images; SPOT 2 . 5 meter resolution images ( references: 50833380811220923092V0 and 50833371012210937422V0 ) , and a Landsat image ( reference L72186056_05620021107 ) . The study area was categorised into the following classes; Agriculture , Forest , Flood plain , Road , Savannah , Swamp and Urban ( Table S1 ) . Classification was conducted in the Object Orientated Image Analysis software eCognition [39] . The resulting maps were validated and corrected where needed following onsite visits in November 2012 . Topography was described using the Shuttle Radar Topography Mission ( SRTM ) digital elevation model [40] , which has a spatial resolution of 90 meters . All topographical variables were derived using the Spatial Analyst extension of the software ArcMap 10 . 1 [41] . For each site we described the mean , standard deviation , minimum , maximum and variety of elevation , in meters above sea level , using SRTM ( Table S1 ) . From the SRTM we calculated the mean , standard deviation , minimum , maximum and variety of the topological slope , in degrees . Flow accumulation is the accumulated number of upstream cells flowing into a point , and ecologically represents the topographical potential for water to accumulate . We derived the mean , standard deviation , maximum and variety of the flow accumulation . We also calculated mean , standard deviation , maximum depth , variety , and proportion of buffer surface area covered by basins . Basins are depressions in the landscape where water is expected to accumulate and , potentially , stagnate , and were detected using the Fill function in Spatial Analyst extension in Arc Map . Stream order indicates the distance from the source of the river , and is a simple index of the type of stream ( 1st order being small streams , larger orders being big rivers ) . Proportion of 1st to 8th order streams , defined by Strahler method [42] , was recorded in each buffer . Finally , wetness index is the topographic potential for water to accumulate . It was derived from the flow accumulation and the slope , according to the Equation 1 , where WI is the wetness index [43] , FA is flow accumulation and S is the topographic slope in degrees . We derived the mean , standard deviation , maximum , and variety of wetness index values , and the proportion of buffer surface area covered by wetness index values which are positive ( relatively wet areas ) and negative ( relatively dry areas ) . ( 1 ) The topography and land cover of the sample sites were described within two different buffers ( Figure 2 ) . These buffers corresponded to local and regional conditions . The first buffer was a 5 km radius circle around the sample site , which was chosen to represent the local conditions . 5 km is , approximately , the flight range of the 5 insect orders sampled [44] , [45] , [46] , [47] . The insects should be able to move throughout this region , be exposed to M . ulcerans , before being captured at the sample site . We describe the land cover and topography within this 5 km buffer and correlate the condition of this region to the proportion of M . ulcerans positive pools in each season . The second buffer was defined using the watershed of the sample site ( Figure 2 ) . The watershed is the upstream catchment area . In principle , all water within this region , and any detritus floating in the water , will eventually flow through the sample site . Watersheds can vary greatly in size , easily being several kilometres long , and detritus from very distant locations can flow quite large distances . M . ulcerans is known to attach to such detritus [24] . This watershed buffer is created using the Watershed tool in ArcMap10 . 1 , Spatial Analyst extension [42] . The 42 variables estimated to describe the landscape were reduced to permit modelling . Principal component analysis ( PCA ) was performed on the landscape variables centred at the mean ( ln ( x ) −ln ( xmean ) ) to summarize the data in the watershed and the 5 km buffer . PCAs were performed with the PCA function in the FactoMineR library in R [48] . This generated two PCAs; a PCA of the 42 environmental variables in the watershed buffer , PCAws , and a PCA of the 42 environmental variables in the 5 km buffer , PCA5 km . In each PCA we examined the orthogonal axes that explained 95% of the variance in the 42 topography and land cover variables . Firstly , 9 principal components explained 95% of the variance in the watershed of the sample site ( PCAws ) . The magnitude and direction of each correlation is given in the supplementary materials ( Tables S1 and S2 ) . We describe PCAws1 as “large watersheds that drain flood plains” , given its strongly positive correlations to watershed surface area and floodplains; PCAws2 as “large watersheds that drain highland agriculture”; PCAws3 as “large watersheds that drain lowland agriculture”; PCAws4 as “small watersheds that drain swamp and forest at flat intermediate elevations”; PCAws5 as “small watersheds that drain highland urban and savannah”; PCAws6 as “small watersheds that drain highland urban and forest”; PCAws7 as “large watersheds that drain lowland forest , savannah and swamp”; PCAws8 as “small watersheds that drain urban and agricultural environments in hilly lowlands”; and PCAws9 as “small watersheds that drain wet swamps in areas that reach from low to high elevations” ( Table S1 ) . Secondly , for the local 5 km circular buffer , 6 principal components ( PCA5 km ) explained 95% of the variance in the data as described in SM2 . Translating these to ecologically meaningful terms , we describe PCA5 km1 as representing “sites surrounded by flat lowland areas with urban , agriculture and the flood plains of large rivers”; PCA5 km2 as representing “sites surrounded by sloped highland areas with urban , agriculture and small rivers”; PCA5 km3 as representing “sites surrounded by sloped highland areas with savannah and large swampy rivers”; PCA5 km4 as representing “sites surrounded by flat lowland areas with savannah and small rivers”; PCA5 km5 as representing “sites surrounded by flat highlands with urban , agriculture and large rivers” , and PCA5 km6 as representing “sites surrounded by lowland hills , with small rivers and many small basins , in unforested environment” , ( Table S2 ) . We allow model selection to choose which of these principal components are most informative in the species distribution , Ywet and Ydry . The dry season general linear models ( GLMs ) and wet season GLMs were fitted separately with glmulti in the glmulti library in R . Glmulti finds the best set of GLMs among all possible combinations of explanatory variables; so for example all possible Ydry∼PCA5 km models were fitted , and each was evaluated with the Akaike information criterion corrected for small sample sizes ( AICc ) . Low AICc scores indicate good performance and reduced overfitting [49] . The best set of these binomial GLMs ( within 2 AICc scores of the best model ) are selected , and the model within this range with the lowest sum of absolute residuals ( best performance ) is selected as the final model ( Figure S1 ) . The response variable changed seasonally , resulting in two response variables , Ydry and Ywet . Along with the PCA5 km and PCAws inputs this resulted in four models; Ydry∼PCA5 km and Ydry∼PCAws in the dry season , and Ywet∼PCA5 km and Ywet∼PCAws in the wet season . This reduces our variables by retaining those that are important . Then , to compare the importance of PCA5 km ( local ) and PCAws ( regional watershed ) in the distribution of the response variable , M . ulcerans abundance , the components retained in these models were included in the final models , Ydry∼PCA5 km+PCAws in the dry season , and Ywet∼PCA5 km+PCAws in the wet season . In this way , by allowing glmulti to retain or drop these variables we can compare the importance of the watershed and local 5 km area variables in the distribution of M . ulcerans . Potential effects of multicolinearity were explored but were deemed minimal , as all pairwise Pearson correlation coefficient R values in the principal components were below 0 . 75 ( Tables S3 and S4 ) . In the initial screen of variables , Ydry∼PCA5 km and Ydry∼PCAws retained PCAws4 , “small watersheds that drain swamp and forest at flat intermediate elevations” , PCAws9 , “small watersheds that drain wet swamps in areas that reach from low to high elevations” and PCA5 km2 , “sites surrounded by sloped highland areas with urban , agriculture and small rivers” . These were included in the model of interest , Ydry∼PCA5 km+PCAws . For the wet season Ywet∼PCA5 km and Ywet∼PCAws retained PCAws1 , “large watersheds that drain flood plains” , PCAws 5 , “small watersheds that drain highland urban and savannah” , PCAws 6 , “small watersheds that drain highland urban and forest” , PCAws 8 , “small watersheds that drain urban and agricultural environments in hilly lowlands” , PCA5 km2 , “sites surrounded by sloped highland areas with urban , agriculture and small rivers” and PCA5 km4 , “sites surrounded by flat lowland areas with savannah and small rivers” , which were included in Ywet∼PCA5 km+PCAws . We interpolate the Akonolinga model within the region of Akonolinga to predict the distribution of suitable habitat , the reservoir , of M . ulcerans . To achieve this , points where streams ( defined using STRM ) flow under or across roads ( defined using satellite images ) were selected . These were termed ‘pour points’ in this article . Selection of the point where streams cross roads was based on the hypothesis that these environments , where contact between humans and the aquatic environment will be high , may be important in infection . This does not mean that infection does not occur in other locations , nor do we speculate on the importance of relative routes of transmission . This will not characterise all the environmental reservoir of the bacterium , but will describe an important part of it . The topography and land cover of the watershed and 5 km buffer of these pour points was characterised , transformed into PCA5 km and PCAws format , and the GLM was predicted . As a summary to describe this distribution , we use Morans Index of spatial autocorrelation , which describes the extent to which the distribution is random , and is here used to describe the distribution of suitable sites . This is implemented using the tool Spatial Autocorrelation Global Moran's I in ArcMap10 . 1 [41] . We extrapolate the Akonolinga wet season model to French Guiana , to understand how the suitable habitat in one region is similar to that in another . For comparability , the wet season model , constructed in Cameroon , was used to predict the positive sites among the 18 sampled sites in French Guiana . Values of PCA5 km and PCAws in French Guiana were generated using the ind . sup option in the PCA function . The Akonolinga wet season model was then predicted into French Guiana using the land cover data provided by the French Ministère de l'Écologie , du Développement Durable et de l'Énergie [50] , and topography derived from SRTM . As discussed above , the choice of error structure is important in the performance of a GLM . We aim to describe the distribution of the bacterium , so preference is given to the model with the lowest residual values in the model , which in this case is Gaussian rather than Binomial error structure . Residuals were much lower in a Gaussian model , as shown in Figures S2 and S3 ( see the observed response versus predicted response for Gaussian and Binomial models and QQ plots for the Gaussian and Binomial models , respectively ) . This difference is an order of magnitude . This was a practical decision – using Gaussian models in this case was based entirely on the desire to clearly predict where this pathogenic bacterium is more likely to occur , in such a case errors of residuals have a greater cost . The wet and dry season watershed Gaussian models were predicted on the pour point data using the predict . glm function in R . The model predictions of habitat suitability at these pour points were then interpolated using Inverse Distance Weighting in the IDW tool of ArcMap 10 [41] .
The final fitted wet season Binomial logit GLM , after stepwise AICc selection , wasThe final GLM suggested that both local and regional effects are substantially correlated to M . ulcerans distribution . Regional effects were represented by PCAws9 , “small watersheds that drain wet swamps in areas that reach from low to high elevations” , and was negatively correlated to M . ulcerans abundance ( correlation coefficient −0 . 37 , p = 0 . 007 ) . This means we expect less M . ulcerans in small watersheds that drain swamps near highlands . The second part of the above equation corresponds to local effects; PCA5 km2 represents “sites surrounded by sloped highland areas with urban , agriculture and small rivers” . This was also negatively correlated to M . ulcerans abundance ( correlation coefficient −0 . 16 , p = 0 . 00214 ) , so we expect less M . ulcerans when the area around the sample site is highland areas with urban and agricultural areas . The spatial distribution of M . ulcerans suitable habitat in the wet season predicted at the pour points was non-random , based on Moran's I spatial autocorrelation ( Moran's Index: 0 . 21 , z-score: 9 . 1 , p<0 . 00001 ) , positive sites tend to cluster together ( Figure 3 ) . The final fitted dry season binomial logit GLM , after stepwise AICc selection , isThe final models on the dry season found that both regional and local effects were substantially correlated to presence of M . ulcerans . Regional effects were represented by PCAws1 , “large watersheds that drain flood plains” , which was marginally negatively correlated to M . ulcerans abundance ( correlation coefficient −0 . 26 , p = 0 . 05210 ) . PCA5 km2 , “sites surrounded by areas with urban , agriculture and small rivers” was positively correlated to M . ulcerans abundance ( correlation coefficient 0 . 09 , p = 0 . 18709 ) though the p value suggests this is not significant , and finally PCA5 km4 , “sites surrounded by areas with savannah and small rivers” , was positively correlated to M . ulcerans abundance , ( correlation coefficient 0 . 38 , p = 0 . 007 ) . The spatial distribution of M . ulcerans suitable habitat in the dry season predicted at the pour points is non-random , based on Moran's I spatial autocorrelation ( Moran's Index: 0 . 33 , z-score: 14 . 32 , p<0 . 00001 ) positive sites tend to cluster together ( Figure 3 ) . Spatial autocorrelation of model residuals can be an issue in GLMs , but this was explored , and it was not the case here . Model residuals were not significantly spatially autocorrelated in the wet season ( Moran's Index: −0 . 285386 , z-score: −1 . 045844 , p = 0 . 295633 ) nor in the dry season ( Moran's Index: 0 . 071225 , z-score: 0 . 655435 , p = 0 . 512187 ) . The AICc of the final dry season Binomial model was 49 . 6 , the absolute sum of the residuals was 11 . 03 . The AICc of the final wet season Binomial model was 67 . 8 , the absolute sum of the residuals was 11 . 95 . We note that Gaussian models had significantly better performance . The AICc of the final dry season Gaussian model was −39 . 8 , the absolute sum of the residuals was 0 . 53 . The AICc of the final wet season Gaussian model was −65 . 5 , the absolute sum of the residuals was 0 . 24 . Model performance is presented in Figure S2 , model residuals were normally distributed ( Figure S3 ) . The Akonolinga wet season model was predicted into 18 sample sites in French Guiana ( Figure 4 , 2nd row ) . The model predicted sites to be positive or negative , and the results of qPCR corroborated these predictions ( Figure 4 ) . Performance of the Binomial model was notably poor , all sites were predicted negative . In contrast , performance of the Gaussian model was better , but accuracy was still poor at 0 . 39 ( Table S5 ) . Sensitivity and negative predictive values are high , indicating that the predictions of presence of the bacterium are likely to be true , specificity and positive predictive values are low; indicating predictions of absence of the bacterium are likely to be incorrect . This is a result of a bias towards Type II errors ( false negatives ) in the Gaussian model . Overall , the model predicts M . ulcerans in Akonolinga , but is sensitive to extrapolation . Extrapolation tends to result in false negative predictions of presence .
The distribution of environmental pathogens needs to be understood to facilitate control . Commonly , local effects in the microhabitats are considered to describe the ecological niche of a pathogen . However our study demonstrates that regional effects are important factors to be considered . Future research on the M . ulcerans would benefit by considering the watershed of potential sample sites , particularly as such data is often quite simple to acquire . The shape , size , and land cover of the watershed correlates with changes in the distribution of M . ulcerans , and useful information is lost if watersheds are ignored . The distribution of swamp in a watershed was found to be an important factor in the suitability of the site for M . ulcerans; though a sample point in the field may be at a location normally considered unsuitable for the bacteria ( e . g . a small swift lentic stream ) , the area upstream may contain an abundance of lotic swamps and be quite suitable for the bacterium , which may be ‘washed out’ downstream towards the sample site . This is an example of the useful information we gain by placing pathogens in an environmental context , rather than regarding them solely in an epidemiological sense .
|
Many pathogens persist in the environment , and an understanding of where they are can assist in disease control , allowing us to identify areas of risk to local human populations . Herein , we use general linear models to describe the distribution of a particular environmental pathogen , Mycobacterium ulcerans , describing the landscape conditions correlated with the presence of this pathogen in local biota , and mapping the distribution of these habitats in a region of Cameroon , Africa . Our findings identify the importance of the watershed as a factor determining the distribution of the bacterium , where landscape conditions upstream of the sample site can influence the abundance of the bacterium in downstream sites . We find that the bacterium has notable seasonal changes in its distribution , between the wet and dry seasons , which may have implications for human health . We also discuss sensitivity of these models to extrapolation , finding that they work well in the African region and underperforming when extrapolated to another region in South America .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"ecology",
"and",
"environmental",
"sciences",
"medicine",
"and",
"health",
"sciences",
"ecological",
"niches",
"ecology",
"neglected",
"tropical",
"diseases",
"biology",
"and",
"life",
"sciences",
"tropical",
"diseases"
] |
2014
|
Topography and Land Cover of Watersheds Predicts the Distribution of the Environmental Pathogen Mycobacterium ulcerans in Aquatic Insects
|
The phosphoinositide-3 kinase ( PI3K ) pathway regulates diverse cellular activities related to cell growth , migration , survival , and vesicular trafficking . It is known that Ebola virus requires endocytosis to establish an infection . However , the cellular signals that mediate this uptake were unknown for Ebola virus as well as many other viruses . Here , the involvement of PI3K in Ebola virus entry was studied . A novel and critical role of the PI3K signaling pathway was demonstrated in cell entry of Zaire Ebola virus ( ZEBOV ) . Inhibitors of PI3K and Akt significantly reduced infection by ZEBOV at an early step during the replication cycle . Furthermore , phosphorylation of Akt-1 was induced shortly after exposure of cells to radiation-inactivated ZEBOV , indicating that the virus actively induces the PI3K pathway and that replication was not required for this induction . Subsequent use of pseudotyped Ebola virus and/or Ebola virus-like particles , in a novel virus entry assay , provided evidence that activity of PI3K/Akt is required at the virus entry step . Class 1A PI3Ks appear to play a predominant role in regulating ZEBOV entry , and Rac1 is a key downstream effector in this regulatory cascade . Confocal imaging of fluorescently labeled ZEBOV indicated that inhibition of PI3K , Akt , or Rac1 disrupted normal uptake of virus particles into cells and resulted in aberrant accumulation of virus into a cytosolic compartment that was non-permissive for membrane fusion . We conclude that PI3K-mediated signaling plays an important role in regulating vesicular trafficking of ZEBOV necessary for cell entry . Disruption of this signaling leads to inappropriate trafficking within the cell and a block in steps leading to membrane fusion . These findings extend our current understanding of Ebola virus entry mechanism and may help in devising useful new strategies for treatment of Ebola virus infection .
Ebola virus , a member of the family Filoviridae , is an emerging infectious agent that causes severe and often fatal hemorrhagic fever in humans and nonhuman primates . In many outbreaks , especially those caused by Zaire Ebola virus ( ZEBOV ) , mortality rates close to 90% have been reported [1] . Currently , no vaccine or therapy is available for Ebola virus hemorrhagic fever . Significant mortality rate , high transmissibility and lack of therapeutic and preventive measures make Ebola virus a potentially serious public health threat . Ebola viruses are filamentous enveloped viruses . The envelope contains two virally-encoded glycoproteins , GP1 and GP2 , which together serve as the primary viral determinant for entry into host cells . The two glycoproteins are produced from a precursor protein ( GP ) , which is cleaved by a furin-like endoprotease to generate surface-bound protein GP1 and the transmembrane protein GP2 , the two proteins , remain associated by a disulfide bridge after cleavage [2] . As with many enveloped viruses , entry of ZEBOV into cells likely involves virus particles binding to host cell receptor ( s ) , followed by endocytosis and trafficking through vesicular compartments , and finally fusion of the virus membrane to that of the endocytic vesicle . This results in release of the viral nucleocapsid into the cytoplasm where the subsequent steps of the replication cycle take place [3] . GP1 is believed to mediate interaction with the host cell receptor , while GP2 is involved in membrane fusion [3] . Membrane fusion is believed to involve a GP2 structural rearrangement triggered by low pH in an endocytic compartment . In addition , intracellular processing of GP1 by endosomal cathepsins is also a prerequisite to membrane fusion [4] . Thus , an important aspect of ZEBOV entry involves endocytic trafficking into the cell . Recent work has shown that many viruses exploit host cell molecules and signaling pathways to facilitate various steps of the entry process . A critical role of focal adhesion kinase and protein kinase C was described for endocytosis and endosomal sorting of West Nile virus in mosquito cells [5] , and recently , Rho A and its upstream tyrosine kinases were implicated in endocytosis and trafficking of poliovirus [6] . However , for most viruses , especially enveloped viruses , information on the requirements of cell signaling for entry is limited . Important advances have been made regarding the entry of Ebola virus and the role of the envelope glycoproteins in cell attachment and endocytosis ( reviewed in [7] ) . However , our understanding of the role of cell signaling in virus entry remains limited . The phosphoinositide-3 kinase ( PI3K ) pathway is an important cell signaling pathway that regulates diverse cellular activities including proliferation , differentiation , apoptosis , migration , metabolism , and vesicular trafficking [8] . PI3Ks ( OMIM#601232; Online Mendelian Inheritance in Man database: http://www . ncbi . nlm . nih . gov/sites/entrezdbomim ) are a family of lipid kinases that are divided into three classes according to their structure and substrate specificity . Of these , class I PI3Ks are the most widely studied . They signal through cell surface protein tyrosine kinase ( PTK ) or G-protein coupled ( GPC ) receptors . Activation of PI3K results in phosphorylation of phosphatidylinositol-bis phosphates to produce phosphatidylinositol-triphosphates , which serve as potent second messengers for downstream signaling . Akt-1 ( OMIM#164730 ) , is a key downstream intermediate in PI3K-dependent signaling . A variety of molecules are directly or indirectly regulated by Akt , and serve as downstream effectors to carry out diverse PI3K-regulated responses [9] . Regulation of vesicular trafficking is one of the oldest recognized functions of PI3Ks [10] . PI3Ks influence a variety of intracellular trafficking events that include cargo selection , vesicle formation , vesicle movement and membrane fusion . This is often through stimulation of actin turnover [11] . Rac1 ( OMIM#602048 ) , along with other Rho family GTPases , is a key effector in this process [12] . Therefore the regulation of Rac1 by class 1 PI3Ks , as seen in many cell types [13]–[16] , provides a mechanism to couple receptor-ligand interaction to induction of endocytosis as well as other actin-mediated functions in the cell . Bacterial pathogens have been shown to take advantage of this mechanism by stimulating phagocytosis and internalization through PI3K activation [17] , [18] . Similarly , interaction of the non-enveloped adenovirus with receptors on the cell surface was shown to activate Rac1 and Cdc42 ( OMIM#116952 ) in a PI3K-dependent manner , and this was required for virus uptake into endosomes [19] . However , similar dependencies for enveloped viruses have not been described . Here we investigated the role of PI3K cell signaling pathway in cellular entry of ZEBOV . The findings indicated that ZEBOV induces activation of PI3K pathway prior to or during entry and that activity of class 1A PI3Ks is critical for entry into host cells . Rac1 GTPase was found to be an important downstream effector in regulating ZEBOV entry . The impact of inhibiting PI3K , Akt or Rac1 was similar , causing an aberrant accumulation of ZEBOV particles in intracellular vesicles , indicating a role of the PI3K-Akt-Rac1 pathway in vesicular trafficking of virus particles .
Several viruses utilize the PI3K-Akt pathway to support replication in host cells [20]–[23] , however , involvement of this pathway in early events of infection such as entry has not been conclusively demonstrated for enveloped viruses . We investigated the effect of LY294002 ( a highly specific inhibitor of PI3K ) on infection by wild type ZEBOV . To determine if the drug was acting early or late in the infection cycle , cells were exposed to drug only during the first 2 h of incubation with the virus . Subsequently , the drug and the unbound virus were removed , and infection was allowed to continue . As compared to untreated cells , the infection of ZEBOV was significantly ( nearly 10-fold ) reduced in cells treated with LY294002 ( p<0 . 01 ) . In contrast , LY294002 exhibited no significant effect ( p>0 . 05 ) on infection by vesicular stomatitis virus ( VSV ) . A similar level of inhibition of ZEBOV infection was observed when a specific Akt inhibitor was used ( Figure 1 ) . These data suggested that the PI3K-Akt pathway plays a role in one or more of the early events in the ZEBOV infection cycle . While the above data indicated that the activity of PI3K is important for early event ( s ) in ZEBOV replication , it was unclear if the basal level activity of the PI3K-Akt pathway was sufficient for infection or if ZEBOV itself was capable of inducing this pathway to promote infection . To address this question , phosphorylation of Akt-1 was measured in cells after incubation with ZEBOV . Akt is a major downstream effector of the PI3K pathway and is phosphorylated ( activated ) following activation of PI3K . Therefore , Akt phosphorylation is often used as an indirect , but reliable measure of PI3K pathway activation [21]–[23] . Serum-starved HEK293 cells were incubated with medium containing no serum ( negative control ) , medium containing 10% fetal bovine serum ( positive control ) , or γ-radiation-inactivated ZEBOV or VSV . Compared to the negative control , ZEBOV caused a marked ( >2-fold ) increase in Akt phosphorylation within 30 min and actually surpassed the level observed after serum stimulation of cells ( Figure 2 ) . In contrast , VSV had no significant effect on phosphorylation of Akt-1 over this time interval . These data suggested that ZEBOV actively and strongly induces the PI3K pathway very early during the infection process . Since γ-radiation-inactivated virus had been used , it was likely that this stimulation was the product of direct GP interaction with cell receptors . The early dependence of ZEBOV infection on PI3K and Akt activity , and a significant induction of Akt phosphorylation by γ-radiation-inactivated ( replication-incompetent ) ZEBOV suggested that the PI3K pathway is likely involved at an early step in infection , most likely entry . To investigate if the PI3K-Akt pathway played a role in ZEBOV entry or was required for some other early , but post-entry step , we adapted a previously described contents mixing assay that allowed rapid and quantitative measurement of entry of diverse enveloped viruses up to and including the point of membrane fusion [24] . The assay , which was originally based on virus pseudotypes , measures release of a recombinant nef-luciferase protein , encapsulated within virus particles . The nef peptide serves to non-specifically target luciferase to cell membranes at the time of particle budding and so , incorporates luciferase within the membrane of new virus particles . After cell-virus membrane fusion , the luciferase becomes accessible to its substrates , previously loaded into cells , and light is emitted . Here the assay was adapted for use , first with ZEBOV GP pseudotyped particles and then with ZEBOV virus-like particles . Both types of virus particles give a measure of GP function but VLPs , which share a filamentous structure with native ZEBOV are likely a better model system for wild type virus . In either case , this is the first time that this assay technology has been adapted to ZEBOV . Each assay was applied to determine if the PI3K-Akt pathway played a role in entry of ZEBOV . To assess their efficiency in an entry assay , luciferase-containing pseudotypes with ZEBOV envelope glycoproteins ( EVP ) or VSV-G protein ( VSVP ) , and VLPs carrying ZEBOV envelope glycoproteins ( ZEBO-VLP ) or VSV-G protein ( VSV-VLP ) were produced , sucrose purified and tested on cells . As compared to particles devoid of envelope glycoproteins , a strong signal was obtained for each of EVP , VSVP , ZEBO-VLP and VSV-VLP ( Table 1 , column 2 ) . The relatively large difference in the signal between EVP and VSVP correlated to differences in pseudotype virus titer , as determined by standard infection assays with a GFP reporter gene ( Table 1 , column 3 ) and likely reflects the potency of the VSV-G relative to ZEBOV GP . Both EVP and ZEBO-VLPs were then further validated for specificity of entry into cells . In each case the activity of the pseudotyped particles reflected that of the VLPs . ZEBOV neutralizing antibody ( KZ52 ) significantly ( ∼70% ) blocked entry of both EVP and ZEBO-VLP , ( Figure 3A ) and correlated to that reported for inhibition of infectious ZEBOV infection at the concentration of the antibody used here [25] while entry of VSVP was unaffected . Secondly , ammonium chloride ( NH4Cl ) and bafilomycin A1 , two well-known inhibitors of endosomal acidification , both inhibited the pH-dependent entry of EVP and VSVP , while they had no effect on entry of a Friend murine leukemia virus pseudotype ( FrVP ) , a pH-independent virus . Similarly , entry of ZEBO-VLP was also inhibited by NH4Cl ( Figure 3B ) . Thirdly , a detailed examination of the entry kinetics of EVPs and ZEBO-VLPs ( Figure 3C ) revealed that the peak of the entry signal was preceded by a pronounced lag and occurred much later than that for VSV-G bearing particles . This timing was similar to that reported previously for a pseudotype infection assay [26] . Of note , both pseudotypes and VLPs entered cells with similar behavior and timings , indicating that the GP dictated the uptake kinetics and pathway used by the virus particle , more than the particle shape . In subsequent studies , the majority of the presented data are from VLPs but similar outcomes were seen with pseudotyped particles and are shown for comparison . To investigate if the PI3K-Akt pathway played a role during the entry steps of ZEBOV infection , LY294002 and Akt inhibitor were tested in the entry assay . In each case , cells treated with the inhibitors exhibited significant reduction in the entry of ZEBOV GP bearing particles but not for particles bearing VSV-G ( Figs . 4A , B ) . As a further independent test , a dominant-negative mutant of the p85 regulatory subunit ( OMIM#171833 ) of class 1A PI3K ( Δp85α ) was used to inhibit PI3K-mediated signaling . Δp85α retains the ability to bind phosphotyrosine residues on upstream receptors that signal through PI3K but lacks the ability to interact with the PI3K catalytic domain [27] . Cells were transiently transfected with either a control plasmid ( pcDNA3 ) or plasmid expressing Δp85α ( pcDNA3:Δp85α ) and entry assays performed using ZEBO-VLPs or VSV-VLPs ( Figure 4C ) . Entry of ZEBO-VLPs was significantly inhibited in cells expressing Δp85α as compared to that in cells transfected with the empty vector , indicating a role for this isoform . In contrast , the entry of VSV-G bearing particles was similar in both cell types ( Figure 4C ) . Δp85α inhibits activity of those PI3K heterodimers that contain the α-isoform of the p85 regulatory subunit . To test the potential involvement of other isoforms , entry assays were performed using pik3R1 ( OMIM#171833 ) knockout ( p85α- , p50α- , p55α-deficient ) or pik3R2 ( OMIM#603157 ) knockout ( p85β-deficient ) mouse embryonic fibroblasts ( MEF ) . Due to lower sensitivity of the VLP-based assay system in these cells , the experiments were performed using the pseudotyped virus-based assay . Furthermore , because of intrinsic resistance of corresponding wild-type MEFs to infection by VSVP , FrVPs were again used as a control . Compared to wild-type MEFs , EVP entry was reduced by >50% in the homozygous knockout cells for pik3R1 , while entry in pik3R2 knockout cells was reduced by 75% . No significant effect on the entry signal of the FrVP was observed in either cell type ( Figure 4D ) . These data indicate that both p85α and p85β containing PI3K heterodimers are involved in entry of ZEBOV . The above findings strongly indicated that inhibition of PI3K-Akt pathway blocked ZEBOV infection up to or including the membrane fusion step . However , this could be due to impaired virus binding to cells or inhibition at a post-binding step such as endocytosis , trafficking , or membrane fusion . To further define the mechanism by which PI3K controls ZEBOV entry , virus binding to cells was measured . Cells were pretreated with LY294002 or Akt-1 inhibitor and pseudotyped viruses were bound for 10 min . Unbound virus was then washed away and cells with residual bound particles were lysed using non-ionic detergent to release virus encapsulated luciferase . Compared to DMSO-treated ( control ) cells , no significant difference was observed in luciferase activity in samples that were treated with either LY294002 or Akt-1 inhibitor , indicating that ZEBOV pseudotype binding to cells was unaffected ( Figure 4E ) . Similarly , no significant difference was observed among p85 wild-type , pik3R1−/− or pik3R2−/−MEFs in their capacity to bind the pseudotyped particles ( data not shown ) . This suggested that inhibition of the PI3K-Akt pathway does not influence levels of accessible receptor on the cell surface . Therefore , the PI3K-Akt pathway most likely plays a role in one or more post-binding steps involved in entry . Among the possible downstream effectors regulated by PI3K-Akt pathway , mTOR ( OMIM#601231 ) and Rac1 are most likely to influence the entry process . mTOR , being a positive regulator of translation [28] , could potentially stimulate synthesis of factors needed during entry . However , a role of mTOR in ZEBOV entry was ruled out as rapamycin , a potent inhibitor of mTOR , had no effect on EVP entry ( data not shown ) . Rac1 , through its regulation of actin polymerization plays a vital role in various steps involved in endocytosis . In many cell types , activity of Rac1 is regulated by the PI3K-Akt pathway [13]–[16] . Therefore , a specific Rac1 inhibitor was tested . A dose dependent inhibition of ZEBO-VLP was observed peaking at >90% inhibition at 400 µM without affecting VSV-VLP entry ( Figure 5A ) . Furthermore , transient expression of a dominant-negative Rac1 mutant ( Rac1-T17N ) in cells reduced entry by >80% ( Figure 5B ) . The extent of ZEBO-VLP entry inhibition corresponded to the number of cells expressing the dominant-negative Rac1 ( Figure 5C , left panel ) . The above findings provided evidence that PI3K-Akt and Rac1 pathways play a role in ZEBOV entry; however , it remained unclear if the two pathways were linked or acted independently . To investigate this , cells were made to express a constitutively active form of Rac1 ( Rac1-G12V ) in the presence of the Akt inhibitor . The effects of the inhibitor were significantly reversed ( Figure 5B ) by the mutant , indicating , that PI3K-Akt and Rac1 act sequentially in a pathway that controls entry of ZEBOV , with PI3K-Akt acting upstream of Rac1 . Again , the extent of the effect of constitutively active Rac1 was proportional to the number of cells expressing the mutant protein ( Figure 5C , middle panel ) . Similar data were obtained when EVPs and VSVPs were used in the assay ( data not shown ) . Inhibitors of PI3K , Akt and Rac1 had no significant effect on EVP binding to cells ( Figure 4E , and data not shown ) indicating that the action of the PI3K-Akt-Rac1 pathway was likely important for endocytosis , trafficking and/or membrane fusion of ZEBOV . To examine this , ZEBOV particles were labeled with a red fluorescent dye ( Alexa Fluor594 ) and incubated with HEK293 cells in the absence or presence of LY294002 , Akt inhibitor or Rac1 inhibitor . Cells were also stained for F-actin to visualize the cell cytoskeleton , and analyzed by confocal microscopy . In untreated cells , particles were found distributed evenly throughout the cell cytoplasm . In contrast , after treatment with each inhibitor , particles accumulated in clusters ( Figure 6A ) . The drugs were also tested with Vero-E6 cells , a widely used , ZEBOV permissive cell line . Again , in untreated cells , individual virus particles were distributed throughout the cell cytoplasm ( Figure 6B ) . Interestingly , image analysis of serial z-sections revealed that most of the individual particles were adjacent to small actin bundles ( Figure 6C , left panel , arrow heads ) supporting a requirement for actin involvement in ZEBOV movement through the cell cytoplasm . Treatment with inhibitors of PI3K , Akt-1 and Rac1 each gave a similar outcome to that seen in the HEK293 cells , with clustering of virus particles within a cytosolic compartment ( Figure 6B ) , possibly of endocytic origin . These observations suggest that in the absence of PI3K activity , virus particles are taken into a vesicular compartment , but further trafficking is blocked .
This study describes a novel role for the PI3K cell signaling pathway in cellular entry of ZEBOV . A number of viruses utilize the PI3K-Akt cell signaling pathway to promote various steps in their replication cycle , such as regulation of gene expression and genome replication . Some bacteria and a few non-enveloped viruses also utilize this pathway to trigger their invasion and endocytosis into cells [22] , [29]–[32] . This report provides evidence that the PI3K pathway plays a critical role in cellular entry of ZEBOV . A previous report suggested that PI3K is involved in early events in Influenza virus infection [33] . However , a detailed analysis of the mechanism of action was not performed and a subsequent study using the same cell type and virus strain failed to show a requirement of PI3K activity for Influenza virus entry into cells [34] . Thus , the present report is the first to show involvement of the PI3K-Akt pathway in entry of an enveloped virus . The data obtained using the knockout cells provided information on which subtype of PI3K was important . Inhibition of pseudotyped ZEBOV entry into pik3R1 knockout ( p85α- , p50α- , p55α-deficient ) or pik3R2 knock out ( p85β-deficient ) cells suggested that class IA PI3Ks played a prominent role . Entry inhibition was more pronounced in pik3R2 knockout cells than in the pik3R1 knockout cells . This was somewhat surprising given that p85α is a major PI3K regulatory subunit , and deletion of the pik3R1 gene has a greater phenotypic impact , including perinatal lethality of homozygous mice accompanied with extensive hepatocytic and brown fat necrosis , enlarged skeletal muscle fibers , calcification of cardiac tissue and impaired B-cell development and proliferation [35] , [36] . However , a few responses such as T-cell proliferation , and insulin-dependent tyrosine phosphorylation of insulin receptor substrate-2 were increased in pik3R2 knockout mice [37] , [38] , indicating that each subtype may have specialized roles in specific cells and tissues . Indeed , each binds different sets of proteins [39] . Individual disruption of pik3R1 or pik3R2 gene was insufficient to confer complete inhibition of ZEBOV entry and may be due to a partial redundancy in the function of each or that other PI3K isoforms may also be involved . Given the relatively slow entry kinetics of ZEBOV , compared to VSV , the rapid phosphorylation of Akt by ZEBOV suggests that induction of the PI3K pathway may be related to a very early event in the entry process , such as receptor/co-receptor engagement . The class 1A PI3Ks are mainly activated by membrane-bound receptor tyrosine kinases ( RTKs ) [40] . Recently , Axl , Dtk , and Mer ( Tyro3 family RTKs ) were shown to serve as important entry factors for Ebola and Marburg viruses [41] . It was suggested that these molecules serve to promote endocytosis of ZEBOV particles; however , the exact mechanism and downstream effectors remained unclear . Interestingly , the inhibition of PI3K or Akt caused virus particles to aberrantly accumulate within the cell cytoplasm . In many cell types tyro3 family members trigger PI3K activation , and physical association of PI3K with Axl , Dtk and Mer has also been demonstrated [42] . ZEBOV interaction with the tyro3 RTK may then directly or indirectly trigger activation of PI3K and downstream effectors leading to virus endocytosis . However , more detailed analyses are required to further test this model . In further studying the mechanism of action , Rac1 was found to be an important downstream effector . PI3K-Akt is one prominent activation pathway for Rac1 in many cell types [13]–[16] . Treatment with PI3K , Akt or Rac1 inhibitors all led to similar intracellular accumulation of ZEBOV particles ( Figure 6 ) , signifying a common block in one of the stages of ZEBOV uptake into cells . A similar clustering of internalized EphA2 receptor in endocytic vesicles was also observed after treatment with inhibitors of PI3K , Akt or Rac1 [43] . The likely role of Rac1 , was through its regulation of actin polymerization , which plays a pivotal role in a variety of actin-dependent cellular processes such as membrane ruffling , receptor-mediated endocytosis and vesicular trafficking [12] . Indirect evidence suggesting that control of actin polymerization may be important for ZEBOV infection came from observations that: ( i ) internalized ZEBOV particles were in close proximity to actin bundles or filaments; ( ii ) inhibitors of PI3K , Akt and Rac1 all caused similar changes in F-actin characterized by loss of membrane ruffling and focal adhesions ( data not shown ) ; and ( iii ) agents that perturb actin dynamics significantly inhibit EVP entry [26] . Early activation of the PI3K-Akt pathway by ZEBOV may also have implications in pathogenesis of Ebola virus hemorrhagic fever . A profound inflammatory response is a key feature of the disease . Macrophages are among the primary targets of ZEBOV infection and respond by producing a number of proinflammatory cytokines and chemokines including TNF-α , IL-6 and IL-8 [44] . This appears to occur in the absence of virus replication as Ebola virus-like particles ( VLPs ) stimulate the same set of cytokines and is dependent on the presence of the Ebola virus envelope glycoproteins [45] . It follows that Ebola virus envelope proteins may play a vital role in the proinflammatory response induced during the infection . There is also evidence that the PI3K-Akt pathway contributes significantly toward regulation of each of these cytokines [46]–[49] . ZEBOV-induced activation of the PI3K-Akt pathway could then directly contribute to the proinflammatory response . Another hallmark of ZEBOV infection is hemorrhage due to increased vascular permeability . Vascular dysregulation has been attributed to both direct invasion and replication of ZEBOV in vascular endothelial cells , and to action of ZEBOV-induced proinflammatory cytokines , especially TNF-α , on vascular endothelial cells [50] . Also , PI3K-Akt pathway activation does lead to increased vascular permeability [51] . Thus , whether the mechanism of vascular dysregulation is through virus replication or action of cytokines , ZEBOV-induced PI3K activation has ability to affect both mechanisms , and thereby can potentially influence and partly explain a mechanism of ZEBOV pathogenesis . The PI3K pathway is vital for regulation of diverse cellular activities , including growth , survival , differentiation and motility . There is mounting evidence that aberrant regulation of the PI3K pathway is central to development and/or progression of many forms of cancer [40] . As a result , considerable effort is currently being focused on developing therapeutic strategies targeting various components of this pathway , including targeting specific isoforms and subunits of the PI3K holoenzyme [52] . Earlier this year a PI3K inhibitor entered phase 1 clinical trials indicating that such drugs are becoming available [53] . The finding that the PI3K pathway is also essential for entry of ZEBOV is therefore highly relevant for design of new therapeutic strategies and provides new potential opportunities where PI3K inhibitors developed for cancer treatments may become equally useful for treatment ZEBOV infection .
HEK293 and HEK293FT human fibroblast-derived cells were purchased from ATCC and Invitrogen , respectively . HEK293-mCAT-1 are a clonal derivative of HEK293 cells that express the mCAT-1 protein which serves as a receptor for ecotropic murine leukemia viruses ( MLV ) , such as the Friend 57 strain of MLV ( Entrez nucleotide #X02794 ) . These cells have similar morphology and growth properties compared to the parental HEK293 cells . Inhibition of ZEBOV entry by PI3K inhibitors was also confirmed in HEK293 cells with similar outcomes ( data not shown ) . Vero-E6 cells were also purchased from ATCC . HEK293 , FT and Vero-E6 cells were maintained in Dulbecco's modified Eagle's ( DMEM ) medium supplemented with 10% fetal bovine serum ( Gemini Bioproducts , GA ) , 1% non-essential amino acids ( Sigma , MO ) and 1% penicillin-streptomycin solution ( Sigma , MO ) . HEK293FT cells were used for pseudotype production and were maintained in the presence of geneticin at 0 . 5 mg/ml . Mouse embryonic fibroblasts ( MEFs ) were isolated from embryos that were heterozygous or homozygous for knockout of the PI3K-family related genes pik3R1 and pik3R2 . Immortalized cells from these embryos were provided by Dr . Lew Cantley ( Harvard Medical School , MA ) and were cultivated in the above medium . ZEBOV-specific KZ52 monoclonal antibody was a gift from Dr . Dennis Burton ( Scripps Research Institute , LaJolla , CA ) . Anti-Akt-1 and anti-phospho-Akt-1 antibodies were purchased from Cell Signaling Technologies ( Danvers , MA ) . Anti-HA ( 12CA5 ) was used as a non-specific control monoclonal antibody ( Roche , IN ) . All plasmids were prepared using Qiagen kits or by CsCl gradient centrifugation following standard procedures . The plasmids encoding HIV-1 gag and polymerase ( pLP1 ) , HIV-1 Rev ( pLP2 ) and VSV-G envelope glycoprotein ( pLP-VSVG ) were purchased from Invitrogen . Construction of the plasmids encoding packageable enhanced green fluorescent protein ( pLenti-EGFP ) and Nef-luciferase fusion protein ( pCDNA3-nef-luc ) has been described previously [24] . Plasmid encoding ZEBOV matrix protein ( VP40 ) ( Entrez gene#NC002549 ) and envelope glycoproteins were kindly provided by Dr . Christopher Basler ( Mount Sinai School of Medicine ) and Dr . Paul Bates ( University of Pennsylvania ) , respectively . The plasmid encoding envelope protein of the Friend 57 strain of MLV ( pFr-Env ) has been described previously [54] . HEK293FT cells were grown to approximately 80% confluence in 10-cm diameter dishes . The cells were simultaneously transfected with plasmids: ( i ) pLP1 ( 3 µg ) , pLP2 ( 2 µg ) , pLenti-EGFP ( 2 µg ) , pcDNA3-Nef-luc ( 1 . 5 µg ) and one of the following envelope protein constructs pLP-VSVG , 2 µg; pFr-Env , 5 µg; pEbola-GP , 0 . 5 µg to yield pseudotyped viruses with VSV , Friend 57 MLV or ZEBOV envelope glycoproteins respectively . Transfection was by calcium-phosphate precipitation [55] . After overnight incubation , culture medium was replenished and the plate was incubated for a further 36 h . At this time , the cell culture supernatant was collected and filtered through a 0 . 45-µm pore size cellulose-acetate filter to remove cell debris . Virus in the filtrate was pelleted by centrifugation through a 20% ( w/v ) sucrose cushion in PBS . Centrifugation was for 3 . 5 h at 25 , 000 rpm in SW28 rotor at 4°C . The virus pellet was resuspended in 0 . 01 volume of DMEM , aliquoted and stored at −80°C until used . ZEBOV-VLPs were produced by co-transecting HEK293 cells with plasmids encoding ZEBOV matrix protein ( VP-40 ) , Zaire Ebola virus ( ZEBOV ) envelope glycoproteins , and Nef-luciferase fusion protein using the calcium phosphate method . For VSV-VLP , plasmid encoding Ebola virus glycoproteins was replaced with one encoding VSV-G . Cell culture supernatant was collected 48 h after transfection and cell debris was cleared by centrifugation ( 1 , 200 rpm for 10 min at 4°C ) . Subsequently , VLPs were purified by centrifugation ( 25 , 000 rpm in SW28 rotor for 3 . 5 h at 4°C ) through a 20% ( w/v ) sucrose cushion in PBS . The VLP pellet was resuspended in 0 . 01 volume of DMEM , aliquoted and stored at 4°C . Assays were performed within 2–3 days after purification of VLPs . Zaire Ebola virus ( Mayinga strain ) , was cultivated on Vero-E6 cells by infection at an MOI of 0 . 1 . Culture supernatants were collected after 10 d and clarified by centrifugation at 2000×g for 15 min . Virus titer was determined by serial dilution in Vero-E6 cells . Cells were incubated with virus for 1 h and then overlaid with 0 . 8% ( w/v ) tragacanth gum in culture medium . 10 d post-infection cells were fixed with formalin , and stained with crystal violet so that plaques could be counted . All experiments with ZEBOV were performed under biosafety level 4 conditions in the Robert E . Shope BSL-4 Laboratory , UTMB . HEK293-mCAT-1 cells were grown in a 96-well plate to approximately 50% confluence . Serial 5-fold dilutions of virus stocks were prepared in DMEM and 50 µl of each dilution was added to cells . After overnight incubation , the medium was replenished and the incubation continued until GFP expressing cells were apparent ( 2 d post-infection ) . The total number of GFP-positive colonies was counted in each well using an inverted epifluorescence microscope and the titer of stock virus was calculated . Cells were removed from plates by trypsin treatment , pelleted by centrifugation and then resuspended in fresh medium . HEK293 cells ( 106 ) were mixed with Nef-luciferase containing pseudotyped virus or VLPs in a volume of 0 . 2 ml and incubated at 37°C on a rotating platform for indicated time intervals . Exposure of cells to low temperatures ( 4°C ) was avoided as this is known to temporarily disrupt endocytosis and receptor trafficking upon return to 37°C . To remove excess virus particles , cells were pelleted by centrifugation at 200×g for 5 min , supernatant containing unbound virus was discarded , and the cell pellet was washed 3 times with DMEM . The final cell pellet was resuspended in 0 . 1 ml of luciferase assay buffer lacking detergent ( Promega , WI ) and luciferase activity measured using a Turner Design TD 20/20 luminometer and expressed as counts/sec . For antibody inhibition assays , the luciferase-containing pseudotyped virus or VLPs were incubated with antibody for 1 h prior to incubation with target cells , which was performed in the continued presence of antibody . To study drug activity on virus entry , cells were pre-treated for 1 h , followed by incubation with pseudotyped virus or VLPs in the continued presence of the drug . Virus entry was then measured as described above . For dominant-negative or constitutively-active mutants , control plasmid ( pcDNA3 ) or plasmid encoding the modified cDNA was transfected into HEK293-mCAT-1 cells by calcium phosphate precipitation as described above . Cells were used for entry assays 36 h after transfection . HEK293 cells were grown to confluence and then serum-starved for 12–14 h . Radiation-inactivated wild type ZEBOV ( Entrez Genome#15507 ) or VSV ( Entrez Genome#10405 ) ( sucrose purified and resuspended in serum-free medium ) was then added at a calculated MOI of 5 . For positive control , cells were treated with 10% fetal bovine serum in medium , while the negative control samples received serum-free medium . All samples were incubated at 37°C for times indicated . After the incubation , cell lysates were applied to 10% polyacrylamide gels and resolved proteins transferred to a nitrocellulose membrane by electroblotting . After blocking the membrane in 5% milk powder in TBST , blots were incubated overnight with anti-phospho-Akt-1 antibody at 4°C , washed and incubated with HRP-conjugated secondary antibody for 1 h . The membrane was then washed and developed using ECL chemiluminescence substrate ( GE life sciences , Piscataway , NJ ) and imaged . Subsequently , the same membrane was stripped and re-probed for total Akt-1 using an anti-Akt-1 antibody . Band densitometry was performed using ImageJ analysis software [56] . ZEBOV was grown on Vero-E6 cells to a titer of 106 pfu/ml . Virus-containing culture supernatant was clarified by pelleting cell debris at 2000×g for 15 min . The virus remaining in the supernatant was then pelleted through 20% sucrose in 10 mM HEPES , pH 7 . 4 by centrifugation at 100 , 000×g for 3 h . The virus pellet was resuspended in 140 mM NaCl in 10 mM HEPES , pH 7 . 4 and inactivated by gamma-radiation ( 5 Mrad ) . Protein content of the virus pellet was determined using a BCA protein assay kit ( Pierce , Rockford , IL ) . An equal volume of 0 . 1 M sodium phosphate , pH 8 . 0 was added and protein concentration adjusted to 2 mg/ml by further addition of this buffer . Of this , 0 . 1 mg of total protein was labeled with 0 . 05 mg of Alexa Fluor594 carboxylic acid , succinimidyl ester ( Invitrogen ) . The reaction was allowed to proceed for 2 h at room temperature at which time it was quenched by addition of 0 . 1 volume of 0 . 1 M glycine . The samples were then dialyzed overnight against PBS at 4°C and then again overnight against DMEM . The virus suspension was then aliquoted and stored at −80°C . HEK293-mCAT-1 or Vero-E6 cells were cultivated overnight on chambered coverglass ( Nunc , Rochester , NY ) at a density of 50% . The following day , cells were incubated with fluorescently-labeled ZEBOV for 3 h . For analysis of drug action , cells were pretreated for 1 h prior to virus addition as described above . Cells were then washed three times in DMEM and fixed in 3 . 5% fresh paraformaldehyde in PBS . After one wash in PBS residual paraformaldehyde was neutralized by addition of 0 . 1 M glycine buffer , pH 7 . 4 and cells were permeabilized using 0 . 1% Triton X-100 for 1 min at room temperature . Cells were stained for F-actin using Alexa488-conjugated phalloidin ( Invitrogen ) for 15 min at room temperature . Cells were imaged using a Leica DMIRB inverted microscope with a 100× oil immersion lens or a Zeiss LSM 510 confocal microscope in the UTMB optical imaging core .
|
Each year , filoviruses such as Ebola virus claim many human lives and decimate gorilla populations in Africa . Infection results in an acute fever often associated with profuse internal and external bleeding and death rates of up to 90% . Due to these symptoms and high pathogenicity , these viruses have been heavily publicized in the media . The first step of infection is entry , where the virus is taken up and penetrates into the cell , from which it spreads throughout the body . While it is known that the cell must engulf the virus by the process of endocytosis , we know little about how the virus triggers this event . Here , we use a novel technology to measure penetration of Ebola virus into the cell in real time and show that Ebola virus stimulates phosphoinositide-3 kinase , a signaling molecule known to induce endocytosis . Importantly , drugs that interfere with this signaling inhibit infection by Ebola virus and block virus spread . This work provides a mechanistic insight into how Ebola virus manipulates the cell to start an infection , may explain part of virus induced pathogenesis , and provides a potential way to treat this deadly disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/animal",
"models",
"of",
"infection",
"virology/antivirals,",
"including",
"modes",
"of",
"action",
"and",
"resistance",
"virology",
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"virology/host",
"invasion",
"and",
"cell",
"entry"
] |
2008
|
Phosphoinositide-3 Kinase-Akt Pathway Controls Cellular Entry of Ebola Virus
|
Although plasma leakage is the hallmark of severe dengue infections , the factors that cause increased vascular permeability have not been identified . As platelet activating factor ( PAF ) is associated with an increase in vascular permeability in other diseases , we set out to investigate its role in acute dengue infection . PAF levels were initially assessed in 25 patients with acute dengue infection to determine if they were increased in acute dengue . For investigation of the kinetics of PAF , serial PAF values were assessed in 36 patients . The effect of dengue serum on tight junction protein ZO-1 was determined by using human endothelial cell lines ( HUVECs ) . The effect of dengue serum on and trans-endothelial resistance ( TEER ) was also measured on HUVECs . PAF levels were significantly higher in patients with acute dengue ( n = 25; p = 0 . 001 ) when compared to healthy individuals ( n = 12 ) . In further investigation of the kinetics of PAF in serial blood samples of patients ( n = 36 ) , PAF levels rose just before the onset of the critical phase . PAF levels were significantly higher in patients with evidence of vascular leak throughout the course of the illness when compared to those with milder disease . Serum from patients with dengue significantly down-regulated expression of tight junction protein , ZO-1 ( p = 0 . 004 ) , HUVECs . This was significantly inhibited ( p = 0 . 004 ) by use of a PAF receptor ( PAFR ) blocker . Serum from dengue patients also significantly reduced TEER and this reduction was also significantly ( p = 0 . 02 ) inhibited by prior incubation with the PAFR blocker . Our results suggest the PAF is likely to be playing a significant role in inducing vascular leak in acute dengue infection which offers a potential target for therapeutic intervention .
Dengue is thought to infect 390 million individuals per year resulting in approximately 96 million clinically apparent infections[1] . The annual burden of dengue has been estimated to be 750 , 000 disability adjusted life years ( DALYs ) [2] which is higher than the global burden of 17 other disease conditions , including upper respiratory tract infections , hepatitis and Japanese Encephalitis[3] . It 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 . Dengue infections are caused by four dengue virus ( DENV ) serotypes that are highly homologous [5] . Infection with any one of these serotypes can lead to asymptomatic infection disease or may manifest as undifferentiated viral fever , dengue fever or result in severe dengue infections in the form of dengue haemarrohagic fever ( DHF ) , dengue shock syndrome ( DSS ) or expanded syndrome of dengue infection . Expanded syndrome of dengue infection is characterized by isolated organ involvement such as liver failure , myocarditic or encephalitis [4] . Although the majority of infections are asymptomatic or cause mild clinical disease , DHF and DSS are associated with a high morbidity and often with fatal outcomes . Increased vascular permeability leading to vascular leak is the hallmark of severe dengue infection [6] . Although the exact timing of vascular leak is not fully known , it is thought to occur early during infection and then substantially increase during the critical phase when it can be detected clinically or by laboratory methods [7] . The critical phase of dengue infection is thought to last for 24 to 48 hours following which the leaked fluid is reabsorbed and the patient recovers [4] . Complications as a result of plasma leakage such as shock , pleural effusions , ascites along with other complications such as liver failure and encephalopathy , also occur during the critical phase [4] . Currently the causes of increased vascular permeability are unknown . However , due to the rapid reversibility of increased vascular permeability , endothelial dysfunction rather than necrosis of the endothelium is thought to be the cause of vascular leak [6] . In fact , in postmortem studies , neither dengue NS1 antigen , viral protein or complement components have been detected in the endothelium , suggesting that endothelium dysfunction is likely mediated by host inflammatory mediators [8] . Cytokines and other mediators such as VEGF , TNFα and MCP-1 have been suggested to contribute to endothelial dysfunction and lead to vascular leak in dengue [9–14]; among these , VEGF has been extensively studied[12 , 13] and it has been documented that plasma VEGF levels correlated with vascular leak [13] . Using Human Endothelial cell lines ( HUVECs ) Appana et al . have shown that factors causing vascular leak are likely to be present in serum of dengue patients . In their experiments , sera from patients with acute dengue have shown to reduce expression of gap junction proteins and disrupt morphology of HUVECs [15] . Apart from the above , lipid mediators such as platelet activating factor ( PAF ) , are known to play a role in increasing vascular permeability in disease conditions such as sepsis and anaphylaxis [16–19] . PAF is believed to be essential for the increase in vascular permeability and associated inflammatory changes seen in cerebral malaria [20] . PAFR−/− mice have shown to be less susceptible in developing severe dengue than the wild type mice [21] . In addition , thrombocytopenia and haemoconcentration observed in the wild type mice was significantly reversed by use of a PAFR blocker [21] . Platelets have been shown to be highly activated in dengue and platelet-monocyte aggregates were found to correlate with thrombocytopenia and increased vascular permeability [22] . Activation of both platelets and complement and release of inflammatory mediators is proposed as an alternate mechanism that causes vasculopathy leading to the plasma leakage [23] . The role of lipid mediators such as PAF has not been studied in dengue infection in humans . However , as PAF is involved in vascular leak in other diseases such as sepsis and anaphylaxis and since there is evidence of its potential role in causing vascular leak in mouse models , it would be crucial to evaluate the role of PAF in triggering vascular leak in acute dengue infection . In this study we found that PAF was significantly increased in patients with DHF and that the PAF levels rose just before the onset of the critical phase of dengue , during which vascular leak is thought to occur . PAF in serum of dengue patients altered expression pattern of tight junction protein ZO-1 and decreased the integrity of human endothelial cell monolayer , as measured by trans-endothelial resistance ( TEER ) . Prior use of PAFR blocker significantly reduced these effects , suggesting that PAF plays a significant role in inducing vascular leak in acute dengue infection .
The study was approved by the Ethics Review Committee of the University of Sri Jayawardanapura . All adult patients provided informed written consent . All clinical features , such as presence of fever , abdominal pain , vomiting , bleeding manifestations , hepatomegaly , blood pressure , pulse pressure and evidence of fluid leakage were recorded several times each day . The full blood counts , the alanine transaminase ( ALT ) and aspartate transaminase ( ALT ) levels were assessed during the course of the illness . Clinical disease severity was classified according to the 2011 WHO dengue diagnostic criteria [4] . The classification of whether the patient had DF or DHF was decided by the attending physician at the time of discharge after carefully reviewing the clinical and laboratory features and complications . Accordingly , patients with a rise in haematocrit above ≥ 20% of the baseline haematocrit or clinical or ultrasound scan evidence of plasma leakage in a patient was classified as having DHF . Shock was defined as having cold clammy skin , along with a narrowing of pulse pressure of ≤ 20 mmHg . According to this definition 25 patients were diagnosed to have DHF and 11 DF . Although some patients had very low platelet counts <25 , 000 cells/mm3 , and high liver enzymes , they were classified as having DF since there was no evidence of fluid leakage . Quantitative PAF , PAF-acetyl hydrolase and secretory PAF receptor levels were done in duplicate on all serum samples by quantitative ELISA . Levels of PAF , PAF-acetyl hydrolase ( PAF-AH ) and PAF receptor levels ( PAFR ) were initially done in the 25 patient samples and also in 12 healthy dengue seropositive individuals to determine if PAF , PAF-AH and PAFR were different in patients and healthy individuals before carrying out these assays in serial serum samples . Following the initial assessment of PAF in the 25 patient samples , the PAF levels were done in duplicate in all 36 serial samples . The levels of PAF , PAF-AH and PAFR levels ( Cusabio , China ) were carried out according to manufacturers’ instructions . Acute dengue infection was confirmed in the serum samples using the NS1 early dengue ELISA ( Panbio , Australia ) . All assays were done in duplicate . Dengue was also confirmed in these patients with a commercial capture-IgM and IgG enzyme-linked immunosorbent assay ( ELISA ) ( Panbio , Brisbane , Australia ) . The ELISA was performed and the results were interpreted according to the manufacturers’ instructions . This ELISA assay has been validated as both sensitive and specific for primary and secondary dengue virus infections [24 , 25] . Human umbilical vein endothelial cells ( HUVECs ) ( Lonza , Switzerland ) were maintained in endothelial cell–based medium 2 ( Lonza , Switzerland ) supplemented with 10% fetal calf serum and growth factors ( human epidermal growth factor , hydrocortisone , human recombinant fibroblast growth factor-beta , vascular endothelial growth factor , Insulin-like growth factor , Ascorbic acid , Heparin , FBS , and Gentamicin/Amphotericin-B ) at 37°C at 5% CO2 . Cells were grown in culture flasks or culture slides ( BD , USA ) pre-coated with 0 . 1% gelatin ( Sigma , UK ) . Pre-coating was carried out by incubating flasks with 100 uL/cm2 of 0 . 1% gelatin at 37°C for 2 hours . PAF and PAF receptor antagonist ( 1- ( N , N-Dimethylcarbamoyl ) -4-ethynyl-3- ( 3-fluoro-4- ( ( 1H-2-methylimidazo[4 , 5-c]pyridin-1-yl ) methyl ) benzoyl ) -indole , HCl ( Calbiochem , Germany ) , ( both from Millipore , Germany ) were used for treatments . Both PAF and PAFR blocker were diluted in dH2O according to the manufacture instructions and aliquoted . PAF was stored in - 20°Ca and PAFR antagonist was stored in 4°C until further use . Endothelial cells were seeded into gelatin-coated eight-well culture slides . The following day , serum samples from dengue patients ( diluted in medium at a ratio of 1:3 ) or PAF were added and incubated for 3 hours at 37°C at 5% CO2 . HUVECs were then immunostained for ZO-1 as described below . In experiments with PAF blockage , PAFR blocker was added to the culture medium one hour prior the addition of PAF or dengue serum . HUVEC cells grown in cell chambers ( BD , USA ) were fixed with 2% paraformaldehyde ( Alfa Aesar , UK ) for 10 minutes and permeabilized ( 0 . 1% Triton X-100; Sigma , UK ) for five minutes at room temperature . The cells were then blocked with 2% BSA and 5% FCS for 45 minutes . Purified rabbit monoclonal anti-human ZO-1 antibody ( Life technologies , USA ) ( 1:200 dilution ) and secondary Alexa Fluor 488 mouse anti-rabbit IgG ( heavy and light chains ) ( Invitrogen , USA ) or Alexa Fluor 568 mouse anti-rabbit IgG ( heavy and light chains ) were used for staining; NucBlue Live ReadyProbes ( Molecular probes , USA ) was used to stain nuclei . Cells were then mounted with Mowiol 4–88 fluorescent mounting medium ( Sigma , UK ) , and the data was acquired on a Zeiss LSM 780 Confocal Inverted Microscope; the image analysis performed using Image J software 1 . 47v ( NIH , USA ) . Confocal images were analysed using an automated FIJI macro . Discrete ZO-1 staining was isolated from images by taking raw image and subtracting Gaussian smoothed ( sigma = 15 ) duplicate of the image . To make segmentation easier of the background-subtracted images , a Gaussian kernel ( sigma = 2 . 0 ) was then applied to remove high-frequency noise from the clusters . Images were then threshold and the resulting binary mask skeletonised using the Fiji ‘skeletonize’ function . The binary fragments were then quantified using the ‘Analyse Particles’ plugin and the area summed to give a measure of total tight junction expression per image . To give an expression level per cell , the area sum value per image was divided by the number of cell nuclei present , as indicated by DAPI nuclear staining . All imaging experiments done in triplicate and five image fields per condition were obtained to include in the analysis . 24 well tissue culture plates and cell inserts ( BD , USA ) were coated with 500μl and 200μl 0 . 1% gelatin , respectively , and incubated for 2 hours at 37°C before adding the cells . The gelatin was washed with PBS and cell inserts kept in a in a companion plate ( 353504 , BD , USA ) and EGM-2 media ( Lonza , Switzerland ) . Both to the insert ( 200μl ) and the companion plate ( 700μl ) were washed before addition of cell suspensions at a concentration of 50 , 000 cells/50μl of media and cultured overnight at 37°C in 5% CO2 . On the following day , the inserts were transferred in to another companion plate with 700μl of EGM-2/well and 250μl of EGM2 was added in the insert prior to measuring TEER using Millicell-ERs ( Fisher Scientific , UK ) . The experiments were carried out when the HUVECs formed a confluent monolayer and plateau of electronic resistance was observed on the 3rd day when the resistance reached 450 ohms . 10 different experiments were carried out in triplicate using serum from healthy individuals and also serum from healthy individuals with PAFR blocker ( 1- ( N , N-Dimethylcarbamoyl ) -4-ethynyl-3- ( 3-fluoro-4- ( ( 1H-2-methylimidazo[4 , 5-c]pyridin-1-yl ) methyl ) benzoyl ) -indole , HCl ( Calbiochem , Germany ) . To determine the effect of dengue sera on TEER , 9 separate experiments with dengue serum in the presence and absence of PAFR blocker were carried out in three biological replicates . In all the experiments the PAFR blocker was added 1 hour prior to adding serum of dengue patients and incubated at 37°C . Statistical analysis was performed using Graph Pad PRISM version 6 . As the data were not normally distributed , differences in means were compared using the Mann-Whitney U test ( two tailed ) ; when three or more groups were compared Kruskal Wallis test was used . Receiver-operator characteristic ( ROC ) curves , showing the area under the curve ( AUC ) were generated to determine the discriminatory performance of the highest serum PAF level detected with regard to severity of illness .
As dengue infection is a very dynamic disease , the patients for determining kinetics of PAF were recruited on a mean of 106 . 2 ( SD±19 . 2 ) hours of illness . As we found that PAF levels were significantly higher in patients with acute dengue , we assessed PAF levels in serial blood samples collected from patients throughout the course of the illness . Patients with DHF had significantly higher PAF throughout the course of the illness when compared to those with DF ( Fig . 2 ) . However , there was a wide variation in the PAF levels in patients from both groups . Except for 3 patients with DF the PAF levels of all other patients ( 8/11 ) with DF never rose to >100 ng/ml throughout the course of the illness . Of these 3 patients who had higher values , one patient had platelet counts that dropped to 30 , 000 cells/mm3 and she also complained of vaginal bleeding in the absence of her usual menstrual period . However , she was classified as having DF as she did not have any clinical or laboratory evidence of fluid leakage . The second patient with DF whose PAF levels rose to 293 . 16 ng/ml , also complained of vaginal bleeding in the absence of menstruation . The other DF patient whose PAF levels rose to 123 . 4 ng/ml only had a mild rise in liver enzymes , no evidence of fluid leakage and no bleeding manifestations . 3/25 patients with DHF had values <100 ng/ml . One of these patients presented to hospital on day 6 of illness and had already progressed to the critical phase . The other 2 patients were not in the critical phase on admission and were admitted to hospital on day 4 . Interestingly , a diurnal variation in PAF levels was observed in the majority of patients with DHF but not in those with DF . As PAF has shown to reduce expression of ZO-1 in HUVECs and increase endothelial permeability , we initially assessed if similar observations were found in our model . As expected , the use of PAF on HUVECs significantly reduced ( p = 0 . 007 ) surface expression of ZO-1 , which was dose dependent ( Fig . 3A ) . ZO-1 expression of HUVECs was significantly up regulated in a dose dependant manner with the use of a PAFR blocker ( Fig . 3B ) . The PAFR blocker ( 1- ( N , N-Dimethylcarbamoyl ) -4-ethynyl-3- ( 3-fluoro-4- ( ( 1H-2-methylimidazo[4 , 5-c]pyridin-1-yl ) methyl ) benzoyl ) -indole , HCl ( Calbiochem , Germany ) , potentially inhibits binding of PAF to its receptor in a competitive manner in equilibrium binding studies . A non-competitive inhibition is reported if the membrane bound PAFR are pre-incubated with this blocker before adding PAF , which is thought to be due to a slower antagonist dissociation rate ( Calbiochem , Germany ) . Since the inhibition of PAF was most significant when the PAFR blocker was used at a concentration of 500ng/ml , this concentration was used for other blocking experiments . The above experiments showed that PAF reduces expression of ZO-1 and this effect was significantly inhibited by the use of a PAFR blocker , we then proceeded to determine the effect of serum from dengue patients on expression of ZO-1 . We also found that serum from patients with DHF significantly downregulated ZO-1 expression ( p = 0 . 004 ) ( Fig . 3C and Fig . 4 ) . Furthermore , HUVEC cells incubated with serum from DHF patients showed disrupted morphology , reduced ZO-1 expression and widening of gap junctions ( Fig . 4 ) . However , the down regulation of ZO-1 expression by dengue sera was significantly inhibited ( p = 0 . 004 ) by incubating HUVECs with a PAFR blocker ( Fig . 3C and Fig . 4 ) . The HUVECs incubated with serum from dengue patients showed disrupted morphology , reduced ZO-1 expression and widening of gap junctions ( Fig . 4 ) . The ZO-1 expression was seen to be restored when HUVECs were pre-incubated with a PAFR antagonist ( Fig . 4 ) . As the above experiments showed that both dengue serum and PAF affected on ZO-1 expressionin a dose dependant manner , which was inhibited by the use of a PAFR blocker , and also the effect of dengue serum of ZO-1 expression was inhibited by a PAFR blocker , we next proceeded to determine the effect of dengue sera on trans-endothelial resistance ( TEER ) . The use of serum from dengue patients significantly reduced TEER ( mean −35 . 82 , SD ± 12 . 93 Ώ ) when compared to use of serum from healthy individuals ( mean 1 . 96 , SD ± 1 . 88 Ώ ) . This reduction in TEER by dengue serum was significantly ( p = 0 . 002 ) inhibited by the use of a PAFR blocker prior to incubation of the HUVECs with dengue sera ( Fig . 5 ) .
In this study we have investigated the role of PAF as a mediator of vascular leak in acute dengue infection . We found that PAF levels were significantly elevated in patients with dengue infection , as well as its principle breakdown enzyme PAF-AH , which suggested that the increase in PAF was likely due to increased production rather than reduced breakdown . We also found that PAF levels were significantly higher in patients with DHF throughout the course of the acute disease when compared to those with DF , although huge inter-individual variations in PAF levels were observed . PAF has been shown to be important in vascular leak in dengue mice models and PAFR−/− mice were shown to have milder clinical disease [21] . The role of PAF in human dengue infection has only been investigated in the context of in vitro studies where mononuclear leucocytes of dengue immune donors were found to produce more PAF than non-immune donors [29] . HUVECs models have been widely used to assess increase in vascular permeability by many mediators and drug molecules as well as to determine changes in trans-endothelial electrical resistance ( TEER ) [11 , 15 , 30] . Experiments carried out by Appanan et al . showed that mediators present in the serum of dengue patients reduced expression of tight junction and adherent junction proteins which are likely to result in increased vascular permeability [15] . We found that PAF reduces expression of ZO-1 in a dose dependent manner and this downregulation of ZO-1 was significantly inhibited by the pre-treatment of HUVECs with a PAFR blocker . Our results further show that similar to the findings of Appanna et al [15] , incubation of HUVECs with serum from dengue patients resulted in down regulation of ZO-1 expression but we here show that this was significantly inhibited by pre-treatment with a PAFR blocker . This suggests that PAF present at high concentrations in serum of dengue patients is likely to contribute to vascular leak by reducing expression of tight junction proteins . In addition to our experiments with HUVECs in assessing ZO-1 expression , we also investigated the effect of dengue serum on TEER in HUVECs . We found that serum from dengue patients significantly reduces the TEER in HUVECs when compared to serum from healthy individuals and this reduction of TEER was significantly inhibited if the HUVECs were pre-treated with a PAFR blocker prior to addition of serum from dengue patients . Therefore , these data further confirm that PAF present in serum of dengue patients reduces TEER in the endothelium , as this reduction was significantly ameliorated by a PAFR blocker . However , although the reduction of TEER in HUVECs was significantly inhibited by the use of a PAFR blocker , the TEER still did not return to normal , suggesting that apart from PAF , other mediators in the serum could also contribute to the vascular leak . PAF is a potent inflammatory lipid mediator rapidly produced by many cells , such as endothelial cells , monocytes , mast cells and leucocytes following cellular stress [31] . It is known to cause hypotension , thrombocytopenia , increased vascular permeability and cardiac dysfunction when experimentally administered to animal models [32–34] . In dengue infection , platelets have been shown to be highly activated and platelet-monocyte aggregates have shown to contribute to the increase in vascular permeability [22] . Although there could be multiple pathways leading to activating of platelets , PAF could be further be contributing significantly to platelet activation and thus the immunopathology of the severe forms of the disease . In this study we also found that PAF varied in the same patient in samples collected in the morning and the afternoon . Since we sampled patients only twice a day , it is difficult to comment if the variation in PAF levels was diurnal or whether such variations are observed more frequently . It has been shown that human monocytes produce PAF in a bi-phasic pattern when stimulated with LPS , which was shown to be due to the effects cytokines such as TNFα and IL-1β [31 , 35] . PAF has been shown to activate transcription of NF-κB resulting in expression of many inflammatory cytokines such as TNFα and IL-1β [31 , 35 , 36] . Since LPS was the main stimulus that resulted in bi-phasic production of PAF and other cytokines , it is possible that LPS plays a similar role in acute dengue infection . For instance , it has been shown that patients who develop plasma leakage have significantly higher levels of LPS than those who did not have plasma leakage [37] . Therefore , the possibility of LPS driving the production of PAF and other cytokines should be further investigated . In summary , our results show that PAF levels were significantly higher in more severe forms of dengue and were associated with a reduced expression of tight junction proteins and reduced cell layer integrity that is likely to result in an increased paracellular leak . Use of PAFR blockers significantly reduced these effects; therefore , our results suggest the PAF is likely to be playing a significant role in inducing vascular leak in acute dengue infections; this has implications for the future management of patients such as use of PAFR blocker in acute dengue infection .
|
Although plasma leakage is the hallmark of severe dengue infections , the factors that cause increased vascular permeability have not been identified . As platelet activating factor ( PAF ) is associated with an increase in vascular permeability in other diseases , we set out to investigate its role in acute dengue infection . In this study , we found that PAF was significantly increased in patients with DHF , and the PAF levels rose just before the onset of the critical phase of dengue , during which vascular leak is thought to occur . PAF in serum of dengue patients was associated with reduced expression of tight junction proteins ( ZO-1 ) and reduction in trans-endothelial resistance ( TEER ) of human endothelial cells . Use of PAFR blockers significantly reduced the down regulation of ZO-1 by serum of dengue patients and also the reduction of TEER , suggesting that PAF plays a significant role in inducing vascular leak in acute dengue infections .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Platelet Activating Factor Contributes to Vascular Leak in Acute Dengue Infection
|
Over time , a population acquires neutral genetic substitutions as a consequence of random drift . A famous result in population genetics asserts that the rate , K , at which these substitutions accumulate in the population coincides with the mutation rate , u , at which they arise in individuals: K = u . This identity enables genetic sequence data to be used as a “molecular clock” to estimate the timing of evolutionary events . While the molecular clock is known to be perturbed by selection , it is thought that K = u holds very generally for neutral evolution . Here we show that asymmetric spatial population structure can alter the molecular clock rate for neutral mutations , leading to either K<u or K>u . Our results apply to a general class of haploid , asexually reproducing , spatially structured populations . Deviations from K = u occur because mutations arise unequally at different sites and have different probabilities of fixation depending on where they arise . If birth rates are uniform across sites , then K ≤ u . In general , K can take any value between 0 and Nu . Our model can be applied to a variety of population structures . In one example , we investigate the accumulation of genetic mutations in the small intestine . In another application , we analyze over 900 Twitter networks to study the effect of network topology on the fixation of neutral innovations in social evolution .
A half-century ago , Zuckerkandl and Pauling [1] discovered that amino acid substitutions often occur with sufficient regularity as to constitute a “molecular clock” . Theoretical support for this observation was provided by Kimura [2] , who argued that observed rates of amino acid substitution could only be explained if the majority of substitutions are selectively neutral . Under simple models of evolution , a single neutral mutation has probability 1/N of becoming fixed in a haploid population of size N . It follows that the rate K of neutral substitution per generation—given by the product of the population size N , the mutation probability u per reproduction , and the fixation probability ρ—is simply equal to u . ( A similar cancellation occurs in diploids , leading again to K = u . ) In other words , for any neutral genetic marker , the rate of substitution at the population level equals the rate of mutation at the individual level [2] . A number of factors can alter the rate of neutral substitution [3 , 4] , including selection , changes in population demography over time , or mutation rates that vary systematically by demographic classes [5 , 6] or sex [7] . The extent to which these factors compromise the applicability of the molecular clock hypothesis to biological sequence data has been intensely debated [3 , 4 , 8–14] . However , it is generally thought that spatial structure alone ( without spatial variation in the mutation rate [7] ) cannot alter the rate of neutral substitution . This consensus is based on analyses [15–31] of a variety of models of spatially structured populations . Each of these analyses found that the fixation probability of a neutral mutation is 1/N , and thus the rate of neutral substitution K = Nuρ again simplifies to u . Here we show that the absence of spatial effects on the rate of neutral substitution in these models does not represent a general principle of evolution . Rather , it is an artifact of two common modeling assumptions: ( i ) all spatial locations are equivalent under symmetry , and ( ii ) mutations are equally likely to arise in each location . While assumption ( i ) is relaxed in a number of models , assumption ( ii ) is made almost universally . Either of these assumptions alone is sufficient to guarantee ρ = 1/N and K = u , as we will show . However , neither of these assumptions is necessarily satisfied in a biological population . In particular , assumption ( ii ) is violated if some spatial locations experience more turnover ( births and deaths ) than others . Since each birth provides an independent opportunity for mutation , the rate at which new mutations appear at a location is proportional to its turnover rate [32] . Thus , even with a constant probability of mutation per birth , mutations may appear with different frequency at different locations . If , in addition , fixation probability depends on a mutant’s initial location , the rate of neutral substitution is altered . Our goal is to identify conditions under which the molecular clock rate is maintained ( K = u ) , accelerated ( K > u ) , or slowed ( K < u ) by spatial population structure . Our main results are as follows ( see also Fig . 1 ) : 10 . 1371/journal . pcbi . 1004108 . g001 Fig 1 How spatial structure affects the molecular clock rate K . Relative to the rate in a well-mixed population ( K = u ) , spatial structure can either accelerate ( K > u ) or slow ( K < u ) the accumulation of neutral substitutions , depending on how birth rates bi and death rates di vary across sites . The rate is unchanged from that of a well-mixed population ( K = u ) if either death rates are uniform across sites ( Result 1 ) , or the birth and death rates are equal at each site ( Result 2 ) . Almost all previous studies of neutral drift in spatially structured populations fall into one of these two categories; thus the effects of spatial structure on the molecular clock rate are unappreciated . We show that , in general , K can take any non-negative value less than Nu ( Result 4 ) . If one adds the constraint that the birth rate is the same at each site , then the molecular clock rate cannot exceed that of a well-mixed population ( K ≤ u; Result 3 ) . Result 1 If the death rate is constant over sites , the molecular clock rate is identical to that of a well-mixed population: K = u . Result 2 If the birth rate equals the death rate at each site , the molecular clock rate is identical to that of a well-mixed population: K = u . Result 3 If the birth rate is constant over sites , the molecular clock rate is less than or equal to that of a well-mixed population: K ≤ u . Result 4 In general ( with no constraints on birth or death rates ) , the molecular clock rate K can take any non-negative value less than Nu .
Our investigations apply to a class of evolutionary models ( formally described in the Methods ) in which reproduction is asexual and the population size and spatial structure are fixed . Specifically , there are a fixed number of sites , indexed i = 1 , … , N . Each site is always occupied by a single individual . At each time-step , a replacement event occurs , meaning that the occupants of some sites are replaced by the offspring of others . Replacement events are chosen according to a fixed probability distribution—called the replacement rule—specific to the model in question . Since we consider only neutral mutations that have no phenotypic effect , the probabilities of replacement events do not depend on the current population state . This class includes many established evolutionary models . One important subclass is spatial Moran processes [23 , 33–35] , in which exactly one reproduction occurs each time-step . This class also includes spatial Wright-Fisher processes , in which the entire population is replaced each time-step [36 , 37] . In general , any subset R ⊂ {1 , … , N} of individuals may be replaced in a given replacement event . Parentage in a replacement event is recorded in an offspring-to-parent map α:R → {1 , … , N} ( see Methods , [32 , 38] ) , which ensures that each offspring has exactly one parent and allows us to trace lineages over time . For a given model in this class , we let eij denote the ( marginal ) probability that the occupant of site j is replaced by the offspring of site i in a single time-step . Thus the expected number of offspring of site i over a single time-step is b i = ∑ j = 1 N e i j . The probability that node i dies ( i . e . , is replaced ) in a time-step is d i = ∑ j = 1 N e j i . The death rate di can also be regarded as the rate of turnover at site i . The total expected number of offspring per time-step is denoted B = ∑ i = 1 N b i = ∑ i = 1 N d i = ∑ i , j e i j . We define a generation to be N/B time-steps , so that , on average , each site is replaced once per generation . We use this framework to study the fate of a single neutral mutation , as it arises and either disappears or becomes fixed . The probability of fixation depends on the spatial structure and the initial mutant’s location . We let ρi denote the probability that a new mutation arising at site i becomes fixed . ( ρi can also be understood as the reproductive value of site i [39] . ) We show in the Methods that the fixation probabilities ρi are the unique solution to the system of equations d i ρ i = ∑ j = 1 N e i j ρ j for i = 1 , … , N , ( 1 ) ∑ i = 1 N ρ i = 1 . ( 2 ) Equation ( 2 ) arises because ρi equals the probability that the current occupant of site i will become the eventual ancestor of the population , which is true for exactly one of the N sites . To determine the overall rate of substitution , we must take into account the likelihood of mutations arising at each site . The rate at which mutations arise at site i is proportional to the turnover rate di , because each new offspring provides an independent chance of mutation . Specifically , if mutation occurs at rate u ≪ 1 per reproduction , then new mutations arise at site i at rate Nudi/B per generation [32] . Thus the fraction of mutations that arise at site i is di/B . The overall fixation probability ρ of new mutations , taking into account all possible initial sites , is therefore ρ = 1 B ∑ i = 1 N d i ρ i . ( 3 ) The molecular clock rate K is obtained by multiplying the fixation probability ρ by the total rate of mutation per generation: K = N u ρ = N u B ∑ i = 1 N d i ρ i . ( 4 ) The units of K are substitutions per generation . Alternatively , the molecular clock can be expressed in units of substitutions per time-step , in which case the formula is K ˜ = B u ρ = u ∑ i = 1 N d i ρ i . How does spatial structure affect the rate of neutral substitution ? In a well-mixed population , each individual’s offspring is equally likely to replace each other individual , meaning that eij is constant over all i and j ( Fig . 2a ) . In this case , the unique solution to Eqs . ( 1 ) – ( 2 ) is ρi = 1/N for all i , and we recover Kimura’s [2] result K = Nu ( 1/N ) = u . Moreover , if each site is equivalent under symmetry , as in Fig . 2b , this symmetry implies that ρi = 1/N for all i and K = u as in the well-mixed case . However , asymmetric spatial structure can lead to faster ( K > u ) or slower ( K < u ) molecular clock rates than a well-mixed population , as shown in Fig . 3 . From Eqs . ( 2 ) and ( 4 ) we can see that K > u is equivalent to the condition d ρ ¯ > d ‾ ρ ‾ , where the bars indicate averages over i = 1 , … , N . This means that the molecular clock is accelerated if and only if di and ρi are positively correlated over sites; that is , if and only if there is a positive spatial correlation between the arrival of new mutations and the success they enjoy . These results led us to seek general conditions on the spatial structure leading to faster , slower , or the same molecular clock rates as a well-mixed population . We first find Result 1 . If the death rates di are constant over all sites i = 1 , … , N , then ρ = 1/N , and consequently K = u . Thus the molecular clock rate is unaffected by spatial structure if each site is replaced at the same rate ( Fig . 4a ) . This result can be seen by noting that if the di are constant over i , then since ∑ i = 1 N d i = B , it follows that di = B/N for each i . Substituting in Eq . ( 3 ) yields ρ = 1/N . Another condition leading to K = u is the following: Result 2 . If the birth rate equals the death rate at each site ( bi = di for all i = 1 , … , N ) , then ρ = 1/N , and consequently K = u . Moreover , bi = di for all i = 1 , … , N if and only if the fixation probability is the same from each site ( ρi = 1/N for all i = 1 , … , N ) . Thus if births and deaths are balanced at each site , then all sites provide an equal chance for mutant fixation ( Fig . 4b ) . In this case the molecular clock is again unchanged from the baseline value . In particular , if dispersal is symmetric in the sense that eij = eji for all i and j then K = u . Result 2 can be obtained by substituting ρi = 1/N for all i into Eq . ( 1 ) and simplifying to obtain bi = di for all i ( details in Methods ) . Alternatively , Result 2 can be obtained as a corollary to the Circulation Theorem of Lieberman et al . [23] . Our third result reveals a “speed limit” to neutral evolution in the case of constant birth rates: Result 3 . If the birth rates bi are constant over all sites i = 1 , … , N , then ρ ≤ 1/N , and consequently K ≤ u , with equality if and only if the death rates di are also constant over sites . In other words , a combination of uniform birth rates and nonuniform death rates slows down the molecular clock . An instance of this slowdown in shown in Fig . 3a . Intuitively , the sites at which mutations occur most frequently are those with high death rates di; because of these high death rates , these sites on the whole provide a reduced chance of fixation . The proof of this result , however , is considerably more intricate than this intuition would suggest ( see Methods ) . Finally , we investigate the full range of possible values for K with no constraints on birth and death rates . We find the following: Result 4 . For arbitrary spatial population structure ( no constraints on eij ) the fixation probability can take any value 0 ≤ ρ < 1 , and consequently , the molecular clock can take any rate 0 ≤ K < Nu . This result is especially surprising , in that it implies that the probability of fixation of a new mutation can come arbitrarily close to unity . Result 4 can be proven by considering the hypothetical spatial structure illustrated in Fig . 5 . Any non-negative value of ρ less than 1 can be obtained by an appropriate choice of parameters ( details in Methods ) . To illustrate the effects of asymmetric dispersal on the molecular clock , we consider a hypothetical population with two subpopulations , labeled “upstream” and “downstream” ( Fig . 6 ) . The sizes of these subpopulations are labeled N↑ and N↓ , respectively . Each subpopulation is well-mixed , with replacement probabilities e↑ for each pair of upstream sites and e↓ for each pair of downstream sites . Dispersal between the subpopulations is represented by the replacement probabilities e→ from each upstream site to each downstream site , and e← from each downstream site to each upstream site . We assume there is net gene flow downstream , so that e→ > e← . Solving Eqs . ( 1 ) – ( 2 ) , we find that the fixation probabilities from each upstream site and each downstream site , respectively , are ρ ↑ = e → N ↑ e → + N ↓ e ← , ρ ↓ = e ← N ↑ e → + N ↓ e ← . ( 5 ) These fixation probabilities were previously discovered for a different model of a subdivided population [40] . Substituting these fixation probabilities into Eq . ( 4 ) yields the molecular clock rate: K = N B N ↑ d ↑ e → + N ↓ d ↓ e ← N ↑ e → + N ↓ e ← u . ( 6 ) Above , d↑ and d↓ are the turnover rates in the upstream and downstream populations , respectively , and B = N↑ d↑+N↓ d↓ is the total birth rate per time-step . In Methods , we show that K > u if and only if d↑ > d↓; that is , the molecular clock is accelerated if and only if there is more turnover in the upstream population than in the downstream population . In the case of unidirectional gene flow , e← = 0 , the molecular clock rate is simply K = ( d↑/B ) Nu . The quantity d↑/B represents the relative rate of turnover in the upstream population , and can take any value in the range 0 ≤ d↑/B < 1/N↑; thus K takes values in the range 0 ≤ K < ( N/N↑ ) u . We note that the upper bound on K is inversely proportional to the size N↑ of the upstream population . The largest possible values of K are achieved when N↑ = 1 , in which case K can come arbitrarily close to Nu . These bounds also hold if there are multiple downstream subpopulations , since for unidirectional gene flow , the spatial arrangement of downstream sites does not affect the molecular clock rate . In particular , if the hub and leaves in Fig . 5 are each replaced by well-mixed subpopulations , then K is bounded above by ( N/NH ) u , where NH is the size of the hub subpopulation . Our results are also applicable to somatic evolution in self-renewing cell populations , such as the crypt-like structures of the intestine . Novel labeling techniques have revealed that neutral mutations accumulate in intestinal crypts at a constant rate over time [41] . The cell population in each crypt is maintained by a small number of stem cells that reside at the crypt bottom and continuously replace each other in stochastic manner ( Fig . 7; [42–44] ) . We focus on the proximal small intestine in mice , for which recent studies [41 , 45] suggest there are ∼ 5 active stem cells per crypt , each replaced ∼ 0 . 1 times per day by one of its two neighbors . In our framework , this corresponds to a cycle-structured population of size 5 with replacement rates 0 . 05/day between neighbors , so that di = 0 . 1/day for all i . Only mutations that arise in stem cells can become fixed within a crypt; thus we need only consider the fixation probabilities and turnover rates among stem cells . By symmetry among the stem cells , ρi = 1/5 for each of the five stem cell sites . The molecular clock rate is therefore K ˜ = u ∑ i = 1 5 d i ρ i = 0 . 1 u substitutions per day . This accords with the empirical finding that , for a neutral genetic marker with mutation rate u ≈ 1 . 1×10−4 , substitutions accumulate at a rate K ˜ ≈ 1 . 1 × 10 − 5 per crypt per day [41] . Does crypt architecture limit the rate of genetic change in intestinal tissue ? Intestinal crypts in mice contain ∼ 250 cells and replace all their cells about once per day [46] . If each crypt were a well-mixed population , the molecular clock rate would be K ˜ = B u / N ≈ u substitutions per day . Thus the asymmetric structure of these epithelial crypts slows the rate of neutral genetic substitution tenfold . Our results can also be applied to ideas that spread by imitation on social networks . In this setting , a mutation corresponds to a new idea that could potentially replace an established one . Neutrality means that all ideas are equally likely to be imitated . To investigate whether human social networks accelerate or slow the rate of idea substitution , we analyzed 973 Twitter networks from the Stanford Large Network Dataset Collection [47] . Each of these “ego networks” represents follower relationships among those followed by a single “ego” individual ( who is not herself included in the network ) . We oriented the links in each network to point from followee to follower , corresponding to the presumed direction of information flow . Self-loops were removed . To ensure that fixation is possible , we eliminated individuals that could not be reached , via outgoing links , from the node with greatest eigenvector centrality . The resulting networks varied in size from 3 to 241 nodes . To model the spread of ideas on these networks , we set eij = 1/L if j follows i and zero otherwise , where L is the total number of links . This can be instantiated by supposing that at each time-step , one followee-follower link is chosen with uniform probability . The follower either adopts the idea of the followee , with probability 1−u , or innovates upon it to create a new idea , with probability u , where u ≪ 1 . With these assumptions , the resulting rate of idea substitution ( as a multiple of u ) depends only on the network topology and not on any other parameters . We found that the mean value of K among these ego networks is 0 . 557u , with a standard deviation of 0 . 222u . 19 of the 973 networks ( 2% ) have K > u . Two networks have K = u exactly; each of these has N = 3 nodes and uniform in-degree di , thus K = u follows from Result 1 for these networks . We found a weak but statistically significant negative relationship between the network size N and value K/u ( slope ≈ −0 . 00164 with 95% confidence interval ( −0 . 0023 , −0 . 001 ) based on the bootstrap method; R ≈ −0 . 45 ) . This negative relationship persists even if small networks with less than 10 nodes are removed ( slope ≈ −0 . 00156 with 95% confidence interval ( −0 . 0023 , −0 . 0009 ) ; R ≈ −0 . 43 ) . In summary , while some Twitter ego-networks accelerate the substitution of neutral innovations , the vast majority slow this rate ( Figs . 8 and 9 ) . One possible explanation for the rarity of networks that accelerate idea substitution has to do with the intrinsic relationship between the turnover rates di and the site-specific fixation probabilities ρi . From Eq . ( 1 ) , we see that ρi can be written as the product ( 1 / d i ) × ( ∑ j = 1 N e i j ρ j ) , where the first factor can be interpreted as the “attention span” of node i and the second can be interpreted as its influence . While these two factors are not strictly independent , we would not necessarily expect a systematic relationship between them in our Twitter network ensemble . In the absence of such a relationship , ρi is inversely related to di , which implies K < u . In other words , the most fertile nodes ( in terms of generating new ideas ) are also the most fickle ( in terms of adopting the ideas of others ) ; thus many new ideas are abandoned as soon as they are generated . This heuristic argument suggests that K > u , while possible , might be an uncommon occurrence in networks drawn from statistical or probabilistic ensembles .
The spatial structure of a population affects its evolution in many ways , for example by promoting cooperative behaviors [36 , 48–54] , genetic variation [55–58] , and speciation [59–61] . Asymmetric spatial structure in particular is known to have important consequences for adaptation [23 , 27 , 28 , 35 , 62–65] and for genetic diversity [66–68] . Our work shows that asymmetric spatial structure also affects the rate of neutral substitution . In light of Results 1 and 2 , we see that the critical factors driving the changes in molecular clock rate are differential probabilities of turnover ( di ) and net offspring production ( bi−di ) across sites . If both di and bi−di differ across sites , the molecular clock rate will in general differ from that of a well-mixed population ( Result 4 ) . If additionally bi is constant across sites , Result 3 guarantees that neutral substitution is slowed relative to the well-mixed case . Our Result 4 shows that the rate of neutral substitution in a population can come arbitrarily close to Nu . However , this result depends on the existence of a single “hub” individual seeding the rest of the population as in Fig . 5 . A similar but more plausible scenario ( especially in sexually reproducing populations ) involves a well-mixed hub subpopulation seeding one or more “leaf” subpopulations . In this case , the upper bound on the molecular clock rate is ( N/NH ) u , where NH is the size of the hub subpopulation . Conditions leading to altered molecular clock rate may occur frequently in natural populations . Asymmetric dispersal may result , for example , from prevailing winds [69 , 70] , water currents [71 , 72] , or differences in elevation [73 , 74] . Differences in habitat quality may lead to variance in birth and death rates across sites ( nonuniform bi and di ) . It is known that such asymmetries in spatial structure have important consequences for adaptation [23 , 62–64] and for genetic diversity [66–68]; our work shows that they also have consequences for the rate of neutral genetic change . In particular , the molecular clock is accelerated if there is greater turnover in “upstream” subpopulations . One important assumption made in our work is that mutation occurs with a constant probability per reproduction . Alternatively , one might suppose that heritable mutations accrue at a constant rate per individual ( e . g . due to germline cell divisions ) . With this alternate assumption , mutations would arise at uniform rates over sites , resulting in a molecular clock rate of K = u for all spatial structures . The applicability of our results thus depends on the mutation process of the population in question , which may in many cases lie between these extremes . In humans , for example , recent evidence suggests that maternal mutations occur with constant probability per reproduction , whereas paternal mutations ( which are more frequent ) increase in probability with the father’s age [75–77] . In general , we expect deviations from K = u to scale with the extent to which mutations in a lineage depend on the number of generations rather than chronological time . Many somatic cell populations have strongly asymmetric patterns of replacement , with a small number of stem cell pools feeding a much larger number of progenitor and differentiated cells . The rate at which mutations accumulate in these populations has significant implications for the onset of cancer [78–80] and the likelihood of successful cancer therapy [81–84] . It is well-known that this rate is proportional to the rate of stem cell division , since only mutations that arise in stem cells can persist [45 , 85–88] . Our work shows this how principle arises from a general analysis of neutral evolution in spatially structured populations . It is important to note , however , that cancer can alter the structure of cell hierarchies; for example , by altering the number and replacement rate of stem cells [41] and/or allowing differentiated cells to revert to stem cells [89] . This restructuring may , in turn , alter the rate of genetic substitution , with further ramifications for cancer progression . The influence of social network topology on the spread of ideas and behaviors is a question of both theoretical and practical interest [90–97] . The neutral substitution rate K on social networks describes how innovations spread when they are equally likely to be imitated as an existing convention . Our finding that most Twitter ego networks have rates less than those of well-mixed populations contrasts with results from epidemiological models , which generally find that the heterogeneity of real-world social networks accelerates contagion [90 , 98 , 99] . We note , however , that this finding is sensitive to the assumption that individuals generate new ideas in proportion to the rate of incoming ideas ( which itself is proportional to the number of individuals followed ) . Since the success of an idea varies according to the node at which it arises , the overall rate of substitution depends on the distribution of new ideas among nodes . For example , if one were to instead assume that each individual generates new ideas at an equal rate , one would find that the network topology has no effect on the rate of substitution . If spatial structure remains constant over time , then the neutral substitution rate K is in all cases a constant multiple of the mutation rate u . In this case , absent other complicating factors such as selection , neutral mutations will accrue at a constant rate that can be inferred from genetic data . However , if the spatial structure changes over time—due , for instance , to changes in climate , tumorogenesis , or social network dynamics [100–103]—the rate of neutral substitutions may change over time as well . In our framework , the molecular clock rate is assumed to depend only on the rate at which mutations arise and their probability of becoming fixed . This approach assumes that the time to fixation is typically shorter than the expected waiting time 1/ ( Nuρ ) for the next successful mutation . If this is not the case , then substitution rates are also affected by fixation times . These fixation times are themselves affected by spatial structure [22 , 30 , 58 , 104] , leading to further ramifications for the molecular clock rate [105] . The starting point of our analysis is that the convention , commonly assumed in evolutionary models , that mutations arise with equal frequency at each site , is not necessarily the most natural choice . If there is a constant probability of mutation per birth , then mutations instead arise in proportion to the rate of turnover at a site . Here we have applied this principle to study the rate of neutral substitution . However , this principle also holds for advantageous and deleterious mutations , as well as those whose effect varies with location . It also applies to frequency-dependent selection [65 , 106] . Re-analyzing existing models using this new mutation convention may reveal further surprises about how spatial structure affects evolution .
In the class of models we consider , there are N sites indexed i = 1 , … , N , each always occupied by a single individual . At each time-step , a replacement event occurs , in which the occupants of some positions are replaced by the offspring of others . A replacement event is identified by a pair ( R , α ) , where R ⊂ {1 , … , N} is the set of sites whose occupants are replaced by new offspring , and α:R → {1 , … , N} is a set mapping indicating the parent of each new offspring . ( This notation was introduced in Ref . [32] . ) A sample replacement event is illustrated in Fig . 10 . A model of neutral evolution is specified by a probability distribution over the set of possible replacement events . We call this probability distribution the replacement rule of the model . The probability of a replacement event ( R , α ) in this distribution will be denoted p ( R , α ) . Neutrality is represented by independence of the probabilities p ( R , α ) from the state of the evolutionary process . The only assumption we place on the replacement rule is that it should be possible for at least one site i to contain the eventual ancestor of the population: Assumption 1 . There is an i ∈ {1 , … , N} , a positive integer n and a finite sequence { ( R k , α k ) } k = 1 n of replacement events such that p ( Rk , αk ) > 0 for all k , and For all individuals j ∈ {1 , … , N} , α k 1 ∘ α k 2 ∘ ⋯ ∘ α k m ( j ) = i , ( 7 ) where k1 , … < km is the maximal subsequence of 1 , … , n such that the compositions in Eq . ( 7 ) are well-defined . We observe that Eq . ( 7 ) traces the ancestors of j backwards in time to i . This assumption is equivalent to saying that there is at least one site i such that mutations arising at site i have nonzero fixation probability . It is also equivalent to the statement that the weighted digraph with edge weights eij is out-connected from at least one vertex . Assumption 1 precludes degenerate cases such as two completely separate subpopulations with no gene flow between them , in which case fixation would be impossible . For a specific replacement event ( R , α ) , the sites that are replaced by the offspring of i is the given by the preimage α−1 ( i ) ⊂ R , i . e . , the set of indices that map to i under α . The number of offspring of site i is equal to ∣α−1 ( i ) ∣ , the cardinality ( size ) of this preimage . Taking all possible replacement events into account , the birth rate ( expected offspring number ) of site i is given by b i = E | α − 1 ( i ) | = ∑ ( R , α ) p ( R , α ) | α − 1 ( i ) | . The death rate ( probability of replacement ) of site i is equal to d i = Pr [ i ∈ R ] = ∑ ( R , α ) i ∈ R p ( R , α ) . The probability that the offspring of i displaces the occupant of j in a replacement event is e i j = Pr [ j ∈ R and α ( j ) = i ] = ∑ ( R , α ) j ∈ R α ( j ) = i p ( R , α ) . We observe that b i = ∑ j = 1 N e i j and d i = ∑ j = 1 N e j i . To study the fixation of new mutations , we consider evolution with two genetic types: mutant ( M ) and resident ( R ) . The type occupying site i in a given state of the evolutionary process is denoted si ∈ {M , R} . The overall state of the process can be recorded as a string s = ( s1 , … , sN ) of length N with alphabet {M , R} . We assume that there is no further mutation after an initial mutant appears; thus offspring faithfully inherit the type of the parent . It follows that if the current state is s = ( s1 , … , sN ) and the replacement event ( R , α ) occurs , then the new state s ′ = ( s 1 ′ , … , s N ′ ) is given by s i ' = s i i ∉ R s α ( i ) i ∈ R . The above assumptions describe a Markov chain on the set of strings of length N with alphabet {M , R} . We call this the evolutionary Markov chain . It is straightforward to show that , from any initial state , this Markov chain will eventually converge upon one of two absorbing states: ( M , … , M ) or ( R , … , R ) [32] . In the former case , we say that the mutant type has gone to fixation; in the latter case we say that the mutant type has disappeared . It is useful to consider a variation on the evolutionary Markov chain called the ancestral Markov chain , denoted 𝓐 . Instead of two types , the ancestral Markov chain has N types , labeled 1 , … , N , which correspond to the N members of a “founding generation” of the population . Evolution proceeds according to the given replacement rule , as described above . The states of the ancestral Markov chain are strings of length N with alphabet {1 , … , N} . The ancestral Markov chain has a canonical initial state a0 = ( 1 , … , N ) , in which the type of each individual corresponds to its location . This initial state identifies the locations of each founding ( t = 0 ) member of the population . The ancestral Markov chain with initial state a0—in our notation , ( 𝓐 , a0 ) —has the useful feature that at any time t ≥ 0 , the state a ( t ) = ( a1 ( t ) , … , aN ( t ) ) indicates the site occupied by each individual’s founding ancestor . In other words , if aj ( t ) = i , then the current occupant of site j is descended from the founder that occupied site i . To relate the evolutionary Markov chains 𝓐 and 𝓜 , consider any set mapping γ:{1 , … , N} → {M , R} . We think of γ ( i ) as giving the genetic type ( M or R ) of each member i = 1 , … , N of the founding generation . The mapping γ induces a mapping γ ˜ from states of 𝓐 to states of 𝓜 , defined by γ ˜ ( a 1 , … , a N ) = ( γ ( a 1 ) , … , γ ( a N ) ) . For any state a ( t ) of ( 𝓐 , a0 ) , the string γ ˜ ( a ( t ) ) ∈ { 1 , … , N } N indicates the genetic type of each individual’s ancestor in the founding generation . Since genetic types are inherited faithfully , it follows that γ ˜ ( a ( t ) ) gives the current genetic type of each individual . Thus if 𝓜 and 𝓐 follow the same replacement rule , we have that for any such mapping γ and any string s ∈ {M , R}N , P ( 𝓐 , a 0 ) [ γ ˜ ( a ( t ) ) = s ] = P ( 𝓜 , γ ˜ ( a 0 ) ) [ s ( t ) = s ] . ( 8 ) We define the fixation probability from site i , ρi , to be the probability that , from an initial state a mutant in site i and residents in all other sites , the mutant type goes to fixation: ρ i = lim t → ∞ P ( 𝓜 , m i ) [ s ( t ) = ( M , … , M ) ] . ( 9 ) Above , mi denotes the initial state consisting of an M in position i and R’s elsewhere . The ordered pair ( 𝓜 , mi ) refers to the Markov chain 𝓜 with initial state mi . P ( 𝓜 , mi ) [s ( t ) = s] denotes the probability that the state of ( 𝓜 , mi ) is s at time t ≥ 0 . We can use the relationship between the ancestral Markov chain 𝓐 and the evolutionary Markov chain 𝓜 to obtain an alternate expression for the site-specific fixation probability ρi: Theorem 1 . ρi = limt → ∞ P ( 𝓐 , a0 ) [a ( t ) = ( i , … , i ) ] . In words , the site-specific fixation probability ρi equals the probability that founding individual i becomes the eventual ancestor of the whole population . Proof . For any i = 1 , … , N , define the set mapping γi:{1 , … , N} → {M , R} by γ i ( j ) = M if j = i R otherwise . ( Intuitively , this mapping describes the case that individual i of the founding generation is a mutant , and all others in the founding generation are residents . ) Note that γ ˜ i ( a 0 ) = m i . Combining Eq . ( 8 ) with the definition of ρi , we obtain ρ i = lim t → ∞ P ( 𝓜 , m i ) [ s ( t ) = ( M , … , M ) ] = lim t → ∞ P ( 𝓐 , a 0 ) [ γ i ˜ ( a ( t ) ) = ( M , … , M ) ] = lim t → ∞ P ( 𝓐 , a 0 ) [ a ( t ) = ( i , … , i ) ] . More generally , we can consider the probability of fixation from an arbitrary set of sites . For any set S ⊂ {1 , … , N} , we let ρS denote the probability that the mutant type becomes fixed , given the initial state mS with mutants occupying the sites specified by S and residents occupying all other sites: ρ S = lim t → ∞ P ( 𝓜 , m S ) [ s ( t ) = ( M , … , M ) ] . Site-specific fixation probabilities are additive in the following sense: Theorem 2 . For any set S ⊂ {1 , …N} of sites , ρ S = ∑ i ∈ S ρ i . ( 10 ) In particular , ∑ i = 1 N ρ i = 1 . ( 11 ) This result has previously been obtained for a number of specific evolutionary processes on graphs [39 , 107 , 108] . Intuitively , the probability of fixation from the initial state described by S equals the probability that one of the individuals in a site i ∈ S becomes the eventual ancestor of the population . Since this cannot be true of more than one site in S , the overall probability ρS is obtained by summing over all i ∈ S the individual probabilities ρi that site i contains the eventual ancestor . Proof . Suppose that we are given a set S ⊂ {1 , … , N} of sites initially occupied by mutants . This situation is described by the set mapping γS:{1 , … , N} → {M , R} , given by γ S ( i ) = M if i ∈ S R otherwise . Invoking the relationship between 𝓐 and 𝓜—in particular , Eq . ( 8 ) and Theorem 1—we have ρ S = lim t → ∞ P ( 𝓜 , γ S ˜ ( a 0 ) ) [ s ( t ) = ( M , … , M ) ] = lim t → ∞ P ( 𝓐 , a 0 ) [ γ S ˜ ( a ( t ) ) = ( M , … , M ) ] = lim t → ∞ P ( 𝓐 , a 0 ) [ a ( t ) = ( i , … , i ) for some i ∈ S ] = ∑ i ∈ S lim t → ∞ P ( 𝓐 , a 0 ) [ a ( t ) = ( i , … , i ) ] = ∑ i ∈ S ρ i . This proves Eq . ( 10 ) . Eq . ( 2 ) now follows from letting S = {1 , … , N} , and noting that , in this case , ρ S = lim t → ∞ P ( 𝓜 , ( M , … , M ) ) [ s ( t ) = ( M , … , M ) ] = 1 . We now derive Eq . ( 1 ) , which allows the fixation probabilities ρi to be calculated from the replacement rates eij . Theorem 3 . For each i = 1 , … , N , d i ρ i = ∑ j = 1 N e i j ρ j . Proof . Considering the change that can occur over a single time-step , we have the following recurrence relation: ρ i = ( 1 − d i ) ρ i + ∑ ( R , α ) p ( R , α ) ρ α − 1 ( i ) . The first term above represents the case that the occupant of i survives the current time-step and becomes the eventual ancestor of the population , while the second term represents the case that one of i’s offspring from the current time-step is the eventual ancestor . Subtracting ( 1−di ) ρi from both sides and applying Theorem 2 with S = ρα−1 ( i ) yields d i ρ i = ∑ ( R , α ) p ( R , α ) ∑ j ∈ α − 1 ( i ) ρ j . Now interchanging the summations on the right-hand side yields d i ρ i = ∑ j = 1 N ρ j ∑ ( R , α ) j ∈ R α ( j ) = i p ( R , α ) . By definition , ∑ ( R , α ) j ∈ R α ( j ) = i p ( R , α ) = e i j . This completes the proof . Theorem 4 ( Result 2 ) . The fixation probabilities from each site are equal to 1/N ( ρi = 1/N for all i = 1 , … , N ) if and only if each site has birth rate equal to death rate ( bi = di for all i = 1 , … , N ) . Proof . Assume first that the fixation probabilities from each site are all equal to 1/N . Substituting ρi = 1/N for all i into Eq . ( 1 ) yields di = bi . This proves the “only if” direction . Next assume that the birth rate is equal to the death rate at each site ( bi = di for all i = 1 , … , N ) . Eq . ( 1 ) can then be rewritten as ∑ j = 1 N e i j ρ i = ∑ j = 1 N e i j ρ j . Clearly , ρi = 1/N for all i satisfies the above equation for all i , and also satisfies ∑ i = 1 N ρ i = 1 . Assumption 1 guarantees that the solution to these equations is unique . Therefore bi = di for all i implies ρi = 1/N for all i , proving the “if” direction . Theorem 5 ( Result 3 ) . If birth rates are constant over sites , bi = 1/N for all i = 1 , … , N , then ρ ≤ 1/N ( and consequently K ≤ u ) with equality if and only if the death rates are also constant over sites . Proof . We separate our proof into six steps . First , we show that there is no loss of generality in assuming that B = 1 . Second , we use the method of Lagrange multipliers to find the critical points of the function ρ = ∑ i = 1 N d i ρ i with respect to the variables { e i j } i , j=1N and { ρ i } i = 1 N and the constraints ∑ j = 1 N e i j = 1 N i ∈ { 1 , … , N } , ∑ j = 1 N e j i ρ i = ∑ j = 1 N e i j ρ j i ∈ { 1 , … , N } , ∑ i = 1 N ρ i = 1 . ( 12 ) We obtain that the critical points are precisely those for which di = 1/N for all i . In the next three steps , we use partial derivatives of Eqs . ( 1 ) , ( 2 ) and ( 3 ) to form a second-order Taylor expansion of ρ in the variables {eij}i ≠ j around the critical points . Finally , we show that the critical points are maxima , completing the proof . We now turn to the full range of possible values for K with no constraints on birth and death rates . Consider the example spatial structure illustrated in Fig . 5 , which consists of a hub with outgoing edges to n leaves , so that the population size is N = n+1 . There is an edge of weight a , 0 ≤ a < 1/ ( N−1 ) , from the hub to each leaf . The hub also has a self-loop of weight 1− ( N−1 ) a , so that B = 1 births are expected per time-step . The death rates are dH = 1− ( N−1 ) a for the hub and dL = a for each leaf . Solving Eqs . ( 1 ) – ( 2 ) we obtain the site-specific fixation probabilities ρL = 0 for each leaf and ρH = 1 for the hub . By Eq . ( 3 ) , the overall fixation probability is equal to the rate of turnover at the hub: ρ = d H = 1 − ( N − 1 ) a . Any value 0 ≤ ρ < 1 can be obtained by an appropriate choice of a , with 0 < a ≤ 1/ ( N−1 ) , specifically , a = ( 1−ρ ) / ( N−1 ) . This proves: Theorem 6 ( Result 4 ) . For arbitrary spatial population structure ( no constraints on eij ) the fixation probability can take any value 0 ≤ ρ < 1 , and consequently , the molecular clock can take any rate 0 ≤ K < Nu . We observe that if a = 1/N then the death rate is 1/N at each site , and therefore ρ = 1/N by Result 1 . If a = 1/ ( N−1 ) then there is no turnover at the hub and thus ρ = 0 . At the other extreme , as a approach zero , ρ comes arbitrarily close to unity . To complement the above arguments , which apply to general population size N , we here derive exact expressions for the overall fixation probability ρ in the cases N = 2 and N = 3 . Our results for node-specific fixation probabilities coincide with those found previous for a different model of evolution in a subdivided population [40] . We now turn to the upstream-downstream model introduced in the Results and in Fig . 6 . Theorem 7 . In the upstream-downstream model , ρ > 1/N , and consequently K > u , if and only if d↑ > d↓ . Proof . From Eq . ( 3 ) we obtain the following expression for the overall fixation probability ρ = 1 B N ↑ d ↑ ρ ↑ + N ↓ d ↓ ρ ↓ = 1 N + 1 B N ↑ d ↑ ρ ↑ − 1 N + N ↓ d ↓ ρ ↓ − 1 N . ( 38 ) Since fixation probabilities sum to one , and since the total population size is N = N↑+N↓ , we have N ↓ ρ ↓ − 1 N = − N ↑ ρ ↑ − 1 N . ( 39 ) Substituting in Eq . ( 38 ) yields ρ = 1 N + ( d ↑ − d ↓ ) N ↑ B ρ ↑ − 1 N . ( 40 ) It follows from Eq . ( 5 ) and from e→ > e← that ρ↑ > ρ↓ . Moreover , Eq . ( 39 ) implies that ρ↑−1/N and ρ↓−1/N have opposite signs , and since ρ↑ > ρ↓ , it follows that ρ↑−1/N must be positive . Thus the second term on the right-hand side of Eq . ( 40 ) has the sign of d↑−d↓ . We therefore conclude that ρ > 1/N , and consequently the molecular clock is accelerated relative to the well-mixed case ( K > u ) , if and only if d↑ > d↓ .
|
Evolution is driven by genetic mutations . While some mutations affect an organism’s ability to survive and reproduce , most are neutral and have no effect . Neutral mutations play an important role in the study of evolution because they generally accrue at a consistent rate over time . This result , first discovered 50 years ago , allows neutral mutations to be used as a “molecular clock” to estimate , for example , how long ago humans diverged from chimpanzees and bonobos . We used mathematical modeling to study how the rates of these molecular clocks are affected by the spatial arrangement of a population in its habitat . We find that asymmetry in this spatial structure can either slow down or speed up the rate at which neutral mutations accrue . This effect could potentially skew our estimates of past events from genetic data . It also has implications for a number of other fields . For example , we show that the architecture of intestinal tissue can limit the rate of genetic substitutions leading to cancer . We also show that the structure of social networks affects the rate at which new ideas replace old ones . Surprisingly , we find that most Twitter networks slow down the rate of idea replacement .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
The Molecular Clock of Neutral Evolution Can Be Accelerated or Slowed by Asymmetric Spatial Structure
|
Fatal Ebola virus infection is characterized by a systemic inflammatory response similar to septic shock . Ebola glycoprotein ( GP ) is involved in this process through activating dendritic cells ( DCs ) and macrophages . However , the mechanism is unclear . Here , we showed that LSECtin ( also known as CLEC4G ) plays an important role in GP-mediated inflammatory responses in human DCs . Anti-LSECtin mAb engagement induced TNF-α and IL-6 production in DCs , whereas silencing of LSECtin abrogated this effect . Intriguingly , as a pathogen-derived ligand , Ebola GP could trigger TNF-α and IL-6 release by DCs through LSECtin . Mechanistic investigations revealed that LSECtin initiated signaling via association with a 12-kDa DNAX-activating protein ( DAP12 ) and induced Syk activation . Mutation of key tyrosines in the DAP12 immunoreceptor tyrosine-based activation motif abrogated LSECtin-mediated signaling . Furthermore , Syk inhibitors significantly reduced the GP-triggered cytokine production in DCs . Therefore , our results demonstrate that LSECtin is required for the GP-induced inflammatory response , providing new insights into the EBOV-mediated inflammatory response .
Ebola virus ( EBOV ) , a member of the family Filoviridae , is the causative agent of severe hemorrhagic fever in humans , which is responsible for the outbreak in West African countries in 2014 [1] . Following EBOV infection , dendritic cells ( DCs ) and macrophages are the early and preferred replication sites of this virus , after which other cell types , including endothelial cells , epithelial cells and hepatocytes , are rapidly infected [2 , 3] . In experimental animal models , excessive production of proinflammatory cytokines and chemokines occurs during lethal EBOV infection , leading to endothelial cell permeability , multiorgan failure , and severe clotting disorders and culminating in a final septic shock-like syndrome [4–6] . More importantly , a fatal outcome in infected patients is also associated with aberrant innate immunity characterized by a “cytokine storm” , with hypersecretion of numerous proinflammatory mediators [3 , 7] , suggesting that the inflammatory response plays an important role in EBOV pathogenesis . Consequently , identification of the molecular mechanisms of the inflammatory response is very important to our understanding of EBOV diseases . EBOV genome consists of seven genes that encode seven structural proteins . Glycoprotein ( GP ) gene is the fourth of seven genes and encodes type I transmembrane GP termed pre-GP via transcriptional RNA editing [8 , 9] . The pre-GP is cleaved by furin into two subunits , GP1 and GP2 , which remain linked by a disulfide bond [10 , 11] . This heterodimer ( GP1 , 2 ) is known to form a trimer on the viral surface . The cleavage of surface GP by tumor necrosis factor-α-converting enzyme ( TACE ) releases a trimeric GP , termed shed GP [12] . During EBOV infection , significant amounts of shed GP can be detected [12] . Recently , it has been demonstrated that shed GP can induce the production of proinflammatory cytokines by activating non-infected DCs and macrophages , which can explain the dysregulated inflammatory host reactions to Ebola infection [13] . In addition , Ebola virus-like particles ( eVLPs ) consisting of virus protein ( VP40 ) and GP are able to induce the activation of DCs [14] . GP is required for eVLPs to induce DCs cytokine production [15 , 16] . All of these results support that GP can induce inflammatory response . However , the molecular mechanism underlying GP-mediated inflammatory responses is unclear . Inflammatory responses are rapidly elicited in response to infection by pathogens [17 , 18] . Innate immune cells including macrophages and DCs play important roles in this process . DCs and macrophages express diverse pattern recognition receptors ( PRRs ) that recognize conserved pathogen-associated molecular patterns ( PAMPs ) to elicit inflammatory immune responses via upregulation of proinflammatory cytokines such as tumor necrosis factor ( TNF ) and IL-6 . C-type lectin receptors ( CLRs ) have been identified as PRRs and play important roles in initiating an innate immune response [19] . The functions of these receptors in immunity as PRRs for carbohydrates present on fungi and some bacterial have been well defined , such as via dectin-1 and DC-SIGN , which signal through Syk [20] and Raf-1 [21] , respectively . However , the role of CLRs in inflammation mediated by virus components is less documented . The lectin LSECtin is encoded in the same chromosomal locus as DC-SIGN and also expressed by human peripheral blood DCs as well as DCs and macrophages generated in vitro [22 , 23] . It has been reported that LSECtin binds exogenous Ebola GP [24 , 25] and mediates its internalization as a PRR [23] . However , it is unclear whether LSECtin initiates specific signaling events and is involved in GP-mediated inflammatory responses . In this study , we report that LSECtin is a DAP12-coupled activating receptor that recognizes Ebola GP . We show that triggering of endogenous LSECtin in DCs by either its mAb or GP activates Syk and ERK and leads to CARD9- and Syk-dependent cytokine production . Collectively , these findings suggest that LSECtin functions as a DAP12-coupled receptor and acts as a functional PRR for Ebola GP .
LSECtin is a C-type lectin receptor and binds Ebola GP as a pattern recognition receptor . To verify whether LSECtin interacts GP , recombinant protein GP1-Fc was prepared and subjected to Coomassie blue staining and Western blotting ( S1A and S1B Fig ) . In addition , we found that under nonreducing conditions , recombinant protein GP1-Fc is in monomeric form ( S1C Fig ) . Using an enzyme-linked immunosorbent assay , our data demonstrated that GP1-Fc binds LSECtin in a dose-dependent way ( S1D Fig ) . LSECtin has a typical carbohydrate recognition domain ( CRD ) and binds Ebola GP in a Ca2+-dependent manner [25] . Amino acid sequence alignment of the CRD of LSECtin with those of other C-type lectins indicates that 2 amino acids , Asn256 and Asn274 , interact with Ca2+ through their carbonyl groups . Thus , we mutated the residues to aspartic acid . The mutant LSECtin ( N256D or N274D ) did not bind Ebola GP , which suggests that these residues are critical for recognition of EBOV GP ( S1D Fig ) . Furthermore , we also performed a cell surface staining assay and demonstrated that Jurkat cells lentivirally transfected with LSECtin rather than mutant LSECtin bind GP ( S1E Fig ) . Next , we explore the expression profile of LSECtin in human blood leukocytes . As shown in S2A Fig , the anti-LSECtin mAb CCB059 did not stain granulocytes , monocytes or lymphocytes . We further investigated the expression of LSECtin on monocyte-derived DCs ( MDDCs ) by culturing monocytes in the presence of GM-CSF and IL-4 ( S2B Fig ) . This treatment resulted in a strong up-regulation of LSECtin . In addition , this result was confirmed by PCR and Western blotting ( S2C and S2D Fig ) . Ebola GP interacts with LSECtin stably expressed on Jurkat cell line . It was thus of interest to investigate whether GP could also bind LSECtin on human MDDCs . To address the issue , MDDCs were transfected with siRNA specific for LSECtin or with control siRNA for 48h ( S3 Fig ) and stained with GP1-Fc . First , we found that Ebola GP can bind MDDCs . More importantly , the activity is partially dependent on LSECtin . These results suggest that LSECtin involves GP binding to MDDCs ( Fig 1A ) . To investigate whether GP/LSECtin interaction can lead to the production of proinflammatory cytokines within the human immune system , MDDCs transfected with siRNA specific for LSECtin or with control siRNA were stimulated with eVLPs and eVP40 which were produced in insect cells . Compared with the stimulation of VP40 , eVLPs significantly enhanced the production of cytokines and chemokines , suggesting that GP is required for eVLPs to activate DCs . Furthermore , we found that after LSECtin “knockdown” , MDDCs stimulated with eVLPs produced less TNF-α , IL-6 , IL-8 , IL-10 and MIP-1α ( Fig 1B ) . Although eVLPs produced in insect cells or mammalian 293T cells exhibit similar DC-stimulating activities [26] , eVLPs were also produced in 293T mammalian cells to determine whether the data are influenced by the insect cell expression system for GP . Similar with the results shown in Fig 1B , eVLPs produced in 293T mammalian cells induced less production of TNF-α , IL-6 , IL-8 , IL-10 and MIP-1α in LSECtin “knockdown” MDDCs ( Fig 1C ) . These results suggest that LSECtin involves the Ebola GP-induced cytokine production whether it was produced in insect cells or human 293T cells . Soluble GP1-Fc did not induce cytokine production ( S4 Fig ) , which is consistent with the previous report [15] . To simulate the configuration and multivalency of GP on eVLPs or shed GP , GP1-Fc was coated on a culture well for stimulation of MDDCs . To directly investigate the role of LSECtin in GP1-mediated proinflammatory cytokine production , control or LSECtin siRNA-transfected MDDCs were stimulated with plate-bound GP1-Fc . Similar to the results as described above , GP1-Fc-induced cytokine production was impaired in LSECtin “knockdown” DCs ( Fig 1D ) . In addition , our results demonstrated that the production of cytokines induced by eVLPs and plate-bound GP1-Fc was inhibited by Pepinh-MYD , a MyD88 inhibitor peptide , which is consistent with the previous reports that eVLP and shed GP induced cytokine production through TLR4/MyD88 signaling ( S5A and S5B Fig ) [13 , 27] . However , the cytokine production induced by LPS is not impacted in LSECtin “knockdown” DCs , suggesting that MyD88 signaling pathway is intact ( Fig 1D ) . Given that the production of cytokines is partially inhibited by LSECtin silencing or MyD88 inhibitory peptide , we next determined whether there is a synergistic efficacy of LSECtin silencing in combination with MyD88 inhibitory peptide . We found that MDDCs treated by double silencing produced less TNF-α and IL-6 than LSECtin silencing or MyD88 inhibitory peptide alone treated cells ( Fig 1E ) . In addition , we also demonstrated that there is a synergistic efficacy of LSECtin and TLR4-induced cytokines after LSECtin/TLR4 double “knockdown” DCs ( S6 Fig ) . These results suggesting that LSECtin and TLR4/MyD88 signaling collaborate to mediate inflammatory response induced Ebola GP . GlcNAcβ1-2Man disaccharide has been demonstrated to be a specific inhibitor of interaction between LSECtin and Ebola GP [25] . Our result also demonstrated that GlcNAcβ1-2Man inhibits the GP binding to MDDCs ( Fig 1A ) . More importantly , the production of TNF-α and IL-6 can be inhibited by the addition of GlcNAcβ1-2Man and the effect was specific for Ebola GP as LPS-induced TNF-α and IL-6 production was unaffected by the presence of the GlcNAcβ1-2Man ( Fig 1F ) . Collectively , these results suggest that LSECtin is selectively expressed in MDDCs and involved in GP-mediated proinflammatory cytokine production . The above results suggest that Ebola GP induced cytokine production by MDDCs through both LSECtin and MyD88 signaling . To specially and clearly explore the LSECtin signaling , we use anti-LSECtin mAbs , including CCA023 , CFD051 and CCB059 , to stimulate LSECtin signaling upon crosslinking in MDDCs . We treated MDDCs with immobilized anti-LSECtin mAbs . The production of TNF-α and IL-6 was significantly increased in MDDCs after 24h of treatment with CFD051 , compared with CCA023 , CCB059 and the control mIgG1 , suggesting that only CFD051-LSECtin engagement promoted cytokine production ( Fig 2A ) . We also observed similar changes in mRNA levels . We found that LSECtin engagement induced rapid but transient mRNA expression for the cytokines IL-6 and TNF-α ( Fig 2B ) . The IL-6 and TNF-α mRNA amounts peaked approximately 3h after LSECtin ligation and subsequently declined to close to baseline 6h after LSECtin crosslinking . In addition , LSECtin engagement increased the maturation of MDDCs , as characterized by increased surface expression of HLA-DR , CD83 and CD86 ( Fig 2C ) . Interestingly , GP/LSECtin interaction also triggers the maturation of DCs as the surface expression of CD40 , CD80 and CD86 decreased in LSECtin “knockdown” DCs ( S7 Fig ) . To determine whether TLR signaling is involved in LSECtin-mediated cytokine production , MDDCs were pretreated with Pepinh-MYD ( a MyD88 inhibitor ) and stimulated the cells with plate-bound CFD051 . Our results show that MyD88 , a crucial adaptor of TLR signaling , was dispensable for LSECtin-mediated cytokine production ( S8 Fig ) . To prove that the cellular effects mediated by LSECtin engagement were specific , we treated the siRNA-transfected MDDCs with CFD051 overnight . We found that after LSECtin “knockdown” , MDDCs stimulated with CFD051 produced less TNF-α and IL-6 ( Fig 2D ) . To exclude the possibility that the TNF-α and IL-6 production was simply due to Fc receptor engagement , we prepared F ( ab′ ) 2 fragments of anti-LSECtin mAb and used them to stimulate MDDCs . As shown in Fig 2E , plate-bound F ( ab′ ) 2 fragments from anti-LSECtin mAb induced the production of TNF-α and IL-6 and markedly increased their production in the presence of LPS ( a TLR4 agonist ) , suggesting cooperation between TLR4 and LSECtin signaling in the MDDCs . The NF-κB factors are held in the cytoplasm in an inactive state complexed with the inhibitory IκBα proteins . Upon stimulation by LPS , IκBα is phosphorylated and subsequently degraded resulting in NF-κB activation . S9 Fig shows that LPS induced a significant reduction in IκBα that last up to 1h . The basal IκBα levels were restored by 2h . However , LPS and LSECtin mAb combined treatment induces IκBα degradation that last up to 2h . The results indicated that LSECtin and TLR4 signaling crosstalks at the level of NF-κB activation . Taken together , our results demonstrated that LSECtin engagement can specially promote TNF-α and IL-6 production and enhance the maturation of MDDCs . The above results showed that LSECtin mediates positive signaling in MDDCs . However , there is no signal transduction motif in this protein’s cytoplasmic tail . Therefore , it is likely that LSECtin is associated with an adaptor molecule to transduce signals . Co-immunoprecipitation and immunoblot analysis showed that LSECtin selectively associated with DAP12 but not with FceRIγ ( Fig 3A ) . We also used reverse IP to show that LSECtin co-precipitated DAP12 ( Fig 3B ) . Importantly , the interaction between endogenous LSECtin and DAP12 was also obvious in MDDCs ( Fig 3C ) . Thus , these results suggested that LSECtin is associated with DAP12 . LSECtin does not possess any positively charged residues in the transmembrane domain that is required for the interaction with DAP12 in many other receptors . The negatively charged amino acid D50 in the transmembrane region of DAP12 is dispensable for the interaction ( S10 Fig ) . However , the interaction of LSECtin and DAP12 was mediated through the transmembrane region of LSECtin ( Fig 3D ) , as deficiency of the transmembrane region abolished its association with DAP12 . Next , we further showed that a short stretch of transmembrane region proximal to the intracellular domain of LSECtin ( amino acids 32–43 ) was required for association with DAP12 ( Fig 3E ) . In addition , the interaction of LSECtin and DAP12 was independent of the only two hydrophilic threonines ( T41 and T42 ) within the transmembrane region of LSECtin ( S10 Fig ) . The previous results in Fig 2 show that immobilized antibody to LSECtin can induce the production of TNF-α and IL-6 . To determine whether LSECtin-mediated signaling is dependent on DAP12 , MDDCs were transfected with siRNA specific for DAP12 or with control siRNA for 72 h ( Fig 3F ) . We found that LSECtin failed to induce the production of TNF-α and IL-6 in DAP12 “knockdown” MDDCs after treatment with CFD051 antibody ( Fig 3G ) , suggesting that LSECtin transduces signaling in a DAP12-dependent manner . LSECtin binds Ebola GP and is required for eVLP-induced cytokine production . To determine whether eVLPs were able to induce tyrosine kinase-based intracellular signals through LSECtin , Jurkat cells were transfected with LSECtin and DAP12 . Jurkat cells do not express LSECtin and DAP12 and refractory to Ebola GP-mediated infection [28] . The LSECtin- and DAP12-transfected cells were either left unstimulated or stimulated with eVLP . Whole-cell extracts were subjected to Western blotting using an anti-phosphotyrosine Ab ( 4G10 ) to detect tyrosine-phosphorylated proteins . Compared with correspondingly stimulated LSECtin or DAP12 transfectants , eVLP-treated LSECtin-DAP12 cells yielded increased amounts of tyrosine-phosphorylated proteins ( Fig 4A ) . LSECtin bearing the two amino acid mutants ( N256D andN274D ) does not bind Ebola GP1-Fc . Consistent with this observation , LSECtin mutants ( N256D and N274D ) did not deliver an activation signal in response to eVLP stimulation , suggesting that LSECtin recognizes its ligand dependently of Ca2+-binding sites ( Fig 4B ) . Signaling through DAP12 is mediated by its ITAM , which relies on phosphorylation of the two tyrosines within the ITAM for propagation of a signal [29] . To determine whether the two tyrosines of DAP12 are required for LSECtin/DAP12-mediated phosphorylation of protein tyrosines , we transduced a lentivirus encoding wild-type ( WT ) or mutant DAP12 in which the ITAM tyrosines at positions 91 and 102 were mutated to phenylalanine ( 2YF ) into Jurkat-LSECtin stable cells . Our results show that eVLP stimulation enhanced the phosphorylation of protein tyrosines in Jurkat-LSECtin cells expressing WT but not mutant DAP12 ( Fig 4C ) , suggesting that the two tyrosines within the ITAM are required for LSECtin/DAP12-mediated phosphorylation of protein tyrosines . We next determine whether ligation of LSECtin results in tyrosine phosphorylation of DAP12 . Jurkat-LSECtin/DAP12 and Jurkat-LSECtin/DAP12 ( Y2F ) stable cells were stimulated with eVLP . To detect DAP12 phosphorylation , DAP12 protein was immunoprecipitated and tyrosine phosphorylation of DAP12 was examined with 4G10 antibody . Our results show that eVLP induced DAP12 phosphorylation in Jurkat-LSECtin cells expressing WT but not mutant DAP12 , although mutant DAP12 can also co-precipitate LSECtin ( Fig 4D ) . More importantly , MDDCs stimulated with eVLP were found to induce DAP12 phosphorylation , but knockdown of LSECtin resulted in a substantial decrease in DAP12 phosphorylation ( Fig 4E ) . In addition , the phosphorylation of DAP12 is independent on TLR4 activation as the effect is not affected after TLR4 “knockdown” in MDDCs ( S11 Fig ) . These results suggest that eVLP-triggered DAP12 phosphorylation is mediated through LSECtin . In addition , we also used plate-coated anti-LSECtin CFD051 mAb to stimulate LSECtin- and DAP12-transfected cells . This treatment also increased the phosphorylation of protein tyrosines in Jurkat cells expressing LSECtin and DAP12 ( Fig 4F ) . And the enhanced phosphorylation of protein tyrosines is dependent on the two tyrosines within the ITAM of DAP12 ( Fig 4G ) . These results indicate that ligation of LSECtin can induce tyrosine kinase-based intracellular signals in the presence of DAP12 . The ITAM in intracellular domain of DAP12 can be phosphorylated and transduce signaling via inducing the phosphorylation of Syk . To determine whether endogenous LSECtin could activate the Syk tyrosine kinase , we stimulated MDDCs with plate-bound anti-LSECtin mAb . Consistent with the results shown in Fig 2A , only plate-bound CFD051 Ab induced phosphorylation of the kinases Syk and ERK in MDDCs ( Fig 5A ) . In addition , the Syk inhibitor piceatannol abrogated the expression of cytokines in MDDCs induced by CFD051 in mRNA and protein levels ( Fig 5B and 5C ) . Identical results were achieved with interfering RNAs ( siRNAs ) against Syk ( S12 Fig ) , confirming specificity of the Syk inhibitor . Syk inhibition by piceatannol also abrogates the enhanced expression of TNF-α and IL-6 by LSECtin-TLR4 cross-talk ( S13 Fig ) . Considering that another C-type lectin , DC-SIGN , has been shown to mediate signal transduction through Raf-1 [21] , we investigated whether Raf-1 is also involved in LSECtin-mediated signaling . The Raf inhibitor GW5074 did not inhibit the expression of cytokines in MDDCs induced by CFD051 ( S14 Fig ) . We next determined whether ligation of LSECtin by eVLP leads to the activation of Syk and ERK . We found that the activation of Syk and ERK induced by eVLP was significantly impaired in LSECtin “knockdown” DCs ( Fig 5D ) . In addition , the phosphorylation of ERK induced by eVLP was also significantly impaired in TLR4 “knockdown” DCs , but the activation of Syk is independent on TLR4 signaling ( S15 Fig ) . These results suggest that eVLP activates Syk through a LSECtin-dependent way . We then examined whether Syk is involved in cytokine expression induced by eVLP . Syk inhibition by piceatannol reduced the production of TNF-α and IL-6 by DCs stimulated with eVLP ( Fig 5E ) , plate-bound GP1-Fc ( Fig 5F ) or eVLPm ( Fig 5G ) . R406 is another specific Syk inhibitor and in clinical trials for human inflammatory diseases [30] . In the presence of R406 , the production of TNF-α and IL-6 by DCs is also significantly suppressed after the stimulation by eVLPs ( Fig 5H ) , plate-bound GP1-Fc ( Fig 5I ) or eVLPm ( Fig 5G ) . As a control , LPS does not activate Syk kinase and the cytokine production induced by LPS is not suppressed in the presence of either piceatannol or R406 ( S16 Fig ) , which suggests that the inhibition of Syk signaling by piceatannol or R406 is specific to the eVLP and GP1-Fc treatment . The adaptor molecule CARD9 is required for Syk-mediated inflammatory responses [31] . We therefore investigated the role of CARD9 in LSECtin-mediated responses in MDDCs transfected with siRNA specific for CARD9 or with control siRNA ( S17 Fig ) . We found that LSECtin failed to induce the production of TNF-α and IL-6 in CARD9 “knockdown” MDDCs after treatment with CFD051 antibody ( Fig 5J ) . More importantly , the production of TNF-α and IL-6 by DCs is significantly reduced in CARD9 “knockdown” MDDCs after the stimulation by eVLPs ( Fig 5K ) , plate-bound GP1-Fc ( Fig 5L ) or eVLPm ( Fig 5M ) . Taken together , these results indicate LSECtin engagement is capable of activating Syk and downstream signaling pathways in DCs , leading to the production of cytokines .
Here , we show that the myeloid C-type lectin receptor LSECtin is a DAP12-coupled activating receptor that induces inflammatory responses by recognizing EBOV GP . LSECtin crosslinked by mAb or ligated with EBOV GP induces the phosphorylation of protein tyrosines and up-regulates the expression of proinflammatory cytokines via Syk and CARD9 . Transduction of these events is dependent on the interaction between LSECtin and DAP12 , which bears an ITAM in its cytoplasmic domain . Signaling transduced by some members of the CLRs is crucial for tailoring immune responses to pathogens [32] . To investigate the role of CLRs in regulation of myeloid cell function , mAbs to selectively trigger surface receptors has been widely used , which provides important insight into the signaling and function of different CLRs [33] . We used mAbs to selectively crosslink LSECtin , inducing Syk- and CARD9-dependent inflammatory cytokine production in DCs . It is noteworthy that the anti-LSECtin mAbs failed to induce cytokine production by DCs after transfection with LSECtin siRNA , which further confirms the specificity of the anti-LSECtin mAbs . Thus , LSECtin signaling by itself is sufficient to induce activation of the Syk/CARD9 pathway and gene expression . We also observed that anti-LSECtin mAb treatment combined with LPS enhanced the production of TNF-α and IL-6 by DCs , which indicates that LSECtin might regulate TLR signaling . C-type lectins comprise a heterogeneous group of transmembrane proteins that recognize various self- and non-self-ligands [19] . These characteristics of CLRs increase the host’s flexibility in recognizing various molecular patterns , including those in exogenous pathogens and endogenous ligands . Our previous studies showed that LSECtin binds activated T cells and inhibits their function through an unidentified endogenous ligand [34] . However , the function of LSECtin as a PRR is still undefined . We have shown here that LSECtin recognizes Ebola GP and transduces an activating signal in DCs . This is contrary to DC-SIGN-mediated immunomodulatory function . For example , DC-SIGN was employed by measles virus to suppress antiviral type I IFN responses and then escape antiviral immunity [35] . It is noteworthy that we used eVLPs or plate-coated GP1-Fc to induce the production of proinflammatory cytokines by MDDCs . Previous studies showed that soluble GP1 alone does not induce cytokine production in human macrophages [15] , and we confirmed this with MDDCs . The data is different from that soluble shed GP which can induce the secretion of cytokines . Shed GP is a trimer , but GP1-Fc is a monomer in our study ( S1 Fig ) . Therefore , the different structures of soluble shed GP and GP1 maybe cause their varied ability of activating DCs . In addition , sera Lectins especially MBL in FBS used in our stimulation systems might also interfere GP binding DCs since MBL present in human sera is capable of affecting the binding of shed GP to cells [13] . DAP12 contains a cytoplasmic ITAM that recruits Syk and promotes activation of ERK [36 , 37] . Piceatannol and R406 , two Syk inhibitors , both significantly inhibit the cytokine production induced by eVLPs or plate-coated GP1-Fc . eVLPs trigger protein tyrosine phosphorylation in LSECtin- and DAP12-co-expressing Jurkat cells , and this effect is dependent on the ITAM of DAP12 . Alignment of the LSECtin amino acid sequence indicates that 2 amino acids within CRD , Asn256 and Asn274 , interact with Ca2+ through their carbonyl groups . Recognition of Ebola GP by LSECtin appears to be dependent on carbohydrates , as eVLPs do not trigger protein tyrosine phosphorylation in mutant LSECtinN256D or N274D- and DAP12-co-expressing Jurkat cells . These results show that LSECtin is a novel DAP12-coupled myeloid CLR that acts as a PRR for Ebola GP . Fatal EBOV infection in humans is associated with severe immune dysregulation and the hypersecretion of numerous proinflammatory cytokines . Recently , it has been demonstrated that trimeric shed GP released from virus-infected cells could activate non-infected DCs and macrophages , causing massive release of pro- and anti-inflammatory cytokines [13] . In addition , Qiu et al . reported that ZMapp , a blend of three EBOV GP-specific mAbs , protected EBOV-infected nonhuman primates [38] . This protection occurred even when ZMapp administered 5 days after infection , a time at which the clinical signs of disease are apparent . However , the mechanisms by which protection is achieved are unclear [39] . Given that GP participates in the production of numerous proinflammatory cytokines , it is reasonable to speculate that ZMapp not only neutralizes EBOV infection but also inhibits the excessive cytokine storm by blocking the interaction between GP and its PRRs , such as TLR4 and LSECtin . Therefore , therapeutic strategies to inhibit the cytokine storm should be considered during treatment for Ebola infection , especially for the patients with obvious clinical symptoms . In this regard , treatment with anti-GP , anti-TLR4 and anti-LSECtin Abs could be used to reduce the inflammatory responses caused by shed GP and may be helpful to alleviate the septic shock-like syndrome observed with EBOV infection .
mAbs to human LSECtin were established by immunization of Balb/C mice with recombinant LSECtin extracellular domain protein . Three independent clones , CCA023 ( IgG2a ) , CFD051 ( IgG1 ) and CCB059 ( IgG2b ) , were established [34] . The anti-human LSECtin mAb CCB059 ( IgG2b ) was selected for staining by flow cytometry . The mouse IgG1 isotype control was from R&D Systems ( Minneapolis , MN , USA ) . mAbs against human HLA-DR , CD83 and CD86 were from eBioscience ( San Diego , CA , USA ) ; mAbs against human CD40 and CD80 were from Biolegend ( San Diego , CA , USA ) ; anti-phosphotyrosine Ab ( 4G10 ) was from Millipore; anti-DAP12 and the other phospho-specific Abs were from Cell Signaling Technology ( Danvers , MA ) . The Syk inhibitors piceatannol and R406 were purchased from Calbiochem ( San Diego , CA , USA ) and Selleckchem ( Houston , TX , USA ) respectively . Raf-1 inhibitor GW5074 was purchased from Calbiochem ( San Diego , CA , USA ) ; The MyD88 inhibitory peptide Pepinh-MYD was from InvivoGen ( San Diego , CA , USA ) . The GlcNAc β1-2Man disaccharide was purchased from Dextra Laboratories ( Reading , UK ) . The Ebola GP1 coding sequence used is from the GP gene of the Zaire EBOV strain Mayinga ( GenBank accession no . AF272001 ) , which contains eight adenosine ( A ) residues at the editing site . The coding sequence was synthesized by TSINGKE Biological Technology . The GP1 cDNA was cloned by PCR and inserted into pIRES2-EGFP-Fc vectors such that the recombinant protein contained the Fc portion of human IgG . The pIRES2-EGFP-GP1-Fc plasmid was transfected into 293T cells , and the supernatants ( free of FBS ) were collected for protein purification using protein A/G agarose ( GE Healthcare ) . To determine the content , purified GP1-Fc was subjected to Coomassie blue staining and Western blotting . The generation of Ebola VLPs in insect cells ( eVLP ) has been described previously [26] . Briefly , recombinant baculoviruses co-expressing Ebola VP40 and GP ( rBV-GP-VP40 ) proteins or only expressing Ebola VP40 ( rBVVP40 ) proteins infect Spodoptera frugiperda Sf9 insect cells at an MOI of 1 . After 48h , the supernants were collected and VP40 and eVLPs proteins were purified in a discontinuous sucrose gradient ( 10–50% ) . A visible band between the 30% and 50% sucrose layers was harvested , concentrated by ultracentrifugation and then resuspended in PBS . Ebola VP40 and GP genes were cloned into pIRES2-EGFP . Mammalian 293T cells were transfected with pIRES2-EGFP-VP40 alone or in combined with pIRES2-EGFP-GP expression vectors at equal DNA concentrations . 48h post-transfection , the supernatants ( free of FBS ) were collected and clarified with a cell spin . VLPs were purified by centrifugation through a sucrose cushion at 26000 rpm in a Beckman SW-28 rotor for 2 h at 4°C . eVLPs were resuspended in PBS . VP40 and eVLPs containing VP40 and GP proteins produced in mammalian 293T cells was designated VP40m and eVLPm respectively . The final concentration of eVLP protein was quantitated using the DC protein assay ( Bio-Rad , Hercules , CA ) . Human peripheral blood mononuclear cells ( PBMCs ) were isolated from buffy coats from healthy donors using a Ficoll-Paque Plus ( GE Healthcare , Piscataway , NJ ) gradient . Monocytes were purified from the PBMCs by adherence for 1h at 37°C in complete medium and were differentiated into MDDCs in the presence of 800U/ml GM-CSF and 400U/ml IL-4 ( PeproTech ) . The DCs were stimulated with plate-bound anti-LSECtin mAb , eVLPs eVLPm or plate-bound GP-Fc ( 10μg/ml ) for the indicated times and then lysed and subjected to Western blotting to detect the phosphorylation of Syk and ERK . RNA was isolated with RNAeasy Mini Kit ( Qiagen , Valencia , CA ) and cDNA was synthesized with First Strand cDNA Synthesis Kit ( Fermentas ) . Quantitative PCR was performed with a SYBR Green PCR kit ( Roche , Laval , Canada ) in an iQ5 ( Bio-Rad ) detection system . The sequences of the primer pairs of TNF-α , IL-6 , CARD9 and TLR4 were described before [40–43] . LSECtin primer pairs were purchased from Qiagen . MDDCs were transfected with 20 nM siRNA using the transfection reagent INTERFERin ( Polyplus Transfection ) as described [44] . Briefly , 5×105 cells were seeded into 6-well plates and then transfected with corresponding siRNAs . After 6 hours , culture medium was replaced with fresh growth medium to reduce cellular toxicity of the transfection reagent . The siRNA sequence was as follows: LSECtin-specific siRNA , 5′-GCGCGAGAACTGTGTCATGAT-3′; DAP12-specific siRNA , 5′- ACAGCGTATCACTGAGACC-3′ [45]; and negative control siRNA , 5′-TTCTCCGAACGTGTCACGTTT-3′ . At 48h after transfection , the cells were stimulated . Syk and TLR4 siRNA was purchased from Dharmacon . CARD9 siRNA was purchased from OriGene . The sequence of the gene encoding human LSECtin was obtained from the National Center for Biotechnology Information’s server ( GenBank accession no . Q9NY25 ) . LSECtin cDNA was cloned by PCR and introduced into the pcDNA3 . 1/Myc-His A vector , which has a Myc tag at the N terminus , as did the different LSECtin mutants . Human FceRIγ and DAP12 were inserted into the pCMV-Flag-Mat-1 vector with a Flag tag at the N terminus . To determine how LSECtin associates with DAP12 , we constructed different LSECtin mutants . Myc-LSECtin ΔICD lacks the entire intracellular domain ( 1-31aa ) . Myc-LSECtin ΔICD&TM lacks the entire intracellular and transmembrane domains ( 1-55aa ) . TMΔ1 ( deletion of 32-43aa ) , TMΔ2 ( deletion of 44-49aa ) and TMΔ3 ( deletion of 50-55aa ) of Myc-LSECtin were confirmed to lack different transmembrane regions , as indicated . A Student t test was used for statistical analysis . Results with a P value of less than 0 . 05 were considered as statistically significant . Peripheral blood mononuclear cells ( PBMC ) are collected from healthy human volunteer donors under approval of Institutional Review Board of Academy of Military Medical Science . The study did not involve any direct contact with human subjects and all samples were anonymized .
|
Ebola virus ( EBOV ) , a highly virulent pathogen , causes a severe hemorrhagic fever syndrome . The fatal infection is characterized by a systemic inflammatory response similar to septic shock . Ebola glycoprotein ( GP ) is thought to contribute to disease pathogenesis , as high amounts of shed GP from virus-infected cells are detected in patients , and activate macrophages and dendritic cells ( DCs ) to produce proinflammatory cytokines . Here , we show that LSECtin plays an important role in GP-mediated inflammatory responses in human DCs . LSECtin is a DAP12-coupled receptor able to initiate specific signaling events in human DCs . LSECtin interacts with Ebola GP and results in DAP12 phosphorylation . LSECtin knockdown impairs the production of proinflammatory cytokines induced by Ebola GP . Thus , this study suggests that LSECtin may contribute to Ebola GP-mediated pathogenicity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"phosphorylation",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"enzyme-linked",
"immunoassays",
"pathology",
"and",
"laboratory",
"medicine",
"molecular",
"probe",
"techniques",
"gene",
"regulation",
"pathogens",
"immunology",
"tropical",
"diseases",
"immunoblotting",
"microbiology",
"ebola",
"hemorrhagic",
"fever",
"viruses",
"developmental",
"biology",
"filoviruses",
"signs",
"and",
"symptoms",
"rna",
"viruses",
"molecular",
"development",
"neglected",
"tropical",
"diseases",
"molecular",
"biology",
"techniques",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"inflammation",
"proteins",
"medical",
"microbiology",
"gene",
"expression",
"immunoassays",
"microbial",
"pathogens",
"molecular",
"biology",
"immune",
"response",
"immune",
"system",
"biochemistry",
"rna",
"ebola",
"virus",
"nucleic",
"acids",
"post-translational",
"modification",
"physiology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"non-coding",
"rna",
"hemorrhagic",
"fever",
"viruses",
"organisms"
] |
2016
|
The Myeloid LSECtin Is a DAP12-Coupled Receptor That Is Crucial for Inflammatory Response Induced by Ebola Virus Glycoprotein
|
Clonorchis sinensis ( C . sinensis ) is considered to be an important parasitic zoonosis because it infects approximately 35 million people , while approximately 15 million were distributed in China . Hepatitis B virus ( HBV ) infection is a major public health issue . Two types of pathogens have the potential to cause human liver disease and eventually hepatocellular carcinoma . Concurrent infection with HBV and C . sinensis is often observed in some areas where C . sinensis is endemic . However , whether C . sinensis could impact HBV infection or vice versa remains unknown . Co-infection with C . sinensis and HBV develops predominantly in males . Co-infected C . sinensis and HBV patients presented weaker liver function and higher HBV DNA titers . Combination treatment with antiviral and anti-C . sinensis drugs in co-infected patients could contribute to a reduction in viral load and help with liver function recovery . Excretory-secretory products ( ESPs ) may , in some ways , increase HBV viral replication in vitro . A mixture of ESP and HBV positive sera could induce peripheral blood mononuclear cells ( PBMCs ) to produce higher level of Th2 cytokines including IL-4 , IL-6 and IL-10 compared to HBV alone , it seems that due to presence of ESP , the cytokine production shift towards Th2 . C . sinensis/HBV co-infected patients showed higher serum IL-6 and IL-10 levels and lower serum IFN-γ levels . Patients with concomitant C . sinensis and HBV infection presented weaker liver function and higher HBV DNA copies . In co-infected patients , the efficacy of anti-viral treatment was better in patients who were prescribed with entecavir and praziquantel than entecavir alone . One possible reason for the weaker response to antiviral therapies in co-infected patients was the shift in cytokine production from Th1 to Th2 that may inhibit viral clearance . C . sinensis/HBV co-infection could exacerbate the imbalance of Th1/Th2 cytokine .
Clonorchiasis , caused by Clonorchis sinensis ( C . sinensis ) , is one of the parasitic zoonosis . It is estimated that approximately 35 million people are infected in Asia , among which approximately 15 million infected people were in China [1–3] . Previous epidemiological data showed that clonorchiasis is endemic in the southeast of China , especially in Guangdong province [4] . The people become infected with C . sinensis by the consumption of raw or undercooked fish that contains C . sinensis metacercariae [5] . The adult worms of C . sinensis , located in the small bile ducts of the liver , lead to mechanical damage , while excretory-secretory products ( ESPs ) of C . sinensis cause chemical damage . Both mechanical damage and chemical damage play a key role in causing hyperplasia and adenomatous changes in the bile ducts[6] . The ESPs are known to be involved in parasite-host interaction and have clinical significance in the diagnosis and pathogenesis [7–10] . Hepatitis B virus ( HBV ) infection is a major public health issue that may develop into cirrhosis , hepatic decompensation and hepatocellular carcinoma ( HCC ) [11] . An estimated 2 billion people have been infected , and more than 350 million are chronic carriers of the virus despite the availability of a prophylactic vaccine[12] . HBV infection situation is even more severe in China where approximately 170 million people are chronically infected with HBV [13 , 14] . Th1 responses seem to be involved in the clearance of HBV , while chronic HBV infection elicits very weak T cell responses [15 , 16] . A relatively smaller population is infected with C . sinensis compared with HBV[17]; concurrent infection with HBV and C . sinensis is often observed in areas where C . sinensis is endemic[18] . In some epidemiological studies , the positive rate of HBV surface-antigen ( HBsAg ) was significantly higher in areas endemic with C . sinensis than non-endemic areas [19–21] . However , C . sinensis infection and chronic HBV infection are two different causes of liver disease in China , and limited data are currently available as to whether there is any association between HBV and C . sinensis infections . There has been no clear discussion as to whether the C . sinensis infection may impact upon HBV infection or vice versa . The aim of our study is to evaluate the impact of C . sinensis infection on HBV infection as well as the response to antiviral therapy in co-infection with C . sinensis and HBV . Our results showed that co-infected individuals presented weaker liver function and higher HBV DNA titers . In co-infected patients , the efficacy of anti-viral treatment was better in patients who were prescribed ETV and PZQ than ETV alone . A possible reason for higher HBV DNA copies and a weaker response to antiviral therapies in co-infected patients was the shift in cytokine production from Th1 to Th2 that may inhibit viral clearance .
The Institutional Review Boards of the Third Affiliated Hospital and Zhongshan School of Medicine , Sun Yat-sen University , approved this study as an exempt study for which informed consent did not need to be sought from subjects . Informed consent was not sought for this study as all information was obtained from the existing medical record , and data were analyzed anonymously . HBV positive sera were obtained from chronic HBV patients , and peripheral blood mononuclear cells ( PBMCs ) were collected from healthy donors or chronic HBV patients from the Third Affiliated Hospital of Sun Yat-sen University . Samples were anonymously coded in accordance with local ethical guidelines ( as stipulated by the Declaration of Helsinki ) , and written informed consent was obtained from patients and healthy volunteers . The work was conducted in strict accordance with the study design as approved by the Clinical Research Ethics Committee of the Third Affiliated Hospital and Zhongshan School of Medicine , Sun Yat-sen University , Guangzhou , China . First , we evaluated HBV patients by screening all consecutive patients at the Third Affiliated Hospital of Sun Yat-sen University between July 2014 and February 2015 . The inclusion criteria for patients who were mono-infected with HBV were the following: men and women aged 18 years and older; HBV surface-antigen ( HBsAg ) -positive; and HBV DNA >20 IU/mL . The inclusion criteria for patients who were co-infected were the following: men and women aged 18 years and older; HBV surface-antigen ( HBsAg ) -positive; HBV DNA >20 IU/mL; and having C . sinensis eggs in the stools . The inclusion criterion for patients who were mono-infected with C . sinensis was having C . sinensis eggs in the stools . The inclusion criteria for healthy subjects were negative for both HBV and C . sinensis . Furthermore , we compared co-infected patients who were treated with entecavir ( ETV , 0 . 5 mg once daily ) alone with those who received a combination treatment of ETV ( 0 . 5 mg once daily ) and praziquantel ( PZQ , 210 mg/kg , 3 times a day , for 3 days ) to determine the impact of C . sinensis on antiviral therapies . The following data were collected from electronic medical records by computer-assisted chart review: age , gender , date of prescription of antiviral drug ETV and PZQ drug , HBV DNA copy numbers , serial liver function test once in 2 weeks , including aspartate aminotransferase ( AST ) , alanine aminotransferase ( ALT ) , and total bilirubin ( TB ) . Patients with the following concomitant conditions were excluded: those co-infected with HIV , hepatitis A , C , D and E , those with type I and type II diabetes , those co-infected with Schistosoma japonicum , or Schistosoma mansoni or other parasites , and those with alcoholic liver , autoimmune diseases , cholestasis , serious heart diseases and pregnant women . Due to the retrospective nature of the study , written informed consent could not be obtained from all patients . All data were de-identified prior to analysis . Biochemical tests were performed using routine automated analyzers . HBsAg was detected by electrochemiluminescence immunoassay with COI >1 . 00 ( COBAS ) . Serum ALT , AST , and TB levels were determined using commercial kits ( Maccura , China ) . Serum levels of HBV DNA were measured by real-time PCR with a lower detection limit of 20 IU/mL ( COBAS ) . ESPs were obtained as previously described [22] . Briefly , the living adult C . sinensis parasites were cultured in DMEM , and the supernatant was collected at 48 h . Fresh whole blood ( 5 ml ) was obtained from health adult volunteers and chronic HBV patients . The blood was mixed with the same volume of phosphate-buffered saline ( PBS ) and layered on 5 ml of lymphocyte separation medium ( TBD , China ) . The sample was centrifuged at 2000 rpm for 20 min at RT . PBMCs from the upper portion of the Ficoll layer were collected , washed with PBS and centrifuged at 1500 rpm for 10 min at RT . The PBMCs were suspended in RPMI 1640 ( Gibco ) with 10% fetal bovine serum ( Gibco ) at a concentration of 2 × 106 cells/ml in endotoxin-free tubes . PBMCs , seeded into 12-well cell culture clusters at a density of 1 . 0 × 106 viable cells per 200 μl of culture medium , were incubated with one of the following conditions: 1 ) HBV DNA-positive sera ( 2 . 0 ×106 HBV DNA IU/mL ) , 2 ) HBV DNA-positive sera ( 2×106 HBV DNA IU/mL ) and ESPs ( 20 μg/mL ) , or 3 ) ESPs ( 20 μg/mL ) alone for 48 h at 37°C in a humidified 5% CO2 incubator . Finally , cells from each culture and their corresponding supernatants were analyzed cytokine mRNA expression and HBV DNA copies , respectively . Total RNA of PBMCs was extracted using Trizol reagent ( Life Technologies , USA ) according to the manufacturer’s protocol . cDNAs were synthesized using a cDNA Synthesis Kit ( TransGen , China ) . Quantitative real-time PCR was performed using a Bio-Rad CFX96 Real-Time System ( Bio-Rad , USA ) to measure SYBR Green ( TRANSGEN BIOTECH , China ) incorporation into double stranded amplicons . Reactions were performed in 20 μl volumes containing forward and reverse primers at a final concentration of 100 nM . Primer sequences are listed in Table 1 . The PCR reaction conditions included a denaturation step at 94°C for 30 sec , then 40 cycles of a three-step cycling reaction as follows: 94°C for 5 sec , then 55°C for 15 sec and 72°C for 10 sec . Melting curve analysis revealed a single peak for each primer set . IL-2 , IL-4 , IL-6 , IL-10 and IFN-γ were measured and normalized relative to β-actin expression . The changes in mRNA expression were analyzed by calculating 2-ΔΔCt . The accession numbers for genes mentioned in the text are listed in S1 Table . Serum samples were obtained by centrifugation at 3000 rpm for 5 min . Serum samples were immediately stored at -80°C and thawed prior to analysis . Cell culture supernatants and serum concentrations of IL-2 , IL-4 , IL-6 , IL-10 and IFN-γ were analyzed by ELISA with commercially available kits ( Elabscience , China ) according to the manufacturer’s instructions . The concentration of each cytokine was determined using a standard curve according to the kit instructions . All data were presented as the mean values±standard error or mean values . Data analyses were carried out using the GraphPad Prism software 5 . 0 . For comparison with more than two groups , one-way ANOVA test was conducted , and if the data were nonparametric , a Kruskal-Wallis test with a confidence interval of 95% was employed . p<0 . 05 was considered statistically significant .
During the study period , there were 701 patients who met the selection criteria , of whom 51 were C . sinensis/HBV co-infected , 53 were C . sinensis mono-infected and 520 were HBV mono-infected . In addition , 77 healthy individuals with a mean age similar to the patient population were included . The patients' characteristics are reported in Table 2 . Patients in the infected groups were predominantly men ( 94% for C . sinensis/HBV co-infected , 73% for mono-HBV , 81% for C . sinensis infected ) . Liver function was assessed by measurement of TB , ALT and AST in plasma . All 3 infected groups of patients showed higher levels of ALT , AST and TB than the healthy control subjects . ALT , AST and TB levels were significantly higher in the co-infected group ( p<0 . 0001 , p<0 . 0001 , p<0 . 0001 , respectively ) compared to HBV mono-infected patients . Furthermore , HBV DNA log copies were also significantly higher in the co-infected patient group ( p <0 . 05 , Table 2 ) . Taken together , these data indicate that patients co-infected with C . sinensis and HBV had weaker liver function than mono-HBV infected and that the presence of C . sinensis may aggravate the disease state . Given that the presence of C . sinensis may aggravate HBV infection disease state , we further investigated whether inhibition of C . sinensis could influence the efficacy of antiviral treatment in co-infected patients clinically . There were 51 co-infected patients , 21 of whom were prescribed ETV and PZQ drugs and 30 of whom were prescribed antiviral ETV drugs only . Because 9 out of 30 patients had not been checked for liver function after treatment , they were excluded from this part of the study . There were no significant differences in HBV DNA copies and the levels of ALT , AST and TB between the two groups before treatment ( Fig 1 ) . After one program of treatment , the level of ALT , and AST and HBV DNA copies were significantly decreased compared to the pretreatment values in both groups , but no significant differences were observed in the levels of ALT and AST between the two groups ( Fig 1A and 1B ) . There was no obvious change in TB level between pre- and post-treatment in C . sinensis/HBV-NONPZQ groups . However , C . sinensis /HBV-PZQ patients demonstrated significantly lower levels of TB than C . sinensis /HBV -NONPZQ patients after one program of treatment ( p<0 . 01 , Fig 1C ) . Additionally , C . sinensis /HBV-PZQ patients had lower levels of HBV DNA log copies compared to C . sinensis /HBV–NONPZQ ( p <0 . 05 , Fig 1D ) . Patients who took PZQ showed C . sinensis eggs negative by Kato-Katz thick stool smear technique ( S1 Fig ) . Together , these results suggested that combined antiviral and anti-clonorchiasis drugs in co-infected patients could contribute to a reduction in viral load and help with liver function recovery . Co-infected patients not only showed weaker liver function but also had significantly higher HBV DNA copies clinically . We reasoned that some metabolites of C . sinensis may directly enhance HBV replication . To address this possibility , we tested HBV DNA copies in the supernatants after co-cultured of PBMCs with ESP and HBV positive patient sera . HBV positive patient serum alone and ESPs alone served as controls . HBV DNA was measured by real-time PCR with a lower detection limit of 20 IU/mL . As expected ( Fig 2 ) , HBV DNA copies were significantly higher in the culture supernatants from the PBMCs co-cultured with ESP and HBV mixture than the control groups ( p <0 . 01 ) . These data suggested that ESPs may , in some ways , promote viral replication . To define and compare the secretion of Th1 cytokines ( IL-2 and IFN-γ ) and Th2 cytokines ( IL-4 , IL-6 and IL-10 ) following in vitro stimulation , we used quantitative RT-PCR to analyze the mRNA levels of each cytokine secreted by PBMCs , which were stimulated with ESPs alone , HBV positive patient serum alone or the mixture of the two , respectively . Data have been normalized for β-actin transcript expression . As showed in Fig 3 , the levels of different cytokine mRNAs varied . IL-4 and IL-10 cytokine mRNA levels were higher in ESP-stimulated PBMCs than in HBV-stimulated PBMCs ( Fig 3B ) , whereas there is no significant difference in IL-2 and IFN-γ cytokine mRNA levels between these two groups ( Fig 3A ) . In particular , IL-6 cytokine mRNA levels were significantly higher in HBV positive sera stimulated PBMCs than in ESP-stimulated PBMCs ( Fig 3B ) . This finding suggested that both stimulators could induce PBMCs to produce Th1 and Th2 cytokines in vitro . Furthermore , in response to the treatment with a mixture of HBV and ESPs , IL-4 and IL-10 mRNA level increased two-fold , and IL-6 mRNA level increased three-fold compared with PBMCs stimulated with HBV positive sera alone , while there were no significant changes in IL-2 and IFN-γ levels . These data suggested that PBMCs stimulated by a mixture of ESP and HBV produced higher level of Th2 cytokines including IL-4 , IL-6 and IL-10 compared to HBV alone , it seems that due to presence of ESP , the cytokine production shift towards Th2 . To validate the real-time PCR results , the protein levels of those cytokines were examined in cell culture supernatant both from stimulated PBMCs from healthy donors and chronic HBV patients ( S1 Fig ) . ESP/HBV stimulated PBMCs secreted a higher level of IL-4 , IL-6 and IL-10 than in the HBV group , and IFN-γ was lower in ESP/HBV than in HBV ( S2 Fig ) . However , there was no significant difference between ESP/HBV and HBV groups with regard to IL-2 level . A similar pattern of cytokine expression could be observed in the stimulated PBMCs from chronic HBV patients ( S3 Fig ) . Note that there is no significant difference in IL-4 and IL-6 in stimulated PBMCs with those from chronic HBV patients . These data confirm that PBMCs stimulated by a mixture of ESPs and HBV mainly produced Th2 cytokines . To verify whether there are changes in serum cytokine levels in C . sinensis/HBV co-infected patients , HBV mono-infected patients and C . sinensis mono-infected patients , we performed cytokine ELISA to examine the levels of IL-2 , IL-4 , IL-6 , IL-10 and IFN-γ . C . sinensis/HBV co-infected patients had both lower IFN-γ and IL-2 levels than both HBV mono-infected and C . sinensis mono-infected patients ( S2 Table ) . All 3 groups of infected patients had higher IL-4 , IL-6 and IL-10 levels than healthy control subjects . IL-6 levels were further increased in C . sinensis/HBV co-infected patients , compared with those in HBV mono-infected patients ( p <0 . 05 ) . C . sinensis/HBV co-infected patients had higher IL-10 levels than HBV mono-infected patients ( p <0 . 05 ) .
In this study , we investigated the relationship between C . sinensis infection and HBV infection in humans and further studied the impact of C . sinensis infection on the efficacy of antiviral treatment . In our study , we provide strong evidence for the existence of an association between HBV infection and C . sinensis infection . Our data showed that a correlation of co-infection C . sinensis and HBV developed predominantly in males . This finding is supported by previous reports that C . sinensis infections in male individuals are usually higher than that in female individuals [23–25] . Additionally , our results showed that co-infected patients had significantly higher liver transaminases levels as well as HBV DNA copies , indicating that concomitant C . sinensis infection aggravated the liver disease . PZQ is known to be very effective and the drug of choice against trematode and cestode infections . Mesan et al . demonstrated that oral PZQ to patients co-infected with schistosomiasis and hepatitis C virus ( HCV ) could help the response to HCV treatment [26] . Patients who received C . sinensis infected liver transplantation who took PZQ experienced improved liver function[27] . The present study provides ample evidence of significant decreases in the levels of TB and HBV DNA copies by using the combination of PZQ during antiviral therapies in co-infected patients . It is theorized that the beneficial effects are likely related to the clearance of C . sinensis worms and subsequent reduction of metabolites of C . sinensis . This result further indicated that the efficacy of HBV antiviral treatment was related to the removal worms in co-infected patients . On the other hand , previous studies suggested that Schistosoma mansoni soluble egg antigens ( SEA ) have the potential to enhance HCV propagation [28] and SEA of Schistosoma Haematobium induces HCV replication in PBMCs [29] , which indicates that some components of trematode could enhance viral replication . ESPs of C . sinensis could cause chemical damage to the host [30 , 31] and induce cell proliferation in vitro [32] . Other findings suggested that in vitro infection of PBMCs with human sera contain HBV particles may be a suitable model to study the early steps of the viral life cycle [33 , 34] . M . Cabrerizo et al showed that the viral DNA can be detected after incubation of PBMCs with human sera containing HBV particles and that HBV is able to infect , replicate and release viral particles in the medium in in vitro infected PBMCs[35] . Therefore , we have used cultures of PBMCs from a healthy donor to test the impact of ESPs on HBV particle replication . We have demonstrated that HBV DNA can be detected in the supernatant 48 h after incubation of PBMCs with human sera containing HBV particles . Additionally , the results revealed that significantly higher level of HBV DNA in the supernatant from cells co-cultured with HBV positive sera and ESPs . This may explain , at least in part , the higher HBV DNA copies observed in co-infected patients . Unfortunately , which components of ESPs are involved were not identified in this study . Additional studies are required to determine which components of ESPs are the key role players . Studies on bile and serum of patients indicated that infection with C . sinensis correlated with Th2 type responses , emphasizing the decrease in concentration in IL-2 and an increase in IL-4 . It was suggested that ESPs are immunogenic , stimulate inflammation and promote proliferation , and suppress apoptosis [32] . Most proteins belonging to ESPs , including CsRNASET2 , CsLAP2 , and CsNOSIP , contributed in eliciting Th2 immune response in mice [36–38] . Cytokines are important mediators in the regulation of the immune response . However , it was not known yet whether ESPs of C . sinensis could stimulate PBMCs to produce cytokines in vitro as well as whether they have an influence on cytokine expression produced by HBV positive sera stimulated PBMCs . Thus , we examined the levels of cytokine specific mRNAs to clarify the cytokine response . We observed that PBMCs stimulated by either ESPs or HBV positive sera could prompt the secretion of Th1 cytokines by enhancing the expression of IL-2 and IFN-γ and Th2 cytokines by enhancing the expression of IL-4 , IL-6 and IL-10 . Additionally , in response to mixtures of HBV positive sera and ESPs , the levels of Th2 cytokines were notably higher compared to HBV positive sera alone , whereas there was no significant change in Th1 cytokine expression level , indicating that HBV/ESP predominantly produced Th2 cytokines . IL-6 exhibits both pro- and anti-inflammatory functions in innate immunity [39] and several studies have shown that IL-6 serum levels are increased in HBV positive patients , significantly higher in patients with severe and acute infections [40 , 41] . Wang et al . suggested that IL-6 is involved in the activation of natural killer cells and cytotoxic T lymphocytes induce the killing of hepatocytes , indicating that IL-6 plays an important role in liver cell necrosis and apoptosis[42] . Combined with our observation , these results suggested that a significantly higher level of IL-6 in response to a mixture of HBV and ESP stimulation , which may explain why the liver function is damaged severely in co-infected patients . In addition , compared to HBV infection alone , the levels of IL-4 and IL-10 were significantly increased , but IFN- γ was not changed in mixed HBV and ESP stimulation , indicating that cytokine production may shift from Th1 response to Th2 response . However , Th1 ( including IL-2 and IFN- γ ) cytokines have been identified to participate in the viral clearance while Th2 cytokine IL-10 serves as a potent inhibitor of Th1 effectors cells[43] . Therefore , one possible reason for the weaker response to antiviral therapies in co-infected C . sinensis/HBV patients was that the shift in cytokine production from Th1 to Th2 that may inhibit viral clearance . Cytokines participate in the induction and effector phases of the immune and inflammatory responses based on the protein level rather than the mRNA level; thus , we determined the protein levels of the above cytokines . Our results suggested that the cytokine pattern were similar between mRNA and protein levels . However , when we evaluated the response of PBMCs from chronic HBV patients to the same stimulators , the pattern of cytokine expression was similar to health subjects’ PBMCs , except for IL-4 and IL-6 level . We reasoned that PBMCs from chronic HBV patients may consist of antigen specific T and B cells , which may interfere with cytokine production . IL-6 plays an important role in host defense against pathogens and mediates anti-parasite protective responses[44] . Additionally , IL-6 may participate in pathological complications of HBV [45] . Our results are consistent with these findings; we found that serum IL-6 were significantly higher in C . sinensis mono-infected patients than both HBV mono-infected and health control subjects; that IL-6 levels were higher in C . sinensis/HBV co-infected patients than in the mono-infected group as well . IL-10 is mainly involved in the regulation of inflammatory response . IL-10 can antagonize Th1 cell responses by inhibiting Th1 cell differentiation and IFN-γ production [46] . Increased levels of IL-10 are relevant to the degree of liver inflammation and lead to disease progression [47 , 48] . Serum IL-10 levels have been reported in patients with chronic hepatitis and cirrhosis [49] and IL-10 can restrain the host’s anti-HBV activity . In this study , the level of IFN-γ in the co-infected patient group was significantly lower than in mono-HBV patients and mono-C . sinensis patients . IFN-γ not only is a cytokine produced by Th1 and NK cells but has a critical role in the suppression of HBV replication [50 , 51] . Studies have shown that chronic HBV patients have a lower level of IFN-γ; so , patients may fail to develop an efficient anti-viral immune response [51 , 52] . In this study , the serum level of IFN-γ in the co-infected patient group was significantly lower than in the mono-HBV patients and mono-C . sinensis patients . Therefore , we assume that co-infection with C . sinensis in HBV infection may suppress the immune response by stimulating IL-10 production as well as inhibiting IFN-γ secretion as a result . In addition , our results showed that C . sinensis seems to induce Th2-related cytokines , with an increase in serum levels of IL4 , IL-6 and IL-10 . Given that serum cytokine levels were fluctuating in C . sinensis/HBV patients , this could partly suggest that C . sinensis infection could exacerbate the imbalances of Th1/Th2 cytokine in HBV patients; however , further analysis is warranted . In conclusion , this study is the first to provide strong evidence for the association between C . sinensis infection and HBV infection . Co-infected individuals presented weaker liver function and higher HBV DNA titers . In co-infected patients , the efficacy of anti-viral treatment was better in patients who were prescribed ETV and PZQ than ETV alone . C . sinensis/HBV co-infection could exacerbate the imbalance of Th1/Th2 cytokine , which may lead to the chronicity of HBV infection , and C . sinensis may play a role in the unresponsiveness to antiviral therapy in co-infected patients . Further investigations are required to address this point .
|
Clonorchiasis and hepatitis B infection are infectious diseases that affect millions of people worldwide , especially in China . These two diseases are caused by two different pathogens , C . sinensis and hepatitis B virus , respectively . Concurrent infection between HBV and C . sinensis is often observed in some areas where C . sinensis is endemic . Both diseases share the same target organ , but there is little known on whether concomitant clonorchiasis could have an impact on HBV infection and the efficacy of antiviral treatment . In this study , we showed for the first time that co-infection with C . sinensis and HBV resulted in significantly higher liver transaminases levels as well as HBV DNA copies , indicating that co-infection with C . sinensis and HBV infection may aggravate the disease state . Combination treatment with antiviral and anti-C . sinensis drugs in co-infected patients could contribute to a reduction in viral load and help with liver function recovery . Furthermore , excretory-secretory products ( ESPs ) of C . sinensis may have a potential role in promoting HBV viral replication . This may explain , at least in part , the higher HBV DNA copies observed in co-infected patients . Additionally , a mixture of ESP and HBV positive sera could induce PBMCs to mainly produce Th2 cytokines such as IL-4 , IL-6 and IL-10 compared to HBV alone . A possible reason for higher HBV DNA copies and a weaker response to antiviral therapies in co-infected patients was the shift in cytokine production from Th1 to Th2 that may inhibit viral clearance .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"pathology",
"and",
"laboratory",
"medicine",
"china",
"antiviral",
"therapy",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"geographical",
"locations",
"hepatitis",
"b",
"virus",
"liver",
"diseases",
"viruses",
"preventive",
"medicine",
"bacterial",
"diseases",
"developmental",
"biology",
"gastroenterology",
"and",
"hepatology",
"molecular",
"development",
"vaccination",
"and",
"immunization",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"tuberculosis",
"medical",
"microbiology",
"microbial",
"pathogens",
"hepatitis",
"viruses",
"viral",
"replication",
"immune",
"system",
"people",
"and",
"places",
"asia",
"virology",
"physiology",
"viral",
"pathogens",
"co-infections",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2016
|
Clonorchis sinensis Co-infection Could Affect the Disease State and Treatment Response of HBV Patients
|
Information on crop pedigrees can be used to help maximise genetic gain in crop breeding and allow efficient management of genetic resources . We present a pedigree resource of 2 , 657 wheat ( Triticum aestivum L . ) genotypes originating from 38 countries , representing more than a century of breeding and variety development . Visualisation of the pedigree enables illustration of the key developments in United Kingdom wheat breeding , highlights the wide genetic background of the UK wheat gene pool , and facilitates tracing the origin of beneficial alleles . A relatively high correlation between pedigree- and marker-based kinship coefficients was found , which validated the pedigree and enabled identification of errors in the pedigree or marker data . Using simulations with a combination of pedigree and genotype data , we found evidence for significant effects of selection by breeders . Within crosses , genotypes are often more closely related than expected by simulations to one of the parents , which indicates selection for favourable alleles during the breeding process . Selection across the pedigree was demonstrated on a subset of the pedigree in which 110 genotyped varieties released before the year 2000 were used to simulate the distribution of marker alleles of 45 genotyped varieties released after the year 2000 , in the absence of selection . Allelic diversity in the 45 varieties was found to deviate significantly from the simulated distributions at a number of loci , indicating regions under selection over this period . The identification of one of these regions as coinciding with a strong yield component quantitative trait locus ( QTL ) highlights both the potential of the remaining loci as wheat breeding targets for further investigation , as well as the utility of this pedigree-based methodology to identify important breeding targets in other crops . Further evidence for selection was found as greater linkage disequilibrium ( LD ) for observed versus simulated genotypes within all chromosomes . This difference was greater at shorter genetic distances , indicating that breeder selections have conserved beneficial linkage blocks . Collectively , this work highlights the benefits of generating detailed pedigree resources for crop species . The wheat pedigree database developed here represents a valuable community resource and will be updated as new varieties are released at https://www . niab . com/pages/id/501/UK_Wheat_varieties_Pedigree .
Information of variety pedigree ( i . e . , ancestry or genealogy ) can be used by breeders to prioritise crosses between highly performing parents whilst maintaining genetic diversity in the offspring for selection . However , development of varieties by commercial breeding companies in recent decades may have resulted in knowledge fragmentation and duplication of resources [1] . We propose that an integrated large-scale wheat ( Triticum aestivum L . ) pedigree would be a valuable resource for the wheat research and breeding communities . Its development would allow inheritance and origins of beneficial genes and alleles to be tracked through the pedigree to identify sources of traits and genetic variation for research and efficient exploitation . For example , as new races of pathogens evolve to overcome variety resistances [2] , sources of resistance could be quickly identified and integrated into breeding programmes . Where genotype data are available on ancestors in a pedigree , genetic identity or estimated breeding values of ungenotyped descendants could be inferred by pedigree based simulations [3] . Within breeding programmes , selection of breeding material can optimise maintenance of genetic diversity with improvement in breeding value [4] . Crosses between genetically distant parents may present a wider genetic variance available for selection [5] and also result in greater potential for heterosis and higher performance of F1 hybrid varieties [6–8] . Information on relatedness among available varieties could also help farmers increase genetic diversity at a farm scale , resulting in resilient systems to deal with climate instability and biotic stresses [9] . Studies of smaller scale pedigrees of crop varieties have often compared calculations of kinship between varieties based on pedigree or genetic marker data . However , these studies commonly used low marker numbers and found low correlations between the two methods for estimating kinship [10–12] . Such studies quickly become outdated: new varieties are released every year , and recent developments in genotyping technologies have meant much higher marker numbers are now available . Other limitations of this comparison include the assumption that a pedigree-based estimation of kinship assumes random inheritance and the absence of selection , which is unlikely to be the case in crop species such as wheat . Large-scale pedigree databases have been developed as research tools in other crops , including oats [13] and rice [14] . Although the International Maize & Wheat Improvement Center ( CIMMYT ) hosts a large wheat database , including wheat pedigree data ( http://wheatpedigree . net/ ) , this pedigree information is only available on a ‘per accession’ basis . Few other pedigree resources are available in wheat . Here , we present a large-scale pedigree of United Kingdom ( UK ) wheat varieties and their ancestors , available in a format suitable for visualisation in software such as Helium ( The James Hutton Institute , Scotland , UK ) [15] , Pedigree Viewer ( University of New England , Biddeford , ME ) [16] , and Pedimap ( Wageningen University , Wageningen , Netherlands ) [17] , representing a valuable resource for the wheat-breeding and research community . We validate the pedigree using a subset of 450 genotyped individuals to compare kinship coefficients calculated by markers and pedigree . We demonstrate evidence of breeder selection by comparison of observed genotype distributions with predictions generated via gene dropping simulations under Mendelian sampling of known founder genotypes [18–19] . We show that ( i ) kinship coefficients calculated from markers or pedigree data show strong positive correlation and that large deviations from this correlation are due to erroneous pedigree or seed source data; ( ii ) within crosses , selection by breeders favours genetic material from the superior parent to which the selected variety will be disproportionally related; and ( iii ) higher than expected linkage disequilibrium ( LD ) in recent varieties and changes in allelic diversity provide evidence of selection by breeders over multiple generations of the pedigree . Details of the lines and genomic regions involved provide insight into selection targets and breeder strategies , and the approaches presented are applicable to many crop species .
To account most effectively for multiple generations of inbreeding used in wheat variety development , kinship calculations were made using an augmented pedigree , including seven intermediate generations of selfing from parents to progeny for each accession with parent information . Using the 15 , 852 entries within the augmented pedigree , 121 , 391 , 571 pairwise comparisons of kinship were calculated; 29% of these comparisons were between entries without known common ancestors in the pedigree that gave a kinship of zero and were omitted from further analysis . Pedigree-based kinship coefficients were compared with a subset of 454 varieties for which genotypic data ( 4 , 009 single nucleotide polymorphisms [SNPs] ) were available . For these , kinship between different varieties based on pedigree varied from 4 . 7 × 10−7 to 0 . 82 and averaged 0 . 11 , whereas kinships calculated by SNPs varied from 0 . 58 to 0 . 99 and averaged 0 . 72 . Although different ranges of kinship values are expected due to differences in the methods used to calculate , a significant correlation ( r = 0 . 63 , P < 0 . 001 ) was found between the two equivalent kinship matrices . Notable , nonrandom deviations from this relationship were found , which facilitated identification of erroneous pedigree information , errors in genotyping , or evidence of strong selection within crosses . Detailed investigation of marker kinships between varieties and their immediate ancestors and descendants revealed clearly erroneous information in either the pedigree or seed source used for genotyping for 40 varieties . When these varieties were removed , an improved correlation of 0 . 68 was found between marker- and pedigree-based kinships . The removal of five additional closely related varieties with information on only one parent improved this correlation to 0 . 71 . Detection of pedigree and/or seed errors is exemplified in S4 Fig , in which there is a notably disproportionate number of marker kinships of approximately 0 . 63 , which were underestimated based on pedigree information . This anomaly is entirely explained by two lines ( ‘Cyber’ and ‘Maris Ensign’ ) , which were mislabelled as spring wheat varieties when they were actually more distantly related winter wheat varieties . The relationship between the pedigree and marker kinship estimates for the 409 lines remaining after removal of all identified lines with erroneous information and missing parental data is shown in Fig 3 . Evidence for selection by breeders within family crosses was found using a combination of pedigree and genetic marker data . Out of a set of 109 ‘simplex families’ with SNP data ( in which a simplex family represents two parents and their one progeny ) , the most closely related parent to the progeny within each family shared a median proportion of genome of 0 . 57 , with 77/109 to a greater extent than expected based on 1 , 000 simulations with gene dropping at the p ≤ 0 . 001 significance threshold of 0 . 54 ( Fig 4 ) . The varieties ‘Robigus’ and ‘Capelle Desprez’ were the most commonly used parents in the pedigree and were always the parent that was more closely related to the progeny , and therefore positively selected , in the five and seven families in which they were included , respectively . Additional long-term evidence for breeders’ selection was found by investigating selection effects across the whole pedigree . Using a subset of the pedigree that included just those founders with genotypic information ( 110 ) , gene dropping simulations compared observed- and simulated-allelic diversity measures for 45 derived varieties released after the year 2000 ( S5 Fig ) . Average values of allelic diversity across all 1 , 821 SNPs were similar between observed ( 0 . 283 ) and simulated ( 0 . 278 ) genotypes . However , when considering each marker separately , 0 . 9% of the markers showed a lower diversity than five standard deviations of the simulated distribution ( equivalent to a Bonferroni corrected P value of 0 . 001 ) , compared to only 0 . 1% showing higher diversity than five standard deviations of the simulated distribution found from 100 gene dropping simulations ( Fig 5; Table 1 ) . Selection can also be inferred from differences in LD decay between observed and simulated genotypes . Pairwise comparisons of LD were found to be much higher in observed than simulated data , across all chromosomes ( Figs 6 and 7 ) . For pairwise LD , the average genetic distance at which R2 fell to 0 . 15 was 25 . 9 cM in the observed data , compared to 11 . 2 cM for the simulated data ( S2 Table ) . Furthermore , the magnitude of difference between observed and simulated data was highly dependent on the cM distance between markers . Average R2obs−R2sim is constant at around 0 . 03 at genetic distances greater than 100 cM and between markers on different chromosomes but linearly increases to 0 . 16 as the distance between markers decreases from 50 cM to 1 cM ( Fig 7 ) . Collectively , this appears to demonstrate that directional selection by breeders has resulted in conservation of haplotype blocks containing beneficial gene combinations .
Using the entire pedigree , more than 121 million kinship coefficients were calculated . A subset of these were compared , with over 110 , 000 kinship coefficients also calculated using SNPs . The relatively high correlation between kinship calculations validates the pedigree and is much greater than coefficients previously found in smaller studies . Laidò and colleagues [27] found coefficients of 0 . 21 and 0 . 23 between pedigree kinships and Diversity Array Technology ( DArT ) or simple sequence repeat ( SSR ) markers , respectively , for a set of 116 durum wheat varieties; Soleimani and colleagues [12] found a coefficient of 0 . 46 for a set of 13 durum wheat varieties . In this study , the greater value of 0 . 71 could be due to the much larger size of the pedigree used , which spans a wide range of geographic origins and histories . This allows for a more complete comparison of pedigree kinships , as common ancestors between distantly related accessions are more likely to be identified . The weakly curvi-linear shape of the correlation found here ( Fig 3 ) is caused by a wide range of marker kinship values at very low pedigree kinship values . This reflects low pedigree information in this region of the graph , i . e . , missing pedigree data in parents , grandparents , and/or great grandparents for many relatively modern varieties in which unconnected founder individuals are assumed to be completely unrelated . An example of this is ‘Robigus’: although it has been commonly used in the pedigree of recent UK elite varieties , both of its parents are of unknown origin . Therefore , ‘Robigus’ and all of its descendants are subject to an underestimated pedigree-based kinship estimate . The same explanation may partly underlie the wider spread of marker kinship estimates above the diagonal line than below . However , on closer inspection , some of the more extreme upward outliers with marker kinship >0 . 9 appear to result from difficulty in correctly calculating pedigree kinship in complex older PBI and CIMMYT pedigrees that often included multiple backcross generations . Nevertheless , extreme deviations from the diagonal in either direction were highly informative in detecting errors , either in the published pedigree information or the material used for genotyping ( S1 Text ) . The high proportion of comparisons with pedigree-based kinship close to 0 . 5 represent varieties with the same parentage . Whilst the advantage of a pedigree-based approach to estimating kinship over a marker-based approach is that a much larger number comparisons can be made without the cost of genotyping , marker-based kinships provide a more informative estimate of genomic relationships for use in practices such as genomic selection [28–29] and QTL mapping studies [30] . In most species , marker relationship matrices are commonly used for trait prediction and association mapping in preference to using the pedigree . However , if marker densities are low or no markers are available , pedigree relationships continue to be used . A recent example in which both wheat marker- and pedigree-based estimates of kinship were fitted simultaneously found the inclusion of the pedigree improved the accuracy of trait prediction [31] . It is becoming more common to fit multiple estimates of relationship , e . g . , derived from partitions of a marker set into separate classes [32] , or for additive and dominance effects ( e . g . , [33] ) . It is pragmatic to include both matrices if available , and their relative merits will be decided within the analysis . An alternative option , not studied here , would be to combine the two so the markers could improve relationship estimates among founders , which are otherwise treated as unrelated , and the pedigree could help estimate relationships among individuals with missing marker data . One of the key assumptions of the pedigree-based approach to calculating kinship is that inheritance is random and in the absence of selection [34] . However , strong selection for traits , including improved yield , height , quality , and disease resistance , has undoubtedly taken place in wheat breeding programmes to achieve the genetic gains over the last century [35] . Here , we tested this assumption using a combination of the pedigree and genetic marker data to perform gene dropping simulations to compare observed variety genotypes against simulations in the absence of selection . By investigating the genetic relatedness of a set of varieties to each of their parents , we found that the majority of varieties demonstrated unequal parental contributions far outside the distribution predicted by simulations . This indicates that whilst the initial F1 from a breeder’s cross will carry exactly half of the alleles from each parent , the subsequent generations of inbreeding and segregation are opportunities for breeders to select segregants with a greater proportion of beneficial alleles that would have come from the superior parent . Our results support this and highlight the effectiveness and intensity of selection performed in wheat breeding programmes over and above simulated genetic drift . We also found that varieties that have been used extensively as parents , such as ‘Robigus’ and ‘Capelle Desprez’ , are also favoured as the dominant parent in subsequent selections . This underlines the historic importance of these varieties and their contribution of beneficial genetic resources to advances in wheat breeding . Our results also have implications for definition of essentially derived varieties ( EDVs ) , defined by the International Union for the Protection of New Varieties of Plants ( UPOV ) as when ‘ ( i ) it is predominantly derived from the initial variety , or from a variety that is itself predominantly derived from the initial variety , while retaining the expression of the essential characteristics that result from the genotype or combination of genotypes of the initial variety , ( ii ) it is clearly distinguishable from the initial variety and ( iii ) except for the differences which result from the act of derivation , it conforms to the initial variety in the expression of the essential characteristics that result from the genotype or combination of genotypes of the initial variety’ [36] . We found that wheat varieties derived from biparental crosses commonly share over 80% of their genetic material with one parent , which is greater than would be expected by backcrossing to a recurrent parent . This highlights the difficulties in defining a threshold of genetic similarity for EDVs in wheat and supports similar findings in other crops [37–38] . An alternative explanation for greatly differing parental relatedness would be that many registered varieties have been derived from incorrectly declared backcrosses . However , this is improbable given the median proportion of genome inherited from the maximally related parent was 0 . 57 , which is substantially below the expected 0 . 75 . Because it is uncommon for wheat varieties to be derived by backcrossing , especially with more modern varieties , we believe the observed excess distortion in favour of one parent is likely due to selection . We demonstrated selection over multiple generations by comparing observed and simulated genotypes in a subset of the pedigree in which 110 genotyped varieties released before the year 2000 were used to predict the distribution of marker alleles in the absence of selection of 45 varieties released after the year 2000 . Amongst these 45 varieties , significant deviations from the expected simulated allelic diversity were used as an indication of breeders’ selection . When directional selection is in favour of one allelic variant , allelic diversity is ultimately reduced . Increased diversity could indicate selection for a more equal balance of alleles in the population , which may be the case if different alleles are favoured in contrasting wheat classes , such as for yield or quality . Alternatively , it could result from a transient polymorphism resulting from a rare allele , which has increased in frequency under selection but has yet to be fixed . Our results indicate that whilst average allelic diversity across all markers was similar to expected from simulations , a small number of lower than expected values were found for individual makers . This gives an indication of the location of genomic regions consistently under selection during the development of varieties released in the 21st century . The regions identified at the stringent significance thresholds used here are not localised to the known major flowering time loci VRN-A1 , VRN-B1 , VRN-D1 ( on the group 5 chromosomes; reviewed by [39] ) or the dwarfing genes RHT-B1 and RHT-D1 ( chromosome 4B and 4D , respectively ) . This is expected , because these loci of major phenotypic effect are already fixed in the materials and time spans used here to investigate selection . However , at the genetic resolution currently available , the major yield QTL on 7AL ( Qyld . csdh . 7AL ) is thought to be principally due to increased grain number per ear [40–41] approximates to the 7A region of decreased diversity identified here , indicating selection for greater yield potential . This possible colocalisation is based on the IWGSC RefSeq version 1 . 0 wheat physical map positions of the peak 7A marker WMC273 identified by [40] and SNP IAAV5268 identified in this study ( 717 . 079 versus 679 . 839 Mbp , respectively ) . Given that this genetic locus ( i ) was identified after the year 2000 ( here we investigated selection in materials pre- and post-2000 ) and ( ii ) has alleles of relatively large phenotypic effect , it is likely that beneficial alleles have been strongly selected at this locus in recent years . This finding highlights the possibility that the additional loci we detected are also under breeder phenotypic selection , as well as supporting the use of this approach for the identification of the genomic regions underlying breeding targets in other crops . It has been suggested that progress in wheat breeding has in part been a result of assemblage of beneficial linked epistatic gene interactions [42] . This is supported by our comparison of observed LD with that expected from gene dropping simulations . It is evident that LD at short genetic distances is considerably higher than expected from simulations . This suggests that breeder selection is favouring conservation of favourable haplotypes and linkage blocks . Rhoné and colleagues [43] found effects in an experimental wheat population grown under natural selection in which selection favoured an important yellow rust resistance gene , increasing LD and reducing diversity around the gene region . However , some of these effects may also be explained by , or be in addition to , ( i ) the strong segregation distortion around common putative introgression fragments identified in the genetic map used here ( constructed using a multiparent mapping population in the absence of intentional selection ) [20] or ( ii ) by inflated map distances . The implications of these findings go some way to explaining the relatively high levels of LD found in association mapping panels of highly selected varieties [26 , 44–45] . Methods are being developed to increase recombination in domesticated crops [46] because there is concern that there is insufficient recombination within the pericentromeric regions of domesticated crop genomes . The results here suggest that care is required: increased recombination in breeders’ germplasm will be beneficial if the extensive LD we have found is predominantly a result of linkage drag or hitchhiking but will be disadvantageous if it breaks up favourable linkages that may have been built up over many generations of selection . The difference between observed and simulated local LD estimates is much higher than that for long-distance LD . Nevertheless , long-distance LD is also weakly elevated in the observed versus simulated data ( 0 . 042 versus 0 . 0141 for off-chromosome averages; see Fig 6 for visual comparison ) . This may be partly generated by directional selection causing segregation distortion ( Fig 4 ) . However , scattered regions within which much higher than expected LD between markers on different chromosomes could be identified ( e . g . , between blocks on chromosomes 1D and 2B , Fig 6 ) likely indicate selection for more distant epistatic genetic relationships or directional selection on polygenic traits [47] . Pedigree-based tests for selection are more common in animals and humans than in plants . Our test for selection within families is essentially the same as the transmission disequilibrium test [48–49] , which can be regarded as a test for the efficiency of selection on progeny in distorting segregation patterns from the parents . In the plant equivalent , no distortion is possible at the F1 , provided the parents are homozygous , but we may detect distortion after several subsequent generations of selfing ( or doubled haploid production ) with accompanying selection . Because we do not have large numbers of simplex families , tests for distortion at individual loci would have low power . However , we have found extensive distortion in favour of one of the parents in many crosses . Larkin and colleagues [50] detected selection in dairy cattle by using approximately 1 million SNPs to reconstruct haplotypes of two elite bulls and comparing observed and expected frequencies in 1 , 149 descendants . They found 49 chromosome segments with strong evidence of selection . Due to the current limit in available wheat genotyping array SNP densities , our data are less extensive , but our approach of using gene-dropping simulations in the pedigree is similar in exploiting the pedigree . Recent work in cattle also suggests that for highly polygenic traits in which selection intensity on any individual locus may be weak , detecting selection in pedigrees may be more appropriate than alternative population based methods , which can have many confounding effects [51] . Here , we have used the available data to illustrate the utility of reconstructing the whole wheat pedigree . We present a pedigree resource for wheat varieties released in the UK up to 2017 . The resource is available at https://www . niab . com/pages/id/501/UK_Wheat_varieties_Pedigree , where it will be periodically updated to incorporate newly released varieties . It is anticipated that further engagement with the wheat breeding community will enable correction of errors in historic pedigrees , as well as provision of pedigree data that is not yet publicly available , thus augmenting the utility of the resource . Whilst the pedigree focuses on UK wheat varieties , significant historic crossover with varieties originating from other countries will facilitate interlinking of fragmented wheat pedigrees maintained in other countries or breeding companies and extend the utility of the resource to the wheat breeding community across the world . Future analysis of the current pedigree will focus on identifying selection on haplotypes . We present a comprehensive wheat pedigree as a resource for the wheat research and breeding community . Recently developed software enables visualisation and navigation of the pedigree , as well as highlights historically important varieties and the diverse origins of elite UK wheat varieties . Comparison of kinship coefficients calculated using the pedigree , as well as genetic markers , validated the pedigree and allowed identification and correction of pedigree and genotyping errors . In conjunction with pedigree and genotypic data , gene dropping simulations demonstrated significant effects of selection within crosses as well as over multiple generations of the pedigree , modulating allelic diversity and conserving LD . These analyses identify the genomic regions controlling putative wheat breeding targets and serve as a model for the identification of genomic regions controlling breeding targets in other crops . The resource developed here will serve as an evolving platform to inform and manage wheat genetic diversity in breeding programmes in the UK and around the world and highlights the need to develop and maintain similar resources in other crop species .
Pedigree information was sourced from publicly available breeders’ records , genebank passport information ( http://genbank . vurv . cz/ewdb/ ) , associated information with commercial varieties released in the UK ( https://cereals . ahdb . org . uk/varieties/ahdb-recommended-lists . aspx ) , textbooks [21 , 52] , wheatpedigree . net ( and references therein ) , and with permission , from breeder’s private records . Genotypic data , available for 454 accessions within the pedigree , were previously generated within the Biotechnology and Biological Sciences Research Council ( BSBRC ) project Wheat Association Genetics for Trait Advancement and Improvement in Lineages ( grant reference BB/J002542/1 ) , using the wheat 90k Illumina iSelect SNP array [53] , following previously described methods [54] . The raw genotype data were sourced from http://www . niab . com/pages/id/326/Resources/ . Pedigrees were visualised using Helium version 1 . 18 . 03 . 15 [15] . Pairwise comparisons of kinship based on the pedigree were calculated using the ‘kinship2’ package [55] in R [56] . The pedigree was augmented to include seven intermediate generations of selfing from parents to progeny for each accession to account for multiple generations of inbreeding used in wheat variety development . This is , of necessity , an approximation and cannot account for lines created from doubled haploids ( information for which is frequently not available ) . The kinship coefficient between two individuals is the probability that a randomly selected allele at a locus in one individual is identical by descent with a randomly selected allele in the other . Individuals with no known common ancestors in the pedigree have a kinship of 0 . Fully inbred sibling lines of inbred parents have a kinship of 0 . 5 , and kinship of an inbred line with itself is 1 . Kinship coefficients based on genetic markers were calculated based on identity by state ( IBS ) allele-sharing implemented in R/EMMA ( http://mouse . cs . ucla . edu/emma/ ) for the 454 accessions that were in common with the pedigree . An IBS approach was chosen for simplicity and ease of comparison with the pedigree estimates , because the assumption that alleles are drawn from a random global population—and the subsequent estimation of allele frequencies—is problematic for calculating genomic relationship matrices in the complexly structured population of inbred lines found in the pedigree . The 26 , 018 available polymorphic Illumina iSelect SNPs were thinned to remove closely linked markers by removal of one of each pair of markers with an absolute correlation >0 . 75 to minimise the effect of the very high levels of marker clustering observed in cereal crops [57] , resulting , e . g . , from commonplace interspecific introgressions [20] and large nonrecombining tracts spanning the centromeres [58] . This resulted in 4 , 009 SNPs for downstream analyses . A comparison of pedigree-based and marker-based kinship coefficients was made for the 102 , 831 pairwise comparisons between accessions with both pedigree and marker data , by calculating the Pearson correlation coefficient between elements of the two kinship matrices . To confirm whether an appropriate number of generations of inbreeding was used to estimate the pedigree kinships , the comparison between pedigree and marker based kinships was also made with pedigree kinships based on 5 , 7 , and 10 generations of inbreeding . Correlation coefficients with marker based kinships differed on average by only 0 . 02% . Of the 454 genotyped accessions , 109 also had genotype data for both parents . For each of these ‘simplex families’ ( i . e . , two parents and their one progeny ) , the proportion of alleles inherited from each parent was calculated . To estimate the variation in this measurement in the absence of selection , Genedrop ( NIAB , Cambridge , UK ) simulation software ( as described in [19] ) was used . Briefly , gene dropping is a permutation analysis , in which multiple simulations are run assuming mendelian inheritance with a 50:50 transmission probability of alleles from parent to offspring of genetic loci defined in the founder generation . The frequency of alleles in the multiple repeated simulations enables definition of probabilities of the occurrence of observed genotypes . Following common practice , we use the term ‘gene dropping’ here to describe the simulation of the descent of multiple loci , with recombination ( based on a provided genetic map ) , through a pedigree , and not just the inheritance of a single locus . One thousand simulations were run using two parents that were polymorphic at all 4 , 009 loci so that the F1 between the parents was completely heterozygous . SNP genetic map positions were sourced from the eight-parent ‘NIAB Elite MAGIC’ genetic map [20] . Seven generations of selfing were included to account for inbreeding in the variety development process . The proportion of alleles inherited from each simulated parent was calculated for each simulation and P = 0 . 01 and P = 0 . 001 significance thresholds determined from this empirical distribution . To investigate selection effects over multiple generations , a subset pedigree was made that included 110 founding accessions with genotype data that were released before the year 2000 . This date was chosen as the approximate median value of the date of release of the varieties with available genotype data . From these genotyped founders released prior to 2000 , 207 descendants could be identified , which do not include ancestry outside of the founder gene pool . Forty-five of the descendants were released after the year 2000 and had genotypic data . One hundred simulations were carried out for this pedigree subset using Genedrop software with the 1 , 821 polymorphic SNPs available with genetic map positions sourced from [20] . Markers were selected to remove one of each pair of markers with an absolute correlation >0 . 75 or that mapped to the same position on the ‘NIAB Elite MAGIC’ genetic map [20] . As with other simulations , seven assumed generations of selfing were included to account for inbreeding . From the 45 descendants , estimates of allelic diversity were calculated for each biallelic marker locus for both observed and simulated genotype data . Diversity [59] was calculated as 1−∑ ( pi2 ) , ( 1 ) where pi is the frequency of the ith allele . Squared correlation coefficients ( R2 ) were calculated for all 311 , 655 pairwise comparisons among 790 markers across the genome for observed and simulated data to estimate LD . These markers were a subset of the 1 , 821 outlined above with a minor allele frequency >0 . 2 . For each chromosome , the simulated and observed LD decay curve was modelled using a loess curve fit , with span smoothing parameter set to 0 . 75 . The fitted curve was used to estimate the genetic distance at which LD fell to 0 . 15 for each chromosome in the observed and simulated data sets . The fitted curve was additionally used to estimate the average LD at different genetic distances along the chromosomes . To anchor genetic markers to the physical map , DNA sequences associated with selected genetic markers were used as queries for BLASTn ( University of Washington , WA ) [60] searches of the wheat cv ‘Chinese Spring 42’ genome assembly ( IWGSC RefSeq version 1 . 0 [58] ) , and the hits were ranked by expectation value ( e-value ) .
|
Breeding activities undertaken in the world’s most important crop species have resulted in large increases in yield potential over the last century . Bread wheat is a key crop for both human and animal nutrition worldwide . To help inform future breeding and research activities , we have developed a pedigree resource of over 2 , 600 bread wheat accessions , originating from 38 countries and representing more than a century of breeding and variety development . Pedigree-based relationships between lines are largely confirmed by genetic marker data . By combining the genetic and pedigree data sets , we are able to identify genetic signatures of selection across the pedigree , identifying genomic regions selected for via modern breeding activities . The resource developed here will serve as an evolving platform to inform and manage wheat genetic diversity in breeding programmes around the world and highlights the utility of developing and exploiting similar resources in other crop species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"ecology",
"and",
"environmental",
"sciences",
"variant",
"genotypes",
"crop",
"genetics",
"genetic",
"mapping",
"cereal",
"crops",
"plant",
"science",
"crops",
"molecular",
"biology",
"techniques",
"molecular",
"genetics",
"plants",
"research",
"and",
"analysis",
"methods",
"ecological",
"metrics",
"grasses",
"crop",
"science",
"gene",
"mapping",
"plant",
"genetics",
"wheat",
"molecular",
"biology",
"species",
"diversity",
"agriculture",
"plant",
"breeding",
"methods",
"&",
"resources",
"agronomy",
"eukaryota",
"ecology",
"heredity",
"genetics",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2019
|
A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders
|
The C-type lectin DC-SIGN ( CD209 ) is known to be the major dengue receptor on human dendritic cells , and a single nucleotide polymorphism ( SNP ) in the promoter region of CD209 ( −336 A/G; rs4804803 ) is susceptible to many infectious diseases . We reason that variations in the DC-SIGN gene might have a broad influence on viral replication and host immune responses . We studied whether the rs4804803 SNP was associated with a susceptibility to dengue fever ( DF ) and/or dengue hemorrhagic fever ( DHF ) through genotyping analysis in a Taiwanese cohort . We generated monocyte-derived dendritic cells ( MDDCs ) from individuals with AA or AG genotype of rs4804803 to study the viral replication and immune responses for functional validation . A total of 574 DNA samples were genotyped , including 176 DF , 135 DHF , 143 other non-dengue febrile illnesses ( OFI ) and 120 population controls . A strong association between GG/AG genotypes of rs4804803 and risk of DHF was found when compared among DF , OFI and controls ( p = 0 . 004 , 3×10−5 and 0 . 001 , respectively ) . The AA genotype was associated with protection against dengue infection compared with OFI and controls ( p = 0 . 002 and 0 . 020 , respectively ) . Moreover , MDDCs from individuals with AG genotype with a higher cell surface DC-SIGN expression had a significantly higher TNFα , IL-12p40 , and IP-10 production than those with AA genotype in response to dengue infection . However , the viral replication in MDDCs with AG genotype was significantly lower than those with AA genotype . With both genotypes , MDDCs revealed an increase in viral replication following the addition of anti-IP-10 neutralizing antibody . The rs4804803 SNP in the CD209 promoter contributed to susceptibility to dengue infection and complication of DHF . This SNP with AG genotype affects the cell surface DC-SIGN expression related to immune augmentation and less viral replication .
Dengue viruses ( DEN ) are arthropod-borne flaviviruses that cause dengue fever ( DF ) with significant morbidity and mortality in tropical and subtropical regions of the world . There are four serotypes of dengue viruses ( DEN types 1–4 ) . Classic DF is a self-limited illness characterized by fever , headache , myalgia , arthralgia , and abdominal pain . Since the 1950s , a more severe form of the disease , dengue hemorrhagic fever ( DHF ) , has been recognized worldwide [1] . Patients who develop DHF typically have initial symptoms similar to those in DF patients , but develop cytokine-storm-like plasma leakage manifested by hemoconcentration , thrombocytopenia , ascites , and pleural effusion near the defeverence stage [2] . DHF pathogenesis has been attributed to viral virulence versus immune enhancement; however , that has been the subject of debate for many years [2] , [3] . The innate immune system is the first line of host defense against pathogens and is involved in early recognition and uptake of microbes by the host's professional phagocytes such as dendritic cells ( DCs ) and macrophages , through germline-encoded receptors , known as pattern recognition receptors ( PRRs ) [4] . These PRRs recognize microbial antigens and initiate innate immune responses followed by adaptive immunity [5] . PRRs are involved in phagocytosis , antigen presentation , and they trigger intracellular signaling and cytokine secretion [5] . PRR polymorphisms may therefore affect virus entry , replication , and immunity . Among the PRRs , the CD209 molecule , also known as DC-SIGN ( dendritic cell-specific intercellular adhesion molecule-3 grabbing non-integrin ) , plays an important role in the early interaction of a pathogen with a dendritic cell [6]–[8] and has a key role in DC-T cell interaction [9] , DC migration [10] , [11] , and pathogen uptake [12] . DC-SIGN is organized into three domains , the N-terminal domain is located in the cytoplasm , the transmembrane domain anchors to the cytoplasmic membrane , and the extracellular domain consists of a neck region formed by seven highly conserved 23 amino acid repeats and a carbohydrate domain for pathogen binding [13] . The CD209 gene is located on chromosome 19p13 . 2–3 and is highly polymorphic . Numerous single nucleotide polymorphisms ( SNPs ) have been reported [14]–[18] . One of these SNPs represents a guanine ( G ) to adenine ( A ) transition at position −336 within the CD209 gene promoter ( rs4804803 ) . This variant has been associated with an increased risk for parenteral acquisition of human immunodeficiency virus ( HIV ) infection [15] , severity of dengue infection [16] , and confered high susceptibility to tuberculosis in a South African cohort [17] . Nevertheless , Vannberg et al . found that G variant allele of rs4804803 was associated with protection against tuberculosis in individuals from sub-Saharan Africa [18] . This variant affects CD209 promoter activity with multiple transcription factor binding sites for the Sp1/GATA1/CACCC- and CAC-binding transcription factors in a transfection study [16] . As an in vitro study of promoter activity might not reflect an actual functional association , we aimed to test whether the rs4804803 SNP in the promoter region of CD209 was associated with the susceptibility to DF and/or DHF in Taiwanese , and whether monocyte-derived DCs from humans with various genotypes of rs4804803 would reveal differences in DC-SIGN membrane expression and implicate the viral replication and immune reactions after DEN infection .
This study was approved by the Institution Review Board ( IRB ) of Chang Gung Memorial Hospital-Kaohsiung Medical Center , Taiwan . The dengue patients were recruited as described previously in the 2002–2003 DEN-2 outbreak in Taiwan [19]–[23] . A larger retrospective cohort was designed and re-approved by an additional IRB review ( Document No . : 97-2111B ) . To validate cell surface expression and immune functions of rs4804803 SNP , we obtained informed consent to collect blood leukocytes from normal volunteers with AA or AG genotypes of rs4804803 . DEN infection was confirmed by clinical dengue symptoms and signs along with detection of DEN-2 RNA by quantitative RT-PCR in blood , detection of IgM to DEN or at least a 4-fold increase in dengue-specific hemagglutination inhibition titers in convalescent serum compared with that in acute-phase serum [20] , [21] . In those with DEN-2 infection , blood was drawn at least once a day subsequent to admission into the hospital to measure the platelet counts and hematocrit levels . A Chest X-ray and abdominal ultrasonography were performed routinely in individuals without evidence of hemoconcentration or hypoalbuminemia to refine the differential diagnosis of DHF vs . DF based on pleural effusion or ascites . A clinical diagnosis of DHF was assigned according to the DHF criteria of the World Health Organization ( WHO ) ; including a reduced platelet count ( <100 , 000/mm3 ) , petechiae , hemorrhagic manifestation , and plasma leakage showing hemoconcentration ( peak hematocrit ≥20% above the mean for the population , or an increase in hematocrit of 20% or more ) , pleural effusion , ascites , or hypoalbuminemia [24] . Patients with DF were defined by detectable DEN-2 RNA by RT-PCR or DEN-specific IgM , but without evidence of DHF . Primary or secondary DEN infections were identified using previously established serologic criteria for IgM/IgG ELISAs [19] . Patients with other non-dengue febrile illnesses ( OFI ) were defined by febrile illness with no detectable DEN-specific IgM , no detectable DEN RNA , and no obvious or reported bacterial etiology for their illness during the same study period . Population controls were healthy , unrelated volunteers from the same community , with neither signs nor previous history of dengue infection , with a DEN IgG sero-positive rate of 1 . 37% ( 1/73 ) . Genomic DNA was isolated from heparin-anticoagulated blood samples using a standard phenol-chloroform extraction followed by 70% alcohol precipitation . Genotyping for the CD209 variant ( −336 A/G; rs4804803 ) was carried out using Custom TaqMan SNP Genotyping Assays ( Applied Biosystems , Foster City , CA , USA ) . The primer sequences were 5′-GGACAGTGCTTCCAGGAACT-3′ ( forward ) and 5′-TGTGTTACACCCCCTCCACTAG-3′ ( reverse ) . The TaqMan minor groove binder probe sequences were 5′-TACCTGCCTACCCTT G-3′ , and 5′-CTGCCCACC CTTG-3′ . The probes were labeled with the TaqMan fluorescent dyes VIC and FAM , respectively . The PCR was conducted in total volume of 15 µL using the following amplification protocol: denaturation at 95°C for 10 min , followed by 40 cycles of denaturation at 94°C for 20 s , followed by annealing and extension at 60°C for one minute . After the PCR , the genotype of each sample was determined by measuring the allele-specific fluorescence in the ABI Prism 7500 Sequence Detection System , using SDS 1 . 1 software for allele discrimination ( both Applied Biosystems ) . To validate the genotyping by real-time PCR analysis , 100 PCR products were subject to restriction fragment length polymorphism ( RFLP ) analysis with MscI restriction enzyme ( New England Biolabs , Beverly , MA , USA ) and showed 100% identical result between these two genotyping systems . Peripheral blood mononuclear cells were collected from peripheral blood of 20 healthy , DEN-specific IgM or IgG seronegative volunteers with AA or AG genotype . CD14+ monocytes were isolated by positive selection according to the manufacturer's specifications using CD14 microbeads and a magnetic cell separator ( MACS ) ( Miltenyi Biotec , Bergisch Gladbach , Germany ) . Enriched CD14+ cells ( purity>95% ) were cultured for 6 days in six-well plates in complete RPMI medium in the presence of 10 ng/mL rhGM-CSF and 5 ng/mL rhIL-4 at 37°C , and 5% CO2 . On day 3 , half of the medium was replaced with fresh medium supplemented with rhGM-CSF and rhIL-4 . Expression of markers was measured by flow cytometer using specific antibodies and their corresponding isotype controls . Unless otherwise stated , monocyte-derived dendritic cells ( MDDCs ) were infected with DEN-2 at a multiplicity of infection ( MOI ) of 5 for 2 h at 37°C and 5% CO2 . Cells were washed twice to remove cell-free virus , and cultured in complete RPMI medium ( without cytokines ) at a density of 2×105 cells/ml in 48-well plates . Cells and supernatants were removed and analyzed at 24 , 48 , and 72 h post-infection . For the neutralization experiments , cells were incubated in the medium alone or in the medium with the addition of anti-human CXCL10/IP-10 antibody ( R&D Systems , Minneapolis , MN , USA ) at 10 µg/mL for 30 min . Cells and supernatants were harvested and analyzed 24 h post-infection . Total RNA extracted from MDDCs was subjected to quantitative RT-PCR to assess levels of mRNA corresponding to CD209 and ß2–microglobulin ( B2MG ) using the ABI PRISM 7500 instrument ( Applied Biosystems ) . The forward primer , reverse primer sequence for detecting CD209 and B2MG were 5′-AACAGCTGAGAGGCCTTGGA-3′ , 5′-GGGACCATGGCCAAGACA-3′ , and 5′-AATTGAAAAAGTGGAGCATTCAGA-3′ , 5′-GGCTGTGACAAAGTCACATGGTT-3′ , respectively . The PCR cycling parameters were 40 cycles of PCR reactions at 94°C for 20 s , and 60°C for one minute . The results were detected in real-time and recorded on a plot showing fluorescence vs . time . RT-PCR products were also visualized on ethidium bromide-stained 1 . 5% agarose ( Pierce Co . , Rockford , IL , USA ) gel with a 100- bp ladder ( Pharmacia Biotech , Piscataway , NJ , USA ) as a reference . To measure the CD209 cell surface expression , MDDCs were stained with FITC-conjugated mAbs specific for DC-SIGN ( R&D Systems , Minneapolis , MN , USA ) . An isotype-matched FITC-labeled control , mouse IgG2b ( clone MOPC195 , Immunotech , Beckman Coulter , Fullerton , CA , USA ) was included in each experiment . Total RNA extracted from MDDCs was subjected to assess DEN-2 RNA viral copies . Fluorescent RT-PCR was performed in an ABI 7500 quantitative PCR machine ( Applied Biosystems ) for 40 cycles using TaqMan technology as previously described [21] . Cytokine/chemokine production and viral replication were determined at 24 , 48 , and 72 h post-infection . Cell-free culture supernatants TNFα and macrophage chemoattractant protein 1 ( MCP-1 ) concentrations were measured using ELISA kits from eBioscience Inc . ( San Diego , CA , USA ) ; IL-12p40 and IFN-inducible protein 10 ( IP-10 ) concentrations were measured using ELISA kits from R&D Systems as per manufacturer's instructions . Data are presented as mean ± SEM values . Alleles and genotypes distribution of rs4804803 are presented as numbers ( percentages ) . Conformance of the allele frequencies to Hardy-Weinberg equilibrium proportions was tested to compare the observed and expected frequencies of heterozygotes and homozygotes . Differences among patients with DEN , DF , DHF , OFI , and population controls were determined using two-sided Chi-Square test or Fisher exact test . Odds ratio ( OR ) values were calculated with 95% confidence intervals ( CI ) . The Student's t-test or Mann-Whitney U test was used for statistical comparisons between continuous variables . The Wilcoxon signed-rank test was used for statistical comparison of the neutralization experiments . All analyses were performed using SPSS 13 . 0 ( SPSS Inc . Chicago , IL , USA ) .
During a large DEN-2 outbreak in southern Taiwan between June 2002 and January 2003 , a hospital-based case-control study was used to identify the risk immune parameters [19]–[23] . Employing the decoded DNA samples from that same cohort of the population that study has been extended to investigate the association of rs4804803 SNP with DF , DHF , viral replication , and immune response . Based on the previous case-control study design , we have included DNA samples from 135 DHF , 176 DF , and 143 OFI patients in this expanded study . The main characteristics of the study population are summarized in Table 1 . There were no significant differences in sex or total leukocyte counts between patients with DF and those with DHF . However , age ( 41 . 7±1 . 6 years vs . 45 . 7±1 . 3 years , p<0 . 001 ) , serum GOT levels ( 70 . 1±8 . 1 U/mL vs . 313 . 8±74 . 6 U/mL , p = 0 . 002 ) and GPT levels ( 67 . 1±11 . 3 U/mL vs . 142 . 7±21 . 9 U/mL , p = 0 . 003 ) were significantly higher in the DHF group ( Table 1 ) . A patient manifested with abdominal pain had ascites as evidenced by abdominal ultrasonography was classified as DF because the patient revealed no thrombocytopenia ( <100 , 000/mm3 ) , petechia or hemorrhagic manifestation during the admission period . We investigated the association of rs4804803 SNP in the promoter region of CD209 with protection from dengue infection and the susceptibility of DHF . Genomic DNA obtained from DEN patients ( n = 311 ) , OFI patients ( n = 143 ) , and population controls ( n = 120 ) was genotyped for rs4804803 SNP . We found that GG/AG genotypes in 16 . 0% of the DEN patients were significantly higher than OFI patients ( 5 . 6% , OR = 3 . 23 , p = 0 . 002 ) and population controls ( 7 . 5% , OR = 2 . 36 , p = 0 . 020; Table 2 ) . Moreover , the GG/AG genotypes were significantly higher in DHF patients ( 23 . 0% ) than OFI patients and population controls ( OR = 5 . 03 and 3 . 68 , p = 3×10−5 and 0 . 001 ) , and also significantly higher than DF patients ( 10 . 8% , OR = 2 . 46; p = 0 . 004; Table 2 ) . Analysis of the allele distribution between DEN and OFI patients or population controls showed that the G allele frequency was higher in DEN patients ( 8 . 4% ) , compared with OFI patients ( 2 . 8% , OR = 3 . 17 , p = 0 . 002 ) or population controls ( 3 . 8% , OR = 2 . 34 , p = 0 . 018; Table 3 ) . Moreover , the frequency of G allele of rs4804803 was significantly higher in DHF patients ( 12 . 2% ) than OFI patients or population controls ( OR = 4 . 84 and 3 . 57 , p = 2×10−5 and 0 . 001 ) , and higher than DF patients ( 5 . 4% , OR = 2 . 44 , p = 0 . 002; Table 3 ) . Few DHF patients ( n = 6 ) had dengue shock syndrome in this cohort; one of them carrying AG genotype . To investigate whether the rs4804803 SNP in the promoter region of CD209 associated with the primary and secondary DEN infection , we used serological methods to detect DEN antibodies for differentiation into primary and secondary dengue infection . Of the 293 DEN patients , 141 ( 48% ) had secondary DEN infections , based on detectable DEN-2 virus RNA and DEN IgG . As shown in Table 1 , secondary DEN-2 infection was more frequently found in patients with DHF than in those with DF ( 65% vs . 36% , p<0 . 001 ) . We found the rs4804803 GG/AG genotypes were found in 12 . 5% of patients with primary DEN infection and 16 . 3% of patients with secondary DEN infection , which did not reach significantly different ( OR = 1 . 36; p = 0 . 352 ) . As shown in Table 4 , there was no association between rs4804803 SNP and primary or secondary dengue infection in DF patients ( OR = 0 . 64; p = 0 . 409 ) , or in DHF patients ( OR = 1 . 80; p = 0 . 251 ) . In addition , there was no association between allele distribution and primary or secondary dengue infection in DF and DHF patients ( data not shown ) . Due to the low frequency of GG genotype in our population ( 2 cases , 0 . 6% ) , we could not recall the patients because the data file had been decoded for identification . We examined DC-SIGN ( CD209 ) expression in both mRNA level of MDDCs and protein level on their cell surface from healthy subjects with AA or AG genotype by quantitative RT-PCR and flow cytometry , respectively . A significant increase in CD209 mRNA expression was detected in the MDDCs from individuals with AG genotype than those from individuals with AA genotype ( p = 0 . 032 , Fig 1B ) . Similarly , individuals with AG genotype had a significantly higher cell surface DC-SIGN expression ( p = 0 . 029; Fig 1D ) . However , the surface DC-SIGN expression declined rapidly along with DEN-2 infection on MDDCs from both genotypes' subjects , which showed no difference at 24 , 48 , and 72 h post-infection ( Fig . 2A ) . To investigate whether the rs4804803 SNP was correlated to viral replication , we measured DEN-2 RNA copies in MDDCs with AA or AG genotype of rs4804803 at 24 , 48 , and 72 h post-infection . DEN-2 replication was significantly higher in MDDCs from individuals with AA genotype than those with AG genotype at 48 h post-infection ( 1 . 07±0 . 45×106 copies/105 cells vs . 3 . 90±0 . 67×106 copies/105 cells , p = 0 . 006 ) and 72 h post-infection ( 4 . 83±0 . 70×105 copies/105 cells vs . 2 . 32±0 . 68×106 copies/105 cells , p = 0 . 003; Fig . 2B ) . Viral replication , as measured at 72 h post-infection , increased more remarkably in MDDCs at MOI of 5 and 10 ( p<0 . 001 and 0 . 002 , respectively; Fig . 2C ) . To investigate whether higher cell surface DC-SIGN expression was correlated with immune response , we investigated kinetic cytokine/chemokine production by MDDCs from individuals with AA or AG genotype of rs4804803 . Results showed that MDDCs with AG genotype had significantly higher TNFα production than those with AA genotype at 24 and 48 hr post-infection ( 303 . 51±66 . 75 pg/mL vs . 143 . 97±68 . 80 pg/mL and 202 . 35±19 . 35 pg/mL vs . 73 . 00±9 . 55 pg/mL; p = 0 . 021 and 0 . 002 , respectively; Fig 3A ) . IL-12p40 production significantly increased by MDDCs with AG genotype than those with AA genotype at 24 , 48 , and 72 h post-infection ( p<0 . 001 , 0 . 007 and 0 . 001 , respectively; Fig 3B ) . We also measured the concentration of two chemokines , MCP-1 and IP-10 , which had been implicated in the recruitment and stimulation of monocytes , macrophages , dendritic cells , NK cells , and T lymphocytes [25] . It was found that IP-10 , but not MCP-1 , production was significantly higher by MDDCs with AG genotype than those with AA genotype at 24 , 48 , and 72 h post-infection ( 620 . 60±175 . 56 pg/mL vs . 243 . 02±41 . 64 pg/mL , 889 . 92±91 . 46 pg/ml vs . 168 . 02±24 . 02 pg/mL , and 614 . 44±49 . 16 pg/mL vs . 322 . 32±69 . 62 pg/mL; p = 0 . 034 , 0 . 009 and 0 . 010 , respectively; Fig 3C ) . The MCP-1 levels peaked at 48 hr in subjects with both genotypes' , but there was no significant difference between AA genotype and AG genotype ( 550 . 72±60 . 73 pg/mL vs . 463 . 92±66 . 80 pg/mL , p = 0 . 157; Fig . 3D ) . IP-10 , produced by non-infected bystander DCs in response to DEN infection , is a potent chemoattractant for activated T and NK cells [26] , and the modulation of adaptive immune response [27] . IP-10 has also been known to inhibit the binding ability of DEN in immortalized cells [28] . In our MDDC model , cells from individuals with AG genotype exhibited an augmented innate immune reaction , showing higher IP-10 production , post-infection ( Figure 3C ) . Based on these results , we hypothesized that DEN-infected MDDCs with AG genotype produced higher levels of IP-10 , which might block viral entry or viral replication in MDDCs . We used an anti-IP-10 neutralizing mAb to block endogenous IP-10 production by MDDCs . With both genotypes , the viral replication 24 h post DEN infection increased significantly more in the presence of neutralizing antibody than in the absence of neutralizing antibody ( p = 0 . 034 and 0 . 040 , respectively; Fig . 4A ) . IP-10 production by MDDCs from individuals with AG genotype significantly decreased ( 795 . 3±368 . 1 pg/mL vs . 273 . 8±87 . 8 pg/mL; p = 0 . 037 ) , but it did not decrease in MDDCs from individuals with AA genotype ( 329 . 8±114 . 2 pg/mL vs . 201 . 8±87 . 0 pg/mL; p = 0 . 091; Fig . 4B ) . These results suggest that IP-10 produced by MDDCs is involved in the viral replication of DEN infection .
DC-SIGN has been shown to be an important receptor for DEN and a number of viruses , including HIV , Helicobacter pylori , and Mycobacterium tuberculosis and hepatitis C virus ( HCV ) [29] . Some studies have demonstrated that genetic variations of CD209 ( rs4804803 ) were associated with the susceptibility to HIV [14] , Mycobacterium tuberculosis [17] , HCV [30] , and dengue [16] . Few studies have demonstrated how the rs4804803 SNP is involved in viral replication or immune response . We are the first in the field to demonstrate the relationship among functional cell surface expression , viral replication , and immune responses in DEN-infected MDDCs from subjects with rs4804803 SNP . Here we found that rs4804803 SNP was strongly associated with the risk of DHF vs . DF and controls . Functional studies have determined that MDDCs from individuals with AG genotype have a significantly higher cell surface DC-SIGN expression than from those with AA genotype . MDDCs with AG genotype produced higher TNFα , IL-12p40 , and IP-10 levels but lower viral replication in response to dengue infection . Because the physiopathology of various manifestations of DHF is not fully understood , several studies have supported the supposition that secondary dengue infection [31] , age [32] , a number of preexisting chronic diseases such as diabetes and bronchial asthma [33] , and host genetic factor [16] , [22] increase the risk of progression to DHF . This indicates that multiple factors are involved in the development of DHF . Our findings regarding rs4804806 SNP associated with DHF vs . OFI control ( p = 3×10−5; Table 2 ) in a case-control association study suggests that rs4804806 SNP contributes in part to the development of DHF . Our study shows that the GG/AG genotypes of rs4804803 were associated with susceptibility to DHF , compared with DF , which is consistent with the observation of Sakuntabhai et al . [16] . In our study , the AA genotype was associated with protection against DHF , compared with OFI and population controls , while G allele was associated with protection against DF in Sakuntabhai's observation . The inconsistency between these studies regarding the protection for DHF or DF may result from two possibilities . First , the frequency of G allele in Chinese population is 3 . 8%; while in Thailand , it is 9 . 5–10 . 4% [16] , [34] . Second , definition of DF and DHF might be also different . We defined DF and DHF according to WHO criteria , while in the study by Sakuntabhai et al . , DF was defined by criteria of severe dengue fever syndrome with hemorrhage but no plasma leakage , excluding patients with flu-like symptoms or those having only fever . Moreover , the rs4804803 SNP was demonstrated to be in linkage disequilibrium with three other intronic polymorphisms in a Thai population , and these might also have contributed to the susceptibility of DHF [16] . Our results suggest that humans carrying the rs4804803 AG genotype have a higher DC-SIGN expression and lower DEN-2 replication in MDDCs . These results differ from a previous study by Loach et al . who demonstrated that the DC-SIGN expression levels on Raji cells after transfection of various DC-SIGN cDNA constructs were significantly correlated to the infection rate of DEN-1 [35] . DC-SIGN is an endocytic receptor shown to induce endocytosis of several pathogens , including dengue [36]–[38] . The difference between these two studies might be due to different cell types and ex vivo culture systems . In our study , it was found that MDDCs from subjects with rs4804803 AG genotype had higher surface DC-SIGN expression with higher production of chemokines such as IP-10 , which could limit DEN-2 replication ( Fig . 4A ) ; however , the higher surface DC-SIGN expression in subjects with AG genotype decreased remarkably 24 h post-infection ( Fig . 2A ) . In the study by Loach et al . , ectopic expression levels of DC-SIGN on Raji cells enhanced DEN-1 replication , which might be related to a higher quantity of receptors or lower production of IP-10 favoring DEN replication . Results from these studies suggest that the correlation of viral replication to higher or lower DC-SIGN expression depends on genetic factors in the host , cell type , and dynamic changes in the receptor following DEN infection . In functional studies of rs4804803 SNP , we determined that MDDCs with AG genotype had a higher DC-SIGN expression correlated to augmented immune responses with higher TNFα , IL-12p40 , and IP-10 , than those with AA genotype , but not MCP-1 production . DEN replication was significantly lower in individuals with AG genotype . The addition of anti-IP-10 neutralizing antibody blocked the production of endogenous IP-10 and significantly enhanced the replication of DEN-2 ( Fig . 4A ) . This suggests that rs4804803 SNP was involved in the DC-SIGN expression associated with augmented immune response , such as the increase in the production of IP-10 that repressed the replication of DEN . This is supported by the fact that altered immune response , but not viral load , was observed in DHF patients [21] , [39] . CLEC5A-mediated DEN infection in animals that was susceptible to DEN hemorrhagic infection also revealed augmented immune response [40] . In contrast , it is interesting to note that the viral replication in MDDCs from individuals with rs4804803 AA genotype was significantly higher than in individuals with AG genotype following DEN-2 infection . The mechanism by which rs4804803 SNP influences DEN replication in MDDCs is currently unknown . Chan et al . showed that certain polymorphisms of L-SIGN , a DC-SIGN homologue , mediated more efficient viral degradation of SARS-CoV [41] . The clinical implications of screening genotypes to prevent DEN infection might be supported if different viral loads could be demonstrated among humans with various genotypes of rs4804803 in future outbreaks of DEN . The outcome of DEN infection is determined by a myriad of interactions among viral , immunological , and human genetic factors , as well as kinetic interactions between innate and adaptive immunity . This study provides new evidence that CD209 rs4804803 SNP , correlated to cell surface expression on dendritic cells , mediates augmented immune responses against DEN-2 infection and is implicated in the susceptibility of DHF . Further studies are warranted , particularly with regard to the genetic variants of CD209 on the DC polarization of adaptive immunity , and how they may promote or protect the development of DHF .
|
Dengue fever ( DF ) is an arthropod-borne disease that is prevalent in tropical and subtropical regions of the world . DC-SIGN [dendritic cell-specific intercellular adhesion molecule 3 ( ICAM-3 ) -grabbing non-integrin] is a major receptor for dengue infection . DC-SIGN , also called CD209 , expresses on dendritic cells ( DCs ) that bind to ICAM-3 , which is expressed on T cells to facilitate the initial interaction between DCs and T cells . Variations in the CD209 promoter ( −336 A/G; rs4804803 ) genotype are involved in the pathogenesis of human infectious diseases . Here we found that patients with dengue hemorrhagic fever ( DHF ) had a higher frequency of the AG or GG genotype of rs4804803 than DF or controls . Functional studies determined that monocyte-derived DCs ( MDDCs ) from individuals with AG genotype had significantly higher cell surface DC-SIGN expression , associated with higher TNFα , IL-12p40 , and IP-10 production , but lower viral replication than those with AA genotype . An increase in DEN-2 replication in MDDCs was observed following the addition of anti-IP-10 neutralizing antibody . These findings highlight the fact that the rs4804803 SNP in the CD209 promoter is associated with DHF and correlated to DC-SIGN expression and immune augmentation .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics/genetics",
"of",
"the",
"immune",
"system",
"immunology/genetics",
"of",
"the",
"immune",
"system",
"infectious",
"diseases/viral",
"infections",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"immunology/immunity",
"to",
"infections"
] |
2011
|
DC-SIGN (CD209) Promoter −336 A/G Polymorphism Is Associated with Dengue Hemorrhagic Fever and Correlated to DC-SIGN Expression and Immune Augmentation
|
Mouse sex determination provides an attractive model to study how regulatory genetic networks and signaling pathways control cell specification and cell fate decisions . This study characterizes in detail the essential role played by the insulin receptor ( INSR ) and the IGF type I receptor ( IGF1R ) in adrenogenital development and primary sex determination . Constitutive ablation of insulin/IGF signaling pathway led to reduced proliferation rate of somatic progenitor cells in both XX and XY gonads prior to sex determination together with the downregulation of hundreds of genes associated with the adrenal , testicular , and ovarian genetic programs . These findings indicate that prior to sex determination somatic progenitors in Insr;Igf1r mutant gonads are not lineage primed and thus incapable of upregulating/repressing the male and female genetic programs required for cell fate restriction . In consequence , embryos lacking functional insulin/IGF signaling exhibit ( i ) complete agenesis of the adrenal cortex , ( ii ) embryonic XY gonadal sex reversal , with a delay of Sry upregulation and the subsequent failure of the testicular genetic program , and ( iii ) a delay in ovarian differentiation so that Insr;Igf1r mutant gonads , irrespective of genetic sex , remained in an extended undifferentiated state , before the ovarian differentiation program ultimately is initiated at around E16 . 5 .
Both the gonads and the adrenal cortex originate from a common structure referred to as the adreno-genital primordium ( AGP ) . In mice , the AGP is visible at embryonic day ( E ) 9 [1] , and is composed of a population of precursor cells expressing the nuclear receptor steroidogenic factor 1 ( SF1 , also named Ftzf1 or Ad4BP; [2] ) . As development proceeds , the AGP separates into two distinct regions [2] . The adrenocortical primordium separates from the gonadal primordium in the rostral region of the AGP at around E10 . 5 , and differentiates into the adrenal cortex in both sexes , ultimately giving rise to the zona glomerula , fascicula and reticularis . In parallel , the bipotential gonadal primordium , composed of primordial germ cells and SF1-positive somatic cells , differentiates into a testis or an ovary depending on the genetic sex . Gonadal differentiation is controlled by a balance of antagonistic pathways . In XY individuals , testis development is initiated by the transient expression of SRY , which , in concert with SF1 , triggers Sox9 upregulation , leading to Sertoli cell commitment and testicular differentiation [3] . Sertoli cell differentiation is a result of the establishment of a positive feedback loop between SOX9 and FGF9 as well as SOX9 and PGD2 secretion [4] , [5] . SOX9/FGF9 also act antagonistically by down regulating female signals such as WNT4 thereby blocking ovarian differentiation [6] . In XX individuals , the bipotential gonad develops as an ovary . Although no morphological differentiation is apparent up until E13 . 5 when germ cells enter meiosis under the influence of retinoic acid [7] , [8] , a robust ovarian-specific genetic program is initiated as early as E11 . 5 [9] , [10] . The R-spondin1/Wnt4/β-catenin pathway and the transcription factor FOXL2 have been shown to act in a complementary manner to promote ovarian development and antagonize the testicular pathway by silencing Sox9 and Fgf9 ( reviewed in [11] ) . As the AGP is the common precursor of both the adrenal cortex and the gonads , mutations in genes important for its initial specification and differentiation usually manifest themselves as defects in the development of both adrenal and gonadal tissues [12] . For example , targeted inactivation of the orphan nuclear receptor SF1 [13] , the Wilms' tumor-suppressor WT1 [14] , the polycomb factor M33 ( CBX2; [15] ) , the transcription co-factor Cited2 [16] , the homeodomain protein PBX1 [17] , and the transcription factor Odd-skipped related 1 ( ODD1; [18] ) lead to adrenal agenesis , impaired thickening of the genital ridges , and subsequent gonadal degeneration and XY sex reversal . Nevertheless , our understanding of the molecular pathways that direct adrenal cortex and gonad development and differentiation remains incomplete , and it has become clear that additional factors and signaling pathways must be involved . Insulin and its related growth factors IGF1 and IGF2 modulate a variety of physiological activities including metabolism , stimulation of cell proliferation , differentiation and survival [19] . The action of these growth factors on target cells is mediated by the insulin receptor ( INSR ) and the IGF type I receptor ( IGF1R ) , two membrane-associated tyrosine kinase receptors . Insulin and IGF1 bind primarily to INSR and IGF1R respectively , while IGF2 seems to act through either IGF1R or the A isoform of INSR ( for review see [20] ) . In recent years , increasing evidence has emerged that the insulin family of growth factors plays an essential role in gonadal development and sex determination . Of particular importance is the observation that insulin/IGF signaling is absolutely required for testis differentiation in mice [21] . However it remains unclear whether the insulin/IGF signaling pathway acts upstream of SRY by affecting adrenogenital precursor cells , or whether it influences Sry expression and the male transcriptional program directly in Sertoli cell precursors . Furthermore , there have been no studies addressing a potential role for insulin/IGF signaling in ovarian differentiation and adreno-cortical development .
A significant constraint in our past research has been the low recovery frequency of XY Insr;Igf1r;Irr triple constitutive ko animals ( 1/32 ) due to the lethal phenotype of single constitutive Insr and Igf1r mutants [21] . To bypass this lethality , and thus generate a large number of constitutive Insr;Igf1r double knockout animals , we crossed mice bearing pairs of loxP-flanked alleles of both Insr [22] and Igf1r [23]; Insrfx/fx;Igf1rfx/fx ) with mice carrying either an oocyte-specific Gdf9:Cre transgene [24] or a spermatogenesis-specific Ngn3:Cre transgene [25] . We found that mice lacking both Insr and Igf1r in either the male germ line ( Ngn3:Cre;Insrfx/fx;Igf1rfx/fx ) or in oocytes ( Gdf9:Cre;Insrfx/fx;Igf1rfx/fx ) have normal reproductive functions ( data not shown and [26] ) . When crossed , these animals produce large numbers of constitutive double knockout embryos ( InsrΔ/Δ;Igf1rΔ/Δ or dko ) , which lack Insr and Igf1r transcripts and their encoded receptors ( Figure 1A , 1B ) . As previously reported [27] , these animals exhibit embryonic growth retardation ( 68% and 75% of control weight at E16 . 5 and P0 , respectively ) , edema and dorsal tail flexion ( Figure 1C–1E and data not shown ) . At E16 . 5 , XY dko gonads morphologically resemble ovaries and are histologically indistinguishable from XX gonads , with no evidence of testis cords and complete absence of Sertoli ( AMH ) and Leydig ( 3βHSD ) cell-specific markers ( Figure 1F–1M ) . In fact , Insr;Igf1r dko embryos recapitulate the sex-reversed phenotype observed in Insr;Igf1r;Irr triple ko animals [21] . This suggested that only Insr and Igf1r , but not Irr , play important roles in testicular differentiation on a mixed genetic background . We next aimed to further dissect the effect of the lack of insulin/IGF signaling on the initiation of the testicular differentiation program . Expression analysis revealed that Sry mRNA expression at ∼E11 . 5 was almost undetectable in dko gonads , which is consistent with an absence of SRY-positive cells in E11 . 5 dko gonads ( Figure 2A–2C ) . By E12 . 5 , Sry transcript and protein were detected in XY dko gonads , but RNA levels were severely reduced compared to controls ( Figure 2A , 2C ) . The expression of Sox9 , a direct target of SRY whose expression is necessary and sufficient to initiate testis differentiation [28] , [29] , was significantly reduced in XY dko gonads at E12 . 5 , both at the transcript and protein levels ( Figure 2D–2F ) . By E13 . 5 , only a few SOX9-positive cells were present in XY dko gonads , which coincided with a complete absence of testis cord formation ( Figure 2F ) . The lack of upregulation of SOX9 downstream genes such as Fgf9 ( Figure 2G , 2H ) , Amh ( Figure 2I , 2J ) , and Ptgds ( data not shown ) suggested that SOX9 expression did not reach the threshold necessary for Sertoli cell commitment . As anticipated , differentiation of Leydig cells and steroidogenesis were not initiated , indicated by the absence of Leydig cell-specific markers such as Insl3 and p450SCC in double mutant gonads at E13 . 5 and E16 . 5 ( Figure 2K , 2L and data not shown ) . Overall , this analysis indicates that Sertoli cell differentiation and therefore the initiation of the testis determination program are disrupted in the absence of insulin/IGF signaling . The testicular and ovarian genetic programs are mutually antagonistic such that genital ridges differentiate into either ovaries or testes . Previous loss-of-function studies have shown that in the absence of testicular differentiation , the female program is initiated and ovarian differentiation occurs [29] , [30] . Key ovarian-determining components include the R-spondin1/WNT4/β-catenin pathway and the FOXL2 transcription factor , which act in a complementary manner to promote the ovarian fate and repress testicular signaling and development [31] , [32] , [33] , [34] . Developing XX dko gonads ( E12 . 5–E16 . 5 ) , although reduced in size , were histologically indistinguishable from XX control gonads ( data not shown ) . As expected , none of the testis-specific markers including SOX9 and p450SCC were expressed , indicating that the testicular pathway was never initiated in XX gonads lacking insulin signaling ( see Figure S1 ) . However , we found that ovarian differentiation was impaired and delayed in both XY and XX dko gonads ( Figure 3 ) . The expression of key ovarian-promoting factors such as Wnt4 and the downstream genes Fst [35] and Irx3 [36] as well as the nuclear mediator of canonical WNT signaling , Lef1 [37] , were either absent or significantly reduced in XX and XY dko gonads ( Figure 3A , 3B , 3D–3J ) . Similarly , Foxl2 transcripts and FOXL2-positive cells were drastically reduced or absent at E12 . 5 and E13 . 5 in XY and XX double mutant gonads ( Figure 3C and 3K–3N , 3Q–3T ) . By E16 . 5 , we observed FOXL2-positive somatic cells in both XX and XY dko gonads ( Figure 3O , 3P , 3U , 3V ) , indicating that ovarian differentiation had initiated both in XX and XY mutant gonads . We next investigated the germ cell fate in XX and XY dko gonads by comparing the expression of the pluripotency marker OCT4 and the meiotic marker SCP3 ( synaptonemal complex protein 3; Figure 4 ) . Approximately the same number of germ cells was present in dko gonads as compared to wild type ( Figure 4A , 4B ) . As expected in XX control gonads , OCT4 was downregulated and SCP3 upregulated in germ cells at E13 . 5 , as they enter meiosis ( compare Figure 4C with 4E ) . In contrast , very few germ cells expressed SCP3 in XX and XY dko gonads at E13 . 5 ( Figure 4F , 4L ) , indicating a delay in the entry to meiosis . It is only later , at E16 . 5 , that the majority of germ cells in XX and XY dko gonads were SCP3-positive , although a few cells still expressed the pluripotency marker OCT4 ( Figure 4H , 4N ) . Overall , these findings suggest that gonads lacking insulin/IGF signaling , irrespective of the genetic sex , remain in an undifferentiated state for several additional days without clear activation of the testicular or ovarian genetic program . Since IGFs stimulate both cell proliferation and differentiation , we investigated whether genital ridge development , its cellular composition and the number of multipotent somatic progenitors were affected in dko embryos prior to ( E10 . 5 ) and around the time ( E11 . 5–E12 . 5 ) of sex determination . We found that the overall body weight and embryonic growth appeared unaffected in dko embryos at E10 . 5 , but began to diverge significantly at E11 . 5 with a 23% reduction , which then increase slightly to 27% at E12 . 5 ( Figure 5A–5D ) . At all these stages ( E10 . 5–E12 . 5 ) , developmental processes such as tail somite formation and limb development were not delayed , suggesting that embryonic developmental processes were not affected despite growth retardation . Similarly , dko genital ridges were normally present at these developmental stages and their cellular composition appeared unaffected with the presence of gonocytes ( OCT4+ cells ) and somatic progenitors ( GATA4+ cells ) in both XX and XY dko genital ridges at E10 . 5 and E11 . 5 ( Figure S2 ) . Analysis of the urogenital anatomy by scanning electron microscopy at E11 . 5 did not reveal clear differences in the size and overall shape of XY dko genital ridges compared to controls ( Figure 5E ) . However , by taking advantage of a transgene expressing eGFP under the control of the mouse Sf1 promoter ( Sf1:eGFP; [38] ) , we found by FACS that the number of SF1+ somatic progenitor cells was reduced by 42% ( p = 0 . 0003 ) and 39% ( p = 0 . 011 ) in dko genital ridges at E10 . 5 and E11 . 5 , respectively ( Figure 5F ) . As evidenced by double anti-GATA4/anti-Ki67 immunofluorescence , we found that the proliferation rates of gonadal progenitor cells ( GATA4+ ) were significantly reduced at E10 . 5 and E11 . 5 in dko genital ridges compared to controls ( Figure 5G–5I ) . In contrast , apoptosis rates did not differ between control and dko genital ridges at any time points examined ( data not shown ) . A reduction in cell proliferation was also observed in other tissues such as the adjacent mesonephros , the somites and the heart suggesting that this effect is not specific to progenitor cells of the AGP but instead represents a more global effect of insulin/IGF signaling ablation ( Figure S3 ) . SF1 is a crucial determinant for the development and differentiation of the AGP . In fact , the onset of adrenal development has been reported to be more sensitive than gonadal development to Sf1 gene dosage and requires a higher SF1 threshold [16] , [39] . Interestingly , we found a significant reduction in Sf1 transcript levels in mutant urogenital ridges between E10 . 5 and E12 . 5 irrespective of the genetic sex ( Figure 6A–6C ) . We therefore investigated whether adrenal development was affected in embryos lacking both Insr and Igf1r . Examination of transverse abdominal sections by hematoxylin and eosin ( H&E ) staining revealed the absence of adrenal glands in dko embryos at E16 . 5 ( compare Figure 6D with 6L ) . This was confirmed by the lack of staining for adrenocortical markers SF1 and 3β-hydroxysteroid dehydrogenase , 3β-HSD , and the chromaffin cell precursor marker tyrosine hydroxylase ( TH ) at E16 . 5 ( compare Figure 6E–6G with 6M–6O ) . Among the large set of dko mice analyzed for the presence of adrenal structures at E16 . 5 , 3 out of 27 embryos , all originating from the same litter , displayed a tiny adrenal structure expressing both steroidogenic ( SF1 , 3β-HSD; Figure 6I , 6J ) and chromaffin cell markers ( TH; Figure 6K ) . This suggested first that insulin/IGF signaling is required for adrenal cell specification but not for adrenocortical differentiation and/or function , and second that the genetic background affect the severity of the phenotype . Based on this striking observation , we next examined the early stages of adrenal development in dko embryos bearing a Sf1;eGFP transgene . Whereas adrenal and gonadal primordia are apparent in control embryos ( arrowhead and arrow respectively in Figure 6P–6S ) , we observed a lack of GFP fluorescence specifically at the expected position of the adrenal primordium in dko embryos ( Figure 6T–6W ) . These studies indicate that the insulin/IGF signaling pathway is indeed required for adrenal primordium specification , possibly by modulating Sf1 gene expression . To obtain a global view of the molecular changes associated with the ablation of insulin/IGF signaling , we performed a genome-wide gene expression analysis using Affymetrix microarrays on isolated SF1+ cells from XX and XY control and mutant gonads at E11 . 5 . We chose this precise developmental stage as it corresponds to the peak of Sry expression and the initiation of both the testicular and ovarian genetic programs [10] , [40] . We found that among the 2147 probesets affected in mutant SF1+ cells , 76% were down-regulated in the mutant gonads ( Figure 7A , Figure S4A ) , revealing a strong negative impact on the transcription of genes associated with metabolic processes and cell cycle ( for additional information see Figure S5 and Table S1 ) . These changes may account for the reduced metabolism and proliferation observed in mutant somatic progenitor cells . In addition , the gene ontology analysis identified other down-regulated genes that are associated with sex determination , gonad development or steroid hormone synthesis . Strikingly , we found that 18% of the genes affected in dko gonads ( 350 annotated genes/397 probesets ) are expressed in a sex-specific manner in SF1+ cells during testicular and ovarian development [10] . In other words , we identified several hundred genes exhibiting an altered expression profile in E11 . 5 dko gonads prior to the establishment of their sexually dimorphic pattern at later stages ( i . e . E12 . 5 and E13 . 5; Figure 7A , Figure S4B ) . This included embryonic testis-specific genes such as Atrx , Cyp26b1 , Cbln4 , Cyp11a1 and Dmrt1 ( Figure S6 and data not shown ) , as well as embryonic ovary-specific genes or female dimorphic genes such as Bmp2 , Cdkn1a , Cdkn1b , Runx1 , Dax1 , and Dmrta1 ( Figure S7 and data not shown ) . All these genes were expressed at lower levels in mutant SF1+ cells regardless of genetic sex . This analysis suggested firstly that testicular and ovarian programs are initiated in the developing bipotential gonads in a gonadal sex-independent manner prior to E11 . 5 , and secondly that initiation of these programs relies , at least partially , on insulin/IGF1 signaling . In order to better characterize the sex-independent transcriptional program established in bipotential genital ridges prior to sex determination , and to investigate the role of insulin/IGF signaling in its initiation , we explored in more detail the expression profiles of genes enhanced and expressed in SF1+ somatic cell progenitors between E10 . 5 and E11 . 5 . For this purpose , we carefully reexamined the transcriptome of SF1+ cells at E10 . 5 , E11 . 0 and E11 . 5 in wild-type mouse embryonic gonads [10] . During this developmental period , 596 genes ( 720 probesets ) were upregulated ( fold change ≥1 . 5 ) both in XY and XX embryonic gonads and constitute what we call the Core Adreno-Gonadal Program ( CAGP - Figure 7B ) . Interestingly , half of these CAGP genes ( 281 genes , 338 probesets ) later exhibited a sexually dimorphic expression pattern , indicating their association with the testicular or ovarian genetic programs . Strikingly , our transcriptomic analysis comparing control and double mutant SF1+ cells at E11 . 5 revealed that the absence of insulin/IGF signaling affects the expression of more than 23% of the CAGP ( 141 out of 596 genes; Figure 7C ) , and 33% of the subset of CAGP genes with subsequent sexually dimorphic expression patterns ( 94 out of 281 genes ) . It includes genes such as Runx1 , Bmp2 and Dax1 in mutant XX embryos and Cyp11a1 , Dmrt1 and Cbln4 in mutant XY embryos . Using our SF1-GFP expression data [10] , Jameson et al . [41] identified a group of 213 genes , named “primed genes” that are initially expressed at identical levels both in XX and XY somatic progenitors prior to sex determination but then become sexually dimorphic when these cells adopt either a male of female fate . This group of 213 genes is different from the CAGP described above , despite a small overlap , but was affected to a similar extent in the absence of Insulin/IGF signaling: expression levels of 27% of primed genes were reduced in the dko gonads ( Figure 7D ) . All together , these data clearly emphasize the essential role played by insulin family growth factors in establishing both the male and female programs in XX and XY somatic progenitors prior to sex determination . To validate these results and confirm that the expression of significant fractions of CAGP and primed genes were affected as early as E10 . 5 in mutant progenitor cells , we developed an assay based on the NanoString Ncounter gene expression system , which captures and counts individual mRNA transcripts without reverse transcription of RNA or any other enzymatic step [42] . We measured the expression profiles of a set of 65 genes in SF1+ cells isolated from XX and XY control or dko gonads between E10 . 5 and E13 . 5 ( Figure 8 , Figure S8 and Table S2 ) . This set of genes included classical genes involved in adrenogonadal development and sex determination as well as a selection of CAGP and primed genes . Analysis of the sources of variation ( ANOVA ) indicated that the most significant factors influencing gene expression variation were the time ( developmental stages ) followed by genotype ( control vs dko ) and sex ( Figure S8A ) . As expected , genes implicated in the testicular program were not upregulated in SF1+ cells from XY dko gonads ( Figure 8D–8F; Figure S8B ) with the notable exception of Sry whose peak of expression was delayed by 2 days ( Figure 8A ) . Similarly , we confirmed that the expression profile of numerous ovarian genes such as Foxl2 , Fst and Lef1 were delayed and reduced in SF1+ cells from both XX and XY dko gonads , compared to XX control ovaries ( Figure 8B , 8C and Fig S8B ) . Finally , we also confirmed that CAGP and primed genes were indeed expressed both in XX and XY SF1+ somatic progenitors from E10 . 5 to E11 . 5 but were reduced or absent in mutant progenitor cells at the same stages . Several representative examples such as Cbln4 , Dmrt1 , Cyp26b1 , Dax1 and Runx1 are shown in Figure 8 . Also of particular interest is Sf1 whose expression was downregulated by ∼67% and ∼38% at E10 . 5 and E11 . 5 , respectively . Overall , we showed that a complex dynamic transcriptional program , entitled the Core Adreno-Gonadal Program ( CAGP ) , is initiated in the bipotential gonadal primordium prior to sex determination and is associated with testicular and ovarian differentiation as well as adrenal specification . The significant alteration in CAGP and primed gene expression in somatic cells of the Insr;Igf1r mutant AGP prior to sex determination may explain the incapacity of this primordium to specify not only the adrenal gland but also to develop into either ovaries or testes in a timely fashion .
Initiation of the testicular pathway requires a threshold level and the correct timing of Sry and Sox9 expression ( for review see [48] ) . SF1 is an essential transcription factor known to promote Sertoli cell differentiation and the testicular pathway by participating in Sry activation and the initiation , upregulation and maintenance of Sox9 transcription in Sertoli cell precursors [48] . We found that Sry expression was drastically reduced and delayed in Insr;Igf1r double ko animals , and was correlated with the lack of upregulation of key testis genes such as Sox9 , Fgf9 , and Ptgds , and the absence of Sertoli cells , Leydig cells and overall testis formation . Interestingly , a few SOX9+ cells were found in E12 . 5 XY dko gonads . These were absent at later stages suggesting that , in addition to Sox9 activation , maintenance of Sox9 expression was also impaired in mutant XY gonads . Recent studies demonstrated that the first event occurring immediately downstream of the onset of SRY expression is the accumulation of glycogen within the precursors of Sertoli cells [49] . This energy storage is critical since disruption of glycogen synthesis and accumulation results in the failure of Sox9 upregulation , testis cord formation and overall testis development . Interestingly , glycogen storage within pre-Sertoli cells appears to be dependent on the activation of the PI3K-AKT pathway , which is known to be activated by both insulin and IGFs [20] . Both expression profiling and Affymetrix analyses showed that genes coding for enzymes involved in the glycogen synthesis pathway , such as hexokinase 2 ( Hxk2 ) , phosphoglucomutase ( Pgm ) and glycogenin ( Gyg ) , were downregulated in dko SF1+ cells at E11 . 5 ( Figure S9 ) . In addition , qRT-PCR performed with RNAs isolated from genital ridges at E11 . 5 showed that glycogen synthase ( GlycoS ) was also down regulated in dko embryos . Absence of insulin/IGF signaling led to a delay in the ovarian program of development , which was reflected at the molecular level by an absence or reduced expression of numerous genes involved in ovarian development . This included FOXL2 , an ovarian determining factor , as well as members of the Wnt4 signaling pathway such as Wnt4 , its downstream gene Fst and the nuclear mediator of canonical WNT signaling , Lef1 . Combined with the failure to initiate the testicular program , Insr;Igf1r mutant gonads remained in an undifferentiated state for several days , until E16 . 5 when the ovarian program was activated in both XX and XY embryos . This delay in differentiation is apparent for both germ and somatic cell lineages in the gonad . Ordinarily , following the initiation of the ovarian differentiation program in the somatic compartment of XX gonads , germ cells enter into prophase of meiosis I around E13 . 5 [50] and upregulate meiotic proteins including the synaptonemal complex 3 ( SCP3 ) . In the fetal ovary , germ cell entry into meiosis is induced by retinoic acid . In the developing testes however , expression of the P450 enzyme CYP26B1 in Sertoli cells , which degrades retinoic acid [7] , [8] , and secretion of FGF9 that directly suppress meiosis , act to maintain pluripotency [51] . Although germ cells were present normally in both XX and XY double mutant gonads , we observed an almost complete absence of SCP3-positive cells at E12 . 5 and E13 . 5 ( Figure 4 ) . Besides the numerous other genes that are affected in the double mutant gonads , several factors regulating retinoic acid ( RA ) metabolism and the correct specification of the germ cell lineage exhibited a marked decrease in mutant SF1+ cells . These included not only Fgf9 and the retinoic acid degrading enzyme Cyp26b1 , but also the aldehyde dehydrogenases Aldh1a1 and Aldh1a7 as well as the alcohol dehydrogenase Adh1 ( Figure S6 ) . These latter enzymes catalyze retinol oxidation , the rate-limiting step in the conversion of retinol to retinoic acid . Consistent with the reduction of Aldh1a1 and Adh , we observed a similar reduction in several RA-regulated genes such as Runx1 , Pbx1 , Bmp2 and Tgfb2 ( data not shown ) . We hypothesize that alteration of both meiosis-suppressing factors ( e . g . Fgf9 , Cyp26b1 ) and meiosis-promoting factors such as key synthesizers of retinoic acid in the mesonephros ( e . g . Aldh1a1 , Aldh1a7 , Adh ) perturbs the initiation of meiosis and germ cell fate in Insr;Igf1r mutant animals . The insulin/IGF family of growth factors acts mainly through INSR and IGF1R to activate two major signaling pathways: the mitogen-activated protein kinase ( MAPK ) pathway and the phosphoinositide 3-kinase ( PI3K ) /Akt pathway [20] . The MAPK pathways , including EKR1/2 , P38 and JNK , regulate cell proliferation , differentiation and apoptosis , while increased phosphatidyl inositol 3 , 4 , 5 triphosphate ( PIP3 ) activates PKB/AKT to prevent apoptosis and to stimulate cellular proliferation and glucose transport . Recently , MAPK pathways have been implicated in testis development: mutations in the MAP3K1 gene cause 46 , XY disorders of sex development with partial or complete gonadal dysgenesis [52] . In addition , loss of function of the Map3k4 gene in mice led to XY gonadal sex reversal [30] . Analysis of mutant gonads revealed a dramatic reduction of Sry and Sox9 expression and a subsequent growth deficit and absence of mesonephric cell migration . Expression analysis of genes coding for proteins involved in insulin/IGF signaling , in particular both the MAPK and PI3K/Akt signaling pathways , revealed that most of these genes were not affected in the double knockout gonads , with the notable exception of Map3k1 , Map2k7 , Jnk3/Mapk10 , Gadd45γ , p38β , p38γ and p38δ ( Figure S10 ) . However , it is expected that many physiological functions of IGFs in developing gonads are mediated through post-translational modifications , such as phosphorylation , of downstream signaling effectors . A major future task will be to define the signaling pathways that mediate these proliferative activities , and that allow these growth factors to specify the adrenal primordium and promote testicular and ovarian differentiation . In conclusion , this study demonstrates the essential role played by the insulin/IGF signaling pathway in mediating different aspects of adrenogonadal development , such as adrenal specification , testicular differentiation and ovarian development . It also sheds light on a crucial , but so far underestimated , signaling pathway underlying sex determination in mice and potentially disorders of sexual development in humans .
All the reagents , antibodies , plasmids and primers used in this study are described in Tables S3 and S4 . Insrflox ( Insrfx/fx ) , Igf1rflox ( Igf1rfx/fx ) , Sf1-eGFP ( Sf1:eGFP ) Ngn3-Cre ( Ngn3:Cre ) and Gdf9-Cre ( Gdf9:Cre ) transgenic mice were provided by R . Kahn , A . Efstratiadis , K . L . Parker , P . L . Herrera and A . J . Cooney respectively and were genotyped at weaning ( P21 ) from tail biopsies by classic PCR as described [22] , [24] , [26] , [38] , [53] . To generate constitutive mutants for both Insr and Igf1r , Insrfx/fx;Igf1rfx/fx;Gdf9:Cre XX mice were mated with Insrfx/fx;Igf1rfx/fx;Ngn3:Cre XY mice . The genotype of gametes produced by both transgenic lines were InsrΔ;Igf1rΔ and the subsequent matings resulted in 100% InsrΔ/Δ;Igf1rΔ/Δ progeny , hereafter referred as “dko” . The genotype of control mice was Insrfx/fx;Igf1rfx/fx . To specifically label SF1 expressing cells in vivo , the Sf1:eGFP transgene [38] was intercrossed with the above mentioned mice to generate Insrfx/fx;Igf1rfx/fx;Sf1:eGFP , Insrfx/fx;Igf1rfx/fx;Gdf9:Cre;Sf1:eGFP and Insrfx/fx;Igf1rfx/fx;Ngn3:Cre;Sf1:eGFP transgenic animals . Embryos were collected from timed matings and staged by designating noon of the day on which the mating plug was detected as E0 . 5 . Accurate staging of embryos between 10 . 5 and 12 . 5 dpc was performed by counting the tail somites ( ts ) . Embryos at 8 ts ( ±2 ts ) were considered as E10 . 5 , 19 ts ( ±2 ts ) as E11 . 5 and 29 ts ( ±3 ts ) as E12 . 5 . Routine sexing of the embryos was determined by Sry PCR [10] . Due to the large number of different transgenes involved in these breedings , the genetic background of dko and control embryos is mixed , although mostly composed of 129 and BL/6 strains . Animals were housed and cared according to the ethical guidelines of the Direction Générale de la Santé of the Canton de Genève ( experimentation ID: 1061/3840/1 ) . Following timed matings , embryos were fixed overnight at 4°C in either 4% paraformaldehyde ( PFA ) or Bouin's fixative , dehydrated in an ethanol series and embedded in paraffin . Five µm-sections were stained with hematoxylin and eosin ( H&E ) or processed for section immunohistochemistry ( IHC ) and immunofluorescence ( IF ) . Section IHC and IF was performed as described [5] . Total protein from E11 . 5 control and dko embryos were mechanically homogenized in ice-cold RIPA buffer . Lysates were cleared by centrifugation and protein content was measured using a BCA protein assay kit . Samples containing 10 µg of total protein were resolved by 10% SDS-PAGE , transferred to nitrocellulose membranes and following staining with antibodies were detected using Lumigen TMA-6 according to manufacturer's instructions . Adult females were time-mated and checked for the presence of vaginal plugs the next morning ( E0 . 5 ) . On the relevant days of gestation ( i . e . E10 . 5 , E11 . 0 , E11 . 5 , E12 . 5 and E13 . 5 ) , pregnant females were sacrificed . XX and XY urogenital ridges from dko and control embryos bearing the EGFP transgene were dissected and digested with trypsin/EDTA . eGFP-positive cells were sorted using a FACS Vantage SE as described [10] . The levels of GFP fluorescence of SF1+ cells were comparable and were not affected by the genetic sex and the genotype of developing embryos ( see Figure S11 ) . We only observed a small and regular increase in the levels of GFP fluorescence based on the developmental stage that did not affect the cell sorting process . RNA was extracted using RNeasy microkit from Qiagen according to the manufacturer's protocol and stored at −80°C until needed . For each of the 4 genotypes ( XX and XY control , XX and XY dko ) , three independent sets of 150 ng of total RNA were isolated and used as a template for probe generation as described [54] . WISH was carried out as described [10] . Briefly , embryos were dissected in PBS , fixed overnight in 4% PFA at 4°C , washed in PBS , and then dehydrated in graded methanol solution and stored at −20°C in 100% methanol . Plasmids containing cDNAs of the relevant candidate genes were linearized and then used as templates to generate digoxigenin-labeled anti-sense riboprobes . Expression profiles were analyzed at E10 . 5 , E11 . 5 , E12 . 5 , E13 . 5 and E16 . 5 using a minimum of three embryos of each sex and genotype at each stage per candidate gene . Total RNAs from E11 . 5 ( 19±2 ts ) and E12 . 5 genital ridges ( together with mesonephroi ) from XX or XY , control or dko embryos were extracted using the RNeasy microkit from Qiagen according to the manufacturer's protocol . For each RNA sample , 15 pairs of genital ridges from the same genotype and stage were pooled . For each condition , three independent pools of RNA were isolated , DNase-treated and converted to 1st strand cDNA using SuperScript II Reverse Transcriptase following the manufacturer's instructions ( Invitrogen Life Technologies ) . Real time PCR was carried out in optical 384-well plates and labeled by using the SYBR green master mix ( Applied Biosystems ) , and the fluorescence was quantified with a Prism 7900 HT sequence detection system ( Applied Biosystems ) . The expression of each gene was assayed in triplicate as previously described [54] . Primers used for qRT-PCR are listed in Table S3 and were designed using the software PRIMER EXPRESS ( Applied Biosystems ) . The statistical significance of fold-changes was determined by a paired Student's t-test . Total RNAs were isolated from purified SF1+ cells originating from XX and XY urogenital ridges at E10 . 5 , E11 . 5 , E12 . 5 and E13 . 5 from both dko and control embryos ( 19±2 ts ) bearing a Sf1:EGFP transgene as described [10] . For each of the 16 conditions , 3 independent sets of total RNA ( each originating from a pool of >6 embryos ) were isolated to minimize the effects of biological variability . For each condition , 100 ng of total RNA was hybridized with multiplexed Nanostring probes and samples were processed according to the published procedure [42] . Barcodes were counted for 1150 fields of view per sample . Background correction was done by subtracting from the raw counts the mean+2 standard deviations of counts obtained with negative controls . Values <1 were fixed to 1 . Positive controls were used as quality assessment: the ratio between the highest and the lowest positive controls average among samples was below 3 . Then counts for target genes were normalized with the geometric mean of the 6 reference genes ( Gapdh , Tuba1b , Gusb , Eef1a1 , Tbp and Rps9 ) selected as the most stable using the geNorm algorithm [60] . Results of a representative experiment are shown and are expressed as means ± SEM of n experiments . The nonparametric unpaired t-test was used for statistical analysis . Differences were considered statistically significant if p was <0 . 05 .
|
Congenital disorders of sexual differentiation are rare diseases in which there is discordance between chromosomal , gonadal , and phenotypic sex . Unfortunately , only a minority of patients clinically diagnosed with disorders of sex development ( DSD ) obtains a molecular diagnosis , indicating that our understanding of the factors and signaling pathways mediating gonadal development and sex determination is far from complete . Using mouse models , we show that the insulin receptor ( INSR ) and the IGF type I receptor ( IGF1R ) are required to mediate different aspects of adrenogonadal development such as adrenal specification , testicular differentiation , and ovarian development . We found that a complex dynamic transcriptional program is initiated in somatic progenitor cells of the bipotential gonadal primordium prior to sex determination . A significant fraction of this genetic program is prematurely altered in the somatic progenitors lacking insulin/IGF signaling , which explains adrenal agenesis and the incapacity of XX and XY mutant gonads to develop into ovaries or testes . This finding sheds light on a crucial , but so far underestimated , signaling pathway underlying sex determination in mice and potentially DSDs in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"cell",
"fate",
"determination",
"functional",
"genomics",
"gene",
"expression",
"genetics",
"biology",
"genomics",
"genetics",
"and",
"genomics",
"cell",
"differentiation",
"dna",
"transcription",
"gene",
"function"
] |
2013
|
Insulin and IGF1 Receptors Are Essential for XX and XY Gonadal Differentiation and Adrenal Development in Mice
|
The orderly packing and precise arrangement of epithelial cells is essential to the functioning of many tissues , and refinement of this packing during development is a central theme in animal morphogenesis . The mechanisms that determine epithelial cell shape and position , however , remain incompletely understood . Here , we investigate these mechanisms in a striking example of planar order in a vertebrate epithelium: The periodic , almost crystalline distribution of cone photoreceptors in the adult teleost fish retina . Based on observations of the emergence of photoreceptor packing near the retinal margin , we propose a mathematical model in which ordered columns of cells form as a result of coupling between planar cell polarity ( PCP ) and anisotropic tissue-scale mechanical stresses . This model recapitulates many observed features of cone photoreceptor organization during retinal growth and regeneration . Consistent with the model's predictions , we report a planar-polarized distribution of Crumbs2a protein in cone photoreceptors in both unperturbed and regenerated tissue . We further show that the pattern perturbations predicted by the model to occur if the imposed stresses become isotropic closely resemble defects in the cone pattern in zebrafish lrp2 mutants , in which intraocular pressure is increased , resulting in altered mechanical stress and ocular enlargement . Evidence of interactions linking PCP , cell shape , and mechanical stresses has recently emerged in a number of systems , several of which show signs of columnar cell packing akin to that described here . Our results may hence have broader relevance for the organization of cells in epithelia . Whereas earlier models have allowed only for unidirectional influences between PCP and cell mechanics , the simple , phenomenological framework that we introduce here can encompass a broad range of bidirectional feedback interactions among planar polarity , shape , and stresses; our model thus represents a conceptual framework that can address many questions of importance to morphogenesis .
Epithelia are one of the basic building blocks from which animals sculpt complex tissues and organs during development [1]–[5] . These sheets of cells are held together by specialized structures—notably apical junctional complexes , including adherens junctions—that allow cells to adhere tightly to their neighbors and ensure the epithelium's mechanical integrity [6]–[9] . In most epithelia , individual cells of distinct identities are packed together in quasi-two-dimensional arrays of varying complexity . Despite the fundamental importance of epithelial organization for many biological functions , the biophysical mechanisms that control cell shape and position in epithelia—and in particular the development of regular , ordered epithelial cell packings—remain only partially understood . In vertebrates , the neural retina exhibits a particularly high degree of epithelial organization , both in the radial direction , where it comprises multiple , stratified layers , and within layers , where the spatial distribution of each class of neuron within the epithelial plane has consistently been shown to be non-random [10] . This planar order is especially pronounced in adult teleost fish , where the cone photoreceptor cells are arranged in a well-defined , periodic pattern—the cone mosaic—that shows strong heterotypic as well as homotypic correlations [11]–[12] . The cone mosaic thus represents a rare vertebrate example of the precise regulation of cell fate and organization at the single cell level ( more instances of which have been described in invertebrate systems [13]–[14] ) . Previous studies have characterized cone mosaic patterns primarily by observing regular spatial arrangements of various individual cone cell subtypes , identified morphologically and/or with specific cell markers [15]–[18] . They have , in contrast , left largely unexplored the complementary question of how cone photoreceptors , together with rod photoreceptors and the apical processes of Müller glia , pack together and occupy space in the epithelial plane . Depending on the species , cone photoreceptors in teleost fish include several morphologically identifiable classes of single cones and double cones that express distinct visual pigments [19]–[20] . For example , the zebrafish , Danio rerio , has four spectral subtypes of cones designated red , green , blue , and ultraviolet ( UV ) , respectively , based on the absorption maxima of their visual pigments [21]–[22] . These cone photoreceptors are distributed in a repeating pattern that has been classically described as a row mosaic ( Fig . 1 ) [11] , [15] , [17] , [23] . The zebrafish retina is a thin , hemispheric sheet that lines the back of the eye . This sheet continues to grow along with the rest of the fish throughout postembryonic larval and adult stages: the diameter of the eye at the end of embryonic development ( ∼3 days post-fertilization [dpf] ) is only ∼0 . 2 mm , but several months later it can reach ∼2 mm or more . From ∼3 dpf onwards , retinal growth is accomplished by the addition of new cone cells at the rim of the retinal hemisphere , where the retinal and ciliary epithelia meet at a circumferential germinal zone of proliferative precursor cells ( Fig . 1 ) [24]–[27] . Due to this particular mode of continuous growth , successive stages of development and cell differentiation are laid out spatially in concentric annuli in a single epithelium: the remnant of the embryonic and larval retina remains in the center of the retina of the adult fish , whereas the majority of the adult retina extending out to the periphery is more recently created tissue [15] . The embryonic/larval remnant is easily distinguished in a flat-mounted preparation of the entire adult retina because neither the cones generated from the embryonic retinal primordium nor those added post-embryonically to the growing larval retina are arrayed in a regular , rectangular mosaic [15] , [18] . Even though the growing retina adds annuli of new cones at the periphery from late embryonic stages onwards , only those born after the end of larval development , at ∼3 weeks post-fertilization ( wpf ) , form an ordered mosaic lattice . Thus , addition of successive annuli of cone photoreceptors at the retinal perimeter is not , by itself , sufficient to produce a crystalline cone mosaic . The appearance of the ordered lattice of cone photoreceptors at ∼3 wpf , on the other hand , does coincide with the completion of significant developmental changes in ocular anatomy . These include the formation and maturation of the anterior segment—that is , the iris , the ciliary epithelium , and the annular ligament , a circular bracket of connective tissue that is thought to give structural support to the front of the eye and that roughly encircles the retinal germinal zone [28] . The maturation of the anterior segment leads to the production of aqueous humor , a fluid secreted by the ciliary epithelium that fills the eyeball . The aqueous humor is maintained at a significant intraocular hydrostatic pressure ( IOP ) relative to the outside environment , and this pressure inflates and stretches the retinal epithelium [29]–[31] . Similar mechanical stresses are known to affect epithelial cell packing in other contexts , but the potential relationship between these tissue-scale influences and the organization of the cone mosaic pattern has not been explored . Another mechanism known to influence cell shape and packing in epithelia is planar cell polarity ( PCP ) —the organization of cellular properties along a preferred direction within the plane of an epithelium [32]–[33] . Such polarization is increasingly recognized as a widespread and important feature of epithelial organization . PCP has not previously been studied in the vertebrate retina , but its molecular mechanisms have been worked out in considerable detail in certain Drosophila model systems [34] , and the same pathway appears to be conserved in some vertebrate systems [35] . One of the major functions of PCP is to introduce anisotropic mechanical stresses in epithelial sheets through modulation of acto-myosin cortical contractility or cell-cell adhesion , leading to polarized cell shape changes and rearrangements [34] , [36]; conversely , PCP can itself be affected by changes in cell shape and packing [37]–[38] and by mechanical stress [39] . Several mathematical models of PCP have been developed , ranging from the relatively molecularly detailed to the more schematic and phenomenological [37]–[38] , [40]–[46] , and the consequences of polarized contractility and adhesion for cell movement have also been examined computationally [47]–[49] . A mathematical model that can capture the full range of interactions between PCP and mechanical forces has , however , so far been lacking . Here , we propose just such a model to explain the developmental mechanisms behind the emergence of the ordered cone mosaic in the adult zebrafish retina . We present the first systematic experimental characterization of the epithelial packing of cone and rod photoreceptors and Müller glia at the apical surface of the retina , and we describe both the evolution of packing order as new cells are generated during retinal growth and the defects in order that accompany photoreceptor regeneration and that occur in a mutant strain of zebrafish with elevated intraocular pressure . Based on our observations , we introduce a mathematical model in which anisotropic , tissue-scale mechanical stresses interact with intrinsic planar cell polarity ( PCP ) in cones to generate cell packing in a rectangular lattice with long-ranged order . We provide morphological observations to verify the existence of the postulated PCP and functional genetic data consistent with the predicted role of anisotropic mechanical stress in the generation of the rectangular cone lattice .
We used immunostaining against the apical junction protein , Zonula Occludens-1 ( ZO-1 ) , which labels the apical cell profiles at the level of the outer limiting membrane ( OLM ) in the vertebrate retina [50] , in order to image the precise cell boundaries and cell arrangements within the photoreceptor cell packing at various stages of retinal growth . To identify the cells whose apical profiles are delimited by ZO-1 we used several transgenic zebrafish lines in which fluorescent reporters ( enhanced green fluorescent protein , EGFP , or a monomeric red fluorescent protein , mCherry ) are driven by cell-specific promoter sequences: sws1 ( ultraviolet opsin ) for ultraviolet cones [51] , sws2 ( blue opsin ) for blue cones [21]; cone alpha-transducin for all cones [52] , rh1 ( rod opsin ) for rod photoreceptors [53]; and gfap ( glial fibrillary acidic protein ) for Müller glia [25] . We first characterized the apical epithelial organization in the photoreceptor layer of adult zebrafish . The photoreceptor mosaic pattern in zebrafish has been previously described by observing the positions of photoreceptor cells in flat-mount retinal preparations [15]–[17] , [23] . Cones in the adult retina are organized in a rectangular lattice consisting of a repeated motif of 12 cells with an internal , reiterative , mirror image symmetry ( Fig . 1A ) . Rows of blue and UV single cones alternate with rows of red and green double cone pairs ( Figs . 1A–B; S1A–C ) . Double cone pairs are tightly apposed along the length of their apical processes ( inner segments ) [17] , [54] . In the orthogonal direction , columns of cones can be separated by rods , which have much smaller profiles ( Figs . 1A , C; S1D–I ) . The adult teleost retina continues to grow by addition of retinal cells in a circumferential germinal zone at the retinal periphery ( Fig . 1D ) , and each column of cones ( Fig . 1A ) represents a cohort of cells that are generated synchronously [15] , [55] and differentiate sequentially [55] . Rods appear after cones differentiate; they continue to accumulate in the adult retina and insert into the epithelial sheet between cone columns ( Fig . S1D–I ) . The earliest born rod photoreceptors insert into the cone pattern at the corners defined by the four-way interface of blue , UV , red , and green cones ( Fig . S1D–F; [16] ) ; as the fish ages , rods also accumulate elsewhere between the cone columns ( Fig . S1G–I ) . Rod photoreceptors have been shown not to be essential for generation of the cone mosaic in goldfish retina [56] , and we subsequently ignore rods in our analysis of cone cell packing . Finally , the numerous , irregular ZO-1-delimited cell profiles at the apical surface of the retina in the germinal zone and in the region of differentiating cones at the peripheral margin are processes of Müller glial cells , which have thin lamellae that completely enwrap the cone and rod photoreceptors as they penetrate through the OLM ( Fig . S1J–L ) . Labeling the cell boundaries with ZO-1 antibodies reveals additional , unexpected details of the apical epithelial packing and shape of cones at the level of the OLM . The two orthogonal directions in cone packing geometry are not equivalent: boundaries between adjacent cones belonging to the same column are straight , particularly the junctions between pairs of red and green double cones within a column ( Figs . 1B–C; S1B , E , H ) . These columns of cones belong to a cohort generated synchronously at the germinal zone , and they remain contiguous; rods penetrate the cone lattice between the columns but rarely between cones within the same column ( Figs . 1C; S1G–I ) . The nonequivalence in the packing geometry of cones is suggestive of a cell-cell adhesion mechanism that operates between cones within a column but not across columns . In contrast to those in the adult retina , cones that differentiate within the first few weeks after fertilization are not organized into long-range , supracellular lines ( Fig . 1E , G ) , and their packing clearly lacks the periodic , repetitive , lattice organization of the adult retina . Although short , linear arrays of alternating blue and UV cones are apparent even in the embryonic remnant ( Fig . 1E ) , they are not aligned in a consistent direction and red-green double cone pairs cannot be recognized at this stage [54] . The embryonic and early larval retina is also known to have relatively more blue and UV cones and relatively few rods compared with the adult retina [15] . In the larval retina , which is formed by addition of cells at the retinal margin ( lower left , Fig . 1G ) , the ordered linear fragments become more prominent , but the long-ranged lattice order is still clearly absent . To obtain a quantitative measure of the regularity of cone cell packing at the level of the apical epithelial surface , we segmented the images of ZO-1 immunolabeled cell profiles and statistically analyzed the data with an orientational order parameter , , that we designed to measure the similarity of the observed packing to an ideal rectangular lattice ( Fig . 2 , Text S1 , and Fig . S2 ) . Compared to a traditional Fourier transform measurement of positional order , is expected to be more sensitive to relatively weak ordering [57] . It also has the advantage that it measures order relatively locally , without the requirement of averaging over a large number of cells , and thus can detect abrupt changes in the degree of ordering such as that observed at the retinal margin ( below ) . Cones of all four subtypes are treated equivalently in the analysis , so that the value of reflects the packing organization only , independent of the distribution of spectral subtypes . The average value of the order parameter is significantly different between the embryonic and adult retinas ( Fig . 1F ) : ( mean SD , ) for embryonic retina , and ( ) for adult retina . is designed to vanish in a truly disordered packing , though finite size effects always give at least a small positive value when it is calculated from real data; the value for embryonic retina thus indicates that there is little or no orientational order . As an alternative measure of crystalline organization , we also computed a conventional positional order parameter from the Fourier transform of the cell centroid positions and found a similar discrepancy between embryonic and adult retinas ( for embryonic retina , for adult retina ) . This difference in is consistent with that observed in simulations of the liquid-solid transition [58]; in particular , is not expected to vanish in a disordered , liquid-like packing . ( See Text S1 and Fig . S3 for details . ) The transition to a rectangular lattice pattern of cone cell packing occurs at the end of larval development , at approximately three weeks of age [28] . The boundary between the larval remnant and the ordered lattice pattern can be visualized in the adult retina ( Fig . 1G ) ; the transition in packing order occurs abruptly , over the scale of a few cones . In order to gain additional insight into the mechanism of cone mosaic formation , we examined the establishment of the cone pattern at the periphery of the retina , where successive cohorts of cone columns are generated in the germinal zone ( Figs . 1D , 3A ) . The spindle-shaped neuroepithelial cells in the germinal zone span the width of the retinal epithelium and form a continuous epithelial sheet with the retinal cells that extend to the apical surface of the differentiated retina , including Müller glia and photoreceptors–rods and cones ( Fig . 3A ) . Rods and cones have an elaborate , cilia-derived extension of their apical surface ( Figs . 3A; S4A–F ) , which includes an inner segment with abundant mitochondria and an outer segment that contains the phototransduction machinery [59] . The apical epithelial cell packing at the boundary between peripheral retina and germinal zone , where newly generated cones are differentiating , shows striking differences between larvae and adults: in the larval retinal margin there is no obvious distinction between the proliferative germinal zone and the differentiated retina , but instead a gradual transition from a region with heterogeneous cell shapes including large , irregular profiles of Müller glia , to the differentiated region where polygonal cone profiles dominate ( Fig . 3B ) . In contrast , a steep transition is clearly visible in the cell profiles in the adult retinal margin from the disordered , heterogeneous proliferative region compared with the crystalline ordered regions of the differentiated retina ( Fig . 3D ) . These distinctions were confirmed by evaluation of the order parameter profile along the direction of growth ( Fig . 3C and 3E , respectively ) . To obtain the value of the order parameter , we did not consider the polygonal , non-convex ZO-1 profiles that are frequently observed in the adult germinal zone , which are filled by processes of Müller cells ( Figs . 3D; S1J–L ) . These profiles were subsequently removed from our analysis based on a convexity index measurement of segmented zones ( Text S1 ) . In order to test our proposal that PCP couples with mechanical interactions to sculpt the cone mosaic , we first looked for experimental evidence of planar polarization in the retinal epithelium . The Crumbs complex–the transmembrane Crumbs2a ( Crb2a ) protein and associated intracellular scaffolding proteins in the MPP5 ( membrane palmitolated protein 5 ) family ( Drosophila ortholog stardust and zebrafish orthologs Nok and Ponli ) –localizes to the subapical region ( SAR ) in the inner segment region of zebrafish cone photoreceptors [77]–[79] . This complex is important in maintaining apical-basal polarity and the integrity of the adherens junctions at the OLM and is thought to mediate cell-cell adhesion both between photoreceptors and Müller glia [50] and between photoreceptors [78] . As cone photoreceptors differentiate , for example in the larval retina from 4 to 10 dpf , the inner segment elongates , as does the SAR , as delimited by Crb2a immunostaining ( Fig . S4A–C ) . In the adult , the SAR interface between inner segments of red-green double cones extends up to 50 µm apical to the OLM ( Fig . S4D–I ) . In contrast , thin lamellar processes of Müller glia surround each cone profile at the level of the OLM , as identified by ZO-1 immunostaining ( Figs . S1J–L , S4D ) , but in the adult retina , these processes extend at most ∼15 µm apically beyond the OLM ( Fig . S4G–I ) . Above the Müller glial processes , the inner segments ( SAR ) of cones have the opportunity for direct cell-cell contacts without intervening glia ( Fig . S4E–H ) . The inner segments/SAR of the red-green double cone pairs , in particular , are tightly apposed and exclude all Müller glial processes from the level of the OLM apically ( Fig . S4I ) . We found that the distribution of Crb2a protein in cone photoreceptors is co-localized with ZO-1 at the level of the zonula adherens in the OLM , but shows planar polarization at the level of the inner segments within the SAR . In this region of direct cell-cell contacts between cones , the Crb2a protein exhibits a polarized distribution aligned with the rectangular cone lattice , as predicted by the mathematical model , with Crb2a enriched along interfaces between adjacent cones within columns compared with interfaces between columns ( Fig . 6A , S4J–L ) . High levels of Crb2a in the SAR are thus correlated with relatively weak interfacial tensions while low levels indicate higher tensions . Importantly , this planar polarization is observed near the retinal margin , before significant numbers of rods that might prevent contacts between cones in different columns have inserted . Indeed , it is interesting to note that rods subsequently insert along interfaces with low Crb2a concentrations , consistent with reports that knockdown of Crumbs favors insertion of transplanted rods into murine retina [80] . One of the motivations for introducing PCP , and hence planar polarized mechanical interactions between cone photoreceptors , into our model was the observed linear fragments of cone columns even in the embryonic or larval retina or remnants ( Figs . 1E , G , 4A ) , where long-ranged order is lacking . The model predicts that similar ordered domains should be observed wherever the cone mosaic is not fully crystalline . To verify this prediction , we examined cone photoreceptor cell packing in regenerated retinal tissue in adult zebrafish . If photoreceptors in a region of the neural retina in adult zebrafish are ablated through exposure to very intense light , the Müller glia in the affected region re-enter the cell cycle and form scattered clusters of neurogenic progenitor cells that regenerate a new complement of cone photoreceptors [75]–[76] . The resulting restored photoreceptor mosaic , however , lacks the crystalline order seen in the undamaged adult retina [27] . Instead , we find that the regenerated retina contains short , curvilinear chains of cones , a single cell wide , with intervening spaces filled with rods ( Fig . 6C ) . The organization of cones in the regenerated retina is closer to simulations of packing of cones being specified simultaneously under a global isotropic stress , where disconnected regions of rectangular order form in all directions ( Fig . 5A ) . It also somewhat resembles that of the larval remnant in the adult retina ( Fig . 1G ) , though the larval remnant has far fewer rods . This striking morphology is strong evidence of anisotropic , polarized interactions between individual cones , independent of any crystalline ordering . Moreover , in the SAR of the regenerated cones , Crb2a localizes preferentially to interfaces within the curvilinear chains of cones ( Fig . 6C ) , suggesting that these groups may be viewed as fragments of cone cell columns organized by PCP-dependent junctional structures , just as the mathematical model predicts . Our model also suggests that perturbations of the mechanical environment of the retina as cones are differentiating should lead to defects in the cone mosaic . Consistent with this prediction , we find that the cone mosaic is disturbed in bugeye mutant fish ( Fig . 6D; compare simulations Fig . 5D , F ) . The bugeye locus encodes the Low density lipoprotein receptor-related protein 2 ( Lrp2 ) , a large transmembrane receptor with multiple identified ligands [81] . In the eye , Lrp2 is expressed in the ciliary epithelium of the anterior segment of the eye and in the retinal pigment epithelium behind the neural retina , but not in the neural retina itself . The phenotype of the bugeye mutants in adult fish includes enlarged eyes , elevated intraocular pressure ( IOP ) , and thinner epithelial layers with decreased photoreceptor density ( consistent with mechanical stretching induced by the IOP ) , but with variable penetrance; the severity and time of onset of the defects varies significantly from one animal to the next and even between the two eyes of a single fish [81] . We detected cone mosaic disruption only in enlarged eyes of bugeye fish ( Fig . 6D ) . When imperfections were observed , they were strongly reminiscent of the defects found in the regenerated regions ( Fig . 6C ) and in simulations of our model in the absence of a global stress anisotropy ( Fig . 5D ) . Because Lrp2 is not expressed in photoreceptor neurons , the effects of the mutation must be transmitted to the photoreceptor layer either through a secondary signal from the retinal pigmented or ciliary epithelium or through changes in mechanical properties like IOP . ( One effect of such a secondary signal might be to affect cell proliferation or death , but no increase in apoptosis is observed in the photoreceptor layer , and , although there is conflicting evidence as to whether and how proliferation is affected , experiments to date do not suggest dramatic enough changes in proliferation to induce the near-complete loss of long-ranged order of Fig . 6D [81]–[82] ) . Although we cannot rule out the possibility of some unknown signal from the non-neural ocular epithelia that is permissive for crystalline mosaics , we favor the latter hypothesis: The loss of long-ranged crystalline order in a mutant with elevated IOP suggests that large changes in the IOP may disrupt the pattern of tissue-scale mechanical stresses necessary for global alignment of the mosaic pattern , as predicted by our mathematical model .
In the crystalline cone mosaic of the adult zebrafish retina , both the spectral fates of cone photoreceptor cells and their shape and packing in the apical plane of the retina exhibit precise patterns . Here , we have focused on the planar packing of cones , which appears to arise roughly simultaneously with the determination of cone cell fate and the differentiation of the SAR as represented by elaboration of the cone inner segment . To gain insights into the mechanisms producing the rectangular lattice packing , we first characterized the arrangement of cone and rod photoreceptors in mature zebrafish retina , which we showed is dominated by circumferential columns of closely apposed cones , a single cell wide , oriented parallel to the site of cone genesis in the germinal zone at the peripheral retinal margin . The cones in a column thus represent a cohort of cells of approximately the same age . Cones in successive columns are also aligned in radiating rows , and rod photoreceptors are mostly constrained to the interstices between cone columns . This rectangular lattice pattern contrasts with the more disordered cone cell packing in the embryonic and larval retina , which largely lacks rods and has reduced numbers of red and green cones [15] , but which nevertheless shows evidence of short , linear chains of cones , reminiscent of the columns in the adult cone mosaic , albeit without long ranged order . We also examined the emergence of epithelial order as new cones are generated at the retinal germinal zone and contrasted this with the relative lack of order in two experimental conditions: 1 ) when cones in light-damaged , adult zebrafish regenerated in central retina from unaligned , scattered neurogenic foci , and 2 ) when cones were generated in the bugeye mutant zebrafish , which has altered intraocular mechanics . Cone photoreceptor genesis in both larval and adult zebrafish occurs only in the germinal zone at the circumference of the retina , and in both cases the retina grows by adding successive annuli of cells at the perimeter . Nonetheless , a truly ordered cone mosaic is only seen in adults , not in larval fish . Thus , progressive , spatially restricted differentiation cannot , by itself , explain the appearance of a crystalline cell packing . Instead , the emergence of regular cone packing coincides with the maturation of the ciliary epithelium in the anterior segment at the end of larval development , with the concomitant production of aqueous humor and the resultant intraocular pressure , and with the simultaneous formation of the annular ligament , a mechanical constraint overlying the retinal germinal zone in the anterior segment [28] . We therefore hypothesize that the tissue-scale mechanical environment may play a central role in regulating local cell shapes and packing in the retinal epithelium as the cone mosaic is formed and that the development of the anterior segment may lead to a significant change in this mechanical environment . Building on this idea and on our experimental observations , we propose that circumferential columns of cones form through a feedback between mechanical tension at cell-cell interfaces and PCP , with an anisotropic mechanical stress , possibly imposed by the annular ligament , providing an overall orientational signal . A mathematical model incorporating both cell mechanics and PCP reveals that such a global mechanical stress , together with progressive growth and addition of cells at the retinal margin , is sufficient to robustly assemble the new cones into a coherent rectangular lattice . The model is supported by our observations of a polarized distribution of Crumbs2a protein in differentiating cones near the retinal margin , as well as in mature cones in the patterned areas of adult fish . The Crumbs transmembrane proteins define the apical membrane of epithelial cells , including photoreceptors in Drosophila and vertebrates , and are implicated in cell-cell adhesion through a poorly understood mechanism [50] , [77] , [83]–[84] . The intracellular domain of Crumbs proteins is associated with a macromolecular complex of scaffolding proteins , including the MPP5 proteins such as zebrafish Nok [83] . Genetic evidence suggests that the Crumbs/Nok apical junctional complex mediates photoreceptor-photoreceptor adhesion in zebrafish: In a zebrafish mutant with nonfunctional N-cadherin , the adherens junctions of the OLM are destabilized and the apicobasal polarity of the retinal epithelium is destroyed , but the photoreceptors nevertheless develop an intrinsic apical-basal polarity and self-associate into small , scattered , spherical “rosettes” with apical surfaces pointing toward the center; in the absence of functional Nok protein , rosettes fail to form [83] . Further , Nok mutant photoreceptors fail to localize Crumbs protein to the SAR of the inner segments , and photoreceptor cells with mutant Nok protein transplanted into a wild type retina show increased mobility ( consistent with reduced cell-cell adhesion ) when viewed by time-lapse microscopy in a living zebrafish embryo [83] . Even more relevant to the present results are recent reports on the subcellular localization of two proteins , a novel Nok/MPP5 family member in zebrafish , Ponli ( Photoreceptor-layer-nok-like ) , and Crumbs2b , both of which are expressed exclusively in red , green , and blue cones and which show polarized localization to the SAR coincident with the localization demonstrated here for Crb2a , i . e . at the interfaces between cones within a column , but not between columns [79] , [84] . In further support of our model , we have here reported fragments of cone cell columns in cones regenerating within the adult retina at a distance from the germinal zone and the annular ligament [75]–[76] and in the bugeye mutant , in which the ocular globe enlarges dramatically as a consequence of increased intraocular pressure [81]; these column fragments exhibit a planar-polarized Crb2a distribution that corresponds very closely to that seen in the cone cell columns of unperturbed retina . Very recently , Zou et al . have observed similar column fragments in transgenic fish with secreted Crb2b extracellular fragment [84] . Additional evidence for strong adhesive interactions between cone photoreceptors comes from morphological observations . The double cones are of special interest: These tightly apposed pairs of cones are found in many vertebrate taxa , though not in placental mammals [85] . Electron microscopy reveals specialized , subsurface , membranous cisternae , located ∼90 Å beneath and parallel to the apposing plasma membranes of the inner segments in the double cone pairs [54] , [86] , i . e . in the region where the Crumbs complex is localized , and electron dense material has been observed in the extracellular space between these apposing membranes [87] . Finally , double cones dissociated from the retina remain physically attached to each other at the interface of their inner segments [88]–[89] . The defining characteristic of the model presented here is the feedback loop encompassing mechanical stresses , cell shape , and PCP: Anisotropic stresses , whether externally imposed on the epithelial sheet or generated internally , tend to deform cells , and the resulting elongated cell shapes in turn favor cell polarization along a particular direction in the epithelial plane . Finally , PCP leads to anisotropic tensions along cell-cell interfaces , closing the loop . Importantly , cells in our model will spontaneously polarize even in the absence of tissue-scale anisotropic stresses imposed by the annular ligament , which serve only to encourage all cells to align in the same direction ( and hence to line up in columns ) ; the model's behavior is thus insensitive to the exact magnitude of the anisotropy . In the presence of a global stress anisotropy , these interactions are sufficient to reproduce the observed packing of cone photoreceptors near the retinal margin , including not only the presence of aligned columns , but also such unexpected features as the tendency of cell-cell interfaces within columns to tilt relative to the average column direction and the rotation of the average direction of the cone cells' long axis as they leave the germinal zone . Consistent with our hypothesis of a central role for PCP in determining cell packing , we have moreover observed planar-polarized protein distributions in adult cone mosaics and a tendency of cone photoreceptors in regenerated retina to form chains , suggestive of planar-polarized interactions between these cells . Our model makes several further predictions that we have not yet been able to test experimentally . Most obviously , we expect that the retinal epithelium supports anisotropic mechanical stresses . On the scale of the entire tissue , this should take the form of different diagonal stress tensor components in the radial and circumferential directions . At the level of individual cells , cell-cell interfaces are expected to have disparate tensions depending on their orientation relative to the direction of the cell's planar polarization . In fact , it should be possible to correlate interfacial tensions with the concentrations of proteins ( including perhaps Crb ) implicated in PCP . In principle , tension anisotropy on both scales can be measured with appropriate forms of laser microsurgery combined with live cell imaging [60] , [64] . In addition , it might be possible to infer the tensions along individual interfaces from a careful analysis of cell shapes and , in particular , of the angles at which interfaces meet at vertices [90] . More broadly , our analysis suggests that changes to the neural retina's overall mechanical environment should disturb the precision of the cone mosaic . Thus , mutations or experimental treatments affecting the eye anterior segment—and especially the annular ligament—might be expected to disrupt the cone mosaic; for example , if it were possible to ablate the annular ligament entirely , we would predict that the crystalline cone mosaic would revert to something closer to the disordered packing seen in the larval retina . Similar effects might also occur as a result of sufficiently large and sustained changes to the intraocular pressure , though in this case global mechanical effects might be difficult to separate from the consequences of physiological stress responses within individual photoreceptor neurons . One striking feature of the cell packing at the retinal margin in the adult is the sudden shift from a haphazard cell array in the germinal zone to the orderly columns characteristic of mature retina . This is to be contrasted with the more gradual rearrangements seen in another prominent example of a crystalline cell packing resulting from front-like growth: In the Drosophila eye imaginal disc , a rough lattice of isolated cells fated to become R8 photoreceptors is first selected from within an essentially disordered cell packing . These R8s then signal to surrounding cells to induce successive waves of differentiation , with significant changes in cell shape and position in most cases coming more gradually and only after fate specification [91]–[92] . The abrupt appearance of columns in fish retina suggests a much tighter integration of spectral fate specification and morphogenesis . Also of note is our observation of planar polarized Crb protein localization in cells near the retinal margin . The Crumbs complex , which includes transmembrane Crumbs proteins and associated intracellular proteins MPP5 and Patj , is thought to work in concert with the Par-3 complex , which includes the cytoplasmic proteins Par-3 ( Bazooka in Drosophila ) , Par-6 , aPKC , and Cdc42 , to define distinct apical membrane domains in epithelial cells [50] , [93]–[95] . More recently , studies in Drosophila have implicated many of these same proteins—including , in one case , Crb [96]—in PCP , especially in systems where PCP is intimately linked with polarized contraction of cell-cell interfaces [48] , [94]–[95] , [97]–[101] . Our results suggest a related role for Crb in fish retina Significantly , in the fly systems , apical proteins have higher concentration on edges with lower tensions , and we likewise observe preferential Crb localization to ( presumptively ) lower tension edges in fish . Our observations thus hint that much of the machinery that establishes apico-basal polarity may be reused to regulate planar polarized cell movements in vertebrates , just as it is in flies . It is well established that PCP can lead to anisotropic tensions along cell-cell interfaces , and thereby to cell shape changes or even to large-scale tissue remodeling . This is perhaps most dramatically seen in convergent extension [48] , but the PCP pathway is also required in other processes , ranging from oriented cell division to the establishment of the hexagonal cell packing in Drosophila wing imaginal discs [13] , [49] or the morphogenesis of Kupffer's vesicle in zebrafish [102] . On the other hand , recent evidence also indicates that both static cell packing defects and the dynamics of cell division and changes in packing topology can influence PCP in wing discs [37]–[38] . Mathematical models have been able to reproduce and explain many of these observations [37]–[38] , [48]–[49] . This prior work , however , focuses largely on unidirectional influences either of PCP on cell mechanics or of cell mechanics on PCP . By including interactions in both directions , we are able to paint a more complete portrait of morphogenetic processes involving PCP . Indeed , the studies just cited show that such feedback loops must exist in wing discs . Likewise , columns of cells reminiscent of those seen in mature fish retina appear in several other systems where PCP is known to be active , most notably in the ventral epidermis of Drosophila embryos , where a similar interplay among PCP , polarized tensions , and cell shape changes may well be at work [96] , [99]–[100] . In convergent extension , it remains unclear how the global PCP orientation is faithfully maintained in the face of large-scale cell movements; as these rearrangements are themselves dependent on PCP , this question can only be addressed with models that integrate the dynamics of PCP and of cell motion . Our model is thus likely to find applications in investigations of many other systems .
Adobe Photoshop CS5 Extended ( Adobe Systems Inc . , San Jose , CA ) and ImageJ 1 . 43u ( http://rsb . info . nih . gov/ij ) were used for post-acquisition processing of digital images . Any digital adjustments to contrast , gain , color , filtering , and layer properties were applied to the entire image . Some figures include maximum projections created from selected layers in confocal Z-stacks . In some cases when overlaying multiple fluorescent channels in a Z-stack projection , selected regions of an individual layer were masked to avoid parallax . The order parameters extracted from experimental images were obtained by segmenting the cell profiles at the level of the OLM with a watershed algorithm . To avoid including Müller cell or rod profiles in the analysis , small cells with area below a cut-off were removed from the segmentation by morphological shrinking . In the margin area , we also removed regions that appeared morphologically distorted; we have verified that those regions are filled with Müller cell processes ( Fig . S1J–L ) . These regions were detected by enforcing a minimal threshold for the ratio between the region area and the area of the convex envelope of the region . From the segmented image , the positions of cell centroids were computed and used to determine order parameters ( Figs . S2 and S3 ) . An orientational order parameter reflecting the four-fold rotational symmetry of a perfect cone mosaic was used to evaluate quantitatively the closeness of an arrangement of cells to a rectangular periodic lattice . Inside each cell , a cross is specified ( Fig . 2A , blue line ) whose orientation reflects the neighbors position . To do so , for every orientation of the cross the plane is separated into four quadrants ( Fig . 2A , dotted line ) and the distance between the arms of the cells and the closest neighbor within the quadrant to which the arm belongs is calculated . The cross orientation is then set by minimizing the sum of these distances . This allows assigning a cross for every cell in the epithelium characterizing the packing geometry ( Fig . 2B ) . An average order parameter for the cross orientations can then be obtained for a group of cells by computing two components , and where refers to the angle with the horizontal of one of the cross arms . This specific functional form is required by the π/2 rotational invariance of the crosses . The magnitude of the order parameter is then given by and satisfies . The value of reflects the level of ordering: corresponds to completely random orientation , whereas reflects a perfect alignment of the crosses ( Fig . 2C ) . Cells assembled on a perfect rectangular lattice would have an order parameter . Our model's basic biological content and mathematical formulation is discussed under Results above . Here , we elaborate on some of the finer technical points .
|
Many tissues and organs , including sensory organs like the vertebrate retina and inner ear , are built from sheets of connected cells called epithelia . The precise arrangement of different types of cells within these epithelia can be essential to their function . ( For example , photoreceptor cells in eyes must be properly spaced to collect an optimal , undistorted signal . ) We combine experimental observations with computational modeling to understand how a particular example of such epithelial organization—the planar crystalline packing of cone photoreceptor cells in the fish retina—is created . Specifically , we introduce a model where the strength of cell-cell adhesion along an interface depends on the orientation of that interface . When a global mechanical compression is applied along one direction , this model can recapitulate observed features of the cone packing and gives qualitatively correct predictions of the cone photoreceptor pattern observed in regenerated and mutant retinas . Our analysis shows that simple local interactions can direct the creation of regular , long-ranged order among epithelial cells , and it also clarifies the mechanical interactions needed to establish and maintain the integrity of the retinal epithelium . Our model may thus ultimately provide a foundation for insights into diseases in which epithelial integrity is lost .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"statistical",
"mechanics",
"neuroscience",
"biomechanics",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"cell",
"mechanics",
"morphogenesis",
"pattern",
"formation",
"developmental",
"neuroscience",
"theoretical",
"biology",
"biophysics",
"theory",
"biology",
"biophysics",
"physics",
"systems",
"biology",
"zebrafish",
"biophysic",
"al",
"simulations",
"computational",
"biology"
] |
2012
|
Coupling Mechanical Deformations and Planar Cell Polarity to Create Regular Patterns in the Zebrafish Retina
|
Zika virus infection is associated with the development of Guillain-Barré syndrome ( GBS ) , a neurological autoimmune disorder caused by immune recognition of gangliosides and other components at nerve membranes . Using a high-throughput ELISA , we have analyzed the anti-glycolipid antibody profile , including gangliosides , of plasma samples from patients with Zika infections associated or not with GBS in Salvador , Brazil . We have observed that Zika patients that develop GBS present higher levels of anti-ganglioside antibodies when compared to Zika patients without GBS . We also observed that a broad repertoire of gangliosides was targeted by both IgM and IgG anti-self antibodies in these patients . Since Zika virus infects neurons , which contain membrane gangliosides , antigen presentation of these infected cells may trigger the observed autoimmune anti-ganglioside antibodies suggesting direct infection-induced autoantibodies as a cause leading to GBS development . Collectively , our results establish a link between anti-ganglioside antibodies and Zika-associated GBS in patients .
Zika virus is an arbovirus ( arthropod-borne ) of the Flaviviridae family , which like dengue viruses and alphavirus chikungunya virus is transmitted by Aedes mosquitoes . Although discovered in 1947 in Uganda , the first large Zika virus outbreak was reported in Micronesia in 2007 [1] followed by the 2014 French Polynesia outbreak [2] and the massive Latin American outbreak in 2015 , which was first reported in Brazil and spread across the Americas [3] . During this outbreak , alarming Zika-associated complications , such as microcephaly and Guillain Barré syndrome ( GBS ) were reported [4 , 5] . GBS is an inflammatory neuropathy and the most common cause of neuromuscular paralysis in the world [6] . The etiology of GBS is unknown but its development has been highly associated with post-infection autoimmune responses against gangliosides in peripheral nerves . Gangliosides are sialated glycosphingolipids found in neuronal membranes and are involved in different neuronal functions . Autoimmune antibodies recognizing gangliosides are found in a high proportion of patients with GBS ( 62% [12] ) and are thought to contribute to neuronal pathology inducing complement-mediated axonal injury and demyelination [6] . Molecular mimicry has been proposed as a likely mechanism in infection-induced GBS , where antibodies generated against microbial antigens with structural similarities to specific gangliosides would cross-react with host gangliosides in neuronal membranes . A classic example is GBS associated with Campylobacter jejuni infection [7] . Zika virus was added to the list of GBS-associated pathogens due to the high incidence reported during the 2015 Latin America outbreak [8]; however , Zika virus-associated GBS shows anti-gangliosides antibodies ( anti-GA1 ) that cannot be attributed to molecular mimicry [9] , as described for C . jejuni [7] , suggesting alternative mechanisms for the generation of autoantibodies as a result of Zika infection . During many autoimmune disorders , such as rheumatoid arthritis , autoantibodies play an essential pathological role in mediating the disease . Interestingly , increased levels of IgG autoantibodies against the ganglioside GD3 have been observed in patients with acute Zika infection and without neurologic manifestations such as GBS [10] . Some GBS manifestations have also been associated with elevated levels of autoantibodies such as anti-ganglioside antibodies that can target peripheral nerves [11 , 12] , but the association of these antibodies with Zika-induced GBS remains unclear . In this study we evaluate the antibody reactivity levels against 17 different glycolipids , including mostly gangliosides , presented in single and combination form , in the plasma of Zika-infected patients from one of the locations of the 2015 outbreak in Salvador , Brazil . We observed that Zika-associated GBS patients have significantly higher levels of plasma anti-glycolipid antibodies compared to non-GBS Zika-infected patients . We also observed a broad repertoire of glycolipids , including gangliosides , that were targeted by both IgM and IgG anti-self antibodies . Collectively , these results established a link between anti-ganglioside antibodies and Zika-associated GBS patients .
This study was approved by the institutional review board of Instituto Gonçalo Moniz-Fiocruz–n°1184454/2015 . All participants were adults , agreed to participate in the study and signed Informed Consent . Cases of GBS and encephalitis associated with arbovirus infection and Zika infection without neurological symptoms were enrolled in a surveillance study in neurological units of two reference hospitals in Salvador , Bahia , Brazil , from May 2015 to April 2016 , during the Zika outbreak in this area [13] . The study population were patients with acute neurological syndromes admitted to neurology sectors of participating hospitals . Patients with Zika infections but no neurological signs were recruited as part of a surveillance program for Zika infections in the same hospitals . All patients with neurological syndromes were evaluated by the researcher neurologist and the diagnosis of GBS was established according to international criteria [14] . The inclusion criteria were: ( 1 ) Patients with symptoms compatible with GBS and its variants or encephalitis . The diagnosis of GBS , Miller-Fisher syndrome ( MFS ) and its variants [14]; and encephalitis [15] was predetermined by disease-specific criteria . [2] Patients that reported acute exantemathous or fever illness in the 4 weeks before onset of neurologic symptoms . Electromyography and nerve conduction studies were performed in patients with GBS . See Table 1 for details regarding the timing of neurologic symptoms and sample collection in relations to symptoms of arbovirus infection . Detection of specific anti-Zika , anti-chikungunya , and anti-dengue IgG antibodies and anti-dengue and anti-chikungunya IgM antibodies were performed using indirect enzyme-linked immunosorbent assays ( ELISAs ) ( Euroimmun , Lüberg , Germany ) , in accordance with the manufacturer protocol . An IgM antibody-capture ELISA ( MAC-ELISA ) , provided by the Arbovirus Reference Collection division of the Centers for Disease Control and Prevention ( CDC ) , was used in accordance with the established CDC protocol . Detection of RNA for Zika , chikungunya and dengue virus were performed by reverse transcriptase-PCR following published methods [16–18] . Patient samples positive for Zika plaque reduction neutralization test ( PRNT ) and/or positive for Zika IgM and negative for dengue IgM by ELISA ( CDC ) were considered positive for Zika infection . Patient 11 was considered positive for Zika infection because it showed positive Zika IgG and negative dengue IgG . Only in patient 15 , which is positive for Zika and dengue IgG , lack of cross-reactivity with anti-dengue antibodies could not be confirmed . Zika infection was considered acute when samples were positive for Zika RNA ( by RT-PCR , [18] and/or Zika IgM ( by ELISA ) . Biological samples , including blood , were collected upon hospital admission or 4 months after the onset of symptoms , as indicated . Data management was performed using REDCap 6 . 18 . 1 - 2018 Vanderbilt University . Costar 3700 384-well ELISA plates were coated with single or mixes of Glycolipids ( Matreya , Sigma ) at 20 μg/ml in 200 proof Molecular Biology ethanol using an Agilent Bravo system in a BSL-2 hood . The lipids used were: sphingomyelin ( SPM ) , phosphatidylserine ( PS ) , sulfatide ( SULF ) , globoside ( GS ) , Trihexosylceramide ( CTH-hydroxi fatty acid ) ( THCH ) , Trihexosylceramide ( CTH non-hydroxy fatty acid ) ( THCHN ) , galactocerebroside ( GALC ) , and the gangliosides GM1 , GM2 , GM3 , GA1 , GD1A , GD1B , GD2 , GD3 , GT1B and GQ1B . Plates were then allowed to evaporate at RT after >16 h of incubation at 4°C . Plates were washed 3 times with PBS 0 . 05% tween 20 and then blocked overnight with PBS supplemented with 3% BSA . Plasma from patients was diluted at 1:100 in blocking buffer and incubated for 2 h at 37 °C . Plates were washed again 3 times and incubated with anti-human IgM/IgG-HRP ( Abcam ) for 1 h at 37 °C . Plates were washed 3 more times and TMB substrate ( BD Biosciences ) was added until desired color was obtained . Reaction was stopped with Stop buffer ( Biolegend ) and absorbance was read at 450 nm . The optical density at 450 nm was compared with the same dilution ( 1:100 ) of a positive plasma sample ( sample ID: 9b ) that was used as reference to calculate relative units ( RU ) . Two negative controls were included in each of the ELISA plates run: ( 1 ) The plasma of a healthy US control donor was used in duplicated wells in the ELISA for each glycolipid and combinations . The average of the 2 determinations for each glycolipid and combinations was used as background for each glycolipid and subtracted from each value . ( 2 ) The reactivity of each plasma sample in wells coated with PBS supplemented with 3% BSA . The value of eight independent wells for each plasma sample was obtained . It was observed that the variation between the eight replicates with each plasma samples was <0 . 02 for all samples and the variation between the average values for the different plasma samples was <0 . 0002 . The reactivity of all plasma samples to BSA was considered constant and was not subtracted from assay values . The reactivity to wells coated with only glycolipids coating buffer ( ethanol ) was not considered since it results in high unspecific background for all samples . The secondary antibody , TMB and stop solution was added using the peristaltic pump on the Biotek EL406 . Washes were also done using the 96-head washer on the EL406 . Validation of the automated 384-well ELISAs was performed using a similar protocol in 96-well plates with manual pipetting . Five different plasma samples ( 9b , 10a , 11 , 14 and 16 ) of GBS patients were tested with five randomly chosen glycolipids ( SPM , GS , GM1 , GD3 and SULF ) . The variation between the two assays was found to be lower than 0 . 008 for each of the glycolipids . Data were analyzed using Prism ( GraphPad Software ) . Unpaired t-test was used to identify statistical differences between groups of samples . For determination of number of antigens recognized per sample and number of positive samples recognizing each ganglioside , reactivity of samples was considered positive if the OD value was at least the average of control background wells plus three times the standard deviation .
Using a novel high throughput ELISA approach , we assessed the plasma of these patients for their reactivity ( IgM and IgG ) against different gangliosides . The reactivity to combinations of glycolipids has been described to be higher than the reactivity to single ones , possibly due to the formation of complex antigenic structures [12] . We therefore analyzed reactivity against 17 lipids , mostly glycolipids including gangliosides described to be associated with GBS [6] , alone or their 139 double combinations . We first performed an overall analysis of all the patient samples to determine the levels of ganglioside reactivity in their plasma using a high throughput ELISA approach to detect anti-ganglioside IgM and IgG antibodies . These assays showed increased anti-ganglioside reactivity in the plasma of Zika-associated GBS patients compared to Zika patients without GBS ( Fig 1A–1C ) . Collectively , these results showed an enriched anti-ganglioside antibody response in the plasma of Zika-associated GBS patients compared to Zika-infected controls . Additionally , we analyzed one sample from a GBS patient of unknown etiology who was negative for Zika , dengue and chikungunya infections , which showed a similar profile to Zika-associated GBS patients . Since a previous report described that the plasma of GBS patients presented higher antibody reactivity to complex glycolipids compared to individual ones , we analyzed the responses to individual versus 2-by-2 combined glycolipids . We did not find any significant differences between the average reactivity of any of the plasma samples to individual or combined glycolipids ( Fig 2 ) . We also analyzed the plasma of two GBS patients with active chikungunya virus infection ( IgM+ ) and previous Zika infection ( IgG+ ) . The plasma from these patients did not present strong reactivity against gangliosides , in contrast to Zika-associated GBS patients ( Fig 3 ) . We further dissected the anti-ganglioside reactivity patterns observed in Zika-infected patients with or without GBS . A detailed analysis of antigen reactivity showed that patients that developed GBS recognized a significantly higher number of antigens ( single and combined ganglioside mixes ) compared to patients without GBS ( Fig 4A ) . The highest number of antigens recognized was found in a GBS patient reacting significantly to >100 different single gangliosides/combinations while all non-GBS patients reacted significantly against 2 or fewer antigens . When we analyzed which specific gangliosides had an enriched reactivity across the Zika infected patients ( either in single or combination form ) we observed a broad reactivity to different gangliosides and other glycolipids ( Fig 4B ) . These results suggest a broad anti-ganglioside antibody response in Zika-associated GBS patients , independent of combinations . Our initial screen of anti-ganglioside reactivity evaluated the presence of overall IgM/IgG antibodies in the plasma of the Zika-associated GBS patients . We further analyzed the samples by validating the responses observed in six highly responsive patients , from the Zika-associated GBS group . We also dissected this response by determining IgM and IgG reactivities separately ( Fig 5 ) . The results of these assays showed high levels of both anti-ganglioside IgM and IgG in the plasma of the patients compared to control plasma . Additionally , these assays validated the strong broad reactivity of the Zika-GBS patient plasma against a high number of gangliosides ( Fig 4 ) . Collectively , these results confirm the presence of IgM and IgG anti-ganglioside antibodies in the plasma of Zika-associated GBS patients . Our findings demonstrate broad reactivity to glycolipids with stronger responses to specific gangliosides .
GBS is one of the most serious complications associated with Zika infection . Neurological symptoms appear shortly after a transient Zika infection resulting in the development of GBS [9 , 11 , 19 , 20] . In this study a median of 10 days before the onset of neurological symptoms was observed , which is similar to a previous report ( 6 days ) [9] . Although Zika virus infection has been reported to lead to development of GBS in patients , little is known about the pathogenesis of this syndrome . Different autoantibodies have been identified as mediators of pathology during different autoimmune disorders , such as anti-nuclear antibodies during Systemic Lupus Erythematosus [21] . Indeed , GBS is considered an autoimmune syndrome due to the immune destruction of peripheral nerve components such as gangliosides [19 , 22 , 23] . Generation of anti-ganglioside antibodies and other autoantibodies has been reported in non-Zika infection-induced GBS , such as the classical one induced by the bacterium C . jejuni [7] . Because anti-ganglioside autoantibodies are implicated in the pathogenesis of GBS [6] , we sought to determine whether anti-ganglioside antibodies were selectively increased in Zika-infected patients with GBS as opposed to Zika-infected controls with self-limited illness . Our initials results show a potent broad reactivity against single and combination of 17 different gangliosides compared to non-GBS Zika infected patients . These anti-ganglioside antibodies were both of the IgM and IgG isotypes . Both anti-ganglioside IgG and IgM have been suggested to have a pathological role during different non-Zika infection induced GBS patients [24] . The mechanism by which these anti-gangliosides lead to pathology is poorly understood . The significant increase in anti-ganglioside IgM/IgG antibodies in patients with GBS compared to non-GBS Zika-infected patients suggests a role for these antibodies in mediating the disease . A recent study assessed a similar relationship of ganglioside reactivity in Zika-associated GBS patients in French Polynesia [9] . This study found an increase in general anti-ganglioside reactivity against a different set of gangliosides tested by a different method of combinatorial glycolipid microarray . Our results show reactivity against different individual and combination of gangliosides hence illustrating the diversity in the anti-ganglioside response induced in a different cohort of Zika-associated GBS patients . We did not observe any enhancement of reactivity whenever specific gangliosides were tested individually or in combination , which may be attributed to the different methods used for detection ( microarray versus ELISA ) . In addition to Zika virus , other arbovirus infections like dengue and chikungunya have also been reported to lead to autoimmunity and neurological problems such as GBS [25–30] . A large percentage of the patients in our study had previous , but not active , dengue infections , as indicated by the differential anti-dengue IgM and IgG reactivity . Previous dengue infections are expected in this area of Brazil where prevalence is 86% in adults and where preexisting high antibody titers to dengue virus have been associated with reduced risk of Zika infection [31] . We also assessed the plasma of two GBS patients with active chikungunya virus infection and previous Zika infection ( IgG+ ) . When we assessed the plasma of these patients for anti-ganglioside reactivity , our results demonstrated levels of anti-ganglioside antibodies significantly lower than GBS patients with an active Zika infection . It is possible that anti-ganglioside antibodies in the circulation induced during a Zika infection decrease over time and are no longer present in these patients . The role of an active chikungunya infection is unclear . It is well established that Zika can infect neurons [32–34] , including peripheral motor nerves/nerve roots , which have high abundance of different gangliosides . Direct infection of neurons would target these cells for phagocytosis by antigen presenting cells , enabling presentation of many auto antigens , such as gangliosides , along with virus antigens , resulting in an antibody response against both . Increased immune recognition of virus-infected neuronal cell antigens could be at the basis of Zika–induced GBS . Immune response against direct neural infection would be consistent with the observation that GBS tends to be an early complication of Zika . Accordingly , a recent studied showed how antiviral CD8+ T-cells mediated nerve damage leading to paralysis in Zika-infected mice [35] . Additionally , immune mediated neurological damage was also reported in fatal cases of Zika-induced microcephaly [36] , providing additional evidence of an immune component contributing to the neuronal damage leading to GBS . Accordingly , plasma from Brazilian Zika-infected patients recognized GD3 from neurons in retina tissues [10] . These patients were found to have high titers of anti-ganglioside antibodies , mainly anti-GD3 IgG antibodies . Nevertheless , mechanistic studies are needed to test this hypothesis and elucidate the role anti-ganglioside antibodies might have in Zika-induced GBS . Although we observed wide reactivity to gangliosides , our results also showed differential reactivity to some gangliosides in the plasma of Zika-associated GBS patients . When we dissected these responses by validating them with single ganglioside ELISAs , we confirmed a strong reactivity against specific gangliosides such as GA1 . Interestingly , GA1 also had the highest reactivity from the French Polynesia study as assessed by a different method of combinatorial glycolipid microarray [9] . However , in a different study in India with Zika-infected GBS patients , the most commonly recognize ganglioside was GT1b [37] . Collectively , our results suggest that , in a minor subset of infected patients , Zika infection causes neuronal damage that triggers an auto-immune antibody response against neuron-derived ganglioside antigens , which contributes to the pathogenesis of GBS .
|
Zika virus infection can trigger the development of Guillain Barré syndrome ( GBS ) , a neurological autoimmune disorder mediated by antibodies recognizing gangliosides in nerve membranes . Mechanisms such as molecular mimicry have been identified as a cause for GBS development in certain infections , such as Campylobacter jejuni , but the broad self reactivity observed during GBS suggests a role for alternative mechanisms . Our finding that Zika patients with GBS present higher levels of anti-ganglioside antibodies compared to uncomplicated Zika patients in Brazil points to these auto-antibodies as a trigger for GBS in these patients . These findings further support infection-induced autoantibodies as a factor contributing to GBS development , adding novel mechanisms for GBS development beyond molecular mimicry .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"glycolipids",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"chikungunya",
"infection",
"sphingolipids",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"viruses",
"clinical",
"medicine",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"antibodies",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"lipids",
"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"immunoassays",
"arboviral",
"infections",
"guillain-barre",
"syndrome",
"biochemistry",
"flaviviruses",
"clinical",
"immunology",
"viral",
"pathogens",
"physiology",
"autoimmune",
"diseases",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"glycobiology",
"organisms",
"zika",
"virus"
] |
2019
|
Anti-ganglioside antibodies in patients with Zika virus infection-associated Guillain-Barré Syndrome in Brazil
|
Morphogenetic gradients are essential to allocate cell fates in embryos of varying sizes within and across closely related species . We previously showed that the maternal NF-κB/Dorsal ( Dl ) gradient has acquired different shapes in Drosophila species , which result in unequally scaled germ layers along the dorso-ventral axis and the repositioning of the neuroectodermal borders . Here we combined experimentation and mathematical modeling to investigate which factors might have contributed to the fast evolutionary changes of this gradient . To this end , we modified a previously developed model that employs differential equations of the main biochemical interactions of the Toll ( Tl ) signaling pathway , which regulates Dl nuclear transport . The original model simulations fit well the D . melanogaster wild type , but not mutant conditions . To broaden the applicability of this model and probe evolutionary changes in gradient distributions , we adjusted a set of 19 independent parameters to reproduce three quantified experimental conditions ( i . e . Dl levels lowered , nuclear size and density increased or decreased ) . We next searched for the most relevant parameters that reproduce the species-specific Dl gradients . We show that adjusting parameters relative to morphological traits ( i . e . embryo diameter , nuclear size and density ) alone is not sufficient to reproduce the species Dl gradients . Since components of the Tl pathway simulated by the model are fast-evolving , we next asked which parameters related to Tl would most effectively reproduce these gradients and identified a particular subset . A sensitivity analysis reveals the existence of nonlinear interactions between the two fast-evolving traits tested above , namely the embryonic morphological changes and Tl pathway components . Our modeling further suggests that distinct Dl gradient shapes observed in closely related melanogaster sub-group lineages may be caused by similar sequence modifications in Tl pathway components , which are in agreement with their phylogenetic relationships .
The embryonic patterning and development of limbs rely on morphogenetic gradients that set up territories of gene expression in a dosage-dependent fashion [1] , [2] . Rather than a static process , cell fate specification normally occurs under dynamically changing environments that involve cell divisions and tissue growth expansion . One important property of morphogenetic gradients is the ability to scale and accommodate tissue cell types despite fluctuations in organismal size , for instance , due to feeding conditions or mutations affecting growth . Scaling is also a fascinating problem in evolutionary biology and can be observed in related species that have dramatically changed in embryo size but kept fixed gene expression domains at relatively similar positions in relation to the whole body [3] . Recent quantitative studies have begun to elucidate the scaling mechanisms of morphogenetic gradients during tissue growth [4] , regeneration [5] , as well as in related species [6]–[8] or artificially selected strains of same species that vary in embryo size [9]–[12] . In particular , studies in Drosophila embryonic gradients stand out as being especially amenable to quantitative analysis and modeling [13] . The relatively simple syncytial organization of the embryo allows precise detection of target gene expression with single cell resolution , and models can be built based on the extensive biochemical data of signaling pathways responsible for gradient formation . Remarkably , the isolation of new closely related species to the Drosophila melanogaster model ( reviewed in [14] ) provides a rich natural repertoire of genetic variations in egg size , cell numbers and gene divergence , which can be used to test the impact of these evolutionary changes on the scaling of gradients . Here we address the question of gradient scaling across related Drosophila species using the embryonic dorso-ventral ( DV ) patterning as a model system . The maternal nuclear concentration gradient of the NF-κB related transcription factor Dorsal ( Dl ) subdivides the embryo into three germ layers: the mesoderm , neuroectoderm and ectoderm . High levels of nuclear Dl in the ventral embryonic side activate expression of mesodermal genes , such as snail ( sna ) , whereas moderate levels in lateral regions activate neuroectodermal genes . Low to negligible levels of nuclear Dl in dorsal regions allow the expression of ectodermal genes such as decapentaplegic ( dpp ) and zerknult ( zen ) , due to the lack of repression that Dl exert on these genes ( reviewed in [14] ) . We recently reported that the Dl gradient has unique distribution profiles in related Drosophilids that vary in embryo size , which result in unequally scaled germ layers [8] . For instance , changes in mesodermal size serve as a mechanism to specify the border of the neuroectoderm and keep it at a constant size . Here we combined experimental approaches and mathematical modeling in order to identify parameters that might be responsible for the modified distributions of Dl gradient across species . Previously , Kanodia et al . ( 2009 ) [15] developed a mathematical model for D . melanogaster that reproduces the dynamics of the Dl gradient formation during cleavage cycles ( Fig . 1A , B ) . Their model consists of differential equations derived from mass balance equations of the main biochemical interactions of the Toll ( Tl ) pathway that lead to Dl transport into the nucleus , which were numerically solved using globally optimized parameters . Briefly , the model simulates the graded nuclear translocation of Dl initiated by the space-dependent dissociation of the cytoplasmic complex formed between Dl and Cactus/Ik-B ( Cact ) . This dissociation is modeled by a reaction rate constant kD and represents the graded activation of Tl receptors along the embryonic DV axis . The Dl-Cact complex prevents Dl from entering the nucleus and its dissociation due to Tl activation frees Dl to enter the nucleus . The model also recreates the geometric arrangement of embryonic nuclei during cleavage cycles , as well as changes in nuclear surface area , which affect Dl nuclear import and export rates . The Kanodia model captures essential properties of the Dl gradient formation and correctly reproduces the dynamics of the gradient formation during early embryonic cycles . However , this model has not yet been formally validated in conditions other than wild type D . melanogaster embryos , or used to simulate the Dl gradient of other species . Kanodia et al . ( 2009 ) [15] employed a genetic algorithm to identify a cloud of dimensionless parameters that satisfied a small dataset of experimental Dl gradient measurements from wild type embryos only . In this work , we built upon this model , and attempted to validate its generality by fixing free parameters using biological measurements , and manipulating only the subset of parameters that were most likely to be biologically relevant . We manipulated a single representative parameter set from this model in order to identify which parameter changes are sufficient to reproduce the experimental Dl gradients from three distinct experimental conditions in D . melanogaster: ( 1 ) embryos with decreased Dl levels , ( 2 ) decreased nuclear size with high nuclear density , and ( 3 ) increased nuclear size with low nuclear density . Once we obtained adjusted parameters for D . melanogaster that also satisfied these extended conditions , we next asked which parameters from this representative set were most likely to be modified in Drosophila species that display distinct Dl gradient shapes . To this end , we selected a divergent species with small embryos , Drosophila busckii , and two additional pairs of species belonging to the melanogaster subgroup , Drosophila simulans/Drosophila sechellia and Drosophila santomea/Drosophila yakuba , which diverged from D . melanogaster between 5 to 6 MYA ( Fig . 1C ) [14] , [16] , [17] . These species give us the unique opportunity to assay the behavior of the Dl gradient in lineages that have undergone a separate speciation event , but share some commonalities . For example , D . sechellia and D . santomea diverged very recently from their ancestral siblings D . simulans and D . yakuba , respectively , at an estimated 0 . 3–0 . 5 MYA . Despite such short divergence time , D . sechellia and D . santomea have much larger embryos than their siblings [6] . The use of modeling gave us insights in the evolution of Dl gradient shapes that are in agreement with the phylogenetic relationships of the species analyzed . We show that although the modified embryonic anatomy of these species influence the Dl gradient distribution , the species-specific Dl gradient shapes also depend on genetic modifications in the Tl pathway , which are shared in closely related species pairs .
We are interested in understanding how the Dl gradient acquired distinct shapes in related Drosophila species . One notable phenotypic difference reported in several Drosophila species is the significant variability in egg size [6] , [18] . In addition , the nuclear size and density also vary in these species [19] . We previously showed that manipulations in nuclei size and density in mutant D . melanogaster embryos can recreate Dl gradient shapes that are found in nature , leading us to hypothesize that nuclei density and size changes might be sufficient to modify the Dl gradient shape . The mutation sesame ( ssm ) generates haploid embryos that undergo an additional round of mitotic division , causing a high nuclear density and decreased nucleus size ( Fig . 2A , B ) [20] . In these mutants , the Dl gradient becomes flattened ( Fig . 2D , E ) . The second mutation used , gynogenetic-2 , gynogenetic-3 ( referred to as gyn [21] ) , generates triploid embryos that stop dividing one cycle earlier causing a lower nuclear density and larger nucleus size compared to the wild type diploid embryos ( Fig . 2C ) . The Dl gradient of gyn embryos is sharper than the wild type ( Fig . 2F ) . These mutations are known to affect ploidy , but are not expected to alter components in the Tl signaling pathway or embryo size . One way of explaining the altered Dl gradients of these mutants would be if the density of nuclei modifies the reading of Tl signal from one nucleus to the next , and consequently the rate of Tl signal decays ( Fig . 2G–J ) . In this scenario , a lower versus higher nuclei density would lead to a steeper and a flatter gradient , respectively ( Fig . 2L ) . In addition , since these mutants have the same amount of maternal Dl protein , the increase in nuclei density would decrease the amount of Dl per cell compartment and flatten the gradient . Another consideration is the differences in nuclei size , which increases the surface area available for Dl transport . Thus , nuclear size may counterbalance the effects of nuclei density . Since a qualitative analysis would not be sufficient to predict all of the combined effects described above , we employed a numerical approach using a modified version of the Kanodia model ( Text S1 ) . We used the same values of a representative parameter set used in the original MATLAB code to reconstruct the original model and run simulations of the wild type gradient formation using Wolfram's Mathematica , which successfully reproduced key features of the model ( Details of state variables , equations and parameters are provided in Text S1 and Tables S1 , S2 , S3 ) . In principle , any set within the restricted cloud of parameter sets identified by Kanodia et al . [15] could be used to model the Dl gradient and investigate qualitative changes to simulate the mutant gradients . We then asked if the shape of ssm and gyn gradients were altered from the onset of the Dl gradient formation , at nuclear cycle 10 ( nc10 ) . One of the Kanodia model findings was that the wild type Dl gradient has a constant shape throughout the nuclear cycles , which matches experimental data [15] . We initially tested the effect of changing nuclear radius in the wild type from nc10 to nc13 over the final gradient shape at the last stage ( nc14 ) . We found that altering the size of nuclei modifies the shape of the Dl gradient at early stages , but does not affect its final shape at nc14 ( Fig . S2 ) . Since we are most interested in the gradient shape at the final cycle nc14 , and not the dynamics of the mutant gradients , this result indicates that the effect of incorrect assumptions about early cycles is minimized . We next attempted to reproduce the Dl gradients from ssm and gyn mutants by using the selected representative set of parameters from Kanodia et al . [15] and adjusting it for nuclei size and density according to our experimental measurements ( Fig . 3; Table S4 ) . Few additional parameters were changed , especially related to early cycles ( Text S2 ) , but given the model robustness these changes did not significantly affect our results . We also normalized the model output to match our experimental data ( see Methods ) , which is restricted to the 30 most ventral cells instead of the entire embryonic cross section ( Fig . 3A–C ) . This ventral region includes the entire mesoderm and few additional cells in wild type and mutant embryos , and encompasses reliably measurable levels of nuclear Dl with distinguishable signal from background noise . This also represents the region where significant variations in the gradient shape are present [8] . With our normalization , we represent the overall shape of the Dl gradient instead of absolute values of Dl concentration ( Fig . 3D–F ) . Unless otherwise noted , the normalized gradient restricted to the 30 most ventral cells is referred to as “Dl gradient” . Given the graded levels of nuclear Dl , we also verified that the variations in the net numbers of mesodermal cells between wild type and mutant embryos do not alter the overall shape of the gradients after normalization ( Fig . S3 ) . In non-normalized graphs , our simulations show that ssm embryos have the highest peak of nuclear Dl concentration , while gyn embryos have the lowest peak ( Fig . 3B ) . Thus , even though ssm has a smaller amount of Dl per cell compartment and nuclear surface area available for Dl translocation , the model predicts that its smaller nuclear volume is the major determinant of the absolute concentration of nuclear Dl . In terms of Dl gradient shape seen in normalized graphs , the model correctly reproduces the flattened ssm gradient , but not the steep gyn gradient , which instead appears with the same shape as wild type ( Fig . 3E–F ) . The fact that the model can reproduce the ssm but not the gyn gradient points to two non-exclusive deductions: ( 1 ) changes in nuclei density and size are sufficient to explain the ssm distorted gradient , but not gyn , i . e . our hypothesis is only partially correct; and ( 2 ) the parameter set used creates a strongly artificial robustness , buffering the effect of our manipulations . To investigate if our manipulations were being buffered , we first tested the individual effects of nuclei density and size on the Dl gradient shape . We found that either higher nuclei density or larger nuclear size result in a flattened gradient ( Fig . 3G , H ) , indicating that the flattened ssm gradient is mostly determined by its higher nuclei density , which overrides the effect of its smaller nuclei . In contrast , the effect of larger nuclei in gyn was only slightly compensated by its reduced nuclei density , resulting in a Dl gradient shape similar to wild type in our simulations , rather than the steep gradient obtained experimentally . The results above suggest that some of the assumptions that apply to wild type and ssm may not apply to gyn . One possibility is that one or more general parameters , such as Dl diffusion rates and Dl nuclear export rates are different from the values employed in the model , but they have a more significant effect under the gyn conditions . We also observe that the wild type simulation is not completely satisfactory , suggesting that this representative parameter set used could be further improved . We next modified the model to determine which parameter combinations could better reproduce our experimental Dl gradients . To increase the model flexibility and allow testing the effects of individual parameter changes , we used dimensionalized equations and focused on the simulation at the last nuclear cycle only ( see Methods ) . In the original model , 9 dimensionless parameters were used ( Table S3 , Text S1 ) , in addition to nuclei radius and density , developmental timing and cell compartment volume at nc14 . In our modified model , a total of 19 parameters can be manipulated independently ( Table 1 ) , and their effects on the Dl gradient shape can be directly analyzed ( Fig . S5 ) . The original values of most of these 19 parameters could be estimated from the representative non-dimensionalized parameter set chosen here , while others were determined by direct measurements and assumptions ( Text S3 ) . Revisions of the parameter values from this set were performed by manually testing a combination of parameters able to reproduce the gradients from wild type , ssm , and finally gyn . To further validate our revised parameter set , we also quantified Dl from D . melanogaster embryos derived from dl−/dl+ heterozygote mothers ( referred to as dl−/dl+ embryos for simplicity ) and tested the model ability to reproduce this mutant Dl gradient . These embryos have normal embryo size and Tl signaling , but only half of normal Dl protein amount . The analysis of dl−/dl+ embryos provided valuable insights about the model parameters . In agreement to a previous report [22] , we verified that these mutants have a flattened Dl gradient ( Fig . 4 ) , which suggests that near the ventral midline , all cytoplasmic Dl is translocated into the nucleus . Therefore , it is reasonable to assume that in the wild type , the Dl nuclear import rate ( ki ) is not the limiting factor for the formation of the gradient peak . In other words , given enough Tl receptor activation and cytoplasmic Dl , peak levels of nuclear Dl can be achieved in the wild type . This result motivated us to increase the ki value ( Table 1 ) . We next asked if decreased Dl levels could simulate the dl−/dl+ gradient shape . However , our model showed that the shape of the Dl gradient is insensitive to the initial concentrations of Dl , Cact and Dl-Cact ( Fig . S5 ) , unless these initial concentrations are zero , in which case the Dl gradient is not formed . This finding suggests that the gradient shape observed in dl−/dl+ embryos is caused by additional parameter changes besides initial Dl concentration . Several studies report that Cact is stabilized in the presence of Dl and that Cact levels are reduced if Dl levels are diminished [23]–[25] . Based on this information , we tested if changes in the rate of Cact degradation ( kDeg ) were able to reproduce the mutant gradient . We found that doubling the wild type kDeg value was not sufficient to completely reproduce the dl−/dl+ flattened gradient . This finding suggests that the relationship between Dl amounts and Cact stabilization is not linear and probably involves cooperativity . Indeed , Dl is reported to form dimers , such that the Dl-Cact complex is formed by one unit of Cactus bound to two units of Dl [23] , [25] . By increasing kDeg four times , our model could correctly reproduce the Dl gradient from dl−/dl+ embryos ( Fig . 4 , Table 1 ) . After implementing this adjusted parameter set , our simulations still failed to reproduce the gyn gradient , unless three additional changes were made: ( 1 ) an increased diffusion rate among compartments , ( 2 ) an increased Dl nuclear export rates , and finally ( 3 ) an increased embryo radius ( Fig . 5 ) . Increasing Dl , Cact and Dl-Cact transport rates between adjacent compartments ( Γ ) from 0 . 03 to 2 sharpened the gyn gradient simulation ( Fig . 5C , simulation 1 ) and still kept a good fit between the simulated and experimentally obtained gradients of wild type , ssm , and dl−/dl+ embryos ( Fig . 5A , B , D ) ( For fit calculations , see Table S5 ) . Indeed , the fit was actually improved for the wild type ( Fig . 5B , black dots; Table S5 ) . The most common value of transport rate within the parameter vectors in the Kanodia model is 0 . 0064 . We verified that the transport rate constant of 2 tested here falls within the parameter vectors found in the Kanodia model , albeit at low frequency [15] . By also increasing the Dl nuclear export rate ( ke ) from 0 . 44 to 1 , the fit for gyn improved significantly ( Fig . 5C , simulation 2 ) without compromising the wild type ( Fig . 5B , black dots ) and ssm simulations ( Fig . 5D , black dots ) , and only having a small increase of the Dl peak levels in the dl−/dl+ gradient simulation ( Fig . 5A , black dots; Table S5 ) . In sum , increasing diffusion across compartments and Dl export rates greatly improved the gyn gradient simulation and did not impact significantly other D . melanogaster mutants and wild type simulations . Finally , an almost perfect fit for the gyn gradient was obtained by increasing the embryo radius ( Fig . 5C , simulation 3 ) . Although the main motivation to use ssm and gyn mutants to test the influence of nuclei size and density was the fact that these mutants should a priori have wild type egg sizes and a normal DV signaling pathway , actual measurements indicate that gyn has a slightly larger radius of 117 µm in comparison to 100 µm in the wild type . The lack of a perfect simulation of the steep gyn gradient may be due to simplifications in the model . For instance , while Dl-Cact dissociation is the main response to Tl activation , the removal of Dl and Cact interaction is insufficient to promote maximum peak levels of nuclear Dl [23] . However , the model does not include alternative pathways for Dl nuclear translocation or possible interactions between Dl and other IkB related proteins . Also , the model does not represent other alternative DV polarizing sources involving components upstream of Toll , but the effect of this second polarizing signal is reported to be subtle and may not necessarily have measurable effects in a wild type background [26] . Together , our simulations nonetheless clearly indicate that embryo morphology affects the Dl gradient shape , and is likely to play an important role in the modifications seen in the other Drosophila species . Since embryo geometry , nuclear density and nucleus radius affect the shape of the Dl gradient ( Fig . S5 ) , we first addressed how this restricted set of parameters act together to generate the species-specific Dl gradients we analyzed previously [8] . Embryos from D . busckii , D . simulans and D . sechellia have different sizes and geometries , as well as distinct nuclear density and size ( Table 2 ) . After adjusting these parameters to the values obtained experimentally , we verified that the model fails to reproduce the species-specific Dl gradients ( Fig . 6 , Table 2 , simulations 1 ) . Furthermore , additional simulations also discarded other parameters relative to morphology with no significant impact to the gradient shape; namely embryonic AP length , width of cortical layer and the total number of nuclei in the entire embryo ( See Fig . S5 for simulations in D . melanogaster ) . We conclude that the evolutionary morphological modifications in these species alone are not sufficient to generate their final Dl gradient shape . We reasoned that the next logical step requiring minimal model manipulations to achieve good gradient fits for the species should involve adjusting parameters that regulate the Tl signaling pathway . This idea is supported by the fact that the Tl pathway is a fast-evolving pathway in Drosophilids , which is required for immune response in addition to DV patterning [27]–[31] ( See Text S5 for selection of parameters ) . Furthermore , we previously showed that this pathway is indeed modified in the species , as seen by their distinct ranges of peak Tl activation levels measured as the percentage of arc-length occupied by the mesodermal marker sna [8] . This variation goes from 21% in D . melanogaster to 17% in D . busckii , 26% in D . sechellia and 27% in D . simulans . We tested the effect of three parameters ( R , S and ξ ) that influence the amplitude and shape of the space-dependent Dl-Cact dissociation rate constant ( kD ) and as such control the range of Tl signaling strength extending dorsally from the midline . By modifying either R or ξ , we could obtain simulations with good fit for each species . For instance , D . sechellia gradient can be reproduced using an R value of 50 , 000 , but D . simulans requires a much larger value of 114 , 000 . However , such large difference in R values is not supported by the experimental data showing these two species have nearly identical mesodermal percent arc-length [8] . Assuming a linear relationship between the percent arc-length of the mesoderm and R , we tested adjusted R values of 12 , 142 ( busckii ) , 19 , 285 ( simulans ) and 18 , 571 ( sechellia ) . These more modest changes in R slightly improve all simulations ( Fig . 6 , simulations 2; Table 2 ) . Most importantly , the gradients are correctly reproduced by few additional changes in Tl pathway parameters , and these changes agree with the phylogenetic relationship of these species . For instance , in the two most closely related species D . simulans and D . sechellia , either increasing Cact degration rates ( kDeg ) or reducing Cact production rates ( PCact ) can correctly simulate their gradients ( Fig . 6C , D , simulations 3; Table 2 ) . In other words , the significantly different gradients observed in these species , which vary in nuclear and embryo size , are generated by changes in the same parameters and place them apart from D . melanogaster . In contrast , the model predicts that D . busckii , a more distantly related species from the melanogaster subgroup , requires an opposite change over Cact regulation , i . e . , a decrease in kDeg or increase in PCact in order to simulate its gradient ( Fig . 6B , simulation 3; Table 2 ) . To gain further insights about the more closely related species D . simulans and D . sechellia , we tested additional parameters that regulate Dl and Cact functions . We found that decreasing binding of Cact to Dl ( kb ) also generates a good fit for these two species ( Fig . 6C , D , simulations 4; Table 2 ) . Another prediction made by the model was that decreasing Dl export rates in both D . simulans and D . sechellia can also improve the simulation of their gradients ( Fig . S6 ) . Finally , by simultaneously modifying more than two Tl-related parameters at a time , we also obtained good fits for D . simulans and D . sechellia ( Fig . 6C , D , simulations 5; Table 2 ) . We also observe the same overall model behavior when using various randomly generated parameter sets within the range of the parameter cloud identified in the original model [15] ( Figure S7 , Table S6 ) . These results indicate that the very distinct Dl gradient shapes found in these closely related species can be correctly simulated by making similar modifications in selected parameters involved in Tl pathway . As seen above , our simulations indicate that making similar adjustments in parameters that affect Cact regulation or Dl export rates generate good fits for D . simulans and D . sechellia . We expected that the model could reveal if there were common evolutionary mechanisms for the formation of the Dl gradient in another pair of sibling species , D . santomea and D . yakuba , which would also set them apart from D . melanogaster . D . santomea emerged as recently as D . sechellia ( Fig . 1C ) and is also reported to have enlarged egg size [6] , but the speciation of these two species took place in geographically distinct regions [14] . We obtained measurements of embryo size , nuclear size and density for these species ( Table 3 ) . Dl quantifications in both D . yakuba and D . santomea reveal an overall gradient shape similar to D . melanogaster and D . sechellia , except for slightly lower peak levels in D . yakuba . Interestingly , the percent arc-length of sna in D . yakuba and D . santomea ( 22 . 06% , SD = 1 . 92 , n = 5; and 20 . 44% , SD = 1 . 54 , n = 5 , respectively ) is similar to D . melanogaster , suggesting that the broadening of Tl range is an innovation in the branch of D . simulans and D . sechellia . After adjusting the model parameters with the D . yakuba and D . santomea measurements of embryo , nuclear size and density , the resulting gradients were sharper than the experimentally measured gradients ( Fig . 7 , simulations 1 ) . We were able to correctly simulate their gradients by modifying parameters related to the Tl pathway , such as decreasing kb , or increasing kDeg to a same value in both species ( Fig . 7 , simulations 3 and 4; Table 3 ) . Decreasing Dl export rates also improves the simulations , but a comparison of Dl protein sequence did not indicate modifications in the Nuclear Export Sequences ( NES ) from D . melanogaster ( see below ) . In sum , our model indicates that in the D . yakuba and D . santomea lineages , the Dl gradient formation appears to depend on similar modifications in Cact regulation , setting these species apart from D . melanogaster as was the case for D . simulans and D . sechellia . To further investigate the biological relevance of Cact regulation and Dl export rates in the formation of the species-specific Dl gradients , we analyzed the amino acid sequences of these proteins from the melanogaster subgroup species . We aligned D . melanogaster Dl with D . simulans and D . sechellia Dl sequences and found that all known functional domains of the protein are conserved , with the exception of the nuclear export sequence 3 ( NES3 ) , which contains 3 amino acid ( aa ) substitutions in D . simulans and D . sechellia ( Fig . S8A ) . These changes could potentially decrease Dl export rates in these species [32] , [33] , as predicted by our model . In contrast , D . yakuba and D . santomea exhibit identical sequences of all NES domains to D . melanogaster . Although the model equations do not capture the full complexity of the Cact degradation pathways in vivo , the comparison of Cact sequences from these species also provided further support for possible changes in its regulation ( Fig . S8B ) . The Cact C-terminal contains six ankyrin repeats [34] which are necessary for its binding to Dl . We found that D . simulans and D . sechellia contain an insertion of 15 aa within the beginning of ankyrin repeat 4 . Using Phyre2 software to predict protein structure [35] , we verified that this insertion does not eliminate this ankyrin motif itself but it may create two α-helixes between ankyrin repeats 3 and 4 , in contrast to only one long helix present in D . melanogaster Cact . Likewise , D . yakuba Cact is also predicted to have two α-helixes in the same region , due to some nearby aa substitutions . It is possible that the alteration nearby the ankyrin domains could modify the binding between Dl and Cact in these species , which would further support the model prediction that using a lower kb rate than D . melanogaster yield good fits of the other species simulations . Another important regulatory region in Cact sequence is located in the N-terminal ( Fig . S8B ) . This region is rich in serine residues that are phosphorylated in response to Tl activation , leading to Cact degradation . D . simulans contains only one serine substitution ( S94R ) in relation to D . melanogaster , but this site has never been tested for its function in vivo . D . yakuba contains more Cact modifications in relation to D . melanogaster , with a total of 18 aa substitutions , including 4 serine substitutions . In addition , D . yakuba Cact has a deletion of 9 aa at positions 124–132 , nearby a domain previously implicated in Cact degradation in vivo [36] . Together , these variations in Cact and Dl suggest that subtle and additive , but possibly biologically relevant changes in components of the Tl pathway are shared by the most closely related species and may contribute to their final Dl gradient shape , as suggested by our model simulations . We next carried out a sensitivity analysis to test how robust the model is to simultaneous changes in the relevant parameters identified above . Instead of an exhaustive test for all possible combinations of parameter values , we focused on the effects on the model output when changing only two concomitant parameters at a time . We observe that for most combinations tested , the simulations stay within robust regions of the model ( Fig . 8A; Fig . S9 K , V–X ) . Two simulations in particular tend to fall within slightly more unstable regions of the parameter range , namely gyn and D . busckii ( Fig . 8B , see also Fig . S9E , L–Q , S , U ) . Our analysis confirmed that the model is indeed sensitive to changes in two key parameters for reproducing the species gradients , Cact degradation ( kDeg ) and Dl-Cact binding rates ( kb ) ( Fig . 8B , D ) . Furthermore , we observe non-linear interactions between kDeg and kb and parameters related to embryonic morphology ( Fig . 8B , D ) . For instance , in D . simulans and D . sechellia , changes in Dl-Cactus binding rates ( kb ) affect the Dl gradient distribution outcome caused by changes in embryonic radius ( Er; Fig . 8D ) and nuclear radius ( r; Fig . S9J ) . In addition , while the model is mostly robust to changes in nuclear Dl export rates ( ke; Fig . 8C , Fig . S9R , T ) , it does display more sensitivity to ke when paired with the species-specific nuclear radius ( Fig . 8C ) and embryo radius variations ( Fig . S9Q ) . Together , these results support an overall robustness of the model simulations and reveal an interaction between morphological modifications and the few selected parameters of Tl pathway regulation that improve the species-specific simulations .
Garcia et al . [10] recently investigated the Dl gradient scaling within the same species using D . melanogaster lines artificially selected to have small or large embryos [9] . Their study indicates that the Dl gradient width is positively correlated with DV axis length and the number of nuclei along the DV axis . Our experimental data from ploidy mutants and mathematical simulations support the claim that an increase in the number of DV nuclei causes a widening of the Dl gradient . Garcia et al . [10] also suggest that changes in the range of Tl signaling could explain the observed scaling of the Dl gradient width within D . melanogaster species . We previously found variations in the range of peak Tl signaling across species [8] , and in this work we provide evidence for species-specific changes within the Tl signaling pathway as a means of influencing the Dl gradient shape . We also show that increasing Dl nuclear export rate and diffusion between cellular compartments more accurately recreates D . melanogaster wild type and mutant Dl gradients ( Fig . 5 ) . With regard to diffusion rates , the majority of parameter sets found in the Kanodia model is in agreement with a cell autonomous steady state behavior , which is supported by live-imaging experiments showing that a GFP-tagged version of Dl has limited diffusion between neighboring compartments [37] . We verified that our adjusted diffusion rates do not exclude the possibility that the embryo is fully compartmentalized , but we also observe that the final Dl gradient shape is influenced by a non-cell-autonomous process ( Text S4 ) . Future work testing native Dl diffusion without GFP may resolve whether the Dl gradient formation is a non-cell-autonomous process with increased lateral diffusion that may be required for scaling the final gradient shapes observed in nature . The difference in embryo morphology across species is also expected to either increase or decrease the diffusion of Dl by itself , as it has been shown before in experiments that measured diffusion constants of injected dextran in species with small and large embryos [3] . These experiments revealed a trend of increased dextran diffusion in large embryos versus decreased diffusion in small embryos . Consistent with this finding , we also note that our calculated diffusion coefficient of Dl , Cact and Dl-Cact slightly increases in larger embryos ( e . g . D . sechellia and D . santomea ) and decreases in smaller embryos ( e . g . D . busckii ) , after doing a unit conversion of Г that inputs the measured values for embryo morphology ( Text S4 ) . Prior work showed a scaling of the antero-posterior gradient Bcd in the inbred D . melanogaster lines mentioned above [11] , [12] and proposed a mechanism in which more maternal bcd mRNA is loaded into larger embryos to compensate for their increased size . With respect to the Dl gradient , an increase in nuclear Dl concentration can occur with or without a corresponding increase in embryo size or altering the maternal contribution of Dl . For instance , we found that D . sechellia and D . santomea do have greater concentrations of Dl in ventral nuclei in relation to their smaller sibling species D . simulans , D . melanogaster and D . yakuba [8] ( Fig . S10 ) . However , despite the fact that D . simulans produces embryos of comparable size to D . melanogaster , the nuclear Dl concentration levels in the former species are more elevated [8] . We show that changes in nuclear size and density , range of peak Tl activation and changes within the Tl signaling pathway provide additional strategies to altering nuclear Dl concentrations and distributions , which can work in conjunction with altering the maternal dosage of Dl . Two interesting properties of this system emerged from our robustness and sensitivity analysis . First , it can be seen that lowering Dl nuclear export rates for D . simulans and D . sechellia allows the model output to change from a flat to a sharp gradient shape after correcting for the species-specific nuclear radius ( Fig . 8C , white arrows ) . A similar non-linear interaction is observed between Dl-Cact binding constant ( kb ) and nuclear radius ( Fig . S8J , “c” and “d” points ) . Second , we notice that for D . simulans and D . sechellia , the simulations stay within robust regions when more than two parameters are modified at a time ( e . g . ke , kDeg and kb , Table 2 , simulations 5 ) . In contrast , simulations that sharply decrease only one parameter at a time in D . simulans , such as decreasing Dl-Cact binding rates ( kb ) ( e . g . Table 2 , simulation 4 ) fall within more unstable regions ( Fig . 8D , yellow arrow; see also Fig . S8F–J , yellow dots ) . In the case of D . busckii , we also note that the simulations fall within more unstable regions of the model upon changes in the parameter of Cact degradation ( kDeg ) only . These results suggest that it is unlikely that these species acquired their Dl gradient shapes by drastic regulatory changes that affect only one component of the Tl pathway . The results obtained from the use of computational modeling revealed important properties about the behavior of gradient formation and evolution of the Tl signaling pathway in the Drosophila species tested . First , our analyses of ssm and gyn mutants demonstrate that the rapid changes in embryo size , nuclear size and density of these species can modify the Dl gradient shape , but those changes alone are not sufficient for the final species-specific Dl gradient shapes . The second significant prediction made by the modeling is that additional changes in the Tl pathway regulation are required for obtaining good fits with the experimental gradient shapes in these species . At first , it was surprising to find that the gradients of the most distantly related species ( i . e . D . sechellia and D . melanogaster ) have an identical distribution , whereas the gradients of the closest related species D . simulans and D . sechellia acquired completely different shapes . However , predictions made by our model reconcile the fact that these quite different gradient shapes can in fact be generated by a similar dynamics of Tl signaling in D . simulans and D . sechellia , after adjusting for their divergent anatomy . First , as suggested in our previous work , the range of peak Tl activation is broader in D . simulans and D . sechellia , compared to D . melanogaster . Second , our present data suggest that additional modifications in components of the Tl pathway affecting Cact regulation and Dl export rates also diverged in the newest species . By altering these parameter to similarly higher ( e . g . increased kDeg ) or lower values ( e . g . decreased ke or kb ) , good fits of the gradients are generated for both D . simulans and D . sechellia ( Fig . 6 , Table 2 , and Table S5 ) . In support of these findings , we verified that D . simulans and D . sechellia share similar changes in amino acid sequences of Cact and Dl within or near domains previously implicated in Dl-Cact binding and Dl nuclear export , respectively ( Fig . S8 ) . The simulations of Dl gradients in another closely related pair , D . yakuba and D . santomea , also suggested shared modifications in Cact regulation . Either lowering Dl-Cact binding rates ( kb ) or increasing Cact degradation ( kDeg ) to same values can generate good fits for the gradients of these species ( Fig . 7 , Table 3 ) . Genomic data available for D . yakuba confirmed the prediction that Cact sequences within domains involved in degradation and Dl binding are indeed modified in relation to D . melanogaster . In contrast , the Dl protein domains in D . yakuba are well conserved in relation to D . melanogaster . We partially sequenced Dl from D . santomea and found that these domains are similar to D . yakuba . In sum , despite the fact that melanogaster subgroup species have particular egg sizes , nuclear size and density , and their Dl gradient shapes appear at odds with their phylogenetic relationships , the use of mathematical modeling reveals that most closely related species share similarly modified regulation of the Tl pathway inherited from their common ancestor .
yw D . melanogaster was used as wild type . Haploid and triploid embryos were generated in our previous work [8] using the mutations sesame ( ssm ) [20] and gynogenetic-2; gynogenetic-3 ( gyn ) [21] . The D . busckii , D . sechellia and D . simulans strains used in [8] were obtained from the Drosophila Species Center at UCSD . The D . yakuba ( tai 6 line ) and D . santomea ( CAR 1495 . 5 line ) stocks were obtained from Daniel Matute ( Univ . of Chicago ) . Quantification of Dorsal gradient and normalization method are described in detail in [8] . Briefly , embryos were stained for anti-Dorsal antibody ( Iowa Hybridoma Bank ) and a Donkey anti-mouse Alexa 647 , manually sliced in cross-sections within trunk region and imaged using a LSM700 Zeiss Confocal microscope . Fluorescent intensity from the 30-most ventral nuclei was obtained using Axiovision software ( Zeiss ) . Position of midline was estimated with a double staining for snail RNA . For nuclei diameter measurement , early-stage embryos stained with anti-Laminin ( Iowa Hybridoma Bank ) were mounted longitudinally with glass beads ( 150–210 µm size , Polysciences ) , to prevent flattening caused by the coverslip . Confocal slices were taken from the embryo surface to its mid-section and nuclei diameter was determined using ImageJ software . In the case of ssm and gyn mutations , some additional measurements were taken from embryos stained with DAPI nuclear dye . The nondimensionalized model of nc10–14 was reproduced as described by Kanodia et al . [15] . Simulations of gyn and ssm gradients employed same equations ( Text S1 ) , with the following genotype-specific changes in the parameter values . Nuclei radius and density along the DV axis at the last nuclear cycle were directly measured as described above and in [8] . Total embryonic nuclei density at final cyles in ssm ( nc15 ) and gyn ( nc13 ) were estimated at 1200 and 3000 , respectively , based on the fact that wild type embryos have an estimated 6000 nuclei at nc14 and on previous data for haploid embryos [38] . The number of nuclei along the DV axis ( n ) at early cycles was obtained as in Kanodia et al . [15] by multiplying n by after each cycle . In our modified model , we adjusted the final number of DV nuclei at nc14 for D . melanogaster wild type from 100 to 92 , as experimentally obtained in [8] . Adjustments for nuclei size in early nuclear cycles and developmental timing in ssm and gyn embryos are explained in Text S2 ( see also Table S4 and Fig . S4 ) , and were estimated based on [39]–[41] . Parameter changes for other Drosophila species were done according to data measured here , and in previous work [8] , [15] , [42] as described in the main text . Dimensionalized equations were written in Mathematica using original mass-balance equations from the Kanodia model ( Table S1 ) . Additionally , to better represent the changes in embryo volume between species , instead of linearizing the cellular compartments as in the original model , those were represented as circular trapezoids organized in a circle ( Fig . S11 ) . The whole cross-section was modeled , with no need for no-flux boundary conditions . Details of the modifications are described in Text S3 . The original Kanodia model was validated here against three mutant conditions within the same species D . melanogaster ( dl−/dl+ , ssm , gyn ) . Manual adjustments in the parameter ki was made for dl−/dl+ , and adjustments of Γ , ke were made for gyn ( See main text ) . Those same values were maintained for a second round of simulations for wild type , ssm and dl−/dl+ , which served as internal validation controls . Fit between experimental and simulation graphs remained roughly similar for ssm and dl−/dl+ , and it was improved for wild type ( Table S5 ) . For fitness comparison , the square root of the square differences between the simulated gradients and respective experimental data was calculated and provided in Table S5 . Standard deviation of the mean ( SD ) are indicated by error bars ( Fig . 2 ) or shadowed areas ( Fig . 4–7 ) . Pink shadowed area in Fig . 3 ( dl−/dl+ mutants ) indicates SD , gray shadow indicates the 99% confidence interval for the experimental mean , as explained in the figure legend . Simulations “1” for D . busckii , D . simulans and D . sechellia ( Fig . 6 ) lie outside of the 99% confidence interval ( not shown ) , and are statistically different from best fit simulations ( black dots ) . Simulations “1” for D . yakuba and D . santomea ( Fig . 7 ) lie within the 99% confidence interval . However , even though there is no statistical significance , simulations “3” and “4” have an improved fit according to our fit calculations . Available coding sequences of Dl and Cact for D . simulans , D . sechellia and D . melanogaster were obtained from FlyBase and aligned using tblastn ( NCBI ) . For D . santomea , genomic DNA was amplified , sequenced and analyzed as described above . Fig . S8 summarizes the comparison for the sequences obtained . Protein structure analysis was done using Phyre2 software ( http://www . sbg . bio . ic . ac . uk/phyre2/html/page . cgi ? id=index ) . Location of Cact and Dl conserved domains was based on previous work [32] , [33] , [36] , [43] , [44] .
|
Embryo size can vary greatly among closely related species . How tissue specification either scales or is modified in the developing embryo in different species is an ongoing investigation in developmental biology . Here we asked how embryo morphology and specific molecular pathways influence tissue specification by altering the distribution of morphogens . Morphogens are molecules that form gradients that regulate gene expression patterns in a dosage-dependent fashion that result in tissue specification , and therefore are a prime target for evolution in order to adjust or maintain tissue proportions in relation to overall embryo size . We used a mathematical model to identify factors that influence the distribution of the Dorsal morphogen gradient that is responsible for patterning the dorsal-ventral axis of the Drosophila fruit fly embryo . We obtained experimental data from mutant conditions and different species of Drosophila to calibrate our model and found an interaction between embryo morphology and regulation of the Toll pathway , which regulates the Dorsal gradient . Furthermore , the model predicts that closely related species share similar modifications in Toll pathway components resulting in their species-specific gradient shapes , which are supported by interspecies amino acid comparison of the components Dorsal and Cactus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"morphogens",
"developmental",
"biology",
"molecular",
"development",
"biology",
"and",
"life",
"sciences",
"cell",
"fate",
"determination",
"evolutionary",
"developmental",
"biology"
] |
2014
|
Modeling of the Dorsal Gradient across Species Reveals Interaction between Embryo Morphology and Toll Signaling Pathway during Evolution
|
Rift Valley fever ( RVF ) is one of the main vector borne zoonotic diseases that affects a wide range of ruminants and human beings in Africa and the Arabian Peninsula . A rapid and specific test for RVF diagnosis at the site of a suspected outbreak is crucial for the implementation of control measures . A first-line lateral flow immunochromatographic strip test ( LFT ) was developed for the detection of the nucleoprotein ( N ) of the RVF virus ( RVFV ) . Its diagnostic performance characteristics were evaluated using reference stocks isolates recovered from different hosts and in geographic regions mimicking clinical specimens and from known RVF negative serum samples . A high level of diagnostic accuracy ( DSe ( 35/35 ) , DSp ( 167/169 ) ) was observed , including the absence of cross-reactivity with viruses belonging to different genera . The fact no specialized reagents and laboratory equipment are needed , make this assay a valuable , first-line diagnostic tool in resource-poor diagnostic territories for on-site RVFV detection , however the staff require training .
Rift Valley fever ( RVF ) is an emerging mosquito-borne disease that affects a wide range of animals and human beings in Africa and the Arabian Peninsula . It is caused by the mosquito-borne Rift Valley fever phlebovirus ( RVFV ) that belongs to the genus Phlebovirus in the family Phenuiviridae of the order Bunyavirales [1] . First identified in the Great Rift Valley in Kenya in 1930 and initially confined to the African continent , it subsequently spreads to Madagascar , the archipelago of Comoros , and the Arabian Peninsula [2] . There is a growing concern that RVFV will extend its current range due to the wide variety of mosquito species able to transmit to several mammal hosts . This includes species distributed in countries outside Africa and Arabian Peninsula where RVFV is not yet known to circulate despite the environmental factors driving and favoring its circulation [3–6] . Recent outbreaks of RVF in Mayotte , Niger , Uganda and Sudan involving human deaths and characterized by mass abortion and high mortality rates of neonates in the ruminant population raised international interests in improving diagnostic and vaccine control strategies [7–10] . When investigating disease outbreaks in animals , the earlier the clinical signs of disease are recognized by the farmer and the earlier the clinical diagnosis is confirmed by laboratory tests , along with rapid reporting to the relevant veterinary authorities , the better the disease will be controlled . Success partly relies on sending samples to a reference laboratory to test for the presence of RVFV , with high levels of viral particles in the serum during the acute phase of the disease . The availability of a ‘pen-side’ diagnostic test would have the advantage of providing additional support for the medical clinical judgment in the first instance and could reduce the time needed to confirm the test results in secondary cases of the disease . The use of rapid diagnostic tests that can be conducted in the field , at the site of the outbreaks , where the infected human and animal populations are , will therefore facilitate earlier and more effective disease control . Conventional techniques for the diagnosis of RVF include virus isolation , detection of specific IgM or IgG antibodies , and detection of RVFV specific nucleic acids . Enzyme-linked immunosorbent assays ( ELISA ) , based on whole virus antigens or the recombinant nucleocapsid protein N have been extensively validated for the serodiagnosis of RVF [11–12] . Conventional and real-time reverse transcriptase polymerase chain reaction ( RT-PCR ) assays are currently the most rapid and sensitive tests for the detection and quantification of RVFV during outbreaks [13–17] . Methods based on next generation sequencing ( NGS ) approaches [18] or colorimetry [19] , TaqMan array cards [20] have been recently developed but most of these techniques are expensive and require dedicated trained personnel and equipped biosafety level 3 laboratories that are often not available in the areas where the disease occurs . There is thus a need for a simpler , inexpensive and reliable pen-side test to facilitate prompt and accurate field diagnosis . Lateral flow tests ( LFT ) also known as immuno-chromatographic strips are rapid , single-use , one-step test devices able to detect at point of care the presence of an analyte in a liquid sample , flowing along a membrane strip encased in a protective plastic frame . The result can be easily seen with the naked-eye ( test line and control line ) . Good examples have already been published for the detection of Ebola , rabies viruses and visceral leishmaniasis [21–23] . In this study , we evaluated the performances of a robust and rapid test for the detection of Rift Valley fever virus that should be a useful diagnostic tool for RVF control as it will rapidly detect the first outbreak thereby limiting disease spread through appropriate surveillance in the framework of a disease management program in developing countries .
No endangered or protected species were involved in the surveys . Farmers in each zone gave their verbal consent to be included in the study . Consent for blood sampling on a herd was obtained from its owner verbally after information was provided in French ( official language ) or Shimaore , Malagasy ( local languages ) . Animals sampled by qualified veterinarians were bled without suffering . The animal serum samples that originated from mainland France , Reunion Island , Tunisia and the Union of Comoros were collected during either a cross-sectional or a longitudinal survey as described previously [24–25] . Animal serum samples from Mayotte were collected under a national disease surveillance system SESAM with the approval of the Direction of Agriculture , Food and Forestry ( DAAF ) of Mayotte [26] . Animal serum samples from Madagascar were collected in collaboration with the Malagasy veterinary services [27] . The test strip was constructed on the principles of immunochromatography using colloidal-gold-labeled Mabs . We used the two Mabs generated against the N protein of RVFV described above: the Mab 8E10-4A4 gold conjugate and the Mab 10H3-4E4-3D5 . Mab 10H3-4E4-3D5 was immobilized onto a nitrocellulose membrane for the test line zone and rabbit anti-mouse antibodies ( Dako , Denmark ) were immobilized for the control line zone to capture unbound Mab . A reagent pad containing colloidal gold-labeled Mab 8E10-4A4 was located in front of the sample hole and overlaid onto the base of the nitrocellulose membrane , parallel to the control and the antibody bands , stuck to the membrane with adhesive cut into 0 . 8 cm wide strips , assembled in a device as described previously [31] . Aliquots ( 150 μl ) of either viral isolates mimicking clinical specimens , or viral supernatants diluted in DMEM , or serum samples were mixed with an equal volume of LFT sample buffer ( 0 , 1% Casein , 20 mM Borat-Borax buffer , 0 , 5% Tween 20 ) and the mixture was applied to the sample pad ( S ) . This resulted in rehydration of the air-dried conjugated gold Mab and and their migration by capillary action along the membrane . If RVFV antigen was present in the sample then the RVFV-Mab-conjugate complex was captured by the immobilized Mab on the membrane at the ‘T’ ( test ) line and resulted in their accumulation , which could be visualized as a red line to signify a positive result . Excess ( or unbound ) Mab-labelled gold particles continued to migrate along the device until being captured by the immobilized rabbit anti-mouse antibody and the formation of a red ‘C’ ( control ) line , to validate the test . The test ( T ) and control ( C ) lines were checked for the development of color after 10 minutes and again after 30 minutes as it might take longer time for weak positives to form a visual band scored subjectively from negative to strong . According to the OIE guidelines [32] , estimates of DSe ( proportion of samples from known infected reference animals that test positive in an assay ) and DSp ( the proportion of samples from known uninfected reference animals that test negative in an assay ) are the primary performance indicators established during validation of an assay . Analytical specificity ( ASp ) defined as the ability of the assay to distinguish the target analyte ( e . g . a viral antigen ) from non-target analytes , including matrix components and analytical sensitivity ( ASe ) defined as the estimated amount of analyte in a specified matrix that would produce a positive result at least a specified percent of the time are the first steps of the validation of an assay . The limit of detection ( LOD ) is a measure of the ASe . Although RVFV is considered as a single genotype and serotype , diagnostic sensitivity ( DSe ) was assessed with sera spiked with different RVF viral isolates from different geographical origins and collected over a period of 69 years to mimic clinical specimens ( serum or fluids from aborted fetuses ) ( n = 25 , Table 1A ) and sera of the ongoing RVF outbreak occurring on Mayotte [10] ( n = 10 , Table 1A ) . Arboviruses belonging to the Phlebovirus genus and viruses belonging to other viral genera but producing the same clinical features in humans and/or ruminants ( i . e . flaviviruses , alphaviruses ) ( n = 9 , Table 1B ) were tested to assess diagnostic specificity ( DSp ) , i . e . the proportion of samples from known uninfected reference animals that test negative in the assay , as they could be considered as the source of possible cross-reactions in diagnostic assays . In addition , animal serum samples known to be seronegative for RVF of different origins ( mainland France , Tunisia , Reunion Island , Mayotte , Union of Comoros , and Madagascar ) ( n = 169 , Table 1B ) were used for this evaluation . To determine ASe , eight titrated suspensions from RVFV of different origins ( Uganda , Madagascar , Mauritania and South Africa ) adapted to cell culture were selected and diluted in RVF negative sheep sera to determine the limit of detection ( LOD ) of the LFT using 10-fold serial dilutions in RVFV negative cattle serum ( Life Technologies , Gibco , France ) mimicking clinical specimens . A volume of 150 μL of each of the dilutions was tested . Samples were scored as true positives when detected positive with the real-time RT-PCR technique [16] , which was considered as the gold standard in our study .
The performances of the RVF LFT are promising for field use , where the test could help to establish rapid preliminary diagnostic results particularly in suspected cases in the field , which would then have to be confirmed using WHO ( World Health Organization ) or OIE ( World Organisation for Animal Health ) recommended tests at central laboratories . The specificity and sensitivity of the evaluated test are lower than the ones of molecular-based techniques ( LAMP , PCR ) but are adequate for specific rapid initial detection of RVF outbreaks or disease surveillance in control programmes . However , there is still room for improvement in LFT performances by changing several parameters i . e . the sample buffer , the type of membrane or the addition of secondary labeled antibodies . This rapid and easy RVF LFT device does not require special laboratory equipment but does require trained staff wearing appropriate biosecure protective clothing . Although the Lateral Flow Test ( LFT ) is easy to use for non-laboratorians outside a biosafety containment reference laboratory normally used for RVF testing , it nevertheless requires appropriate training to avoid any accidental infection during the removal and handling of a potentially infected sample of body fluid ( serum being the ideal type of sample in the case of RVF outbreak or fluid from aborted fetuses ) for LFT testing . LFT can be performed in the field where epizootics occur . RVF LFT will be particularly valuable in remote areas or in territories where there are no diagnostic facilities but it is important to underline that staff needs to be trained to handle it safely under the highest possible biosecurity conditions .
|
Rift Valley fever ( RVF ) is a viral disease that affects a wide range of animals and human beings in Africa and the Arabian Peninsula involving low case fatality rates . A rapid and specific test for RVF diagnosis at the site of a suspected outbreak is crucial for the implementation of control measures . Here , we report the development and the evaluation of the diagnostic performance characteristics of a pen-side test found to be a highly accurate and valuable first-line diagnostic tool for on-site RVF detection .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"rift",
"valley",
"fever",
"virus",
"enzyme-linked",
"immunoassays",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"viruses",
"rift",
"valley",
"fever",
"nucleoproteins",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"antibodies",
"immunologic",
"techniques",
"bunyaviruses",
"veterinary",
"science",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"veterinary",
"diseases",
"zoonoses",
"proteins",
"medical",
"microbiology",
"epidemiology",
"microbial",
"pathogens",
"immunoassays",
"biochemistry",
"diagnostic",
"medicine",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"organisms"
] |
2019
|
Development and validation of a pen side test for Rift Valley fever
|
Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine . Notwithstanding , a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design . Thus , future success of these devices depends on obtaining circuits with scalable complexity and reusable parts . Here we show how to build complex computational devices using multicellular consortia and space as key computational elements . This spatial modular design grants scalability since its general architecture is independent of the circuit’s complexity , minimizes wiring requirements and allows component reusability with minimal genetic engineering . The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs , thus demonstrating the scalability and flexibility of this method . The potential implications of our results are outlined .
Synthetic biological devices have been built to perform a variety of functions [1–3] . Currently , the creation of complex logic circuits capable of integrating a high number of different inputs and of performing non-trivial decision making processes is one of the major challenges of synthetic biology [4–8] . Examples of synthetic gene circuits used to perform digital computation are switches [9 , 10] , logic gates [11 , 12] , oscillators [13] , band-pass filters [14] , classifiers [15] and memory devices [16] . However , despite the enormous efforts devoted to developing such devices , the results obtained are far from the level of complexity needed for applications [17 , 18] . Limitations in the design of some of these devices and the lack of reusability of the genetic modules strongly constrains the degree of scalability and complexity necessary for industrial , environmental or biomedical applications [19] . In general , the implementation of biological devices that are capable of performing complex logical computations in response to a growing number of input signals involves complex genetic engineering with limited reusability . Usually , the circuits are obtained ( or designed ) by connecting basic logic gates following standard combinatorial logic , inspired by the circuit analogies applied to understanding genetic networks [20–24] . Dedicated efforts have been oriented towards the exploration of such combinatorial scheme within synthetic biology [25 , 26] . In accordance with this standard architecture , the functional complexity of a circuit will scale up with both the number of different logic gates and the number of wires that connect them ( i . e . circuit connectivity ) . Both elements limit the scalability and complexity of these devices [19] . One of the most restrictive constraints is the so-called wiring problem . While wiring is not a major problem in standard electronics , in biological systems is a key limiting factor . This limitation arises from the fact that each connection ( wire ) requires a different biochemical entity and that crosstalk needs to be prevented [27] . In spite of the efforts aimed at standardization of genetic components in synthetic biology , serious limitations still exist [28] . This limits both scalability and the potential reuse of genetic components . Along with the wiring problem , novel strategies towards synthetic biological computation seem required to overcome these problems . In this context , the implementation of circuits using multicellular consortia instead of single cells allows for a reduction in the genetic engineering required in a particular cell [29 , 30] and the reusability of the components . In this scenario , each cell carries a particular engineered design that , when combined with other cells of the consortia , performs the final computation ( hereafter distributed computation ) [27] . Furthermore , when this approach is combined with distributed output production [31] or spatial segregation [32] it allows the attainment of logic circuits with a significant reduction in the number of wires and genetic manipulations required . Noteworthy , both in nature and engineering , space is used as an added dimension of information processing , such as in intracellular network computation [33] , amorphous computing [34] , cell-cell interaction [35] , pattern formation [36–38] , or in ant colonies [39 , 40] . Nevertheless , spatial segregation has never been fully exploited as a key computational parameter in the building of synthetic biological devices [17–19] . Here we present a novel methodology that allows designing biological devices based on the combination of three elements: multicellular consortia , distributed output production and spatial segregation . A major reason to adopt this approximation is the division of labor already present in tissues and organs , where different cell types perform different functions while communicating through signaling molecules . Such segregation of functions , combined with integration of signals is a universal design principle of multicellular systems . Our approach uses engineered cells ( our cell types ) implementing one-input one-output logic gates organized in several consortia , connecting cells of each consortium with a single wire , and allowing each consortium to produce the final output independently of the others . This systematic and simplified distribution of the computation , together with spatial isolation in modules of the different multicellular consortia , permits the building of complex logic circuits . Modules and cells can be reorganised to obtain different computations . Notably , only one wiring molecule and minimal genetic engineering is used . An important property of this architecture is that it does not depend on the complexity of the circuit to be implemented; thus , scalability is ensured because the required number of cell types and modules are bounded . As a proof of principle , we have built several logic circuits in eukaryotic cells with increasing complexity that respond to up to 6 inputs ( such as a 4-to-1 multiplexer ) . Furthermore , we focused on two particular , but very relevant , types of biological devices with diverse outputs . The first type of devices produce an output with a determined function that is secreted into the medium ( e . g . hormone , secretable enzyme ) . Such devices could be used in bioreactors for the production of enzyme , metabolites , or recombinant proteins , as well as in biomedical industry for the production of pharmaceutical products like hormones or drugs . The second type , called transducers ( e . g . biosensors ) transform a combination of different external inputs into a single signal that can be easily quantified with reader devices . Those are devices that could be used for example in diagnostic kits , microbiological assays or biodetectors . In both scenarios , this novel architecture allows the construction of modular biocomputers in a flexible , robust and scalable manner .
With the aim of reducing wiring requirements and minimizing in vivo genetic manipulations , we designed a new logic architecture for use in biological circuits . The basis of this architecture is the combination of multiple consortia with distributed computation [31] with the use of spatial confinement [32] . In general , the behavior of a given logic circuit responding to N inputs can be defined by a logic Boolean function described by the so-called truth table . This table defines all possible combinations of inputs and the associated outputs . According to Boolean algebra , the so-called canonical form of the Boolean function can also describe the truth table of a given logic circuit . Independently on the particular circuit analyzed , this canonical form follows the general expression: f=∑i=1M[∏j=1Nϕij ( xj ) ] Here Σ represents the OR operator and Π the AND operator . The function ϕij is either a logic representation of the presence of a molecular input xj ( Identity function ) or of its absence ( NOT function ) . Finally , M is the maximum number of terms present in the Boolean function , which depends on the complexity of the function , but the condition M ≤ 2N-1 is always satisfied [41] . In general , the expression of a Boolean function f can be reduced by the systematic application of standard rules of simplification , such as Karnaugh maps [42] or the Quine-McCluskey algorithm [43] . However , in the biological context easier implementations can be achieved modifying the canonical expression of the Boolean function to obtain an expression involving only OR logic ( the simpler logic in a cellular implementation ) . To reach this goal we propose an alternative formulation of the Boolean function based on the Inverted Logic Formulation ( ILF ) . This formulation minimizes biological constraints ensuring scalability ( see S1 Text for a detailed mathematical description ) . The canonical form of a general Boolean function can be rewritten applying a double negation , i . e . f=f¯¯=∑i=1M[∏j=1Nϕij ( xj ) ]¯¯ Applying the Morgan’s Laws [44] , {a OR b¯=a¯ AND b¯a AND b¯=a¯ OR b¯ the Boolean function can be expressed as f=f¯¯=∑i=1M[∑j=1Nθij ( xj ) ¯] where θij ( xj ) =ϕij ( xj ) ¯ ( see S1 Text for a detailed mathematical description ) . Hence , the Boolean function results in the OR combination of several computational modules ψi , i . e . : f=∑i=1Mψi Each module ψi is the inversion of OR combinations ( symbol Σ ) of inverted terms θij , i . e . ψi=∑i=1Nθij ( xj ) ¯ where θij ( xj ) can be chosen among NOT or Identity functions , i . e . θij ( xj ) ={xj¯orxj depending on the specific function to be implemented by the circuit . This formalism can be easily translated into a biological implementation . Fig 1 shows a schematic representation of the proposed architecture . In the general basic design , a particular logic circuit is composed of M different multicellular consortia ( Fig 1A ) located in physically isolated chambers {ψ1 , ψ2 , … ψM} . Here , each module ψi that conform the Boolean function can be biologically implemented by a different multicellular consortia located in a physically isolated chamber . Each consortium contains two different layers of cells , namely the Input Layer ( IL ) and the Output Layer ( OL ) . The Input Layer is composed of several cell types that sense single external inputs {x1 , x2 , …xN} and secretes a wiring molecule ω according to a particular internal logic , Identity ( ID ) or NOT ( NOT ) logic implementing the θij ( xi ) functions . When the wiring molecules ω secreted by each cell are mixed in the medium , the OR function ( Σ ) is implicitly implemented . The OL consists in a single cell type that responds to the wiring molecule ( ω ) producing the output β according to a NOT logic , i . e . the output molecule ( β ) is produced only in the absence of the wiring molecule ( ω ) . Of note , only two types of elementary logic responses are implemented in engineered cells ( ID or NOT ) , yet the logic circuitry is much more sophisticated thanks to the spatial segregation of the consortia . In each consortium , the IL cells produce the same wiring molecule in a shared environment thus implementing an implicit OR logic gate . Combining this OR gate with the NOT gate of the OL cells results in a multi-input NOR gate . Remarkably , only one wire ( ω ) is needed . The output of the whole circuit is the OR combination of each consortium output . This OR combination can be easily implemented by connecting the chambers that contain each consortium and mixing the output ( β ) produced using an integrated device . Hence this architecture is optimal for systems where the output is a secreted molecule ( e . g . hormone or enzyme ) . Because the circuit is based on distributed computation , in the presence of a given combination of inputs , the output ( β ) can be produced in one or more consortia at the same time . Therefore the final output concentration can be different depending on the number of consortia producing it . Despite being a digital approach , in which only the presence ( logic state 1 ) or the absence ( logic state 0 ) of the output molecule is relevant , in a real applied system the total amount of output production could be meaningful . Hence , the total amount of output production should not be dependent on the specific combination of inputs that induces their production . This problem can be solved introducing a buffer cell ( BUF cell ) that senses β secreted by the different consortia and produces the final output of the circuit according to identity logic . This buffer cell has to be designed that responds at maximum when senses the presence of the output signal ( β ) from a single consortium . Hence , higher levels of β will not be translated into differentiated output levels ( Fig 1B , upper panel ) . Alternatively , in devices where β is a simple readout of the computation like “transducer circuit” ( e . g . biosensors ) , the final OR could be assessed by quantification of a reporter ( e . g . fluorescence ) directly expressed in the OL cells using a reader device ( e . g . FACS or microscopy ) . In these cases , a positive signal in any consortia can account as the final positive output of the computation bypassing the need for an integrated device and a buffer cell ( Fig 1B , bottom panel ) . The main feature of this architecture is its general design , i . e . for a given number ( N ) of inputs any arbitrary circuit can be built by using the same architecture , independently of its complexity . A simple calculation reveals that , while the upper bound size of the cell library necessary to implement circuits integrating N different inputs scales linearly as Z = 2N+1 ( ID and NOT for each input plus the OL cell ) and the maximum number of spatial modules ( ¬i = 1 , …M ) increases according to M = 2N-1 , the number of implementable different logic functions B grows super-exponentially as B = 22N ( Fig 1C ) . For instance , to implement all the 5-input functions ( B = 4 . 294 . 967 . 296 ) , only 11 different cell types and , in the most complex scenario , 16 independent modules ( M = 16 ) are needed . Of note , we are here referring to a single output ( β ) circuit . In multiple-output circuits the upper bound on the number of OL cells is equal to the number of circuit’s output . When using the optional Buffer layer ( BL ) only one cell type and one additional module are required . Notably , the simple combination of ID and NOT logic cells , when spatially segregated , defines a functional complete set that guarantees that any logic circuit can be built by combining these elements ( a formal demonstration of this design and a detailed description of a systematic methodology for logic circuit implementation are included in S1 Fig and in S1 Text . Therefore , any arbitrary logic function can be encoded in a number M of different consortia and in the particular combinations of IL cells in each of these consortia ( Z ) ( Fig 1C ) . Increasing functional complexity of the logic circuits is translated into an increase in the number of consortia and the corresponding chambers , but not in the number of cell types or wires . In order to demonstrate that our architecture allows scalability together with the minimization of the genetic engineering requirements , we built several logic circuits in eukaryotic cells that respond to up to 6 inputs . In order to implement modular biocomputing in vivo , we created a minimal library of engineered yeast cells required for the ILs and OLs of the circuits ( Fig 2A and S2–S4 Figs ) . The IL library consists of six pairs of cells that respond to extracellular stimuli , namely: doxycycline ( DOX ) , progesterone ( PRO ) , aldosterone ( ALD ) , mating α-factor from C . albicans ( αCa ) , 17-β-estradiol ( EST ) and dexamethasone ( DEX ) . The detection of those hormones is done by expressing the specific receptor in each cell type ( see full description in S1 Text ) . Each pair of cells consists of two different types of cells that respond to the same stimulus but with a different logic , either ID or NOT logic , and that secrete a wiring molecule , the S . cerevisiae α-factor pheromone ( αSc ) accordingly . ID cells secrete the wiring molecule upon stimulation by expressing the MF ( α ) 1 gene from a specific promoter that responds to a defined stimulus . The corresponding pair-wise cell with the NOT logic express the LacI repressor from the same stimulus-specific promoter . NOT cells express the MF ( α ) 1 gene under a modified TEF1 promoter that contains LacI binding sites ( PTEF1i ) and thus , inhibits αSc expression in the presence of stimuli ( Fig 2A and S2A Fig ) . Cells in the OL respond to the αSc that is secreted by IL cells , and subsequently express , or do not express a protein ( β ) ( the output of the module ) , according to NOT logic . In the general architecture , β can be a secreted molecule that performs a determined function ( e . g . an enzyme or a hormone ) or a fluorescent protein ( i . e . GFP or mCherry ) . Briefly , OL able to secrete molecules ( OL3 ) consist in a cell that constitutively expresses the C . albicans α–factor ( αCa ) gene , CaMF ( a ) 1 , under the TEF1i promoter . The αCa is secreted in the culture media mimicking hormones in biomedical application or protein production in bioreactors . The LacI repressor is transcribed from the FUS1 promoter . Therefore , in the presence of S . cerevisiae α-factor ( i . e . the wiring molecule ) the LacI repressor is produced and represses the expression of C . albicans α-factor ( Fig 2A and S2C Fig ) . Alternatively , fluorescent OL cells ( OL1 and OL2 ) constitutively express a modified version of a fluorescent protein ( yEGFP or mCherry ) fused to a degradation tag ( ssrA ) under the TEF1i promoter [45] . The presence of the wiring molecule induces LacI expression , which leads to down-regulation of fluorescent protein expression . Pheromone ( αSc ) also stimulates degradation of the fluorescent protein by induction of the ClpXP protease complex that recognizes and degrades ssrA-tagged proteins ( Fig 2A and S2B Fig ) . Given that the output of the circuit is distributed in different consortia the concentration of the secreted molecule ( e . g . αCa ) can differ according to the number of consortia simultaneously producing it . In case that the level of the secreted molecule needs to be constrained within given thresholds , we engineered a Buffer Layer cell ( BUF ) , which is designed to produce GFP in the presence of αCa according to Identity logic . This cell contains GFP integrated into the FUS1 gene locus under its promoter ( FUS1::GFP-KanMX ) . BL cells also express the C . albicans pheromone receptor ( CaSTE2 ) so that they can sense the secreted pheromone ( Fig 2A and S2D Fig ) . The buffer cell has been designed to give the maximum response when it detects αCa from a single consortium in a sharp step-like function ( S7B Fig ) . Hence , higher levels of αCa will not be translated into different output levels . The genotype and the graphical notation of the logic function performed by each cell ( Input , Output and Buffer Layers ) of the library are depicted in Fig 2 and S3 and S4 Figs and in S1 Text . Once the library of cells was built , we coupled one cell from the IL with one of the OL in the presence or in the absence of the input in order to demonstrate that the wire connection works properly . Two possible scenarios are described: computation of the IL cells in the presence ( Fig 2B ) or the absence ( Fig 2C ) of the optional BL cells . Similar output results were obtained using both strategies by measuring the fluorescence of single cells using flow cytometry . The autofluorescence and the percentage of cells able to produce a positive output signal that were fluorescent positive cells were calculated ( S5 Fig ) . These results showed a clear separation between 0 and 1 logic states in the response of the cells . We then extended the analysis to all of the cell types by measuring their transfer function ( i . e . the relationship between different input concentrations and the corresponding output production ) ( S6 and S7 Figs ) . Briefly , the transfer function of OL cells was characterized by incubation with increasing concentrations of the input synthetic αSc and measurement of the output fluorescence by FACS ( OL1 and OL2 ) , or the output fluorescence resulting from secretion of the αCa ( OL3 ) after incubation with BUF cells . Similarly , the transfer function of the BL cells was characterized by incubation with increasing concentration of synthetic αCa . The transfer function of IL cells was assessed by measurement of output fluorescence upon exposure to increasing levels of each stimulus in the presence of the OL1 cells . These experimental results indicate that all of the cells exhibit a proper behavior that allows definition of a clear separation between 0 and 1 logic states . Applying the same methodology used in electronics , we defined a threshold . Cells producing an output below the threshold are in the 0 logic state , whereas if the output is above this threshold is considered in the 1 logic state . This threshold is the same for all the cells and circuits analyzed . Based on these results , we established the concentration of inputs used in the circuits ( see below ) so that they were clearly above the threshold in order to guarantee a correct response of the cells . Also , based on these transfer functions results , we determined a specific range of time for the response to input signals used in the circuits to ensure a robust response . When working with cellular consortia , it is critical for the system to work robustly that cells within a consortium display similar growth rates . Thus , we assessed the growth rate of each cell type and found no major differences within the entire cell library ( S8 and S9 Figs ) suggesting that the different consortia should not display unbalanced cell growth of any of the components . A potential thread to implement complex biological circuits is crosstalk between cells . We therefore assessed crosstalk between the IL cells in response to different single inputs or to all of the inputs combined . Each IL cell type was mixed with OL1 cells and then treated separately with every input . The percentage of GFP positive cells was measured using FACS . Each cell type responds only to its own stimulus and secretes the pheromone only in the presence ( ID ) or in the absence ( NOT ) of the specific input . Finally , we incubated every cell type with all of the inputs to which it should not respond ( ALL-I ) . Even in this scenario , no significant crosstalk was observed ( Fig 2D ) . Therefore the crosstalk between the IL cells upon different inputs was not significant . We then implemented a number of 2-input logic gates to test the combination of several cell types from the library . Here , just to test the cells we measured the output of the logic circuits as the GFP production of the OL1 cell ( S10 Fig ) and found that the cells computed correctly when an AND , NOR or N-IMPLIES gates were assessed . As an example of how this modular architecture works we built the majority rule device ( Fig 3 ) , by testing it in circuits with a secretable output . This three inputs circuit is a decision-making system used in electronics as a security device against failure in redundant systems . The formal representation of the circuits is shown in S14A and S14B Fig . Using our library of cells , we implemented it as a device that detects when at least two molecules out of three are present . Determine the unbalance between molecules concentrations could be of interests in biomedical applications . The equivalent , single-cell type design of a majority rule would be very difficult to build in vivo [46] . To define the best cell combination from the library within the different modules , the design of the logic circuit is first done in silico which ensure the use of the correct combination of cells ( see S1 Text for a detail description of a systematic methodology for logic circuit implementation ) . Following the basic architecture described above , implementation required just three different multicellular consortia , ψ1 , ψ2 and ψ3 , formed combining three IL cell types with the OL3 cell . Production of the secretable molecule from the independent modules was sensed by the BL cell ( Fig 3A , left ) . A key element in the proposed architecture is the spatial segregation of the different modules . Here , the final OR computation is done physically connecting the modules and collecting the output ( i . e . a secreted molecule ) . To this purpose we built an open-flow computing device ( Fig 3B ) with physically isolated chambers and able to collect and integrate the outputs ( here αCa ) from the different consortia of the circuit . The different consortia were assembled in three independent computational chambers ( ψ1 , ψ2 and ψ3 ) and exposed to the same combination of the three inputs ( x1 = DEX , x2 = EST , x3 = PRO ) . The buffer cells were incubated in the Buffer chamber ( BUF ) . After a transitory computational time , the device is programmed to gather the fluxes of αCa produced by the independent consortia in the Buffer chamber , thereby performing the final OR computation . IL cells were prevented to enter into the OR chamber by positioning a filter before the Buffer chamber . The final output of the circuit , stored in the Buffer chamber , was quantified as % of GFP positive BL cells using both microscopy and flow cytometry ( Fig 3C , grey bars ) . All the eight possible combinations of inputs were tested and the final outcome of the computation was as expected for a majority rule circuit: only when at least two of the inputs were present there was a positive output . The open-flow computing device is an example of an integrated system able to implement in vivo circuits with a secreted output . The second type of devices that can be implemented with the this architecture is the transducers circuits . These circuits are devoted to translate a complex combination of multiple external inputs into a single output signal . The architecture of transducer circuits is simpler because they do not require the final output integration . These circuits can be built as an array of separated modules ( chambers ) that produce the same fluorescent protein as an output which , in turns , is measured by an external readout system . More specifically , we assessed the output by direct quantification of the OL1 or OL2 fluorescence using microscopy and flow cytometry as reader devices . The final output of the circuit was considered positive whenever any consortia gave a positive fluorescent signal above the 1 logic state threshold , bypassing the need of a full system integration . As a first example of a transducer , we measured the output of the same majority rule circuit using OL1 instead of OL3 ( Fig 3A , right ) mimicking a biosensing circuit . Fig 3C , green bars , shows that the circuit responded similar to the open-flow computing device even using a different type of OL cell . Thus , a combination of cell types with the proper design resulted in a device that was capable of implementing a majority rule circuit in vivo . The final result of the computation of the circuit is given as % of GFP cells with fluorescence below ( 0 state ) or above ( 1 state ) the logic threshold previously defined . Alternatively , the output could be measured in terms of total GFP fluorescence ( in arbitrary units ) . We demonstrate that for our library of cells , both metrics are qualitatively equivalent ( S12 Fig and S1 Text ) . A detailed description of the measurement procedures and outcome circuit production is included in the Material and Methods section and S11 Fig . To show the flexibility and robustness of the cells library , we built the same circuit using IL7 cells , which respond to DOX , instead of IL12 cells , which respond to DEX . S13 Fig shows that the logic circuit responded similarly and reliably even swapping cells from the library to respond to different inputs with the same logic , indicating the robustness of the circuit response to cell variation . To explore the potentiality of this approach , we investigated whether more complex devices could be achieved using biosensing devices as reference , since their implementation is simpler in the laboratory when all the input combinations need to be measured . We increased circuit complexity by creating a circuit that responds to four different inputs by producing two different outputs . We chose a 2-bit magnitude comparator , which permits the comparison of two binary numbers , A and B , each having two bits ( A = {a1 , a0} and B = {b1 , b0} ) . Comparators are at the heart of most central processing units ( CPUs ) in computers and perform a large portion of the logical operations . The circuit is able to respond to 4 inputs , upon 16 entries , and yields three different outcomes from the computation ( A>B , A<B and A = B ) . The formal representation of the circuits is shown in S14C and S14D Fig . The implementation of such a circuit in vivo required six different consortia and different combinations of four pairs of IL cells . Cells respond to four stimuli ( DOX , EST , PRO and DEX ) , where EST and DOX encoded A , and PRO and DEX encoded B . Of note , this circuit has an additional level of complexity because it requires two different outputs to distinguish between A<B , A>B and A = B . Therefore , we used two OL cell types , that express green ( GFP , OL1 ) or red ( mCherry , OL2 ) reporters ( Fig 4A ) . The expected output would be green when A<B , red when A>B and no signal when A = B ( Fig 4B ) . After incubation with the inputs , the fluorescence of the cell consortia was assessed and the final computation was calculated by measuring the percentage of mCherry positive cells present in the first three chambers ( A>B ) and the percentage of GFP positive cells in the last three chambers ( A<B ) . All 16 combinations yielded the expected outcome , supporting the notion that multiple functions can be constructed from a small library of reusable cells . To demonstrate the scalability of this modular approach and exploit the capability of our library of cells , we implemented a highly complex multiplexer involving 6 inputs . Of note , such computational complexity has never been reached so far in biological circuits . A multiplexer permits the sharing of one device by several signals thereby avoiding the necessity of having one device per input signal . The MUX 4-to-1 is a circuit that responds to 6 inputs . 2 of these 6 inputs are called selectors because they allow the “selection” of which one of the other 4 inputs will determine the final output . Here , PRO and DOX are the selectors ( S0-S1 ) and ALD , αCa , EST and DEX are the inputs ( I0-I3 ) . For example , when both PRO and DOX are equal to zero ( S0 = 0 , S1 = 0 ) , the selected input is ALD ( I0 ) as indicated in the true table ( Fig 5A , bottom ) . Thus , the circuit will produce the 0 output when ALD is equal to 0 , and an output of 1 when ALD is equal to 1 ( violet row in the truth table , Fig 5C , bottom ) . Thus , a total of 64 combinations of inputs are possible . The formal representation of the circuits is shown in S14E and S14F Fig . This circuit , which would represent an enormous effort if it was built in a single cell using standard design methods ( e . g . S15 Fig ) , can be assembled by involving just eight IL cells and one OL cell combined in four spatially independent consortia ( Fig 5B ) . Similarly , we directly measured the output from the modules using the fluorescent OL1 cells and microscopy and flow cytometry as reader devices . A mixture of the six inputs was simultaneously added to the four chambers and , after incubation , the fluorescence of the consortia was measured using FACS and microscopy . All the 64 combinations of inputs were tested and the final computation was assessed as before . Although the complexity of the circuit required differential outputs to 64 different input combinations , the in vivo results clearly showed the expected response ( Fig 5C ) .
A major challenge in the field of synthetic biology is the construction of flexible , scalable and complex logic circuits using engineered cells . Many different strategies have been implemented to create logic circuits in biological systems over the last decade [6] . However , several problems , including those derived from wiring requirements , pose a serious limitation on scalability [6] . Some approaches have been advanced to overcome these obstacles , including the use of multicellular distributed computation [31] and the use of spatially restricted computational modules [32 , 38 , 47] . Here , we propose a novel alternative to the standard architecture that combines three elements to create new circuits in a strategic manner: 1 ) the use of multicellular consortia , 2 ) spatial segregation and 3 ) distributed output computation . On top of this , circuit design does not follow electronic standard methodology but rather we implemented a new method that permitted to obtain the maximum benefits of the combination of the three elements ( i . e . inverted logic ) . This approach uses the simplest logic devices , i . e . one-input one-output logic gates , connects the cells of each consortium with a single wire , and allows each consortium to produce the final output independently of the others . This new architecture has several appealing properties . On one hand , using a minimal library of cells , several combinations of multicellular consortia can be assembled ( modularity ) . Modular biocomputing profits from the enormous potential of combining a limited number of building blocks ( IL and OL cells ) , which is comparable with the combinatory richness of standard microelectronics . Once the library of cells has been built , different combinations of the same cells can create novel circuits without additional engineering , thereby pushing the concept of reusability of parts one step further . This combination of cells allows an exponential increase in the number of different circuits available without additional engineering . There are however many aspects that need to be taken into account when designing and implementing logic circuits with biological consortia . An extensive cell characterization is important to create proper cell libraries . It is important that the cells within the same module display similar dynamic responses that can be easily deciphered by the characterization of transfer functions . A possible alternatively , could be to introduce a certain nonlinear circuit such as a toggle in the IL cells [48] . Also , it is essential that each cell responds to only one input and thus crosstalk has to be avoided . The balance of cells within the consortia is also a key point for long term circuit responses . It is critical that no cellular imbalances occur and thus , cellular growth should be similar among cells in a module . After library construction and characterization , that serves to create cells responding to the desired input , logic design of the circuit can be established by defining the best cell combination from the library within the different modules . In general , a given circuit can be implemented by different cells and modules combination , which is optimized in silico to reduce the number of cells and modules to facilitate in vivo implementation . The reusability of the cells within a library depends on the input that needs to be sensed , however , our data indicates that thanks to the simple logic of each cell ( ID and NOT ) , cells can be created and swapped easily if they maintain similar dynamic responses as described before . A crucial property of this architecture is that it does not depend on the complexity of the circuit to be implemented , thereby ensuring virtual unlimited scalability , yet maintaining minimal genetic engineering requirements . For instance , a library that responds to six inputs as reported here is sufficient to create up to 1 . 8x1019 different circuits , with a maximum of 32 modules in the worst complex scenario . As a proof of principle , we have built several logic circuits in eukaryotic cells that respond to up to 6 inputs ( such as a multiplexer 4-to-1 ) , and that reach an increase in complexity that has not been implemented before . This design shows how a modular biocomputer can be constructed in a flexible , robust and scalable manner . In addition , computation performed by multicellular consortia opens the door to exploration of circuits obtained by combining different cellular species and the synergies that can be derived from this coexistence . Remarkably , modular biocomputing is flexible to different types of applications; for instance , it can be use to build circuits that function as biosensors and the output of the computation is assessed through a reporter system . These are devices where the circuit outcome can be assessed by microscopy ( e . g . microfluidic devices ) , or biodetectors , analytical and microbiological assays , and diagnostic kits . Still , there are scenarios where the output is a secreted molecule with a biological function , e . g . recombinant proteins or enzymes produced in bioreactors , chemical compounds and metabolites in industrial biotechnology or pharmaceutical products ( hormones or drugs ) in biomedical applications . By expanding the library with only two simple cells we showed how our design can be extended to such applications as well . Finally , we built an open-flow computing apparatus as a proof of principle of an integrated device that upgrades the potential of our architecture to circuits with a secreted output . Depending on the application , the user may require different devices with different proprieties and a diverse level of spatial isolation within each module . For example , in biotechnological applications , the production of toxic by-products by one cell type in a module may inhibit other cell type , thus affecting the computational capability of the consortia . In such cases , the implementation will require isolation of the different cell types in each module where the toxic product is trapped but the wiring molecule can flow . Also , unbalanced growth rates of different cells types calls for devices where the culture growth can be maintain constant like in a chemostat . All together these concerns are pushing the field towards a personalized device technology where users design and build their own devices specifically optimized for the desired application . Lately , many possible micro-environments , such as microfluidics devices [49–51] , cell microcapsules [52] , micro-fabricated implantable arrays [53] and cell culture patterns [54] have been improved . Recent advancements in photolithography , plastic molding and , recently , in 3D-printing might lead to custom-designed microdevices easily available for biomedical applications . Coupling these technologies with modular biocomputing design can provide a general and robust way of exploring the landscape of living computational devices .
Yeast W303 ( ade2-1 his3-11 , 15 leu2-3 , 112 trp1-1 ura3-1 can1-100 ) cells were genetically modified so that they could produce αSc from an inducible promoter ( IL cells ) , control output expression ( fluorescent proteins or αCa ) in response to the αSc ( OL cells ) , or produce a fluorescent protein in response to αCa ( Buffer cells ) . Schematic genotypic characteristics of each cell and plasmid used are summarized in S1 Text , S3 and S4 Figs and S1 and S2 Tables . The cells within a consortium can be followed by specific markers or the presence of fluorescent reporters . Overnight cultures were diluted to OD660 nm ≈ 0 . 2 and were grown at 30°C in YPD or selective medium . We followed standard electronics for defining a positive signal from a circuit as described [31] . As shown in Figs 3–5 , in our biological devices the resolution of the 1 logic is more than 60% and 0 logic is less than 20% of the maximal value , indicating that these circuits are comparable with electronics in terms of resolution . However , this separation between logic states is a necessary but not a sufficient condition to guarantee that multicellular circuits can be implemented that connect different cells acting as logic blocks . A proper characterization of the library of engineered cells is necessary to analyze the so-called Transfer Function , i . e . the cellular response with respect to different input levels . An adequate Transfer Function should be characterized by several key features [55 , 56]: i ) a step-like shape , ii ) linear or higher gain ranges in order to ensure that the signal will not be degraded from input to output in a single cell , iii ) the noise margins must be adequate , without overlap between the high and the low state , and iv ) each cell must respond properly only to the specific inputs and must ignore the rest of inputs of the circuit . All these aspects have been experimentally addressed in the set of engineered cells of the library . S6 and S7 Figs show the full set of transfer functions for each cell . Experimental data were fitted to a Hill equation as described in S1 Text and S3 Table . All these curves exhibit the proper shape to be logic blocks for a multicellular implementation . This procedure allows characterization not only of cellular behavior but also of the wire efficiency . Cells were grown in selective media or YPD to mid exponential phase and were then diluted to OD660 nm ≈ 0 . 2 . Input Layer ( NOT ) cells were washed to remove the αSc that was produced o/n and were resuspended in YPD . Each Input Layer cell was mixed with the GFP Output Layer cell ( OL1 ) at a 4:1 ratio and the mixture was subjected to different concentrations of input ( S6 Fig ) . OL3 cells were washed to remove the αCa that was produced o/n and were resuspended in YPD . OL3 cells were then mixed with the Buffer Layer cells ( BL ) at a 4:1 ratio and were subjected to different concentrations of αSc ( S7A Fig ( bottom ) ) . Various concentrations of αSc were added to OL1 and OL2 cells ( S7A Fig ( top ) ) and different concentrations of αCa factor were added to BL cells ( S7B Fig ) . Samples were incubated for 4 h at 30°C and were analyzed using flow cytometry . Data are expressed as the percentage of GFP positive cells . The transfer function represents the mean and standard deviation of three independent experiments . All of the cells exhibit a proper behavior that allows definition of a clear threshold between 0 and 1 logic states . Based on these results , we established the concentration of inputs used in the circuits ( arrow in S6 and S7 Figs ) to be clearly above the threshold . Output of the circuits , transfer function and crosstalk were analyzed after 4 h incubation at 30°C with a combination of inputs unless specified differently . Samples were diluted in PBS and analyzed using flow cytometry ( BD LSRFortessa ) . A total of 10 . 000 cells were collected from each sample . Constitutive fluorescence in Output Layer cells ( mCherry for OL1 and YFP for OL2 ) , was used to differentiate them from Input Layer cells ( S5B and S5D Fig ) . In S7A Fig , bottom , constitutive fluorescence in the Buffer Layer cells ( mCherry ) was used to differentiate them from the OL3 cells . Specific emission in the fluorescence channel of the subsets of Output or Buffer Layer cells was measured versus autofluorescence ( PerCP-Cy5-5-A channel for GFP and YFP , PerCP-Cy7 channel for mCherry ) . Autofluorescence in a wild type strain without carrying any reporter was measured as a reference ( S5A Fig ) . A gate was set to subtract autofluorescence and cells inside the gate were considered as GFP positive cells . Data are expressed as percentage of fluorescent positive cells ( GFP for OL1 and BL , mCherry for OL2 ) ( S5C and S5E Fig ) . Also measured in a shift on total fluorescence ( S5F Fig ) . An output expression below the 20% of GFP positive cells corresponded to the 0 logic state ( low threshold ) and above the 60% of GFP positive cells corresponded to the 1 logic state ( high threshold ) . In all the circuits , we use the same low and high threshold values . Data were analyzed using FlowJo or BD FACSDiva software . A representative FACS plot of our quantification method is presented in S5 and S11 Figs . For microscopic analyses , cells were harvested and resuspended in Low Fluorescent Media . Images were collected with a Nikon Eclipse Ti Microscope using NIS elements Software ( Nikon ) and were analyzed using ImageJ . Fig 2D shows the individual cellular response of each IL cell in response to the different single inputs they encounter within a circuit or to all of the inputs combined . Cells were grown in selective media or YPD to mid exponential phase ( OD660 nm ≈ 0 . 2 ) . Input Layer ( ID ) cells were mixed with the GFP Output Layer cells at a 2:1 ratio . Input Layer ( NOT ) cells were washed to remove the S . cerevisiae alpha factor that was produced o/n , were resuspended in YPD and were mixed with the GFP Output Layer cells at a 3:1 ratio . Each mixture was subjected to all 6 inputs individually , to all 6 inputs together ( ALL ) and to all inputs except for the specific input ( ALL-I ) . Samples were incubated for 4 h at 30°C and were analyzed using flow cytometry . Data are expressed as the percentage of GFP positive cells . The experimental data shows that there is no undesired crosstalk and that each cell responds only to the expected input . The open flow device ( Fig 3B ) is composed of three parts: the computational chambers ( ψ1 , ψ2 , ψ3 ) , the valves , and the Buffer chamber ( BUF ) . The computational chambers are tanks with 4 . 5ml liquid storage and a cup allowing pneumatic actuation of the fluids ( Microfluidic ChipShop ) . 1 . 6 mm tygon tubes connect the air pump ( CellASIC ONIX Control System ) with the tanks cup using male mini-luer connectors . The fluidic interface is realized as female luer connector . Valves ( Discofix Braun ) can be turned in three different positions ( p1: waste , p2: closed and p3: Buffer ) according to the different experimental steps . The Buffer chamber is a 4 . 5ml tank with a pneumatic cap carrying three male mini luers . The interconnection between the components is enables by 1 . 6 mm tygon tubes , male and female luers and mini luers . To prevent the cells mixture to enter the OR chamber , but still allowing the transferring of the supernatant , a 0 . 22 mm Millipore filter is plugged in before the OR chamber . Finally , a device carrier has been designed and built to hold the apparatus . Fig 2B shows the single cell computation in the presence of the optional Buffer Layer cells . Input Layer cells were mixed with OL3 cells in the absence or presence of the specific input . After 4 h of computation the supernatant of the mix was added to the Buffer Layer cells , incubated for 4 h and the percentage of GFP positive BL cells was analyzed using FACS . Fig 3B and 3C show the implementation of the major rule circuit using the optional BL and the open flow device . The appropriate combinations of IL cells were mixed proportionally into the three chambers , together with the OL3 cells , and exposed to the same combination of the three inputs . After 7h of incubation stirring at RT ( transitory time ) , we pumped into the chambers fresh media with the corresponding combination of inputs ( psi: 0 . 5 , valve: p1 , minutes: 3 ) . The valve was then turned to p2 and the cells mixture was incubated for 10 h stirring at RT ( computational time ) . Finally , the αCa produced by the independent modules was automatically collected in the Buffer chamber ( psi: 5 , valve: p3 , minutes: 1 ) and incubated with the BL cells for 4 h at 30°C . Samples were analyzed using FACS and microscopy . We repeated the same experiment in triplicate for each combination of inputs of the majority rule . Circuits in Figs 3–5 were built mixing proportionally the appropriate combination of IL and OL cells in different tubes ( i . e . the consortia ) . The same mixture of inputs was simultaneously added to each consortium . All the possible combinations of inputs were tested . After 4h of computation at 30°C , for each combination of inputs , the percentage of GFP positive cells in each module was analyzed using FACS and microscope . A positive signal ( more than 60% ) in any consortia accounts for a 1 as the final output of the circuit . When more than one consortium gave a positive fluorescent signal we choose the highest value . The same was done for negative ( less than 20% ) outputs ( 0 ) ( S11 Fig ) . Data represent the mean and standard error of three independent experiments .
|
Synthetic biological circuits have been built for different purposes . Nevertheless , the way these devices have been designed so far present several limitations: complex genetic engineering is required to implement complex circuits , and once the parts are built , they are not reusable . We proposed to distribute the computation in several cellular consortia that are physically separated , thus ensuring implementation of circuits independently of their complexity and using reusable components with minimal genetic engineering . This approach allows an easy implementation of multicellular computing devices for secretable inputs or biosensing purposes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"pathogens",
"sociology",
"microbiology",
"social",
"sciences",
"light",
"microscopy",
"logic",
"circuits",
"fungi",
"model",
"organisms",
"microscopy",
"fungal",
"pathogens",
"genetic",
"engineering",
"research",
"and",
"analysis",
"methods",
"microfluidics",
"fluidics",
"saccharomyces",
"mycology",
"transfer",
"functions",
"mathematical",
"functions",
"medical",
"microbiology",
"fluorescence",
"microscopy",
"mathematical",
"and",
"statistical",
"techniques",
"microbial",
"pathogens",
"consortia",
"yeast",
"candida",
"biology",
"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"saccharomyces",
"cerevisiae",
"organisms",
"candida",
"albicans",
"electronics",
"engineering"
] |
2016
|
Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.